Evaluation of Compensation Data Collected Through the EEO-1 Form (2023) / Chapter Skim
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7 Are Component 2 Pay Data Useful for Investigating Individual Establishments and Local Labor Markets?
7 Are Component 2 Pay Data Useful for Investigating Individual Establishments and Local Labor Markets?
Pages 257-290
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From page 257... ...
Component 2 data could be suitable for employer self-assessment of raw pay gaps but not for finer-grained pay-equity or job-segregation analyses. Component 2 data present the following challenges: hourly pay cannot be reliably computed, pay bands are too wide to measure important pay disparities, and calculating pay gaps is hindered when there is a lack of demographic and pay diversity.
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From page 258... ...
Specifically, it provides an analysis of sex and race/ethnicity pay gaps in a single labor market, defined by industry and geography: the technology sector in Silicon Valley. Unadjusted sex and race/ ethnicity pay gaps at the local labor-market level and at four hypothetically targeted establishments are examined.
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From page 259... ...
Another benefit of focusing on the Silicon Valley technology sector is that establishments are sufficiently large to avoid coverage issues, which are more serious among small establishments. However, the panel notes that focusing on the technology sector may provide an overly optimistic view of data utility, particularly in estimating raw gaps at the local labor-market level, as coverage in the Silicon Valley technology sector is expected to be relatively high, based on industry coverage patterns discussed in Chapter 4.
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From page 260... ...
These issues were assessed in the Silicon Valley technology sector sample by examining the number of pay bands per SRO cell by establishment characteristics, occupation, sex, and race/ethnicity. As shown in Table 7-1, the typical Silicon Valley technology establishment shows sufficient variation in wages to calculate pay gaps, with an average of three pay bands used per SRO cell.
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From page 261... ...
NOTE: Excludes data based on all rules in Appendix 6-1. Data limited to Silicon Valley in the NAICS codes specified in Appendix 7-1.
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From page 262... ...
As discussed in Chapter 3, further differentiation in pay is in order, especially for high-paying occupations, to identify within-job pay gaps by sex and race/ethnicity. Table 7-2 also shows that women in the Silicon Valley technology sector are concentrated in slightly fewer pay bands than men, and most workers of color (including Black workers, Hispanic workers, and workers from other race groups)
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From page 263... ...
Data limited to Silicon Valley in the NAICS codes specified in Appendix 7-1. Minimum and maximum pay-band values for all characteristics shown are 1 and 12, respectively, with the exception of the executives job category (maximum value is 11)
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From page 264... ...
The category of professionals, for instance, is a heterogenous job category in the technology sector; the aggregation of many job titles into the professional category may lead to the identification of raw pay gaps due largely to differences in job titles and duties, thus contributing to overestimation of gaps. The executive category, in contrast, is less heterogeneous and may post smaller pay gaps simply because of greater similarity in job titles and duties, as well as the use of fewer pay bands.
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From page 265... ...
RESULTS Local Labor-Market Profile: Silicon Valley Technology Sector Table 7-3 provides summary statistics on the Component 2 sample representing the Silicon Valley technology sector, as defined by geographical and industrial codes. Summary statistics are provided for each focal EEO-1 job category by 10 sex and race/ethnicity groups: White nonHispanic men, White non-Hispanic women, Black men, Black women, Hispanic men, Hispanic women, Asian men, Asian women, men of all other race groups, and women of all other race groups.
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From page 266... ...
22,365 34.2 $239,311.3 $239,243.2 $0.0 $148.1 $911.7 $0.0 0.0 White Women 9,019 13.8 $224,603.1 $193,600.5 –$14,708.2 $131.3 $467.5 –$16.8 –6.2 Black/African American 716 1.1 $201,726.9 $133,828.4 –$37,584.4 $118.8 $263.3 $29.3 –15.7 Men
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From page 267... ...
