Appendix This Appendix provides detailed regression results from Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data. Please see the complete study for corresponding methodology and details of the data. I. United States Variable Table A1. Overall U.S. Results for Base Pay: Regression of Log Base Salary on Various Individual, Job and Employer Characteristics No Controls for controls for industry, age, education occupation, company- and years of state, year specific experience. and firm size. job- title- specific Gender Pay Gap (%) 0.241*** 0.192*** 0.080*** 0.068*** 0.054*** (0.002) (0.001) (0.001) (0.001) (0.001) Age 0.054*** 0.050*** 0.051*** 0.044*** (0.001) (0.001) (0.001) (0.001) Education High School - 0.026*** - 0.002 0.006** 0.007*** (0.004) (0.003) (0.003) (0.002) Bachelor's Degree 0.302*** 0.141*** 0.105*** 0.076*** (0.003) (0.003) (0.002) (0.002) Master's Degree 0.545*** 0.277*** 0.210*** 0.157*** (0.004) (0.003) (0.003) (0.002) J.D. 0.825*** 0.394*** 0.298*** 0.236*** (0.010) (0.008) (0.008) (0.007) M.B.A. 0.655*** 0.358*** 0.275*** 0.206*** (0.005) (0.003) (0.003) (0.003) 1
Variable No Controls for controls for industry, age, education occupation, company- and years of state, year specific experience. and firm size. job- title- specific M.D. 0.773*** 0.419*** 0.333*** 0.238*** (0.019) (0.015) (0.013) (0.012) Ph.D. 0.543*** 0.349*** 0.275*** 0.213*** (0.006) (0.005) (0.004) (0.004) Experience 0.069*** 0.050*** 0.046*** 0.038*** (0.000) (0.000) (0.000) (0.000) Experience Squared - 0.002*** - 0.001*** - 0.001*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 505,438 505,438 505,438 505,438 505,438 Adjusted R- squared 0.040 0.365 0.654 0.743 0.793 Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 2
Table A2. Overall U.S. Results for Total Compensation: Regression of Log Total Compensation on Various Individual, Job and Employer Characteristics Variable for controls for industry, age, education occupation, company- job- title- No and years of state, year specific specific Controls experience. and firm size. Gender Pay Gap (%) 0.270*** 0.222*** 0.113*** 0.093*** 0.074*** (0.002) (0.002) (0.002) (0.002) (0.001) Age 0.051*** 0.053*** 0.054*** 0.045*** (0.001) (0.001) (0.001) (0.001) Education High School 0.002 0.006 0.014*** 0.014*** (0.005) (0.004) (0.004) (0.004) Bachelor's Degree 0.337*** 0.161*** 0.128*** 0.097*** (0.004) (0.004) (0.004) (0.003) Master's Degree 0.539*** 0.293*** 0.230*** 0.175*** (0.004) (0.004) (0.004) (0.004) J.D. 0.832*** 0.435*** 0.337*** 0.273*** (0.013) (0.012) (0.012) (0.011) M.B.A. 0.687*** 0.392*** 0.309*** 0.238*** (0.006) (0.005) (0.005) (0.005) M.D. 0.741*** 0.441*** 0.356*** 0.258*** (0.024) (0.021) (0.020) (0.020) Ph.D. 0.510*** 0.389*** 0.314*** 0.251*** (0.008) (0.007) (0.007) (0.006) Experience 0.070*** 0.052*** 0.048*** 0.041*** (0.000) (0.000) (0.000) (0.000) 3
Variable for controls for industry, age, education occupation, company- job- title- No and years of state, year specific specific Controls experience. and firm size. Experience Squared - 0.002*** - 0.001*** - 0.001*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 505,438 505,438 505,438 505,438 505,438 Adjusted R- squared 0.037 0.267 0.490 0.571 0.615 Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A3. U.S. Gender Pay Gap by Industry: Regression of Base Pay on Gender x Industry Interaction Terms and Individual, Job and Employer Controls (β!"#$ + β!"#$!!"#$%&'( ) Percentage Gender Pay Gap Signif.? Industry Base Pay (p < 0.10) Sample Size Health Care 7.2% *** 22,386 Insurance 7.2% *** 14,210 Mining & Metals 6.8% 401 Transportation & Logistics 6.7% ** 7,414 Media 6.6% *** 14,348 Arts, Entertainment & Recreation 6.6% * 4,053 Finance 6.4% *** 44,251 Retail 5.9% *** 56,916 Construction, Repair & Maintenance 5.9% 5,625 4
