What the Business Community Needs to Know i

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

A Compass for Understanding and Using 

American Community Survey Data

What the Business Community Needs to Know

Issued

October 2008
Acknowledgments Ken Hodges, Nielsen Claritas, drafted this handbook for the U.S. Census 

Bureau’s American Community Survey Offi ce. Kennon R. Copeland and 

John H. Thompson of National Opinion Research Center at the University 

of Chicago drafted the technical appendixes. Edward J. Spar, Executive 

Director, Council of Professional Associations on Federal Statistics, 

Frederick J. Cavanaugh, Executive Business Director, Sabre Systems, Inc., 

Susan P. Love, Consultant, Linda A. Jacobsen, Vice President, Domestic 

Programs, Population Reference Bureau, and Mark Mather, Associate Vice 

President, Domestic Programs, Population Reference Bureau, provided initial 

review of this handbook.

Deborah H. Griffi n, Special Assistant to the Chief of the American 

Community Survey Offi ce, provided the concept and directed the 

development and release of a series of handbooks entitled A Compass for 

Understanding and Using American Community Survey Data. Cheryl V. 

Chambers, Colleen Flannery, Cynthia Davis Hollingsworth, Susan L. 

Hostetter, Pamela D. Klein, Clive R. Richmond, Enid Santana, Anna M. 

Owens, and Nancy K. Torrieri contributed to the planning and review of 

this handbook series.

The American Community Survey program is under the direction of 

Arnold A. Jackson, Associate Director for Decennial Census, Daniel H. 

Weinberg, Assistant Director for the American Community Survey and 

Decennial Census, and Susan Schechter, Chief, American Community Survey 

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

Frank Ambrose, Maryam Asi, Arthur Bakis, Genora Barber, Michael 

Beaghen, Judy Belton, Lisa Blumerman, Scott Boggess, Ellen Jean 

Bradley, Stephen Buckner, Whittona Burrell, Edward Castro, Cheryl 

Chambers, Gary Chappell, Michael Cook, Russ Davis, Carrie Dennis,

Jason Devine, Joanne Dickinson, Barbara Downs, Maurice Eleby,

Colleen Flannery, Sirius Fuller, Dale Garrett, Yvonne Gist, Marjorie 

Hanson, Greg Harper, William Hazard, Steve Hefter, Douglas Hillmer,

Frank Hobbs, Cynthia Davis Hollingsworth, Susan Hostetter, Todd 

Hughes, Trina Jenkins, Nicholas Jones, Anika Juhn, Donald Keathley,

Wayne Kei, Karen King, Debra Klein, Pamela Klein, Vince Kountz,

Ashley Landreth, Steve Laue, Van Lawrence, Michelle Lowe, Maria 

Malagon, Hector Maldonado, Ken Meyer, Louisa Miller, Stanley Moore,

Alfredo Navarro, Timothy Olson, Anna Owens, Dorothy Paugh, Marie 

Pees, Marc Perry, Greg Pewett, Roberto Ramirez, Dameka Reese,

Katherine Reeves, Patrick Rottas, Lil Paul Reyes, Clive Richmond,

Merarys Rios, J. Gregory Robinson, Anne Ross, Marilyn Sanders,

Enid Santan-Ortiz, Nicole Scanniello, David Sheppard, Joanna Stancil,

Michael Starsinic, Lynette Swopes, Anthony Tersine, Nancy Torrieri,

Carrie Werner, Edward Welniak, Andre Williams, Steven Wilson,

Kai Wu, and Matthew Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

Division, Francis Grailand Hall, Chief, provided publications management, 

graphics design and composition, and editorial review for the print and 

electronic media. Claudette E. Bennett, Assistant Division Chief, and 

Wanda Cevis, Chief, Publications Services Branch, provided general direction 

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued October 2008

U.S. Department of Commerce

Carlos M. Gutierrez, 

Secretary

John J. Sullivan, 

Deputy Secretary

Economics and Statistics Administration

Cynthia A. Glassman, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock, 

Director

What the Business Community Needs to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What the Business Community 

Needs to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2008.

Economics and Statistics 

Administration

Cynthia A. Glassman,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock,

Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What the Business Community Needs to Know iii

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Contents Foreword...................................................................................................... iv

What Is the American Community Survey? ...................................................1

Why Is the American Community Survey Important? ....................................1

The Bottom Line on ACS and Advantages and Limitations ...........................2

ACS Data Collection ......................................................................................3

How ACS Data Are Provided ..........................................................................3

How the ACS Diff ers From Other Data Sources .............................................5

What ACS Data Products Are Available? .......................................................7

How to Access ACS Data ...............................................................................9

Confi dentiality, Statistical Reliability, and

 the Reporting of ACS Data ...................................................................... 12

Accuracy of ACS Data .................................................................................. 14

Single-Year and Multiyear Period Estimates ............................................... 15

ACS Data and Business Applications .......................................................... 18

Implications for Private Sector Value Added Products ............................... 19

Glossary...................................................................................................... 21

Appendixes ................................................................................................A-1

 Appendix 1. Understanding and Using Single-Year and Multiyear Estimates .......A-1

 Appendix 2. Diff erences Between ACS and Decennial Census Sample Data ........A-8

 Appendix 3. Measures of Sampling Error ..........................................................A-11

 Appendix 4. Making Comparisons ...................................................................A-18

 Appendix 5. Using Dollar-Denominated Data ...................................................A-22

 Appendix 6. Measures of Nonsampling Error ...................................................A-24

 Appendix 7. Implications of Population Controls on ACS Estimates ..................A-26

 Appendix 8. Other ACS Resources ...................................................................A-27
iv What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Foreword The American Community Survey (ACS) is a nationwide survey designed to 

provide communities with reliable and timely demographic, social, economic, and 

housing data every year. The U.S. Census Bureau will release data from the ACS in 

the form of both single-year and multiyear estimates. These estimates represent 

concepts that are fundamentally diff erent from those associated with sample 

data from the decennial census long form. In recognition of the need to provide 

guidance on these new concepts and the challenges they bring to users of ACS 

data, the Census Bureau has developed a set of educational handbooks as part of 

The ACS Compass Products. 

We recognize that users of ACS data have varied backgrounds, educations, 

and experiences. They need diff erent kinds of explanations and guidance to 

understand ACS data products. To address this diversity, the Census Bureau 

worked closely with a group of experts to develop a series of handbooks, each of 

which is designed to instruct and provide guidance to a particular audience. The 

audiences that we chose are not expected to cover every type of data user, but 

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in Spanish)

Business community Public Use Microdata Sample (PUMS) data

 users

Researchers Users of data for rural areas

Federal agencies State and local governments

Media Users of data for American Indians and 

 Alaska Natives

The handbooks diff er intentionally from each other in language and style. Some 

information, including a set of technical appendixes, is common to all of them. 

However, there are notable diff erences from one handbook to the next in the 

style of the presentation, as well as in some of the topics that are included. We 

hope that these diff erences allow each handbook to speak more directly to its 

target audience. The Census Bureau developed additional ACS Compass Products

materials to complement these handbooks. These materials, like the handbooks, 

are posted on the Census Bureau’s ACS Web site: <www.census.gov/acs/www>.

These handbooks are not expected to cover all aspects of the ACS or to provide 

direction on every issue. They do represent a starting point for an educational 

process in which we hope you will participate. We encourage you to review these 

handbooks and to suggest ways that they can be improved. The Census Bureau 

is committed to updating these handbooks to address emerging user interests as 

well as concerns and questions that will arise. 

A compass can be an important tool for fi nding one’s way. We hope The ACS 

Compass Products give direction and guidance to you in using ACS data and that 

you, in turn, will serve as a scout or pathfi nder in leading others to share what 

you have learned. 
What the Business Community Needs to Know 1

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The American Community Survey (ACS) is the biggest 

change in the U.S. decennial census since businesses 

became major users of decennial census data in the 

early 1970s. In recent decades the census has consisted of a “short form” that was sent to most households, asking just a few basic questions, and a “long 

form,” sent to a sample of households, gathering data 

on many detailed characteristics. Long-form data have 

been especially popular in business applications as the 

source of information on income, education, language, 

commuting, housing value, and many other items. The 

provision of data for very small areas has been a key to 

much of this popularity. 

Beginning with the 2010 Census, the census will collect information only on the basic short-form items. 

There will be no long form in 2010, but the data that 

businesses have come to expect will still be provided. 

Instead of collecting the data as part of the 

2010 Census, the Census Bureau now is collecting 

this information throughout the decade using the ACS. 

“Long-form replacement” is a key ACS objective, but 

ACS data are not a literal replacement for long-form 

data. Content is similar to the long form, but ACS data 

are diff erent enough that users should not simply plug 

them into existing applications without a basic understanding of this new data source. The ACS provides 

a new, wide range of data products. To make eff ective use of the ACS, businesses need to understand 

how ACS data are collected and reported, and their 

advantages and limitations relative to the long-form 

data previously collected during the decennial census. 

A glossary and a series of technical appendixes—for 

those interested in more advanced ACS issues—are 

included at the back of this handbook.

What Is the American Community Survey?

Note to new data users:

This handbook makes frequent references to census long-form sample data, and it describes 

how ACS data diff er from data previously provided by the census. These comparisons are 

for the benefi t of those who have been using census sample data and who need to transition 

to the ACS. In contrast, new users can start fresh, focusing on the ACS for what it is—a very 

large survey that will provide regularly updated estimates of a wide variety of demographic 

and socioeconomic variables that are valuable for many business applications. 

While new users need not be concerned with comparisons to traditional long-form data, 

they will want to pay close attention to the handbook’s explanation of the nature of ACS 

estimates. Of particular importance are the descriptions of ACS “period estimates,” which 

are unlike the “point-in-time” estimates provided by most data sources, and the measures of 

statistical uncertainty involved in ACS estimates. 

Why Is the American Community Survey 

Important?

The ACS is important to business for the same reasons that census long-form data have been important. 

The census collects information for federal program 

purposes, but businesses have been creative in fi nding 

prolifi c uses for census data—especially data from the 

long form. Census data have become a foundation for 

the research, planning, and evaluation work of many 

businesses. 

Business uses of demographic and socioeconomic data 

fall into two major categories—market/site evaluation and consumer segmentation. These applications 

often require data for very small areas, and the census 

has been the most authoritative source of small area 

data available. And while many businesses now use 

sophisticated and proprietary site and segmentation 

products provided by private fi rms, these products are 

built largely on a statistical and geographic foundation 

provided by the census. The Decennial Census 

Program, now including the American Community 

Survey, remains a critical resource to the companies 

that build site and segmentation tools and to the companies that use them. 
2 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

One could fi ll a book on the topic of ACS advantages 

and limitations relative to the decennial census long 

form. ACS data involve some basic tradeoff s that can 

be summarized as follows. 

ACS data have the advantage of being more current 

than census long-form data because they are collected 

and disseminated throughout the decade. Unlike the 

census, ACS data will never be 10 years out of date. 

With content similar to the long form, the ACS will provide updates of items, such as household composition 

(for example, single-parent households with children), 

that are often used by businesses but have not been 

frequently updated for small areas. There is also an 

advantage in quality, as information from nonresponding ACS households is gathered by highly trained and 

experienced interviewers, in contrast with the temporary workers who followed up on households that did 

not return census long forms. 

Several factors off set these ACS advantages. First, the 

ACS sample is smaller than that of the Census 2000 

long form. About 18.3 million housing units were in 

the Census 2000 long-form sample, while 5 years of 

ACS sample addresses will sum to around 15 million. 

Because the ACS follows up on only a subsample of 

nonresponding addresses, the number of housing units 

actually interviewed over 5 years is expected to be 

about 10 million. Second, the once-per-decade longform data were controlled to actual census counts of 

population and housing, while the annual ACS data are 

weighted to conform to the Census Bureau population 

and housing estimates that update the census counts 

every year. The uncertainty in the Census Bureau’s 

population and housing estimates contributes to uncertainty in ACS data. (Appendix 7 describes the implications of the use of controls based on Census Bureau 

estimates.) Third, while long-form data described 

characteristics approximating a point in time or a 

fi xed reference period, ACS data refl ect population and 

housing characteristics over a period of time—1 year, 

3 years, or 5 years. Consequently, the interpretation of 

ACS estimates is not as straightforward as some users 

might like. 

The eff ects of these diff erences may be largest for the 

small area data that are common to business applications. Individual users will have to weigh these impacts 

to determine the suitability of ACS data for their applications. For example, the impact on site evaluation 

work may be minimal, as unreliable block group estimates are aggregated to trade areas large enough to 

have low sampling error. But segmentation applications 

that depend on accurate statistical portraits for specifi c 

small areas—such as individual block groups—may be 

impaired. 

