Try and Make Me!:

Why Corporate Diversity Training Fails

Alexandra Kalev, Sociology, Tel Aviv University akalev@post.tau.ac.il

Frank Dobbin, Sociology, Harvard University

frank_dobbin@harvard.edu

Abstract

Diversity training consumes the lion’s share of the corporate diversity budget yet studies suggest that it may do little to change attitudes or behaviors. We develop locus-of-motivation theory to suggest that organizational practices can be structured to signal individual, organizational, or extra-organizational motivation, and that extra-organizational motivation will elicit resistance and thereby backfire. Three features of diversity training signal extra-organizational (regulatory) motivation – making it mandatory (implying a legal requirement), special manager training (implying an effort to stop illegal treatment), and legal/procedural curriculum. By contrast, voluntary training signals individual choice, while all-worker training and cultural curriculum signal organizational motivations. Individual and organizational motivation should bring about more positive responses and lead to increases in managerial diversity. Yet, we argue, the effects of regulation are complex. Accountability theory suggests that training effects will be moderated by evaluation apprehension; training should have positive effects where decision-makers believe that federal regulators, and diversity managers, may scrutinize their decisions. Predictions are tested using data on 805 workplaces over more than thirty years. Findings have implications for theory and practice, shedding light on why inequality persists and providing insights for policymakers and employers seeking to counter discrimination.

Prejudice reduction efforts date back to the race-relations workshops required by federal agencies in the late 1960s. Today corporate diversity training is pervasive in the Fortune 500 and common even among small to middling firms. Diversity training is often required in discrimination suit settlements; proposed by plaintiff attorneys and approved by judges (Schlanger and Kim 2014). Yet there is little evidence that prejudice-reduction training reduces bias or discrimination. A survey of 985 studies found weak evidence of short- or long-term effects of training on prejudice or behavior (Paluck and Green 2009). Field studies show that while diversity mentoring and taskforces have strong, consistent effects on the representation of white women and minorities in management, diversity training has weak, inconsistent, effects (Dobbin, Kalev, and Kelly 2007; Kalev, Dobbin, and Kelly 2006).

We build on a growing body of research that brings together insights from laboratory and field studies to understand workplace inequality (e.g. Legault et al. 2011; Kaiser et al. 2013; Castilla and Benard 2010). We begin with self-determination theory, which suggests that people rebel against efforts to control them (Deci and Ryan 1985, 2002). Thus when subjects asked to read anti-prejudice materials are encouraged to think they are motivated to reduce prejudice by external norms, rather than autonomous desire, they show increased prejudice (Legault et al. 2011). We argue that the locus of motivation shapes behavior in important ways, and that organizational practices can signal that the motive for training is extra-organizational (legal compliance), organizational (workgroup efficacy and inclusion), or individual (autonomous). When they signal extra-organizational motivation, we predict, training will backfire and lead to reductions in managerial diversity. When they signal organizational motivation or individual voluntarism, trainees will internalize training lessons and managerial diversity will rise. Employers typically structure training in ways that, we predict, will backfire.

Our theory could be read to suggest that innovations adopted to signal compliance with federal anti-discrimination regulations will backfire generally, but previous studies show positive effects of those regulations (Leonard 1989, 1990; Kalev et al. 2006). We argue that regulation may have a positive effect by activating accountability (Kalev et al. 2006; Kalev 2014; Tetlock 1992; Castilla forthcoming; Rissing and Castilla 2014). When subjects in laboratory studies believe that they may be asked to account for their decisions, evaluation apprehension (Cottrell 1972) leads them to scrutinize their own actions and censor bias (Barley, Gordon, and Gash 1988; Kruglanski and Freund 1983; Bodenhausen, Kramer, and Susser 1994). We posit that negative responses to training will be moderated where managers are subject to monitoring by Department of Labor officials who oversee federal contractors or by internal diversity managers.

We suggest, then, that the effects of regulation are complex; when managers think that external legal controls drive equity efforts they may rebel and exercise bias, yet when they think that federal officials or diversity managers may call them to account they may censor their own biases.

We use data from the Equal Employment Opportunity Commission’s annual demographic census of private-sector work establishments, combined with a retrospective survey of characteristics of corporate diversity training programs. We test predictions using a national sample of 805 work establishments for the period 1971-2003. What effects do new training programs have on white, black, Hispanic, and Asian men and women in management? It may be that employers who face legal problems will tie training to compliance, thus we control for discrimination charges, lawsuits, compliance reviews of federal contractors, and a wide range of other organizational factors. We pool the data in panel models with fixed effects to assess the

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consequences of training programs and federal contractor status. We conduct a series of robustness tests. THE ORIGINS OF DIVERSITY TRAINING

Corporate anti-discrimination training was stimulated by civil rights regulations of the 1960s, beginning with John F. Kennedy’s 1961 executive order requiring federal contractors to take “affirmative action” to prevent discrimination (Lawrence 1965, p. 139). By the end of 1971, the Social Security Administration had put fifty thousand staffers through training (Robertson 1971). By 1976, 60% of big companies offered equal-opportunity training (Bureau of National Affairs 1976, p. 9). A 2005 study reported that 65% of large firms offered what had come to be called diversity training (Esen 2005). In our 2002 survey of medium and large private-sector employers we find that 40% of employers offer diversity training, making it the most popular diversity program. The popularity of training has been fueled by the rise of white women in management, who advocate for training (Dobbin, Kim, and Kalev 2011). In the 1980s, diversity training acquired a new business rationale (Kelly and Dobbin 1998). Consultants and federal agencies began to argue that the workforce was becoming majority-minority with the growth of women and racial and ethnic minorities, and that training could help employees to work together (Johnston and Packer 1987). Some consultant/trainers still focused on legal compliance, while others came to focus on the business case for inclusion. Our respondents reported little variation in the method of training (97% involved live workshops), but substantial variation in whether training was mandatory or voluntary, whether it was for managers or the whole workforce, and whether the curriculum covered legal compliance or cultural inclusion. WHY DOESN’T DIVERSITY TRAINING WORK BETTER? Next we review evidence that training is generally ineffective, and discuss three explanations that have been put forward; that training reinforces stereotypes and heightens bias, that it fosters complacency, and that it makes whites feel excluded.

In their exhaustive review of 985 published and unpublished studies of prejudice reduction interventions, Paluck and Green (2009) conclude that there is little evidence that training reduces bias (e.g. Kirkpatrick and Kirkpatrick 2006; Kraiger et al. 1993). In their review of 31 organizational diversity-training studies that used either pre-test/post-test assessments or a post-test control group, Kulik and Roberson (2008) identify twenty-seven that document improved knowledge of, or attitudes toward, diversity. Yet improvements were modest, and most studies that assessed multiple behavioral and attitudinal indicators found change in only one or two. None looked at changes beyond one year out. In their review of 39 studies, Bezrukova, Joshi, and Jehn (2008) identify only five that examine long-term effects, two showing positive effects, two negative, and one no effect. Studies of the effects of diversity training on workplace outcomes, beyond attitudes or self-reported behaviors, are rare. Rynes and Rosen (1995) find that only a third of 765 human resources experts think that their diversity training has positive effects. A study of federal agencies found no effect of diversity training on the careers of women or minorities (Kellough and Naff 2004; Naff and Kellough 2003). Kalev and colleagues (2006) found that corporate diversity training had mixed, and modest, effects on management diversity.

Why does research suggest that training is ineffective? One possibility is that training activates bias, rather than giving participants the tools to suppress bias. Field and laboratory studies suggest that training may have such adverse effects, reinforcing stereotypes and eliciting backlash from white men (Egan and Bendick 2008; Naff and Kellough 2003; Sidanius et al.

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2001; MacDonald 1993; Rynes and Rosen 1995; Galinsky and Moskowitz 2000; Kulik, Perry, and Bourhis 2000). Anand and Winters (2008:361) conclude that trainees often leave “confused, angry, or with more animosity toward” other groups than they began with. Experienced trainers report that they often encounter anger and resistance (Kulik et al. 2007).

A second possibility is that training makes managers complacent by inspiring unrealistic confidence in their employers’ anti-discrimination efforts. Several studies suggest this may be the case. In the lab, Kaiser and colleagues (2013) find that when whites and men are told that their employers have pro-diversity measures such as training, they presume that the workplace is free of bias and react harshly to claims of discrimination. In the same vein, Castilla and Benard (2010) find that when an employer signals that it is meritocratic, subjects do not censor their own gender biases. These studies suggest that diversity training may lead decision-makers to lower scrutiny of the workplace and self-control of bias.

If training fails solely because it activates bias, or breeds complacency, we might expect training of different sorts to have similar adverse effects. However, if training also fails because it signals that the locus of motivation is extra-organizational, eliciting resistance, formats that signal extra-organization motivation should be most likely to backfire.

A third possibility is that training makes white decision-makers feel excluded. In a series of laboratory studies, Plaut and colleagues (2011) find that as compared to the message of color-blindness, the message of diversity and multiculturalism makes whites feel excluded and makes them less supportive of diversity. If training fails principally because it activates sentiments of exclusion among whites, we might expect cultural awareness training to be less effective than legal training, which emphasizes color- and gender-blindness. However, if training also fails because it signals extra-organizational motivation, legal training should be more likely to fail. TRAINING AND THE LOCUS OF MOTIVATION We draw insights from self-determination theory, which suggests that external control of behavior can backfire, causing people to think and behave in opposition to the designs of the controller. This finding has broad implications for the organizational and management literatures, where the command-and-control view still plays a large role. We develop an organizational-sociology version of locus-of-motivation theory, which suggests that external motivation for pursuing a goal is less effective than autonomous motivation (deCharms 1968; Ryan and Connell 1989; Malhotra, Galletta, and Kirsch 2008). In the workplace, there are multiple perceived sources of motivation. When the source is organizational, we suggest, managers may react positively -- organizational motivation may foster internalization and increase the efficacy of practices.

Self-determination theory suggests that autonomous choice contributes to internalization (Deci and Ryan 1985, 2002). Whether subjects control their own prejudice is, in part, a function of whether the desire to control prejudice is self-determined -- voluntary (Devine et al. 2002; Legault et al. 2007; Plant and Devine 1998). People who are autonomously motivated to control their own racial prejudice show lower levels of prejudice than people who are motivated by an external incentive or norm (Amodio, Harmon-Jones, and Devine 2003; Legault, Green-Demers, and Eadie 2009; Plant, Devine, and Peruche 2010). Plant and Devine (2001) find that whites resent pressure to control prejudice against blacks (see also Kaiser et al. 2013). Moreover, instructing subjects to suppress stereotypes backfires, making stereotypes more cognitively accessible (Galinsky and Moskowitz 2000a), and causing subjects to discriminate (Kulik, Perry, and Bourhis 2000a). Thus, external efforts to control prejudice may prevent both internalization

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and realization of the desired effect. These findings are consistent with reactance theory (Brehm and Brehm 1981), which links defiance to threats to autonomy.