Percentage Gender Pay Gap Signif.? Industry Base Pay (p < 0.10) Sample Size Information Technology 5.9% *** 84,514 Real Estate 5.8% 4,123 Non Profit 5.7% 4,613 Oil, Gas, Energy & Utilities 5.6% 8,944 Business Services 5.5% * 71,476 Government 4.7% 11,345 Telecommunications 4.6% 16,801 Consumer Services 4.5% 2,670 Manufacturing 4.0% 45,821 Education 3.3% ** 27,839 Restaurants, Bars & Food Service 3.2% * 13,262 Travel & Tourism 3.0% * 8,708 Biotech & Pharmaceuticals 3.0% * 8,852 Agriculture & Forestry 2.5% 611 Aerospace & Defense 2.5% ** 8,391 Notes: Based on Glassdoor salary data. The omitted reference category for industry fixed effects is Accounting and Legal Services. Percentages are the sum of the coefficient on the male x industry interaction term and the overall coefficient on male. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A4. U.S. Gender Pay Gap by Occupation: Regression of Base Pay on Gender x Occupation Interaction Terms and Individual, Job and Employer Controls (β!"#$ + β!"#$!!""#$%&'!( ) Percentage Gender Pay Gap Signif.? Occupation Base Pay (p < 0.10) Sample Size Computer Programmer 28.3% *** 138 Chef 28.1% *** 511 Dentist 28.1% ** 61 C Suite 27.7% *** 870 Psychologist 27.2% *** 91 Pharmacist 21.8% *** 904 CAD Designer 21.5% *** 1,044 Physician 18.2% ** 151 Optician 17.3% ** 160 Pilot 16.0% ** 532 Game Artist 15.8% *** 1,149 Driver 14.9% *** 2,156 Information Security Specialist 14.7% *** 911 Retail Representative 14.6% *** 10,966 Medical Technician 14.4% *** 1,471 GIS Specialist 14.2% ** 291 Data Specialist 13.6% ** 491 Patient Care Technician 13.6% *** 1,899 Real Estate Broker 12.6% ** 722 5
Percentage Gender Pay Gap Signif.? Occupation Base Pay (p < 0.10) Sample Size Dealer 12.6% 152 Revenue Manager 12.3% *** 2,048 Technical Consultant 12.0% ** 915 Branch Manager 11.8% *** 52,084 Military 11.5% * 723 Client Development Manager 11.4% *** 2,326 Professor 11.2% ** 1,760 Administrative 11.1% *** 6,141 Software Architect 10.6% ** 2,330 Caregiver 10.6% 596 Regulatory Affairs Manager 10.5% 181 Technical Support 10.4% ** 4,005 Customer Service 10.4% *** 13,272 SEO Strategist 10.2% 214 Deputy Manager 9.9% ** 1,923 Legal 9.7% * 2,112 Front End Engineer 9.7% ** 3,525 Other 9.7% ** 2,984 Database Engineer 9.7% 1,055 Program Manager 9.6% ** 5,437 Designer 9.4% ** 3,849 Corporate Controller 9.3% 366 Computer Sales 9.2% 838 Sharepoint Developer 9.2% 272 Claims 9.1% * 2,159 Business Development 9.1% * 2,552 Production Associate 9.0% 712 Project Coordinator 8.8% 116 Valuation Associate 8.7% 154 Technical Sales 8.5% 1,286 Teacher 8.4% 5,646 Dean 8.4% 92 SAP Developer 8.4% 292 Systems Technician 8.3% 3,690 Client Services 8.1% 1,364 Business Operations 7.9% 882 Marketing Manager 7.7% 13,265 Field Sales Manager 7.6% 6,394 Nursing 7.6% 2,865 Construction 7.6% 781 Bioinformatics Scientist 7.5% 305 Graphic Designer 7.4% 3,116 Geologist 7.3% 313 Teller 7.2% 3,978 Analytics 7.2% 2,173 Technical Advisor 7.1% 446 6
