U.S. CENSUS MONITORING BOARD

Presidential Members

Final Report to Congress

September 1, 2001

U.S. Census Monitoring Board

Presidential Members

4700 Silver Hill Road

Suite 1250 – 3

Suitland, MD 20746

(301) 457-9900

www.cmbp.gov

email: comment@cmbp.census.gov
U.S. Census Monitoring

Board

4700 Silver Hill Road

Suite 1250-3

Suitland, MD 20746

PRESIDENTIAL MEMBERS

Phone: (301) 457-9900

Fax: (301) 457-9901

Gilbert F. Casellas

Co-Chair

Cruz M.Bustamante

Everett M. Ehrlich

Lorraine Green

Margarita Roque

Executive Director

September 1, 2001

The Honorable Richard Cheney

President

United States Senate

Washington, D.C. 20510

The Honorable J. Dennis Hastert

Speaker

U.S. House of Representatives

Washington, D.C. 20515

Mr. President and Mr. Speaker:

Pursuant to Public Law 105-119, the undersigned members of the U.S. Census

Monitoring Board (CMB) hereby transmit our final report to Congress.

The following report represents over three years of dedicated service to the cause of an

accurate Census 2000. It embodies not only our legacy, but also our belief that the census, as a foundation of democracy, should count every single individual in our great

nation. It is also our hope that this final report will serve as a useful roadmap for

Census 2010. 

We would like to thank President Clinton, House Democratic Leader Richard Gephardt

and Senate Majority Leader Tom Daschle for providing us with the opportunity to

serve. The Constitution of the United States provided for a decennial census for the

purpose of apportioning seats in the House of Representatives. As Presidential

Members of the Board, we were honored to have been a part of the historic process for

Census 2000. 

We also wish to acknowledge the heroes of Census 2000 – the thousands of individuals,

local governments and community organizations who tried so hard to achieve a complete count. To the degree that Census 2000 was a success, the Census 2000 partners

deserve the lion’s share of the credit. We enjoyed working with these and other census

stakeholders over the past three years through all phases of the operation. In addition,

we are very grateful for the assistance provided to us by the Commerce Department, the

Government Printing Office, many Members of Congress and their staffs, and, of

course, our own staff and expert consultants at the CMB. 

Finally, we want to thank the Bureau for providing office space to the CMB and for

accommodating our oversight efforts in Washington and in the more than 50 local

offices in all 12 census regions in the country.

Census 2000 posed many challenges, including how to use new technology to achieve

a more accurate count, how to stem the tide of non-participation, and how to adjust for

the differential undercount evident since the 1940 census. 

While our experience with the Bureau was generally good, we must note that the spirit of transparency and cooperation that marked the CMB’s relations with the Bureau

under former Acting Director James Holmes and former Director Kenneth Prewitt dissolved following the change of Administration in January of 2001. 

Our efforts to monitor census operations in accordance with our statutory mandate

were met with resistance from the Bureau. Several requests for data – to which we

were legally entitled under the CMB’s authorizing statute – were delayed or simply

ignored. As a result, we were unable to conduct additional research beyond that which

is included in this report. 

The most significant set of data delayed by the Bureau related to the Local Update 

of Census Addresses (LUCA), one of the three major initiatives the Bureau cited as

having contributed to the operational success of Census 2000. We strongly felt that

review of this data and analysis of its effectiveness would have proven invaluable. 
This final report details the operational milestones achieved in Census 2000, a set of recommendations for

future censuses, a comprehensive analysis of the 2000 Census conducted by Dr. Eugene Ericksen of Temple

University, an examination of demographic analysis by Dr. Jeffrey Passel of the Urban Institute, an in-depth

forecast of lost Federal funding as a result of the undercount provided by PricewaterhouseCoopers, a brief

overview of the Census Partnership Program and a listing of activities conducted by the CMBP. 

Since the recommendation of the Executive Steering Committee for A.C.E. Policy (ESCAP) was announced

and made official by the Secretary of Commerce, Don Evans, we have attempted to analyze the rationale

behind the decision. The ESCAP committee cited inconsistencies between the Accuracy and Coverage

Evaluation (A.C.E.) estimates and demographic analysis (DA), and the lack of time available to the committee to resolve these differences. 

Dr. Jeffrey Passel, a noted expert in the field, concluded that the Bureau’s DA estimate failed to incorporate

the best available estimates of immigration and therefore was seriously flawed. Dr. Passel said that had the

base DA been somewhat different, "there would have been more attention to the areas of agreement rather than

disagreement between the two measures and more attention to the potential deficiencies in Census 2000

itself."

Additionally, in an effort to better comprehend the decision, CMBP invited the participation of noted experts

Dr. Stephen Fienberg and Dr. Jay Kadane to join the team of Ericksen and Passel. Their findings were made

public and provided the basis for understanding the ESCAP decision. These scientists concluded that given the

results of their combined research and the information currently available from the Census Bureau, a more

accurate census would have been achieved by adjusting. 

