Equal Employment Opportunity Commission v. Chicago Miniature Lamp Works
This text of 622 F. Supp. 1281 (Equal Employment Opportunity Commission v. Chicago Miniature Lamp Works) is published on Counsel Stack Legal Research, covering District Court, N.D. Illinois primary law. Counsel Stack provides free access to over 12 million legal documents including statutes, case law, regulations, and constitutions.
Opinion
FINDINGS OF FACT AND CONCLUSIONS OF LAW
SHADUR, District Judge.
Equal Employment Opportunity Commission (“EEOC”) and Ed Randolph (“Randolph”) have sued Chicago Miniature Lamp Works (“Chicago Miniature”), charging race-based discrimination against blacks as *1286 a class and, in Randolph’s case, individually. After a bench trial the parties have supplemented their extensive pretrial submissions by tendering proposed post-trial findings of fact and conclusions of law.
In accordance with Fed.R.Civ.P. (“Rule”) 52(a), this Court finds the facts specially as set forth in the following Findings of Fact (“Findings”) and states the following Conclusions of Law (“Conclusions”). To the extent if any of the Findings as stated reflect legal conclusions, they shall be deemed Conclusions; to the extent if any of the Conclusions as stated reflect factual findings, they shall be deemed Findings.
Findings of Fact
Parties
1. EEOC is the agency of the United States charged with administration and enforcement of Title VII of the Civil Rights Act of 1964 (“Act”), 42 U.S.C. §§ 2000e to 2009e-17 (“Title VII”) (Stip. ¶ 3). 1
2. Randolph is an individual citizen of the United States and a resident of the Northern District of Illinois. Randolph’s race is black (Stip. ¶ 4).
3. Until October 1980 Chicago Miniature was a corporation organized and existing under the laws of the State of Illinois, with its only office and factory located at 4433 North Ravenswood Avenue, Chicago, Cook County, Illinois. Since then Chicago Miniature has been, and it now is, a division of General Instrument Corporation (“General Instrument”), a corporation organized and existing under the laws of the State of Delaware 2 (Stip. ¶¶ 1, 10). Jurisdiction and Venue
4. Chicago Miniature has been and is engaged in the manufacture and sale of miniature and sub-miniature incandescent and neon lamps and associated components and subassemblies. Its lamp products are sold primarily to original equipment manufacturers for a variety of industrial and consumer product applications (Stip. ¶ 9). Chicago Miniature was and is now an employer in an industry affecting commerce within the meaning of Sections 2000e(g) and (h) (Stip. ¶ 2).
5. Randolph was hired and first employed by Chicago Miniature as a “programmer analyst” (Randolph Tr. 147). He remained employed by Chicago Miniature (receiving one promotion and three salary increases) from November 17, 1975 through approximately February 6, 1978 (Stip. ¶ 16). On March 9, 1978 Randolph filed a timely Charge of Discrimination (the “Charge,” P.Ex. 2) with EEOC’s Chicago District Office, alleging Chicago Miniature had discriminated against him in denying him a promotion to the position of Data Processing Manager because of his race (Stip. ¶ 5).
6. In investigating the Charge EEOC discovered and investigated evidence indicating Chicago Miniature discriminated against blacks as a class, on account of their race, in recruitment, hiring and promotions (Morgan Tr. 1518-20).
7. On November 9, 1978 EEOC found reasonable cause to believe:
(a) Chicago Miniature had discriminated against Randolph by failing to promote him to the position of Data Processing Manager because of his race.
(b) Chicago Miniature discriminated against blacks as a class, on account of their race, in recruitment, hiring and promotions.
EEOC’s findings of reasonable cause to believe were set forth in its November 9 “Letter of Determination” mailed to Chicago Miniature and Randolph (Morgan Tr. 1518-19; P.Ex. 6).
8. EEOC filed this action June 8, 1979. On November 9, 1979 the Court granted Randolph leave to intervene in his own behalf as a party plaintiff.
*1287 9. EEOC elected to proceed to trial only with respect to (a) Chicago Miniature’s discrimination against blacks as a class in recruitment and hiring for entry-level factory jobs and (b) Chicago Miniature’s refusal and failure to promote Randolph because of his race (FPTO Att. I).
10. Jurisdiction and venue are not disputed by the parties (Tr. 2391-97; Stip. ¶ 8).
11. From sometime in 1977 through October 1979 William Curran (“Curran”) was chief executive and operating officer of Chicago Miniature, holding the title of Executive Vice-President (Stip. ¶ 12).
12. From about 1972 through August 1980 Armella Simon (“Simon”) was employed by Chicago Miniature as its Personnel Manager (Stip. ¶ 13). Since then Magalis Trueva (“Trueva”) has succeeded Simon as the person with primary responsibility for filling openings for entry-level employees at Chicago Miniature (Trueva Tr. 1540).
13. From and after May 16, 1979 Donald Howard (“Howard”) was employed by Chicago Miniature as its Director of Human Resources (Stip. ¶ 14).
14. Daniel Hoeh (“Hoeh”) was employed at Chicago Miniature as Data Processing Manager for a period of approximately eight years ending in October 1977. Hoeh’s responsibilities as Data Processing Manager included the hiring of personnel for the data processing department. It was Hoeh who, after interviewing Randolph, hired him as a Programmer Analyst (Hoeh Tr. 17, 25, 58; P.Exs. 9, 37).
Chicago Miniature’s Reporting of Its Work Force Composition
15. In accordance with Title VII, Chicago Miniature filed with EEOC EEO-1 reports, signed by authorized Chicago Miniature management personnel (including Simon and Howard), for each of the years 1966 through 1984 (Stip. ¶ 19; P.Exs. 81-95, 172-74). Those EEO-1 reports set forth, by race, sex and national origin, the number of persons employed by Chicago Miniature, as of a given payroll date in each year, in each of nine job categories identified in the reports (Stip. ¶ 20); Elkhanialy 1/16 Tr. 44-45). Entry-level factory jobs are shown in the “operative,” “laborer” (none reported) and “service worker” job categories in the EEO-1 reports (Stip. ¶ 23; Simon Tr. 997).
16. All data in EEO-1 reports is 3 “employer-generated”: It is the employer, not EEOC, that determines the race, national origin and sex of its employees, decides under which of the job classifications employees are reported and makes the numerical count of the employees in each category (Stip. ¶¶ 19, 21-22; Elkhanialy 1/16 Tr. 45; P.Ex. 124 at 2-3).
17. For the years 1970 through 198J Chicago Miniature’s work force composition, as reported in its EEO-1 reports, was as set forth in the table attached as Appendix (“App.”) 1 (P.Ex. 124, Table I; P.Exs. 84-95 [EEO-1 reports]; Elkhanialy 1/16 Tr. 45-58).
18. For the years 1982-83 (after this action was filed), not reflected in App. 1, the number of blacks employed by Chicago Miniature in entry-level operative and service job categories continued to decline.
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FINDINGS OF FACT AND CONCLUSIONS OF LAW
SHADUR, District Judge.
Equal Employment Opportunity Commission (“EEOC”) and Ed Randolph (“Randolph”) have sued Chicago Miniature Lamp Works (“Chicago Miniature”), charging race-based discrimination against blacks as *1286 a class and, in Randolph’s case, individually. After a bench trial the parties have supplemented their extensive pretrial submissions by tendering proposed post-trial findings of fact and conclusions of law.
In accordance with Fed.R.Civ.P. (“Rule”) 52(a), this Court finds the facts specially as set forth in the following Findings of Fact (“Findings”) and states the following Conclusions of Law (“Conclusions”). To the extent if any of the Findings as stated reflect legal conclusions, they shall be deemed Conclusions; to the extent if any of the Conclusions as stated reflect factual findings, they shall be deemed Findings.
Findings of Fact
Parties
1. EEOC is the agency of the United States charged with administration and enforcement of Title VII of the Civil Rights Act of 1964 (“Act”), 42 U.S.C. §§ 2000e to 2009e-17 (“Title VII”) (Stip. ¶ 3). 1
2. Randolph is an individual citizen of the United States and a resident of the Northern District of Illinois. Randolph’s race is black (Stip. ¶ 4).
3. Until October 1980 Chicago Miniature was a corporation organized and existing under the laws of the State of Illinois, with its only office and factory located at 4433 North Ravenswood Avenue, Chicago, Cook County, Illinois. Since then Chicago Miniature has been, and it now is, a division of General Instrument Corporation (“General Instrument”), a corporation organized and existing under the laws of the State of Delaware 2 (Stip. ¶¶ 1, 10). Jurisdiction and Venue
4. Chicago Miniature has been and is engaged in the manufacture and sale of miniature and sub-miniature incandescent and neon lamps and associated components and subassemblies. Its lamp products are sold primarily to original equipment manufacturers for a variety of industrial and consumer product applications (Stip. ¶ 9). Chicago Miniature was and is now an employer in an industry affecting commerce within the meaning of Sections 2000e(g) and (h) (Stip. ¶ 2).
5. Randolph was hired and first employed by Chicago Miniature as a “programmer analyst” (Randolph Tr. 147). He remained employed by Chicago Miniature (receiving one promotion and three salary increases) from November 17, 1975 through approximately February 6, 1978 (Stip. ¶ 16). On March 9, 1978 Randolph filed a timely Charge of Discrimination (the “Charge,” P.Ex. 2) with EEOC’s Chicago District Office, alleging Chicago Miniature had discriminated against him in denying him a promotion to the position of Data Processing Manager because of his race (Stip. ¶ 5).
6. In investigating the Charge EEOC discovered and investigated evidence indicating Chicago Miniature discriminated against blacks as a class, on account of their race, in recruitment, hiring and promotions (Morgan Tr. 1518-20).
7. On November 9, 1978 EEOC found reasonable cause to believe:
(a) Chicago Miniature had discriminated against Randolph by failing to promote him to the position of Data Processing Manager because of his race.
(b) Chicago Miniature discriminated against blacks as a class, on account of their race, in recruitment, hiring and promotions.
EEOC’s findings of reasonable cause to believe were set forth in its November 9 “Letter of Determination” mailed to Chicago Miniature and Randolph (Morgan Tr. 1518-19; P.Ex. 6).
8. EEOC filed this action June 8, 1979. On November 9, 1979 the Court granted Randolph leave to intervene in his own behalf as a party plaintiff.
*1287 9. EEOC elected to proceed to trial only with respect to (a) Chicago Miniature’s discrimination against blacks as a class in recruitment and hiring for entry-level factory jobs and (b) Chicago Miniature’s refusal and failure to promote Randolph because of his race (FPTO Att. I).
10. Jurisdiction and venue are not disputed by the parties (Tr. 2391-97; Stip. ¶ 8).
11. From sometime in 1977 through October 1979 William Curran (“Curran”) was chief executive and operating officer of Chicago Miniature, holding the title of Executive Vice-President (Stip. ¶ 12).
12. From about 1972 through August 1980 Armella Simon (“Simon”) was employed by Chicago Miniature as its Personnel Manager (Stip. ¶ 13). Since then Magalis Trueva (“Trueva”) has succeeded Simon as the person with primary responsibility for filling openings for entry-level employees at Chicago Miniature (Trueva Tr. 1540).
13. From and after May 16, 1979 Donald Howard (“Howard”) was employed by Chicago Miniature as its Director of Human Resources (Stip. ¶ 14).
14. Daniel Hoeh (“Hoeh”) was employed at Chicago Miniature as Data Processing Manager for a period of approximately eight years ending in October 1977. Hoeh’s responsibilities as Data Processing Manager included the hiring of personnel for the data processing department. It was Hoeh who, after interviewing Randolph, hired him as a Programmer Analyst (Hoeh Tr. 17, 25, 58; P.Exs. 9, 37).
Chicago Miniature’s Reporting of Its Work Force Composition
15. In accordance with Title VII, Chicago Miniature filed with EEOC EEO-1 reports, signed by authorized Chicago Miniature management personnel (including Simon and Howard), for each of the years 1966 through 1984 (Stip. ¶ 19; P.Exs. 81-95, 172-74). Those EEO-1 reports set forth, by race, sex and national origin, the number of persons employed by Chicago Miniature, as of a given payroll date in each year, in each of nine job categories identified in the reports (Stip. ¶ 20); Elkhanialy 1/16 Tr. 44-45). Entry-level factory jobs are shown in the “operative,” “laborer” (none reported) and “service worker” job categories in the EEO-1 reports (Stip. ¶ 23; Simon Tr. 997).
16. All data in EEO-1 reports is 3 “employer-generated”: It is the employer, not EEOC, that determines the race, national origin and sex of its employees, decides under which of the job classifications employees are reported and makes the numerical count of the employees in each category (Stip. ¶¶ 19, 21-22; Elkhanialy 1/16 Tr. 45; P.Ex. 124 at 2-3).
