Best PPractices oor BBest GGuesses? Assessing tthe ... · John Meyer, Trond Peterson, Daniel...

Lists of ldquobest practicesrdquo in diversity man-agement have proliferated recently

Everyone seems to have a list from the EqualEmployment Opportunity Commission (1998)

to the Presidential Glass Ceiling Commission(1995) the womenrsquos business advocacy groupCatalyst (1998) and the Society for HumanResources Management (2004) These lists are

Best PPractices oor BBest GGuesses Assessing tthe EEfficacy oof CCorporate Affirmative AAction aand DDiversity PPolicies

Alexandra Kalev Frank DobbinUniversity of California Berkeley Harvard University

Erin KellyUniversity of Minnesota

Employers have experimented with three broad approaches to promoting diversity Some

programs are designed to establish organizational responsibility for diversity others to

moderate managerial bias through training and feedback and still others to reduce the

social isolation of women and minority workers These approaches find support in

academic theories of how organizations achieve goals how stereotyping shapes hiring

and promotion and how networks influence careers This is the first systematic analysis

of their efficacy The analyses rely on federal data describing the workforces of 708

private sector establishments from 1971 to 2002 coupled with survey data on their

employment practices Efforts to moderate managerial bias through diversity training

and diversity evaluations are least effective at increasing the share of white women

black women and black men in management Efforts to attack social isolation through

mentoring and networking show modest effects Efforts to establish responsibility for

diversity lead to the broadest increases in managerial diversity Moreover organizations

that establish responsibility see better effects from diversity training and evaluations

networking and mentoring Employers subject to federal affirmative action edicts who

typically assign responsibility for compliance to a manager also see stronger effects

from some programs This work lays the foundation for an institutional theory of the

remediation of workplace inequality

AMERICAN SOCIOLOGICAL REVIEW 22006 VVOL 771 ((August589ndash617)

Direct correspondence to Alexandra Kalev RWJScholars Program University of California 140Warren Hall MC7360 Berkeley CA 94720(akalevberkeleyedu) The authors thank RonaldEdwards and Bliss Cartwright of the EqualEmployment Opportunity Commission for sharingtheir data and expertise Nicole Esparza and LeslieHinkson for help with data collection Kevin DobbinJohn Donohue Lauren Edelman Joshua GuetzkowHeather Haveman Jer ry A Jacobs Seema

Jayachandran Lawrence Katz Jordan MatsudairaJohn Meyer Trond Peterson Daniel Schrage PaulSegal Robin Stryker Donald Tomaskovic-DeveyBruce Western Chris Winship and four anonymousreviewers for suggestions and Randi Ellingboe fortechnical and editorial assistance Supported byNational Science Foundation grant 0336642 andRussell Sage Foundation grant 87-02-03 and par-tially supported by the Robert Wood Johnson Scholarsin Health Policy Research Program

Delivered by Ingenta to University of California Berkeley

Mon 25 Sep 2006 165130

loosely based on academic theories that point tocauses of workplace inequality ranging fromunwitting bias (Lemm and Banaji 1999) todependence on networks for hiring and pro-motion (Reskin and McBrier 2000) Whereasthere has been a great deal of research on thesources of inequality there has been little on theefficacy of different programs for countering itAt best ldquobest practicesrdquo are best guesses Weknow a lot about the disease of workplaceinequality but not much about the cure

We examine the effects of seven commondiversity programsmdashaffirmative action plansdiversity committees and taskforces diversitymanagers diversity training diversity evalua-tions for managers networking programs andmentoring programsmdashon the representation ofwhite men white women black women andblack men in the management ranks of privatesector firms Each of these programs may wellincrease diversity To date there has been littleevidence one way or the other This is surpris-ing given the popularity and cost of the pro-grams Our contribution is to bring to bear richnew data to theoretically distinguish three typesof diversity programs and to show that organi-zational structures allocating responsibility forchange may be more effective than programs tar-geting either managerial bias or the social iso-lation of disadvantaged groups

Previous empirical studies of antidiscrimi-nation and diversity programs have been limit-ed by data constraints Economists f irstcompared employers who are subject to affir-mative action requirements with those who arenot (Ashenfelter and Heckman 1976 Heckmanand Wolpin 1976 Leonard 1984) They lackeddata on employer programs Sociologists andeconomists studying employer programs exam-ine data at one or two points in time (but seeBaron Mittman and Newman 1991) analyzingthe effects of some programs without account-ing for others These studies indicate that someprograms may be effective but their findings areinconsistent (Baron et al 1991 Edelman andPetterson 1999 Holzer and Neumark 2000Konrad and Linnehan 1995 Leonard 1990Naff and Kellough 2003) Gender and racialsegregation has declined remarkably since the1970s when employers first adopted antidis-crimination programs (Jacobs 1989a King1992 Tomaskovic-Devey et al 2006) but there

is no hard evidence that these programs playeda role

We obtained the federal establishment-leveldata that economists have used (ie the annu-al EEO-1 reports that private sector establish-ments submit to the Equal EmploymentOpportunity Commission [EEOC]) We thensurveyed a sample of these establishments on thehistory of their personnel and diversity pro-grams so that we could analyze program effectson diversity

A strength of the EEO-1 reports is that theydetail annual employment by race ethnicityand gender in all medium and large private sec-tor workplaces A limitation is that they coveronly nine broad job categories collapsing intoldquomanagementrdquo all jobs above that of first-linesupervisor (Baron and Bielby 1985 Smith andWelch 1984) We know from previous researchthat women and African Americans are crowd-ed in the lowest ranks of management Even aswomen moved into management in the 1970sand 1980s ldquowomen managers continued to trailtheir male counterparts in both earnings andauthorityrdquo (Jacobs 1992) Thus our analysesindicate which diversity programs help womenand African Americans move at least into thebottom ranks of management and importantlywhich do not They cannot tell us whether anyof these practices help women and minorities tomove into the executive ranks

We find a clear pattern in the data Structuresestablishing responsibility (affirmative actionplans diversity committees and diversity staffpositions) are followed by significant increas-es in managerial diversity Programs that targetmanagerial stereotyping through education andfeedback (diversity training and diversity eval-uations) are not followed by increases in diver-sity Programs that address social isolationamong women and minorities (networking andmentoring programs) are followed by modestchanges The effects of these initiatives varyacross groups with white women benefitingmost followed by black women Black menbenefit least We also find that responsibilitystructures make training performance evalua-tions networking and mentoring programsmore effective Federal aff irmative actionrequirements which typically lead to assign-ment of responsibility for compliance also cat-alyze certain programs

590mdashndashAMERICAN SSOCIOLOGICAL RREVIEW


Mon 25 Sep 2006 165130

These findings support an institutional the-ory of inequality remediation that builds on keyprecepts of organizational sociology As Weber(1978 [1968]) argues executives must appointspecialists and give them authority to achievespecialized goals Thus remedies targeting indi-vidual bias or network isolation may be lesseffective than remedies that establish responsi-ble parties As neo-institutionalists (Meyer andRowan 1977) note new programs decoupledfrom everyday practice often have no impactTherefore appointing a manager or committeewith responsibility for change is likely to bemore effective than annual diversity trainingperiodic diversity evaluations or decentralizednetworking and mentoring programs As struc-tural theorists of organizational inequality claim(Baron 1984) there is more to segregation thanrogue managers exercising bias Thus appoint-ing special staff members and committees torethink hiring and promotion structures may bemore effective than training managers not to asktheir secretaries to make coffee and not toexclude minorities from football pools

The argument that organizations should struc-ture responsibility for reducing inequality mayseem commonsensical but todayrsquos populardiversity programs often focus on changingindividuals In the academy generally and inmanagement studies particularly methodolog-ical individualism now holds sway Theoristsprescribe solutions that change incentives forand beliefs of individuals with the idea thatmost problems of management are problemsof motivation rather than structure Thus themost popular program that is not federally man-dated is diversity training designed to attackbias Managerial bias is also the target of diver-sity evaluations that offer feedback to man-agers Networking and mentoring programsmay appear to operate at the collective level butthey are designed to ldquofixrdquo a lack of specifichuman and social capital in individual workers

Next we describe the three categories ofdiversity practices link them to theories ofinequality and summarize the (scant) evidenceabout the effects of workplace antidiscrimina-tion programs Then we review the research onthe effects of the Civil Rights Act and presi-dential affirmative action edicts on employ-mentmdashhitherto the main body of research on theeffectiveness of antidiscrimination measuresAfter a discussion of data and methods we

present the results from analyses of white menwhite women black women and black men inmanagement

THREE AAPPROACHES TTO IINCREASINGMANAGERIAL DDIVERSITY

Scholars often presume that practices designedto attack known causes of inequality actuallywill reduce it as Reskin (2003) argues makinga leap of faith between causes and remediesThus for example although we know fromexperimental psychology that unconscious biasis endemic and likely contributes to workplaceinequality we can only hope that the prevailingtreatmentsmdashdiversity training and diversityevaluationsmdashdiminish inequality Under-standing the cause of malaria and understand-ing its treatment are two different thingsWhether a prescription for inequality is effec-tive is an inherently empirical question Currentprescriptions are not based in evidence

Our goal is to take a first step toward devel-oping an empirically based theory of remedia-tion for organizational inequality We sketchthree mechanisms for remediating workplaceinequality rooted in different social science lit-eratures and discuss the popular humanresources (HR) measures thought to put thesetheories to work One mechanism based inarguments from Max Weber and organization-al institutionalists is the creation of special-ized positions as the way to achieve new goalsAnother mechanism based in theories of stereo-typing and bias involves training and feedbackas the way to eliminate managerial bias and itsoffspring inequality A third mechanism basedin theories of social networks involves pro-grams that target the isolation of women andminorities as a way to improve their careerprospects

ORGANIZATIONAL CHANGE SSTRUCTURES OF

RESPONSIBILITY

We begin with a canonical insight from orga-nizational theory Organizational sociologistsand psychologists find that workers ignorenewly announced organizational goals and con-tinue to pursue old goals with old routines Thedecoupling of formal goals and daily practicemay occur because individuals face informationoverload and thus stick to the familiar or

CORPORATE AAFFIRMATIVE AACTION AAND DDIVERSITY PPOLICIESmdashndash591


Mon 25 Sep 2006 165130

because the old ways of doing things have beenimbued with meaning and value over time(Orton and Weick 1990 Selznick 1949)Institutionalists argue that decoupling is com-mon in programs responsive to regulatorydemands such as civil rights programs (Dobbinet al 1988 Edelman and Petterson 1999 Scott2001 Sutton and Dobbin 1996) Thus forinstance academic departments have abandonedthe old-boy system of hiring in favor of open jobadvertisement but department chairs still asktheir pals for leads Some argue that managersmay simply not perceive it as in their interest topromote gender and racial integration of jobs(Jacobs 1989b) Decoupling is particularly like-ly when there is no office or expert to monitorprogress as Max Weber (1978 [1968]) hintedwhen he argued that executives should appointspecialists to pursue specialized goals

If Weber and the institutionalists are correctwhere diversity efforts are everyonersquos respon-sibility but no onersquos primary responsibility theyare more likely to be decoupled In organizationsthat do not assign responsibility for diversitygoals to a specific office person or groupthese goals may fall by the wayside as line man-agers juggle competing demands to meet pro-duction quotas financial targets and the like(Edelman 1990 Meyer and Rowan 1977)Scholars (Reskin 2003 Sturm 2001) and con-sultants (Winterle 1992) alike advise ongoingcoordination and monitoring of diversityprogress by dedicated staff members or taskforces Three common approaches can be usedto establish responsibility for diversity as dis-cussed in the following sections

RESPONSIBILITY AND AFFIRMATIVE ACTION

PLANS Assign responsibility for setting goalsdevising means and evaluating progress thiswas Weberrsquos advice to bureaucrats The agencyLyndon Johnson set up in 1965 to monitor affir-mative action among federal contractors encour-aged this approach In 1971 the Office ofFederal Contract Compliance (OFCC whichlater gained a P for ldquoprogramsrdquo to becomeOFCCP) ordered contractors to write affirma-tive action plans in which they annually evalu-ate their own workforces specify goals for thefair representation of women and minoritiesbased on labor market analyses and sketchtimetables for achievement of these goals(Shaeffer 197366)

The order also specifies that firms shouldassign responsibility to a staff member ldquoHe orshe must have the authority resources supportof and access to top management to ensure theeffective implementation of the affirmativeaction programrdquo (US Department of Labor2005) By collecting and reviewing local infor-mation annually the affirmative action officercan track ldquounderutilizationrdquo of women andminorities and keep managers informed abouttheir departmentsrsquo progress (Linnehan andKonrad 1999410 Reskin 200313) or initiateldquoconstructive dialoguerdquo about making furtherprogress (Sturm 2001)

The few studies that examine effects of affir-mative action plans are inconclusive Baron etal (1991) studying annual data from 89California state agencies between 1975 and1981 found that all else being equal agencieswith affirmative action programs made signif-icantly slower progress in gender desegrega-tion of jobs Yet those agencies were moreintegrated originally so it may be that preex-isting affirmative action programs had left lit-tle room for improvement (see also Edelman andPetterson 1999126 Leonard 199065) In astudy of 3091 federal contractors with affir-mative action plans Jonathan Leonard (1985b)shows that the goals employers set for hiringwhite women black women and black men didhave positive effects although the goals werewildly optimistic Goals apparently do not actas quotas because virtually no employer everachieves its written goals

Federal contractors are required to write affir-mative action plans but contractor status doesnot correspond perfectly with the presence of aplan Many contractors fail to write plans or toupdate them (Bureau of National Affairs 1986Leonard 199055) Up to one fourth of firmswith affirmative action plans are not contractorsThey create plans to bid for contracts or to setdiversity goals (Bureau of National Affairs1986 Reskin 1998) In our sample 7 percent ofcontractors never had a plan and 20 percent offirms that had never had a contract wrote plans

