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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

> fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

> NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from Business
28. yrsexp: Experience
29. yrsftexp: FT Experience
30. yrsptexp: PT Experience
31. yrsptexpsq: PT Experience^2
32. yrsftexpsq: FT Experience^2
33. yrsExpSq: Experience^2
34. yrsexpfz: Experience (filling in zeros)
35. yrsftexpfz: FT Experience (filling in zeros)
36. yrsptexpfz: Years of Part-Time Experience (Filling in zeros)
37. yrsexpfzsq: Experience^2 (filling in zeros)
38. yrsftexpfzsq: FT Experience^2 (filling in zeros)
39. wtrgov: Works in Government (dummy variable)
40. selfemp: selfEmpLbl =1 If Self Employed for ANY Job in the Current Wave. Everyone gets a zero for this variable because our wage analyses only include wage and salary workers.
41. predict98: Total Experience must be predicted for 1998
42. predictft98: FT Experience must be predicted for 1998
43. predict00: Total Experience must be predicted for 2000
44. predictft00: Experience must be predicted for 2000
45. predict02: Total Experience must be predicted for 2002
46. predictft02: FT Experience must be predicted for 2002
47. predict04: Total Experience must be predicted for 2004
48. predictft04: FT Experience must be predicted for 2004
49. predict06: Total Experience must be predicted for 2006
50. predictft06: FT Experience must be predicted for 2006
51. predict08: Total Experience must be predicted for 2008
52. predictft08: FT Experience must be predicted for 2008
53. predict1: Total Experience must be predicted for 2010
54. predictft10: FT Experience must be predicted for 2010
55. origage:
56. origannHrsHD:
57. origannHrsWF:
58. origannLabIncHD:
59. origannLabIncWF:
60. origannWeeksHD:
61. origannWeeksWF:
62. origcurrHrWkHD:
63. origcurrHrWkWF:
64. origdegreeHD:
65. origdegreeWF:
66. origemp: ER34116L
67. origeverwrkHD07: ER36351L BC62 WTR EVER WORKED
68. origeverwrkHD09: ER42376L BC62 WTR EVER WORKED
69. origeverwrkHD11: ER47689L BC62 WTR EVER WORKED
70. origeverwrkHD99: ER13476L C4 EVER WORKED? (HD-U)
71. origeverwrkWF07: ER36609L DE62 WTR EVER WORKED
72. origeverwrkWF09: ER42628L DE62 WTR EVER WORKED
73. origeverwrkWF11: ER47946L DE62 WTR EVER WORKED
74. origeverwrkWF99: ER13988L E4 EVER WORKED? (WF-U)
75. origfamWgt:
76. origfarmInc:
77. origindHD:
78. origindWF:
79. origmarSt: ER47323L
80. orignumChld: ER47320L
81. origoccHD:
82. origoccWF:
83. origraceHD: ER51904L
84. origraceWF: ER51810L
85. origregion: ER52398L
86. origrelHead: ER34103L
87. origsch: ER34119L
88. origschfamHD07: ER41037L COMPLETED ED-HD
89. origschfamHD09: ER46981L COMPLETED ED-HD
90. origschfamHD11: ER52405L COMPLETED ED-HD
91. origschfamHD81: V8039L M28 EDUCATION-HD
92. origschfamHD99: ER16516L COMPLETED ED-HD
93. origschfamWF07: ER41038L COMPLETED ED-WF
94. origschfamWF09: ER46982L COMPLETED ED-WF
95. origschfamWF11: ER52406L COMPLETED ED-WF
96. origschfamWF81: V7998L L2 EDUCATION-WF
97. origschfamWF99: ER16517L COMPLETED ED-WF
98. origsexHead: ER47318L
99. origspanHD: ER51903L Spanish Descent Head
100. origspanWF: ER51809L Spanish Descent Wife
101. origstopw~DE299: ER13307L B53 STOP WRK OTR EMP H-E
102. origstopw~DE399: ER13388L B92 STOP WRK XJOB1 (H-E)
103. origstopw~DE499: ER13413L B104 STOP WORK XJOB2 H-E
104. origstopw~DE599: ER13437L B116 STOP WRK XTRA JOB3
105. origstopw~DE699: ER13461L B128 STOP WRK XTRA JOB4