17. For the years 1970 through 198J Chicago Miniature’s work force composition, as reported in its EEO-1 reports, was as set forth in the table attached as Appendix (“App.”) 1 (P.Ex. 124, Table I; P.Exs. 84-95 [EEO-1 reports]; Elkhanialy 1/16 Tr. 45-58).
18. For the years 1982-83 (after this action was filed), not reflected in App. 1, the number of blacks employed by Chicago Miniature in entry-level operative and service job categories continued to decline. Only because of an overall decline in the work force, their percentage level of representation increased slightly, as follows (Exs. 172-74):
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Curran’s Recognition of Black Underrepresentation
19. Curran acknowledged that in his opinion there were not, during his tenure as chief executive officer of Chicago Miniature, enough blacks within the work force of Chicago Miniature, and that was true on a company-wide basis (Curran Tr. 909). “Recruitment” by Chicago Miniature
20. Chicago Miniature did not and does not advertise to obtain applicants for entry-level (“operative” and “service”) factory jobs (Stip. ¶ 26; Simon Tr. 999, 1020). It did however utilize newspaper advertising to recruit applicants for clerical and typist jobs (Simon Tr. 999-1001).
21. Nor did Chicago Miniature advertise in any form of media “targeted” to black audiences in order to recruit black job applicants or to seek to increase the level of black representation in its work force (Cur-ran Tr. 909-10).
22. Chicago Miniature seldom utilized, and it received few (less than 5%, in Simon’s opinion) job applicants by referral from, the State of Illinois unemployment office (Simon Tr. 1002).
23. Instead Chicago Miniature utilized and relied primarily upon word-of-mouth to recruit applicants for entry-level factory jobs. In Simon’s opinion approximately 75% of Chicago Miniature’s job applicants come to it through word-of-mouth recruiting (Simon Tr. 998, 1025). Most of Chicago Miniature's applicants and employees identified in Findings 52 and 53, and employees testifying for Chicago Miniature, learned of Chicago Miniature through word-of-mouth.
24. Statistical analysis of its applicant flow data confirms Chicago Miniature’s heavy reliance upon word-of-mouth recruiting. Analysis shows blacks, who have historically been underrepresented in Chicago Miniature’s work force, continue to be underrepresented in its applicant flow, even when the analysis is restricted to nearby areas, and it shows the reverse with respect to Hispanics, who have been heavily represented. It also shows that the geographical distribution of applicants is clustered within an artificially small area (Elkhanialy Tr. 741-43).
25. Chicago Miniature’s heavy utilization of and reliance upon word-of-mouth recruiting resulted in the exclusion of blacks from the network of information concerning jobs at Chicago Miniature, gross underrepresentation of blacks in Chicago Miniature’s applicant flow, and perpetuation of the gross underrepresentation of blacks in, and their exclusion from, Chicago Miniature’s entry-level work force (Elkhanialy Tr. 741-43, 1325-27; P.Ex. 124 at 22, 24, 31, 41).
Relevant Labor Market: Relevant Definitions
26. Generally speaking a “labor market” is an economically integrated geographic unit (a) from which employers operating in that unit draw their work forces and (b) within which a significant number of workers may change jobs (“labor mobility”) in response to changing economic conditions without necessarily changing their places of residence (Elkhanialy 1/16 Tr. 2; P.Ex. 124 at 29). For a particular employer such as Chicago Miniature the “relevant labor market” is the area from which it may be expected to draw its job applicants and employees (Elkhanialy 1/16 Tr. 8; P.Ex. 124 at 29).
27. “Standard Metropolitan Statistical Area” (“SMSA”) is a geographic area comprising a city having a population of 50,000 or more, the county in which the city is *1289 located and the outlying counties that have a high degree of economic and social integration with the nucleus (Elkhanialy 1/16 Tr. 4; P.Ex. 124 at 29). “Chicago SMSA” comprises the City of Chicago (“Chicago”), Cook County (in which Chicago is located) and the outlying counties of Lake, McHenry, Kane, DuPage and Will (Elkhanialy 1/16 Tr. 4, Tr. 1046; P.Exs. 126, 127).
28. Since passage of the Civil Rights Act of 1964 an SMSA or a sub-area of it (such as a city, a county or a group of counties) has usually been considered the relevant labor market for occupations requiring no specialization, such as clerks or operatives (P.Ex. 124 at 29). 4
Relevant Labor Market: Methodology
29. Whether the relevant labor market for entry-level factory jobs at a Chicago employer, such as Chicago Miniature, is the Chicago SMSA as a whole or some lesser part of the SMSA is determined by analysis of (a) the location of the employer within the SMSA, (b) the accessibility of the employer, (c) commuting patterns and (d) in appropriate cases, the employer’s applicant flow (Elkhanialy 1/16 Tr. 8, Tr. 1320; P.Ex. 124 at 29-30).
Relevant Labor Market: Use of Applicant Flow Data
30. If an employer’s applicant flow is not tainted by discriminatory recruiting practices and is not otherwise biased or distorted, that applicant flow is one of the reliable indicators of the employer’s relevant labor market. Untainted applicant flow data shows both (a) the area from which the employer has historically drawn applicants and (b) the racial and ethnic composition of the employer’s actual applicant pool. Untainted applicant flow may also serve as a surrogate for underlying factors that are more directly determinative of the employer’s relevant labor market (such as the distance applicants and employees are willing to travel to work, accessibility of the employer and relevant transportation systems) (Elkhanialy 1/16 Tr. 19-20; P.Ex. 124 at 30-31).
31. But Chicago Miniature’s applicant flow is clearly not a reliable indicator of its relevant labor market. Except in the unreal world sought to be constructed by Chicago Miniature’s “hired gun” expert, Dr. Chiswick (discussed in Findings 105-16), blacks have been grossly underrepresented in Chicago Miniature’s work force. As already found, Chicago Miniature has relied in principal part upon word-of-mouth recruiting to generate its applicant flow. As a result, Chicago Miniature’s applicant flow is simply a self-fulfilling prophecy reflecting the characteristics of its work force. It does not reflect (a) either the area from which it would be expected to draw applicants in an unbiased environment or (b) the racial and ethnic composition of the civilian labor force in that area (Elkhanialy 1/16 Tr. 20-23; P.Ex. 124 at 30-32).
32. Finding 31 as to the total unreliability of Chicago Miniature’s applicant flow as an indicator of its relevant labor market or the racial and ethnic composition of that market is compelled by the fact that — even without regard to Chicago Miniature’s recruiting practices — the applicant flow is not remotely representative of the racial and ethnic composition of the civilian work force in any area that might be deemed the relevant labor market. As later Findings reflect, the applicant flow is totally at odds with the composition of the civilian labor force in (a) Chicago, (b) the 12 Zip Code Area (within approximately five miles) from which Chicago Miniature has drawn more than 90% of its applicants, (c) the 5 Zip Code Area (within approximately three to four miles) from which it has drawn more than 70% of its applicants or (d) its own Home Zip Code Area 60640 from which it has drawn nearly 30% of its applicants (Elkhanialy 1/16 Tr. 20-23; P.Ex. 124 at 31).
33. Because the recruiting process Chicago Miniature utilized to generate its applicant flow is itself being challenged as discriminatory, it is conceptually circular to *1290 utilize the result of that process (applicant flow) to determine the labor market against which the process (recruiting) is tested (Elkhanialy 1/16 Tr. 23; see Chiswick Tr. 2140). By definition a result measured against a warped yardstick will itself be skewed, and Finding 31’s reference to a “self-fulfilling prophecy” compels rejection of Chicago Miniature’s reliance on its applicant flow.
Relevant Labor Market: Location
34. Chicago Miniature is located at 4433 North Ravenswood Avenue, Chicago, Cook County, Illinois, in zip code 60640 (Stip. ¶ 1; Elkhanialy Tr. 1047).
35. Chicago is the center or core of the Chicago SMSA. It is a viable, economically integrated, densely populated, almost self-sufficient economic unit with a population of approximately 3 million (Elkhanialy 1/16 Tr. 13-14, Tr. 1047, 1051; P.Ex. 124 at 30). It provides employment for 82% of its residents. This is more true for blacks (85% of whom work in Chicago) than for whites or Hispanics (80% and 77% of whom, respectively, work in Chicago). Though Chicago also provides jobs for 23% of the suburbanites, they are more likely to have jobs at the upper end of the occupational hierarchy rather than in entry-level factory jobs (Elkhanialy 1/16 Tr. 14; Tr. 1047-48, 1091; P.Ex. 124 at 30).
36. During the past 10 years Chicago has been losing entry-level manufacturing jobs, and the labor for such jobs has been moving from Chicago to the suburbs, not vice versa. It is not likely that suburban residents of the Chicago SMSA will seek entry-level factory jobs in Chicago (Elkhanialy 1/16 Tr. 14-15; Tr. 1047-48, 1068). Conversely it is far more likely that Chicago residents seeking entry-level factory jobs will seek such jobs within Chicago rather than in the suburbs (Elkhanialy 1/16 Tr. 1086-87). Indeed, virtually all applicants for entry-level factory jobs at Chicago Miniature have been Chicago residents (Chiswick Tr. 1896).
37. Areas of the Chicago SMSA outside of Chicago are not part of Chicago Miniature’s relevant labor market for entry-level factory jobs (Elkhanialy 1/16 Tr. 14-15; P.Ex. 124 at 30). Conversely Chicago Miniature’s location is such that residents from throughout Chicago are likely to apply there for work (Elkhanialy 1/16 Tr. 1397). Though of course it is not equally likely that every member of the Chicago labor force — whatever the location of his or her residence — would apply for an entry-level factory job at Chicago Miniature, there is no way identified by either party’s expert to develop statistics that would factor in the variables (residence location or a host of other factors) affecting such likelihood in individual cases — except of course the already-discredited self-fulfilling use of applicant flow data (see Findings 31-33 and 73-104). As Finding 115 reflects, analysis of the statistics for Chicago and successively smaller areas (without attempting to weight each set of figures by those imponderable variables), coupled with the overwhelming nature of the figures involved, compels the conclusion Chicago Miniature engaged in race discrimination.
Relevant Labor Market: Commuting Patterns
38. Blacks who both live and work in Chicago commute, on the average, 38 minutes to work, compared to 29 minutes for whites and Hispanics. Black males who live in Chicago but work in the suburbs commute, on the average, 44.4 minutes by private vehicle and 58.8 minutes by public transportation, while the average time (a) for white males is 33.8 minutes by private vehicle and 46.9 minutes by public transportation and (b) for Hispanic males is 37.3 minutes by private vehicle and 45.2 minutes by public transportation. For black females who live in Chicago but work in the suburbs, the mean commuting time is 42.4 minutes by private vehicle and 60.3 minutes by public transportation, while the mean times (a) for white females are 27.8 minutes and 44.3 minutes respectively and (b) for Hispanic females are 36.3 minutes and 45.3 minutes respectively (Elkhanialy 1/16 Tr. 24-28, Tr. 1108-09; P.Ex. 124 at 32-33).
39. In Chicago zip codes in which the civilian labor force is 90% or more black, *1291 the median commuting time is approximately 45 minutes, and approximately 25% of the workers residing there commute more than an hour. In those areas, workers who commute at least 45 minutes (including those who commute an hour or more) make up approximately 45% of the labor force (Elkhanialy 1/16 Tr. 28-29).
40. Disparities in commuting time reflected in Findings 38 and 39 are not, of course, the result of mere chance. High rates of unemployment among blacks and the declining number of jobs available in black areas of Chicago are demographic trends that operate to increase commuting times for blacks (Elkhanialy 1/16 Tr. 29-30, Tr. 1071-76, 1305, 1309; P.Ex. 124 at 34-37).
41. In summary, blacks generally spend more time commuting than non-blacks (Elkhanialy 1/16 Tr. 29, Tr. 1111, 1305; Chiswick Tr. 1915, 2142-44).
42. For applicants for entry-level factory jobs at Chicago Miniature, a reasonable commuting time is certainly up to and including approximately an hour. There are a substantial number of potential applicants (principally blacks) for whom a reasonable commuting time would be as much as approximately IV2 hours (Elkhanialy 1/16 Tr. 31; Tr. 1106, 1121-22, 1135, 1340; P.Ex. 124 at 32-37).
Relevant Labor Market: Accessibility of Chicago Miniature
43. Chicago Miniature is located immediately adjacent to the Chicago Transit Authority (“CTA”) Ravenswood elevated train tracks and is about a five-minute walk from the Montrose Avenue station on that line (Stip. ¶ 27; Elkhanialy 1/16 Tr. 32). CTA’s Ravenswood line connects with the North-South Howard Street-Jackson Park/Englewood elevated line at two elevated stations on Chicago’s North Side and, via the North-South line in the Loop, with other elevated and underground lines serving Chicago (Stip. ¶ 28; P.Ex. 64).
