Meeting 21st Century Demographic

Data Needs—Implementing the

American Community Survey

U.S.Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

Issued May 2004

Report 5: Comparing Economic Characteristics With Census 2000
ACKNOWLEDGEMENTS

This report was prepared by Theresa

F. Leslie, Joan K. Broadwater,

Deborah H. Griffin, Susan P. Love,

and David A. Raglin.

Members of the American Community

Survey Research and Evaluation

Steering Committee who sponsored and

reviewed this report include: Preston

J. Waite, Chair, William R. Bell,

Cynthia Z.F. Clark, Robert E. Fay, III,

Nancy M. Gordon, Deborah H.

Griffin, David L. Hubble, Ruth Ann

Killion, John F. Long, Elizabeth A.

Martin, Lawrence S. McGinn,

Rajendra P. Singh, Carol M. Van

Horn, Daniel H. Weinberg, and

Tommy Wright.

Other individuals who contributed to

the review and release of this report

include: Alemayehu Bishaw, Celia G.

Boertlein, Wendy D. Hicks, Jane L.

Dye, Philip M. Gbur, Douglas W.

Hillmer, David L. Hubble, Mia A.

Huff, Mary C. Kirk, Theresa

McCane, Louisa F. Miller, Charles T.

Nelson, Thomas J. Palumbo, Kirby

G. Posey, J. Gregory Robinson,

Phillip A. Salopek, Rajendra P.

Singh, Sharon M. Stern, Daniel H.

Weinberg, Mai A. Weismantle, and

Edward J. Welniak, Jr.

Kim D. Ottenstein and Everett L.

Dove of the Administrative and

Customer Services Division, Walter C.

Odom, Chief, provided publications

and printing management, graphics 

design and composition, and editorial 

review for print and electronic media. 

General direction and production 

management were provided by 

James R. Clark, Assistant Division

Chief, and Susan L. Rappa, Chief, 

Publications Services Branch.
Meeting 21st Century Demographic

Data Needs—Implementing the

American Community Survey Issued May 2004

Report 5: Comparing Economic Characteristics With Census 2000

U.S. Department of Commerce

Donald L. Evans,

Secretary

Vacant,

Deputy Secretary

Economics and Statistics Administration

Kathleen B. Cooper,

Under Secretary

for Economic Affairs

U.S. CENSUS BUREAU

Charles Louis Kincannon,

Director
Economics

and Statistics

Administration

Kathleen B. Cooper,

Under Secretary

for Economic Affairs

U.S. CENSUS BUREAU

Charles Louis Kincannon,

Director

Hermann Habermann,

Deputy Director and Chief Operating Officer

Preston J. Waite, 

Associate Director for Decennial Census

SUGGESTED CITATION

U.S. Census Bureau,

Meeting 21st Century Demographic

Data Needs—Implementing

the American Community Survey

Report 5: Comparing 

Economic Characteristics 

With Census 2000

Washington, DC,

2004
i

TABLE OF CONTENTS

EXECUTIVE SUMMARY ............................................................................................................ v

1. OVERVIEW AND PURPOSE ................................................................................................. 1

2. BACKGROUND ...................................................................................................................... 2

3. INTRODUCTION ..................................................................................................................... 3

3.1 Economic characteristics from the Census 2000 Sample provide vital 

information about the economic status of our Nation ............................................ 4

3.2 ACS estimates of selected economic characteristics will provide critical 

information throughout the decade ........................................................................ 4

3.3 Some differences are expected between the Census 2000 Sample and the C2SS 4

4. METHODOLOGY ................................................................................................................... 5

4.1 Methods were developed to identify differences ................................................... 6

4.2 The design and implementation of the C2SS and the Census 2000 Sample 

methods were examined ......................................................................................... 9

5. RESULTS ............................................................................................................................... 16

5.1 Employment Status .............................................................................................. 16

5.2 Commuting to Work ............................................................................................ 20

5.3 Occupation, Industry, and Class of Worker ......................................................... 24

5.4 Income and Poverty .............................................................................................. 29

6. CONCLUSIONS ..................................................................................................................... 38

7. REFERENCES ....................................................................................................................... 39
ii

APPENDICES

Appendix A. Item Allocation Rates by Data Collection Mode

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Appendix C. Federal Uses of Selected Economic Characteristics

Appendix D. Characteristics of Counties Used in Sub-National Comparisons

Appendix E. Summary of County-Level Differences
iii

TABLES

Table 1. Employment Status, National-Level Distributions (C2SS compared with the

Census 2000 Sample) ........................................................................................... 18

Table 2. Commuting to Work, National-Level Distributions (C2SS compared with the

Census 2000 Sample) ........................................................................................... 21

Table 3a. Occupation, National-Level Distributions (C2SS compared with the Census 2000

Sample) ................................................................................................................ 25

Table 3b. Industry, National-Level Distributions (C2SS compared with the Census 2000

Sample) ................................................................................................................ 26

Table 3c. Class of Worker, National-Level Distributions (C2SS compared with the

Census 2000 Sample) ........................................................................................... 27

Table 4a. Household Income, National-Level Distributions (C2SS compared with the

Census 2000 Sample) ........................................................................................... 32

Table 4b. Type of Income, National-Level Distributions (C2SS compared with the

Census 2000 Sample) in 1999 Dollars ................................................................. 32

Table 4c. Family Income, National-Level Distributions (C2SS compared with the

Census 2000 Sample) in 1999 Dollars ................................................................. 33

Table 4d. Percent Below Poverty, National-Level Distributions (C2SS compared with the

Census 2000 Sample) ........................................................................................... 34
iv

FIGURES

Figure A. Distributions of Total Weighted Census 2000 Sample and C2SS Housing Unit

Responses by Week ............................................................................................ 15 

Figure 1. Percent of Household Population 16+ Employed, Census 2000 Sample and C2SS

County-Level Estimates ...................................................................................... 19 

Figure 2. Percent of Workers 16+ Who Carpooled, Census 2000 Sample and C2SS

County-Level Estimates ....................................................................................... 22

Figure 3. Percent Workers Employed in Educational, Health, and Social Services Industry,

Census 2000 Sample and C2SS County-Level Estimates .................................. 28

Figure 4a. Median Household Income, Census 2000 Sample and C2SS County-Level

Estimates ............................................................................................................. 35

Figure 4b. Percent of Female-Householder Families with Related Children Under 18 in

Poverty, Census 2000 Sample and C2SS County-Level Estimates .................... 36
v

EXECUTIVE SUMMARY

The American Community Survey (ACS) is one of three program components required to

achieve the 2010 Census reengineering strategy goals. The ACS replaces the Census Sample,

also known as the census long form, the once-a-decade collection of detailed demographic,

housing, and socioeconomic data, that occurs as part of the decennial census, with an ongoing

survey that produces annual and multi-year estimates of these same characteristics. The

Census 2000 Supplementary Survey (C2SS) was conducted as part of Census 2000 to

demonstrate the operational feasibility of ACS methods. To date reports have been issued

addressing questions of conducting the ACS coincident with the decennial census, technical

performance, and the implications of changing the ACS to a voluntary survey. In this report, we

compare Census 2000 Sample estimates to those produced by the C2SS to look for substantive

differences, possible explanations, and supporting evidence about which is likely to be better in

the event we find differences. Specifically, the report includes comparisons of the economic

profile characteristics for:

• Employment Status; 

• Commuting to Work; 

• Occupation, Industry, and Class of Worker; and

• Income and Poverty. 

We produced this report to educate users of these economic data and ease the transition from the

decennial census sample estimates to the ACS estimates.

Major Findings 

At the national level, distributions of economic profile estimates from the C2SS were very

similar to those produced from the Census 2000 Sample. Although three-fourths of the

economic profile estimates differ significantly between the C2SS and the Census 2000 Sample 

at the 90 percent confidence limit, in most cases the differences were not substantive differences. 

While about one-third of all the estimates differ by more than 0.5 percentage points, less than

one-tenth differ by one or more percentage points. Thus, published C2SS data were very

consistent with the Census 2000 Sample estimates. 

A review of selected sub-national results identified few substantive differences. A review of

data for 18 selected counties suggests few substantive differences for subnational levels. Less

than 30 percent of the county-level estimate differences were statistically significant and only

8 percent of these differences varied by 2 or more percentage points. This is important to

understand as data users begin to move from the decennial census sample to the ACS for the

collection of the selected economic items discussed in this report. Data users would in general

come to similar conclusions, implement similar programs, and allocate funds in a similar way

regardless of which data set they used. For example, analysis of data for the 18 selected counties

confirms that if the Census 2000 Sample said a county had a high employment rate, the C2SS
vi

also said this. However, more analysis is needed to understand the findings at the subnational

level; for example, analysis should be conducted of state-level data.

This analysis provided an opportunity to identify a few minor differences that may exist in

ACS estimates relative to decennial census sample estimates that data users should be

aware of when transitioning from the Census 2000 Sample to the ACS. These differences

likely result from differences in the reference periods for the C2SS and the Census 2000

Sample that result from differences in the timing of data collection for these two surveys

(see pages 20 and 37). Employment Status was collected in the Census 2000 Sample and the

C2SS using the reference period “last week”. The results refer to the period of time before the

date on which the data were collected. Census 2000 data collection covered a period from

March until August 2000. In contrast, C2SS data collection was continuous throughout the year. 

A person’s response to employment status determined if they were asked the commuting to

work, occupation, industry, and class of worker questions; thus, these items were also affected. 

The Census 2000 Sample collected income data for calendar year 1999 while the C2SS collected

these data for the “last 12 months”. Thus, the difference in the timing of data collection for these

two surveys also affected the income and poverty estimates; the Census 2000 Sample produced

income estimates for 1999 and the C2SS, after adjusting the 12-monthly samples of data for

inflation, produced income data for a different period, reported in 2000 dollars. Users of these

income data need to understand this difference. 

 

The well-trained interviewers and automated instruments used for the C2SS

Computer-Assisted Telephone Interviewing (CATI) and Computer-Assisted Personal

Interviewing (CAPI) operations appear to have had a positive effect on the level of data

completeness for the economic characteristics examined in this report (See Appendix A). 

While the national-level distributions were very similar, some statistically significant differences

did exist. One possible explanation was the differing levels of item nonresponse for data

collected in the Census 2000 Sample and the C2SS nonresponse followup operations. For some

items such as Wages and Salary Income, almost 33 percent of followup responses in the Census

2000 Sample were missing compared with 22 percent of C2SS responses. Some of this

difference may be the result of well-trained interviewers and quality control in the automated

data collection instruments used for followup in the C2SS, minimizing the possibility of

questions being overlooked by the interviewer. Range checks in the instruments also guarded

against interviewer and response error.

Additional research is recommended to further our understanding of current ACS

methods. This report looks at differences for items and categories in the economic

characteristics profile table between Census 2000 Sample and the C2SS at the national level and

for 18 counties across the country. Given that we find few substantive differences between the

Census 2000 Sample and the C2SS at either the national or county level, additional analysis

should focus on developing a further understanding of all methodological aspects of the ACS

including such things as the effect of using multiple modes for data collection. Similarly, data

should be examined by sex, age, race, Hispanic origin, and other key demographic items. 
1

1. OVERVIEW AND PURPOSE

This report is one in a series of reports designed to document the differences that exist between

the C2SS and the Census 2000 estimates. The first report, Comparing General Demographic and

Housing Characteristics, compared results for sex, age, relationship, Hispanic origin, race,

tenure, and housing occupancy status (U.S. Census Bureau, 2004). This report focuses on the

comparison of distributions for selected economic characteristics such as Employment status and

Income for the C2SS and the Census 2000 Sample. In this analysis we compare the C2SS

distributions to the Census 2000 Sample distributions, look for substantive differences, and for

those found, look for possible explanations and supporting evidence. This report also helps

educate users of these economic data to make the transition from the decennial census sample

estimates to the ACS estimates.

This report compares tables in the Census 2000 Sample Profile of Selected Economic

Characteristics (Table DP-3) with the comparable C2SS data profile tables. The analysis is

restricted to data for the household population; excluding the group quarters population. Some

derived measures such as means and per-capita income were not calculated for this comparison

study although they appear in the individual profile reports. Comparisons include single-year

(2000) estimates at the national level and for selected counties. The county-level analysis was

done in an effort to begin to understand what happens to economic data at sub-national levels. 

We describe the methods used for this analysis in detail in Section 4 of this report. 

Data on economic characteristics such as the employment status of working-age people, the jobs

they have, how they commute to work, and their income provide critical information needed by

federal, state, and local planners. Federal budget formulation and fund allocation require these

data. State and local governments, non-profit organizations, and businesses use data about these

items to plan, budget, and pay benefits. Corporations and individuals use data on occupation,

industry, and class of worker to develop business plans and to determine the demographic

characteristics of areas where they might want to expand or start businesses. In addition, local

business proprietors use income data to determine demographic and economic trends in their

service areas and to adjust their product lines or services to meet the current economic

conditions. It is therefore important for users of these economic data to understand how the ACS

data might differ from the data historically produced from the decennial census sample.

In 2004, the Census Bureau will release these additional comparison reports:

• A detailed comparison of the Census 2000 General Demographic and Housing

Characteristics (Table DP-1) with the C2SS at the national level. This profile includes such

items as sex, age, relationship, Hispanic origin, race, and tenure (See U.S. Census Bureau,

2004). 

• A detailed comparison of the Census 2000 Sample Profile of Selected Social Characteristics

(Table DP-2) with the C2SS at the national level. This profile includes such items as school

enrollment and disability status. 
2

• A detailed comparison of the Census 2000 Sample Profile of Selected Physical and Financial

Characteristics of Housing (Table DP-4) with the C2SS at the national level. This profile

includes such items as units in structure and mortgage status.

• A detailed comparison of all Census 2000 Profile Table estimates with three-year ACS

estimates for the 36 ACS test counties and their tracts. 

• A detailed comparison of quality measures between Census 2000 Sample estimates and

three-year ACS estimates for the 36 test counties. It includes estimates of self-response, unit

and item nonresponse, and sample completion.