OVERSIGHT VIA STAFF POSITIONS AND DEPART-MENTS Following the classic bureaucratic dic-tum (Weber 1978 [1968]) some organizationsappoint full-time staff members or create depart-ments to monitor diversity instead of leaving thetask to line managers or assigning it to staffers



Mon 25 Sep 2006 165130

with other responsibilities As a newly appoint-ed diversity manager in a high tech companyexplained to us in 2001 ldquoAs the organization hasstarted to grow they realized they needed some-one in there to really pay attention to affirma-tive action and compliance and || efforts ondiversity||| So the position was created at thebeginning of this yearrdquo

Big military contractors were the first to cre-ate special positions in the wake of Kennedyrsquosinitial affirmative action order in 1961 Edelmanand Petterson (1999) show that equal opportu-nity departments do not increase gender andracial diversity on their own but that they doexpand diversity recruitment programs whichin turn improve diversity We include a measurefor recruitment programs to isolate the effectsof diversity staff positions

OVERSIGHT AND ADVOCACY VIA COMMITTEESFrom the late 1980s experts have advisedemployers to appoint diversity committees andtask forces comprising people from differentdepartments professional backgrounds andmanagerial levels Committees typically arecharged with overseeing diversity initiativesbrainstorming to identify remedies and moni-toring progress The diversity task force at theaccounting and consulting giant Deloitte ampTouche for instance created a series of ongo-ing groups responsible for analyzing the gendergap recommending remedial steps and estab-lishing systems for monitoring results and ensur-ing accountability (Sturm 2001492)

These three strategies share a focus onresponsibility An organization with any one ofthese has assigned responsibility for progress toa person or groupmdashan affirmative action offi-cer a diversity manager or department or acommittee or task force That person or groupmonitors progress regularly Affirmative actionofficers also write explicit annual goals forprogress as do some staffers and committees

BEHAVIORAL CHANGE RREDUCING BIAS

THROUGH EDUCATION AND FEEDBACK

Social psychologists trace inequality to biasamong managers Stereotyping is a natural cog-nitive mechanism It is inevitable given ourinnocent tendency to make associations betweencategories and concepts (Gorman 2005Heilman 1995 Lemm and Banaji 1999) The

implicit associations we make between racegender ethnicity and social roles can have theeffect of reproducing existing patterns ofinequality (Jost Banaji and Nosek 2004)Managers may unwittingly select women forjobs traditionally dominated by women andmen for jobs dominated by men with the effectof preserving between-group differencesMoreover in-group preference is widespread(Tajfel and Turner 1979) and may likewise con-taminate managerial judgment (Baron andPfeffer 1994 Reskin 2000) Rosabeth MossKanter (1977) sketches the early research on in-group preference to support her theory ofhomosocial reproductionmdashwhite men promot-ing their clones Kanter argues that managersprefer to hire their own for reasons of commu-nication and trust

Two corporate initiatives are thought to count-er stereotyping and in-group preferenceDiversity training is thought to make managersaware of how bias affects their actions and thoseof subordinates Diversity evaluations arethought to provide managers with feedbackshowing the effects of their decisions on diver-sity

EDUCATION VIA DIVERSITY TRAINING Socialpsychological research shows that giving peo-ple information about out-group members andabout stereotyping may reduce bias (Fiske 1998Nelson Acker and Melvin 1996) Diversitytraining provides managers with such informa-tion It can be traced to the equal opportunityldquosensitivityrdquo training programs that a handful ofmajor corporations put together in the mid-1970s in response to the first equal opportuni-ty consent decrees and court orders (Shaeffer1973) By the late 1980s quite a few corporatetrainers and psychologists had developed train-ing modules designed to familiarize employeeswith antidiscrimination law to suggest behav-ioral changes that could address bias and toincrease cultural awareness and cross-culturalcommunication (Bendick Egan and Lofhjelm1998)

Employers usually offer training either to allmanagers or to all employees We look at theeffects of training offered at least to all man-agers Some studies of diversity training suggestthat it may activate rather than reduce bias(Kidder et al 2004 Rynes and Rosen 1995Sidanius Devereux and Pratto 2001) Research



Mon 25 Sep 2006 165130

on diversity training programs has seldomexplored their effects on workforce compositionbut one study of federal agencies (Naff andKellough 2003) did show that a broad diversi-ty program had a negative effect on the pro-motion of minorities (Krawiec 2003514)

FEEDBACK VIA PERFORMANCE EVALUATIONSFeedback is thought to reduce bias by directingmanagerial attention and motivation (Reskin2003325) Laboratory experiments show thatwhen subjects know that their decisions willbe reviewed by experimenters they show lowerlevels of bias in assigning jobs (Salancik andPfeffer 1978 Tetlock 1985) Evaluating man-agers on their diversity performance createsoversight and provides feedback As early as1973 the Harvard Business Review noted thatldquoas one criterion of a line managerrsquos perform-ance appraisal some companies have includedhis success in effectively implementing equalopportunity programsrdquo (Fretz and Hayman1973137) By the mid-1980s a study of nineexemplary firms found that managers in eachfirm received regular equal opportunity per-formance evaluations (Vernon-Gerstenfeld andBurke 198559ndash60) To our knowledge no stud-ies assess the effects of diversity evaluations

TREATING SOCIAL ISOLATION NNETWORKING

AND MENTORING

Mark Granovetter (1974) brought insights aboutsocial networks pioneered by both sociologistsand psychologists to the study of how peoplefind jobs Students of inequality have sincespeculated that differential network contactsand differential resources accruing from thesecontacts may explain part of the continuinginequality between whites and blacks andbetween men and women (Blair-Loy 2001 Burt1998 Ibarra 1992 1995 McGuire 2000Petersen Saporta and Seidelm 1998) Whitemen are more likely than others to find goodjobs through network ties because their net-works are composed of other white men whodominate the upper tiers of firms (Burt 1998Reskin and McBrier 2000 but see Fernandezand Fernandez-Mateo 2006 Mouw 2003)Social networks also encourage trust supportand informal coaching (Baron and Pfeffer 1994Castilla 2005 Kanter 1977) Networking andmentoring programs designed specifically for

women and minorities are thought to provideuseful contacts and information (Thomas 2001)Both types of programs were pioneered in the1970s and then revived in the 1990s as part ofdiversity management efforts (Wernick 199425Winterle 199221)

NETWORKING PROGRAMS Diversity network-ing programs for women and minorities vary instructure Some take the form of regular brown-bag lunch meetings whereas others include lav-ish national conferences (Crow 2003) Theseprograms may be initiated by employees or byHR managers They provide a place for mem-bers to meet and share information and careeradvice Some networks also advocate policychanges such as those involving family policiesand domesticndashpartner benefits (Briscoe andSafford 2005) Although networking may occurwithout any organizational impetus we exam-ine formal networking programs that employ-ers support through release time for participantsmeeting space funding newsletters and emaillists

MENTORING PROGRAMS In 1978 the HarvardBusiness Review published an article titledldquoEveryone Who Makes It Has a Mentorrdquo thatmade mentors a must-have for aspiring man-agement trainees (Lunding Clements andPerkins 1979 see also Roche 1979) Proponentsof formal mentoring programs argue that theycan level the playing field giving women andminorities the kinds of relationships that whitemen get through the old-boy networkMentoring programs match aspiring managerswith senior mentors with the two meeting forcareer counseling and informal adviceEmpirical studies such as Burke and McKeenrsquos(1997) survey of university graduates suggesta relationship between mentoring and careersuccess among women but do not rule out thepossibility that ambitious women seek men-tors One study of random mentor assignmentwithin a single firm found that in generalmentees have improved social networks andtactical knowledge which may help their careers(Moore 2001) Others have found that cross-racementoring relationships often fail (Thomas2001) and that same-sex mentoring does nothave a positive effect on job placement in aca-



Mon 25 Sep 2006 165130

demic departments of economics (Neumarkand Gardecki 1996)

ADVERSE EFFECTS OF DIVERSITY PRACTICES

Some argue that affirmative action and diver-sity programs can backfire (Bond and Pyle1988 Linnehan and Konrad 1999) First exec-utives may believe that women and minoritiesbenefit from reverse discrimination and thusmay not deserve their positions (HeilmanBlock and Stathatos 1997 but see Taylor 1995)Second because of the elusive nature of cogni-tive bias ldquoconscious attempts at thought regu-lationrdquomdashsuch as diversity training and diversityevaluationsmdashldquomay even backfire leading toexaggerated stereotyping under conditions ofdiminished capacity or when self-regulationefforts are relaxedrdquo (Nelson et al 199631)Indeed management consultants and researchersfind mixed reactions to diversity managementamong white males who report that they areldquotired of being made to feel guilty in every dis-cussion of diversity || of being cast as oppres-sorsrdquo (Hemphill and Haines 1997) Thirdcoworkers and executives may have negativereactions when they perceive minorities ldquoasattempting to obtain power by individual andcollective meansrdquo (Ragins 1995106) and exec-utives may fear that networking will lead tounion organizing (Bendick et al 1998 Carter2003 Friedman and Craig 2004 Miller1994443 Society for Human ResourcesManagement 2004) Finally some studies findthat racially diverse work groups communicateless effectively and are less coherent (Baugh andGraen 1997 Townsend and Scott 2001 Vallas2003 Williams and OrsquoReilly 1998) Takentogether this research suggests that diversityprograms may inhibit management diversityparticularly for blacks

THE CCIVIL RRIGHTS AACT AAFFIRMATIVEACTION EEDICTS AAND DDIVERSITYPRACTICES

Although there is little research on the effectsof corporate diversity programs the Civil RightsAct and presidential affirmative action ordershave been shown to increase diversity The CivilRights Act covers virtually all employers mak-ing research on its effects difficult (Donohue andHeckman 1991) The effects of presidential

affirmative action orders can be examined bycomparing federal contractors subject to theseorders with noncontractors Six studies usingEEOC data for periods of 4 to 6 years between1966 and 1980 show that black employmentgrew more quickly among contractors(Ashenfelter and Heckman 1976 Goldstein andSmith 1976 Heckman and Payner 1989Heckman and Wolpin 1976) Affirmative actionhad negligible effects on white women (Leonard198965) Contractor effects on blacks espe-cially black women declined from the early1980s (Leonard 199058) coincident with theReagan administrationrsquos policy of deregulationThese studies do not look at whether federalcontractors increased black employment byadopting antidiscrimination practices The twoexceptions are a study by Leonard (1985b)showing that employers who set high recruit-ment goals see more change and a study byHolzer and Neumark (2000) showing thatemployers subject to affirmative action lawexpand recruitment efforts and hire more appli-cants from disadvantaged groups We examinethe effect of affirmative action orders andexplore the possibility that being subject to suchorders (by being a federal contractor) renders theseven diversity programs more effective

In summary we expect the different sorts ofdiversity programs to vary in efficacy If assign-ing organizational responsibility is more effec-tive than targeting the behavior of individualsthen affirmative action plans diversity com-mittees and full-time diversity staff will be fol-lowed by broader increases in diversity thanwill either diversity training and diversity eval-uations or networking and mentoring programsBy the same logic the latter four programs maybe more effective when implemented in organ-izations with responsibility structures Finallywe examine whether affirmative action oversightrenders programs more effective

ALTERNATIVE SSOURCES OOF CCHANGEIN TTHE MMANAGERIAL WWORKFORCE

We include in the analyses other factors thoughtto affect management diversity We cannotinclude factors that do not vary with time suchas industry or location because our fixed-effectsmodels account for such stable traits



Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT

Legal enforcement through OFCCP compli-ance reviews lawsuits and EEOC chargesshould increase employersrsquo hiring and promo-tion of women and minorities (Baron et al19911386 Donohue and Siegelman 1991Kalev and Dobbin forthcoming Leonard 1984Skaggs 2001)

ORGANIZATIONAL STRUCTURES

Organizational size and the availability of man-agerial jobs create new opportunities (Baron etal 1991) but also more competition Konradand Linnehan (1995) and Leonard (199052)find that increased demand for managers favorswhite women but not African AmericansUnionization tends to preserve segregation byfavoring old timers through seniority provisions(Blau and Beller 1992 Milkman 1985 but seeKelly 2003 Leonard 1985a) Formalization ofpersonnel systems can reduce favoritism(Dobbin et al 1993 Reskin and McBrier 2000)although it also can create separate career tra-jectories for different groups (Baldi and McBrier1997 Baron and Bielby 1985 Elvira andZatzick 2002) Legal counsel may sensitizeemployers to diversity in promotion decisionsand recruitment systems targeting women andminorities can increase diversity (Edelman andPetterson 1999 Holzer and Neumark 2000)Finally workfamily policies may remove obsta-cles to the promotion of women (Williams2000)

TOP MANAGEMENT COMPOSITION

The diversity of the top management team mayaffect managerial hires through homosocialreproduction or social closure (Kanter 1977Tomaskovic-Devey 1993)

LABOR MARKET AND ECONOMIC ENVIRONMENT

Firms can more easily increase managerialdiversity when internal and external labor poolsare diverse (Cohen Broschak and Haveman1998 Shenhav and Haberfeld 1992) Demandfor workers from underrepresented groups maybe higher in industries with more federal con-tractors In hard economic times black men andto a lesser extent women are more vulnerablethan white men to being laid off (Elvira and

Zatzick 2002 Kletzer 1998) Finally growingindustries can offer more attractive jobs andboth women and minorities have historicallybeen relegated to less attractive sectors (Reskinand Roos 1990298)

DATA AAND MMETHODS

We conducted a fixed-effects analysis of lon-gitudinal data on the workforce composition of708 establishments to assess changes in mana-gerial composition after the adoption of each ofseven diversity practices The data cover theperiod 1971ndash2002 Fixed-effect models accountimplicitly for organizationsrsquo unobserved char-acteristics that do not vary over time and thatmay affect diversity

