J Bus Psychol (2013) 28:411–424 DOI 10.1007/s10869-013-9292-y
What Factors Influence Judges’ Rulings About the Legality of Affirmative Action Plans? Jennifer L. Thompson • Scott B. Morris
Published online: 16 March 2013 Ó Springer Science+Business Media New York 2013
Abstract Purpose When implementing affirmative action programs involving race and gender, human resource practitioners must balance efforts to increase workforce diversity against the need to avoid illegal reverse discrimination. The tension between non-discrimination law and preferential treatment is explored. In reverse discrimination case law, affirmative action plans are evaluated by judges along two dimensions: remedial need and limiting harm. The legal literature specifies certain factors such as statistical imbalance, employee qualification, and duration of plan that are usually examined within these two dimensions. Methodology A content analysis of 80 federal court cases was conducted to quantitatively analyze the weight and importance of these factors within judicial rulings as well as contextual factors (e.g., judge’s political affiliation, beneficiary of program) that may influence the outcome of affirmative action lawsuits. Results It was found that remedial need can be demonstrated by large statistical disparities in the workforce, and was also more likely to be found by Democratic than Republican judges. Limiting harm is more likely to be supported by plans that are of limited duration and do not use reserved slots, or quotas.
J. L. Thompson (&) Business Psychology Department, The Chicago School of Professional Psychology, 325 North Wells Avenue, Chicago, IL 60654, USA e-mail:
[email protected] S. B. Morris College of Psychology, Illinois Institute of Technology, Chicago, IL, USA
Implications The study provides empirically based recommendations for the design of legally defensible affirmative action plans that involve preferential treatment. Keywords Affirmative action Reverse discrimination Employment law Personnel selection Strict scrutiny
Introduction Affirmative action policies are commonplace in organizations as a means of seeking to promote workforce diversity and to comply with federal regulations. Despite their widespread adoption, considerable controversy persists around the use of affirmative action policies (Crosby et al. 2003; Kravitz et al. 1997). This controversy creates an ambiguous situation for human resource professionals seeking to develop fair and legally defensible employment practices. Much of the difficulty in implementing affirmative action programs stems from the tension between non-discrimination laws and affirmative action requirements. Title VII of the Civil Rights Act of 1964 and 1991 prohibits discrimination on the basis of five variables: race, gender, color, national origin, and religion. This law prevents discrimination in any direction, based on any ethnicity, any religion, and either gender. Many have argued non-discrimination alone is insufficient to overcome the lingering effects of historical discrimination (Crosby et al. 2003). Affirmative action goes beyond non-discrimination, and involves special efforts to increase the representation of underutilized groups. These efforts may, in some circumstances, involve preferential treatment of persons from minority groups. This article will focus on affirmative action based on race or gender. Affirmative action programs exist for other groups as well, including individuals with disabilities
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(Americans with Disabilities Act of 1990) and veterans (Vietnam Era Veterans’ Readjustment Assistance Act of 1974). However, affirmative action involving race and gender is distinct in that there is a potential tension between affirmative action goals and civil rights laws that prohibit consideration of race or gender in employment decisions. In the development and implementation of employment tests, training programs and performance management systems, human resource professionals must consider non-discrimination laws outlined by Title VII (Gutman 2000). This must be balanced with the organization’s affirmative action goals. A major concern for human resource professionals is that non-discrimination, or refraining from using factors such as race or gender, and increasing representation of certain groups by these same factors can be incompatible goals (Haley 1990; Walworth and DiChristina 1994). Affirmative action can be implemented through a large variety of practices within organizations. The different types of affirmative action are not often differentiated, especially in human resource vernacular. There is also a lack of understanding of when each type of affirmative action is appropriate to use. This research describes the types of affirmative action, the conditions under which affirmative action can involve preferential treatment, and a resolution of the conflict between non-discrimination laws and preferential treatment plans. The legal literature (Kilberg 1995; Walworth and DiChristina 1994) has developed general principles identifying when an affirmative action plan involving preferential treatment is legally defensible. Such plans must satisfy the two prongs of strict scrutiny: establishing a remedial need and limiting harm to non-beneficiaries. However, less is known about how these principles are interpreted by judges in the context of specific plans. While guided by legal precedent, judges have considerably discretion to determine the relevant factors on which to base their judgments. Though a content analysis of federal affirmative cases, the current research examines the weight judges give to various factors when determining the legality of preferential treatment plans. The analysis will also examine contextual factors, such as the political affiliation of the judge or the type of job, that are not part of the conceptual framework, yet nevertheless may impact judicial decisions. Types of Affirmative Action Affirmative action can be defined as any organizational policy or procedure that attempts to increase the representation of underutilized groups in an organization (Doverspike et al. 2006). There are three typical reasons to create an affirmative action plan: (1) because the organization is forced
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to by court order or consent decree, (2) because the organization would like to do business with the government and must submit to the rules of E.O. 11246, or (3) because the organization voluntarily wishes to increase minority representation. For organizations that have been found guilty of discrimination, affirmative action may be mandated by court order. Similarly, companies may agree to an affirmative action plan as part of a consent decree to avoid going to trial over a past discrimination claim. Many organizations create affirmative action plans to comply with E.O. 11246, which established affirmative action requirements for organizations receiving federal contracts. These organizations are required to monitor workforce demographics, identify groups that are underutilized relative to the available labor market, and develop plans to increase the representation of these groups in the workforce. These requirements (monitoring, analyzing, developing action plans) are very similar to actions an organization might take if they voluntarily wish to increase underutilized groups. Voluntary affirmative action plans are often a component of an organization’s diversity program, which may reflect a broader set of initiatives aimed at creating an organizational culture that values and supports people from diverse backgrounds (Kalev et al. 2006; Kravitz 2008). For the purpose of this article, we will consider affirmative action to consist of only those practices that are related to employment decisions (e.g., recruiting, selection, termination, etc.). Although some literature classifies affirmative action according to voluntary or involuntary status (Doverspike et al. 2000; Gray 1992; Kleiman and Faley 1988), the real question is whether race or gender was used in the personnel decision, not whether the plan was imposed or court-ordered (Walworth and DiChristina 1994). Some plans do not use race or gender in job-related decisions, but rather afford training, recruitment, and supporting resources to traditional minority group members. Targeted recruiting strategies might include placing job ads in specialty ethnic publications or recruiting job applicants from historically minority-serving colleges. Other examples of race and gender-neutral strategies are Pepsi’s leadership training programs for minorities and women and Deloitte’s networking groups for minorities and women (Fuller et al. 2007; Weisberg and Carey 2007). This type of affirmative action can be classified as race or gender-neutral affirmative action. This means that race and gender are not used as factors in personnel decisions such as hiring, promotion, or termination. This type of affirmative action cannot be brought to court as reverse discrimination because it does not violate any non-discrimination law, such as Title VII (Gutman 2000). Any
