Affirmative Action and the Occupational Advancement of Minorities [PDF]

Affirmative Action and the Occupational Advancement of Minorities and Women During 1973–2003 FIDAN ANA KURTULUS*

The share of minorities and women comprising high-paying skilled occupations such as management, professional, and technical occupations has been increasing since the 1960s, while the proportion of white men in such occupations has been declining. What has been the contribution of affirmative action to the occupational advancement of minorities and women from low-wage unskilled occupations into high-wage skilled ones in U.S. firms? I examine this by comparing the occupational position of minorities and women at firms holding federal contracts, and thereby mandated to implement affirmative action, and noncontracting firms, over the course of 31 years during 1973–2003. I use a new longitudinal dataset of over 100,000 large private-sector firms across all industries and regions uniquely suited for the exploration of this question obtained from the U.S. Equal Employment Opportunity Commission. My key findings show that the share of minorities and women in highpaying skilled occupations grew more at federal contractors subject to affirmative action obligation than at noncontracting firms during the three decades under study, but these advances took place primarily during the pre- and early Reagan years and during the decade following the Glass Ceiling Act of 1991.

Introduction THE PURPOSE OF AFFIRMATIVE ACTION LEGISLATION IS NOT ONLY TO MOVE MINORITIES and women into employment but also to facilitate their move up the job ladder * The author’s affiliation is Department of Economics, University of Massachusetts, Amherst, MA. E-mail: [email protected]. The author thanks the editor and two anonymous referees, as well as Sami Alpanda, Michael Ash, Lee Badgett, Fran Blau, Jed DeVaro, Ron Ehrenberg, Nancy Folbre, Richard Freeman, Ira Gang, Doug Kruse, Ron Oaxaca, Lisa Saunders, Wayne Vroman, Tom Weisskopf, Stephen Woodbury, Myeong-Su Yun, and session participants at the 2012 Annual Meetings of the Allied Social Sciences Associations for insightful comments and suggestions. JEL Classifications: J15, J16, J21, J7, J8, K31, N32, N42, M51. INDUSTRIAL RELATIONS, Vol. 51, No. 2 (April 2012).  2012 Regents of the University of California Published by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford, OX4 2DQ, UK.

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into higher-paying skilled positions where they have historically been starkly underrepresented. Long-term trends indicate that the share of minorities and women comprising highly skilled occupations such as management, professional, and technical positions at large U.S. firms has been increasing since the 1960s, while the proportion of white men in such occupations has been declining (Figure 1).1 What has been the contribution of affirmative action to the occupational advancement of minorities and women from lowwage unskilled occupations into high-wage skilled ones in U.S. firms over the past decades? i explore this question by comparing the occupational position of minorities and women at firms holding federal contracts and thereby mandated to implement affirmative action, and noncontracting firms, over the three decades spanning 1973–2003 using a new large national dataset uniquely suited for the exploration of this topic obtained from the U.S. Equal Employment Opportunity Commission. my key findings show that the share of black women and white women in professional occupations, and Hispanic women and black men in technical occupations increased more on average at federal contractors subject to affirmative action obligation during 1973–2003, and these results are robust to controlling for firm size, corporate and occupational structure, industry-specific shocks, region-specific shocks, economy-wide shocks, and firm fixed effects. I also uncover some important results on how the impact of affirmative action has evolved over the three decades under study. For example, affirmative action had a positive effect on moving black women, Hispanic women, white women, and black men into high-skill high-pay management, professional, and technical occupations during the 1970s and early 1980s, but the impact of affirmative action on advancing minorities and women into the top echelons of firm structures subsided during the Reagan years. Another key finding is that during the decade following the Glass Ceiling Act of 1991, affirmative action resurfaced as an important factor moving Hispanic women and white women into managerial occupations, black women and Hispanic women into professional occupations, and black men into professional and technical occupations. Affirmative action in the labor market has generated heated debate ever since its incorporation into federal law during the Civil Rights Movement. Several states have prohibited affirmative action in public employment in recent years, and the future of affirmative action in the United States is uncertain. Rhetoric abounds on both sides of the affirmative action debate with little 1 These trends are based on data from the U.S. Equal Employment Opportunity Commission. Nationally representative data on the share of employed women and men by race aged 16 and over from Current Population Survey confirm these trends. Data on the share of women and non-whites in managerial, professional, and technical occupations reported by Donohue and Siegelman (1991) based on Census data also match these trends.

0.4

0.4

0.4

0.03

0.02 0.03

0.01

0.35

0.04

0.01

0.25

0.03

0.01

Female Sales

white

0.15 0.2

0.02 black

0

Female Professionals white

0.25 0.3

black

0.03

0.02

hispanic

Female Technicians

0.04

0.35 0.6

0.3 0.5

white

black

0.02

hispanic

0

0.3 0.8

0.2 0.7

0.1

0.6

white

black

hispanic

0.01

0

OF

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Female Managers

RACE SHARES

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0.25

AND

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

MEAN GENDER 0.9

0.8

0

0.7

0.9

0.03

0.015

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0.3

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Affirmative Action and Occupational Advancement / 215 FIGURE 1 OCCUPATIONS AT

0.05

0.04

U.S. FIRMS: 1973–2003

0.8

Male Managers

0.7

0.6

white

0.04 0.03 hispanic

0.02 black

hispanic 0.01 0

0.7

Male Professionals

0.6

0.025

0.5

white

0.02

hispanic

0.015

0.01 black

0.005

0

Male Technicians white

hispanic

0.03 black

0.02

0.01

0

Male Sales

white

0.025

hispanic

0.02

0.01

black

0.005

0

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KURTULUS FIGURE 1 (Cont.)

0.8

0.2

Female Clerical

Male Clerical

0.76

0.15 w itie wh

0.72 w iti e wh

0.68 0.1

blackk

0.08

0.012

0.06 hispaniic

0.04

hispanic

0.008 0.004

0.02 1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

0

0.2

black

0.016

0.6

Female Manual

Male Manua l

0.58 w it wh ie

0.15

0.56 0.54

0.06

w iti e wh

0.52 0.16 hispp ann ic

black

0.04

0 .11 2 0.08

hisp p anic

0.02

black

0.04 0

0.3 0.25

0.5

Female Serv r ice w iitt e wh

0.2 0.12 0.1

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

blacck

0.4

Male Serv r ice w it wh ie

0.2 0.16

0.08

0.12

black

0 .0 06 hispanicc

0.08 0 .0 04

0

0 1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0.02

hispanic

1973 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0.04

SOURCE: U.S. Equal Employment Opportunity Commission EEO-1 Reports. In each graph, the y-axis illustrates the mean share of a given occupation category comprised of each gender-race group, and year on x-axis depicts year. The diamond-shaped, square-shaped, and triangle-shaped lines trends pertain to whites, blacks, and Hispanics, respectively. In each graph, there is a break in the y-axis such that the white shares at the top are at a greater scale than the black and Hispanic shares at the bottom.

hard evidence brought to bear to inform policy discussions. As Blau and Winkler (2005) put it, ‘‘After four decades, we are still debating how much impact affirmative action can and should have on opportunities and outcomes at work…. in all the controversy and rancor, there is one question that is less often asked and even less frequently answered: Does affirmative action in employment actually work?’’

Affirmative Action and Occupational Advancement / 217 This study presents evidence on the effects of affirmative action on the occupational advancement of minorities and women using a large national longitudinal database containing information on both federal contractors bound by affirmative action obligation and noncontractor firms in all industries and regions of the United States. This is also one of the first studies of how the effect of affirmative action has evolved over the course of three decades, during political administrations with drastically different views about affirmative action. The Equal Employment Opportunity Commission firm reports have only recently become available to scientific researchers for the first time since the early 1980s, presenting a unique opportunity to investigate the long-term effects of affirmative action. Indeed, with over 100,000 firms over 31 years, these data constitute the largest and longest available panel of U.S. firms with information on gender and race composition by occupation.

Institutional Background Affirmative action in the labor market was incorporated into federal law in 1961 by President John F. Kennedy with Executive Order 10925, which required that government contractors not discriminate against employees or job applicants, mandated that contractors ‘‘take affirmative action to ensure that applicants are employed and employees are treated during employment without regard to their race, creed, color, or national origin,’’ and established the Committee on Equal Employment Opportunity. Kennedy’s executive order was strengthened with Executive Order 11246 in 1965 by President Lyndon B. Johnson by expanding affirmative action to cover women and establishing the Office of Federal Contract Compliance Programs (OFCCP), a branch of the Department of Labor responsible for affirmative action and nondiscrimination enforcement. Johnson’s executive order clarified the guidelines for affirmative action implementation; it stipulated that firms with government contracts must prepare annual written affirmative action plans identifying under-utilization of women and minorities relative to their availability in the labor market from which employees are recruited, and required that these written affirmative action plans lay out procedures, placement goals, and timetables firms will follow in recruitment, evaluation, hiring, training, and promotion of minority and female employees at every level of the workplace.2 Johnson’s executive order also mandated that government contractors are subject to compliance reviews by the Office of Federal Contract 2

Detailed guidelines for affirmative action and equal employment opportunity implementation are provided by the OFCCP in their Federal Contract Compliance Manual, which states that the geographic area used to determine labor availability of protected groups may vary from local to nationwide as the skill level required for the job increases (U.S. Department of Labor, 1998, chapter 2, section G).