EEOC DDATA

The Civil Rights Act of 1964 as amendedrequires private employers with more than 100employees and government contractors withmore than 50 employees and contracts worth$50000 to file annual EEO-1 reports Thesereports detail the race ethnicity and gender ofemployees in nine broad occupational cate-gories There are no better data on workforcecomposition (for a methodological discussion onusing EEO-1 reports see Robinson et al 2005)We obtained the data from the EEOC throughan Intergovernmental Personnel Act (IPA) agree-ment

Some argue that employers reclassified jobsin the 1970s moving women and minoritiesinto management categories to improve theirfederal reports (Smith and Welch 1984)Leonard (199053) notes that ldquopure reclassifi-cation would cause black losses in the loweroccupations [in the EEO data] which is gener-ally not observedrdquo Jacobs (1992298) shows adeclining gender earnings gap consistent withreal progress noting that ldquothe predominant trendhas been toward real if slow progress into man-agement on the part of womenrdquo In our samplefew firms show sudden increases for women orblacks in management but we checked resultsfor robustness by eliminating these cases andthe results did not change We also eliminatedestablishment-year spells from before 1990 asdiscussed later and the findings held up



Mon 25 Sep 2006 165130

ORGANIZATIONAL SURVEY DATA

We drew a random sample of establishmentsfrom the EEO-1 database for our organization-al survey For that sample we constructed adataset comprising all EEO-1 reports for theyears 1971ndash2002 interpolating for the missingyears of 1974 1976 and 1977 Establishmentsenter the dataset when they begin filing EEO-1 reports To ensure that we would be able to fol-low establishments over time we chose half ofthe sample from establishments that had beenin the dataset since 1980 and half from those thathad been in the dataset since 1992 We alsostratified by size selecting 35 percent of estab-lishments with fewer than 500 employees in1999 and by industry to represent the manu-facturing service and trade sectors We sampledfrom food chemicals computer equipmenttransportation equipment wholesale trade retailtrade insurance business services and healthservices Corporate diversity can be influencedby acquisitions spin-offs and plant closings sowe sampled establishments selecting no morethan one per parent firm

We conducted a longitudinal survey ofemployment practices at each establishmentcovering the years 1971ndash2002 in collaborationwith the Princeton Survey Research Center Wedrew on the experiences of others who had con-ducted organizational surveys of employmentpractices (particularly Kalleberg et al 1996Kelly 2000 Osterman 1994 2000) We com-pleted 833 interviews for a response rate of 67percent which compares favorably with therates of those other organizational surveys Inpreparation we conducted 41 in-person inter-views with HR managers from randomly sam-pled organizations in four different regions and20 pilot phone interviews Data from thoseinterviews are not included in the analysesreported in this discussion

We began by writing to the HR director ateach establishment We asked for permission toconduct an interview and for the name of theperson who could best answer questions aboutthe establishmentrsquos history of HR practices Thetypical interviewee was an HR manager with 11years of tenure We scheduled phone interviewsat the convenience of the interviewees andexplained in advance the nature of the infor-mation needed We asked whether the estab-lishment had ever used each personnel programwhen it was adopted and whether and when it

had been discontinued Program discontinuationwas rare When a respondent could not answera question we sent a copy of that question byemail or fax asked that she consult records andcolleagues and called back to fill in the blanksDuring our in-person pilot interviews respon-dents routinely pulled out manuals with copiesof policies and lists of adoption and revisiondates Nonetheless because responses aboutevents long past may be inaccurate we repli-cated the analyses using only establishment-year spells for 1990 to 2002 as discussed later

We matched survey data for each establish-ment with annual EEO-1 records creating adataset with annual establishment-year spellsAfter excluding 10 cases that had EEO-1 dataavailable for fewer than 5 years 13 cases withexcessive numbers of missing values for EEO-1 or survey data and 102 cases that were miss-ing the adoption date for at least one keyprogram our final dataset included 708 casesand 16265 establishment-year cells with amedian of 25 years of data per establishment aminimum of 5 years and a maximum of 32years We collected data on national state andindustry employment from the Bureau of LaborStatistics

Because of our stratified sampling designand the response pattern we were concernedthat respondents might not represent the popu-lation of establishments that file EEO-1 reportsin the sampled industries We constructedweights based on the inverse probability that anestablishment from each stratum (industry bysize and by time in the EEO-1 dataset) wouldcomplete the survey We replicated all reportedanalyses using weights and the results remainedintact We report unweighted results in the fol-lowing discussion (Winship and Radbill 1994)We also were concerned that employers whorefused to participate might systematically dif-fer on factors affecting diversity from thosewho participated We included in the modelspredicted values from a logistic regression esti-mating the probability of response (Heckman1979) This did not change our resultsCovariates in that model were industry estab-lishment status (headquarters subunit stand-alone status) size contractor status managerialdiversity and contact personrsquos position The lastvariable was obtained in the initial contact theothers from the EEO-1 data



Mon 25 Sep 2006 165130

DEPENDENT VARIABLES

The dependent variables are the log odds thatmanagers are white men white women blackwomen and black men For each group oddsare calculated as the proportion of managersfrom that group divided by the proportion notfrom that group (proportion(1 ndash proportion))Figure 1 presents the trends in percents in oursample Between 1971 and 2002 managementjobs held by white men decline from 81 to 61percent in the average establishmentManagement jobs held by white women risefrom 16 to 26 percent whereas those held byblack women rise from 04 to 2 percent andthose held by black men rise from 1 to 31 per-cent There also is a significant rise in the rep-resentation of other groups notably Hispanicsduring this period which is why the percentagesdo not sum up to 100 percent

Black women and men showed dramaticchanges in their proportions in managementrelative to the baseline quadrupling and triplingrespectively but saw small changes in percent-age points Because the absolute changes forblacks are relatively small we log the depend-ent variables We use log odds rather than logproportion because the distribution is close tonormal (Fox 199778)1 In a sensitivity analy-sis log proportion performed very similarlyThe dependent variable is measured annuallyone year after the independent variablesChanging the lag to 2 3 or 4 years does not alterthe findings Our sample is designed to inves-tigate the effects of diversity programs on work-force composition in private sectorestablishments large enough to file EEO-1reports We do not claim to describe the nationrsquosmanagerial workforce Nationally representativesamples such as the Current Population Surveyinclude the public and nonprofit sectors inwhich the gains of women and minorities have

been larger Furthermore national figures reflectthe change in womenrsquos representation in man-agement associated with service sector growth(eg Jacobs 1992) whereas our data track arelatively stable set of firms

AFFIRMATIVE ACTION PLANS AND DIVERSITY

PRACTICES

Figure 2 shows the prevalence of all seven diver-sity programs among the 708 employers ana-lyzed later By 2002 affirmative action planswere used in 63 percent of the workplaces westudy followed by training in 39 percent diver-sity committees in 19 percent networking pro-grams (for women and minorities) in 19 percentdiversity evaluations for managers in 19 percentdiversity staff in 11 percent and mentoring pro-grams (for women and minorities) in 11 percentThe bivariate correlations and joint frequen-cies of the seven programs are not shown here(see Online Supplement ASR Web site httpwww2asanetorgjournalsasr2006toc052html)

In the analyses reported in the following dis-cussion we use binary variables to represent thepresence of the seven diversity programs For sixprograms we asked whether the organizationhad ever had the program when it was firstadopted and when (if ever) it was discontinuedFor the seventh practice diversity training weasked when it was first and last offered If anemployer had gone for 3 years without trainingwe treated the program as defunct We collect-ed additional information about diversity train-ing because our in-person interviews suggestedthat it varied across organizations more thanthe other programs but we found significantsimilarities in training programs In 70 percentof the establishments with training for man-agers training was mandatory Included in 80percent of the training programs was a discus-sion on the legal aspects of diversity and 98 per-cent were conducted with live facilitators asopposed to being offered exclusively via theWeb or video Although some organizationsoffered training not only to managers but alsoto all employees we report effects of trainingfor managers because managers made promo-tion decisions Training for all employees hadnearly identical effects in the models

Because the measures are binary coded 1for all the years the program is in place programeffects are estimated for the entire period of


1 Because log-odds (logit) is undefined at valuesof 0 and 1 we substituted 0 with 12Nj and 1 with1-12Nj where Nj is the number of managers inestablishment j (Hanushek and Jackson 1977 Reskinand McBrier 2000) The results were robust to dif-ferent substitutions for 0 We chose the one that keptthe distribution unimodal and closest to normal Toensure that the substitution does not drive the find-ings we include a binary variable for no group mem-bers in management


Mon 25 Sep 2006 165130


Figure 1 Percent of Managers White Men and Women and Black Men and Women 1971ndash2002

Note Based on EEO-1 reports 1971ndash2002 sampled for Princeton University Human Resources Survey 2002Varying N Maximum N = 708 EEO = equal employment opportunity

Figure 2 Percent of Private-Sector Workplaces with Affirmative Action Plans and Diversity Programs 1971ndash2002

Note Based on Princeton University Human Resources Survey 2002 Varying N Maximum N = 708


Mon 25 Sep 2006 165130

the programrsquos existence (not merely for the yearafter initiation)

For six of the programs between 2 and 4percent of the respondents who reported theprogramrsquos adoption could not tell us the exactyear For the seventh practice affirmative actionplan the figure was 8 percent We eliminatedcases with missing data on any of these vari-ables The results were virtually identical whenwe imputed missing data for variables of inter-est and retained these cases in the analysisMissing adoption dates for control variableswere imputed using ordinary least squares(OLS) regression with industry age of estab-lishment and type of establishment as covari-ates Omitting cases with imputed data did notsubstantially alter the findings

CONTROL VARIABLES

All measures included in the analyses varyannually Table 1 presents definitions and datasources for key variables as well as means andstandard deviations (based on all organization-al spells) Descriptive statistics for the entire listof control variables are not shown here (seeOnline Supplement ASR Web site) Becausethe fixed-effects method estimates variationwithin the organization it captures change overtime For example in the models the variableorganizational size captures the effect of achange in size on change in managerial diver-sity These models effectively ignore measuresthat do not change such as industry but cross-case variation in those measures is captured bythe fixed effects

LEGAL ENVIRONMENT We include a binaryvariable based on the EEO-1 reports indicatingwhether the establishment is a federal contrac-tor subject to affirmative action regulationLegal enforcement is measured using three sur-vey variables that capture the establishmentrsquosexperience with Title VII lawsuits EEOCcharges and affirmative action compliancereviews Each is coded 1 from the year of thefirmrsquos first enforcement experience More thanone third of establishment-year spells had pre-viously faced a lawsuit more than one thirdhad faced an EEOC charge and nearly 15 per-cent had faced a compliance review (only con-tractors are subject to compliance reviews)

ORGANIZATIONAL STRUCTURES Organi-zational size and availability of managerial jobsare measured using EEO-1 data on the totalnumber of employees in the establishment andthe number of managerial employeesUnionization is coded 1 when the establish-ment has at least one contract Substitutingwith a measure of core job unionization doesnot alter the results Formal HR policies involvea count of hiring promotion and dischargeguidelines job descriptions promotion lad-ders performance evaluations pay grade sys-tem and internal job posting Legal counsel ismeasured with a binary variable for the pres-ence of an in-house attorney Targeted recruit-ment policy is a binary measure of specialdiversity recruitment efforts Workndashfamily sup-port counts paid maternity leave paid paterni-ty leave flextime policies and top managementsupport for workndashfamily programs as assessedby our respondents

TOP MANAGEMENT COMPOSITION Top man-agement team diversity is measured with thepercentage of the top 10 positions held bywomen andor African Americans based onsurvey data We asked about the percentage at10-year intervals and interpolated values forthe intervening years

LABOR MARKET AND ECONOMIC ENVIRONMENTThe diversity of the establishmentrsquos internallabor pool is measured with two variables basedon the EEO-1 reports the percent of the focalgroup in nonmanagerial jobs and the percent inthe core job To determine the EEO-1 categorythat held the core job we asked respondentsabout the single biggest job in the organiza-tion We include a variable coded 1 when thereare no members of the focal group in manage-ment Diversity of the establishmentrsquos externallabor pool is captured by two sets of variableson industry and state labor forces from theCurrent Population Survey Industry employ-ment variables are logged We use the industryrsquospercent of government contractors (based onEEO-1 data) to measure demand for underrep-resented workers in affirmative action sectorsEconomic conditions are measured with theyearly state unemployment rate and industrysize is measured as total annual industry



Mon 25 Sep 2006 165130

CORPORATE AAFFIRMATIVE AACTION AAND DDIVERSITY PPOLICIESmdashndash601T

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Out

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e V

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Man

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700

236

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Mon 25 Sep 2006 165130

employment both from the Current PopulationSurvey

METHODS

We use pooled cross-sectional time-series mod-els with fixed effects for both establishment andyear (Hicks 1994 Hsiao 1986) We use fixedeffects for establishments to account for unmea-sured time-invariant characteristics that mightaffect outcome variables (for recent empiricalexamples of these methods applied to individ-uals see Budig and England 2001 Western2002) This specification achieved by sub-tracting the values of each observation fromthe establishment mean (Hsiao 198631)strengthens our causal inferences about theeffects of affirmative action plans and diversi-ty practices by ruling out the possibility thatorganizations that adopted those practices hadstable unobserved preferences for diversity Tocapture environmental changes such as legaland cultural shifts we use a binary variable foreach year omitting 1971 The large number ofparameters involved in estimating fixed-effectsmodels renders them less efficient than otherestimators However we prefer these to alter-native models because they provide the moststringent tests of our hypotheses The estab-lishment and year fixed effects also offer anefficient means of dealing with nonconstantvariance of the errors (heteroskedasticity) stem-ming from the cross-sectional and temporalaspects of the pooled data