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company, public or private, is allowed to engage in these diversity efforts for any reason. Second, there is preferential treatment, where race and gender are taken into account when making personnel decisions. Race or gender-conscious efforts (e.g., quotas, score adjustments, banding with diversity preference or plus factor) may open the organization to reverse discrimination claims, because preferential treatment of any ethnic or gender group violates equal employment opportunity laws such as Title VII (Coil and Rice 1993). When affirmative action is mentioned, preferential treatment is often the image that is conjured. While this is believed to occur more often than it does (Carrell and Mann 1993), it can only occur legally under very limited circumstances. These circumstances are directly related to past discrimination committed by the same organization, either documented through statistical imbalance, a consent decree or acts of past discrimination (Day 2001). In practice, there is possibly a misconception among lessinformed organizations that ‘‘diversity’’ means hiring minorities and women out of the applicant pool for their minority status, or using their minority status as a ‘‘plus factor’’ or tie-breaker (Carrell and Mann 1993). Before 1991, employers were lawfully allowed to consider race and gender as a plus factor in employment decisions (Coil and Rice 1993). However, when Congress passed the Civil Rights Act of 1991, which updated Title VII, this practice was banned. The use of plus factor violates the spirit and original intent of Title VII, which is to refrain from taking into account race or gender when making an employment decision. This is a distinction that legal experts and many industrial/ organizational psychologists may know, but not all human resource employees or hiring managers understand the difference between diversity efforts and preferential treatment. Larger organizations typically have the means to develop more sophisticated and legally defensible affirmative action programs that avoid preferential treatment. Organizations that do not have these means, as well as uninformed managers making individual personnel decisions, can fall prey to using preferential treatment when unwarranted. It is crucial that all organizations understand the difference between affirmative action programs that promote diversity efforts and affirmative action that involves preferential treatment. Organizations that have not committed or documented past discrimination should only engage in race and gender/neutral diversity efforts (Coil and Rice 1993; Kleiman and Faley 1988). Diversity efforts for most organizations should center on advertising, recruitment, and training. Non-discrimination Law v. E.O. 11246 In circumstances where there is a strong remedial need (e.g., a history of discrimination), some form of preferential
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treatment based on race or gender may be allowed, or even required. At the same time, these affirmative action plans are constrained by laws that prohibit discrimination (Title VII, 5th or 14th amendments of the Constitution). Non-discrimination laws make it illegal to knowingly using one of the protected categories (race, gender, religion, etc.) in any personnel decision, regardless of which group benefits. Affirmative action plans may be challenged under both the Constitution and the Title VII. Constitutional standards apply to all government jobs, because those employees are covered under the equal protection clause of the 5th amendment (federal workers) and 14th amendment (state and local government workers). Title VII protects all private sector employees, as well as government employees. Constitutional standards are seen as more stringent, although there is much debate as to whether this is true, was will be discussed further. The conflicting requirements of preferential treatment and non-discrimination can put organizations in a bind. Without some form of special consideration for minority candidates, an organization, especially one with a past history of discrimination, may be unable to achieve affirmative action goals. At the same time, if an organization gives too much special treatment to minority candidates, the organization could be found guilty of reverse discrimination. Through the evolution of case law, general principles have developed regarding when preferential treatment is allowed and how it should be administered. The two general requirements are demonstrating a remedial need for the plan and showing that the plan limits harm toward the majority group (Kilberg 1995; Walworth and DiChristina 1994). Following these two principles should help balance the opposing requirements of preferential treatment and non-discrimination. This study examines factors related to judges’ decisions on plans that demonstrate remedial need and limiting harm to the majority group. Remedial Need This first step inquires as to whether there is sufficient justification to give preferential treatment. There are three factors frequently considered by the courts: statistical imbalance, specific illegal acts of past discrimination, and the use of selection tools that have adverse impact (Day 2001; Kleiman and Faley 1988). What is not clear is how statistical imbalance should be calculated and which acts of past discrimination, from adverse impact in a selection component to intentional acts of past discrimination, will warrant the judicial ruling of having met the remedial need standard. Statistical imbalance refers to data showing under-representation of a minority group, relative to the availability
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of minorities in the labor pool. Calculating statistical imbalance is not always straightforward (Biddle 2006; Siskin and Trippi 2005). First, there is not a uniform method used to calculate the imbalance. Second, the correct relevant population must be used in calculating the statistical imbalance, which may be difficult to determine. Third, whether statistical imbalance alone can prove remedial need is questionable (Day 2001). For a further discussion of these issues, see Day (2001). The second factor within remedial need is to document past history of discrimination. But what level of action is considered past discrimination? The more obvious evidence is an admission of past illegal actions (Local No. 93 of International Association of Firefighters v. City of Cleveland 1986), prior court decisions against the employer (United States v. Paradise 1987), or consent decrees to resolve past claims of discrimination. Prior court decisions are uncontested proof. Consent decrees indicate that the organization was involved in a lawsuit where past discrimination was being charged, but was settled out of court. Organizations enter into consent decrees to avoid expensive lawsuits, especially if the organization does not believe they have a good chance of winning in court. Although a consent decree is not the same proof of past discrimination as a court finding, it can be viewed as recognition by the organization that there is some practice in need of remediation (Kleiman and Faley 1988). What is less obvious is whether the presence of adverse impact in past selection procedures constitutes a history of discrimination. Adverse impact indicates a disproportionate rejection of minority applicants in a selection or promotion decision. Adverse impact can be a symptom of more subtle past discrimination, but it does not necessarily indicate past discrimination, as long as the selection system is validated and found to be job-related. However, it may suffice to establish a history of underutilizing a protected group (Coil and Rice 1993). Adverse impact without validated tests would indicate lack of awareness, while adverse impact with validated tests may represent efforts to avoid discrimination. To summarize, the standard of remedial need is concerned with proving that past discrimination has occurred in an organization. The three factors that influence this standard are the statistical imbalance, prior court activity around past allegations, and adverse impact. We know from the legal literature and case law that judges examine these three factors. What passes and what fails to meet these three factors is explored further in this analysis. Limiting Harm If phase one of remedial need has been met, then judges examine how narrowly tailored the affirmative action plan
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is to limit harm to the majority group. The purpose of having sufficient limitations on affirmative action plans is to discourage, not encourage the use of racial classifications (Robinson et al. 1998). Specifically, the three issues that Title VII and the Constitution have in common when examining whether limiting harm has been met are flexibility, duration, and magnitude of the program’s relief (Day 2001). The first key element of a successful affirmative action plan is flexibility. Courts do not look favorably upon employers hiring, promoting, or transferring employees unnecessarily to achieve diversity goals. A plan should not sacrifice employee qualifications by hiring or promoting unqualified employees, which is never acceptable under any affirmative action plan. Some plans do not require the employer to hire the most qualified person, but employers must always hire a qualified person (Kleiman and Faley 1988). Organizations can document their commitment to employee qualifications by including statements of qualification and waiver provisions as part of the affirmative action plan (Robinson et al. 1998). Statements of qualification are claims put into a plan stating that the organization will not allow the hiring or promotion of unqualified employees. Any proof that documents that employees were minimally qualified could be used as evidence of flexibility. Waiver provisions are safeguards built into allow affirmative action goals to be modified as circumstances change. Waiver provisions consider factors that would make it impossible to reach an affirmative action goal and allows for this failure. Waivers can also allow for an affirmative action plan to be modified when needed. An example of not having a waiver provision in place would be letting a spot remain vacant until a minority is hired or hiring an unqualified minority. The presence of a waiver provision often draws the line between what is a reserved slot, or quota and what is a goal (Kleiman and Faley 1988). Therefore, the factor of flexibility looks at employee qualification criteria, statements of employee qualification, waiver provisions, and other elements that may inform the judge whether the plan is flexible or not. What is not clear is whether judges accept these statements or whether they look for evidence of these principles in action, and what constitutes this evidence. The second common standard is the duration of the plan. The main question is whether the projected time of preferential treatment is in alignment with the severity of remedial need. Courts do not look favorably upon undefined interim periods. They also do not look favorably upon using preferential treatment to maintain diversity goals or statistical parity (Day 2001). One way to limit the duration of the plan would be to apply preferential treatment only
