Conclusion INT-4.1: The 2020 Census was implemented in light of severe and unprecedented operational challenges. Faced with extremely difficult decisions and reconciling operational demands with strong and appropriate concern for public health conditions, the professional staff of the Census Bureau generally made principled and reasoned choices in adjusting 2020 Census operations to the COVID-19 pandemic, natural disasters, and other disruptions. The basic fact that the 2020 Census was completed, as close to schedule as it was, is itself a major accomplishment, and the Census Bureau and its staff (and the responding American public) deserve commendation for heroic effort amidst the difficult circumstances.

Conclusion INT-4.2: The ability of the Census Bureau to complete the 2020 Census amidst its difficult circumstances depended critically on early commitment in the preceding decade to a general design for the 2020 Census, premised on targeted development work in a tractable number of priority innovation areas: increased field automation, wider use of administrative records data in census processes, modernized address list development, and Internet response.

Conclusion INT-4.3: The fact that the 2020 Census was completed under difficult circumstances is not the same, and is not meant to be interpreted, as a broader statement that the 2020 Census and its data products are high quality and credible. While that may be the case, the evidence base for such a definitive statement on 2020 Census quality is still in development, and such statements require careful attention to bases of comparison.

Conclusion INT-4.4: The 2020 Census labored under formidable challenges, disruptions, and impediments, many of which were forced upon the Census Bureau in the sense that they were purely beyond the Bureau’s control. But it must also be acknowledged that some of the disruptions and impediments to the 2020 Census arose from design decisions and factors within the Census Bureau’s control.

Conclusion INT-4.5: The 2020 Census partnership and communication programs and the mobilization of complete count committees and other outreach/get-out-the-count efforts (including those with states and philanthropic bodies) were intended to boost public attention to the census, but there has been little attention to the return on investment and effectiveness of these support operations.

Conclusion INT-4.6: It will not be possible for this panel (or any other evaluator) to understand and characterize the quality of the 2020 Census unless the Census Bureau is forthcoming with informative data quality metrics, including new measures based on operational/process paradata, at substate levels and small-domain spatiotemporal resolution, unperturbed by noise infusion.

Recommendation INT-4.1: The Census Bureau should work on ways to make 2020 Census data quality metrics publicly available at small-domain spatiotemporal resolutions, unperturbed by disclosure avoidance, to bolster confidence in the published tabulations. The Census Bureau should also develop ways to enable qualified researchers to access a full range of data quality metrics and report their findings.

Recommendation INT-4.2: The Census Bureau should cast its operational assessments and evaluations of the 2020 Census as evidence of the effectiveness of census operations and key inputs to the research agenda for the 2030 Census, rather than purely procedural and documentary of the 2020 experience. Historically, the Census Bureau’s operational assessments and evaluations have been limited to high-level tabulations; the 2020 program should examine 2020 Census operations at finer levels of spatiotemporal and demographic resolution.

Recommendation 2.1: In addition to completing its own program of evaluation and assessment reports, the U.S. Census Bureau should complete and publish a comprehensive procedural history of the 2020 Census.

Conclusion 3.1: Age heaping (excess responses of ages ending in 0 or 5) is a well-recognized indicator of data quality in censuses. Age heaping was much more prevalent in the 2020 Census than in 2010, by an estimated factor of at least 2.5 for ages between 23 and 62 years, for which heaping is most common. This increase was almost completely due to increases in the rate of heaping during Nonresponse Followup (NRFU)—especially from proxy respondents but also from NRFU household respondents. Age heaping was rare in both censuses for self-responses, whether by paper or internet (in 2020 only), and there was no evidence of age heaping associated with administrative records.

Conclusion 3.2: Findings about age heaping raise concerns about the overall quality of data collected during 2020 Nonresponse Followup (NRFU), especially from proxy respondents but even from some household members. Other quality indicators, including item nonresponse rates, numbers of people requiring imputation of all characteristics, and coverage errors, reinforce the difficulties of obtaining accurate data in NRFU and indicate that the difficulties increased in 2020 compared with 2010. The greater extent of quality problems in NRFU in 2020 compared with 2010 may be attributable to the COVID-19 pandemic (including the resulting delay in the NRFU schedule), but other factors might also be involved, which would need to be addressed in 2030 Census planning.