Because our dependent variables are meas-ured as parts of the same whole (the wholebeing management jobs) we expect their errorterms to be correlated Ordinary least squareswould thus produce unbiased and consistent butinefficient estimators We use seemingly unre-lated regression which takes into accountcovariance between the errors and producesunbiased efficient estimators (Felmlee andHargens 1988 Greene 1997 Zellner 1962)Simultaneous estimation also allows us to com-pare the effect of each diversity practice acrossgroups with formal chi-square tests (Kallebergand Mastekaasa 2001 Zellner 1962)

FINDINGS

The analysis shows substantial variation in theeffectiveness of diversity programs Someincrease managerial diversity across the board

whereas others have meager effects or posi-tive effects for some groups and negative effectsfor others The most effective practices are thosethat establish organizational responsibility affir-mative action plans diversity staff and diver-sity task forces Attempts to reduce socialisolation among women and African Americansthrough networking and mentoring programsare less promising Least effective are programsfor taming managerial bias through educationand feedback

DIVERSITY PROGRAMS AT WORK

In Table 2 we report models of managerialdiversity (Selected control variables are pre-sented the remaining coefficients can be seenon the Online Supplement ASR Web site) Eachdependent variable is the (natural) log odds ofmanagers being from a certain group To trans-form the coefficient from representing changein log odds to representing percentage changein odds it should be exponentiated [exp() ndash1]100 Once exponentiated in this way thecoefficient represents the average percentagechange in the odds that managers are from a cer-tain group associated with a change in the inde-pendent variable In the discussion below we uselsquoodds for [group]rsquo as a shorthand We also pro-vide an illustrative summary of the results inproportion terms

The R2 figures for these fixed-effects mod-els represent the percentage of the varianceexplained by the predictors when the uniqueeffects of each establishment are excluded A loglikelihood ratio test shows that the variablesreported in Table 2 significantly improve themodel fit (chi(28) = 40566 p lt 001) as com-pared with the baseline models that have novariables representing diversity programs (avail-able on request)

ORGANIZATIONAL RESPONSIBILITY Coeffi-cients for the diversity programs represent thechange in the log odds that managers are froma certain group that is attributable to the pres-ence of a practice averaged across all years ofthe programrsquos existence After employers set upaffirmative action plans the odds for white menin management decline by 8 percent the oddsfor white women rise by 9 percent and the oddsfor black men rise by 4 percent These numbersrepresent the estimated average difference



Mon 25 Sep 2006 165130


Table 2 Fixed Effects Estimates of the Log Odds of White Men and Women and Black Women and Men inManagement 1971ndash2002

White Men White Women Black Women Black Men

Organizational ResponsibilitymdashAffirmative action plan ndash078 086 005 039mdash (017) (017) (014) (015)mdashDiversity committee ndash081 175 242 114mdash (028) (029) (024) (026)mdashDiversity staff ndash055 104 123 128mdash (033) (034) (028) (030)Managerial BiasmdashDiversity training ndash038 ndash001 ndash066 031mdash (021) (022) (018) (019)mdashDiversity evaluations 028 061 ndash027 ndash081mdash (027) (028) (023) (025)Social IsolationmdashNetworking programs ndash083 080 012 ndash096mdash (027) (028) (023) (024)mdashMentoring programs ndash011 ndash004 213 037mdash (033) (035) (029) (031)Legal EnvironmentmdashGovernment contract 032 006 ndash039 ndash027mdash (019) (019) (016) (017)mdashCompliance review ndash083 077 020 081mdash (020) (020) (017) (018)mdashTitle VII lawsuit ndash107 141 044 029mdash (015) (016) (013) (014)mdashEEOC charge ndash007 014 019 034mdash (016) (017) (014) (015)Organizational StructuresmdashProportion managers in establishment ndash896 309 ndash4499 ndash3989mdash (108) (112) (092) (099)mdashEstablishment size (log) ndash021 ndash023 ndash661 ndash515mdash (012) (012) (010) (011)mdashUnion agreement ndash053 ndash068 ndash007 ndash029mdash (033) (034) (028) (030)mdashFormal personnel policies ndash002 ndash003 ndash016 ndash015mdash (004) (004) (003) (003)mdashIn-house attorney ndash100 126 ndash040 021mdash (023) (024) (020) (021)mdashTargeted recruitment policy ndash071 108 131 099mdash (021) (021) (018) (019)mdashWork-family accommodations ndash078 065 026 004mdash (008) (009) (007) (008)Top Management CompositionmdashProportion minorities in top management ndash002 ndash002 007 012mdash (001) (001) (001) (001)mdashProportion women in top management ndash002 004 002 ndash002mdash (001) (001) (001) (001)mdashmdashR2 (64 parameters) 3335 3146 3636 2799

Note Log likelihood ratio test 2 (28) = 40566 p lt 001 Data shown are coefficients from seemingly unrelatedregression with standard errors in parentheses Variables included in the analyses but not shown here are 8 vari-ables for proportion of each group in non-managerial jobs and in core job in each establishment 4 binary vari-ables for no workers from a group in management 8 variables for proportion of each group in state and industrylabor forces proportion of contractor firms in industry industry employment and state unemployment rate (fullresults on Online Supplement ASR Web site httpwww2asanetorgjournalsasr2006toc052html) Analysesalso include establishment and year fixed effects All independent variables are lagged by 1 year excludingproportion of managerial jobs N (organization-year) = 16265 N (organizations) = 708 EEOC = EqualEmployment Opportunity Commission p lt 05 p lt 01 (two tailed test)


Mon 25 Sep 2006 165130

between having a plan and the counterfactualcondition of not having a plan for the entireperiod of the planrsquos existence These results areconsistent with Leonardrsquos (1990) finding thataffirmative action plan goals are effective Notethat the coefficient for black women is not sig-nificant here When we introduced industryinteractions we discovered that in manufactur-ing (computers electronics transportation)affirmative action plans had negative effectson black women whereas in service (retailinsurance business services) affirmative actionplans had positive effects (results available uponrequest) Creating a diversity committee increas-es the odds for white women across the periodof the committeersquos existence by 19 percentThe odds for black women rise 27 percent andthe odds for black men rise 12 percentEmployers who appoint full-time diversity staffalso see significant increases in the odds forwhite women (11 percent) black women (13percent) and black men (14 percent) in man-agement

As noted the coefficients in Table 2 representthe average changes in log odds that managersare from a certain group The effect of eachprogram on the percent of women and minori-ties in management will vary depending onwhere organizations begin (Fox 199778) Forexample an 8 percent decrease in the odds ofmanagers being white men resulting from adop-tion of affirmative action plan would translateto a decline of 26 percent in the percent ofwhite men in management if they constituted 70percent before adoption but it would mean alarger decline of 43 percent if they made uponly 50 percent at the baseline (Petersen1985311)

PROGRAMS FOR REDUCING MANAGERIAL BIASPrograms designed to reduce managerial biasthrough education (diversity training) and feed-back (diversity evaluations) show one modestpositive effect and two negative effects acrossthe three disadvantaged groups Diversity train-ing is followed by a 7 percent decline in the oddsfor black women Diversity evaluations are fol-lowed by a 6 percent rise in the odds for whitewomen but an 8 percent decline in the odds forblack men These mixed effects are anticipatedin the literature As noted laboratory studies andsurveys often show adverse reactions to train-ing (Bendick et al 1998 Nelson et al 1996)

Moreover critics argue that trainers definediversity broadly to include groups not coveredby federal civil rights law (parents smokers)and thereby draw attention away from protect-ed groups (Edelman Fuller and Mara-Drita2001 Kochan et al 2003 Konrad and Linnehan1995)

PROGRAMS FOR REDUCING SOCIAL ISOLATIONNetworking and mentoring programs designedto counter social isolation show modest effectson managerial diversity Networking is followedby a rise in the odds for white women and adecline in the odds for white men and blackmen The negative coefficient for black men isanticipated by qualitative research (Carter 2003Friedman and Craig 2004) showing that whitescan develop negative attitudes toward African-American organizing In contrast mentoringprograms show a strong positive effect on theodds for black women These findings suggestthat having personal guidance and support atwork can facilitate career development (Castilla2005) for black women whereas networking ismore effective for white women

GENDER AND RACIAL PATTERNS Overall itappears that diversity programs do most forwhite women and more for black women thanfor black men Black men gain significantlyless from affirmative action than do whitewomen (chi-sq(1) = 415 p lt 05) and signif-icantly less from diversity committees than doblack women (chi-sq(1) = 2247 plt 01) Threeprograms show negative effects on AfricanAmericans whereas no program shows a neg-ative effect on white women We hesitate tooverinterpret this pattern but note that there issomething of a trade-off among groups

Table 3 evaluates the magnitude of the effectsof programs on the proportion of each group inmanagement based on the coefficients in Table2 ldquoProportion in year of adoptionrdquo is the meanproportion of each group in managementamong adopters in their actual years of programadoption (ie just before treatment) ldquoEstimatedproportion with practicerdquo shows the predictedmean proportion after the practice is in placeThus for example the proportion of whitewomen among managers in the average estab-lishment adopting an affirmative action pro-gram was 0132 and the net effect of the



Mon 25 Sep 2006 165130

program with control for other factors is toraise white women proportion to 0142Similarly the proportion of black women amongmanagers was 0014 in the average firm adopt-ing a diversity committee and adoption bringsblack women to 0018 an increase of almost30 The third row based on the first two rowsreports the percentage change over the baselineresulting from program adoption

Tables 2 and 3 support our contention thatprograms establishing organizational responsi-bility are more broadly effective than those thataddress managerial bias or social isolationamong women and African AmericansOrganizations that structure responsibility seeconsistent positive effects for white womenblack women and black men

Coefficients for control variables are con-sistent with expectations with one possibleexception The negative effect of formal per-

sonnel policies is not consistent with the ideathat bureaucracy impedes cronyism or bias inpromotion decisions (Reskin and McBrier2000) but is consistent with the argument thatformalization leads to the needless inflation ofeducational prerequisites (Collins 1979) andwith findings that the determinants of promo-tion differ systematically for whites and blackseven when formal personnel systems exist(Baldi and McBrier 1997) Other coefficients ofcontrol variables show that although growthand unionization have not improved diversityand although legal staff had only limited effectstargeted recruitment programs workfamilyaccommodations and top management teamdiversity show positive effects on managerialdiversity Coefficients for the labor market andeconomic environment measures not shownhere are in the expected direction as well (seeOnline Supplement ASR Web site)


Table 3 Estimated Average Differences in Managerial Composition Due to Adoption of Affirmative Action andDiversity Practices


Affirmative Action PlanmdashProportion in year of adoption 783 132 017 024mdashEstimated proportion with practice 769 142 017 025mdashPercent difference due to adoption ndash18 76 0 42Diversity CommitteemdashProportion in year of adoption 630 230 014 020mdashEstimated proportion with practice 611 262 018 022mdashPercent difference due to adoption ndash30 139 298 100Diversity StaffmdashProportion in year of adoption 724 157 014 021mdashEstimated proportion with practice 713 171 016 024mdashPercent difference due to adoption ndash15 89 143 143Diversity TrainingmdashProportion in year of adoption 687 194 017 022mdashEstimated proportion with practice 679 194 016 023mdashPercent difference due to adoption ndash12 0 ndash59 45Diversity EvaluationsmdashProportion in year of adoption 720 160 017 024mdashEstimated proportion with practice 726 168 017 022mdashPercent difference due to adoption 8 50 0 ndash83Networking ProgramsmdashProportion in year of adoption 702 193 014 020mdashEstimated proportion with practice 684 206 014 018mdashPercent difference due to adoption ndash26 67 0 ndash100Mentoring ProgramsmdashProportion in year of adoption 690 216 017 021mdashEstimated proportion with practice 688 215 021 022mdashPercent difference due to adoption ndash3 ndash5 235 48

Note Estimates based on coefficients presented in Table 2 p lt 05 p lt 01 (two tailed test)


Mon 25 Sep 2006 165130

DOES ORGANIZATIONAL RESPONSIBILITY

IMPROVE PROGRAM EFFECTIVENESS

It is possible that some programs work best incombination with others (MacDuffie 1995Perry-Smith and Blum 2000) Our finding thatorganizational responsibility structures havebroader effects than other programs suggeststhat perhaps training evaluation mentoringand networking would be more successful incombination with responsibility structures Weundertake several analyses of program combi-nations

First we explore the possibility that the sim-ple number of programs matters Perhaps ourmeasures capture not the effects of discrete pro-grams so much as an orientation toward chang-ing workplace demography We introduce threebinary variables representing the presence of anyone two and three or more programs Acrossthe 16265 organization-year spells of data 49percent had no programs 34 percent had oneprogram 10 percent had two programs and 7percent had three or more programs In the toppanel of Table 4 we report the effects of the


Table 4 Fixed-Effects Estimates of the Log Odds of White Men and Women and Black Women and Men inManagement with Bundles of Programs 1971ndash2002