Percentage Gender Pay Gap Signif.? Occupation Base Pay (p < 0.10) Sample Size Lab Specialist 7.0% 853 Account Executive 7.0% 8,329 Scientist 6.9% 1,813 Clinical Research 6.9% 785 Unskilled Labor 6.9% 1,803 Quality Assurance 6.8% 4,171 Technical Manager 6.8% 4,198 Systems Administrator 6.8% 8,730 Accounting 6.5% 5,654 Personal Trainer 6.5% 367 Solution Specialist 6.5% 1,317 Maintenance 6.5% 1,015 Product Support 6.4% 1,008 Finance Specialist 6.4% 10,796 Program Coordinator 6.4% 4,002 Researcher 6.2% 5,871 Contract Manager 6.2% 337 Management Consulting 6.1% 14,145 Software Engineer 6.0% 35,050 Project Manager 6.0% 13,461 Police Security Officers 5.9% 1,844 Wall Street 5.9% 2,496 Executive Secretary 5.7% 2,361 Store Manager 5.6% 15,342 Corporate Attorney 5.5% 1,488 Collections Representative 5.5% 790 Recruiter 5.5% 3,696 HR Specialist 5.4% 4,889 Guest Relations 5.4% 686 Civil Engineer 5.3% 862 Operations 5.3% 2,275 Logistics Associate 5.2% 761 Programmer Developer 5.2% 2,199 Community Manager 5.0% 956 Editor 4.9% 3,033 Sales Representative 4.9% 21,982 Analyst 4.8% 16,616 Facility Administrator 4.8% 181 Facility Administrator 4.8% 181 Inventory Specialist 4.8% 815 Beauty 4.7% 1,267 Appointee 4.7% 1,816 Producer 4.6% 1,460 IT 4.6% 6,192 Environmental Specialist 4.5% 580 Banker 4.5% 1,238 7
Percentage Gender Pay Gap Signif.? Occupation Base Pay (p < 0.10) Sample Size Compliance Consultant 4.5% 610 Front Desk 4.4% 1,791 Product Manager 4.3% 6,601 Pharmacy Technician 4.2% 1,465 Trainer 4.0% 1,823 Mechanical Engineer 3.6% 5,084 Corporate Account Manager 3.5% 9,450 Skilled Labor 3.4% 1,658 Veterinary 3.3% 272 Loss Prevention 3.3% 1,102 Server 3.2% 1,394 Business Analyst 3.2% 7,582 Tax Specialist 3.2% 2,039 Underwriter 3.2% 2,099 Insurance Agent 3.0% 646 Technology Specialist 2.9% 1,164 Mobile Developer 2.9% 147 Stock Clerk 2.3% 1,569 Engineer 2.2% 14,679 Actuarial Consultant 2.0% 539 Technical Staff 2.0% 1,644 Auditor 1.9% 3,903 Hardware Engineer 1.9% 4,194 Field Services 1.4% 1,810 Clinical Dietitian 1.3% 100 Academic Counselor 1.0% 2,780 Supply Chain Specialist 1.0% 2,004 Student 0.9% 1,592 Technical Coordinator 0.7% 207 Internal Medicine Resident 0.6% 101 Food Services 0.4% * 2,782 Logistics Manager 0.4% 1,600 Event Coordinator 0.2% 488 Therapist - 0.5% 997 Business Coordinator - 0.5% * 1,237 Procurement - 0.8% * 608 Health Educator - 0.9% 129 Social Media - 1.9% ** 1,102 Communications Associate - 2.2% ** 805 Physician Advisor - 2.4% 91 Purchasing Specialist - 5.5% *** 2,095 Research Assistant - 6.6% *** 3,899 Merchandiser - 7.6% *** 2,173 Social Worker - 7.8% *** 1,283 Notes: Based on Glassdoor salary data. The omitted reference category for occupation fixed effects is Beauty. Percentages are the sum of the coefficient on the male x occupation interaction term and the overall coefficient on male. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 8
Table A5. U.S. Gender Pay Gap by Age: Regression of Base Pay on Gender x Age Interaction Terms and Individual, Job and Employer Controls (β!"#$ + β!"#$!!"#!"#$% ) Percentage Gender Pay Gap Age Group Base Pay Signif.? Total Comp. Signif.? (p < 0.10) Sample Size 18-24 years 2.2% *** 5.0% *** 26,029 25-34 years 3.3% *** 4.9% *** 230,253 35-44 years 6.2% 8.4% 146,083 45-54 years 9.5% *** 12.0% *** 72,679 55-64 years 10.5% *** 13.3% *** 26,623 65+ years 9.7% *** 11.4% * 3,535 Notes: Based on Glassdoor salary data. The omitted reference category for age group fixed effects is 35 44 years. Percentages are the sum of the coefficient on the male x age group interaction term and the overall coefficient on male. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A6. U.S. Gender Pay Gap by Year: Regression of Base Pay on Gender x Year Interaction Terms and Individual, Job and Employer Controls Percentage Gender Pay Gap Year Base Pay Signif.? (p < 0.10) Sample Size 2007 4.0% ** 4,185 2008 4.4% *** 19,544 2009 5.2% *** 48,233 2010 5.3% *** 67,513 2011 6.1% *** 31,910 2012 5.7% *** 64,240 2013 5.7% *** 83,094 2014 4.3% *** 95,131 2015 5.0% *** 90,965 Notes: Based on Glassdoor salary data. The omitted reference category for year fixed effects is 2006. Percentages are the sum of the coefficient on the male x year group interaction term and the overall coefficient on male. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 9
II. United Kingdom Table A7. Overall U.K. Results for Base Pay: Regression of Log Salary on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, city, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.229*** 0.150*** 0.086*** 0.071*** 0.055*** (0.009) (0.007) (0.005) (0.006) (0.005) Age 0.139*** 0.095*** 0.094*** 0.083*** (0.004) (0.004) (0.004) (0.004) Education High School - 0.021-0.014 0.031 0.028 (0.030) (0.022) (0.024) (0.023) Bachelor's Degree 0.176*** 0.057*** 0.094*** 0.075*** (0.028) (0.021) (0.023) (0.022) Master's Degree 0.326*** 0.124*** 0.135*** 0.106*** (0.029) (0.022) (0.023) (0.022) J.D. 0.666*** 0.318*** 0.144* 0.057 (0.099) (0.075) (0.076) (0.073) M.B.A. 0.556*** 0.243*** 0.235*** 0.197*** (0.032) (0.024) (0.025) (0.025) M.D. 0.449*** 0.163** 0.247*** 0.199*** (0.086) (0.064) (0.064) (0.063) Ph.D. 0.300*** 0.180*** 0.167*** 0.140*** (0.034) (0.026) (0.027) (0.026) Experience 0.079*** 0.062*** 0.058*** 0.050*** (0.001) (0.001) (0.001) (0.001) Experience Squared - 0.002*** - 0.002*** - 0.001*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 22,468 22,468 22,468 22,468 22,468 Adjusted R- squared 0.031 0.385 0.658 0.738 0.774 10
Variable for age, for industry, education and occupation, city, company- job- No years of year and firm specific title- specific Controls experience. size. Worker Demographics City Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A8. Overall U.K. Results for Total Compensation: Regression of Log Total Compensation on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, city, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.251*** 0.172*** 0.119*** 0.102*** 0.084*** (0.010) (0.009) (0.008) (0.009) (0.009) Age 0.133*** 0.098*** 0.104*** 0.092*** (0.006) (0.005) (0.006) (0.006) Education High School 0.020 0.009 0.062* 0.052 (0.039) (0.033) (0.036) (0.037) Bachelor's Degree 0.213*** 0.080** 0.128*** 0.096*** (0.037) (0.031) (0.035) (0.035) Master's Degree 0.348*** 0.147*** 0.166*** 0.125*** 11
Variable for age, for industry, education and occupation, city, company- job- No years of year and firm specific title- specific Controls experience. size. (0.038) (0.032) (0.035) (0.035) J.D. 0.704*** 0.342*** 0.114-0.005 (0.131) (0.110) (0.116) (0.117) M.B.A. 0.613*** 0.285*** 0.286*** 0.236*** (0.043) (0.036) (0.039) (0.039) M.D. 0.467*** 0.184* 0.323*** 0.258** (0.113) (0.095) (0.099) (0.101) Ph.D. 0.277*** 0.201*** 0.199*** 0.157*** (0.044) (0.038) (0.041) (0.042) Experience 0.081*** 0.065*** 0.061*** 0.052*** (0.002) (0.002) (0.002) (0.002) Experience Squared - 0.002*** - 0.002*** - 0.002*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 22,468 22,468 22,468 22,468 22,468 Adjusted R- squared 0.025 0.271 0.493 0.576 0.604 Worker Demographics City Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 12