Conclusion 4.1: Comparing the 2010 and 2020 Census net population coverage estimates from Demographic Analysis (DA):

• For total population, both censuses, based on the middle series of DA estimates, closely approximated the estimated population total (census erroneous enumerations almost offset census omissions).
• For age and sex, the 2020 Census undercounted children ages 0–4 more than the 2010 Census. The 2020 Census also overcounted

Recommendation 4.1: For the Demographic Analysis (DA) coverage evaluation method, the U.S. Census Bureau should conduct research on:

• The reasonableness of the assumption for incompleteness of Medicare enrollment in the high series, which produced coverage estimates that diverged greatly from the low and middle series for people ages 75 and older, through an appropriate match study;
• The suitability of Medicare data for coverage evaluation of the entire population ages 65 and older as in the 2010 Census and for improving the census count of this age group in 2030;
• The reasonableness of the methods for assigning Hispanic and non-Hispanic births for coverage estimates for children and young adults, which produced wide differences between the low and high DA series in 2020;
• Methods for narrowing estimates of net international migration, which affect estimates for Hispanic people and the total population; and
• Methods for developing experimental subnational DA estimates, starting with young children.

Recommendation 4.2: The U.S. Census Bureau should conduct additional analyses of the 2020 Post-Enumeration Survey (PES) to learn as much as possible about census omissions. One such analysis should include P-sample and E-sample households with at least one member in common, to assess not only erroneous census enumerations but also omissions and how they vary with household characteristics and type of enumeration (e.g., proxy interview, internet response). In addition, the Census Bureau should perform discriminant or similar analyses to identify variables that contributed to census component errors (e.g., omission, duplication) for 2020 P-sample and E-sample addresses, households, and individuals, which could identify census processes to target for improvement in 2030.

Recommendation 4.3: For the Post-Enumeration Survey (PES) method, the U.S. Census Bureau should:

• Perform sensitivity analyses, based on plausible assumptions, to put error bounds around such key operations as matching and imputation of match and enumeration status, to evaluate the quality of the 2020 PES and plan such analyses from the outset for the 2030 PES;

Conclusion 5.1: The U.S. Census Bureau’s approach of retaining a fluid Master Address File database and filtering operational extracts from that database is a sound one, provided that the filters are continually studied and updated to improve their efficacy.

Conclusion 5.2: The U.S. Census Bureau’s reliance on the U.S. Postal Service’s Delivery Sequence File as a primary update source between censuses is a good one, though it necessitates a reliable update source in areas without mail delivery.

Conclusion 5.3: With respect to the user-input address-building operations of Local Update of Census Addresses, Count Review, and New Construction, both participant governments and the U.S. Census Bureau erred on the side of inclusion—with local authorities submitting many more addresses for review than were actually in the operation’s scope and the Census Bureau injecting many of those submitted addresses into the enumeration process to let fieldwork determine their accuracy. Consequently, these user-input operations appear to have added more bad (unenumerated) addresses to the 2020 Census than good (enumerated) addresses.

Conclusion 5.4: The Office-Based Address Verification operation for resolving respondent-supplied addresses from Non-ID Processing was an effective innovation in the 2020 Census, both in terms of its potential for resolving address listing issues with limited field work and for informing revisions to Master Address File filters.

Recommendation 5.1: The U.S. Census Bureau should continue to research and refine the filters it applies to the Master Address File to derive functional operational extracts, with the intent to reduce the number of addresses cancelled during collection (i.e., flagged as deletes in Nonresponse Followup and other field operations). Along those lines, the Census Bureau should continue to study ways to partner with and more fully utilize U.S. Postal Service data (including the Delivery Sequence File) and potential sources for address addition and revision information between censuses, such as address data recoverable from administrative records extracts, with particular eye toward more regular updating in areas without mail delivery.

Recommendation 5.2: The rules and scope of participant-input address-building operations of the census require clarification, including the Local Update of Census Addresses, New Construction, and Count Review. The U.S. Census Bureau should continue to improve ways to work with user-supplied input or data resources, and make it easier for state, local, and tribal authorities to supply input in usable form, while more clearly laying out the expectations for those address-building operations.