White White Black BlackMen Women Women Men

Adoption of One or More AA Plans amp Diversity ProgramsmdashOnly one program ndash043 056 ndash009 026mdash (016) (016) (013) (014)mdashTwo programs ndash091 121 020 024mdash (023) (023) (019) (021)mdashThree or more programs ndash158 232 127 046mdash (029) (030) (025) (027)mdashmdashR2 (60 parameters) 3323 3124 3569 2767mdashInteraction with Responsibility StructuresmdashResponsibility structures ndash063 081 007 042mdash (017) (017) (014) (015)mdashDiversity training ndash026 ndash064 ndash046 026mdash (036) (038) (031) (033)mdashmdash Responsibility structure ndash026 132 044 040mdash (042) (043) (036) (038)mdashDiversity evaluations 294 ndash042 ndash065 ndash077mdash (057) (059) (049) (052)mdashmdash Responsibility structure ndash326 136 057 009mdash (061) (063) (053) (057)mdashNetworking programs ndash090 163 ndash026 ndash172mdash (050) (052) (043) (046)mdashmdash Responsibility structure ndash003 ndash088 073 118mdash (056) (058) (048) (051)mdashMentoring programs 140 ndash101 ndash042 127mdash (066) (068) (057) (061)mdashmdash Responsibility structure ndash183 133 344 ndash108mdash (074) (076) (063) (068)

mdashR2 (66 parameters) 3347 3136 3602 2785

Note Data shown are coefficients from 2 seemingly unrelated regression analyses with standard errors in paren-theses Responsibility Structures include affirmative action plans diversity committees and diversity staff Theanalyses include establishment and year fixed effects and all the control variables included in the models present-ed in Table 2 (for coefficients of control variables see Online Supplement ASR Web site httpwww2asanetorgjournalsasr2006toc052html) N (organization-year) = 16265 N (organizations) = 708 p lt 05 p lt 01 (two tailed test)


Mon 25 Sep 2006 165130

number of programs in models parallel to thosepresented in Table 2 (results for the control vari-ables are available on the Online SupplementASR Web site) We compared coefficients for thebinary count variables using t tests For whitewomen the sheer number of programs mattersone is better than zero two better than one andthree or more are better than two For whitemen we find the opposite pattern suggestingthat each additional program reduces the oddsfor white men For black women having one ortwo programs is not significantly different fromhaving none Having three is significantly dif-ferent For black men none of the count vari-ables show an effect significantly different fromhaving no programs Hence for white womenthe more programs the better For blacks thenumber of programs matters less than the con-tent of the programs This is not surprising giventhat some practices in Table 2 show no effectsor even negative effects on blacks

Although each additional program regardlessof content does not always translate into greaterdiversity particular bundles of programs mightoperate well together To test this idea we ran(in models otherwise identical to those in Table2) all two-way interactions between affirma-tive action plan diversity committee diversitystaff training evaluation networking and men-toring (The bivariate correlations and joint fre-quencies of the seven programs are presented onthe Online Supplement ASR Web site) Thetwo-way interactions among training evalua-tion networking and mentoring did not indicatethat any pairs operated better than individualprograms But two-way interactions withresponsibility structures did render trainingevaluation networking and mentoring moreeffective For ease of presentation we collapsethe three responsibility structures into a singlevariable interacting it with the four other pro-gram variables The second panel in Table 4includes estimates from models with these inter-actions (results for the control variables are pre-sented on the Online Supplement ASR Website)

Diversity training evaluation networkingand mentoring programs are more effective infirms with responsibility structures With diver-sity training and evaluations the responsibilitystructure interaction positively affects whitewomen With networking the responsibilitystructure interaction positively affects black

men and with mentoring it positively affectsblack women Note that the noninteracted vari-able responsibility structure continues to showthe expected effects for white men whitewomen and black men The overall pattern isstriking and suggests that these authority struc-tures render the other programs more effectiveYet even with responsibility structures in placenone of these programs show the sort of con-sistent pattern across outcomes that we findfor say diversity committee

DO AFFIRMATIVE ACTION ORDERS MEDIATE

PROGRAM EFFICACY

In Table 2 we also examine whether affirma-tive action enforcement shows direct effectsEmployers who sign a government contractand thereby become subject to affirmative actionregulation do not see increases in managerialdiversity as a direct result When we interactedcontractor status with the period 1971ndash1980 theresults did not support early researchersrsquo find-ings that contractors experienced faster growthin black employment in the 1970s Of courseeffects found in earlier studies were quite smalland it may be that they were concentrated inindustries we do not sample For the entire peri-od we find a decline in the odds for blackwomen after the approval of a government con-tract This may be because employers who striveto improve their numbers before seeking gov-ernment work improve more slowly afterreceiving contracts (Baron et al 19911389Leonard 199065) Government contractor sta-tus does not show positive effects even when weexclude programs that may be associated withcontractor status the seven diversity measuresformal HR policies workndashfamily policies andcompliance reviews (results available onrequest)

Unlike contractor status antidiscriminationenforcement shows effects Federal compliancereviews which 32 percent of the contractors inour data faced increased representation of whitewomen and black men Leonard (1985b) alsofound effects of compliance reviews in his studyof the 1970s When we interacted compliancereview with the period 1971ndash1980 our results(available upon request) replicated his findingfrom the 1970s as well (see also Kalev andDobbin forthcoming) Discrimination lawsuitsincrease the odds for all three groups in man-



Mon 25 Sep 2006 165130

agement (Skaggs 2001) and EEOC chargesincrease the odds for black men

The natural follow-up question is whetheraffirmative action oversight mediates the effi-cacy of the seven affirmative action and diver-sity measures Theory suggests that programimplementation may be taken more seriously infirms subject to regulatory scrutiny Those firmstypically assign responsibility for compliance toan office or person In Table 5 we add interac-tion terms between programs and contractorstatus to the model presented in Table 2Coefficients for control variables are availableon the Online Supplement ASR Web site A

log-likelihood test shows a significant improve-ment in fit over that of the model presented inTable 2 The interaction coeff icients showwhether effects are significantly different amongcontractors and noncontractors We also exam-ine the linear combination of the interactioncomponents (using Lincom in Stata) to assesswhether programs have signif icant effectsamong contractors

Diversity training shows the greatest differ-ence in effects on all four groups Whereasamong noncontractors training decreases therepresentation of white and black women inmanagement among contractors it is followed


Table 5 Fixed-Effects Estimates of the Log Odds of White Men and Women and Black Women and Men inManagement with Government Contractor Interactions 1971ndash2002


Affirmative Action Plan ndash050 086 000 007(023) (023) (019) (021)

mdash Government contract ndash050 003 000 053(028) (029) (024) (026)

Diversity Committee ndash096 173 270 076(038) (040) (033) (035)

mdash Government contract 029 ndash006 ndash050 074(053) (055) (046) (049)

Diversity Staff ndash076 018 205 240(058) (060) (050) (053)

mdash Government contract 024 120 ndash127 ndash145(066) (068) (056) (060)

Diversity Training 005 ndash094 ndash116 ndash016(027) (028) (023) (025)


Diversity Evaluations 049 090 ndash097 ndash063(039) (041) (034) (036)

mdash Government contract ndash041 ndash035 118 ndash027(050) (051) (042) (045)

Networking Programs ndash133 171 ndash034 ndash035(038) (039) (033) (035)

mdash Government contract 111 ndash195 069 ndash113(051) (052) (043) (046)

Mentoring Programs 028 ndash053 179 070(046) (047) (039) (042)

mdash Government contract ndash081 086 057 ndash056(063) (065) (054) (058)

R2 (71 parameters) 3341 3165 3650 2811

Note Log likelihood ratio test 2 (28) = 13586 p lt 001 Data shown are coefficients from seemingly unrelatedregression with standard errors in parentheses The analyses include establishment and year fixed effects and allthe control variables included in the models presented in Table 2 (for coefficients of control variables see OnlineSupplement ASR Web site httpwww2asanetorgjournalsasr2006toc052html) N (organization-year) =16265 N (organizations) = 708 p lt 05 p lt 01 (two tailed test)


Mon 25 Sep 2006 165130

by a significant decline in the odds for whitemen ( = 086 SE = 004) and significantincreases among white women ( = 103 SE =030) and black men ( = 083 SE = 027)Diversity evaluations also are less likely to back-fire among contractors where the effect onblack women is now zero

Affirmative action plans show significantlylarger effects for black men among contractorsfurther supporting Leonardrsquos (1990) findingsThe coefficients for diversity staff in the mod-els for black women and men although signif-icantly smaller among contractors are stillpositive and significant (B = 078 SE = 032 andB = 095 SE = 034 respectively) Networkingprograms help white women in noncontractorestablishments at the expense of white men butthis effect disappears among contractors andblack men see negative effects for reasons thatare not clear

FURTHER ANALYSES

A key challenge in analysis of nonexperimen-tal data is to account for heterogeneity thatstems from nonrandom selection into the ldquotreat-mentrdquo (in our case adopting a program)Heterogeneity may bias casual inference Ourmodel specification with fixed effects for eachyear and each establishment and with controlvariables measuring organizational structureslabor pool composition and economic and legalenvironment is designed to minimize this pos-sibility

We conducted three additional robustnesstests (results available on request) First weadded binary variables as proxies for unspeci-fied unobserved events (impending lawsuitlocal news coverage) that may have causedemployers both to implement new antidiscrim-ination programs and to hire more women andAfrican Americans We created proxies for eachof the seven programs We re-ran the analysis14 times with proxies measured 2 and 3 yearsbefore program adoption in models parallel tothose presented in Table 2 These proxy variablesdid not substantially alter the coefficients orstandard errors for affirmative action and diver-sity programs and most did not show signifi-cant effects This adds to our confidence that theobserved relationships between diversity pro-grams and managerial diversity are not spurious

(Rossi Lipsey and Freeman 2004 Snyder2003)

Second program adopters may be differentfrom nonadopters in ways that are not absorbedby the establishment fixed effects Perhapsadopters change faster than nonadopters interms of management fads and demographicsWe therefore re-ran the analyses in Table 2seven times each time only with establishmentsthat ever adopted a particular program (once foraffirmative action plan adopters then for diver-sity committee etc) If the effects in Table 2 areattributable to differences between adopters andnonadopters then program effects should dis-appear when we exclude nonadopters Theresults of our ldquoadopters onlyrdquo analyses are sub-stantively similar to those in Table 2

Third we were concerned that because thedataset is not rectangular (some establishmentsenter the data after 1971) unobserved hetero-geneity might distort the results if establish-ments are missing in early years for reasons(eg organizational size or age) associated withthe outcome variables We thus replicated theanalysis using a rectangular subsample of estab-lishments The results were substantially simi-lar to those reported in this discussion

To examine the robustness of the results towithin-unit serial correlation we corrected forthe possibility that each error is partially depend-ent on the error of the previous year (AR[1])with the CochranendashOrcutt method (available inState using xtregar not the seemingly unrelat-ed regression) This transforms the data by sub-tracting from the equation for time t the equationfor time t-1 multiplied by the autocorrelationcoefficient The AR(1) results are substantial-ly similar to those reported in Table 2 (availableon the Online Supplement ASR Web site) Theone exception is that affirmative action plan issignificant for whites only at the p lt 01 levelWe report seemingly unrelated regression mod-els in Table 2 because they account for related-ness of outcome variables and are thus moreefficient and because they allow us to comparecoefficients for different groups

Because our analyses cover more than threedecades we also explored two theories of tim-ing and program efficacy (results available onrequest) to rule out the possibility that some pro-grams showing no effects in the aggregate actu-ally were effective at certain points in timeOne theory is that employer practices are more



Mon 25 Sep 2006 165130

effective under active regulatory regimes Wethus added to the model reported in Table 2interaction terms between each of the practicesand the Reagan and first Bush era (1981ndash1992)as well as the Bill Clinton and George W Bushera (1993ndash2002) The comparison period 1971to 1980 encompassed the activist Nixon admin-istration the brief Ford administration and theactivist Carter administration (Skrentny 1996)A finding that programs were more effectiveduring the 1970s might help to explain whyresearch on the period (eg Leonard 1990)found the greatest increases in black employ-ment among contractors We find no evidencethat programs operated differently across peri-ods

The second timing argument is that earlyprogram adopters are those most committed tochange (Tolbert and Zucker 1983) We lookedat whether the effects of each practice werestronger among the first 15 25 and 40 percentof eventual adopters Our analyses showed thatpractices are no more effective among earlyadopters

We also explored whether some programsshowed weak effects in the models because theyhad differential effects by establishment sizeor industry With regard to size interactionssome negative program effects were neutral-ized in very large establishments but the pro-grams that proved ineffective in general were noteffective among large or small organizations Inindustry interactions most program effects werestable in direction if not in magnitude acrossindustries One notable pattern was that theeffect of aff irmative action plans on blackwomen was negative in manufacturing and pos-itive in service as discussed earlier

Finally we were concerned that surveyrespondent reports of early program dates mightbe inaccurate which could cause us to under-estimate program effects by including post-treatment values (ie that reflect changesattributable to a program) as pretreatment dataWe were particularly concerned about resultsshowing weak effects for training evaluationsnetworking and mentoring Correlationsbetween respondent tenure and adoption yearswere small and not significant the one excep-tion being for networking (correlation of ndash020p lt 005) To evaluate the effects of measure-ment error we re-ran Table 2 models elimi-nating establishment-year spells before 1990

thus excluding from the analysis possibly erro-neous information on early years of adoptionUsing fixed-effects models to analyze only datafor 1990ndash2002 would prevent us from evaluat-ing the effects of programs adopted any timebefore 1990 so we first replicated the full analy-sis (for the entire period) without fixed estab-lishment effects replacing differenced variableswith undifferenced variables The results weresimilar to those presented in Table 2 Then usingthe undifferenced variables we re-ran the mod-els eliminating all establishment-year spellsbefore 1990 We lost many spells but the sub-stantive results held up (for results see OnlineSupplement on ASR Web site) This increasesour confidence in the models and particularlyin the weak effects of training evaluations net-working and mentoring

CONCLUSION

The antidiscrimination measures we study havebecome popular among employers HR man-agers lawyers and advocacy groups despite theabsence of hard evidence that they work (Bisom-Rapp 1999 Krawiec 2003) Employers use thesepractices to defend themselves in court andthe courts in many cases accept them as goodfaith efforts to stamp out discrimination(Edelman et al 2005) There are reasons tobelieve that employers adopt antidiscrimina-tion measures as window dressing to inoculatethemselves against liability or to improvemorale rather than to increase managerial diver-sity In the final analysis however the measureof these programsmdashfor scholars practitionersand the courtsmdashshould be whether they do any-thing to increase diversity Using EEO-1 reportswe cannot examine whether these programshelp women and African Americans to move upfrom the bottom rungs of management But wecan show that some popular diversity programsat least help women and African Americans toclimb into the ranks of management Other pop-ular programs do not do even that

