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Pages 200-227

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From page 200...
... APPENDIX 5-2  Continued 200 Firms Establishments Within Firms Cells Within Establishments Firm, Establishment, and Employee Characteristic Red Orange Green Red Orange Green Red Orange Yellow Green Craft Workers – – – – – – 6.4 11.4 5.4 4.9 Operatives – – – – – – 13.3 1.5 7.5 7.7 Laborers and Helpers – – – – – – 6.3 6.2 6.8 5.4 Service Workers – – – – – – 16.3 8.4 10.4 11.7 Establishment Quality Red – – – 100.0 0.0 0.0 57.2 4.3 0.5 0.1 Orange – – – 0.0 100.0 0.0 42.8 46.6 75.8 5.1 Green – – – 0.0 0.0 100.0 0.0 49.0 23.7 94.8 Overall 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 SOURCE: Panel generated Component 2 employer, establishment, and employee files for 2017 and 2018. NOTE: # Indicates rounds to zero.
From page 201...
... the match rates on how successful the two datasets can be matched based on the ID variables and additional information (e.g., addresses, NAICS code, and zip code)
From page 202...
... Component 1 & 1 Data 2 data files for Numerator: the number of establishments that 2017 and 2018 can be matched across the two years Denominator 1: the number of establishments that appeared in the 2017 Component 2 data Denominator 2: the number of establishments that appeared in the 2018 Component 2 data a The Type 6 reports and establishments in the firms that failed the Walmart rule (i.e., with a size larger than Walmart) are excluded in the calculation of the match rate.
From page 203...
... Establishments with unique matches across the two years then can be merged. Match Rate Match rate among establishments, after excluding outliers (based on the Walmart rule)
From page 204...
... 5) Merge based on unique matches of HDQ_NBR+ZIPCODE+NAICS: for establishments that failed to be matched in the steps above, they were matched based on the zip code from the standardized ad dresses and the NAICS code (all six digits)
From page 205...
... The overall match rate is 66.5 percent, which equals to 597,361 divided by 897,770. Match rate in 2018 among establishments, after excluding large outliers (based on the Walmart rule)
From page 206...
... of them can be matched with establishments that can be matched across the two components in 2018. These 527,379 establishments account for 58.7 percent of the total 897,770 establishments in the 2017 Component 2 Data and 56.4 percent of the total 935,610 establishments in the 2018 Component 2 data.
From page 207...
... APPENDIX 5-4 Inconsistent Zeros for Number of Employees Comparing Component 2 Data for 2017 and 2018 at the SRO Level Number Percent Firm and Establishment >0 Employees; >0 Employees; >0 Employees; >0 Employees; >0 Employees; >0 Employees; Characteristic 2017 Only 2018 Only Both Years 2017 Only 2018 Only Both Years Administration Mode             Online-Entry 243,256 277,659 1,482,817 12.1 13.9 74.0 Data-Upload 849,513 896,738 4,498,270 13.6 14.4 72.0 Establishment Size             Fewer than 100 729,700 789,222 3,290,336 15.2 16.4 68.4 100–249 231,687 245,237 1,543,519 11.5 12.1 76.4 250–499 85,843 89,616 658,356 10.3 10.7 79.0 500–999 29,070 31,948 276,911 8.6 9.5 81.9 1,000 or More 16,469 18,302 211,965 6.7 7.4 85.9 Establishment Quality Red 3,401 4,052 18,288 13.2 15.7 71.0 Orange 71,705 77,498 361,755 14.0 15.2 70.8 Green 1,017,663 1,092,847 5,601,044 13.2 14.2 72.6 Overall 1,092,769 1,174,397 5,981,087 13.2 14.2 72.5 SOURCE: Panel generated from Component 2 employer and establishment files for 2017 and 2018.
From page 209...
... This issue exists even after the application of data-quality filters discussed in Chapter 5, which was limited to addressing outliers in employee counts. At the national level, there is enough variation across pay bands to estimate pay gaps across sex and race/ethnicity.
From page 210...
