New Challenges and New 

Opportunities for the Federal 

Statistical System

Katharine G. Abraham, University of Maryland

Federal Committee on Statistical Methodology Conference

October 24, 2023
Envisioning the future of the Federal statistical system

• In December 2018, Congress passed the Foundations for EvidenceBased Policymaking Act (aka the Evidence Act)

– Incorporated many of the recommendations of the Commission on EvidenceBased Policymaking related to data access and use

– When making those recommendations, Commission members had the 

Federal statistical system very much in mind

• Take approaching 5-year Evidence Act anniversary as opportunity to 

think about the future of the Federal statistical system

• Will argue that there is both a need and, thanks in part to the 

Evidence Act, an opportunity to rethink and strengthen the federal 

statistical system infrastructure 
Federal statistical system faces 

growing challenges
Foundations for U.S. social and economic statistics 

infrastructure laid in mid-20th century

• Probability surveys of households and businesses at core

– Samples designed to represent target population

– Questionnaires designed to collect desired information

– Standard measures produced on a regular schedule

• Census and administrative benchmarks for survey data

– Decennial censuses for social statistics

– Economic censuses and administrative data for economic statistics

• Tasks allocated across multiple statistical agencies, each with its own 

separate set of assigned responsibilities
Existing statistical infrastructure has served nation 

well, but subject to growing pressures

• Increasing difficulty of obtaining survey responses

• Expanding demands from data users
Source: Meyer, Mok and Sullivan (2015); agency websites

Unit response rates, selected household surveys, 

1984-2022
Unit response rates, selected Census Bureau business 

surveys, 2005-2019

Source: U.S. Census Bureau
Unit response rates, selected Bureau of Labor Statistics 

business surveys, 2002-2022

Source: Bureau of Labor Statistics.
Imputation rates for selected items, American 

Community Survey, 2000 and 2022

Item 2000 Allocation Rate 2022 Allocation Rate

Own versus rent, occupied housing units 1.4% 2.2%

Age 2.4% 1.4%

Marital status (age 15 plus) 2.3% 7.5%

Place of birth 6.4% 12.3%

Citizenship 0.5% 9.8%

Educational attainment (age 3 plus) 4.8% 11.9%

Weeks worked past 12 months (age 16 plus and worked) 9.6% 17.8%

Self-employment income (age 15 plus) 6.3% 14.4%

Some or all income imputed (age 15 plus) 23.9% 31.8%

Source: U.S. Census Bureau
Existing statistical infrastructure has served nation 

well, but subject to growing pressures

• Increasing difficulty of obtaining survey responses

• Expanding demands from data users
What do today’s data users want?

• Timely data of appropriate quality 

• Data that are compatible across agencies and programs

• Data more disaggregated along multiple dimensions

– More detailed demographic groups

– State, metropolitan area, county, customized geographies

• Data to answer both long-standing and emerging questions
Administrative data already helping 

statistical agencies meet users’ needs
Administrative records

• Administrative records contain information collected for the purpose 

of administering government programs. 

• Examples of administrative records:

– Individual and business income tax returns

– Unemployment insurance reports

– Medicare and Medicaid records

– SNAP applications and benefit receipt histories

– Customs declarations
Long history of administrative data serving 

significant statistical purposes

• A number of statistical agencies use administrative data as sampling 

frames; others produce statistics based exclusively on administrative data 

as core part of mission

• Federal law directs that, where possible, Census Bureau use administrative 

and other existing data rather than survey data

– Title 13(9)(c): “To the maximum extent possible and consistent with the kind, timeliness, quality and scope of the statistics required, the [Census Bureau] 

shall acquire and use information available from [existing federal, state or 

private data resources]... instead of conducting direct inquiries.”

• OMB M-14-06: Guidance for Providing and Using Administrative Data for 

Statistical Purposes

– “...the Federal Government has the opportunity to create ... statistical information 

more efficiently through greater use of information that the Federal Government 

has already collected for programmatic and regulatory purposes...”
Census Bureau: Using administrative data to 

substitute for missing survey data

• For many business surveys, Census uses administrative data to fill in 

for missing survey data

• Preferred response rate cited for these surveys reflects data obtained 

both through surveys and from administrative records

– Total quantity response rate (TQRR): Share of estimated (weighted) total for 

a data item represented by survey responses or sources deemed to be of 

equivalent quality

• Administrative data an important contributor to effective overall 

response rate
NCHS: Enhancing health surveys with linkages 

to administrative records 

• National Center for Health Statistics (NCHS) data linkage program 

dates to the early 1990s

• Both National Health Interview Survey (NHIS) and National Health 

and Nutrition Examination Survey (NHANES) have been linked to:

– National Death Index (date and cause of death)

– Medicare and Medicaid claims records

– Social Security benefit records

• Linked records have contributed to assessments of the accuracy of 

health statistics and a better understanding of the determinants of 

health outcomes 
Census Bureau: Producing small area poverty 

estimates

• Census Bureau required to produce estimates of poverty for states, 

counties and school districts, but direct American Community Survey 

estimates for many jurisdictions are noisy

• In its Small Area Income and Poverty Estimates (SAIPE) program, 

Census Bureau has produced modeled estimates that rely in part on 

relationship between (noisy) poverty measure and other variables by 

geography. For example, county model includes logs of:

– Number of exemptions claimed on tax returns where reported income places 

the unit below the poverty line

– Total number of exemptions claimed on tax returns

– Number of SNAP beneficiaries

– Total number of people in poverty as of the 2000 Census

– Estimated total population 
Other places where administrative data could help 

to improve agency data products?

• Census: Use of administrative data to produce better estimates of 

income and program participation

– Building on prior work by academic researchers, National Experimental Wellbeing Statistics (NEWS) project is actively exploring how to do this

• Bureau of Labor Statistics: Linkages of National Longitudinal Survey 

records to administrative records such as educational histories, 

earnings, program participation, and interactions with the criminal 

justice system
Use of private sector data 

becoming more important
Naturally occurring private data have proliferated

• Naturally occurring private data are generated by private actors for 

purposes other than statistical purposes. 

• Many types of natively digital private data. A few examples:

– Retail scanner data

– Online information about prices and product characteristics

– Credit card transactions data

– Payroll processing and scheduling data

– Electronic health records 

– Sensor data (e.g., satellite imaging, traffic cameras)
BLS: Using big data to improve the Consumer Price 

Index (CPI)

• CPI price data collected by surveying businesses and rental units

– Commodities and Services Survey: ~94,000 prices per month

– Housing Survey: ~8,000 rental housing unit quotes per month

– Majority of data collected by personal visit

• Efforts underway to substitute data from alternative sources where 

feasible and cost-effective

– Currently identified data sources could replace prices for up to 22 percent of 

the CPI market basket

– Similar efforts underway in several countries
Census Bureau: Using big data to produce state-level retail 

sales estimates

• Monthly retail sales data collected from a survey sample of ~13,000 retail 

and food services businesses

– Data collected at company level; no geographic component to design

• Census has explored use of point-of-sale data from 3rd party vendor NPD to 

reduce respondent burden and improve national estimates

• NPD data also used for new experimental monthly state-level estimates

– Top-down estimates: Allocation of national sales

– Bottom-up estimates: Sum of sales for survey reporters operating in a single state, 

pre-selected multi-unit businesses from NPD, and imputed values for other retailers

– Composite estimates: Weighted sum of two estimates; weights based on relative 

variances
Bureau of Economic Analysis: Using private data to 

improve early estimates of GDP

• First estimates of GDP often rely on “judgmental trend” 

• Where possible BEA uses an extrapolator until data until Census data on 

receipts become available. Some examples: 

– For purchased software, information on company receipts from SEC filings

– For various components, data from trade sources (e.g., intercity bus transportation, 

intracity mass transportation, motion picture theaters, accommodations, mining 

exploration). 

– BEA researchers have explored more sophisticated “nowcasting” models 

based on machine learning methods for projecting Quarterly Services 

Survey data not available for first estimates

– Best models used BLS employment and credit card data

– Nowcasting results have been incorporated into production; most often used for 

healthcare services and software investment
Other places where private data could help to 

improve agency data products?