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Compliance, with penalties for noncompliance ranging from revocation of current government contracts to suspension of the right to bid on future contracts. The affirmative action climate changed dramatically in the 1980s. Government enforcement of affirmative action was severely weakened beginning in 1981 when the Office of Federal Contract Compliance came under new leadership that was neither committed to the organization nor to affirmative action. During the presidency of Ronald Reagan, a serious effort was made to rescind Executive Order 11246 and when that failed, numerous steps were taken to weaken affirmative action enforcement (Anderson 1996). During the Reagan years, the OFCCP continued to conduct compliance reviews but rarely issued sanctions for noncompliance (Leonard 1990, 1996). After having risen sharply during the 1970s, the number of employment discrimination lawsuits plateaued in the 1980s, the number of class action discrimination suits fell, and most employment discrimination cases involved termination rather than hiring (Bergmann 1996; Donohue and Siegelman 1991). Enforcement activity increased somewhat beginning in 1989 when President George Bush took office, and the Civil Rights Act of 1991 allowed plaintiffs trial by jury in discrimination lawsuits and to recover compensatory and punitive damages. Title II of the Civil Rights Act of 1991, also called ‘‘The Glass Ceiling Act,’’ established the Federal Glass Ceiling Commission to review and prepare recommendations for the President and business leaders on the progression of protected groups into the top echelons of the corporate ladder (U.S. Glass Ceiling Commission 1995a, 1955b).3 With the inauguration of President Bill Clinton in 1993, OFCCP enforcement accelerated and the number of debarments because of affirmative action noncompliance increased dramatically.4 Recent years have witnessed efforts to rescind affirmative action at the state level, with California prohibiting affirmative action in public employment in 1996, Washington in 1998, Michigan in 2006, Nebraska in 2008, Arizona in 2010, and legislation pending in several other states.

Previous Literature In previous work, I found that affirmative action had a positive effect on increasing the overall employment of minority women during 1973–2003 (Kurtulus 2010). The focus of this paper is the role of affirmative action on the 3 The Glass Ceiling Act also established a Presidential Award given annually to a business that made significant efforts to remove obstacles to career growth and provided advancement of opportunities to women and minorities. 4 See Holzer and Neumark (2000) for a detailed review of affirmative action legislation and enforcement since the 1960s.

Affirmative Action and Occupational Advancement / 219 advancement of minorities and women into high-paying skilled occupations. It is in skilled occupations like management and professional jobs that protected groups have historically been the most underrepresented relative to white males but where the impact on increasing the incomes of protected groups is likely to be greatest.5 There is a vast literature documenting gender and race disparities in the hiring and promotion of women and minorities into the top echelons of job hierarchies (Bertrand, Goldin, and Katz 2010; Bertrand and Hallock 2001; Blau and DeVaro 2007; Blau, Ferber, and Winkler 2010; Ransom and Oaxaca 2005; Smith and Welch 1986, U.S. Glass Ceiling Commission 1995a, 1995b). The few past studies that have specifically looked at the role of affirmative action were conducted in the late 1970s and early 1980s using EEOC records and found that contractors were more successful than noncontractors in hiring protected groups into relatively unskilled blue-collar positions during the late 1960s and early 1970s, but that contractors were more effective than noncontractors in advancing minorities and women into white-collar occupations during the late 1970s. Ashenfelter and Heckman (1976) found that between 1966 and 1970, affirmative action led to increases in black men’s employment share relative to white men in operative, sales, and laborer occupations, while black men’s relative share decreased in managerial and professional occupations more rapidly in the contracting sector during this time. Heckman and Wolpin (1976) found that between 1972 and 1973, black male employment gains were concentrated in blue-collar occupations in the contracting sector and that contractors did not promote minorities into white-collar jobs at a greater rate than noncontractors. Leonard (1984) found that the black male share of managerial, technical, sales, clerical, craft, and service occupations increased faster among contractors than noncontractors between 1974 and 1980. He also found evidence of a twist in demand at contractors vis-a`-vis noncontractors toward nonblack minority males in white-collar occupations, particularly in sales and clerical positions, and away from these groups in operative and laborer positions; black women increased their employment in all occupations except technical and craft positions faster in the contractor sector; and white women’s representation in managerial and professional jobs grew faster in the contractor sector, while their presence in clerical, sales, and operative occupations diminished faster in that sector.6,7 My study contributes to 5 As seen in Figure 1, minority and female representation relative to that of white men has also been low in blue-collar jobs, as well as in sales, clerical, and service jobs, though not as low as in management and professional occupations. Figure 1 also illustrates the great strides white women have made in management occupations in particular, where their mean share increased from 14 percent in 1973 to nearly 30 percent in 2003. 6 Brown (1982), Donohue and Heckman (1991), and Blau and Winkler (2005) provide a critical review of some of these studies. 7 As we will see later in the paper, the current paper’s findings pertaining to the years common with these early studies are consistent with their findings.

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this literature by updating our knowledge about the effect of affirmative action on the occupational advancement of minorities and women since these early studies, an exercise that has not been possible until now because of the unavailability of appropriate data to examine this important topic.

Data and Methodology The firm-level data used in the empirical analyses come from confidential annual EEO-1 Employer Information Reports for 1973 and each year in 1978– 2003 that have been collected by the U.S. Equal Employment Opportunity Commission as mandated by Title VII of the U.S. Civil Rights Act of 1964.8 EEO-1 reports describe the race and gender composition of employees by occupation across all U.S. private-sector firms with one hundred or more employees and private-sector federal contractors with fifty or more employees.9,10 These data are exceptional for numerous reasons. First, they contain records on over 100,000 firms over 1973–2003. Furthermore, a great advantage of these data is their longitudinal nature, allowing us to follow firms over time and thereby use panel regression methods to control for unobserved attributes of firms that may be correlated with female and minority representation and derive sharper econometric estimates of the effect of affirmative action. EEO-1 reports have only recently become available to scientific researchers, and I have gained access to these data through use of an Inter-Government Personnel Act Agreement with the Equal Employment Opportunity Commission. EEO-1 reports provide employment counts at each firm by gender and five race or ethnic groups: White, Black, Hispanic, Asian or Pacific Islander, American Indian or Alaskan Native, distributed across the following occupational categories: Managers and Officers, Professionals, Technicians, Sales Workers, Office and Clerical Workers, Manual Workers, and Service Workers. The managerial and professional jobs generally require a college degree or more, while technician, sales, clerical, manual, and service occupations typically require only

8 EEO-1 records for the years 1974–1977 were unavailable from the U.S. Equal Employment Opportunity Commission. 9 As a robustness check, the baseline regressions were also estimated restricting the sample to firms with one hundred or more employees and the results were very similar to those reported in the paper. 10 Prior to 1983, the EEO-1 reporting requirement was such that firms with fifty or more employees and federal contractors with twenty-five or more employees had to submit records. As a robustness check, the baseline regressions were also estimated restricting the pre-1983 sample to firms with one hundred or more employees and federal contractors with fifty or more employees to match the post-1983 EEO-1 reporting requirement and the results matched those reported in the paper very closely.

Affirmative Action and Occupational Advancement / 221 an education level of 2 years of junior college or less.11 Firms are instructed not to include temporary or casual employees hired for a specified period of time or for the duration of a specified job in their reports but to include leased employees as well as both part-time and full-time employees. Robinson et al. (2005) compare employment covered in the EEO-1 data to employment estimates from the U.S. Bureau of Labor Statistics and report EEO-1 coverage to typically be between 40 and 50 percent of all U.S. private-sector employment, with higher proportions in industries comprised of larger firms such as manufacturing and transportation. The EEO-1 reports also include information on the firm’s industry, geographic location, whether or not the firm is a federal contractor, and whether or not the firm is a multi-establishment organization. The main independent variable in my empirical analysis is federal contractor status. Firms with government contracts are mandated to implement affirmative action and are subject to compliance reviews by the Office of Federal Contract Compliance, with penalties for noncompliance ranging from revocation of current government contracts to suspension of the right to bid on future contracts. Therefore the empirical approach is to investigate the relationship between firm federal contractor status and changes in female and minority shares within occupational categories in U.S. firms to study the impact of affirmative action. Use of contracting status to understand the effects of affirmative action was also the approach taken in the earlier studies that used EEO-1 records (Ashenfelter and Heckman 1976; Heckman and Wolpin 1976; Leonard 1984, 1986).12,13 The longitudinal nature of the EEO-1 data is utilized to estimate fixed effects regressions of the relationship between firm federal contractor status and the shares of women and men of different races within each occupational category. Identification of the federal contractor effects comes from variation 11

Detailed descriptions of these occupational categories and examples of jobs they comprise are in Appendix A. I consolidated three categories—‘‘craft workers,’’ ‘‘operatives,’’ and ‘‘laborers,’’ which all pertain broadly to manual work of varying skill levels and which yielded similar trend and regression results—into the category ‘‘manual workers’’ in this paper. 12 Note that in these earlier studies, the unit of analysis was an establishment, while in mine the unit of analysis is a firm. However, since the entity being awarded a government contract is the firm and not individual establishments within the firm, there is no variation at the establishment level within a given firm in my main independent variable, and so the firm is the more appropriate unit of analysis for the purposes of the current study. My methodology also differs from these early studies in that I control for firm fixed effects, industry-specific, region-specific, and economy-wide shocks in my empirical models. 13 An additional element that would have enriched the analysis but which I do not have data on is which contracting firms underwent formal OFCCP compliance reviews. It has been argued, however, that the threat of enforcement can actually have a larger effect than enforcement action (Heckman and Wolpin 1976; Leonard 1985, 1996). Moreover, survey evidence also shows that fear of litigation or debarment from government contracting is a strong deterrent against violation of affirmative action laws even in the absence of OFCCP reviews (Badgett 1995).