Recommendation 5.3: The U.S. Census Bureau should extend and improve during-the-decade programs, akin to the Geographic Support Systems Initiative of the 2010s, with the goal of continuous address- and map-database refinement. Such

efforts should include engagement with state, local, and tribal partners, including on the question of whether some addresses can safely be removed from the Master Address File.

Recommendation 5.4: Lessons learned from the Office-Based Address Verification operation should be documented and incorporated into the tools and techniques for ongoing, continuous address and map data refinement (e.g., continuous In-Office Address Canvassing) as well as revisions of the Non-ID Processing operation.

Recommendation 5.5: The U.S. Census Bureau should research and test approaches to increase the proportion of dwellings that can be mailed to while still being associated with a physical geographical location. This would reduce the proportion of addresses in Update Leave and expand the use of approaches yielding higher Self-Response for more dwellings in the 2030 Census.

Conclusion 6.1: Going from a paper-only 2010 Census to a majority Internet Self-Response 2020 Census is a commendable achievement, and it is particularly commendable that the U.S. Census Bureau’s internally developed Primus system was able to be swapped in immediately prior to the census with excellent performance throughout the count. (Primus was the backup system pressed into primary service after concerns surfaced regarding the outsource-developed system.) The distinction between Internet First and Internet Choice contact strategies was a reasonable starting position.

Conclusion 6.2: With available return rate and operational data, it was impossible to fully evaluate the 2020 Census mailing and contact strategy beyond gross effects, such as somewhat earlier elevated levels of paper-questionnaire takeup in Internet Choice areas, and it was also impossible to disentangle effects of messaging, delivery, and response mode choices brought about by operations during the COVID-19 pandemic.

Conclusion 6.3: Mode configurations varied substantially across geography, and this variation was associated with social, economic, and housing characteristics. People in areas with higher concentrations of minority populations, lower socioeconomic status, and less-stable housing were more likely to be enumerated in Nonresponse Followup and by modalities with high item nonresponse. The quality of 2020

Census data (as indicated by higher item nonresponse) is poorer in areas with higher concentrations of minorities, lower socioeconomic status, and less stable housing. This raises concerns about data equity, with progressively less accurate and less complete data being collected differentially by race and ethnicity, socioeconomic status, and housing tenure.

Conclusion 6.4: The magnitude of uncertainty in terms of the accuracy of counts of population characteristics as a function of item nonresponse is unknown. The variation in enumeration modality associated with social, economic, and housing characteristics results in differences in data quality by these characteristics. We do not understand the overall impact of item nonresponse and the associated imputation of values on the accuracy of data for the total population or subpopulations. A better understanding of the differential impact of imputation across subpopulations would potentially enable prioritized targeting of groups, to improve self-reporting.

Recommendation 6.1: The U.S. Census Bureau should continue to investigate the performance of the contact strategies used in the 2020 Census, to increase overall self-response (particularly via the internet) in 2030, more in concordance with international experience with online census response. This investigation should include review of the effectiveness of the 2020 Census’s segmentation into Internet First and Internet Choice strategies, as well as the timing and the operative contact/enumeration strategy (e.g., Internet First or Internet Choice mailings or Update Leave delivery) underlying successful Non-ID enumerations in 2020.

Recommendation 6.2: The U.S. Census Bureau should engage in further research to more specifically identify social, economic, and housing characteristics associated with less-than-accurate forms of enumeration, and should also research related communication and operational strategies that would improve self-response and associated data accuracy for those population and housing segments associated with poorer-quality census data in 2020.

Recommendation 6.3: The U.S. Census Bureau should assess the magnitude of the impact of imputation on the accuracy of 2020 Census data. Assessment of the differential impact of imputation on subpopulations should be used to prioritize research into strategies to improve self-response.

Conclusion 7.1: Analysis of the resolution of Nonresponse Followup (NRFU) cases by operational phase does not, in itself, show signs that 2020 Census data collection was unduly hastened to completion. Enumeration by proxy report or by administrative records (if records information of sufficient quality was available) was invoked in all stages of NRFU, but not in a way suggestive of an egregious rush to closeout.