Where characteristics such as age, income, length of 

residence, or ethnicity are involved, private sector 

products built from consumer household databases 

might be preferred as a means to target individual 

block groups or individual households. However, for 

characteristics such as language, employment status, 

disability, and many others, the ACS (or products built 

from the ACS) may be the only source of small area 

data. Users of these data for individual small areas 

need to understand the relatively large sampling errors 

that can be involved and that are reported with all ACS 

estimates. 

In assessing the suitability of small area ACS data for 

their applications, business users should keep in mind 

that long-form data for individual tracts and block 

groups also were subject to relatively large sampling 

errors and were best used for aggregated areas. Longform data errors were less apparent, as margins of 

error were not reported with the published estimates, 

and the once-per-decade schedule masked the fl uctuations that would have been observed if long-form data 

had been provided annually. Knowing the variability 

and reliability of the data, although it may require more 

understanding and analysis, should help businesses 

arrive at better decisions. 

Examples of types of questions that can be addressed by the ACS: 

• How many high-income households with children live near a specifi c store location? 

• Where can one fi nd concentrations of people of Polish ancestry in the Pittsburgh metropolitan area?

• How has the age composition of Phoenix changed in recent years? 

• Which Midwestern metropolitan areas have the highest percent of foreign-born population? 

• Where can one fi nd concentrations of housing held for seasonal, recreational, or occasional use? 

• Which Chicago neighborhoods have concentrations of people who speak Spanish at home? 

• How many workers in Atlanta leave home for work before 6 a.m.? 

The Bottom Line on ACS Advantages and 

Limitations
ACS Data Collection

The Census Bureau started testing the ACS in 1996 and 

went to the full nationwide sample in 2005. In contrast to the once-per-decade census, the ACS collects 

data continuously during the calendar year, with about 

250,000 housing units added to the ACS sample each 

month. 

The ACS questionnaire is similar to that of the census 

long form, but some questions are asked diff erently, 

and content has evolved in recent years. Copies of ACS 

questionnaires for recent years can be reviewed at the 

ACS Web site. Go to <www.census.gov/acs/www

/SBasics/SQuest/SQuest1.htm> to fi nd the link. 

The ACS is a mailout/mailback survey. Each month the 

ACS questionnaire is sent to about 250,000 addresses 

sampled from the Census Bureau’s Master Address File. 

About 6 weeks later, the Census Bureau follows up by 

telephone, when possible, on addresses that have not 

returned a completed questionnaire. For addresses 

that still have not responded, a subsample, ranging 

between 1-in-2 and 1-in-3, is selected for additional 

follow-up. Sample addresses that could not be mailed 

are subsampled at a rate of 2-in-3. Census Bureau fi eld 

interviewing staff visit these addresses in person and 

conduct interviews. 

Starting in 2006, in a separate operation, data are collected from people who live in group quarters facilities, 

such as college/university student housing, military 

barracks, nursing home facilities, and correctional 

facilities. In this process, a sample of group quarters 

facilities is selected. Census Bureau fi eld staff visit each 

selected facility, interview a contact person (such as an 

administrator) to collect facility level information, and 

then select and interview a sample of residents living 

at the facility. The ACS group quarters questionnaires 

can be reviewed at <www.census.gov/acs/www

/SBasics/SQuest/SQuest1.htm>.

Response to the ACS is required by law, just as it is for 

the decennial census. As with the census, responses 

to the ACS are strictly confi dential, and the Census 

Bureau takes great care to preserve this confi dentiality 

in ACS data products. 

How ACS Data Are Provided

Although ACS data collection is going on nearly every 

day, specifi c data products are updated once each year 

and released over several months, beginning in late 

August through December. Three basic types of data 

will be released—estimates refl ecting 1 year of data 

collection, 3 years of data collection, and 5 years of 

data collection. One year of data collection refl ects an 

initial sample of around 3 million addresses, 3 years 

nearly 9 million, and the 5-year data refl ect close to 15 

million addresses. However, because the ACS follows 

up on only a subsample of nonresponding addresses, 

the number of completed interviews is smaller than the 

initial sample sizes indicated above, close to 2 million 

housing unit interviews and 150,000 interviews with 

group quarters residents every year. 

The population size of a geographic area determines 

the type of ACS estimates that are provided. Estimates 

that are based on 1 year of data collection are provided 

for geographic areas with populations of 65,000 or 

more and 3-year estimates are provided for areas of 

20,000 or more. Only 5-year ACS estimates will be provided for areas of the smallest population size—including small towns, census tracts, and block groups. It is 

these 5-year data that come closest to replacing the 

small area census long-form sample data that many 

businesses have used in recent decades.

ACS data are provided (or will be provided) for a variety of geographic areas including those identifi ed in 

Table 1 below. This table gives you a sense of which 

types of areas are large enough to receive 1-year and 

3-year estimates. As the number of areas in column 1 

makes clear, the vast majority of geographic areas will 

receive only 5-year estimates. 

The Census Bureau plans to release new or updated, 

sets of 1-year, 3-year, and 5-year ACS data every year. 

Since 2005 was the fi rst year of ACS data collection 

at full sample, the fi rst set of 1-year estimates was 

released in 2006. The fi rst set of 3-year estimates 

will be released in 2008, and the fi rst set of 5-year 

estimates will be released in 2010. Following 2010, 

the Census Bureau will release new 1-year, 3-year, and 

5-year estimates every year. 

Table 2 summarizes the data collection periods and 

year of release for ACS data products (through 2011), 

as data collection builds to permit the annual release 

of 1-year, 3-year, and 5-year data. 

What the Business Community Needs to Know 3

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
4 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

* When originally designed, each PUMA contained a population of about 100,000. Over time, some of these PUMAs have gained or lost 

population. However, due to the population displacement in the greater New Orleans areas caused by Hurricane Katrina in 2005, Louisiana 

PUMAs 1801, 1802, and 1805 no longer meet the 65,000-population threshold for 1-year estimates. With reference to Public Use Microdata 

Sample (PUMS) data, records for these PUMAs were combined to ensure ACS PUMS data for Louisiana remain complete and additive.

Source: U.S. Census Bureau, 2008. This tabulation is restricted to geographic areas in the United States. It was based on the population sizes of 

geographic areas from the July 1, 2007, Census Bureau Population Estimates and geographic boundaries as of January 1, 2007. Because of the 

potential for changes in population size and geographic boundaries, the actual number of areas receiving 1-year, 3-year, and 5-year estimates 

may diff er from the numbers in this table.

 

 Type of geographic area 

Total 

number of 

areas

Percent of total areas receiving . . .

1-year, 

3-year, 

& 5-year 

estimates

3-year & 

5-year 

estimates 

only

5-year 

estimates 

only

States and District of Columbia 51 100.0 0.0 0.0

Congressional districts 435 100.0 0.0 0.0

Public Use Microdata Areas* 2,071 99.9 0.1 0.0

Metropolitan statistical areas 363 99.4 0.6 0.0

Micropolitan statistical areas 576 24.3 71.2 4.5

Counties and county equivalents 3,141 25.0 32.8 42.2

Urban areas 3,607 10.4 12.9 76.7

School districts (elementary, secondary, and unifi ed) 14,120 6.6 17.0 76.4

American Indian areas, Alaska Native areas, and 

Hawaiian homelands 607 2.5 3.5 94.1

Places (cities, towns, and census designated places) 25,081 2.0 6.2 91.8

Townships and villages (minor civil divisions) 21,171 0.9 3.8 95.3

ZIP Code tabulation areas 32,154 0.0 0.0 100.0

Census tracts 65,442 0.0 0.0 100.0

Census block groups 208,801 0.0 0.0 100.0

 Table 1. Major Geographic Areas and Type of ACS Estimates Received

 Table 2. Release Schedule for ACS Data

Year of Data Release

Data Population 2006 2007 2008 2009 2010 2011 2012 2013

product threshold 

 Year(s) of Data Collection

 1-year 

 estimates 65,000+ 2005 2006 2007 2008 2009 2010 2011 2012

 3-year 2005– 2006– 2007– 2008– 2009– 2010–

 estimates 20,000+ 2007 2008 2009 2010 2011 2012

 5-year 2005– 2006– 2007– 2008–

 estimates All areas* 2009 2010 2011 2012

*Five-year estimates will be available for areas as small as census tracts and block groups. 

Source: U.S. Census Bureau.
What the Business Community Needs to Know 5

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

What about ZIP Codes? 

The Census Bureau also plans to release ACS data for 

ZIP Code Tabulation Areas (ZCTAs). First defi ned by the 

Census Bureau for Census 2000 data products, ZCTAs 

are aggregations of census blocks that approximate 

areas corresponding to ZIP Codes. The emphasis is on 

“approximate” because ZIP Codes are numeric codes 

for mail delivery and often do not have clear geographic boundaries. Because most ZCTAs are small, 

the Census Bureau will only release them in the form 

of 5-year estimates. Because ZIP Codes are subject 

to change, the Census Bureau plans to update ZCTA 

defi nitions periodically. However, ZCTA defi nitions will 

not be updated every year, so users will need to pay 

close attention to the “vintage” of the ZCTA defi nitions 

in ACS products and understand that these defi nitions 

may diff er from ZIP Codes as defi ned by the U.S. Postal 

Service or by private data suppliers.

How the ACS Diff ers From Other Data Sources

While “long-form replacement” is an ACS objective, it is 

not a replacement in a literal sense, as there are fundamental diff erences between ACS and census long-form 

data. The census long-form sample was a large survey 

taken once a decade as part of the decennial census, 

while the ACS is a large survey conducted throughout 

the decade—with a new sample of around 250,000 

addresses introduced each month. The collected 

information is aggregated and processed once a year, 

and the resulting ACS estimates refl ect population and 

characteristics over periods of time—either 1, 3, or 5 

years, as described above. The complications of these 

“period estimates” are described in a later section. 

The ACS uses a diff erent defi nition than the census of 

who lives in a housing unit. The census counts people 

according to their “usual residence,” or where they live 

most of the year. The ACS includes people according 

to a “2-month rule” of “current residence.” People are 

considered residents of a sample unit if their length 

of stay has been or will be more than 2 months when 

contacted by the ACS. This diff erence is important 

for businesses that need to know if their data include 

seasonal populations who reside in an area only a few 

months of the year. For the census, the answer is “No,” 

but for the ACS, the answer is not a clear “Yes” or “No.” 

For example, in parts of Florida, ACS data collection in 

winter months will interview people who reside there 

only during the winter if their stay exceeds 2 months. 

ACS data products describe the characteristics of the 

population of an area throughout the entire year (or 

years) of data collection based on stays of more than 2 

months, and thus refl ect a mix of seasonal highs and 

lows. 

The census is focused on population counts, and for 

purposes of congressional apportionment and redistricting, it is important that people be counted only 

once and in the right place. The ACS is not about population counts but about characteristics. For example, 

the ACS does not determine the estimate of the total 

population of Fargo, North Dakota. The total population for Fargo provided in ACS tables is based on the 

Census Bureau’s Population Estimates Program. However, if one wants to know the percent of Fargo’s 

population that is widowed or divorced, the ACS is the 

source for that estimate. 

Most ACS characteristics are similar in defi nition to 

their counterparts from the census long-form sample, but users should be aware of some diff erences. 

Because so many businesses rely on estimates of 

income and home value, these users need to understand how the ACS handling of these estimates diff ers 

from what they have been using from the long form. 

The Census 2000 long form asked respondents to 

report income received during the previous calendar 

year, so income reported by Census 2000 refl ected 

income received during 1999. In contrast, the ACS 

asks respondents to report income received during the 

previous 12 months. With data collected throughout 

the year, the incomes reported refl ect a range of 12 

diff erent 12-month income reference periods. People 

interviewed in March 2007 reported income received 

from March 2006 through February 2007, while those 

interviewed in November 2007 reported income 

received from November 2006 through October 2007. 

Among “2007” ACS respondents, reference periods 

span 23 months (from January 2006 through 

November 2007). 

When dealing with ACS income data, it is important 

to keep in mind that the dollar amounts are infl ation 

adjusted to refl ect changes in the national consumer 

price index (CPI)—with results expressed in dollar 

values of the most current data collection year. For 

example, each of the 12 diff erent income reference 

periods for 2007 ACS respondents are infl ation adjusted to represent 2007 dollars. When combining multiple 

ACS samples, as is done to create 3-year and 5-year 

estimates, the earlier year ACS respondent incomes are 

further infl ation adjusted to the most recent ACS year 

of the multiyear period. Five-year income estimates 

based on data collected from 2005 through 2009 will 
6 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

refl ect the incomes reported during that period adjusted 

to 2009-dollar values. Table 3 summarizes the diff erences between income data from Census 2000 and 

5-year ACS estimates refl ecting data collected during 

2005 through 2009. Similar adjustments are made for 

3-year estimates. 