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in a given firm’s occupational race and gender composition as the firm’s contractor status changes.14 Note that during the sample period of 1973–2003, a firm was observed for 8.1 years on average. Around 8 percent of noncontractors switched to being contractors the following year, and around 10 percent of contractors became noncontractors the following year. Federal contractors held their contractor status for 5.9 years on average. The following equation is estimated: 0 b þ hi þ kt þ Industryi  kt þ Regioni  kt þ ei;t % OccðgÞi;t ¼ aFebi;t þ Xi;t

ð1Þ

Here, %Occ(g)i,t is the percentage of workers in a given occupational category at firm i belonging to demographic group g in year t, where the demographic groups to be examined are g={White Female, Black Female, Hispanic Female, White Male, Black Male, Hispanic Male}. The main dependent variable, Fedi,t, is a dummy variable equaling 1 if firm i is a federal contractor in year t. My primary interest lies in estimating a, or the coefficient on Fedi,t, which measures the total change in the share of occupation Occ composed of demographic group g associated with becoming a federal contractor on average during 1973–2003. Xi,t is a vector that includes a constant term and an array of time-varying firm i controls, including firm size in year t, whether the firm is a multi-establishment organization in year t, and the percentage of workers at the firm in year t who are in occupation Occ; hi is a firm fixed effect; kt is a year fixed effect; Industryi * kt represents interactions between industry dummies and year dummies; and Regioni * kt represents interactions between Census region dummies and year dummies. The objective is to estimate the effect of federal contractor status on the race and gender distribution of occupations at the firm net of economy-wide and firm-specific factors that may also be influencing the evolution of race and gender diversity within occupations at the firm. Including firm fixed effects in equation (1) allows us to control for any time-invariant unobserved firm attributes, which may influence changes in the firm’s occupation distribution of minorities and women. Furthermore, year fixed effects are included to control for any economy-wide shocks and general trends affecting female and minority representation within occupations symmetrically across all firms, such as the increasing supply of highly educated women and minorities in labor markets

14

Firms that are never contractors are also included in the analysis sample; these firms help identify the other coefficients in the regression model.

Affirmative Action and Occupational Advancement / 223 since the 1960s that high-skill occupations like management and professional occupations draw from.15 It is likely that there are also factors influencing the share of women and minorities in skilled occupations that vary within the firm’s industry and the firm’s geographic region over time which could bias my estimates of the relationship between contractor status and female and minority representation if such factors do not change at a national level uniformly and get picked up by the year fixed effects in my model. Therefore it would be desirable to additionally control for such industry-specific and region-specific factors that may also be serving to increase the firm’s share of women and minorities within certain occupations over time. I incorporate interactions of industry dummies with year dummies to account for industry-specific shocks to female and minority representation within occupations. For example, many firms in a given industry may respond to a high-profile lawsuit on gender discrimination in promotion into management brought against a similar firm by enacting a policy of increasing the share of women in managerial positions over a period of time. Including industry-year dummies allows us to flexibly control for such phenomena and get more accurate estimates of the influence of federal contractor status net of any industry trends toward higher levels of gender and race diversity within certain occupations. In a similar fashion, interactions of region dummies with year dummies (hi * kt) are also incorporated to account for region-specific changes in available female and minority labor pools that firms face and thus influence the extent to which firms can implement affirmative action hiring. However, even after we account for firm fixed effects, year fixed effects, industry-specific time effects, and region-specific time effects, there may still remain differences across firms in factors such as management practices that vary over time and that influence the evolution of the occupational position of minority and female workers at the firm, biasing the estimates of the effect of affirmative action on minority and female occupational advancement. To alleviate this potential source of bias, the regression model additionally controls for a set of observable time-varying firm characteristics that are likely to be correlated with unobservable factors like management practices and that may 15

Bound and Freeman (1992) and Freeman (1976), for example, showed that the share of black men who graduated college increased greatly during the 1960s, 1970s, and 1980s. Donohue and Heckman (1991), Heckman and Payner (1989), and Smith and Welch (1986) present further evidence on the increasing educational attainment of black men and women vis-a`-vis whites during the 1960s and 1970s, particularly in the South. Also, the educational attainment of women has been on the rise since the 1960s, though the rise in black male educational attainment has slowed since the 1980s (Blau, Ferber, and Winkler 2010; McDaniel et al. 2011). Finally, the quality of education blacks have been receiving since the 1960s also increased as documented by Smith and Welch (1986) and Card and Krueger (1992a, 1992b).

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influence the effect of contractor status on the occupational upgrading of protected groups at the firm. For example, firm size is likely to be important: large firms are more likely to have formalized personnel policies and recruitment programs that may reduce barriers to the hiring and promotion of women and minorities.16 Therefore, larger firms may have better affirmative action track records. At the same time contractor status may be positively correlated with firm size. In this case, a positive revealed relationship between contractor status and female and minority occupational advancement might be spurious; it might be picking up the correlation between promotion of protected groups and firm size. The regression model therefore includes controls for firm size and whether the firm is a multi-establishment organization. The regression model also controls for the share of workers at the firm in the occupational group relevant to each specification to account for the possibility that a smaller occupational group may allow more flexibility to grow to accommodate the hiring and promotion of minorities and women to that occupational category.17 In the second part of the empirical analysis, the evolution of the relationship between federal contractor status and the occupational upgrading of minorities and women over the three decades during 1973–2003 is examined. To do this, equation (1) is modified by replacing the federal contractor indicator with interactions of each of the year dummies with federal contractor status (kt *Fedi,t), allowing us to study the effect of affirmative action by year. Table 1 presents descriptive statistics for key variables in the pooled sample. Some 43 percent of firms hold federal contracts. The contractor firms are on average bigger and more likely to be multi-establishment organizations than noncontractors. They are more likely than noncontractors to be located in the U.S. South and West and relatively less likely to be in the Northeast and Midwest. They are also more likely than noncontractors to be in the construction, manufacturing, mining, finance, and transportation and utilities industries, and relatively less likely to be in services, wholesale and retail trade, and agriculture. Contractors also have higher proportions of employment comprised of managerial, professional, technical, clerical, and manual occupations but lower proportions comprised of sales and service occupations. Shares of women in all occupational categories have generally been lower at contractors on average during the 31 years under study, except in the case of black women and Hispanic women in clerical occupations. Black and Hispanic men’s shares of professional, technical, clerical, and service occupations have been higher at 16

A number of past studies have found a positive relationship between employer size and the rate of black and female employment since the 1970s, including Holzer (1998) and Carrington, McCue, and Pierce (2000). 17 Variables used in the empirical analyses are defined in greater detail in the Appendix A.

Affirmative Action and Occupational Advancement / 225 TABLE 1 DESCRIPTIVE STATISTICS

BY

FEDERAL CONTRACTOR STATUS

Fed = 1

Size (100) Growth Multi-establishment Agriculture Mining Construction Manufacturing Transportation Wholesale Retail Finance Service Northeast Midwest South West % Managers % Professionals % Technicians % Sales workers % Clerical workers % Manual workers % Service workers % White female managers % White female professionals % White female technicians % White female sales % White female clerical % White female manual % White female service % Black female managers % Black female professionals % Black female technicians % Black female sales % Black female clerical % Black female manual % Black female service % Hispanic female managers % Hispanic female professionals % Hispanic female technicians % Hispanic female sales % Hispanic female clerical % Hispanic female manual % Hispanic female service % White male managers % White male professionals

Fed=0

Mean

Std. Dev.

Obs

Mean

Std. Dev.