Conclusion 7.2: Though the logic of the U.S. Census Bureau’s split of Nonresponse Followup (NRFU) into distinct operational phases is generally sound, it is impossible to assess the effectiveness of the phased approach as originally designed because the circumstances of the 2020 Census precluded the planned winnowing of NRFU staff. The determination of change from one NRFU phase to another was data driven, based on resolution of a specified percentage of cases in a particular geographic area, which is appropriate. However, the specific operational geography at which NRFU phases were determined—Census Field Supervisor Areas—was so operational and tactical in nature, and so divorced from familiar tabulation geography, that it is unworkable from an evaluation standpoint and raises concerns that it might be difficult for census managers to appreciate actual completion coverage during live field operations.

Conclusion 7.3: The information available for the panel’s analysis did not permit assessment of the potential effects of the delays in 2020 Nonresponse Followup (NRFU) and other field operations in 2020 on the quality of results, such as recall biases and the coding of accurate occupancy statuses of dwellings (especially later in the NRFU period).

Conclusion 7.4: Nonresponse Followup (NRFU) enumerations result in more item nonresponse and poorer coverage than self-responses. NRFU item nonresponse for race and Hispanic origin exceeded 15% in the 2020 Census. Proxy enumerations had greater than 35% item nonresponse for race and Hispanic origin. Results suggest that census tracts with higher proportions of people with lower socioeconomic status (SES) characteristics were more likely to be enumerated using modes that resulted in higher item nonresponse. The net result is that 2020 Census data on race, Hispanic origin, and age are less accurate in those census tracts than in those with higher proportions of higher SES characteristics. This result is most relevant for redistricting. State and local officials are defining political districts with data that vary in how accurately they reflect the age and racial and ethnic composition of the population. Census data are particularly prone to error in areas

with high percentages of people who are Black, Hispanic, or American Indian and Alaska Native living on reservations.

Recommendation 7.1: As a goal for the 2030 Census, the U.S. Census Bureau should consider a major reduction in the use of proxy interviewing for enumeration, if not the elimination of proxy reporting in all but very limited circumstances. Work toward this goal should be predicated on the results of research that only the Census Bureau can conduct, reexamining and comparing proxy reports in the 2020 Census with the information that would have been available from administrative records or third-party data resources.

Recommendation 7.2: The U.S. Census Bureau should consider conducting research on the quality, and factors associated with variation in the quality, of proxy and count-only responses, with the aim of minimizing the occurrence of these factors.

Recommendation 7.3: The U.S. Census Bureau should conduct fine-grained analysis of Nonresponse Followup enumerations (separately for household, proxy, and administrative records responses) for small geographic areas and population groups as part of its research to develop and test ways to improve the quality of enumeration in the 2030 Census. The Census Bureau should publish the results of its analysis as an aid to users of 2020 Census data.

Conclusion 8.1: Based on the information currently available, the U.S. Census Bureau deserves commendation for the meaningful steps made in the limited use of administrative records as an alternative to the enumeration of some nonresponding households in the 2020 Census. The Census Bureau acted with appropriate levels of both boldness and caution in settling on conditions under which a nonresponding household at a designated residence would be enumerated using records information after only one enumerator visit rather than six, yet still ensuring that single in-person visit. Moreover, this change in approach was preceded by an extensive history of high-quality experimentation and testing.

Conclusion 8.2: Moving to the use of administrative records to enumerate a substantial proportion of the population without a contact attempt does not seem feasible for the 2030 Census. Rather, it appears

to be a long-term proposition, requiring extensive testing, research, and engagement with Congress, other stakeholders, and the public. Not only are there operational and technical issues to resolve and data-quality standards to determine and assess for geographic areas and population subgroups, but there is also the need to consider the ramifications of switching from a household/address-based focus to a person-based focus and to address issues of legal, constitutional, and public acceptability. It will be essential for the U.S. Census Bureau to be as transparent as possible about its research and testing and to have open and constructive dialogue with all parties.

