NLSY79

Attachment 102: State FIPS Codes

This attachment lists Federal Information Processing Standards (FIPS) codes used to code respondents' state of birth and state of residence. The FIPS codes are taken from the Federal Information Processing Standards Publication 5-1, published June 15, 1970, FIPS 5-1, which supersedes FIPS 5, published November 1, 1963.

States of the United States (including the District of Columbia) with their assigned codes
Outlying areas of the United States with their assigned codes
Alabama through Montana FIPS codes by county
Nebraska through Wyoming FIPS codes by county

Specifications for states and outlying areas of the United States

Name of Standard. States and Outlying Areas of the United States.
Category of Standard. Federal General Data Standard, Representations and Codes.
Explanation. This standard provides names, abbreviations and codes for representing the 50 States, the District of Columbia, and the outlying areas, all of which are considered to be "first order subdivisions" of the United States.
Qualifications. In the application of this standard, any of the three forms of representation (i.e., names, abbreviations, or codes) may be used to represent those data elements having values (data items) of States or outlying areas of the United States. Names and abbreviations are preferred when human considerations and input reliability are important. The two character numeric code may be used in those applications where machine and numeric sorting considerations are more important, since a sort on the numeric code arranges the States in alphabetical sequence. The codes from FIPS 10, Countries, Dependencies, and Areas of Special Sovereignty, are listed in this standard for the outlying areas of the United States. The form of representation used (i.e., names, abbreviations, or codes) should be identified in format descriptions when data are interchanged.
Special Information. Changes in this revision of the standard are noted by an asterisk in the left margin. Also, the number of changes which have occurred to an item since its original publication is noted by a superscript character following the code. All changes are explained by footnotes.
Specifications. See following tables.

Table 1: States of the United States (including the District of Columbia) with their assigned codes.
Name	Abbreviation	Code
Alabama	AL	01
Alaska	AK	02
Arizona	AZ	04
Arkansas	AR	05
California	CA	06
Colorado	CO	08
Connecticut	CT	09
Delaware	DE	10
District of Columbia	DC	11
Florida	FL	12
Georgia	GA	13
Hawaii	HI	15
Idaho	ID	16
Illinois	IL	17
Indiana	IN	18
Iowa	IA	19
Kansas	KS	20
Kentucky	KY	21
Louisiana	LA	22
Maine	ME	23
Maryland	MD	24
Massachusetts	MA	25
Michigan	MI	26
Minnesota	MN	27
Mississippi	MS	28
Missouri	MO	29
Montana	MT	30
Nebraska	NE	31
Nevada	NV	32
New Hampshire	NH	33
New Jersey	NJ	34
New Mexico	NM	35
New York	NY	36
North Carolina	NC	37
North Dakota	ND	38
Ohio	OH	39
Oklahoma	OK	40
Oregon	OR	41
Pennsylvania	PA	42
Rhode Island	RI	44
South Carolina	SC	45
South Dakota	SD	46
Tennessee	TN	47
Texas	TX	48
Utah	UT	49
Vermont	VT	50
Virginia	VA	51
Washington	WA	53
West Virginia	WV	54
Wisconsin	WI	55
Wyoming	WY	56

The following codes are reserved for possible future use in identifying:

American Samoa (03)
Canal Zone (07)
Guam (14)
Puerto Rico (43)
Virgin Islands (52)

Note: In addition to the FIPS county codes, the following codes were used when the place name was not specific enough or the place name could not be found:

Place	City/Area	Code
Mexico		99090
New York	New York City	500
	APO New York	500
	Attica (ns)	300
	Long Island	600
Oklahoma	Tulsa (ns)	400
Wisconsin	Milwaukee	600
Missouri	Kansas City	700
Texas	Corpus Christi (ns)	702
	Richardson (ns)	701
	Amarillo (ns)	700
	Pleasant Grove (ns)	703
West Virginia	Huntington (ns)	200
Louisiana	Cut Off (ns)	200
Oregon	Salem (ns)	200
Alabama	Laton Hill	200
Arkansas	Bow and Arrow	200

Table 2: Outlying areas of the United States with their assigned codes.
Name	State Code	Country Code	Post Office	Notes
American Samoa	60	AQ		U.S. territory in the Pacific.
Canal Zone	61	PQ	CZ	Territory in Panama leased by U.S.
Canton and Enderbury Islands	62	EQ		Under common U.S.-U.K. administration.
* See #5 Special Information above	63¹ Note 2.1
* See #5 Special Information above	64¹ Note 2.2
* See #5 Special Information above	65¹ Note 2.2
Guam	66	GQ	GU	U.S. territory in the Pacific.
Johnston Atoll	67	JQ		U.S. territory in the Pacific. Includes Sand Island.
* See #5 Special Information above	68¹ Note 2.2
* See #5 Special Information above	69¹ Note 2.1
* See #5 Special Information above	70¹ Note 2.1
Midway Islands	71	MQ		U.S. territory in the Pacific.
Puerto Rico	72	RQ	PR	Commonwealth associated with the U.S.
Ryukyu Islands, Southern	73	YQ		U.S. administered islands in the Pacific south of 27° 52? No. Lat.; 128° 14 E. Long. and the Daito-jima.
Swan Islands	74	SQ		U.S. territory in the Caribbean.
* Trust Territories of the Pacific Islands See #5 Special Information above	75¹ Note 2.3	TQ		U.S. administered: Includes Caroline, Mariana, and Marshall island groups.
U.S. Miscellaneous Caribbean Islands	76	BQ		Includes Navassa Islands, Quito, Sueno Bank, Roncador Cay, Serrana Bank, and Serranilla Bank.
U.S. Miscellaneous Pacific Islands	77	IQ		Includes Kingman Reef, Howland, Baker and Jarvis Islands, and Palmyra Atoll.
Virgin Islands	78	VQ	VI	U.S. territory in the Caribbean.
Wake Island	79	WQ		U.S. territory in the Pacific.
Note: Post Office Abbreviations have not been assigned to all outlying areas of the United States. Note 2.1: 63¹, 69¹, and 70¹. Deleted FIPS 5-1,F June 15, 1970. Previously assigned to the Caroline, Mariana, and Marshall Islands respectively. These islands are now included under the Trust Territories of the Pacific Islands with the new numeric code 75 and the alpha Country Code TQ (as assigned in FIPS 10, 'Countries, Dependencies and Areas of Special Sovereignty.') Note 2.2: 64¹, 65¹, and 68¹. Deleted FIPS 5-1, June 15, 1970. Previously assigned to the Cook Islands, the Gilbert and Ellice Islands, and Line Islands, Southern respectively. These islands are no longer considered to be outlying areas of the United States and are identified and coded in FIPS 10, 'Countries, Dependencies and Areas of Special Sovereignty.' Note 2.3: 75¹ Revised FIPS 5-1, June 15, 1970. Previously assigned to the Tokelau Islands which are no longer considered to be an outlying area of the United States. These islands are identified and coded in FIPS 10, 'Countries, Dependencies and Areas of Special Sovereignty.' U.S. Government Printing Office: 1970 O - 397-525

Click below to expand and collapse the FIPS tables.

Alabama through Montana with County Codes

01 ALABAMA (AL)

001 Autauga
003 Baldwin
005 Barbour
007 Bibb
009 Blount
011 Bullock
013 Butler
015 Calhoun
017 Chambers
019 Cherokee
021 Chilton
023 Choctaw
025 Clark
027 Clay
029 Cleburne
031 Coffee
033 Colbert
035 Conecuh
037 Coosa
039 Covington
041 Crenshaw
043 Cullman
045 Dale
047 Dallas
049 DeKalb
051 Elmore
053 Escambia
055 Etowah
057 Fayette
059 Franklin
061 Geneva
063 Greene
065 Hale
067 Henry
069 Houston
071 Jackson
073 Jefferson
075 Lamar
077 Lauderdale
079 Lawrence
081 Lee
083 Limestone
085 Lowndes
087 Macon
089 Madison
091 Marengo
093 Marion
095 Marshall
097 Mobile
099 Monroe
101 Montgomery
103 Morgan
105 Perry
107 Pickens
109 Pike
111 Randolph
113 Russell
115 St. Clair
117 Shelby
119 Sumter
121 Talladega
123 Tallapoosa
125 Tuscaloosa
127 Walker
129 Washington
131 Wilcox
133 Winston

02 ALASKA (AK)

Note: The official political units of Alaska are called boroughs. There are 10 organized boroughs and an unorganized borough. Unfortunately, for statistical purposes, the organized boroughs cover only a very small percentage of the land area of the state and have the further disadvantage of excluding military and Indian reservations which lie within their boundaries. Consequently, the unorganized borough is too large to provide a basis for suitable statistical analysis. For these reasons the boroughs are not usable for most statistical purposes.

The Census Bureau, in conjunction with the State of Alaska, has established Census Divisions for Alaska which provide a more effective basis for statistical purposes. The Census Divisions are stable geographic statistical areas with defined boundaries that encompass the entire state and generally comprise the election districts. The State of Alaska has requested that these Census Divisions be utilized for statistical purposes.

Two sets of codes are therefore provided for the geopolitical subdivisions of Alaska; namely, the Census Divisions and the Bureaus. The Census Division codes all have a zero in the lower order (last) position. Also listed next are the codes for the boroughs. These can be used in those applications having definite requirements to identify these political units. It should be noted that the low order position of these codes are other than the character zero. Also, the State of Alaska defines the Unorganized Borough to be the remainder of the State other than the 10 organized boroughs. Accordingly, those systems using this definition of the Unorganized Borough would code the military and Indian reservations as 999.

0101¹ Aleutian Islands Division
0201¹ Anchorage Division
0301¹ Angoon Division
0401¹ Barrow Division
0501¹ Bethel Division
0601¹ Bristol Bay Borough Division
0701¹ Bristol Bay Division
0801¹ Cardova-McCarthy Division
0901¹ Fairbanks Division
1001¹ Haines Division
1101¹ Juneau Division
1201¹ Kenai-Cook Inlet Division
1301¹ Ketchikan Division
1401¹ Kobuk Division
1501¹ Kodiak Division
1601¹ Kuskok Wim Division
1701¹ Matanuska-Susitna Division
1801¹ Nome Division
1901¹ Outer Ketchikan Division
2001¹ Prince of Wales Division
2101¹ Seward Division
2201¹ Sitka Division
2301¹ Skagway-Yakutat Division
2401¹ Southeast Fairbanks Division
2501¹ Upper Yukon Division
2601¹ Valdez-Chitina-Whittier Division
2701¹ Wade Hampton Division
2801¹ Wrangell-Petersburg Division
2901¹ Yukon-Koyukuk Division
8091¹ Bristol Bay Borough
8181¹ Fairbanks North Star Borough
8271¹ Greater Anchorage Area Borough
8361¹ Greater Juneau Borough
8451¹ Greater Sitka Borough
8541¹ Haines Borough
8631¹ Kenai Peninsula Borough
8721¹ Ketchikan Gateway Borough
8811¹ Kodiak Island Borough
8911¹ Matanuska-Susitna Borough
9991¹ Unorganized Borough

010¹ Aleutian Islands Division through 9991, Unorganized Borough-Revised FIPS 6-1, June 15, 1970. Original codes assigned are listed below:

001 Aleutian Islands Division
003 Angoon Division
005 Barrow Division
007 Bethel Division
009 Bristol Bay Borough
011 Bristol Bay Division
013 Cordova-McCarthy Division
015 Gateway Borough
017 Greater Anchorage Area Borough
019 Greater Juneau Borough
021 Greater Sitka Borough
023 Kenai Peninsula Borough-NW
025 Kenai Peninsula Borough-SE
027 Kobuk Division
029 Kodiak Island Borough
031 Kuskokwim Division
033 Lynn Canauicy Straits Division
035 Matanuska-Susitna Borough
037 Nome Division
039 North Star Borough
041 Outer Ketchikan Division
043 Prince of Wales Division
045 Southeast Fairbanks Division
047 Upper Yukon Division
049 Valdez-Chitina-Whittier Division
051 Wade Hampton Division
053 Wrangell-Petersburg Division
055 Yukon-Koyukuk Division

04 ARIZONA (AZ)

001 Apache
003 Cochise
005 Coconino
007 Gila
009 Graham
011 Greenlee
012 La Paz
013 Maricopa
015 Mohave
017 Navajo
019 Pima
021 Pinal
023 Santa Cruz
025 Yavapai
027 Yuma

05 ARKANSAS (AR)

001 Arkansas
003 Ashley
005 Baxter
007 Benton
009 Boone
011 Bradley
013 Calhoun
015 Carroll
017 Chicot
019 Clark
021 Clay
023 Cleburne
025 Cleveland
027 Columbia
029 Conway
031 Craighead
033 Crawford
035 Crittenden
037 Cross
039 Dallas
041 Desha
043 Drew
045 Faulkner
047 Franklin
049 Fulton
051 Garland
053 Grant
055 Greene
057 Hempstead
059 Hot Spring
061 Howard
063 Independence
065 Izard
067 Jackson
069 Jefferson
071 Johnson
073 Lafayette
075 Lawrence
077 Lee
079 Lincoln
081 Little River
083 Logan
085 Lonoke
087 Madison
089 Marion
091 MIller
093 Mississippi
095 Monroe
097 Montgomery
099 Nevada
101 Newton
103 Ouachita
105 Perry
107 Phillips
109 Pike
111 Poinsett
113 Polk
115 Pope
117 Prairie
119 Pulaski
121 Randolph
123 St. Francis
125 Saline
127 Scott
129 Searcy
131 Sebastien
133 Sevier
135 Sharp
137 Stone
139 Union
141 Van Buren
143 Washington
145 White
147 Woodruff
149 Yell

06 CALIFORNIA (CA)

001 Alameda
003 Alpine
005 Amador
007 Butte
009 Calaveras
011 Colusa
013 Contra Costa
015 Del Norte
017 El Dorado
019 Fresno
021 Glenn
023 Humboldt
025 Imperial
027 Inyo
029 Kern
031 Kings
033 Lake
035 Lassen
037 Los Angeles
039 Madera
041 Marin
043 Mariposa
045 Mendocino
047 Merced
049 Modoc
051 Mono
053 Monterey
055 Napa
057 Nevada
059 Orange
061 Placer
063 Plumas
065 Riverside
067 Sacramento
069 San Benito
071 San Bernadino
073 San Diego
075 San Francisco
077 San Joaquin
079 San Luis Obispo
081 San Mateo
083 Santa Barbara
085 Santa Clara
087 Santa Cruz
089 Shasta
091 Sierra
093 Siskiyou
095 Solano
097 Sonoma
099 Stanislaus
101 Sutter
103 Tehama
105 Trinity
107 Tulare
109 Tuolumne
111 Ventura
113 Yolo
115 Yuba

08 COLORADO (CO)

001 Adams
003 Alamosa
005 Arapahoe
007 Archuleta
009 Baca
011 Bent
013 Boulder
015 Chaffee
017 Cheyenne
019 Clear Creek
021 Conejos
023 Constilla
025 Crowley
027 Custer
029 Delta
031 Denver
033 Dolores
035 Douglas
037 Eagle
039 Elbert
041 El Paso
043 Fremont
045 Garfield
047 Gilpin
049 Grand
051 Gunnison
053 Hinsdale
055 Huerfano
057 Jackson
059 Jefferson
061 Kiowa
063 Kit Carson
065 Lake
067 La Plata
069 Larimer
071 Las Animas
073 Lincoln
075 Logan
077 Mesa
079 Mineral
081 Moffat
083 Montezuma
085 Montrose
087 Morgan
089 Otero
091 Ouray
093 Park
095 Phillips
097 Pitkin
099 Prowers
101 Pueblo
103 Rio Blanco
105 Rio Grande
107 Routt
109 Saguache
111 San Juan
113 San Miguel
115 Sedgwick
117 Summit
119 Teller
121 Washington
123 Weld
125 Yuma

09 CONNECTICUT (CT)

001 Fairfield
003 Hartford
005 Litchfield
007 Middlesex
009 New Haven
011 New London
013 Tolland
015 Windham

10 DELAWARE (DE)

001 Kent
003 New Castle
005 Sussex

11 DISTRICT OF COLUMBIA (DC)

001 District of Columbia

12 FLORIDA (FL)

001 Alachua
003 Baker
005 Bay
007 Bradford
009 Brevard
011 Broward
013 Calhoun
015 Charlotte
017 Citrus
019 Clay
021 Collier
023 Columbia
025 Dade
027 DeSoto
029 Dixie
031 Duval
033 Escambia
035 Flagler
037 Franklin
039 Gadsden
041 Gilchrist
043 Glades
045 Gulf
047 Hamilton
049 Hardee
051 Hendry
053 Hernando
055 Highlands
057 Hillsborough
059 Holmes
061 Indian River
063 Jackson
065 Jefferson
067 Lafayette
069 Lake
071 Lee
073 Leon
075 Levy
077 Liberty
079 Madison
081 Manatee
083 Marion
085 Martin
087 Monroe
089 Nassau
091 Okaloosa
093 Okeechobee
095 Orange
097 Osceola
099 Palm Beach
101 Pasco
103 Pinellas
105 Polk
107 Putnam
109 St. Johns
111 St. Lucie
113 Santa Rosa
115 Sarasota
117 Seminole
119 Sumter
121 Suwannee
123 Taylor
125 Union
127 Volusia
129 Wakulla
131 Walton
133 Washington

13 GEORGIA (GA)

001 Appling
003 Atkinson
005 Bacon
007 Baker
009 Baldwin
011 Banks
013 Barrow
015 Bartow
017 Ben Hill
019 Berrien
021 Bibb
023 Bleckley
025 Brantley
027 Brooks
029 Bryan
031 Bulloch
033 Burke
035 Butts
037 Calhoun
039 Camden
043 Candler
045 Carroll
047 Catoosa
049 Charlton
051 Chatham
053 Chattahoochee
055 Chattooga
057 Cherokee
059 Clarke
061 Clay
063 Clayton
065 Clinch
067 Cobb
069 Coffee
071 Cloquitt
073 Columbia
075 Cook
077 Coweta
079 Crawford
081 Crisp
083 Dade
085 Dawson
087 Decatur
089 DeKalb
091 Dodge
093 Dooly
095 Dougherty
097 Douglas
099 Early
101 Echols
103 Effingham
105 Elbert
107 Emanuel
109 Evans
111 Fannin
113 Fayette
115 Floyd
117 Forsyth
119 Franklin
121 Fulton
123 Gilmer
125 Glascock
127 Glynn
129 Gordon
131 Grady
133 Greene
135 Gwinnett
137 Habersham
139 Hall
141 Hancock
143 Haralson
145 Harris
147 Hart
149 Heard
151 Henry
153 Houston
155 Irwin
157 Jackson
159 Jasper
161 Jeff Davis
163 Jefferson
165 Jenkins
167 Johnson
169 Jones
171 Lamar
173 Lanier
175 Laurens
177 Lee
179 Liberty
181 Lincoln
183 Long
185 Lowndes
187 Lumpkin
189 McDuffie
191 McIntosh
193 Macon
195 Madison
197 Marion
199 Meriwether
201 Miller
205 Mitchell
207 Monroe
209 Montgomery
211 Morgan
213 Murray
215 Muscogee
217 Mewton
219 Oconee
221 Oglethorpe
223 Paulding
225 Peach
227 Pickens
229 Pierce
231 Pike
233 Polk
235 Pulaski
237 Putnam
239 Quitman
241 Rabun
243 Randolph
245 Richmond
247 Rockdale
249 Schley
251 Screven
253 Seminole
255 Spalding
257 Stephens
259 Stewart
261 Sumter
263 Talbot
265 Taliaferro
267 Tattnall
269 Taylor
271 Telfair
273 Terrell
275 Thomas
277 Tift
279 Toombs
281 Towns
283 Treutlen
285 Troup
287 Turner
289 Twiggs
291 Union
293 Upson
295 Walker
297 Walton
299 Ware
301 Warren
303 Washington
305 Wayne
307 Webster
309 Wheeler
311 White
313 Whitfield
315 Wilcox
317 Wilkes
319 Wilkinson
321 Worth

15 HAWAII (HI)

001 Hawaii
003 Honolulu
005 Kalawao
007 Kauai
009 Maui

16 IDAHO (ID)

001 Ada
003 Adams
005 Bannock
007 Bear Lake
009 Benewah
011 Bingham
013 Blaine
015 Boise
017 Bonner
019 Bonneville
021 Boundary
023 Butte
025 Camas
027 Canyon
029 Caribou
031 Cassia
033 Clark
035 Clearwater
037 Custer
039 Elmore
041 Franklin
043 Fremont
045 Gem
047 Gooding
049 Idaho
051 Jefferson
053 Jerome
055 Kootenai
057 Latah
059 Lemhi
061 Lewis
063 Lincoln
065 Madison
067 Minidoka
069 Nez Perce
071 Oneida
073 Pwyhee
075 Payette
077 Power
079 Shoshone
081 Teton
083 Twin Falls
085 Valley
087 Washington

17 ILLINOIS (IL)

001 Adams
003 Alexander
005 Bond
007 Boone
009 Brown
011 Bureau
013 Calhoun
015 Carroll
017 Cass
019 Champaign
021 Christian
023 Clark
025 Clay
027 Clinton
029 Coles
031 Cook
033 Crawford
035 Cumberland
037 DeKalb
039 De Witt
041 Douglas
043 DuPage
045 Edgar
047 Edwards
049 Effingham
051 Fayette
053 Ford
055 Franklin
057 Fulton
059 Gallatin
061 Greene
063 Grundy
065 Hamilton
067 Hancock
069 Hardin
071 Henderson
073 Henry
075 Iroquois
077 Jackson
079 Jasper
081 Jefferson
083 Jersey
085 Jo Daviess
087 Johnson
089 Kane
091 Kankakee
093 Kendall
095 Knox
097 Lake
099 La Salle
101 Lawrence
103 Lee
105 Livingston
107 Logan
109 McDonough
111 McHenry
113 McLean
115 Macon
117 Macoupin
119 Madison
121 Marion
123 Marshall
125 Mason
127 Massac
129 Menard
131 Mercer
133 Monroe
135 Montgomery
137 Morgan
139 Moultrie
141 Ogle
143 Peoria
145 Perry
147 Piatt
149 Pike
151 Pope
153 Pulaski
155 Putnam
157 Randolph
159 Richland
161 Rock Island
163 St. Clair
165 Saline
167 Sangamon
169 Schuyler
171 Scott
173 Shelby
175 Stark
177 Stephenson
179 Tazewell
181 Union
183 Vermilion
185 Wabash
187 Warren
189 Washington
191 Wayne
193 White
195 Whiteside
197 Will
199 Williamson
201 Winnebago
203 Woodford

18 INDIANA (IN)

001 Adams
003 Allen
005 Bartholomew
007 Benton
009 Blackford
011 Boone
013 Brown
015 Carroll
017 Cass
019 Clark
021 Clay
023 Clinton
025 Crawford
027 Daviess
029 Dearborn
031 Decatur
033 De Kalb
035 Delaware
037 Dubois
039 Elkhart
041 Fayette
043 Floyd
045 Fountain
047 Franklin
049 Fulton
051 Gibson
053 Grant
055 Greene
057 Hamilton
059 Hancock
061 Harrison
063 Hendricks
065 Henry
067 Howard
069 Huntington
071 Jackson
073 Jasper
075 Jay
077 Jefferson
079 Jennings
081 Johnson
083 Knox
085 Kosciusko
087 Lagrange
089 Lake
091 La Porte
093 Lawrence
095 Madison
097 Marion
099 Marshall
101 Martin
103 Miami
105 Monre
107 Montgomery
109 Morgan
111 Newton
113 Noble
115 Ohio
117 Orange
119 Owen
121 Parke
123 Perry
125 Pike
127 Porter
129 Posey
131 Pulaski
133 Putnam
135 Randolph
137 Ripley
139 Rush
141 St. Joseph
143 Scott
145 Shelby
147 Spencer
149 Starke
151 Steuben
153 Sullivan
155 Switzerland
157 Tippecanoe
159 Tipton
161 Union
163 Vanderburgh
165 Vermilion
167 Vigo
169 Wabash
171 Warren
173 Warrick
175 Washington
177 Wayne
179 Wells
181 White
183 Whitley

19 IOWA (IA)

001 Adair
003 Adams
005 Allamakee
007 Appanoose
009 Audobon
011 Benton
013 Black Hawk
015 Boone
017 Bremer
019 Buchanan
021 Buena Vista
023 Butler
025 Calhoun
027 Carroll
029 Cass
031 Cedar
033 Cerro Gordo
035 Cherokee
037 Chickasaw
039 Clarke
041 Clay
043 Clayton
045 Clinton
047 Crawford
049 Dallas
051 Davis
053 Decatur
055 Delaware
057 Des Moines
059 Dickinson
061 Dubuque
063 Emmet
065 Fayette
067 Floyd
069 Franklin
071 Fremont
073 Greene
075 Grundy
077 Guthrie
079 Hamilton
081 Hancock
083 Hardin
085 Harrison
087 Henry
089 Howard
091 Humboldt
093 Ida
095 Iowa
097 Jackson
099 Jasper
101 Jefferson
103 Johnson
105 Jones
107 Keokuk
109 Kossuth
111 Lee
113 Linn
115 Louisa
117 Lucas
119 Lyon
121 Madison
123 Mahaska
125 Marion
127 Marshall
129 Mills
131 Mitchell
133 Monona
135 Monroe
137 Montgomery
139 Muscatine
141 O'Brien
143 Osceola
145 Page
147 Palo Alto
149 Plymouth
151 Pocahontas
153 Polk
155 Pottawattamie
157 Poweshiek
159 Ringgold
161 Sac
163 Scott
165 Shelby
167 Sioux
169 Story
171 Tama
173 Taylor
175 Union
177 Van Buren
179 Wapello
181 Warren
183 Washington
185 Wayne
187 Webster
189 Winnebago
191 Winneshiek
193 Woodbury
195 Worth
197 Wright

20 KANSAS (KS)

001 Allen
003 Anderson
005 Atchinson
007 Barber
009 Barton
011 Bourbon
013 Brown
015 Butler
017 Chase
019 Chautauqua
021 Cherokee
023 Cheyenne
025 Clark
027 Clay
029 Cloud
031 Coffey
033 Comanche
035 Cowley
037 Crawford
039 Decatur
041 Dickinson
043 Doniphan
045 Douglas
047 Edwards
049 Elk
051 Ellis
053 Ellsworth
055 Finney
057 Ford
059 Franklin
061 Geary
063 Gove
065 Graham
067 Grant
069 Gray
071 Greeley
073 Greenwood
075 Hamilton
077 Harper
079 Harvey
081 Haskell
083 Hodgeman
085 Jackson
087 Jefferson
089 Jewell
091 Johnson
093 Kearney
095 Kingman
097 Kiowa
099 Labette
101 Lane
103 Leavenworth
105 Lincoln
107 Linn
109 Logan
111 Lyon
113 McPherson
115 Marion
117 Marshall
119 Meade
121 Miami
123 Mitchell
125 Montgomery
127 Morris
129 Morton
131 Nemaha
133 Neosho
135 Ness
137 Norton
139 Osage
141 Osborne
143 Ottawa
145 Pawnee
147 Phillips
149 Pottawatomie
151 Pratt
153 Rawlins
155 Reno
157 Republic
159 Rice
161 Riley
163 Rooks
165 Rush
167 Russell
169 Saline
171 Scott
173 Sedgwick
175 Seward
177 Shawnee
179 Sheridan
181 Sherman
183 Smith
185 Stafford
187 Stanton
189 Stevens
191 Sumner
193 Thomas
195 Trego
197 Wabaunsee
199 Wallace
201 Washington
203 Wichita
205 Wilson
207 Woodson
209 Wyandotte

21 KENTUCKY (KY)

001 Adair
003 Allen
005 Anderson
007 Ballard
009 Barren
011 Bath
013 Bell
015 Boone
017 Bourbon
019 Boyd
021 Boyle
023 Bracken
025 Breathitt
027 Breckinridge
029 Bullitt
031 Butler
033 Caldwell
035 Calloway
037 Campbell
039 Carlisle
041 Carroll
043 Carter
045 Casey
047 Christian
049 Clark
051 Clay
053 Clinton
055 Crittenden
057 Cumberland
059 Daviess
061 Edmonson
063 Elliott
065 Estill
067 Fayette
069 Fleming
071 Floyd
073 Franklin
075 Fulton
077 Gallatin
079 Garrard
081 Grant
083 Graves
085 Grayson
087 Green
089 Greenup
091 Hancock
093 Hardin
095 Harlan
097 Harrison
099 Hart
101 Henderson
103 Henry
105 Hickman
107 Hopkins
109 Jackson
111 Jefferson
113 Jessamine
115 Johnson
117 Kenton
119 Knott
121 Knox
123 Larue
125 Laurel
127 Lawrence
129 Lee
131 Leslie
133 Letcher
135 Lewis
137 Lincoln
139 Livingston
141 Logan
143 Lyon
145 McCracken
147 McCreary
149 McLean
151 Madison
153 Magoffin
155 Marion
157 Marshall
159 Martin
161 Mason
163 Meade
165 Menifee
167 Mercer
169 Metcalfe
171 Monroe
173 Montgomery
175 Morgan
177 Muhlenberg
179 Nelson
181 Nicholas
183 Ohio
185 Oldham
187 Owen
189 Owsley
191 Pendleton
193 Perry
195 Pike
197 Powell
199 Pulaski
201 Robertson
203 Rockcastle
205 Rowan
207 Russell
209 Scott
211 Shelby
213 Simpson
215 Spencer
217 Taylor
219 Todd
221 Trigg
223 Trimble
225 Union
227 Warren
229 Washington
231 Wayne
233 Webster
235 Whitley
237 Wolfe
239 Woodard

22 LOUISIANA (LA)

001 Acadia
003 Allen
005 Ascension
007 Assumption
009 Avoyelles
011 Beauregard
013 Bienville
015 Bossier
017 Caddo
019 Calcasieu
021 Caldwell
023 Cameron
025 Catahoula
027 Claiborne
029 Concordia
031 De Soto
033 East Baton Rouge
035 East Carroll
037 East Feliciana
039 Evangeline
041 Franklin
043 Grant
045 Iberia
047 Iberville
049 Jackson
051 Jefferson
053 Jefferson Davis
055 Lafayette
057 Lafourche
059 La Salle
061 Lincoln
063 Livingston
065 Madison
067 Morehouse
069 Natchitoches
071 Orleans
073 Ouachita
075 Plaquemines
077 Pointe Coupee
079 Rapides
081 Red River
083 Richland
085 Sabine
087 St. Bernard
089 St. Charles
091 St. Helena
093 St. James
095 St. John the Baptist
097 St. Landry
099 St. Martin
101 St. Mary
103 St. Tammany
105 Tangipahoa
107 Tensas
109 Terrebonne
111 Union
113 Vermilion
115 Vernon
117 Washington
119 Webster
121 West Baton Rouge
123 West Carroll
125 West Feliciana
127 Winn

23 MAINE (ME)

001 Androscoggin
003 Aroostook
005 Cumberland
007 Franklin
009 Hancock
011 Kennebec
013 Knox
015 Lincoln
017 Oxford
019 Penobscot
021 Piscataquis
023 Sagadahoc
025 Somerset
027 Waldo
029 Washington
031 York

24 MARYLAND (MD)

001 Alleghany
003 Anne Arundel
005 Baltimore
009 Calvert
011 Caroline
013 Carroll
015 Cecil
017 Charles
019 Dorchester
021 Frederick
023 Garrett
025 Harford
027 Howard
029 Kent
031 Montgomery
033 Prince George's
035 Queen Anne's
037 St. Mary's
039 Somerset
041 Talbot
043 Washington
045 Wicomico
047 Worcester
510 Baltimore City

25 MASSACHUSETTS (MA)

001 Barnstable
003 Berkshire
005 Bristol
007 Dukes
009 Essex
011 Franklin
013 Hampden
015 Hampshire
017 Middlesex
019 Nantucket
021 Norfolk
023 Plymouth
025 Suffolk
027 Worcester

26 MICHIGAN (MI)

001 Alcona
003 Alger
005 Allegan
007 Alpena
009 Antrim
011 Arenac
013 Baraga
015 Barry
017 Bay
019 Benzie
021 Berrien
023 Branch
025 Calhoun
027 Cass
029 Charlevoix
031 Cheboygan
033 Chippewa
035 Clare
037 Clinton
039 Crawford
041 Delta
043 Dickinson
045 Eaton
047 Emmet
049 Genesee
051 Gladwin
053 Gogebic
055 Grand Traverse
057 Gratiot
059 Hillsdale
061 Houghton
063 Huron
065 Ingham
067 Ionia
069 Iosca
071 Iron
073 Isabella
075 Jackson
077 Kalamazoo
079 Kalkaska
081 Kent
083 Keweenaw
085 Lake
087 Lapeer
089 Leelanau
091 Lenawee
093 Livingston
095 Luce
097 Mackinac
099 Macomb
101 Manistee
103 Marquette
105 Mason
107 Mecosta
109 Menominee
111 Midland
113 Missaukee
115 Monroe
117 Montcalm
119 Montmorency
121 Muskegon
123 Newaygo
125 Oakland
127 Oceana
129 Ogemaw
131 Ontonagon
133 Osceola
135 Oscoda
137 Otsego
139 Ottawa
141 Presque Isle
143 Roscommon
145 Saginaw
147 St. Clair
149 St. Joseph
151 Sanilac
153 Schoolcraft
155 Shiawassee
157 Tuscola
159 Van Buren
161 Washtenaw
163 Wayne
165 Wexford

27 MINNESOTA (MN)

001 Aitkin
003 Anoka
005 Becker
007 Beltrami
009 Benton
011 Big Stone
013 Blue Earth
015 Brown
017 Carlton
019 Carver
021 Cass
023 Chippewa
025 Chisago
027 Clay
029 Clearwater
031 Cook
033 Cottonwood
035 Crow Wing
037 Dakota
039 Dodge
041 Douglas
043 Faribault
045 Fillmore
047 Freeborn
049 Goodhue
051 Grant
053 Hennepin
055 Houston
057 Hubbard
059 Isanti
061 Itasca
063 Jackson
065 Kanabec
067 Kandiyohi
069 Kittson
071 Koochiching
073 Lac qui Parle
075 Lake
077 Lake of the Woods
079 Le Sueur
081 Lincoln
083 Lyon
085 McLeod
087 Mahnomen
089 Marshall
091 Martin
093 Meeker
095 Mille Lacs
097 Morrison
099 Mower
101 Murray
103 Nicollet
105 Nobles
107 Norman
109 Olmsted
111 Otter Tail
113 Pennington
115 Pine
117 Pipestone
119 Polk
121 Pope
123 Ramsey
125 Red Lake
127 Redwood
129 Renville
131 Rice
133 Rock
135 Roseau
137 St. Louis
139 Scott
141 Sherburne
143 Sibley
145 Stearns
147 Steele
149 Stevens
151 Swift
153 Todd
155 Traverse
157 Wabasha
159 Wadena
161 Waseca
163 Washington
165 Watonwan
167 Wilkin
169 Winona
171 Wright
173 Yellow Medicine