Obs

16.55322 0.05997 0.59751 0.00742 0.01278 0.08009 0.32475 0.05883 0.05347 0.04305 0.18907 0.23054 0.24019 0.26269 0.30792 0.18921 0.13716 0.11747 0.06229 0.05057 0.22073 0.35725 0.05453 0.22302 0.28685 0.23629 0.25327 0.71204 0.14034 0.19067 0.01453 0.02151 0.03040 0.01631 0.06531 0.03361 0.07526 0.00845 0.01099 0.01370 0.01161 0.04248 0.02699 0.02591 0.69117 0.60099

93.03280 1.09825 0.49040 0.08580 0.11232 0.27144 0.46828 0.23531 0.22498 0.20296 0.39156 0.42118 0.42720 0.44009 0.46163 0.39168 0.08007 0.16379 0.09736 0.10536 0.22878 0.32605 0.15536 0.19800 0.26679 0.25813 0.27796 0.21836 0.19967 0.26358 0.04858 0.06966 0.09246 0.06765 0.11112 0.08387 0.17091 0.03351 0.04829 0.05770 0.05581 0.09161 0.07901 0.09816 0.23098 0.29925

434,076 373,852 434,076 432,802 432,802 432,802 432,802 432,802 432,802 432,802 432,802 432,802 433,414 433,414 433,414 433,414 434,075 434,075 434,075 434,075 434,075 434,075 434,075 432,742 361,505 322,241 261,477 430,693 344,071 250,022 432,742 361,505 322,241 261,477 430,693 344,071 250,022 432,742 361,505 322,241 261,477 430,693 344,071 250,022 432,742 361,505

7.35677 0.05535 0.47254 0.01069 0.00932 0.03344 0.30080 0.05367 0.06356 0.12701 0.07074 0.33077 0.24459 0.29057 0.30068 0.16416 0.10148 0.10485 0.05657 0.09145 0.15322 0.35456 0.13787 0.26735 0.38461 0.32220 0.30706 0.72826 0.17083 0.28289 0.01735 0.02598 0.04141 0.02150 0.05999 0.04084 0.10100 0.00912 0.01152 0.01422 0.01541 0.04176 0.03151 0.03551 0.64293 0.50633

45.02975 1.31109 0.49925 0.10282 0.09611 0.17979 0.45860 0.22536 0.24396 0.33299 0.25639 0.47049 0.42985 0.45402 0.45856 0.37042 0.06841 0.15259 0.09225 0.17429 0.16061 0.33458 0.24903 0.23518 0.32222 0.32531 0.31299 0.23310 0.22463 0.30583 0.05493 0.08095 0.11676 0.07703 0.11167 0.10007 0.19001 0.03502 0.04884 0.06052 0.06539 0.09658 0.09338 0.10620 0.26297 0.34217

581,291 497,443 581,291 579,956 579,956 579,956 579,956 579,956 579,956 579,956 579,956 579,956 580,622 580,622 580,622 580,622 581,283 581,283 581,283 581,283 581,283 581,283 581,283 578,648 458,815 409,445 369,518 573,312 493,798 367,563 578,648 458,815 409,445 369,518 573,312 493,798 367,563 578,648 458,815 409,445 369,518 573,312 493,798 367,563 578,648 458,815

226 /

FIDAN

ANA

KURTULUS TABLE 1 (Cont.) Fed = 1

% % % % % % % % % % % % % % % % % % %

White male technicians White male sales White male clerical White male manual White male service Black male managers Black male professionals Black male technicians Black male sales Black male clerical Black male manual Black male service Hispanic male managers Hispanic male professionals Hispanic male technicians Hispanic male sales Hispanic male clerical Hispanic male manual Hispanic male service

Fed=0

Mean

Std. Dev.

Obs

Mean

Std. Dev.

Obs

0.59861 0.67182 0.12649 0.57766 0.46665 0.02055 0.01667 0.03863 0.01489 0.01487 0.09970 0.14342 0.01976 0.01687 0.03543 0.01677 0.01150 0.08353 0.07448

0.30496 0.31144 0.13790 0.30011 0.35287 0.05496 0.05166 0.08933 0.05754 0.03853 0.15259 0.23522 0.05730 0.06004 0.09867 0.06323 0.03609 0.15598 0.18617

322,241 261,477 430,693 344,071 250,022 432,742 361,505 322,241 261,477 430,693 344,071 250,022 432,742 361,505 322,241 261,477 430,693 344,071 250,022

0.51621 0.60465 0.12207 0.54159 0.36538 0.02148 0.01445 0.03223 0.01710 0.01298 0.09589 0.12002 0.02215 0.01522 0.03325 0.01928 0.01130 0.09011 0.07042

0.35269 0.34528 0.14850 0.31055 0.33461 0.05480 0.05417 0.09120 0.05879 0.03679 0.15313 0.21226 0.06648 0.06696 0.10793 0.07031 0.03887 0.17199 0.17627

409,445 369,518 573,312 493,798 367,563 578,648 458,815 409,445 369,518 573,312 493,798 367,563 578,648 458,815 409,445 369,518 573,312 493,798 367,563

NOTES: Based on the full EEO sample of N = 1,015,881 firm-years during 1973–2003. Descriptive statistics based on the samples used in all of the regressions in the paper closely match the above figures. Differences in means between the Fed = 1 and Fed = 0 subsamples are statistically significant at the 1% level for all variables except in the case of Growth, which is statistically significant at the 10% level.

contractors, on average; on the other hand, contractors’ shares of management and sales occupations have been lower. Furthermore, black men’s share of manual occupations has been higher at contractors, while Hispanic men’s share of such occupations has been lower. Lastly, the proportion of white men in all occupational categories has been higher at contractors, on average.

Results Overall Effects of Affirmative Action. To examine the average total effect of affirmative action on employment shifts within disaggregated occupations during 1973–2003, equation (1) is estimated for each demographic group g and occupation category Occ. The main results from this large amount of information are condensed into Table 2, where each entry corresponds to the estimated coefficient on federal contractor status from a regression of the share of each gender and race group within each occupation category. For example,

Affirmative Action and Occupational Advancement / 227 the entry in the first row and first column is the estimated coefficient on contractor status in a regression of the proportion of managers who are white women. Focusing on the coefficients that are statistically significant, we see that becoming a federal contractor was associated with a 0.183 percentage point increase on average during 1973–2003 in white women’s share of professional occupations, and a 0.052 percentage point increase in black women’s share of professional occupations. As shown in the trend plots in Figure 1, the mean share of black women in professional occupations across firms increased from 1.35 percent in 1973 to 3.37 percent in 2003. Thus, in terms of the implied contribution of affirmative action in federal contracting to the overall trends in the status of black women in professional jobs, the 0.052 percentage point increase because of affirmative action represents a 3.9 percent increase from its 1973 level and constitutes 2.6 percent of the total increase over 1973– 2003. Likewise, given that the share of white women in professional occupations advanced from 25 percent in 1973 to 36.8 percent in 2003, the 0.183 percentage point increase because of affirmative action represents a 7.3 percent increase from its 1973 level, and 1.6 percent of the total advancement from 1973 to 2003. Another interesting finding from Table 2 is that Hispanic women and black men made inroads into technical occupations at federal contractors, increasing their share by 0.058 percentage points and 0.109 percentage points, respectively, at firms that became federal contractors. Given that the mean share of Hispanic women in technical occupations across firms increased from 0.75 percent in 1973 to 1.85 percent in 2003, and the mean share of black men increased from 2.6 percent to 4.1 percent, this means that affirmative action increased the 1973 shares by 7.7 percent for Hispanic women and by 4.2 percent for black men (amounting to 5.3 percent of the 31 year increase for Hispanic women technicians and 7.3 percent of the 31 year increase for black male technicians). At the same time, white men’s share of professional, technical, and clerical occupations declined substantially at federal contractors in contrast to noncontractors, although contractor status was not associated with analogous declines in white male managerial representation. We also see that, on average, black women increased their share of clerical and manual occupations by 0.191 and 0.089 percentage points at firms that became contractors (amounting to a 5.83 percent increase and a 2.29 percent increase, respectively, from their 1973 mean values of 3.28 percent and 3.88 percent), and black men increased their share of manual occupations by 0.120 percentage points at firms that became contractors (amounting to a 1.1 percent increase from its 1973 value of 10.7 percent). Finally, Table 2 illustrates that Hispanic men did not benefit from affirmative action on average during 1973–2003: the coefficients on federal contractor status in the Hispanic male regressions are

0.00098* (0.00058) 0.00003 (0.00019) )0.00043** (0.00019) 1,007,807 123,184

)0.00252*** (0.00097) )0.00016 (0.00022) )0.00002 (0.00026) 817,732 103,823

0.00183** (0.00092) 0.00052* (0.00027) 0.00013 (0.00020)

GENDER

)0.00221* (0.00116) 0.00109** (0.00043) )0.00014 (0.00042) 729,432 95,275

0.00097 (0.00103) 0.00036 (0.00037) 0.00058** (0.00026)

Technicians (3)

ON

AND

)0.00069 (0.00116) 0.00016 (0.00026) 0.00007 (0.00029) 628,738 88,380

0.00055 (0.00111) 0.00002 (0.00029) )0.00005 (0.00025)

Sales (4)