44. Chicago Miniature is located within walking distance of three CTA bus routes: the Montrose Avenue (4400 North) route, the Lawrence Avenue (4800 North) route and the Damen Avenue (2000 West) route. Via said routes, connections may be made with other bus routes serving other areas of Chicago (Stip. ¶ 29; P.Ex. 64).
45. Chicago has an extensive public transportation system, including elevated and subway train lines and buses. Every part of Chicago is easily accessible to every other part of Chicago by public transportation (Elkhanialy 1/16 Tr. 31; P.Ex. 124 at 37; P.Ex. 64).
46. Public transportation commuting times from points (selected by EEOC) at the centers of Chicago’s South (95th and Dan Ryan) and West Sides (Madison and Kostner), both predominantly black, to Chicago Miniature are both less than an hour (Elkhanialy 1/16 Tr. 32-33, Tr. 1341; P.Ex. 124 at 37). Public transportation commuting times from five Chicago points (selected by Chicago Miniature) to Chicago Miniature are as follows:
(Elkhanialy 1/16 Tr. 33-36; D.Ex. LL) Despite the artificiality of Dr. Chiswick’s division of Chicago into Areas I through V (as an examination of the map discloses), and despite the fact those five selected points paint a kind of worst-case picture, the commuting times even from those outlying points are within reasonable limits (see Findings 42 and 52).
*1292 47. Chicago has an extensive network of expressways and arterial streets, rendering every part of Chicago easily accessible to every other part of Chicago by automobile (Elkhanialy 1/16 Tr. 32). There is a limited (but meaningful) amount of free on-street automobile parking available across the street from Chicago Miniature and elsewhere within walking distance of the facility on a first-come first-served basis (Stip. ¶[ 30; Elkhanialy 1/16 Tr. 38).
48. In summary, Chicago Miniature is accessible within a reasonable commuting time by public transportation and private vehicle from all parts of Chicago (Elkhanialy 1/16 Tr. 36-39; P.Ex. 124 at 37).
49. There are large concentrations of blacks, in areas ranging from 10% to 90% black, within 5 to 10 miles of Chicago Miniature, from which the commuting time to Chicago Miniature is less than an hour and thus (see Finding 42) certainly within reasonable limits (Elkhanialy 1/16 Tr. 39-41; P.Ex. 124 at 37).
Relevant Labor Market: Chicago Miniature’s Own View
50. In its own Affirmative Action Plan for the period June 1, 1977 to May 31, 1978 Chicago Miniature described its relevant labor market as follows:
The basic recruiting area for our company is the whole of the Chicago area and surrounding suburbs, since public transportation is available to the front door with easy access by car from expressway and main streets.
(Curran Tr. 904). In that same Affirmative Action Plan, Chicago Miniature identified its “recruitment area” for entry-level jobs of “assembler,” “warehouse” and “jani tor” — the very jobs at issue in this litigation — as “Chicago” (Curran Tr. 906-08).
51. In newspaper advertising utilized by Chicago Miniature to recruit applicants for typist jobs, Chicago Miniature repeatedly described its location as being “easily accessible by El or CTA buses” (Simon Tr. 1000-02; P.Exs. 71, 72, 73).
Relevant Labor Market: Applicants and Employees
52. Several blacks who had applied for jobs at Chicago Miniature but were not hired were called as witnesses by EEOC. They testified they (a) lived on both the South and West Sides of Chicago, (b) had held jobs throughout Chicago (including areas of the North Side more distant from their homes than Chicago Miniature), (c) commuted an hour to work on the average and (d) in numerous instances commuted IV2 hours to work. Their testimony is summarized in the three-page table attached as App. 2.
53. Chicago Miniature called the chief union stewardess at its plant as a witness. She identified 15 black employees of Chicago Miniature who lived on Chicago’s South and West Sides and commuted to work at Chicago Miniature as follows (Gailes Tr. 1566-81):
*1293 Chicago Miniature also called another plant employee as a witness. She identified two additional black South Side residents who are or were employees of Chicago Miniature and commute there to work as follows (R. Porter Tr. 1620-21):
Bailey 4400 S. Vincennes CTA
Harding 51st & Union Auto
Relevant Labor Market: Conclusion
54. Taking into account all the facts found in Findings 26-53, this Court finds Chicago is the relevant labor market for entry-level factory jobs at Chicago Miniature (Elkhanialy 1/16 Tr. 13, 37). As later Findings reflect, even if the relevant labor market were assumed to be any of several lesser areas within Chicago, analysis still demonstrates a discriminatory underrepresentation of blacks in Chicago Miniature’s recruitment and hiring for entry-level factory jobs.
Available Statistical Data
55. From documents produced by Chicago Miniature during pretrial discovery, EEOC constructed a computerized data base containing information (name, race, address, zip code, job applied for, whether hired or rejected, etc.) respecting all applicants for whom Chicago Miniature produced documentation (Donovan Tr. 1420, 1426-28, 1473-77; P.Exs. 117/Alpha, 117/Doc. No.). EEOC made its data base available to Chicago Miniature before trial, in both computer magnetic tape and print-out form, and then incorporated into its data base all changes requested by Chicago Miniature (Donovan Tr. 1426-28, 1451, 1469-77; Tr. 2417-20; and see Exhibits annexed to EEOC’s Response to Chicago Miniature’s Motion for Continuance of Trial filed Jan. 24, 1985).
Hiring Practices
56. No blacks were employed in Chicago Miniature’s personnel department or performed personnel functions during Simon’s tenure (Simon Tr. 1012-13; Randolph Tr. 184; Hoeh Tr. 65). Nor was any of the persons responsible for hiring entry-level factory workers at Chicago Miniature black (Simon Tr. 1012-13; Hoeh Tr. 65-66).
57. Chicago Miniature did not maintain or utilize objective written hiring procedures (Simon Tr. 1013). It had no education requirements for hire into entry-level factory jobs (Stip. ¶ 32; Simon Tr. 997-98), nor has it required any prior job experience as a minimum qualification for hire into such jobs (Simon Tr. 998). Instead, basic manual dexterity and the ability to speak some English have been the only minimal qualifications required by Chicago Miniature for hire into such jobs (Simon Tr. 997).
58. Chicago Miniature tested the manual dexterity of some applicants for entry-level factory jobs by administration of a “peg board” test. Applicants with ordinary manual dexterity, and no particular educational or job experience, were uniformly able to pass the very easy peg board test (Simon Tr. 997-98; Trueva Tr. 1542-43). No record evidence dealt with Chicago Miniature’s measures (if any) to determine applicants’ English-speaking ability, but satisfaction of the minimal requirements could readily be ascertained orally at the time applicants came to the plant.
59. Numerous entry level job applications of blacks have the letter “B” written by hand on them. Neither Simon nor Trueva was able to account for or explain that obvious race-coding of the applications of blacks (Simon Tr. 1004-08; Trueva Tr. 1544-45). 5
*1294 Computerized Analyses of Hiring Decisions
60. P.Exs. 122, 122-A and 122-B are computerized analyses of EEOC’s data base. Each page of each exhibit “matches” (a) one or more black entry level applicants) who applied on a particular date and was or were not hired with (b) one or more non-black(s) who applied for such jobs within four weeks before or eight weeks after the rejected black applicants) and was or were hired within eight weeks after the black applicants) had applied (Donovan Tr. 1432-37).
61. Chicago Miniature objected that a comparative analysis, as explained in Finding 60, was not appropriate as to black applicant Barbara Jackson (Tr. 1480-81, D.Ex. SSS at 1), non-black hire James Garcia (Tr. 1481-83, D.Ex. SSS at 2), non-black hire Charles Allison (D.Ex. SSS at 2), non-black hire Chi Song (D.Ex. SSS at 3), black applicant Annette Anderson (Tr. 1484-87), non-black hire P. Alexander (D.Ex. SSS at 5, 26), black applicant Charles Fenner (Tr. 1492-93, D.Ex. SSS at 13), black applicant Lillie Redmond (Tr. 1496-97, D.Ex. SSS at 14), non-black hire Margarita Plaza (D.Ex. SSS at 15), non-black hire Ana Morales (Tr. 1498-99), non-black hire Mona Noureldin (D.Ex. SSS at 16), non-black hire Scott Baier (D.Ex. SSS at 23), black applicant Helen Perkins (Tr. 1500-01), black applicant Jim H. Brown, Sr. (Tr. 1504-05), non-black hire Maria Patino (Tr. 1505-06) and non-black hire Ofelia Bucid (D.Ex. SSS at 25). Although a number of Chicago Miniature’s objections are dubious at best, this Finding will exclude all the objected-to individuals purely arguendo. Nevertheless there remain 20 separate and specifically identifiable instances during the 1978-81 period in which Chicago Miniature rejected a black applicant and, soon thereafter, hired a non-black applicant who applied at the same time as or within four weeks before or eight weeks after the rejected black (P.Exs. 122, 122-A, 122-B). Included within that group of 20 are Carolyn Johnson (Evans), Jessie Carr and Demetrius Woods, all of whom testified at trial that they applied for entry level jobs at Chicago Miniature and were not hired.
62. Chicago Miniature also objected that the pattern of discrimination plainly evident from the facts in Finding 61 is somehow vitiated by the fact that some of the non-black hires identified in P.Exs. 122, 122-A and 122-B applied slightly “earlier in time” (see Tr. 1483-84) than the comparably rejected black applicants. That objection is, spurious and is flatly rejected. P.Exs. 122, 122-A and 122-B show Chicago Miniature did not hire in order of application and, to the contrary, frequently hired without any regard whatever for date or order of application. To select only a few examples:
(a) Choi Ja applied August 1, 1978 and was hired 48 days later on September 18, 1978, while Margarita DeLeon, who applied a month and a half later on September 12, 1978, was hired September 13, 1978 (P.Ex. 122, Period=18AUG78);
(b) Milagro Fahiari applied November 27, 1978 and was hired 77 days later on February 12, 1979, while Charles Allison applied January 25, 1979 and was hired January 29, 1979 and Han Un applied December 4, 1978 and was hired January 24, 1979 (P.Ex. 122, Period=20DEC78);
(c) Dobrilla Naumov applied October 11, 1980 and was hired 44 days later on November 24, 1980, while Huong Dang applied November 14,1980 and was hired November 17, 1980 (P.Ex. 122, Period=020CT80);
(d) Carmen Gomez applied November 14, 1980 and was hired four days later on November 18, 1980, while G. Alvarez applied November 17, 1980 and was hired the very same day she applied (D.Ex. 122-B, Period=27OCT80).
Hiring Analysis
63. Chicago Miniature seeks to make much of a higher comparative hiring rate among black applicants than among non-blacks. As the following Findings reflect, that argument is spurious because it improperly seeks to attribute meaningful significance to the extremely small number of *1295 black entry-level applicants and hires during 1978-81. To assist in reviewing those Findings, the table attached as App. 3 (P.Ex. 124-A at 1) sets forth Chicago Miniature’s hiring data (applicants and hires for entry-level factory jobs) by race for the years 1978-81 inclusive. Where EEOC’s data base differed from Chicago Miniature’s data base, EEOC’s data is indicated by the row heading “P” and Chicago Miniature’s data is indicated by the row heading “D” (Elkhanialy Tr. 705-06).
64. Because of the very small number of black entry-level applicants and hires (see Finding 63), the statistical weight of any single black hire is vastly different from that of any single non-black hire, and any meaningful statistical comparison of black and non-black hiring rates (percentage of black applicants hired vs. percentage of non-black applicants hired) is impossible (Elkhanialy Tr. 706-17; P.Ex. 124 at 16-19; P.Ex. 124-A at 1; see Chiswick Tr. 1953-56, 2242-49, 2251-58).
65. What is significant in determining whether Chicago Miniature’s hiring was or was not race-discriminatory is rather a comparison of Chicago Miniature’s actual hiring with what would normally be expected from the relevant labor market. In that light, it is highly probative that Chicago Miniature’s extremely small number of black entry-level hires, coupled with the gross underrepresentation of blacks in Chicago Miniature’s work force since at least 1970, resulted in the continued severe underrepresentation of blacks in, and their exclusion from, Chicago Miniature’s entry-level factory jobs during the 1978-81 period (P.Ex. 124 at 19 Table I; P.Ex. 124-A at 1-2). Both facets of that underrepresentation are dealt with in statistical terms in the following Findings.
66. In 1970 35% of Chicago’s civilian labor force employed in operative and service job categories was black; in 1980 the proportion was 36.4% (Elkhanialy Tr. 698, 718; P.Ex. 124 at 6-7; P.Ex. 124-A at 5). Had Chicago Miniature’s recruitment and hiring processes during that period been race-neutral, it would be expected that— even using the lower 1970 level of black representation in the labor market — a total of approximately 51 blacks (35% of 146 total hires) would have been hired for entry-level factory jobs between 1978 and 1981. Instead the actual number of black entry-level hires during the 1978-81 period was only 9 (Elkhanialy Tr. 718; P.Ex. 124 at 19; P.Ex. 124-A at 3).