28 MISSISSIPPI (MS)

001 Adams
003 Alcorn
005 Amite
007 Attala
009 Benton
011 Bolivar
013 Calhoun
015 Carroll
017 Chickasaw
019 Choctaw
021 Clairborne
023 Clarke
025 Clay
027 Coahoma
029 Copiah
031 Covington
033 DeSoto
035 Forrest
037 Franklin
039 George
041 Greene
043 Grenada
045 Hancock
047 Harrison
049 Hinds
051 Holmes
053 Humphreys
055 Issaquena
057 Itawamba
059 Jackson
061 Jasper
063 Jefferson
065 Jefferson Davis
067 Jones
069 Kemper
071 Lafayette
073 Lamar
075 Lauderdale
077 Lawrence
079 Leake
081 Lee
083 Leflore
085 Lincoln
087 Lowndes
089 Madison
091 Marion
093 Marshall
095 Monroe
097 Montgomery
099 Neshoba
101 Newton
103 Noxubee
105 Oktibbeha
107 Panola
109 Pearl River
111 Perry
113 Pike
115 Pontotoc
117 Prentiss
119 Quitman
121 Rankin
123 Scott
125 Sharkey
127 Simpson
129 Smith
131 Stone
133 Sunflower
135 Tallahatchie
137 Tate
139 Tippah
141 Tishomingo
143 Tunica
145 Union
147 Walthall
149 Warreb
151 Washington
153 Wayne
155 Webster
157 Wilkinson
159 Winston
161 Yalobusha
163 Yazoo

29 MISSOURI (MO)

001 Aitkin
003 Anoka
005 Becker
007 Beltrami
009 Benton
011 Big Stone
013 Blue Earth
015 Brown
017 Carlton
019 Carver
021 Cass
023 Chippewa
025 Chisago
027 Clay
029 Clearwater
031 Cook
033 Cottonwood
035 Crow Wing
037 Dakota
039 Dodge
041 Douglas
043 Faribault
045 Fillmore
047 Freeborn
049 Goodhue
051 Grant
053 Hennepin
055 Houston
057 Hubbard
059 Isanti
061 Itasca
063 Jackson
065 Kanabec
067 Kandiyohi
069 Kittson
071 Koochiching
073 Lac qui Parle
075 Lake
077 Lake of the Woods
079 Le Sueur
081 Lincoln
083 Lyon
085 McLeod
087 Mahnomen
089 Marshall
091 Martin
093 Meeker
095 Mille Lacs
097 Morrison
099 Mower
101 Murray
103 Nicollet
105 Nobles
107 Norman
109 Olmsted
111 Otter Tail
113 Pennington
115 Pine
117 Pipestone
119 Polk
121 Pope
123 Ramsey
125 Red Lake
127 Redwood
129 Renville
131 Rice
133 Rock
135 Roseau
137 St. Louis
139 Scott
141 Sherburne
143 Sibley
145 Stearns
147 Steele
149 Stevens
151 Swift
153 Todd
155 Traverse
157 Wabasha
159 Wadena
161 Waseca
163 Washington
165 Watonwan
167 Wilkin
169 Winona
171 Wright
173 Yellow Medicine

30 MONTANA (MT)

001 Beaverhead
003 Big Horn
005 Blaine
007 Broadwater
009 Carbon
011 Carter
013 Cascade
015 Chouteau
017 Custer
019 Daniels
021 Dawson
023 Deer Lodge
025 Fallon
027 Fergus
029 Flathead
031 Gallatin
033 Garfield
035 Glacier
037 Golden Valley
039 Granite
041 Hill
043 Jefferson
045 Judith Basin
047 Lake
049 Lewis and Clark
051 Liberty
053 Lincoln
055 McCone
057 Madison
059 Meagher
061 Mineral
063 Missoula
065 Musselshell
067 Park
069 Petroleum
071 Phillips
073 Pondera
075 Powder River
077 Powell
079 Prairie
081 Ravalli
083 Richland
085 Roosevelt
087 Rosebud
089 Sanders
091 Sheridan
093 Silver Bow
095 Stillwater
097 Sweet Grass
099 Teton
101 Toole
103 Treasure
105 Valley
107 Wheatland
109 Wibaux
111 Yellowstone
113 Yellowstone National Park

Nebraska through Wyoming with County Codes

31 NEBRASKA (NE)

001 Adams
003 Antelope
005 Arthur
007 Banner
009 Blaine
011 Boone
013 Box Butte
015 Boyd
017 Brown
019 Buffalo
021 Burt
023 Butler
025 Cass
027 Cedar
029 Chase
031 Cherry
033 Cheyenne
035 Clay
037 Colfax
039 Cuming
041 Custer
043 Dakota
045 Dawes
047 Dawson
049 Deuel
051 Dixon
053 Dodge
055 Douglas
057 Dundy
059 Fillmore
061 Franklin
063 Frontier
065 Furnas
067 Gage
069 Garden
071 Garfield
073 Gosper
075 Grant
077 Greeley
079 Hall
081 Hamilton
083 Harlan
085 Hayes
087 Hitchcock
089 Holt
091 Hooker
093 Howard
095 Jefferson
097 Johnson
099 Kearney
101 Keith
103 Keya Paha
105 Kimball
107 Knox
109 Lancaster
111 Lincoln
113 Logan
115 Loup
117 McPherson
119 Madison
121 Merrick
123 Morrill
125 Nance
127 Nemaha
129 Nucktolls
131 Otoe
133 Pawnee
135 Perkins
137 Phelps
139 Pierce
141 Platte
143 Polk
145 Red Willow
147 Richardson
149 Rock
151 Saline
153 Sarpy
155 Saunders
157 Scotts Bluff
159 Seward
161 Sheridan
163 Sherman
165 Sioux
167 Stanton
169 Thayer
171 Thomas
173 Thurston
175 Valley
177 Washington
179 Wayne
181 Webster
183 Wheeler
185 York

32 NEVADA (NV)

001 Churchill
003 Clark
005 Douglas
007 Elko
009 Esmerelda
013 Humboldt
015 Lander
017 Lincoln
019 Lyon
021 Mineral
025¹
027 Pershing
029 Storey
031 Washoe
033 White Pine
510¹ Carson City

025¹ and 510¹ Carson City, Revised FIPS 6-1, June 15, 1970
Code 025 was previously assigned to Ormsby County which has been incorporated as the independent city of Carson City.

33 NEW HAMPSHIRE (NH)

001 Belknap
003 Carroll
005 Chesire
007 Coos
009 Grafton
011 Hillsborough
013 Merrimack
015 Rockingham
017 Strafford
019 Sullivan

0111 Hillsborough, Revised FIPS 6-1, June 15, 1970. May also be spelled "Hillsboro."

34 NEW JERSEY (NJ)

001 Atlantic
003 Bergen
005 Burlington
007 Camden
009 Cape May
011 Cumberland
013 Essex
015 Gloucester
017 Hudson
019 Hunterdon
021 Mercer
023 Middlesex
025 Monmouth
027 Morris
029 Ocean
031 Passaic
033 Salem
035 Somerset
037 Sussex
039 Union
041 Warren

35 NEW MEXICO (NM)

001 Bernalillo
003 Catron
005 Chaves
006 Cibola
007 Colfax
009 Curry
011 DeBaca
013 Dona Ana
015 Eddy
017 Grant
019 Guadelupe
021 Harding
023 Hidalgo
025 Lea
027 Lincoln
028 Los Alamos
029 Luna
031 McKinley
033 More
035 Otero
037 Quay
039 Rio Arriba
041 Roosevelt
043 Sandoval
045 San Juan
047 San Miguel
049 Santa Fe
051 Sierra
053 Socorro
055 Taos
057 Torrance
059 Union
061 Valencia

36 NEW YORK (NY)

001 Albany
003 Alleghany
005 Bronx
007 Broome
009 Cattaraugus
011 Cayuga
013 Chautauqua
015 Chemung
017 Chenango
019 Clinton
021 Columbia
023 Cortland
025 Delaware
027 Dutchess
029 Erie
031 Essex
033 Franklin
035 Fulton
037 Genesee
039 Greene
041 Hamilton
043 Herkimer
045 Jefferson
047 Kings
049 Lewis
051 Livingston
053 Madison
055 Monroe
057 Montgomery
059 Nassau
061 New York
063 Niagara
065 Oneida
067 Onondaga
069 Ontario
071 Orange
073 Orleans
075 Oswego
077 Otsego
079 Putnam
081 Queens
083 Rensselaer
085 Richmond
087 Rockland
089 St. Lawrence
091 Saratoga
093 Schenectady
095 Schoharie
097 Schuyler
099 Seneca
101 Steuben
103 Suffolk
105 Sullivan
107 Tioga
109 Tompkins
111 Ulster
113 Warren
115 Washington
117 Wayne
119 Westchester
121 Wyoming
123 Yates

37 NORTH CAROLINA (NC)

001 Alamance
003 Alexander
005 Alleghany
007 Anson
009 Ashe
011 Avery
013 Beaufort
015 Bertie
017 Bladen
019 Brunswick
021 Buncombe
023 Burke
025 Cabarrus
027 Caldwell
029 Camden
031 Carteret
033 Caswell
035 Catawba
037 Chatham
039 Cherokee
041 Chowan
043 Clay
045 Cleveland
047 Columbus
049 Craven
051 Cumberland
053 Currituck
055 Dare
057 Davidson
059 Davie
061 Duplin
063 Durham
065 Edgecombe
067 Forsyth
069 Franklin
071 Gaston
073 Gates
075 Graham
077 Granville
079 Greene
081 Guilford
083 Halifax
085 Harnett
087 Haywood
089 Henderson
091 Hertford
093 Hoke
095 Hyde
097 Iredell
099 Jackson
101 Johnston
103 Jones
105 Lee
107 Lenoir
109 Lincoln
111 McDowell
113 Macon
115 Madison
117 Martin
119 Mecklenburg
121 Mitchell
123 Montgomery
125 Moore
127 Nash
129 New Hanover
131 Northampton
133 Onslow
135 Orange
137 Pamlico
139 Pasquotank
141 Pender
143 Perquimans
145 Person
147 Pitt
149 Polk
151 Randolph
153 Richmond
155 Robeson
157 Rockingham
159 Rowan
161 Rutherford
163 Sampson
165 Scotland
167 Stanly
169 Stokes
171 Surry
173 Swain
175 Transylvania
177 Tyrrell
179 Union
181 Vance
183 Wake
185 Warren
187 Washington
189 Watauga
191 Wayne
193 Wilkes
195 Wilson
197 Yadkin
199 Yancey

38 NORTH DAKOTA (ND)

001 Adams
003 Barnes
005 Benson
007 Billings
009 Bottineau
011 Bowman
013 Burke
015 Burleigh
017 Cass
019 Cavalier
021 Dickey
023 Divide
025 Dunn
027 Eddy
029 Emmons
031 Foster
033 Golden Valley
035 Grand Forks
037 Grant
039 Griggs
041 Hettinger
043 Kidder
045 LaMoure
047 Logan
049 McHenry
051 McIntosh
053 McKenzie
055 McLean
057 Mercer
059 Morton
061 Mountrail
063 Nelson
065 Oliver
067 Pembina
069 Pierce
071 Ramsey
073 Ransom
075 Renville
077 Richland
079 Rolette
081 Sargent
083 Sheridan
085 Sioux
087 Slope
089 Stark
091 Steele
093 Stutsman
095 Towner
097 Traill
099 Walsh
101 Ward
103 Wells
105 Williams

39 OHIO (OH)

003 Allen
005 Ashland
007 Ashtabula
009 Athens
01 Adams
011 Auglaize
013 Belmont
015 Brown
017 Butler
019 Carroll
021 Champaign
023 Clark
025 Clermont
027 Clinton
029 Columbiana
031 Coshocton
033 Crawford
035 Cuyahoga
037 Darke
039 Defiance
041 Delaware
043 Erie
045 Fairfield
047 Fayette
049 Franklin
051 Fulton
053 Gallia
055 Geauga
057 Greene
059 Guernsey
061 Hamilton
063 Hancock
065 Hardin
067 Harrison
069 Henry
071 Highland
073 Hocking
075 Holmes
077 Huron
079 Jackson
081 Jefferson
083 Knox
085 Lake
087 Lawrence
089 Licking
091 Logan
093 Lorain
095 Lucas
097 Madison
099 Mahoning
101 Marion
103 Medina
105 Meigs
107 Mercer
109 Miami
111 Monroe
113 Montgomery
115 Morgan
117 Morrow
119 Muskingum
121 Noble
123 Ottawa
125 Paulding
127 Perry
129 Pickaway
131 Pike
133 Portage
135 Preble
137 Putname
139 Richland
141 Ross
143 Sandusky
145 Scioto
147 Seneca
149 Shelby
151 Stark
153 Summit
155 Trumbull
157 Tuscarawas
159 Union
161 Van Wert
163 Vinton
165 Warren
167 Washington
169 Wayne
171 Williams
173 Wood
175 Wyandot

40 OKLAHOMA (OK)

001 Adair
003 Alfalfa
005 Atoka
007 Beaver
009 Beckham
011 Blaine
013 Bryan
015 Caddo
017 Canadian
019 Carter
021 Cherokee
023 Choctaw
025 Cimarron
027 Cleveland
029 Coal
031 Comanche
033 Cotton
035 Craig
037 Creek
039 Custer
041 Delaware
043 Dewey
045 Ellis
047 Garfield
049 Garvin
051 Grady
053 Grant
055 Greer
057 Harmon
059 Harper
061 Haskell
063 Hughes
065 Jackson
067 Jefferson
069 Johnston
071 Kay
073 Kingfisher
075 Kiowa
077 Latimer
079 Le Flore
081 Lincoln
083 Logan
085 Love
087 McClain
089 McCurtain
091 McIntosh
093 Major
095 Marshall
097 Mayes
099 Murray
101 Muskogee
103 Noble
105 Nowata
107 Okfuskee
109 Oklahoma
111 Okmulgee
113 Osage
115 Ottawa
117 Pawnee
119 Payne
121 Pttsburg
123 Pontotoc
125 Pottawatomie
127 Pushmataha
129 Roger Mills
131 Rogers
133 Seminole
135 Sequoyah
137 Stephens
139 Texas
141 Tillman
143 Tulsa
145 Wagoner
147 Washington
149 Washita
151 Woods
153 Woodward

41 OREGON (OR)

001 Baker
003 Benton
005 Clackamas
007 Clatsop
009 Columbia
011 Coos
013 Crook
015 Curry
017 Deschutes
019 Douglas
021 Gilliam
023 Grant
025 Harney
027 Hood River
029 Jackson
031 Jefferson
033 Josephine
035 Klamath
037 Lake
039 Lane
041 Lincoln
043 Linn
045 Malheur
047 Marion
049 Morrow
051 Multnomah
053 Polk
055 Sherman
057 Tillamook
059 Umatilla
061 Union
063 Wallowa
065 Wasco
067 Washington
069 Wheeler
071 Yamhill

42 PENNSYLVANIA (PA)

001 Adams
003 Allegheny
005 Armstrong
007 Beaver
009 Bedford
011 Berks
013 Blair
015 Bradford
017 Bucks
019 Butler
021 Cambria
023 Cameron
025 Carbon
027 Centre
029 Chester
031 Clarion
033 Clearfield
035 Clinton
037 Columbia
039 Crawford
041 Cumberland
043 Dauphin
045 Delaware
047 Elk
049 Erie
051 Fayette
053 Forest
055 Franklin
057 Fulton
059 Greene
061 Huntington
063 Indiana
065 Jefferson
067 Juniata
069 Lackawanna
071 Lancaster
073 Lawrence
075 Lebanon
077 Lehigh
079 Luzerne
081 Lycoming
083 Mc Kean
085 Mercer
087 Mifflin
089 Monroe
091 Montgomery
093 Montour
095 Northampton
097 Northumberland
099 Perry
101 Philadelphia
103 Pike
105 Potter
107 Schuylkill
109 Snyder
111 Somerset
113 Sullivan
115 Susquehanna
117 Tioga
119 Union
121 Venango
123 Warren
125 Washington
127 Wayne
129 Westmoreland
131 Wyoming
133 York

44 RHODE ISLAND (RI)

001 Bristol
003 Kent
005 Newport
007 Providence
009 Washington

45 SOUTH CAROLINA (SC)

001 Abbeville
003 Aiken
005 Allendale
007 Anderson
009 Bamberg
011 Barnwell
013 Beaufort
015 Berkeley
017 Calhoun
019 Charleston
021 Cherokee
023 Chester
025 Chesterfield
027 Clarendon
029 Colleton
031 Darlington
033 Dillon
035 Dorchester
037 Edgefield
039 Fairfield
041 Florence
043 Georgetown
045 Greenville
047 Greenwood
049 Hampton
051 Horry
053 Jasper
055 Kershaw
057 Lancaster
059 Laurens
061 Lee
063 Lexington
065 McCormick
067 Marion
069 Marlboro
071 Newberry
073 Oconee
075 Orangeburg
077 Pickens
079 Richland
081 Saluda
083 Spartanburg
085 Sumter
087 Union
089 Williamsburg
091 York

46 SOUTH DAKOTA (SD)

003 Aurora
005 Beadle
007 Bennett
009 Bon Homme
011 Brookings
013 Brown
015 Brule
017 Buffalo
019 Butte
021 Campbell
023 Charles Mix
025 Clark
027 Clay
029 Codington
031 Corson
033 Custer
035 Davison
037 Day
039 Deuel
041 Dewey
043 Douglas
045 Edmunds
047 Fall River
049 Faulk
051 Grant
053 Gregory
055 Haakon
057 Hamlin
059 Hand
061 Hanson
063 Harding
065 Hughes
067 Hutchinson
069 Hyde
071 Jackson
073 Jerauld
075 Jones
077 Kingsbury
079 Lake
081 Lawrence
083 Lincoln
085 Lyman
087 McCook
089 McPherson
091 Marshall
093 Meade
095 Mellette
097 Miner
099 Minnehaha
101 Moody
103 Pennington
105 Perkins
107 Potter
109 Roberts
111 Sanborn
113 Shannon
115 Spink
117 Stanley
119 Sully
121 Todd
123 Tripp
125 Turner
127 Union
129 Walworth
135 Yankton
137 Ziebach

47 TENNESSEE (TN)

001 Anderson
003 Bedford
005 Benton
007 Bledsoe
009 Blount
011 Bradley
013 Campbell
015 Cannon
017 Carroll
019 Carter
021 Cheatham
023 Chester
025 Claiborne
027 Clay
029 Cocke
031 Coffee
033 Crockett
035 Cumberland
037 Davidson
039 Decatur
041 DeKalb
043 Dickson
045 Dyer
047 Fayette
049 Fentress
051 Franklin
053 Gibson
055 Giles
057 Grainger
059 Greene
061 Grundy
063 Hamblen
065 Hamilton
067 Hancock
069 Hardeman
071 Hardin
073 Hawkins
075 Haywood
077 Henderson
079 Henry
081 Hickman
083 Houston
085 Humphreys
087 Jackson
089 Jefferson
091 Johnson
093 Knox
095 Lake
097 Lauderdale
099 Lawrence
101 Lewis
103 Lincoln
105 Loudon
107 McMinn
109 McNairy
111 Macon
113 Madison
115 Marion
117 Marshall
119 Maury
121 Meigs
123 Monroe
125 Montgomery
127 Moore
129 Morgan
131 Obion
133 Overton
135 Perry
137 Pickett
139 Polk
141 Putnam
143 Rhea
145 Roane
147 Robertson
149 Rutherford
151 Scott
153 Sequatchie
155 Sevier
157 Shelby
159 Smith
161 Stewart
163 Sullivan
165 Sumner
167 Tipton
169 Trousdale
171 Unicoi
173 Union
175 Van Buren
177 Warren
179 Washington
181 Wayne
183 Wealey
185 White
187 Williamson
189 Wilson

48 TEXAS (TX)

001 Anderson
003 Andrews
005 Angelina
007 Aransas
009 Archer
011 Armstrong
013 Atascosa
015 Austin
017 Bailey
019 Bandera
021 Bastrop
023 Baylor
025 Bee
027 Bell
029 Bexar
031 Blanco
033 Borden
035 Bosque
037 Bowie
039 Brazoria
041 Brazos
043 Brewster
045 Briscoe
047 Brooks
049 Brown
051 Burleson
053 Burnet
055 Caldwell
057 Calhoun
059 Callahan
061 Cameron
063 Camp
065 Carson
067 Cass
069 Castro
071 Chambers
073 Cherokee
075 Childress
077 Clay
079 Cochran
081 Coke
083 Coleman
085 Collin
087 Collingsworth
089 Colorado
091 Comal
093 Comanche
095 Concho
097 Cooke
099 Coryell
101 Cottle
103 Crane
105 Crockett
107 Crosby
109 Culberson
111 Dallam
113 Dallas
115 Dawson
117 Deaf Smith
119 Delta
121 Denton
123 DeWitt
125 Dickens
127 Dimmit
129 Donley
131 Duval
133 Eastland
135 Ector
137 Edwards
139 Ellis
141 El Paso
143 Erath
145 Falls
147 Fannin
149 Fayette
151 Fisher
153 Floyd
155 Foard
157 Fort Bend
159 Franklin
161 Freestone
163 Frio
165 Gaines
167 Galveston
169 Garza
171 Gillespie
173 Glasscock
175 Goliad
177 Gonzales
179 Gray
181 Grayson
183 Gregg
185 Grimes
187 Guadalupe
189 Hale
191 Hall
193 Hamilton
195 Hansford
197 Hardeman
199 Hardin
201 Harris
203 Harrison
205 Hartley
207 Haskell
209 Hays
211 Hemphill
213 Henderson
215 Hidalgo
217 Hill
219 Hockley
221 Hood
223 Hopkins
225 Houston
227 Howard
229 Hudspeth
231 Hunt
233 Hutchinson
235 Irion
237 Jack
239 Jackson
241 Jasper
243 Jeff Davis
245 Jefferson
247 Jim Hogg
249 Jim Wells
251 Johnson
253 Jones
255 Karne
257 Kaufman
259 Kendall
261 Kenedy
263 Kent
265 Kerr
267 Kimble
269 King
271 Kinney
273 Kleberg
275 Knox
277 Lamar
279 Lamb
281 Lampasas
283 La Salle
285 Lavaca
287 Lee
289 Leon
291 Liberty
293 Limestone
295 Lipscomb
297 Live Oak
299 Llano
301 Loving
303 Lubbock
305 Lynn
307 McCulloch
309 McLennan
311 McMullen
313 Madison
315 Marion
317 Martin
319 Mason
321 Matagorda
323 Maverick
325 Medina
327 Menard
329 Midland
331 Milam
333 Mills
335 Mitchell
337 Montague
339 Montgomery
341 Moore
343 Morris
345 Motley
347 Nacogdoches
349 Navarro
351 Newton
353 Nolan
355 Nueces
357 Ochiltree
359 Oldham
361 Orange
363 Palo Pinto
365 Panola
367 Parker
369 Parmer
371 Pecos
373 Polk
375 Potter
377 Presidio
379 Rains
381 Randall
383 Reagan
385 Real
387 Red River
389 Reeves
391 Refugio
393 Roberts
395 Robertson
397 Rockwall
399 Runnels
401 Rusk
403 Sabine
405 San Augustine
407 San Jacinto
409 San Patricio
411 San Saba
413 Schleicher
415 Scurry
417 Shackelford
419 Shelby
421 Sherman
423 Smith
425 Somervell
427 Starr
429 Stephens
431 Sterling
433 Stonewall
435 Sutton
437 Swisher
439 Tarrant
441 Taylor
443 Terrell
445 Terry
447 Throckmorton
449 Titus
451 Tom Green
453 Travis
455 Trinity
457 Tyler
459 Upshur
461 Upton
463 Uvalde
465 Val Verde
467 Van Zandt
469 Victoria
471 Walker
473 Waller
475 Ward
477 Washington
479 Webb
481 Wharton
483 Wheeler
485 Wichita
487 Wilbarger
489 Willacy
491 Williamson
493 Wilson
495 Winkler
497 Wise
499 Wood
501 Yoakum
503 Young
505 Zapata
507 Zavala

49 UTAH (UT)

001 Beaver
003 Box Elder
005 Cache
007 Carbon
009 Daggett
011 Davis
013 Duchesne
015 Emery
017 Garfield
019 Grand
021 Iron
023 Juab
025 Kane
027 Millard
029 Morgan
031 Piute
033 Rich
035 Salt Lake
037 San Juan
039 Sanpete
041 Sevier
043 Summit
045 Tooele
047 Uintah
049 Utah
051 Wasatch
053 Washington
055 Wayne
057 Weber

50 VERMONT (VT)

001 Addison
003 Bennington
005 Caledonia
007 Chittenden
009 Essex
011 Franklin
013 Grand Isle
015 Lamoille
017 Orange
019 Orleans
021 Rutland
023 Washington
025 Windham
027 Windsor

51 VIRGINIA (VA)

001 Accomack
003 Albemarle
005 Alleghany
007 Amelia
009 Amherst
011 Appomattox
013 Arlington
015 Augusta
017 Bath
019 Bedford
021 Bland
023 Botetourt
025 Brunswick
027 Buchanan
029 Buckingham
031 Campbell
033 Caroline
035 Carroll
036 Charles City
037 Charlotte
041 Chesterfield
043 Clarke
045 Craig
047 Culpeper
049 Cumberland
051 Dickenson
053 Dinwiddie
057 Essex
059 Fairfax
061 Fauquier
063 Floyd
065 Fluvanna
067 Franklin
069 Frederick
071 Giles
073 Gloucester
075 Goochland
077 Grayson
079 Greene
081 Greensville
083 Halifax
085 Hanover
087 Henrico
089 Henry
091 Highland
093 Isle of Wight
095 James City
097 King and Queen
099 King George
101 King William
103 Lancaster
105 Lee
107 Loudoun
109 Louisa
111 Lunenburg
113 Madison
115 Mathews
117 Mecklenburg
119 Middlesex
121 Montgomery
125 Nelson
127 New Kent
131 Northampton
133 Northumberland
135 Nottoway
137 Orange
139 Page
141 Patrick
143 Pittsylvania
145 Powhatan
147 Prince Edward
149 Prince George
153 Prince William
155 Pulaski
157 Rappahannock
159 Richmond
161 Roanoke
163 Rockbridge
165 Rockingham
167 Russell
169 Scott
171 Shenandoah
173 Smyth
175 Southampton
177 Spotsylvania
179 Stafford
181 Surry
183 Sussex
185 Tazewell
187 Warren
191 Washington
193 Westmoreland
195 Wise
197 Wythe
199 York
510 Alexandria city
515¹ Bedford city
520 Bristol city
530 Buena Vista city
540 Charlottesville city
550 Chesapeake city
560 Clifton Forge city
570 Colonial Heights city
580 Covington city
590 Danville city
595 Emporia city
600 Fairfax city
610 Falls Church city
620 Franklin city
630 Fredericksburg city
640 Galax city
650 Hampton city
660 Harrisonburg city
670 Hopewell city
678 Lexington city
680 Lynchburg city
690 Martinsville city
700 Newport News city
710 Norfolk city
720 Norton city
730 Petersburg city
740 Portsmouth city
750 Radford city
760 Richmond city
770 Roanoke city
775 Salem city
780 South Boston city
790 Staunton city
800 Suffolk city
810 Virginia Beach city
820 Waynesboro city
830 Williamsburg city
840 Winchester city

515¹ Bedford. Independent City, Revised FIPS 6-1, June 15, 1970.
Bedford, formerly part of Bedford County, has been incorporated as an independent city.

53 WASHINGTON (WA)

001 Adams
003 Asotin
005 Benton
007 Chelan
009 Clallam
011 Clark
013 Columbia
015 Cowlitz
017 Douglas
019 Ferry
021 Franklin
023 Garfield
025 Grant
027 Grays Harbor
029 Island
031 Jefferson
033 King
035 Kitsap
037 Kittitas
039 Klickitat
041 Lewis
043 Lincoln
045 Mason
047 Okanogan
049 Pacific
051 Pend Oreille
053 Pierce
055 San Juan
057 Skagit
059 Skamania
061 Snohomish
063 Spokane
065 Stevens
067 Thurston
069 Wahkiakum
071 Walla Walla
073 Whatcom
075 Whitman
077 Yakima

54 WEST VIRGINIA (WV)

001 Barbour
003 Berkeley
005 Boone
007 Braxton
009 Brooke
011 Cabell
013 Calhoun
015 Clay
017 Doddridge
019 Fayette
021 Gilmer
023 Grant
025 Greenbrier
027 Hampshire
029 Hancock
031 Hardy
033 Harrison
035 Jackson
037 Jefferson
039 Kanawha
041 Lewis
043 Lincoln
045 Logan
047 McDowell
049 Marion
051 Marshall
053 Mason
055 Mercer
057 Mineral
059 Mingo
061 Monongalia
063 Monroe
065 Morgan
067 Nicholas
069 Ohio
071 Pendleton
073 Pleasants
075 Pocahontas
077 Preston
079 Putnam
081 Raleigh
083 Randolph
085 Ritchie
087 Roane
089 Summers
091 Taylor
093 Tucker
095 Tyler
097 Upshur
099 Wayne
101 Webster
103 Wetzel
105 Wirt
107 Wood
109 Wyoming

55 WISCONSIN (WI)

009 Brown
011 Buffalo
013 Burnett
015 Calumet
017 Chippewa
019 Clark
021 Columbia
023 Crawford
025 Dane
027 Dodge
029 Door
031 Douglas
033 Dunn
035 Eau Claire
037 Florence
039 Fond du Lac
041 Forest
043 Grant
045 Green
047 Green Lake
049 Iowa
051 Iron
053 Jackson
055 Jefferson
057 Juneau
059 Kenosha
061 Kewaunee
063 La Crosse
065 Lafayette
067 Langlade
069 Lincoln
071 Manitowoc
073 Marathon
075 Marinette
077 Marquette
078 Menominee
079 Milwaukee
081 Monroe
083 Oconto
085 Oneida
087 Outagamie
089 Ozaukee
091 Pepin
093 Pierce
095 Polk
097 Portage
099 Price
101 Racine
103 Richland
105 Rock
107 Rusk
109 St. Croix
111 Sauk
113 Sawyer
115 Shawano
117 Sheboygan
119 Taylor
121 Trempealeau
123 Vernon
125 Vilas
127 Walworth
129 Washburn
131 Washington
133 Waukesha
135 Waupaca
137 Waushara
139 Winnebago
141 Woo

56 WYOMING (WY)

001 Albany
003 Big Hortn
005 Campbell
007 Carbon
009 Converse
011 Crook
013 Fremont
015 Goshen
017 Hot Springs
019 Johnson
021 Laramie
023 Lincoln
025 Natrona
027 Niobrara
029 Park
031 Platte
033 Sheridan
035 Sublette
037 Sweetwater
039 Teton
041 Uinta
043 Washakie
045 Weston

Read more about Attachment 102: State FIPS Codes

Attachment 101: Country Codes

Click below to see the country codes for PAPI and CAPI years:

Foreign country codes: 1979-1992 PAPI
Code	Country
101	Afghanistan
102	Albania
103	Algeria
104	Antigua
105	Argentina
106	Armenia
107	Australia
108	Austria
109	Bahamas
110	Bangladesh (East Pakistan)
111	Barbados
112	Belgium
113	Benin
114	Bermuda
115	Bolivia
116	Brazil
117	Bulgaria
118	Burma
119	Cambodia
120	Canada
121	Quebec
122	Ceylon (Sri Lanka)
123	Chile
124	China
125	Colombia
126	Costa Rica
127	Cuba
128	Curacao
129	Cyprus
130	Czechoslovakia
131	Denmark
132	Dominican Republic
133	Ecuador
134	Egypt
135	El Salvador
136	England
138	Ethiopia
139	Finland
140	France
141	French Guiana
142	Gambia
143	Germany
144	Ghana
145	Greece
146	Guadeloupe
147	Guatemala
148	Guinea
149	Guinea-Bisseau
150	Guyana
151	Haiti
152	Honduras
153	Hong Kong
154	Hungary
155	Iceland
156	India
157	Indonesia
158	Iran
159	Iraq
160	Ireland (Eire)
161	Israel
162	Italy
163	Ivory Coast
164	Jamaica
165	Japan
166	Jordan
167	Korea
168	Laos
170	Lebanon
171	Liberia
172	Libya
173	Liechtenstein
175	Luxembourg
176	Malaysia
177	Malta
178	Martinique
179	Mexico
180	Monaco
181	Morocco
182	Netherlands
183	New Guinea (Papua)
184	New Zealand
185	Nicaragua
186	Nigeria
187	Northern Ireland (Ulster)
188	Norway
189	Pakistan
190	Panama
191	Paraguay
192	Philippines
193	Peru
194	Poland
195	Portugal
196	Rhodesia
197	Romania
200	Saudi Arabia
201	Scandinavia
202	Scotland
203	Senegal
204	Sierra Leone
205	Singapore
206	South Africa
207	Spain
208	Surinam
209	Switzerland
210	Syria
211	Taiwan
212	Thailand
213	Togo
214	Trinidad & Tobago
215	Tunisia
216	Turkey
218	Uganda
219	Uruguay
220	U.S.S.R.
221	Venezuela
222	Vietnam
223	Virgin Islands (not U.S.)
224	Wales
225	Yugoslavia
300	United States

Foreign country codes: 1979-1992 PAPI (Other countries not mentioned above)
Code	Country
301	in Asia
302	in Africa (sub-Saharan)
303	in the Caribbean
304	in Europe
305	in the Middle East
306	in the Pacific Islands
400	Other (List)

Foreign country codes: 1993-2022 CAPI
Code	Country
102	Afghanistan
104	Albania
106	Algeria
108	Antigua
110	Argentina
112	Armenia
114	Australia
115	Austria
116	Azerbaijan
130	Bahamas
132	Bangladesh (East Pakistan)
134	Barbados
136	Belgium
138	Byelorussia (Belorusse)
140	Benin
142	Bermuda
144	Bolivia
146	Bosnia-Hercegovina
148	Brazil
150	Bulgaria
152	Myanmar (formerly Burma)
170	Kampuchea (formerly Cambodia)
172	Canada
174	Quebec (Canada)
176	Ceylon (Sri Lanka)
178	Chile
180	China
182	Colombia
184	Costa Rica
186	Croatia
188	Cuba
190	Curacao
192	Cyprus
194	Czech Republic (formerly part of Czechoslovakia)
210	Denmark
212	Dominican Republic
230	Ecuador
232	Egypt
234	El Salvador
236	England
238	Estonia
240	Ethiopia
250	Finland
252	France
254	French Guiana
270	Gambia
272	Germany
274	Georgia
276	Ghana
278	Greece
280	Guadeloupe
282	Guatemala
284	Guinea
286	Guinea-Bisseau
288	Guyana
300	Haiti
302	Honduras
304	Hong Kong
306	Hungary
320	Iceland
322	India
324	Indonesia
326	Iran
328	Iraq
330	Ireland (Eire)
332	Israel
334	Italy
336	Ivory Coast
350	Jamaica
352	Japan
354	Jordan
370	Khazakhstan
372	Korea
374	Kurdizia
390	Laos
392	Latvia
394	Lebanon
396	Liberia
398	Libya
400	Liechtenstein
402	Lithuania
404	Luxembourg
420	Macedonia
422	Malaysia
424	Malta
426	Martinique
428	Mexico
430	Moldavia
432	Monaco
434	Morocco
436	Myanmar (formerly Burma)
450	Netherlands
452	New Guinea (Papua)
454	New Zealand
456	Nicaragua
458	Nigeria
460	Northern Ireland (Ulster)
462	Norway
480	Pakistan
482	Panama
484	Paraguay
486	Philippines
488	Peru
490	Poland
492	Portugal
510	Rhodesia
512	Romania
514	Russia
530	Saudi Arabia
532	Scandinavia
534	Scotland
536	Senegal
538	Sierra Leone
540	Singapore
542	Slovak Republic (formerly part of Czechoslovakia)
544	Slovenia
546	South Africa
548	Spain
550	Surinam
552	Sweden
554	Switzerland
556	Syria
570	Taiwan
572	Tajikhistan
574	Thailand
576	Togo
578	Trinidad & Tobago
580	Tunisia
582	Turkey
584	Turkomenistan
600	Uganda
602	Ukraine
604	United States
606	Uzbekhistan
620	Venezuela
622	Vietnam
624	Virgin Islands (not U.S.)
640	Wales
650	Yugoslavia
660	Other country - Asia
662	Other country - Africa (sub-Saharan)
664	Other country - Caribbean
666	Other country - Europe
668	Other country - Middle East
670	Other country - Pacific Islands
672	Other (SPECIFY)

Read more about Attachment 101: Country Codes

Attachment 100: Geographic Regions

Table: States by region
Census Division	States
Region 1: Northeast	Connecticut Maine Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont
Region 2: North Central	Illinois Indiana Iowa Kansas Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin
Region 3: South	Alabama Arkansas Delaware District of Columbia Florida Georgia Kentucky Louisiana Maryland Mississippi North Carolina Oklahoma South Carolina Tennessee Texas Virginia West Virginia
Region 4: West	Alaska Arizona California Colorado Hawaii Idaho Montana Nevada New Mexico Oregon Utah Washington Wyoming

Read more about Attachment 100: Geographic Regions

Appendix 10: Geocode Documentation

Documentation for the NLSY79 Geocode data
1979-1988 Geocode data creation procedures
1989-1994 Geocode data creation procedure
1996-2002 Geocode data creation procedure
2004-2022 Geocode data creation procedure
Supplementary created variables
Missing data
Use of the file

Documentation for the NLSY79 Geocode data files

Geocode data format

This document provides a discussion on the creation of the variables available on the NLSY79 restricted-use Geocode data. The geocode data are now being released as comma-delimited ASCII data with support files. Because of the volume of variables, the data have been split into five sets of files based on content:

Location data for respondent
Data from survey responses and created variables
Computed distance measures between addresses at each survey point for each respondent
County and SMSA level data for 1979-1992 (County and City Data Book variables)
County and SMSA level data for 1993-2002 (County and City Data Book variables)

The geocode data also includes text/ASCII data files as well as SPSS, SAS and STATA programs to read them. In addition, the geocode data contains documentation files (user's guide and codebook supplement) for the NLSY79. The data files contain variables that are only available on the restricted-use Geocode data release, plus the identification number to allow merging with the main 1979-2022 public data.

