In "Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau's Use of Differential Privacy," boyd and Sarathy argue that empirical evaluations of the Census Disclosure Avoidance System (DAS), including our published analysis (Kenny et al., 2021b), failed to recognize that the benchmark data against which the 2020 DAS was evaluated is never a ground truth of population counts. In this commentary, we explain why policy evaluation, which was the main goal of our analysis, is still meaningful without access to a perfect ground truth. We also point out that our evaluation leveraged features specific to the decennial census and redistricting data, such as block-level population invariance under swapping and voter file racial identification, better approximating a comparison with the ground truth. Lastly, we show that accurate statistical predictions of individual race based on the Bayesian Improved Surname Geocoding, while not a violation of differential privacy, substantially increases the disclosure risk of private information the Census Bureau sought to protect. We conclude by arguing that policymakers must confront a key trade-off between data utility and privacy protection, and an epistemic disconnect alone is insufficient to explain disagreements between policy choices.
This article introduces the 50stateSimulations, a collection of simulated congressional districting plans and underlying code developed by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. The 50stateSimulations allow for the evaluation of enacted and other congressional redistricting plans in the United States. While the use of redistricting simulation algorithms has become standard in academic research and court cases, any simulation analysis requires non-trivial efforts to combine multiple data sets, identify state-specific redistricting criteria, implement complex simulation algorithms, and summarize and visualize simulation outputs. We have developed a complete workflow that facilitates this entire process of simulation-based redistricting analysis for the congressional districts of all 50 states. The resulting 50stateSimulations include ensembles of simulated 2020 congressional redistricting plans and necessary replication data. We also provide the underlying code, which serves as a template for customized analyses. All data and code are free and publicly available. This article details the design, creation, and validation of the data.
We study presidential patronage as a form of distributive politics. To do so, we introduce comprehensive data on supervisory personnel in the executive branch between 1925 and 1959 and link each bureaucrat to the congressional representative from their home district. We identify testable hypotheses regarding the impact of electoral considerations, partisanship, and legislative support on the distribution of bureaucratic appointments across districts. Results from a variety of fixed-effects estimation strategies are consistent with several forms of presidential patronage. Our results provide initial evidence about the mechanisms through which patronage appointments are administered in the executive branch and illustrate how presidential politics affects the composition of the federal government.Effective use of the personnel system is a key component of presidents' influence over the bureaucracy. For example, presidents can often advance their policy agendas (e.g., Moe 1985) and electoral interests (e.g., Lewis 2008) by appointing political allies to bureaucratic positions. 1 The allocation of bureaucratic positions, however, is a perennial source of controversy, as critics often allege bias, mismanagement, and nepotism when they perceive that presidents have elevated political or personal connections over qualifications and expertise. The findings from recent scholarship suggest that these concerns may not be without merit (e.g.,
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