Recommendation 8.1: The U.S. Census Bureau should continue its research and development program concerning the best ways to use administrative records as a supplement to decennial census operations. Potential uses of administrative records include expanding enumeration of limited subsets of the 2030 Census population in the Nonresponse Followup workload, reducing proxy responses and whole-person imputations, and possibly redressing the long-standing net undercount of children ages 0–4.

Conclusion 9.1: The enumeration of nonhousehold group quarters (GQ) locations in the 2020 Census experienced major shortcomings, some of which can be directly attributed to COVID-19 pandemic adjustments to the data-collection scheduling (e.g., the scuttling of prearranged plans for in-person enumeration worked out in the Group Quarters Advance Visit operation). Yet several important operational and conceptual shortcomings cannot be attributed solely to the pandemic, such as the mismatch between census content and the U.S. Department of Education’s interpretation of permissible data provision under federal privacy law, the shortfalls in the enumeration of people in military GQs, as well as the fluctuation in status (and resulting problems in the address list) in more fluid GQ types like group homes.

Recommendation 9.1: The U.S. Census Bureau’s steps to make the Master Address File an ongoing, continuous inventory of all living quarters—including group quarters (GQs)—should continue, with an eye toward developing updated information for GQs on par with the regular updating of conventional housing units. This should include a separate inventory of GQ types that have longstanding address provenance and those that

need to be tracked more closely because they are continually changing.

Recommendation 9.2: Group quarters (GQs) require more than updated address information; they require continually updated contact strategies as well. The U.S. Census Bureau should develop “selected advance contact” protocols with GQ facilities by type (and their parent companies, as appropriate), akin to follow-ups performed with businesses, and state, local, and tribal partners should be engaged in this update activity.

Recommendation 9.3: In light of the 2020 experience, the U.S. Census Bureau should convene group quarters (GQ) stakeholders in a discussion of more effective and useful options for electronic provision of data for GQ residents, and on the issues involved in obtaining access to administrative records for GQ facilities. The completeness of such records should also be assessed by GQ type.

Conclusion 10.1: The 2020 Census depicted a more diverse nation than did the 2010 Census. The White Alone population declined significantly, particularly among Hispanic people; the Hispanic population increased; and the Two or More Races population increased substantially, particularly among Hispanic people. It is unknown the extent to which these changes were due to demographic changes (e.g., more multiracial children); changes in self-identification; the provision of write-in origins (perhaps not intended to indicate racial identification) made possible by the format and processing changes to the 2020 Census race question; and other effects of the 2020 Census format and processing changes.

Conclusion 10.2: The 2020 Census had poorer quality data on race and ethnicity compared with the 2010 Census in terms of coverage error (net overcounts and undercounts), rates of missing and imputed responses, and the noise infused by the 2020 Census Disclosure Avoidance System.

• The 2020 Census exhibited larger coverage errors compared with 2010 for race and ethnic groups. There were increases in net overcounts for some groups, particularly non-Hispanic White Alone people and Asian people, and increases in net undercounts for other groups, particularly Black people, Hispanic people, and American Indians and Alaska Natives. Consequently, differences among groups widened significantly in 2020, with

• adverse implications for uses of census data to allocate fixed resources, such as representation, funding, and services.
• The 2020 Census had higher imputation rates (to account for missing and inconsistent responses) for race and ethnicity compared with 2010, particularly for responses not provided by a household member via the internet, a mailed-back paper questionnaire, or telephone. Moreover, the 2020 Census had much higher imputation rates for race and ethnicity for people in group quarters compared with the already high rates in 2010.
• The 2020 Disclosure Avoidance System injected noise of considerable magnitude into the Redistricting File for small race and ethnicity groups in small governmental jurisdictions, such as incorporated places and American Indian tribal lands and Alaska Native villages.

Recommendation 10.1: The U.S. Census Bureau should conduct research to determine how the changes in format and processes that were made in the 2020 Census and in the American Community Survey beginning in 2020 affected the distributions of race and ethnicity. Such research should use qualitative, quantitative, and simulation methods to ascertain: how respondents viewed and used the 2010 and 2020 formats; trends in multirace reporting by age, sex, race, and ethnicity; how samples of 2020 respondents would have been categorized using the 2010 format, data capture, and coding; and the implications of differences in write-ins by response mode (e.g., more write-ins for internet responses) on race distributions among geographic areas. The Census Bureau should communicate the results of this research to data users to assist them to understand the implications of the changes for time series, and should also use the findings to inform 2030 Census planning.

