This study quantitatively evaluates the incidence and magnitude of errors made by attorneys and their clients in unsuccessful settlement negotiations. The primary study analyzes 2,054 contested litigation cases in which the plaintiffs and defendants conducted settlement negotiations, decided to reject the adverse party's settlement proposal, and proceeded to arbitration or trial. The parties' settlement positions are compared with the ultimate award or verdict, revealing a high incidence of decision‐making error by both plaintiffs and defendants. This study updates and enhances three prior studies of attorney/litigant decision making, increasing the number of cases in the primary data sets more than threefold, adding 72 explanatory variables from 19 classes, applying a multivariate analysis, presenting an historical review of error rates during the 1964–2004 period, and comparing the primary study error rates with error rates in cases where the parties are represented by attorney‐mediators. Notwithstanding these enhancements, the incidence and relative cost of the decision‐making errors in this study are generally consistent with the three prior empirical studies, demonstrating the robustness of the earlier works by Samuel Gross and Kent Syverud, and Jeffrey Rachlinski. The multivariate analysis, moreover, shows that the incidence of decision‐making error is more significantly affected by “context” variables (e.g., case type and forum) than by “actor” variables (e.g., attorney gender and experience level).
This study argues that the aggregative specifications often used to examine wage diferentials fail to control for important demographic variations in wage patterns. In testing how postal wages compare to wages in the private sector, the authors therefore introduce interaction terms to control for gender and race differentials by industry. Their analysis of data from the May 1979 Current Population Survey indicates that average wages are higher in the Postal Service than in many private sector industries because the Postal Service pays nonwhites and women wages similar to those it pays comparable white men, whereas gender and race differentials are common in the private sector. The findings also indicate that the postal wage for white men is about the same as the average wage paid to comparable white men in other sectors of the economy, a relationship that the authors argue should be the key criterion for wage comparability in any public agency that follows a nondiscriminatory wage policy.
Almost two decades of research on the exact relationship between socio-economic variables and the rate of incarceration has produced highly divergent results. Some of these inconsistencies may be due to the various models specified (some use total crime rather than violent, and some control for system level variables while others do not). Virtually all of the previous research has focused on the direct effect only. Utilizing 1990 cross-sectional state level data this study examines the direct and indirect effects of socio-economic variables on imprisonment rates while controlling for arrests or crimes rates and system level variables (e.g., prison admissions and releases). Models using arrest rate data rather than crime rates were included to capture the effect of the recent "war on drugs" on imprisonment which is not included in the Index Crime rate data. These data reveal that percent nonwhite has a significant and direct effect on imprisonment levels across the U.S., but less of a direct effect in nonsouthern states. The data also document that percent nonwhite has a substantial indirect effect. The indirect effect of economic inequality is greater than the direct effect. Finally, there was no variation in the effects of extra-legal variables between those models which controlled for system level data and those which did not.
This study examines the direct and indirect effects of race and income inequality on imprisonment rates across states. The analysis is designed to: 1) investigate whether race and income inequality are significantly related to imprisonment when controlling for crime, 2) assess the relative magnitudes of the direct and indirect effects; and 3) assess the relative magnitudes of race and income inequality. Crime is found to be the strongest predictor of incarceration rates in five of the six equations estimated. Income inequality is significantly related to incarceration rates in two of the six equations. There was no clear evidence of a direct race effect. The indirect effect of race was greater than the direct effect in four of the six equations.
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