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once, at a single point in time on one particular employment decision. Alternatively, an organization might specify as part of the affirmative action plan that preferential treatment will be discontinued once the diversity goals have been met. The third common standard is magnitude, which refers to the relationship between the program’s numerical goals and the statistical disparity that has been demonstrated. The courts examine how strong the preferential treatment is relative to the strength of the remedial need. Two factors are examined. First, goals are examined for ‘‘fit’’ with the amount of statistical imbalance or remedial need. In theory, if there is a 10 % disparity, the goal should not be over 10 %. Second, are plans and goals reviewed often to make sure that the goal has remained correctly aligned? If 5 % of the 10 % goal is met in the first year, this should be reviewed and the goal should be changed to reflect the remaining 5 % disparity, rather than continuing at 10 % (Day 2001). Therefore, to determine the factor of magnitude, judges would examine any evidence that reflects tailoring the goals to the prior discrimination and review of these goals to make sure they are updated. Constitutional standards further require that race-neutral alternatives be examined first, before considering preferential treatment (Robinson et al. 1997). Examples of raceneutral alternatives are recruiting, advertising, training, and revising the selection system to remove artificial barriers. Revising the selection system can include decreasing an unnecessarily high passing score, removing a non-jobrelated test, removing bias from test items, and validating the test or selection system. When considering alternatives, the courts could try to determine whether race or gender-neutral alternatives were examined, to what extent they were examined and how feasible these alternatives would have been to implement over race or gender-conscious measures (Day 2001). When looking at whether alternatives were examined or not, some courts simply look to see if at least some effort was made. Some courts look to see if the success of alternatives was considered or based on other’s success using the same alternatives. Whether there is a uniform standard across courts remains to be seen. To summarize, both Title VII and Constitutional law allow for preferential treatment under very stringent conditions. First, both standards demand some evidence of a remedial need. Second, both refer to the same concept of limiting harm against the majority class. The most important difference between Title VII and Constitutional law is the severity of proof to evidence remedial need. Some scholars suggest that constitutional standards require more evidence of past discrimination than just statistical disparity to demonstrate a remedial need (A. Gutman, personal communication, July, 2003). While some courts only
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require a statistical disparity, other courts require an actual violation of the law. Constitutional standards also examine limiting harm more closely by taking into consideration the extent to which neutral alternatives were considered.
Contextual Variables While the legal literature provides considerable guidance of the characteristics of defensible affirmative action plans, there is still considerable ambiguity regarding how these principles should be interpreted in light of the complex set of factors present in each case. Roehling (1993) noted that in the study of judicial rulings, it is important to recognize the multitude of factors that affect judges’ decisions. Decisions may be included by legal nuances (e.g., under which law a claim was filed), procedural details (e.g., time limits for filing a claim), or factors unrelated to the merits of the case (e.g., the political climate). These contextual factors are not explicitly identified in the legal standards, yet may nevertheless still influence outcomes. Failure to take into account contextual factors can result in misleading findings if those factors are confounded with the variables of interest. Two types of contextual factors will be discussed: characteristics of the courts and characteristics of the cases. Characteristics of the courts that could theoretically impact the outcome of judicial ruling are political appointment of the judge, year of case, the law the suit was brought under, and the court level. Political ideology is operationalized through the political appointment of the judge(s). Democratic judges, compared to Republican judges, are expected to be more likely to rule in favor of affirmative action plans (Aliotta 1988; Robinson and Fleishman 2001). Legal standards are not fixed, but are constantly evolving as courts interpret the law (Roehling 1993). Furthermore, changes to the law (e.g., the Civil Rights Act of 1991) can require adjustments to the interpretation of legal standards. Therefore, it is important to the analysis to control for the year the case was decided, as this could reflect systematic shifts in the law’s interpretation. There may be differences in cases due to court level (district or appellate) that rendered the final decision. There are two differences that occur between these two types of court. First, there can be differences between individual and group decision-making processes. In a district court, there is one judge that evaluates and makes a decision regarding a case. In an appellate court, there is a panel of judges that evaluate and make a decision regarding a case. There could potentially be a difference in the decision-making process between an individual and a group (Roehling 1993). Second, there are also possible differences between the decision-making process of deciding facts and deciding
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issues of law. Trial courts are charged with deciding issues of fact, which involves investigating testimonies, depositions, and records to determine what facts are considered ‘‘true.’’ Appellate courts are charged with deciding issues of law, which involves determining the legal principles that will be applied and what will be precedent for future cases (Roehling 1993). In addition to characteristics of the court, there are also characteristics of the case that may influence the outcome. Three identified here are type of personnel decision, beneficiary of the affirmative action program, and job preparation. It may make a difference whether a personnel decision involves giving a new opportunity or taking away an existing one. Hiring decisions involve giving a new job to someone who was previously not employed by the organization. In contrast, firing or layoffs involve someone losing a job. It is expected that personnel decisions involving selection are more likely to pass legal scrutiny than those that involve firing or layoffs. The beneficiaries of the affirmative action plans (i.e., women vs. ethnic minorities) could impact the outcome. Attitudes toward racism or sexism are theorized to influence reactions to affirmative action for different beneficiary groups. It is expected from the psychological research and legal research that there will be a lighter standard for gender-only preferential treatment. Psychological research has shown opinion polls to favor women as suffering more discrimination in the past (Kravitz et al. 1997; Smith and Kleugel 1984). Affirmative action based on gender bears intermediate scrutiny, unlike strict scrutiny for race, which means that one must show ‘‘important governmental objectives’’ versus ‘‘compelling government interest’’ (Dallas Fire Fighters v. City of Dallas 1995). The type of job might also impact the evaluation of affirmative action plans. The higher the job level, they are more likely to require more education, the extent that discrimination affects access to those prior experiences, the need for affirmative action may be greater at higher level jobs. This study sought to verify and clarify the characteristics of a legally defensible affirmative action plan through an analysis of the factors related to court decisions concerning reverse discrimination. To summarize, the research questions are: (1)
(2)
Does statistical imbalance, past history or adverse impact/validated tests influence the outcome of remedial need? Does qualification, duration, magnitude, use of reserved slots or race-neutral alternatives influence the outcome of limiting harm? Does political appointment of judge, year of case, law, court level, personnel decision, beneficiary, or job preparation influence the outcomes of remedial need or limiting harm?
