Although there is now a considerable literature on how law and legal principles serve as institutionalized models for organizations, there has been far less attention to how organizational practices may serve as institutionalized models for courts. This article offers a theoretical and empirical analysis of legal endogeneity -a subtle yet powerful process through which institutionalized organizational structures and practices influence judicial conceptions of legality and compliance with antidiscrimination law. We argue that, irrespective of their effectiveness, organizational structures, such as grievance procedures, anti-harassment policies, evaluation procedures, and formal hiring procedures, become symbolic indicators of compliance with anti-discrimination laws, first within organizations, but eventually in the judicial realm as well. As organizational structures become increasingly institutionalized, lawyers and judges become more likely to associate them with rationality and fairness and to infer nondiscrimination from the mere presence of those structures. Legal endogeneity has observable manifestations: judges increasingly refer to organizational structures in their opinions, find them relevant to determinations of legal liability, and ultimately defer to those structures by inferring nondiscrimination from their presence. We test legal endogeneity theory by analyzing a random sample of 1024 federal employment discrimination decisions from 1965-1999. We find that legal endogeneity has increased over time; that judicial deference is most likely when plaintiffs lack social and economic clout; and that judicial deference is most likely when the legal theories require judges to rule on organizational attributes that are not directly observable. We suggest that legal endogeneity weakens the impact of law as judges come to view organizational structures as indicators of legal compliance even when those structures are ineffective in combating discrimination.2

This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 The maximum likelihood estimator (MLE) is an alternative to the minimum variance unbiased estimator (MVUE). For many estimation problems, the MVUE does not exist. Moreover, when it does exist, there is no systematic procedure for nding it. In constrast, the MLE does not necessarily satisfy any optimality criterion, but it can almost always be computed, either through exact formulas or numerical techniques. For this reason, the MLE is one of the most common estimation procedures used in practice. The MLE is an important type of estimator for the following reasons: 1. The MLE implements the likelihood principle. 2. MLEs are often simple and easy to compute. 3. MLEs have asymptotic optimality properties (consistency and eciency). 4. MLEs are invariant under reparameterization. 5. If an ecient estimator exists, it is the MLE. 6. In signal detection with unknown parameters (composite hypothesis testing), MLEs are used in implementing the generalized likelihood ratio test (GLRT). This module will discuss these properties in detail, with examples.

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Several problems often encountered in research using log-linear models for categorical response variables are discussed. The issues covered are (a) determining the degrees of freedom for a model, (b) analyzing sparse data, (c) analyzing weighted data, (d) modeling rates, and (e) interpreting results.

A rich theoretical literature describes the disadvantages facing plaintiffs who suffer multiple, or intersecting, axes of discrimination. This article extends extant literature by distinguishing two forms of intersectionality: demographic intersectionality, in which overlapping demographic characteristics produce disadvantages that are more than the sum of their parts, and claim intersectionality, in which plaintiffs who allege discrimination on the basis of intersecting ascriptive characteristics (e.g., race and sex) are unlikely to win their cases. To date, there has been virtually no empirical research on the effects of either type of intersectionality on litigation outcomes. This article addresses that lacuna with an empirical analysis of a representative sample of judicial opinions in equal employment opportunity (EEO) cases in the U.S. federal courts from 1965 through 1999. Using generalized ordered logistic regression and controlling for numerous variables, we find that both intersectional demographic characteristics and legal claims are associated with dramatically reduced odds of plaintiff victory. Strikingly, plaintiffs who make intersectional claims are only half as likely to win their cases as plaintiffs who allege a single basis of discrimination. Our findings support and elaborate predictions about the sociolegal effects of intersectionality.