• NCHS: Private sector health records could in principle augment or 

replace collection of some information through surveys

• Census Bureau: Exploring whether data from private sources plus 

online permit data could replace its Building Permit Survey

• Census Bureau: Exploring whether satellite images could be used to 

measure building starts, completions, and selected unit 

characteristics, replacing measurement through its Survey of 

Construction
Don’t mean to suggest that data 

integration is simple!
Different approaches to combining administrative 

or private data with survey data

• Record linkage

– Deterministic or probabilistic matching; fill in missing or deficient survey values with 

information for same individual or unit

• Imputation

– Statistical matching; fill in missing or deficient survey values with values for similar 

individuals or units

• Modeling 

– Extrapolate to produce current estimates

– Exploit auxiliary information to produce estimates for population subgroups or 

small geographic areas

• Successful projects have had to address many technical challenges

– Won’t always be the case that use of alternative data is the best path forward
Beyond the technical challenges, existing 

infrastructure not conducive to data integration

• Administrative data files typically not structured for statistical analysis 

and often poorly documented

• Holders of administrative often reluctant to share

– Concerns about legal and other risks, especially as related to data protection

– Absence of benefit to sharing agency or state

• Owners of private data voice similar concerns

– Data may be viewed as sensitive or proprietary

– Data can be viewed as an asset to be monetized

• Negotiation of memoranda of understanding a time-consuming caseby-case process
Evidence Act an important step towards 

lowering barriers to data integration
Key Evidence Act provisions

• Enhance protections for confidential and sensitive data

– Reinforce need for legal protection of confidential data 

– Create requirement for risk assessment prior to data release

• Enhance capacity for secure access to confidential data

– Direct creation of data inventories

– Direct creation of standard application process

– Create presumption of access to data for evidence-building purposes unless 

prohibited by law

• Encourage use of data for evidence-building 

– Require learning agendas and evaluation plans
Key Evidence Act provisions (continued)

• Require the assignment of leaders to guide the development, sharing 

and use of data within the federal government

– Chief Data Officers

– Statistical Officials

– Chief Evaluation Officers

• Mandate the formation of the Advisory Committee on Data for 

Evidence Building, charged with making recommendations for 

creation of a National Secure Data Service
How will all of this affect statistical agencies moving 

forward?

• Increased access to administrative data that can help them to

– Reduce respondent burden

– Assess and potentially improve the quality of survey estimates

– Support the production of more disaggregated estimates

– Augment data items collected on surveys to answer new questions

• New options for accessing private data 

– Research on access and linkage modalities carried out as part of the National 

Secure Data Service Demonstration 
How will all of this affect statistical agencies moving 

forward? (continued)

• New roles vis a vis other government agencies

– Chief Data Officers given responsibility for data stewardship, but Statistical 

Officials (and their staffs) have a critical role to play in ensuring that data are 

collected, captured and documented in a fashion that makes them useful for 

statistical analysis

– New emphasis on evidence building will encourage partnerships between 

statistical agencies and program agencies, both within and outside of their 

departments
Example of collaboration with program 

agencies: NCHS-HUD partnership

• Department of Housing and Urban Development (HUD) approached the 

National Center for Health Statistics (NCHS) about linking their 

administrative data to NCHS’ NHIS and NHANES survey data

– Interested in learning about health of housing program participants

• Once NCHS expressed interest, several issues to work through

– Was project consistent with both agencies’ missions and with the consent obtained 

from NCHS survey respondents?

– How would data be shared? Where would they be stored? 

• Processing and cleaning HUD administrative data a cooperative effort

• Project has yielded information that is of interest to NCHS and invaluable 

to HUD

– One important finding: Lead hazard control regulations in HUD properties 

associated with significantly lower blood lead levels in resident children; 

led HUD to seek to tighten those rules
Statistical agencies will need help to fully realize 

their expanded potential

• Leadership from the Office of Management and Budget (OMB) and 

the Interagency Council on Statistical Policy (ICSP)

– In the near term, especially looking forward to the release of the draft 

“presumption of access” regulation

– Development of a plan for a functioning National Secure Data Service that 

can augment existing capacities

• Necessary staffing and other resources

• Ultimately, changes in laws that constraint use of data for statistical 

purposes
How the statistical agencies view themselves also 

will be important

• Openness to innovation

– Work at agencies including NCHS, Census, and the Bureau of Economic 

Analysis, among others, on data integration and experimental data products 

has produced exciting results

– New data products that provide insights about important questions may be 

valuable even if they don’t meet traditional quality standards

• Effective communications about data quality will be important!

• System perspective versus silo perspective

– Contributions to evidence-building that fall outside agencies’ traditional role 

but can help to inform policy likely to grow in importance

• New opportunities are there to be seized. I hope you will do so!
Katharine G. Abraham

University of Maryland

kabraham@umd.edu