Table A9. Oaxaca- Blinder Decomposition of the U.K. Male- Female Pay Gap Log Base Pay Log Total Compensation Mean Male Pay 10.623 10.748 Mean Female Pay 10.394 10.498 Uncontrolled Gender Pay Gap - 0.229-0.251 Explained Pay Gap (Due to Differences in Worker Characteristics) - 0.146-0.137 Percentage Explained 64% 55% Education & Experience ("Human Capital") 26% 25% Choice of Occupation and Industry 38% 30% Unexplained Pay Gap (Due to Differences in Regression Coefficients) - 0.083-0.114 Percentage Unexplained 36% 45% Observations 22,468 22,468 Worker Demographics City Year Industry Occupation Company Size Notes: Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 13
III. Australia Table A10. Overall Australia Results for Base Pay: Regression of Log Salary on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.173*** 0.120*** 0.060*** 0.053*** 0.039*** (0.015) (0.012) (0.011) (0.012) (0.012) Age 0.089*** 0.074*** 0.083*** 0.082*** (0.008) (0.007) (0.008) (0.008) Education High School - 0.038-0.059-0.107** - 0.043 (0.048) (0.042) (0.052) (0.054) Bachelor's Degree 0.061-0.007-0.065-0.038 (0.045) (0.039) (0.050) (0.052) Master's Degree 0.109** 0.025-0.036-0.012 (0.046) (0.040) (0.050) (0.052) J.D. 0.531*** - 0.022-0.029 0.129 (0.191) (0.198) (0.251) (0.230) M.B.A. 0.212*** 0.095** 0.023 0.009 (0.053) (0.046) (0.056) (0.057) M.D. 0.068-0.022-0.156-0.046 (0.191) (0.163) (0.152) (0.142) Ph.D. - 0.001-0.023-0.111* - 0.060 (0.061) (0.055) (0.063) (0.065) Experience 0.068*** 0.057*** 0.057*** 0.051*** (0.002) (0.002) (0.002) (0.002) Experience Squared - 0.002*** - 0.001*** - 0.001*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 4,044 4,044 4,044 4,044 4,044 Adjusted R- squared 0.031 0.402 0.580 0.668 0.738 14
Variable for age, for industry, education and occupation, company- job- No years of state, year and specific title- specific Controls experience. firm size. Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A11. Overall Australia Results for Total Compensation: Regression of Log Total Compensation on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.178*** 0.126*** 0.088*** 0.074*** 0.054*** (0.018) (0.015) (0.014) (0.016) (0.016) Age 0.093*** 0.076*** 0.080*** 0.082*** (0.010) (0.010) (0.011) (0.011) Education High School 0.032-0.010-0.052-0.012 (0.060) (0.055) (0.069) (0.075) Bachelor's Degree 0.071 0.008-0.048-0.031 (0.057) (0.052) (0.066) (0.072) Master's Degree 0.096* 0.034-0.019-0.010 (0.057) (0.053) (0.066) (0.073) 15
Variable for age, for industry, education and occupation, company- job- No years of state, year and specific title- specific Controls experience. firm size. J.D. 0.454* - 0.010-0.005 0.138 (0.239) (0.262) (0.330) (0.319) M.B.A. 0.236*** 0.120** 0.015 0.004 (0.066) (0.060) (0.074) (0.079) M.D. 0.758*** 0.726*** 0.579*** 0.704*** (0.239) (0.215) (0.201) (0.197) Ph.D. - 0.055-0.019-0.098-0.056 (0.077) (0.073) (0.083) (0.090) Experience 0.069*** 0.058*** 0.061*** 0.056*** (0.003) (0.003) (0.003) (0.003) Experience Squared - 0.002*** - 0.001*** - 0.002*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 4,044 4,044 4,044 4,044 4,044 Adjusted R- squared 0.024 0.302 0.45 0.57 0.623 Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 16
Table A12. Oaxaca- Blinder Decomposition of the Australia Male- Female Pay Gap Log Base Pay Log Total Compensation Mean Male Pay 11.429 11.516 Mean Female Pay 11.256 11.338 Uncontrolled Gender Pay Gap - 0.173-0.178 Explained Pay Gap (Due to Differences in Worker Characteristics) - 0.106-0.086 Percentage Explained 61% 48% Education & Experience ("Human Capital") 24% 24% Choice of Occupation and Industry 38% 24% Unexplained Pay Gap (Due to Differences in Regression Coefficients) - 0.067-0.092 Percentage Unexplained 39% 52% Observations 4,044 4,044 Worker Demographics State Year Industry Occupation Company Size Notes: Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 17