Occupation

RACE SHARES OF

)0.00169*** (0.00055) 0.00007 (0.00015) 0.00001 (0.00014) 1,000,479 122,520

)0.00015 (0.00071) 0.00191*** (0.00031) )0.00044 (0.00028)

Clerical (5)

)0.00120 (0.00082) 0.00120** (0.00047) )0.00119*** (0.00045) 834,972 106,384

)0.00005 (0.00061) 0.00089*** (0.00032) 0.00010 (0.00026)

Manual (6)

OCCUPATIONS DURING 1973–2003

)0.00122 (0.00141) )0.00097 (0.00100) 0.00011 (0.00078) 615,663 84,522

0.00105 (0.00116) 0.00067 (0.00071) )0.00010 (0.00049)

Service (7)

NOTES: Each entry represents the coefficient estimate on federal contractor status from a regression of the share of women and men of different races within each occupational category at the firm on federal contractor status and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industryspecific time effects. Robust standard errors clustered by firm are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Observations Number of firms

Hispanic male

Black male

Men: White male

Hispanic female

Black female

)0.00051 (0.00052) )0.00012 (0.00017) 0.00008 (0.00011)

Professionals (2)

FEDERAL CONTRACTOR STATUS

ANA

Women: White female

OF

FIDAN

Managers (1)

THE EFFECT

TABLE 2

228 / KURTULUS

Affirmative Action and Occupational Advancement / 229 either not statistically significant at conventional levels or are statistically significantly negative.18,19 Evolution of Affirmative Action Effects. The estimates presented in the previous section pertain to the average effects of affirmative action over the 31 years during 1973–2003, but also of interest is the evolution of this effect during this period. To examine this, the baseline regressions in Table 2 (equation 1) are modified by replacing the basic federal contractor indicator with interactions of each of the year dummies with the contractor indicator. Here, the estimated coefficients on the YearN*Fed interactions measure the average difference within each year in demographic group shares of occupations between federal contractors and noncontractors, i.e., the marginal effect of federal contractor status in each year. For expositional purposes, it is useful to plot the estimated coefficients on the YearN*Fed interactions over 1973–2003 to illustrate the evolution of the effects of federal contractor status; this is done in Figures 2–7 for each demographic group in turn, where the solid lines depict the coefficient estimates and the dashed lines represent the robust 95 percent confidence intervals (clustered at the firm level) for each point estimate. One should be cautious about making strong inferences from the point estimates with wide confidence interval bands. Of primary interest in these figures is to discern whether there are regions of rapid increase (or decrease) in the contractor coefficient for the relevant demographic-occupation group, indicating that that particular type of employment grew (or shrunk) faster at contractors than at noncontractors during that period. Furthermore, the steeper the slope, the more rapid was the relative growth (or decline).20 A number of interesting findings emerge from Figures 2 to 7. The effect of affirmative action on advancing black, Hispanic, and white women into management, professional, and technical occupations occurred primarily during the 1970s and early 1980s (pre- and early-Reagan years); during this period, contractors grew their shares of these groups more rapidly than noncontractors and decreased their relative shares of white men in management, professional and technical occupations. For example, as seen in Figure 2, between 1973 and 1982, the share of white women in management went from being 0.524

18 These results are robust to excluding firms in the retail industry, which has the lowest share of firms that are contractors. 19 It is important to note that some of the estimated coefficients in Table 2 are statistically significant only at the 10-percent level and should be evaluated with caution since the presence of many regressors in each model may create multiple inference bias. 20 The regression estimates on which these figures are based are available from the author upon request.

Clerical 0.008 0.006 0.004 0.002 0 -0.002 -0.004 -0.006 -0.008 -0.01

0.02 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02 -0.025 -0.03

Professionals

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Manual

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

Technicians

Service

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

Sales

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of white women on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

-0.015

-0.01

-0.005

0

0.005

0.01

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANA

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

FIDAN

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.008 0.006 0.004 0.002 0 -0.002 -0.004 -0.006 -0.008 -0.01

FIGURE 2

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON WHITE FEMALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

230 / KURTULUS

Clerical

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

0.005

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

Professionals

Manual 0.012 0.01 0.008 0.006 0.004 0.002 0 -0.002 -0.004 -0.006 -0.008

0.005 0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004

Technicians

Service

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

Sales

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of black women on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

0.006 0.005 0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.0015 0.001 0.0005 0 -0.0005 -0.001 -0.0015 -0.002 -0.0025 -0.003

FIGURE 3

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON BLACK FEMALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

Affirmative Action and Occupational Advancement / 231

Clerical

Manual

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004 -0.005

0.0025 0.002 0.0015 0.001 0.0005 0 -0.0005 -0.001 -0.0015 -0.002 -0.0025

Professionals

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

Technicians

Service

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

Sales

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of Hispanic women on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004 -0.005

-0.0015

-0.001

-0.0005

0

0.0005

0.001

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANA

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

FIDAN

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.0015

FIGURE 4

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON HISPANIC FEMALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

232 / KURTULUS

0.02

0.01

Clerical

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

Professionals

Manual

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.03 0.025 0.02 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02

Technicians

Service

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

Sales

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of white men on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, regionspecific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

-0.01

-0.005

0

0.005

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.015

FIGURE 5

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON WHITE MALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

Affirmative Action and Occupational Advancement / 233

0.002

Clerical

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.0025 0.002 0.0015 0.001 0.0005 0 -0.0005 -0.001 -0.0015 -0.002 -0.0025 -0.003

Professionals

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Manual

-0.015

-0.01

-0.005

0

0.005

0.01

0.006 0.005 0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004

Technicians

Service

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

Sales

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of black men on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, regionspecific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

-0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANA

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

FIDAN

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.0015

FIGURE 6

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON BLACK MALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

234 / KURTULUS

Clerical

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

Professionals

Manual 0.01 0.008 0.006 0.004 0.002 0 -0.002 -0.004 -0.006 -0.008

0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004 -0.005

Tehnicians

Service

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

Sales

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

NOTE: In each graph, the solid line illustrates the estimated coefficients on the YearN*Fed interactions over the years 1973–2003 in a regression of the percentage of the particular occupational group comprised of Hispanic men on YearX*Fed interactions and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. The dashed lines depict the robust firm-clustered 95 percent confidence interval around the point estimates.

-0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

Managers

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 1973 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

0.003

FIGURE 7

THE EFFECT OF FEDERAL CONTRACTOR STATUS ON HISPANIC MALE SHARES OF OCCUPATIONS DURING 1973–2003, BY YEAR

Affirmative Action and Occupational Advancement / 235

236 /

FIDAN

ANA

KURTULUS

percentage points lower to being over 0.234 percentage points higher at contractors than at contractors.21 After the early 1980s, the positive impact of affirmative action on white, black, and Hispanic women in management, professional, and technical occupations decelerated or vanished entirely. It is interesting to note that the slowdown in the occupational advancement of minorities and women into high-skill high-pay management, professional, and technical jobs at federal contractors was concurrent with the major shifts in political attitudes toward affirmative action that began when President Reagan took office, including efforts to rescind affirmative action legislation and severe cuts in EEOC and OFCCP budgets as described in the Institutional Background section. There was, however, a resurgence in the case of Hispanic female managers in the early 1990s concurrent with the passage of the Glass Ceiling ⁄ Civil Rights Act of 1991, as seen in Figure 4, and in the case of white female managers and black and Hispanic female professionals in the late 1990s and early 2000s, as seen in Figures 2–4. Figure 6 shows that analogous trends took place also for black men in professional, technical, and sales occupations. Turning to blue-collar jobs, we see that during the 1970s and early 1980s the share of black and white women in manual occupations also grew faster at contractors relative to noncontractors, while the relative share of Hispanic women, white men, black men, and Hispanic men shrunk more rapidly.22 During the 1990s and early 2000s on the other hand, contractors experienced declining representation in manual occupations of white women, Hispanic women, black men, and Hispanic men, concurrent with increasing white male representation in such jobs. The trends in service jobs were similar but less pronounced. Several other interesting trends emerge. For example, during the 1990s, white, black, and Hispanic women left clerical jobs, traditionally a female domain, more rapidly at contracting firms, while white, black, and Hispanic men increased their representation in the clerical positions of contractors faster during this time. Also, the main benefits of affirmative action on Hispanic male employment occurred in professional and sales occupations primarily during the early 1990s.

21

Note that these findings are consistent with previous research on this period, notably Leonard’s (1984) analysis of the years 1974–1980 showing the share of black and white women in management and professional occupations had increased faster at contractors relative to non-contractors. 22 Leonard’s (1984) analysis of the 1974–1980 period also found that black women’s share of blue collar occupations increased faster, and Hispanic men’s share of blue collar occupations decreased faster, at contractors.