106. origstopw~DU299: ER13560L C45 STOP WRK OTR EMP H-U
107. origstopw~DU399: ER13641L C84 STOP WORK XJOB1 H-U
108. origstopw~DU499: ER13665L C96 STOP WORK XJOB2 H-U
109. origstopw~DU599: ER13689L C108 STOP WRK XTRA JOB3
110. origstopw~DU699: ER13713L C120 STOP WORK XTRA JOB4
111. origstopw~FE299: ER13819L D53 STOP WRK OTR EMP W-E
112. origstopw~FE399: ER13900L D92 STOP WRK XJOB1 (W-E)
113. origstopw~FE499: ER13925L D104 STOP WRK XJOB2 W-E
114. origstopw~FE599: ER13949L D116 STOP WRK XTRA JOB3
115. origstopw~FE699: ER13973L D128 STOP WRK XTRA JOB4
116. origstopw~FU299: ER14072L E45 STOP WRK OTR EMP W-U
117. origstopw~FU399: ER14153L E84 STOP WORK XJOB1 W-U
118. origstopw~FU499: ER14177L E96 STOP WORK XJOB2 W-U
119. origstopw~FU599: ER14201L E108 STOP WRK XTRA JOB3
120. origstopw~FU699: ER14225L E120 STOP WRK XTRA JOB4
121. origtotYrsFTHD: ER51956L
122. origtotYrsFTWF: ER51862L
123. origtotYrsHD: ER51955L
124. origtotYrsWF: ER51861L
125. origunJobHD: ER47491L
126. origunJobWF: ER47748L
127. origwrkPriorJ~D: ER47453L
128. origwrkPriorJ~F: ER47710L
129. origwtrNewHD: ER51865L
130. origwtrNewWF: ER51771L
131. origyrNewHD: this is the year the family acquired a new head
132. origyrNewWF: this is the year the family acquired a new wife
133. predict97: Total Experience must be predicted for 1997
134. predictft97: FT Experience must be predicted for 1997
135. predictfz97: fz Total Experience must be predicted for 1997
136. predictftfz97: fz FT Experience must be predicted for 1997
137. predictfz98: fz Total Experience must be predicted for 1998
138. predictftfz98: fz FT Experience must be predicted for 1998
139. predict1999: Total Experience must be predicted for 1999
140. predictft1999: FT Experience must be predicted for 1999
141. predictfz99: fz Total Experience must be predicted for 1999
142. predictftfz99: fz FT Experience must be predicted for 1999
143. predictfz00: fz Total Experience must be predicted for 2000
144. predictftfz00: fz FT Experience must be predicted for 2000
145. predict01: Total Experience must be predicted for 2001
146. predictft01: FT Experience must be predicted for 2001
147. predictfz01: fz Total Experience must be predicted for 2001
148. predictftfz01: fz FT Experience must be predicted for 2001
149. predictfz02: fz Total Experience must be predicted for 2002
150. predictftfz02: fz FT Experience must be predicted for 2002
151. predict03: Total Experience must be predicted for 2003
152. predictft03: FT Experience must be predicted for 2003
153. predictfz03: fz Total Experience must be predicted for 2003
154. predictftfz03: fz FT Experience must be predicted for 2003
155. predictfz04: fz Total Experience must be predicted for 2004
156. predictftfz04: fz FT Experience must be predicted for 2004
157. predictfz06: fz Total Experience must be predicted for 2006
158. predictftfz06: fz FT Experience must be predicted for 2006
159. predict2007: Total Experience must be predicted for 2007
160. predictft2007: FT Experience must be predicted for 2007
161. predictfz07: fz Total Experience must be predicted for 2007
162. predictftfz07: fz FT Experience must be predicted for 2007
163. predictfz08: fz Total Experience must be predicted for 2008
164. predictftfz08: fz FT Experience must be predicted for 2008
165. predict2009: Total Experience must be predicted for 2009
166. predictft2009: FT Experience must be predicted for 2009
167. predictfz09: fz Total Experience must be predicted for 2009