67. Statistical comparison of the figures in Finding 66 shows the actual number of black entry-level hires during the 1978-81 period was 7.2 standard deviation units below the expected (Elkhanialy Tr. 718; P.Ex. 124 at 19; P.Ex. 124-A at 3). In terms of statistical probability, the likelihood of an expected value being 3 standard deviation units above or below an actual or observed value is 1 in 741, of 4 standard deviation units is 1 in 31,546, and of 5 standard deviation units is 1 in 3,487,967 (Elkhanialy Tr. 693-97, 700, 718; P.Ex. 124 at 10-11 Table 11).
68. In summary, the statistical probability of Chicago Miniature’s hiring so few blacks into entry-level jobs in the 1978-81 period, in the absence of racial bias against blacks in recruitment and hiring, is virtually zero (Elkhanialy Tr. 718-19; P.Ex. 124 at 19-20). This Court finds Chicago Miniature’s actual hiring pattern demonstrates such a racial bias in both recruitment and hiring.
Work Force Analysis
69. Given the 35% level of black representation in operative and service jobs in Chicago in 1970, it would be expected Chicago Miniature would then have had approximately 112 blacks in those entry-level job categories (Elkhanialy Tr. 698-99; P.Ex. 124 at 14 Table 4). Given the corresponding 36.4% level of black representation in such jobs in Chicago in 1980, it would be expected Chicago Miniature would then have had approximately 98 blacks in those entry-level job categories (Elkhanialy Tr. 700-02; P.Ex. 124 at 14 Table 4). Instead Chicago Miniature’s actual numbers of black operative and service *1296 workers were 19 in 1970 (Elkhanialy Tr. 699; P.Ex. 124 at 14 Table I, Table 4) and 16 in 1980 (Elkhanialy Tr. 700-02; P.Ex. 124 at 14 Table I, Table 4).
70. Statistical comparison of the figures in Finding 69 shows the actual number of black entry-level workers at Chicago Miniature was 10.9 standard deviation units below the expected number in 1970 (Elkhanialy Tr. 700; P.Ex. 124 at 14 Table 4) and 10.4 standard deviation units below the expected number in 1980 (Elkhanialy Tr. 702; P.Ex. 124 at 14, Table 4). For all intervening years between 1970 and 1980, and using the lower (and thus more favorable to Chicago Miniature) 1970 level of black representation in the Chicago labor market as the basis of comparison, the actual number of Chicago Miniature’s black entry-level workers was always at least 9 standard deviation units below the expected number (Elkhanialy Tr. 703).
71. In summary (and as was true as to Chicago Miniature’s hiring), the statistical probability of Chicago Miniature’s having so few blacks in entry-level jobs in its work force during the 1970-81 period, in the absence of racial bias against blacks in recruitment and hiring, is also virtually zero (see Finding 67) (Elkhanialy Tr. 697-704, 722; P.Ex. 124 at 14-15). This Court finds Chicago Miniature’s actual work force composition demonstrates such a racial bias in both recruitment and hiring.
Applicant Flow Analysis: Actual Figures
72. Attached App. 4 reflects Chicago Miniature’s applicant flow for entry-level factory jobs during the 1978-81 inclusive period (“1978-81”). Where EEOC’s and Chicago Miniature’s data bases differed from each other, EEOC’s data is indicated by the heading “P” and Chicago Miniature’s data is indicated by the heading “D” (P.Ex. 124-A at 2; Elkhanialy Tr. 722-41).
Applicant Flow Analysis: Chicago as “Relevant Labor Market”
73. In 1981 (after this lawsuit was filed) Chicago Miniature had, both in absolute terms and proportionally, the largest number of black applicants. 6 Findings 74-75 reflect that even on those 1981 figures most favorable to Chicago Miniature it fails dismally, while the following Findings confirm the same results whatever relevant time period is used.
74. Even in the year 1981 the expected number of black applicants for entry-level factory jobs at Chicago Miniature would have been 294, according to EEOC’s data base (36.4% [the black representation in Chicago operative and service labor force according to the 1980 census] X 807 applicants), 298 according to Chicago Miniature’s data base (36.4% x 820 applicants) and 289 according to data furnished to EEOC by Chicago Miniature during pretrial proceedings (36.4% x 794 applicants). Instead the actual number of black applicants in that year was 32 according to EEOC’s data base, 35 according to Chicago Miniature’s data base and 36 according to Chicago Miniature’s pretrial data (P.Ex. 124 at 23; P.Ex. 124-A at 2, 4; Elkhanialy Tr. 722-41).
75. Statistical comparison of the figures in Finding 74 based on Chicago Miniature’s figures (those most favorable to it) shows that in 1981 the actual number of black applicants for entry-level factory jobs at Chicago Miniature (a) calculated on the basis of 36 of 794 applicants being black was 18.6 standard deviation units below the expected number and (b) calculated on the basis of 35 of 820 applicants being black was 19.1 standard deviation units below the expected number (P.Ex. 124 at 24; P.Ex. 124-A at 4; Elkhanialy Tr. 722-41).
76. In 1980 (also after this lawsuit was filed) the expected number of black appli *1297 cants for entry-level factory jobs at Chicago Miniature would have been 320, according to Chicago Miniature’s own data base (36.4% X 880 applicants). Instead the actual number of black applicants was 33, again according to Chicago Miniature’s own data base (P.Ex. 124-A at 2, 4; Elkhanialy Tr. 722-41).
77. Statistical comparison of the figures in Finding 76 based on Chicago Miniature’s figures shows that in 1980 the actual number of black applicants for entry-level factory jobs at Chicago Miniature was 20.1 standard deviation units below the expected number (P.Ex. 124-A at 4; Elkhanialy Tr. 722-41).
78. During 1978-81 the expected number of black applicants for entry-level factory jobs at Chicago Miniature would have been 842, according to EEOC’s data base (36.4% x 2314 applicants) (because Chicago Miniature did not submit different applicant figures for 1979, no comparable calculation can be made on its aggregate figures) (P.Ex. 124-A at 2; Elkhanialy Tr. 722-41). Instead the actual number of black applicants during the 1978-81 inclusive period was 55 according to EEOC’s data base and 75 according to Chicago Miniature’s data base (P.Ex. 124-A at 2).
79. Statistical comparison of the figures in Finding 78 shows that during 1978-81 the actual number of black applicants for entry-level factory jobs at Chicago Miniature (a) calculated from EEOC’s data base was 34 standard deviation units below the expected number and (b) calculated from Chicago Miniature’s data as to the aggregate number of black applicants was 33.1 standard deviation units below the expected number (P.Ex. 124-A at 2).
80. Omitting 1979 (the year for which Chicago Miniature did not submit different applicant figures) the expected number of black applicants for entry-level factory jobs at Chicago Miniature for the years 1978, 1980 and 1981 would have been 760 according to EEOC’s data base (36.4% X 2087 applicants), and 765 according to Chicago Miniature’s data base (36.4% X 2101 applicants). Instead the actual number of black applicants for those years was 53 according to EEOC’s data base and 73 according to Chicago Miniature’s data base (P.Ex. 124-A at 2).
81. Statistical comparison of the figures in Finding 80 shows that for the years 1978, 1980 and 1981 the actual number of black applicants for entry-level factory jobs at Chicago Miniature (a) calculated from EEOC’s data base was 32.2 standard deviation units below the expected number and (b) calculated from Chicago Miniature’s data base was 31.5 standard deviation units below the expected number (P.Ex. 124-A at 2).
Applicant Flow Analysis: “12 Zip Code Area” as Assumed “Relevant Labor Market”
82. During 1978-81 Chicago Miniature received approximately 91% of its applications for entry-level factory jobs from 12 zip code areas located within an approximately five mile radius around its facility (the “12 Zip Code Area”): Zip codes 60640, 60625, 60613, 60647, 60618, 60622, 60660, 60657, 60626, 60651, 60659 and 60639. Each zip code within that area was the source of at least 1% of Chicago Miniature’s applications (Elkhanialy Tr. 726-27, 736-38; P.Ex. 124 at 25-26; P.Ex. 124-A at 5).
83. In the 1980 census year, black representation in the civilian labor force within the 12 Zip Code Area was 8.7% (Elkhanialy Tr. 728-29; P.Ex. 124 at 25; P.Ex. 124-A at 7).
84. Given the level of black representation referred to in Finding 83, the expected number of black entry-level job applicants from the 12 Zip Code Area during 1978-81 would have been 183 (8.7% x 2,109 entry-level applications received by Chicago Miniature from the 12 Zip Code Area). Instead the actual number of black applicants from that area was 19 (Elkhanialy Tr. 728-29; P.Ex. 124 at 26 Table 6).
85. Statistical comparison of the figures in Finding 84 shows that in 1978-81 the actual number of black applicants for entry-level jobs at Chicago Miniature from *1298 the 12 Zip Code Area was 12.7 standard deviation units below the expected number (Elkhanialy Tr. 729; P.Ex. 124 at 26).
86. Application of the same statistical methodology to Chicago Miniature’s data base for the post-lawsuit-filing years 1980 and 1981 shows that the actual number of black applicants for entry-level jobs at Chicago Miniature from the 12 Zip Code Area was 9.1 standard deviation units below the expected number (Elkhanialy Tr. 735-39; P.Ex. 124-A at 7).
Applicant Flow Analysis: “5 Zip Code Area” as Assumed “Relevant Labor Market”
87. During 1978-81 Chicago Miniature received 72.6% of its applications for entry-level factory jobs from five zip code areas located within an approximately three to four mile radius around its facility (the “5 Zip Code Area”): Zip codes 60640, 60625, 60613, 60647 and 60618. Each zip code within that area was the source of at least 5% of Chicago Miniature’s applications (Elkhanialy Tr. 729-30; P.Ex. 124 at 26-27 Table 8).
88. In the 1980 census year, black representation in the civilian labor force within the 5 Zip Code Area was 4.8% (Elkhanialy Tr. 730; P.Ex. 124 at 27 Table 8).
89. Given the level of black representation referred to in Finding 88, the expected number of black entry-level job applicants from the 5 Zip Code Area during 1978-81 would have been approximately 81 (4.8% X 1,680 entry-level applications received by Chicago Miniature from 5 Zip Code Area). Instead the actual number of black applicants from that area was 12 (Elkhanialy Tr. 730; P.Ex. 124 at 27 Table 8).
90. Statistical comparison of the figures in Finding 89 shows that in 1978-81 the actual number of black applicants for entry-level jobs at Chicago Miniature from the 5 Zip Code Area was 7.9 standard deviation units below the expected number (Elkhanialy Tr. 730; P.Ex. 124 at 27 Table 8).
91. Application of the same statistical methodology to Chicago Miniature’s data base for the post-lawsuit-filing years 1980 and 1981 shows that the actual number of black applicants for entry-level jobs at Chicago Miniature from the 5 Zip Code Area was 5.1 standard deviation units below the expected number (Elkhanialy Tr. 735-39; P.Ex. 124-A at 8).
Applicant Flow Analysis: “Home Zip Code Area” as Assumed “Relevant Labor Market”
92. During 1978-81 Chicago Miniature received 29.4% of its applications for entry-level factory jobs from its own “home” zip code area (60640) (the “Home Zip Code Area”) (Elkhanialy Tr. 733-34; P.Ex. 124 at 27-28 Table 8).
93. In the 1980 census year, black representation in the civilian work force within the Home Zip Code Area was 12.2% (Elkhanialy Tr. 734; P.Ex. 124 at 27).
94. Given the level of black representation referred to in Finding 93, the expected number of black entry-level job applicants from the Home Zip Code Area during 1978-81 would have been more than 83 (12.2% x 683 applications). Instead the actual number of black applicants from that area was 8 (Elkhanialy Tr. 734; P.Ex. 124 at 27-28 Table 8).
95. Statistical comparison of the figures in Finding 93 shows that in 1978-81 the actual number of black applicants for entry-level jobs at Chicago Miniature from the Home Zip Code Area was 8.8 standard deviation units below the expected number (Elkhanialy Tr. 734; P.Ex. 124 at 28).
96. Application of the same statistical methodology to Chicago Miniature’s data base for the post-lawsuit-filing years 1980 and 1981 shows that the actual number of black applicants for entry-level jobs at Chicago Miniature from the Home Zip Code Area was 6.4 standard deviation units below the expected number (Elkhanialy Tr. 735-39; P.Ex. 124-A at 9).
Applicant Flow Analysis: Zip Code Area” as Assumed “Relevant Labor Mar ket”
97. At this Court’s request, EEOC’s expert Dr. Elkhanialy compared Chicago Miniature’s applicant flow to the civilian labor *1299 force of a four zip code area (the “4 Zip Code Area”) utilized by Chicago Miniature’s expert Dr. Chiswick for certain comparisons: Zip codes 60640, 60625, 60618 and 60613. Chicago Miniature is located near the center of the four zip codes (Tr. 2163-66, 2383-84; P.Ex. 178).