Affirmative Action and Occupational Advancement / 237

Exploring the Role of Selection and Stickiness This section investigates the role of selection and stickiness in the relationship between contractor status and minority and female representation. It may be argued that the positive relationship between federal contractor status and protected group representation found earlier reflects selection rather than contractor response to affirmative action obligation. That is, it may be that firms which had high minority and female representation in the first place were more likely to apply for and be awarded government contracts than those that were not as diverse, and it may be that firms which lost their contracts were those that were decreasing their female and minority representation. To explore the potential role of selection into and out of contractor status in my earlier findings, the baseline regression model (equation 1) is augmented with dummy variables indicating that the firm is going to gain contractor status in the next year and that the firm is going to lose contractor status in the next year. If selection into and out of contractor status plays an important role, then the coefficients on these dummies in the minority and female regressions should be statistically significantly positive and negative, respectively. It may also be argued that firms do not reduce their female and minority presence upon losing contractor status, i.e., gaining and losing contractor status may not imply symmetric gains and losses in minority and female shares, respectively, but rather there may be persistence or ‘‘stickiness’’ in minority and female representation even after the firm is no longer a federal contractor. To explore the presence of such stickiness, equation (1) is also augmented with a dummy variable indicating the year following the loss of contractor status; a statistically significant non-negative coefficient estimate will indicate stickiness. Estimates from this augmented model are presented in Table 3 for each demographic-occupation group. We see that that majority of the estimated coefficients on the dummy variable indicating the year prior to contract gain (Pre1fed) are not statistically significant at conventional levels, suggesting that selection into contractor status is not a major concern. There is also little evidence to support the idea that there is selection out of contractor status; only four of the 35 coefficients on the dummy variable indicating the last year of contract status (Last1fed) in the minority and female regressions are statistically significantly negative, while the remaining are either statistically insignificant at conventional levels or significantly positive contrary to the selection hypothesis. As for the persistence of contractor status, Table 3 reveals that the majority of the coefficients on the dummy variable denoting the year after contract loss (Post1fed) are not statistically significant, indicating that firms

Observations Number of firms

Post1fed

Last1fed

Fed

Hispanic female Pre1fed

Post1fed

Last1fed

Fed

Black female Pre1fed

Post1fed

Last1fed

EFFECT

Technicians (3)

1973–2003

)0.00037 (0.00031) )0.00026 (0.00035) 0.00039 (0.00028) )0.00023 (0.00032) 581,160 77,713

)0.00054 (0.00038) )0.00016 (0.00040) 0.00024 (0.00031) 0.00031 (0.00038)

)0.00103 (0.00129) 0.00097 (0.00162) )0.00031 (0.00116) 0.00121 (0.00127)

Sales (4)

FEDERAL CONTRACTOR STATUS

Dependent variable: % white female Occ 0.00077 )0.00038 0.00063 (0.00063) (0.00111) (0.00124) )0.00091 0.00101 0.00101 (0.00075) (0.00131) (0.00149) 0.00085* )0.00047 )0.00066 (0.00047) (0.00090) (0.00101) )0.00001 )0.00231** )0.00005 (0.00061) (0.00109) (0.00121) Dependent variable: % black female Occ 0.00000 0.00035 )0.00032 (0.00021) (0.00033) (0.00047) )0.00019 0.00083** 0.00027 (0.00024) (0.00038) (0.00053) )0.00000 0.00014 )0.00043 (0.00016) (0.00027) (0.00038) 0.00004 0.00118*** )0.00040 (0.00022) (0.00034) (0.00046) Dependent variable: % Hispanic female Occ )0.00028* 0.00011 0.00063* (0.00014) (0.00025) (0.00033) )0.00002 0.00018 0.00082** (0.00016) (0.00028) (0.00038) 0.00011 0.00015 )0.00019 (0.00011) (0.00021) (0.00027) 0.00004 0.00034 0.00012 (0.00014) (0.00025) (0.00033) 930,153 757,674 675,632 106,015 92,081 84,813

OF

ON

AND

)0.00030 (0.00032) )0.00038 (0.00040) )0.00027 (0.00027) )0.00012 (0.00032) 923,351 105,534

0.00092** (0.00038) 0.00238*** (0.00045) )0.00064* (0.00033) 0.00115*** (0.00039)

)0.00138 (0.00085) )0.00079 (0.00100) )0.00045 (0.00071) )0.00348*** (0.00085)

OF

)0.00002 (0.00029) 0.00018 (0.00037) )0.00016 (0.00025) 0.00053* (0.00030) 770,836 91,753

0.00107*** (0.00039) 0.00104** (0.00046) 0.00054* (0.00030) 0.00073* (0.00038)

)0.00023 (0.00074) )0.00042 (0.00088) 0.00073 (0.00059) )0.00064 (0.00071)

0.00030 (0.00056) 0.00026 (0.00069) 0.00007 (0.00050) 0.00132** (0.00056) 569,230 73,601

)0.00087 (0.00081) 0.00083 (0.00102) )0.00185** (0.00072) )0.00104 (0.00079)

0.00178 (0.00131) 0.00120 (0.00168) 0.00176 (0.00118) 0.00200 (0.00128)

Service (7)

OCCUPATIONS DURING

Manual (6)

RACE SHARES

Clerical (5)

GENDER

ANA

Fed

IN THE

Professionals (2)

STICKINESS

Managers (1)

AND

FIDAN

White female Pre1fed

Panel A: Women

EXPLORING SELECTION

TABLE 3

238 / KURTULUS

Professionals (2)

Technicians (3)

Dependent variable: % white male Occ 0.00003 )0.00099 )0.00061 (0.00069) (0.00116) (0.00137) 0.00160* )0.00181 )0.00232 (0.00084) (0.00138) (0.00165) )0.00085 )0.00028 )0.00084 (0.00053) (0.00096) (0.00115) 0.00002 0.00117 )0.00163 (0.00067) (0.00115) (0.00135) Dependent variable: % black male Occ 0.00013 0.00052* 0.00086 (0.00022) (0.00029) (0.00053) 0.00014 0.00011 0.00141** (0.00026) (0.00030) (0.00060) 0.00006 )0.00037 0.00068 (0.00018) (0.00024) (0.00045) 0.00022 )0.00022 0.00120** (0.00022) (0.00028) (0.00052) Dependent variable: % Hispanic male Occ )0.00011 )0.00038 0.00050 (0.00023) (0.00032) (0.00052) )0.00030 )0.00011 )0.00023 (0.00028) (0.00037) (0.00059) )0.00045** 0.00013 0.00043 (0.00018) (0.00027) (0.00046) )0.00020 0.00025 0.00006 (0.00023) (0.00032) (0.00054) 930,153 757,674 675,632 106,015 92,081 84,813

Managers (1)

0.00031 (0.00035) 0.00035 (0.00041) 0.00003 (0.00032) 0.00084** (0.00034) 581,160 77,713

)0.00011 (0.00032) 0.00021 (0.00036) )0.00009 (0.00030) 0.00019 (0.00034)

0.00182 (0.00133) )0.00113 (0.00169) )0.00035 (0.00122) )0.00271** (0.00133)

Sales (4)

)0.00000 (0.00017) 0.00016 (0.00019) 0.00007 (0.00015) 0.00050*** (0.00018) 923,351 105,534

0.00030 (0.00020) 0.00032 (0.00021) )0.00052*** (0.00016) 0.00021 (0.00019)

0.00018 (0.00067) )0.00183** (0.00077) 0.00110** (0.00056) 0.00131* (0.00067)

Clerical (5)

)0.00061 (0.00051) )0.00145** (0.00065) 0.00032 (0.00044) 0.00024 (0.00051) 770,836 91,753

0.00082 (0.00053) 0.00170** (0.00067) )0.00073 (0.00047) 0.00044 (0.00053)

)0.00081 (0.00095) )0.00106 (0.00118) )0.00134* (0.00080) )0.00151 (0.00094)

Manual (6)

)0.00071 (0.00091) )0.00051 (0.00110) )0.00009 (0.00086) )0.00112 (0.00089) 569,230 73,601

)0.00094 (0.00113) )0.00194 (0.00142) 0.00032 (0.00109) )0.00196* (0.00109)

0.00123 (0.00163) )0.00010 (0.00202) )0.00003 (0.00154) 0.00072 (0.00159)

Service (7)

NOTE: All models include controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. Robust standard errors clustered by firm are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Observations Number of firms

Lost1fed

Last1fed

Fed

Hispanic male Pre1fed

Post1fed

Last1fed

Fed

Black male Pre1fed

Post1fed

Last1fed

Fed

White male Pre1fed

Panel B: Men

TABLE 3 (Cont.)

