Evaluation of Compensation Data Collected Through the EEO-1 Form (2023) / Chapter Skim
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6 Are Component 2 Pay Data Useful for Examining National Pay Differences?
6 Are Component 2 Pay Data Useful for Examining National Pay Differences?
Pages 209-256
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From page 209... ...
However, the top and bottom pay bands are very large, and some job categories encompass too many workers at divergent levels of pay. EEO-1 job categories are too coarse to capture important pay differences across occupations.
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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)
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. There are differences in the distributions of workers across EEO-1 job categories between the two datasets with, for example, fewer professionals 3 See Chapter 2 for a detailed description of eligible filers.
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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)
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7 In this report, "job category" is used to refer to the 10 job categories used in the Compo nents 1 and 2 data collections. "Occupation" is used as a more generic term and with reference to the SOC codes that provide a more detailed categorization of occupations.
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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.
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From page 215... ...
6.b . 6.b Basic Pay Differences by Race/Ethnicity The first set of results comes from a version of Eq.
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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)
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From page 217... ...
In particular, pay disparities between non-White women and White men are larger than for White women and White men for all groups except Asian workers. Relative to White men, Black men earn 44 percent less, Hispanic men earn 38 percent less, Native Hawaiian or Other Pacific Islander men earn 29 percent less, American Indian/Alaska Native men earn 41 percent less, and men of two or more races earn 27 percent less.
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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.
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FIGURE 6-4 Pay differentials by sex and race/ethnicity in the ACS with sequential controls (natural log)
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codes has a very large effect on the male-female annual pay differential, reducing it from 35 to 23 percent; adding industry controls and then state controls reduces the differential further but by just 2.8 percentage points. Controlling for geographic variation across states increases the relative pay premium for White workers for all race/ethnicity groups, and notably, for Asian workers, a positive pay gap relative to White workers becomes negative.
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From page 221... ...
when EEO-1 job categories are used than when SOC occupation codes are used. The same basic pattern holds for each race/ethnicity group: sorting across occupations as defined by SOC codes explains far more of the pay differentials by race/ethnicity than does sorting across EEO-1 job categories; and sorting 13 The coefficient estimates and standard errors corresponding to Figure 6-5 are in Appendix 6-6.
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-0.4 -0.2 0.0 0.2 FIGURE 6-5 Pay differentials by sex and race/ethnicity in the ACS with EEO'd occupations (natural log)
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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.
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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.
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All differences from White males are statistically significant except those bars that are not filled in. These bars show the estimated differences based on the following progressively more detailed specifications of the models: basic, followed by adding occupations, industry, geography (states)
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From page 226... ...
6.a and 6.b without additional controls, and this figure can be compared to ACS results in Figure 6-3.19 Women in the Component 2 data earn 23 percent less than men; compared to White workers, Black workers earn 34 percent less; Hispanic workers earn 30 percent less; Native Hawaiian and Other Pacific Islander workers earn 29 percent less; American Indian/Alaska Native workers earn 28 percent less; and those reporting two or more races report 34 percent less. Asian workers out-earn White workers by four percent.
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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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Figure 6-9 reports results from regressions that control for EEO-1 job category, and then additional controls for industry and state were added FIGURE 6-8 Intersectional pay differentials in Component 2 data (natural log)
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NOTE: Excludes data based on all rules in Appendix 6-1. Percent differences from White males (natural log)
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Moreover, the fact that the pay differentials across the two datasets are similar for Black and Hispanic workers but diverge for other workers (e.g., Asian workers) does not mean that Component 2 data are of higher quality for detecting pay differences between White workers and Black or Hispanic workers than between White and Asian workers.
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From page 231... ...
It increased the pay disadvantage for Asian workers by six percentage points, and it reduced the pay gap for those with two or more races by seven percentage points. The results shown in Figure 6-6 are similar, but given that job categories are substantially less predictive of pay differences than SOC codes, it is not surprising that controlling for age and education had a somewhat larger impact when data were EEO'd.