98. In the 1980 census year, black representation in the civilian labor force within the 4 Zip Code Area was 5.41% (P.Ex. 178 at 1).
99. Given the level of black representation referred to in Finding 98, the expected number of black entry-level job applicants from the 4 Zip Code Area during 1978-81 would have been approximately 90 (5.41% X 1,495 entry level applications received by Chicago Miniature from 4 Zip Code Area). Instead the actual number of black applicants from that area was 12 (P.Ex. 178 at 1).
100. Statistical comparison of the figures in Finding 99 shows that in 1978-81 the actual number of black applicants for entry-level jobs at Chicago Miniature from the 4 Zip Code Area was 7.9 standard deviation units below the expected number (P.Ex. 178 at 1).
101. Application of the same statistical methodology to Chicago Miniature’s data base for the post-lawsuit-filing years 1980 and 1981 shows that the actual number of black applicants for entry-level jobs at Chicago Miniature from the 4 Zip Code Area was 5.2 standard deviation units below the expected number (P.Ex. 178 at 2).
Applicant Flow Analysis: Summary
102. Findings 72 through 101 demonstrate that from every perspective the statistical probability of Chicago Miniature having so few black applicants for entry-level factory jobs in 1978-81, in the absence of racial bias against blacks in recruiting and hiring, is virtually zero (see also Finding 67) (Elkhanialy Tr. 725-41; P.Ex. 124 at 28).
103. For purposes of statistical analysis of Chicago Miniature’s applicant flow, and for purposes of determining whether Chicago Miniature’s recruiting and hiring practices for entry-level factory jobs discriminated against blacks, it does not make any material difference:
(a) which of the geographical areas defined and discussed in prior Findings (Chicago, 12 Zip Code Area, 5 Zip Code Area, Home Zip Code Area or 4 Zip Code Area) is deemed to be Chicago Miniature’s “relevant labor market,”
(b) which years or aggregations of years are'subjected to statistical analysis, or
(c) whether EEOC’s or Chicago Miniature’s data base is utilized as the source of the underlying data.
In every instance the actual number of black applicants for entry-level factory jobs at Chicago Miniature is so many standard deviation units below the expected number, in the absence of racial bias against blacks in either recruitment or hiring or both, as to produce a statistical probability of virtually zero. Indeed the statistical probabilities of random (unbiased) causes of those actual figures are so remote that the introduction of other variables or other assumptions would make no difference (unless those variables or assumptions were themselves enormously improbable).
104. In summary, there can be no other conclusion from Chicago Miniature’s applicant flow analysis but that its recruiting and hiring practices discriminated against blacks, and this Court so finds. That really makes it unnecessary to address the contrary testimony of Chicago Miniature’s expert Dr. Chiswick. But his testimony provides such a graphic illustration of the use of statistics to make the worse appear the better cause, and to produce results totally at war with common sense, that the following Findings deal briefly with that testimony-
Dr. Chiswick’s Testimony: Absence of Recruitment Analysis
105. Dr. Chiswick arrived at no statistically-based conclusion whatever as to whether Chicago Miniature had or had not engaged in discrimination in recruitment (Chiswick Tr. 1866).
*1300 Dr. Chiswick’s Testimony: Hiring Analysis and the “Relevant Labor Market”
106. In his hiring analysis Dr. Chiswick determined the percentage level of black representation in what he defined as Chicago Miniature’s “relevant labor market” in the following manner (Chiswick Tr. 2171-72, 2219):
(a) Chicago Miniature’s “applicant flow” (the percentage of applications it received from each “606” zip code area) was determined. If no applications were received from a particular zip code area, it was deemed to have a value of zero.
(b) Next the percentage level of black representation in the total labor force (not restricted to operative and service occupations) was determined from census data for all “606” zip code areas.
(c) Then the percentage of blacks in the total labor force of each zip code area (Finding 106(b)) was multiplied by the applicant flow percentage from that zip code area (Finding 106(a)).
(d) Finally the products calculated under Finding 106(c) were added. Their sum was deemed to be the percentage level of black representation in Chicago Miniature’s relevant labor market.
107. One consequence of Dr. Chiswick’s obviously flawed methodology was that, because of the total absence of applications from blacks in certain zip code areas, the entire black labor force in numerous Chicago zip code areas was totally excluded from what he defined as Chicago Miniature’s relevant labor market. For example:
(a) In Dr. Chiswick’s 1980 analysis (entitled “1980 All”), 25 of the 60 “606” zip code areas were excluded entirely, so that all black members of the labor force in those zip code areas were allocated a value of zero.
(b) In his 1978 analysis (entitled “1978 All”), 37 of the 60 “606” zip codes were excluded entirely, so that all black members of the labor force in those zip code areas were allocated a value of zero.
(c) In his 1981 analysis (entitled “1981 A11”), 24 of the 60 “606” zip code areas were excluded entirely, so that all black members of the labor force in those zip code areas were allocated a value of zero.
(Chiswick Tr. 2169-82) By the same process, various other zip code areas were given disproportionately minimal weighting (though not zero values).
108. Dr. Chiswick did not include in his calculations, or modify the results of those calculations by, any of the following obviously relevant factors:
(a) commuting times within an area;
(b) whether an area was near or far from public transportation;
(c) whether an area was near or far from major arterial streets;
(d) whether an area was near or far from expressways; or
(e) distance.
(Chiswick Tr. 2219-21) Instead he argued solely from results — a self-fulfilling prophecy. Just how much Dr. Chiswick’s presentation was at odds with any logical analysis is graphically demonstrated by the hopscotch pattern his “relevant labor market” reflects — for example, jumping over (excluding entirely) areas with highly convenient public transportation to Chicago Miniature’s plant, then including areas substantially farther away (both geographically and in commuting time). That hopscotching confirms the unrepresentative nature of Chicago Miniature’s applicant flow (see Findings 73-104).
109. Despite the patent invalidity of Chicago Miniature’s applicant flow as the predicate for further analysis, the only data utilized by Dr. Chiswick in determining the level of black representation in what he defined as Chicago Miniature’s relevant labor market was that applicant flow. As this Court has already found, Chicago Miniature’s applicant flow is not a reliable indicator of Chicago Miniature’s relevant labor market. Dr. Chiswick of course had to know the invalidity of his presentation — any scientist confronted with such an obviously flawed result would reexamine his premises to see where they *1301 were flawed. It does a well-credentialed scholar no credit to offer such testimony, and Dr. Chiswick destroyed his own credibility by the fact he not only accepted uncritically the obviously biased results but actually promoted them enthusiastically.
110. In sharp contrast to his unwillingness to acknowledge obviously relevant factors that should have changed his analytical method entirely (thus wholly revising his quantitative statistical analysis), Dr. Chiswick maintained that shift preference and immigrant status, and a related lack of English language fluency, were factors that would “qualitatively” [sic] decrease the proportion of black representation in Chicago Miniature’s relevant labor market (Chiswick Tr. 2108). Those factors do not alter this Court’s already-stated findings as to the discriminatory patterns shown by Chicago Miniature’s hiring, work force and applicant flow (and see Finding 114). Nonetheless they will be touched on in Findings 111-13.
111. As to shift preference the evidence was that:
(a) No person ever refused an entry-level job at Chicago Miniature because of shift preference (Trueva Tr. 1546).
(b) Chicago Miniature did not distinguish between (1) applicants who preferred one shift but would have accepted a job on another shift and (2) applicants who were only willing to work a particular shift (Trueva Tr. 1547-49).
(c) Shift preference was never discussed with many applicants (see App. 2).
(d) Chicago Miniature’s employment application forms did not have a space for the applicant to indicate a preference for a particular shift (Chiswick Tr. 2238-41).
(e) There is no expectation that shift preference would vary by race (Chiswick Tr. 2241-42).
(f) There was no systematic recording of the shift preferences of applicants by Chicago Miniature, and Dr. Chiswick did not utilize the shift preference notes appearing on some applications for any purpose (Chiswick Tr. 2241).
(g) Chicago Miniature’s Trial Brief admitted that (1) at least one-half of the employment applications it received had no notations whatever regarding shift preference (Br. 5), (2) one of its three shifts was eliminated entirely in 1980 and (3) since mid-1981 Chicago Miniature has had only one shift (Br. 2).
(h) Dr. Chiswick admitted it is not possible to modify the estimated proportion of blacks in what he termed Chicago Miniature’s relevant labor market to account for shift preference (Chiswick Tr. 1951).
112. As to immigrant status and language fluency the evidence was that:
(a) Absence of an English language fluency requirement for hire at Chicago Miniature would have no impact upon the number of blacks applying for work at Chicago Miniature (Chiswick Tr. 2134).
(b) Dr. Chiswick’s analysis of data as to language fluency and immigrant status was restricted to the years 1980 and 1981, after suit was filed (Chiswick Tr. 2103).
(c) Chicago Miniature’s data as to immigrant status and language fluency of hires (how many hires spoke Spanish) does not show how many hires did not speak English (Chiswick Tr. 2264).
(d) As with shift preference, Dr. Chiswick admitted he could not quantify in any way the information concerning either alien status or the language difficulties of job applicants to Chicago Miniature to adjust his calculations as to the level of black representation in Chicago Miniature’s relevant labor market (Chiswick Tr. 2103, 2107, 2108).
113. In view of the foregoing, Dr. Chiswick’s testimony as to the “qualitative” impact of shift preference, language skills and immigrant status was sheer speculation and of no probative value. Indeed that testimony had essentially no foundation in the evidence. As already stated, by Dr. Chiswick’s own admission he did not and could not factor those matters into his calculations of the percentage of black representation in Chicago Miniature’s relevant *1302 labor market for entry-level factory jobs. Dr. Chiswick’s testimony on those issues to support Chicago Miniature’s position did not even rise to the level of a “pretextual” defense.
114. This Court of course recognizes that a principal thrust of Dr. Chiswick’s testimony as to shift preference, language skills and immigrant status was not so much an attempt to buttress his own testimony as an effort to discredit that of Dr. Elkhanialy (whose testimony also did not take those factors into account). But this Court does not find Dr. Chiswick’s testimony probative for that purpose:
(a) As Findings 110-13 reflect, it was wholly speculative, and its merely suggestive nature cannot serve to vitiate Dr. Elkhanialy’s quantitative statistical analysis.
(b) As Finding 103 reflects, the statistical improbability of a nondiscriminatory explanation for Chicago Miniature’s performance is so great as to contain a substantial “cushion” for the introduction of other variables such as those suggested by Dr. Chiswick.
Dr. Chiswick’s Testimony: Summary
115. There are no reported decisions in which any court, in applying Title VII, has “weighted” an employer’s relevant labor market by the sole use of applicant flow, as Dr. Chiswick has done, when (a) the weighting is to be done with respect to areas inside the geographical boundaries of a city, (b) the employer is located within the city, (c) the city has an extensive public transportation system, (d) the jobs involved are entry-level jobs and (e) the employer’s applicant flow is challenged (and persuasively so) as being the result of discriminatory recruiting practices (see Chiswick Tr. 2362-63). This Court recognizes that some artificiality is introduced by the assumption (inherent in Dr. Elkhanialy’s analysis) that all persons located in the geographical area providing the relevant labor market are equally likely to apply for employment. However, the universality of the results produced by each of the progressively smaller areas testified to by Dr. Elkhanialy (including the 4 Zip Code Area suggested by a portion of Dr. Chiswick’s testimony), coupled with the virtually zero probability of a nondiscriminatory explanation (and the corresponding cushion already referred to in Findings 103 and 114(b)), leads this Court to conclude that any rational method of weighting — when applied within any relevant geographical area— would not alter the findings already made by this Court.
116. It is frankly distressing to encounter a scholar of stature (such as Dr. Chiswick) so willing to serve as a “hired gun” —to prostitute himself by espousing views and methodology he had to recognize as invalid from the results they produced. No “expert” is entitled to divorce himself from the real world implications of his testimony, and thereby to avoid reexamination of the premises underlying that testimony. Dr. Chiswick has unfortunately lent weight to, and tended to legitimize, the canard that there are (in ascending order) “liars, damned liars and statisticians.”
Discrimination by Chicago Miniature: Summary
117. In summary, this Court finds as a matter of fact that Chicago Miniature has since at least from the early 1970s through 1981 engaged in a pattern and practice of class discrimination against blacks, on account of their race, in both recruitment and hiring for entry-level factory jobs. As of the time of trial Chicago Miniature had engaged in no entry-level hiring since 1981. Randolph’s Prior Qualifications and His Claims in This Action
118. Randolph’s educational background includes the equivalent of two years of college education, which he obtained while in the Army. None of that college coursework related to computers or data processing (Randolph Tr. 209, 1625). Randolph’s experience and skills in writing computer programs and then in designing computer systems were gained in his handling of his job responsibilities both before and at Chicago Miniature (Randolph Tr. 1624-27, 1635-38, 1654-55).