Black/African American 473 0.7 $202,745.5 $121,056.2 –$36,565.8 $119.3 $251.1 –$28.8 –15.3 Women Hispanic Men 2,069 3.2 $209,527.0 $144,027.1 –$29,784.3 $142.4 $532.3 –$5.7 –12.5 Hispanic Women 1,066 1.6 $193,205.0 $129,813.7 –$46,106.3 $135.5 $389.3 –$12.6 –19.3 Asian Men 20,152 30.8 $220,251.6 $271,850.9 –$19,059.7 $136.8 $863.3 –$11.3 –8.0 Asian Women 8,074 12.3 $218,259.1 $191,000.1 –$21,052.1 $134.4 $595.0 –$13.7 –8.8 All Other Races Men 889 1.4 $212,256.8 $118,719.6 –$27,054.5 $152.0 $408.5 $3.9 –11.3 All Other Races Women 563 0.9 $196,042.6 $112,422.4 –$43,268.6 $125.3 $237.6 –$22.8 –18.1 Professionals 209,739 100.0 $172,851.1 $273,363.1 –$14,205.5 $109.2 $613.7 –$2.1 –7.6 White Men (reference) 56,931 27.1 $187,056.6 $333,157.3 $0.0 $111.3 $639.9 $0.0 0.0 White Women 20,537 9.8 $151,461.0 $217,141.6 –$35,595.6 $93.3 $456.7 –$17.9 –19.0 Black/African American 2,775 1.3 $136,396.1 $150,405.6 –$50,660.5 $93.8 $330.6 –$17.5 –27.1 Men Black/African American 1,469 0.7 $120,809.8 $124,531.7 –$66,246.8 $85.5 $233.1 –$25.8 –35.4 Women Hispanic Men 6,258 3.0 $156,745.8 $168,115.5 –$30,310.8 $97.0 $305.9 –$14.3 –16.2 Hispanic Women 3,358 1.6 $129,719.5 $129,074.4 –$57,337.1 $80.5 $195.4 –$30.8 –30.7 Asian Men 77,176 36.8 $183,677.7 $375,161.6 –$3,378.9 $121.0 $1,017.5 $9.7 –1.8 Asian Women 35,938 17.1 $155,806.3 $263,661.3 –$31,250.3 $98.8 $555.6 –$12.5 –16.7 All Other Races Men 3,321 1.6 $150,894.2 $134,495.3 –$36,162.4 $97.0 $246.2 –$14.3 –19.3 continued 267
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From page 268... ...
NOTE: Excludes data based on all rules in Appendix 6-1. Data limited to Silicon Valley in the NAICS codes specified in Appendix 7-1.
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From page 269... ...
of executive positions in the Silicon Valley technology sector, Asian men are about a quarter (25%) of executives, White women represent about 13 percent, and Asian women represent 7 percent.
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From page 270... ...
Executive pay gaps relative to White men are larger for women than for men of the same race/ethnicity group, as shown in Figures 7-1 and 7-2. Officials and Managers Among officials and managers in the Silicon Valley technology sector, White men (34%)
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From page 271... ...
The average annual pay for Silicon Valley technology sector professionals is $172,851. Despite being second to Asian men in terms of representation, White men still maintain the highest annual pay ($187,057)
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From page 272... ...
Targeted Analysis: Profiling Four Establishments Having identified aggregate pay disparities for sex and race/ethnicity groups relative to White men in the Silicon Valley technology sector, the next step was to focus on four targeted establishments. The objective of this exercise was to simulate the type of analysis EEOC investigators might initially conduct using Component 2 data when employees bring complaints of pay discrimination.
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From page 273... ...
These data are meant to be an initial tool for identifying situations requiring further research, but EEOC as part of its normal enforcement processes would collect more detailed data from those establishments. Examining Pay Gaps at Targeted Establishments Turning to the four targeted establishments, the last four columns of Table 7-4 present annual pay gaps for each sex and race/ethnicity group relative to White men, for SRO cells with sufficient data.
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From page 274... ...