Laboratory studies have focused on external versus internal (autonomous) motivation. We argue that in the workplace, people make a distinction between goals imposed by the external environment and those chosen by the organization. In the case of training, it is one thing to signal that workforce diversity is a goal of distant regulators. It is another thing to signal that workforce inclusion and diversity are important to the success of the firm. We argue that the framing of practices such as training can influence whether people perceive motivation to be autonomous, organizational, or extra-organizational, and that extra-organizational motivation will be most likely to backfire. This intuition is supported by a lab study finding that when legal regulations and sanctions are framed as the motive for a diversity program, white MBA students are more resistant than when the organization’s need to manage diversity is framed as the motive (Kidder et al. 2004; see also Ely and Thomas 2001).

In the organizational context, the motivation for a new management practice can be signaled by features of implementation. While early self-determination studies depended on naturally occurring anti-prejudice motivation, contrasting it norms activated in the lab, Legault, Gutsell, and Inzlicht (2011) show that autonomous and external motivation can be manipulated with contextual cues. One implication is that the design of diversity training may influence the perceived locus of motivation. Thus, for instance, we expect that by making attendance at diversity training voluntary, employers encourage participants to think motivation is autonomous (individual) – and autonomous choice is a strong predictor of internalization of norms. Mandatory Training and Extra-Organizational Motivation

We examine three characteristics that signal the motivation for training. First, mandatory/voluntary status, we argue, distinguishes extra-organizational from autonomous motivation. There are three arguments for making training mandatory. First, research suggests that in mandatory courses, top executives model desired behavior for underlings because everyone participates (Wiggenhorn 1990; Goldstein 1991; Noe and Ford 1991). Second, HR executives whose companies mandate diversity training assess it as more effective (Rynes and Rosen 1995). Third, voluntarism brings in the converted, and people knowledgeable about diversity, but not the skeptics; as uber-consultant R. Roosevelt Thomas argues, diversity training is “required, and if you can’t deal with that then we have to ask you to leave” (quoted in Johnson 2008, p. 412; see also Kulik et al. 2007).

But, we suggest, making training mandatory signals that the motive for offering training is extra-organizational, based in the belief that regulators and judges favor mandatory training. That belief is well founded; high-profile settlements in discrimination lawsuits, such as those federal judges approved in cases against both Texaco and Coca-Cola, require mandatory diversity training (Herman et al. 2003; Williamson et al. 2002). According to our locus-of-motivation theory, by signaling that the motive is extra-organizational, firms may elicit rebellion on the part of managers.

Building on self-determination and locus-of-motivation theories, we suggest that by making participation voluntary employers encourage trainees to perceive their participation as autonomously determined. This should reduce bias, for Legault and colleagues (2011) show that perceived motivation for reading anti-prejudice materials can be influenced and that perceived voluntarism decreases bias. Thus employers who make training voluntary should encourage internalization and see positive effects on workforce diversity.

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Overall, 29% of firms in our sample offered manager training – 80% of those courses were mandatory. Nineteen percent of firms offered all-worker training – 50% of those courses were mandatory. Another survey showed similar results; 79% of managers-only courses, and 53% of all-worker courses, were mandatory (Esen 2005). Thirteen percent offered both special manager training and all-worker training. Hypothesis 1: Mandatory diversity training sessions will lead to reductions in managerial

diversity; voluntary training sessions will lead to increases in managerial diversity. Manager Training and Extra-Organizational Motivation

The second and third training characteristics distinguish organizational from extra-organizational motivation. The second concerns the target group for training. Employers with manager training signal external, legal, motivation, by indicating that training is offered to prevent management decisions that could give rise to litigation. Court-approved discrimination settlements, indeed, typically specify manager training to prevent discrimination (Herman et al. 2003; Williamson et al. 2002). There is a long history of manager training to prevent illegal discrimination, beginning in the 1960s and early 1970s at employers such as Western Electric and the Department of Health, Education, and Welfare (Robertson 1971; Dobbin 2009). Employers who offer training for the entire workforce, by contrast, send the signal that training is motivated by management goals of improving inclusion and workgroup efficacy (Ely and Thomas 2001). We expect manager training to backfire, but all-worker training to encourage internalization and thereby promote diversity. Hypothesis 2: Manager training will lead to reductions in managerial diversity; all-worker

training will lead to increases in managerial diversity. Legal Curriculum and Extra-Organizational Motivation

The third training characteristic concerns curriculum. Legal and procedural curriculum, focusing on the risk of litigation and practices that can prevent lawsuit, signals extra-organizational motivation. Cultural awareness curriculum signals an organizational motive; improved communication and collaboration among workers. Kochan and colleagues (2003) argue that interest in promoting cultural awareness to improve workplace relations rose after the 1987 publication of Workforce 2000 (Johnston and Packer 1987), a report sponsored by Reagan’s Secretary of Labor that predicted a dramatic rise in the racial, ethnic, and gender diversity of the workforce. Yet when surveyed in the late 1990s, HR managers overwhelmingly listed legal protection as the first reason for using diversity training (Jordan 1998). While the motive for training in most organizations may in fact be legal compliance, trainer/consultants can signal this with legal curriculum, or downplay it with cultural curriculum.

According to our locus-of-motivation theory, by signaling extra-organizational motivation, legal curriculum will inhibit internalization and elicit rebellion. This prediction is consistent with case studies by Robin Ely and David Thomas (2001), who find that diversity programs operating under the discrimination and fairness paradigm, emphasizing the external legal case, are less effective than programs operating under the learning and effectiveness paradigm, emphasizing the business case, or hybrid programs (see also Thomas and Ely 1996; Anand and Winters 2008:262).

In our survey, we asked employers about the curriculum of training programs for the most common form of training – mandatory manager training. Among 207 workplaces, 25% had cultural awareness curriculum, 24% had legal and procedural curriculum, and 52% had mixed curriculum. We expect that any degree of legal content will signal that the motive for prejudice aversion is external, and will prove counterproductive.

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Hypothesis 3: Legal training curriculum will lead to reductions in managerial diversity; cultural curriculum will lead to increases in diversity.

Accountability and Motivation Thus far our theory suggests that legal regulation can be counterproductive when

managers perceive extra-organizational motivation for diversity interventions. But accountability theory suggests that external oversight can have a different effect, eliciting evaluation apprehension that leads people to scrutinize their own behavior for signs of bias (Cottrell 1972). We consider the effects of both regulatory and managerial accountability. The prospect of having to account for one’s decisions leads subjects to pay attention to the appearance of their own behavior, censoring actions that might be perceived as biased (Tetlock 1992). People censor demographic biases in making judgments (Kruglanski and Freund 1983; Gordon, Rozelle, and Baxter 1988, 1989; Bodenhausen et al. 1994). Accountability has been shown to reduce bias in both laboratory (Lerner and Tetlock 1999; Dencker 2008) and field studies (Hirsh 2009; Castilla 2008; Castilla forthcoming; Rissing and Castilla 2014; Kalev 2014). Monitoring of managers by diversity experts has been shown to catalyze particular diversity reforms and increase workforce integration (Kalev et al. 2006; Sturm 2001). Thus while locus-of-motivation theory suggests that extra-organizational motivation will activate resistance, and exacerbate discrimination, we expect that organizational and regulatory monitoring will dampen discrimination. Organizational and Regulatory Accountability

We consider two factors that may elicit evaluation apprehension; the presence of a local diversity manager and the presence of a federal contract, subjecting the employer to Department of Labor oversight.

From the early 1960s, large firms hired full-time managers to handle compliance with state equal-opportunity laws and with John F. Kennedy’s 1961 presidential order requiring federal contractors to practice “affirmative action” in the pursuit of equality. While some argue that these positions are merely symbolic (Edelman 1992), studies suggest that diversity managers improve the efficacy of diversity programs by activating accountability (Hirsh and Kmec 2009; Dencker 2008; Kalev et al. 2006) and protect underrepresented groups in downsizings (Kalev 2014). We expect that the presence of a diversity manager will help to inoculate firms against negative effects of training programs by eliciting evaluation apprehension.

The federal government has held contractors accountable for preventing employment discrimination since Kennedy’s 1961 edict. Today the Office of Federal Contract Compliance Programs (OFCCP) in the Department of Labor is charged with conducting on-site compliance reviews at federal contractor workplaces, at its discretion (Anderson 1996; Leonard 1984b; Baron, Mittman, and Newman 1991:1386; Skaggs 2001, 2008). We suggest that federal regulatory monitoring will positively moderate the effects of training programs. Some have found that actual compliance reviews have positive effects on black employment (Goldstein and Smith 1976; Kalev and Dobbin 2006; Leonard 1984c, 1984b), despite evidence that federal agencies do not review the firms that most need oversight (Leonard 1984a). But accountability theory suggests that it is not actual scrutiny of behavior that causes people to change; it is apprehension. We predict that even controlling for actual compliance reviews, and the interaction of compliance reviews with diversity training, the presence of a federal contract will elicit evaluation apprehension and positively moderate the effects of training. Hypothesis 4: Corporate diversity officers and federal contracts will positively moderate the

effects of training programs on managerial diversity.

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DATA AND METHODS We examine 805 establishments between 1971 and 2002, conducting a fixed effects

analysis of managerial composition to evaluate the effects of different sorts of diversity training programs. The dependent variables are the log odds of white, black, Hispanic, and Asian men and women in management. Our pooled time-series, cross-sectional models estimate the effects of new training programs on the log odds of each group in management. Dependent variables are lagged by one year. Fixed effects for establishments are achieved with binary variables for each workplace, using Stata’s AREG procedure. Fixed effects for year are achieved with binary variables for each but the first year. We use time-varying controls for organizational, regulatory, and labor market factors thought to affect managerial diversity, which are discussed below. Coefficients for controls are reported in appendices. We include post-program-change spells in the models, so that the coefficients represent the average effects of program changes across subsequent years. The modal training program is observed for 10 years. Data Our data come from three sources. The managerial composition data and select organizational variables come from the annual EEO-1 reports employers submit to the government. The data on training and most other organizational practices come from our own retrospective employer survey. Data on external labor market characteristics come from Bureau of Labor Statistics surveys.

EEO1 data on managerial composition. Private sector employers with more than 100 workers, and federal contractors with more than 50, are required to file annual EEO-1 reports detailing the race, ethnicity, and gender of their workers, broken down into 9 occupational categories. State and local agencies, schools, and colleges file different reports (EEOC n.d.). There are no better data on workforce composition at the organizational level (see Robinson et al. 2005). Ronald Edwards and Bliss Cartwright at the EEOC graciously gave us access to these confidential data through an Intergovernmental Personnel Act (IPA) agreement.

We examine the effects of training on different status groups in management, as the best measure of whether groups are moving into positions of authority and prestige. While the EEOC data do not distinguish different levels within management, previous studies show white women and minorities to be clustered in the lower ranks. In figures 1 and 2 we present trends in the sample between 1971 and 2002. White men held 81% of management jobs in the average establishment in 1971 and 61% in 2002. White women rose from 16% to 26%; black women from 0.4% to 1.8%, black men from 1% to 3.1%; Hispanic men from 0.6% to 2.5%; Hispanic women from 0.1% to 0.9%; Asian men from 0.5% to 1.8%; and Asian women from 0.1% to 0.7%. These figures understate national gains for underrepresented groups because our sample excludes groups of employers where gains were substantial; small firms, newly established firms, and the public sector.

Figures 1 and 2 About Here To ensure that our estimates are robust to extreme values, we take the log of the odds of each group being in management. We log the odds rather than the proportion because the conditional distribution of log odds is closer to normal (Fox 1997:78).1 We interpolate workforce data for three missing years — 1974, 1976, and 1977. The results are not sensitive to data interpolation.