There is a rich tradition of theory and researchon the causes of workplace inequality We con-tend that this work may not always hold clearimplications for remedies The question of howto reduce inequality is just as deserving of atten-tion Our conceptualization of different types ofdiversity programs and our analyses of theireffects lay the groundwork for research and the-



Mon 25 Sep 2006 165130

ory on the remediation of inequality in work-places

Broadly speaking our findings suggest thatalthough inequality in attainment at work maybe rooted in managerial bias and the social iso-lation of women and minorities the best hopefor remedying it may lie in practices that assignorganizational responsibility for change Ourown theory of the remediation of inequalitybuilds on classical organizational sociologyrather than on theories of cognitive bias or socialnetworks (see also Blum Fields and Goodman1994)

Structures that embed accountability author-ity and expertise (affirmative action plansdiversity committees and taskforces diversitymanagers and departments) are the most effec-tive means of increasing the proportions ofwhite women black women and black men inprivate sector management Moreover theyshow effects even in the presence of controls forthe specific initiatives that specialists oftenimplement from formal hiring and promotionrules to workndashfamily programs Responsibilitystructures also catalyze the other diversity pro-grams rendering each a bit more effective forone group Some programs also prove moreeffective among federal contractors likelybecause legal requirements encourage employ-ers to assign responsibility for compliance

Practices that target managerial bias throughfeedback (diversity evaluations) and education(diversity training) show virtually no effect inthe aggregate They show modest positiveeffects when responsibility structures are alsoin place and among federal contractors Butthey sometimes show negative effects other-wise Research to date from HR experts and psy-chologists suggests that interactive trainingworkshops of the kind we examine often gen-erate backlash Finally programs designed tocounter the social isolation of women andminorities through mentoring and networkingare disappointing although mentoring doesappear to help black women

The poor performance of practices thataddress socialndashpsychological and socialndashrela-tional sources of inequality should not be takenas evidence that these forces do not producesocial inequality A preponderance of empiricalresearch shows that bias and poor network con-nections contribute to inequality Further

research is needed to determine why these pro-grams do not live up to their promise

Much management theorizing from law andeconomics scholars (Becker 1968 Gray andShadbegian 2005 Posner 1992 see alsoSimpson 2002) and psychologists (eg Tetlock1985) suggests that corporate behavior is bestcontrolled by doling out incentives to individ-ual managers and shaping their attitudes Thisapproach is rooted in a sort of methodologicalindividualism that is prominent in managementresearch and practice However when it comesto addressing corporate inequality we find thatthe strategies designed to change individualsare less effective than the conventional man-agement solution of setting goals and assigningresponsibility for moving toward these goals

That said the three programs we found to bemost effective likely operate in somewhat dif-ferent ways Whereas affirmative action plansand diversity staff both centralize authority overand accountability for workforce compositiondiversity committees locate authority andaccountability in an interdepartmental task forceand may work by causing people from differentparts of the organization to take responsibilityfor pursuing the goal of integration

In this study we examine managers alone Itis important for both theory and practice toextend this research to other occupationalgroups Yet for employers seeking solutions tothe problem of gender and racial segregationour analyses offer hope Most employers dosomething to promote diversitymdash76 percenthad adopted one of these seven programs by2002mdashbut do they do what is most effectiveDiversity committees have been quite effectiverequiring neither additional staff nor expensiveconsultants Less than 20 percent of the estab-lishments we studied had them by 2002Diversity staff are also quite effective but only11 percent of establishments had them On theother hand diversity training which 39 percentof establishments had adopted and which canbe quite costly was not very effective andshowed adverse effects among noncontractors

Even the programs that work best have mod-est effects particularly for African Americanswho are poorly represented to begin withDiversity committees raise the proportion ofblack women in management by a remarkable30 percent on average but from a baseline ofonly 14 percent Appointing full-time diversi-



Mon 25 Sep 2006 165130

ty staffer raises the proportion of black men bya healthy 14 percent but from a baseline ofonly 21 percent These programs alone willnot soon change the look of management Notehowever that our sample of large private firmshas changed less quickly than the economy asa whole In young start-up firms and in the pub-lic sector these practices may be even moreeffective than they are in our sample

The effects of these programs should not beconflated with the effects of antidiscriminationlegislation First as we demonstrate federalaffirmative action regulations clearly mediatethe efficacy of diversity evaluations and train-ing Our findings thus go against the popularclaim that antidiscrimination regulation is nolonger needed because diversity programs havegained a life of their own (Fisher 1985Liberman 2003) Moreover it was federal reg-ulations that led employers to first establishaffirmative action plans the most commonintervention and one of the most effective

Second enforcement has been effectiveregardless of corporate policies As researchhas shown and as our findings support Title VIIlawsuits and affirmative action compliancereviews led to increases in womenrsquos and minori-tiesrsquo share of management jobs especially inperiods and judicial circuits wherein civil rightsenforcement was strong (Kalev and Dobbinforthcoming Leonard 1989 1990 Skaggs2001)

Finally to assess the impact of antidiscrimi-nation legislation on employment inequalityone needs to consider broader political socialand cultural changes associated with the CivilRights Act affirmative action and related laws(Burstein 2000) Yet if the effects of governmentantidiscrimination measures have slowed assome observers suggest then we should wasteno time sorting out which corporate programsare effective

Alexandra Kalev received her PhD from Princetonin 2005 Her dissertation examines how workplacerestructuring (ldquohigh performancerdquo systems anddownsizing) affects the careers of women and minori-ties Kalev is a postdoctoral fellow in the RobertWood Johnson Scholars in Health Policy ResearchProgram at UC Berkeley studying gender and racialdisparities in work related injuries and illnessesKalev has published with Frank Dobbin on civilrights law enforcement in the face of deregulation(Law and Social Inquiry) and with Erin Kelly on how

companies manage flexible schedules (Socio-Economic Review)

Frank Dobbin is Professor of Sociology at HarvardHe edited The New Economic Sociology A Reader(Princeton University Press) and The Sociology ofthe Economy (Russell Sage Foundation) both pub-lished in 2004 He is continuing work with Kalev andKelly on the effects of employer policies on workforcediversity and is spending the 2006ndash2007 academicyear at the Radcliffe Institute for Advanced Study withfellowships from Radcliffe and from the John SimonGuggenheim Foundation

Erin L Kelly is Assistant Professor of Sociology atthe University of Minnesota Her research on thedevelopment diffusion and implementation of fam-ily-supportive policies has appeared in the AmericanJournal of Sociology and the SocioEconomic ReviewShe and Phyllis Moen are conducting a multimethodstudy of whether and how flexible work initiativesaffect organizational cultures the experiences ofworkers on the job and the health and well-being ofworkers and their families That project is part of theNational Institutes of Healthrsquos research network onwork family health and well-being

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Baldi Stephane and Debra Branch McBrier 1997ldquoDo the Determinants of Promotion Differ forBlacks and Whites Evidence from the US LaborMarketrdquo Work and Occupations 24478ndash97

Baron James N 1984 ldquoOrganizational Perspectiveson Stratificationrdquo Annual Review of Sociology1037ndash69

Baron James N and William T Bielby 1985ldquoOrganizational Barriers to Gender Equality SexSegregation of Jobs and Opportunitiesrdquo Pp233ndash251 in Gender and the Life Course edited byA S Rossi New York Aldine de Gruyter

Baron James N Brian S Mittman and Andrew ENewman 1991 ldquoTargets of OpportunityOrganizational and Environmental Determinantsof Gender Integration within the California CivilServices 1976ndash1985rdquo American Journal ofSociology 961362ndash401

Baron James N and Jeffrey Pfeffer 1994 ldquoTheSocial Psychology of Organizations andInequalityrdquo Social Psychology Quarterly57190ndash209

Baugh Gayle S and George B Graen 1997 ldquoEffectsof Team Gender and Racial Composition onPerceptions of Team Performance in Cross-



Mon 25 Sep 2006 165130

Functional Teamsrdquo Group and OrganizationManagement 22366

Becker Gary 1968 ldquoCrime and Punishment AnEconomic Approachrdquo Journal of PoliticalEconomy 76 169ndash217

Bendick Mark Jr Mary Lou Egan and Suzanne MLofhjelm 1998 The Documentation andEvaluation of Antidiscrimination Training in theUnited States Washington DC Bendick and EganEconomic Consultants

Bisom-Rapp Susan 1999 ldquoBulletproofing theWorkplace Symbol and Substance in EmploymentDiscrimination Law Practicerdquo Florida StateUniversity Law Review 26959ndash1049

Blair-Loy Mary 2001 ldquoItrsquos Not Just What You KnowItrsquos Who You Know Technical KnowledgeRainmaking and Gender among FinanceExecutivesrdquo Research in the Sociology of Work1051ndash83

Blau Francine D and Andrea Beller 1992 ldquoBlackWhite Earnings over the 1970s and 1980s GenderDifferences in Trendsrdquo The Review of Economicsand Statistics 74276ndash86

Blum Terry C Dail L Fields and Jodi S Goodman1994 ldquoOrganization-Level Determinants ofWomen in Managementrdquo Academy ofManagement Journal 37241ndash68

Bond Megan A and Jean L Pyle 1988 ldquoDiversityDilemmas at Workrdquo Journal of ManagementInquiry 7252ndash69

Briscoe Forrest and Sean Safford 2005 ldquoAgency inDiffusion Activism Imitation and the Adoptionof Domestic Partner Benefits among the Fortune500rdquo Unpublished paper Pennsylvania StateUniversity University Park PA

Budig Michelle J and Paula England 2001 ldquoTheWage Penalty for Motherhoodrdquo AmericanSociological Review 66204ndash25

Bureau of National Affairs 1986 Work and FamilyA Changing Agenda Washington DC Bureau ofNational Affairs

Burke Ronald J and Carol A McKeen 1997 ldquoNotEvery Managerial Woman Who Makes It Has aMentorrdquo Women in Management Review 12136ndash9

Burstein Paul 2000 ldquoThe Impact of EEO Law ASocial Movement Perspectiverdquo Pp 129ndash155 inLegacies of the 1964 Civil Rights Act edited by BGrofman Charlottesville VA University ofVirginia Press

Burt Ronald S 1998 ldquoThe Gender of Social CapitalrdquoRationality and Society 105ndash46

Carter John 2003 Ethnicity Exclusion and theWorkplace London Palgrave Macmillan Press

Castilla Emilio J 2005 ldquoSocial Networks andEmployee Performance in a Call Centerrdquo AmericanJournal of Sociology 1101243ndash84

Catalyst 1998 Advancing Women in Business - theCatalyst Guide Best Practices from the CorporateLeaders San Francisco Jossey-Bass

Cohen Lisa E Joseph P Broschak and Heather AHaveman 1998 ldquoAnd Then There Were More TheEffect of Organizational Sex Composition on theHiring and Promoting of Managersrdquo AmericanSociological Review 63711ndash27

Collins Randall 1979 The Credential Society AHistorical Sociology of Education andStratification New York Academic Press

Crow Kelly 2003 ldquoReaching Out Staying Focusedon Diversity Goalsrdquo New York Times October 28p G2

Dobbin Frank Lauren B Edelman John W MeyerW Richard Scott and Ann Swidler 1988 ldquoTheExpansion of Due Process in Organizationsrdquo Pp71ndash100 in Institutional Patterns andOrganizations Culture and Environment editedby L G Zucker Cambridge MA Ballinger

Dobbin Frank John R Sutton John W Meyer andW Richard Scott 1993 ldquoEqual Opportunity Lawand the Construction of Internal Labor MarketsrdquoAmerican Journal of Sociology 99396ndash427

Donohue John J and James Heckman 1991ldquoContinuous Versus Episodic Change The Impactof Federal Civil Rights Policy on the EconomicStatus of Blacksrdquo Journal of Economic Literature291603ndash43

Donohue John J III and Peter Siegelman 1991ldquoThe Changing Nature of EmploymentDiscrimination Litigationrdquo Stanford Law Review43983ndash1033

Edelman Lauren B 1990 ldquoLegal Environments andOrganizational Governance The Expansion ofDue Process in the American WorkplacerdquoAmerican Journal of Sociology 951401ndash40

Edelman Lauren B Sally Riggs Fuller and IonaMara-Drita 2001 ldquoDiversity Rhetoric and theManagerialization of the Lawrdquo American Journalof Sociology 1061589ndash641

Edelman Lauren B Linda Hamilton Krieger ScottEliason and K T Albiston 2005 ldquoJudicialDeference to Institutionalized ComplianceStrategiesrdquo Working Paper Thesis Boalt HallSchool of Law University of California BerkeleyCA

Edelman Lauren B and Stephen M Petterson 1999ldquoSymbols and Substance in OrganizationsrsquoResponse to Civil Rights Lawrdquo Research in SocialStratification and Mobility 17107ndash35

Elvira Marta M and Christopher D Zatzick 2002ldquoWhorsquos Displaced First The Role of Race inLayoff Decisionsrdquo Industrial Relations 41329ndash61

Equal Employment Opportunity Commission(EEOC) 1998 ldquoBest Practices of Private SectorEmployersrdquo Washington DC Equal EmploymentOpportunity Commission Retrieved April 192004 (httpwwweeocgovabouteeoctask_reportspracticehtml)

Felmlee D H and L L Hargens 1988 ldquoEstimationand Hypothesis Testing for Seemingly Unrelated



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Regression A Sociological Applicationrdquo SocialScience Research 17384ndash99

Fernandez Roberto M and Isabel Fernandez-Mateo2006 ldquoNetwork Race and Hiringrdquo AmericanSociological Review 7142ndash71

Fisher Anne B 1985 ldquoBusinessmen Like to Hire bythe Numbersrdquo Fortune September 16 pp 26ndash30

