Identifying bureaus with substantial personnel change during the Trump administration: A Bayesian approach.

Presidents and executive branch agencies often have adversarial relationships. Early accounts suggest that these antagonisms may have been deeper and broader under President Trump than under any recent President. Yet careful appraisals have sometimes shown that claims about what President Trump has done to government and politics are over-stated, require greater nuance, or are just plain wrong. In this article, we use federal employment records from the Office of Personnel Management to examine rates of entry and exit at agencies across the executive branch during President Trump's term. A key challenge in this endeavor is that agencies vary in size dramatically, and this variability makes direct comparisons of rates of entry and exit across agencies problematic. Small agencies are overrepresented among agencies with large and small rates. Yet small agencies do important work and cannot simply be ignored. To address such small-area issues, we use a Bayesian hierarchical model to generate size-adjusted rates that better reflect the fundamental uncertainty about what is happening in small agencies as well as the substantial likelihood that these entities are less unusual than raw statistics imply. Our analysis of these adjusted rates leads to three key findings. First, total employment at the end of the Trump administration was largely unchanged from where it began in January of 2017. Second, this aggregate stability masks significant variation across departments, with immigration-focused bureaus and veterans-affairs bureaus growing significantly and certain civil-rights focused bureaus exhibiting signs of stress. Finally, compared to the first terms of Presidents Bush and Obama, separation rates under President Trump were markedly higher for most agencies.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Libgober, Richardson. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)