1 Where the proportion of managers for a group was 0 or 1, we substituted !

!! for 0 and

1− !!!

for 1, where N is the total number of managers in the firm (Reskin and McBrier 2000).

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Survey data on organizational factors. To learn about employer diversity training programs, we drew a stratified random sample from the 1999 EEO-1 files and administered a survey. We selected half of that sample from establishments that were in the dataset, which begins in 1971, since at least 1980, and half from those that were in the files since at least 1992. Thus the youngest establishments in the sample existed in 1992. We selected 35% of the sample from establishments with less than 500 employees in 1999, to ensure some variation in size. We sampled from a representative group of industries: food, chemicals, electronics equipment, transportation equipment, wholesale trade, retail trade, insurance, business services, and health services, sampling no more than one establishment per parent firm. We follow establishments through changes in ownership.

The Princeton Survey Research Center surveyed human resource managers or, in firms without HR managers, general managers. We built on experience from previous surveys of human resources practices, asking about the history of practices in the target establishment (Kalleberg et al. 1996; Kelly 2000; Osterman 2000). In the first contact, we asked for the name of the person most knowledgeable about the history of human resources programs. We conducted the interview with that person. The modal respondent had 11 years of tenure.

We asked respondents when their establishments had adopted, and terminated, each of several dozen personnel practices. At the end of each interview we asked the respondent to consult records or colleagues to fill in any blanks and scheduled a follow-up call. We completed 833 interviews for a response rate of 67%, which compares favorably with other employer surveys (Kalleberg et al. 1996; Kelly 2000; Osterman 1994). By matching the survey data and the EEO-1 data, we created a dataset with establishment-year spells. We exclude 28 cases with large numbers of missing key variables, or with missing data on training program characteristics, for a final dataset of 805 establishments and 16,570 establishment-years. We set a minimum of five years of data per establishment. The maximum is thirty-two years, and the median is twenty-five years.

In Table 1 we report means and standard deviations for key variables. For missing values on the dependent variables in scattered years, we interpolate. We impute missing values for years of adoption of independent variables using OLS regression based on industry, establishment age, and headquarters status. We do not impute missing values for training programs or their features.

Table 1 About Here Data on state unemployment, industry size, and the composition of both industry and

state labor markets come from the Bureau of Labor Statistics. Data on federal contractor status and proportion of industry establishments with contracts come from EEO-1 reports.

In the survey, we first asked “Has (organization name) ever had a training program about diversity issues for all workers?” and then asked “Has (organization name) ever had a diversity-training program for managers?” Figure 3 presents the proportion of firms in the sample with diversity training for managers, and for all workers, in 2002. Nineteen percent of firms had manager training, 6% had all-worker training, and 13% had both. We followed up the all-worker-training question by asking “Is diversity training mandatory?” and the manager-training question with “Is this training mandatory for managers?” Based on the pilot, which showed manager training to be more common than all-worker training, we estimated that cell sizes would

Results are robust to other substitutions, and we include a dummy variable for firms with no group members in management.

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be adequate for analysis of curriculum variation for manager training. Thus we followed up the manager-training questions with “Which of the following topics best describes” what is covered, allowing respondents to choose one, two, or three answers from “the legal context of discrimination,” “procedures for fair recruitment and selection”, and “cultural information on minorities, immigrants, and other groups.” Most respondents who chose “legal context” also chose “procedures” and so we combined those answers into one, binary, variable.

In Figure 4 we report the characteristics of manager training programs in 2002. Twenty percent of firms made manager training voluntary. Cell sizes are too small to analyze training curriculum within those that make training voluntary. But 80% of firms with manager training make it mandatory; half of those offer mixed curriculum, a quarter offer cultural curriculum, and a quarter offer legal curriculum.

We have time-varying data on training target groups (managers versus all workers) but asked about mandatory/voluntary and curriculum only for the survey year because in the pilot, we found that respondents rarely reported change over time in these characteristics. This may introduce some error, because employers may have changed the mandate or curriculum. But the effect of measurement error should be to increase standard errors, and make significant effects more difficult to discover, and below we report a clear pattern of significant coefficients.

Figure 3 About Here Figure 4 About Here

Controls for Other Factors In addition to using fixed effects for establishments to control factors that do not vary

over time, and for year to control environmental shifts that affect all employers, we account for factors that vary across firms or sectors with controls representing the organizational, labor market, and regulatory forces shown to matter in previous studies.

Organizational structures. We control for the percent of the workforce in management jobs, and firm size, because growth in management jobs and firm growth signal that a company offers promotion opportunities, and increase competition for jobs – research suggests that white men are favored in situations of expanded competition (Reskin and Roos 1990). Growth also creates opportunities for increasing management diversity through hiring (Baron et al. 1991). White women have been shown to benefit from the growth in management positions (Konrad and Linnehan 1995; Leonard 1990, p. 52). Unionization has been found to slow integration through seniority provisions that favor incumbent employees for management posts (but see Blau and Beller 1992; Kelly 2003; Milkman 1985; Leonard 1985). Unionization is coded 1 when the establishment has a contract. Personnel formalization is thought to advantage women and minorities (Reskin and McBrier 2000). Formal human resources policies is a count of hiring, promotion, and discharge guidelines; job descriptions; promotion ladders; performance evaluations; pay grade systems; and internal job posting. Firms with human resources departments and legal counsel have been more attentive to issues of equal opportunity (Edelman and Petterson 1999; Holzer and Neumark 2000). We control for non-union grievance procedures which provide an avenue for civil-rights complaints (Edelman 1990), and for skill tests for managers, which are thought to prevent discrimination. Attorneys are thought to be attentive to civil rights law, and so we control for both in-house attorneys and attorneys on retainer.

Diversity practices. We control for the diversity-management initiatives examined in previous work (Kalev et al. 2006); special recruitment for women and minority managers, work-family accommodations, affirmative action plan, diversity manager, diversity committee/taskforce, diversity ratings for managers, affinity networking programs, diversity

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mentoring. Work-family policies counts paid maternity leave, paid paternity leave, flextime policies and top management support for work-family programs as assessed by survey respondents (Williams 2000).

Legal environment. Department of Labor compliance reviews of federal contractors are controlled with a binary variable representing past review activity. Discrimination lawsuits and discrimination charges to the government are also controlled in a binary variable. Reviews, lawsuits, and charges have been shown to raise diversity at the firm and industry level (Baron et al. 1991; Kalev and Dobbin 2006; (Skaggs 2001, 2008) Goldstein and Smith 1976; Leonard 1984a; Leonard 1984b; Leonard 1984c). More than a third of establishment-spells had previously faced at least one lawsuit, more than a third had faced an EEOC charge, and nearly 15% had faced a compliance review (only contractors are subject to compliance reviews).

Top management composition. Firms with diverse top management teams appear to hire more diverse managers (Elliot and Smith 2004; Kanter 1977; Tomaskovic-Devey 1993). Percent of top managers who are minorities and who are women are calculated based on the top ten managers. We asked respondents about the percent at 10 year intervals and interpolated values for intervening years.

Labor market and economy. Corporate diversity is affected by internal and external labor pools (Cohen, Broschak, and Haveman 1998; Shenhav and Haberfeld 1992). The diversity of the establishment’s internal labor pool is measured with two variables based on the EEO-1 reports: the proportion of the status group in non-managerial jobs and in the core job. The core job is the single biggest job category in the establishment. We include a variable coded 1 when there are no members of the focal group in management. For the external labor pool, we control for the representation of each of six groups in the industry labor market (2-digit Standard Industrial Code) and in the state labor market, from the Current Population Survey. The six groups are white, black, and Hispanic men and women. We omit Asia men and women because the CPS data do not cover Asians for the entire period under observation. However, we tested for the sensitivity of models to the exclusion of labor market data, by excluding data for all groups, and results were robust.

We use the industry’s proportion of government contractors (based on EEO-1 data) to measure demand for underrepresented workers in industries subject to affirmative-action regulations. Industry employment captures changing demand for workers. Growing industries can offer new job opportunities, yet women and minorities have historically been relegated to less dynamic sectors (Hodson and Kaufman 1982). We control for state unemployment, because in recessions, minorities and women are less likely to be hired. Method

We pool the cross-sectional observations, using time series models (Hicks 1994; Hsiao 1986). To account for stable establishment characteristics we introduce fixed effects (Budig and England 2001; Western 2002), using the Stata AREG procedure. Fixed effects for year are achieved with binary variables for each year but the first, 1971. The establishment fixed effects help us to isolate the effects of changes in causal variables, and to deal with heteroskedasticity arising from the cross-sectional and temporal aspects of the pooled data.

Because the eight dependent variables are related, their error terms are likely correlated. Our results are robust to taking into account covariance between the errors FINDINGS

We present three sets of models, examining the most common combinations of manager/all-worker training, mandatory/voluntary training, and cultural/hybrid/legal training

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curriculum. For combinations we omit, estimated cell sizes based on the pilot were too small to permit analysis (hence we omitted subquestions), or actual cell sizes were too small. The sequence of models permits us to test all hypotheses.

In the first set of models we compare manager training with all-worker training. We expect that manager training signals that the impetus is extra-organizational (regulatory compliance) and that this elicits resistance and reduces managerial diversity. We expect that all-worker training signals that the impetus is organizational, and that this will have positive effects on managerial diversity. In the second set of models, we compare voluntary versus mandatory versions of manager training. We expect that voluntarism will encourage people to think that their motivation to participate is autonomous and that mandatory training will encourage them to think the motivation is extra-organizational, or regulatory. In the third set of models, we compare legal, cultural, and mixed curriculum for the most common form of training, mandatory manager training. We expect that any mention of the law will signal extra-organizational motivation, and will lead training to backfire.

We interact training characteristics in each set of models with federal contractor status and the presence of a diversity manager to test whether regulatory, and organizational, accountability positively moderate the effects of diversity training. About fifty percent of establishment-year spells report federal contracts, and about ten percent report diversity managers. In each set of models, a significant positive coefficient indicates that a group’s share of management jobs increases in the years following the new training program. A significant negative coefficient indicates that the group loses. Significant coefficients consistent with predictions are bold, and those inconsistent are bold italic.

Table 2 reports changes in management diversity following the adoption of manager training and all-worker training. Without accountability, manager training shows negative effects on white and black women and on Asian men and women. The interaction with government contract produces positive coefficients that eliminate the negative effects on Asian women and turns the effect on Asian men to positive and significant (.063 p<.05). We use the LINCOM procedure in STATA to estimate the joint effects of factors included in interactions. The interaction produces an unexpected negative coefficient for Hispanic women, but the net effect of manager training plus its interaction is non-significant. A contract turns the effect of diversity training positive only for Asian Men.

The interaction with diversity manager produces positive effects on Hispanic men and women and Asian women. For Hispanic men, the net effect of manager training plus its interaction with diversity manager is non-significant. For Hispanic women, the net effect is a coefficient of .240 (p<.001). For Asian women, net effect of manager training plus its interaction with diversity is non-significant. The presence of a diversity manager eliminates some negative effects, but turns the effect positive only for Hispanic women.

