Evaluation of Compensation Data Collected Through the EEO-1 Form (2023) / Chapter Skim
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Pages 254-264
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From page 254... ...
hr.) ✓ SOURCE: Panel generated from ACS, 2018.
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* Pacific Islander (0.0046)
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Dummies ✓ ✓ SOURCE: Panel generated from Component 2 employer, establishment, and employee files for 2018. NOTE: Excludes data based on all rules in Appendix 6-1.
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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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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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Analysis was restricted to establishments and sex-race/ethnicity-occupation-pay (SROP) cells with "green flags," as defined in Chapter 5, to eliminate SROP cells with implausible values.
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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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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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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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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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