This project investigates appointment politics and practices on the U.S. District Courts in the current historically lengthy (1969-2000) period of divided government. This research extends the PIs prior work developing a linked dataset that spans the evolution of the courts from 1789. The district courts are ideal loci for studies of divided versus unified control of government, neo-institutionalism, and social background theory among others. Three types of data are included: the legislative history behind the creation of each court and seat, information on the officeholders and the circumstance of their appointment and departure that will enable the investigators to track each position and the judges occupying it over time, and data on their backgrounds an career experiences. With this database, scholars will have access to a wide range of information on all judges from the founding of the Republic. The dataset is large enough (N nearly = 2,500 judges) to allow investigators to use statistical analysis of data meaningfully. %%% This project investigates appointment politics and practices on the U.S. District Courts in the current historically lengthy (1969-2000) period of divided government. This research extends the PIs prior work developing a linked dataset that spans the evolution of the courts from 1789. The district courts are ideal loci for studies of divided versus unified control of government, neo-institutionalism, and social background theory among others. Three types of data are included: the legislative history behind the creation of each court and seat, information on the officeholders and the circumstance of their appointment and departure that will enable the investigators to track each position and the judges occupying it over time, and data on their backgrounds an career experiences. With this database, scholars will have access to a wide range of information on all judges from the founding of the Republic. The dataset is large enough (N nearly = 2,500 judges) to allow investigators to use statistical analysis of data meaningfully.