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The clear advantage in using EEO-1 pay data to explain pay gaps is the inclusion of establishment and firm information in those data. SUMMARY This chapter first described an assessment of the estimation of pay differentials by sex and race/ethnicity in ACS data, using reported information on pay, occupation, industry, and geography, and then alternatively when using EEO-1 measures of occupation (job categories)
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From page 233... ...
EEO-1 job categories are too coarse to capture important pay differences across occupations that are associated with the sorting of workers into occupations by sex and race/ethnicity. In the panel's opinion, future data collections should move to the collection of SOC-based occupation information.
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From page 234... ...
However, this would not be the case for examinations of pay differentials that are narrower in scope. In particular, the lowest and highest pay bands are so wide as to encompass large fractions of employees, particularly in certain EEO-1 job categories, so that no measurable differences of pay are available for many workers.
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Larger than average drops in employee counts were seen for Hispanic workers (dropping to 41% of baseline) , Native Hawaiian or Other Pacific Islander workers (36%)
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The biggest differences in adjustments involved variables for job category, establishment size, and industry. The final sample of establishments for the regression analysis reports 76 billion hours worked (Appendix 6-3)
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EXAMINING NATIONAL PAY DIFFERENCES 237 APPENDIX BOX 6-1 Sequence of Steps Followed to Create Analysis File for Regressionsa Decision rule Rationale Discussion Reflected in Preliminary edits 1 Exclude Type 6 Type 6 reports do Chapter 2 Appendix reports not include sex, 6-2 and 6-3, race/ethnicity, occu- Column 1 pation, or pay data needed for analysis 2 Exclude firms with Such counts exceed Chapter 3 Appendix more than 1.4 the largest employer 6-2 and 6-3, million employees in the United States Column 1 reported in EEO-1 Component 2 data Edits to employee counts 3 Exclude Component Component 1 data Chapter 5 Appendix 6-2, 2 employee data appeared to be Column 2 that are more than more accurate, and nine times the Com- such large changes ponent 1 value for within the same the same year and year seem unlikely the difference be tween Component 1 and Component 2 for the same year was at least 400 4 Exclude employee Such counts are Chapter 5 Appendix 6-2, counts for establish- larger than the Column 2 ments larger than largest known 60,000 employees establishment 5 Exclude Component Without Component Chapter 5 Appendix 6-2, 2 employee data 1 data, data ac- Column 3 that have no Com- curacy cannot be ponent 1 match verified 6 Exclude employee Create a dataset Chapter 6 Appendix 6-2, cells based on that will produce Column 4 issues with hours uniform counts worked (see below) for all regression models continued
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From page 238... ...
238 COMPENSATION DATA COLLECTED THROUGH THE EEO-1 FORM APPENDIX BOX 6-1 Continued Decision rule Rationale Discussion Reflected in 7 Exclude cells with Create a dataset Chapter 6 Appendix 6-2, missing data on that will produce Column 4 regression variables uniform counts for all regression models Edits to number of hours worked 8 Exclude hours- At this level, em- Chapter 4 Appendix 6-3, worked cells ployees would be Column 2 showing average working more than hours worked per 16 hours per day, employee greater 365 days per year than 5,840 9 Exclude hours- Such outliers Chapter 4 Appendix 6-3, worked cells more appear likely to Column 3 than three standard contain errors deviations from the mean for the SROP 10 Exclude cells that Without employee Chapter 4 Appendix 6-3, lack employee counts, data are Column 4 counts meaningless 11 Exclude hours- Create a dataset Chapter 4 Appendix 6-3, worked cells based that will produce Column 5 on issues with the uniform counts employee counts for all regression models 12 Exclude cells with Create a dataset Chapter 6 Appendix 6-3, missing data on that will produce Column 5 regression variables uniform counts for all regression models SOURCE: Panel edits based on data generated from Component 2 employer, establishment, and employee files for 2018. a These rules were created to quickly subset to a dataset that is relatively free of errors.
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From page 239... ...