All-worker training shows no effects in the absence of accountability, but the contractor interaction produces positive coefficients for black and Asian men. For both groups, the combination of all-worker training plus its interaction with contract is also significant. Moreover, the combination of these variables for White Men is negative (0.085 p<.05). The diversity-manager interaction produces no significant effects, but the combination of all-worker training and its interaction with diversity manager is significant (0.163 p<.05). In support of Hypothesis 2, manager training appears to elicit backlash. The finding for all-worker training does not support the hypothesis – all-worker training does not promote diversity. In support of Hypothesis 4, both regulatory and organizational accountability appear to positively moderate

12

the effects of training, eliminating some negative effects of manager training, and catalyzing positive effects for both types of training for certain groups.

In Table 3, we examine the effects of mandatory versus voluntary diversity training for managers. Eighty percent of the 258 manager-training programs were mandatory. In the first row, we see that mandatory manager training had negative effects on all underrepresented groups but Hispanic women. Again, accountability moderates those effects. The government contract interaction produces significant positive effects for black and Hispanic men, as well as for Asian men and women. LINCOM results show that a government contract eliminates the negative effect on black men, reduces the effect on black women to -.079 (p<.01), eliminates the negative effect on Hispanic men, turns the negative effect on Asian men positive (.057, p<.05), and eliminates the negative effect on Asian women. In firms with government contracts, in other words, mandatory manager training has one negative effect (black women) and one positive effect (Asian men). In interaction with diversity manager, mandatory manager training shows significant positive effects on Hispanic men and women and on Asian women. The net (combined) effect of mandatory manager training and diversity manager is to increase Hispanic women in management (.292, p<.001) and reduce Asian men (-.162, p<.01).

Voluntary manager training shows positive effects on black men and Hispanic women. The interaction with government contract produces an unanticipated negative effect on black men (and a non-significant combined effect), as well as significant positive effects for Asian men and women, as well as significant linear combinations of effects that similar in magnitude and significant to the interactions. The interaction with diversity manager produced no significant effects. All-worker diversity training shows positive effects for black and Asian men which, we saw in Table 2, were brought about by the joint presence of all-worker training and a federal contract.

When we broke all-worker diversity training into mandatory and voluntary, with and without federal contract and diversity manager, cell sizes became very small. Only 25 firms had voluntary all-worker training, and only 4 of those had diversity managers. Few coefficients were significant, and there was no clear pattern. We do not report those results.

In Table 4 we examine variation in curriculum for the most common form of diversity training; mandatory manager training. The effects of curriculum are striking. In the absence of accountability, cultural training shows one negative effect, on Asian women. Legal and cultural curriculum combined show negative effects on all minority groups but Hispanic women. Legal curriculum by itself shows a positive effect on White men, and negative effects on all other groups but black men and Hispanic women. Without accountability, it appears that any legal curriculum in mandatory manager training produces broad backlash.

Regulatory and organizational accountability eliminate a number of negative effects and produce some positive net effects. First, the interaction between cultural training and federal contract produces positive coefficients for black and Asian men – the linear combination of coefficients black men is .213 (p<.001) and the combination for Asian men is .112 (p<.05). Moreover, the linear combination of cultural curriculum and its interaction with government contract produces a significant negative effect for white men (-.174, p<.05) and a significant ositive effect for white women (.280, p<.01). Second, the interaction cultural curriculum*diversity manager produces two unanticipated effects, positive for white men and negative for white women, but the linear combinations of these with “cultural curriculum” are non-significant. The interaction with diversity manager produces positive coefficients for Hispanic men and women and for Asian women, and significant positive combined effects, at

13

.585 (p<.001), .488 (p<.001), and .546 (p<.001) respectively. Third, for legal and cultural curriculum, interactions with government contract produce

one unanticipated negative coefficient (Hispanic women) and one positive effect (Asian men). But combined, legal/cultural curriculum and its interaction with contract show only 2 significant effects in the LINCOM results, for black men (-.149, p<.001) and black women (-.107, p<.01). Thus, a government contract eliminates negative effects for Hispanic men and Asian men and women. Fourth, the interaction legal/cultural curriculum*diversity manager produces positive effects for Hispanic and Asian women. Combined, legal/cultural curriculum and its interaction with diversity manager produces negative effects on black women (-.165, p<.05) and Asian men (-.216, p<.01) and a positive effect on Hispanic Women (.278, p<.001). In short, diversity managers eliminate negative effects of legal/cultural curriculum for black men, Hispanic men, and Asian women and create a positive effect for Hispanic women.

Fifth, government contract produces significant positive coefficients in interaction with legal curriculum for white women, black men, Hispanic men, and Asian men and women. When we combine the coefficients in LINCOM, only the coefficient for white women remains significant (-.106, p<.05). Government contract thus eliminates five of the six adverse effects of legal curriculum, and reduces the sixth. Finally, the interaction legal-curriculum*diversity-manager produces a significant positive coefficient for black men, and the combined coefficients (LINCOM) suggest that under a diversity manager, legal curriculum has four adverse effects (increasing white men, and decreasing white women and Asian men and women) rather than the six we saw for legal curriculum on its own.

It appears that when diversity training emphasizes the law, managers resist the goal of diversity and actually hire more white men and fewer women and minorities for management than they would have without the training. By contrast, when training curriculum emphasizes only cultural awareness, managers are less likely to resist. Both regulatory and organizational accountability appear to dampen the negative effects of legal curriculum.

In this model, the non-interacted voluntary manager training shows positive effects on black and Hispanic as well as Asian and women. All-worker diversity training continues to show positive effects on black and Asian men.

Across the models there are 61 significant coefficients that support our hypotheses. Six scattered coefficients run counter to hypotheses. None of these six is significant at p<.01, and the significant interactions do not produce significant effects in their linear combinations with the main training program effects.

Controls are reported in the appendices. Other diversity measures show considerably stronger, and more consistent, effects than even the best-designed diversity training program. The other reforms are also measured with binary variables. It appears that special recruitment programs, diversity task forces, and mentoring programs do more to promote diversity than training does. Diversity managers themselves have strong positive effects, net of the effects of the programs included in the models, as we see in tables 2, 3, and 4. Robustness Tests

Fixed effects account for unmeasured differences across years and establishments. To further examine whether firms that adopt specific training programs differ from their peers in unmeasured ways that affect diversity, we re-ran models separately for each training program using only firms that at some point adopted the program. The results reported in tables 2, 3, and 4 held up, suggesting that unobserved differences between adopters and non-adopters of particular training programs are not driving the results.

14

To examine the robustness of the results to within-unit serial correlation, we tested whether each error is partially dependent on the error of the previous year (AR(1)). We found no evidence that serial correlation affects the findings.

We considered the possibility that some of the non-effects resulted from including large and small firms or different regulatory periods in the same analysis. Interactions did not reveal size-specific or presidential-administration-specific effects.

We also looked into the possibility that an unmeasured change at the firm level, such as a new CEO, brought about new training programs changes in diversity. For each reform, we omitted post-adoption years and ran identical models, adding a placebo binary variable T equal to 1 in the three years before policy adoption (Heckman and Hotz 1989). This approach offers a stringent test for selection bias. If T shows a significant effect in the same direction as the program effect, unobserved differences between program adopters and non-adopters may be responsible for observed policy effects. T is significant for several reform/group combinations but the overall pattern of our findings is robust. CONCLUSION We argue that managerial pursuit of organizational goals is shaped by the perceived locus of motivation for those goals, which may be extra-organizational (e.g. regulatory), organizational (maangement initiative), or individual (autonomous choice). Psychologists have shown that when the perceived locus of motivation for pursuing a goal is individual, subjects are more likely to internalize the goal than when it is external. People frequently rebel against perceived external control efforts, as self-determination theorists have found in the lab and job-autonomy theorists have found in the field (Deci and Ryan 1985, 2002; Hodson 1996). We argue that characteristics of organizational practices, such as diversity training, often signal loci of motivation. Thus special manager training indicates that the goal is to prevent litigation, mandatory training signals that the goal is to appease judges, and legal curriculum suggests that the goal is compliance. These formats, we suggest, will make trainees think the motivation for training is extra-organizational and resist the message. By contrast, voluntary training will elicit sentiments of autonomous, individual choice, encouraging internalization. And both all-worker training and cultural curriculum will signal organizational, not regulatory, motivation, also encouraging internalization. We thus expand locus-of-motivation theory for the organizational context, suggesting that employees will internalize goals they perceive as important to their organization as well as those they perceive as autonomously chosen. We draw implications from this theory for the pattern of effects of diversity training, suggesting that mandatory training, manager training, and legal curriculum will lead to reductions in managerial diversity. Voluntary and all-worker training, and cultural curriculum, are more likely to promote diversity. The findings support this pattern. Others have suggested different reasons for the failure of training. First, that training strengthens stereotypes, rather than giving people tools to fight them (Egan and Bendick 2008; Sidanius, Devereux, and Pratto 2001; Galinsky and Moskowitz 2000b; Kulik, Perry, and Bourhis 2000b). Second, that training breeds complacency, which causes organizational decision-makers to let down their guard and practice or permit discrimination (Kaiser et al. 2013; Castilla and Benard 2010). Third, that multicultural training makes white decision-makers feel excluded, and reduces their commitment to diversity (Plaut et al. 2011). If stereotype-activation or complacency were the sole mechanisms causing diversity training to fail, we would expect to find different training formats and curricula to be equally ineffective. If white sentiments of exclusion based on the message of multiculturalism were the sole mechanism, we would expect cultural curriculum to be less

15

effective than legal training. Instead, there is a clear pattern of effects across the different formats and curricula that supports our organizational locus-of-motivation theory. We hypothesized that accountability would positively moderate the effects of training programs among employers with federal contracts or full-time diversity managers by eliciting “evaluation apprehension,” which makes managers sensitive to how their decisions appear to monitors. Results supported this hypothesis as well. In fact, if it were not for accountability, the typical training program would have significant negative effects. Mandatory manager training with legal curriculum accounts for over half of training programs, and without accountability, such programs have broad adverse effects on managerial diversity. Accountability to federal regulators and local diversity managers moderates these negative effects in many firms, and in those firms training is a wash. In our sample, 57% (89) of employers with mandatory manager training and legal curriculum held federal contracts, and 17% (26) had full-time diversity officers. That diversity training appears to do little to promote diversity in the management ranks, and frequently backfires, is troubling given the role it plays in equal opportunity programs backed by both the private sector and government. Training is regularly at the top of private best-diversity-practices lists (Catalyst 1998; Society for Human Resources Management 2004). Mandatory legal training is also at the top of the Equal Employment Opportunity Commission’s best-practices list for racial equality, which begins: “Train Human Resources managers and all employees on EEO laws” (EEOC 2015).

Attorneys and judges have also championed mandatory training in the law and in procedures to prevent discrimination. High-profile settlements in discrimination cases, designed by attorneys and approved by judges, frequently allocate the lion’s share of the budget for remedial action to training. The court-approved 2000 settlement in the $192 million Coca-Cola race case called for mandatory “Equal Opportunity Training” for managers and, separately, diversity awareness training for all employees (Herman et al. 2003). Our results suggest that Coca-Cola would have been well advised to either skip training or, if intent on proceeding with it, to not offer special training to managers, not focus on equal opportunity laws, and not make training mandatory.