See U.S. Census Bureau (2004) for a complete explanation of the comparison studies project.

2. BACKGROUND

The ACS replaces the decennial census national sample survey that evolved over many decades

for collection of general demographic and housing data and more detailed social, economic, and

housing data from selected people and housing units across the nation. The decennial census

sample has been in existence for seven censuses and for each of these decennial censuses has had

unique questionnaires, data collection procedures, and sampling and estimation methods. 

In the 1940 Census, a 5 percent probability sample of the population was introduced in an effort

to collect more information without a noticeable increase in respondent burden. Since the census

contacted the entire population one time every 10 years, it provided the perfect opportunity to

introduce sampling and conduct a large national survey. The modern decennial census sample

was introduced in 1960, when the primary sampling unit was changed to the housing unit and the

sample increased to 25 percent. Data from these samples were provided for areas as small as

tracts, and the more extensive use of sampling introduced moderate amounts of sampling error

into the estimates. In an attempt to control the variance, ratio estimation of the sample data to

the full census counts was introduced instead of the simple weighting by probabilities of

selection used previously. 

Interpenetrating samples of 15 and 5 percent were used in the 1970 census but have not been

used since. Differential sampling was introduced in 1980, selecting 1 in 2 units in sparsely

populated areas instead of 1 in 6 to produce more reliable estimates. A third rate of 1 in 8 was

introduced in the 1990 census and a fourth rate of 1 in 4 was added in 2000 (See Griffin, Love,

and Obenski, 2003 for more details). 

The ACS is the next chapter of this 70-year history of census samples. It represents a major

innovative step in meeting the nation’s need for the kind of information that has only been

available through the decennial census samples. The ACS will produce estimates of social,

economic, and housing characteristics of the Nation annually by adopting the concept of

continuous measurement and spreading a sample of about 3 million housing units every year
3

over twelve months. The ACS will use the best mail survey techniques combined with

computer-assisted technology and a permanent interviewing staff.

The C2SS and Census 2000 used similar methods of data collection but adapted them to meet

their unique goals and very different environments. Census 2000 relied heavily on the mail to

enumerate the population in housing units. Followup interviews were conducted by personal

visit to complete the enumeration of unresponsive households and vacant housing units. The

mailout and enumerator delivery of pre-addressed short and long form questionnaires occurred

in March of 2000, and field followup operations took place from the end of April through

August. All mailed-back questionnaires were returned to one of four processing centers for data

capture, and raw data files were sent to Census Bureau headquarters for post-capture processing. 

The general demographic and housing characteristics, or “100 percent” data, derived from

responses found on both short form and long form questionnaires, were captured and processed

first to meet the legal deadlines for providing apportionment and redistricting counts to Congress

and the states. The capture of “sample” data collected on Census long form questionnaires was

completed once the “100 percent” capture was finished. 

The C2SS used the following ACS methods: questionnaire mailout, telephone, and personal

visit data collection methods over a rolling three-month time period, collecting data from twelve

independent monthly samples of addresses every year. Each month a unique national sample of

addresses receives an ACS questionnaire. Addresses that do not respond are telephoned during

the second month of collection when a phone number is available, and personal visits are

conducted during the third and last month of data collection for a subsample of nonresponding

units. Data are collected and captured continuously throughout the year, and data products are

released every year, including single-year, 3-year, and 5-year accumulations of survey estimates,

depending on the size of geographic areas. 

The distributions shown in this report come from information collected in the year 2000. Two

distinct ACS data collection activities took place during this time: (1) a national sample of

1,203 counties was selected and surveyed using ACS methods, and (2) the ongoing collection

from 36 ACS test counties. Together, these data for an initial sample of almost 900,000

households produced the C2SS estimates compared with the Census 2000 Sample estimates in

this report. 

3. INTRODUCTION

This report documents the comparison of the C2SS and the Census 2000 Sample, also know as

the census long form, estimates for selected economic characteristics for the household

population of the Nation in 2000. 
4

3.1 Economic characteristics from the Census 2000 Sample provide vital information

about the economic status of our Nation

The economic questions included in the Census 2000 Sample provide a vital measure of general

economic circumstances in the United States. For example, these data are used to determine

poverty status and to assess the need for various types of assistance. In addition, these data are

used in federal allocation formulas. At the community level, these data guide funding for social

services distributed to local agencies, identify local areas eligible for grants to run job training

and other employment programs, and are used to allocate funds to areas requiring housing

assistance and home energy aid. These data are also used at the local level to distribute funds to

improve the education of economically disadvantaged children. Currently this information is

only available every 10 years. 

3.2 ACS estimates of selected economic characteristics will provide critical information

throughout the decade

Having annual data on economic characteristics from the ACS will give federal, state, and local

planners more current data for monitoring the economic situation in their jurisdiction over time. 

This will enable them to use resources more effectively and secure adequate funding for federal,

state, and local projects, better assisting those most in need. For example, the ACS will provide

estimates of the number of elderly in poverty, data on levels and types of occupations by race,

and information on the economic characteristics of state and local areas on a yearly basis. 

Collecting these data continuously throughout the decade will allow planners in all jurisdictions

to track changes in these and other important socioeconomic distributions. 

3.3 Some differences are expected between the Census 2000 Sample and the C2SS

An enumeration of the entire population and housing which includes a large survey for one-sixth

of the units is very different from a stand-alone sample survey of detailed housing, and

socioeconomic characteristics. The different purposes and relative sizes of the undertakings

guided the methodologies used to collect and process data. Before discussing differences, we

should say here that both the decennial census and the ACS serve similar purposes of providing

data to meet legal and programmatic needs. It is important to note that both the Census 2000

Sample and the C2SS were quite successful. The Census 2000 Sample achieved higher mail

return rates (Stackhouse and Brady, 2003) than the C2SS but the unit nonresponse, item

allocation, and completeness rates were better in the C2SS than those achieved by the Census

2000 Sample (U.S. Census Bureau, 2001). 

Census 2000 officially enumerated the Nation’s entire population as directed by the Constitution.

The results are used for apportionment, redistricting, and to support important legislation such as

the Civil Rights Act and the Voting Rights Act. Securing a complete count as of Census Day

(April 1 in 2000) is the primary goal of the decennial census and priority is given to designing a

census that facilitates this count and ensures that key data are produced by the legal deadlines. 

At the same time, the decennial census also collected detailed social, economic, and housing

characteristics for a sample of households to provide legally-mandated data needed for federal
5

programs. While Census 2000 benefitted from the publicity and perceived importance of a

decennial census which is often described as the “census environment”, it’s design had to

accommodate the tremendous workload and tight operational scheduling constraints; for

example, paper questionnaires were used for almost all Census 2000 data collection operations

and all data were collected between March and August 2000. As a last resort, Census 2000

allowed proxy responses from people who were not members of the household, such as

neighbors, to collect critical count data by the required deadlines. 

In contrast, the ACS is designed to collect these same detailed economic, social and housing data

to measure the characteristics of all areas as a yearly average. The C2SS was based on an initial

housing unit sample of approximately 900,000 and used ACS methods and residence rules to

collect data throughout the year using a combination of mail-out/mail-back questionnaires,

Computer-Assisted Telephone Interviewing (CATI), and Computer-Assisted Personal

Interviewing (CAPI). The large yearly sample size was broken down into manageable monthly

workload assignments that could be completed by our permanent field staff. The ACS uses a

unique concept of “current residence”given the monthly samples distributed throughout the year,

rather than the census concept of “usual residence” as of April 1. 

ACS methods require that information collected from sample households must come from a

household member. Unlike the decennial census, no proxy respondents, such as neighbors, are

allowed to answer for a sample household. However, like the decennial census, one household

member (called a within-household proxy respondent) could answer the survey for all household

members in the ACS. The use of within-household proxy respondents may contribute to

differences in Census 2000 Sample and C2SS estimates of economic characteristics when the

respondent answers in error for others in the household. 

The Census 2000 Sample and the C2SS data have levels of both sampling and nonsampling error

associated with them. The following section describes the methods used to conduct this

comparison study, and how different designs and methods may explain observed differences

between C2SS and the Census 2000 Sample estimates.

4. METHODOLOGY

This section describes the methods used to compare the C2SS and the Census 2000 Sample

estimates. The tables included in this report compare final published C2SS estimates with final 

Census 2000 Sample estimates for the household population only. The final published C2SS

estimates were controlled to the Census 2000 counts of population and housing at the county and

sampling stratum levels. Specifically, population controls increased the national C2SS survey

estimate of the household population by about 3.2 percent and the estimate of total housing by

about 0.4 percent. See U.S. Census Bureau (2000) for a more detailed discussion of the use of

population and housing controls in the C2SS. 

Comparisons consist of percentage point differences between the two distributions. Differences

are displayed, along with margins of error representing the 90 percent confidence interval of the
1

In general, all people are classified as living either in housing units or in group quarters. A housing unit is

defined as a house, apartment, a mobile home or trailer, a group of rooms or a single room occupied as a

separate living quarters or, if vacant, intended for occupancy as a separate living quarters. While the C2SS

did not collect data from group quarters, the ACS will when the survey moves to full implementation.

6

differences. C2SS estimates that differ from the Census 2000 Sample estimates beyond

sampling error are identified. Although only national data tables are included, selected

sub-national comparisons are geographically displayed for 18 of the 36 counties included in the

ACS test sites for the past several years. We examined these data to start looking at how C2SS

and Census 2000 Sample estimates compare at lower geographic levels. 

We examined C2SS and Census 2000 methods to assess the potential effects of nonsampling

error on either the Census 2000 Sample estimates or the C2SS estimates. Coverage,

nonresponse, processing, and measurement errors were studied to learn if observed differences

reflect problems inherent in the design of the ACS. In addition, the effect of methodological

differences such as residence rules, reference periods, and the time frame for data collection

were considered. However, because of the interdependencies among types of errors and

methods, the relative effects of these differences cannot be determined. Consequently, this

report does not definitively attribute identified differences to specific methods or practices.

4.1 Methods were developed to identify differences

This report contains tables comparing the C2SS and the Census 2000 Sample estimates for:

• Employment Status;

• Commuting to Work;

• Occupation, Industry, and Class of Worker; and 

• Income and Poverty.

Before conducting this comparison, we considered two factors. First, unlike Census 2000, the

C2SS did not include interviews of the group quarters population.1 To make appropriate

comparisons, the group quarters population data were removed from the Census 2000 files

resulting in tables that included only the household population. Second, since the Census 2000

Sample and the C2SS, as surveys, were subject to sampling error, comparisons using these

estimates had to take into account sampling variability. Tests for statistical significance of the

differences in the estimates were conducted and the results are shown in the tables. At the

national level, the Census 2000 Sample and C2SS variances were quite small, resulting in many

statistically significant differences between the Census 2000 Sample and the C2SS profile

distributions, although most differences are not substantive (See Section 4.1.1 for more details). 

4.1.1 National distributions of characteristics from the C2SS and the Census 2000 Sample

were compared

The scope of this report is a comparison between tables in the Census 2000 Sample Profile of

Selected Economic Characteristics (Table DP-3) and the comparable C2SS data profile tables. 
7

The analysis includes data for the household population and excludes data for the group quarters

population. Some derived measures such as means and per-capita income were not calculated

for this comparison study although they appear in the individual profile reports. The table stubs

are reproduced as they appear in the C2SS Profile tables. This section describes the contents of

those tables, how they were produced, and how they should be interpreted. 

An example of the table for commuting to work follows. The first row of the table, which is

shaded, shows the target populations rounded to the nearest 100,000. This is the universe used 

to calculate the percentages in the other rows. The distribution of the various groups or

categories across this target population fall down the columns. The “Census 2000 Sample

Estimate" column is the distribution for each specified group based on the Census 2000

household population. In the sample table that follows, 2.5 percent of workers 16 or older in the

household population reported walking as their primary means of transportation to work in the

Census 2000 Sample. The “C2SS Estimate” column contains the same information from the

C2SS; in this case, 2.7 percent. The “C2SS-Census 2000 Sample” column is the difference

between the C2SS and the Census 2000 Sample percent distributions for that row. After

calculating these differences, the percentages were rounded to avoid over emphasizing very

small and insignificant differences in these distributions and for this reason the “Difference”

shown may not always be the same as “C2SS Estimate” minus “Census 2000 Sample Estimate". 

A difference of 0.0 does not necessarily mean there was no difference–it means that the

difference was less than 0.05 percent. 

Example Table. Commuting to Work, National-Level Distributions (C2SS compared with the Census 2000

Sample) 

Commuting to Work

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference 

(C2SS-Census

2000 Sample

in percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Workers 16 years and over 126,900,000 127,700,000

Car, truck, or van - - drove

alone 76.2 76.3 0.1 ± 0.3 No

Car, truck, or van - - carpooled 12.2 11.2 -1.0 ± 0.1 Yes

Public transportation (including

taxicab) 4.7 5.2 0.5 ± 0.1 Yes

Walked 2.5 2.7 0.2 ± 0.1 Yes

Other means 1.2 1.4 0.3 ±0.0 Yes

Worked at home 3.2 3.2 -0.0 ± 0.1 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.

To determine if differences were statistically significant, variances were estimated using

methods designed for a complex sample design, and statistical tests were conducted. The ACS

uses replication methods to compute direct estimates of the standard error and controls are used

in calculating these estimates. For Census 2000, the generalized variance formula, a simple
8

random sample formula multiplied by a design effect, was used for all proportions. In this report

we use a confidence level of 90 percent as the dividing line for statistical significance, and show 

the resulting margins of error of the differences. A margin of error of the difference of 0.0

indicates that the actual margin of error was less than 0.05 with a negative value indicating a

larger Census 2000 Sample estimate. In the last column of the table, we identify when the

measured percentage point difference was not within the margin of error. 