168. predictftfz09: fz FT Experience must be predicted for 2009
169. predictfz10: fz Total Experience must be predicted for 2010
170. predictftfz10: fz FT Experience must be predicted for 2010
171. predict2011: Total Experience must be predicted for 2011
172. predictft2011: FT Experience must be predicted for 2011
173. predictfz11: fz Total Experience must be predicted for 2011
174. predictftfz11: fz FT Experience must be predicted for 2011
175. origAdvHD: Adv is advanced degree
176. origAdvWF:
177. origBAHD: BA is bachelor’s degree
178. origBAWF:
179. origannWeeksHDE: annWeeks is annual weeks worked E means currently employed
180. origannWeeksHDR: R means currently retired
181. origannWeeksHDU: U means currently not employed
182. origannWeeksWFE:
183. origannWeeksWFR:
184. origannWeeksWFU:
185. origindHDE: ind is industry
186. origindWFE:
187. origindHDU:
188. origindWFU:
189. origindHDR:
190. origindWFR:
191. origoccHDE: occ is occupation
192. origoccHDR:
193. origoccHDU:
194. origoccWFE:
195. origoccWFR:
196. origoccWFU:
197. origrace: race is race
198. origschHD: sch is years of schooling
199. origschWF:
200. origyrHghstDe~D: yrHghstDegHD is year of highest degree for head
201. origyrHghstDe~F:
202. origwtrCollDe~D: whether college degree
203. origwtrCollDe~F:
204. origwtrCollHD: whether attended college
205. origwtrCollWF:
206. predict: ==1 if Logit Prediction Needed for ANY gap year
207. predictft: ==1 if FT Logit Prediction Needed for ANY gap year
208. smsa: SMSA dummy variable variable
209. perconexp: T-1 Personal Consumption
210. Expenditure:
211. hrwage: hourly wage
212. annhrs2: alternate measure of annual hours worked
213. expendbase10: level of National Income and Products Account Personal Consumption Expenditures (PCE) price deflator for 2010
214. inflate: inflation factor to multiply earnings by in order to convert to 2010 dollars
215. realhrwage: Real Hourly Wage in 2010 PCE corrected dollars
216. immigrantsamp: Immigrant Sub-Sample (zero for everyone since we don’t use the immigrant subsample)
217. northeast: Region: North-East
218. northcentral: Region: North-Central
219. south: Region: South
220. west: Region: West, Alaska and Hawaii
221. lnrealwg: Log(Real Hourly Wage)
222. ft: full time work dummy variable
223. potexp: potential experience (age-years of schooling-6) truncated to be between 0 and age-18
224. potexp2: potential experience squared
225. ba: bachelor's Degree
226. adv: advanced Degree
227. military: zero for everyone since we study civilians
228. basesamp: this is base sample, which is 1 for everyone in this data set
229. wagesamp: this is wage sample
230. female:
231. ind2: 2-digit Industry
232. occ2: 2-digit Occupation
233. occ2name:
234. Agriculture:
235. miningconstru~n: Ind: Mining and Construction
236. durables: Ind: Durables Manufacturing
237. nondurables: Ind: Non-durables Manufacturing
238. Transport: Ind: Transport
239. Utilities: Ind: Utilities
240. Communications: Ind: Communications
241. retailtrade: Ind: Retail Trade
242. wholesaletrade: Ind: Wholesale Trade
243. finance: Ind: Finance
244. SocArtOther: Ind: Social Work, Arts and Recreation, Other Services
245. hotelsrestaur~s: Ind: Hotels and Restaurants
246. Medical: Ind: Medical
247. Education: Ind: Education
248. professional: Ind: Professional Services
249. publicadmin: Ind: Public Administration
250. sumind: this is the sum of industry dummy variables, which is 1 for everyone
251. manager: Manager
252. business: Business Operations Specialists