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Method Sample of Cases The data for this study were obtained from a content analysis of federal court cases involving claims of reverse discrimination. When conducting a review of judicial rulings, it is important to identify the appropriate time frame for the analysis, so that the pertinent laws reflect the legal standards that are currently in effect. A review of the literature shows that Weber set the major standards for affirmative action in 1979 for both Title VII and Constitutional cases. For federal Fifth Amendment cases, there was a significant change in the interpretation of the law after the 1997 Adarand decision. Accordingly, federal court cases that date from 1980 to October 2011 were obtained from Westlaw electronic database, and 5th Amendment cases before 1997 were excluded. The initial search terms were ‘‘reverse discrimination’’ and ‘‘affirmative action.’’ Other search terms with similar meanings were used such as past discrimination, set-asides, preferential treatment, and various combinations of all search terms. This search yielded 815 cases, of which 80 were deemed eligible according to four inclusion criteria. First, the case had to deal with reverse discrimination, and had to include a ruling on remedial need or limiting harm. Second, the cases had to be tried at the federal court level. Cases that were tried in state courts were not included. Third, the case must involve an employment-related decision. Cases that dealt with reverse discrimination in education or awarding of contracts, such as Minority Business Enterprise (MBE’s) or Women Business Enterprise (WBE’s), were excluded. All these criteria were used to avoid contamination of evaluating irrelevant cases (back pay, fines, ‘‘regular’’ discrimination), inconsistent laws (across states), and cases not pertaining to employment discrimination. Finally, cases that were appealed were followed until the verdict on the issue of reverse discrimination was determined. Possibly, the verdict could be determined at the appellate level, or it could be remanded back to the district level or some decisions (such as remedial need) could be decided at the appellate level and other issues (such as limiting harm) could be remanded back to the district court. Measures Outcome Variables Remedial Need This dichotomous outcome variable indicates whether the defendant established a remedial need (coded 1) or not (coded 0). A case was coded as
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establishing remedial need if the case report explicitly mentioned finding remedial need to justify preferential treatment. 75 % of the cases established remedial need, 18.8 % of the cases did not establish remedial need, and 6.3 % of the cases the judge did not specifically state the decision on remedial need. Limiting Harm This dichotomous variable indicates whether the defendant’s preferential treatment was found to limit harm to an acceptable degree (coded 1), as indicated by explicit statements that the plan was narrowly tailored or limited harm or not (coded 0). 55 % of cases established limiting harm, 31.3 % did not establish limiting harm, and 13.8 % of the cases the judge did not specifically state the decision on limiting harm. Predictors of Remedial Need Statistical Imbalance This dichotomous variable measures whether the judge concluded that the defendant was able to prove that statistical disparity existed in the job in question (coded 1) or not (coded 0). 72.5 % of the cases proved statistical disparity that amounted to statistical imbalance. Adverse Impact and Validated Tests This categorical variable measures whether the defendant made any attempt to minimize discrimination using tests that did not have adverse impact, or by validating tests to make sure they are job-related. Adverse impact refers to any component of the selection system that had disproportionate rate of nonselection against minorities or women. Validated tests examine whether the defendant provided evidence that all components of a selection system were directly related to job requirements. Disparate impact case law dictates that employment practices are illegal if they have adverse impact and are not job-related (Gutman 2000). Because there was too little data to include both of these as separate variables, we chose to code them together, resulting in two possible categories: (1) legally challengeable selection systems where adverse impact was found and tests were not validated (88.8 %; coded 0) or (2) legally defensible selection systems where either no adverse impact found or adverse impact was found and tests were validated (11.3 %; coded 1). Predictors of Limiting Harm Qualification This dichotomous variable indicates what measures were taken to guarantee minimal employee qualification. Plans that ignored qualifications (no mention of qualification standards or statement that race or gender was the sole criteria used) were coded as 0. Plans that
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demonstrated qualification was considered (other criteria besides race were used, statement within the plan ensuring qualification, banding that went outside of the top band to select a minority, or waiver provision if no qualified minorities, specific passing rate on an exam, using minority status as a plus factor or tie-breaker among equally qualified candidates, or banding that did not go outside the top band) were coded 1. There were 15.8 % of the cases that ignored qualifications and 84.2 % of the cases considered qualification. Duration This variable indicates what evidence there was that the defendant made provisions for a plan to be temporary. The first level is no evidence, where the duration was not mentioned or there was a ruling that there was no specified end point to a plan. The second level is a specified endpoint through either termination of a plan once goals have been reached or a date set and plan dissolved. The third level is a single set, or a one-time action. These three categories were combined into a scale. No end point was coded as 0, when set goal was reached was coded as 1 and single set was coded as 2. There were 53.8 % of the cases at the no evidence level, 38.8 % of the cases at the specified endpoint level, and 7.5 % at the single set level. Magnitude This measure combined fit of goal and review of goal to indicate the care in which the plan was crafted. Fit of goal indicates whether or not there was evidence of goals being tailored to the amount of statistical disparity. Evidence of fit of goal could be demonstrated by the judge’s conclusion, or actual comparison of the goal being equal to the statistical disparity. Evidence of lack of fit of goal could be demonstrated by the judge’s conclusion or comparison of the goal being more than the statistical disparity. The second factor indicates whether the organization planned or conducted a review of progress toward updating minority staffing goals. Goals were either reviewed at some point, such as 5–10 years, or consistently (every 1 or 2 years) or there was no evidence of ever being reviewed. These two variables were combined where 0 indicated no review and no fit and 1 indicated review and/ or fit. There were 25 % of the cases that did not have fit or review and 75 % that had review and/or fit. Reserved Slots This dichotomous variable indicates whether slots were reserved exclusively for minorities or women (coded 1) or not (coded 0). An example of reserved slots is giving a certain percentage of positions to minorities/women. 22.5 % of the cases used reserved slots. Race-Neutral Alternatives This categorical variable indicates whether race-neutral alternatives or validated
