Collaborative Research: Building a Multi-User Database of District Court Decisions

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Despite the obvious importance of the Federal District Courts, less scholarly attention has been directed to these courts, particularly as compared to studies of the U.S. Supreme Court or the U.S. Courts of Appeal. In part this is because available databases have not been disseminated in the most user-friendly format and because they lack important variables. Foremost among these underutilized datasets is the Integrated Database (IDB) of federal district court decisions distributed by the Federal Judicial Center in conjunction with the Administrative Office of the United States Courts. However, even though the IDB has been publicly available since the 1980s, it is almost never used by social scientists studying the courts because IDB does not contain information on the identity of the judges presiding over trials nor on the litigants of the case. Because of this, scholars are unable to incorporate attitudinal/ideological and litigant characteristic variables in models of judicial behavior. This research addresses this shortcoming by supplying judge and litigant information for a sample of the IDB, thus rendering the database more useful to scholars and providing researchers with the opportunity to utilize the IDB to examine decision making at the federal trial court level. The research will use the new data to examine and test party capability theory, which asserts that the "haves" come out ahead. The research will examine the conditions under which federal district court judges render decisions according to the characteristics and resources of the litigants and the attitude/ideology of the judge. The research uses a two-prong strategy to code this additional information. Initially, the plan will code a random sample stratified by year of 4000 cases per year for ten years, to collect information regarding litigant type using the comprehensive categories developed by Songer for the Courts of Appeals database. Because this initial stratified random sample is likely to include a vast majority of procedural terminations decided before the case goes to trial, the plan will code an additional "over-sample" of 1000 merits terminations per year. This will provide a dataset of 5000 observations per year, across ten separate years, containing both procedural terminations (generated through the initial random sample) and merits terminations (based on the initial random sample and subsequent over-sample). Moreover, instead of picking a single ten-year period, the research will code the sample of 5000 cases from every other year over a 20-year period, 1995 through 2015. This time span allows the tracking of institutional changes to the district courts across four presidential administrations. Collecting this information will allow scholars to conduct various analyses on the preliminary motions and evidentiary motions potentially leading to settlements rather than trials, as well as the situations in which the merits of a dispute are litigated in a trial.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.