This repository includes two STATA .do files, one R script file, and an assortment of datasets. All were used to perform the calculations for a paper by Paula England, Andrew Levine, and Emma Mishel entitled “Is the Gender Revolution Stalled? An Update” published in PNAS in 2020. The STATA .do file named “PNAS_Paper_CPS_Cleaning_FinalRevision” cleans CPS data for employment and wage analyses. The STATA .do file named "PNAS_Paper_EdOcc_AdjustedEmpWage_Analyses_FinalRevision" conducts education, occupational segregation, and adjusted employment and wage analyses. The R script named "PNAS_Paper_Figures_FinalRevision.R" performs unadjusted employment and wage analyses and creates all figures presented in the paper. We suggest working through each code file in this order, although we provide the cleaned CPS data here. The R script pulls on various reformatted datasets to ease the construction of figures. These include the files named "PNAS_Num_Degrees", "PNAS_Degree_Ratios", "PNAS_Field_Segregation", "PNAS_Occupational_Segregation", "PNAS_CPS_Cleaned", "PNAS_Paper_Predicted_Values_02092020", "PNAS_Paper_Predicted_Quantiles_02092020". Data for education analyses are reformatted from raw data obtained from NCES (https://nces.ed.gov/programs/digest/current_tables.asp). Specifically, we use data from tables 318.10 (for total number of men and women receiving BAs and Doctorates) and 325.10, 325.15, 325.20, 325.25, 325.30, 325.35, 325.40, 325.45, 325.50, 325.55, 325.60, 325.65, 325.70, 325.80, 325.85, 325.90, and 325.95 (for field segregation analyses). These data are reformatted in the Excel sheets named "BA_MASTER table by year field and sex", "BA_MASTER table by year field and sex_ForATest", "PHD_MASTER table by year field and sex", and "PHD_MASTER table by year field and sex_forAtest". Reformatted education datasets are provided here. Data for occupational segregation analyses are derived from IPUMS samples available at https://usa.ipums.org/usa/index.shtml. Samples are Census 1% state form 1 for 1970, 5% state for 1980, 5% for 2000, and 1% ACS samples for 2001 through 2017. In addition to required variables, download, YEAR, SEX, EDUC, OCC1990, EMPSTAT, AGE, CLASSWKR, and PERWT. IPUMS provides code to label the downloaded data. Labeled data are too large to store here; download, label, and save and then load in "PNAS_Paper_EdOcc_AdjustedEmpWage_Analyses_FinalRevision" code to perform occupational segregation analyses. We collapse OCC1990 occupations into Grusky's microclass occupational scheme, provided here as "Grusky_occ_class_scheme_13.dta". The .do file merges this scheme with the Census/ACS occupation data. The datasets "Year x Occupation x Gender x Educ (Employed Last Week, 25-54)" and "Year x Occupation x Gender x Educ (Employed Last Week, 25-54) A_INDEX_ANALYSIS" are cleaned occupational datasets used in D and A segregation analyses, respectively. Finally, employment and wage analyses use data from IPUMS CPS ASEC samples for 1970 to 2018. These data are avaialble here: https://cps.ipums.org/cps. In addition to required variables, we downloaded YEAR, CPI99, ASECWT, AGE, SEX, RACE, HISPAN, EMPSTAT, CLASSWKR, EDUC, AHRSWRKT, WKSWORK1, WKSWORK2, UHRSWORKLY, and INCWAGE. IPUMS provides code to label downloaded data. We also provide the labeled data here with the file named "PNAS_CPS_Cleaned".