Note that income is the only variable for which such 

adjustments are made for the 1-year estimates. For all 

other data items reported as dollar values, infl ation 

adjustments are made only for the 3-year and 5-year 

estimates. The adjustments are made to refl ect dollar 

values of the latest collection year in the period.

See Appendix 2 for additional detail on diff erences 

between ACS and decennial census sample data. 

Appendix 5 provides detail on the use of dollardenominated ACS data. 

The ACS also diff ers in important ways from the 

Current Population Survey (CPS), which also is used as 

a source of demographic and socioeconomic information. The ACS sample of nearly 3 million addresses 

per year is much larger than the CPS sample of about 

50,000 households per month. While both the CPS and 

ACS collect data throughout the year, the CPS collects 

demographic and income detail in the Annual Social 

and Economic Supplement (ASEC), based on a sample 

of about 100,000 addresses initially interviewed in 

CPS during February, March, and April. With its larger 

sample, the ACS is designed to provide data for much 

smaller areas than the CPS. However, for some topics, 

the CPS asks more detailed questions and may produce results that are preferable for some applications. 

Thus for seemingly similar data items, such as median 

income or poverty rates, the ACS and the CPS will 

sometimes report diff erent results at national and state 

levels. 

 Table 3. Diff erences in Income Reported by Census 2000 and the ACS

Census 2000 Long Form ACS 2005–2009

Total Households As of April 2000 Averaged over collection period

Collection period Spring and summer, 2000 Every month—January 2005 through December 

2009

Income reporting Income received in 1999 Income received during 12-month period prior 

to interview

Infl ation adjustments None Income infl ation adjusted to the most current 

data collection year

Source: U.S. Census Bureau.
What the Business Community Needs to Know 7

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Despite the diff erences between ACS and census longform data, experienced census data users should feel 

very much at home with ACS data products and new 

users should fi nd them easy to get to know. All ACS 

products can be accessed through the American 

FactFinder on the Census Bureau’s Web site: <http://

factfi nder.census.gov>. 

Current products include Detailed Tables that provide 

the most detailed information on all ACS subjects for 

all geographic levels. Users can think of these tables as 

similar to those from the Census 2000 Summary Files 

(such as SF1 and SF3). One diff erence is that the ACS 

tables report measures of sampling error called margins of error for every estimate, while the 

Census 2000 sample data products provided no such 

measures. The exception is ACS estimates that are 

controlled to independent Census Bureau estimates. 

Measures of sampling error are not provided because 

these estimates are not subject to sampling error. 

The detailed table below shows the estimates and 

margins of error of sex by educational attainment for 

Los Angeles County, California, in 2006. The data are 

produced for the population aged 25 and over.

What ACS Data Products Are Available?

 Figure 1. Example of a Detailed Table

Source: U.S. Census Bureau, 2006 American Community Survey, accessed at <http://factfi nder.census.gov>.
8 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 2. ACS Data Sets Page

Figure 2 is a screenshot of the American FactFinder 

data sets page. This page lists the specifi c ACS data 

sets (for example, 2007 ACS or 2005–2007 ACS) and 

the associated ACS data products. Some of these products are described in greater detail below.

• Data profi les are preformatted reports summarizing characteristics for selected areas. They are similar to the data profi les provided for Census 2000 

data. For all areas meeting the required population 

thresholds, one can fi nd data profi le reports summarizing information on the following topics, as 

well as others. Narrative profi les can be accessed 

from these data profi les and provide information 

in a user-friendly text and graphic format that puts 

various topics found in the data profi le into words 

for the general user. 

o Social: education, marital status, household 

type, grandparents 

o Economic: income, employment, occupation, 

commuting 

o Housing: housing value, owner/renter, mortgage status 

o Demographic: age, sex, race, Hispanic origin 

• Selected population profi les summarize basic 

data for a generous list of race, Hispanic origin, and 

ancestry groups. For example, a user could quickly 

access a report summarizing the characteristics 

of people who marked both White and Asian in 

responding to the race question. Beginning in 2008, 

these selected population profi les will include country of birth.

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.

• Comparison profi les make it easy to compare 

data for 2 sequential years. They are new in 2007 

and will present 2006 ACS and 2007 ACS estimates 

and display the results of statistical testing to 

assess if the diff erences in these 2 years are statistically diff erent. 

• Ranking tables allow users to rank states (plus the 

District of Columbia and Puerto Rico) on key characteristics. For example, a fi nancial services company 

that organizes sales territories by groups of states 

might rank states on variables such as percent of 

households with retirement income or the median 

value of owner-occupied housing units. 

• Subject tables provide expanded detail in tables 

arranged by subject. For example, a business targeting a Spanish-speaking clientele could defi ne the 

area of interest and then choose among a variety of 

tables presenting data related to language spoken 

at home. 

• Detailed tables provide the most detailed information on all ACS subjects for all geographic levels. 

Users can think of these tables as similar to those 

from the Census 2000 Summary Files (such as SF1 

and SF3). 
What the Business Community Needs to Know 9

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

• Geographic comparison tables complement 

the ranking tables by providing data with more 

geographic detail. For example, if the business 

described above is surprised by Florida’s ranking 

on median home value, it can run a geographic 

comparison table to see how median value is distributed across the state’s counties. 

• Thematic maps present the state level information from ranking tables on maps. 

• The ACS Summary File (SF) includes all ACS 

detailed tables for all published geographic levels. 

Businesses that use large quantities of ACS data, 

or that use ACS data in the development of nationwide marketing information products, may fi nd 

the Summary File the most effi cient way to acquire 

and store the ACS data they use. Unlike decennial 

census products, which provide multiple summary 

fi les (such as Summary Files 1, 2, 3, and 4), there is 

only one ACS Summary File. 

• Public Use Microdata Samples (PUMS) provide 

anonymous respondent or record level data for a 

sample of all housing units and residents of group 

quarters interviewed by the ACS. To protect confi dentiality, PUMS data are not provided for small 

areas—only for large areas known as Public Use 

Microdata Areas (PUMAs). PUMAs have populations 

of about 100,000. Because individual level responses are provided, PUMS enables users to generate tables that are not published by the Census 

Bureau. For example, a research fi rm developing 

weights for use in a survey of Asian householders 

might need estimates of Asian householders by age 

and place of birth for all states. The ACS detailed 

tables do not provide this cross-tabulation, but the 

research fi rm can produce it from the ACS PUMS 

product. PUMS also provides the option of producing separate tables for people living in households 

and people living in group quarters. 

 As with traditional census products, ACS PUMS data 

involve a tradeoff . PUMS data provide great fl exibility in data content—enabling users to build data 

tables that are not available in the published data 

products (such as the detailed tables). However, 

the tradeoff is that PUMS data provide much less 

geographic detail than the published products. 

• Custom tabulations are an option for users 

needing data not provided by ACS detailed tables, 

and which cannot be produced with PUMS. For 

example, a publisher seeking to identify concentrations of highly educated foreign-born women with 

children might be disappointed to fi nd that the ACS 

does not provide a detailed table with this combination of characteristics. If the publisher needs the 

data for counties or census tracts (to aggregate to 

market areas), the data cannot be produced from 

PUMS. A user in this situation can pay the Census 

Bureau to produce a “custom tabulation” of the data 

from the ACS detail fi les. Although attractive for 

some users, this option involves time and expense. 

The requested tabulation must be approved by the 

Census Bureau’s Disclosure Review Board to ensure 

that it does not put the confi dentiality of individual 

data at risk. To further guard against disclosure, 

custom tabulation data are subject to rounding and 

suppression. 

Diff erent ACS products will appeal to diff erent businesses. Many ACS products function best as a lookup 

tool. The user has a few areas in mind, wants specifi c 

data and maps for those areas, and runs the relevant 

ACS product. For example, a small Des Moines, Iowa, 

based retailer could go to the Census Bureau Web 

site (see details below on how to access the Web site) 

and obtain selected detailed tables, data profi les, and 

subject tables for the Sioux City, Omaha, and Lincoln 

metropolitan areas to determine which is the best 

candidate for an initial expansion. However, companies 

that need continuous and complex analyses of markets 

across the United States would need to acquire large 

volumes of ACS data—either from a download center 

provided through American FactFinder or the ACS 

Summary File. A real estate fi rm could download ACS 

data every year to help monitor the demographics in a 

large number of sites defi ned as aggregations of block 

groups. A fi nancial services provider with data on the 

age and mortgage status of its customers might use 

ACS PUMS data to build a model suggesting the likelihood that its customers have children approaching college age. Another fi rm might use ACS data on housing 

costs as a percent of income to identify neighborhoods 

where homeowners face diffi culty in making house 

payments. 

How to Access ACS Data

It remains to be seen how private sector data fi rms will 

provide and incorporate ACS data into their products, 

but data already are available on the Census Bureau’s 

Web site <www.census.gov>. On the Census Bureau 

homepage, clicking “American Community Survey” 

takes the data user to the ACS “main page,” which provides links to a wide range of information, from introductory material to product descriptions and release 

schedules to in-depth explanations of topics including 

data collection and error measures. 
10 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Once familiar with the ACS and its products, many 

users will access ACS data through the American 

FactFinder, the Census Bureau Web site’s function 

for providing access to data products. The American 

FactFinder main page can be accessed from the Census 

Bureau Web site’s homepage or at <http://factfi nder

.census.gov>. 

Clicking on “Data Sets” and then on “American 

Community Survey” opens a page where users choose 

the desired ACS data set and specifi c data product 

(such as detailed tables, ranking tables, subject tables, 

thematic maps). Figure 2 displays the 2007 Data Sets 

page. Note that the user by default is directed to the 

most recent data set. In this example, the 2005–2007 

ACS 3-year estimates are shown as the default. This is 

what users will see when these 3-year estimates are 

released in December 2008. Using the radio buttons, 

users can change this selection to another data set 

including the 2007 ACS 1-year estimates or 1-year 

estimates for 2005 or 2006. Depending on the product 

selected, the user is then prompted for the specifi c 

geographic area of interest and the specifi c table or 

a keyword or subject in order to search for a table. 

Figure 3 is a screenshot of the page within American 

FactFinder that prompts the user to identify the geographic area.

 Figure 3. Selecting Geographies in American FactFinder

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
What the Business Community Needs to Know 11

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

See below for a step-by-step example of how to access an ACS table from the American FactFinder. 

The American FactFinder is most useful as a way to 

quickly look up a few ACS estimates for a few areas, 

but it is not practical for applications requiring large 

volumes of data for large numbers of small areas—

such as income, education, and language data for all 

block groups nationwide. Users who need large quantities of ACS data may want to acquire the ACS Summary 

File (available for download from the Census Bureau’s 

Web site). Like the Census 2000 Summary Files, the 

ACS Summary File provides a large number of data 

tables for a range of geographic levels. 

To retrieve an ACS Detailed Table, follow the steps below. 

In this example, you will retrieve a place of birth table for Bexar County, Texas, from the 2006 ACS.

- Go to the Census Bureau’s Web site <www.census.gov>.

- Click on “American FactFinder.” 

- Under “Getting Detailed Data,” select “American Community Survey – get data.”

- Under “Data Sets,” select “2006 American Community Survey.” 

- Select “Detailed Tables.” 

- Under “Select a geographic type,” select “County.” 

- At the prompt, select “Texas” and then “Bexar County.” 

- Click “Add” to place this county in the selection box.

- Click “Next.” 

- Search by “Subject” (for example, search under social characteristics—birthplace, nativity, 

and year of entry) or “Keyword” (for example, place of birth) to fi nd the specifi c table.

- Select “Table B05002. Place of Birth by Citizenship Status.”

- Click “Add” to place the table in the selection box.

- Click “Show Result” to view the table (see table below).

B05002. Place of Birth by Citizenship Status

2006 American Community Survey

 Figure 4. Example 1

Source: U.S. Census Bureau, 2006 American FactFinder, accessed at <http://factfi nder.census.gov>.

As an alternative to the Summary File, users who know 

the specifi c tables and geographic levels they want can 

go to the “Download Center,” which lists ACS and other 

data products available for download from the Census 

Bureau’s Web site. For example, a business needing 

home value data for all counties could download the 

data to a fi le as described in the example below. 
12 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Downloaded fi les make it easier to use ACS data for 

large numbers of small area geographies but leave it to 

users to aggregate the data according to their interests. Some businesses may fi nd it more convenient to 

acquire ACS data for aggregations from private suppliers, whose products are geared to dissemination for 

user-defi ned areas. 

Note—ACS PUMS fi les are not available at the Download Center. To download ACS PUMS products, go to 

To download an ACS table from the Download Center, follow the steps below. 

In this example, you will download a fi le providing home value for all counties based on the 2006 

ACS. 