Alternatively, if EEOC were to receive a similar charge from a White woman manager in Establishment 2 and observed the much smaller –2.6 percent pay gap relative to White men, staff could place lower priority on this case or require more information to be convinced that the pay gap is meaningful. As "professional" is the largest and most sex and race/ethnicity diverse occupation in the Silicon Valley technology sector sample, it is not surprising that calculating sex and race/ethnicity pay gaps across the four target establishments is most feasible for professionals.
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From page 275... ...
0 0.0 –– 0.0 0.0 0.0 White Women –10,777 –3.9 –– 2.4 0.0 –– Black/African American –2,889 –1.0 –– –– –– –– Men Black/African American –33,365 –12.1 –– –– –– –– Women Hispanic Men –8,345 –3.0 –– –– –– –– Hispanic Women –12,481 –4.5 –– –– –– –– Asian Men –4,476 –1.6 –– –– 0.0 –2.5 Asian Women –17,440 –6.3 –– –– 0.0 –– All Other Races Men –12,013 –4.4 –– –– –– –– All Other Races Women –25,929 –9.4 –– –– –– –– Managers –13,613 –5.7 –5.9 –5.8 –0.9 –4.5 White Men (reference) 0 0.0 0.0 0.0 0.0 0.0 White Women –14,708 –6.1 –16.9 –2.6 –– –– Black/African American –37,584 –15.7 –– –– – – –– Men Black /African American –36,566 –15.3 –– –– –– –– Women Hispanic Men –29,784 –12.4 –– – – –– –– Hispanic Women –46,106 –19.3 –– –– –– –– Asian Men –19,060 –8.0 –3.3 –– 5.1 5.4 Asian Women –21,052 –8.8 –16.8 –37.6 11.5 –24.2 All Other Races Men –27,054 –11.3 –– –– –– –– All Other Races Women –43,269 –18.1 –– –– –– –– Professionals –14,205 –7.6 –10.0 8.4 –8.1 –19.5 White Men (reference)
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From page 276... ...
0 0.0 –– 0.0 0.0 0.0 White Women –17,494 –21.1 –– –5.2 –– –– Black/African American –9,294 –11.2 – – –– 9.6 –– Men Black/African American –24,229 –29.2 –– –– –– –– Women Hispanic Men –8,853 –10.7 –– –– –– –22.2 Hispanic Women –23,885 –28.8 –– 5.6 –7.7 –– Asian Men –5,873 –7.1 –2.8 –– –33.9 –29.1 Asian Women –15,015 –18.1 –27.1 19.6 –52.4 –24.5 All Other Races Men –9,528 –11.5 –– – – –– –– All Other Races Women –21,772 –26.3 –– –– –– –– SOURCE: Panel generated from Component 2 employer, establishment, and employee files for 2018, "green flags" only. NOTE: Excludes data based on all rules in Appendix 6-1.
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From page 277... ...
The objective of this exercise was to illustrate how Component 2 data visualizations might enhance EEOC's systemic enforcement efforts. Figure 7-3 provides density plots of the ratio of each sex and race/ethnicity group's annual pay compared to that of White men for each of the four target occupations in the Silicon Valley technology sector.
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From page 278... ...
NOTE: Excludes data based on all rules in Appendix 6-1. Data limited to Silicon Valley in the NAICS codes specified in Appendix 7-1.The statistics shown are the natural log of the ratio of a group's pay compared to that of White males.
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From page 279... ...
CONCLUSIONS AND RECOMMENDATIONS 279 Professionals Technicians FIGURE 7-3 Continued
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From page 280... ...
For example, among professionals in the Silicon Valley technology sector, the curve for Black women is centered to the left of zero, with a substantially larger portion of the tail falling in the 50 percent or less region, as compared to data for other sex and race/ethnicity groups of professionals. This might flag technology professionals as a high pay-disparity context for Black women, and might draw regulatory attention to those establishments in which Black women are making more than 50 percent less than similarly skilled White men.
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From page 281... ...
Table 7-4 enables such comparisons between the four hypothetically targeted establishments and Silicon Valley technology sector labor-market averages. The first two columns of Table 7-4 provide mean pay gaps, expressed in dollars and percentages, for each sex and race/ethnicity group relative to White men for the Silicon Valley technology sector sample, taken from the baseline gaps in Table 7-3.
