Book Review

Quantitative Methods in Law represents the efforts of one legal scholar to apply mathematical probability and statistics to the solution of a wide range of legal problems. Michael O. Finkelstein has republished in book form a collection of his articles, beginning with his most famous and most widely cited: the application of mathematical probability to jury discrimination cases. After leading the reader through a series of fascinating applications of statistical problem solving to an impressively wide range of legal situations, the book concludes with the final words of one of the most engaging battles among legal scholars in recent years: the exchange between Michael Finkelstein and Laurence Tribe on the use of Bayes\u27 theorem in a criminal trial to assist the jury in integrating probabilistic evidence with nonnumerical testimony

Let the Damages Fit the Wrong: An Immodest Proposal for Reforming Personal Injury Damages

Rather than comment on the wisdom of piecemeal reform, this article questions the premise of compensatory damages and takes the position that make-whole recovery is an unnecessary consequence of liability and does not necessarily achieve just results...I propose that civil damages should fit the wrong.6 Compensatory damages should abandon the make-whole premise and be measured by three factors: the degree of the wrongfulness of the tort, the severity of the harm, and the extent to which the risky conduct was directed at the plaintiff—which I call connectedness

Book Review: The Burden of Brown. by Raymond Wolters.

Book review: The Burden of Brown. By Raymond Wolters. Knoxville: The University of Tennessee Press. 1984. Pp. 346. Reviewed by: Elaine W. Shoben

Book Review: The Burden of Brown. by Raymond Wolters.

Book review: The Burden of Brown. By Raymond Wolters. Knoxville: The University of Tennessee Press. 1984. Pp. 346. Reviewed by: Elaine W. Shoben

Book Review

The Burden of Brown by Raymond Wolters is a long book with a very short message: integration is bad, but desegregation is not. The distinction between the two is crucial to Wolters\u27s analysis. Desegregation is the prohibition of officially sanctioned separation of the races. Integration, on the other hand, is the compelled mixing of the races for the sake of mixing. The burden of Brown v. Board of Education, according to Wolters, is that the Supreme Court has blurred this distinction and erroneously requires integration instead of merely prohibiting segregation. Wolters\u27s thesis is that Brown had two prongs: one said that officially sanctioned separation of the races offended the Constitution, the other that exclusion of blacks from the company of whites caused psychological harm that also offended the Constitution. The fact that the Supreme Court interlocked these concepts led to a common usage of these terms as synonymous. It has also, Wolters says, led to tragically misdirected attempts to reform the nation\u27s schools by integration orders

The Use of Statistics to Prove Intentional Employment Discrimination

Two decades after the once fiery debate about the meaning of discrimination in employment under Title VII of the Civil Rights Act of 1964, the issue has recently been rekindled. In simplest form, the question is whether the type of discrimination statutorily prohibited is only purposeful exclusions, or whether it includes unintended exclusions caused by tests or requirements that disproportionately affect a group defined by race, sex, or ethnicity. The Supreme Court\u27s decision in Griggs v. Duke Power Co. resolved the question in one major area, thus causing the issue to lie dormant since 1971. Griggs held that liability under Title VII does not require a showing that an employer acted purposefully to exclude; instead, liability could be premised upon a showing that an employment screening device, such as a requirement of a high school education or a minimum score on an aptitude test, disproportionately excludes a group protected by the Act without the justification of business necessity. The difference between intentional and effect discrimination still poses a question today in the area of subjective interviews. Objective hiring criteria, such as aptitude tests or education requirements, are distinguishable from formless subjective hiring processes. Is it sufficient to use a Griggs-based proof of adverse impact when challenging the disproportionate effect of subjective interviews, or must the claimant show purposeful exclusion in such a case? The courts of appeal are floundering on this issue. A decision in favor of an intent requirement in such cases does not necessarily foreclose the use of statistical proof. Statistics have been the major focus of disparate impact cases following Griggs, as well as being relevant to proof of intentional discrimination.8 Statistical proof, however, should have a greater role than it currently does in cases alleging intentional discrimination. Statistics should play an important role in two ways. One is to probe whether a discriminatory pattern of hiring through subjective interviews is so unlikely to happen by chance alone that purposeful exclusion can be inferred. The other is to show the impact of a requirement such that the employer must have been aware of the exclusionary effect. The continued use of a selection process with a known exclusionary effect is a reckless disregard of rights which satisfies a statutory intent requirement. The burden then shifts to the defendant to show good faith belief in the validity of the requirement

Employee Recruitment by Design or Default: Uncertainty Under Title VII

The employment of every new worker is the result of a two-stage process: recruitment of applicants and selection from the applicant pool. A personnel officer may evaluate only John and Jane Worker because Juan and Juanita Worker are not in the applicant pool. What active or passive acts by the company establish the applicant pool? The issue becomes particularly troublesome when Juan and Juanita Worker are members of one minority group and John and Jane Worker are members of another racial or ethnic minority group. May employers legally recruit more actively from one group than another? This Article examines the types of cases involving recruitment issues and proposes a model for analyzing recruitment practices. The model distinguishes between restrictive recruitment practices, such as admitting applicants from a single source or requiring applications at a single time, and open recruitment practices, where an employer is willing to accept and consider all applications. Restrictive practices are those where the source of the application becomes virtually an employment requirement; the application is not considered unless it comes from an acceptable source or at an acceptable time. Thus, they should be treated by the same legal standards as selection requirements

In Defense of Disparate Impact Analysis Under Title VII: A Reply to Dr. Cohn

The preceding article by Dr. Richard M. Cohn\u27 concerning the use of statistics in Title VII employment discrimination cases makes three basic points. First, Cohn rejects the methods used to assess disproportionate differences between groups on tests, such as ability tests. He finds fault both with the approach of the Uniform Guidelines on Employee Selection Procedures and with the method based on finding statistical significance that I have advocated. Second, he also rejects the approach courts have adopted for evaluating the relative exclusion of groups defined by race, sex, or national origin in the employer\u27s work force. He argues that this method of comparing the composition of the employer\u27s work force to the relevant labor pool is an inaccurate probe of whether discrimination has occurred. Finally, Cohn offers an alternative method for analyzing employment records which he believes should more accurately detect discrimination. This reply contends that Cohn\u27s arguments are based on a misunderstanding of the legally relevant questions under Title VII and thus that he proposes the use of particular quantitative methods that provide answers to the wrong questions. The exceptions that this reply takes with each of the three sections of Cohn\u27s article are not concerning any disagreement over statistics. To the contrary, we are in perfect agreement as to the underlying assumptions that must be met with situations using statistical inference. Rather, our disagreements revolve around questions of law and logic

Probing the Discriminatory Effects of Employee Selection Procedures with Disparate Impact Analysis Under Title VII

Last term the Supreme Court handed down three decisions in which it defined with some precision the proper use of statistics in Title VII cases. Those decisions filled a void that had existed since Griggs v. Duke Power Co., but they left some questions unanswered. In this article Professor Shoben discusses those decisions and addresses the issues still unresolved. She proposes a structured framework for the systematic analysis of disparate impact cases that is consistent with, yet builds upon, the three recent decisions. In addition, Professor Shoben considers whether allowing a plaintiff to establish a prima facie case with statistics alone violates the Act\u27s guarantee that it does not require preferential hiring