Affirmative Action and Occupational Advancement / 239

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generally do not reduce their female and minority shares once they are no longer contractors; in fact, in some cases, the estimates are statistically significantly positive indicating that firms continue to diversify even after their federal contract has ended.23,24,25,26

Conclusion Using longitudinal data on more than 100,000 large private-sector firms from all industries and regions from the U.S. Equal Employment Opportunity Commission, this study finds that minority and female shares of higher-paying skilled occupations grew more at federal contractors subject to affirmative action obligation over the three decades spanning 1973–2003, and this result is robust to controlling for firm size, corporate and occupational structure, industry-specific shocks, region-specific shocks, economy-wide shocks, and firm fixed effects. Specifically, becoming a federal contractor increased white women’s share of professional occupations by 0.183 percentage points, or by 7.3 percent, on average during these three decades, and increased black women’s share by 0.052 percentage points (or by 3.9 percent). Becoming a federal contractor also increased Hispanic women’s and black men’s share of technical occupations on average by 0.058 and 0.109 percentage points, respectively (or by 7.7 and 4.2 percent). These represent a substantial contribution of affirmative action to overall trends in the occupational advancement of women and minorities over the three decades under study. Affirmative action also had a smaller impact on race and gender diversity in blue-collar occupations, by increasing the share of black women and black men in manual occupations during 1973–2003. Auxiliary checks indicate that selection was not 23 Note that the estimated coefficients on federal contractor status are generally qualitatively similar to those reported in Table 2. 24 Note that also adding additional dummy variables indicating 2 years before gaining contract status, 2 years until the end of contract status, and 2 years after the end of contract status resulted in qualitatively very similar results to those reported above. 25 As a further check on the role of selection, the baseline regressions in Table 2 were re-estimated limiting the sample to firms that were ever contractors (i.e., excluding firms that never held a federal contract during the 31 years under study). Similar results between these and Table 2’s baseline results would indicate selection in and out of contractor status does not bias the results. Indeed, the results using only those firms that were ever contractors are very similar to the baseline results reported in Table 2, suggesting that selection into contractor status is not a major concern. These additional results are reported in Table B1. 26 An event study-type specification was also estimated, with dummy variables indicating the year prior to contract status, first year of contract status, second year of contract status, third year of contract status, etc. The estimates revealed that most of the increases in minority and female shares occurred fairly continuously within the first 5 years of contract status for the specifications which also had statistically significant coefficients on Fed in the baseline specification in Table 2. These results are available from the author.

Affirmative Action and Occupational Advancement / 241 driving these results and also that contractors maintained their increased minority and female shares even after they no longer held a federal contract. The paper also reveals some important results on how the impact of affirmative action evolved over the three decades under study. For example, affirmative action had a positive effect on moving black women, Hispanic women, white women, and black men into high-skill high-pay management, professional, and technical occupations during the 1970s and early 1980s, but the impact of affirmative action on advancing minorities and women into the top echelons of firm structures subsided during the Reagan years. Another key finding is that during the decade following the Glass Ceiling Act of 1991, affirmative action resurfaced as an important factor moving Hispanic women and white women into managerial occupations, black women and Hispanic women into professional occupations, and black men into professional and technical occupations. Affirmative action in the labor market continues to generate heated debate, yet there is little hard evidence brought to bear to inform policy discussions. This paper has presented large-sample evidence that minority and female representation in high-wage skilled occupations increased more on average at federal contractors subject to affirmative action obligation during 1973–2003. Recent years have also witnessed much discussion surrounding the glass ceiling phenomenon and ways to reduce barriers that women and minorities face in achieving positions at high levels of firms. The current study suggests that government policy plays an important role in cracking the glass ceiling for women and minorities in the workplace. REFERENCES Anderson, Bernard. 1996. ‘‘The Ebb and Flow of Enforcing Executive Order 11246.’’ American Economic Review Papers and Proceedings 86(2): 298–301. Ashenfelter, Orley, and James Heckman. 1976. ‘‘Measuring the Effect of an Anti-discrimination Program.’’ In Evaluating the Labor Market Effects of Social Programs, edited by Orley Ashenfelter, and James Blum, pp. 46–89. Princeton, NJ: Princeton University, Industrial Relations Section. Badgett, M. V. Lee. 1995. ‘‘Affirmative Action in a Changing Legal and Economic Environment.’’ Industrial Relations 34(4): 489–506. Bergmann, Barbara. 1996. In Defense of Affirmative Action. New York: Basic Books. Bertrand, Marianne, Claudia Goldin, and Lawrence Katz. 2010. ‘‘Dynamics of the Gender Gap for Young Professionals in the Financial and Corporate Sectors.’’ American Economic Journal: Applied Economics 2(3): 228–55. —––—, and Kevin Hallock. 2001. ‘‘The Gender Gap in Top Corporate Jobs.’’ Industrial and Labor Relations Review 55: 3–21. Blau, Francine D., and Jed L. DeVaro. 2007. ‘‘New Evidence on Gender Differences in Promotion Rates: An Empirical Analysis of a Sample of New Hires.’’ Industrial Relations 46(3): 511–50. —––—, Marianne Ferber, and Anne Winkler. 2010. The Economics of Women, Men, and Work, 6th ed. Upper Saddle River, NJ: Prentice-Hall.

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—––—, and Anne Winkler. 2005. ‘‘Does Affirmative Action Work?’’ Regional Review. The Federal Reserve Bank of Boston Q1: 38–40. Bound, John, and Richard B. Freeman. 1992. ‘‘What Went Wrong? The Erosion of Relative Earnings and Employment Among Young Black Men in the 1980s.’’ Quarterly Journal of Economics 107(1): 201– 32. Brown, Charles. 1982. ‘‘The Federal Attack on Labor Market Discrimination: The Mouse that Roared?’’ In Research in Labor Economics, Vol. 5, edited by Ronald Ehrenberg, pp. 33–68. New York, NY: JAI Press. Card, David, and Alan B. Krueger. 1992a. ‘‘School Quality and Black-White Relative Earnings: A Direct Assessment.’’ Quarterly Journal of Economics 107(1): 151–200. —––—, and —––—. 1992b. ‘‘Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States.’’ Journal of Political Economy 100(1): 1–40. Carrington, William J., Kristin McCue, and Brooks Pierce. 2000. ‘‘Using Establishment Size to Measure the Impact of Title VII and Affirmative Action.’’ Journal of Human Resources 35(3): 503–23. Donohue, John J., and James J. Heckman. 1991. ‘‘Continuous Versus Episodic Change: The Impact of Civil Rights Policy on the Economic Status of Blacks.’’ Journal of Economic Literature 29(4): 1603–43. —––—, and Peter Siegelman. 1991. ‘‘The Changing Nature of Employment Discrimination Litigation.’’ Stanford Law Review 43: 483–1033. Freeman, Richard B. 1976. Black Elite. New York: McGraw-Hill. Heckman, James J., and Brook S. Payner. 1989. ‘‘Determining the Impact of Federal Antidiscrimination Policy on the Economic Status of Blacks: A Study of South Carolina.’’ American Economic Review 79(1): 138. —––—, and Kenneth I. Wolpin. 1976. ‘‘Does the Contract Compliance Program Work? An Analysis of Chicago Data.’’ Industrial and Labor Relations Review 29(4): 544–64. Holzer, Harry J. 1998. ‘‘Why Do Small Establishments Hire Fewer Blacks Than Large Ones?’’ Journal of Human Resources 4(33): 896–914. Holzer, Harry J., and David Neumark. 2000. ‘‘Assessing Affirmative Action.’’ Journal of Economic Literature 38(3): 483–568. Kurtulus, Fidan Ana. 2010. ‘‘The Impact of Affirmative Action on the Employment of Minorities and Women Over Three Decades: 1973–2003.’’ Working Paper. Amherst, MA: University of Massachusetts. Leonard, Jonathan S. 1984. ‘‘Employment and Occupational Advance under Affirmative Action.’’ Review of Economics and Statistics 66: 377–85. —––—. 1985. ‘‘What Promises Are Worth: The Impact of Affirmative Action Goals.’’ Journal of Human Resources 20(1): 3–20. —––— 1986. ‘‘The Effectiveness of Equal Employment Law and Affirmative Action Regulation.’’ In Research in Labor Economics, Vol. 8, edited by R. G. Ehrenberg, pp. 318–50. Greenwich, CT: JAI Press. —––—. 1990. ‘‘The Impact of Affirmative Action Regulation and Equal Employment Law on Black Employment.’’ Journal of Economic Perspectives 4(4): 47–63. —––—. 1996. ‘‘Wage Disparities and Affirmative Action in the 1980s.’’ American Economic Review Papers and Proceedings 86(2): 285–89. McDaniel, Anne, Thomas A. DiPrete, Claudia Buchmann, and Uri Shwed. 2011. ‘‘The Black Gender Gap in Educational Attainment: Historical Trends and Racial Comparisons.’’ Demography 48: 889–914. Ransom, Michael., and Ronald L. Oaxaca. 2005. ‘‘Intrafirm Mobility and Sex Differences in Pay.’’ Industrial and Labor Relations Review 58(2): 219–37. Robinson, Corre L., Tiffany Taylor, Donald Tomaskovic-Devey, Catherine Zimmer, and Matthew Irwin. 2005. ‘‘Studying Race or Ethnic and Sex Segregation at the Establishment Level: Methodological Issues and Substantive Opportunities Using EEO-1 Reports.’’ Work and Occupations 32(1): 5–38. Smith, James, and Finis Welch. 1986. ‘‘Closing the Gap. Forty Years of Economic Progress for Blacks.’’ Rand Corporation Publication Series, R-2296.