While three-fourths of differences in this report were statistically significant, not all differences

that are statistically significant, especially at the 90 percent level, are important or even

substantive. Since the two samples responsible for producing the estimates being analyzed in

this report are extremely large and therefore likely produce significantly differences, we

developed a yardstick to use in determining if the differences are also substantive. In this report,

we focus the analysis of one or more percentage point differences when discussing national-level

comparisons and we consider significant differences of 0.5 percentage points or less as not

important since this small amount of difference should not effect funding or programmatic

decisions. We developed and used this yardstick to help focus our analysis. This tool, however,

is subjective and users can apply their own standards to interpret the data presented in this report.

4.1.2 County-Level data were analyzed to assess sub-national results

In an attempt to make a preliminary assessment of what national findings might imply for lower

levels of geography, we selected a subset of 36 counties where ACS methods have been tested

since 1999 and for which the sample design is consistent with the design planned for full

implementation. These counties represent a diverse set of areas that vary in size geographically

and demographically, reflecting both urban and rural areas. We selected 18 of the 36 ACS test

counties for inclusion because they contain sufficient sample sizes for producing reliable

single-year estimates. Details of these 18 counties can be found in Appendix D. For this

analysis, an attempt was made to determine if national findings held at the county level for these

selected counties or if national-level results masked important county-level results. National

findings of no major differences could mean that some counties differed in one direction while

others differed in the opposite direction, netting to no difference at the national level. We also

expected that minor differences at the national level could mean that some counties had no

differences while others had very large differences. 

Methods used to produce the national summary tables were also used to produce comparison

data for these 18 counties. We calculated county-level Census 2000 Sample and C2SS

distributions for housing units only and conducted statistical testing to identify significant

differences at the county level. To summarize county-level results, we produced detailed tables

for all items and a series of graphs to highlight a subset of the major findings; summary tables

are in Appendix E and some graphs are presented in the results section. 

The graphs included in the results section depict both the degree of differences between the

Census 2000 Sample and C2SS estimates and the specific values of the differences. The

counties are ordered on the y-axis, by population size. Sevier, TN, the smallest county, is the

closest to the origin and Broward, FL, the largest county, is the farthest from the origin. A “C”
9

symbol marks the Census 2000 Sample value and a “>” marks the C2SS value. Whenever the

difference between the two estimates was determined to be statistically significant, the symbols

for both the Census 2000 Sample and the C2SS estimates are bolded. All graphs show a range

of 50 percentage points, from zero to 50 percent, unless otherwise noted in the report. We

should note here that the larger sample sizes in the largest counties make it more likely that

differences could be identified as statistically significant than the differences in the smaller

counties. Using a slightly modified yardstick to determine if statistically significant differences

are substantive, county-level differences of less than 2 percentage points are not considered

substantive in this analysis (See Salvo, Lobo and Calabrese, 2004). We used this yardstick as an

analysis tool; users may develop their own standards for evaluating the data. 

Appendix E includes county-level tables similar to the profile tables in the results section. The

C2SS and the Census 2000 Sample estimates however, are not provided. The difference

between the two estimates (C2SS-Census 2000 Sample) was determined and only the

statistically significant differences are displayed. A positive value indicates that the C2SS

estimate was greater than the Census 2000 Sample estimate while a negative value means that

the C2SS has a lower estimate for this item than the Census 2000 Sample estimate.

4.2 The design and implementation of the C2SS and the Census 2000 Sample methods

were examined

This report systematically reviews C2SS and Census 2000 methods to assess whether these

methods may have led to differences in results. The fundamentally different purposes of the

ACS and Census 2000 led to critical differences in the choice of methods. For example, since

the ACS is an on-going monthly survey that has a manageable workload, the Census Bureau uses

experienced permanent interviewers, equipped with automated data collection instruments, to

collect information from nonresponding units. This was not practical in a decennial census

given the need to hire hundreds of thousands of temporary staff to complete this one-time data

collection effort in a very short time period. This report considers how well data collection and

processing activities were implemented. Nonsampling error (coverage, nonresponse,

measurement, and processing errors) and methodological differences such as reference periods

may explain some C2SS and Census 2000 Sample differences and are discussed below.

4.2.1 Coverage error was considered

Coverage error--excluding or duplicating a certain group of people or housing units from the

survey--is addressed by measures known as completeness rates. These rates indicate the overall

adjustments that were needed to bring Census 2000 Sample enumerations placed in the

Census 2000 Sample and successful C2SS interviews to the level of the full Census 2000 counts.

Completeness rates have been calculated for the total household population count and for the

total housing unit count. The Census 2000 Sample housing unit completeness rate is the ratio of

the total housing units placed in the census sample (those meeting the minimal data

requirement), weighted by the inverse of their expected probability of selection, to the total

Census 2000 housing unit count. Similarly, the Census 2000 Sample household population
10

completeness rate is the ratio of the number of people enumerated in the housing units placed in

the census sample, weighted by the inverse of the housing unit’s expected probability of

selection, to the total Census 2000 household population count. The housing unit completeness

rate for the C2SS is the ratio of the survey’s interviewed units, weighted by the inverse of their

initial probability of selection and subsampling factor if applicable, to the full Census 2000 total

housing unit count, while the C2SS household population completeness rate is the ratio of the

survey’s population in interviewed housing units, weighted by the inverse of the inverse of the

housing unit’s probability of selection and subsampling factor if applicable, to the full Census

2000 household population count. 

The total housing unit completeness rate for the Census 2000 Sample was estimated to be

91.2 percent. The C2SS rate was 93.4 percent. The household population completeness rate for

the Census 2000 Sample was estimated to be 91.4 percent, and the comparable C2SS rate was

91.2 percent. These measures show very similar levels of estimated coverage and thus we do not

see evidence that coverage error played a role in the differences between the C2SS and

Census 2000 Sample estimates examined in this report.

4.2.2 Levels and treatment of unit nonresponse were considered

Unit nonresponse is the failure to obtain sufficient information from a sample unit for it to be

considered an interview–a responding unit. Noninterviews are the most commonly recognized

form of unit nonresponse. Sample units were not interviewed for reasons ranging from a

household's absence during the interview period to its refusal to participate or to provide answers

to sample questions. 

The level and treatment of unit nonresponse differed between the Census 2000 Sample and the

C2SS. The C2SS national weighted unit nonresponse rate was 4.9 percent, which translates into

a survey response rate of 95.1 percent. The comparable rate for the Census 2000 Sample was a

unit nonresponse rate of 8.8 percent, or a survey response rate of 91.2 percent (Griffin, Love, and

Obenski, 2003). To reduce the amount of nonresponse bias introduced into C2SS estimates,

sample units that were not successfully interviewed after all three phases of data collection were

adjusted for by a series of weighting factors in the estimation process. These adjustments took

into account geography and mode of collection. The Census 2000 Sample estimation process

did not use a separate weighting step to adjust for noninterview units as was used in the C2SS. 

Subsampling for nonresponse is used in the final personal interviewing stage of data collection

for the ACS. A sample of about one-third of the C2SS units that did not respond by mail or

CATI were selected for personal visit interviewing. Units were systematically selected and 

removed from the sample as an operational design to reduce costs. This subsampling resulted in

larger variances on survey estimates, especially of the population and housing characteristics

heavily represented in the final data collection stage, but it does not introduce a potential bias

into the overall results. Research is planned to assess the effect of this subsampling on important

survey estimates (Love and Griffin, 2003). 
11

4.2.3 The completeness of data collected at the item level was considered

Item nonresponse is the failure of a responding unit to provide complete and usable information

for a data item. It occurs in all types of data collection modes and often for very different

reasons. A respondent may omit specific questions or entire sections of the questionnaire,

intentionally or unintentionally, resulting in an incomplete mail form. Followup interviewers

may find an otherwise cooperative respondent unwilling to provide them with sensitive

information, such as income. Both the C2SS and the Census 2000 Sample defined item

nonresponse in the same way.

Item allocation rates are often used as a measure of the level of item nonresponse, and are

included in this report (see Attachment A). These rates are computed as the ratio of the number

of eligible people or housing units for which a value was allocated for a specific item to the

number of people or housing units eligible to have responded to that item. Only the response

records that were considered "interviews" -- those that meet the minimal data criteria --

participated in the edit and allocation process and contributed to the item allocation rates. Both

the C2SS and Census 2000 sample data files included an allocation variable for every item that

reported the type of edit actions taken on each item and how often they were taken. The

information provided by this variable indicated whether the answer to the item was used “as

reported,” was assigned based on other information on the same record, or was allocated from

another record. 

Allocation rates were computed for each of the economic items discussed in this report for the

C2SS and Census 2000 Sample. These rates are shown separately by mode of data collection in

Appendix A. As a rule of thumb when judging levels of item imputation, this analysis considers

allocation rates of less than 5 percent as having little influence on final estimates, rates from

5 percent to 10 percent as possibly but not probably influencing final estimates, and rates higher

than 10 percent as likely influencing the results (See Schneider, 2004 for details). Appendix A

contains tables of item allocation rates related to each profile table. As these data show, item

allocation rates were consistently lower in the C2SS than in the Census 2000 Sample. 

The C2SS used several specialized methods designed to reduce item nonresponse that were not

used in Census 2000. These included the use of the telephone to follow up with households that

returned their C2SS mail questionnaire with missing or inconsistent responses. In addition,

current surveys like the ACS use computerized data collection instruments and experienced

interviewers to conduct interviews. The C2SS interviewers were trained on techniques to help

convince reticent respondents to cooperate with the survey, and used computer-assisted

instruments (used for telephone and personal visit followup). These instruments were

programmed with internal edits to assess consistency and reasonableness of responses and to

automatically skip to the correct question based on answers provided during the actual

interviews. These checks of related information during the interview process decreased the

amount of inconsistent and missing data that the final content edit and allocation programs had

to correct. For some items, the instruments were very successful in reducing the need for

imputation in the C2SS CATI and CAPI modes (Love, 2004).
12

The Census Bureau’s subject-matter experts designed the program edits for those instances in

which allocation was required. While some of the edit and allocation methods used in the C2SS

differed from those used in the Census 2000 Sample, the basic edits were very similar. For

example, the edits used for income were the same for C2SS and the Census 2000 Sample;

however, pre-edits were added for Census 2000 Sample income entries to ensure that the optical

character recognition (OCR) equipment and the keyers interpreted responses similarly. These

additional edits were not required for the C2SS as keyers completed all data capture activities

(Posey, Welniak, and Nelson, 2003). Similarly, different methods were used to code industry

and occupation entries for the C2SS and Census 2000 Sample. Details of these differences are

discussed in the results section.

4.2.4 Measurement and processing errors may explain some observed differences 

Measurement and processing errors can occur for a variety of reasons and are the consequence of

errors during the data collection and data processing stages of the survey. Biemer et al. (1991)

describe measurement error as having four primary sources: the questionnaire, the mode of data

collection, the interviewer, and the respondent. This report considered each of these sources as

possible explanatory information when differences were detected. Specifically, we considered 

different question wording, different interviewer training, and different respondent tools for

completing a form or interview. For example, an instruction booklet was mailed with each C2SS

mail questionnaire to help respondents answer the question but this booklet was not used for

Census 2000. 

Measurement error manifests itself in two broad ways–response and interviewer errors. 

Response error occurs if a respondent does not interpret the meaning of a question as intended,

or fails to recall the information accurately. Interviewer error can also be a source of systematic

measurement error if interviewers are not properly trained, if they misinterpret their procedures,

or if they implement procedures incorrectly. Response error, in the form of variance or bias, can

result because of questionnaire design or because respondents simply find the concepts complex

and undefined. Questionnaire presentation, the way a question is asked, and the response

categories provided can affect, either individually or in tandem, how a respondent answers a

question. Differences in presentation and wording of some questions existed between the C2SS

and the Census 2000 Sample, and may contribute to differences in estimates. For example, the

format of the response categories for the commuting to work item differed for space reasons; we

do not know for sure if this difference played a part in the differences seen in the distributions

but they may have had an effect. Appendix B includes facsimiles of the economic questions as

they appeared on the C2SS and Census 2000 Sample mail questionnaires, the Census 2000

Sample followup questionnaires, and the C2SS data collection instruments. 

Response error can also occur when the person who provides the information is not the best

source. There were two ways this error could have manifested itself. For Census 2000

nonresponse followup interviewers took responses from non-household members such as

neighbors (referred to as “proxy” responses) as a last resort to complete data collection. In

Census 2000, about 15 percent of the occupied Sample nonresponse followup enumerations were

based on proxy respondents (Moul, 2002). The C2SS did not accept proxy interviews. For both
13

Census 2000 and C2SS, one household-member provided information for all household

members. Error may occur if the person interviewed does not provide accurate information for

each household member, whether done intentionally or unintentionally. Response error is of

particular concern for this report given that data on employment and income may be more

difficult for one person to answer for all household members. 

Interviewer error is another source of measurement error that could have contributed to

differences. Because of the on-going nature of the ACS, the C2SS interviewers were more

intensively trained and generally have more experience than interviewers recruited for the

decennial census. In addition, the C2SS interviewers also had the benefit of automated

instruments that reduced the potential for interviewers to skip questions in error or to collect

inconsistent data. Refer to Census Bureau (2004) for more details.

Processing error is recognized as a form of systematic error that can be introduced when systems

or programs designed to capture, edit, and tabulate data induce error. Such errors can be

attributed to problems in specifications, in programming, or in implementation. For example,

the C2SS data were keyed from mail returns and into computer-assisted instruments and the

Census 2000 Sample data were captured and interpreted using an Optical Mark Recognition

(OMR) and OCR processes. Processing error can occur if the OCR equipment misreads Census

2000 Sample income entries or if a data entry clerk keyed the wrong information during data

capture. Processing errors may be a factor to consider when analyzing income data as will be

discussed in the results section. Similarly, since coding was used for the Census 2000 Sample

and C2SS industry and occupation items, it is possible that coding errors were made. Errors

introduced during the editing and file creation process are another possible source of processing

error, which may be the result of errors in specification (e.g., incomplete, unclear, or incorrect

specifications) or in programming. We reviewed processing methods and procedures as part of

this analysis. 