Are in Place Race/Ethnicity Hispanic 18,190,363 9,814,302 8,138,649 7,462,194 White 67,771,550 38,379,821 32,054,088 29,687,614 Black/African American 16,235,123 9,764,824 8,114,521 7,492,356 Native Hawaiian or Other 654,370 300,699 262,793 235,994 Pacific Islander Asian 6,655,007 4,004,308 3,574,248 3,330,861 Native American/Alaska Native 699,606 356,940 300,430 274,964 Two or More Races 2,461,857 1,535,154 1,226,151 1,132,361 Sex Male 57,753,516 32,767,875 27,525,706 25,429,036 Female 54,914,360 31,388,173 26,145,174 24,187,308 Job Category Executive 3,867,316 1,115,682 851,482 733,882 First/Midlevel 11,625,579 6,488,531 5,372,463 5,008,445 Professionals 21,791,200 13,056,010 11,257,033 10,342,682 Technicians 6,936,129 3,719,137 3,203,644 2,925,548 Sales Workers 10,561,595 7,372,567 6,093,798 5,878,204 Administrative Support 14,003,620 7,929,296 6,691,626 6,229,372 Craft Workers 5,971,061 3,342,690 2,824,998 2,558,991 Operatives 11,710,199 6,111,329 5,291,923 4,865,954 Laborers and Helpers 8,451,228 4,706,811 3,868,123 3,502,298 Service Workers 17,749,949 10,313,995 8,215,790 7,570,968 Number of Employees Less than 100 33,035,190 16,889,911 13,409,200 12,558,409 100–249 28,253,879 14,937,493 13,517,771 12,446,269 continued
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240 COMPENSATION DATA COLLECTED THROUGH THE EEO-1 FORM Component Component 2 (Unedited) 2 (Maxima (Only Exclude Compo- Applied Final Edited the Extreme nent 2 and Non- Data When Employee and Establishment Firm Size (Maxima Matches All Edits Characteristic Outliers)
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aExtreme firm size outliers refer to firms reporting more than 1.4 million employees -- a number larger than the largest U.S. employer.
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Place Race/Ethnicity Hispanic 18,150,016.3 14,363,473.9 14,360,785.3 11,130,930.9 White 70,823,667.6 59,239,283.7 59,236,570.5 46,873,143.6 Black/African American 17,093,983.6 13,241,643.5 13,239,450.8 10,361,008.0 Native Hawaiian or 526,555.0 490,994.3 488,079.1 344,441.3 Other Pacific Islander Asian 9,173,269.9 6,667,425.2 6,663,142.7 5,409,958.1 Native American/ 587,385.2 565,463.9 561,771.2 399,786.7 Alaska Native Two or More Races 2,070,934.5 1,904,799.2 1,902,649.9 1,420,137.4 Sex Male 67,793,486.7 52,017,945.1 52,010,078.2 41,587,992.4 Female 50,632,325.3 44,455,138.6 44,442,371.3 34,351,413.6 Job Category Executive 2,004,510.9 1,830,656.7 1,830,366.3 1,368,153.7 First/Midlevel 17,904,978.2 11,797,384.8 11,796,786.7 9,356,221.5 Professionals 24,059,856.6 22,088,797.0 22,084,175.4 17,160,826.6 Technicians 6,164,942.3 5,760,872.7 5,755,649.1 4,581,158.4 Sales Workers 9,347,089.2 8,954,726.5 8,953,204.8 7,370,338.4 Administrative 13,617,845.4 12,053,291.8 12,051,603.1 9,718,183.4 Support Craft Workers 10,024,720.9 5,948,193.1 5,946,140.8 4,708,263.8 Operatives 14,672,154.0 10,674,486.4 10,673,977.1 8,559,719.4 Laborers and Helpers 7,459,483.2 6,203,739.1 6,201,678.2 4,685,316.5 Service Workers 13,170,231.4 11,160,935.5 11,158,868.1 8,431,224.2
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a Red Flag) Orange Flag)
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Place Public Ad92 163,937.7 127,018.6 126,949.7 79,832.8 ministration 99 Unclassified 380,467.2 265,174.2 265,005.3 66,936.4 SOURCE: Panel generated from Component 2 employer, establishment, and employee files for 2018. aExtreme firm size outliers refer to firms reporting more than 1.4 million employees -- a number larger than the largest U.S.
