On January 31, the Census Bureau will release a Proof of Concept for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) for public review and feedback. This Proof of Concept will be based on 2010 Census data. 

The 30-day public feedback period on the Proof of Concept will conclude on March 2. The Proof of Concept applies a new disclosure avoidance methodology to 2010 Census data. This allows a side-by-side comparison of the proposed 2020 Detailed DHC-A table specifications and Disclosure Avoidance System (DAS) algorithm parameters against the published 2010 Census tables. The final Detailed DHC-A based on 2020 Census data is slated for release in September 2023 (previously scheduled for August 2023). 

What is the Detailed DHC-A? 

The 2020 Census Detailed DHC-A is the successor to the 2010 Census Summary File 2 and the 2010 Census American Indian and Alaska Native Summary File. Improvements in the design of the 2020 Census race and ethnicity questions, as well as improved processing and coding procedures, enabled us to collect more detailed race and ethnicity data in 2020 than ever before.  

As a result, the Detailed DHC-A data product provides population counts and age and sex statistics for approximately 370 detailed racial and ethnic groups and 1,200 American Indian and Alaska Native tribes and villages. Data will be available for the nation, states, counties, tracts, places, and American Indian/Alaska Native/Native Hawaiian (AIANNH) areas.  

How the Detailed DHC-A Differs from Other 2020 Census Data Products  

The requirements for the Detailed DHC-A are different than those for the 2020 Census Redistricting Data (Public Law 94-171) Summary File, Demographic and Housing Characteristics File (DHC), and Demographic Profile. Unlike the Detailed DHC-A, those already released products required the creation of a privacy-protected microdata file, postprocessing and consistency through aggregation (summed totals of lower-level geographies must equal higher-level geography totals). They are protected using the TopDown Algorithm (TDA), which meets those requirements.  

The Detailed DHC-A uses an algorithm called SafeTab-P, which produces privacy-protected tabulations directly from the confidential data. Unlike the products protected by the TopDown Algorithm, the Detailed DHC-A does not generate a privacy-protected microdata file, postprocessing and consistency through aggregation (summed totals of lower-level geographies must equal higher-level geography totals). This significantly reduces post-processing bias but changes the way census products are typically used. For example, state population counts for a given ethnic or racial group will not sum to the total national count within this particular data product.   

Data Planned for the Detailed DHC-A 

The product uses an adaptive design to determine the amount of data produced based on each racial and ethnic group’s population size for a given geography. It includes total population counts and sex by age data as follows:  

• Total population: Detailed racial and ethnic groups and American Indian and Alaska Native (AIAN) tribal and village population groups will receive total population data at the national and state levels. Groups that meet minimum population counts will also receive data for counties, tracts, places, and AIANNH areas. Note that 2010 Summary File 2 and American Indian and Alaska Native Summary File used minimum population thresholds of 100 for all tables in all geographies. SafeTab-P uses adaptive population thresholds, all of which are less than 100 in the Proof of Concept product. 

• Sex by Age: There are three sex by age tables that differ in table granularity – either four, nine, or 23 age categories. Population size for a group at a given level of geography determines whether the group receives a sex by age table and the level of detail in those tables.  

The Proof of Concept will not include accompanying summary files or a full run of the 2010 Census data. Rather, we are asking users to evaluate the data’s fitness-for-use based on the proposed thresholds and margins of error using metrics and example data.