Recommendation 10.2: To improve the quality of reporting on race and ethnicity in the census, American Community Survey (ACS), and other federal data collections, the U.S. Office of Management and Budget (OMB) should revise Statistical Policy Directive No. 15 to adopt a combined, check-more-than-one race/ethnicity classification with both Hispanic and Middle Eastern or North African as main categories, in addition to White, Black, American Indian and Alaska Native, Asian, and Native Hawaiian and Other Pacific Islander. OMB should formalize any changes to the race and ethnicity reporting standards as soon as practicable, to permit the U.S. Census Bureau to implement the new race/ethnicity question in the

ACS and in the 2030 Census. OMB should allow agencies to collect data only for the major categories in the combined question when added detail (as in the 2020 Census) would impose undue administrative burdens. A decision by the Census Bureau on including added detail and write-in spaces, along with expanded data capture and coding procedures, for a combined race/ethnicity question for the ACS and the 2030 Census should await completion of research on such matters as how respondents viewed the 2020 question format and the effects of response-mode differences in write-in responses on diversity among geographic areas.

Recommendation 10.3: The U.S. Census Bureau should produce a crosswalk or bridge between the 2010 and 2020 Census race and ethnicity questions and responses. Similarly, if a combined race/ethnicity question (with Hispanic and Middle Eastern or North African categories) is adopted as the standard by the U.S. Office of Management and Budget, the Census Bureau should produce a crosswalk or bridge between the 2020 version and revised race and ethnicity questions and responses, as soon as the revisions are implemented in the American Community Survey and then in the 2030 Census. The Census Bureau should involve data users in this important work.

Recommendation 10.4: The U.S. Census Bureau should consult as early as possible with the redistricting community, which includes state legislators, redistricting commissions, political parties, and political consultants, among others, to determine the optimum set of tabulations to include on the 2030 Redistricting File, whether the current race and ethnicity categories are retained or the proposed revisions take effect. The consultation should include consideration of streamlining the number of race/ethnicity categories in the file and combining blocks with small populations (using input from localities) to maximize the ability to protect confidentiality while maintaining accuracy.

Conclusion 11.1: In an era of Big Data, linkage technology, and other aspects of today’s computational and data environment, it is difficult for the decennial census to balance the need for accurate data for small areas and small population groups with an adequate

level of confidentiality protection. When evaluating methods and levels of confidentiality protection, stewardship of census data for the public good requires balancing societal needs for the information—particularly for equity in representation, fund allocation, and many other uses—with plausible harms of breaches of confidentiality.

Conclusion 11.2: The decision of the U.S. Census Bureau to respond to the threats to confidentiality protection at a very late date in 2020 Census planning with a new, more complex Disclosure Avoidance System (DAS) using differential privacy-based algorithms went counter to long-standing principles of decennial census planning. The approach had not been tested in a census environment nor had the ability of the algorithms to handle critical user data needs been assessed; the Census Bureau had no backup plan should implementation of the new DAS prove challenging. The decision to continue to deploy the new DAS in the face of serious implementation problems has resulted in marked delays in delivery of data products, with some variables and types of geographic units of questionable utility, and other variables and geographies not provided at all. In addition, it is not clear that the chosen privacy budgets for the various 2020 Census data products, with high values of the ∊ parameter that trade off accuracy with confidentiality protection, provide much actual protection.

Conclusion 11.3: Sustained, interactive communication with data users in advance of major changes to data products from a census or long-running survey is critical for a statistical agency to maintain credibility with and serve its user community. The late decision to adopt new confidentiality-protection techniques for the 2020 Census led to untimely and inadequate user communication on the part of the U.S. Census Bureau. The comment periods after the release of each demonstration file for the Redistricting and Demographic and Housing Characteristics Files were valuable, but many users lacked the staff and resources to respond adequately.