4.2.5 The effect of differing residence rules, reference periods, and data collection time

frames were considered

Residence Rules

Differences in residence rules may have contributed to variation in the level of occupancy,

household membership, and universes on which the economic characteristics depend. The

Census 2000 residence rules count the population as of April 1, 2000, while the ACS residence

rules collect representative information on a wide range of topics continuously over 12 months,

and produce yearly average distributions of these characteristics for all kinds of areas. 

Census 2000 residence rules reflect the principle of usual residence as of April 1, 2000. These

rules are premised on the need to establish one and only one residence for each respondent. 

Establishing one usual residence is critical to minimizing the chance that a respondent will be

counted in more than one location. Additionally, the usual residence concept links to the

Constitutional requirement of a census to support apportionment. In contrast, the ACS methods

call for some mode of interviewing nearly every day of the year. Thus, the ACS adopted a
2

The question on mobility asked “Did this person live in this house or apartment 5 years ago (on April 1,

1995)?

3

The C2SS used the following specific reference periods: “last 3 months” for school enrollment, “last 12

months” for income, and “1 year ago” for whether the person was living in house or apartment.

14

current residence rule. Using this residence rule approach produced data that ultimately

provided an estimate of the average characteristic for every area in the nation each year.

The ACS “current residence” concept is based on a 2-month length of stay that includes the day

that the unit is contacted. This rule recognizes that people can have more than one place where

they live or stay over the course of a year, and that estimates of the characteristics of the 

population for some areas are affected by these people. Thus, a different set of residence rules

was adopted. 

The differences in C2SS and Census 2000 Sample estimates caused by the residence rules were

most likely minimal for most of the economic data discussed in this report. However, for certain

segments of the population the usual and current residence concepts can result in different

residence decisions. Appreciable differences may occur in areas where large numbers of people

spend several months of the year in what would not be considered their residences under the

census usual residence concept. In particular, estimated distributions of certain characteristics

for states like Florida and Arizona, and for areas like beach, lake, or mountain vacation spots

may differ appreciably between the census and the ACS because of their large seasonal

populations. Similarly, areas with large colleges or universities may see differences in

household population distributions due to the more de facto nature of the ACS current residence

rule.

Reference Dates and Periods

Reference date or period refers to the time frame about which the question asks for information. 

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

assumption being that the remaining “100 percent” items are also reflecting that date, regardless

of whether the enumeration is conducted by mail in March and April or by followup operations

in July. However, only one sample question on the Census 2000 Sample referenced this

April 1, 2000 date.2 The remaining sample questions either had no specific reference period or

provided a specific reference period such as “last week” (for employment status), “Since

February 1" (for school enrollment), “calendar year 1999", (for income questions), or 5 years

ago (for person living in this house or apartment).3 This implies that the decennial census

sample estimates, with the exception of these items, primarily reflect status in the months of

April, May, and tailing out into August. They could be influenced by delivery dates for the mail

questionnaires and the length of time data are collected from followup operations. 

The ACS estimates of characteristics reflect the conditions as of the day the data are collected, or

they reflect a specific time period referenced in individual questions. The ACS data, except for

income which is collected for the last 12 months, tend to be equally spread across each month of
15

the year, with peaks of information within each month that reflect the receipt of mail return

questionnaires. 

The figure below illustrates this diversity at the national level. The weighted estimate of total

housing units in the Census 2000 Sample and the C2SS are graphed according to the week in

which the data were collected. Census Day – April 1, 2000 – is the last day of week 13. As the

figure shows, Census 2000 data collection occurred between weeks 10 and 32 while C2SS data

collection occurred throughout the year.

Several of the economic estimates would obviously be affected by the interview date and the

distribution of the amount of data collected over the interview time frame. For example, the

reference period for employment status is “last week” for both the Census 2000 Sample and the

C2SS. However, since the timing of data collection for the Census 2000 Sample and the C2SS

differed, “last week” had different meaning for the respondents to the two surveys. Since the

answers to the employment status questions determine the universe for many other economic

characteristics discussed in this paper including commuting to work, occupation, industry, and

class of worker, this difference also affected differences in these estimates. 

The reference period for the income questions also differed for the Census 2000 Sample and the

C2SS. The Census 2000 Sample asks about income in “Calendar Year 1999" whereas the C2SS

referenced “the last 12 months”. Again, since data collection for the C2SS is continuous through

the year, the reference periods differ. For income estimates, C2SS estimates can be thought of as

a 12-month estimate centered around December 15, 1999 while the Census 2000 Sample
4 ACS interviews in calendar year 2000 had income reference periods ranging from January through

December 1999 (for those interviewed in January 2000) to December 1999 through November 2000 (for

those interviewed in December 2000). The combined reference period for all 12 interviews is 23 months

(January 1999 through November 2000). The midpoint of this period is 11 1/2 months after the starting date

of January 1 (December 15, 1999). For Census 2000, the midpoint is July 1, 1999 since the reference

period for everyone is calendar year 1999.

16

estimate is a 12-month estimate centered around July 1, 1999.4 As will be discussed in the

results section, analysts believe many of the differences in the C2SS and Census 2000 Sample

estimates discussed in this report are affected by the difference in the reference periods.

5. RESULTS

This section documents the comparison of C2SS and Census 2000 Sample distributions for the

following items that appear in the economic profile:

• Employment Status, 

• Commuting to Work, 

• Occupation, Industry, and Class of Worker, 

• Income and Poverty. 

The C2SS estimates compared in this report reflect the use of final population and housing

controls and are limited to the household population only (that is, they exclude the group

quarters population). The Census 2000 Sample estimates are based on data from about 1 in

6 households nationally who completed Census 2000 Sample forms. For each of the economic

items, this section provides background on the uses of the data and how the questions were

asked. Two sets of data are provided - tables comparing the national-level C2SS and Census

2000 Sample estimates, and graphs showing selected county-level comparisons. This section

identifies areas in which improvements in ACS methods or additional research are recommended

as well as real differences that may exist in ACS estimates relative to those produced from the

Census 2000 Sample. A complete summary of statistically significant sub-national results for

18 counties can be found in Appendix E.

5.1 Employment Status

5.1.1 Description of Item

The ACS includes a series of questions on employment status that classify the entire

working-age population (those ages 16 and older) into categories to characterize the employment

status of the American workforce. The categories make it possible to calculate important

statistics about employment conditions, such as the unemployment rate and the ratio of those

employed to the total working-age population of a state, county or local jurisdiction. These
17

questions also identify specific segments of the population like the civilian labor force which is

used to provide critical occupational data for equal employment opportunity programs. The

Department of Health and Human Services uses these data in each state to allocate Community

Food and Nutrition funds. The Civil Rights Act of 1964 and Work Investment Act of 1998 are

just two examples of legal authorization for data about employment status. For a more complete

list of federal uses, refer to Appendix C.

The official estimates of employment status are produced from the Current Population Survey

(CPS). The C2SS and Census 2000 Sample provide estimates of employment status “last week”. 

Although these are not the official estimates for employment status, these data are collected for

important reasons. They provide estimates for places too small for reliable CPS estimates and

the Bureau of Labor Statistics uses these estimates as benchmarks in the small-area estimates

program. In addition, these data serve as the screener and universe-setting item for additional

items including commuting to work, industry, occupation and class of worker, and veteran

status. The collection of these data permits more detailed cross-tabulations of employment

status by other characteristics than is possible in the CPS.

The wording of the questions used to collect data on employment status was identical in the

C2SS and the Census 2000 Sample for the mail and nonresponse followup operations. 

Appendix B includes facsimiles of the Employment status questions asked on the C2SS and

Census 2000 Sample mail questionnaires, the Census 2000 Sample nonresponse followup

questionnaire, and the C2SS CATI and CAPI followup instruments. 

5.1.2 National-Level Comparisons

Table 1 compares the distribution of the household population 16 years and older by

employment status in the C2SS and the Census 2000 Sample. All estimates are in percentages.

The national results presented in Table 1 show fairly consistent results when comparing the

distributions. However, we see a higher rate of labor force participation and unemployment

estimated by the C2SS when compared with the Census 2000 Sample. These differences appear

when looking at the entire household population 16 years and older and the female household

population 16 years and older. Both Census 2000 and the C2SS report the same percent of

people in the Armed Forces. 

The largest substantive difference was the difference for the percent of “own children under age

six where all parents are in the labor force”. This estimate is a measure of the proportion of

pre-school age children who live with two parents (or their only parent for those who live with

only one parent) in the labor force and indicates the potential demand for child-care in an area. 

The C2SS estimated that 60.7 percent of “own children under age six who had all parents in the

labor force” compared with 58.6 percent in the Census 2000 Sample. This 2.2 percentage point

difference may be a result of differences in the timing of data collection for the two surveys; that

is, since the Census 2000 estimate covers the time period from March through August, it is

possible that the labor force composition may vary for spring and summer compared to the entire

year. Another possible concern is issues of coverage given that the relationship question, used to

determine the universe, differed for the Census 2000 Sample and the C2SS. In the Census 2000
18

Sample, there was greater detail, providing more ways to determine the subfamily relationships

in a household. More research is necessary to uncover the reasons. 

Table 1. Employment Status, National-Level Distributions (C2SS compared with the Census 2000 Sample) 

Employment Status

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Household Population 16 years

and over

209,600,000 209,600,000

In labor force 65.2 66.2 1.0 ± 0.1 Yes

Civilian labor force 64.8 65.8 1.0 ± 0.1 Yes

Employed 61.4 62.3 0.9 ± 0.1 Yes

Unemployed 3.4 3.5 0.1 ± 0.0 Yes

Percent unemployed 5.2 5.3 0.1 ± 0.1 Yes

Armed Forces 0.4 0.4 -0.0 ± 0.0 No

Not in labor force 34.8 33.8 -1.0 ± 0.1 Yes

Universe:

Females 16 years and older 109,000,000 108,900,000

In labor force 58.3 59.1 0.8 ± 0.1 Yes

Civilian labor force 58.2 59.0 0.8 ± 0.1 Yes

Employed 55.1 55.7 0.6 ± 0.2 Yes

Universe:

Own children under 6 years 21,800,000 21,900,000

All parents in family in labor

force 58.6 60.7 2.2 ± 0.5 Yes

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.

5.1.3 County-Level Comparisons

Sub-national data for a set of 18 counties were analyzed to give a preliminary indication of

whether the national findings held at lower levels of geography. Figure 1 summarizes the

percent of the household population ages 16 and older employed for these 18 counties. Note that

the scale starts at 25 percent, not zero. This graph shows the same phenomenon observed

nationally - the percent employed was slightly higher in the C2SS (shown as a triangle) than in

the Census 2000 Sample (shown as a circle). Differences in 10 of the 18 counties were

statistically significant although only five differences were substantive using the 2.0 percentage

point difference yardstick. There was a higher rate of employment in the C2SS in Sevier, TN,

Jefferson, AR, Yakima, WA, Tulare, CA, and Bronx, NY when compared with the Census 2000

Sample. Based on these results, we believe local planners in these 18 counties would implement

programs in their areas in similar ways whether using the Census 2000 Sample or the C2SS data.
19

Figure 1. Percent of Household Population 16+ Employed

Census 2000 Sample and C2SS County-Level Estimates

KEY: 1. The universe is restricted to the 2000 Household Population.

2. Census 2000 Sample county-level estimates are shown as circles; C2SS county-level estimates are shown as triangles.

3. Whenever the difference between the two estimates was determined to be statistically significant, the symbols for both the

Census 2000 Sample and the C2SS estimates are bolded.

Appendix E, Table 1, displays the county-level data for the employment status categories shown

in Table 1 above. Only the statistically significant differences are shown. The most variability

in the selected county-level data examined for this report appears for this economic item, with

over 20 percent of the estimates differing by at least 2 percentage points. The C2SS has a higher

rate of labor force participation when looking at the county-level data for seven of the

18 counties. These include: Sevier, TN, Jefferson, AR, Black Hawk, IA, Yakima, WA,

Tulare, CA, San Francisco, CA, and Bronx, NY. The variability is especially noticeable when

looking at the statistics for females 16 years and over. As was the case with the national-level

comparison, the greatest variability is for the percent of own children under 6 where all parents

are in the labor force. The difference is substantive for seven counties including Calvert, MD,

Jefferson, AR, Tulare, CA, Hampden, MA, Pima, AZ, Bronx, NY and Broward, FL. In all cases

except Calvert, MD, the C2SS estimated a higher percentage than did the Census 2000 Sample. 
20

5.1.4 Analysis

The official estimates of employment, including the critical economic measure of the

unemployment rate, are produced by the CPS. The CPS estimates employment status each

month for the week that includes the twelfth day and yearly estimates are also produced using

these data. The 2000 estimate of civilian unemployment was 4.0 percent (Bureau of Labor

Statistics, 2001). The Census 2000 Sample and C2SS estimates of the civilian unemployment

rate (3.4 percent in the Census 2000 Sample and 3.5 percent in the C2SS) are fairly comparable

with each other but Census Bureau analysts continue to conduct research to understand why

these estimates differ from the official CPS estimate.

Limiting the analysis to the profile tables, as is done in this report, has limitations when looking

at estimates of employment. Census Bureau analysts also examine data on work history in the

past 12 months, weeks worked, and usual hours worked per week to help understand

employment statistics. These items have not been analyzed in this report. 

The comparison of the C2SS and the Census 2000 Sample national and sub-national

employment status data indicate a slightly higher proportion of labor force participation and

unemployment in the C2SS estimates. This difference may be the result of a number of factors. 