- Go to the Census Bureau’s Web site <www.census.gov>.

- Click on “American FactFinder.” 

- Click on “Download Center.” 

- Under “Select a Data Set,” select “2006 American Community Survey.” 

- Under “Select a Geographic Summary Level,” select “All Counties.” 

- For “Select a Download Method,” use the default option—“Selected Detailed Tables.” 

- Click “Go.” 

 - Search by “Subject” (for example, Housing Characteristics—Value of Home) or “Keyword” (for 

example, home value) to fi nd the specifi c table. 

- Select “Table B25075. Value for Owner-Occupied Housing Units.” 

- Click “Add” to place the table in the selection box. 

- Click “Next.” 

- Click “Start Download” to initiate the download.

the ACS main page and using the Access Data tab, 

access the PUMS products link. Once you select the 

specifi c PUMS fi les of interest, you can download the 

PUMS data and view documentation. A page opens that 

prompts you to select specifi cations for the PUMS data 

to be downloaded. For more information about using 

PUMS data, refer to the handbook in this series for 

PUMS data users. 

Confi dentiality, Statistical Reliability, and 

the Reporting of ACS Data

The Census Bureau has indicated that Census 2000 

long-form products can be used as a guide to the data 

that will be reported from the ACS and their geographic levels. Tables such as Households by Income, 

which Census 2000 reported for block groups, will be 

reported for block groups in the ACS. Tables such as 

Ancestry that were reported only at the census tract 

level will be limited to tract data in the ACS. 

Confi dentiality

Like the decennial census, the ACS is a mandatory 

survey, with response required by law. Like the census, 

the same law that requires response mandates that 

these responses be kept confi dential. Individual data 

from the ACS are not shared with anyone outside the 

Census Bureau and the Census Bureau takes great care 

to prevent the disclosure of information on individual 

respondents through released ACS data products. 

Similar to the census, the ACS prevents disclosure 

by not reporting data for individuals and households 

but by releasing aggregate statistics for geographic 

areas. The one exception is the Public Use Microdata 

Samples (PUMS) product, which provides a sample of 

individual level records but preserves confi dentiality by 

not identifying their detailed geographic location. The 

individual level data reported on PUMS fi les are anonymous—no names or addresses are identifi ed, and data 

are reported only for large Public Use Microdata Areas 

(PUMAs). Since PUMAs have a minimum population of 

100,000, one knows little about where these respondents live, and confi dentiality is well protected. 

The Census Bureau also guards against the risk of 

disclosing individual information in aggregated data. 

Rare characteristics are “swapped” from one geography to another to add uncertainty. ACS block group 

data refl ect only a sample of people living in the block 
What the Business Community Needs to Know 13

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

group over a 5-year period, so the identity of individuals—even those with rare characteristics—is very well 

protected by this disclosure avoidance process. 

Statistical Reliability and the Reporting of ACS 

Data

The ACS also limits the release of some data on the 

basis of expected sampling error (see discussion of 

sampling error below). Two versions of many of the 

detailed tables have been designed and are produced—

a table that shows the information in full detail, and a 

version with fewer categories, known as a collapsed 

version. The 1-year and 3-year ACS tables are subject 

to a test of statistical reliability. For a given geographic 

area, if a table does not pass the reliability test, a collapsed version of the table that reports fewer categories may be provided. If the collapsed table fails to 

pass the reliability test, the data are not reported for 

that geographic area. 

For example, the full Household Income table reports 

16 categories of income and is illustrated in Figure 5 

with estimates refl ecting 2006 ACS data for the nation. 

If, for a smaller area, the full table does not pass the 

reliability check, data would be provided only for the 

collapsed version of the table shown in Figure 6 (again 

shown with national level data) that reports only 10 

categories of income—as long as this table passes the 

reliability check. When the full detailed table passes, 

the collapsed version of the table is also published. 

One might expect the reliability checks to have an 

especially severe impact on the 5-year data, which are 

released for small areas, but the 5-year data are not 

subject to reliability tests or to collapsing or suppression. The reason is that the 5-year data are regarded as 

the primary ACS products for long-form replacement, 

and it is understood that aggregations to user-defi ned 

areas require the provision of all building-block geographies, regardless of reliability. 

Collapsing and suppression may make the 1-year and 

3-year data diffi cult for some users, as availability may 

not be consistent. For example, a business examining 

income by age of householder for a set of cities may 

fi nd that the data are suppressed for some, and that 

only the collapsed data are available for others. Yearto-year disruptions can be expected, as an area might 

pass the reliability test in some years but not others. In 

rare cases, the data for a specifi c city might be provided 1 year, suppressed the next, provided in collapsed form the following year, and then released in 

full form the year after that. 

For applications requiring the availability of all tables 

for all areas in all release years, users should look to 

the 5-year ACS data. 

 Figure 5. Example of a Full Detailed Table

—2006 Households by Income, 

U.S. Total

Source: U.S. Census Bureau, 2006 American FactFinder, accessed at 

<http://factfi nder.census.gov>.

 Figure 6. Example of a Collapsed Detailed 

Table—2006 Households by Income, 

U.S. Total

Source: U.S. Census Bureau, 2006 American FactFinder, accessed at 

<http://factfi nder.census.gov>.
Accuracy of ACS Data

ACS data are estimates and are subject to error and 

uncertainty. This is nothing new, as previous census 

long-form data were also estimates. However, the 

frequent releases with annual fl uctuations, and the 

widespread use of ACS data make it especially important that users understand the nature and degree of 

error that can be involved. 

Error occurs when the characteristics described by a 

survey diff er from those of the actual population. For 

example, the ACS might indicate that a county has a 

median household income of $49,500 when the actual 

median is $48,200. Because the actual median is 

unknown, there is no way to know exactly how much 

the survey estimate diff ers from the truth, but there 

are ways to estimate the amount of error one could 

expect in ACS estimates. 

All estimates, including those from the ACS, are subject to two major types of error—sampling error and 

nonsampling error. 

Sampling error results when estimates for an 

entire population are based on information gathered from a sample or portion of that population. 

Despite eff orts to collect data from a sample that 

is representative of the whole population, sample 

results will always diff er somewhat from those 

that would be obtained by interviewing everyone. 

Sampling error is the only source of error that 

can be easily quantifi ed and thus reported in the 

margins of error shown in ACS data products.

Nonsampling error results from all other sources 

and would exist even if information were collected from the entire population. For example, a 

respondent might—intentionally or unintentionally—inaccurately report their age, income, occupation, or educational attainment. Or they might 

provide their best guess of the year their housing unit was built. Nonsampling error also can 

result from nonresponse. A person who declines 

to report their “means of transportation to work” 

might have a “Drove alone” response imputed, 

when in fact they ride a bicycle to work. Additional error can result from the process of controlling ACS responses to refl ect the Census 

Bureau’s independent county population and 

housing estimates. If the Census Bureau estimates a county’s population to be 127,400, but 

in reality it is 125,100, the error in the population 

estimate contributes additional error to ACS estimates. As noted earlier, measures of nonsampling 

error are not included in the margins of error 

displayed with ACS estimates. See Appendix 6 for 

further detail on nonsampling error. 

The precise error in specifi c ACS estimates is never 

known, as it is not possible to measure the accuracy 

of each individual response, the error of the Census 

Bureau population estimates used as controls, or the 

error introduced by a specifi c sample of the population. However, there are formulas for estimating 

sampling error, which is larger for small samples, and 

would be zero if the sample included all people and 

households. Nonsampling error is harder to measure, 

and important information on nonsampling error is 

provided on the ACS Web site—under “Quality Measures” <http://www.census.gov/acs/www/UseData

/sse/> and “Accuracy of the Data” <http://www

.census.gov/acs/www/UseData/Accuracy/Accuracy1

.htm>. 

When considering sampling error, statisticians think 

not of the error of a single sample but of the distribution of errors that one would expect if we could 

complete many surveys based on many samples of 

a given size for the area in question. In general, the 

larger the sample, the smaller the expected sampling 

error. So, for a county of 100,000 households, samples 

of 20,000 households would produce estimates with 

less sampling error than samples of 10,000 households. A specifi c sample of 20,000 households might 

fi nd that 40 percent are families with children. Because 

we do not know the actual percent for this county, we 

do not know how much the 40 percent estimate (from 

this sample) is in error. However, one can estimate 

the “standard error” expected for samples of 20,000 

households out of 100,000. 

Based on the “standard deviation” measure that users 

may recall from statistics courses, the standard error 

describes how often estimates based on a given sample size are likely to diff er by specifi c amounts from 

the actual values they seek to measure. Again, the 

larger the sample, the smaller the expected error. See 

Appendix 3 for more detailed descriptions of sampling 

error and the derivation of sampling error measures. 

The Census Bureau computes standard errors for all 

ACS estimates and reports them on ACS products as 

the easier to interpret “margins of error.” These margins of error refl ect a 90-percent confi dence level and 

are easily translated into confi dence intervals around 

the ACS estimates. For example, if a median estimate 

of $48,200 has a margin of error of plus or minus 

$1,000, there is a 90 percent likelihood that the actual 

(but unknown) median income is between $47,200 

and $49,200. We would have greater confi dence in this 

estimate than a median income estimate of $48,200 

for another county where the margin of error is plus or 

minus $1,500. 

14 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
What the Business Community Needs to Know 15

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The Census Bureau recommends that users take note 

of the margins of error reported with ACS data to guard 

against drawing erroneous conclusions. For example, 

before committing to build a store that sells upscale 

merchandise, a business might require that the metropolitan market have at least 50,000 households with 

incomes over $75,000 and a householder over age 55. 

The most recent ACS estimate might indicate 53,000 

such households in a market but with a margin of error 

of +/- 5,000. In other words, there is a 90 percent 

chance that the observed estimate of 53,000 target 

households is within 5,000 of the actual number. The 

actual number could be less than 50,000, so the business must consider this level of uncertainty in deciding 

whether or not to establish a store in this market. 

The margins of error reported with ACS data can alert 

users to high levels of uncertainty, identifying where 

they should be especially cautious in drawing conclusions. However, low margins of error do not guarantee 

high accuracy. In the hypothetical market described 

above, if the estimate of 53,000 target households was 

associated with a margin of error of +/- 500, a business might feel highly confi dent that the real number is 

above the 50,000 minimum they require. Users should 

keep in mind that unmeasured nonsampling error is 

another source of uncertainty. 

While it is important that users keep the uncertainties of ACS data in mind, they should not despair over 

them. Census long-form data have always had both 

sampling and nonsampling error, and businesses have 

long put these data to eff ective use without knowledge 

of the level of error. While there are multiple sources 

of potential error, they are not necessarily cumulative. In some areas, these errors may off set each other 

to produce more accurate estimates. For example, 

sampling error might result in the under-estimation of 

a county’s foreign-born population, but this error could 

be off set if the data are controlled to a county population estimate that is too high. But while it is important 

to understand that sampling and nonsampling errors 

can off set, it is a chance occurrence that one cannot 

count on. 

Users might also consider if ACS period estimates 

based on the 2-month residence rule might impair 

their usefulness for applications that require estimates 

approximating a single point in time and refl ecting census residence rules. For many applications, businesses 

may fi nd these diff erences to be inconsequential, but 

for others they could be a factor for businesses to 

consider. Even if sampling and nonsampling error could 

be eliminated, users would still have to determine the 

extent to which ACS estimates are well-suited for their 

applications. 

Businesses that use ACS data aggregated to custom 

geographic areas might seek confi rmation that error 

levels for these aggregations are substantially lower 

than for the block groups from which they were built. 

The Census Bureau has provided formulas for calculating the margins of error for such aggregations. The 

formulas are reasonably straightforward, but some care 

is required, as there are separate formulas for data that 

are aggregate counts, proportions, ratios, or medians. 

The formulas are provided and described in Appendix 3. 

Single-Year and Multiyear Period Estimates

Period Versus Point-in-Time Estimates 

Although census long-form data were collected over 

several months of nonresponse follow-up, they have 

been used as approximate “snapshots” of April 1 of 

the census year. In contrast, the ACS provides “period 

estimates” refl ecting data collected over periods of 1, 

3, and 5 years. 

Single-year estimates are based on data collected over 

a particular calendar year. The diff erence with traditional census data is illustrated by the data below, 

which show the monthly fl uctuation in percent of the 

population in the labor force for a hypothetical town. 

Traditional census data, focused on April 1, would 

report 60 percent of the town’s population in the labor 

force. However, a 1-year period estimate averages data 

across all 12 months to an estimate of about 45 percent—a perfectly valid period estimate but a percentage that is not observed for any one month during the 

year. Actual ACS 1-year estimates are more complex 

 Table 4. Period Estimates Example—Percent of Population in the Labor Force

Month

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.