Conclusion 11.4: By not releasing unperturbed operational quality metrics for census tracts, the U.S. Census Bureau missed an opportunity to inform the public of quality differences in the 2020 Census among substate areas and to gain the assistance of interested stakeholders in planning for 2030. Other than the theoretical concept that the release of any piece of information about an individual increases the risk of disclosure, the Census Bureau has not justified its decision to release census tract metrics that are too noisy for meaningful analysis.

Conclusion 11.5: For a major change in data products such as the new confidentiality-protection techniques for the 2020 Census, it is

essential for a statistical agency to document fully and publicly the need for the change and the implications for the products. In the case of the 2020 Census, there has not yet been peer review of the crucial 2016–2018 reconstruction-reidentification study that was the public basis for the decision to adopt a formal privacy approach for protecting confidentiality at a late date in 2020 Census planning. Similarly, there has not been an actual demonstration of why more-than-minimal confidentiality protection is needed for substate operational quality metrics (e.g., self-return rates for census tracts and counties) or why it is not possible to provide as-collected population counts to local governments with a reasonable level of confidentiality protection for other data.

Recommendation 11.1: For 2030 Census data products, the U.S. Census Bureau should adopt the risk-utility framework recommended by the Advisory Commission on Data for Evidence Building, which accepts that disclosure risk is a continuum, that not all data items are equally sensitive, and that federal statistical data need to be accessible and useful for a wide range of users and uses.

Recommendation 11.2: At a minimum, the 2030 Census should provide as-collected (i.e., unaltered) total population counts for all governmental units (states, counties and equivalents, minor civil divisions, incorporated places, recognized tribal areas, school districts) and quasi-governmental units (census county divisions, census-designated places, and tribal statistical areas), no matter how small. In addition, census block population totals should add up to block groups, census tracts, counties, and states.

Recommendation 11.3: For the 2030 Census data product plan, the U.S. Census Bureau should begin immediately on a multipronged research program with ample testing and opportunities for feedback and dialogue with the data user and stakeholder community, broadly defined. The goal should be an end-to-end plan by 2027–2028 for producing a suite of 2030 data products that serve user needs, are appropriately protected, and meet the time schedule of the 1990–2010 Censuses.

Recommendation 11.4: U.S. Census Bureau research and dialogue with the user community, broadly defined, on the end-to-end plan for the 2030 Census suite of data products should include:

• A review of the 2020 Census experience, including rigorous assessment of the Census Bureau’s reconstruction-reidentification studies under plausible scenarios of potential attack and comprehensive debriefing of users.
• Consultation with the redistricting community on race/ethnicity and geographic detail (accounting for the possibility of a combined race/ethnicity question) that best trade off accuracy and confidentiality protection for the 2030 Redistricting File.
• Issuance of challenges to the research community, in addition to focused contracts, for research and development on a range of confidentiality-protection methods and understandable metrics of risk and accuracy to accompany those methods.
• Research on practical methods for users to account for noise injected into 2030 Census data by the selected confidentiality-protection techniques.
• Research on the application of cost-benefit and risk-utility analysis for making tradeoffs between confidentiality protection and utility of census data products.
• Research on the sensitivity of individual data items to relevant communities at differing levels of geographic aggregation and the implications for confidentiality protection.

Recommendation 11.5: The U.S. Census Bureau should welcome initiatives to add language to appropriate legislative vehicles, such as the Foundations for Evidence-Based Policymaking Act of 2018, that prescribes responsibilities and penalties for data users, in addition to agency staff, for willful, harmful disclosure of confidential information.

Recommendation 12.1: To prioritize its testing and development of both procedures and technical systems for the 2030 Census, the U.S. Census Bureau should take the following as primary objectives:

• Maximize self-response to the census, including better matching of contact and communication strategies to the desired response mode, with particular attention to hard-to-reach, at-risk populations;

• Improve the quality of data in Nonresponse Followup, including reducing if not eliminating the use of low-quality proxy reporting when there is an alternative available;
• Reduce gaps in coverage and data quality associated with race, ethnicity, and socioeconomic status;
• Improve the quality of address listings and contact strategies for all living quarters, including group quarters; and
• Realign the balance between utility, timeliness, and confidentiality protection in 2030 Census data products.