First, the timing of data collection differed for the Census 2000 Sample and the C2SS. While

employment status was determined by a series of questions asking about “last week”, data

collection occurred at different times which makes the reference periods different. As discussed,

Census 2000 Sample data collection occurred between March and August of 2000. This could

lead to a different pattern of employment than estimated from the C2SS or CPS where data

collection occurred constantly throughout the year and may more accurately affect cycles of

employment through the entire year. Analysts continue to do research to understand this

difference of timing of data collection on the employment estimates. Differences in item

allocation may be a factor to consider. As Table 1 in Appendix A shows, the amount of item

allocation for employment status is problematic for the Census 2000 Sample and moderate for

the C2SS using our yardstick; the rate was 11.1 percent for the Census 2000 Sample compared

with 6.0 percent for the C2SS. The largest difference in the amount of allocation appears in the

interviewer-collected data (9.2 percent in the Census 2000 Sample compared with 2.6 percent in

the C2SS). 

5.2 Commuting to Work

5.2.1 Description of Item

Data on Commuting to Work describe the types of transportation commuters use to get from

home to work and are used with data on Place of Work to describe key characteristics of

commuter travel. These data are needed to design programs to conserve energy, reduce

pollution, and ease traffic problems, including planning highway improvements and developing

public transportation services. For a more complete list of federal uses, refer to Appendix C.
21

The wording of the questions used to collect data on Commuting to work was identical in the

C2SS and Census 2000 Sample questionnaires although the format of the response categories

differed on the paper forms. To reduce the number of pages of the C2SS mail questionnaire

(which is 24 pages), the C2SS triple-banks questions for a person on a questionnaire page. 

Using this format, the response categories for this question are double banked and motorcycle,

bicycle, walked, worked at home, and other method appear at the top of column 2. The

Census 2000 Sample questionnaire booklets used for mail and followup (39 pages long) asked

the detailed population questions in two columns on each page. Using this format, the

Census 2000 Sample response categories for this questions are in one continuous list. 

Appendix B includes facsimiles of the Commuting to work questions discussed in this report as

asked on C2SS and Census 2000 Sample mail questionnaires, the Census 2000 Sample

nonresponse followup questionnaire, and the C2SS CATI and CAPI followup instruments. 

5.2.2 National-Level Comparisons

Commuting to work includes questions about the means of transportation to work, carpool

occupancy, and average travel time but the profile tables analyzed in this report include only

information about the means of transportation to work and carpool occupancy (recoded to "drove

alone" and "carpooled"). The national-level comparisons for these items appear in Table 2. The

distributions look very similar and while there are statistically significant differences, they

differed by less than one percentage point. The Census 2000 Sample and the C2SS estimated

very similar percentages for every response category except the percent of workers who

carpooled and the percent who used public transportation. The C2SS estimated about one

percentage point fewer people who carpooled to work than the Census 2000 Sample. 

Table 2. Commuting to Work, National-Level Distributions (C2SS compared with the Census 2000 Sample) 

Commuting to Work

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample

in percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Workers 16 years and over 126,900,000 127,700,000

Car, truck, or van - - drove

alone 76.2 76.3 0.1 ± 0.3 No

Car, truck, or van - - carpooled 12.2 11.2 -1.0 ± 0.1 Yes

Public transportation (including

taxicab) 4.7 5.2 0.5 ± 0.1 Yes

Walked 2.5 2.7 0.2 ± 0.1 Yes

Other means 1.2 1.4 0.3 ± 0.0 Yes

Worked at home 3.2 3.2 -0.0 ± 0.1 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.
22

Figure 2. Percent of Workers 16+ Who Carpooled

Census 2000 Sample and C2SS County-Level Estimates

5.2.3 County-Level Comparisons

Sub-national results are summarized in Figure 2 for the percent of workers 16 and older who

carpooled. This was the means of transportation category with the greatest number of

statistically significant differences at the county level. Eight of the 18 counties had significantly

different proportions of workers in this category. In all instances except one, the C2SS estimate

(shown as a triangle) was slightly lower than the Census 2000 Sample estimate (shown as a

circle). In Tulare, CA the percent of workers who carpooled in the C2SS was over three

percentage points lower than the Census 2000 Sample estimate and in Madison, MS, the percent

of workers who carpooled in the C2SS was almost five percentage points lower than the

Census 2000 estimate. Broward, FL was the only county where the C2SS had a higher

proportion of people who carpooled when compared with the Census 2000 Sample. 

KEY: 1. The universe is restricted to the 2000 Household Population.

2. Census 2000 Sample county-level estimates are shown as circles; C2SS county-level estimates are shown as triangles.

3. Whenever the difference between the two estimates was determined to be statistically significant, the symbols for both the

Census 2000 Sample and the C2SS estimates are bolded.
23

Appendix E, Table 2, displays the county-level data for all means of transportation categories. 

Only the statistically significant differences are shown. This table shows that many of the

national findings hold when looking at the county-level data for these specific sites. The

selected county-level data examined look fairly consistent with national data. In Madison, MS,

Tulare, CA, and Franklin, OH, the C2SS estimated fewer people who carpooled and more who

drove alone when compared with the Census 2000 Sample. In Bronx, NY, the C2SS estimated

fewer people who drove, whether alone or in a carpool, and more people who used public

transportation when compared with the Census 2000 Sample. In Broward, FL, there was a

different phenomenon. In this county, the C2SS estimated fewer people who drove alone and

more people who carpooled when compared with Census 2000 Sample estimates. 

In general, the only national-level finding that does not hold up well is for driving alone. While

nationally the differences were not significant, there were statistically significant differences for

six of the 18 counties and these differences were not small. For example, the C2SS estimate was

over five percentage points higher than the Census 2000 Sample estimate in Madison, MS. The

C2SS estimate was also higher in Tulare, CA (3.1 percentage points) and Franklin, OH

(2.5 percentage points). The Census 2000 Sample estimate was about two percentage points

higher in Rockland, NY, Bronx, NY, and Broward, FL, than the C2SS estimate. This is an

example of where differences may exist at the county level but net out at the national level. We

need to look at this more closely, producing cross-tabulations or doing other analysis, to

understand this difference. 

5.2.4 Analysis

As the data in Table 2 show, the C2SS estimated that fewer workers carpooled and more workers

used public transportation, walked, or used other means to get to work than did the Census 2000

Sample. The differences are small and may be a result of design differences, namely the

reference period, for the two data collection efforts. This is probable given that answers to the

employment status questions define the universe for the commuting to work questions. 

Allocation rates also may be a factor. As Tables 2a and 2b in Appendix A show, the allocation

rates for mail returns were consistent for the Census 2000 Sample and the C2SS; however, the

allocation rates for the Census 2000 Sample were higher, and more problematic using our

yardstick, than the C2SS for interviewer-collected data. Thirteen percent of the Census 2000

data for means of transportation to work were allocated compared with about just over three

percent of the C2SS data.

The slightly higher reporting of “Other” in the C2SS compared with the Census 2000 Sample

may be the result of the formatting of the response categories; that is, Motorcycle, Bicycle, and

Other may have been more visible at the top of the double-banked list on the C2SS form than at

the bottom of the long list of Census 2000 Sample categories. This is just speculation on our

part; there has been no formal testing of this hypothesis.
24

5.3 Occupation, Industry, and Class of Worker

5.3.1 Description of Item

Questions on Occupation, Industry, and Class of Worker describe the occupational skills and

work activity of the American labor force. Occupation describes the kind of work people do and 

Industry provides information on the kinds of businesses where people work. Class of Worker

identifies whether the worker is employed by a private or government agency, is self-employed,

or working as an unpaid family worker. These data are used to formulate employment policy,

programs, and training and are needed to measure compliance with anti-discrimination policies. 

The Department of Education uses these data to analyze career trends and options in the United

States. For a more complete list of federal uses, refer to Appendix C.

The wording of the questions used to collect data on Occupation, Industry, and Class of Worker

was identical in the C2SS and Census 2000 Sample paper questionnaires. The C2SS took

advantage of the computer technology when asking these questions in the followup instruments,

using branching techniques to ask the Class of Worker question. Respondents were given a

limited number of response categories including government and self-employed. If the

respondent chose one of those two categories, they were asked additional questions to identify

the type of government employee–local, state or federal, and whether the worker’s company was

incorporated or not. 

The one real difference was the order in which the questions were asked in the two surveys. On

the Census 2000 Sample forms, Industry was asked first, followed by Occupation, and Class of

Worker. In the C2SS, Class of Worker was asked first, before Industry. The order of questions

used for the C2SS was mirrored after work done in the 1990's when the CPS CAPI instrument

was developed. Analysts believed asking about Class of Worker first set the context for asking

the Industry and Occupation questions and thus this sequence was used for all three modes of

data collection in the C2SS. The decennial census has asked about Industry, Occupation, and

Class of Worker questions in this order since the 1980 Census. Census Bureau analysts would

like to do additional testing on the sequence of these questions before making a final

recommendation on the sequence for use in the ACS. Appendix B includes facsimiles of the

these questions asked on the C2SS and Census 2000 Sample mail questionnaires, the Census

2000 Sample nonresponse followup questionnaire, and the C2SS CATI and CAPI followup

instruments. 

5.3.2 National-Level Comparisons

Tables 3a, 3b, and 3c show the national-level comparisons for Occupation, Industry, and Class of

Worker between the Census 2000 Sample and the C2SS. The Census 2000 Sample and C2SS

distributions for these items are very comparable; all differences are quite small and not

substantive. In terms of Occupation (Table 3a), there are no substantive differences. Table 3b

shows data on the industry classification. Again, few differences are seen. The Census 2000

Sample reported about 0.5 percentage points more people working in educational, health, and
25

social services than did the C2SS. When analyzing the Class of Worker data (Table 3c) several

small, meaningless differences are seen. 

Table 3a. Occupation, National-Level Distributions (C2SS compared with the Census 2000 Sample)

Occupational Group

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference 

(C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Employed civilian population

16years and older

128,700,000 130,600,000

Management, professional, and

related occupations 33.7 33.3 -0.4 ± 0.2 Yes

Service occupations 14.8 15.1 0.3 ± 0.1 Yes

Sales and office occupations 26.6 26.6 -0.0 ± 0.1 No

Farming, fishing, and forestry

occupations 0.7 0.8 0.0 ± 0.1 Yes

Construction, extraction, and

maintenance occupations 9.5 9.5 -0.0 ± 0.1 No

Production, transportation, and

material moving occupations 14.7 14.7 0.0 ± 0.1 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.
26

Table 3b. Industry, National-Level Distributions (C2SS compared with the Census 2000 Sample)

Industry

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

(C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Employed civilian population

16 years and older

128,700,000 130,600,000

Agriculture, forestry, fishing

and hunting, and mining 1.9 1.9 0.0 ± 0.1 No

Construction 6.8 6.8 -0.0 ± 0.1 No

Manufacturing 14.2 14.2 0.0 ± 0.2 No

Wholesale trade 3.6 3.8 0.2 ± 0.1 Yes

Retail trade 11.7 11.9 0.2 ± 0.1 Yes

Transportation and

warehousing, and utilities 5.2 5.2 -0.0 ± 0.1 No

Information 3.1 3.1 0.0 ± 0.1 No

Finance, insurance, real estate,

and rental and leasing 6.9 6.8 -0.1 ± 0.1 Yes

Professional, scientific,

management, administrative,

and waste management services 9.3 9.3 -0.1 ± 0.1 No

Educational, health and social

services 19.7 19.3 -0.5 ± 0.1 Yes

Arts, entertainment, recreation,

accommodation, and food

services 7.8 8.0 0.2 ± 0.1 Yes

Other services (except public

administration) 4.9 4.9 0.0 ± 0.1 No

Public administration 4.8 4.8 -0.1 ± 0.1 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.
27

Table 3c. Class of Worker, National-Level Distributions (C2SS compared with the Census 2000 Sample) 

Class of Worker

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Employed civilian population

16 years and older

128,700,000 130,600,000

Private wage and salary

workers 78.5 78.5 0.0 ± 0.2 No

Government workers 14.6 14.2 -0.3 ± 0.1 Yes

Self-employed workers in own

not incorporated business 6.7 6.9 0.3 ± 0.1 Yes

Unpaid family workers 0.3 0.3 0.0 ± 0.0 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.

5.3.3 County-Level Comparisons

Sub-national data were analyzed to determine if the national findings held at lower levels of

geography, which they generally did. Figure 3 summarizes the percent of workers employed in

the educational, health, and social services industry. This is the response category that had the

most variation at the national level. This graph confirms the national trend; there were

statistically-significant differences for 10 of the 18 counties but there were only substantive

differences in reporting of this industry in 4 of the 18 counties shown. In all four of these

counties, the Census 2000 Sample reported slightly more people working in this industry than

did the C2SS.
28

Figure 3. Percent of Workers Employed in the Educational, Health and Social Services Industry

Census 2000 Sample and C2SS County-Level Estimates

KEY: 1. The universe is restricted to the 2000 Household Population.

2. Census 2000 Sample county-level estimates are shown as circles; C2SS county-level estimates are shown as triangles.

3. Whenever the difference between the two estimates was determined to be statistically significant, the symbols for both the

Census 2000 Sample and the C2SS estimates are bolded.

Appendix E summarizes county-level results for all of the Occupation, Industry, and Class of

Worker categories in Tables 3a, 3b, and 3c. Table 3a shows the statistically significant

differences for Occupation categories. As this table shows, there were very few differences and

most the differences follow the direction of the national comparison; that is, the C2SS has a

slightly lower proportion of workers working in management, professional, and related

occupations and higher proportion of workers working in services occupations when compared

with the Census 2000 Sample. When looking at Table 3b we see only four counties where there

seem to be any substantive differences–Black Hawk, IA, Schuylkill, PA, Rockland, NY, and

Tulare, CA. In terms of Class of Worker, there are very few substantive differences at the

county level, as we see in Table 3c.
5

The 1980 SOC was the only code book available when testing for ACS started in 1996. Starting in 2002,

the ACS switched to the Census 2000 coding procedures.