Geocode data source files and content

County and City Data Books

For survey years 1979-2002, selected variables from the County and City Data Books from various years are provided along with geographic variables from the NLSY79 main data file. No variables from the County and City Data Books are included for survey years 2004-2022.

The county and state of residence for each NLSY79 respondent for each survey year between 1979 and 2002 were matched with the county and state variables in the specific County and City Data Book data files used for each year. Selected county-level or SMSA-level environmental variables were extracted from those files and included in the geocode data. The County and City Data Book data files were prepared by the U.S. Census Bureau. Related printed matter for each of these data files can be found in the County and City Data Book for the specified year. These books are also published by the Census Bureau.

The following is a brief description of the various NLSY79 geocode data for specific survey years and the County and City Data Book data files that were merged with the different years of NLSY79 data:

The 1979-1982 geocode data include county-level and SMSA-level variables from the County and City Data Book, 1972 data file, which provides data from the 1970 Census of the Population and Housing, the 1972 Economic Census, and the 1969 Census of Agriculture, and other data derived from a variety of federal government and private agencies.
The 1979-1982 geocode data include county-level and SMSA-level variables from the County and City Data Book, 1977 data file, which provides data from the 1970 Census of the Population and Housing, the 1972 Economic Census, and the 1974 Census of Agriculture, and other data derived from a variety of federal government and private agencies.
The 1983-1987 geocode data include county-level variables from the County and City Data Book, 1983 data file, which provides data from the 1980 Census of the Population and Housing, the 1977 Economic Census, the 1978 Census of Agriculture, and other data derived from a variety of federal government and private agencies.
The 1988-1996 geocode data includes county-level variables from the County and City Data Book, 1988 data file which provides data from the 1980 Census of the Population and Housing, the Current Population Surveys, and other data derived from a variety of federal government and private agencies.
The 1998-2002 geocode data includes county-level variables from the County and City Data Book, 1994 data file, which provides data from the 1990 Census of the Population and Housing, the Current Population Surveys, and other data derived from a variety of federal government and private agencies.

Variables from the 1988 County and City Data Book data file were selected with an eye toward comparability with the 1983 County and City Data Book variables. Similar considerations were made between the 1994 and 1988 variables. In the absence of updated information from the 1988 County and City Data Book data file, the 1983 County and City Data Book variables were retained. However, some differences do exist between similar variables selected from the various County and City Data Book data files.

The 1983 County and City Data Book data file variables for MSA/NECMA and CMSA have been combined into one 4-digit variable in the 1988 County and City Data Book data file. Therefore, the 1988 County and City Data Book geographic variables correspond to the 1983 County and City Data Book geographic variables in the following manner:

The MSA/NECMA codes that existed in the 1983 data are identical in the 1988 data.
Six MSAs were added, one MSA was expanded, and one CMSA was expanded in the 1988 data. The MSAs that were added have their own unique 4-digit code.
The 1983 CMSA variable was recorded with a new unique 4-digit code for each CMSA in the 1988 combined variable. The 1983 PMSA variable was retained and is identical to the 1988 PMSA variable. Therefore, each CMSA and PMSA is still identifiable in the same manner they were with the separate 1983 CMSA variable.
Two 1983 CMSAs were redefined as MSAs in the 1988 data. These are Kansas City and St. Louis. They have been recorded with their own unique 4-digit code.
From 1979-1987, respondents living in New England were excluded from merging with the County and City Data Book, since the SMSA/MSA variable on the County and City Data Book, 1983 data file is the New England County Metropolitan Areas (NECMA) code. NECMA residents were not excluded when merging with the 1988 County and City Data Book data file. In the 1988 County and City Data Book data file, the MSA/NECMA and the CMSA variables found in the 1983 County and City Data Book data file were combined into one 4-digit variable. The addition of a "Record Type" variable in the County and City Data Book 1988 data makes it possible to distinguish separately between MSAs, NECMAs, and CMSAs. This "Record Type" variable classifies cases in the 1988 data combined MSA/NECMA/CMSA variable according to whether they provide information for the U.S., States, MSAs, NECMAs, CMSAs, or a Nonmetropolitan County. The use of this variable allows the user to exclude any of these groups from the analysis without having to conduct a county-by-county or state-by-state determination of NECMA/non-NECMA status. Respondents residing in NECMAs are found in the New England states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont.

The population by age variables from the 1988 County and City Data Book data file are estimates made for the National Cancer Institute by the Census Bureau. These figures suppress data for counties in which the population is under 20,000. Users should keep this in mind during analysis.

City Reference Files

Another type of data file, the City Reference File (CRF) for various years, was also merged with the NLSY79 data in order to identify the SMSA/MSA for each respondent according to zip code. The City Reference File data files, prepared by the U.S. Census Bureau, contain the Federal Information Process Standards (FIPS) county and state codes, zip codes, and SMSA/MSA codes.

The following is a list of the various City Reference Files that were merged with the different years of NLSY79 data to identify the SMSA/MSA for each respondent:

The 1979-1982 NLSY79 data was merged with the City Reference File, 1973, which contains the SMSA codes as defined by the Office of Management and Budget (OMB) as of August 15, 1973.
The 1983 NLSY79 data was merged with the City Reference File, 1982, which contains the SMSA codes defined by OMB prior to June 30, 1983.
The 1984-1987 NLSY79 data was merged with the City Reference File, 1983, which contains the MSA codes as defined by OMB as of June 30, 1983.
The 1988-1992 NLSY79 data was merged with the City Reference File, 1987, which contains the MSA codes as defined by OMB as of June 30, 1987.
The 1993-1998 NLSY79 data was merged with the City Reference File, 1993, which contains the MSA codes as defined by OMB as of July 31, 1993.

Local Exchange Routing Guides

Between 1989 and 1994, a third type of data file was used to verify the geocode information provided by NORC for each respondent. The Local Exchange Routing Guide (LERG) data file is constructed by Bell Communications Research (BELLCORE) and contains address information for the "switches" which regulate each telephone area code and exchange.

The LERG data file used for the 1989 NLSY79 geocodes was updated through October 1989.
The LERG file used for the 1990 and 1991 NLSY79 geocodes was updated through January 20, 1992.
The LERG file used for the 1992 NLSY79 geocodes was updated through March 1, 1993.
The LERG file used for the 1993-94 NLSY79 geocodes was updated through August 1, 1994.

Geostatistical mapping software

Beginning in 1996, geostatistical mapping software was employed in the geocoding process to assign latitude and longitude coordinates and other geographical codes. Basic standard geographic information such as latitude and longitude was linked to each respondent's address. This was accomplished by matching address data to information in the software database. Matching records were appended with the matching address, coordinates, Census information, and FIPS (Federal Information Processing Standards) codes for state, county, MCD (Minor Civil Division), and MSA (Metropolitan Statistical Area). The software packages used in specific survey years are listed below.

Matchmaker for Windows, V2.5 was used in survey years 1996 and 1998.
Maptitude V4.2 was used in survey year 2000.
Maptitude V4.6 was used in survey years 2002 and 2004.
ArcGIS (ArcMap) V9.2 was used in survey years 2006-2012.
Maptitude 2022 was used in survey year 2022.

1979-1988 Geocode data creation procedures

The following briefly outlines the procedures used to create the 1979-1988 NLSY79 geocode data.

State, county, and zip codes are reported by each NLSY79 respondent. Missing information was hand-edited whenever possible. A discussion of the hand-editing process follows. The variable GEO10 was created to indicate the type of hand-editing that was done on each case. The creation of GEO10 is described in this document.
The state, county, and zip codes were then matched with the CRF. For those cases where the NLSY79 state, county, and zip codes matched with a state, county, and zip code from the CRF, the SMSA/MSA from the CRF was added to each respondent's record.
Between 1983 and 1987, for cases missing an SMSA/MSA because there was no match with the CRF, a match was made based on the NLSY79 county and state and the CRF county and state so that an SMSA/MSA could be provided.
The NLSY79 data, with SMSA/MSA added when there was a match on all three residence variables, was then merged with the County and City Data Book data files. In the 1987 and 1988 NLSY79 data, if there was not an exact match on the state, county, and zip codes, two additional steps were taken. First, a match was attempted on the state and zip codes. If the state and zip codes matched, then the county and SMSA/MSA codes from the CRF were added to the respondent's record (assigned an edit code of 10 in GEO10). Second, if there was no match on state and zip codes, but there was a match on the zip code only, then the state, county, and SMSA/MSA codes from the CRF were added to the respondent's record (assigned an edit code of 11 in GEO10).

Hand-edits and changes in matching procedures

1979-1982

More than 1,000 hand-edits for each survey year from 1979 through 1982 were performed to constrain respondents' reported state, county, and zip codes so that they conformed to legitimate state-county-zip combinations. In some cases, this involved making estimates for one or more of the above items. The state and county codes from the main NLSY79 data for each year that are included in the NLSY79 geocode data are the original, unedited values for the respondents. The hand-edited versions of the state and county codes were used to match with the CRF and with the City and County Data Book data files for these years.

In compiling the 1981 geocode information, a systematic review of hand-edited state, county, and zip codes was also undertaken. All cases that required a hand-edit in any of the three survey years were included in this inspection. The point of this review was: (1) to check for consistency in hand-editing decision rules over the three years, and (2) where possible, to use the respondent's reported geocodes in subsequent years to check on the accuracy of hand-edits performed in preceding years (this was possible for those cases that required hand-edits in early years and which showed no change of residence over the period).

The results of these consistency checks were very encouraging. Only 13 cases turned up that seemed to be in error. These cases had their geocodes revised accordingly. While doing this review, several dozen other cases with keypunch or coding errors in the hand-edit code variables were also uncovered. These errors were also corrected. In any case, this procedure provides substantial validation to the overall hand-editing process.

1983-1988

In 1983-1987, the majority of hand-edits involved the derivation and addition of a zip code. Changes in the matching strategy were made because the zip code was more accurate than either the county or state geocodes taken individually. Some mismatching, however, did occur because the zip code was in error rather than the county or state code, but this error rate was smaller than another matching algorithm not requiring case by case hand edits. It is probable that some mismatching did occur because the county itself was in error. Nevertheless, we are confident that matching by zip code improved the quality of the match.

Users are cautioned that matching by state and zip code or by zip code only may result in a higher moving rate between 1987 and the previous interview year than might actually have occurred. We suspect that some NLSY79 county and state geocodes were not updated if the respondent reported an address change prior to the 1987 interview or the previous interview. If the geocodes were not updated in a previous interview, then there would have been an under-reporting of moving to a new county and/or state in that interview year that would now show up with the 1987 NLSY79 data because of the improved matching algorithm. In the 1987 NLSY79 geocode data, if the zip code and state did not match but the zip code alone matched, the state and county were added to the record.

Because the 1988 procedure required both the zip code and state to match, some cases in which the zip code alone matched, and which were possibly in error in 1987, may have been hand-edited in 1988. This may affect mobility rates between 1987 and 1988 to the extent that those inaccurate zip codes in 1987 may have been corrected in the 1988 NLSY79 data.

In 1988, more than 1,000 hand-edits were performed. Approximately 56.6% of these involved the derivation and addition of a zip code, while approximately 48.4% involved correction of the state of residence.

We believe that the requiring a zip code and state correspondence further improved the accuracy of the resulting matches. In support of this assumption, the cases that were actually hand-edited, produced only approximately 6% with an invalid county. The possibility of zip codes continuing across adjacent counties suggests that this may even be an overestimate of the actual error occurring.

1981 changes in SMSA designations

For those using these data to track the mobility of respondents over the 1979-81 survey years an additional caution applies. In June of 1981, the OMB announced the designation of 36 new SMSAs, the disqualification of one pre-existing SMSA, and the merger of two pre-existing SMSAs into one new area. The 1973 CRF file was updated by CHRR to reflect these changes, and the updates were applied beginning with the 1980 interview place of residence in the 1980 geocode data file. One consequence of these changes is that when attempting to match places of residence for respondents using data from the 1979 geocode data and separate 1980 updated geocode data, some respondents give the appearance of moving into (or out of) an SMSA between 1979 and 1980 when in fact they may not have moved at all. This faulty inference of mobility would be reached if one compared changes in SMSA designation between the separate 1979 and 1980 updated geocode data.

Users ordering a full complement of geocode data at any given point should not find this discrepancy in mobility. This applies only to those who ordered a 1979 geocode data and then updated that data with single year files in subsequent years. As single year files were no longer available after the 1979-89 release, recent purchasers of the geocode data would have received all available years of the geocode data, and should not detect the discrepancy resulting from the 1981 SMSA changes between the 1979 and 1980 separate data files. A variable representing the 1981 SMSA designation (if applicable) of place of residence at interview is currently present in the geocode data for all survey years, including 1979.

It is possible, however, that the created variable based upon SMSA of residence and found in the main NLSY79 data file named KEYVARS ("Is R's Current Residence in SMSA?"), would give a false impression of mobility in and out of an SMSA for respondents living in the same location for which the SMSA designation was changed between survey years. (See Appendix 6: Urban-Rural and SMSA-Central City Variables in the public-use file Codebook Supplement for further details on the creation of this variable.)

Note that all other SMSA environmental variables for those living in these new areas remain NA, since the County and City Data Book, 1972 and 1977 data files did not contain information for these SMSAs.

Rewrite of 1979-1982 Geocode data

In 1989, work was undertaken to reduce the number of variables provided in the 1979-1982 NLSY79 geocode data so that the number and type of variables included in these data more closely resembled the geographic data available for the 1983 and subsequent survey years. The previous 1979-1982 NLSY79 geocode data file contained 2,245 variables. This number was reduced to 545 variables with county-level and SMSA-level data retained. In addition, four new variables were included in the 1979-1982 NLSY79 geocode data. These variables provide data on the "Continuous Unemployment Rate for the Labor Market of Current Residence" for each survey year. This reduction in the number of variables made it possible to better document the geocode variables and to produce codebooks like the ones produced for the main NLSY79 data.

1989-1994 Geocode data creation procedure

A new procedure was implemented in 1989 as an initial step in verifying the county and state of residence by using address information from the "switch" associated with each area code and exchange. In the hand-editing process for the 1988 geocode data, reported telephone information was found to be very accurate, even in cases for which some or all of the address information was in error. Thus the telephone information presented itself as a reliable, independent source of verification for the address information. The state and county generated from the phone number are compared to the state and county in the NORC address file for each respondent. Cases in which the telephone information would indicate a different state and/or county from that in the address file are identified through this process. This procedure helped identify respondents with incorrect or inconsistent records. Cases that produced such a non-match were checked for accuracy and hand-edited if necessary.

The following briefly outlines the procedures used to create the 1989-1994 geocode data.

For 1989-1991, an initial data set was constructed containing state, county, and zip code information for the "switch" which regulates each area code and exchange (the "PHONE" data set). The procedures for the creation of the 1992-1994 geocode data were streamlined, particularly in terms of the hand-editing required on individual cases. A new locator database was created containing the most recent information on each respondent's residence at the time of the survey. Wherever possible, a code was then assigned for each state, county, and country (if applicable). The information in the new locator database was then compared to locator information from the previous interview year. This included an electronic comparison of the character strings entered for street addresses of respondents. If the state, county, and zipcode information matched that from the previous year, and the address strings matched in whole or in significant part (indicating probable typos or keypunch errors), the same state and county geocodes were assigned to a case as were assigned in the previous year. Cases for which a partial or full mismatch occurred, or for which information was missing from any field, were identified during this process and were hand-edited wherever necessary. These cases were then assigned a state and county code based upon the hand-edited data. The procedure of electronic matching of address strings has considerably reduced the number of cases requiring individual hand-editing. Data from each respondent's locator record was then matched by zip code to the state and county from the CRF data file (the "ZIP CODE" data) and by area code and extension to the state and zip from the Local Exchange Routing Guide (LERG) data file (the "PHONE" data).
A second data set was constructed containing the state, county, zip code, and telephone information reported by the respondent ("ADDRESS" data) and the state and county information from the CRF for the respondent's reported zip code ("ZIP CODE" data).
The state variables from each of these sources were then compared and a "quality of match" variable (GEO10 - see discussion in this document) was computed based upon the extent to which the "PHONE" state, the "ZIP CODE" state and the "ADDRESS" state match. The highest quality match exists if the "PHONE" state, the "ZIP CODE" state and the "ADDRESS" state all match. For 1989-1991, if a non-match occurred between these state variables, then the geocode information was represented by data matching the "PHONE" information. For 1992-1994, cases in which erroneous or missing zipcode and/or phone information could not be assigned and, in turn, which prevented assignment of state and county geocodes from either the "ZIP CODE" or the "PHONE" data files, were hand-edited as necessary. The matching procedure was then repeated.
In 1989, the state and county established through this matching and verification procedure were then compared to the state and county reported by NORC for each respondent. In 1990 and 1991, the comparison was made to the state and county reported in the previous survey year. Cases for which a non-match occurred between states and/or counties were examined individually. These cases were hand-edited if possible.

From this point, the procedures closely follow those applied in constructing the geocode data files in prior survey years, with minor modifications. The CRF matching was based upon state and county only for the purposes of the final matching of information from the County and City Data Book data files. As metropolitan statistical area information is based upon county delineations (except in New England), matching on cleaned state and county data should not affect the assignment of respondent MSAs.

The state and county were then matched with the CRF. For those cases where the NLSY79 state and county (and zip code in 1990/1991) matched with a state and county (and zip code in 1990/1991) from the CRF, the SMSA/MSA from the CRF was added to each respondent's record.
The NLSY79 data, with SMSA/MSA added when there was a match on county and state of residence was then merged with the County and City Data Book data files.

Hand-edits and changes in matching procedures

In creating the 1989-1994 geocode data, the same logical procedures were applied in identifying cases requiring individual examination. However, the automation of the decision rules and procedures to check for and identify such cases resulted in a substantial reduction in the number of cases requiring hand-editing.

1989-1994

The effect of the 1989 phone verification procedures on the ability to detect errors in the NORC geocode data may also affect mobility rates between 1988 and 1989. Due to time and personnel constraints, it was not possible to examine every case that did not initially match on the state, county, and zip codes.

In the 1989 procedure the geocodes established by the phone number were compared to the geocodes received directly from NORC. By using the 1989 CHRR-edited versions of the geocodes for comparison, updates and corrections that were made to the geocodes during the 1989 hand-editing processes were incorporated. This reduced the number of mismatches between the geocode information based upon the current phone number and the respondent-reported geocode information and increased the amount of consistency observed between survey years. The number of cases requiring individual examination was thereby reduced.

From this point, the procedures closely follow those applied in constructing the 1988 geocode data, with minor modifications. For 1989, CRF matching was based upon state and county only for the purposes of the final matching of information from the County and City Data Book data. A match on state, county, and zip was also required to construct a variable reflecting a respondent's SMSA/non-SMSA residence status for inclusion in the NLSY79 main data file. This match, which was included in the geocode procedures prior to 1989, was done separately for the 1989 release when the new set of initial procedures was instituted. To streamline programming tasks, however, the zip information was reinserted in the CRF matching program for 1990. Therefore, the CRF matching for the 1990 geocode data was again based upon state, county, and zip code, as it had been prior to 1989.

In earlier survey years, residence information was usually collected by NORC interviewers only when there was a change in that information from the previous interview. In 1990, however, an effort was made to get current information for all respondents. Many of the cases in this current update information also included counties that have been inconclusive (even in case-by-case hand-editing) in previous years. These are generally cases in which a zip code spans more than one county, and for which valid county data is missing from the respondent's reported residence information. For such cases, the possibility existed in the 1989 (and prior) data that counties assigned based upon such multiple-county zip codes might be in error in a small number of cases. This would result in the assignment of a county adjacent to the county in which the respondent actually lived. To the extent that current update information for the county of residence in 1990 showed the assigned county in 1989 to be in error, mobility determinations may have been affected. In contrast, using the 1989 CHRR-edited versions of the geocodes for comparison with the current geocode information should have improved the accuracy of mobility ratings. This is a more dependable confirmation of past geocode information, eliminating the need to make individual determinations in many cases with multiple-county zip codes as discussed above.

1996-2002 Geocode data creation procedure

The procedures for the creation of the 1996 and subsequent geocode data changed from those used in previous years. Software packages were used to create the data for 1996-2008. The following briefly outlines the process used in these survey years.

Although different software packages were used, the procedures for data creation were essentially the same across these years. Three graduated matching methods were applied, depending on the quality of the address data available.

An automated match was done between the respondent's address data and the software database. Address records with matching street segments were appended with the matching address, coordinates (latitude and longitude values for a specific location), Census information (county, tract, and block group codes for an address), FIPS codes for state, county, MCD, and MSA. In some cases, addresses had to be cleaned before software matching could be done. Cleaning involves steps such as standardizing the address format, correcting obvious misspellings, identifying apartment numbers and locating them in the correct field, etc. It does not include any changes that might result in a change in the actual address location.
For some addresses the procedure outlined in step #1 failed to produce a match between the respondent's address data and the software database. For these cases in 1996-1998, individual respondent addresses were temporarily corrected in order to match them to the software database. By correcting obvious errors and referring to lists of valid address components, a map display, and commercial maps, a temporary working address was constructed; this was used to assign geocodes to these cases. These temporary address corrections were made in a working file to test the improvements in matching to the software database. The original address data remained unchanged. Successful address corrections were matched by this method and geocodes assigned accordingly. In 2000-2008, for cases failing Step #1, geocode staff used software to locate the correct street. If the street number could be located along this street, the latitude and longitude were assigned. However, some streets in software databases do not include information about street numbers. If this is the case, the address is manually located in the center of the street. The street is then classified as either a short street or a long street. Long streets cross Census tract or block group boundaries while short streets do not. As a result, the level of certainty about geographical information is much higher for short streets than for long streets.
Addresses unmatched by either of the first two procedures were assigned latitude and longitude coordinates and related Census data according to a 5-digit ZIP centroid. A centroid is essentially the mid-point of a ZIP Code area. Centroid matches were made only for addresses that could not been matched by any other means. Addresses with ZIP codes that were no longer current were appended with latitude and longitude coordinates only.

The procedures outlined in steps #2 and #3 approximate the hand-editing process described in previous survey years for records with different degrees of matched address data.

2004-2022 Geocode data creation procedure

For survey years 2004-2012, a software called ArcGIS (ArcMap 9.2 in 2006 - 2012) was used in the creation of the geocode data, while Maptitude 2014 was used for survey year 2014. Maptitude software updated annually was used to create geocode data for each survey year, through the current round. The procedures for data creation were essentially the same as those used in 1996-2002.

Three graduated matching methods were applied, depending on the quality of the address data available.

An automated match was done between the respondent's locating address data and the ArcGIS/Maptitude database. In some cases, addresses had to be cleaned before they could be matched by ArcGIS/Maptitude. Cleaning involves steps such as standardizing the address format, correcting obvious misspellings and locating them in the correct field, etc. It does not include any changes that might result in a change in the actual address location.
For some addresses, the procedure outlined in Step #1 failed to produce a match between the respondent's address data and the ArcGIS/Maptitude database. In these cases, geocode staff used ArcGIS/Maptitude to locate the correct street. If the street number could be located along this street, the latitude and longitude were assigned. However, some streets in the ArcGIS/Maptitude database do not include information about street numbers. If this is the case, the address is manually located in the center of the street. The street is then classified as either a short street or a long street. Long streets cross Census tract or block group boundaries while short streets do not. As a result, the level of certainty about geographical information is much higher for short streets than for long streets.
Addresses unmatched by either of the first two procedures were assigned latitude and longitude coordinates according to a 5-digit zip centroid. A centroid is essentially the midpoint of a zip code area. The geographic information is less certain for respondents located using the zip centroid method.

Researchers can identify the method used to locate the respondent's address by using the variable "GEO10" which provides information about the quality of the geographic match. This variable differentiates between addresses located based on the actual address, in the center of a short street, in the center of a long street, or using the zip centroid method. This variable can be used to determine the level of certainty for the respondent's geographic data.

Supplementary created variables

Urban-Rural and SMSA/CBSA-Central City residence variables

The procedures for creating the Urban-Rural and SMSA/CBSA-Central City residence variables (released in the KEYVARS area of interest) were modified for the 2000 public release. In 2000 and later survey years, these variables were created with the same software used to create the other geocode data.

For 2004-present, the Urban-Rural and CBSA-principal city variables. If the respondent's residence was located using a street name match (method 2 above) or a zip centroid match (method 3), the CBSA-Principal City and Urban/Rural variables are further evaluated. For the CBSA-Principal City variable, if the street or zip code falls completely inside or outside the boundaries of the CBSA and the principal city, then the respondent is assigned to the appropriate status. If the street or zip code crosses the boundaries of the CBSA and the central city, then the respondent is coded as CBSA, principal city status unknown. Similarly, respondents are only assigned to an urban or rural status if their entire street or zip code lies within an urban or rural area. If the street or zip code crosses an urban/rural boundary, the respondent is assigned to an unknown status.

For further discussion of these variables, see Appendix 6: Urban-Rural and SMSA/CBSA-Central City Variables in the main file NLSY79 Codebook Supplement.

Migration History variables

In NLS79 survey years 2000-2022, respondents who had moved to a different county or state since the date of last interview were asked to report each address and the dates of each move. The FIPS code for the state and county of each address are included in the 2022 geocode data. The address items collected are found in the geocode files titled "survey_and_created_variables" and in the questionnaire and codebook, with question names beginning with "MIGR_". Similar migration histories were collected in several early survey years of the NLSY79.

Distance measures variables

A series of variables was added with the 2006 geocode release containing the collapsed distance between each pair of residential addresses reported by the respondent for all survey years and indicating whether the respondent changed zipcode between each pair of addresses. These data have been updated for the 2022 release with more information for many address pairs in various survey years added.

Editing/quality of match variables

A variable named "GEO10" provides information about the quality of the respondent's address match and the method used to locate the address. In 1994 and prior years, GEO10 contains information on the degree of match between different address elements. Between 1996 and 2004, GEO10 identifies whether the county was assigned based on the respondent-provided address or the zip centroid method. In 2006-2022, this variable differentiates between addresses located based on the actual address, the center of a short street, in the center of a long street or using a zip centroid method. This variable can be used to determine the level of certainty for the respondent's geographic data.

National Death Index (NDI) data

The round 30 (2022) data release contains information regarding cause, dates and location of death for deceased respondents for whom a matching death certificate was returned from an NDI search. Variables depicting the cause, year and region of death are restricted to the Geocode data release. Variables containing the month and state, territory or country of death are further restricted to the Zipcode data release. For further information on available variables and access requirements for restricted data, see the Health section for information on NDI related variables. In addition, NLSY79 Attachment 8: Health Codes, contains an explanation of the matching process and coding for underlying cause of death.

Missing data

The missing data values for all items on the geocode data files are-3, -4 and -5. The -5 values indicate a noninterview for a given year. -3 codes in the data after 1996 indicate respondents whose latitude and longitude of current residence could not be determined. Respondents who have a -4 value in the data for any variables from the County and City Data Book or other residence indicators fall into the following categories:

Respondents who were in the military or who had an APO address
Respondents who were residing outside of the United States
Respondents whose state or county codes could not be determined
Respondents who reside in a county or SMSA/MSA for which the County and City Data Book is missing data for that geographic location for that specific item
Respondents who do not reside in an SMSA for any survey year 1979-1982 will be missing SMSA level environmental variables for that year
Respondents whose state, county, and zip codes for any survey year 1979-1982 do not lead to an unambiguous SMSA designation. This generally applies only to a small number of respondents living in New England.
Respondents residing in the New England states of Connecticut, Maine Massachusetts, New Hampshire, Rhode Island and Vermont who did not match on county, state and zip code on the 1982 or 1983 CRF are coded -4 on all of the metropolitan statistical area variables with NECMA codes for any survey years 1983-1987 that they resided in those areas.
In the 1988-2000 NLSY79 geocode data, respondents residing in the New England states of Connecticut, Maine Massachusetts, New Hampshire, Rhode Island and Vermont who did not match on county, state and zip code on the CRF are coded -4 on all of the 1983 metropolitan statistical area variables with NECMA codes for the survey years 1988-1998.

Use of the file

Finally, we have a few notes and suggestions concerning the use of these NLSY79 geographic data.

The NLSY79 geocode data should not be used in any fashion that would endanger the confidentiality of any sample member. Only those users who have signed a written licensing agreement consenting to protect respondent confidentiality and to other conditions, who agree not to make, or allow to be made, unauthorized copies of the geocode file, and who also agree to indemnify the Center for Human Resource Research for all claims arising from misuse of the file may use these data.

The data and the accompanying documentation should be used in conjunction with the printed versions of the 1972, 1977, 1983, 1988, and 1994 County and City Data Books that correspond to each variable desired in order to have complete information regarding variable descriptions and coding idiosyncrasies. No variables from the County and City Data Books have been included in the geocode data after 2002. Users wishing to attach specific individual items from that or other sources may do so by using the state, county and/or various MSA variables to merge data.

Edited variables describing the location of each respondent's residence are created as a result of this matching process. The first two variables, question names "GEO1" and "GEO2", provide the FIPS code for the respondent's county and state of residence. Two versions of the county and state of residence variables are included in the geocode data for most survey years from 1979-92. The state and county variables appearing at the beginning of each year's variable listing are the edited versions that incorporate all revisions deemed necessary in the hand-editing process for each year. These edited variables are used in the construction of the final geocode data. The state and county variables appearing near the end of the variable listing for most of those years are the unedited version, as received directly from NORC. It is generally recommended that users employ the edited version as these contain corrected geocodes based upon the most current available information.

Researchers are encouraged to use caution during analyses because several modifications were made since 1987 in the programming procedures that create the geocode data files. Please refer to this document for discussion of specific modifications of note.

In years for which zip code centroids were assigned, users should note that there is some small possibility that a respondent's county may be misassigned using the centroid method in cases where more than one county is represented in a given ZIP code. In these cases, it is possible that a respondent might live in one county but that the center of the ZIP code area is in another county. However, since ZIP codes infrequently cross county lines and less than a quarter of respondents' counties were assigned using the ZIP centroid method, the number of counties incorrectly assigned should be quite small.

Read more about Appendix 10: Geocode Documentation

Appendix 7: Unemployment Rates

The NLSY79 unemployment rate variables are constructed using state and area labor force data from the May publication of Employment and Earnings for the month of March of each survey year. Employment and Earnings is published by the U.S. Department of Labor, Bureau of Labor Statistics and lists the civilian labor force and number of unemployed persons for every state and selected metropolitan areas.

The figures provided for the state and selected metropolitan and micropolitan statistical areas are used to compute the unemployment rate for the portion or balance of the state that is not represented in a metropolitan or micropolitan statistical area. If a respondent resides in a metropolitan or micropolitan area that is listed in Employment and Earnings, then the unemployment rate is the unemployment rate for that metropolitan or micropolitan area. Otherwise, the unemployment rate is the computed balance of state unemployment rate for the state in which the respondent resides.