253. financialop: Financial Operations Specialists
254. computer: Computer and Math Technicians
255. architect: Architects an Engineers
256. scientist: Life, Physical and Social Sciences
257. socialworker: Community and Social Workers
258. postseceduc: Post-secondary educators
259. legaleduc: Other Education, Training, Library and Legal Occupations
260. artist: Arts, Design, Entertainment, Sports and Media
261. lawyerphysician: Physicians and Dentists
262. healthcare: Nurses and HealthCare Practitioners and Technicians
263. healthsupport: Healthcare Support Occupations
264. protective: Protective Service Occupations
265. foodcare: Food Preparation and Serving and Personal Care Services
266. building: Building and Grounds Cleaning and Maintenance
267. sales: Sales and Related
268. officeadmin: Office and Administrative Support
269. farmer:
270. constructextr~l: Construction, Extraction, Installation
271. production: Production
272. transport: Transportation and Materials Moving
273. sumocc: this is sum of the occupation dummy variables which is 1 for everyone
274. LEHS: High School or Less

CPS variables:

> NOTES: VARIABLES WITH A q AT THE BEGINNING ARE DATA QUALITY FLAGS. ANY VALUE GREATER THAN ZERO INDICATES SOME ISSUE WITH DATA QUALITY. THE EARNINGS DATA WITH ZEROS WAS ONLY USED DURING THE CREATION OF THIS VARIABLE. DUE TO LACK OF DATA AVAILABILITY for 1981, ALL OF THE HOURS AND WEEKS DATA WERE FORCED TO USE REGARDLESS OF THE DATA QUALITY FLAG. THE ORIGINAL CPS VARIABLES HAVE BEEN KEPT. OCCUPATION AND INDUSTRY WERE NOT USED IN THE CPS ANALYSIS. THE VARIABLES WITH tc AT THE BEGINNING INDICATE TOPCODED VALUES.

1. year: Survey year
2. serial: Household serial number
3. numprec: Number of person records following
4. hwtsupp: hwtsupp_lbl Household weight, Supplement
5. gq: gq_lbl Group Quarters status
6. region: region_lbl Region and division
7. statefip: statefip_lbl State (FIPS code)
8. metro: metro_lbl Metropolitan central city status
9. metarea: metarea_lbl Metropolitan area
10. county: FIPS county code
11. farm: farm_lbl Farm (1=this is a farm, 2= it’s not a farm)
12. month: month_lbl Month
13. pernum: Person number in sample unit
14. wtsupp: Supplement Weight
15. relate: relate_lbl Relationship to household head (Head/hous=101, Spouse=201, Child=301, Stepchild=303, Parent=501, Sibling=701, Grandchil=901, Other rel=1001, Partner/r=1113, Unmarried=1114, Housemate=1115, Roomer/bo=1241, Foster ch=1242, Other non=1260)
16. age: age_lbl Age
17. sex: sex_lbl Sex (1=male, 2=female)
18. race: raceLbl Race (White nonhisp=1, Black nonhisp=2, Hispanic=3, Other nonhisp=4)
19. marst: marst_lbl Marital status (Married, spouse present=1, Married, spouse absent=2, Separated=3, Divorced=4, Widowed=5, Never mar=6)
20. popstat; popstat_lbl Adult civilian, armed forces, or Child (1 for everyone—we include only civilian adults)
21. bpl: bpl_lbl Birthplace
22. yrimmig: yrimmig_lbl Year of immigration
23. citizen: citizen_lbl Citizenship status
24. mbpl: mbpl_lbl Mother's birthplace
25. fbpl: fbpl_lbl Father's birthplace
26. nativity: nativity_lbl Foreign birthplace or parentage
27. hispan: hispan_lbl Hispanic origin
28. sch: educLbl Educational attainment recode (None=0, 1=1, Grades 1=2, 2.5=2.5, 3=3, 4=4, Grades 5=5, 5.5=5.5, 6=6, Grades 7=7, 7.5=7.5, 8=8, Grade 9=9, Grade 10=10, Grade 11=11, Grade 12=12, Some Coll=13, Assoc.=14, BA=16, Adv. Degr=18)