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tests were part of the preferential treatment plan. The first category is no attempt or no evidence. The second category is race-neutral methods that increase the pool, such as recruiting or advertising, or increase skill, such as training. The third category is race-neutral methods that change selection standards, such as validating tests as part of the plan, lowering cut scores, or revising the selection system. If a case used both increasing the pool/skill and changing selection standards, the case was coded as changing selection standards, as successive categories are seen as more effort in providing race-neutral alternatives. These three categories were effect coded. 60 % of the cases made no attempt at race-neutral alternatives, 13.8 % either increased the pool or increased skill, and 26.3 % modified the selection system. Contextual Variables Political Appointment This continuous variable indicates whether a Republican or Democratic president made the political appointment of the judge or judges. This variable is highly correlated to a judge’s political affiliation (Alliota, March 2003, personal communication). For multiple judges with both Republican and Democratic appointments, the proportion of Republicans was used. For cases with multiple trials (i.e., due to appeals and remands), the appointment of the judge(s) who made the final decision was coded. This variable was coded 1 for Republican appointment and 0 for Democratic appointment. There was 100 % agreement for judge’s names among the coders. Republican presidents appointed 61 % of the judges. Year This continuous variable indicated what year the case was decided. The range of this variable was 1980–2011. This variable was coded 0 for 1980, 1 for 1981 and so on. The mean was 11.68 (SD 7.72), representing the year 1992, rounded up. Law This dichotomous variable indicates whether reverse discrimination was brought under Title VII or the Constitutional law (14th Amendment, 5th Amendment after 1997, Section 1981 or Section 1983). If both types of law were used, then the case was coded as Constitutional law, as it may possibly be more stringent. Title VII was coded as 0 and all Constitutional laws were coded as 1. 21.3 % of the cases were brought under Title VII only and 78.8 % of the cases were brought under Constitutional law or a combination of Title VII and Constitutional law. Court Level This dichotomous variable indicates whether the most current judicial decision was made at the lowest level of federal court (district level) by an individual, or whether it was made at an appellate level (circuit court or
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Supreme Court) by a group. This reflects the status as of the date of sampling. If the case was tried by both types of courts, these cases were combined into the appellate level, reflecting that group decision-making processes occurred at some point during the case. District cases were coded as 0 and appellate cases were coded as 1. 50 % of the cases were at the appellate level. Personnel Decision This dichotomous variable indicates whether the personnel decision involved in the case was providing something to the minority (hiring, promotion, or preference in training), or taking something from the majority (firing, demotion, or salary reduction). This variable was coded as 0 for providing something to the minority and 1 for taking something away from the majority. 93.8 % of the cases involved a personnel decision that granted something to the minority and 6.3 % of the cases involved a personnel decision that took away from the majority. Beneficiary This dichotomous variable indicates whether the target of past discrimination, thus now the beneficiary of affirmative action, was based on race or a combination of race and gender, or gender only. Beneficiaries that involve race are subject to the strict scrutiny standard, which is more restrictive than the intermediate scrutiny standard for beneficiaries that involve gender (Dallas Fire Fighters v. City of Dallas 1995). Race only and a combination of race and gender were both coded as 0, while gender only was coded as 1. Only 5 % of the cases involved gender only as the beneficiary. Job Preparation This continuous variable indicated the job zone each job was classified in. Job zones were taken from O*NET, the electronic web version of the Dictionary of Occupational Titles (DOT) that provides an extensive list of job descriptions. The levels of job zones are (1) occupations that need little or no preparation (e.g., custodian), (2) occupations that need some preparation (e.g., firefighter), (3) occupations that need medium preparation (e.g., maintenance mechanic; police officer), (4) occupations that need considerable preparation (e.g., civil engineer; secondary education teacher; public safety first-line supervisor), and (5) occupations that need extensive preparation (e.g., lawyer, professor). The author then recorded the appropriate job zone for each job category. The mean for this variable was 3.19 (SD 1.18) and the majority of the cases were at ‘‘considerable preparation’’ (61.3 %). Coding The initial coding scheme was developed by the authors from a review of the literature and approximately half of
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Table 1 Correlation and agreement statistics of study variables Political affiliation Political affiliation
Years
Law
Court level
Personnel decision
Beneficiary
Job preparation
Remedial need
Statistical imbalance
(1.00/1.00)
Year
0.18
Law
0.17
(1.00/1.00) 0.50**
(0.71/0.90)
Court level
-0.02
-0.13
-0.09
(1.00/1.00)
Personnel decision
-0.08
-0.04
-0.12
0.05
Beneficiary
-0.01
-0.05
-0.16
0.11
0.18
Job preparation
-0.13
-0.11
0.06
0.14
0.05
-0.23*
Remedial need
-0.27*
-0.11
-0.02
0.03
-0.27*
0.12
0.00
Statistical imbalance
0.09
-0.23*
-0.05
0.11
-0.19
0.14
0.12
AI/validated tests
-0.01
0.10
0.18
-0.12
-0.09
0.10
0.01
Limiting harm
-0.02
-0.13
0.02
-0.06
-0.20
0.06
0.13
0.63**
0.51**
Qualification Duration
0.21 -0.15
0.12 0.02
0.11 -0.09
-0.04 -0.02
-0.40** -0.06
-0.10 0.17
0.12 0.15
0.23 0.28*
0.31** 0.08
0.30**
0.42**
(0.74/0.97) (1.00/1.00) (1.00/1.00) (0.63/0.85) 0.43** -0.02
(0.68/0.86) 0.04
Magnitude
0.03
-0.24*