IV. Germany Table A13. Overall Germany Results for Base Pay: Regression of Log Salary on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.225*** 0.170*** 0.118*** 0.053** 0.055** (0.030) (0.025) (0.023) (0.027) (0.026) Age 0.054*** 0.049*** 0.035* 0.022 (0.015) (0.014) (0.018) (0.017) Education High School 0.082 0.120-0.040-0.176 (0.101) (0.087) (0.152) (0.164) Bachelor's Degree 0.262*** 0.222*** - 0.018-0.160 (0.095) (0.082) (0.148) (0.158) Master's Degree 0.392*** 0.310*** 0.064-0.103 (0.096) (0.083) (0.148) (0.158) J.D. 0.047 0.086-0.261-0.593** (0.152) (0.136) (0.224) (0.287) M.B.A. 0.438*** 0.326*** 0.056-0.095 (0.103) (0.090) (0.155) (0.163) M.D. 0.770*** 0.502** 0.052-0.065 (0.290) (0.247) (0.308) (0.272) Ph.D. 0.466*** 0.430*** 0.099-0.080 (0.108) (0.094) (0.159) (0.169) Experience 0.076*** 0.060*** 0.061*** 0.061*** (0.005) (0.005) (0.005) (0.006) Experience Squared - 0.002*** - 0.001*** - 0.002*** - 0.002*** (0.000) (0.000) (0.000) (0.000) Observations 1,603 1,603 1,603 1,603 1,603 Adjusted R- squared 0.034 0.338 0.547 0.704 0.809 18
Variable for age, for industry, education and occupation, company- job- No years of state, year and specific title- specific Controls experience. firm size. Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A14. Overall Germany Results for Total Compensation: Regression of Log Total Compensation on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.212*** 0.150*** 0.098*** 0.047 0.046 (0.040) (0.036) (0.036) (0.049) (0.050) Age 0.055** 0.053** 0.030-0.002 (0.022) (0.022) (0.033) (0.033) Education High School 0.216 0.253* 0.202-0.142 (0.147) (0.139) (0.278) (0.318) Bachelor's Degree 0.339** 0.281** 0.114-0.107 (0.139) (0.131) (0.270) (0.307) Master's Degree 0.465*** 0.370*** 0.193-0.063 (0.140) (0.132) (0.271) (0.307) 19
Variable for age, for industry, education and occupation, company- job- No years of state, year and specific title- specific Controls experience. firm size. J.D. 0.090 0.168-0.053-0.217 (0.221) (0.216) (0.410) (0.557) M.B.A. 0.532*** 0.423*** 0.188 0.002 (0.151) (0.143) (0.283) (0.317) M.D. 0.775* 0.519-0.036-0.131 (0.423) (0.394) (0.563) (0.528) Ph.D. 0.631*** 0.568*** 0.248 0.042 (0.158) (0.150) (0.290) (0.328) Experience 0.082*** 0.064*** 0.075*** 0.078*** (0.007) (0.007) (0.010) (0.012) Experience Squared - 0.002*** - 0.002*** - 0.002*** - 0.003*** (0.000) (0.000) (0.000) (0.001) Observations 1,603 1,603 1,603 1,603 1,603 Adjusted R- squared 0.016 0.213 0.358 0.448 0.598 Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 20
Table A15. Oaxaca- Blinder Decomposition of the Germany Male- Female Pay Gap Log Base Pay Log Total Compensation Mean Male Pay 10.917 11.034 Mean Female Pay 10.692 10.823 Uncontrolled Gender Pay Gap - 0.225-0.212 Explained Pay Gap (Due to Differences in Worker Characteristics) - 0.114-0.135 Percentage Explained 51% 64% Education & Experience ("Human Capital") 22% 28% Choice of Occupation and Industry 28% 36% Unexplained Pay Gap (Due to Differences in Regression Coefficients) - 0.111-0.077 Percentage Unexplained 49% 36% Observations 1,603 1,603 Worker Demographics State Year Industry Occupation Company Size Notes: Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 21