*1303 119. On February 16, 1977 Gary Matthews (“Matthews”) was hired by Chicago Miniature as its Systems Manager (Matthews Tr. 1973-74). On October 31, 1977 Larry Goerges (“Goerges”) was hired by Chicago Miniature as its Data Processing Manager (Stip. ¶ 8; P.Ex. 29). Randolph contends that, in the absence of race discrimination, he would have been promoted to each of those positions.
120. Randolph’s Charge, filed with EEOC March 9, 1978, referred only to Chicago Miniature’s having denied his promotion to the position of Data Processing Manager (Stip. ¶ 5). It made no reference to Randolph’s not having been promoted over a year earlier to the Systems Manager position.
Selection of Chicago Miniature’s First Systems Manager
121. Curran returned to Chicago Miniature in February 1976 as its chief operating officer and Executive Vice-President, having previously served as its Vice-President of Marketing (Curran Tr. 914). On his return he found the company’s prior difficulty in obtaining data processing service for non-financial functions was still a problem (Curran Tr. 918-21, 927).
122. Based on his prior favorable experience with Matthews at another subsidiary of General Instrument, Curran requested the assistance of Matthews, who was still working as a troubleshooting consultant with General Instrument’s industrial components staff (Curran Tr. 944). After considerable efforts to correct the existing deficiencies in Chicago Miniature's data processing systems, Curran decided to fire Treasurer-Controller Earl Erickson (“Erickson”) (who had been non-cooperative in those efforts, Curran Tr. 954-55, Matthews Tr. 1979-80).
123. At that point Curran decided any solution of the problem referred to in Finding 121 called not for the simple replacement of Erickson, but instead for a fundamental alteration of the corporate structure (Curran Tr. 859). Chicago Miniature’s new controller Gene Nelson was to deal solely with financial matters (Randolph Tr. 158-59). Curran created the new position of “Systems Manager,” to be filled by a high-level corporate official to report directly to Curran (Curran Tr. 859). That official’s function was to define and assign priority to the systemic needs for data processing support, based on consultation with Chicago Miniature’s other top management officials.
124. In January 1977 Curran discussed the proposed Systems Manager position with Erickson (Curran Tr.’ 859, 936-37). Curran did not tell Erickson of his imminent firing but did tell Erickson he wanted to take away the systems responsibility from Erickson and to have the upgraded position of Systems Manager report directly to Curran. Curran and Erickson disagreed about the role of the new position (Randolph Tr. 1690-91).
125. Erickson later recommended Randolph for the position of Systems Manager, and Curran asked Erickson for information about Randolph and the work he had done (Curran Tr. 949-50). Curran reviewed Randolph’s job application (P. Ex. 9) and samples of Randolph’s written work and spoke with Randolph about the position and Randolph’s qualifications for it (Curran Tr. 951-52; Randolph Tr. 1649-50).
126. Curran’s decision to hire Matthews as Chicago Miniature’s first Systems Manager was based on his belief (reasonably founded in light of Matthews’ background and experience, and in light of Curran’s own favorable past and current experience in working with Matthews) that Matthews was the best-qualified candidate for the position (Curran Tr. 944-47). Curran also considered the hiring of Matthews as Systems Manager to be consistent with Chicago Miniature’s policy of promotion from within (Curran Tr. 948).
127. In summary, Randolph’s race did not affect Curran’s decision to create the position of Systems Manager or to hire Matthews (and not to hire Randolph) to fill that position. Though Randolph’s claim in that respect was barred as a matter of law (see Conclusion 4), it also provides no pro *1304 bative evidence in support of Randolph’s timely claim as to his not being promoted to the Data Processing Manager position.
128. On Erickson’s termination in April 1977, Chicago Miniature’s data processing department no longer reported to the Treasurer-Controller, but instead reported directly to Systems Manager Matthews (Hoeh Tr. 47-48). Hoeh was then Data Processing Manager (Curran Tr. 955). Non-Promotion of Randolph to the Data Processing Manager Position
129. Later in 1977 Curran and Matthews decided Hoeh should be terminated for what they considered his unwillingness to cooperate in the development of new computer systems at Chicago Miniature (Curran Tr. 955-56; Matthews Tr. 1993-94). Matthews was primarily responsible for hiring Hoeh’s replacement (Matthews Tr. 1994-95). Curran had concurring responsibility, and independent consultant Howard Kanter (“Kanter”) evaluated and made recommendations regarding the technical qualifications of candidates (Matthews Tr. 1995-2000; Kanter Tr. 286-87, 290).
130. Matthews’ standards for Hoeh’s replacement called for a person with strong manufacturing systems background; experience on large mainframe computers; project management and project administration responsibilities; substantial experience in the implementation of inventory management systems; experience in a multiplant environment; and a college degree (Matthews Tr. 1994-95). Though a college degree (which Randolph lacked) was not an essential prerequisite for the job in objective terms, it cannot be considered either an unreasonable or a pretextual requirement. Nothing suggests Matthews’ establishment of that or any other criterion was race-motivated.
131. Matthews first considered Chicago Miniature employees, including Randolph (whom Hoeh had recommended), for the position of Data Processing Manager (Matthews Tr. 1995). In Matthews’ opinion no one at Chicago Miniature was qualified for the position. Matthews also looked elsewhere within the General Instrument organization for a candidate (Matthews Tr. 1996). Though that search produced one possible candidate, negotiations broke down because the employee was not willing to relocate from New Jersey to Chicago in view of Chicago’s housing prices (Matthews Tr. 1996-97).
132. Matthews later retained an executive search firm, CompuPro, recommended by Kanter (P. Ex. 39; Matthews Tr. 1997-98; Kanter Tr. 288). Randolph May (“May”) was the CompuPro account manager with whom Matthews dealt, and Kenneth Ashley (“Ashley”) was the CompuPro consultant who worked with May on the assignment.
133. After Matthews and Kanter met with May in October 1977 to discuss the Data Processing Manager job and its requirements (May Tr. 1598-1600), May conferred with Ashley (Ashley Tr. 1752). They discussed some seven people whose resumes appeared to meet the job requirements (Ashley Tr. 1753) and concluded three of them were qualified (Ashley Tr. 1756). Goerges was one of the three candidates they considered qualified.
134. CompuPro gave Matthews resumes of approximately eight candidates (Matthews Tr. 1998; May Tr. 1603), 7 from among whom Matthews chose three or four (including Goerges) for interviews (Matthews Tr. 1999; see P. Ex. 29). Matthews and Kanter interviewed Goerges at CompuPro’s offices (Matthews Tr. 1999). After the interview Kanter told Matthews Goerges was qualified technically for the Data Processing Manager position, and Matthews considered his managerial and experience qualifications were satisfactory (Matthews Tr. 2000) and his resume was impressive (id. at 2002).
*1305 135. Matthews then told Curran he had narrowed the field to two candidates (one of whom was Goerges), and Curran interviewed each of them (Matthews Tr. 2001-02; Curran Tr. 887). Curran also reviewed Goerges’ resume (Curran Tr. 888).
136. Matthews decided to hire Goerges as Data Processing Manager because of his stated experience in implementing inventory management systems, previous work on large IBM mainframe computers, experience in on-line and multiplant computer systems, college degree and Certificate of Data Processing (Matthews Tr. 2003). Matthews thought Goerges had excellent experience in the specific qualifications for which Matthews was looking (Matthews Tr. 2002). Curran approved hiring Goerges on the basis of his stated managerial experience, technical experience in the systems and computer area and educational background (Curran Tr. 957).
137. It turned out Goerges did not live up to his paper qualifications, and indeed a more careful check of his prior references by CompuPro (whose job that was) or even by Matthews (though he reasonably relied on CompuPro to do that) would have given evidence of Goerges’ problems that later surfaced with Chicago Miniature. But second-guessing an employer’s honest (and non-race-motivated) mistakes is not the function of a Title VII case. Randolph himself acknowledged that, based on his personal review of Matthews’ notes as to Goerges’ prior experience, Goerges appeared more qualified than Randolph to be Chicago Miniature’s Data Processing Manager (Randolph Tr. 1677).
138. No evidence suggests Matthews’ negative evaluation of Randolph’s experience and qualifications for the Data Processing Manager job (or his positive evaluation of Goerges’) was a pretext for race discrimination. Randolph himself recognizes the normal progression in the data processing industry is to become a systems analyst before becoming a data processing manager (Randolph Tr. 1671). Matthews did promote Randolph to a systems analyst position at about the same time Goerges was hired as Data Processing Manager (Matthews Tr. 2006-07; Randolph Tr. 1655).
139. In summary, Randolph’s race did not affect (a) Matthews’ decision to hire Goerges or (b) Curran’s concurrence in that decision. Whether or not in retrospect Randolph’s abilities to perform at least part of the responsibilities of Data Processing Manager might be considered as greater than Goerges’, that hindsight comparison is in part a function of what proved to be Goerges’ deficiencies rather than of any highly superior qualifications on Randolph’s part. In any event, Chicago Miniature’s perception of the relative qualifications of Goerges and Randolph at the time Goerges was hired was reasonable and cannot give rise to even an inference of discriminatory intent on the part of Chicago Miniature.
Conclusions of Law
In General
1. This Court has jurisdiction over the parties and the subject matter of this Title VII action. Venue in this District is proper under 28 U.S.C. § 1391(b). See also Finding 4.
2. Actions under Title VII require the acts of alleged discrimination to have been the subject of a timely-filed charge of discrimination with EEOC. Delaware State College v. Ricks, 449 U.S. 250, 256, 101 S.Ct. 498, 503, 66 L.Ed.2d 431 (1980); Act § 2000e-5(f)(1). In a state (such as Illinois, Ill.Rev.Stat. ch. 68, ¶¶ 1-101 et seq. (formerly ch. 48, ¶¶ 851-867)) with its own administrative remedies for employment discrimination, EEOC filing must take place no more than 300 days after the unlawful employment practice occurs. Act § 2000e-5(e).
3. Randolph’s March 9, 1978 Charge alleged a single act of discrimination: Chicago Miniature’s refusal to promote him to Data Processing Manager October 26, 1977 (less than 300 days before Randolph filed the Charge of discrimina *1306 tion). This Court has jurisdiction over that claim.
4. Randolph now also seeks to assert the claim Chicago Miniature discriminatorily failed to promote him to the position of Systems Manager on February 16, 1977. Randolph never filed a charge with EEOC on that score, and the different Charge he did file (see Conclusion 3) was more than 300 days after the allegedly discriminatory February 16 act. This Court may not grant relief based on Randolph’s claim referred to in this Conclusion. United Airlines, Inc. v. Evans, 431 U.S. 553, 558, 97 S.Ct. 1885, 1889, 52 L.Ed.2d 571 (1977).
5. This Court has already concluded the Charge also provides a basis for jurisdiction over EEOC’s class-wide recruitment and hiring claims because EEOC’s claims “developed in the course of a reasonable investigation of [Randolph’s] charge.” EEOC v. Chicago Miniature Lamp Works, 526 F.Supp. 974, 974-75 (N.D.Ill.1981) (quoting EEOC v. General Electric Co., 532 F.2d 359, 366 (4th Cir.1976)). No conclusion is reached as to the period for which Chicago Miniature has liability based on EEOC’s claims (a subject addressed in Chicago Miniature’s proposed Conclusions of Law but not EEOC’s). That matter will be dealt with after entry of today’s order in the remedies phase of this action.
EEOC’s Claims
A. Elements of a Pattern-or-Practice Suit
6. Section 2000e-6(a) authorizes EEOC 8 to bring a civil action in the district courts:
[w]henever [it] has reasonable cause to believe that any person or group of persons is engaged in a pattern or practice of resistance to the full enjoyment of any of the rights secured by [Title VII], and that the pattern or practice is of such a nature and is intended to deny the full exercise of the rights herein described____
To prevail in a pattern-or-practice suit, EEOC must prove discrimination was Chicago Miniature’s “standard operating procedure — the regular rather than the unusual practice.” International Brotherhood of Teamsters v. United States, 431 U.S. 324, 336, 97 S.Ct. 1843, 1855, 52 L.Ed.2d 396 (1977). Such actions may be brought under either the disparate-treatment or disparate-impact model of discrimination, according to the “particular set of facts” involved. Id. at 335-36 n. 15, 349, 97 S.Ct. at 1861-62.
B. Disparate-Treatment Model
7. In a disparate-treatment case the ultimate issue is discriminatory motive. Plaintiff bears “[t]he ultimate burden of persuading the trier of fact that the defendant intentionally discriminated against the plaintiff____” Texas Department of Community Affairs v. Burdine, 450 U.S. 248, 253, 101 S.Ct. 1089, 1093, 67 L.Ed.2d 207 (1981). But the Supreme Court has recognized employers are most likely to act out of a discriminatory motive without advertising as much (Furnco Construction Co. v. Waters, 438 U.S. 567, 577, 98 S.Ct. 2943, 2949-50, 57 L.Ed.2d 957 (1978) (emphasis in original)):
[W]e know from our experience that more often than not people do not act in a totally arbitrary manner, without any underlying reasons, especially in a business setting. Thus, when all legitimate reasons for rejecting an applicant have been eliminated as possible reasons for the employer’s actions, it is more likely than not the employer, who we generally assume acts only with some reason, based his decision on an impermissible consideration such as race.