-0.09
0.00
0.03
-0.13
0.09
Reserved slots
0.04
-0.05
0.06
-0.06
-0.02
0.15
0.12
-0.03
0.00
-0.03
-0.05
0.17
0.02
-0.19
0.07
-0.13
0.01
-0.01
0.01
0.07
0.15
-0.10
-0.20
0.02
-0.11
-0.01
0.08
Increase pool Revise selection
AI/validated tests AI/validated tests Limiting harm Qualification
(0.71/0.86) -0.16 0.00
Limiting harm
Qualification
Duration
Magnitude
Reserved slots
Increase pool
Revise selection
(0.67/0.80) 0.29*
(0.54/0.89)
Duration
-0.18
0.34**
0.18
Magnitude
-0.07
0.14
0.25*
(0.69/0.81) 0.08
(0.50/0.82)
Reserved slots
0.09
-0.20
-0.19
-0.08
-0.24*
Increase pool
0.12
-0.07
-0.01
-0.22
0.03
-0.11
(0.75/0.91)
Revise selection
0.37**
-0.24*
-0.05
-0.24*
0.08
0.07
(0.46/0.73) 0.71**
(1.00/1.00)
Numbers in parentheses reflect inter-rater agreement (kappa/proportion agreement) * p \ 0.05; ** p \ 0.01
the cases. All information that could be pertinent to this research was organized into a coding sheet where coders indicated whether the information was present or absent. A training manual was developed that included definitions of terms used on the coding sheet as well as quotes from cases reflecting examples of what text should be coded with each term. Coder training was provided including practice cases with feedback. All cases were coded by at least two individuals who were law students specializing in employment
law. When discrepancies were found between the first two coders, a third independent coder was used and the majority response was chosen. Inter-rater agreement was assessed between the first two coders. Kappa and proportion agreement statistics are presented on the diagonal in Table 1. Good inter-rater agreement was found on most variables, but was only moderate for qualification (89 % agreement, kappa = 0.54), magnitude (82 % agreement, kappa = 0.50), and race-neutral
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alternatives (73 % agreement, kappa = 0.46). It should be noted that the agreement statistics reflect the reliability of an individual coder. As the final set of codes reflects the combined judgment of 2–3 coders, the reliability of the final scores is expected to be higher than indicated by these statistics. Past History and Score Adjustment were excluded from further analysis due to low agreement.
Results Inter-correlations among the study variables are presented in Table 1. First, remedial need and limiting harm were examined to see if they predicted whether the judge ruled in favor of the plaintiff (against the plan) or for the defendant (for the plan). An examination of the data revealed that there was perfect prediction. If a plan did not pass both the remedial need and the limiting harm tests, the ruling was against the plan. Please see Table 2. Remedial Need Analysis The variables that composed remedial need were examined through logistic regression. This analysis contained remedial need as the outcome variable, all contextual variables, statistical imbalance, and adverse impact/validated tests as predictor variables (see Table 3). There were 75 cases where the judge specifically ruled on whether remedial need was met. When all of the variables were entered simultaneously into the equation, the likelihood ratio v2 for the model was 28.06 (df = 9), p \ 0.01. The model was able to correctly classify 84 % of the cases, 4 % higher than the null model. Statistical imbalance was a significant predictor of whether a plan passed remedial need (b = 2.76, Wald = 9.81, p \ 0.01). The odds ratio for having evidence of statistical imbalance versus having no evidence was 15.77, which indicates a large effect size. To better understand the nature of this effect, we calculated the predicted outcome for each level of statistical imbalance, while setting all categorical predictors at the referent category (e.g., the category coded zero) and all other predictors set at their means. The predicted probability of finding remedial need increased from
Table 2 Ruling as a function of remedial need and limiting harm Remedial need
Limiting harm
Ruling for plan
Yes
Yes
40
No No
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Ruling against plan
10
Yes
1
No
13
0.41 to 0.92 by demonstrating evidence of statistical imbalance. Political appointment was a significant predictor of whether a plan passed remedial need (b = -3.69, Wald = 7.19, p \ 0.01). The odds of passing remedial need with a Republican versus a Democratic judge was 0.03 which a large effect size. Figure 1 shows the effect of political appointment on remedial need. The probability of passing remedial need increased from 0.14 with a Republican judge or panel of judges to 0.50 with a panel of 50 % Republican and 50 % Democratic judges. The probability of passing remedial need further increased to 0.86 with a Democratic judge or panel of Democratic judges. In addition, personnel decision, which indicates whether something was given to the beneficiaries or something was taken away from the majority group, approached significance, (b = 3.26, Wald = 3.63, p = 0.06). Remedial need was found more often when plans did not remove benefits to the majority group. The probability of passing remedial need increased from 0.04 when firing or demotion was the type of personnel decision to 0.54 when hiring or promotion was the type of personnel decision used. The univariate v2 for personnel decisions was significant v2 (1, N = 75) = 5.36, p \ 0.05) and the pattern of results was similar across both analyses. This indicates a consistent result and gives credibility to this finding. Limiting Harm Analysis For this analysis, logistic regression was used to assess the importance of the predictor variables (qualification, duration, magnitude, reserved slots, and race-neutral alternatives) on the outcome variable of limiting harm (see Table 4). Contextual variables were included first in the model as well. As two vectors represented race-neutral alternatives, increase pool/skill, and revise selection, these variables were entered as a separate block of the analysis to determine the significance of the variable as a whole. There were 69 cases where the judge specifically ruled on whether limiting harm was met. The likelihood ratio v2 for the model including all predictors was 27.40 (df = 13), p \ 0.05. The model was able to correctly classify 79.7 % of the cases, almost 16 % higher than the null model. The likelihood ratio v2 for the block of race-neutral vectors was 3.43 (df = 2), p = 0.18, indicating this variable was not significant. The shorter the duration of the plan, the more likely the plan would pass limiting harm (b = 1.42, Wald = 4.40, p \ 0.05). The odds ratio was 4.14, which is a medium effect size. Figure 2 shows the effect of duration on limiting harm when all other variables in the equation were set at their mean or for dichotomous variables, at zero. The probability of passing limiting harm increased from 0.35
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421
Table 3 Logistic regression for remedial need Univariate V2
Predictor
B
S.E.