20 20 40 60 60 60 60 60 60 50 30 20

Source: U.S. Census Bureau, Artifi cial Data.
16 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

than this simple average, but these hypothetical data 

illustrate how period estimates diff er from point-intime estimates. 

ACS 3-year and 5-year estimates are produced in a 

similar manner. Data obtained from 3 years of interviews and 5 years of interviews are combined into a 

single data set and reweighted. They are not averages 

of the 1-year estimates that were produced for those 

years. These new combined estimates are controlled to 

the average of the Census Bureau’s most recent population estimates for the years in the period. 

Further descriptions of ACS single-year and multiyear 

estimates are provided in Appendix 1. 

Transition to Period Estimates 

As users transition from traditional census data to 

ACS period estimates, some key diff erences will be 

encountered. The ACS often presents users with a 

choice between 1-year, 3-year, and 5-year estimates. 

For small areas, only 5-year estimates are released, 

but for larger areas, each annual release will provide 

1-year, 3-year, and 5-year estimates. For example, in 

2010, there will be three sets of commuting data for 

San Diego County—1-year estimates for 2009, 3-year 

estimates refl ecting 2007–2009, and 5-year estimates 

for the period 2005–2009. Users must decide which is 

the most appropriate for their application. 

In making this choice, users should consider the tradeoff between currency and reliability. The 1-year estimates for an area tend to have higher sampling error 

than the 3- and 5-year estimates for that area because 

they are based on only 1 year of data collection (i.e., a 

smaller sample). But currency is enhanced because that 

1 year is the most recent year. In contrast, the 3-year 

and 5-year estimates for an area have larger samples 

and lower sampling error than the 1-year estimates for 

that area, but they are less current because the larger 

samples draw from data collected in earlier years. 

Consider this trade-off as faced by a small packaged 

goods business whose products target households 

with young children. The year is 2010, and the company seeks to expand to metropolitan markets in 

Indiana. For the large Indianapolis market and a common characteristic such as presence of young children, 

1-year data refl ecting 2009 might be preferred because 

they are the most recent and suffi ciently reliable. For 

smaller markets, such as Muncie, the company might 

want the added reliability of the 3-year estimates, even 

though they refl ect data collected as far back as 2007. 

Even in Indianapolis, the company might prefer 3-year 

or 5-year estimates if the target is smaller populations, 

such as households with young children and an Asian 

householder. 

No absolutes dictate when 1-year, 3-year, and 5-year 

data would be preferred, but the margins of error 

provided with ACS data will help our hypothetical packaged goods company weigh its options in the trade-off 

between currency and reliability. If the company needs 

to compare Indianapolis to Muncie and other potential 

markets, it must be careful to use consistent data. If 

3-year estimates are used for Muncie and other smaller 

markets, they should be compared with 3-year estimates for Indianapolis. Comparisons of 1-year data 

for the large market with 3-year data for the smaller 

market, could lead to erroneous conclusions. 

Users also might consider the benefi ts of examining all 

three estimates—the 1-year, 3-year, and 5-year estimates. If all three point to the same conclusion, confi dence in the related business decision is increased, 

but if the estimates suggest diff erent conclusions, this 

may signal the need for caution and further investigation. Similar to getting a second and third opinion on a 

medical diagnosis, cross-checking multiple ACS estimates allows thoroughness that was not an option with 

the one-number long-form census data. 

Regardless of which sets of ACS estimates are chosen, 

the interpretation of period estimates can be a challenge—especially for the 3-year and 5-year multiyear 

estimates. The ACS provides one number for each 

multiyear estimate, but this one number does not 

refl ect one point in time. A 5-year estimate of households with children (based on data from 2005 through 

2009) does not refl ect this characteristic for 2005, 

2007, 2009, or any single year. It is probably best to 

think of this estimate as the average over the period 

from 2005 through 2009. If a characteristic is changing at a steady pace, the average for that area might 

be a reasonable estimate for the middle year (2007 in 

this example) but if the percentage was stable through 

2008 and changed only in 2009, the average would not 

be a good estimate for 2007. One cannot generically 

describe 5-year estimates as refl ecting the middle year 

of data collection and in fact should never be used to 

describe a particular year—only a period of time. 

For example, in 2010, if a business needs to know the 

percent of households with children for aggregations 

of block groups, and the estimates have to be for the 

year 2009, the ACS will not provide such estimates. 

Aggregations of block groups require 5-year estimates, which at that time, will refl ect data collected 

from 2005–2009. However, if the business needs to 

know which of these market areas have had—in recent 

years—the highest percent of households with children, the ACS will answer that question very well. 

See Appendix 1 for further guidance on the use of ACS 

single-year and multiyear data. 
What the Business Community Needs to Know 17

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Tracking Change With ACS estimates 

Tracking change with ACS data requires care. Changes 

in 1-year estimates are relatively straightforward, but 

users need to watch margins of error closely to distinguish actual change from sampling variability or 

“noise.” For example, if the 2006 ACS reports that a 

county had a median household income of $53,000 

(with a margin of error of +/- $1,200) and the 2007 

ACS reports a median of $51,900 in 2007 (margin of 

error +/- $1,100), is the county really experiencing a 

decline in income, or might the decline be a function of 

sampling variability? The ACS estimate alone does not 

provide a defi nitive answer, but the margins of error 

can be used to make this determination. To assist users 

in tracking change, the Census Bureau will provide 

Comparison Profi les for all areas receiving 

1-year estimates. Beginning with the 2007 ACS products, the Comparison Profi le will provide data for 

multiple years (one column per year), with asterisks 

identifying changes that are statistically signifi cant at 

the 90-percent confi dence level. 

Tracking change with multiyear estimates is more 

complicated because the data overlap for successive 

years. For example, 5-year estimates released in 2010 

will refl ect data collected from 2005 though 2009, and 

5-year estimates released the following year will refl ect 

data collected from 2006 through 2010. Both estimates 

share data collected in 2006, 2007, 2008, and 2009. 

Put another way, only about 1/5, or 20 percent, of the 

data in the “new” estimate is really new. 

For this reason, change is best-measured using estimates refl ecting nonoverlapping time periods. For 

example, 5-year estimates based on data collected 

during the period 2005 through 2009 (and released in 

2010) are best compared with 5-year estimates based 

on data collected during the period 2010 through 2014 

(and released in 2015). 

Many businesses will not want to wait 5 years to 

measure change based on 5-year estimates. Such businesses should keep in mind that census long-form data 

required a wait of 10 years, and that once the initial 

pair of nonoverlapping 5-year ACS estimates becomes 

available (when the 2015 release can be compared with 

the 2010), a new pair will follow every year. Five-year 

estimates released in 2016 can be compared with 

2011; those released in 2017 can be compared with 

2012, and so on. 

One could argue that year-to-year diff erences in multiyear estimates provide potentially useful information, 

and the Census Bureau acknowledges that a series 

of overlapping estimates can smooth out short-term 

fl uctuations that can obscure longer-term trends. The 

key is interpreting the data correctly and with realistic 

expectations. For example, a business might watch 

annual changes in 5-year data on the Spanish-speaking 

population to see which of its trade areas are stable, 

and which are growing with respect to this characteristic. Understanding that these are annual changes in 

5-year averages, the business realizes that it is not 

pinpointing exact rates of change but rather getting 

valuable information on the overall pattern of change 

that the census long-form data could not provide. 

The complications described above illustrate why users 

will have to take care in transitioning to ACS data. But 

while ACS data require that users become knowledgeable about some issues, the payoff is frequent updates 

to a wide range of information. In the absence of the 

ACS, the hypothetical companies described above 

would have had to rely on 10-year old data from 

Census 2000, and patterns of change limited to the 

10-year span between censuses. 

Appendix 4 provides additional detail on comparisons 

of ACS data—both with other data sources and across 

time periods. 
18 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The ACS data are intended to take the place of census 

long-form data, so the suitability of ACS data for business applications is similar to the suitability of census 

long-form data. 

Businesses with occasional or limited data needs will 

be well served by the prepackaged ACS data products 

on the Census Bureau Web site, while large scale users 

may prefer to acquire ACS summary fi les or download 

detailed tables. Users also can expect to acquire ACS 

data in value-added form from private suppliers.

For many business applications, the focus is not on 

the ACS data themselves but on marketing products 

built from them. For example, a business may be less 

interested in the ACS estimates of income and age of 

householder than in the estimates of sales potential 

for products (such as consumer electronics) that are 

derived from them. Whatever the channel for acquiring ACS data, and whether the focus is on ACS data or 

derived products, business users should be aware of 

the nature of ACS data. 

The fact that the ACS has a smaller sample and larger 

sampling error than the Census 2000 long-form sample 

is important to business users, who typically need data 

for very small areas. The suitability of ACS data for 

these businesses depends on the application and the 

level of accuracy required. An important distinction is 

between applications that use small area data as building blocks to larger areas and those that focus on the 

characteristics of a specifi c small area. 

Using ACS Data as Building Blocks for Larger 

Geographic Areas

Applications that use small areas as building blocks to 

business-specifi c trade areas should be well served by 

ACS data. Companies may use data supplied by private 

organizations to examine demographic data for custom 

areas such as 3-mile radii around store locations, 

20-minute drive times to bank branches, or telecommunications service areas. The private suppliers’ systems build aggregations from block groups, and even 

when ACS estimates for individual block groups have 

large errors, aggregations to larger areas will reduce 

sampling error to lower levels. 

Using ACS Data for Small Geographic Areas

While aggregations have always been the best way to 

use small area census data (including long-form data), 

some businesses have used data for individual block 

groups or defi ned markets consisting of one or a few 

block groups. For example, a fast food business might 

consider the size and characteristics of the population 

in a 3-block group area to be critical to the success of 

a location. However, ACS age, income, and employment data may be too uncertain to support a decision to establish a franchise at that location. Indeed, 

census long-form data may have had too much error 

for this application as well. But with the ACS, the user 

can examine the margins of error reported for the 

three block groups (and calculate the margin of error 

for the aggregation) to gauge the level of uncertainty 

involved. 

There also is a history of businesses using block 

group characteristics as a proxy for data on individual 

consumers. A publisher might have the name, address, 

and billing history of current subscribers to one magazine but need to append income, presence of children, 

and education data to these subscriber records to 

target prospects for a new publication. Because each 

subscriber’s address corresponds to a block group, 

the publisher might infer these characteristics on a 

probability basis based on the characteristics of the 

block groups where subscribers live. This approach 

is error prone even if block group data are perfectly 

accurate, and the error associated with census or ACS 

data adds considerable uncertainty. For these reasons, 

many businesses tap the resources of household level 

databases that are available from commercial suppliers. These databases have their own errors and often 

limited content, so the temptation to use the characteristics of individual block groups persists. 

From a statistical perspective, applications of this 

type appear reckless. The ACS might indicate that 58 

percent of households in a block group have a child 

when the (unknown) reality is only 29 percent. Clearly, 

it would be a mistake to market to this block group as 

one with many children. But marketing eff orts are not 

confi ned to a single block group, and a business might 

conclude that, across thousands of block groups, 

inferences of this type might work well enough often 

enough to outperform alternatives such as targeting 

based on county data or untargeted blanket mailings. 

It is up to individual businesses to know the requirements of their applications and the limitations of ACS 

data before committing to applications of this type. 

Using Data on ZIP Codes

ZIP Code data are widely used by businesses and merit 

a special note. First, even though the Census Bureau 

plans to provide ACS data for ZIP Code Tabulation 

Areas (see the description of ZCTAs above), annual 

updates to ZCTA defi nitions are said to be unlikely. For 

this reason, ZIP Code estimates produced by private 

ACS Data and Business Applications
What the Business Community Needs to Know 19

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

suppliers are likely to have a strong following. Second, 

while some proprietary ZIP Code data are aggregations 

from household level records (such as customer databases), private suppliers will have to aggregate ACS 

data to ZIP Codes from the block groups for which 

they are reported. Most ZIP Codes will benefi t from the 

reduced error of block group aggregation, but some are 

very small, and will have high levels of sampling error. 

Using Data on Population and Housing Counts

Many businesses, such as commercial developers, seek 

areas of rapid population growth for their untapped 

potential and attractive demographics. Such users need 

to understand that the ACS is not about population 

counts but rather the characteristics of the population, 

households, and housing. 

At the county level and above, ACS population and 

housing totals are forced to agree with the Census 

Bureau’s population and housing estimates. Below the 

county level, the Census Bureau’s Master Address File 

(MAF) determines the distribution of sampled addresses 

but not the fi nal housing or population totals. For 

example, the MAF count might refl ect a block group’s 

recent growth from 200 addresses in 2005 to 2,000 

addresses in 2009, but the 5-year estimate will refl ect 

addresses sampled over the 5-year period, including 

years when the address count was much lower. The 

resulting housing and population totals are further 

decreased or increased as they are weighted for consistency with the Census Bureau’s county population and 

housing estimates. 