The court-approved $176 million settlement in the 1997 Texaco race case called for mandatory diversity training for all employees, and then ongoing voluntary diversity training for supervisors. In its final report, the court-appointed external task force argued that the ongoing supervisor training should be mandatory, “ChevronTexaco actively encourages diversity training; however, those who need it most may not be the most likely to respond to such encouragement. We therefore reiterate our strong belief that ongoing diversity training for existing supervisors is critical, and we encourage the Company to provide such training on a mandatory basis” (Williamson et al. 2002:9). Our results suggest that Texaco should not heed that advice.

The Coca-Cola and Texaco settlements are far from unique. A study of 502 class-action consent decrees between 2000 and 2008 found that 89% required diversity training – typically mandatory training. The other popular remedies were posting the anti-discrimination policy (89%), writing an anti-discrimination policy (62%), and creating a grievance procedure (38%) (Hegewisch, Deitch, and Murphy 2011:26; see also Schlanger and Kim 2013). Courts appear to put a lot of confidence in diversity training. Our results suggest that they should not.

These findings should serve as a wake-up call to proponents of workplace equity because they show that many training programs have done more harm than good. One estimate put the

16

annual cost of diversity programs at $8 billion (Fay 2003), and our analyses suggest that much of that money is wasted on training that elicits resistance. Companies can, however, tweak their training programs and turn the tide. Some studies suggest that employers have noticed that mandatory training can elicit backlash (Esen 2005; Kulik and Roberson 2008). Still, in our sample 80% of employers with manager training make it mandatory, and other recent studies show that 60% to 70% make it mandatory (Esen 2005; The New York Times Company 2007). We see little evidence of a groundswell of support for making training voluntary, and leading diversity consultants such as R. Roosevelt Thomas and DiversityInc still insist that it must be obligatory to be effective (Frankel 2007; Johnson 2008:412) Some companies have also recognized resistance to training about legal compliance. Bell and Kravitz (2008, p. 303) find that many employers feel that they “must provide training about antidiscrimination law” and question whether diversity training is “more likely to have positive effects if it is separated from the antidiscrimination training temporally and through the use of different trainers.” Many companies have separated legal compliance and diversity management functions, and some, like the food service giant Sodexo, have separated diversity from legal compliance training (Anand and Winters 2008, p. 264). Whether that will quell backlash is an open question, but our findings would seem to suggest that any legal training is too much, for firms that combine cultural with legal training see only negative effects. There is still much debate among practitioners about the most effective training strategies, and new strategies appear regularly. Some experts have recently advocated educating managers about implicit bias research (Banaji et al. 1993; Greenwald and Krieger 2006; Greenwald et al. 2003) as a way to help them understand how cognition shapes our response to people (Egan and Bendick 2008; Stewart et al. 2008). Google has taken up the challenge by developing a diversity training program for its 49,000 employees based on implicit association research (Manjoo 2014). This strategy may make managers aware of their own unconscious biases. Future research might look into whether this, or other alternatives now being put into place, leads to actual changes in workforce composition. Our tort and regulatory systems are rooted in the assumption that legal threat will cause individuals to pursue societal goals. If legal threat actually impedes internalization of those goals, as our research suggests it may, then this regulatory strategy may be misguided. The psychological literature suggests that we may overestimate the importance of homo economicus, and underestimate the role of social and psychological factors in shaping the behavior of managers and workers (Heath 1999). Some have discounted this area of research as tantamount to social engineering, and yet it will be key to understanding the failure of regulatory interventions to achieve the desired results. Regulators need to pay greater heed to social science research on the effects of public policies and the organizational reforms they stimulate (Apfelbaum and Sommers 2013).

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Table 1: Means and Standard Deviations of Key Variables Used in the Analysis. N=16,570.2

2 Descriptive statistics for control variables are listed in Appendix Table A.

Outcome VariablesProportion of Managers who are:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic WomenAsian MenAsian Women

Diversity Training VariablesManager Training Mandatory Manager Training Voluntary Manager Training Mandatory Manager Training, CulturalMandatory Manager Training, Legal/culturalMandatory Manager Training, LegalAll-worker Training

Diversity ManagerFederal Contract

Mean S.D. Min Max Type

0.702 0.236 0.000 1 Continuous 0.219 0.213 0.000 1 Continuous 0.024 0.057 0.000 1 Continuous 0.013 0.040 0.000 0.667 Continuous 0.017 0.050 0.000 0.714 Continuous 0.005 0.021 0.000 0.500 Continuous 0.012 0.042 0.000 0.851 Continuous 0.004 0.018 0.000 0.500 Continuous

0.094 0.292 0 1 Binary0.075 0.264 0 1 Binary0.019 0.135 0 1 Binary0.017 0.130 0 1 Binary0.042 0.200 0 1 Binary0.016 0.127 0 1 Binary0.055 0.228 0 1 Binary

0.061 0.239 0 1 Binary0.491 0.500 0 1 Binary

SurveySurvey

Survey

SurveySurvey

EEO-1EEO-1EEO-1EEO-1EEO-1

Data

EEO-1EEO-1EEO-1

Survey

Survey

Survey

EEO-1

18

Table 2: Fixed-Effects Estimates of the Log Odds of Groups in Management following Diversity Training Programs, 1971-2002. Controls Presented in Appendix Table B.

Diversity TrainingManager Training -0.012 -0.071* 0.018 -0.116*** -0.031 0.050 -0.125*** -0.101*** (0.033) (0.034) (0.031) (0.029) (0.030) (0.027) (0.030) (0.026)

* Federal Contract 0.015 0.050 -0.015 0.049 0.061 -0.073* 0.188*** 0.126***(0.043) (0.044) (0.040) (0.037) (0.038) (0.034) (0.038) (0.034)

White Men

White Women

Black Men

Black Women

Hispanic Men

Hispanic Women

Asian Men

Asian Women

* Diversity Manager 0.042 0.033 0.028 2.64E-04 0.125* 0.190*** 0.039 0.118*(0.067) (0.068) (0.063) (0.058) (0.060) (0.054) (0.060) (0.053)

All-Worker Training -0.003 0.029 -0.036 0.039 -0.017 -0.064 -0.031 -0.010(0.043) (0.044) (0.040) (0.037) (0.038) (0.034) (0.038) (0.034)

* Federal Contract -0.081 0.013 0.134** -0.038 0.049 0.020 0.131** 0.025(0.054) (0.055) (0.051) (0.047) (0.048) (0.043) (0.048) (0.043)

* Diversity Manager 0.091 -0.072 0.040 0.124 -0.018 0.089 -0.027 0.116(0.077) (0.078) (0.072) (0.066) (0.068) (0.062) (0.068) (0.061)

MonitoringFederal Contract 0.033 0.010 -0.024 -0.048** -0.076*** -0.041** -0.069*** -0.034*

(0.019) (0.019) (0.017) (0.016) (0.017) (0.015) (0.017) (0.015)

Diversity Manager -0.084** 0.128*** 0.180*** 0.096*** 0.012 0.128*** 0.090** 0.069**(0.032) (0.032) (0.030) (0.028) (0.028) (0.026) (0.028) (0.025)

Constant 0.141 -0.686 2.130*** 2.930*** 2.714*** 2.925*** 2.923*** 3.216***(0.346) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.867 0.873 0.819 0.859 0.859 0.881 0.852 0.886

***p<0.001; ** p<0.01; * p<0.05; (two tailed test)

Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 76. N (organization-year; organizations)= 16,570; 805.

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Table 3: Fixed-Effects Estimates of the Log Odds of Groups in Management following Diversity Training Programs, 1971-2002. Controls Presented in Appendix Table C.

Diversity TrainingMandatory Manager Training -0.004 -0.080* -0.083* -0.144***-0.068* 0.016 -0.195***-0.154*** (0.035) (0.036) (0.033) (0.030) (0.031) (0.028) (0.031) (0.028)

* Federal Contract -0.007 0.058 0.088* 0.065 0.080* -0.050 0.252*** 0.121***(0.041) (0.042) (0.039) (0.036) (0.037) (0.033) (0.037) (0.032)

White Men

White Women

Black Men

Black Women

Hispanic Men

Hispanic Women

Asian Men

Asian Women

* Diversity Manager 0.094 0.007 0.067 0.058 0.143** 0.276*** 0.032 0.236***(0.055) (0.056) (0.052) (0.048) (0.049) (0.044) (0.049) (0.043)

Voluntary Manager Training 0.012 -0.035 0.207*** -0.020 0.037 0.109* -0.020 -0.013(0.061) (0.062) (0.057) (0.053) (0.054) (0.049) (0.054) (0.048)

* Federal Contract -0.034 0.040 -0.171* -0.080 0.106 -0.088 0.189* 0.288***(0.088) (0.090) (0.083) (0.076) (0.078) (0.071) (0.078) (0.070)

* Diversity Manager 0.016 -0.085 0.111 0.134 -0.005 0.035 0.120 -0.121(0.132) (0.135) (0.124) (0.115) (0.118) (0.106) (0.118) (0.105)

All-Worker Training -0.037 0.028 0.064* 0.043 0.015 -0.037 0.053* 0.031(0.029) (0.030) (0.027) (0.025) (0.026) (0.023) (0.026) (0.023)

Constant 0.138 -0.670 2.171*** 2.932*** 2.728*** 2.940*** 2.944*** 3.207***(0.347) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.867 0.873 0.819 0.859 0.859 0.882 0.852 0.886

***p<0.001; ** p<0.01; * p<0.05; (two tailed test)

Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 77. N (organization-year; organizations)= 16,570; 805.

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Table 4: Fixed-Effects Estimates of the Log Odds of Groups in Management following Diversity Training Programs, 1971-2002. Controls Presented in Appendix Table D.