29

5.3.4 Analysis

The Census 2000 Sample and C2SS had very comparable estimates for the Occupation, Industry,

and Class of Worker items. Some of the differences seen may be the result of the different

sequence in which the questions were asked in the Census 2000 Sample and the C2SS. The

sequence used in the Census 2000 Sample (and the 1980 and 1990 censuses) was industry,

occupation, and class of worker. Research conducted while developing the CPS CAPI

instrument led analysts to recommend asking the Class of Worker question before the questions

on Industry and Occupation as they felt that the Class of Worker question set the context of the

next two questions. Analysts understand that there are some differences in the distributions and

they would like to do additional testing to determine which sequence to use for the ACS;

however, given that the distributions are so similar, this should only be considered if it can be

added to a planned test and is not cost prohibitive.

There were some operational differences for these two surveys. The Industry and Occupation

responses were coded differently for the Census 2000 Sample and the C2SS. The Census 2000

Sample Industry and Occupation responses were first coded using an automated coder and then

clerical coding was used to code responses that could not be autocoded by a staff of temporary 

coders hired for Census 2000. This staff used the 2000 Standard Occupation Classification

(SOC) code book, updated in 1998, for this operation. The C2SS Industry and Occupation

responses were coded monthly by a small staff of permanent Census Bureau employees in

Jeffersonville, IN who used the 1980 SOC code book to code these responses.

5

 This staff coded

CPS and C2SS responses each month using the 1980 SOC code book which was used for the

1990 Census. 

Since responses to the employment status questions determined the universe for these questions, 

the differences in employment status universes discussed in the analysis of employment status

(Section 5.1.4) may also affect these results.

5.4 Income and Poverty 

5.4.1 Description of Item

Data on income provide vital measures of the general economic circumstances in the United

States and determine poverty status. Questions on income have been asked in the census since

the first sample introduced in 1940. The Department of Education uses these data to allocate

grants in high poverty areas and Department of Housing and Urban Development uses these data

to help allocate low-income housing assistance. For a more complete list of federal uses, refer to

Appendix C.
6

The CPI-U stands for the Consumer Price Index for all Urban Consumers. It’s the Bureau of Labor

Statistic’s basic cost of living measure and is used to produce a calendar-year estimate for income by

adjusting monthly ACS data. The ACS data processing includes a step to convert all income dollar

amounts from a 12-month reference period into a calendar-year estimate for the year of interview by using

the difference between the average CPI-U for the household's 12-month reference period and the calendar

year of the interview. For example, a C2SS household that was interviewed in July of 2000 had an income

reference period of July 1999-June 2000. Income estimates for this household were expressed in calendaryear 2000 dollars, based on the difference between the average CPI-U for July 1999-June 2000 and January

2000-December 2000. For this analysis, the C2SS income estimates were converted to 1999 dollars based

on the difference between the annual average CPI-U in 1999 and 2000.

30

The wording of the questions used to collect data on income was identical in the C2SS and

Census 2000 Sample but the reference periods differed. The Census 2000 Sample and the C2SS

split the income question into a series of questions on eight specific sources of income to help

respondents remember all sources of income. The reference period for the C2SS was “during the

past 12 months” while the reference period for the Census 2000 Sample was “calendar year

1999.” The C2SS CATI and CAPI instruments included online edits to verify income values

that seemed suspicious. These edits were not available on the Census 2000 Sample

questionnaires or the C2SS mail questionnaire. Appendix B includes facsimiles of the income

questions asked on the C2SS and Census 2000 Sample mail questionnaires, the Census 2000

Sample nonresponse followup questionnaire, and the C2SS CATI and CAPI followup

instruments. 

5.4.2 National-Level Comparisons

Tables 4a-4c show the national-level comparisons of household income, type of income, and

family income in constant 1999 dollars. The C2SS collected data throughout the year on an

on-going, monthly basis and asks for a respondent’s income over the “past 12 months.” To

produce yearly estimates using C2SS data, the C2SS estimates were adjusted using the CPI-U

inflation factor6 to convert 12 monthly estimates into one yearly estimate in calendar-year 2000

dollars. The Census 2000, however, collected the income data for a fixed period of time--

“during 1999” and no inflation adjustments were needed to compute an annual estimate. Thus,

the Census 2000 income estimates were in calendar-year 1999 dollars while the C2SS income

estimates were in calendar-year 2000 dollars. To make these two distributions comparable for 

this analysis, we deflated the C2SS estimates using the CPI-U inflation factor to produce two

income distributions in calendar-year 1999 dollars. 

Table 4a shows the comparison for household income and Table 4c shows the comparison for

family income. Since the data in these two tables tell the same story, we will only discuss

Table 4a. As these data show, the C2SS had higher percentage point estimates of income below

$50,000 and lower percentage point estimates of income above $50,000 when compared with the

Census 2000 Sample estimates. Median household income was $41,994 in the Census 2000

Sample compared with $40,137 in the C2SS. The percentage point differences were statistically

significant for all categories and substantive for the $15,000-$24,999 category where the C2SS

estimate was one percentage point higher than the Census 2000 Sample estimate.
31

Table 4b shows household income by type. As the table shows, 80.5 percent of the Census 2000

Sample and C2SS households had earnings income and close to 17 percent of these households

had retirement income. In the C2SS, there was a slightly higher reporting of Social Security

Income, and a slightly lower reporting of Supplemental Security Income and income from public

assistance than in the Census 2000 Sample.

Poverty statistics are derived from income data. Table 4d shows the percent of families and

individuals in poverty for the past 12 months. The top section of the table shows the comparison

of poverty statistics for families while the bottom section shows the comparison of poverty

statistics for individuals. When looking at the family-level poverty statistics, the C2SS reported

higher rates of poverty (although differences are small) for all four measures. The largest

difference is for the female-householder families with related children under 18 years. The

C2SS reported 1.1 percentage points more “female-householder families with related children

under 18 years” in poverty than did the Census 2000 Sample. 

The C2SS and Census 2000 Sample estimated the same percent of individuals in poverty (see the

bottom section of Table 4d). Small differences emerged when this category was sub-divided by

demographic characteristics such as sex, age, and whether or not household members were

related. The C2SS estimated a higher percent of individuals in poverty for all categories except

individuals 18 years and over and unrelated individuals 15 years and older when compared with

the Census 2000 Sample. 
32

Table 4a. Household Income, National-Level Distributions (C2SS compared with the Census 2000 Sample) in

1999 Dollars

Income Groupings

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Households 105,500,000 104,800,000

Less than $10,000 9.5 10.1 0.5 ± 0.1 Yes

$10,000 to $14,999 6.3 6.9 0.6 ± 0.1 Yes

$15,000 to $24,999 12.8 13.9 1.0 ± 0.1 Yes

$25,000 to $34,999 12.8 13.1 0.3 ± 0.1 Yes

$35,000 to $49,999 16.5 16.8 0.2 ± 0.1 Yes

$50,000 to $74,999 19.5 18.6 -0.9 ± 0.1 Yes

$75,000 to $99,999 10.2 9.7 -0.5 ± 0.1 Yes

$100,000 to $149,000 7.7 7.1 -0.7 ± 0.1 Yes

$150,000 to $199,999 2.2 2.0 -0.2 ± 0.0 Yes

$200,000 or more 2.4 2.0 -0.4 ± 0.0 Yes

Median Household Income (in

dollars) $41,994 $40,137 -$1,854 $178 Yes

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.

Table 4b. Type of Income, National-Level Distributions (C2SS compared with the Census 2000 Sample) in

1999 Dollars

Percent of Households with

Type of Income

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

 Universe:

Households 105,500,000 104,800,000

With earnings 80.5 80.5 -0.0 ± 0.3 No

With Social Security Income 25.7 26.5 0.9 ± 0.1 Yes

With Supplemental Security

Income 4.4 3.8 -0.6 ± 0.1 Yes

With public assistance Income 3.4 2.6 -0.8 ± 0.0 Yes

With retirement Income 16.7 16.8 0.0 ± 0.1 No

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.
33

Table 4c. Family Income, National-Level Distributions (C2SS compared with the Census 2000 Sample) in

1999 Dollars

Income Groupings

Census

2000

Sample

Estimate

(in percent)

C2SS

Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample in

percentage

points)

Margin of

Error of

Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe:

Families 72,300,000 71,000,000

Less than $10,000 5.8 6.0 0.3 ± 0.1 Yes

$10,000 to $14,999 4.3 4.8 0.5 ± 0.1 Yes

$15,000 to $24,999 10.7 11.8 1.0 ± 0.1 Yes

$25,000 to $34,999 12.0 12.4 0.4 ± 0.1 Yes

$35,000 to $49,999 17.1 17.5 0.4 ± 0.1 Yes

$50,000 to $74,999 22.3 21.6 -0.7 ± 0.2 Yes

$75,000 to $99,999 12.5 12.0 -0.5 ± 0.1 Yes

$100,000 to $149,000 9.6 8.9 -0.7 ± 0.1 Yes

$150,000 to $199,999 2.7 2.6 -0.2 ± 0.1 Yes

$200,000 or more 2.9 2.5 -0.5 ± 0.1 Yes

Median Family Income

 (in dollars) 50,046 48,014 -2,032 275 Yes

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.
34

Table 4d. Percent Below Poverty, National-Level Distributions (C2SS compared with the Census 2000

Sample)

Poverty Status

Census 2000

Sample

Estimate

(in percent)

C2SS Estimate

(in percent)

Difference

 (C2SS-Census

2000 Sample

in percentage

points)

Margin of Error

of Difference 

(in percentage

points)

Is the

Difference

Statistically

Significant?

Universe: 

Families 72,300,000 71,000,000

Families in poverty 9.2 9.3 0.2 ± 0.1 Yes

With related children under

18 years 13.6 14.3 0.7 ± 0.2 Yes

Families with female

householder, no husband

present 26.5 27.3 0.8 ± 0.4 Yes

With related children under

18 years 34.3 35.4 1.1 ± 0.5 Yes

Universe:

Individuals 272,700,000 272,500,000

Individuals in poverty 12.2 12.2 0.0 ± 0.2 No

18 years and over 10.7 10.5 -0.2 ± 0.1 Yes

65 years and over 9.5 9.7 0.2 ± 0.2 Yes

Related children under

18 years 16.1 16.8 0.7 ± 0.3 Yes

Related children 5 to 17 years 15.4 15.9 0.5 ± 0.3 Yes

Unrelated individuals 15 years

and over 21.8 21.5 -0.4 ± 0.3 Yes

KEY: The univers es have been round ed to the n earest 100,000 an d all estima tes are rou nded to one de cimal place. 

A value of 0.0 indicates an estimate is less than 0.05.

5.4.3 County-Level Comparisons

Sub-national data were analyzed to determine if the national findings held at lower levels of

geography. Figure 4a shows the median household income for 18 counties. Note that the scale

starts at 25,000 dollars, not zero. This graph shows that the national finding is also seen when

we look at selected county-level data. There were statistically significant differences in 12 of the

18 counties and in all cases the C2SS estimate of median household income was significantly

lower than the Census 2000 Sample estimate. 

Figure 4b shows the percent of female-householder families with related children under 18 years

in poverty for the 18 counties. Note that the scale starts at 10 percent, not zero. At the national

level, the C2SS reported 1.1 percentage points more of these families in poverty than did the

Census 2000 Sample. However, we see more variation when looking at the selected

county-level data. While the differences are only statistically different for five of the

18 counties, these percentage point differences are large. In Tulare, CA, Douglas, NE, Lake, IL,

and Franklin, OH, the C2SS estimated a higher percent in poverty (6.9, 4.6, 6.1, and
35

Figure 4a. Median Household Income

Census 2000 Sample and C2SS County-Level Estimates

4.0 percentage points differences respectively) than did the Census 2000 Sample. In Sevier, TN

the Census 2000 estimate was almost 13 percentage points higher than the C2SS estimate. These

differences would not likely lead to different programmatic decisions.

KEY: 1. The universe is restricted to the 2000 Household Population.

2. Census 2000 Sample county-level estimates are shown as circles; C2SS county-level estimates are shown as triangles.

3. Whenever the difference between the two estimates was determined to be statistically significant, the symbols for both the

Census 2000 Sample and the C2SS estimates are bolded.
36

Figure 4b. Percent of Female-Householder Families with Related Children under 18 in Poverty

Census 2000 Sample and C2SS County-Level Estimates

KEY: 1. The universe is restricted to the 2000 Household Population.

2. Census 2000 Sample county-level estimates are shown as circles; C2SS county-level estimates are shown as triangles.

3. Whenever the difference between the two estimates was determined to be statistically significant, the symbols for both the

Census 2000 Sample and the C2SS estimates are bolded.

Appendix E, Tables 4a-4c, display the comparable county-level data for income. As the data

show, the county-level comparisons tell the same story as the national-level comparisons. There

are few substantive differences but when they exist, the C2SS estimate for income is slightly

lower than the Census 2000 Sample estimate for these selected counties. 

Table 4d in Appendix E shows the comparable county-level data for poverty for the family-level

statistics only. The national-level differences are small but statistically significant. When we

look at the county-level data, we see that there are no differences between the C2SS and

Census 2000 poverty estimates in half of the counties analyzed. But, the differences that do

exist are larger than we’ve seen when looking at the other items. For example, when we look at

the percent of families with female-householders, differences are statistically different for 7 of

the 18 counties and these differences are large. In Tulare, CA, Douglas, NE, Lake, IL, and

Franklin, OH, the C2SS estimated a higher percent in poverty (6.0, 4.8, 4.7, and 3.1 percentage
37

points, respectively) than did the Census 2000 Sample. In Sevier, TN, Madison, MS, and

Broward, FL, the Census 2000 Sample estimate was higher than the C2SS estimate.

5.4.4 Analysis

Official national estimates of income and poverty come from the Annual Social and Economic

Supplement of the CPS. The 1999 CPS estimates for median household income was $40,816

and $49,940 for families (U.S. Department of Commerce, 2000). These estimates fall in

between the Census 2000 Sample and C2SS estimates produced for this analysis. The C2SS

estimates of median household income were generally lower than Census 2000 Sample estimates

at the national level and these estimates matched up more closely with the CPS income estimates

(Posey, Welniak, and Nelson, 2003). This could be a result of differences in the timing of data

collection for the two surveys discussed earlier. Since ACS interviews in calendar year 2000 had

income reference periods ranging from January through December 1999 (for those interviewed

in January 2000) to December 1999 through November 2000 (for those interviewed in

December 2000), the midpoint of this period would be December 15, 1999 compared with

July 1, 1999 for the Census 2000 Sample. Thus, the C2SS estimates may more accurately reflect

the economic change over 2000 that could not have been captured by Census 2000. 