From 1979-2002, the respondent's metropolitan statistical area was assigned based on the county, state, and zip code of current residence. Beginning in 2004, the metropolitan or micropolitan statistical area is assigned based on the latitude and longitude of current residence. (For a detailed discussion of the hand-editing and merging process with other data files used to create the geographic variables, including the metropolitan statistical area, see Appendix 10: Geocode Documentation)

As with other geographic based variables, respondents who are in the military, who are living outside of the United States, or who have invalid geographic data for a given survey year are valid skips on these variables.

Read more about Appendix 7: Unemployment Rates

NLSY79/97 Cross-Cohort Data

An NLSY79/NLSY97 cross-cohort beta release is now available on Investigator. See Appendix 13: Cross-Cohort NLSY79/97 Overview in the NLSY97 User Guide for information.

Read more about NLSY79/97 Cross-Cohort Data

NLSY79 Appendix 30: Programs for Assorted Created NLSY79 Variables

This appendix contains programs that produce a number of created variables in the current round (R30), survey year 2022.

Click below to view programming code.

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/
CURDATE~M_2022		                 CURMO
CURDATE~Y_2022		                 CURYR
Q1-3_A~M_1979		                 BRTHMO79
Q1-3_A~M _1979		                 BRTHYR79
Q1-3_A~M_1981		                 BRTHMO81
Q1-3_A~Y_1981		                 BRTHYR81

/*--------------------------------------------------------------*/
curmo=CURDATE_M;
curday=CURDATE_D;
curyr=CURDATE_Y;
if brthyr81 >= -4 then do; 
	birthmo=brthmo81;
	birthda=brthda81;
	birthyr=brthyr81+1900;
end;
else do;
	birthmo=brthmo79;
	birthda=brthda79;
	birthyr=brthyr79+1900;
end;
intmo=curmo;
intyr=curyr;
intyr = curyr;
ageatint=-4;
if (intyr > 0 and birthyr > 0 and intmo > 0 and birthmo > 0) then do;
        if (intmo > birthmo) then ageatint = intyr - birthyr;
        else if (intmo <= birthmo) then ageatint = intyr - birthyr - 1;
end;
else ageatint = -5;

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/
 CURDATE~Y_2022                      CURDATEY   
 Q2-1_2022                           Q2_1       
 Q2-2_2022                           Q2_2       
 Q2-3-0A_2022                        NEEDDATE   
 Q2-3-0B~M_2022                      MO_MSINCORR
 Q2-3-0B~Y_2022                      YR_MSINCORR
 Q2-3A_2022                          Q2_3A      
 Q2-4AC_2022                         CURMARNOCHG
 Q2-4A_2022                          CURMARCHG  
 Q2-5A1B.01_2022                     LSTMARREPL1
 Q2-5A1B.02_2022                     LSTMARREPL2
 Q2-5A1B.03_2022                     LSTMARREPL2
 Q2-5A1.01_2022                      CHGL1_SDW  
 Q2-5A1.02_2022                      CHGL2_SDW  
 Q2-5A1.03_2022                      CHGL3_DRW  
 Q2-5A1_A.01_2022                    CHGL1_DRW  
 Q2-5A1_A.02_2022                    CHGL2_DRW  
 Q2-5B1.01~M_2022                    MOCHG1A    
 Q2-5B1.01~Y_2022                    YRCHG1A    
 Q2-5B2.01~M_2022                    MOCHG1B    
 Q2-5B2.01~Y_2022                    YRCHG1B    
 Q2-5B2.02~M_2022                    MOCHG2B    
 Q2-5B2.02~Y_2022                    YRCHG2B    
 Q2-5B2.03~M_2022                    MOCHG3B    
 Q2-5B2.03~Y_2022                    YRCHG3B    
 Q2-5B3.01~M_2022                    MOCHG1C    
 Q2-5B3.01~Y_2022                    YRCHG1C    
 Q2-5B3.02~M_2022                    MOCHG2C    
 Q2-5B3.02~Y_2022                    YRCHG2C    
 Q2-5B3.03~M_2022                    MOCHG3C    
 Q2-5B3.03~Y_2022                    YRCHG3C    
 Q2-5C1.01_2022                      ANYCHGAFT1 
 Q2-5C1.02_2022                      ANYCHGAFT2 
 Q2-5C1.03_2022                      ANYCHGAFT3 
 Q2-5C1BB.01_2022                    MSAFTC1    
 Q2-5C1BB.02_2022                    MSAFTC2    
 Q2-5C1BB.03_2022                    MSAFTC3    
 Q2-5E_2022                          Q2_5E      
 Q2-5F_2022                          Q2_5F      
 SYMBOL_MARCODE_2022                 MARCODE    
 SYMBOL_OLDMARCODE_2022              OLDMARCODE 
 SYMBOL_MARCODEDLI_2022              MARCODEDLI
/*--------------------------------------------------------------*/
intv2022=1;
if CURDATEY=-5 then intv2022=0;
/* 1st marital status change code */
marst_chng1_type = -4;
if (CHGL1_SDW > -4) then marst_chng1_type = CHGL1_SDW;
else if (CHGL1_DRW > -4) then marst_chng1_type = CHGL1_DRW;
else if (MOCHG1A > -4) then marst_chng1_type = 1;
else if (MOCHG1B > -4) then marst_chng1_type = 5;
else if (CHGL1_SDW = -4 & MOCHG1A = -4 & MOCHG1B = -4) then marst_chng1_type = -4;
else marst_chng1_type = -3;
/* 1st marital status change date */
if (MOCHG1A > -4) then do;
marst_chng1_date_m = MOCHG1A;
marst_chng1_date_y = YRCHG1A;
end;
else if (MOCHG1B > -4) then do;
marst_chng1_date_m = MOCHG1B;
marst_chng1_date_y = YRCHG1B;
end;
else if (MOCHG1C > -4) then do;
marst_chng1_date_m = MOCHG1C;
marst_chng1_date_y = YRCHG1C;
end;
else if (MOCHG1A = -4 & MOCHG1B = -4 & MOCHG1C = -4) then do;
marst_chng1_date_m = -4;
marst_chng1_date_y = -4;
end;
/* 2nd-3rd marital status change code */
CHGL3_SDW=-4; MOCHG2A=-4; MOCHG3A=-4; YRCHG2A=-4; YRCHG3A=-4;
array mschgcd (n) marst_chng2_type marst_chng3_type;
array q2_5a1  (n) CHGL2_SDW CHGL3_SDW;
array q2_5a1a (n) CHGL2_DRW CHGL3_DRW;
array q2_5b1m (n) MOCHG2A MOCHG3A;
array q2_5b2m (n) MOCHG2B MOCHG3B;
do n=1 to 2;
if (q2_5a1 > -4) then mschgcd = q2_5a1;
else if (q2_5a1a > -4) then mschgcd = q2_5a1a;
else if (q2_5b1m > -4) then mschgcd = 5;
else if (q2_5b2m > -4) then mschgcd = 5;
else if (q2_5a1 = -4 & q2_5b1m = -4) then mschgcd = -4;
else mschgcd = -3;
end;
/* 2nd-3rd marital status change date */
array mschmo  (n) marst_chng2_date_m marst_chng3_date_m;
array mschyr  (n) marst_chng2_date_y marst_chng3_date_y;
*array q2_5b1m (n) MOCHG2A MOCHG3A;
array q2_5b1y (n) YRCHG2A YRCHG3A;
*array q2_5b2m (n) MOCHG2B MOCHG3B;
array q2_5b2y (n) YRCHG2B YRCHG3B;
array q2_5b3m (n) MOCHG2C MOCHG3C;
array q2_5b3y (n) YRCHG2C YRCHG3C;
do n=1 to 2;
if (q2_5b1m > -4) then do;
        mschmo = q2_5b1m;
        mschyr = q2_5b1y;
        end;
else if (q2_5b2m > -4) then do;
        mschmo = q2_5b2m;
        mschyr = q2_5b2y;
        end;
else if (q2_5b3m > -4) then do;
        mschmo = q2_5b3m;
        mschyr = q2_5b3y;
        end;
else if (q2_5b1m = -4 & q2_5b2m = -4 & q2_5b3m = -4) then do;
        mschmo = -4;
        mschyr = -4;
        end;
end;
array finvars   marst_chng1_type marst_chng1_date_m marst_chng1_date_y
                marst_chng2_type marst_chng2_date_m marst_chng2_date_y
                marst_chng3_type marst_chng3_date_m marst_chng3_date_y;
do i=1 to dim(finvars);
   if intv2022=0 then finvars[i]=-5;
end;
MARRCHG122=marst_chng1_type; 
MARRCHG1MO=marst_chng1_date_m; 
MARRCHG1YR=marst_chng1_date_y; 
MARRCHG222=marst_chng2_type; 
MARRCHG2MO=marst_chng2_date_m; 
MARRCHG2YR=marst_chng2_date_y; 
MARRCHG322=marst_chng3_type; 
MARRCHG3MO=marst_chng3_date_m; 
MARRCHG3YR=marst_chng3_date_y;

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/
Q2-4AC_2022                          CURMARNC  
Q2-5C1BB.01_2022                     MARCHNG1  
Q2-5C1BB.02_2022                     MARCHNG2  
Q2-5C1BB.03_2022                     MARCHNG3
RELSPPTR22                           SPPTRTYPE
/*--------------------------------------------------------------*/
/****************************************************************/
/*Construction for key marital status variables                 */
/****************************************************************/
array changes MARCHNG1 MARCHNG2 MARCHNG3;
finchange=.;
do i=1 to 3;
  if changes[i]>=-2 then finchange=changes[i];
end;
newmarstat=CURMARNC;
if CURMARNC=-4 then newmarstat=finchange;
MARSTAT_KEY=newmarstat;
if newmarstat in (4 5) then MARSTAT_KEY=1;
MARSTAT_COL=3;
if MARSTAT_KEY=0 then MARSTAT_COL=1;
else if MARSTAT_KEY=1 and spptrtype=1 then MARSTAT_COL=2;
else if MARSTAT_KEY=-5 then MARSTAT_COL=-5;

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/
CASEID                               NORCID   
CURDATE~D                            CURDAY   
CURDATE~M                            CURMO    
CURDATE~Y                            CURYR    
SYMBOL_PCY_YEAR                      PCYYRA   
SYMBOL_PCY_YEARNUM                   PCYYRN  

/*--------------------------------------------------------------*/ 
length pcyyra $4.;               
if (norcid               = .) then norcid                = -4;
if (curyr                = .) then curyr                 = -4;
if (curmo                = .) then curmo                 = -4;
if (curday               = .) then curday                = -4;
if (pcyyrn               = .) then pcyyrn                = -4;
past_calendar_year = curyr; 
if (pcyyra = '2021') then past_calendar_year = 2021;
else if (pcyyra = '2022') then past_calendar_year = 2022;

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/                                    
CURDATE~D                            CURDATE_D                        
CURDATE~M                            CURDATE_M                        
CURDATE~Y                            CURDATE_Y                        
CASEID                               norcid                           
PUBLIC_ID                            pubid                            
HHI_FINAL_RELCODE.01                 HHI_FINAL_RELCODE_01                   
HHI_FINAL_RELCODE.02                 HHI_FINAL_RELCODE_02                   
HHI_FINAL_RELCODE.03                 HHI_FINAL_RELCODE_03                   
HHI_FINAL_RELCODE.04                 HHI_FINAL_RELCODE_04                   
HHI_FINAL_RELCODE.05                 HHI_FINAL_RELCODE_05                   
HHI_FINAL_RELCODE.06                 HHI_FINAL_RELCODE_06                   
HHI_FINAL_RELCODE.07                 HHI_FINAL_RELCODE_07                   
HHI_FINAL_RELCODE.08                 HHI_FINAL_RELCODE_08                   
HHI_FINAL_RELCODE.09                 HHI_FINAL_RELCODE_09                   
HHI_FINAL_RELCODE.10                 HHI_FINAL_RELCODE_10                   
HHI_FINAL_STATCODE.01                HHI_FINAL_STATCODE_01                  
HHI_FINAL_STATCODE.02                HHI_FINAL_STATCODE_02                  
HHI_FINAL_STATCODE.03                HHI_FINAL_STATCODE_03                  
HHI_FINAL_STATCODE.04                HHI_FINAL_STATCODE_04                  
HHI_FINAL_STATCODE.05                HHI_FINAL_STATCODE_05                  
HHI_FINAL_STATCODE.06                HHI_FINAL_STATCODE_06                  
HHI_FINAL_STATCODE.07                HHI_FINAL_STATCODE_07                  
HHI_FINAL_STATCODE.08                HHI_FINAL_STATCODE_08                  
HHI_FINAL_STATCODE.09                HHI_FINAL_STATCODE_09                  
HHI_FINAL_STATCODE.10                HHI_FINAL_STATCODE_10 

/*--------------------------------------------------------------*/                 
array varsall  (m) CURDATE_D CURDATE_M CURDATE_Y norcid pubidf HHI_FINAL_RELCODE_01 HHI_FINAL_RELCODE_02 HHI_FINAL_RELCODE_03
                   HHI_FINAL_RELCODE_04 HHI_FINAL_RELCODE_05 HHI_FINAL_RELCODE_06 HHI_FINAL_RELCODE_07 HHI_FINAL_RELCODE_08 HHI_FINAL_RELCODE_09
                   HHI_FINAL_RELCODE_10 HHI_FINAL_STATCODE_01 HHI_FINAL_STATCODE_02 HHI_FINAL_STATCODE_03 HHI_FINAL_STATCODE_04
                   HHI_FINAL_STATCODE_05 HHI_FINAL_STATCODE_06 HHI_FINAL_STATCODE_07 HHI_FINAL_STATCODE_08 HHI_FINAL_STATCODE_09 HHI_FINAL_STATCODE_10;
do over varsall;
	if missing(varsall) then varsall=-4;
end;
* ==============================================================================;
* Compute family sizes;
* ==============================================================================;
/* --- Compute "family size '22" and "spouse '22" --- */
spou22=0;
famsize=1;
nonfamsize=0;
if (hhi_final_relcode_01 = -5) then famsize = -5;
array relation (i) 	hhi_final_relcode_01 - hhi_final_relcode_10;
array statc    (i) 	hhi_final_statcode_01 - hhi_final_statcode_10;
/* Go thru all the relations and
    (1) if there is a relation (blood or by law), increase the family size, foster relations don't count
    (2) check whether one of them is a spouse */
/* does 57 "husband or brother-in-law" and 58 "wife or sister-in-law" count as spouse?  */
/* 75 is husband, 76 is wife */
do i = 1 to 10;
    if (spou22 = 0  & relation = 1 ) then spou22=1;
	  if  ((relation <= 0) | (33 <= relation <= 36) | relation = 45 | relation = 46
          | (50 <= relation <= 54) | (relation > 66))
    then famsize = famsize;
    else famsize = famsize + 1;
end;
do i = 1 to 10;
 if  ((33 <= relation <= 36) | relation = 45 | relation = 46
       | (50 <= relation <= 54) | (relation > 66)) then nonfamsize = (nonfamsize +1);
end;

/************************ Variables Used ************************/
/**Variable Names in data release    Variable Names in program**/
QES-76Q.## 			                 qes_76q_##
QES-76S.## 			                 qes_76s_##
QES-76T.## 			                 qes_76t_##
HRP## 				                 hrp##
HRP#_WHRLY2 		                 hrp_whrly#

/*--------------------------------------------------------------*/
array ophrly   (i)  qes_76q_01-qes_76q_14;
array ohrpot   (i)  qes_76s_01-qes_76s_14;
array ohrp     (i)  qes_76t_01-qes_76t_14;
array hrp      (i)  hrp1-hrp14;
array hrp_rec  (i)  hrp_rec1-hrp_rec14;
array hrp_w    (i)  hrp1_whrly2 hrp2_whrly2 hrp3_whrly2 hrp4_whrly2 hrp5_whrly2 hrp6_whrly2
 hrp7_whrly2 hrp8_whrly2 hrp9_whrly2 hrp10_whrly2 hrp11_whrly2 hrp12_whrly2
 hrp13_whrly2 hrp14_whrly2;
do i=1 to 14;
	if ophrly=1 then do;
		if ohrpot>-4 then hrp_rec=ohrpot;
		else if ohrp>-4 then hrp_rec=ohrp;
	end;
	hrp_w=-4;
	if hrp_rec>0 then hrp_w=hrp_rec;
	else if (hrp_rec=0|hrp_rec=-1| hrp_rec=-2|hrp_rec=-3) and hrp<0 then hrp_w=hrp_rec;
	else if (hrp_rec=0|hrp_rec=-1| hrp_rec=-2|hrp_rec=-3) and hrp>0 then hrp_w=hrp;
	else hrp_w=hrp;
end;
run;

/************************ Variables Used ************************/
/**Variable Names in program(## = 01/10)/Variable Names in data release(## = 01/10)**/
BUSOWN_LINT_22 BUSOWN-LINT (XRND)
BUSOWN_1_22/xr BUSOWN-1 (XRND)
BUSOWN_2_22/xr BUSOWN-2 (XRND)
BUSOWN_5_##_Y_22/xr BUSOWN-5. ##~Y (XRND)
BUSOWN_6_##_M_22/xr BUSOWN-6. ##~M (XRND)
BUSOWN_6_##_Y_22/xr BUSOWN-6. ##~Y (XRND)
BUSOWN_7_##_22/xr BUSOWN-7. ## (XRND)
BUSOWN_9_##_22/xr BUSOWN-9. ## (XRND)
BUSOWN_10_##_0_22/xr BUSOWN-10. ##_000000 (XRND)
BUSOWN_10_##_1_22/xr BUSOWN-10. ##_000001 (XRND)
BUSOWN_10_##_2_22/xr BUSOWN-10. ##_000002 (XRND)
BUSOWN_10_##_3_22/xr BUSOWN-10. ##_000003 (XRND)
BUSOWN_10_##_4_22/xr BUSOWN-10. ##_000004 (XRND)
BUSOWN_10_##_5_22/xr BUSOWN-10. ##_000005 (XRND)
BUSOWN_10_##_6_22/xr BUSOWN-10. ##_000006 (XRND)
BUSOWN_10_##_7_22/xr BUSOWN-10. ##_000007 (XRND)
BUSOWN_11_##_22/xr BUSOWN-11. ## (XRND)
BUSOWN_11_UNF1_##_22/xr BUSOWN-11_UNF1. ## (XRND)
BUSOWN_11_UNF5_##_22/xr BUSOWN-11_UNF5. ## (XRND)
BUSOWN_11_UNF6_##_20/xr BUSOWN-11_UNF6. ## (XRND)
BUSOWN_11_UNF7_##_20/xr BUSOWN-11_UNF7. ## (XRND)
BUSOWN_12_##_22/BUSOWN-12. ## (XRND)
BUSOWN_14_##_22/BUSOWN-14. ## (XRND)
BUSOWN_15_##_22/BUSOWN-15. ## (XRND)
BUSOWN_16_##_22/BUSOWN-16. ## (XRND)
BUSOWN_16_TRUNC_##_22/BUSOWN-16_TRUNC. ## (XRND)
BUSOWN_17_##_22/BUSOWN-17. ## (XRND)
BUSOWN_18_##_Y_22/BUSOWN-18. ##~Y (XRND)
BUSOWN_19_##_22/BUSOWN-19. ## (XRND)
BUSOWN_20_##_22/BUSOWN-20. ## (XRND)
BUSOWN_21_##_22/BUSOWN-21. ## (XRND)
BUSOWN_22_##_22/BUSOWN-22. ## (XRND)
BUSOWN_23A_22/BUSOWN-23A (XRND)
BUSOWN_23B_22/BUSOWN-23B (XRND)
BUSOWN_24A_22/BUSOWN-24A (XRND)
BUSOWN_24B_22/BUSOWN-24B (XRND)
BUSOWN_25_22/BUSOWN-25 (XRND)
BUSOWN_27_22/BUSOWN-27 (XRND)
BUSOWN_28A_22/BUSOWN-28A (XRND)
BUSOWN_28B_22/BUSOWN-28B (XRND)
BUSOWN_29_22/BUSOWN-29 (XRND)
 
    BUSOWN_LINT_22       = BUSOWN_LINT;        
    BUSOWN_1_22          = BUSOWN_1;           
    BUSOWN_2_22          = BUSOWN_2;           
    BUSOWN_5_01_Y_22     = BUSOWN_5_01_Y;      
    BUSOWN_5_02_Y_22     = BUSOWN_5_02_Y;  
    BUSOWN_5_03_Y_22     = BUSOWN_5_03_Y;      
    BUSOWN_5_04_Y_22     = BUSOWN_5_04_Y;  
    BUSOWN_5_05_Y_22     = BUSOWN_5_05_Y;      
    BUSOWN_5_06_Y_22     = BUSOWN_5_06_Y;  
    BUSOWN_5_07_Y_22     = BUSOWN_5_07_Y;      
    BUSOWN_5_08_Y_22     = BUSOWN_5_08_Y;  
    BUSOWN_5_09_Y_22     = BUSOWN_5_09_Y;      
    BUSOWN_5_10_Y_22     = BUSOWN_5_10_Y;
 
    BUSOWN_6_01_M_22     = BUSOWN_6_01_M;      
    BUSOWN_6_01_Y_22     = BUSOWN_6_01_Y;      
    BUSOWN_6_02_M_22     = BUSOWN_6_02_M;      
    BUSOWN_6_02_Y_22     = BUSOWN_6_02_Y;   
    BUSOWN_6_03_M_22     = BUSOWN_6_03_M;      
    BUSOWN_6_03_Y_22     = BUSOWN_6_03_Y;   
    BUSOWN_6_04_M_22     = BUSOWN_6_04_M;      
    BUSOWN_6_04_Y_22     = BUSOWN_6_04_Y;   
    BUSOWN_6_05_M_22     = BUSOWN_6_05_M;      
    BUSOWN_6_05_Y_22     = BUSOWN_6_05_Y;   
    BUSOWN_6_06_M_22     = BUSOWN_6_06_M;      
    BUSOWN_6_06_Y_22     = BUSOWN_6_06_Y;   
    BUSOWN_6_07_M_22     = BUSOWN_6_07_M;      
    BUSOWN_6_07_Y_22     = BUSOWN_6_07_Y;   
    BUSOWN_6_08_M_22     = BUSOWN_6_08_M;      
    BUSOWN_6_08_Y_22     = BUSOWN_6_08_Y;   
    BUSOWN_6_09_M_22     = BUSOWN_6_09_M;      
    BUSOWN_6_09_Y_22     = BUSOWN_6_09_Y;   
    BUSOWN_6_10_M_22     = BUSOWN_6_10_M;      
    BUSOWN_6_10_Y_22     = BUSOWN_6_10_Y;   
 
    BUSOWN_7_01_22       = BUSOWN_7_01;        
    BUSOWN_7_02_22       = BUSOWN_7_02;  
    BUSOWN_7_03_22       = BUSOWN_7_03;        
    BUSOWN_7_04_22       = BUSOWN_7_04;  
    BUSOWN_7_05_22       = BUSOWN_7_05;        
    BUSOWN_7_06_22       = BUSOWN_7_06;  
    BUSOWN_7_07_22       = BUSOWN_7_07;        
    BUSOWN_7_08_22       = BUSOWN_7_08;  
    BUSOWN_7_09_22       = BUSOWN_7_09;        
    BUSOWN_7_10_22       = BUSOWN_7_10;  
    BUSOWN_9_01_22       = BUSOWN_9_01;        
    BUSOWN_9_02_22       = BUSOWN_9_02;   
    BUSOWN_9_03_22       = BUSOWN_9_03;        
    BUSOWN_9_04_22       = BUSOWN_9_04;   
    BUSOWN_9_05_22       = BUSOWN_9_05;        
    BUSOWN_9_06_22       = BUSOWN_9_06;   
    BUSOWN_9_07_22       = BUSOWN_9_07;        
    BUSOWN_9_08_22       = BUSOWN_9_08;   
    BUSOWN_9_09_22       = BUSOWN_9_09;        
    BUSOWN_9_10_22       = BUSOWN_9_10;   
 
    BUSOWN_10_01_0_22    = BUSOWN_10_01_000000;     
    BUSOWN_10_01_1_22    = BUSOWN_10_01_000001;     
    BUSOWN_10_01_2_22    = BUSOWN_10_01_000002;     
    BUSOWN_10_01_3_22    = BUSOWN_10_01_000003;     
    BUSOWN_10_01_4_22    = BUSOWN_10_01_000004;     
    BUSOWN_10_01_5_22    = BUSOWN_10_01_000005;     
    BUSOWN_10_01_6_22    = BUSOWN_10_01_000006;     
    BUSOWN_10_01_7_22    = BUSOWN_10_01_000007;     
    BUSOWN_10_02_0_22    = BUSOWN_10_02_000000;     
    BUSOWN_10_02_1_22    = BUSOWN_10_02_000001;     
    BUSOWN_10_02_2_22    = BUSOWN_10_02_000002;     
    BUSOWN_10_02_3_22    = BUSOWN_10_02_000003;     
    BUSOWN_10_02_4_22    = BUSOWN_10_02_000004;     
    BUSOWN_10_02_5_22    = BUSOWN_10_02_000005;     
    BUSOWN_10_02_6_22    = BUSOWN_10_02_000006;     
    BUSOWN_10_02_7_22    = BUSOWN_10_02_000007; 
    BUSOWN_10_03_0_22    = BUSOWN_10_03_000000;     
    BUSOWN_10_03_1_22    = BUSOWN_10_03_000001;     
    BUSOWN_10_03_2_22    = BUSOWN_10_03_000002;     
    BUSOWN_10_03_3_22    = BUSOWN_10_03_000003;     
    BUSOWN_10_03_4_22    = BUSOWN_10_03_000004;     
    BUSOWN_10_03_5_22    = BUSOWN_10_03_000005;     
    BUSOWN_10_03_6_22    = BUSOWN_10_03_000006;     
    BUSOWN_10_03_7_22    = BUSOWN_10_03_000007;     
    BUSOWN_10_04_0_22    = BUSOWN_10_04_000000;     
    BUSOWN_10_04_1_22    = BUSOWN_10_04_000001;     
    BUSOWN_10_04_2_22    = BUSOWN_10_04_000002;     
    BUSOWN_10_04_3_22    = BUSOWN_10_04_000003;     
    BUSOWN_10_04_4_22    = BUSOWN_10_04_000004;     
    BUSOWN_10_04_5_22    = BUSOWN_10_04_000005;     
    BUSOWN_10_04_6_22    = BUSOWN_10_04_000006;     
    BUSOWN_10_04_7_22    = BUSOWN_10_04_000007;     
    BUSOWN_10_05_0_22    = BUSOWN_10_05_000000;     
    BUSOWN_10_05_1_22    = BUSOWN_10_05_000001;     
    BUSOWN_10_05_2_22    = BUSOWN_10_05_000002;     
    BUSOWN_10_05_3_22    = BUSOWN_10_05_000003;     
    BUSOWN_10_05_4_22    = BUSOWN_10_05_000004;     
    BUSOWN_10_05_5_22    = BUSOWN_10_05_000005;     
    BUSOWN_10_05_6_22    = BUSOWN_10_05_000006;     
    BUSOWN_10_05_7_22    = BUSOWN_10_05_000007;     
    BUSOWN_10_06_0_22    = BUSOWN_10_06_000000;     
    BUSOWN_10_06_1_22    = BUSOWN_10_06_000001;     
    BUSOWN_10_06_2_22    = BUSOWN_10_06_000002;     
    BUSOWN_10_06_3_22    = BUSOWN_10_06_000003;     
    BUSOWN_10_06_4_22    = BUSOWN_10_06_000004;     
    BUSOWN_10_06_5_22    = BUSOWN_10_06_000005;     
    BUSOWN_10_06_6_22    = BUSOWN_10_06_000006;     
    BUSOWN_10_06_7_22    = BUSOWN_10_06_000007;     
    BUSOWN_10_07_0_22    = BUSOWN_10_07_000000;     
    BUSOWN_10_07_1_22    = BUSOWN_10_07_000001;     
    BUSOWN_10_07_2_22    = BUSOWN_10_07_000002;     
    BUSOWN_10_07_3_22    = BUSOWN_10_07_000003;     
    BUSOWN_10_07_4_22    = BUSOWN_10_07_000004;     
    BUSOWN_10_07_5_22    = BUSOWN_10_07_000005;     
    BUSOWN_10_07_6_22    = BUSOWN_10_07_000006;     
    BUSOWN_10_07_7_22    = BUSOWN_10_07_000007;     
    BUSOWN_10_08_0_22    = BUSOWN_10_08_000000;     
    BUSOWN_10_08_1_22    = BUSOWN_10_08_000001;     
    BUSOWN_10_08_2_22    = BUSOWN_10_08_000002;     
    BUSOWN_10_08_3_22    = BUSOWN_10_08_000003;     
    BUSOWN_10_08_4_22    = BUSOWN_10_08_000004;     
    BUSOWN_10_08_5_22    = BUSOWN_10_08_000005;     
    BUSOWN_10_08_6_22    = BUSOWN_10_08_000006;     
    BUSOWN_10_08_7_22    = BUSOWN_10_08_000007;     
    BUSOWN_10_09_0_22    = BUSOWN_10_09_000000;     
    BUSOWN_10_09_1_22    = BUSOWN_10_09_000001;     
    BUSOWN_10_09_2_22    = BUSOWN_10_09_000002;     
    BUSOWN_10_09_3_22    = BUSOWN_10_09_000003;     
    BUSOWN_10_09_4_22    = BUSOWN_10_09_000004;     
    BUSOWN_10_09_5_22    = BUSOWN_10_09_000005;     
    BUSOWN_10_09_6_22    = BUSOWN_10_09_000006;     
    BUSOWN_10_09_7_22    = BUSOWN_10_09_000007;     
    BUSOWN_10_10_0_22    = BUSOWN_10_10_000000;     
    BUSOWN_10_10_1_22    = BUSOWN_10_10_000001;     
    BUSOWN_10_10_2_22    = BUSOWN_10_10_000002;     
    BUSOWN_10_10_3_22    = BUSOWN_10_10_000003;     
    BUSOWN_10_10_4_22    = BUSOWN_10_10_000004;     
    BUSOWN_10_10_5_22    = BUSOWN_10_10_000005;     
    BUSOWN_10_10_6_22    = BUSOWN_10_10_000006;     
    BUSOWN_10_10_7_22    = BUSOWN_10_10_000007;
     
    BUSOWN_11_01_22      = BUSOWN_11_01;       
    BUSOWN_11_02_22      = BUSOWN_11_02;       
    BUSOWN_11_03_22      = BUSOWN_11_03;       
    BUSOWN_11_04_22      = BUSOWN_11_04;       
    BUSOWN_11_05_22      = BUSOWN_11_05;       
    BUSOWN_11_06_22      = BUSOWN_11_06;       
    BUSOWN_11_07_22      = BUSOWN_11_07;       
    BUSOWN_11_08_22      = BUSOWN_11_08;       
    BUSOWN_11_09_22      = BUSOWN_11_09;       
    BUSOWN_11_10_22      = BUSOWN_11_10;       
    BUSOWN_11_UNF1_01_22 = BUSOWN_11_UNF1_01;  
    BUSOWN_11_UNF1_02_22 = BUSOWN_11_UNF1_02;  
    BUSOWN_11_UNF1_03_22 = BUSOWN_11_UNF1_03;  
    BUSOWN_11_UNF1_04_22 = BUSOWN_11_UNF1_04;  
    BUSOWN_11_UNF1_05_22 = BUSOWN_11_UNF1_05;  
    BUSOWN_11_UNF1_06_22 = BUSOWN_11_UNF1_06;  
    BUSOWN_11_UNF1_07_22 = BUSOWN_11_UNF1_07;  
    BUSOWN_11_UNF1_08_22 = BUSOWN_11_UNF1_08;  
    BUSOWN_11_UNF1_09_22 = BUSOWN_11_UNF1_09;  
    BUSOWN_11_UNF1_10_22 = BUSOWN_11_UNF1_10;
  
    BUSOWN_12_01_22      = BUSOWN_12_01;       
    BUSOWN_12_02_22      = BUSOWN_12_02; 
    BUSOWN_12_03_22      = BUSOWN_12_03;       
    BUSOWN_12_04_22      = BUSOWN_12_04; 
    BUSOWN_12_05_22      = BUSOWN_12_05;       
    BUSOWN_12_06_22      = BUSOWN_12_06; 
    BUSOWN_12_07_22      = BUSOWN_12_07;       
    BUSOWN_12_08_22      = BUSOWN_12_08; 
    BUSOWN_12_09_22      = BUSOWN_12_09;       
    BUSOWN_12_10_22      = BUSOWN_12_10;
 
    BUSOWN_14_01_22      = BUSOWN_14_01;       
    BUSOWN_14_02_22      = BUSOWN_14_02;    
    BUSOWN_14_03_22      = BUSOWN_14_03;       
    BUSOWN_14_04_22      = BUSOWN_14_04;    
    BUSOWN_14_05_22      = BUSOWN_14_05;       
    BUSOWN_14_06_22      = BUSOWN_14_06;    
    BUSOWN_14_07_22      = BUSOWN_14_07;       
    BUSOWN_14_08_22      = BUSOWN_14_08;    
    BUSOWN_14_09_22      = BUSOWN_14_09;       
    BUSOWN_14_10_22      = BUSOWN_14_10;
    
    BUSOWN_15_01_22      = BUSOWN_15_01;       
    BUSOWN_15_02_22      = BUSOWN_15_02;   
    BUSOWN_15_03_22      = BUSOWN_15_03;       
    BUSOWN_15_04_22      = BUSOWN_15_04;   
    BUSOWN_15_05_22      = BUSOWN_15_05;       
    BUSOWN_15_06_22      = BUSOWN_15_06;   
    BUSOWN_15_07_22      = BUSOWN_15_07;       
    BUSOWN_15_08_22      = BUSOWN_15_08;   
    BUSOWN_15_09_22      = BUSOWN_15_09;       
    BUSOWN_15_10_22      = BUSOWN_15_10;
   
    BUSOWN_16_01_22      = BUSOWN_16_01; 
    BUSOWN_16_02_22      = BUSOWN_16_02; 
    BUSOWN_16_03_22      = BUSOWN_16_03; 
    BUSOWN_16_04_22      = BUSOWN_16_04; 
    BUSOWN_16_05_22      = BUSOWN_16_05; 
    BUSOWN_16_06_22      = BUSOWN_16_06; 
    BUSOWN_16_07_22      = BUSOWN_16_07; 
    BUSOWN_16_08_22      = BUSOWN_16_08; 
    BUSOWN_16_09_22      = BUSOWN_16_09; 
    BUSOWN_16_10_22      = BUSOWN_16_10;
 