Diversity TrainingVoluntary Manager Training -0.007 -0.019 0.127** -0.047 0.092* 0.066 0.091* 0.126*** (0.043) (0.043) (0.040) (0.037) (0.038) (0.034) (0.038) (0.034)

Mandatory Manager TrainingCultural Curriculum -0.070 0.073 0.014 -0.038 0.103 -0.024 -0.056 -0.106*

(0.066) (0.067) (0.062) (0.057) (0.059) (0.053) (0.059) (0.052)

* Federal Contract -0.105 0.108 0.200* 0.020 -0.101 -0.066 0.168* 0.024(0.085) (0.086) (0.080) (0.074) (0.075) (0.068) (0.076) (0.067)

White Men

White Women

Black Men

Black Women

Hispanic Men

Hispanic Women

Asian Men

Asian Women

* Diversity Manager 0.269* -0.259* -0.008 0.166 0.481*** 0.512*** 0.178 0.546***(0.112) (0.115) (0.106) (0.098) (0.100) (0.090) (0.100) (0.089)

Legal & Cultural Curriculum -0.051 -0.044 -0.142***-0.184***-0.101* 0.033 -0.198***-0.096**(0.044) (0.045) (0.041) (0.038) (0.039) (0.036) (0.039) (0.035)

* Federal Contract 0.055 0.004 -0.007 0.077 0.075 -0.097* 0.214*** 0.079(0.055) (0.056) (0.052) (0.048) (0.049) (0.044) (0.049) (0.043)

* Diversity Manager 0.040 0.109 0.010 0.018 0.102 0.245*** -0.018 0.172**(0.069) (0.071) (0.065) (0.060) (0.062) (0.056) (0.062) (0.055)

Legal Curriculum 0.225** -0.377***-0.092 -0.151* -0.154* 0.014 -0.379***-0.365***(0.080) (0.081) (0.075) (0.069) (0.071) (0.064) (0.071) (0.063)

* Federal Contract -0.162 0.271** 0.168* 0.064 0.207* 0.022 0.440*** 0.327***(0.091) (0.093) (0.085) (0.079) (0.081) (0.073) (0.081) (0.072)

* Diversity Manager 0.079 -0.008 0.263* 0.099 -0.081 0.127 0.106 0.054(0.142) (0.144) (0.133) (0.123) (0.126) (0.114) (0.126) (0.112)

All-Worker Training -0.020 0.003 0.076** 0.044 0.013 -0.035 0.052* 0.021(0.030) (0.030) (0.028) (0.026) (0.026) (0.024) (0.026) (0.023)

MonitoringFederal Contract 0.030 0.013 -0.021 -0.048** -0.070*** -0.041** -0.061***-0.027

(0.019) (0.019) (0.017) (0.016) (0.017) (0.015) (0.017) (0.015)

Diversity Manager -0.078* 0.122*** 0.179*** 0.107*** 0.015 0.133*** 0.094*** 0.075**(0.031) (0.032) (0.029) (0.027) (0.028) (0.025) (0.028) (0.025)

Constant 0.165 -0.712* 2.195*** 2.908*** 2.688*** 2.925*** 2.932*** 3.172***(0.347) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.868 0.873 0.820 0.859 0.860 0.882 0.852 0.886

***p<0.001; ** p<0.01; * p<0.05; (two tailed test)

Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 81. N (organization-year; organizations)= 16,570; 805.

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Figure 1: White Men and Women in Management

Figure 2: Minority Men and Women in Management

22

Figure 3 Prevalence of Manager and All-Worker Training

19%

13%

6%

62%

Manager Training

Manager Plus All-Worker

All-Worker Training

No Training

23

Figure 4 Characteristics of Training Programs for Managers

20%

20%

41%

19%

Voluntary

Mandatory, Cultural

Mandatory, Cultural & Legal

Mandatory, Legal

24

Appendix Table A. Means and Standard Deviations of Control Variables: N=16,570.

Continued

Organizational StructuresPercent Managers Establishment sizeUnion agreementFormal HR policiesa

HR DepartmentGrievance ProcedureJob Tests for ManagersLegal DepartmentAttorney on Retainer

Diversity ProgramsSpecial Recruitment, Women/MinoritiesAffirmative Action PlanDiversity TaskforceDiversity Evaluations for ManagersNetworking ProgramMentoring ProgramWork-family Supportsb

Legal EnvironmentEEOC Charge/Title VII LawsuitCompliance ReviewTop Management Compositionc

Percent of Top Managers, MinorityPercent of Top Managers, Women

Labor Market and EconomyProportion of Non-Managers:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic WomenAsian MenAsian Women

0.123 0.088 0.001 0.789 Continuous 746 923 12 14195 Continuous

0.258 0.437 0 1 Binary4.151 2.362 0 8 Count0.830 0.376 0 1 Continuous 0.362 0.481 0 1 Binary0.090 0.287 0 1 Count0.288 0.453 0 1 Count0.343 0.475 0 1 Count

0.167 0.373 0 1 Binary0.050 0.218 0 1 Binary0.069 0.253 0 1 Binary0.067 0.250 0 1 Binary0.037 0.188 0 1 Binary0.940 0.991 0 4 Binary0.464 0.499 0 1 Count

0.157 0.364 0 1 Binary0.447 0.497 0 1 Binary

3.244 9.549 0 100 Continuous16.249 23.374 0 100 Continuous

0.411 0.253 0 1 Continuous 0.380 0.251 0 1 Continuous 0.052 0.088 0 0.940 Continuous 0.058 0.098 0 0.893 Continuous 0.052 0.137 0 1.724 Continuous 0.034 0.096 0 1.340 Continuous 0.015 0.043 0 1.074 Continuous 0.015 0.037 0 0.953 Continuous

EEO-1

SurveySurveySurveySurvey

Survey

EEO-1SurveySurvey

Survey

SurveySurvey

EEO-1

EEO-1SurveySurvey

Survey

Survey

Survey

EEO-1EEO-1EEO-1EEO-1

SurveySurvey

EEO-1EEO-1EEO-1

25

Appendix Table A Continued

Proportion in Core Job:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic WomenAsian MenAsian Women

No Managers are:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic WomenAsian MenAsian Women

Proportion Industry Labor Force:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic Women

Proportion State Labor Force:White MenWhite WomenBlack MenBlack WomenHispanic MenHispanic Women

Proportion Contractors in Industry

Industry Employment (millions)State Unemployment Rate

c Percents were obtained in 10 years intervals (2002, 1992 and 1982). Values for the years in between were interpolated using a linear function.

a Includes adoption of a formal HR department, written hiring, promotion and discharge guidelines, written job description, written promotion ladder, written performance evaluations, pay b Includes paid maternity leave, paid paternity leave, policy allowing flexible work hours and top management support for work-family balance (support was asked at 10-year intervals and interpolated).

0.389 0.317 0 1 Continuous 0.384 0.319 0 1 Continuous 0.055 0.106 0 0.963 Continuous 0.061 0.114 0 1 Continuous 0.045 0.115 0 1 Continuous 0.031 0.081 0 0.757 Continuous 0.014 0.041 0 0.819 Continuous 0.016 0.046 0 0.560 Continuous

0.007 0.081 0 1 Binary0.120 0.325 0 1 Binary0.542 0.498 0 1 Binary0.706 0.456 0 1 Binary0.657 0.475 0 1 Binary0.813 0.390 0 1 Binary0.701 0.458 0 1 Binary0.839 0.367 0 1 Binary

0.445 0.153 0.145 0.742 Continuous 0.324 0.145 0.103 0.624 Continuous 0.041 0.019 0.009 0.106 Continuous 0.042 0.025 0.004 0.119 Continuous 0.064 0.037 0.006 0.231 Continuous 0.044 0.023 0.000 0.141 Continuous

0.387 0.061 0.116 0.595 Continuous 0.353 0.063 0.093 0.496 Continuous 0.043 0.030 0 0.186 Continuous 0.048 0.034 -0.004 0.201 Continuous 0.052 0.064 0.001 0.286 Continuous 0.037 0.046 0.001 0.249 Continuous

0.492 0.226 0.061 0.821 Continuous

3.738 2.821 0.996 11.458 Continuous 6.101 2.023 2.000 18.000 Continuous

c Percents were obtained in 10 years intervals (2002, 1992 and 1982). Values for the years in between were interpolated using a linear function.

EEO-1

EEO-1EEO-1EEO-1EEO-1

EEO-1

EEO-1EEO-1EEO-1EEO-1

EEO-1EEO-1EEO-1EEO-1

EEO-1

EEO-1

CPSCPS

CPS

BLS

CPS

CPSCPS

a Includes adoption of a formal HR department, written hiring, promotion and discharge guidelines, written job description, written promotion ladder, written performance evaluations, pay

CPSCPSCPS

EEO-1

CPS

CPSCPSCPS

b Includes paid maternity leave, paid paternity leave, policy allowing flexible work hours and top management support for work-family balance (support was asked at 10-year intervals and interpolated).

26

Appendix Table B: Coefficients for Control Variables for Table 2

Continued

White Men

White Women

Black Men

Black Women

Hispanic Men

Hispanic Women

Asian Men

Asian Women

Organizational StructuresPercent Managers -0.939*** 0.451*** -3.804*** -4.279*** -3.914*** -4.710*** -4.365*** -4.603***

(0.109) (0.112) (0.102) (0.094) (0.097) (0.087) (0.097) (0.086)

Establishment Size -0.022 -0.064*** -0.499*** -0.662*** -0.616*** -0.722*** -0.656*** -0.731***(0.012) (0.012) (0.011) (0.010) (0.011) (0.010) (0.011) (0.009)

Union Agreement 0.004 -0.053 -0.107*** -0.063* 0.050 0.028 0.002 -0.024(0.034) (0.035) (0.032) (0.029) (0.030) (0.027) (0.030) (0.027)

Formal HR Policies 0.007 -0.003 -0.001 -0.003 -0.002 0.001 -0.004 -0.006(0.004) (0.004) (0.004) (0.004) (0.004) (0.003) (0.004) (0.003)

HR Department -0.029 -0.064** -0.056** -0.040* -0.052** -0.069*** -0.074*** -0.069***(0.020) (0.021) (0.019) (0.018) (0.018) (0.016) (0.018) (0.016)

Grievance Procedure 0.015 -0.053** -0.072*** -0.043** -0.051** -0.039** -0.087*** -0.028*(0.017) (0.018) (0.016) (0.015) (0.016) (0.014) (0.016) (0.014)

Job Tests for Managers -0.013 -0.063* -0.123*** -0.076*** -0.080*** -0.124*** -0.052* -0.109***(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Legal Department -0.117*** 0.177*** 0.045* -0.027 0.019 0.020 0.032 0.017(0.024) (0.024) (0.022) (0.021) (0.021) (0.019) (0.021) (0.019)

Attorney on Retainer 0.027 -0.057** 0.007 -0.031 -0.019 -0.053*** -0.039* -0.042**(0.018) (0.018) (0.017) (0.016) (0.016) (0.015) (0.016) (0.014)

Diversity ProgramsSpecial Recruitment for -0.065** 0.090*** 0.097*** 0.122*** 0.035* 0.042** 0.091*** 0.064*** Women or Minorities (0.020) (0.020) (0.019) (0.017) (0.018) (0.016) (0.018) (0.016)

Affirmative Action Plan -0.051** 0.021 0.036* 0.013 0.037* 0.018 0.038** -0.006(0.016) (0.017) (0.015) (0.014) (0.015) (0.013) (0.015) (0.013)

Diversity Taskforce -0.111*** 0.165*** 0.108*** 0.215*** 0.116*** 0.120*** 0.259*** 0.188***(0.027) (0.027) (0.025) (0.023) (0.024) (0.021) (0.024) (0.021)

Diversity Evaluations for 0.051 0.040 -0.079** -0.010 0.051* 0.016 -0.031 -0.005 Managers (0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.024) (0.021)

Networking Program -0.068** 0.081** -0.078** 0.002 0.067** 0.018 0.022 0.018(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Mentoring Program -0.026 0.016 0.055 0.173*** 0.080** 0.184*** 0.165*** 0.178***(0.033) (0.034) (0.031) (0.029) (0.030) (0.027) (0.030) (0.026)

Work-family Supports -0.051*** 0.039*** 0.004 0.026*** 0.029*** 0.030*** 0.044*** 0.038***(0.008) (0.009) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007)

Legal EnvironmentEEOC Charge/Title VII Suit -0.065*** 0.072*** 0.016 0.009 0.031* 0.026* 0.014 0.012

(0.015) (0.015) (0.014) (0.013) (0.013) (0.012) (0.013) (0.011)

Compliance Review -0.047* 0.038 0.073*** 0.014 -0.002 0.023 0.063*** 0.051***(0.019) (0.020) (0.018) (0.017) (0.017) (0.016) (0.017) (0.015)