8. In a case charging individual discrimination, the acts that give rise to an inferential prima facie case (see McDonnell Douglas Corp. v. Green, 411 U.S. 792, 802, 93 S.Ct. 1817, 1824, 36 L.Ed.2d 668 (1973)) *1307 may be put in a nondiscriminatory light if the employer can “articulate some legitimate, nondiscriminatory reason for the employee’s rejection” (id.). Burdine, 450 U.S. at 253-55, 101 S.Ct. at 1093-95 (citation omitted) teaches the actual burdens on a plaintiff and a defendant in such individual discrimination cases are initially only those of creating and dispelling inferences:
The burden of establishing a prima facie case of discrimination is not onerous. The plaintiff must prove by a preponderance of the evidence that she applied for an available position for which she was qualified, but was rejected under circumstances which give rise to an inference of unlawful discrimination____
The burden that shifts to the defendant, therefore, is to rebut the presumption of discrimination by producing evidence that the plaintiff v/as rejected, or someone else was preferred, for a legitimate, nondiscriminatory reason. The defendant need not persuade the court that it was actually motivated by the proffered reasons____ It is sufficient if the defendant’s evidence raises a genuine issue of fact as to whether it discriminated against the plaintiff.
9. In a pattern-or-practice case, however, proof of disparate treatment does not usually follow the McDonnell DouglasBurdine model. “Statistics are equally competent in proving employment discrimination.” Teamsters, 431 U.S. at 339, 97 S.Ct. at 1856. That is so because, as Teamsters, id. at 340 n. 20, 97 S.Ct. at 1856 said:
[A]bsent explanation, it is ordinarily to be expected that nondiscriminatory hiring practices will in time result in a work force more or less representative of the racial and ethnic composition of the population in the community from which employees are hired. Evidence of longlasting and gross disparity between the composition of a work force and that of the general population thus may be significant even though [Section 2000e-2(j)] makes clear that Title VII imports no requirement that a work force mirror the general population.
Hazelwood School District v. United States, 433 U.S. 299, 307-08, 97 S.Ct. 2736, 2741-42, 53 L.Ed.2d 768 (1977) therefore held “gross statistical disparities” alone may “constitute prima facie proof of a pattern or practice of discrimination.” See also Babrocky v. Jewel Food Co., 773 F.2d 857, 867 (7th Cir.1985) (“The Supreme Court has explicitly adopted the use of statistics to establish a prima facie case,” citing Teamsters, 431 U.S. at 339, 97 S.Ct. at 1856). Mozee v. Jeffboat, Inc., 746 F.2d 365, 372 (7th Cir.1984) put it this way:
We note that statistics are an effective means of measuring the cumulative effects of employment actions and that statistically significant disparities in the treatment of comparably situated persons are often quite probative of unlawful discrimination.
10. Thus the nature of a statistical prima facie case in a pattern-or-practice disparate-treatment suit is logically different from a McDonnell Douglas-Burdine prima facie case. Overwhelming statistical proof of discrimination — a showing of disparities in recruiting or hiring so gross that chance could have played no part — is not subject to rebuttal merely through articulation of a legitimate, nondiscriminatory reason. What this Court has elsewhere termed the McDonnell Douglas-Burdine ping-pong approach “works poorly, if at all, where a plaintiff class loads overwhelming force into its opening shot” via a showing of gross statistical disparity. Vuyanich v. Republic National Bank of Dallas, 521 F.Supp. 656, 661 (N.D.Tex.1981), vacated and remanded on other grounds, 723 F.2d 1195 (5th Cir.1984); see Lewis v. Blooms-burg Mills, Inc., 773 F.2d 561, 572 (4th Cir.1985) (approving Vuyanich’s rejection of “articulated reason” rebuttal in statistically-proved pattern-or-practice cases).
11. Accordingly a prima facie case established by “statistical evidence demonstrating substantial disparities in the application of employment actions as to minorities, ... buttressed by evidence of general policies or specific instances of discrimination,” Coates v. Johnson & John *1308 son, 756 F.2d 524, 532 (7th Cir.1985), can be rebutted only by a showing the statistics are “either inaccurate or insignificant” or by providing a “nondiscriminatory explanation for the apparently discriminatory result” (id., quoting Teamsters, 431 U.S. at 360, 361 n. 46, 97 S.Ct. at 1867-68).
12. Statistical proof relevant in pattern-or-practice disparate-treatment suits involves a showing of “significant statistical disparity” between the racial composition of the employer’s work force (or applicant flow or hiring pattern) and the racial composition of the community from which the workers are drawn. Markey v. Tenneco Oil Co., 635 F.2d 497, 499 (5th Cir.1981) (“Markey I ”). Obviously the probative value of “a significant statistical disparity” depends upon the figures being compared. Hazelwood, 433 U.S. at 310, 97 S.Ct. at 2743. Racial balance of an employer’s work force must be compared with the racial composition of its “relevant labor market,” the population of workers having the skills required by the employer living within the geographical area from which the employer’s job applicants are drawn. Id. at 311, 97 S.Ct. at 2743. As Markey I, 635 F.2d at 499 said:
Thus, the proper identification of the relevant labor market is particularly important, and may often determine whether a plaintiff has established a prima facie case.
See Clark v. Chrysler Corp., 673 F.2d 921, 927 (7th Cir.1982) (applying Hazelwood comparison is “sometimes problematic, however, because of the difficulty of defining the ‘relevant labor market’ ”).
13. At the threshold of the “relevant labor market” inquiry, it is necessary to identify the segment of the minority population that, based on any qualifications required for the job, can legitimately be considered as prospective applicants. Where the skills demanded by the employer are ones “many persons possess or can fairly readily acquire,” the full percentage of blacks in the relevant geographical labor market may be used as the figure for comparison, without regard to skill levels within that black population. Hazelwood, 433 U.S. at 308 n. 13, 97 S.Ct. at 2742 (comparing the skill required in Teamsters —ability to drive a truck — with the skill required in Hazelwood — ability to teach in a public school). Reliance on general population data is “not misplaced” where there is no reason to assume a characteristic (such as, in this case, the ability to perform entry-level work) is not shared by the general population. Dothard v. Rawlinson, 433 U.S. 321, 330, 97 S.Ct. 2720, 2727, 53 L.Ed.2d 786 (1977). Here it is clearly appropriate to utilize general population figures concerning racial demographics in Chicago Miniature’s relevant labor market area (see Finding 57).
14. With any limiting factor based on required skills thus eliminated, the “relevant labor market” inquiry turns to a determination of the proper geographical boundaries of that market — a factual issue for the trial court. Hazelwood, 433 U.S. at 312, 97 S.Ct. at 2744. By definition “the relevant labor market should include only those areas from which employees are actually drawn.” Clark, 673 F.2d at 928. And of course an employer is unlikely to draw its work force in precisely equal proportions from all geographical segments of its labor market area, so that (id.):
In the case of an employer that traditionally draws employees primarily from the county in which it is located, but that has also drawn a small percentage of employees from peripheral areas, it may be more accurate to weight workforce availability statistics to reflect primarily the demographics of the closer geographic areas from which most employees are actually taken. 9
15. Weighting the demographics of the relevant labor market according *1309 to historic applicant flow has therefore been accepted by some courts as an appropriate means of “fine-tuning” statistical comparisons. Compare, e.g., Markey I, 635 F.2d at 499-500 (improper to weight demographics according to current employees’ places of residence) with Markey v. Tenneco Oil Co., 707 F.2d 172, 174-75 (5th Cir.1983) (“Markey II”) (proper to weight demographics according to current applicants’ places of residence). But an applicant-flow model of the relevant labor market poses obvious dangers. As Clark, 673 F.2d at 928 points out:
The danger of weighting, of course, is that geographic recruiting and employment patterns may themselves be tainted by racial discrimination. The appropriateness of determining the relevant labor market by the actual composition of an employer’s work force or applicant pool has been questioned when recruitment practices are themselves challenged as discriminatory.
If the process by which applicants are drawn to an employer acts to filter out minorities, it “might not adequately reflect the actual potential applicant pool____” Dothard, 433 U.S. at 330, 97 S.Ct. at 2727. Therefore historical applicant-flow data cannot be assumed automatically to constitute an accurate picture of the relevant labor market. Castaneda v. Pickard, 648 F.2d 989, 1003 (5th Cir.1981); see also Wheeler v. City of Columbus, Mississippi, 686 F.2d 1144, 1152 (5th Cir.1982) (applicant flow data is “often distorted by inadequate or excessive recruiting efforts, improper deterrence] of applicants ... or improper groupings”). “Relevant labor market” includes not only those who did apply for jobs but also those who would have applied for jobs but for a suspect employment practice. Dothard, 433 U.S. at 330, 97 S.Ct. at 2727. For that reason, the percentage of minority residents in a given segment of the area from which an employer draws its applicants should not be weighted negatively for purposes of the Teamsters-Hazelwood comparison merely because few applicants have in fact come from that segment, if the message as to the existence of job openings was not designed to reach that segment.
16. Chicago Miniature relied heavily on word-of-mouth recruiting to attract applicants (see Finding 23). Word-of-mouth hiring, if relied on extensively by an employer, is a suspect employment practice because it “circumscribe[s the] web of information” about job opportunities. United States v. Georgia Power Co., 474 F.2d 906, 926 (5th Cir.1973). Absent proof to the contrary, it is usually assumed word-of-mouth hiring tends to perpetuate the existing racial composition of an employer’s work force, precisely because word of job openings may never reach parts of the labor market where existing employees do not live. Barnett v. W.T. Grant Co., 518 F.2d 543, 549 (4th Cir.1975); but contrast Clark, 673 F.2d at 929 (despite employer’s use of word-of-mouth hiring, no imbalance in applicant flow was shown). In this case the statistical imbalance has been shown to be far more than substantial — “gross” is a better label. Whether the relevant labor market was viewed as the City of Chicago, the “12 Zip Code Area,” the “5 Zip Code Area,” the “4 Zip Code Area” or the “Home Zip Code Area,” the statistical variation of the number of black applicants from the expected number of black applicants was never less than 5.1 standard deviation units (see Findings 81, 85, 86, 90, 91, 95, 96, 100, 101). Those statistical variations in the range of 5+ standard deviation units were only in the post-lawsuit-filing years (all other statistical variations were substantially greater). Even 5 standard deviation units represents a probability (a euphemism under the circumstances) of 1 in 3.5 million (see Finding 67).
17. This Court therefore concludes historic applicant-flow data cannot be considered in defining the relevant labor market for Chicago Miniature. It considers the racial demographics of the entire City of Chicago are relevant for purposes of the statistical comparison mandated by Teamsters and Hazelwood, though the conclusion is the same whatever area is chosen. It is clear EEOC has made out a prima *1310 facie case of an intentional pattern or practice of discrimination (see Findings 102-04).
18. Chicago Miniature has attempted to rebut EEOC’s showing by introduction of its own statistical analysis designed to demonstrate EEOC’s statistics are “either inaccurate or insignificant.” Teamsters, 431 U.S. at 360, 97 S.Ct. at 1867. When plaintiff and defendant present conflicting statistical studies and the trial court determines each is of limited probative value due to methodological flaws, it would not be clearly erroneous to discount the probative value of all the statistical evidence. Coates, 756 F.2d at 547. But in this case this Court has found Chicago Miniature’s statistics rife with methodological flaws, while EEOC’s are basically sound (see Findings 105-16). Chicago Miniature’s expert, Dr. Chiswick, has not Succeeded in showing EEOC’s statistics are “inaccurate or insignificant,” and thus Chicago Miniature’s rebuttal attempt has failed. EEOC has proved its case of an intentional pattern or practice of discrimination.
C. Disparate-Impact Model
19. Griggs v. Duke Power Co., 401 U.S. 424, 430, 91 S.Ct. 849, 853, 28 L.Ed.2d 158 (1971) established the rationale for disparate-impact cases under Title VII:
Under [Title VII], practices, procedures, or tests neutral on their face, and even neutral in terms of intent, cannot be maintained if they operate to “freeze” the status quo of prior discriminatory employment practices.