29. educ99: educ99_lbl Educational attainment, 1990, available for 1999 and later (No school=1, 1st-4th g=4, 5th-8th g=5, 9th grade=6, 10th grad=7, 11th grad=8, 12th grad=9, High scho=10, Some coll=11, Associate=13, Associate=14, Bachelors=15, Masters d=16, Professio=17, Doctorate=18)
30. schlcoll: schlcoll_lbl School or college attendance; available only in 2013 (High school full time=1, High school part time=2, College or univ full time=3, College or univ part time=4, Does not attend school=5)
31. empstat: empstat_lbl Employment status (At work=10, Has job, not at work now=12)
32. labforce: labforce_lbl Labor force status everyone gets a 2—in the labor force
33. occ: occ_lbl Occupation
34. occ1990: occ1990_lbl Occupation, 1990 basis
35. ind1990: ind1990_lbl Industry, 1990 basis
36. occ1950: occ1950_lbl Occupation, 1950 basis
37. ind: ind_lbl Industry
38. ind1950: ind1950_lbl Industry, 1950 basis
39. classwkr: classwkr_lbl Class of worker (Self-empl=10, Wage/salary, private sector=21, Wage/salary, government=24, Federal govt employee=25, State govt employee=27, Local govt employee=28, Unpaid family worker=29)
40. occly: occly_lblOccupation last year
41. occ50ly: occ50ly_lbl Occupation last year, 1950 basis
42. indly: indly_lbl Industry last year
43. ind50ly: ind50ly_lbl Industry last year, 1950 basis
44. classwly: classwly_lbl Class of worker last year (Self-employed=14, Wage/salary private=22, Federal govt=25, State gov=27, Local gov=28, Unpaid family worker=29)
45. wkswork1: wkswork1_lbl Weeks worked last year
46. wkswork2: wkswork2_lbl Weeks worked last year, intervalled
47. hrswork: hrswork_lbl Hours worked last week
48. uhrswork: uhrswork_lbl Usual hours worked per week (last yr)
49. union: union_lbl Union membership (only available for outgoing rotation group) (NIU=0, No union coverage=1, Member of labor union=2, Covered by union but not a member=3)
50. incwage: incwage_lbl Wage and salary income
51. incbus: incbus_lbl Non-farm business income
52. incfarm: incfarm_lbl Farm income
53. inclongj: inclongj_lbl Earnings from longest job
54. oincwage: oincwage_lbl Earnings from other work included wage and salary earnings
55. srcearn: srcearn_lbl Source of earnings from longest (1=wage and salary; 4=without pay) job
56. ftype: ftype_lbl Family Type (Primary family=1, Nonfamily householder=2, Related subfamily=3, Unrelated subfamily=4, Secondary individual=5)
57. quhrswor: quhrswor_lbl Data quality flag for UHRSWORK
58. qwkswork: qwkswork_lbl Data quality flag for WKSWORK1 and WKSWORK2
59. qincbus: qincbus_lbl Data quality flag for INCBUS
60. qincfarm: qincfarm_lbl Data quality flag for INCFARM
61. qinclong: qinclong_lbl Data quality flag for INCLONGJ
62. qincwage: qincwage_lbl Data quality flag for INCWAGE
63. qsrcearn: qsrcearn_lbl Data quality flag for SRCEARN
64. o_numprec: Original Number of person records following'
65. o_hwtsupp: Original Household weight, Supplement'
66. o_gq: Original Group Quarters status'
67. o_region: Original Region and division'
68. o_statefip: Original State (FIPS code)'
69. o_metro: Original Metropolitan central city status'
70. o_metarea: Original Metropolitan area'
71. o_county: Original FIPS county code'
72. o_farm: Original Farm'
73. o_month: Original Month'
74. o_pernum: Original Person number in sample unit'
75. o_wtsupp: Original Supplement Weight'
76. o_relate: Original Relationship to household head'
77. o_age: Original Age'
78. o_sex: Original Sex'
79. o_race: Original Race'
80. o_marst: Original Marital status'