Top Mgt. CompositionProportion Minorities in 0.097 -0.085 0.911*** 0.483*** -0.177 -0.361** -0.356** -0.432*** Top Management -0.145 -0.147 -0.137 -0.126 -0.130 -0.116 -0.130 -0.115

Proportion Women in Top -0.025 0.120 -0.182** 0.221*** 0.071 0.003 -0.069 0.039 Management -0.074 -0.075 -0.070 -0.064 -0.066 -0.060 -0.066 -0.059

27

Appendix Table B Continued

Labor Market & EconomyProportion of Group in 1.309*** 1.116*** 1.349*** 2.095*** 0.717*** 0.389** 0.391 2.122*** Non-managerial Jobs (0.086) (0.090) (0.232) (0.192) (0.119) (0.131) (0.384) (0.297)

Proportion of Group in -0.066 -0.174*** 0.291 -0.977*** 0.286 0.151 2.876*** 0.808*** Core Job (0.045) (0.047) (0.156) (0.122) (0.152) (0.156) (0.341) (0.235)

None from Group in Mgt. -0.536*** -0.565*** -0.630*** -0.680*** -0.550*** -0.551*** -0.608*** -0.591***(0.072) (0.018) (0.013) (0.013) (0.012) (0.013) (0.012) (0.013)

Industry Labor Force (log) White Men 0.085 -0.057 0.380*** 0.088 0.242** 0.078 0.334*** 0.100 (0.091) (0.093) (0.086) (0.079) (0.081) (0.073) (0.082) (0.072)

White Women -0.079 0.117 0.197** 0.129* 0.064 0.054 0.127* 0.149** (0.069) (0.071) (0.065) (0.060) (0.061) (0.055) (0.061) (0.054)

Black Men -0.104*** 0.092*** 0.018 0.026 0.012 -0.015 0.036 0.012(0.026) (0.026) (0.024) (0.022) (0.023) (0.021) (0.023) (0.020)

Black Women -0.089*** 0.042 0.093*** 0.011 0.058** 0.024 -0.008 0.044** (0.021) (0.022) (0.020) (0.019) (0.019) (0.017) (0.019) (0.017)

Hispanic Men -0.057** 0.049* 0.092*** 0.115*** 0.018 0.045** 0.003 0.082*** (0.021) (0.021) (0.019) (0.018) (0.018) (0.017) (0.019) (0.016)

Hispanic Women -0.027 0.006 0.005 0.007 0.003 -0.010 0.011 0.007 (0.017) (0.018) (0.016) (0.015) (0.015) (0.014) (0.015) (0.014)

State Labor Force White Men 0.233 -0.453 -0.242 -0.510 -1.184*** -0.632* -0.801* -0.402 (0.392) (0.400) (0.369) (0.341) (0.349) (0.315) (0.351) (0.311)

White Women -0.320 -0.030 -1.576*** -1.789*** -2.428*** -2.610*** -2.340*** -2.006*** (0.434) (0.443) (0.407) (0.377) (0.386) (0.349) (0.388) (0.345)

Black Men 1.658* -0.879 -4.516*** -2.962*** -2.504*** -1.360* -0.827 -0.546 (0.777) (0.792) (0.730) (0.675) (0.692) (0.625) (0.694) (0.616)

Black Women 0.161 0.002 1.550* 1.860** -2.065*** -2.374*** -2.056*** -1.545** (0.674) (0.688) (0.634) (0.587) (0.600) (0.542) (0.603) (0.535)

Hispanic Men -0.338 -0.888 -1.038 -1.005 -0.509 -2.023*** -2.647*** -1.288* (0.691) (0.704) (0.649) (0.600) (0.615) (0.555) (0.617) (0.548)

Hispanic Women 1.448 -1.549 -1.383 -0.046 0.614 2.842*** 2.457** 1.280 (0.837) (0.854) (0.786) (0.727) (0.745) (0.674) (0.748) (0.663)

Contractors in Industry 0.681*** -0.817*** 0.132 -0.371*** 0.112 -0.209* -0.190 -0.323**(0.129) (0.131) (0.121) (0.111) (0.115) (0.103) (0.115) (0.102)

Industry Employment 0.040*** -0.045*** -0.010* -0.001 -0.015*** -0.003 -0.015*** 0.017***-(0.004) -(0.005) -(0.004) -(0.004) -(0.005) -(0.004) -(0.005) -(0.004)

State Unemployment Rate 0.010* -0.018*** 0.004 -0.011** -0.013*** -0.008* -0.013*** -0.010**(0.004) (0.004) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003)

Constant 0.141 -0.686 2.130*** 2.930*** 2.714*** 2.925*** 2.923*** 3.216***(0.346) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.867 0.873 0.819 0.859 0.859 0.881 0.852 0.886Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 76. N (organization-year; organizations)= 16,570; 805.

28

Appendix Table C: Coefficients for Control Variables for Table 3

Continued

Organizational StructuresPercent Managers -0.939***0.451*** -3.797***-4.277***-3.913*** -4.704*** -4.363***-4.597***

(0.109) (0.112) (0.102) (0.094) (0.097) (0.087) (0.097) (0.086)

Establishment Size -0.022 -0.064***-0.499***-0.662***-0.616*** -0.722*** -0.656***-0.731***(0.012) (0.012) (0.011) (0.010) (0.011) (0.010) (0.011) (0.009)

Union Agreement 0.005 -0.054 -0.111*** -0.064* 0.046 0.026 -0.003 -0.032(0.034) (0.035) (0.032) (0.029) (0.030) (0.027) (0.030) (0.027)

Formal HR Policies 0.007 -0.003 ####### -0.003 -0.001 0.001 -0.004 -0.005(0.004) (0.004) (0.004) (0.004) (0.004) (0.003) (0.004) (0.003)

HR Department -0.029 -0.064** -0.057** -0.041* -0.052** -0.069*** -0.075***-0.070***(0.020) (0.021) (0.019) (0.018) (0.018) (0.016) (0.018) (0.016)

Grievance Procedure 0.015 -0.053** -0.072***-0.042** -0.052*** -0.039** -0.087***-0.030*(0.017) (0.018) (0.016) (0.015) (0.016) (0.014) (0.016) (0.014)

Job Tests for Managers -0.012 -0.063* -0.125***-0.076***-0.082*** -0.124*** -0.056* -0.114***(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Legal Department -0.116***0.177*** 0.045* -0.027 0.017 0.021 0.030 0.014(0.024) (0.024) (0.022) (0.021) (0.021) (0.019) (0.021) (0.019)

Attorney on Retainer 0.028 -0.057** 0.006 -0.030 -0.019 -0.053*** -0.040* -0.042**(0.018) (0.018) (0.017) (0.016) (0.016) (0.015) (0.016) (0.014)

Diversity ProgramsSpecial Recruitment for -0.066** 0.091*** 0.100*** 0.122*** 0.037* 0.042** 0.095*** 0.067*** Women or Minorities (0.020) (0.020) (0.019) (0.017) (0.018) (0.016) (0.018) (0.016)

Affirmative Action Plan -0.051** 0.021 0.036* 0.014 0.036* 0.017 0.038** -0.008(0.016) (0.017) (0.015) (0.014) (0.015) (0.013) (0.015) (0.013)

Diversity Taskforce -0.111*** 0.166*** 0.113*** 0.216*** 0.121*** 0.124*** 0.264*** 0.197***(0.027) (0.027) (0.025) (0.023) (0.024) (0.021) (0.024) (0.021)

Diversity Evaluations for 0.052 0.041 -0.078** -0.008 0.053* 0.017 -0.030 -0.001 Managers (0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.024) (0.021)

Networking Program -0.066* 0.080** -0.075** 0.006 0.067** 0.019 0.024 0.021(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Mentoring Program -0.024 0.016 0.057 0.174*** 0.081** 0.187*** 0.164*** 0.182***(0.033) (0.034) (0.031) (0.029) (0.030) (0.027) (0.030) (0.026)

Work-family Supports -0.052***0.039*** 0.004 0.026*** 0.029*** 0.030*** 0.044*** 0.038***(0.008) (0.009) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007)

Legal EnvironmentEEOC Charge/Title VII Suit -0.064***0.073*** 0.016 0.009 0.031* 0.026* 0.014 0.012

(0.015) (0.015) (0.014) (0.013) (0.013) (0.012) (0.013) (0.011)

Compliance Review -0.048* 0.038 0.074*** 0.013 -0.003 0.023 0.064*** 0.050**(0.019) (0.020) (0.018) (0.017) (0.017) (0.016) (0.017) (0.015)

Top Mgt. CompositionProportion Minorities in 0.101 -0.075 0.937*** 0.492*** -0.161 -0.345** -0.340** -0.406*** Top Management -0.145 -0.148 -0.137 -0.126 -0.130 -0.117 -0.130 -0.115

Proportion Women in Top -0.026 0.118 -0.195** 0.213*** 0.064 0.003 -0.082 0.026 Management -0.074 -0.076 -0.070 -0.064 -0.066 -0.060 -0.066 -0.059

29

Appendix Table C Continued

Labor Market & EconomyProportion of Group in 1.307*** 1.116*** 1.349*** 2.100*** 0.724*** 0.391** 0.365 2.027*** Non-managerial Jobs (0.086) (0.090) (0.232) (0.192) (0.119) (0.131) (0.384) (0.297)

Proportion of Group in -0.065 -0.174***0.295 -0.977***0.282 0.160 2.850*** 0.894*** Core Job (0.045) (0.047) (0.156) (0.122) (0.152) (0.156) (0.341) (0.235)

None from Group in Mgt. -0.539***-0.565***-0.632***-0.680***-0.551*** -0.550*** -0.610***-0.589***(0.072) (0.018) (0.013) (0.013) (0.012) (0.013) (0.012) (0.013)

Industry Labor Force (log)

White Men 0.084 -0.057 0.387*** 0.089 0.245** 0.082 0.340*** 0.105 (0.091) (0.093) (0.086) (0.079) (0.081) (0.073) (0.082) (0.072)

White Women -0.080 0.118 0.195** 0.126* 0.063 0.055 0.123* 0.146** (0.069) (0.071) (0.065) (0.060) (0.061) (0.055) (0.061) (0.054)

Black Men -0.105***0.092*** 0.021 0.027 0.013 -0.014 0.038 0.013(0.026) (0.026) (0.024) (0.022) (0.023) (0.021) (0.023) (0.020)

Black Women -0.089***0.042 0.095*** 0.011 0.059** 0.024 -0.007 0.045** (0.021) (0.022) (0.020) (0.019) (0.019) (0.017) (0.019) (0.017)

Hispanic Men -0.057** 0.049* 0.091*** 0.114*** 0.017 0.046** 0.002 0.080*** (0.021) (0.021) (0.019) (0.018) (0.018) (0.017) (0.019) (0.016)

Hispanic Women -0.027 0.006 0.005 0.007 0.003 -0.010 0.011 0.008 (0.017) (0.018) (0.016) (0.015) (0.015) (0.014) (0.015) (0.014)

State Labor Force White Men 0.227 -0.468 -0.276 -0.522 -1.194*** -0.648* -0.814* -0.402 (0.393) (0.400) (0.369) (0.341) (0.349) (0.316) (0.351) (0.311)