    BUSOWN_17_01_22      = BUSOWN_17_01;       
    BUSOWN_17_02_22      = BUSOWN_17_02; 
    BUSOWN_17_03_22      = BUSOWN_17_03;       
    BUSOWN_17_04_22      = BUSOWN_17_04; 
    BUSOWN_17_05_22      = BUSOWN_17_05;       
    BUSOWN_17_06_22      = BUSOWN_17_06; 
    BUSOWN_17_07_22      = BUSOWN_17_07;       
    BUSOWN_17_08_22      = BUSOWN_17_08; 
    BUSOWN_17_09_22      = BUSOWN_17_09;       
    BUSOWN_17_10_22      = BUSOWN_17_10;
 
    BUSOWN_18_01_Y_22    = BUSOWN_18_01_Y;     
    BUSOWN_18_02_Y_22    = BUSOWN_18_02_Y;     
    BUSOWN_18_03_Y_22    = BUSOWN_18_03_Y;     
    BUSOWN_18_04_Y_22    = BUSOWN_18_04_Y;     
    BUSOWN_18_05_Y_22    = BUSOWN_18_05_Y;     
    BUSOWN_18_06_Y_22    = BUSOWN_18_06_Y;     
    BUSOWN_18_07_Y_22    = BUSOWN_18_07_Y;     
    BUSOWN_18_08_Y_22    = BUSOWN_18_08_Y;     
    BUSOWN_18_09_Y_22    = BUSOWN_18_09_Y;     
    BUSOWN_18_10_Y_22    = BUSOWN_18_10_Y;
     
    BUSOWN_19_01_22      = BUSOWN_19_01;       
    BUSOWN_19_02_22      = BUSOWN_19_02;       
    BUSOWN_19_03_22      = BUSOWN_19_03;       
    BUSOWN_19_04_22      = BUSOWN_19_04;       
    BUSOWN_19_05_22      = BUSOWN_19_05;       
    BUSOWN_19_06_22      = BUSOWN_19_06;       
    BUSOWN_19_07_22      = BUSOWN_19_07;       
    BUSOWN_19_08_22      = BUSOWN_19_08;       
    BUSOWN_19_09_22      = BUSOWN_19_09;       
    BUSOWN_19_10_22      = BUSOWN_19_10;
       
    BUSOWN_20_01_22      = BUSOWN_20_01; 
    BUSOWN_20_02_22      = BUSOWN_20_02; 
    BUSOWN_20_03_22      = BUSOWN_20_03; 
    BUSOWN_20_04_22      = BUSOWN_20_04; 
    BUSOWN_20_05_22      = BUSOWN_20_05; 
    BUSOWN_20_06_22      = BUSOWN_20_06; 
    BUSOWN_20_07_22      = BUSOWN_20_07; 
    BUSOWN_20_08_22      = BUSOWN_20_08; 
    BUSOWN_20_09_22      = BUSOWN_20_09; 
    BUSOWN_20_10_22      = BUSOWN_20_10;
 
    BUSOWN_21_01_22      = BUSOWN_21_01; 
    BUSOWN_21_02_22      = BUSOWN_21_02; 
 
    BUSOWN_22_01_22      = BUSOWN_22_01;       
    BUSOWN_22_02_22      = BUSOWN_22_02;       
    BUSOWN_23A_22        = BUSOWN_23A;         
    BUSOWN_23B_22        = BUSOWN_23B;         
    BUSOWN_24A_22        = BUSOWN_24A;         
    BUSOWN_24B_22        = BUSOWN_24B;  
    BUSOWN_25_22         = BUSOWN_25;          
    BUSOWN_29_22         = BUSOWN_29;          
if busown_1_22>-4 then do;
	busown_1_xr=busown_1_22;
	busown_sourceyr=2022;
end;
if busown_2_22>-4 then busown_2_xr=busown_2_22;
array	bus5_22	    (i)  busown_5_01_y_22 busown_5_02_y_22 busown_5_03_y_22 busown_5_04_y_22 busown_5_05_y_22   
	    				 busown_5_06_y_22 busown_5_07_y_22 busown_5_08_y_22 busown_5_09_y_22 busown_5_10_y_22;
array	bus5_xr	    (i)  busown_5_01_y_xr busown_5_02_y_xr busown_5_03_y_xr busown_5_04_y_xr busown_5_05_y_xr   
	    				 busown_5_06_y_xr busown_5_07_y_xr busown_5_08_y_xr busown_5_09_y_xr busown_5_10_y_xr;
array	bus6m_22	(i)  busown_6_01_m_22 busown_6_02_m_22 busown_6_03_m_22 busown_6_04_m_22 busown_6_05_m_22   
	    				 busown_6_06_m_22 busown_6_07_m_22 busown_6_08_m_22 busown_6_09_m_22 busown_6_10_m_22;
array	bus6m_xr	(i)  busown_6_01_m_xr busown_6_02_m_xr busown_6_03_m_xr busown_6_04_m_xr busown_6_05_m_xr   
	    				 busown_6_06_m_xr busown_6_07_m_xr busown_6_08_m_xr busown_6_09_m_xr busown_6_10_m_xr;
array	bus6y_22	(i)  busown_6_01_y_22 busown_6_02_y_22 busown_6_03_y_22 busown_6_04_y_22 busown_6_05_y_22   
	    				 busown_6_06_y_22 busown_6_07_y_22 busown_6_08_y_22 busown_6_09_y_22 busown_6_10_y_22;
array	bus6y_xr	(i)  busown_6_01_y_xr busown_6_02_y_xr busown_6_03_y_xr busown_6_04_y_xr busown_6_05_y_xr   
	    				 busown_6_06_y_xr busown_6_07_y_xr busown_6_08_y_xr busown_6_09_y_xr busown_6_10_y_xr;
array	bus7_22	    (i)  busown_7_01_22 busown_7_02_22 busown_7_03_22 busown_7_04_22 busown_7_05_22   
	    				 busown_7_06_22 busown_7_07_22 busown_7_08_22 busown_7_09_22 busown_7_10_22;
array	bus7_xr	    (i)  busown_7_01_xr busown_7_02_xr busown_7_03_xr busown_7_04_xr busown_7_05_xr   
	    				 busown_7_06_xr busown_7_07_xr busown_7_08_xr busown_7_09_xr busown_7_10_xr;
array	bus9_22		(i)  busown_9_01_22 busown_9_02_22 busown_9_03_22 busown_9_04_22 busown_9_05_22   
	    				 busown_9_06_22 busown_9_07_22 busown_9_08_22 busown_9_09_22 busown_9_10_22;
array	bus9_xr		(i)  busown_9_01_xr busown_9_02_xr busown_9_03_xr busown_9_04_xr busown_9_05_xr   
	    				 busown_9_06_xr busown_9_07_xr busown_9_08_xr busown_9_09_xr busown_9_10_xr;
array	bus100_22	(i)  busown_10_01_0_22 busown_10_02_0_22 busown_10_03_0_22 busown_10_04_0_22 busown_10_05_0_22   
	    				 busown_10_06_0_22 busown_10_07_0_22 busown_10_08_0_22 busown_10_09_0_22 busown_10_10_0_22;
array	bus100_xr	(i)  busown_10_01_0_xr busown_10_02_0_xr busown_10_03_0_xr busown_10_04_0_xr busown_10_05_0_xr   
	    				 busown_10_06_0_xr busown_10_07_0_xr busown_10_08_0_xr busown_10_09_0_xr busown_10_10_0_xr;
array	bus101_22	(i)  busown_10_01_1_22 busown_10_02_1_22 busown_10_03_1_22 busown_10_04_1_22 busown_10_05_1_22   
	    				 busown_10_06_1_22 busown_10_07_1_22 busown_10_08_1_22 busown_10_09_1_22 busown_10_10_1_22;
array	bus101_xr	(i)  busown_10_01_1_xr busown_10_02_1_xr busown_10_03_1_xr busown_10_04_1_xr busown_10_05_1_xr   
	    				 busown_10_06_1_xr busown_10_07_1_xr busown_10_08_1_xr busown_10_09_1_xr busown_10_10_1_xr;
array	bus102_22	(i)  busown_10_01_2_22 busown_10_02_2_22 busown_10_03_2_22 busown_10_04_2_22 busown_10_05_2_22   
	    				 busown_10_06_2_22 busown_10_07_2_22 busown_10_08_2_22 busown_10_09_2_22 busown_10_10_2_22;
array	bus102_xr	(i)  busown_10_01_2_xr busown_10_02_2_xr busown_10_03_2_xr busown_10_04_2_xr busown_10_05_2_xr   
	    				 busown_10_06_2_xr busown_10_07_2_xr busown_10_08_2_xr busown_10_09_2_xr busown_10_10_2_xr;
array	bus103_22	(i)  busown_10_01_3_22 busown_10_02_3_22 busown_10_03_3_22 busown_10_04_3_22 busown_10_05_3_22   
	    				 busown_10_06_3_22 busown_10_07_3_22 busown_10_08_3_22 busown_10_09_3_22 busown_10_10_3_22;
array	bus103_xr	(i)  busown_10_01_3_xr busown_10_02_3_xr busown_10_03_3_xr busown_10_04_3_xr busown_10_05_3_xr   
	    				 busown_10_06_3_xr busown_10_07_3_xr busown_10_08_3_xr busown_10_09_3_xr busown_10_10_3_xr;
array	bus104_22	(i)  busown_10_01_4_22 busown_10_02_4_22 busown_10_03_4_22 busown_10_04_4_22 busown_10_05_4_22   
	    				 busown_10_06_4_22 busown_10_07_4_22 busown_10_08_4_22 busown_10_09_4_22 busown_10_10_4_22;
array	bus104_xr	(i)  busown_10_01_4_xr busown_10_02_4_xr busown_10_03_4_xr busown_10_04_4_xr busown_10_05_4_xr   
	    				 busown_10_06_4_xr busown_10_07_4_xr busown_10_08_4_xr busown_10_09_4_xr busown_10_10_4_xr;
array	bus105_22	(i)  busown_10_01_5_22 busown_10_02_5_22 busown_10_03_5_22 busown_10_04_5_22 busown_10_05_5_22   
	    				 busown_10_06_5_22 busown_10_07_5_22 busown_10_08_5_22 busown_10_09_5_22 busown_10_10_5_22;
array	bus105_xr	(i)  busown_10_01_5_xr busown_10_02_5_xr busown_10_03_5_xr busown_10_04_5_xr busown_10_05_5_xr   
	    				 busown_10_06_5_xr busown_10_07_5_xr busown_10_08_5_xr busown_10_09_5_xr busown_10_10_5_xr;
array	bus106_22	(i)  busown_10_01_6_22 busown_10_02_6_22 busown_10_03_6_22 busown_10_04_6_22 busown_10_05_6_22   
	    				 busown_10_06_6_22 busown_10_07_6_22 busown_10_08_6_22 busown_10_09_6_22 busown_10_10_6_22;
array	bus106_xr	(i)  busown_10_01_6_xr busown_10_02_6_xr busown_10_03_6_xr busown_10_04_6_xr busown_10_05_6_xr   
	    				 busown_10_06_6_xr busown_10_07_6_xr busown_10_08_6_xr busown_10_09_6_xr busown_10_10_6_xr;
array	bus107_22	(i)  busown_10_01_7_22 busown_10_02_7_22 busown_10_03_7_22 busown_10_04_7_22 busown_10_05_7_22   
	    				 busown_10_06_7_22 busown_10_07_7_22 busown_10_08_7_22 busown_10_09_7_22 busown_10_10_7_22;
array	bus107_xr	(i)  busown_10_01_7_xr busown_10_02_7_xr busown_10_03_7_xr busown_10_04_7_xr busown_10_05_7_xr   
	    				 busown_10_06_7_xr busown_10_07_7_xr busown_10_08_7_xr busown_10_09_7_xr busown_10_10_7_xr;
array	bus11_22	(i)  busown_11_01_22 busown_11_02_22 busown_11_03_22 busown_11_04_22 busown_11_05_22   
	    				 busown_11_06_22 busown_11_07_22 busown_11_08_22 busown_11_09_22 busown_11_10_22;
array	bus11_xr	(i)  busown_11_01_xr busown_11_02_xr busown_11_03_xr busown_11_04_xr busown_11_05_xr   
	    				 busown_11_06_xr busown_11_07_xr busown_11_08_xr busown_11_09_xr busown_11_10_xr;
array	bus11u1_22	(i)  busown_11_unf1_01_22 busown_11_unf1_02_22 busown_11_unf1_03_22 busown_11_unf1_04_22 busown_11_unf1_05_22   
	    				 busown_11_unf1_06_22 busown_11_unf1_07_22 busown_11_unf1_08_22 busown_11_unf1_09_22 busown_11_unf1_10_22;
array	bus11u1_xr	(i)  busown_11_unf1_01_xr busown_11_unf1_02_xr busown_11_unf1_03_xr busown_11_unf1_04_xr busown_11_unf1_05_xr   
	    				 busown_11_unf1_06_xr busown_11_unf1_07_xr busown_11_unf1_08_xr busown_11_unf1_09_xr busown_11_unf1_10_xr;
array	bus12_22	(i)  busown_12_01_22 busown_12_02_22 busown_12_03_22 busown_12_04_22 busown_12_05_22   
	    				 busown_12_06_22 busown_12_07_22 busown_12_08_22 busown_12_09_22 busown_12_10_22;
array	bus12_xr	(i)  busown_12_01_xr busown_12_02_xr busown_12_03_xr busown_12_04_xr busown_12_05_xr   
	    				 busown_12_06_xr busown_12_07_xr busown_12_08_xr busown_12_09_xr busown_12_10_xr;
array	bus14_22	(i)  busown_14_01_22 busown_14_02_22 busown_14_03_22 busown_14_04_22 busown_14_05_22   
	    				 busown_14_06_22 busown_14_07_22 busown_14_08_22 busown_14_09_22 busown_14_10_22;
array	bus14_xr	(i)  busown_14_01_xr busown_14_02_xr busown_14_03_xr busown_14_04_xr busown_14_05_xr   
	    				 busown_14_06_xr busown_14_07_xr busown_14_08_xr busown_14_09_xr busown_14_10_xr;
array	bus15_22	(i)  busown_15_01_22 busown_15_02_22 busown_15_03_22 busown_15_04_22 busown_15_05_22   
	    				 busown_15_06_22 busown_15_07_22 busown_15_08_22 busown_15_09_22 busown_15_10_22;
array	bus15_xr	(i)  busown_15_01_xr busown_15_02_xr busown_15_03_xr busown_15_04_xr busown_15_05_xr   
	    				 busown_15_06_xr busown_15_07_xr busown_15_08_xr busown_15_09_xr busown_15_10_xr;
array	bus16_22	(i)  busown_16_01_22 busown_16_02_22 busown_16_03_22 busown_16_04_22 busown_16_05_22   
	    				 busown_16_06_22 busown_16_07_22 busown_16_08_22 busown_16_09_22 busown_16_10_22;
array	bus16_xr	(i)  busown_16_01_xr busown_16_02_xr busown_16_03_xr busown_16_04_xr busown_16_05_xr   
	    				 busown_16_06_xr busown_16_07_xr busown_16_08_xr busown_16_09_xr busown_16_10_xr;
array	bus17_22	(i)  busown_17_01_22 busown_17_02_22 busown_17_03_22 busown_17_04_22 busown_17_05_22   
	    				 busown_17_06_22 busown_17_07_22 busown_17_08_22 busown_17_09_22 busown_17_10_22;
array	bus17_xr	(i)  busown_17_01_xr busown_17_02_xr busown_17_03_xr busown_17_04_xr busown_17_05_xr   
	    				 busown_17_06_xr busown_17_07_xr busown_17_08_xr busown_17_09_xr busown_17_10_xr;
array	bus18_22	(i)  busown_18_01_y_22 busown_18_02_y_22 busown_18_03_y_22 busown_18_04_y_22 busown_18_05_y_22   
	    				 busown_18_06_y_22 busown_18_07_y_22 busown_18_08_y_22 busown_18_09_y_22 busown_18_10_y_22;
array	bus18_xr	(i)  busown_18_01_y_xr busown_18_02_y_xr busown_18_03_y_xr busown_18_04_y_xr busown_18_05_y_xr   
	    				 busown_18_06_y_xr busown_18_07_y_xr busown_18_08_y_xr busown_18_09_y_xr busown_18_10_y_xr;
array	bus19_22	(i)  busown_19_01_22 busown_19_02_22 busown_19_03_22 busown_19_04_22 busown_19_05_22   
	    				 busown_19_06_22 busown_19_07_22 busown_19_08_22 busown_19_09_22 busown_19_10_22;
array	bus19_xr	(i)  busown_19_01_xr busown_19_02_xr busown_19_03_xr busown_19_04_xr busown_19_05_xr   
	    				 busown_19_06_xr busown_19_07_xr busown_19_08_xr busown_19_09_xr busown_19_10_xr;
array	bus20_22	(i)  busown_20_01_22 busown_20_02_22 busown_20_03_22 busown_20_04_22 busown_20_05_22   
	    				 busown_20_06_22 busown_20_07_22 busown_20_08_22 busown_20_09_22 busown_20_10_22;
array	bus20_xr	(i)  busown_20_01_xr busown_20_02_xr busown_20_03_xr busown_20_04_xr busown_20_05_xr   
	    				 busown_20_06_xr busown_20_07_xr busown_20_08_xr busown_20_09_xr busown_20_10_xr;
array	bus21_22	(i)  busown_21_01_22 busown_21_02_22 busown_21_03_22 busown_21_04_22 busown_21_05_22   
	    				 busown_21_06_22 busown_21_07_22 busown_21_08_22 busown_21_09_22 busown_21_10_22;
array	bus21_xr	(i)  busown_21_01_xr busown_21_02_xr busown_21_03_xr busown_21_04_xr busown_21_05_xr   
	    				 busown_21_06_xr busown_21_07_xr busown_21_08_xr busown_21_09_xr busown_21_10_xr;
array	bus22_22	(i)  busown_22_01_22 busown_22_02_22 busown_22_03_22 busown_22_04_22 busown_22_05_22   
	    				 busown_22_06_22 busown_22_07_22 busown_22_08_22 busown_22_09_22 busown_22_10_22;
array	bus22_xr	(i)  busown_22_01_xr busown_22_02_xr busown_22_03_xr busown_22_04_xr busown_22_05_xr   
	    				 busown_22_06_xr busown_22_07_xr busown_22_08_xr busown_22_09_xr busown_22_10_xr;
do i=1 to 10;
	if bus5_22>-4 then bus5_xr=bus5_22;
	if bus6m_22>-4 then bus6m_xr=bus6m_22;
	if bus6y_22>-4 then bus6y_xr=bus6y_22;
	if bus7_22>-4 then bus7_xr=bus7_22;
	if bus9_22>-4 then bus9_xr=bus9_22;
	if bus101_22>-4 then bus101_xr=bus101_22;
	if bus102_22>-4 then bus102_xr=bus102_22;
	if bus103_22>-4 then bus103_xr=bus103_22;
	if bus104_22>-4 then bus104_xr=bus104_22;
	if bus105_22>-4 then bus105_xr=bus105_22;
	if bus106_22>-4 then bus106_xr=bus106_22;
	if bus107_22>-4 then bus107_xr=bus107_22;
	if bus100_22>-4 then bus100_xr=bus107_22;
	if bus11_22>-4 then bus11_xr=bus11_22;
	if bus11u1_22>-4 then bus11u1_xr=bus11u1_22;
	if bus12_22>-4 then bus12_xr=bus12_22;
	if bus14_22>-4 then bus14_xr=bus14_22;
	if bus15_22>-4 then bus15_xr=bus15_22;
	if bus16_22>-4 then bus16_xr=bus16_22;
	if bus17_22>-4 then bus17_xr=bus17_22;
	if bus18_22>-4 then bus18_xr=bus18_22;
	if bus19_22>-4 then bus19_xr=bus19_22;
	if bus20_22>-4 then bus20_xr=bus20_22;
	if bus21_22>-4 then bus21_xr=bus21_22;
	if bus22_22>-4 then bus22_xr=bus22_22;
end;
if busown_23a_22>-4 then busown_23a_xr=busown_23a_22;
if busown_23b_22>-4 then busown_23b_xr=busown_23b_22;
if busown_24a_22>-4 then busown_24a_xr=busown_24a_22;
if busown_24b_22>-4 then busown_24b_xr=busown_24b_22;
if busown_25_22>-4  then busown_25_xr=busown_25_22;
if busown_29_22>-4  then busown_29_xr=busown_29_22;
array pastuid22	(2) 		pastjobuid22_1_1 pastjobuid22_2_1;
array qual22    (2) 		quality22_1-quality22_2;
array pastuid	(2) 		pastjobuid_1_1 pastjobuid_2_1;
array qual      (2) 		quality_1-quality_2;
array yr        (2) 		matchyear_1-matchyear_2;
do i=1 to 2;
	if qual22(i)~=. then do;
		qual(i)=qual22(i);
		pastuid(i)=pastuid22(i);
		yr(i)=2022;
	end;
end;
keep norcid busown_sourceyr 
   busown_1_xr busown_2_xr
   busown_5_01_y_xr busown_5_02_y_xr busown_5_03_y_xr busown_5_04_y_xr busown_5_05_y_xr   
   busown_5_06_y_xr busown_5_07_y_xr busown_5_08_y_xr busown_5_09_y_xr busown_5_10_y_xr              
   busown_6_01_m_xr busown_6_02_m_xr busown_6_03_m_xr busown_6_04_m_xr busown_6_05_m_xr   
   busown_6_06_m_xr busown_6_07_m_xr busown_6_08_m_xr busown_6_09_m_xr busown_6_10_m_xr              
   busown_6_01_y_xr busown_6_02_y_xr busown_6_03_y_xr busown_6_04_y_xr busown_6_05_y_xr   
   busown_6_06_y_xr busown_6_07_y_xr busown_6_08_y_xr busown_6_09_y_xr busown_6_10_y_xr              
   busown_7_01_xr busown_7_02_xr busown_7_03_xr busown_7_04_xr busown_7_05_xr   
   busown_7_06_xr busown_7_07_xr busown_7_08_xr busown_7_09_xr busown_7_10_xr                    
   busown_9_01_xr busown_9_02_xr busown_9_03_xr busown_9_04_xr busown_9_05_xr   
   busown_9_06_xr busown_9_07_xr busown_9_08_xr busown_9_09_xr busown_9_10_xr                    
   busown_10_01_0_xr busown_10_02_0_xr busown_10_03_0_xr busown_10_04_0_xr busown_10_05_0_xr   
   busown_10_06_0_xr busown_10_07_0_xr busown_10_08_0_xr busown_10_09_0_xr busown_10_10_0_xr           
   busown_10_01_1_xr busown_10_02_1_xr busown_10_03_1_xr busown_10_04_1_xr busown_10_05_1_xr   
   busown_10_06_1_xr busown_10_07_1_xr busown_10_08_1_xr busown_10_09_1_xr busown_10_10_1_xr          
   busown_10_01_2_xr busown_10_02_2_xr busown_10_03_2_xr busown_10_04_2_xr busown_10_05_2_xr   
   busown_10_06_2_xr busown_10_07_2_xr busown_10_08_2_xr busown_10_09_2_xr busown_10_10_2_xr        
   busown_10_01_3_xr busown_10_02_3_xr busown_10_03_3_xr busown_10_04_3_xr busown_10_05_3_xr   
   busown_10_06_3_xr busown_10_07_3_xr busown_10_08_3_xr busown_10_09_3_xr busown_10_10_3_xr           
   busown_10_01_4_xr busown_10_02_4_xr busown_10_03_4_xr busown_10_04_4_xr busown_10_05_4_xr   
   busown_10_06_4_xr busown_10_07_4_xr busown_10_08_4_xr busown_10_09_4_xr busown_10_10_4_xr           
   busown_10_01_5_xr busown_10_02_5_xr busown_10_03_5_xr busown_10_04_5_xr busown_10_05_5_xr   
   busown_10_06_5_xr busown_10_07_5_xr busown_10_08_5_xr busown_10_09_5_xr busown_10_10_5_xr           
   busown_10_01_6_xr busown_10_02_6_xr busown_10_03_6_xr busown_10_04_6_xr busown_10_05_6_xr   
   busown_10_06_6_xr busown_10_07_6_xr busown_10_08_6_xr busown_10_09_6_xr busown_10_10_6_xr           
   busown_10_01_7_xr busown_10_02_7_xr busown_10_03_7_xr busown_10_04_7_xr busown_10_05_7_xr   
   busown_10_06_7_xr busown_10_07_7_xr busown_10_08_7_xr busown_10_09_7_xr busown_10_10_7_xr           
   busown_11_01_xr busown_11_02_xr busown_11_03_xr busown_11_04_xr busown_11_05_xr   
   busown_11_06_xr busown_11_07_xr busown_11_08_xr busown_11_09_xr busown_11_10_xr   
   busown_11_unf1_01_xr busown_11_unf1_02_xr busown_11_unf1_03_xr busown_11_unf1_04_xr busown_11_unf1_05_xr   
   busown_11_unf1_06_xr busown_11_unf1_07_xr busown_11_unf1_08_xr busown_11_unf1_09_xr busown_11_unf1_10_xr  
   busown_12_01_xr busown_12_02_xr busown_12_03_xr busown_12_04_xr busown_12_05_xr   
   busown_12_06_xr busown_12_07_xr busown_12_08_xr busown_12_09_xr busown_12_10_xr              
   busown_14_01_xr busown_14_02_xr busown_14_03_xr busown_14_04_xr busown_14_05_xr   
   busown_14_06_xr busown_14_07_xr busown_14_08_xr busown_14_09_xr busown_14_10_xr                 
   busown_15_01_xr busown_15_02_xr busown_15_03_xr busown_15_04_xr busown_15_05_xr   
   busown_15_06_xr busown_15_07_xr busown_15_08_xr busown_15_09_xr busown_15_10_xr              
   busown_16_01_xr busown_16_02_xr busown_16_03_xr busown_16_04_xr busown_16_05_xr   
   busown_16_06_xr busown_16_07_xr busown_16_08_xr busown_16_09_xr busown_16_10_xr                
   busown_17_01_xr busown_17_02_xr busown_17_03_xr busown_17_04_xr busown_17_05_xr   
   busown_17_06_xr busown_17_07_xr busown_17_08_xr busown_17_09_xr busown_17_10_xr                 
   busown_18_01_y_xr busown_18_02_y_xr busown_18_03_y_xr busown_18_04_y_xr busown_18_05_y_xr   
   busown_18_06_y_xr busown_18_07_y_xr busown_18_08_y_xr busown_18_09_y_xr busown_18_10_y_xr          
   busown_19_01_xr busown_19_02_xr busown_19_03_xr busown_19_04_xr busown_19_05_xr   
   busown_19_06_xr busown_19_07_xr busown_19_08_xr busown_19_09_xr busown_19_10_xr                  
   busown_20_01_xr busown_20_02_xr busown_20_03_xr busown_20_04_xr busown_20_05_xr   
   busown_20_06_xr busown_20_07_xr busown_20_08_xr busown_20_09_xr busown_20_10_xr                  
   busown_21_01_xr busown_21_02_xr                
   busown_22_01_xr busown_22_02_xr  
   busown_23a_xr busown_23b_xr
   busown_24a_xr busown_24b_xr
   busown_25_xr 
   busown_29_xr
   pastjobuid_1_1 pastjobuid_2_1
   quality_1-quality_2;
run;

Read more about NLSY79 Appendix 30: Programs for Assorted Created NLSY79 Variables

NLSY79 Appendix 29: Date of Interview Current Status Variables

The NLSY79 public release data contains a set of variables for survey years 1980-2022 with information on the respondent's labor force status at each date of interview. These variables are:

Question Name	Variable Title
DOI_EMPLOYED	DATE OF INTERVIEW STATUS – EMPLOYED
DOI_HOURS_WORKED	DATE OF INTERVIEW STATUS – HOURS WORKED
DOI_RETIRED	DATE OF INTERVIEW STATUS – RETIRED
DOI_DISABLED	DATE OF INTERVIEW STATUS – DISABLED

Below is a description of these variables and the process by which they were constructed.

Variable description

In survey years 1979-1998 and 2006, the NLSY79 included a heavily used variable called Employment Status Recode (ESR) for each year. These variables represented the respondent's employment status during the survey week and were based on a very specific set of questions that mirrored the actual monthly Current Population Survey. Because ESR can only be computed from those specific questions, it cannot be computed in a strictly equivalent way in survey years in which the CPS section was not fielded as part of the NLSY79.

Many inquiries have been received concerning the missing ESR variables in non-CPS survey years. The Date of Interview Current Status (DOI) variables are intended to help provide a similar concise picture of a respondent's current status on the date of interview. The DOI variables provide a snapshot, capturing whether on the interview date the respondent:

was employed (DOI_EMPLOYED)
worked more or less than 30 hours per week (DOI_HOURS_WORKED)
considered herself/himself retired or reported current retirement status during the course of the survey, regardless of employment status (DOI_RETIRED)
considered herself/himself disabled, or reported current disability status in the course of the survey, regardless of employment status (DOI_DISABLED)

Users should note that the working/retired/disabled statuses are not necessarily mutually exclusive within single survey years, or continuous through multiple survey years. For example, a respondent may report that s/he is retired and disabled, but also working in some capacity at a single interview point. Likewise, a respondent who reports herself/himself disabled at one point in time may not be disabled (or provides no evidence of disability) at a later interview date. Similarly, someone who reports retirement at an earlier survey date may be working and not be identified as retired at a later survey date. For instance, respondents who have reported retirement from the military or a civil service/law enforcement position at a relatively early age may go on to a second full-time career and be identified only as employed in later years. An examination of current status variables in multiple years is necessary to determine if a respondent ever identified as retired/disabled, even if their status variables in the most current survey years do not reflect these reports.

Users should note that because the Date of Interview Current Status variables are based on weekly arrays, users may encounter a subset of cases that are assigned an "employed" status in these variables but report that they are "not currently working" in the actual survey.

Variable creation

DOI_EMPLOYED – DATE OF INTERVIEW STATUS – EMPLOYED

These variables are based on codes in the Work History WEEKLY LABOR STATUS array. If a job number or a code for active military enlistment is present in the array for the week of interview, the respondent is assigned a current status of "1 - working." If the array for that week contains a code for unemployed or out of the labor force, the respondent is assigned a current status of "0 - not working."

DOI_HOURS_WORKED – DATE OF INTERVIEW STATUS – HOURS WORKED

The hours worked are determined from the Work History HOURS WORKED array. Those with 0 hours worked are assigned a -4 code. Respondents who worked between 1-29 hours during the interview week are assigned a code of "0 – less than 30 hours." Respondents who worked 30 or more hours that week are assigned a code of "1 – 30 hours or more."

DOI_RETIRED – DATE OF INTERVIEW STATUS - RETIRED

Current retirement status at the date of each interview was established by using available questions that allow respondents to specifically report retirement in a response category. There are only one or two such question opportunities in the NLSY79 in any given year, and they are not necessarily asked of the entire sample. For later survey years (1994-2022) comments entered during the interview were available for staff. Comments were examined to identify respondents who reported current retired status outside the structure of the proscribed interview questions. Questions used to determine retired status, with examples from 1990, 1998, 2012 and 2018, are:

1990 (QES-23A.##)
1998 (QES-23A.##, ESR_KEY)
2012 (QES-23A.##, RETIRE_EXP_P2_1A)
2018 (QES-23A.##, Q7-19.##, Q7-38)

DOI_DISABLED – DATE OF INTERVIEW STATUS - DISABLED

Similar to retirement status, current disability status at the date of each interview was established by using available questions that allow respondents to specifically report disability/inability to work in a response category. NLSY79 questionnaires offer various opportunities in any given year to report disability, although all are not necessarily asked of the entire sample. Questions used to determine disabled status, with examples from 1990, 2012 and 2018, are:

1990 (QES-23A.##, ESR_KEY, QES-43.##.##, Q7-19.##, Q11-3)
2012 (RETIRE_EXP_P2_1A, QES-23A.##, QES-43.##.##, Q7-19.##, Q11-3)
2018 (QES-23A.##, QES-43.##.##, QES-33.##.##, Q7-19.##, Q7-38, Q11-3)

As mentioned for the retirement variables above, comments entered during the interview were available to staff for survey years 1994-2022. Comments for these later years were examined to identify respondents who reported current disability status outside the structure of the proscribed interview questions as listed above.

Click below to view programming code.