ACS housing and population totals derive less from the 

survey itself, than from the Census Bureau’s Population 

Estimates Program since the Census Bureau’s estimates 

of population by race/ethnicity and age are used as 

controls. For data on housing and population totals, or 

for age, sex, race, and Hispanic ethnicity, the Census 

Bureau recommends using data from the Census 

Bureau’s Population Estimates Program. 

Implications for Private Sector Value Added Products

Since the 1970s, a number of fi rms have functioned 

as census data intermediaries—processing and providing data in value-added forms convenient to business 

users. These data companies often provide more 

than census data, but for decades, their census-based 

products have operated on a 10-year cycle coinciding 

with the decennial census. With each new census, they 

reconfi gure their data reporting, consumer segmentation, mapping, and other products to incorporate the 

new census data and geography. Private data sources 

and industry-specifi c geography are always changing, 

but census data and geography have provided a fi xed 

foundation for a 10-year period. 

The transition to the ACS will aff ect the data industry’s 

cycle for census-based products. The ACS now will 

update demographic, socioeconomic, and housing 

characteristics every year. It remains to be seen exactly 

how private suppliers will incorporate the torrent of 

new data into their product off erings. 

It also remains to be seen if, in reporting ACS data, 

the data companies will include the margins of error. 

If there is demand among business users, there will 

be incentive to provide margins of error, but for some 

providers, the massive increase in database size (providing another number with every number) may be an 

obstacle. Private companies must decide whether to 

report the ACS’s published margins of error and whether to build into their products the ability to calculate 

margins of error for ACS data that users may aggregate 

to custom geographic areas. 

The private data companies produce small area estimates and projections of population and household 

totals, as well as characteristics, such as age, race, and 

income. They produce these estimates for all block 

groups nationwide, once per year, and in some cases, 

once per quarter. But as ambitious as the suppliers’ 

eff orts have been, they have not typically attempted 

estimates of characteristics such as education, occupation, language, disability, commuting, and many others 

formerly provided by the census long form. With the 

ACS releasing new block-group level data for these 

items every year, private suppliers will be challenged 

to incorporate them into their products. In doing so, 

they will have to decide whether to provide the 5-year 

period estimates “as is,” or attempt to project the data 

forward to something refl ecting more conventional 

“current year” point-in-time estimates. 

The suppliers also will have to consider the impact of 

the ACS on the update schedule for neighborhood level 

segmentation systems. Although somewhat overshadowed in recent years by segmentation systems based 

on household-level proprietary sources, the “geodemographic” systems remain popular for some applications. 

These systems assign block groups to “lifestyle clusters” 

defi ned on block group census data. The assignment 

of block groups to the lifestyle segments can change 

during the decade based on private data sources, or 

estimates, but the segments themselves are typically 

defi ned once a decade following the release of new census data. With the annual release of ACS characteristics 

data for block groups, data companies can reconsider 

the frequency with which it makes sense to update 

geodemographic segmentation products. 

What the Business Community Needs to Know 21

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the diff erence between 

the survey estimate and the true (unknown) value. 

Attributes are measured in terms of sources of error 

(for example, coverage, sampling, nonresponse, 

measurement, and processing). 

American Community Survey Alert. This periodic 

electronic newsletter informs data users and other 

interested parties about news, events, data releases, 

congressional actions, and other developments 

associated with the ACS. See <http://www.census

.gov/acs/www/Special/Alerts/Latest.htm>.

American FactFinder (AFF). An electronic system 

for access to and dissemination of Census Bureau 

data on the Internet. AFF off ers prepackaged data 

products and user-selected data tables and maps 

from Census 2000, the 1990 Census of Population 

and Housing, the 1997 and 2002 Economic 

Censuses, the Population Estimates Program, annual 

economic surveys, and the ACS. 

Block group. A subdivision of a census tract (or, 

prior to 2000, a block numbering area), a block 

group is a cluster of blocks having the same fi rst 

digit of their four-digit identifying number within 

a census tract. 

Census geography. A collective term referring 

to the types of geographic areas used by the 

Census Bureau in its data collection and tabulation 

operations, including their structure, designations, 

and relationships to one another. See <http://www

.census.gov/geo/www/index.html>.

Census tract. A small, relatively permanent 

statistical subdivision of a county delineated by a local 

committee of census data users for the purpose of 

presenting data. Census tract boundaries normally 

follow visible features, but may follow governmental 

unit boundaries and other nonvisible features; they 

always nest within counties. Designed to be relatively 

homogeneous units with respect to population 

characteristics, economic status, and living conditions 

at the time of establishment, census tracts average 

about 4,000 inhabitants. 

Coeffi cient of variation (CV). The ratio of the 

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

deviation). The lower the CV, the higher the relative 

reliability of the estimate. 

Comparison profi le. Comparison profi les are 

available from the American Community Survey for 

1-year estimates beginning in 2007. These tables 

are available for the U.S., the 50 states, the District of 

Columbia, and geographic areas with a population of 

more than 65,000.

Confi dence interval. The sample estimate and its 

standard error permit the construction of a confi dence 

interval that represents the degree of uncertainty about 

the estimate. A 90-percent confi dence interval can be 

interpreted roughly as providing 90 percent certainty 

that the interval defi ned by the upper and lower 

bounds contains the true value of the characteristic. 

Confi dentiality. The guarantee made by law (Title 

13, United States Code) to individuals who provide 

census information, regarding nondisclosure of that 

information to others. 

Consumer Price Index (CPI). The CPI program of 

the Bureau of Labor Statistics produces monthly data 

on changes in the prices paid by urban consumers for 

a representative basket of goods and services.

Controlled. During the ACS weighting process, the 

intercensal population and housing estimates are used 

as survey controls. Weights are adjusted so that ACS 

estimates conform to these controls. 

Current Population Survey (CPS). The CPS is 

a monthly survey of about 50,000 households 

conducted by the Census Bureau for the Bureau of 

Labor Statistics. The CPS is the primary source of 

information on the labor force characteristics of the 

U.S. population.

Current residence. The concept used in the ACS to 

determine who should be considered a resident of a 

sample address. Everyone who is currently living or 

staying at a sample address is considered a resident of 

that address, except people staying there for 2 months 

or less. People who have established residence at the 

sample unit and are away for only a short period of 

time are also considered to be current residents. 

Custom tabulations. The Census Bureau off ers a 

wide variety of general purpose data products from the 

American Community Survey (ACS). These products 

are designed to meet the needs of the majority of data 
22 What the Business Community Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

users and contain predefi ned sets of data for standard 

census geographic areas, including both political and 

statistical geography. These products are available on 

the American FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

data from ACS microdata fi les. They vary in size, 

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

of a geographic area over the entire 5-year period and 

will be published for all geographic areas down to the 

census block group level. 

File Transfer Protocol (FTP) site. A Web site that 

allows data fi les to be downloaded from the Census 

Bureau Web site.

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

Group quarters (GQ) facilities. A GQ facility is a 

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation 

must be based on other techniques such as using 

answers from other people in the household, other 

responding housing units, or people believed to have 

similar characteristics. Such donors are refl ected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

an MOE instead of confi dence intervals. An MOE is the 

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

by adding the margin of error to the estimate (for the 

upper bound) and subtracting the margin of error from 

the estimate (for the lower bound). All published ACS 

margins of error are based on a 90-percent confi dence 

level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

a population of 20,000 or more. Five-year estimates 

will be published for all geographic areas down to the 

census block group level. 

Narrative profi le. A data product that includes easyto-read descriptions for a particular geography. 

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
What the Business Community Needs to Know 23

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Period estimates. An estimate based on information 

collected over a period of time. For ACS the period is 

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population estimates program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

release: 1) initial sample size and fi nal interviews; 

2) coverage rates; 3) response rates, and; 4) item 

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

refer to the day of the interview; others refer to “the 

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

(if anyone) is considered to be a resident of a sample 

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

or ethnic group (for example, Alaska Natives) or other 

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

likely to be from random chance (sampling error) alone. 

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

percent level” if their diff erence is large enough to infer 

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

of ACS data. These estimates are meant to refl ect the 

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

geographic areas with a population of 20,000 or more.

Appendix A-1

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

an area over a specifi c time period. In the case of ACS 

single-year estimates, the period is the calendar year 

(e.g., the 2007 ACS covers January through December 

2007). In the case of ACS multiyear estimates, the 

period is either 3 or 5 calendar years (e.g., the 2005–

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

the 1-year estimates are not calculated as an average of 

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

the average of 3 or 5 single-year estimates. Rather, the 

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

over a specifi c time period—1 year, 3 years, or 5 years. 

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

for areas over an entire time frame, ACS single-year 

and multiyear estimates contrast with “point-in-time” 

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

upon data collected around April 1, 2000, and thus its 

data refl ect a narrower time frame than ACS data. The 

monthly CPS collects data for an even narrower time 

frame, the week containing the 12th of each month.

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

that would be obtained from a “point-in-time” survey 

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

(even for a single-year) may noticeably diff er from 

“point-in-time” estimates. The impact will be more 

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

area. This logic can be extended to better interpret 3-

year and 5-year estimates where the periods involved 

are much longer. If, over the full period of time (for 

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

could diff er from a “point-in-time” estimate within the 

year is provided in Table 1. Imagine a town on the Gulf 

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

(similar in concept to a period estimate), a “point-intime” estimate for any particular month would yield 

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

Source: U.S. Census Bureau, Artifi cial Data.

Month

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.

20 20 40 60 60 60 60 60 60 50 30 20
A-2 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

any specifi c day or period within the year, while ACS 

multiyear estimates describe the population and 

characteristics of an area for the full 3- or 5-year 

period, not for any specifi c day, period, or year within 

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

populations of 20,000 or more. Beginning in 2010, 

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

that fi nal set of estimates can be released. This means 

that in 2008 only 1-year and 3-year estimates are 

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

the 2006–2008 ACS estimates) and consecutive 5-year 

estimates contain 4 years of overlapping coverage. 

 Table 2. Sets of Sample Cases Used in Producing ACS Multiyear Estimates

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

 estimates 2005–2007 2006–2008 2007–2009 2008–2010 2009–2011

5-year

 estimates Not Available Not Available 2005–2009 2006–2010 2007–2011

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

Single-year estimates provide more current information about areas that have changing population and/or 

housing characteristics because they are based on the 

most current data—data from the past year. In contrast, 

multiyear estimates provide less current information 

because they are based on both data from the previous 

year and data that are 2 and 3 years old. As noted earlier, for many areas with minimal change taking place, 

using the “less current” sample used to produce the 

multiyear estimates may not have a substantial infl uence on the estimates. However, in areas experiencing 

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

same because they are based on data from two different time periods. This will be true even if the ACS 

single year is the midyear of the ACS multiyear period 

(e.g., 2007 single year, 2006–2008 multiyear).

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

shows a hypothetical set of 1-year and 3-year estimates. Comparing data by release year shows that for 

an area such as this with steady growth, the 3-year 

estimates for a period are seen to lag behind the estimates for the individual years. 

Reliability

Multiyear estimates are based on larger sample sizes 

and will therefore be more reliable. The 3-year estimates are based on three times as many sample cases 

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

Source: U.S. Census Bureau.
Appendix A-3

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Table 3. Example of Diff erences in Single- and Multiyear Estimates—Percent of Population

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

and multiyear ACS estimates, should be taken into consideration when deciding which set of estimates to use.

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

only option. For the very smallest of these areas (less 

than 20,000 population), the only option will be to 

use the 5-year ACS estimates. Users have a choice of 

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

20,000. Both 3-year and 5-year ACS estimates will be 

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

they also have higher sampling variability. One measure, the coeffi cient of variation (CV) can help you 

determine the fi tness for use of a single-year estimate 

in order to assess if you should opt instead to use the 

multiyear estimate (or if you should use a 5-year estimate rather than a 3-year estimate). The CV is calculated as the ratio of the standard error of the estimate 

to the estimate, times 100. A single-year estimate with 

a small CV is usually preferable to a multiyear estimate 

as it is more up to date. However, multiyear estimates 

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

than 65,000. Users may think that they only need to 

use multiyear estimates when they are working with 

small areas, but this isn’t the case. Estimates for large 

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

as building blocks to produce estimates for meaningful higher levels of geography. These aggregations will 

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

Three primary uses of ACS estimates are to understand the characteristics of the population of an area 

for local planning needs, make comparisons across 

areas, and assess change over time in an area. Local 

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

Source: U.S. Census Bureau, Artifi cial Data.
A-4 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

where Spanish is spoken at home for ACS test counties Broward, Florida, and Lake, Illinois. The standard 

errors and CVs associated with those estimates are also 

shown.