Fiske Susan T 1998 ldquoStereotyping Prejudice andDiscriminationrdquo Pp 357ndash411 in StereotypingPrejudice and Discrimination edited by D TGilbert S T Fiske and G Lindzey New YorkMcGraw-Hill

Fox John 1997 Applied Regression Analysis LinearModels and Related Methods Thousand OaksCA Sage

Fretz C F and Joanne Hayman 1973 ldquoProgress forWomen Men Are Still More Equalrdquo HarvardBusiness Review SeptemberndashOctober pp133ndash142

Friedman Raymond A and Kellina M Craig 2004ldquoPredicting Joining and Participating in MinorityEmployee Network Groupsrdquo Industrial Relations43793ndash816

Goldstein Morris and Robert S Smith 1976ldquoChanges in Labor Market for Black Americans1948ndash1970rdquo Industrial and Labor RelationsReview 29523ndash43

Gorman Elizabeth 2005 ldquoGender StereotypesSame-Gender Preferences and OrganizationalVariation in the Hiring of Women Evidence fromLaw Firmsrdquo American Sociological Review70702ndash28

Granovetter Mark 1974 Getting a Job A Study ofContracts and Careers Chicago University ofChicago Press

Gray Wayne B and Ronald J Shadbegian 2005ldquoWhen and Why Do Plants Comply Paper Millsin the 1980srdquo Law and Policy 27 238ndash60

Greene W H 1997 Econometric Analysis UpperSaddle River NJ Prentice-Hall

Hanushek Erin A and John E Jackson 1977Statistical Methods for Social Scientists NewYork NY Academic Press

Heckman James and Kenneth Wolpin 1976 ldquoDoesthe Contract Compliance Program Work AnAnalysis of Chicago Datardquo Industrial and LaborRelations Review 29544ndash64

Heckman James J 1979 ldquoSample Selection Bias asa Specification Errorrdquo Econometrica 45153ndash61

Heckman James J and Brook S Payner 1989ldquoDetermining the Impact of FederalAntidiscrimination Policy on the Economic Statusof Blacks A Study of South Carolinardquo AmericanEconomic Review 79138ndash77

Heilman Madeline E 1995 ldquoSex Stereotypes andTheir Effects in the Workplace What We Knowand What We Donrsquot Knowrdquo Journal of SocialBehavior and Personality 103ndash26

Heilman Madeline E Caryn J Block and Peter

Stathatos 1997 ldquoThe Affirmative Action Stigmaof Incompetence Effects of PerformanceInformation Ambiguityrdquo Academy of ManagementJournal 40603ndash25

Hemphill Hellen and Ray Haines 1997Discrimination Harassment and the Failure ofDiversity Training What to Do Now WestportCT Quorum Books

Hicks Alexander M 1994 ldquoIntroduction to PoolingrdquoPp 169ndash188 in The Comparative PoliticalEconomy of the Welfare State edited by T Janoskiand A M Hicks New York Cambridge UniversityPress

Holzer Harry J and David Neumark 2000 ldquoWhatDoes Affirmative Action Dordquo Industrial andLabor Relations Review 53240ndash71

Hsiao Cheng 1986 Analysis of Panel Data vol11 Cambridge England Cambridge UniversityPress

Ibarra Herminia 1992 ldquoHomophily and DifferentialReturns Sex Differences in Network Structureand Access in an Advertising Firmrdquo AdministrativeScience Quarterly 34422ndash47

mdashmdashmdash 1995 ldquoRace Opportunity and Diversityof Social Circles in Managerial NetworksrdquoAcademy of Management Journal 38673ndash703

Jacobs Jerry A 1989a ldquoLong-Term Trends inOccupational Segregation by Sexrdquo AmericanJournal of Sociology 95160ndash73

mdashmdashmdash 1989b Revolving Doors Sex Segregationand Womenrsquos Careers Stanford CA StanfordUniversity Press

mdashmdashmdash 1992 ldquoWomenrsquos Entry into ManagementTrends in Earnings Authority and Values amongSalaried Managersrdquo Administrative ScienceQuarterly 37282ndash301

Jost John T Mahzarin Banaji and Brian A Nosek2004 ldquoA Decade of System-Justification TheoryAccumulated Evidence of Conscious andUnconscious Bolstering of the Status QuordquoPolitical Psychology 25881ndash919

Kalev Alexandra and Frank Dobbin ForthcomingldquoEnforcement of Civil Rights Law in PrivateWorkplaces The Effects of Compliance Reviewsand Lawsuits over Timerdquo Law and Social Inquiry

Kalleberg Arne L David Knoke Peter V Marsdenand Joe L Spaeth 1996 Organizations inAmerica Analyzing Their Structures and HumanResource Practices Thousand Oaks CA Sage

Kalleberg Arne L and Arne Mastekaasa 2001ldquoSatisfied Movers Committed Stayers The Impactof Job Mobility on Work Attitudes in NorwayrdquoWork and Occupations 28183ndash209

Kanter Rosabeth Moss 1977 Men and Women of theCorporation New York NY Basic Books

Kelly Erin 2000 ldquoCorporate Family Policies in USOrganizations 1965ndash1997rdquo PhD dissertationDepartment of Sociology Princeton UniversityPrinceton NJ



Mon 25 Sep 2006 165130

mdashmdashmdash 2003 ldquoThe Strange History of Employer-Sponsored Childcare Interested ActorsUncertainty and the Transformation of Law inOrganizational Fieldsrdquo American Journal ofSociology 109606ndash49

Kidder Deborah L Melenie J Lankau DonnaChrobot-Mason Kelly A Mollica and RaymondA Friedman 2004 ldquoBacklash toward DiversityInitiatives Examining the Impact of DiversityProgram Justif ication Personal and GroupOutcomesrdquo International Journal of ConflictManagement 1577ndash104

King Mary C 1992 ldquoOccupational Segregation byRace and Sex 1940ndash1988rdquo Monthly Labor Review11530

Kletzer Lori 1998 ldquoJob Displacementrdquo Journal ofEconomic Perspective 12115ndash37

Kochan Thomas A Katerina Bezrukova Robin JEly Susan Jackson Aparna Joshi Karen JehnJonathan S Leonard David I Levine and DavidA Thomas 2003 ldquoThe Effect of Diversity onBusiness Performance Report of the DiversityResearch Networkrdquo Human ResourcesManagement 423ndash21

Konrad Alison M and Frank Linnehan 1995ldquoFormalized HRM Structures Coordinating Equal-Employment Opportunity or ConcealingOrganizational Practicesrdquo Academy ofManagement Journal 38787ndash820

Krawiec Kimberly D 2003 ldquoCosmetic Complianceand the Failure of Negotiated GovernancerdquoWashington University Law Quarterly 81487ndash544

Lemm Kristi and Mahzarin R Banaji 1999ldquoUnconscious Attitudes and Beliefs about Menand Womenrdquo Pp 215ndash35 in Perceiving andPerforming Gender edited by U Paseor and FBraun Opladen Germany Westdutscher Verlag

Leonard Jonathan S 1984 ldquoThe Impact ofAffirmative Action on Employmentrdquo Journal ofLabor Economics 2439ndash63

mdashmdashmdash 1985a ldquoUnions and the Employment ofBlacks Hispanics and Womenrdquo Industrial andLabor Relations Review 39115ndash32

mdashmdashmdash 1985b ldquoWhat Promises are Worth TheImpact of Affirmative Action Goalsrdquo The Journalof Human Resources 203ndash20

mdashmdashmdash 1989 ldquoWomen and Affirmative ActionrdquoThe Journal of Economic Perspectives 361ndash75

mdashmdashmdash 1990 ldquoThe Impact of Affirmative ActionRegulation and Equal Employment OpportunityLaw on Black Employmentrdquo The Journal ofEconomic Perspectives 447ndash63

Liberman Vadim 2003 ldquoWorkplace Diversity ItrsquosAll in the Mixrdquo Across the Board XL51ndash2

Linnehan Frank and Alison Konrad 1999 ldquoDilutingDiversity Implications for Intergroup inOrganizationsrdquo Journal of Management Inquiry8399ndash413

Lunding F S C E Clements and D S Perkins

1979 ldquoEveryone Who Makes It Has a MentorrdquoHarvard Business Review 5689ndash101

MacDuffie John Paul 1995 ldquoHuman ResourceBundles and Manufacturing PerformanceOrganizational Logic and Flexible ProductionSystems in the World Auto Industryrdquo Industrialand Labor Relations Review 482

McGuire G M 2000 ldquoGender Race Ethnicity andNetworks The Factors Affecting the Status ofEmployeesrsquo Network Membersrdquo Work andOccupations 27500ndash23

Meyer John W and Brian Rowan 1977ldquoInstitutionalized Organizations Formal Structureas Myth and Ceremonyrdquo American Journal ofSociology 83340ndash63

Milkman Ruth 1985 ldquoWomen Workers Feminismand the Labor Movement since the 1960srdquo inWomen Work and Protest A Century of WomenrsquosLabor History edited by R Milkman BostonMA Routledge and Kegan Paul

Miller Joanne 1994 Corporate Responses toDiversity New York Center for the New AmericanWorkplace at Queens College

Moore Paul Cameron 2001 ldquoThe Transfer of Humanand Social Capital Employee DevelopmentThrough Assigned Peer Mentoringrdquo PhD disser-tation Stanford Graduate School of BusinessStanford CA

Mouw Ted 2003 ldquoSocial Capital and Finding a JobDo Contacts Matterrdquo American SociologicalReview 68868ndash98

Naff Katherine C and J Edward Kellough 2003ldquoEnsuring Employment Equity Are FederalDiversity Programs Making a DifferencerdquoInternational Journal of Public Administration261307ndash36

Nelson Thomas E Michele Acker and ManisMelvin 1996 ldquoIrrepressible Stereotypesrdquo Journalof Experimental Social Psychology 3213ndash38

Neumark David and Rosella Gardecki 1996ldquoWomen Helping Women Role Model andMentoring Effects on Female PhD Student inEconomicsrdquo Working Paper National Bureau ofEconomic Research Cambridge MA

Orton Douglas J and Karl E Weick 1990 ldquoLooselyCoupled Systems A ReconceptualizationrdquoAcademy of Management Review 15203ndash23

Osterman Paul 1994 ldquoHow Common is WorkplaceTransformation and Who Adopts Itrdquo Industrialand Labor Relations Review 47173ndash88

mdashmdashmdash 2000 ldquoWork Reorganization in an Era ofRestructuring Trends in Diffusion and Effects onEmployee Welfarerdquo Industrial and Labor RelationsReview 53179ndash96

Perry-Smith Jill E and Terry C Blum 2000ldquoWorkndashFamily Human Resource Bundles andPerceived Organizational Performancerdquo Academyof Management Journal 431107ndash17

Petersen Trond 1985 ldquoA Comment on Presenting



Mon 25 Sep 2006 165130

Results from Logit and Probit Modelsrdquo AmericanSociological Review 50130ndash131

Petersen Trond Ishak Saporta and Marc DavidSeidelm 1998 ldquoOffering a Job Meritocracy andSocial Networksrdquo American Journal of Sociology106763ndash816

Posner Richard A 1992 Economic Analysis of Law4th edition Boston Little Brown and Company

Presidential Glass Ceiling Commission 1995 ASolid Investment Making a Full Use of ourNationsrsquo Human Capital Washington DC USDepartment of Labor Glass Ceiling Commission

Ragins Belle Rose 1995 ldquoDiversity Power andMentorship in Organizations A CulturalStructural and Behavioral Perspectiverdquo Pp 91ndash132in Diversity in Organizations New Perspectives fora Changing Workplace edited by M M ChemersS Oskamp and M A Costanzo Thousand OaksCA Sage

Reskin Barbara F 1998 The Realities of AffirmativeAction in Employment Washington DC AmericanSociological Association

mdashmdashmdash 2000 ldquoThe Proximate Causes ofEmployment Discriminationrdquo ContemporarySociology 29319ndash28

mdashmdashmdash 2003 ldquoIncluding Mechanisms in OurModels of Ascriptive Inequalityrdquo AmericanSociological Review 681ndash21

Reskin Barbara F and Debra B McBrier 2000ldquoWhy Not Ascription OrganizationsrsquoEmploymentof Male and Female Managersrdquo AmericanSociological Review 65210ndash33

Reskin Barbara F and Patricia Roos 1990 JobQueues Gender Queues Explaining WomenrsquosInroads into Male Occupations Philadelphia PATemple University Press

Robinson Corre Tiffany Taylor Donald Tomaskovic-Devey Catherine Zimmer and Matthew W IrvineJr 2005 ldquoStudying RaceEthnic and SexSegregation at the Establishment-LevelMethodological Issues and SubstantiveOpportunities Using EEO-1 Reportsrdquo Work andOccupations 325ndash38

Roche Gerard R 1979 ldquoMuch Ado about MentorsrdquoHarvard Business Review 5714

Rossi Peter H Mark W Lipsey and Howard EFreeman 2004 Evaluation A SystematicApproach Thousand Oaks CA Sage

Rynes Sara and Benson Rosen 1995 ldquoA FieldSurvey of Factors Affecting the Adoption andPerceived Success of Diversity TrainingrdquoPersonnel Psychology 48247ndash70

Salancik Gerald R and Jeffrey Pfeffer 1978ldquoUncertainty Secrecy and the Choice of SimilarOthersrdquo Social Psychology 41246ndash55

Scott W Richard 2001 Institutions andOrganizations Thousand Oaks CA Sage

Selznick Philip 1949 TVA and the Grass RootsBerkeley CA University of California Press

Shaeffer Ruth G 1973 ldquoNondiscrimination inEmployment Changing Perspectives 1963ndash1972rdquoNew York The Conference Board