81. o_popstat: Original Adult civilian, armed forces, or child'
82. o_bpl: Original Birthplace'
83. o_yrimmig: Original Year of immigration'
84. o_citizen: Original Citizenship status'
85. o_mbpl: Original Mother's birthplace'
86. o_fbpl: Original Father's birthplace'
87. o_nativity: Original Foreign birthplace or parentage'
88. o_hispan: Original Hispanic origin'
89. o_educ: Original Educational attainment recode'
90. o_educ99: Original Educational attainment, 1990'
91. o_schlcoll: Original School or college attendance'
92. o_empstat: Original Employment status'
93. o_labforce: Original Labor force status'
94. o_occ: Original Occupation'
95. o_occ1990: Original Occupation, 1990 basis'
96. o_ind1990: Original Industry, 1990 basis'
97. o_occ1950: Original Occupation, 1950 basis'
98. o_ind: Original Industry'
99. o_ind1950: Original Industry, 1950 basis'
100. o_classwkr: Original Class of worker'
101. o_occly: Original Occupation last year'
102. o_occ50ly: Original Occupation last year, 1950 basis'
103. o_indly: Original Industry last year'
104. o_ind50ly: Original Industry last year, 1950 basis'
105. o_classwly: Original Class of worker last year'
106. o_wkswork1: Original Weeks worked last year'
107. o_wkswork2: Original Weeks worked last year, intervalled'
108. o_hrswork: Original Hours worked last week'
109. o_uhrswork: Original Usual hours worked per week (last yr)'
110. o_union: Original Union membership'
111. o_incwage: Original Wage and salary income'
112. o_incbus: Original Non-farm business income'
113. o_incfarm: Original Farm income'
114. o_inclongj: Original Earnings from longest job'
115. o_oincwage: Original Earnings from other work included wage and salary earnings'
116. o_srcearn: Original Source of earnings from longest job'
117. o_ftype: Original Family Type'
118. o_quhrswor: Original Data quality flag for UHRSWORK'
119. o_qwkswork: Original Data quality flag for WKSWORK1 and WKSWORK2'
120. o_qincbus: Original Data quality flag for INCBUS'
121. o_qincfarm: Original Data quality flag for INCFARM'
122. o_qincwage: Original Data quality flag for INCWAGE'
123. origrace: race_lbl Race
124. white: white nonhispanic dummy variable
125. black: black nonhispanic dummy variable
126. hisp: Hispanic dummy variable
127. othrace: other race dummy variable
128. educorig: original education codes (see CPS documentation site mentioned above)
129. ba: bachelor’s degree dummy variable
130. adv: advanced degree dummy variable
131. groupquar: group quarters dummy variable. Zero for everyone.
132. potexp: age-yrs of schooling-6
133. potexp2: potexp squared
134. selfemp: self employment dummy variable. Zero for everone.
135. military: =1 if military (Based on popstat variable). Variable is zero for everyone.
136. employed: equals 1 for everyone
137. annhrs: annual work hours
138. ft: full time work dummy variable
139. niincwage: Not Imputed incwage
140. incwageman: Manually Created INCWAGE
141. tcoincwage
142. tcinclongj
143. tcincwage: True Topcoded INCWAGE (Includes Imputed Values)
144. hrwage: hourly wage
145. perconexp: T-1 Personal Consumption Expenditure
146. expendbase10: 2010 PCE value
147. inflate: inflation factor for expressing wages in 2010 dollars
148. realhrwage: Real Hourly Wage, inflated to 2010 dollars
149. uncenrealhrwage: Real Hourly Wage (same as realhrwage)
150. lnrwg: log of real hourly wage
151. hdwfcoh: Head/Wife/Cohabitator Indicator
152. notalloc: not allocated wage. Equals 1 for everyone.