In this illustration, the CV for the single-year estimate 

in Broward County is 1.0 percent (0.2/19.9) and in 

Lake County is 1.3 percent (0.2/15.9). Both are suffi ciently small to allow use of the more current singleyear estimates.

Single-year estimates for small subpopulations (e.g., 

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

larger CVs. In general, multiyear estimates are preferable to single-year estimates when looking at estimates 

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

had an estimated population of 76,632 in 2004 according to the Population Estimates Program. This population is larger than the Census Bureau’s 65,000-

population requirement for publishing 1-year estimates. However, many subpopulations within this 

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

but only 1,883 families with a female householder, no 

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

precision using the 3-year or 5-year estimate. In this 

example, the 5-year estimate of 40.2 percent has an 

SE of 4.9 percentage points that yields a CV of 12.2 

percent (4.9/40.2), and the 3-year estimate of 40.4 percent has an SE of 6.8 percentage points which yields a 

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

estimate as a way to assess “fi tness for use.” The CV 

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

broader categories) can allow you to improve the reliability of the aggregate estimate, bringing the CVs to a 

more acceptable level.

 Table 4. Example of How to Assess the Reliability of Estimates—Percent of Population 

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

 Table 5. Percent in Poverty by Family Type for Sevier County, TN

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

 With related children under 18 years 9,067 15.3 1.5 16.5 2.4 17.8 4.5

Married-couple families 17,320 5.8 0.7 5.4 0.9 7.9 2.0

 With related children under 18 years 6,633 7.7 1.2 7.3 1.7 12.1 3.9

Families with female householder, no husband 3,433 27.2 3.0 26.7 4.8 19.0 7.2

 With related children under 18 years 1,883 40.2 4.9 40.4 6.8 38.3 13.0

Source: U.S. Census Bureau, Multiyear Estimates Study data.

Source: U.S. Census Bureau, Multiyear Estimates Study data.
Appendix A-5

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

will do much of the statistical work to identify statistically signifi cant diff erences between the 2007 ACS and 

the 2006 ACS. 

As noted earlier, caution is needed when using multiyear estimates for estimating year-to-year change 

in a particular characteristic. This is because roughly 

two-thirds of the data in a 3-year estimate overlap with 

the data in the next year’s 3-year estimate (the overlap is roughly four-fi fths for 5-year estimates). Thus, 

as shown in Figure 1, when comparing 2006–2008 

3-year estimates with 2007–2009 3-year estimates, 

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

diff erence in these two estimates describes the diff erence between 2009 and 2006. While the interpretation 

of this diff erence is diffi cult, these comparisons can be 

made with caution. Users who are interested in comparing overlapping multiyear period estimates should 

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

be in the form of rankings or of specifi c pairs of comparisons. Whenever you want to make a comparison 

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

type. That is, 1-year estimates should only be compared with other 1-year estimates, 3-year estimates 

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

100,000 but you should use the data set that they have 

in common. In this example you could use the 3-year 

or the 5-year estimates because they are available for 

areas of 30,000 and areas of 100,000.

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

Source: U.S. Census Bureau.
A-6 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 2. Civilian Veterans, County X Single-Year, Multiyear Estimates

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

(near the 65,000-publication threshold) and small subgroups within even large areas may limit the ability to 

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

be used to assess the “lagging eff ect” in the multiyear 

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

the period. However, this could be the case under some 

specifi c conditions, as is the case when an area is experiencing growth in a linear trend.

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

Source: U.S. Census Bureau. Based on data from the Multiyear Estimates Study.
Appendix A-7

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

single-year estimates have large CVs or when the precision of the estimates is more important than the currency of the data. Multiyear estimates should also be 

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

Single-year estimates should, in general, be used for 

larger geographies and populations when currency is 

more important than the precision of the estimates. 

Single-year estimates should be used to examine yearto-year change for estimates with small CVs. Given the 

availability of a single-year estimate, calculating the CV 

provides useful information to determine if the singleyear estimate should be used. For areas believed to be 

experiencing rapid changes in a characteristic, singleyear estimates should generally be used rather than 

multiyear estimates as long as the CV for the singleyear estimate is reasonable for the specifi c usage.

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

when they fi rst occur. Single-year estimates, if associated with suffi ciently small CVs, can be very valuable 

in identifying and studying such phenomena. Graphing trends for such areas using single-year, 3-year, and 

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

estimates could be graphed together to better understand local area variations.

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

data used for single-year estimates will be used in the 

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

 Figure 3. Proportion of Population With Bachelor’s Degree or Higher, City X Single-Year, 

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

Source: U.S. Census Bureau. Based on data from the Multiyear Estimates Study.
A-8 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

of the population were collected using a “long-form” 

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

most questions are identical or nearly identical. Differences in the design and implementation of the two 

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

characteristics for the same time frame as the decennial census estimates, which are interpreted to be a 

snapshot of April 1 of the census year. Additional differences are described below. 

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

the census, and their timing, led to important diff erences in the choice of data collection methods. For 

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

household membership. Defi ning the rules in a dissimilar way can aff ect those two very important estimates. 

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

principle of “usual residence” on April 1, 2000, in keeping with the focus of the census on the requirements 

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

concept since data are collected continuously throughout the entire year with responses provided relative 

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

The ACS yearly samples, spread over 12 months, collect information that is anchored to the day on which 

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

“last week” or “the last 12 months” all begin the reference period as of this interview date. Even the information on types and amounts of income refers to the 12 

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

period. The 1-year estimates refl ect the full calendar 

year; 3-year and 5-year estimates refl ect the full 36- or 

60-month period. 

Most decennial census sample estimates are anchored 

in this same way to the date of enumeration. The most 

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

the time period used to collect data for each singleyear ACS estimate. But a more important diff erence is 

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

Census Bureau analysts have compared sample estimates from Census 2000 with 1-year ACS estimates 

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

fi ndings and can be found at <http://www.census

.gov/acs/www/AdvMeth/Reports.htm>. In general, 

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

Residence rules determine which individuals are considered to be residents of a particular housing unit or group 

quarters. While many people have defi nite ties to a single 

housing unit or group quarters, some people may stay 

in diff erent places for signifi cant periods of time over the 

course of the year. For example, migrant workers move 

with crop seasons and do not live in any one location for 

the entire year. Diff erences in treatment of these populations in the census and ACS can lead to diff erences in 

estimates of the characteristics of some areas. 

For the past several censuses, decennial census residence rules were designed to produce an accurate 

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

count of the population as of Census Day, April 1, 

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

units, the decennial census uses a “usual residence” rule 

to enumerate people at the place where they live or stay 

most of the time as of April 1. The ACS uses a “current 

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

it is visited by an ACS interviewer is eligible to be sampled and interviewed for the survey. Further information 

on residence rules can be found at <http://www.census 

.gov/acs/www/AdvMeth/CollProc/CollProc1.htm>.

The diff erences in the ACS and census data as a consequence of the diff erent residence rules are most likely 

minimal for most areas and most characteristics. However, for certain segments of the population the usual 

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

lake, or mountain vacation areas may diff er appreciably between the census and the ACS if populations live 

there for more than 2 months.

More on Reference Periods

The decennial census centers its count and its age distributions on a reference date of April 1, the assumption 

being that the remaining basic demographic questions 

also refl ect that date, regardless of whether the enumeration is conducted by mail in March or by a fi eld followup in July. However, nearly all questions are anchored to 

the date the interview is provided. Questions with their 

own reference periods, such as “last week,” are referring 

to the week prior to the interview date. The idea that 

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

are, in many ways, similar to those in the census in that 

they refl ect the circumstances on the day the data are 

collected and the individual reference periods of questions relative to that date. However, the ACS estimates 

represent the average characteristics over a full year (or 

sets of years), a diff erent time, and reference period than 

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

Users should consider the potential impact these diff erent reference periods could have on distributions when 

comparing ACS estimates with Census 2000. 

Those who are interested in more information about differences in reference periods should refer to the Census 

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

Census 2000 with those used in the ACS. See <http://

www.census.gov/acs/www/UseData/compACS.htm>. 

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

prior to the interview date as the reference period. Thus, 

while Census 2000 collected income information for 

calendar year 1999, the ACS collects income information for the 12 months preceding the interview date. The 

responses are a mixture of 12 reference periods ranging 

from, in the case of the 2006 ACS single-year estimates, 

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

person had “at any time in the last 3 months attended 

a school or college.” A consistent 3-month reference 

period is used for all interviews. In contrast, 

Census 2000 asked if a person had “at any time since 

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

short as about 6 weeks or as long as 7 months. 

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

ACS. The census asked for annual costs, while the ACS 

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

home. Census 2000 included installment loan costs in 
A-10 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

the total annual costs but the ACS does not. In this 

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

Census 2000. Please see <http://www.census.gov/acs

/www/AdvMeth/Reports.htm> for detailed comparisons of these measures of survey quality. 
Appendix A-11

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

interval, and coeffi cient of variation. As the ACS estimates are based on a sample survey of the U.S. population, information about the sampling error associated 

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

they diff er and how each should be used. It is intended 

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

A standard error (SE) measures the variability of an estimate due to sampling. Estimates derived from a sample 

(such as estimates from the ACS or the decennial 

census long form) will generally not equal the population value, as not all members of the population were 

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

from the sample survey can be expected to deviate from this population value. It is the foundational 

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

to determine whether the diff erences between the estimates can be said to be statistically signifi cant.

A very basic example of the standard error is a population of three units, with values of 1, 2, and 3. The average value for this population is 2. If a simple random 

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

with values of 1 and 2 selected), 2 (units with values 

of 1 and 3 selected), or 2.5 (units with values of 2 and 

3 selected). In this simple example, two of the three 

samples yield estimates that do not equal the population value (although the average of the estimates 

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

the sample size used for the survey. In general, the 

larger the sample size, the smaller the standard error 

of the estimates produced from the sample. This relationship between sample size and SE is the reason ACS 

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

A margin of error (MOE) describes the precision of the 

estimate at a given level of confi dence. The confi dence 

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

(the MOE) from the population value. Confi dence levels 

of 90 percent, 95 percent, and 99 percent are commonly used in practice to lessen the risk associated 

with an incorrect inference. The MOE provides a concise measure of the precision of the sample estimate 

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

data is to use a 90-percent confi dence level. Thus, the 

MOEs published with the ACS estimates correspond 

to a 90-percent confi dence level. However, users may 

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

for the specifi c application, as a 90-percent confi dence 

level implies a 10 percent chance of an incorrect inference, in contrast with a 1 percent chance if using a 

99-percent confi dence level. Thus, if the impact of an 

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

incomes for each of 100 counties in a state, using a 

90-percent confi dence level would imply that 10 of the 

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

99-percent confi dence level would reduce the likelihood of this kind of false inference.

Measures of Sampling Error

Appendix 3.
A-12 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

where is the positive value of the ACS published MOE for the estimate.

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

from the 2006 ACS is +12,357. The SE for the estimate 

would be derived as 

Confi dence Intervals

A confi dence interval (CI) is a range that is expected 

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

confi dence” or “confi dence level.” CIs are useful when 

graphing estimates to display their sampling variabilites. The sample estimate and its MOE are used to 

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

To construct a CI at the 90-percent confi dence level, 

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

For example, if an estimate of 20,000 had an MOE 

at the 90-percent confi dence level of +1,645, the CI 

would range from 18,355 (20,000 – 1,645) to 21,645 

(20,000 + 1,645).

For CIs at the 95-percent or 99-percent confi dence 

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

 is the positive value of the MOE for the estimate at the desired confi dence level.

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

If you want to use an MOE corresponding to a confi -

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

dence level is 95 percent, then the factor is equal to 

1.960/1.645.1

 If the desired confi dence level is 99 

percent, then the factor is equal to 2.576/1.645. 

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

Virginia is +12,357. The MOE corresponding to a 95-

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

an estimate, the SEs of the estimates are needed. To 

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

confi dence intervals from the margins of error. For example, a small 

population estimate may have a calculated lower bound less than zero. 

A negative number of persons doesn’t make sense, so the lower bound 

should be set to zero instead.

1

 The value 1.65 must be used for ACS single-year estimates for 2005 

or earlier, as that was the value used to derive the published margin of 

error from the standard error in those years.

2

 If working with ACS 1-year estimates for 2005 or earlier, use the 

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 645.1

960.1

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  723,14357,12 645.1

960.1 MOE95 r r 

645.1

MOEACS SE 

512,7 645.1

357,12 SE 

CL XL  MOECL ˆ

CL XU  MOECL ˆ

MOECL

  CL ULCI CLCL , .

MOEACS

MOEACS

Xˆ

MOE MOEACS 645.1

576.2

99 
Appendix A-13

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

For example, to construct a CI at the 95-percent 

confi dence level for the number of civilian veterans in 

the state of Virginia in 2006, one would use the 2006 

estimate (771,782) and the corresponding MOE at the 

95-percent confi dence level derived above (+14,723).