Posey, Welniak, and Nelson (2003), when comparing the Census 2000 Sample and C2SS income

estimates identify the reference period, the Census 2000 data capture methodology,

inflation-adjustment procedures, and allocation as possible reasons for the differences in the

C2SS and the Census 2000 Sample income estimates. In addition, a fundamental difference

between the two surveys was the method used for data capture. In Census 2000, optical

character recognition, or OCR, was used to interpret the write-in entries. Edits were added for

Census 2000 to ensure that OCR interpreted handwritten income entries accurately but errors

may still exist. All C2SS write-in responses were keyed which also has error. 

These analysts recommended looking at differences in allocation rates as a potential source of

C2SS/Census 2000 Sample differences. Both surveys had high income allocation rates–around

30 percent in the Census 2000 Sample compared with 24 percent in the C2SS (Posey, Welniak,

and Nelson, 2003). Appendix A, Tables 6a through 6h provides the item allocation rates for

each C2SS and Census 2000 Sample income item. As these tables show, the item allocation

rates are especially high and problematic for income data collected during nonresponse followup

operations. Using our yardstick, this amount of item imputation may affect the final Census

2000 Sample and C2SS estimates.

Another possible explanation for the differences in the distributions seen is that the C2SS may in

part reflect true economic change over 2000 that could not have been captured by Census 2000. 

This is speculation on our part and additional research is necessary to better understand these

differences; although this is difficult given that these are both surveys and we do not have a

measurement of “truth” for comparison. 

Poverty statistics are derived from income data. Given the lower estimates of income in the

C2SS, the increase in family-level poverty estimates seen in the C2SS seem reasonable. The
38

individual poverty estimates were the same. These results concur with additional findings from

Census 2000 that showed that the number of families in poverty declined when compared with

1989 (Bishaw and Iceland, 2003). One possible explanation for differences may be the

difference in the timing of data collection which has already been discussed at length in this

report. 

6. CONCLUSIONS

To understand the effect of replacing the decennial census sample data collection with the ACS,

we would ideally compare ACS estimates, as measured by the C2SS in 2000, to measures of

truth. However, since this was not possible, we compared the C2SS with the Census 2000

Sample distributions for the nation and a selected group of counties for several economic items. 

The results of this analysis lead us to conclude that the C2SS national-level distributions of the

selected economic characteristics examined in this report were fundamentally the same as those

produced from the Census 2000 Sample. Published C2SS data were very consistent with the

Census 2000 Sample results and a review of selected county-level data confirmed these findings. 

Less than 10 percent of the differences were deemed to be substantive at the county level. Given

these results, data users would likely come to similar conclusions and therefore would be likely

to implement support programs and allocate funds in a similar fashion, regardless of whether

they used the Census 2000 Sample or the C2SS data. For example, if a program needed to

allocate funds to support low-income families for these selected counties, planners would make

similar decisions whether they used the Census 2000 Sample or the C2SS data. 

Some differences were noted and this report concludes that the key methodological difference

between the Census 2000 Sample and the C2SS that affects these selected economic items is the

difference in the reference periods that results from the differences in the timing of data

collection for the two surveys. As this report discusses, these differences particularly affect the

employment status and the income and poverty estimates. Over three-fourths of the substantive

national-level differences are for these items. 

The sequence of the Occupation, Industry, and Class of Worker questions differed between the

C2SS and Census 2000 Sample as was discussed in Section 5.3 of this report. Census Bureau

analysts recommend conducting additional testing of the sequence of these questions to

determine the optimal sequence to use in the ACS. 

This report is a first step at understanding the differences in Census 2000 Sample and C2SS

estimates for these selected economic items. As mentioned, at the national level and for the

selected sub-national analysis done, the results are promising. Additional research is

recommended to further our understanding of current ACS methods. Given that we find few

substantive differences between the Census 2000 Sample and the C2SS at either the national or

county level, additional analysis should focus on developing a further understanding of all

methodological aspects of the ACS including such things as the effect of using multiple modes
39

for data collection. Similarly, data should be examined by sex, age, race, Hispanic origin, and

other key demographic items. 

7. REFERENCES

Biemer, Paul, Robert Groves, Lars Lyberg, Nancy Mathiowetz, and Seymour Sudman. (Eds)

1991. Measurement Error In Surveys. New York: John Wiley & Sons.

Bishaw, Alemayehu and John Iceland. 2004. “Census 2000 Brief: Poverty:1999.” May. 

Bureau of Labor Statistics. 2003. http://www.bls.gov/cps/prev_yrs.htm.

Bureau of Labor Statistics. 2000. “THE EMPLOYMENT SITUATION: DECEMBER 2000--

Technical information: USDL 01-02 Household data.” released Friday, January 5.

Griffin, Deborah, Susan Love, and Sally Obenski. 2003. “Can the American Community Survey

Replace the Census Long Form?” Paper presented to the annual meeting of the American

Association for Public Opinion Research, Nashville.

Lessler, Judith. 1984. “Measurement Errors in Surveys,” in C.F. Turner, and E. Martin (eds.), 

Surveying Subjective Phenomena, Vol. 2, pp. 405-440.

Love, Susan and Deborah Griffin. 2003. “A Closer Look at the Quality of Small Area Estimates

from the American Community Survey.” Paper presented to the annual meeting of the

American Statistical Association, San Francisco.

Love, Susan. 2004. “Comparison of Item Allocation Levels for Comparable Census 2000

Sample and Census 2000 Supplementary Survey Items, by Data Collection Mode.”

Internal Census Bureau Memorandum dated February 5.

Moul, Darlene. 2002. “Census 2000 Evaluation H-5.” Washington, DC, U.S. Bureau of the

Census, unpublished report.

Navarro, Alfredo. 2002. “Coverage in the 2001 American Community Survey and 2001

Supplementary Survey,” Internal memorandum for A. Tupek dated October 30.

Posey, Kirby, Edward Welniak, and Charles Nelson. 2003. “Income in the American

Community Survey: Comparisons to Census 2000.” Paper presented to the annual

meeting of the American Statistical Association, San Francisco.

Salvo, Joseph, Peter Lobo and Timothy Calabrese. 2004. “Replacing the Census Long Form

with the American Community Survey: An Evaluation of ACS Estimates for

Neighborhoods in the Bronx Test Site”. Paper presented at the annual meeting of the

Population Association of America, Boston.
40

Schindler, Eric, Richard Griffin, and Carolyn Swan. 1992. “Weighting the 1990 Census

Sample.” Paper presented to the annual meeting of the American Statistical Association. 

Schneider, Paula. 2004. “Census 2000 Topic Report Number 12: Content and Data Quality in

Census 2000.” http://www.census.gov/pred/www/rpts/TR12.pdf. January 29.

Stackhouse, Hubert and Sarah Brady. 2003. “Census 2000 Mail Return Rates.” Census 2000

Evaluation A.7.B dated January 30.

Starsinic, Michael and Keith Albright. 2002. “Coverage and Completeness in the Census 2000

Supplementary Survey.” Paper presented to the annual meeting of the American

Statistical Association, New York..

U.S. Census Bureau. 2004. Meeting 21st Century Demographic Data Needs - Implementing the

American Community Survey Report 4: Comparing General Demographic and Housing 

Characteristics to Census 2000. May.

U.S. Census Bureau. 2002. Meeting 21st Century Demographic Data Needs - Implementing the

American Community Survey Report 2: Demonstrating Survey Quality. May. 

U.S. Census Bureau. 2001. Meeting 21st Century Demographic Data Needs - Implementing the

American Community Survey Report 1: Demonstrating Operational Feasibility. July. 

U.S. Census Bureau. 2000. “Accuracy of the Data.” 

http://www.census.gov/acs/www/downloads/ACS/Accuracy00.html

U.S. Census Bureau. 1997. “Census Brief: Children with single parents–how they fare,”

September. 

U.S. Census Bureau. 1998. “Census Brief: Increase in at-home workers reversed earlier trend,”

March. 

U.S. Department of Commerce. 2000. Money Income in the United States 1999: Current

Population Reports, Consumer Income, P60-209. Issued September.
A-1

Appendix A. Item Allocation Rates by Data Collection Mode

Section 1. Employment Status

Table 1. Employment Status, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 11.1 6.0

 Mail* 10.2 8.2

 Interviewer 9.2 2.6

 CATI NA 2.5

 CAPI NA 2.6

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Section 2. Commuting to Work

Table 2a. Means of Transportation to Work, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 7.6 4.6

 Mail* 5.4 5.5

 Interviewer 13.0 3.3

 CATI NA 2.9

 CAPI NA 3.4

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 2b. Carpool Size, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 10.0 5.8

 Mail* 8.4 7.0

 Interviewer 14.1 4.0

 CATI NA 3.6

 CAPI NA 4.1

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.
A-2

Appendix A. Item Allocation Rates by Data Collection Mode

Section 3. Occupation, Industry, and Class of Worker

Table 3a. Occupation, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 14.9 9.5

 Mail* 13.2 11.1

 Interviewer 19.2 7.1

 CATI NA 4.8

 CAPI NA 7.8

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 3b. Industry, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 14.9 9.4

 Mail* 13.3 11.1

 Interviewer 18.7 7.0

 CATI NA 4.5

 CAPI NA 7.7

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 3c. Class of Worker, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 17.0 8.3

 Mail* 16.0 10.4

 Interviewer 19.5 5.2

 CATI NA 4.0

 CAPI NA 5.5

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.
A-3

Appendix A. Item Allocation Rates by Data Collection Mode

Section 4. Income

Table 4a. Wages and Salary Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 20.0 16.4

 Mail* 15.0 13.0

 Interviewer 32.6 21.4

 CATI NA 18.5

 CAPI NA 22.2

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 4b. Self-Employment Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 9.9 6.3

 Mail* 8.0 7.3

 Interviewer 14.5 4.9

 CATI NA 4.4

 CAPI NA 5.0

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 4c. Interest, Net Rental Income, etc, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 20.8 13.3

 Mail* 19.1 15.5

 Interviewer 25.1 9.8

 CATI NA 10.9

 CAPI NA 9.5

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.
A-4

Appendix A. Item Allocation Rates by Data Collection Mode

Table 4d. Other Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 18.3 10.6

 Mail* 16.4 13.9

 Interviewer 23.2 5.6

 CATI NA 5.0

 CAPI NA 5.8

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 4e. Social Security or Railroad Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 8.7 11.7

 Mail* 7.0 14.3

 Interviewer 13.0 7.7

 CATI NA 7.9

 CAPI NA 7.6

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 4f. Supplemental Security Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 19.0 10.2

 Mail* 17.4 13.5 

 Interviewer 23.0 5.2

 CATI NA 4.4

 CAPI NA 5.5

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.
A-5

Appendix A. Item Allocation Rates by Data Collection Mode

Table 4g. Public Assistance Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 18.2 10.5

 Mail* 16.4 14.0

 Interviewer 22.7 5.3

 CATI NA 4.5

 CAPI NA 5.6

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.

Table 4h. Retirement Income, Allocation Rates by Data Collection Mode

Mode

Census 2000 Sample

(in percent)

C2SS

(in percent)

All 18.8 11.0

 Mail* 17.0 14.2

 Interviewer 23.2 6.2

 CATI NA 5.8

 CAPI NA 6.3

KEY: *C2SS m ail includes improvements due to content follow-up.

CATI - Computer Assisted Telephone Interviewing

CAPI - Computer Assisted Personal Visit Interviewing

NA - Not applicable

Values less than 0.05 appear as 0.0.
B-1

Figure 1a. C2SS M ail Form

Figure 1a. C2SS M ail Form

Figure 1a. C2SS M ail Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Employment Status Questions
B-2

Figure 1b. C2SS CATI/CAPI Instrument

Figure 1b. C2SS CATI/CAPI Instrument

Figure 1b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Employment Status Questions
B-3

Figure 1b. C2SS CATI/CAPI Instrument

Figure 1c. Cen sus 200 0 Mail Sample Form

Figure 1c. Cen sus 200 0 Mail Sample Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Employment Status Questions
B-4

Figure 1d. Census 20 00 Interviewer Sam ple Form

Figure 1d. Census 20 00 Interviewer Sam ple Form

Figure 1d. Census 20 00 Interviewer Sam ple Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Employment Status Questions
B-5

Figure 2a. C2SS M ail Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Commuting to Work Questions
B-6

Figure 2b. C2SS CATI/CAPI Instrument

Figure 2b. C2SS CATI/CAPI Instrument

Figure 2b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Commuting to Work Questions
B-7

Figure 2c. Cen sus 200 0 Mail Sample Form Figure 2c. Cen sus 200 0 Mail Sample Form

Figure 2d. Census 20 00 Interviewer Sam ple Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Commuting to Work Questions
B-8

Figure 3a. C2SS M ail Form

Figure 3a. C2SS M ail Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Occupation, Industry, and Class of Worker Questions
B-9

Figure 3b. C2SS CATI/CAPI Instrument

Figure 3b. C2SS CATI/CAPI Instrument

Figure 3b. C2SS CATI/CAPI Instrument

Figure 3b. C2SS CATI/CAPI Instrument

Figure 3b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Occupation, Industry, and Class of Worker Questions
B-10

Figure 3b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Occupation, Industry, and Class of Worker Questions
B-11

Figure 3c. Cen sus 200 0 Mail Sample Form

Figure 3c. Cen sus 200 0 Mail Sample Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Occupation, Industry, and Class of Worker Questions
B-12

Figure 3d. Census 20 00 Interviewer Sam ple Form

Figure 3d. Census 20 00 Interviewer Sam ple Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Occupation, Industry, and Class of Worker Questions
B-13