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From page 282... ...
The question whether an individual has been treated improperly takes precedence over general patterns of pay disparity, though the presence of such patterns would also be informative. DISCUSSION Implications for Suitability of Component 2 Data for Intended Enforcement Uses As presented here for the Silicon Valley technology sector and four target establishments, Component 2 pay data can be used to calculate average annual wages and pay gaps between sex and race/ethnicity groups by occupation at the local labor-market and establishment levels.
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From page 283... ...
For the Silicon Valley technology sector, 21 percent (264/1,210) of establishments were filtered out due to data quality issues.
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From page 284... ...
. In the Silicon Valley technology sector context, lack of pay differentiation was most problematic for computing pay gaps for executives.
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From page 285... ...
Sex and race/ethnicity representation: the lack of representation of specific sex, race, and ethnicity groups, especially when examining sex and race/ethnicity intersectionally, impacts the utility of Component 2 data for enforcement purposes. In job categories with low sex and race/ethnicity diversity, such as executives in the Silicon Valley technology sector, it is impossible to calculate pay gaps for targeted establishments due to few or no workers in specific SRO cells.
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From page 286... ...
Using Component 2 annual pay data, average pay gaps between specific sex and race/ethnicity groups relative to White men in four job categories were calculated for the entire Silicon Valley technology sector labor market and for the four targeted establishments. At the local labor-market level, all sex and race/ethnicity groups earned less than White men in the same occupation and women in each race/ethnicity group earned less than their race/ethnicity male peers.
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From page 287... ...
to calculate raw annual pay gaps for establishments under investigation by individual charges; (2) to make comparisons between investigated establishments and peer establishments in the same industry and metropolitan area, county, and/or core-based statistical area; and (3)
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From page 288... ...
288 COMPENSATION DATA COLLECTED THROUGH THE EEO-1 FORM CHAPTER APPENDIXES APPENDIX 7-1 NAICS Codes Used for Selecting High Tech Sample 4-Digit Code Industry Label 3254 Pharmaceutical and Medicine Manufacturing 3333 Commercial and Service Industry Machinery Manufacturing 3341 Computer and Peripheral Equipment Manufacturing 3342 Audio and Video Equipment Manufacturing 3344 Semiconductor and Other Electronic Component Manufacturing 3345 Navigational, Measuring, Electrometrical, and Control In struments Manufacturing 3346 Manufacturing and Reproducing Magnetic and Optical Media 3364 Aerospace Product and Parts Manufacturing 3391 Medical Equipment and Supplies Manufacturing 5112 Software Publishers 5179 Other Telecommunications 5191 Other Information Services 5413 Architectural, Engineering, and Related Services 5415 Computer Systems Design and Related Services 5417 Scientific Research and Development Services 5419 Other Professional, Scientific, and Technical Services
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From page 289... ...
95120 (San Jose) 94024 (Los Altos, Los Altos Hills)
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From page 290... ...
(n = 946) Mean number of employees 275.19 314.16 Mean number of pay bands 7.63 8.08 Percent distributions Sex Male 70.47 70.51 Female 29.53 29.49 Race/ethnicity Asian 50.90 50.40 Black/African American 2.03 2.01 Hispanic 4.98 4.99 White 39.57 40.05 American Indian/Alaska Native, Native Hawaiian or 2.52 2.54 Other Pacific Islander, and Two or More Races Job categories Executive 3.48 3.30 First/midlevel 22.21 22.02 Professionals 69.80 70.61 Technicians 4.52 4.07 Federal contractor Yes 85.72 86.45 No 14.28 13.55 Submission status Single establishment 7.24 5.02 Consolidated report 92.76 94.98 Mode of administration Web 19.77 17.31 Upload 80.23 82.69 Industry Manufacturing 39.06 38.74 Information 30.64 31.36 Professional, Scientific, and Technical Services 30.29 29.90 SOURCE: Panel generated table from EEO-1 Component 2 data, 2018.
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