Compute 2022 (Round 30) DOI_EMPLOYED
Compute 2022 (Round 30) DOI_HOURS_WORKED
Compute 2022 (Round 30) DOI_RETIRED & DOI_DISABLED

Compute 2022 (Round 30) DOI_EMPLOYED

/************************ Variables Used ************************/
/**Variable Names in program    Variable Names in data release**/
CURWK22			                CURRINT_WK#_2022
STATUS_WK#### (2334-2384)	    STATUS_WK_NUM#### (2334-2384)
/*--------------------------------------------------------------*/
array status   (i)  	_1-_2333 status_wk2334-status_wk2384;
do i=2334 to 2384; 
	if i=CURWK22 then do;
		empstat_wbi=status;
	end;
end;
if empstat_wbi>0 & empstat_wbi<=5 then doi_employed=0;
else if empstat_wbi=7 then doi_employed=1;
else if empstat_wbi>=3000 then doi_employed=1;
else if empstat_wbi=0 then doi_employed=-3;
if CURWK22=-4 then doi_employed=-5;

Compute 2022 (Round 30) DOI_HOURS_WORKED

/************************ Variables Used ************************/
/**Variable Names in program    Variable Names in data release**/
CURWK20                         CURRINT_WK#_2020
HRS_WK#### (2230-2296)          HRS_WORKED_WK_NUM#### (2230-2296)
/*--------------------------------------------------------------*/ 
array hrs   (i)  _1-_2229 hrs_wk2230-hrs_wk2295;
 
do i=2230 to 2295; 
   if i=CURWK20 then do;
hrs_doi=hrs;
   end;
end;
 
if doi_employed=0 then doi_hours_worked=-4;
else if doi_employed=1 then do;
   if hrs_doi<0 & hrs_doi>-4 then doi_hours_worked=-3;
   else if hrs_doi>=0 & hrs_doi<30 then doi_hours_worked=0;
   else if hrs_doi>=30 then doi_hours_worked=1;
end;
else if doi_employed=-3 then doi_hours_worked=-3;
else if doi_employed=-5 then doi_hours_worked=-5;

Compute 2022 (Round 30) DOI_RETIRED & DOI_DISABLED

/***************************** Variables Used *****************************/
/**Variable Names in program              Variable Names in data release**/
CURDATE_D                                 CURDATE~D
CURDATE_M                                 CURDATE~M
CURDATE_Y                                 CURDATE~Y
QES_B                                     QES-B
QES_23A_## (1-5)                          QES-23A.## (1-5)
QES_23A_2_## (1-5)                        QES-23A_2.## (1-5)
QES_31_##_##_D (job 1-5, gap 1-3)         QES-31.##.##~D (job 1-5, gap 1-3)
QES_31_##_##_M (job 1-5, gap 1-3)         QES-31.##.##~M (job 1-5, gap 1-3)
QES_31_##_##_Y (job 1-5, gap 1-3)         QES-31.##.##~Y (job 1-5, gap 1-3)
QES_33_##_## (job 1-5, gap 1-3)           QES-33.##.## (job 1-5, gap 1-3)
QES_43_##_## (job 1-3, gap 1-3)           QES-43.##.## (job 1-3, gap 1-3)
Q7_11_##_D (1-4)                          Q7-11.##~D (1-4)
Q7_11_##_M (1-4)                          Q7-11.##~M (1-4)
Q7_11_##_Y (1-4)                          Q7-11.##~Y (1-4)
Q7_19_## (1-4)                            Q7-19.## (1-4)
Q7_38                                     Q7-38
Q11_3                                     Q11-3
Q11_4                                     Q11-4
Q11_5                                     Q11-5
Q11_5A                                    Q11-5A
Q11_7                                     Q11-7
Q11_8_M                                   Q11-8~M
Q11_8Y_                                   Q11-8~Y
Q15_9A_1                                  Q15-9A~000001
Q15_9A_2                                  Q15-9A~000002
Q15_9A_3                                  Q15-9A~000003
Q15_9A_4                                  Q15-9A~000004
Q15_9A_5                                  Q15-9A~000005
Q15_9A_6                                  Q15-9A~000006
Q15_9A_7                                  Q15-9A~000007
RNI                                       RNI
/*--------------------------------------------------------------*/
WORK DOI_EMPLOYED
HOURS30 DOI_HOURS_WORKED
EMP_STOP_##_D (1-13) EMPLOYER_STOPDATE.##~D (1-13)
EMP_STOP_##_M (1-13) EMPLOYER_STOPDATE.##~M (1-13)
EMP_STOP_##_Y (1-13) EMPLOYER_STOPDATE.##~Y (1-13)
 
data three;
 set one;
 
 *why leaving a job;
array stopd (*) EMP_STOP_01_D EMP_STOP_02_D EMP_STOP_03_D EMP_STOP_04_D EMP_STOP_05_D;
array stopm (*) EMP_STOP_01_M EMP_STOP_02_M EMP_STOP_03_M EMP_STOP_04_M EMP_STOP_05_M;
array stopy (*) EMP_STOP_01_Y EMP_STOP_02_Y EMP_STOP_03_Y EMP_STOP_04_Y EMP_STOP_05_Y;
array qes23a (*)  qes_23a_01 qes_23a_02 qes_23a_03 qes_23a_04 qes_23a_05;
 *within job gap;
array qes31d (*)  QES_31_01_01_D QES_31_01_02_D QES_31_01_03_D
QES_31_02_01_D QES_31_02_02_D QES_31_02_03_D
QES_31_03_01_D QES_31_03_02_D QES_31_03_03_D
QES_31_04_01_D QES_31_04_02_D QES_31_04_03_D
QES_31_05_01_D ES_31_05_02_D dum;
array qes31m (*) QES_31_01_01_M QES_31_01_02_M QES_31_01_03_M
QES_31_02_01_M QES_31_02_02_M QES_31_02_03_M
QES_31_03_01_M QES_31_03_02_M QES_31_03_03_M
QES_31_04_01_M QES_31_04_02_M QES_31_04_03_M
QES_31_05_01_M QES_31_05_02_M dum;
array qes31y (*) QES_31_01_01_Y QES_31_01_02_Y QES_31_01_03_Y
QES_31_02_01_Y QES_31_02_02_Y QES_31_02_03_Y
QES_31_03_01_Y QES_31_03_02_Y QES_31_03_03_Y
QES_31_04_01_Y QES_31_04_02_Y QES_31_04_03_Y
QES_31_05_01_Y QES_31_05_02_Y dum;
array qes33 (*)  QES_33_01_01 QES_33_01_02 QES_33_01_03
QES_33_02_01 QES_33_02_02 QES_33_02_03
QES_33_03_01 QES_33_03_02 QES_33_03_03
QES_33_04_01 QES_33_04_02 QES_33_04_03
QES_33_05_01 QES_33_05_02 dum;
array qes43     (*)  qes_43_01_01 qes_43_01_02 qes_43_01_03
                     qes_43_02_01 qes_43_02_02 qes_43_03_03
                     qes_43_03_01 qes_43_03_02 qes_43_03_03
                     dum dum dum
dum dum dum;
*between job gaps;
array q711d (*) Q7_11_01_D Q7_11_02_D Q7_11_03_D Q7_11_04_D;
array q711m (*) Q7_11_01_M Q7_11_02_M Q7_11_03_M Q7_11_04_M;
array q711y (*) Q7_11_01_Y Q7_11_02_Y Q7_11_03_Y Q7_11_04_Y;
array q719  (*)  q7_19_01 q7_19_02 q7_19_03 q7_19_04;
 
***** retire or not at interview date;
if qes_23a_01=-5 then qes23aR=-5;
else do;
 qes23aR = 0;
 q719R=0; q719R3=0; 
 do i = 1 to dim(qes23a);
  if (qes23a(i) = 17) then qes23aR = (qes23aR + 1);
 end;
 do i = 1 to dim(q719);
  if q719(i) = 13 & (qes_b in (0 -4) or (q711d(i)=curdate_d & q711m(i)=curdate_m & q711y(i)=curdate_y)) then q719R = (q719R + 1);
 end;
 if q719R=0 then do i = 1 to dim(q719);
  if q719(i) = 13 & (q711d(i)  in (-1 -2 -3) or q711m(i) in (-1 -2 -3) or q711y(i) in (-1 -2 -3)) then q719R3 = (q719R3 + 1);
 end; 
end;
 
if qes_23a_01=-5 then retire=-5;
else do;
 retire=0;
 if (q7_38 = 5) then do; retire = 1; flag_r0=1; end;
 else if qes23aR > 0 then do; retire = 1; flag_r1=1; end;
 else if q719R>0 then do;   retire = 1; flag_r2=1; end; 
 else if q719R3>0 then do;  retire = 1; flag_r23=1; end;
end; 
 
***** disabled or not at interview date;
if qes_23a_01=-5 then do;
 qes23aD=-5;
 qes43D=-5;
 q719D=-5;
 q113D=-5;
end;
else do;
 qes23aD=0; qes23aD3=0;
 qes43D=0; qes43D3=0;
 q719D=0; q719D3=0;
 q113D=0;
 
 do i = 1 to dim(qes23a);
  if work=0 & qes23a(i) = 10 & ( (0<stopy(i) &  stopy(i)<curdate_y) or (stopy(i)=curdate_y & 0<stopm(i) & stopm(i)<curdate_m)
     or (stopy(i)=curdate_y & stopm(i)=curdate_m & 0<stopd(i) & stopd(i)<curdate_d))  then qes23aD = qes23aD + 1;
 end;
 if qes23aD=0 then do i = 1 to dim(qes23a);
  if work=0 & qes23a(i) = 10 & (stopy(i) in (-1 -2 -3) or stopm(i) in (-1 -2 -3) or stopd(i) in (-1 -2 -3)) then qes23aD3= qes23aD3+1;
 end;
 
 do i = 1 to dim(qes43);
  if (qes43(i) = 2 or qes33(i)=9) & (qes31d(i)=curdate_d & qes31m(i)=curdate_m & qes31y(i)=curdate_y) then qes43D = (qes43D + 1);
 end;
 if qes43D=0 then do i = 1 to dim(qes43);
  if (qes43(i) = 2 or qes33(i)=9) & (qes31d(i) in (-1 -2 -3) or qes31m(i) in (-1 -2 -3) or qes31y(i) in (-1 -2 -3)) & work=0 then qes43D3=qes43D3 + 1;
 end;
 
 do i = 1 to dim(q719);
  if q719(i) = 9 & (qes_b in (0 -4) or (q711d(i)=curdate_d & q711m(i)=curdate_m & q711y(i)=curdate_y)) then q719D = (q719D + 1);
 end;
 if q719D=0 then do i = 1 to dim(q719);
  if q719(i) = 9 & (q711d(i)  in (-1 -2 -3) or q711m(i) in (-1 -2 -3) or q711y(i) in (-1 -2 -3)) then q719D3 = (q719D3 + 1);
 end; 
 
 if q11_3=1 then q113D=1;
end;
 
if qes_23a_01=-5 then disable=-5;
else do;
 disable=0;
 if (q7_38 = 4) then do; disable = 1; flag_d0=1; end;
 else if qes23aD>0 then do; disable=1; flag_d1=1; end;
 else if qes43D>0 then do;  disable=1; flag_d2=1; end;
 else if q719D>0 then do;   disable=1; flag_d3=1; end;
 else if q113D>0 then do;   disable=1; flag_d4=1; end;
 else if qes23aD3>0 then do; disable=1; flag_d13=1; end;
 else if qes43D3>0 then do;  disable=1; flag_d23=1; end;
 else if q719D3>0 then do;   disable=1; flag_d33=1; end;
end;

Read more about NLSY79 Appendix 29: Date of Interview Current Status Variables

NLSY79 Appendix 28: NLSY79 Employer History Roster

Introduction
Roster construction
Employer order
Roster content
Roster structure
Linking to non-roster employer variables
User notes

The Employer History Roster (EHR) has been available to public data users since 2013. This sizeable set of variables incorporates a substantial amount of information about each employer into a single record for each. See Appendix 13: Development of Questionnaires and Codebooks for a description of rosters as a data structure.

Programs that created the EHR can be accessed at the bottom of this page.

Introduction

Throughout NLSY79 history, it has been a common exercise for users to track employer records through multiple survey years. The traditional means of linking job records is somewhat involved, requiring multiple steps and identification of job numbers in current, previous and subsequent survey years (See Appendix 9: Linking Employers Through Survey Years).

The EHR is intended to alleviate if not eliminate the burden of linking jobs for users. In the EHR, employer records through survey years have already been linked into a single continuous cumulative record for each employer ever reported by a respondent. The roster currently contains data for up to 67 employers (up to 67 records). This means that at least one respondent has reported 67 jobs over the course of the NLSY79's 30 survey rounds, spanning 44 years. Most respondents of course will have a much smaller number of jobs reported since 1979 and only a single respondent has reported 67 jobs.

While the EHR does not contain all information ever reported about each employer, it does contain a substantial amount of commonly used data items that are consistently found through the survey's history. Users can also more easily incorporate job-related data items that are not currently on the roster. Instructive examples of how the roster data can be used appear below. Variables that are part of the EHR are available through the NLS Investigator search and extraction tool. These variables can be found in Investigator by using the areas of interest that start with 'EMPLOYER HISTORY,' question names that start with 'EMPLOYERS_ALL' or reference numbers that start with 'E.' The variables in this roster are classified as 'XRND', meaning that they are not linked to any specific survey year (see the Glossary of NLS Terms for a full definition of XRND). Instead, they are updated each round with the most current cumulative data available.

Please note: Variables referenced in the following discussion are described in Table 1, which lists the contents of the Employer History Roster.

Roster construction

The process of constructing the historical EHR began by establishing preliminary linkages similar to that described in Appendix 9. Employers were tracked between current, previous and subsequent survey years using multiple employer numbers in each survey year. After preliminary matches were established, linkages and discrepancies were examined in detail, often using in-house data not available publicly. This might include items such as occupation and industry descriptions and interviewer comments made during the course of an interview, in conjunction with employer names. Examining these items could in some cases serve to clarify connections between jobs reported in various years. Consequently, at least some employer records built in the EHR may be of higher quality than those that can be established using publicly available data.

Employer order

Employers are listed on the EHR in chronological order by the earliest start date reported for a job. For instance:

Employer #1 will be the job with the earliest start date ever reported for any job.
Employer #4 will be the job with the 4^th earliest start date. There will be three jobs with earlier start dates in the roster, numbered Employers #1, #2 and #3.

Please note that the order of an employer on the roster (from 1-67 possible) is based solely on the original start date (the earliest start date available) at the first report of that job. It is not based on the survey year in which the job was first reported. Consider the following example:

A respondent has reported 3 jobs through the 1987 survey year. These jobs with original start dates ranging from 1978 to 1984, have the following pattern:
- Employer first reported 1979, original start date = Jan. 1978
- Employer first reported 1983, original start date = Nov. 1982
- Employer first reported 1987, original start date = Jan. 1987

During the 1988 interview, the respondent reports a 4th employer that s/he failed to report at an earlier interview with an original start date of March 1985. While this job will be numbered by the 'unique identification number' as a job reported in the 1988 survey year, its order on the roster will precede the job reported in 1987. Although the 1987 job was reported first, it actually started later. In this simple example, the order of employers on the roster would appear as:

Employer #1: first reported 1979, original start date = Jan. 1978, unique id = 19790100
Employer #2: first reported 1983, original start date = Nov. 1982, unique id = 19830100
Employer #3: first reported 1988, original start date = Mar. 1985, unique id = 19880100
Employer #4: first reported 1987, original start date = Jan. 1987, unique id = 19870100

These types of reporting irregularities along with possible isolated errors in recording and data entry errors are not common but are recurrent throughout the life of the survey. As many inconsistencies as possible were resolved in the construction of the EHR.

Roster content

The vast majority of variables currently included on the EHR are directly copied from responses collected during the survey or from data items already coded or created separately for the public release dataset. For example, start and stop dates, currently working status and class of worker are all responses collected during the interview. Occupation and industry variables are coded from responses collected during the interview. Start and stop weeks and array job numbers are variables created by the Work History programs (see Appendix 18: Work History Data).

Table 1 lists the contents of the Employer History Roster by area of interest and question name. The EHR currently contains data for up to 67 jobs ever reported.

Table 1: Employer Roster
EMPLOYER HISTORY	Question names	Description	Source	Variables included for:
JOB CHARACTERISTICS	EMPLOYERS_ALL_GOVJOB_[YEAR].[JOB#]	Was this job a government job?	Direct survey responses	Each job for each survey year through 1987 (a)
	EMPLOYERS_ALL_CPSJOB_[YEAR].[JOB#]	Was this job the CPS (current/most recent) job?	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_UNION_[YEAR].[JOB#]	R covered by union or employee contract on the job?	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_CURWK_[YEAR].[JOB#]	R currently working for employer at date of interview?	Direct survey responses	Each job for each survey year (a)
JOB EMPLOYER IDS	EMPLOYERS_ALL_NUM_ARRAY_[YEAR].[JOB#]	Number loaded into Work History Labor Force Status array	Created by Work History Programs	Each job for each survey year (a)
	EMPLOYERS_ALL_PREVID_[YEAR].[JOB#]	Job number of employer from previous interview	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_ID_[YEAR].[JOB#]	ID number of job in survey year	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_UID.[JOB#] (c)	Unique identification number for each job, consisting of survey year job first reported, appended with job number in that survey year * 100 (e.g. job #2 first reported in 1980 = 19800200)	Created for EHR	Each job (b)
JOB HOURS WORKED	EMPLOYERS_ALL_HOURSDAY_[YEAR].[JOB#] User Note: These variables are not available for self-employed jobs.	Hours per day usually worked at job	Direct survey responses	Each job for each survey year (a)
JOB HOURS WORKED	EMPLOYERS_ALL_HOURSWEEK_[YEAR].[JOB#]	Hours per week usually worked at job	Direct survey responses	Each job for each survey year (a)
JOB INDUSTRY, OCCUPATION AND CLASS OF WORKER	EMPLOYERS_ALL_IND_[YEAR].[JOB#] User note: The coding frame for these variables is different for survey years 1979-2000 and for 2002 through current data releases. The 1970 Census coding frame was used for variables through the 2000 survey year. The 2000 Census coding frame was applied in 2002 and subsequent survey years.	Type of business or industry for employer	Coded from survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_OCC_[YEAR].[JOB#] User note: The coding frame for these variables is different for survey years 1979-2000 and for 2002 through current data releases. The 1970 Census coding frame was used for variables through the 2000 survey year. The 2000 Census coding frame was applied in 2002 and subsequent survey years.	Occupation for employer	Coded from survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_COW_[YEAR].[JOB#] User note: Researchers using these variables across multiple survey rounds should take careful note that the response categories changed in 1994. Categories are consistent from 1979-1993, and from 1994 through the current data release.	Class of worker for employer	Coded from survey responses	Each job for each survey year (a)
JOB ORIGINAL STARTDATES AND MOST RECENT STOPDATES	EMPLOYERS_ALL_STOPDATE_MOST_RECENT.[JOB#]~[D/M/Y] (d)	Most recent stopdates for employer	Created from survey responses for EHR	Each job (b)
JOB ORIGINAL STARTDATES AND MOST RECENT STOPDATES	EMPLOYERS_ALL_STARTDATE_ORIGINAL.[JOB#]~[D/M/Y] (c)	Original startdate for employer	Created from survey responses for EHR	Each job (b)
JOB PAYRATES AND TIME UNITS	EMPLOYERS_ALL_TIMERATE_[YEAR].[JOB#]	Time unit for rate of pay	Survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_PAYRATE_[YEAR].[JOB#]	Payrate for employer	Coded from survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_HRLY_WAGE_[YEAR].[JOB#]	Hourly rate of pay for employer	Coded from survey responses	Each job for each survey year (a)
JOB START DATES	EMPLOYERS_ALL_STARTDATE_[YEAR].[JOB#]~[D/M/Y]	Startdate for employer	Direct survey responses	Each job for each survey year (a)
JOB START WEEKS	EMPLOYERS_ALL_STARTWEEK_[YEAR].[JOB#]	Week number of start date for job	Created by Work History Programs	Each job for each survey year (a)
JOB STOP DATES	EMPLOYERS_ALL_STOPDATE_[YEAR].[JOB#]~[D/M/Y]	Stop date for employer	Direct survey responses	Each job for each survey year (a)
JOB STOP WEEKS	EMPLOYERS_ALL_STOPWEEK_[YEAR].[JOB#]	Week number of stop date for job	Created by Work History Programs	Each job for each survey year (a)
JOB TENURE AND PRETENURE	EMPLOYERS_ALL_PRETEN_[YEAR].[JOB#]	Months worked for employer before date of last interview	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_TENURE_[YEAR].[JOB#]	Total weeks tenure with employer	Created by Work History Programs	Each job for each survey year (a)
	EMPLOYERS_ALL_PAST_[YEAR].[JOB#]	R work for employer before date of last interview?	Direct survey responses	Each job for each survey year (a)
JOB WHY LEFT	EMPLOYERS_ALL_WHYLEFT_[YEAR].[JOB#] User note: Researchers using these variables across multiple survey rounds should take careful note that the response categories and assignments of response codes have changed a number of times over the 30 survey rounds. For example, response code "2" may mean 'Discharged or fired' in one survey year and 'Plant closed' in another. Likewise, 'Layoff' is grouped with 'Plant closed, or end of temporary or seasonal job' in some survey years and is listed as a separate response category in others.	Reason left job	Direct survey responses	Each job for each survey year (a)
	EMPLOYERS_ALL_WHYLEFT_MOST_RECENT.[JOB#] User note: This variable contains the most recent reason why the respondent left the job, even if the respondent has since returned and is currently working at that job. Also please note: Response categories for these variables incorporate all possible responses across survey years. For instance, 'Layoff' is listed separately from 'Layoff, plant closed, or end of temporary or seasonal job', because both of these responses categories existed in different survey years.	Reason left job most recent time left	Created from survey responses for EHR	Each job (b)
	EMPLOYERS_ALL_WHYLEFT_MOST_RECENT_COL.[JOB#] User note: Response categories for these variables collapses all possible categories in the EMPLOYERS_ALL_WHYLEFT_MOST_RECENT.[JOB#] variables into 6 broad categories. This variable contains the most recent reason why the respondent left the job, even if the respondent has since returned and is currently working at that job.	Reason left job most recent time left - collapsed	Created from survey responses for EHR	Each job (b)
WITHIN JOB GAPS REASON NOT WORKING	EMPLOYERS_ALL_WHYNOWK_[YEAR].[JOB#].[GAP#]	Reason not working for within job gap	Direct survey responses	Each job for each gap for each survey year (e)
WITHIN JOB GAPS START DATES	EMPLOYERS_ALL_PERSTART_[YEAR].[JOB#].[GAP#]	Week number of start dates for within job gap	Created by Work History Programs	Each job for each gap for each survey year (e)
WITHIN JOB GAPS STOP DATES	EMPLOYERS_ALL_PERSTOP_[YEAR].[JOB#].[GAP#]	Week number of stop dates for within job gap	Created by Work History Programs	Each job for each gap for each survey year (e)
WITHIN JOB GAPS WEEKS LOOKING	EMPLOYERS_ALL_LOOK_[YEAR].[JOB#].[GAP#]	Any weeks looking for work during within job gap	Direct survey responses	Each job for each gap for each survey year (e)
WITHIN JOB GAPS WEEKS NOT WORKING	EMPLOYERS_ALL_NOTLOOK_[YEAR].[JOB#].[GAP#]	Number of weeks not looking for work during within job gap	Direct survey responses	Each job for each gap for each survey year (e)
WITHIN JOB GAPS WEEKS NOT WORKING	EMPLOYERS_ALL_WKSNOTWK_[YEAR].[JOB#]	Any weeks not working for employer	Direct survey responses	Each job for each gap for each survey year (e)

Variables created for 'each job for each survey year' will have one variable picked up for each survey year for each job the respondent has reported. For instance, the EMPLOYERS_ALL_CURWK_[YEAR].[JOB#] set of variables will include the 'currently working' status for each job reported, in each survey year that a job was active. If a respondent reports employer #1, spanning from 1980-1989, a variable will be present for employer #1 for each survey year (EMPLOYERS_ALL_CURWK_1980.01, EMPLOYERS_ALL_CURWK_1981.01, EMPLOYERS_ALL_CURWK_1982.01...EMPLOYERS_ALL_CURWK_1989.01). If the respondent reports employer #2, spanning from 1985-1990, another set of those variables would be available for employer #2 for each of those survey years (EMPLOYERS_ALL_CURWK_1985.02, EMPLOYERS_ALL_CURWK_1986.02, etc.). Users should note that the EMPLOYERS_ALL_GOVJOB_[YEAR].[JOB#] set of variables only exist through 1987, when that question was dropped from the survey.
Variables created for 'each job' will have one variable per employer. EMPLOYERS_ALL_UID.[JOB#], EMPLOYERS_ALL_STARTDATE_ORIGINAL.[JOB#]~[D/M/Y] and EMPLOYERS_ALL_STOPDATE_MOST_RECENT.[JOB#]~[D/M/Y] are all examples of variables that appear only once for each job. Each employer has only one unique identification number, one original start date and one most recent stop date. These variables will be present for each reported employer, regardless of the survey years in which they were reported.
Once assigned, the unique id and original start date for an employer will not change except under two conditions: 1) an error is found in the reporting record that necessitates either a correction to an original start date or a reordering of employers, and/or 2) more employers are recovered from archives that necessitate a chronological reordering of employers for a respondent.
The most recent stop date for an employer will be updated with each successive interview during which that employer is reported, until the respondent leaves the employer and does not return.
Variables created for 'each job for each gap for each survey' are similar to the variables in group (b) above, but are present for up to 4 gaps reported within the tenure of each job. For example, if a respondent reports two gaps within the tenure for employer #2 and one gap within the tenure for employer #3 in 1986, gap variables would be present for each employer for that survey year. EMPLOYERS_ALL_WHYNOWK_[YEAR].[JOB#].[GAP#] variables in that case would be EMPLOYERS_ALL_WHYNOWK_1986.02.01 and EMPLOYERS_ALL_WHYNOWK_1986.02.02 (gaps within tenure with employer #2), and EMPLOYERS_ALL_WHYNOWK_1986.03.01 (gap within tenure with employer #3).

Roster structure

The Employer History Roster is structured with a single 'record' for each employer. The information from survey year to survey year for the same employers has already been linked into this single record. This allows researchers to look at the record for an employer for the entire time the job was reported. Information for each specific job can be identified using the following search criteria:

Question Name (enter search term) + starts with + 'EMPLOYERS_ALL'
Word in Title (enter search term) + contains + 'JOB ##'

The NLS Investigator search captured in Figure 1 below will produce all variables on the roster pertaining to the first job a respondent ever reported, regardless of when that job was first reported. Some respondents may have reported their first job in 1979 while others may not have reported their first job until 1985.

Figure 1. Investigator search to produce all variables pertaining to first job reported

Select study = NLSY79
Click the VARIABLE SEARCH tab
Next, click the SEARCH sub-tab
In the criteria section:
1. Select QUESTION NAME (enter search term) from the first drop-down menu,
2. select "starts with" in the second drop-down menu,
3. type EMPLOYERS_ALL in the text box, and
4. click ADD
In the next row of criteria:
1. Select WORD IN TITLE (enter search term) from the first drop-down menu,
2. select "contains" in the second drop-down menu,
3. type Job 01 in the text box, and
Click DISPLAY VARIABLES

Each successive survey year presents the opportunity for a respondent to report additional employers. Consider a respondent with the reporting record depicted in Table 2:

Table 2: Hypothetical respondent's reporting record
Interview date	Order in EHR	Original start date	Most recent stop date	Unique ID	Employer information reported in survey years	Examples of variables present for each job
Feb. 1, 1979	4	Mar. 1, 1978	Feb. 2, 1980	19790100	1979, 1980	EMPLOYERS_ALL_UID.04, EMPLOYERS_ALL_STARTDATE_ORIGINAL.04~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.04~[D/M/Y], EMPLOYERS_ALL_CURWK_1979.04, EMPLOYERS_ALL_ID_1979.04, EMPLOYERS_ALL_IND_1979.04, EMPLOYERS_ALL_OCC_1979.04, EMPLOYERS_ALL_CURWK_1980.04, EMPLOYERS_ALL_ID_1980.04, EMPLOYERS_ALL_IND_1980.04, EMPLOYERS_ALL_OCC_1980.04
	3	Feb. 15, 1978	Dec. 15, 1979	19790200	1979, 1980	EMPLOYERS_ALL_UID.03, EMPLOYERS_ALL_STARTDATE_ORIGINAL.03~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.03~[D/M/Y], EMPLOYERS_ALL_CURWK_1979.03, EMPLOYERS_ALL_ID_1979.03, EMPLOYERS_ALL_IND_1979.03, EMPLOYERS_ALL_OCC_1979.03, EMPLOYERS_ALL_CURWK_1980.03, EMPLOYERS_ALL_ID_1980.03, EMPLOYERS_ALL_IND_1980.03, EMPLOYERS_ALL_OCC_1980.03
	1	Feb. 15, 1977	Jan. 15, 1979	19790300	1979	EMPLOYERS_ALL_UID.01, EMPLOYERS_ALL_STARTDATE_ORIGINAL.01~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.01~[D/M/Y], EMPLOYERS_ALL_CURWK_1979.01, EMPLOYERS_ALL_ID_1979.01, EMPLOYERS_ALL_IND_1979.01, EMPLOYERS_ALL_OCC_1979.01
	2	Apr. 1, 1977	Dec. 31, 1978	19790400	1979	EMPLOYERS_ALL_UID.02, EMPLOYERS_ALL_STARTDATE_ORIGINAL.02~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.02~[D/M/Y], EMPLOYERS_ALL_CURWK_1979.02, EMPLOYERS_ALL_ID_1979.02, EMPLOYERS_ALL_IND_1979.02, EMPLOYERS_ALL_OCC_1979.02
Jan. 15, 1980	5	Jun. 15, 1979	Aug. 1, 1981	19800100	1980, 1981, 1982	EMPLOYERS_ALL_UID.05, EMPLOYERS_ALL_STARTDATE_ORIGINAL.05~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.05~[D/M/Y], EMPLOYERS_ALL_CURWK_1980.05, EMPLOYERS_ALL_ID_1980.05, EMPLOYERS_ALL_IND_1980.05, EMPLOYERS_ALL_OCC_1980.05, EMPLOYERS_ALL_CURWK_1981.05, EMPLOYERS_ALL_ID_1981.05, EMPLOYERS_ALL_IND_1981.05, EMPLOYERS_ALL_OCC_1981.05, EMPLOYERS_ALL_CURWK_1982.05, EMPLOYERS_ALL_ID_1982.05, EMPLOYERS_ALL_IND_1982.05, EMPLOYERS_ALL_OCC_1982.05
Jan. 15, 1980	6	Sep. 15, 1979	Jun. 1, 1980	19800200	1980, 1981	EMPLOYERS_ALL_UID.06, EMPLOYERS_ALL_STARTDATE_ORIGINAL.06~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.06~[D/M/Y], EMPLOYERS_ALL_CURWK_1980.06, EMPLOYERS_ALL_ID_1980.06, EMPLOYERS_ALL_IND_1980.06, EMPLOYERS_ALL_OCC_1980.06, EMPLOYERS_ALL_CURWK_1981.06, EMPLOYERS_ALL_ID_1981.06, EMPLOYERS_ALL_IND_1981.06, EMPLOYERS_ALL_OCC_1981.06
Mar. 4, 1981	8	Nov. 15, 1980	Nov. 15, 1981	19810100	1981, 1982	EMPLOYERS_ALL_UID.08, EMPLOYERS_ALL_STARTDATE_ORIGINAL.08~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.08~[D/M/Y], EMPLOYERS_ALL_CURWK_1981.08, EMPLOYERS_ALL_ID_1981.08, EMPLOYERS_ALL_IND_1981.08, EMPLOYERS_ALL_OCC_1981.08, EMPLOYERS_ALL_CURWK_1982.08, EMPLOYERS_ALL_ID_1982.08, EMPLOYERS_ALL_IND_1982.08, EMPLOYERS_ALL_OCC_1982.08
	7	Aug. 15, 1980	Jun. 30, 1981	19810200	1981, 1982	EMPLOYERS_ALL_UID.07, EMPLOYERS_ALL_STARTDATE_ORIGINAL.07~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.07~[D/M/Y], EMPLOYERS_ALL_CURWK_1981.07, EMPLOYERS_ALL_ID_1981.07, EMPLOYERS_ALL_IND_1981.07, EMPLOYERS_ALL_OCC_1981.07, EMPLOYERS_ALL_CURWK_1982.07, EMPLOYERS_ALL_ID_1982.07, EMPLOYERS_ALL_IND_1982.07, EMPLOYERS_ALL_OCC_1982.07
	9	Dec. 1, 1980	Feb. 28, 1981	19810300	1981	EMPLOYERS_ALL_UID.09, EMPLOYERS_ALL_STARTDATE_ORIGINAL.09~[D/M/Y], EMPLOYERS_ALL_MOST_RECENT_STOPDATE.09~[D/M/Y], EMPLOYERS_ALL_CURWK_1981.09, EMPLOYERS_ALL_ID_1981.09, EMPLOYERS_ALL_IND_1981.09, EMPLOYERS_ALL_OCC_1981.09
Feb. 2, 1982						Please note: Jobs with unique ids 19800100, 19810100 and 19810200 would still be active during the period between the 1981 and 1982 interview and would therefore have information reported during the 1982 interview.

In the example in Table 2, the respondent has reported four employers at the initial 1979 interview, two additional employers at the 1980 interview, and three more new employers at the 1981 interview.

The original start date, most recent stop date and unique id variables fall into the category of variables in Table 1 created for 'each job'. They will be present for all jobs reported by a respondent. If by 1990, the respondent has reported 13 jobs, 13 original start dates, 13 original stop dates and 13 unique ids will be present in the EHR for that respondent.
In the Table 2 example, those variables falling into the other categories in Table 1 (variables present for each job (and each gap if applicable) for each survey year in which the job is reported), will be present only for the survey years listed in column 6 ('Employer information reported in survey years'), as those are the survey years in which information on the specific jobs would have been collected. Examples of the variables that would be present for the respondent and jobs listed in Table 2 are in the final column ('Examples of variables present for each job').

Variables for all jobs will not be present for all respondents. For example:

A respondent reporting only 5 jobs through 2010 will have no data at all for jobs 6 through 67.
Likewise, a respondent reporting their 28^th employer starting in 1990 will have no data for that 28^th employer picked up from survey years 1979-1989. In this instance, variables EMPLOYERS_ALL_UID.28, EMPLOYERS_ALL_STARTDATE_ORIGINAL.28~[D/M/Y], and EMPLOYERS_ALL_MOST_RECENT_STARTDATE.28~[D/M/Y] will be present but variables from the specific survey years 1979-89 (for instance, EMPLOYERS_ALL_OCC_1979.28, EMPLOYERS_ALL_OCC_1980.28, EMPLOYERS_ALL_OCC_1981.28, etc.) will not be present. Those variables will appear for 1990 when the job is first reported and be present for each subsequent survey year in which the job is reported. If this employer is reported in 1990, 1991 and 1992, EMPLOYERS_ALL_OCC_1990.28, EMPLOYERS_ALL_OCC_1991.28 and EMPLOYERS_ALL_OCC_1992.28 will be present for that respondent for job 28. Occupation codes for all other survey years prior and subsequent will be missing for that job for that respondent.

In addition, different series of variables may have missing items, depending on the reporting pattern. For instance:

Users may find that there is data for jobs 1-33 and jobs 36-40 in 1994, but no data for jobs 34-35. Such a pattern would indicate that no respondents reported what would be their 34^th and 35^th job during the 1994 interview.
There are also instances in which specific variable series have similar gaps. For instance, in a specific year, one might find industry and occupation codes for jobs 1-33 and 36-40, but again not for the 34^th and 35^th job. Such a pattern could result because no one reported their 34^th and 35^th job that year. Alternatively if several respondents reported a 34^th/35^th job that survey year, but those jobs did not meet the criteria (based on hours worked and length of time) that would have led to collection of industry and occupation descriptions, there would be no industry and occupation data from that survey year.

Linking to non-roster employer variables

While a large number of commonly used employer-related variables have been incorporated into the EHR, there are some variables in various survey years that have not. Users can link employer variables from a specific survey year that are not currently on the EHR to the cumulative record for an employer on the EHR by using the EMPLOYERS_ALL_ID_[YEAR].[JOB#] variables. These variables contain the identification number of a job in a specific survey year in which it was reported. The following paragraphs describe several examples of linking scenarios.

Imagine that a researcher wants to link data on promotions asked in 1989 to the cumulative employer record found in the EHR. Using the EMPLOYERS_ALL_ID_1989.[JOB#] variables, one can link the promotions data for the correct employer in 1989 to the cumulative employer record in the EHR. To accomplish this, the value of EMPLOYERS_ALL_ID_1989.[JOB#] would be checked for each employer on the EHR. If that value is '1,' the promotion variables for job #1 in 1989 would be linked. If the value of EMPLOYERS_ALL_UID_1989.[JOB#] is '2,' promotion variables for job #2 in 1989 would be linked, and so on.

