Exploring the Use of External Data 

in the American Community Survey: 

Opportunities and Challenges

Amy O’Hara and Deborah Stempowski

December 2, 2015

FCSM Research Conference
Overview

 Objective

 Findings

 Challenges

 Next Steps

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Agility in Action -- Key Action Areas

• Using administrative records

• Reducing follow-up contact 

attempts

• Testing messaging in mail materials

• Modifying survey questions

• Asking less frequently or of fewer 

respondents

• Communicating with respondents 

• Promoting awareness of data uses

• Engaging subject-matter experts

• Advocating for respondents

• Leveraging a communications 

strategy

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http://www.census.gov/programssurveys/acs/operations-and-administration/2015-16-

survey-enhancements.html

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Findings

 Year Built

 Phone Service

 Condominium

 Income

 Residence One Year Ago

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Year Built

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Year Built: MAF ID and BSA Data 

Linkages

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Unedited 2012 ACS records able to link to vendor data, link to vendor year built, and link to same year built category

772,892 

ACS and 

vendor 

agree

1,064,027 

with 

valid 

vendor 

year built

1,401,531 

linked on 

MAFID to 

vendor 

data

2,375,715 

total ACS 

records

1,774,559 

linked on 

BSA to 

vendor 

data

1,315,091 

with 

valid 

vendor 

year built

899,970 

ACS and 

vendor 

agree

0%

20%

40%

60%

80%

100%

MAFID Linkage BSA Linkage

Source: Unedited 2012 ACS and 2006-2010 vendor data
Phone Service

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Phone Service

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Unedited ACS phone service responses (2012) by 2015 CF (shaded cells indicate agreement between data sources)

Administrative Records Contact Frame

Phone Service

No phone 

service Total

ACS 

Respon

se

Phone service 1,877,788 202,915 2,080,703

No phone service 44,813 11,077 55,890

Total 1,922,601 213,992 2,136,593

Source: 2012 edited unswapped American Community Survey with unedited phone 

service responses data and 2015 Contact Frame
Condominium

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Condominium

Source National Coverage Unit Level Data Available Condo Fee Data Available

Veterans Administration/Federal 

Housing Authority Loan Guarantee 

Programs

No No No

Fannie Mae/Freddie Mac Loan 

Guarantee Programs

No No No

National Flood Insurance Plan No No No

Internal Revenue Service No No No

State Databases Partial No No

CoreLogic No No No

CondoSafe (product of CoreLogic) Partial Yes Yes

Real Estate Websites (e.g., Zillow,

Redfin)

Partial Yes Partial

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Income

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Income

 Among ACS persons age 15 and older, 94% 

have sufficient identifying information to 

allow linkage to administrative records. 

 88% of linkable persons age 18 to 64 have at 

least one of the eight common types of IRS 

information returns that are currently 

available to the Census Bureau. 

 Of those 65 or older, 98% have an information 

return.

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Income - Wages

 Linkable ACS persons age 15 and over had wage 

reporting (zero or positive) that agreed with IRS 

W-2s (absent or present) in 87% of cases. 

 In this group, only 6% had reported wages in the 

ACS but did not have any W-2. 

 This type of さfalse positiveざ disIrepaミIy マay He due 

to income type confusion, matching error, or the 

misalignment of reference period or scope. 

 Wages were present only on the W-2 for another 6% 

of this universe, revealing underreporting or reference 

period misalignment.

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Income - Pensions

 87% of linkable ACS persons age 15 and over 

had ACS-reported retirement, survivor, and 

disability pension (RSDP) receipt that agreed 

with the absence or presence of an IRS 1099-R 

record. 

 RSDP income was present only in ACS for 2% 

of this universe, but was present only on the 

1099-R for 12%, possibly reflecting 

underreporting.

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Residence One Year Ago

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Residence One Year Ago

 94% of ACS 2012 persons are validated for linkage

 71% ACS reference persons matched a TY2011 1040

 51% of the matched ACS-1040 records were at the 

same MAFID

 90% of IRS 1040s match MAF

 679k movers in 2012 ACS, look for match in TY2010

 Focus on reference persons (218k) and primary filers

 68% match to a TY2010 return (148k)

 50% are in same county (108k)

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Challenges

 Data availability

 Concept and timing alignment

 Resources

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Next Steps

 Subject matter teams extend and refine 

findings to date

 New analyses on acreage, real estate taxes, 

tenure, number of rooms and bedrooms, selfemployment earnings, mortgage, sale of 

agricultural products, facilities, fuel type, 

property value, social security benefits, and 

Supplemental Security Income

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