Using Small Area 

Estimation to Create 

New Tools for 

Community Resilience

2023 Federal Committee on Statistical Methodology 

(FCSM) Research and Policy Conference

Hyattsville, MD

October 24-26, 2023

Bethany DeSalvo

Heather King

Social, Economic, and Housing Statistics Division

U. S. Census Bureau
In March of 2020, the COVID-19 pandemic struck 

the world and highlighted a need for high quality 

data on the vulnerability of our communities. 

The Census Bureau’s Community Resilience Estimates (CRE)

provide a precise, understandable, and easy to use measure of 

social vulnerability.

What are you going to learn today?
The Census Bureau and COVID-19

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COVID-19 pandemic reiterated 

the need for usable information 

about the population.

Many groups reached 

out to the Census 

Bureau for data.

Overwhelming need 

for a single, easy to 

understand metric.
What is resilience?

Community resilience is a measure of the ability of individuals and 

households within a community to cope with the external stresses of 

the impacts of a disaster.

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What is the CRE’s social vulnerability measure?

• Number and percentage of population that 

are at low, moderate, and high risk of being 

unable to absorb, endure, and recover from 

impacts of a disaster or emergency

• Annual estimates for all states, counties, and 

tracts

• Uses survey data from the nation’s largest 

and most comprehensive survey, the 

American Community Survey (ACS), along 

with decennial census data and population 

estimates

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CRE Low, Moderate, and High-Risk Metric

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• Defined by the concentration of population with risk factors 

that increase vulnerability

• CRE provides the number and percent of population in social 

vulnerability risk factor groups for each geography

 Low: 0 risk factors

 Moderate: 1-2 risk factors 

 High: 3-10 risk factors
CRE Social Vulnerability Risk Factors

1. Income-to-Poverty Ratio less than 130 

percent

2. Single or Zero Caregiver Household (only 

one or no individuals who are 18-64)

3. Crowding (more than 0.75 persons per 

room)

4. Communication Barrier (limited Englishspeaking households or no one over the 

age of 16 with high school diploma)

5. Households without Full-time, Yearround Employment

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6. Disability

7. No Health Insurance

8. Age 65+

9. No Vehicle Access

10. No Broadband Internet Access
National 2021 CRE Estimates

0 Risk Factors

Low Risk

1-2 Risk Factors

Moderate Risk

3+ Risk Factors

High Risk

112,885,613

(+/-1,618,652)

144,981,486

(+/-1,772,974)

69,111,026

(+/-1,470,280)

34.5% 

(+/-0.5%)

44.3% 

(+/-0.5%)

21.1% 

(+/-0.5%)

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Note: Percentages may not total 100% due to rounding.
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2021 CRE: Percent of County Population at “High Risk” 

Percent of 

population 

with 3+ risk 

factors

> 45 – 100

> 37 – 45

> 30 – 37

> 22 – 30

> 0 – 22
2021 CRE: Percent of Census Tract Population at 

“High Risk” in Washington, DC 

• CRE estimates of social 

vulnerability provide 

granular data at the 

Census tract level

• Tracts are small areas 

within counties with 

limited survey data 

available 

• How is this level of 

geographic detail 

possible in the CRE?

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Percent of 

population 

with 3+ risk 

factors
What is a “Small Area”?

• Small Geographic Area

 County 

 Municipality

 School District

• Small Domain

 Specific socio-demographic group

 Ex. Low-income Hispanic women 

aged 40 to 64 in Indiana

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Buckeye 

Union 

High 

School 

District

Maricopa County, 

Arizona
Why Small Area Estimation?

• Censuses have limited scope.

• Sample surveys provide reliable statistics for large areas or 

populations.

• Growing demand for reliable small area statistics, but sample 

sizes are too small to provide survey (or area specific) 

estimators with acceptable accuracy.

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Why Small Area Estimation?

• Estimates can be released for all 

counties, regardless of population size, 

and for many detailed domains

• Reduction in year-to-year volatility 

relative to the survey estimates

• Improvement in precision (margins of 

error) relative to the survey estimates

• Timely release of annual estimates not 

possible by using survey data alone

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Counties Published in the 2017 American Community 

Survey (ACS) 1-Year Estimates
Small Area Estimation in CRE 

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• Blend ACS direct survey estimates 

with related information from 

external data sources (auxiliary 

data)

• Use information from related 

geographies, times, and/or groups 

to increase the precision of “small 

area” estimates 

The CRE small area model is a weighted average of 

the direct survey estimate and an indirect 

estimate derived from pulling in additional related 

data called auxiliary data

Indirect 

estimate

Direct 

estimate

More weight given 

to direct estimate 

with high reliability
Indirect

Estimate

ACS

Estimate

Survey data has high relative 

precision (due to large sample)

Indirect estimate has low 

relative precision

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More weight

Less weight

Areas with large 

sample sizes

Small Area Estimation in CRE 
Indirect

Estimate

ACS

Estimate

Survey data has low relative 

precision (due to small 

sample)

Indirect estimate has high 

relative precision

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More weight

Less weight

Areas with small 

sample sizes

Small Area Estimation in CRE 
Small Area Estimation in 2021 CRE: 

Auxiliary Data Sources

• 2020 decennial census counts at the tract level by age group, race, and 

Hispanic origin and living arrangement (households, group quarters like 

college dorms or nursing homes)

• 2021 official county population estimates from the Census Bureau by same 

demographic groups and living arrangements

• Census and population estimates are combined to produce synthetic 

population counts in 2021 for all tracts by same detail 

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Indirect 

Estimate

ACS 

Estimate

Weighted average between survey estimate and indirect estimate: 

Risk Factor Group Proportions 

Small Area Estimation in CRE 

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Proportion of 

population with 0 

risk factors, 1-2 risk 

factors, and 3-10 risk 

factors tabulated 

from ACS at 

geography of interest

• Risk factor group 

proportions from 

poststratification

• Joint distribution of 

risk factor 

prevalence 

estimated at higher 

levels of geography 

(Census division by 

increasing levels of 

urbanicity) and by 

demographic groups 

(Age, race, and 

Hispanic origin) 

applied to tract 

population estimates 
Percent Reduction in the Coefficient of Variation 

for High-Risk Population in Populated Census Tracts: 

CRE vs Direct ACS Estimate

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Source: 2019 CRE Connected to Restricted Complete 

2019 1-Year ACS Estimates

CRE Reduces Error, Improves Data Quality
CRE Data Products

Published

• 2018, 2019, & 2021 CRE on Social Vulnerability 

• 2019 CRE for Equity Supplement (2019 CRE on Social Vulnerability + 2019 5-Year ACS 

Estimates for social context)

• 2019 CRE for Puerto Rico

• 2019 CRE for Heat

Coming soon (within 1 year)

• 2022 CRE

• 2021 CRE for Puerto Rico

• 2021 CRE for Heat

• 2021 CRE on Capacity 

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Recap

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CRE provides the following benefits

• Timeliness (1-year ACS)

• Accuracy & reliability (small area estimation  reduces sampling error)

• Granularity (outputs estimates down to census tracts)

• Accessibility (interactive tools)

• Adaptability/Growth (geographic coverage, research topic, indicators, 

additional data sources)
Thank You!

Contacts

Bethany DeSalvo

bethany.desalvo@census.gov

Heather King

heather.king@census.gov

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