White Women -0.309 -0.053 -1.604***-1.778***-2.447*** -2.616*** -2.365***-2.016*** (0.434) (0.443) (0.407) (0.376) (0.386) (0.349) (0.387) (0.344)

Black Men 1.668* -0.873 -4.481***-2.941***-2.508*** -1.347* -0.828 -0.577 (0.777) (0.793) (0.730) (0.676) (0.692) (0.625) (0.694) (0.615)

Black Women 0.185 -0.036 1.463* 1.867** -2.110*** -2.399*** -2.126***-1.576** (0.674) (0.688) (0.633) (0.587) (0.600) (0.542) (0.603) (0.534)

Hispanic Men -0.316 -0.888 -1.084 -0.992 -0.523 -2.046*** -2.679***-1.294* (0.691) (0.704) (0.648) (0.600) (0.615) (0.555) (0.617) (0.547)

Hispanic Women 1.428 -1.602 -1.358 -0.039 0.584 2.848*** 2.481*** 1.256 (0.836) (0.853) (0.784) (0.726) (0.744) (0.673) (0.747) (0.662)

Contractors in Industry 0.675*** -0.812***0.148 -0.373***0.124 -0.203* -0.172 -0.303**(0.129) (0.131) (0.121) (0.111) (0.115) (0.103) (0.114) (0.102)

Industry Employment 0.040*** -0.045***-0.011* -0.001 -0.015*** -0.003 -0.015***0.017***-(0.005) -(0.005) -(0.005) -(0.004) -(0.005) -(0.004) -(0.005) -(0.004)

State Unemployment Rate 0.010* -0.018***0.004 -0.011** -0.013*** -0.008* -0.013***-0.010**(0.004) (0.004) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003)

Constant 0.138 -0.670 2.171*** 2.932*** 2.728*** 2.940*** 2.944*** 3.207***(0.347) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.867 0.873 0.819 0.859 0.859 0.882 0.852 0.886

** p<0.01; * p<0.05; (two tailed test)

Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 77. N (organization-year; organizations)= 16,570; 805.

30

Appendix Table D: Coefficients for Control Variables for Table 4

Continued

White Men

White Women

Black Men

Black Women

Hispanic Men

Hispanic Women

Asian Men

Asian Women

Organizational StructuresPercent Managers -0.942***0.451*** -3.791***-4.278***-3.924*** -4.706*** -4.362*** -4.605***

(0.109) (0.112) (0.102) (0.094) (0.097) (0.087) (0.097) (0.086)

Establishment Size -0.023 -0.063***-0.501***-0.663***-0.616*** -0.722*** -0.655*** -0.729***(0.012) (0.012) (0.011) (0.010) (0.011) (0.010) (0.011) (0.009)

Union Agreement 0.007 -0.058 -0.111*** -0.063* 0.050 0.028 -5.40E-05 -0.026(0.034) (0.035) (0.032) (0.029) (0.030) (0.027) (0.030) (0.027)

Formal HR Policies 0.008 -0.004 -0.001 -0.003 -0.002 0.001 -0.004 -0.005(0.004) (0.004) (0.004) (0.004) (0.004) (0.003) (0.004) (0.003)

HR Department -0.031 -0.061** -0.053** -0.038* -0.051** -0.070*** -0.073*** -0.071***(0.020) (0.021) (0.019) (0.018) (0.018) (0.016) (0.018) (0.016)

Grievance Procedure 0.014 -0.051** -0.076***-0.045** -0.054*** -0.040** -0.090*** -0.031*(0.017) (0.018) (0.016) (0.015) (0.016) (0.014) (0.016) (0.014)

Job Tests for Managers -0.014 -0.060* -0.125***-0.074** -0.080*** -0.125*** -0.053* -0.111***(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Legal Department -0.116***0.178*** 0.046* -0.027 0.021 0.021 0.033 0.017(0.024) (0.024) (0.022) (0.021) (0.021) (0.019) (0.021) (0.019)

Attorney on Retainer 0.026 -0.055** 0.010 -0.029 -0.019 -0.054*** -0.039* -0.043**(0.018) (0.018) (0.017) (0.016) (0.016) (0.015) (0.016) (0.014)

Diversity ProgramsSpecial Recruitment for -0.068***0.093*** 0.101*** 0.120*** 0.033 0.041* 0.090*** 0.063*** Women or Minorities (0.020) (0.021) (0.019) (0.017) (0.018) (0.016) (0.018) (0.016)

Affirmative Action Plan -0.054***0.025 0.035* 0.013 0.038** 0.016 0.040** -0.004(0.016) (0.017) (0.015) (0.014) (0.015) (0.013) (0.015) (0.013)

Diversity Taskforce -0.113***0.165*** 0.105*** 0.210*** 0.109*** 0.123*** 0.263*** 0.196***(0.027) (0.027) (0.025) (0.023) (0.024) (0.022) (0.024) (0.021)

Diversity Evaluations for 0.054* 0.035 -0.074** -0.012 0.050* 0.022 -0.033 -0.003 Managers (0.027) (0.027) (0.025) (0.023) (0.024) (0.021) (0.024) (0.021)

Networking Program -0.064* 0.077** -0.073** 0.006 0.070** 0.020 0.027 0.023(0.026) (0.027) (0.025) (0.023) (0.023) (0.021) (0.023) (0.021)

Mentoring Program -0.007 -0.008 0.048 0.177*** 0.088** 0.194*** 0.168*** 0.187***(0.034) (0.034) (0.031) (0.029) (0.030) (0.027) (0.030) (0.027)

Work-family Supports -0.052***0.039*** 0.007 0.027*** 0.029*** 0.030*** 0.044*** 0.038***(0.008) (0.009) (0.008) (0.007) (0.008) (0.007) (0.008) (0.007)

Legal EnvironmentEEOC Charge/Title VII Suit -0.065***0.074*** 0.015 0.009 0.031* 0.026* 0.015 0.014

(0.014) (0.015) (0.014) (0.013) (0.013) (0.012) (0.013) (0.011)

Compliance Review -0.048* 0.038 0.069*** 0.012 -0.001 0.022 0.068*** 0.056***(0.019) (0.020) (0.018) (0.017) (0.017) (0.016) (0.017) (0.015)

Top Mgt. CompositionProportion Minorities in 0.099 -0.079 0.915*** 0.499*** -0.139 -0.341** -0.312* -0.373** Top Management -0.145 -0.148 -0.137 -0.126 -0.130 -0.117 -0.130 -0.114

Proportion Women in Top -0.057 0.158* -0.185** 0.221*** 0.076 -0.001 -0.058 0.046 Management -0.075 -0.076 -0.070 -0.065 -0.067 -0.060 -0.067 -0.059

31

Appendix Table D Continued

Labor Market & EconomyProportion of Group in 1.300*** 1.114*** 1.316*** 2.104*** 0.712*** 0.375** 0.340 1.987*** Non-managerial Jobs (0.086) (0.090) (0.232) (0.192) (0.119) (0.131) (0.384) (0.297)

Proportion of Group in -0.061 -0.169***0.310* -0.974***0.281 0.179 2.874*** 0.875*** Core Job (0.045) (0.046) (0.156) (0.122) (0.152) (0.156) (0.341) (0.235)

None from Group in Mgt. -0.538***-0.565***-0.632***-0.681***-0.552*** -0.549*** -0.608*** -0.589***(0.072) (0.018) (0.013) (0.013) (0.012) (0.013) (0.012) (0.013)

Industry Labor Force (log)

White Men 0.078 -0.051 0.397*** 0.083 0.237** 0.087 0.339*** 0.104 (0.091) (0.093) (0.086) (0.080) (0.081) (0.073) (0.082) (0.072)

White Women -0.079 0.115 0.205** 0.129* 0.064 0.058 0.125* 0.143** (0.069) (0.071) (0.065) (0.060) (0.061) (0.055) (0.061) (0.054)

Black Men -0.106***0.092*** 0.022 0.026 0.011 -0.014 0.037 0.012(0.026) (0.026) (0.024) (0.022) (0.023) (0.021) (0.023) (0.020)

Black Women -0.087***0.039 0.093*** 0.010 0.056** 0.023 -0.009 0.042* (0.021) (0.022) (0.020) (0.019) (0.019) (0.017) (0.019) (0.017)

Hispanic Men -0.055** 0.045* 0.091*** 0.114*** 0.016 0.046** 0.002 0.079*** (0.021) (0.021) (0.019) (0.018) (0.018) (0.017) (0.019) (0.016)

Hispanic Women -0.028 0.007 0.005 0.007 0.002 -0.011 0.011 0.007 (0.017) (0.018) (0.016) (0.015) (0.015) (0.014) (0.015) (0.014)

State Labor Force White Men 0.224 -0.463 -0.295 -0.516 -1.192*** -0.628* -0.847* -0.406 (0.392) (0.400) (0.368) (0.341) (0.349) (0.315) (0.351) (0.310)

White Women -0.356 0.010 -1.578***-1.757***-2.428*** -2.611*** -2.351*** -2.011*** (0.434) (0.442) (0.407) (0.376) (0.386) (0.349) (0.387) (0.344)

Black Men 1.589* -0.783 -4.472***-2.942***-2.566*** -1.416* -0.842 -0.611 (0.778) (0.793) (0.730) (0.676) (0.692) (0.625) (0.694) (0.615)

Black Women 0.170 -0.025 1.475* 1.919** -2.043*** -2.363*** -2.079*** -1.521** (0.675) (0.688) (0.633) (0.587) (0.600) (0.542) (0.603) (0.534)

Hispanic Men -0.354 -0.838 -1.117 -0.990 -0.502 -2.054*** -2.691*** -1.263* (0.691) (0.704) (0.648) (0.600) (0.615) (0.555) (0.617) (0.547)

Hispanic Women 1.293 -1.411 -1.210 0.031 0.671 2.864*** 2.585*** 1.318* (0.836) (0.853) (0.784) (0.726) (0.744) (0.673) (0.747) (0.662)

Contractors in Industry 0.695*** -0.838***0.134 -0.376***0.122 -0.198 -0.178 -0.306**(0.129) (0.131) (0.121) (0.112) (0.115) (0.104) (0.115) (0.102)

Industry Employment 0.040*** -0.046***-0.010* -0.001 -0.016*** -0.003 -0.016*** 0.015***-(0.005) -(0.005) -(0.005) -(0.004) -(0.005) -(0.004) -(0.005) -(0.004)

State Unemployment Rate 0.010* -0.018***0.004 -0.011** -0.013*** -0.008* -0.013*** -0.010**(0.004) (0.004) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003)

Constant 0.165 -0.712* 2.195*** 2.908*** 2.688*** 2.925*** 2.932*** 3.172***(0.347) (0.357) (0.325) (0.303) (0.309) (0.279) (0.310) (0.275)

R-sq 0.868 0.873 0.820 0.859 0.860 0.882 0.852 0.886

** p<0.01; * p<0.05; (two tailed test)

Note: Data shown are coefficients from OLS regression with standard errors in parentheses. The analyses include establishment and year fixed effects (parameters for 30 binary variables for the years 1972-2001 are not shown, 1971 is the omitted year and 2002 is included only for calculating the outcome variable). All independent variables are lagged by one year, excluding proportion of managerial jobs. Number of parameters is 81. N (organization-year; organizations)= 16,570; 805.

32

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