Shenhav Yehouda A and Yitchak Haberfeld 1992ldquoOrganizational Demography and InequalityrdquoSocial Forces 71123ndash43

Sidanius Jim Erik Devereux and Felicia Pratto2001 ldquoA Comparison of Symbolic Racism Theoryand Social Dominance Theory as Explanationsfor Racial Policy Attitudesrdquo Journal of SocialPsychology 132377ndash95

Simpson Sally S 2002 Corporate Crime Law andSocial Control New York Cambridge UniversityPress

Skaggs Sheryl 2001 ldquoDiscrimination LitigationImplications for Women and Minorities in RetailSupermarket Managementrdquo PhD dissertationDepartment of Sociology North Carolina StateUniversity Raleigh NC

Skrentny John D 1996 The Ironies of AffirmativeAction Politics Culture and Justice in AmericaChicago University of Chicago Press

Smith James P and Finis Welch 1984 ldquoAffirmativeAction and the Labor Marketsrdquo Journal of LaborEconomics 2269ndash301

Snyder Lori D 2003 ldquoAre Management-BasedRegulations Effective Evidence from StatePollution Prevention Programsrdquo Regulatory PolicyProgram Working Paper RPP-2003-21 Center forBusiness and Government John F KennedySchool of Government Harvard UniversityCambridge MA

Society for Human Resources Management 2004ldquoWhat Are The Components of a SuccessfulDiversity Initiativerdquo Alexandria VA Society forHuman Resources Management Retrieved April18 2004 (httpwwwshrmorgdiversitycomponentsasp)

Sturm Susan 2001 ldquoSecond-GenerationEmployment Discrimination A StructuralApproachrdquo Columbia Law Review 101459ndash568

Sutton John R and Frank Dobbin 1996 ldquoThe TwoFaces of Governance Responses to LegalUncertainty in American Firms 1955ndash1985rdquoAmerican Sociological Review 61794ndash811

Tajfel Henri and John C Turner 1979 ldquoAnIntegrative Theory of Intergroup Conflictrdquo Pp33ndash47 in The Social Psychology of IntergroupRelations edited by W G Austin and S WorchelMonterey CA BrooksCole

Taylor Marylee 1995 ldquoWhite Backlash toWorkplace Affirmative Action Peril or MythrdquoSocial Forces 731385ndash414

Tetlock E Philip 1985 ldquoAccountability A SocialCheck on the Fundamental Attribution ErrorrdquoSocial Psychology Quarterly 48227ndash36

Thomas David A 2001 ldquoThe Truth about MentoringMinorities Race Mattersrdquo Harvard BusinessReview April pp 99ndash107



Mon 25 Sep 2006 165130

Tolbert Pamela S and Lynne G Zucker 1983ldquoInstitutional Sources of Change in the FormalStructure of Organizations The Diffusion of CivilService Reform 1880ndash1935rdquo AdministrativeScience Quarterly 2822ndash39

Tomaskovic-Devey Donald Catheine R ZimmerKevin Stainback Corre Robinson Tiffany Taylorand Tricia McTague 2006 ldquoDocumentingDesegregation Segregation in AmericanWorkplaces by Race Ethnicity and Sex1966ndash2003rdquo American Sociological Review71565ndash588

Tomaskovic-Devey Donald D 1993 Gender andRacial Inequality at Work The Sources andConsequences of Job Segregation Ithaca NYIndustrial and Labor Relations Press

Townsend Anthony M and Dow K Scott 2001ldquoTeam Racial Composition Member Attitudesand Performance A Field Studyrdquo IndustrialRelations 40317ndash37

US Department of Labor 2005 ldquoPlacement GoalsCode of Federal Regulations Pertaining toEmployment Standards Administration Title 41Part 60-2 subpart Brdquo Washington DC USDepartment of Labor

Vallas Steven P 2003 ldquoRediscovering the ColorLine within Work Organizations The `Knittingof Racial Groupsrsquo Revisitedrsquo Work andOccupations 30379ndash400

Vernon-Gerstenfeld Susan and Edmund Burke 1985ldquoAffirmative Action in Nine Large Companies AField Studyrdquo Personnel 6254ndash60

Weber Max [1968] 1978 ldquoSociological Categoriesof Economic Actionrdquo Pp 63ndash211 in Economyand Society vol 1 edited by R Guenther and CWittich Berkeley CA University of CaliforniaPress

Wernick Ellen D 1994 Preparedness CareerAdvancement and the Glass Ceiling WashingtonDC US Department of Labor Glass CeilingCommission

Western Bruce 2002 ldquoThe Impact of Incarcerationon Wage Mobility and Inequalityrdquo AmericanSociological Review 67477ndash98

Williams Joan 2000 Unbending Gender WhyFamily Work Conflict and What to Do about ItNew York Oxford University Press

Williams Katherine Y and Charles A OrsquoReilly1998 ldquoDemography and Diversity inOrganizationsrdquo Pp 77ndash140 in Research inOrganizational Behavior vol 20 edited by B MStaw and L L Cummings Greenwich CT JAI

Winship Christopher and Larry Radbill 1994ldquoSampling Weights and Regression AnalysisrdquoSociological Methods and Research 23230ndash57

Winterle Mary J 1992 Work Force DiversityCorporate Challenges Corporate Responses NewYork Conference Board

Zellner Alfred 1962 ldquoAn Efficient Method ofEstimating Seemingly Unrelated Regressions andTests for Aggregation Biasrdquo Journal of theAmerican Statistical Association 57348ndash68



Mon 25 Sep 2006 165130

loosely based on academic theories that point tocauses of workplace inequality ranging fromunwitting bias (Lemm and Banaji 1999) todependence on networks for hiring and pro-motion (Reskin and McBrier 2000) Whereasthere has been a great deal of research on thesources of inequality there has been little on theefficacy of different programs for countering itAt best ldquobest practicesrdquo are best guesses Weknow a lot about the disease of workplaceinequality but not much about the cure

We examine the effects of seven commondiversity programsmdashaffirmative action plansdiversity committees and taskforces diversitymanagers diversity training diversity evalua-tions for managers networking programs andmentoring programsmdashon the representation ofwhite men white women black women andblack men in the management ranks of privatesector firms Each of these programs may wellincrease diversity To date there has been littleevidence one way or the other This is surpris-ing given the popularity and cost of the pro-grams Our contribution is to bring to bear richnew data to theoretically distinguish three typesof diversity programs and to show that organi-zational structures allocating responsibility forchange may be more effective than programs tar-geting either managerial bias or the social iso-lation of disadvantaged groups

Previous empirical studies of antidiscrimi-nation and diversity programs have been limit-ed by data constraints Economists f irstcompared employers who are subject to affir-mative action requirements with those who arenot (Ashenfelter and Heckman 1976 Heckmanand Wolpin 1976 Leonard 1984) They lackeddata on employer programs Sociologists andeconomists studying employer programs exam-ine data at one or two points in time (but seeBaron Mittman and Newman 1991) analyzingthe effects of some programs without account-ing for others These studies indicate that someprograms may be effective but their findings areinconsistent (Baron et al 1991 Edelman andPetterson 1999 Holzer and Neumark 2000Konrad and Linnehan 1995 Leonard 1990Naff and Kellough 2003) Gender and racialsegregation has declined remarkably since the1970s when employers first adopted antidis-crimination programs (Jacobs 1989a King1992 Tomaskovic-Devey et al 2006) but there

is no hard evidence that these programs playeda role

We obtained the federal establishment-leveldata that economists have used (ie the annu-al EEO-1 reports that private sector establish-ments submit to the Equal EmploymentOpportunity Commission [EEOC]) We thensurveyed a sample of these establishments on thehistory of their personnel and diversity pro-grams so that we could analyze program effectson diversity

A strength of the EEO-1 reports is that theydetail annual employment by race ethnicityand gender in all medium and large private sec-tor workplaces A limitation is that they coveronly nine broad job categories collapsing intoldquomanagementrdquo all jobs above that of first-linesupervisor (Baron and Bielby 1985 Smith andWelch 1984) We know from previous researchthat women and African Americans are crowd-ed in the lowest ranks of management Even aswomen moved into management in the 1970sand 1980s ldquowomen managers continued to trailtheir male counterparts in both earnings andauthorityrdquo (Jacobs 1992) Thus our analysesindicate which diversity programs help womenand African Americans move at least into thebottom ranks of management and importantlywhich do not They cannot tell us whether anyof these practices help women and minorities tomove into the executive ranks

We find a clear pattern in the data Structuresestablishing responsibility (affirmative actionplans diversity committees and diversity staffpositions) are followed by significant increas-es in managerial diversity Programs that targetmanagerial stereotyping through education andfeedback (diversity training and diversity eval-uations) are not followed by increases in diver-sity Programs that address social isolationamong women and minorities (networking andmentoring programs) are followed by modestchanges The effects of these initiatives varyacross groups with white women benefitingmost followed by black women Black menbenefit least We also find that responsibilitystructures make training performance evalua-tions networking and mentoring programsmore effective Federal aff irmative actionrequirements which typically lead to assign-ment of responsibility for compliance also cat-alyze certain programs



Mon 25 Sep 2006 165130









RESPONSIBILITY




Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130














Mon 25 Sep 2006 165130




AND MENTORING







Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130









RESPONSIBILITY




Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130














Mon 25 Sep 2006 165130




AND MENTORING







Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130














Mon 25 Sep 2006 165130




AND MENTORING







Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130














Mon 25 Sep 2006 165130




AND MENTORING







Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130




AND MENTORING







Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130

LEGAL ENVIRONMENT









DATA AAND MMETHODS


EEOC DDATA





Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130

DEPENDENT VARIABLES





PRACTICES







Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130



CONTROL VARIABLES








Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130


able

1

Sel

ecte

d V

aria

bles

Use

d in

Ana

lysi

s of

Man

ager

ial W

orkf

orce

Com

posi

tion

Mea

nS

tand

ard

Dev

iati

onM

inim

umM

axim

umTy

peD

ata

Out

com

e V

aria

bles

(pe

rcen

t)mdash

Man

ager

s w

ho a

re w

hite

men

700

236

010

0C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re w

hite

wom

en22

221

20

100

Con

tinu

ous

EE

O-1

mdashM

anag

ers

who

are

bla

ck w

omen

14

42

066

7C

onti

nuou

sE

EO

-1mdash

Man

ager

s w

ho a

re b

lack

men

24

59

010

0C

onti

nuou

sE

EO

-1A

ffir

mat

ive

Act

ion

and

Div

ersi

ty M

easu

res

mdashA

ffir

mat

ive

acti

on p

lan

422

494

01

Bin

ary

Sur

vey

mdashF

ull t

ime

EE

Od

iver

sity

sta

ff0

452

060

1B

inar

yS

urve

ymdash

Div

ersi

ty c

omm

itte

e0

522

220

1B

inar

yS

urve

ymdash

Div

ersi

ty tr

aini

ng0

642

440

1B

inar

yS

urve

ymdash

Div

ersi

ty e

valu

atio

ns o

f m

anag

ers

102

303

01

Bin

ary

Sur

vey

mdashN

etw

orki

ng p

rogr

ams

064

244

01

Bin

ary

Sur

vey

mdashM

ento

ring

pro

gram

s0

331

790

1B

inar

yS

urve

yL

egal

Env

iron

men

tmdash

Aff

irm

ativ

e ac

tion

sta

tus

(gov

ernm

ent c

ontr

act)

455

498

01

Bin

ary

EE

O-1

mdashC

ompl

ianc

e re

view

149

356

01

Bin

ary

Sur

vey

mdashD

iscr

imin

atio

n la

wsu

its

341

474

01

Bin

ary

Sur

vey

mdashE

EO

C c

harg

es3

144

640

1B

inar

yS

urve

yO

rgan

izat

iona

l Str

uctu

res

mdashPe

rcen

t man

ager

s in

est

abli

shm

ent

124

090

002

789

Con

tinu

ous

EE

O-1

mdashE

stab

lish

men

t siz

e70

282

710

128

66C

onti

nuou

sE

EO

-1mdash

Uni

on a

gree

men

t2

544

360

1B

inar

yS

urve

ymdash

Form

al H

R p

olic

ies

491

72

516

09

Cou

ntS

urve

ymdash

In-h

ouse

att

orne

y2

774

480

1C

ount

Sur

vey

mdashS

peci

al r

ecru

itm

ent f

or w

omen

and

min

orit

ies

156

363

01

Bin

ary

Sur

vey

mdashW

ork-

fam

ily a

ccom

mod

atio

ns9

129

780

4C

ount

Sur

vey

Top

Man

agem

ent C

ompo

siti

on (

perc

ent)

mdashTo

p m

anag

ers

who

are

min

orit

ies

347

110

239

010

0C

onti

nuou

sS

urve

ymdash

Top

man

ager

s w

ho a

re w

omen

164

4523

575

010

0C

onti

nuou

sS

urve

y

Not

eN

= 1

626

5 L

abor

mar

ket a

nd e

cono

mic

env

iron

men

t var

iabl

es a

re in

clud

ed in

the

anal

yses

but

not

sho

wn

here

See

not

e to

Tab

le 2

for

a d

etai

led

list

of

vari

able

s no

t sho

wn

here

(se

e en

tire

list

of

cont

rol v

aria

bles

on

Onl

ine

Sup

plem

ent

ASR

Web

sit

e h

ttp

w

ww

2as

anet

org

jou

rnal

sas

r20

06t

oc05

2ht

ml)

EE

O =

equ

al e

mpl

oym

ent o

ppor

tuni

ty

HR

= h

uman

res

ourc

es


Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130


METHODS



FINDINGS









Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130







Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130










Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130











Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130












Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130






PROGRAM EFFICACY





Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130






















R2 (71 parameters) 3341 3165 3650 2811



Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130



FURTHER ANALYSES










Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130






CONCLUSION





Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130













Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130









REFERENCES










Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130


































Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130





































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130



































Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130


















Mon 25 Sep 2006 165130

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