Discriminatory motive is therefore irrelevant in disparate-impact cases. Teamsters, 431 U.S. at 336 n. 15, 97 S.Ct. at 1855. In disparate-impact terms, a prima facie case is established when a plaintiff shows the “facially neutral standards in question select applicants for hire in a significantly discriminatory pattern.” Dothard, 433 U.S. at 329, 97 S.Ct. at 2726-27. That prima facie case may be rebutted by the employer’s proving the challenged requirement is “job related.” Id. 10 If the employee proves job relatedness, the plaintiff may still prevail if he or she can show other selection standards not having a discriminatory impact “would also ‘serve the employer’s legitimate interest in “efficient and trustworthy workmanship.” ’ ” Id., quoting Albemarle Paper Co. v. Moody, 422 U.S. 405, 425, 95 S.Ct. 2362, 2375, 45 L.Ed.2d 280 (1975), in turn quoting McDonnell Douglas, 411 U.S. at 801, 93 S.Ct. at 1823-24. Because disproportionate impact is not measured in an individual case but upon a minority group, statistics are naturally the key to getting a disparate-impact case off the ground.
20. From the very nature of the disparate-impact model, its most appropriate use is to challenge “a specific procedure, usually a selection criterion for employment, that can be shown to have a causal connection to a class based imbalance in the work force.” Pouncy v. Prudential Insurance Co., 668 F.2d 795, 800 (5th Cir.1982); 11 see also United States v. *1311 City of Chicago, 549 F.2d 415, 427 (7th Cir.1977) (applying disparate-impact analysis to police hiring and promotion examinations). Here Chicago Miniature’s principal means of attracting applicants was word-of-mouth (Finding 23). Word-of-mouth hiring is facially neutral (although it may be used intentionally by an employer to screen out minorities). But that method of drawing applicants can operate as a “built-in headwind” under Griggs. Georgia Power, 474 F.2d at 925. Markey II, 707 F.2d at 174 observed “common sense indicates” word-of-mouth hiring could be predicted to “replicate” an already segregated work force, though it was not inevitable the practice should have that effect — as indeed it did not on the facts presented to the court there. Though about one-fifth of Tenneco’s work force in that case was black, about two-fifths of the applicants were so.
21. In this case the statistics tell a dramatically different tale. Between 1982 and 1984 fewer than one-tenth of Chicago Miniature’s work force of entry-level employees was black (Finding 18). Between 1970 and 1981 the figure never exceeded one-fifteenth (6.5% in 1981) (Finding 17). At the same time Chicago Miniature’s black applicant flow was only about 1% in 1978 and 1979, 2% (according to EEOC’s figures) or 4% (according to Chicago Miniature’s) in 1980, and 4% in 1981 (after this lawsuit was filed 12 ) (Findings 72, 73). Chicago Miniature’s performance graphically confirms the common-sense prediction that word-of-mouth hiring can be expected to perpetuate existing imbalances in applicant flow, with an obviously corresponding effect on minority hiring (Findings 25, 70, 71). See Domingo, 727 F.2d at 1436 (where word-of-mouth recruiting was by whites, “[predictably, nearly all of those hired were white”). Even if it were assumed (contrary to this Court’s earlier Findings and Conclusions) Chicago Miniature did not intend to attract fewer blacks than whites, its reliance on word-of-mouth produced that effect. EEOC has thus made out a prima facie Griggs case.
22. Chicago Miniature has not even sought to offer any evidence tending to show its reliance on word-of-mouth recruiting is business-related. 13 Nor, in view of the fact word-of-mouth recruiting tended artificially to cluster applicants within a small area (Finding 24), has Chicago Miniature really asserted any credible interest in employing mostly entry-level workers who live close by the plant. EEOC’s prima facie case of disparate impact stands unrebutted.
D. Summary as to EEOC’s Claims
23. In sum, whether analyzed under a disparate-treatment or disparate-impact model or both, EEOC has demonstrated Chicago Miniature engaged in a pattern or practice of discrimination against blacks in entry-level jobs.
Randolph’s Claim
24. Randolph’s claim of intentional race-based discrimination calls for his introduction of evidence sufficient to establish that (Burdine, 450 U.S. at 253 n. 6,101 *1312 S.Ct. at 1093, adhering to McDonnell Douglas, 411 U.S. at 802, 93 S.Ct. at 1824):
(a) Randolph belongs to a racial minority (in this case, black).
(b) He was available and qualified for the position of Data Processing Manager.
(e) Chicago Miniature rejected him despite his qualifications.
(d) Chicago Miniature continued to seek applicants from persons having the same qualifications.
If Randolph satisfies that prima facie requirement, Chicago Miniature must articulate a legitimate non-discriminatory reason for its decision. McDonnell Douglas, 411 U.S. at 802, 93 S.Ct. at 1824. If it does so, Randolph must show its articulated explanation is a mere pretext for discrimination. Id. at 804, 93 S.Ct. at 1825. Although those successive steps involve shifts in the burden of production, the ultimate burden of persuasion rests at all times on Randolph. Burdine, 450 U.S. at 253, 101 S.Ct. at 1093.
25. Despite this Court’s lack of ability to grant relief on Randolph’s claim he was discriminatorily denied a promotion to the position of Systems Manager (see Conclusion 4), it has considered the facts regarding that claim as relevant background evidence as to the alleged later act of discrimination as to which it clearly could grant relief if the claim were valid.
26. As to the Systems Manager position:
(a) Randolph did not prove he was qualified for the new Systems Manager position, an executive management position reporting directly to Chicago Miniature’s chief executive and operating officer Curran. Curran considered Randolph and concluded he was not qualified for the position.
(b) Even had Randolph proved himself qualified, Chicago Miniature has articulated a legitimate non-discriminatory reason for its decision to hire Matthews (see Findings 122, 126).
(c) Randolph failed to show the articulated reasons for hiring Matthews were pretextual. Based on the relative qualifications and experience of Matthews and Randolph, Curran reasonably believed Matthews was more qualified for the Systems Manager position than Randolph.
Because Randolph’s race was not a factor in Chicago Miniature’s decision to hire Matthews as Systems Manager, that prior act of alleged discrimination is entitled to no weight in considering the claim over which this Court does have jurisdiction.
27. As to Randolph’s claim of discrimination in his nonpromotion to the Data Processing Manager position:
(a) Randolph clearly satisfies the three elements of a prima facie case other than his qualification for the position. As to that element, it is arguably possible to view the objective requirements for the job as different from the nominal qualifications established (however reasonably) by Matthews. Thus a college degree, for example, was not a true “qualification” for the work of Data Processing Manager, even though Matthews was not necessarily unreasonable in setting that up as one prerequisite. Accordingly it will be assumed arguendo Randolph has also met the condition that he was qualified for the position, thus establishing a prima facie case.
(b) Even so, Chicago Miniature has articulated a legitimate non-discriminatory reason for its decision to hire Goerges (see Findings 130, 136).
(c) Randolph failed to show the articulated reasons for hiring Goerges were pretextual. Based on Chicago Miniature’s then-existing understanding of the relative qualifications and experience of Goerges and Randolph, Matthews and Curran reasonably believed Goerges was more qualified for the Data Processing Manager position than Randolph.
Because Randolph’s race was not a factor in Chicago Miniature’s decision to hire Goerges as Data Processing Manager, it did not intentionally discriminate against Randolph on the basis of race. See *1313 McDonnell Douglas, 411 U.S. at 804, 93 S.Ct. at 1825.
* * * * * *
It is ordered and adjudged that:
1. Plaintiff-intervenor Ed Randolph shall take nothing and his action shall be and is dismissed on the merits.
2. Defendant Chicago Miniature Lamp Works is liable for (a) having discriminated against blacks as a class, on account of their race, in both recruitment and hiring for entry-level factory jobs, and having engaged in a pattern and practice of such disparate treatment of blacks and (b) having established and carried out policies and practices of recruitment as to such jobs that had a disparate adverse impact upon blacks and constituted a pattern and practice of discrimination, all in violation of Title VII.
*1314
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" 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" 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" width="1345"/>
*1317
*1318
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" width="1365"/>
*1319
*1320
Source: Defendant’s exhibit FF for defendant’s figures (D). Plaintiff’s exhibits no. 118 and no. 120 for Plaintiff’s figures (P).
. All "Stip. ¶ — ’’ references are to the parties’ Stipulation of Uncontested Facts in the Final Pretrial Order (“FPTO”). All further citations to Title VII will simply take the form "Section — ” and will refer to numbering within Title 42, rather than to Title VII’s internal numbering.
. Though the Stip. identifies General Instrument as a New Jersey corporation, Chicago Miniature’s proposed Finding 6 n. 1 says that is in error.
. This Court of course recognizes "data” is really a plural noun. But common usage of the term in a singular sense is by now so deeply entrenched that it no longer offends the ear or the eye. These Findings and Conclusions will therefore follow the current practice of coupling "data” with singular verbs.
. See, e.g., Hamer v. City of Atlanta, 450 F.Supp. 771, 783 (N.D.Ga.1978).
. See EEOC v. Detroit Edison Co., 515 F.2d 301, 313 (6th Cir.1975):
The use of racial coding of applications and heavy reliance upon subjective judgments of interviewers were found to discriminate against black applicants.
. As to the weight to be given attempted correetions of Title VII violations after suit is filed, see Jenkins v. United Gas Cotp., 400 F.2d 28, 33 (5th Cir.1968) ("Such actions in the face of litigation áre equivocal in purpose, motive and permanence"); Parham v. Southwestern Bell Telephone Co., 433 F.2d 421, 426 (8th Cir.1970) ("The trial court erred in completely absolving the Company of unlawful employment practices on the basis of changes in the appellee’s recruitment practices and increased hiring of blacks subsequent to the institution of this lawsuit”).
. Though Ashley, May and Matthews differed somewhat in their recollections of the total number of resumes CompuPro gave Matthews, those differences are not at all material to the issues here.
. Section 2000e-6(a) originally authorized the Attorney General to bring "pattern or practice” suits, but pursuant to Section 2000e-6(c) that Attorney General function was transferred to EEOC.
. If an employer draws applicants from two counties of equal population, one of which has a 10% black population and the other 90% black, the overall percentage of black residents in the two counties is 50%. But if 90% of all job applicants come from the first county, it would plainly be highly unlikely that 50% of the total applicants would be black.
. Of course an employer may also challenge the accuracy and validity of plaintiffs statistics in a disparate-impact case. As these Conclusions already indicate, EEOC's statistics are not open to rebuttal under the disparate-impact model any more than under the disparate-treatment model.
. Chicago Miniature argues Pouncy precludes disparate-impact liability here, because the Pouncy court held the disparate-impact model “is not ... the appropriate vehicle from which to launch a wide ranging attack on the cumulative effect of a company’s employment practices” (668 F.2d at 800). Pouncy, id. insisted a disparate-impact case required proof that a racial imbalance in the work force was caused by "a specific procedure,” so use of the approach was not appropriate simply on a showing of racial imbalance. Other circuits have taken a far broader approach, see, e.g., Lewis, At 573 n. 16 (“[claimants, particularly class claimants, may well simply allege and offer proof of a significant discriminatory effect, knowing only that the effect is there”); Segar v. Smith, 738 F.2d 1249, 1270-71 (D.C.Cir.1984) (Pouncy is “unpersuasive” and "not compelling” because employers have “knowledge far superior to that of the plaintiff as to precisely how its employment practices affect employees”). But even if Pouncy were to represent the correct view of Griggs (a doubtful proposition), EEOC has clearly met even that restrictive approach by identifying Chicago Miniature’s reliance on word-of- *1311 mouth as the cause of the disparity in recruiting and hiring. See Domingo v. New England Fish Co., 727 F.2d 1429, 1436 n. 3 (9th Cir.1984) (Pouncy concerns met by showing word-of-mouth caused racial imbalance).
. As the cases cited in n. 6 reflect, post-lawsuit statistics tend to be suspect because of the obvious possibility the employer may have taken steps to put its house in order once a lawsuit puts it on notice of its imminent exposure to liability. Where as here word-of-mouth is essentially the sole means of recruitment, an opposing party’s discovery as to the existence of any such curative efforts is especially difficult. This Court ascribes no weight to the apparent increase in post-lawsuit numbers (particularly where even they continued to refute Dr. Chiswick’s notion that market forces would tend to eliminate any bias caused by a racially skewed work force, although word-of-mouth was the principal recruitment method).
. In relation to the unskilled entry-level jobs at issue in this case, any business-related purpose would be extraordinary indeed. Contrast Furnco Const. Corp. v. Waters, 438 U.S. 567 at 570-71, 98 S.Ct. 2943 at 2946, 57 L.Ed.2d 957 (word-of-mouth hiring served "‘critical’ necessity of insuring that only experienced and highly qualified fire-bricklayers were employed”).
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Cite This Page — Counsel Stack
622 F. Supp. 1281, 1985 U.S. Dist. LEXIS 14340, 38 Empl. Prac. Dec. (CCH) 35,709, 39 Fair Empl. Prac. Cas. (BNA) 297, Counsel Stack Legal Research, https://law.counselstack.com/opinion/equal-employment-opportunity-commission-v-chicago-miniature-lamp-works-ilnd-1985.