... These shortcomings include the fact that the data are aggregated in various ways; they use wide pay bands for annual earnings (see Chapter 3) ; annual hours worked are aggregated for all employees in each of the pay bands (see Chapter 3)
From page 211...
... Some differences exist across the two datasets that need to be taken into account when considering this chapter's analyses, but the datasets are comparable enough to make the analyses informative for evaluating multiple dimensions of quality in the Component 2 data. Relative to Component 2 data, workers in the ACS sample have lower average annual pay.
From page 212...
... , or of reporting differences between the identification of job categories by workers and firms.6 The first comparison assessed whether individual-level ACS data yield similar results regarding pay gaps by sex and race/ethnicity as the less-detailed data arranged in a manner following that collected by the Component 2 instrument (called the "EEO'd" data)
From page 213...
... , 2018 ACS data on pay for workers in each of those pay bands were used. State-specific medians were calculated and assigned to workers in the upper and lower pay bands based on state of residence.
From page 214...
... in Earnings, as noted as error. Earnings, previously, noted were the approximate midpoints of each of the pay bands, with the exception previously, were the approximate midpoints of each of the pay bands, with of the upper and lower bands.the exception of the upper and lower bands.
From page 215...
... . Theutilizes 8 ACS estimated sex and race/ethnicity coefficients and associated standard errors (th the federal standard naming convention "American Indian/Alaska Native." estimates of β1 and The comparable EEOCβ2 race and category their standard errors)
From page 216...
... Figure 6-3 reports results on pay gaps by race/ethnicity for men and women that arise from estimating a version of Eq. 6.b that allows for interactions between the sex indicator and the race/ethnicity indicators, but that FIGURE 6-2  Basic pay differentials in ACS data by sex and race/ethnicity (natural log)
From page 217...
... White women earn 41 percent less than White men and, except for Asian women, all other non-White women earn even less relative to White men (69% less for Hispanic women, 60% for Black women, 45% for Native Hawaiian or Other Pacific Islander women, 67% for American Indian/ Alaska Native women, and 55% for women of two or more races)
From page 218...
... Moreover, there is spatial variation of race/ethnicity in the United States and geographic variation in pay. Second, these estimated pay gaps can be compared to those obtained when EEO'd data were used, by employing the 10 EEO-1 job categories rather than the detailed SOC codes.
From page 219...
... . SOURCE: Panel generated from ACS, 2018.
From page 220...
... .11 Estimating Differences in Wage Rates Across Workers Results presented thus far give differences in annual pay by workers' sex and race/ethnicity. Given that many employees do not work full-time for the full year in the establishments under consideration, these pay gaps may not represent differences in underlying wage rates.
From page 221...
... . ACS Data in EEO'd Form Controlling for the 819 detailed SOC codes, as reported in Figure 6-4, meaningfully reduces estimated pay gaps across workers.
From page 222...
... . SOURCE: Panel generated from ACS, 2018.
From page 223...
... estimated pay differentials. For example, the pay gap for women is 25 percent when occupation is EEO'd but 10 percent when SOC codes are used, and the pay gap for Black workers is 16 percent when EEO-1 job categories are used but 9 percent when SOC codes are used.14 Occupational sorting matters to estimated pay gaps in ways that cannot be captured by combining sorting across EEO-1 job categories with sorting across industries and geography.
From page 224...
... Due to this disproportionate concentration of workers in certain pay bands, it is impossible to examine pay differences by sex or race/ethnicity for the lowest-earning workers once pay data are EEO'd. This problem is even more serious when examining workers within job categories.
From page 225...
... . SOURCE: Panel generated from ACS, 2018.
From page 226...
... While detailed SOC codes are not available for Component 2 data, the establishment to which a SROP cell belongs is known. That additional detail allows regressions to be run that control for the establishment of the workers, allowing "within"-establishment pay differentials across workers to be estimated (also known as regressions with establishment fixed effects)
From page 227...
... For some groups, the pay gaps are also similar in magnitude across the two datasets. Indeed, the pay differentials for Hispanic workers and Black workers in the Component 2 data are within two to four percentage points of the estimates in the ACS data.


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