The 95-percent CI can thus be expressed as the range 

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

given the MOE at the 90-percent confi dence level used 

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

between 18,355 and 21,645. This CI can be represented visually as

Coeffi cients of Variation

A coeffi cient of variation (CV) provides a measure of 

the relative amount of sampling error that is associated with a sample estimate. The CV is calculated as 

the ratio of the SE for an estimate to the estimate itself 

and is usually expressed as a percent. It is a useful 

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

analysis. The method for obtaining the SE for an estimate was described earlier.

The CV is a function of the overall sample size and the 

size of the population of interest. In general, as the 

estimation period increases, the sample size increases 

and therefore the size of the CV decreases. A small CV 

indicates that the sampling error is small relative to the 

estimate, and thus the user can be more confi dent that 

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

to the use of a 1-year estimate that doesn’t meet this 

desired level of precision.

For example, if an estimate of 20,000 had an SE of 

1,000, then the CV for the estimate would be 5 percent ([1,000 /20,000] x 100). In terms of usability, 

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95  059,757723,14782,771

U95  505,786723,14782,771

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

of aggregation. Combining estimates across geographic areas or collapsing characteristic detail can 

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

2006, one would use the 2006 estimate (771,782), 

and the SE derived previously (7,512).

This means that the amount of sampling error present 

in the estimate is only one-tenth of 1 percent the size 

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u 

X

SE CV

Xˆ

%1.0100

782,771

512,7 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

.1 645

MOE ACS SE 

100 ˆ u 

X

SE CV

SE

CI X MOE X MOE   CL CL CL    , 
A-14 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Calculating Margins of Error for Derived 

Estimates

One of the benefi ts of being familiar with ACS data is 

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

(e.g., aggregate estimates for a three-county area or for 

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

of error (MOEs) (2005 and later) at the 90-percent 

confi dence level. However, when derived estimates are 

generated (e.g., aggregated estimates, proportions, 

or ratios not available in AFF), the user must calculate 

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

at the 90-percent confi dence level. If an MOE with a 

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

4) Take the square root of the sum of the squared 

 MOEs.

The result is the MOE for the aggregated count. Algebraically, the MOE for the aggregated count is calculated as:

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

cities that border Washington, DC, is 89,008, and the 

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

denominator (e.g., the proportion of single person 

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

6) Subtract the result of (5) from the squared MOE of 

the numerator.

7) Take the square root of the result of (6).

8) Divide the result of (7) by the denominator of the 

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Fairfax Arlington  XXXX ˆˆˆˆ Alexandria 

MOEFairfax r 303,3 , 

)854,1()011,2()303,3( 2 2 2 MOEagg r 

r ¦cMOEagg MOEc

2

MOEc

th

c

  008,89190,17464,19354,52

 011 MOEArlington r ,2 , 

r r 289,4246,391,18

MOEAlexandria r ,1854
Appendix A-15

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The result is the MOE for the derived proportion. Algebraically, the MOE for the derived proportion is calculated as:

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

the value under the square root will be negative. If that 

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

age and older in Fairfax County, Virginia, with a graduate degree based on the 2006 ACS.

 The estimated proportion is:

where is the ACS estimate of Black females 25 

years of age and older in Fairfax County with a graduate degree and is the ACS estimate of Black 

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

1461.0

713,31

634,4

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

r 989 MOEnum , 601 MOEden r 

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County, Virginia, is 0.1461, and the 

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

The numerator of a ratio is not a subset (e.g., the ratio 

of females living alone to males living alone). To calculate the MOE for derived ratios:

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

6) Add the result of (5) to the squared MOE of the 

 numerator.

7) Take the square root of the result of (6).

8) Divide the result of (7) by the denominator of 

 the ratio.

The result is the MOE for the derived ratio. Algebraically, the MOE for the derived ratio is calculated as:

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0311.0

373,31

1.985

373,31

7.408,970 r 

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ )* 2 2 2 r  ( ˆ )* 2 2 2 MOEnum  p MOEden 

     ]601*1461.0[989 2 2 2  

  7.408,9703.712,7121,978

den

num den

R

X

MOE R MOE

MOE ˆ

)* ˆ ( 2 2 2 r  
A-16 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

County, Virginia, with a graduate degree to the number of Black males 25 years of age and older in Fairfax 

County, Virginia, with a graduate degree is 0.7200, and 

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

3) Multiply the fi rst squared estimate by the second estimate’s squared MOE.

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

5) Add the results from (3) and (4).

6) Take the square root of (5).

The result is the MOE for the product. Algebraically, the 

MOE for the product is calculated as:

where A and B are the fi rst and second estimates, 

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

over in Fairfax County, Virginia, who used public transportation to commute to work, based on the 2006 ACS.

To apply the method, the proportion (0.134) needs to 

be used instead of the percent (13.4). The estimated 

product is 50,624 × 0.134 = 6,784. The MOE is calculated by:

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

6,784, and the MOE of the estimate is ±1,405.

MOEnum r 989, 328 MOEden r ,1

2135.0

440,6

2.375,1

440,6

1.259,891,1 r 

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

 over (fi rst estimate) 50,624 +2,423

Percent of Black workers 16 

years and over who commute by public transportation (second estimate)

13.4% +2.7%

423,2134.0027.0624,50 2 2 2 2 MOE uBA r uu

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

7200.0

440,6

634,4

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

2 22 2 MOE uBA A MOEB B uur MOEA

MOEA

MOEB

r 405,1

)* ˆ ( 2 2 2 MOEnum  R MOEden 

     ]328,1*7200.0[989 2 2 2  

 1.259,891,11.318,913121,978
Appendix A-17

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

Finally, the MOE of the percent change is the MOE of 

the ratio, multiplied by 100 percent, or 4.33 percent.

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

The “percent change” or “percent diff erence” between 

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

The example below shows how to calculate the margin of error of the percent change using the 2006 and 

2005 estimates of the number of persons in Maryland 

who lived in a diff erent house in the U.S. 1 year ago.

The percent change is:

For use in the ratio formula, the ratio of the two estimates is:

The MOEs for the numerator ( ) and denominator 

( ) are:

Add the squared MOE of the numerator (MOE2) to the 

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

0521.1

475,762

210,802

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0433.0

475,762

3.038,33

475,762

529,528,091,1 r 

r r MOER 

r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

1

12

ˆ

ˆˆ *%100

X

XX

Percent Change  

ˆ

ˆˆ *%100

1

12  X

XX

Percent Change

%21.5

475,762

475,762210,802 *%100  ̧ 

1

·  ̈

©

§ 

MOE2 = +/-22,866, MOE1= +/-22,666 

)* ˆ ( 2

1

22 MOE2  R MOE 

     ]666,22*0521.1[866,22 2 2 2

 

529,528,091,1

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

areas within the same time period (e.g., comparing the 

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

geographic area across time periods (e.g., comparing 

the proportion of people below the poverty level in a 

county for 2006 and 2007); and 3) comparisons of ACS 

estimates with the corresponding estimates from past 

decennial census samples (e.g., comparing the proportion of people below the poverty level in a county for 

2006 and 2000).

A number of conditions must be met when comparing survey estimates. Of primary importance is that 

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

presented in the next section. Considerations associated with the various types of comparisons that could 

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

diff erence is likely due to chance (and thus is not statistically signifi cant) or likely represents a true diff erence that exists in the population as a whole (and thus 

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

data, SE is defi ned by the positive value of the 

margin of error (MOE) divided by 1.645).4

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

7) Compare the absolute value of the result of (6) 

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

8) If the absolute value of the result of (6) is greater than the critical value, then the diff erence 

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

 is the SE for the estimate i (=1,2)

 is the critical value for the desired confi dence 

level (=1.645 for 90 percent, 1.960 for 95 percent, 

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

in 2006 with one or more people of age 65 and older 

between State A (estimated percentage =22.0, SE=0.12) 

and State B (estimated percentage =21.5, SE=0.12) is 

statistically signifi cant. Using the formula above:

Since the test value (2.90) is greater than the critical 

value for a confi dence level of 99 percent (2.576), the 

diff erence in the percentages is statistically signifi cant 

at a 99-percent confi dence level. This is also referred 

to as statistically signifi cant at the alpha = 0.01 level. 

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

people aged 65 and older between State A and State B.

Making Comparisons

Appendix 4.

4

 NOTE: If working with ACS single-year estimates for 2005 or earlier, 

use the value 1.65 rather than 1.645. 

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   12.012.0

5.210.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

90.2

173.0

5.0

03.0

5.0

015.0015.0

5.0 

Ei
Appendix A-19

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

By contrast, if the corresponding estimates for State C 

and State D were 22.1 and 22.5, respectively, with standard errors of 0.20 and 0.25, respectively, the formula 

would yield

Since the test value (1.25) is less than the critical value 

for a confi dence level of 90 percent (1.645), the difference in percentages is not statistically signifi cant. 

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

time period (e.g., from the same year or the same 

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

period, the areas or groups will generally be nonoverlapping (e.g., comparing estimates for two diff erent 

counties). In this case, the two estimates are independent, and the formula for testing diff erences is statistically correct.

In some cases, the comparison may involve a large 

area or group and a subset of the area or group (e.g., 

comparing an estimate for a state with the corresponding estimate for a county within the state or comparing an estimate for all females with the corresponding 

estimate for Black females). In these cases, the two 

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

be correct, but a fi nding of a lack of statistical signifi -

cance based on the formula may be incorrect. If it is 

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

approaches for accounting for the correlation in performing the statistical test of signifi cance.

   25.020.0

1.225.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

25.1

320.0

4.0

1025.0

4.0

0625.004.0

4.0 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

multiyear estimates (e.g., comparing 2006 with 2007–

2009) and against comparing multiyear estimates of 

diff ering lengths (e.g., comparing 2006–2008 with 

2009–2014), as they are measuring the characteristics 

of the population in two diff erent ways, so diff erences 

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

When comparing estimates from two diff erent singleyear periods, one prior to 2006 and the other 2006 or 

later (e.g., comparing estimates from 2005 and 2007), 

the user should recognize that from 2006 on the ACS 

sample includes the population living in group quarters (GQ) as well as the population living in housing 

units. Many types of GQ populations have demographic, 

social, or economic characteristics that are very different from the household population. As a result, 

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

a substantial GQ population. For housing characteristics or characteristics published only for the household 

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 

2006–2008 with estimates from 2009–2011). The comparison of two estimates for diff erent, but overlapping 

periods is challenging since the diff erence is driven by 

the nonoverlapping years. For example, when comparing the 2005–2007 ACS with the 2006–2008 ACS, data 

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 
A-20 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

many areas whose only source of data will be 5-year 

estimates will not want to wait until 2015 to evaluate 

change (i.e., comparing 2005–2009 with 2010–2014). 

When comparing two 3-year estimates or two 5-year 

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

where C is the fraction of overlapping years. For example, the periods 2005–2009 and 2007–2011 overlap for 

3 out of 5 years, so C=3/5=0.6. If the periods do not 

overlap, such as 2005–2007 and 2008–2010, then C=0.

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

method outlined in the previous section with one modifi cation; substitute for 

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

estimates with decennial census estimates. For example, given the reference period diff erences, seasonality 

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

or characteristic that might be aff ected by these diff erences.5

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

census sample estimates have sampling error associated with them and that the standard errors for both 

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

estimates. To derive the SEs of census sample estimates, use the method described in Chapter 8 of either 

the Census 2000 Summary File 3 Technical Documentation <http://www.census.gov/prod/cen2000/doc/sf3

.pdf> or the Census 2000 Summary File 4 Technical 

Documentation <http://www.census.gov/prod

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

the Census 2000 estimate is the same as that determined for the ACS estimate. The result of this approach 

would be that a fi nding of statistical signifi cance can be 

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

site at <http://www.census.gov/acs/www/UseData/compACS.htm>.

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2

2

2

1 1  SESEC

2

2

2

1  SESE
Appendix A-21

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

where is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of infl ation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

for which infl ation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

income infl ation adjustments are made. Monthly CPIU-RSs are used to infl ation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

936,38$700,37$ 7.286

1.296 ˆ

X ,2005 Adj u 

1

1

2

,1 ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

not refl ect diff erences that may exist across characteristics such as energy and housing costs.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

 is the published ACS estimate for the earlier time 

period (P1).

080,35$000,32$ 1.270

1.296 ˆ

X  ,20032001 Adj u 

1

1

2

,1 ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units diff er from the characteristics of responding units.

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

<http:/www.census.gov/acs/www/UseData/sse/>.

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

 The sample size measures for housing units (number of initial addresses 

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

.census.gov/acs/www/UseData/compACS.htm> Tables 

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.