V. France Table A16. Overall France Results for Base Pay: Regression of Log Salary on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.143*** 0.096*** 0.069*** 0.037 0.063** (0.032) (0.025) (0.024) (0.029) (0.030) Age 0.150*** 0.116*** 0.092*** 0.115*** (0.020) (0.019) (0.024) (0.023) Education High School 0.075 0.105 0.198 0.573*** (0.129) (0.126) (0.179) (0.196) Bachelor's Degree 0.091 0.026 0.095 0.527*** (0.118) (0.114) (0.169) (0.196) Master's Degree 0.120 0.016 0.086 0.525*** (0.118) (0.115) (0.169) (0.196) J.D. 0.662* - 0.009 0.052 0.488 (0.349) (0.420) (0.370) (0.336) M.B.A. 0.259** 0.119 0.233 0.618*** (0.124) (0.120) (0.177) (0.204) M.D. 0.065-0.032 0.035 0.488** (0.202) (0.186) (0.223) (0.243) Ph.D. 0.052 0.033 0.235 0.586*** (0.136) (0.131) (0.186) (0.209) Experience 0.054*** 0.049*** 0.054*** 0.042*** (0.005) (0.005) (0.006) (0.006) Experience Squared - 0.001*** - 0.001*** - 0.001*** - 0.001*** (0.000) (0.000) (0.000) (0.000) Observations 1,049 1,049 1,049 1,049 1,049 Adjusted R- squared 0.018 0.395 0.571 0.768 0.858 22
Variable for age, for industry, education and occupation, company- job- No years of state, year and specific title- specific Controls experience. firm size. Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. Table A17. Overall France Results for Total Compensation: Regression of Log Total Compensation on Various Individual, Job and Employer Characteristics Variable No Controls for age, education and years of experience. for industry, occupation, state, year and firm size. company- specific job- title- specific Gender Pay Gap (%) 0.161*** 0.108** 0.094** 0.126* 0.062 (0.050) (0.045) (0.046) (0.073) (0.091) Age 0.171*** 0.131*** 0.037 0.060 (0.035) (0.037) (0.060) (0.072) Education High School 0.116 0.023 0.051 1.026* (0.228) (0.242) (0.453) (0.603) Bachelor's Degree 0.064-0.041 0.017 0.989 (0.208) (0.220) (0.427) (0.601) Master's Degree 0.058-0.105-0.010 0.986 23
Variable for age, for industry, job- education and occupation, company- title- No years of state, year and specific specific Controls experience. firm size. (0.208) (0.220) (0.428) (0.602) J.D. 0.453-0.345-0.733 0.464 (0.616) (0.808) (0.935) (1.030) M.B.A. 0.223-0.005 0.150 1.071* (0.218) (0.232) (0.448) (0.626) M.D. - 0.045-0.155-0.171 0.860 (0.356) (0.358) (0.563) (0.744) Ph.D. - 0.073-0.109 0.227 0.982 (0.240) (0.251) (0.469) (0.641) Experience 0.067*** 0.064*** 0.068*** 0.057*** (0.009) (0.009) (0.015) (0.019) Experience Squared - 0.002*** - 0.001*** - 0.002*** - 0.002** (0.000) (0.000) (0.001) (0.001) Observations 1,049 1,049 1,049 1,049 1,049 Adjusted R- squared 0.009 0.228 0.349 0.391 0.452 Worker Demographics State Year Industry Occupation Company Size Employer IDs Job Title IDs Notes: Standard errors in parentheses. Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 24
Table A18. Oaxaca- Blinder Decomposition of the France Male- Female Pay Gap Log Base Pay Log Total Compensation Mean Male Pay 10.780 10.929 Mean Female Pay 10.637 10.768 Uncontrolled Gender Pay Gap - 0.143-0.161 Explained Pay Gap (Due to Differences in Worker Characteristics) - 0.102-0.135 Percentage Explained 71% 84% Education & Experience ("Human Capital") 21% 30% Choice of Occupation and Industry 50% 53% Unexplained Pay Gap (Due to Differences in Regression Coefficients) - 0.041-0.026 Percentage Unexplained 29% 16% Observations 1,049 1,049 Worker Demographics State Year Industry Occupation Company Size Notes: Based on Glassdoor salary data. See Andrew Chamberlain (2016) Demystifying the Gender Pay Gap: Evidence from Glassdoor Salary Data for complete details. 25