Decoding the Census: Why Data Design Matters More Than Noisy Measurements

The design of census data products is crucial because it directly impacts the utility and accuracy of the data used for critical functions such as political representation and resource allocation. While differential privacy, particularly through the use of Noisy Measurement Files (NMFs), aims to protect individual privacy, the way these NMFs are integrated into broader data products determines their effectiveness. Optimizing the query workload output, such as the Redistricting Data (P.L. 94-171) Summary File, can lead to more effective management of the privacy-loss budget, fewer noisy measurements, and reduced post-processing bias, ultimately enhancing the reliability of census data. Emphasizing thoughtful design ensures that the data meets diverse user needs while upholding stringent confidentiality standards, as seen in discussions at the December 2019 Committee on National Statistics (CNSTAT) workshop on the 2020 Census Disclosure Avoidance System (DAS).

Noisy Measurement Files (NMFs) are raw statistics altered to ensure privacy in census data. They are the direct output of a differential privacy system, such as the one used for the 2020 Census. The utility of NMFs depends heavily on how these measurements are integrated into broader data products, like the Redistricting Data (P.L. 94-171) Summary File. While NMFs provide detailed data, including information on every high-order interaction, their primary purpose is to protect individual privacy by introducing noise into the measurements. The scholarly community's engagement in the design of decennial census data products underscores the importance of balancing privacy with the need for accurate and reliable statistics.

The Redistricting Data (P.L. 94-171) Summary File is significant because it is crucial for creating accurate, equal-population voting districts that comply with the 1965 Voting Rights Act. This file is used to support redistricting legislative bodies. The official 2020 Redistricting Data (P.L. 94-171) Summary File contains about 1.5 billion linearly independent statistics; the redistricting NMF expands this to approximately 16 billion linearly independent statistics by including data for the race and ethnicity of all persons and adults living in major group quarters types. Optimizing this output can lead to more effective management of the privacy-loss budget, fewer noisy measurements, and reduced post-processing bias, ultimately enhancing the accuracy and reliability of census data.

The U.S. Decennial Census of Population and Housing serves numerous critical functions, but three stand out due to their constitutional and statutory foundations. These include the apportionment of the House of Representatives, statistical support for redistricting legislative bodies, and support for the Census Bureau's Population Estimates Program. These functions heavily influence how modern U.S. censuses are structured and assessed for accuracy. The redistricting data NMF and the demographic and housing characteristics NMF are groundbreaking as the first publications by any statistical agency to offer the raw output of a confidentiality protection system.

Beyond privacy, several other factors should be considered when designing census data products. These include meeting diverse user needs, ensuring data accuracy and reliability, and promoting transparency. The design should facilitate the creation of accurate, equal-population voting districts that comply with the 1965 Voting Rights Act. User feedback and collaboration among census officials, data scientists, policymakers, and community stakeholders are essential to unlocking the full potential of census data. This collaborative approach can inform evidence-based decision-making and promote a more equitable society, ensuring the census data effectively serves its intended purposes.