In another example, one might want to link the EHR cumulative employer record with the information on the respondent's employee status (regular, consultant, contractor, temp worker) asked in 1994, 1996 and 1998. One would again use the EMPLOYERS_ALL_ID_1994.[JOB#], EMPLOYERS_ALL_ID_1996.[JOB#] and EMPLOYERS_ALL_ID_1998.[JOB#] variables in the same way described above to link to the appropriate employee status variables in each year. If the value of EMPLOYERS_ALL_ID_1994.24 is '1' then the employee status variables for employer #1 in 1994 would be linked to the cumulative record for job #24 on the EHR. If the value of EMPLOYERS_ALL_ID_1994.30 is '1' then those variables would be linked to the cumulative record for job #30 on the EHR. Following a similar procedure, the employee status variables for 1996 and 1998 could be linked to the appropriate cumulative job record on the EHR.

In addition, it is now possible to identify which cumulative job records on the EHR link to specific job numbers on the Work History Labor Force Status and Dual Jobs arrays. The EHR includes a set of variables called EMPLOYERS_ALL_NUM_ARRAY_[YEAR].[JOB#]. These variables contain the job number assigned by the Work History programs to each job for each survey year. The job number assigned by the Work History programs consists of the survey round and the number of the job for that survey year. So for instance, the 2^nd job reported in 1993 (round 15 of the survey) would appear as job #1502 on the Labor Force Status and/or Dual Job arrays. If this same job was reported as job #1 in 1994 (round 16 of the survey), it would appear as job #1601 in the Labor Force Status array. The EMPLOYERS_ALL_NUM_ARRAY_[YEAR].[JOB#] variables allow users to identify and track a specific employer through the Work History job arrays more easily. For instance, if a user wants additional information on the first job a respondent reported after a gap, and the Work History array job number is 1701, one would want to identify which of the employers in the EHR (1-59) contains 1701 in the EMPLOYERS_ALL_NUM_ARRAY_1996.[JOB#] variable. Establishing this link to the EHR then provides an expanded series of variables depicting the entire history of the employer.

User notes

The Employer History Roster was made available after a considerable effort and was a long time in the making. Improvements will continue to be made as time allows. Some possible areas of improvement and expansion are discussed below:

Each survey year there are a small number of cases that report more than five employers. Prior to 1993, jobs 6+ were provided and stored on separate media. With the many technological transformations that have taken place in the past two decades, much of the information on this small set of jobs in each older survey year has become very difficult to recover from original sources, and is generally not included in the EHR. Reporting of more than five jobs per survey year is not common.

As mentioned earlier, the EHR includes a great deal, but not all, information collected on employers in each survey. For instance, some information on employee status (regular, temp worker, contractor, etc.), starting wages and hours of a job, fringe benefits applying to a job, etc. have been collected in various survey years but are not included on the EHR. Users can access this additional data by linking to individual Employer Supplements in the appropriate survey years.

Employer History Roster programs

Click a topic below to view programming code. Note: ## index reference variables for employers 1-67 in the Employer History Roster.

Variable names in programs and NLSY79 data releases
Linkjobs79_22
Endjob22
Stopdate22_final
Employers_roster_7922_job
Employers_roster_22_uid_name_stadate
Employers_roster_22_id
Employers_roster_22_others
Employers_roster_22_stodate_whyleft
Employers_roster_22_stdate_stweek
Employers_roster_22_wksnotworked

Variable names in programs and NLSY79 data releases

/****************************** Variables Used ******************************/
/**Variable Names in program            Variable Names in data release**/
UID_## (1-67)				EMPLOYERS_ALL_UID.## (1-67)
STAD_ORI_## (1-67)			EMPLOYERS_ALL_STARTDATE_ORIGINAL.##~D (1-67)
STAM_ORI_## (1-67)			EMPLOYERS_ALL_STARTDATE_ORIGINAL.##~M (1-67)
STAY_ORI_## (1-67)			EMPLOYERS_ALL_STARTDATE_ORIGINAL.##~Y (1-67)
STOD_FIN_## (1-67)			EMPLOYERS_ALL_STOPDATE_MOST_RECENT.##~D (1-67)
STOM_FIN_## (1-67)			EMPLOYERS_ALL_STOPDATE_MOST_RECENT.##~M (1-67)
STOY_FIN_## (1-67)			EMPLOYERS_ALL_STOPDATE_MOST_RECENT.##~Y (1-67)
REASONLEFT_## (1-67)			EMPLOYERS_ALL_WHYLEFT_MOST_RECENT.## (1-67)
REASONLEFTC_## (1-67)			EMPLOYERS_ALL_WHYLEFT_MOST_RECENT_COL.## (1-67)
ID_2022_## (1-67)			EMPLOYERS_ALL_ID_2022.## (1-67)
STAD_2022_## (1-67)			EMPLOYERS_ALL_STADATE_2022.##~D (1-67)
STAM_2022_## (1-67)			EMPLOYERS_ALL_STADATE_2022.##~M (1-67)
STAY_2022_## (1-67)			EMPLOYERS_ALL_STADATE_2022.##~Y (1-67)
STOD_2022_## (1-67)			EMPLOYERS_ALL_STOPDATE_2022.##~D (1-67)
STOM_2022_## (1-67)			EMPLOYERS_ALL_STOPDATE_2022.##~M (1-67)
STOY_2022_## (1-67)			EMPLOYERS_ALL_STOPDATE_2022.##~Y (1-67)
STAWK_2022_## (1-67)			EMPLOYERS_ALL_STARTWEEK_2022.## (1-67)
STOWK_2022_## (1-67)			EMPLOYERS_ALL_STOPWEEK_2022.## (1-67)
COW_2022_## (1-67)			EMPLOYERS_ALL_COW_2022.## (1-67)
CPSJOB_2022_## (1-67)			EMPLOYERS_ALL_CPSJOB_2022.## (1-67)
CURWK_2022_## (1-67)			EMPLOYERS_ALL_CURWK_2022.## (1-67)
HRP_2022_## (1-67)			EMPLOYERS_ALL_HRLY_WAGE_2022.## (1-67)
HRSDAY_2022_## (1-67)			EMPLOYERS_ALL_HOURSDAY_2022.## (1-67)
HRSWK_2022_## (1-67)			EMPLOYERS_ALL_HOURSWEEK_2022.## (1-67)
IND_2022_## (1-67)			EMPLOYERS_ALL_IND_2022.## (1-67)
JOBNUM_A_2022_## (1-67)			EMPLOYERS_ALL_NUM_ARRAY_2022.## (1-67)
MOSLI_2022_## (1-67)			EMPLOYERS_ALL_PRETEN_2022.## (1-67)
OCC_2022_## (1-67)			EMPLOYERS_ALL_OCC_2022.## (1-67)
PREVID_2022_## (1-67)			EMPLOYERS_ALL_PREVID_2022.## (1-67)
RELEFT_2022_## (1-67)			EMPLOYERS_ALL_WHYLEFT_2022.## (1-67)
ROP_2022_## (1-67)			EMPLOYERS_ALL_PAYRATE_2022.## (1-67)
TENURE_2022_## (1-67)			EMPLOYERS_ALL_TENURE_2022.## (1-67)
TUROP_2022_## (1-67)			EMPLOYERS_ALL_TIMERATE_2022.## (1-67)
UNI_2022_## (1-67)			EMPLOYERS_ALL_UNION_2022.## (1-67)
WBEFDI_2022_## (1-67)			EMPLOYERS_ALL_PAST_2022.## (1-67)
UNEM_2022_## (1-67)			EMPLOYERS_ALL_WKSNOTWK_2022.## (1-67)
STAWK_GAP1_2022_## (1-67)		EMPLOYERS_ALL_PERSTAR_2022.##.01 (1-67)
STAWK_GAP2_2022_## (1-67)		EMPLOYERS_ALL_PERSTAR_2022.##.02 (1-67)
STAWK_GAP3_2022_## (1-67)		EMPLOYERS_ALL_PERSTAR_2022.##.03 (1-67)
STAWK_GAP4_2022_## (1-67)		EMPLOYERS_ALL_PERSTAR_2022.##.04 (1-67)
STOWK_GAP1_2022_## (1-67)		EMPLOYERS_ALL_PERSTOP_2022.##.01 (1-67)
STOWK_GAP2_2022_## (1-67)		EMPLOYERS_ALL_PERSTOP_2022.##.02 (1-67)
STOWK_GAP3_2022_## (1-67)		EMPLOYERS_ALL_PERSTOP_2022.##.03 (1-67)
STOWK_GAP4_2022_## (1-67)		EMPLOYERS_ALL_PERSTOP_2022.##.04 (1-67)
REA_GAP1_2022_## (1-67)			EMPLOYERS_ALL_WHYNOWK_2022.##.01 (1-67)
REA_GAP2_2022_## (1-67)			EMPLOYERS_ALL_WHYNOWK_2022.##.02 (1-67)
REA_GAP3_2022_## (1-67)			EMPLOYERS_ALL_WHYNOWK_2022.##.03 (1-67)
REA_GAP4_2022_## (1-67)			EMPLOYERS_ALL_WHYNOWK_2022.##.04 (1-67)
LOOK_GAP1_2022_## (1-67)		EMPLOYERS_ALL_LOOK_2022.##.01 (1-67)
LOOK_GAP2_2022_## (1-67)		EMPLOYERS_ALL_LOOK_2022.##.02 (1-67)
LOOK_GAP3_2022_## (1-67)		EMPLOYERS_ALL_LOOK_2022.##.03 (1-67)
LOOK_GAP4_2022_## (1-67)		EMPLOYERS_ALL_LOOK_2022.##.04 (1-67)
NLOOK_GAP1_2022_## (1-67)		EMPLOYERS_ALL_NOTLOOK_2022.##.01 (1-67)
NLOOK_GAP2_2022_## (1-67)		EMPLOYERS_ALL_NOTLOOK_2022.##.02 (1-67)
NLOOK_GAP3_2022_## (1-67)		EMPLOYERS_ALL_NOTLOOK_2022.##.03 (1-67)
NLOOK_GAP4_2022_## (1-67)		EMPLOYERS_ALL_NOTLOOK_2022.##.04 (1-67)

Linkjobs79_22

did2022=SYMBOL_CURDATE_D;
dim2022=SYMBOL_CURDATE_M;
diy2022=SYMBOL_CURDATE_Y;
stay20221=EMPLOYER_STARTDATE_01_Y;
stay20222=EMPLOYER_STARTDATE_02_Y;
stay20223=EMPLOYER_STARTDATE_03_Y;
stay20224=EMPLOYER_STARTDATE_04_Y;
stay20225=EMPLOYER_STARTDATE_05_Y;
stay20226=EMPLOYER_STARTDATE_06_Y;
stay20227=EMPLOYER_STARTDATE_07_Y;
stay20228=EMPLOYER_STARTDATE_08_Y;
stay20229=EMPLOYER_STARTDATE_09_Y;
stay202210=EMPLOYER_STARTDATE_10_Y;
stay202211=EMPLOYER_STARTDATE_11_Y;
stay202212=EMPLOYER_STARTDATE_12_Y;
stay202213=EMPLOYER_STARTDATE_13_Y;
stay202214=EMPLOYER_STARTDATE_14_Y;
array stay (i)		stay20221-stay20229 stay202210-stay202214;
array curf (i)		EMPLOYER_CURFLAG_01-EMPLOYER_CURFLAG_14;
array curwk (i)		curwk20221-curwk20229 curwk202210-curwk202214;
totemp2022=0;
do i=1 to 14;
	if stay~=. then do;
		totemp2022=totemp2022+1;
		if curf~=. then curwk=curf;
		else if curf=. then curwk=-3;
	end;
	else if stay=. then curwk=-4;
end;
array intyear (*) diy1979 diy1980 diy1981 diy1982 diy1983 diy1984 diy1985 diy1986 diy1987 diy1988 diy1989 diy1990 
				  diy1991 diy1992 diy1993 diy1994 diy1996 diy1998 diy2000 diy2002 diy2004 diy2006 diy2008 diy2010 
				  diy2012 diy2014 diy2016 diy2018 diy2020;
array intrd   (*) i1-i29 (1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 
				  1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020);
if dim2022=. then dim2022=-5;
if did2022=. then did2022=-5;
if diy2022=. then diy2022=-5;
do i=dim(intyear) to 1 by -1;
	if (intyear(i)>0) then do;
		linty=intrd(i);
		leave;
	leave;
	end;
end;
array curwk22	    curwk20221-curwk20229 curwk202210-curwk202014;
do over prev;
	if diy2022=-5 then do;
		prev=-5;
		curwk22=-5;
		totemp2022=-5;
	end;
end;
array job2022 	   (j)  job2022_job2022_1-job2022_job2022_14;
array curwk2022	   (j)  curwk20221-curwk20229 curwk202210-curwk202214;
array jobnum2022   (j)  job_num_a2022_1-job_num_a2022_14;
totemp_wh2022=0;
do j=1 to 14;
	if curwk2022=-5 then do;
		job2022=-5;
		totemp_wh2022=-5;
	end;
	else if curwk2022>-4 then job2022=j;
	else job2022=-4;
	if jobnum2022>0 then totemp_wh2022=totemp_wh2022+1;
end;
array previd    (14)		previd2022_1-previd2022_14;
array check		(14)		check1-check14;
do h=1 to 14;
	do k=1 to 14;
		if h~=k then do;
			if previd(h)>0 and previd(h)=previd(k) then check(h)=k;
		end;
	end;
end;
%macro cc(linty);
	%do a=1 %to 19;
		%do i=1 %to 14;
			if previd2022_&i=&a then do;
				if linty=&linty and job&linty._job&linty._&a~=-4 then job&linty._job2022_&i=&a;
				if linty=&linty and job&linty._job&linty._&a=-4 then flag_jmatch2022_&i=1;
			end;
			else if previd2022_&i=-4 then do;
				job2020_job2022_&i=-4;
			end;
			else if previd2022_&i=-5 then do;
				job2020_job2022_&i=-5;
				flag_jmatch2022_&i=-5;
			end;
			if job&linty._job2022_&i=. then job&linty._job2022_&i=-4;
			if flag_jmatch2022_&i=. then flag_jmatch2022_&i=0;
		%end;
	%end;
%mend cc;
%macro lint;
	%do linty=2020 %to 1994 %by -2;
		%cc(&linty);
	%end;
	%do linty=1993 %to 1979 %by -1;
		%cc(&linty);
	%end;
%mend lint;
%lint;
*Have to break it down since there is not enough memory to run from 1979 to 2018 because of the macros;
*Link job# in 22 to job# in 1994-2018;
%macro cc(a,linty);
	%if job&linty._job2022_&a>0 %then %do;
		%do yr=2018 %to 1994 %by -2;
			%if &linty>&yr %then %do;
				%do i=1 %to 14;
					%do j=1 %to 14;
						if job&linty._job2022_&a=&i and job&yr._job&linty._&i=&j then job&yr._job2022_&a=&j;
						else if job&linty._job2022_&a=-5 then job&yr._job2022_&a=-5;
					%end;
				%end;
			%end;
		%end;
	%end;
%mend cc;
%macro job(linty);
	%do a=1 %to 14;
		%cc(&a,&linty);
	%end;
%mend job;
%macro lint;
	%do linty=2020 %to 1996 %by -2;
		%job(&linty);
	%end;
%mend lint;
%lint;
*Link job# in 22 to job# in 1987-1993;
%macro cc(a,linty);
	%if job&linty._job2022_&a>0 %then %do;
		%do yr=1993 %to 1987 %by -1;
			%if &linty>&yr %then %do;
				%do i=1 %to 14;
					%do j=1 %to 14;
						if job&linty._job2022_&a=&i and job&yr._job&linty._&i=&j then job&yr._job2022_&a=&j;
						else if job&linty._job2022_&a=-5 then job&yr._job2022_&a=-5;
					%end;
				%end;
			%end;
		%end;
	%end;
%mend cc;
%macro job(linty);
	%do a=1 %to 14;
		%cc(&a,&linty);
	%end;
%mend job;
%macro lint;
	%do linty=2020 %to 1994 %by -2;
		%job(&linty);
	%end;
	%do linty=1993 %to 1988 %by -1;
		%job(&linty);
	%end;
%mend lint;
%lint;
*Link job# in 22 to job# in 1983-1986;
%macro cc(a,linty);
	%if job&linty._job2022_&a>0 %then %do;
		%do yr=1986 %to 1983 %by -1;
			%if &linty>&yr %then %do;
				%do i=1 %to 14;
					%do j=1 %to 14;
						if job&linty._job2022_&a=&i and job&yr._job&linty._&i=&j then job&yr._job2022_&a=&j;
						else if job&linty._job2022_&a=-5 then job&yr._job2022_&a=-5;
					%end;
				%end;
			%end;
		%end;
	%end;
%mend cc;
%macro job(linty);
	%do a=1 %to 14;
		%cc(&a,&linty);
	%end;
%mend job;
%macro lint;
	%do linty=2020 %to 1994 %by -2;
		%job(&linty);
	%end;
	%do linty=1993 %to 1984 %by -1;
		%job(&linty);
	%end;
%mend lint;
%lint;
*Link job# in 22 to job# in 1979-1982;
%macro cc(a,linty);
	%if job&linty._job2022_&a>0 %then %do;
		%do yr=1982 %to 1979 %by -1;
			%if &linty>&yr %then %do;
				%do i=1 %to 14;
					%do j=1 %to 14;
						if job&linty._job2022_&a=&i and job&yr._job&linty._&i=&j then job&yr._job2022_&a=&j;
						else if job&linty._job2022_&a=-5 then job&yr._job2022_&a=-5;
					%end;
				%end;
			%end;
		%end;
	%end;
%mend cc;
%macro job(linty);
	%do a=1 %to 14;
		%cc(&a,&linty);
	%end;
%mend job;
%macro lint;
	%do linty=2020 %to 1994 %by -2;
		%job(&linty);
	%end;
	%do linty=1993 %to 1980 %by -1;
		%job(&linty);
	%end;
%mend lint;
%lint;
if diy2000>0 then diy2000=2000;
if diy2002>0 then diy2002=2002;
if diy2004>0 then diy2004=2004;
if diy2006>0 then diy2006=2006;
if diy2008>0 then diy2008=2008;
if diy2010>0 then diy2010=2010;
if diy2012>0 then diy2012=2012;
if diy2014>0 then diy2014=2014;
if diy2016>0 then diy2016=2016;
if diy2018>0 then diy2018=2018;
if diy2020>0 then diy2020=2020;
if diy2022>0 then diy2022=2022;
%macro cc(a);
	%do j=1994 %to 1979 %by -1;
		if (job&j._job2022_&a >0 and job&j._job2022_&a <=9) and diy&j>0 then do;
			staj1_job2022_&a=put(diy&j,4.)||'0'||put(job&j._job2022_&a,1.);
		end;
		else if job&j._job2022_&a >=10 and diy&j>0 then do;
			staj1_job2022_&a=put(diy&j,4.)||put(job&j._job2022_&a,2.);
		end;
	%end;
	%do j=2022 %to 1996 %by -2;
		if (job&j._job2022_&a >0 and job&j._job2022_&a <=9) and diy&j>0 then do;
			staj2_job2022_&a=put(diy&j,4.)||'0'||put(job&j._job2022_&a,1.);
		end;
		else if job&j._job2022_&a >=10 and diy&j>0 then do;
			staj2_job2022_&a=put(diy&j,4.)||put(job&j._job2022_&a,2.);
		end;
	%end;
%mend cc;
%macro job;
	%do a=1 %to 14;
		%cc(&a);
	%end;
%mend job;
%job;
%macro dd(b);
	%do i=2 %to 1 %by -1;
		if staj&i._job2022_&b ~=' ' then do;
			staj_job2022_&b=staj&i._job2022_&b;
		end;
	%end;
%mend dd;
%macro jb;
	%do b=1 %to 14;
		%dd(&b);
	%end;
%mend jb;
%jb;
array staj_job2022 (k) 	staj_job2022_1-staj_job2022_14;
array job2022 	   (k) 	job2022_job2022_1-job2022_job2022_14;
do k=1 to 14;
	if staj_job2022=. and job2022=-5 then staj_job2022=-5;
	else if staj_job2022=. then staj_job2022=-4;
end;
array staj2022 	(*)		staj_job2022_1-staj_job2022_14;
do i=1 to 14;
	do j=1 to 14;
		if i~=j then do;
			if staj2022(i)>0 and staj2022(i)=staj2022(j) then check=1;
		end;
	end;
end;

Endjob22

%macro id(yra,yrb);
	%do j=1 %to 19;
		%do i=1 %to 14;
			if job&yra._job&yrb._&j=&i then job&yrb._job&yra._&i=&j;
			else if job&yra._job&yrb._&j=-4 and job&yrb._job&yra._&i=. then job&yrb._job&yra._&i=-4;
			else if diy&yrb=-5 or diy&yra=-5 then job&yrb._job&yra._&i=-5;
		%end;
	%end;
%mend id;
%macro rd(yrb);
    %do yra = 1979 %to 1994;
        %id(&yra,&yrb);
    %end; 
    %do yra = 1996 %to 2022 %by 2;
        %id(&yra,&yrb);
    %end; 
%mend rd;
%macro rds;
    %do yrb = 1979 %to 1994;
        %rd(&yrb);
    %end; 
    %do yrb = 1996 %to 2022 %by 2;
        %rd(&yrb);
    %end; 
%mend rds;
%rds;
if diy2000>0 then diy2000=2000;
if diy2002>0 then diy2002=2002;
if diy2004>0 then diy2004=2004;
if diy2006>0 then diy2006=2006;
if diy2008>0 then diy2008=2008;
if diy2010>0 then diy2010=2010;
if diy2012>0 then diy2012=2012;
if diy2014>0 then diy2014=2014;
if diy2016>0 then diy2016=2016;
if diy2018>0 then diy2018=2018;
if diy2020>0 then diy2020=2020;
if diy2022>0 then diy2022=2022;
%macro dd(a,yrb);
	%do j=1979 %to 1994 %by 1;
		if &j>=&yrb then do;
			if (job&j._job&yrb._&a>=1 and job&j._job&yrb._&a<=9) and diy&j>0 then do; 
				endj1_job&yrb._&a=put(&j,4.)||'0'||put(job&j._job&yrb._&a,1.);
			end;
			else if job&j._job&yrb._&a>=10 and diy&j>0 then do; 
				endj1_job&yrb._&a=put(&j,4.)||put(job&j._job&yrb._&a,2.);
			end;
		end;
	%end;
	%do j=1996 %to 2022 %by 2;
		if &j>=&yrb then do;
			if (job&j._job&yrb._&a>=1 and job&j._job&yrb._&a<=9) and diy&j>0 then do; 
				endj2_job&yrb._&a=put(&j,4.)||'0'||put(job&j._job&yrb._&a,1.);
			end;
			else if job&j._job&yrb._&a>=10 and diy&j>0 then do; 
				endj2_job&yrb._&a=put(&j,4.)||put(job&j._job&yrb._&a,2.);
			end;
		end;
	%end;
%mend dd;
%macro job(yrb);
	%do a=1 %to 19;
		%dd(&a,&yrb);
	%end;
%mend job;
%macro rds;
    %do yrb = 1979 %to 1994;
        %job(&yrb);
    %end; 
    %do yrb = 1996 %to 2022 %by 2;
        %job(&yrb);
    %end; 
%mend rds;
%rds;
%macro ee(b,yrc);
	%do i=1 %to 2 %by 1;
		if endj&i._job&yrc._&b ~=' ' then do;
			endj_job&yrc._&b=endj&i._job&yrc._&b;
		end;
	%end;
%mend ee;
%macro jb(yrc);
	%do b=1 %to 19;
		%ee(&b,&yrc);
	%end;
%mend jb;
%macro rd;
    %do yrc = 1979 %to 1994;
        %jb(&yrc);
    %end; 
    %do yrc = 1996 %to 2022 %by 2;
        %jb(&yrc);
    %end; 
%mend rd;
%rd;

Stopdate22_final

%macro id(a,yra,yrb);
	%do i=1 %to 9;
		if endj_job&yra._&a=put((&yrb.0&i),6.) then do;
			stod_final&yra._&a=stod&yrb._&i;
			stom_final&yra._&a=stom&yrb._&i;
			stoy_final&yra._&a=stoy&yrb._&i;
		end;
	%end;
	%do i=10 %to 14;*14 jobs in 2022;
		if endj_job&yra._&a=put((&yrb&i),6.) then do;
			stod_final&yra._&a=stod&yrb._&i;
			stom_final&yra._&a=stom&yrb._&i;
			stoy_final&yra._&a=stoy&yrb._&i;
		end;
	%end;
%mend id;
%macro jb(yra,yrb);
	%do a=1 %to 19;
		%id(&a,&yra,&yrb);
	%end;
%mend jb;
%macro rd(yrb);
    %do yra = 1979 %to 1994;
        %jb(&yra,&yrb);
    %end; 
    %do yra = 1996 %to 2022 %by 2;
        %jb(&yra,&yrb);
    %end; 
%mend rd;
%macro rds;
    %do yrb = 2022 %to 2022;
        %rd(&yrb);
    %end; 
%mend rds;
%rds;

Employers_roster_7922_job

array staj  (236)            staj_job1979_1-staj_job1979_5 staj_job1980_1-staj_job1980_5 staj_job1981_1-staj_job1981_5 staj_job1982_1-staj_job1982_5
                                                             staj_job1983_1-staj_job1983_5 staj_job1984_1-staj_job1984_5 staj_job1985_1-staj_job1985_5 staj_job1986_1-staj_job1986_5
                                                             staj_job1987_1-staj_job1987_5 staj_job1988_1-staj_job1988_5 staj_job1989_1-staj_job1989_5 staj_job1990_1-staj_job1990_5
                                                             staj_job1991_1-staj_job1991_5 staj_job1992_1-staj_job1992_5 staj_job1993_1-staj_job1993_5 staj_job1994_1-staj_job1994_5
                                                             staj_job1996_1-staj_job1996_5 staj_job1998_1-staj_job1998_12 staj_job2000_1-staj_job2000_10 staj_job2002_1-staj_job2002_9 
                                                             staj_job2004_1-staj_job2004_12 staj_job2006_1-staj_job2006_11 staj_job2008_1-staj_job2008_11 staj_job2010_1-staj_job2010_9
                                                             staj_job2012_1-staj_job2012_9 staj_job2014_1-staj_job2014_14 staj_job2016_1-staj_job2016_8 staj_job2018_1-staj_job2018_19
                                                             staj_job2020_1-staj_job2020_13 staj_job2022_1-staj_job2022_14;

do i=1 to 236;
            do j=1 to 236;
                        if i~=j then do;
                                    if staj(i)=-4|staj(i)=-5 then staj(i)=' ';
                                    else if staj(j)=-4| staj(j)=-5 then staj(j)=' ';
                                    else if staj(j)=staj(i) then staj(j)=' ' ;
                        end;
            end;
end;

array job          (*)        job1-job236;

count=0;
do i=1 to 236;
            if staj(i)~=' ' then do;
                        count=count+1;
                        j=count;
                        job(j)=staj(i);
            end;
end;


array stad        (j)                    stad1979_1-stad1979_5  stad1980_1-stad1980_5  stad1981_1-stad1981_5  stad1982_1-stad1982_5
                                                            stad1983_1-stad1983_5  stad1984_1-stad1984_5  stad1985_1-stad1985_5  stad1986_1-stad1986_5
                                                            stad1987_1-stad1987_5  stad1988_1-stad1988_5  stad1989_1-stad1989_5  stad1990_1-stad1990_5
                                                            stad1991_1-stad1991_5  stad1992_1-stad1992_5  stad1993_1-stad1993_5  stad1994_1-stad1994_5
                                                            stad1996_1-stad1996_5  stad1998_1-stad1998_12 stad2000_1-stad2000_10 stad2002_1-stad2002_9 
                                                            stad2004_1-stad2004_12 stad2006_1-stad2006_11 stad2008_1-stad2008_11 stad2010_1-stad2010_9
                                                            stad2012_1-stad2012_9  stad2014_1-stad2014_14 stad2016_1-stad2016_8 stad2018_1-stad2018_19
                                                            stad2020_1-stad2020_13 stad2022_1-stad2022_14;
array stam       (j)                    stam1979_1-stam1979_5  stam1980_1-stam1980_5  stam1981_1-stam1981_5  stam1982_1-stam1982_5
                                                            stam1983_1-stam1983_5  stam1984_1-stam1984_5  stam1985_1-stam1985_5  stam1986_1-stam1986_5
                                                            stam1987_1-stam1987_5  stam1988_1-stam1988_5  stam1989_1-stam1989_5  stam1990_1-stam1990_5
                                                            stam1991_1-stam1991_5  stam1992_1-stam1992_5  stam1993_1-stam1993_5  stam1994_1-stam1994_5
                                                            stam1996_1-stam1996_5  stam1998_1-stam1998_12 stam2000_1-stam2000_10 stam2002_1-stam2002_9 
                                                            stam2004_1-stam2004_12 stam2006_1-stam2006_11 stam2008_1-stam2008_11 stam2010_1-stam2010_9
                                                            stam2012_1-stam2012_9  stam2014_1-stam2014_14 stam2016_1-stam2016_8 stam2018_1-stam2018_19
                                                            stam2020_1-stam2020_13 stam2022_1-stam2022_14;
array stay        (j)                    stay1979_1-stay1979_5  stay1980_1-stay1980_5  stay1981_1-stay1981_5  stay1982_1-stay1982_5
                                                            stay1983_1-stay1983_5  stay1984_1-stay1984_5  stay1985_1-stay1985_5  stay1986_1-stay1986_5
                                                            stay1987_1-stay1987_5  stay1988_1-stay1988_5  stay1989_1-stay1989_5  stay1990_1-stay1990_5
                                                            stay1991_1-stay1991_5  stay1992_1-stay1992_5  stay1993_1-stay1993_5  stay1994_1-stay1994_5
                                                            stay1996_1-stay1996_5  stay1998_1-stay1998_12 stay2000_1-stay2000_10 stay2002_1-stay2002_9 
                                                            stay2004_1-stay2004_12 stay2006_1-stay2006_11 stay2008_1-stay2008_11 stay2010_1-stay2010_9
                                                            stay2012_1-stay2012_9  stay2014_1-stay2014_14 stay2016_1-stay2016_8 stay2018_1-stay2018_19
                                                            stay2020_1-stay2020_13 stay2022_1-stay2022_14;
array stod        (j)                    stod1979_1-stod1979_5  stod1980_1-stod1980_5  stod1981_1-stod1981_5  stod1982_1-stod1982_5
                                                            stod1983_1-stod1983_5  stod1984_1-stod1984_5  stod1985_1-stod1985_5  stod1986_1-stod1986_5
                                                            stod1987_1-stod1987_5  stod1988_1-stod1988_5  stod1989_1-stod1989_5  stod1990_1-stod1990_5
                                                            stod1991_1-stod1991_5  stod1992_1-stod1992_5  stod1993_1-stod1993_5  stod1994_1-stod1994_5
                                                            stod1996_1-stod1996_5  stod1998_1-stod1998_12 stod2000_1-stod2000_10 stod2002_1-stod2002_9 
                                                            stod2004_1-stod2004_12 stod2006_1-stod2006_11 stod2008_1-stod2008_11 stod2010_1-stod2010_9
                                                            stod2012_1-stod2012_9  stod2014_1-stod2014_14 stod2016_1-stod2016_8 stod2018_1-stod2018_19
                                                            stod2020_1-stod2020_13 stod2022_1-stod2022_14;
array stom       (j)                    stom1979_1-stom1979_5  stom1980_1-stom1980_5  stom1981_1-stom1981_5  stom1982_1-stom1982_5
                                                            stom1983_1-stom1983_5  stom1984_1-stom1984_5  stom1985_1-stom1985_5  stom1986_1-stom1986_5
                                                            stom1987_1-stom1987_5  stom1988_1-stom1988_5  stom1989_1-stom1989_5  stom1990_1-stom1990_5
                                                            stom1991_1-stom1991_5  stom1992_1-stom1992_5  stom1993_1-stom1993_5  stom1994_1-stom1994_5
                                                            stom1996_1-stom1996_5  stom1998_1-stom1998_12 stom2000_1-stom2000_10 stom2002_1-stom2002_9 
                                                            stom2004_1-stom2004_12 stom2006_1-stom2006_11 stom2008_1-stom2008_11 stom2010_1-stom2010_9
                                                            stom2012_1-stom2012_9  stom2014_1-stom2014_14 stom2016_1-stom2016_8 stom2018_1-stom2018_19
                                                            stom2020_1-stom2020_13 stom2022_1-stom2022_14;
array stoy        (j)                    stoy1979_1-stoy1979_5  stoy1980_1-stoy1980_5  stoy1981_1-stoy1981_5  stoy1982_1-stoy1982_5
                                                            stoy1983_1-stoy1983_5  stoy1984_1-stoy1984_5  stoy1985_1-stoy1985_5  stoy1986_1-stoy1986_5
                                                            stoy1987_1-stoy1987_5  stoy1988_1-stoy1988_5  stoy1989_1-stoy1989_5  stoy1990_1-stoy1990_5
                                                            stoy1991_1-stoy1991_5  stoy1992_1-stoy1992_5  stoy1993_1-stoy1993_5  stoy1994_1-stoy1994_5
                                                            stoy1996_1-stoy1996_5  stoy1998_1-stoy1998_12 stoy2000_1-stoy2000_10 stoy2002_1-stoy2002_9 
                                                            stoy2004_1-stoy2004_12 stoy2006_1-stoy2006_11 stoy2008_1-stoy2008_11 stoy2010_1-stoy2010_9
                                                            stoy2012_1-stoy2012_9  stoy2014_1-stoy2014_14 stoy2016_1-stoy2016_8 stoy2018_1-stoy2018_19
                                                            stoy2020_1-stoy2020_13 stoy2022_1-stoy2022_14;
array stadp1 (j)           stadp1_1979_1-stadp1_1979_5  stadp1_1980_1-stadp1_1980_5  stadp1_1981_1-stadp1_1981_5  stadp1_1982_1-stadp1_1982_5
                                                            stadp1_1983_1-stadp1_1983_5  stadp1_1984_1-stadp1_1984_5  stadp1_1985_1-stadp1_1985_5  stadp1_1986_1-stadp1_1986_5
                                                            stadp1_1987_1-stadp1_1987_5  stadp1_1988_1-stadp1_1988_5  stadp1_1989_1-stadp1_1989_5  stadp1_1990_1-stadp1_1990_5
                                                            stadp1_1991_1-stadp1_1991_5  stadp1_1992_1-stadp1_1992_5  stadp1_1993_1-stadp1_1993_5  stadp1_1994_1-stadp1_1994_5
                                                            stadp1_1996_1-stadp1_1996_5  stadp1_1998_1-stadp1_1998_12 stadp1_2000_1-stadp1_2000_10 stadp1_2002_1-stadp1_2002_9 
                                                            stadp1_2004_1-stadp1_2004_12 stadp1_2006_1-stadp1_2006_11 stadp1_2008_1-stadp1_2008_11 stadp1_2010_1-stadp1_2010_9
                                                            stadp1_2012_1-stadp1_2012_9  stadp1_2014_1-stadp1_2014_14 stadp1_2016_1-stadp1_2016_8 stadp1_2018_1-stadp1_2018_19
                                                            stadp1_2020_1-stadp1_2020_13 stadp1_2022_1-stadp1_2022_14;
array stamp1 (j)          stamp1_1979_1-stamp1_1979_5  stamp1_1980_1-stamp1_1980_5  stamp1_1981_1-stamp1_1981_5  stamp1_1982_1-stamp1_1982_5
                                                            stamp1_1983_1-stamp1_1983_5  stamp1_1984_1-stamp1_1984_5  stamp1_1985_1-stamp1_1985_5  stamp1_1986_1-stamp1_1986_5
                                                            stamp1_1987_1-stamp1_1987_5  stamp1_1988_1-stamp1_1988_5  stamp1_1989_1-stamp1_1989_5  stamp1_1990_1-stamp1_1990_5
                                                            stamp1_1991_1-stamp1_1991_5  stamp1_1992_1-stamp1_1992_5  stamp1_1993_1-stamp1_1993_5  stamp1_1994_1-stamp1_1994_5
                                                            stamp1_1996_1-stamp1_1996_5  stamp1_1998_1-stamp1_1998_12 stamp1_2000_1-stamp1_2000_10 stamp1_2002_1-stamp1_2002_9 
                                                            stamp1_2004_1-stamp1_2004_12 stamp1_2006_1-stamp1_2006_11 stamp1_2008_1-stamp1_2008_11 stamp1_2010_1-stamp1_2010_9
                                                            stamp1_2012_1-stamp1_2012_9  stamp1_2014_1-stamp1_2014_14 stamp1_2016_1-stamp1_2016_8 stamp1_2018_1-stamp1_2018_19
                                                            stamp1_2020_1-stamp1_2020_13 stamp1_2022_1-stamp1_2022_14;
array stayp1 (j)           stayp1_1979_1-stayp1_1979_5  stayp1_1980_1-stayp1_1980_5  stayp1_1981_1-stayp1_1981_5  stayp1_1982_1-stayp1_1982_5
                                                            stayp1_1983_1-stayp1_1983_5  stayp1_1984_1-stayp1_1984_5  stayp1_1985_1-stayp1_1985_5  stayp1_1986_1-stayp1_1986_5
                                                            stayp1_1987_1-stayp1_1987_5  stayp1_1988_1-stayp1_1988_5  stayp1_1989_1-stayp1_1989_5  stayp1_1990_1-stayp1_1990_5
                                                            stayp1_1991_1-stayp1_1991_5  stayp1_1992_1-stayp1_1992_5  stayp1_1993_1-stayp1_1993_5  stayp1_1994_1-stayp1_1994_5
                                                            stayp1_1996_1-stayp1_1996_5  stayp1_1998_1-stayp1_1998_12 stayp1_2000_1-stayp1_2000_10 stayp1_2002_1-stayp1_2002_9 
                                                            stayp1_2004_1-stayp1_2004_12 stayp1_2006_1-stayp1_2006_11 stayp1_2008_1-stayp1_2008_11 stayp1_2010_1-stayp1_2010_9
                                                            stayp1_2012_1-stayp1_2012_9  stayp1_2014_1-stayp1_2014_14 stayp1_2016_1-stayp1_2016_8  stayp1_2018_1-stayp1_2018_19
                                                            stayp1_2020_1-stayp1_2020_13 stayp1_2022_1-stayp1_2022_14;
array code (j)  $  a1-a236 
                                                            ('197901' '197902' '197903' '197904' '197905'
                                                             '198001' '198002' '198003' '198004' '198005'
                                                             '198101' '198102' '198103' '198104' '198105'
                                                             '198201' '198202' '198203' '198204' '198205'
                                                             '198301' '198302' '198303' '198304' '198305'
                                                             '198401' '198402' '198403' '198404' '198405'
                                                             '198501' '198502' '198503' '198504' '198505'
                                                             '198601' '198602' '198603' '198604' '198605'
                                                             '198701' '198702' '198703' '198704' '198705'
                                                             '198801' '198802' '198803' '198804' '198805'
                                                             '198901' '198902' '198903' '198904' '198905'
                                                             '199001' '199002' '199003' '199004' '199005'
                                                             '199101' '199102' '199103' '199104' '199105'
                                                             '199201' '199202' '199203' '199204' '199205'
                                                             '199301' '199302' '199303' '199304' '199305'
                                                             '199401' '199402' '199403' '199404' '199405'
                                                             '199601' '199602' '199603' '199604' '199605'
                                                             '199801' '199802' '199803' '199804' '199805' '199806' '199807' '199808' '199809' '199810'
                                                             '199811' '199812'
                                                             '200001' '200002' '200003' '200004' '200005' '200006' '200007' '200008' '200009' '200010'
                                                             '200201' '200202' '200203' '200204' '200205' '200206' '200207' '200208' '200209'
                                                             '200401' '200402' '200403' '200404' '200405' '200406' '200407' '200408' '200409' '200410'
                                                             '200411' '200412'
                                                             '200601' '200602' '200603' '200604' '200605' '200606' '200607' '200608' '200609' '200610'
                                                             '200611'
                                                             '200801' '200802' '200803' '200804' '200805' '200806' '200807' '200808' '200809' '200810'
                                                             '200811'
                                                             '201001' '201002' '201003' '201004' '201005' '201006' '201007' '201008' '201009'
                                                             '201201' '201202' '201203' '201204' '201205' '201206' '201207' '201208' '201209'                                       
                                                             '201401' '201402' '201403' '201404' '201405' '201406' '201407' '201408' '201409' '201410'
                                                             '201411' '201412' '201413' '201414'
                                                             '201601' '201602' '201603' '201604' '201605' '201606' '201607' '201608'
                                                             '201801' '201802' '201803' '201804' '201805' '201806' '201807' '201808' '201809' '201810'
                                                             '201811' '201812' '201813' '201814' '201815' '201816' '201817' '201818' '201819'
                                                             '202001' '202002' '202003' '202004' '202005' '202006' '202007' '202008' '202009' '202010'
                                                             '202011' '202012' '202013'                                         
                                                             '202201' '202202' '202203' '202204' '202205' '202206' '202207' '202208' '202209' '202210'
                                                             '202211' '202212' '202213' '202214');                                               