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7.466 0.520 7.132 1.506 Male 0.553 0.513 Female 0.447 0.487 White (Non-Hispanic) 0.602 0.598 Hispanic 0.193 0.150 Black/African American 0.118 0.151 Native Hawaiian or Other Pacific Islander 0.002 0.005 Asian 0.060 0.067 American Indian/Alaska Native & Not Hispanic 0.005 0.006 Two or More Races 0.022 0.023 Executives 0.008 0.015 Managers 0.093 0.101 Professionals 0.165 0.208 Technicians 0.027 0.059 Sales Workers 0.124 0.118 Administrative Support Workers 0.149 0.126 Craft Workers 0.088 0.052 Operatives 0.122 0.098 Laborers and Helpers 0.061 0.071 Service Workers 0.164 0.153 N (total workers)
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APPENDIX 6-5 Percentage of Employees in Each Job Category Who Are in Each Pay Band $19,239 $19,240– $24,440– $30,680– $39,000– $49,920– $62,920– $80,080– $101,920– $128,960– $163,800– $208,000 Total % of and Less $24,439 $30,679 $38,999 $49,919 $62,919 $80,079 $101,919 $128,959 $163,799 $207,999 and More Workers COMPONENT 2 Totals 27.94 6.23 7.82 9.75 10.18 9.44 8.65 6.94 4.84 3.18 1.88 3.15 100.00 Executive 2.66 0.64 0.78 1.14 1.80 2.72 4.41 6.63 8.61 11.14 12.18 47.30 1.48 Midlevel 3.97 1.41 1.99 3.87 7.98 11.24 13.28 14.25 13.25 11.16 7.18 10.42 10.09 Professionals 9.80 2.35 3.13 4.91 8.75 13.47 16.87 15.01 10.81 6.40 3.38 5.12 20.85 Technicians 29.03 5.02 7.61 11.31 13.72 12.63 10.05 6.01 2.94 1.11 0.35 0.23 5.90 Sales 51.30 8.84 8.26 7.05 5.57 4.52 3.58 3.02 2.50 2.05 1.36 1.97 11.85 Admin. 24.31 8.45 12.90 19.04 17.06 9.97 5.07 2.01 0.69 0.26 0.11 0.12 12.56 Support Craft 12.64 3.51 5.34 9.42 14.34 16.46 15.92 11.78 6.47 2.64 0.95 0.53 5.16 Operatives 19.70 6.04 9.93 15.69 17.34 13.92 9.27 5.60 1.81 0.52 0.12 0.06 9.81 Laborers/ 44.91 9.93 13.30 14.29 9.47 4.52 2.08 0.98 0.31 0.10 0.04 0.07 7.06 Helpers Service 58.09 11.22 11.28 9.15 5.27 2.58 1.28 0.52 0.22 0.11 0.07 0.21 15.26 Workers ACS Totals 24.96 9.08 11.71 9.48 10.80 10.69 8.41 5.43 3.29 2.55 1.49 2.11 100.00 Executive 2.40 0.98 1.58 1.56 3.00 5.58 7.38 10.28 10.33 12.24 13.84 30.82 0.76 247 continued
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NOTE: Excludes data based on all rules in Appendix 6-1.
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* Native Hawaiian or Other –0.175 0.017 0.020 –0.052 –0.091 –0.038 –0.286 –0.050 –0.044 –0.116 –0.159 –0.093 Pacific Islander (0.024)
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Native Hawaiian 0.240 0.146 0.139 0.137 0.148 0.118 or Other Pacific Islander (0.048)
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(0.003) Native Hawaiian or Other Pacific Islander –0.175*
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NOTE: Standard errors in parentheses: *
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(0.003) Native Hawaiian or Other Pacific Islander –0.186*
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NOTE: Standard errors in parentheses: *
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(0.0008) Native Hawaiian or Other –0.2882*
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From page 256... ...
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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