153. basesamp: base sample; 1 for everyone
154. wagesamp: wage sample dummy variable
155. occ_orig:
156. adj_occ: in some years, occupation has four digits and in others it has 3. This expresses occupation in 3 digits
157. occ_2010_orig
158. ind_orig
159. adj_ind
160. ind_2002_orig
161. ind_2007_orig
162. occ_81
163. ind_81
164. occ_2000female: 2000 Occupation Code
165. unmatched_fe~81
166. occ_2000male: 2000 Occupation Code
167. unmatched_ma~81
168. ind_2000
169. occ2000_81: 1981 occupation codes converted to 2000 codes
170. ind2000_81: 1981 industry codes converted to 2000 codes
171. occ_1990
172. ind_1990
173. occ_1999
174. ind_1999
175. unmatched_oc~90
176. occ2000_90: 1990 occupation codes converted to 2000 codes
177. unmatched_in~90
178. ind2000_90: 1990 industry codes converted to 2000 codes
179. indname2000_90
180. unmatched_oc~99
181. occ2000_99: 1990 occupation codes converted to 2000 codes
182. unmatched_in~99
183. ind2000_99: 1990 industry codes converted to 2000 codes
184. indname2000_99
185. un_lnrealwg: Log of Real Hourly Wage
186. northeast: Region: North-East
187. northcentral: Region: North-Central
188. south: Region: South
189. west: Region: West, Alaska and Hawaii
190. female
191. adj_ind2
192. adj_occ2
193. adj_occ2name
194. Agriculture
195. miningconstru~n: adj_ind: Mining and Construction
196. durables: adj_ind: Durables Manufacturing
197. nondurables: adj_ind: Non-durables
198. Manufacturing
199. Transport: adj_ind: Transport
200. Utilities: adj_ind: Utilities
201. Communications: adj_ind: Communications
202. retailtrade: adj_ind: Retail Trade
203. wholesaletrade: adj_ind: Wholesale Trade
204. finance: adj_ind: Finance
205. SocArtOther: adj_ind: Social Work, Arts and Recreation, Other Services
206. hotelsrestaur~s: adj_ind: Hotels and Restaurants
207. Medical: adj_ind: Medical
208. Education: adj_ind: Education
209. professional: adj_ind: Professional Services
210. publicadmin: adj_ind: Public Administration
211. sumadj_ind: sum of industry dummy variables
212. manager: Manager
213. business: Business Operations Specialists
214. financialop: Financial Operations Specialists
215. computer: Computer and Math Technicians
216. architect: Architects an Engineers
217. scientist: Life, Physical and Social Sciences
218. socialworker: Community and Social Workers
219. postseceduc: Post-secondary educators
220. legaleduc: Other Education, Training, Library and Legal adj_occupations
221. artist: Arts, Design, Entertainment, Sports and Media
222. lawyerphysician: Physicians and Dentists
223. healthcare: Nurses and HealthCare Practitioners and Technicians
224. healthsupport: Healthcare Support adj_occupations
225. protective: Protective Service adj_occupations
226. foodcare: Food Preparation and Serving and Personal Care Services
227. building: Building and Grounds Cleaning and Maintenance
228. sales: Sales and Related
229. officeadmin: Office and Administrative Support
230. farmer
231. constructextr~l: Construction, Extraction,
232. Installation
233. production: Production
234. transport: Transportation and Materials
235. Moving
236. sumadj_occ: sum of occupation dummy variables
237. LEHS: dummy for less than or equal to high school

Acknowledgements

• Journal Article: Blau, Francine D. Kahn, Lawrence M. The Gender Wage Gap: Extent, Trends, and Explanations Journal of Economic Literature 55 3 789-865 2017