Wald
Exp(B)
Political appointment
5.52*
-3.69
1.38
7.19**
0.02
Year
0.84
-0.01
0.06
0.05
0.99
Law
0.02
1.22
1.41
0.75
3.39
Court level
0.05
-0.29
0.76
0.15
0.75
Personnel decision
5.36*
-3.26
0.76
3.63
0.04
Beneficiary
1.06
19.60
0.76
0.00
0.00
Job preparation
0.00
-0.19
0.76
0.29
0.82
Statistical imbalance
13.91**
Adverse impact/validated tests
0.03
Constant
2.76
0.76
9.81**
-1.57
0.76
2.11
15.78 0.21
0.24
0.76
0.03
1.27
* p \ 0.05; ** p \ 0.01; df = 7
Probability of Passing Remedial Need
1.00 0.90
0.86
Therefore, qualification did not have a unique contribution to the outcome.
0.80 0.70 0.60
Discussion 0.50
0.50 0.40 0.30 0.20 0.10
0.14
0.00
Republican
Neutral
Democratic
Fig. 1 Probability of passing remedial need by political appointment
with no limited duration to 0.65 when there was a specific goal, and reached 0.90 for a one-time action. Reserved slots was significant, b = -1.99, Wald = 4.40, p \ 0.05, indicating that an affirmative action plan that did not use reserved slots had a stronger likelihood of passing limiting harm. The odds ratio for not using reserved slots versus using reserved slots was 0.14, which indicates a medium effect size. The predicted probability of passing limiting harm (with all other variables in the equation set at their mean or for categorical variables, at zero) increased from 0.14 to 0.54 by not using reserved slots. Qualification was initially significant in univariate analysis, v2 (1, N = 69) = 5.89, p \ 0.05. Demonstrating that some effort had been made to ensure qualifications (waiver provisions, statements, minimum cut scores on selection instruments) would increase the probability of passing limiting harm from 0.54 to 0.82. However, after controlling for other variables, qualification was no longer significant in the multivariate analysis. This is likely due to a moderate correlation between qualification and personnel decisions (r (78) = -0.40, p \ 0.001), where termination decisions were less likely to have evidence of qualification.
In the design of affirmative action practices, organizations must balance non-discrimination with special efforts to increase representation of underutilized groups. To explore where this balance should be, this research examined roughly the last 30 years of court cases where affirmative action plans had been challenged as constituting reverse discrimination. By analyzing these lawsuits, some factors that influenced the legality of affirmative action plans were discovered. Consistent with the legal literature (Day 2001; Gutman 2000; Kleiman and Faley 1988; Robinson et al. 1998), our review focused on two issues: remedial need and limiting harm. Remedial need demonstrates that there is a reason to give preferential treatment, such as when the organization has a history of discrimination. Limiting harm demonstrates that the preferential treatment plan is narrowly tailored to remedy past discrimination and causes minimal harm to the majority group. When deciding remedial need, statistical imbalance was a significant factor. Statistical imbalance demonstrates underutilization of minorities or women in a particular job. The variable of past discrimination was dropped from the analysis due to low agreement in the two cases where there was a question of a lack of past history. It is important to keep in mind that 90 % of the cases did show past history, which reflected an admission by the employer of historical or intentional discrimination, a court finding, a consent decree, or a reported violation by the Equal Employment Opportunity Commission (EEOC). Therefore, it may be useful to view the significance of statistical imbalance in light of the presence of past history.
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Table 4 Logistic regression of limiting harm S.E.
Wald
Exp(B)
0.67
1.02
0.44
1.96
1.12
-0.08
0.06
2.12
0.92
0.03 0.27
0.40 -0.23
1.07 0.65
0.14 0.12
1.49 0.79
2.76
-3.37
2.12
2.54
0.03
0.23
1.86
1.72
1.17
6.44
1.08
0.46
0.32
2.03
1.58
Qualification
5.89*
1.35
1.30
1.07
3.86
Duration
7.93**
1.42
0.71
4.05*
4.14
Magnitude
1.41
-0.24
0.90
0.07
0.79
Reserved slots
2.73
-1.99
0.95
4.40*
0.14
Increase pool/skill
3.43
0.29
0.70
0.18
1.34
Revise selection
3.43
-0.81
0.53
2.36
0.45
-1.31
1.53
0.74
0.27
Predictor
Univariate V2
Political appointment
0.03
Year Law Court level Personnel decision Beneficiary Job preparation
Constant
B
Probability of Passing Limiting Harm
* p \ 0.05; ** p \ 0.01; df = 13
1.00 0.90
0.90 0.80 0.70
0.69
0.60 0.50 0.40 0.30
0.35
0.20 0.10 0.00 No End Point
When Goal Reached
Single Set
Fig. 2 Probability of passing limiting harm by duration
This finding supports the recommendations in the legal literature. Proof of a statistical imbalance and evidence of past history of discrimination for each specific job or level of jobs (such as professionals) should be clearly established before ever embarking on designing a preferential treatment plan. Positions that do not have statistical imbalance and/or past history of discrimination should not be targeted for affirmative action plans that involve preferential treatment. Human resources should pay careful attention to documenting statistical imbalance and past history of discrimination to demonstrate remedial need. In contrast, whether there was adverse impact or validated tests did not impact the ruling of remedial need. Thus, it appears that this type of evidence caries less weight than statistical imbalance. When further examining limiting harm, duration was the most important factor. Duration refers to the time period of
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the preferential treatment plan. It is very important to set a specific end to a plan. As the level of specificity increased in the duration of the plan, so did the favorability in regards to passing limiting harm. This confirms the legal standard of temporary duration from the literature. It also clarifies that one-time actions are favored over terminating the plan once goals are reached, which is favored over no termination point. Not surprisingly, plans with strict quotas or reserved slots were less likely to pass the limiting harm test. As others have suggested, plans that do not have sufficient flexibility and that do not ensure qualification are not accepted by the courts (Kleiman and Faley 1988). Affirmative action plans must establish flexible goals, promoting the employment of underutilized minorities, but not at the cost of hiring qualified workers. Surprisingly, plans that attempted to build in flexibility by attempting race- or gender-neutral alternatives first before turning to preferential treatment did not fare better than plans that only used preferential treatment. This does not mean that such approaches are not useful. If affirmative action goals can be achieved by neutral methods, then preferential treatment would not be required, and the organization could avoid the risk of a reverse discrimination claim. Of the contextual variables, political appointment impacted judicial rulings. Political appointment of a judge does have an influence on a defendant passing the standard of remedial need. Democratic judges are more likely to rule in favor of remedial need. This is confirmed in the political science literature as Democratic judges have been shown to be more likely to rule in favor of individual needs versus business needs (Aliotta 1988). This is also confirmed in the psychological literature. Democratic judges are also more likely to view African-Americans as being disadvantaged and believe in government interventions to social problems (Robinson and Fleishman 2001). Political appointment did not impact the standard of limiting harm. It is unclear why political ideology would impact one standard and not another or the overall ruling. Possibly, the standards for statistical imbalance under remedial need are less clear (i.e., Republican judges scrutinize the method of calculating statistical imbalance and Democratic judges do not). Contrary to expectations, the type of personnel decision approached significantly predicting remedial need, but was not related to limiting harm. The legal literature has indicated that firing and layoffs are viewed unfavorably (Robinson et al. 1998), and we expected that this variable would be negatively related to judgments of limiting harm. In contrast, we found that decision involving terminations were less likely to be seen as having a remedial need. This may suggest that judgments regarding remedial need and limiting harm are not entirely independent.