Affirmative Action and Occupational Advancement / 243 U.S. Department of Labor, Employment Standards Administration, Office of Federal Contract Compliance Programs. 1998. Federal Contract Compliance Manual, http://www.dol.gov/ofccp/regs/compliance/ fccm/fccmanul.htm (accessed April 6, 2010). U.S. Glass Ceiling Commission. 1995a. Good for Business: Making Full Use of the Nation’s Human Capital. Washington, DC: U.S. Government Printing Office. U.S. Glass Ceiling Commission. 1995b. A Solid Investment: Making Full Use of the Nation’s Human Capital. Washington, DC: U.S. Government Printing Office.

Appendix A Occupational Category Descriptions. Managers and Officers: Administrative and managerial occupations comprised of personnel who set broad policies, exercise overall responsibility for execution of these policies, and direct individual departments or special phases of the firm’s operations. Examples include officers, executives, middle management, plant managers, department managers, superintendents, salaried supervisors who are members of the management, ship captains, and farm operators and managers. Professionals: Occupations requiring a college degree or experience providing a comparable background. Examples include accountants and auditors, airplane pilots and navigators, architects, artists, chemists, designers, dietitians, editors, engineers, lawyers, librarians, mathematicians, natural scientists, registered professional nurses, personnel and labor relations specialists, physical scientists, physicians, social scientists, teachers, and surveyors. Technicians: Occupations requiring a combination of basic scientific knowledge and manual skill which can be obtained through 2 years of post-high school education, such as is offered in technical institutes and junior colleges, or through equivalent on-the-job training. Examples include computer programmers; drafters; engineering aides; junior engineers; mathematical aides; licensed, practical, or vocational nurses; photographers; radio operators; scientific assistants; technical illustrators; medical and dental technicians; and electronic technicians. Sales Workers: Occupations engaging wholly or primarily in direct selling, including advertising agents and sales workers, insurance agents and brokers, real estate agents and brokers, stock and bond sales workers, sales workers and sales clerks, grocery clerks, and cashiers. Office and Clerical Workers: All clerical-type work regardless of level of difficulty, where the activities are predominantly nonmanual work. Examples include bookkeepers, messengers and office helpers, office machine and computer operators, shipping and receiving clerks, stenographers, typists and secretaries, telephone operators, and legal assistants. Manual Workers: Manual occupations of all skill levels, including skilled craft workers, semiskilled machine operatives, and unskilled laborers. Examples include hourly paid supervisors and lead operators who are not members of management, mechanics and repairers, electricians, mine operatives and laborers, motor operators, truck and tractor drivers, welders, farm workers, and laborers performing lifting and loading operations.

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Service Workers: Workers in service occupations, including attendants in hospitals and other institutions, nurse aides, barbers, cleaners, cooks, counter workers, elevator operators, firefighters, guards, door-keepers, stewards, janitors, police officers and detectives, porters, waiters, amusement and recreation facilities attendants, guides, ushers, and public transportation attendants.

Variable Definitions. % White Female Managers: Percentage of managerial workers at the firm who are white women % White Female Professionals: Percentage of professional workers at the firm who are white women % White Female Technicians: Percentage of technicians at the firm who are white women % White Female Sales: Percentage of sales workers at the firm who are white women % White Female Clerical: Percentage of clerical workers at the firm who are white women % White Female Manual: Percentage of manual workers at the firm who are white women % White Female Service: Percentage of service workers at the firm who are white women The occupation shares are defined analogously for Black Females, Hispanic Females, White Males, Black Males, and Hispanic Males. Fed: Dummy variable equaling 1 if the firm is a federal contractor, 0 otherwise Pre1fed: Dummy variable indicating the year prior to contract gain, 0 otherwise Last1fed: Dummy variable indicating the last year of contract status, 0 otherwise Post1fed: Dummy variable indicating the year following contract loss, 0 otherwise Size(100): Total number of workers at the firm in year t in 100s Growth: Percentage growth in the total number of workers in the firm from year t to year t + 1 Multi-establishment: Dummy variable equaling 1 if the firm is a multi-establishment organization, 0 otherwise % Managers: Percentage of employees at the firm who are managerial workers % Professionals: Percentage of employees at the firm who are professional workers % Technicians: Percentage of employees at the firm who are technicians

Affirmative Action and Occupational Advancement / 245 % Sales: Percentage of employees at the firm who are sales workers % Clerical: Percentage of employees at the firm who are clerical workers % Manual: Percentage of employees at the firm who are manual workers % Service: Percentage of employees at the firm who are service workers YearN: Dummy variables indicating year N = (1973, 1978–2003) Industry-Year Interactions (9): Agriculture*YearN: (Dummy variable equaling 1 if the industry of the firm is Agriculture, Forestry, and Fishing, 0 otherwise)*(YearN) Mining*YearN: (Dummy variable equaling 1 if the industry of the firm is Mining, 0 otherwise)*(YearN) Construction*YearN: (Dummy variable equaling 1 if the industry of the firm is Construction, 0 otherwise)*(YearN) Manufacturing*YearN: (Dummy variable equaling 1 if the industry of the firm is Manufacturing, 0 otherwise)*(YearN) Transportation*YearN: (Dummy variable equaling 1 if the industry of the firm is Transportation, Communications, Electric, Gas and Sanitary Services, 0 otherwise)*(YearN) Wholesale*YearN: (Dummy variable equaling 1 if the industry of the firm is Wholesale Trade, 0 otherwise)*(YearN) Retail*YearN: (Dummy variable equaling 1 if the industry of the firm is Retail Trade, 0 otherwise)*(YearN) Finance*YearN: (Dummy variable equaling 1 if the industry of the firm is Finance, Insurance, and Real Estate, 0 otherwise)*(YearN) Service*YearN: (Dummy variable equaling 1 if the industry of the firm is Services, 0 otherwise)*(YearN) Region-Year Interactions: Northeast*YearN: (Dummy variable equaling 1 if the firm’s headquarters are located in the Northeast region of the U.S. Census Bureau’s primary geographic region classification, 0 otherwise)*(YearN) Midwest*YearN: (Dummy variable equaling 1 if the firm’s headquarters are located in the Midwest region of the U.S. Census Bureau’s primary geographic region classification, 0 otherwise)*(YearN)

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South*YearN: (Dummy variable equaling 1 if the firm’s headquarters are located in the South region of the U.S. Census Bureau’s primary geographic region classification, 0 otherwise)*(YearN) West*YearN: (Dummy variable equaling 1 if the firm’s headquarters are located in the West region of the U.S. Census Bureau’s primary geographic region classification, 0 otherwise)*(YearN)

Appendix B TABLE B1 THE EFFECT

OF

FEDERAL CONTRACTOR STATUS

DURING 1973–2003 USING

THE

SUBSAMPLE

ON OF

GENDER

AND

RACE SHARES

OF

OCCUPATIONS

FIRMS THAT WERE EVER CONTRACTORS Occupation

Managers Professionals Technicians (1) (2) (3) Women: White female

)0.00049 0.00192** 0.00101 (0.00052) (0.00092) (0.00104) Black female )0.00010 0.00055** 0.00036 (0.00017) (0.00027) (0.00037) Hispanic female 0.00009 0.00015 0.00059** (0.00011) (0.00020) (0.00026) Male: White male 0.00089 )0.00265*** )0.00231** (0.00058) (0.00097) (0.00116) Black male 0.00002 )0.00018 0.00108** (0.00019) (0.00022) (0.00043) Hispanic male )0.00040** )0.00003 )0.00010 (0.00019) (0.00026) (0.00042) Observations 717,236 596,142 533,922 Number of firms 72,380 64,180 59,390

Sales (4)

Clerical (5)

Manual (6)

Service (7)

0.00065 )0.00017 )0.00005 0.00106 (0.00112) (0.00071) (0.00061) (0.00116) 0.00005 0.00187*** 0.00085*** 0.00065 (0.00029) (0.00031) (0.00032) (0.00071) )0.00004 )0.00046 0.00007 )0.00008 (0.00025) (0.00028) (0.00026) (0.00049) )0.00077 )0.00162*** )0.00111 (0.00116) (0.00055) (0.00082) 0.00014 0.00008 0.00121** (0.00026) (0.00015) (0.00047) 0.00006 0.00001 )0.00121*** (0.00029) (0.00014) (0.00046) 447,751 712,957 591,296 52,749 72,141 62,929

)0.00113 (0.00142) )0.00113 (0.00100) 0.00015 (0.00078) 434,103 51,122

NOTE: This table re-estimates the specifications in Table 2 using the subsample of firms that were ever contractors during 1973–2003. Each entry represents the coefficient estimate on federal contractor status from a regression of the share of women and men of different races within each occupational category at the firm on federal contractor status and controls for firm size, corporate structure, occupation share, firm fixed effects, year fixed effects, region-specific time effects, and industry-specific time effects. Robust standard errors clustered by firm are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.