do over stay;
            sta_d=stad;
            sta_m=stam;
            sta_y=stay;
            sto_d=stod;
            sto_m=stom;
            sto_y=stoy;
            gapsta_d=stadp1;
            gapsta_m=stamp1;
            gapsta_y=stayp1;

            jobcode=code;
            output;
end;

array dido (k)              did1979 did1980 did1981 did1982    did1983 did1984 did1985 did1986 did1987 did1988 
                                                            did1989 did1990         did1991 did1992 did1993 did1994 did1996 did1998 did2000 did2002
                                                            did2004 did2006 did2008 did2010 did2012 did2014 did2016 did2018 did2020 did2022;

array dimo (k)             dim1979 dim1980 dim1981 dim1982 dim1983 dim1984 dim1985 dim1986 dim1987 dim1988
                                                            dim1989 dim1990      dim1991 dim1992 dim1993 dim1994 dim1996 dim1998 dim2000 dim2002
                                                            dim2004 dim2006 dim2008 dim2010 dim2012 dim2014 dim2016 dim2018 dim2020 dim2022;

array diyo (k)              diy1979 diy1980 diy1981 diy1982    diy1983 diy1984 diy1985 diy1986 diy1987 diy1988
                                                            diy1989 diy1990         diy1991 diy1992 diy1993 diy1994 diy1996 diy1998 diy2000 diy2002
                                                            diy2004 diy2006 diy2008 diy2010 diy2012 diy2014 diy2016 diy2018 diy2020 diy2022;

array dlido (k)             dlid1979 dlid1980 dlid1981 dlid1982 dlid1983 dlid1984 dlid1985 dlid1986 dlid1987 dlid1988
                                                            dlid1989 dlid1990 dlid1991 dlid1992 dlid1993 dlid1994 dlid1996 dlid1998 dlid2000 dlid2002
                                                            dlid2004 dlid2006 dlid2008 dlid2010 dlid2012 dlid2014 dlid2016 dlid2018 dlid2020 dlid2022;

array dlimo (k)                        dlim1979 dlim1980 dlim1981 dlim1982       dlim1983 dlim1984 dlim1985 dlim1986 dlim1987 dlim1988
                                                            dlim1989 dlim1990 dlim1991 dlim1992 dlim1993 dlim1994 dlim1996 dlim1998 dlim2000 dlim2002
                                                            dlim2004 dlim2006 dlim2008 dlim2010 dlim2012 dlim2014 dlim2016 dlim2018 dlim2020 dlim2022;

array dliyo (k)             dliy1979 dliy1980 dliy1981 dliy1982 dliy1983 dliy1984 dliy1985 dliy1986 dliy1987 dliy1988
                                                            dliy1989 dliy1990 dliy1991 dliy1992 dliy1993 dliy1994 dliy1996 dliy1998 dliy2000 dliy2002
                                                            dliy2004 dliy2006 dliy2008 dliy2010 dliy2012 dliy2014 dliy2016 dliy2018 dliy2020 dliy2022;
array code (k)  $  a1-a30
                                                            ('1979' '1980' '1981' '1982' '1983' '1984' '1985' '1986' '1987' '1988' 
                                                             '1989' '1990' '1991' '1992' '1993' '1994' '1996' '1998' '2000' '2002'
                                                             '2004' '2006' '2008' '2010' '2012' '2014' '2016' '2018' '2020' '2022');                                       
do over dido;
            dim=dimo;
            did=dido;
            diy=diyo;
            dlim=dlimo;
            dlid=dlido;
            dliy=dliyo;

            year=code;
            output;
end;

if sta_y=0 or sta_y=1900 then do;
            sta_d=-3;
            sta_m=-3;
            sta_y=-3;
end;

lintdate=mdy(dlim, dlid, dliy);
startdate=mdy(sta_m, sta_d, sta_y);
gapdate=mdy(gapsta_m, gapsta_d, gapsta_y);
stopdate=mdy(sto_m, sto_d, sto_y);
intdate=mdy(dim, did, diy);

if (sta_d<0 and sta_m>0 and sta_y>0) then do;
            sta_d1=1;
            sta_m1=sta_m;
            sta_y1=sta_y;
end;

else if (sta_d<0 and sta_m<0 and sta_y>0) then do;
            sta_d1=1;
            sta_m1=1;
            sta_y1=sta_y;
end;

else if (sta_d>0 and sta_m<0 and sta_y>0) then do;
            if stopdate>0 and sta_y<sto_y and sta_y>dliy then do;
                        sta_m1=1;
                        sta_d1=sta_d;
                        sta_y1=sta_y;
            end;
            else if stopdate>0 and sta_y<sto_y and sta_y=dliy then do;
                        sta_m1=dlim+3;
                        sta_d1=sta_d;
                        sta_y1=sta_y;
            end;
            else if stopdate>0 and sta_y=sto_y and sta_y>=dliy then do;
                        sta_m1=sto_m-1;
                        sta_d1=sta_d;
                        sta_y1=sta_y;
            end;

            else if stopdate=. and sto_y>0 and dliy<sto_y then do;
                        sta_m1=1;
                        sta_d1=sta_d;
                        sta_y1=sta_y;
            end;
end;


else if (sta_y<0) then do;
            if gapsta_y>0 then do;
                        if gapsta_d<0 and gapsta_m<0 then do;
                                    sta_d1=1;
                                    sta_m1=8;
                                    sta_y1=2004;
                        end;
                        else if gapsta_d<0 and gapsta_m>0 then do;
                                    gapsta_d=1;
                                    a=((mdy(gapsta_m, gapsta_d, gapsta_y))-(lintdate))/2;
                                    b=lintdate+a;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                        end;
                        else if gapsta_d>0 and gapsta_m>0 then do;;
                                    if year=1979 then do;
                                                sta_d1=gapsta_d;
                                                sta_m1=gapsta_m;
                                                sta_y1=gapsta_y;
                                    end;
                                    else if year~=1979 then do;
                                    a=(gapdate-lintdate)/2;
                                    b=lintdate+a;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                                    end;
                        end;
            end;
            else if gapsta_y<0 then do;
                        if year=1979 then do;
                                    b=intdate-180;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                        end;
                        else if stopdate<lintdate and stopdate~=. then do;
                                    b=stopdate-90;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                        end;
                        else if stopdate<intdate and stopdate~=. then do;
                                    a=(stopdate-lintdate)/2;
                                    b=lintdate+a;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                        end;
                        else if stopdate=intdate or stopdate=. then do;
                                    a=(intdate-lintdate)/2;
                                    b=lintdate+a;
                                    sta_d1=day(b);
                                    sta_m1=month(b);
                                    sta_y1=year(b);
                        end;
            end;
end;
else do;
            sta_d1=sta_d;
            sta_m1=sta_m;
            sta_y1=sta_y;
end;

if sta_y1<0 then do;
            c=intdate-365;
            sta_d1=day(c);
            sta_m1=month(c);
            sta_y1=year(c);
end;

startdate1=mdy(sta_m1, sta_d1, sta_y1);

if diy>=2014 then do;
            if startdate1<lintdate then do;
                        sta_m1=dlim;
                        sta_d1=dlid+1;
                        sta_y1=dliy;
            end;
end;

proc sort; by norcid sta_y1 sta_m1 sta_d1;
run;dliy then do;
		sta_m1=1;
		sta_d1=sta_d;
		sta_y1=sta_y;
	end;
	else if stopdate>0 and sta_y0 and sta_y=sto_y and sta_y>=dliy then do;
		sta_m1=sto_m-1;
		sta_d1=sta_d;
		sta_y1=sta_y;
	end;
	else if stopdate=. and sto_y>0 and dliy0 then do;
		if gapsta_d<0 and gapsta_m<0 then do;
			sta_d1=1;
			sta_m1=8;
			sta_y1=2004;
		end;
		else if gapsta_d<0 and gapsta_m>0 then do;
			gapsta_d=1;
			a=((mdy(gapsta_m, gapsta_d, gapsta_y))-(lintdate))/2;
 			b=lintdate+a;
			sta_d1=day(b);
			sta_m1=month(b);
			sta_y1=year(b);
		end;
		else if gapsta_d>0 and gapsta_m>0 then do;;
			if year=1979 then do;
				sta_d1=gapsta_d;
				sta_m1=gapsta_m;
				sta_y1=gapsta_y;
			end;
			else if year~=1979 then do;
			a=(gapdate-lintdate)/2;
 			b=lintdate+a;
			sta_d1=day(b);
			sta_m1=month(b);
			sta_y1=year(b);
			end;
		end;
	end;
	else if gapsta_y<0 then do;
		if year=1979 then do;
 			b=intdate-180;
			sta_d1=day(b);
			sta_m1=month(b);
			sta_y1=year(b);
		end;
		else if stopdate=2014 then do;
	if startdate1

Employers_roster_22_uid_name_stadate

%macro id(a,yra);
	%do i=1 %to 9;
		if job&a=put((&yra.0&i),6.) then do;
			unique_id_&a=put((&yra.0&i),6.)||'00';
			if empname&yra._&i~=' ' then empname_&a=empname&yra._&i;
			else if empname&yra._&i=' ' then empname_&a='MISSING NAME';
			if (stad&yra._&i~=-4|stam&yra._&i~=-4|stay&yra._&i~=-4) then do;
				stad_ori_&a=stad&yra._&i;
				stam_ori_&a=stam&yra._&i;
				stay_ori_&a=stay&yra._&i;
			end;
			else if (stad&yra._&i=-4 and stam&yra._&i=-4 and stay&yra._&i=-4) then do;
				stad_ori_&a=-3;
				stam_ori_&a=-3;
				stay_ori_&a=-3;
			end;
		end;
	%end;
	%do i=10 %to 14;
		if job&a=put((&yra&i),6.) then do;
			unique_id_&a=put((&yra&i),6.)||'00';
			if empname&yra._&i~=' ' then empname_&a=empname&yra._&i;
			else if empname&yra._&i=' ' then empname_&a='MISSING NAME';
			if (stad&yra._&i~=-4|stam&yra._&i~=-4|stay&yra._&i~=-4) then do;
				stad_ori_&a=stad&yra._&i;
				stam_ori_&a=stam&yra._&i;
				stay_ori_&a=stay&yra._&i;
			end;
			else if (stad&yra._&i=-4 and stam&yra._&i=-4 and stay&yra._&i=-4) then do;
				stad_ori_&a=-3;
				stam_ori_&a=-3;
				stay_ori_&a=-3;
			end;
		end;
	%end;
%mend id;
%macro jb(yra);
	%do a=1 %to 67;
		%id(&a,&yra);
	%end;
%mend jb;
%macro rds;
    %do yra = 2022 %to 2022;
        %jb(&yra);
    %end; 
%mend rds;
%rds;

Employers_roster_22_id

%macro id(yra);
	%do a=1 %to 67;
		%do i=1 %to 9;
			if job&a=put((&yra.0&i),6.) and job2022_job&yra._&i>0 then id_2022_&a=job2022_job&yra._&i;
			else if job&a=put((&yra.0&i),6.) and job2022_job&yra._&i=-5 then id_2022_&a=-5;
		%end;
		%do i=10 %to 19;
			if job&a=put((&yra&i),6.) and job2022_job&yra._&i>0 then id_2022_&a=job2022_job&yra._&i;
			else if job&a=put((&yra&i),6.) and job2022_job&yra._&i=-5 then id_2022_&a=-5;
		%end;
	%end;
%mend id;
%macro rd;
    %do yra = 1979 %to 1994;
        %id(&yra);
    %end; 
    %do yra = 1996 %to 2022 %by 2;
        %id(&yra);
    %end; 
%mend rd;
%rd;

Employers_roster_22_others

%macro att(yrb,b);
	%do l=1 %to 14;
		if id_&yrb._&b=&l then do;
			*CPS job;
			if cpsemp&yrb._&l~=-4 and cpsemp&yrb._&l~=. then cpsjob_&yrb._&b=cpsemp&yrb._&l;
			else if cpsemp&yrb._&l=-4|cpsemp&yrb._&l=. then cpsjob_&yrb._&b=-4;
			*occupation code;
			if occ&yrb._&l~=-4 and occ&yrb._&l~=. then occ_&yrb._&b=occ&yrb._&l;
			else if occ&yrb._&l=-4|occ&yrb._&l=. then occ_&yrb._&b=-4;
			*industry code;
			if ind&yrb._&l~=-4 and ind&yrb._&l~=. then ind_&yrb._&b=ind&yrb._&l;
			else if ind&yrb._&l=-4|ind&yrb._&l=. then ind_&yrb._&b=-4;
			*class of worker;
			if cow&yrb._&l~=-4 and cow&yrb._&l~=. then cow_&yrb._&b=cow&yrb._&l;
			else if cow&yrb._&l=-4|cow&yrb._&l=. then cow_&yrb._&b=-4;
			*rate of pay;
			if rop&yrb._&l~=-4 and rop&yrb._&l~=. then rop_&yrb._&b=rop&yrb._&l;
			else if rop&yrb._&l=-4|rop&yrb._&l=. then rop_&yrb._&b=-4;
			*time unit to interpret payrate;
			if tu&yrb._&l~=-4 and tu&yrb._&l~=. then turop_&yrb._&b=tu&yrb._&l;
			else if tu&yrb._&l=-4|tu&yrb._&l=. then turop_&yrb._&b=-4;
			*hourly rate of pay;
			if hrp&yrb._&l~=-4 and hrp&yrb._&l~=. then hrp_&yrb._&b=hrp&yrb._&l;
			else if hrp&yrb._&l=-4|hrp&yrb._&l=. then hrp_&yrb._&b=-4;
			*union;
			if uni&yrb._&l~=-4 and uni&yrb._&l~=. then uni_&yrb._&b=uni&yrb._&l;
			else if uni&yrb._&l=-4|uni&yrb._&l=. then uni_&yrb._&b=-4;
			*hours worked per day;
			if hrsday&yrb._&l~=-4 and  hrsday&yrb._&l~=. then hrsday_&yrb._&b=hrsday&yrb._&l;
			else if hrsday&yrb._&l=-4|hrsday&yrb._&l=. then hrsday_&yrb._&b=-4;
			*hours worked per week;
			if hrswk&yrb._&l~=-4 and hrswk&yrb._&l~=. then hrswk_&yrb._&b=hrswk&yrb._&l;
			else if hrswk&yrb._&l=-4|hrswk&yrb._&l=. then hrswk_&yrb._&b=-4;
			*months worked for employer before last int;
			if mosli&yrb._&l~=-4 and mosli&yrb._&l~=. then mosli_&yrb._&b=mosli&yrb._&l;
			else if mosli&yrb._&l=-4|mosli&yrb._&l=. then mosli_&yrb._&b=-4;
			*tenure;
			if ten&yrb._&l~=-4 and ten&yrb._&l~=. then tenure_&yrb._&b=ten&yrb._&l;
			else if ten&yrb._&l=-4|ten&yrb._&l=. then tenure_&yrb._&b=-4;
			*working at job before di;
			if (wbefdi&yrb._&l~=-4 and wbefdi&yrb._&l~=.) then wbefdi_&yrb._&b=wbefdi&yrb._&l;
			if (wbefdi&yrb._&l=-4|wbefdi&yrb._&l=.) then wbefdi_&yrb._&b=-4;
			*previous employer ID;
			if (previd&yrb._&l~=-4 and previd&yrb._&l~=.) then previd_&yrb._&b=previd&yrb._&l;
			if (previd&yrb._&l=-4|previd&yrb._&l=.) then previd_&yrb._&b=-4;
			*job number loaded into A array;
			if job_num_a&yrb._&l~=-4 and job_num_a&yrb._&l~=. then jobnum_a_&yrb._&b=job_num_a&yrb._&l;
			else if job_num_a&yrb._&l=-4|job_num_a&yrb._&l=. then jobnum_a_&yrb._&b=-4;
			*currently woring at job;
			if curwk&yrb._&l~=-4 and curwk&yrb._&l~=. then curwk_&yrb._&b=curwk&yrb._&l;
			else if curwk&yrb._&l=-4|curwk&yrb._&l=. then curwk_&yrb._&b=-4;
			*reason left job;
			if (curwk&yrb._&l>=-3 and curwk&yrb._&l<=0) and (releft&yrb._&l~=-4 and releft&yrb._&l~=.) then releft_&yrb._&b=releft&yrb._&l;
			else if (curwk&yrb._&l>=-3 and curwk&yrb._&l<0) and (releft&yrb._&l=-4|releft&yrb._&l=.) then releft_&yrb._&b=-4;
			else if curwk&yrb._&l=0 and (releft&yrb._&l=-4|releft&yrb._&l=.) then releft_&yrb._&b=-3;
			else if curwk&yrb._&l=-4 and (releft&yrb._&l=-4|releft&yrb._&l=.) then releft_&yrb._&b=-4;
			else if curwk&yrb._&l=1 then releft_&yrb._&b=-4;
		end;
		else if id_&yrb._&b=-5 then do;
			cpsjob_&yrb._&b=-5;
			occ_&yrb._&b=-5;
			ind_&yrb._&b=-5;
			cow_&yrb._&b=-5;
			rop_&yrb._&b=-5;
			turop_&yrb._&b=-5;
			hrp_&yrb._&b=-5;
			uni_&yrb._&b=-5;
			hrsday_&yrb._&b=-5;
			hrswk_&yrb._&b=-5;
			mosli_&yrb._&b=-5;
			tenure_&yrb._&b=-5;
			wbefdi_&yrb._&b=-5;
			previd_&yrb._&b=-5;
			jobnum_a_&yrb._&b=-5;
			curwk_&yrb._&b=-5;
			releft_&yrb._&b=-5;
		end;
	%end;
%mend att;
%macro rd(b);
    %do yrb = 2022 %to 2022;
        %att(&yrb,&b);
    %end; 
%mend rd;
%macro job;
	%do b=1 %to 67;
		%rd(&b);
	%end;
%mend job;
%job;

Employers_roster_22_stodate_whyleft

%macro id(a,yra,yrb);
	%do i=1 %to 9;
		if endj_job&yra._&a=put((&yrb.0&i),6.) then do;
			if curwk&yrb._&i>=-3 & curwk&yrb._&i<=0 then do;
				reasonleft&yra._&a=whyleft_recode&yrb._&i;
				reasonleftC&yra._&a=whyleft_recodeC&yrb._&i;
			end;
			else if (curwk&yrb._&i>=-3 & curwk&yrb._&i<0) & (whyleft_recode&yrb._&i=.) then do;
				reasonleft&yra._&a=-4;
				reasonleftC&yra._&a=-4;
			end;
			else if curwk&yrb._&i=0 & (whyleft_recode&yrb._&i=.) then do;
				reasonleft&yra._&a=-3;
				reasonleftC&yra._&a=-3;
			end;
		end;
	%end;
	%do i=10 %to 14;
		if endj_job&yra._&a=put((&yrb&i),6.) then do;
			if curwk&yrb._&i>=-3 & curwk&yrb._&i<=0 then do;
				reasonleft&yra._&a=whyleft_recode&yrb._&i;
				reasonleftC&yra._&a=whyleft_recodeC&yrb._&i;
			end;
			else if (curwk&yrb._&i>=-3 & curwk&yrb._&i<0) & (whyleft_recode&yrb._&i=.) then do;
				reasonleft&yra._&a=-4;
				reasonleftC&yra._&a=-4;
			end;
			else if curwk&yrb._&i=0 & (whyleft_recode&yrb._&i=.) then do;
				reasonleft&yra._&a=-3;
				reasonleftC&yra._&a=-3;
			end;
		end;
	%end;
%mend id;
%macro jb(yra,yrb);
	%do a=1 %to 19;
		%id(&a,&yra,&yrb);
	%end;
%mend jb;
%macro rd(yrb);
    %do yra = 1979 %to 1994;
        %jb(&yra,&yrb);
    %end; 
    %do yra = 1996 %to 2022 %by 2;
        %jb(&yra,&yrb);
    %end; 
%mend rd;
%macro rds;
    %do yrb = 2022 %to 2022;
        %rd(&yrb);
    %end; 
%mend rds;
%rds;
%macro id(a,yra);
	%do i=1 %to 9;
		if job&a=put((&yra.0&i),6.) then do;
			if (stod_final&yra._&i~=-4|stom_final&yra._&i~=-4|stoy_final&yra._&i~=-4) then do;
				stod_fin_&a=stod_final&yra._&i;
				stom_fin_&a=stom_final&yra._&i;
				stoy_fin_&a=stoy_final&yra._&i;
			end;
			else if (stod_final&yra._&i=-4 and stom_final&yra._&i=-4 and stoy_final&yra._&i=-4) then do;
				stod_fin_&a=-3;
				stom_fin_&a=-3;
				stoy_fin_&a=-3;
			end;
			reasonleft_&a=reasonleft&yra._&i;
			reasonleftC_&a=reasonleftC&yra._&i;
		end;
	%end;
	%do i=10 %to 14;
		if job&a=put((&yra&i),6.) then do;
			if (stod_final&yra._&i~=-4|stom_final&yra._&i~=-4|stoy_final&yra._&i~=-4) then do;
				stod_fin_&a=stod_final&yra._&i;
				stom_fin_&a=stom_final&yra._&i;
				stoy_fin_&a=stoy_final&yra._&i;
			end;
			else if (stod_final&yra._&i=-4 and stom_final&yra._&i=-4 and stoy_final&yra._&i=-4) then do;
				stod_fin_&a=-3;
				stom_fin_&a=-3;
				stoy_fin_&a=-3;
			end;
			reasonleft_&a=reasonleft&yra._&i;
			reasonleftC_&a=reasonleftC&yra._&i;
		end;
	%end;
%mend id;
%macro jb(yra);
	%do a=1 %to 67;
		%id(&a,&yra);
	%end;
%mend jb;
%macro rds;
    %do yra = 1979 %to 1994;
        %jb(&yra);
    %end; 
    %do yra = 1996 %to 2022 %by 2;
        %jb(&yra);
    %end; 
%mend rds;
%rds;

Employers_roster_22_stdate_stweek

%macro att(yrb,b);
	%do l=1 %to 14;
		if id_&yrb._&b=&l then do;
			*Start day;
			if stad&yrb._&l~=-4 then stad_&yrb._&b=stad&yrb._&l;
			else if stad&yrb._&l=-4 then stad_&yrb._&b=-3;
			*Start month;
			if stam&yrb._&l~=-4 then stam_&yrb._&b=stam&yrb._&l;
			else if stam&yrb._&l=-4 then stam_&yrb._&b=-3;
			*Start year;
			if stay&yrb._&l~=-4 then stay_&yrb._&b=stay&yrb._&l;
			else if stay&yrb._&l=-4 then stay_&yrb._&b=-3;
			*Stop day;
			if stod&yrb._&l~=-4 then stod_&yrb._&b=stod&yrb._&l;
			else if stod&yrb._&l=-4 then stod_&yrb._&b=-3;
			*Stop month;
			if stom&yrb._&l~=-4 then stom_&yrb._&b=stom&yrb._&l;
			else if stom&yrb._&l=-4 then stom_&yrb._&b=-3;
			*Stop year;
			if stoy&yrb._&l~=-4 then stoy_&yrb._&b=stoy&yrb._&l;
			else if stoy&yrb._&l=-4 then stoy_&yrb._&b=-3;
			*Start week;
			if stawk&yrb._&l~=-4 then stawk_&yrb._&b=stawk&yrb._&l;
			else if stawk&yrb._&l=-4 then stawk_&yrb._&b=-3;
			*Stop week;
			if stowk&yrb._&l~=-4 then stowk_&yrb._&b=stowk&yrb._&l;
			else if stowk&yrb._&l=-4 then stowk_&yrb._&b=-3;
		end;
		else if id_&yrb._&b=-5 then do;
			stad_&yrb._&b=-5;
			stam_&yrb._&b=-5;
			stay_&yrb._&b=-5;
			stod_&yrb._&b=-5;
			stom_&yrb._&b=-5;
			stoy_&yrb._&b=-5;
			stawk_&yrb._&b=-5;
			stowk_&yrb._&b=-5;
		end;
	%end;
%mend att;
%macro rd(b);
    %do yrb = 2022 %to 2022;
        %att(&yrb,&b);
    %end; 
%mend rd;
%macro job;
	%do b=1 %to 67;
		%rd(&b);
	%end;
%mend job;
%job;

Employers_roster_22_wksnotworked

%macro att(b);
	%do l=1 %to 19;
		%do k=1 %to 4;
			if id_2022_&b=&l and unemsdli2022_&l=1 then do;
				unem_2022_&b=1;
				if stawkgap&k._2022_&l~=-4 and stawkgap&k._2022_&l~=. then stawk_gap&k._2022_&b=stawkgap&k._2022_&l;
				if stowkgap&k._2022_&l~=-4 and stowkgap&k._2022_&l~=. then stowk_gap&k._2022_&b=stowkgap&k._2022_&l;
				if reap&k._2022_&l~=-4 and reap&k._2022_&l~=. then rea_gap&k._2022_&b=reap&k._2022_&l;
				if lookp&k._2022_&l~=-4 and lookp&k._2022_&l~=. then look_gap&k._2022_&b=lookp&k._2022_&l;
				if nlookp&k._2022_&l~=-4 and nlookp&k._2022_&l~=. then nlook_gap&k._2022_&b=nlookp&k._2022_&l;
			end;
		%end;
	%end;
%mend att;
%macro job;
	%do b=1 %to 67;
		%att(&b);
	%end;
%mend job;
%job;

Read more about NLSY79 Appendix 28: NLSY79 Employer History Roster

NLSY79 Appendix 27: IRT Item Parameter Estimates, Scores and Standard Errors

Item Response Theory (IRT) item parameter estimates, scores and standard errors have been calculated for several attitudinal scales administered in multiple NLSY79 survey years as well as in other NLS cohorts. The following PDF files provide detailed descriptions of the creation procedures for these scales:

Read more about NLSY79 Appendix 27: IRT Item Parameter Estimates, Scores and Standard Errors

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NLSY79

Specifications for states and outlying areas of the United States

Alabama through Montana FIPS codes

01 ALABAMA (AL)

02 ALASKA (AK)

04 ARIZONA (AZ)

05 ARKANSAS (AR)

06 CALIFORNIA (CA)

08 COLORADO (CO)

09 CONNECTICUT (CT)

10 DELAWARE (DE)

11 DISTRICT OF COLUMBIA (DC)

12 FLORIDA (FL)

13 GEORGIA (GA)

15 HAWAII (HI)

16 IDAHO (ID)

17 ILLINOIS (IL)

18 INDIANA (IN)

19 IOWA (IA)

20 KANSAS (KS)

21 KENTUCKY (KY)

22 LOUISIANA (LA)

23 MAINE (ME)

24 MARYLAND (MD)

25 MASSACHUSETTS (MA)

26 MICHIGAN (MI)

27 MINNESOTA (MN)

28 MISSISSIPPI (MS)

29 MISSOURI (MO)

30 MONTANA (MT)

Nebraska through Wyoming FIPS codes

31 NEBRASKA (NE)

32 NEVADA (NV)

33 NEW HAMPSHIRE (NH)

34 NEW JERSEY (NJ)

35 NEW MEXICO (NM)

36 NEW YORK (NY)

37 NORTH CAROLINA (NC)

38 NORTH DAKOTA (ND)

39 OHIO (OH)

40 OKLAHOMA (OK)

41 OREGON (OR)

42 PENNSYLVANIA (PA)

44 RHODE ISLAND (RI)

45 SOUTH CAROLINA (SC)

46 SOUTH DAKOTA (SD)

47 TENNESSEE (TN)

48 TEXAS (TX)

49 UTAH (UT)

50 VERMONT (VT)

51 VIRGINIA (VA)

53 WASHINGTON (WA)

54 WEST VIRGINIA (WV)

55 WISCONSIN (WI)

56 WYOMING (WY)

Foreign countries (1979-1992 PAPI-collected coding)

Foreign countries (1993-2022 CAPI-collected coding)

Documentation for the NLSY79 Geocode data files

Geocode data format

Geocode data source files and content

County and City Data Books

City Reference Files

Local Exchange Routing Guides

Geostatistical mapping software

1979-1988 Geocode data creation procedures

Hand-edits and changes in matching procedures

1979-1982

1983-1988

1981 changes in SMSA designations

Rewrite of 1979-1982 Geocode data

1989-1994 Geocode data creation procedure

Hand-edits and changes in matching procedures

1989-1994

1996-2002 Geocode data creation procedure

2004-2022 Geocode data creation procedure

Supplementary created variables

Urban-Rural and SMSA/CBSA-Central City residence variables

Migration History variables

Distance measures variables

Editing/quality of match variables