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Judges in cases involving termination may set a higher standard for establishing remedial need, and therefore cases involving termination are less likely to pass this phase of the judgment. However, it should be noted that only 8 % of the cases involved firing or layoffs. Due to the limited data on conditions with job loss, combined with the marginal significance, this result should be interpreted with caution. Law did not have any significant findings. The legal literature in this area pointed to a possible difference between the laws in that Constitutional law may require stronger proof of past discrimination than just statistical imbalance (Day 2001). The Constitutional standards also require race-neutral alternatives. However, this variable was non-significant as well. Over time, more cases fell under a combination of the laws, and this may have caused the standards applied in the two types of law to converge. To summarize, evidence of statistical imbalance, avoiding reserved slots and shorter duration of plans were the most important factors in designing an affirmative action plan. Evidence of these factors aided in the success of affirmative action plans standing up to reverse discrimination suits in court. Human resource practitioners can balance increasing workforce diversity and avoiding illegal discrimination by doing three specific things. First, they must take care in documenting statistical imbalances. It is best that this calculation is done as specifically as possible. It should be tied to the job in question, rather than a more inclusive job group or even use of general workforce population statistics. For example, if the job in question is a manager of accounting, that specific job title should be used rather than all accounting jobs. It is also best to follow current best practices for demonstrating statistical disparity (Biddle 2006; Day 2001; Siskin and Trippi 2005). Statistical imbalance for the job in question is a strong piece of evidence to support a remedial need. Second, affirmative action plans must avoid using reserved slots, or quotas. Reserved slots can be a strong indicator that qualification of employees was not considered, which would fail to uphold the principle of limiting harm. Proof of qualification can be documented by having a waiver provision that allows the organization to skip hiring a beneficiary if there is not a qualified beneficiary available. Furthermore, proof of qualification can be demonstrated by a statement that the organization will only hire qualified individuals. Qualification and lack of reserved slots is best shown by a ‘‘cut score’’ on any quantitative selection instruments. This draws a clear line as to what is qualified and what is not, rather than leaving it to more subjective measures. Third, constraining the use of preferential treatment to a single decision shows the organization’s seriousness of limiting the duration of their plan. If that is not feasible, plans of a few years can also demonstrate limited duration, particularly when coupled with a yearly review to assess
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progress toward goals and adjust the end date accordingly. Plans with no specific date for dissolution should be avoided. Although this study included the entire population of reverse discrimination lawsuits that tested affirmative action plans, the number of cases was still small and there were some limitations to this research. All power estimates were well below the desired 0.80. This indicates that these analyses were only able to detect large effects in the data. Therefore, this research was not able to detect small or medium effects and some patterns in the data may have gone undetected. As mentioned previously, there is also a limitation in the generalizability of our study due to the selection of cases that are heard by the court. Information was not included where cases were either settled out of court or not taken on by a lawyer. Because lawyers on both sides will be familiar with the standards discussed in the legal literature, cases that clearly favor one party or the other will typically be resolved before reaching trial. This likely produces a set of cases with restricted range on the key variables, and may limit the ability of research like this study to detect determinants of judicial outcomes. Another limitation that should be mentioned is that judicial opinions do not necessarily correlate perfectly with the facts of a case. Information gleaned from these cases must first pass through the filter of the judge and what is reported is what she/he thinks is important to the case. Another related problem is that case reports often include limited information about the facts as the judge only reports those details needed to support the ruling. Furthermore, opinions may not accurately reflect the factors that drive the judge’s decision (Roehling 1993). Judges may lack insight into what factors actually do drive their decisions. They also may not be willing to record what factors influence their decisions (affect, court behavior, etc.). Opinions may also contain bias, stating factors that build evidence for a decision and omitting evidence that is contrary to the decision. Finally, sometimes the judges do not write the opinions themselves. Unfortunately, the judicial opinion is often the only information that is available to analyze and its limitations must be taken into consideration when generalizing results. Trying to impose structure and scientific rigor on a complex and possibly subjective process can be very challenging. To have high reliability between the coders, coders could only code exact words or phrases that the judge said. This rule could have also potentially restricted the collection of information that would be important to this study. This limited the ability to look at details of affirmative action plans and only allowed the analysis of general classifications. In spite of these difficulties, this study was the first of its kind to attempt to quantitatively analyze the standards of affirmative action.
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Future research should attempt to provide a more detailed examination of the types of evidence related to judicial rulings. For example, in the assessment of remedial need, it would be useful to compare methods of computing statistical imbalance (e.g., what statistics were used? what was the reference population?). Related to limiting harm, it would be useful to compare different methods of preferential treatment that place more or less weight on minority status relative to qualifications. Unfortunately, until more cases are available, it is unlikely that these questions can be addressed through an empirical analysis. Despite the limitations, this provides a foundation for evidence-based practice when designing affirmative action plans. The results confirm recommendations from the legal literature that affirmative action plans can involve preferential treatment, but only under limited circumstances. Preferential treatment is legally permissible only after establishing a remedial need through evidence of a statistical imbalance or history of discrimination. Furthermore, the preferential treatment plan must limit harm to the majority group, by avoiding reserved slots and setting a limited duration. Acknowledgments This research was supported by the Psychology Research Fund from the Institute of Psychology, Illinois Institute of Technology, and FDCP Complex Faculty Grant from The Chicago School. We are also grateful to Richard Gonzalez for his assistance and support, as well as Roya Ayman and Nambury Raju. We thank Shani Austin,Angela Bartels, Porcia Beasley, Sarju Bharucha, Katie Cisneros, Justin Greenfield, Hatton Greer, Eunseon Ha, Rebecca Kohn, Suzanne Seiler, Phillip Terrazzino and Tyler Vandermeeden for their invaluable assistance with the coding of cases.
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