Replication Data for: Experiential Learning and Presidential Management of the U.S. Federal Bureaucracy: Logic and Evidence from Agency Leadership Appointments

Presidents become increasingly effective at managing the bureaucracy because of the information and expertise that they acquire from on-the-job experience. In their appointment choices, this theory predicts that presidents become better at reducing information asymmetries incurred from the bureaucracy (Agent Selection Learning), improve the vertical balance of leadership agent traits between top supervisory positions and subordinates directly beneath them (Agent Monitoring Learning), and place a greater relative premium on loyalty in response to horizontal policy conflict between the White House and the Senate (Common Agency Learning). This logic obtains empirical support from the analysis of bureaucratic agent traits for Senate confirmed presidential appointees serving in leadership positions covering 39 U.S. federal government agencies from 1977-2009. Presidents' appointment strategies reflect their increasing effectiveness at managing the bureaucracy, thus complementing their increasing reliance on administrative mechanisms to achieve policy objectives as their tenure in office rises. Note: Given that there are numerous files, please consult the README document to best navigate the replication file materials uploaded. One set of files pertain to the contents of the Measurement Model Appendix (MMA) document, the other pertain to the Manuscript/Article and Supporting Information (SI) documents. In addition, codebook and ancillary analysis files are also included with these replication file materials.