Figure 4a. C2SS M ail Form

Figure 4a. C2SS M ail Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Income (In 1999 Inflation-adjusted) Dollars Questions
B-14

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Income (In 1999 Inflation-adjusted) Dollars Questions
B-15

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Figure 4b. C2SS CATI/CAPI Instrument

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Income (In 1999 Inflation-adjusted) Dollars Questions
B-16

Figure 4c. Cen sus 200 0 Mail Sample Form Figure 4c. Cen sus 200 0 Mail Sample Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Income (In 1999 Inflation-adjusted) Dollars Questions
B-17

Figure 4d. Census 20 00 Interviewer Sam ple Form Figure 4d. Census 20 00 Interviewer Sam ple Form

Appendix B. Facsimiles of C2SS and Census 2000 Sample Questions

Income (In 1999 Inflation-adjusted) Dollars Questions
Appendix C. Federal Uses of Selected Economic Characteristics 

C-1

Selected Economic Items Federal Uses of the Data

Employment Status # Used by the Department of Justice to comply with

provisions of the Civil Rights Act to determine the

availability of individuals for work

# Helps the Department of Labor to plan Federal farm

worker programs and to construct the Consumer Price

Index

# Needed to provide reliable data to determine the

employment resources available when considering

government programs that provide Federal assistance to

areas 

# Needed to characterize workers by full-time/part-time

and full-year/part-year status

# Used to collect accurate income data by defining the

universe of persons who should have earnings as part of

their total income 

Commuting to Work # Needed to plan highway improvements, develop public

transportation services, design programs to ease traffic

problems, conserve energy, and reduce pollution

# Needed to develop standards to reduce work-related

vehicle trips and increase passenger occupancy during

peak periods of travel

# Used to forecast vehicle miles of travel in areas that are

designated “non-attainment” with respect to ambient air

quality standards under the Clean Air Act Amendments

of 1990

# Used (place of work) by the Office of Management and

Budget, under the authority of the Paperwork Reduction

Act, as part of the criteria to define metropolitan areas
Appendix C. Federal Uses of Selected Economic Characteristics 

Selected Economic Items Federal Uses of the Data

C-2

Occupation, Industry, and

Class of Worker

# Used to formulate policy and programs for employment,

career development, and training

# Needed to measure compliance with anti-discrimination

policies

# Used in analysis for mandated Congressional reports on

the labor force

# Used by the National Center for Health Statistics to

compute vital statistics

# Required by the Bureau of Economic Analysis to develop

its state per capita income estimates which are used in the

allocation formulas or eligibility criteria of more than 20

Federal programs such as Medicaid

# Used by the Equal Employment Opportunity

Commission, under the Civil Rights and Equal Pay Acts,

in litigation where employment discrimination is alleged

# Helps the Environmental Protection Agency, under the

Toxic Substances Control Act, to identify occupations

and industries that expose people to harmful chemicals

and that adversely affect the environment

Income # Provides a vital measure of general economic

circumstances

# Used to determine poverty status

# Used to assess the need for various types of assistance

# Included in Federal allocation formulas for nearly all

governmental areas
7

Persons per square kilometer 

D-1

Appendix D. Characteristics of Counties Used in Sub-National Comparisons

These counties represent areas with populations that meet the 65,000 minimum required for

yearly data-release. They are a diverse set of areas that were chosen to be test sites because they

vary geographically and demographically. They reflect both urban and rural areas and range in

household population size from 70,533 in Sevier, TN to over 1.6 million in Broward, FL. 

Population density also varies from 20 persons per square kilometer in Yakima, WA to nearly

12,000 persons per square kilometers in Bronx, NY. 

The table below summarizes geographic, demographic, and economic characteristics for the

18 counties used in this report. Geographic data are based on Census 2000 counts. The

remaining data are based on the 2001 ACS. As the data show, between 57 and 72 percent of the

population 16 and older are in the labor force in these sites. For most of these sites, public

transportation is not the primary means used to get to work. The exceptions are Bronx, NY and

San Francisco, CA. The median household income ranges from $27,000 in Bronx, NY to

$71,000 in Calvert, MD. The percent of related children ages 5-18 in poverty ranges from

3 percent in Calvert, MD to 41 percent in Bronx, NY. 

AC S Te st Site Squa re

Kilometers

Census

2000 

Househo ld

Population

Den sity7 Percent of

population

16+ in labor

force 

Percent of labor 

forc e using public

transportation to

get to work

Median household

income (in nearest

thousand dollars)

Percen t of 

related 

child ren in

Pov erty

Sevier TN 1534 70533 46 68 0 35,000 19

M ad is on , MS 1863 72615 39 70 1 47,000 15

C alvert MD 557 73982 133 72 2 71,000 3

Jefferson, AR 2292 78989 34 63 1 38,000 31

Black Hawk IA 1470 121535 83 68 1 36,000 17

Schuylkill PA 2017 143110 71 61 1 34,000 17

Y akim a WA 11127 218844 20 66 0 33,000 29

Rockland NY 451 279104 619 67 10 69,000 12

Tulare CA 12495 361980 29 62 0 32,000 33

H ampden MA 1602 441799 276 66 2 40,000 21

Douglas NE 857 451878 527 72 1 43,000 12

Lake, IL 1160 623378 538 71 5 69,000 8

Multnomah OR 1127 643798 571 71 12 41,000 17

San Francisco CA 121 756976 6258 69 30 60,000 11

Pima AZ 23794 821712 35 62 2 36,000 21

Franklin OH 1399 1046872 749 72 3 44,000 15

Bronx NY 109 1285415 11793 57 57 27,000 41

Broward FL 3131 1603094 512 64 2 41,000 15
E-1

Appendix E. Summary of County-Level Differences

The difference between the C2SS and Census 2000 Sample estimates was determined (C2SS minus Census 2000 Sample) and only

the statistically significant differences are displayed in these tables. A positive value indicates the C2SS estimate was greater than

the Census 2000 Sample estimate. A negative value indicates that the C2SS had a lower estimate for this characteristic than the

Census 2000 Sample.

Table 1. Employment Status, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample) 

Household Population 16 years and over Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

In labor force ..................................................... 4.0 2.9 1.1 2.0 1.1 4.0 1.4

Civilian labor force ........................................ 4.1 2.8 1.1 2.0 1.3 4.0 1.4

Employed .................................................. 2.5 3.0 -1.6 3.3 4.2 1.6 1.6 0.8 5.3 1.3

Unem ployed .............................................. -1.9 0.9 1.2 0.8 -3.4 -0.5 0.6 0.4 0.6 0.3 -1.3

Armed Forces ................................................ -0.2

Not in labor force .............................................. -4.0 -2.9 -1.1 -2.0 -1.1 -4.0 -1.4

Females 16 years and over

Civilian labor force ........................................ 5.2 -2.3 2.7 -2.2 1.5 1.1 2.9 1.5

Employed .................................................. 4.5 4.3 -2.6 4.0 -2.2 3.6 1.3 3.8 1.4

Ow n children und er 6 ye ars: 

 All parents in family in labor force ................... -9.2 11.5 5.7 6.1 6.4 4.6 3.8
E-2

Appendix E. Summary of County-Level Differences

Table 2. Commuting to Work, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Workers 16 years and over Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Car, truck, or van -- drove alone ........................ 5.2 -2.1 3.1 2.5 -1.9 -2.1

Car, truck, or van -- carpooled ........................... -4.7 -3.2 -0.8 -1.6 -1.4 -1.5 -1.8 1.3

Public transportation (including taxicab) ........... 1.0 1.5 0.8 -0.5 3.5 0.4

Walked .............................................................. -0.5

Other means ...................................................... 2.7 0.7 0.9 0.5 0.4 0.5

Worked at home ................................................ -1.4 -1.1

Table 3a. Occupation, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Employed Civilian Population 16 years 

and over Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Man agement, professional, and related

occupations .......................................................

-2.5 -1.4 -1.3

Service occupations ........................................... 3.8 2.7 0.9 1.5 1.1

Sales and office occupations .............................. 2.0

Farming, fishing, and forestry occupations ........ 0.5 2.0 1.7

Construction, extraction, and maintenance

occupations .......................................................

-1.7 -1.1 0.6 -0.9 -0.7

Production, transportation, and material

moving occupations ..........................................

-2.4 1.0 1.1 1.5
E-3

Appendix E. Summary of County-Level Differences

Table 3b. Industry, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Employed Civilian Population 16 years 

and over Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Agricu lture, forestry, fishing and hunting, 

and mining ........................................................

-0.6 -0.6 2.0 -0.1

Construction ...................................................... -1.0

Manufacturing ................................................... 2.2 1.5 0.8

Wholesale trade ................................................. 0.8 -0.5 -0.4

Retail trade ........................................................ 1.7 -1.4 0.9 0.7

Transportation and warehousing, and utilities .... -1.1

Information ....................................................... 0.8 -0.5

Fin ance, insuran ce, real estate, and rental 

and leasing ........................................................

1.2 -1.5 -1.1 0.6 1.0

Professional, s cientific, managem ent,

administrative, and waste management services

-1.0

Educational, health, and social services ............. -2.7 -2.9 -2.5 -1.9 -0.9 -1.5 1.1 -0.9 -1.8 -0.9

Arts, entertainment, recreation,

accommodation, and food services ....................

-1.8 1.0 0.9 0.7

Other services (except public administration) .... 0.5

Public administration ........................................ 0.7
E-4

Appendix E. Summary of County-Level Differences

Table 3c. Class of Worker, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Employed Civilian Population 16 years 

and over Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Private wage and salary workers ........................ 2.0 1.9 2.0

Governm ent workers ......................................... -2.3 -1.7 -0.6

Self-employed workers in own not incorporated

business ............................................................

-1.2 1.0

Unp aid family workers ...................................... -0.2 -0.2

Table 4a. Household Income, D Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Total Households Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Less than $10,000 ............................................. -2.0 1.4 2.1 0.6 1.0 1.4 1.1 0.6

$10,000 to $14,999 ........................................... 3.4 -1.0 1.6 1.6 2.8 1.2 1.1 0.7 1.6 1.0

$15,000 to $24,999 ........................................... 1.2 2.2 1.1 1.3 0.8 1.5 1.1

$25,000 to $34,999 ........................................... 2.8 -1.4 0.7

$35,000 to $49,999 ........................................... 2.2 -1.0

$50,000 to $74,999 ........................................... -2.0 -1.8 -2.2 -1.7 -1.9 -1.7 -1.8 -1.2 -1.1

$75,000 to $99,999 ........................................... -1.3 -0.9 -0.8 -0.6 -0.6

$100,000 to $149,999 ....................................... -1.2 -1.3 -2.3 -0.7 -0.5 -1.0 -0.8 -1.0 -0.7 -1.3

$150,000 to $199,999 ....................................... -0.7 -1.5 -0.4 -0.5 -0.4 -0.3

$200,0 00 or more .............................................. -0.7 -0.5 -0.5 -0.4 -0.5 -0.3 -0.5 -0.6

Med ian household income (in dollars) ............ -4,366 -2,578 -1,773 -2,395 -2,891 -2,532 -2,990 3,138 -2,268 -2,046 -2,441 -1,857
E-5

Appendix E. Summary of County-Level Differences

Table 4b. Type of Income, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Total Households Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

With Earnings ................................................... 2.1 -1.1 -1.1 -0.8

With Social Security .......................................... 1.5 1.3 -1.1 1.5 1.7

With Supplemental Security Income ................. 1.9 -1.5 -0.9 -1.0 -1.2 -0.6 -0.4 -1.1 -0.5 -0.8 -0.6

With Public Assistance Income ......................... -2.3 -1.1 -1.8 -0.5 -0.6 -1.4 -1.1 -1.0 -0.5 -1.0

With Retirement Income ................................... 1.9 -0.7

Table 4c. Family Income, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Family Households Sevier, TN

Madison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Less than $10,000 ............................................. -0.9 1.8 1.1 0.9 0.9

$10,000 to $14,999 ........................................... 2.9 2.4 -0.6 2.4 1.3 0.9 0.7 1.3 1.3

$15,000 to $24,999 ........................................... 1.7 2.3 2.4 1.7 1.0 1.1

$25,000 to $34,999 ........................................... -2.7 2.6 -0.8 0.9

$35,000 to $49,999 ........................................... 4.1 1.8 1.1 1.1

$50,000 to $74,999 ........................................... -2.9 -2.0 -1.4 -2.5 -1.9 -1.1

$75,000 to $99,999 ........................................... -1.8 -1.4 1.2 -1.3 -0.8

$100,000 to $149,999 ....................................... -1.4 -1.9 -1.2 -2.0 -0.8 -1.3 -0.7 -1.5

$150,000 to $199,999 ....................................... -0.6 -1.7 -0.6 -0.5 -0.6

$200,0 00 or more .............................................. -0.8 -0.5 -0.5 -0.8 -0.6 -0.5 -0.6 -0.7

Med ian family income (in dollars) .................... -3,376 -2,650 -2,734 -2,478 -2,717 -2,122 -903 -2,785 -2,032
E-6

Appendix E. Summary of County-Level Differences

Table 4d. Poverty, Statistically Significant Differences in County-Level Estimates (C2SS minus Census 2000 Sample)

Sevier, TNMadison, MSCalvert, M

D

Jefferson, A

R

Black Hawk, IASchuylkill, PAYakima, W

A

Rockland, N

Y

Tulare, CAHampden, M

A

Douglas, NELake, ILMultnomah, O

R

San Francisco, CAPima, AZFranklin, O

H

Bronx, N

Y

Broward, FL

Families ......................................................... -1.8 1.8 1.0 1.0 0.9

With related children under 18 years ................ -2.8 2.3 2.6 1.7 2.0 1.9

Families with female householder, no

husband present ............................................. -9.1 -8.5 6.0 4.8 4.7 3.1 -2.7

With related children under 18 years ................ -12.9 6.9 4.6 6.1 4.0