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Pages 228-253

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From page 228...
... . For men, some estimated race/ethnicity pay gaps relative to White men are similar in Component 2 and ACS data (e.g., for Black and Hispanic workers)
From page 229...
... . SOURCE: Panel generated from Component 2 employer, establishment, and employee files for 2018.
From page 230...
... , and given that, as demonstrated using ACS data, controlling for high(er) quality hours-worked data does not meaningfully change estimated pay gaps for most groups.
From page 231...
... Figure 6-4 shows that including information on education and age in the detailed ACS regressions had mixed impacts on estimated pay gaps. Including this information did not meaningfully change estimated pay gaps for many groups relative to men or White workers, but it did reduce the pay gap for Hispanic workers relative to White workers by five percentage points.
From page 232...
... 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)
From page 233...
... Measurement or data-quality issues in ACS data could affect comparisons of pay differentials. These include (but are not limited to)
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.
From page 235...
... 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%)
From page 236...
... 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)
From page 237...
... 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
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.
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
From page 240...
... Are in Place 250–499 14,821,209 9,930,597 8,855,726 8,183,381 500–999 12,424,701 7,055,524 6,363,225 5,901,549 1,000 or More 24,132,897 15,342,523 11,524,958 10,526,736 Federal Contractor         Yes 51,812,201 33,387,367 28,709,616 26,798,137 No 60,348,558 30,734,407 24,930,847 22,793,330 NAICS Code Industry Sector         11 Agriculture, 968,465 307,219 244,843 199,990 Forestry, Fishing, and Hunting 21 Mining, Quarrying, 979,252 484,726 388,871 344,543 and Oil and Gas Extraction 22 Utilities 552,805 495,574 454,737 441,728 23 Construction 3,289,431 1,795,913 1,453,977 1,262,796 31–33 Manufacturing 19,131,122 10,104,097 8,812,210 8,016,794 42 Wholesale Trade 4,905,647 2,077,105 1,644,062 1,506,046 44–45 Retail Trade 13,260,679 10,229,311 8,809,144 8,561,894 48–49 Transportation and 4,803,385 3,086,063 2,641,680 2,475,126 Warehousing 51 Information 3,018,710 2,098,462 1,876,352 1,800,435 52 Finance and 7,943,392 4,575,968 3,830,759 3,690,200 Insurance 53 Real Estate and 1,872,925 737,595 570,950 529,665 Rental and Leasing 54 Professional, 6,222,468 4,284,129 3,654,007 3,406,106 Scientific, and Technical Services 55 Management of 2,562,559 811,486 697,481 670,014 Companies and Enterprises
From page 241...
... aExtreme firm size outliers refer to firms reporting more than 1.4 million employees -- a number larger than the largest U.S. employer.
From page 242...
... 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
From page 243...
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From page 244...
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From page 245...
... 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.
From page 246...
... 1,004,526 49,616,344 SOURCE: Panel generated from ACS, 2018; and Component 2 employer, establishment, and employee files for 2018. NOTE: Excludes data based on all rules in Appendix 6-1.
From page 247...
... 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
From page 248...
... APPENDIX 6-5  Continued 248 $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 Midlevel 6.90 3.45 5.68 6.42 10.01 13.70 14.40 12.23 9.24 7.73 4.79 5.44 9.29 Professionals 11.68 4.08 5.96 6.32 10.13 13.55 14.28 11.20 7.73 6.18 3.43 5.45 16.54 Technicians 13.12 6.82 11.27 11.53 16.52 17.32 12.62 5.64 2.41 1.11 0.64 0.99 2.71 Sales 33.62 8.52 9.62 7.64 8.98 9.41 7.37 5.01 2.95 2.79 1.78 2.32 12.35 Admin. Support 27.25 11.01 15.63 13.88 12.79 9.42 5.25 2.41 1.00 0.63 0.32 0.40 14.91 Craft 12.76 7.12 12.80 11.42 15.27 16.40 12.80 6.61 2.69 1.41 0.41 0.31 8.76 Operatives 20.98 10.74 14.91 11.92 13.89 13.05 8.20 3.72 1.46 0.65 0.25 0.23 12.17 Laborers/ 36.23 13.83 16.62 10.96 9.82 6.78 3.28 1.41 0.52 0.29 0.12 0.15 6.10 Helpers Service Workers 48.25 14.76 14.69 8.43 6.59 4.03 1.85 0.69 0.30 0.19 0.07 0.16 16.41 SOURCE: Panel generated from Component 2 employer, establishment, and employee files for 2018 and ACS, 2018.
From page 249...
... * 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)
From page 250...
... hr.) ✓ ✓ SOURCE: Panel generated from ACS, 2018.
From page 251...
... (0.003) Native Hawaiian or Other Pacific Islander –0.175*
From page 252...
... hr.) ✓ SOURCE: Panel generated from ACS, 2018.
From page 253...
... (0.003) Native Hawaiian or Other Pacific Islander –0.186*


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