In conclusion, despite being unable to access and analyze data withheld by the Bureau, we are nonetheless

proud of what we were able to accomplish. In particular, CMBP played a leading role in the discovery, analysis, documentation, and publication of some of the most critical census issues, including: 

• The social, political and economic effects of a census undercount; 

• The release of estimates of the 2000 Census undercount for all 50 states and many of the 

country’s counties; 

• The discovery of the larger-than-expected immigrant population and; 

• The vast number of errors, imputations and potential duplications in Census 2000. 

Our hope is that the volume of scientific research provided as a result of this initiative will be utilized to gain

a better understanding of Census 2000. 

And finally, we would be remiss if we did not reaffirm our belief that overall, the Census Bureau’s workforce

is among the most skilled, dedicated, and nonpartisan in all of government. They perform a tremendous and

essential public service, not just every 10 years, but year in and year out. 

We are confident that the legacy of the Board will provide Congress and the nation with a valuable perspective leading into Census 2010.

Respectfully submitted, 

Gilbert F. Casellas

Co-Chair,Presidential Members 

Cruz M. Bustamante

Presidential Member

Everett M. Ehrlich

Presidential Member

Lorraine A. Green

Presidential Member
TABLE OF CONTENTS

1. Census 2000 and Census Monitoring Board Milestones . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Recommendations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3. An Evaluation of the 2000 Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Table 1. Comparison of Direct and Synthetic Dual Systems Estimates for 1990 in 

Manhattan and Ulster County, New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Table 2. Comparisons of Undercount Rates and Differences, 1990 and 2000 Census . . . 29

Table 3. Net Undercount Rates for Post-Strata Groups . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Table 4. Comparison of A.C.E. Synthetic Estimates of Undercount of States . . . . . . . . . 32

Table 5. Comparison of Non-Hispanic Black and Non-Hispanic White Rates of 

Undercount in Large and Small Metropolitan Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Table 6. Comparison of Hispanic and Non-Hispanic White Rates of Undercount in 

Large and Small Metropolitan Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Table 7. Rates of Net Undercount for Counties Sorted by State and Region . . . . . . . . . . 38

Table 8. Projected and Actual Growth in Large Metropolitan Counties, 1999-2000. . . . . 40

Table 9. Effects of Poverty and Racial Composition Estimated Rates on Undercount 

for Census Tracts — 1990 Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Table 10. Rates of Undercount and Other Indicators of Census Error 

by Post-Stratum Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Appendix A. Estimated Rates of Net Undercount and Number of Persons Missed 

by County Sorted by Selected State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4. Demographic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Table 1. Demographic Analysis Estimates of Percent Net Census Undercount, 

by Race, for the United States: 1940–1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Table 2. Population and Percent Net Census Undercount, by Race According to 

Alternative Measures, for the United States: 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Table 3. Components of Estimates of Undocumented Immigrant Population 

Based on Different Data Sources, for All Undocumented Immigrants and 

Those Born in Mexico: 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Table 4. Alternative Estimates of Change in the Undocumented Population,

for the United States: 1990 to 2000 (in thousands). . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Table 5. Suggested Modifications to “Base” Demographic Analysis Estimate, A.C.E., 

and Census 2000, by Sex and Race/Ethnicity (in millions). . . . . . . . . . . . . . . . . . . . . 109

Table 6. Amount and Percent Net Census Undercount based on “Base” 

Demographic Analysis, A.C.E., and Modified Demographic Analysis, 

by Sex and Race/Ethnicity: Census 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5. Effect of Census 2000 Undercount on Federal Funding To 

States and Selected Counties, 2002-2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Appendix A. 2000 Population Counts and Estimated Undercounts of Persons 

Over and Under 18 Years of Age by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Appendix B. 2000 Population Undercount by Selected County . . . . . . . . . . . . . . . . . . . 134

Appendix C. Federal Program Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Appendix D. Estimated Funding Effect by State Program . . . . . . . . . . . . . . . . . . . . . . . 142

Appendix E. Estimated Funding Effect by Selected County . . . . . . . . . . . . . . . . . . . . . 151
6. Census 2000 Partnership Program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Best Practices in Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Best Practices in California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

7. CMB-P Activities, 1998-2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

CONTENTS (continued)
Introduction

Planning for a census begins long before the actual census day or even census year. Each planning

cycle brings about its own set of challenges. Census 2000 was confronted by a persistent undercount

of minorities and the poor, a number which had increased in the 1990 census. Additionally, 1990 was

the first census since 1940 that had not improved coverage over the previous census. The data

revealed that of the 4 million net undercount, 4.4 percent of Blacks, 5 percent of Hispanics, 2.3 percent of Asians and Pacific Islanders, and 4.5 percent of American Indians were missed compared to

0.7 percent of non-Hispanic whites. 

The 1990 results generated several lawsuits, the formation of committees, research studies and reports

on how best to remedy the persistent differential undercount. After due consideration of all the findings presented, Dr. Barbara Bryant, Director of the Census Bureau, along with the majority of the

Bureau’s Undercount Steering Committee, who had assessed the accuracy of the data, recommended

that the 1990 census be statistically adjusted. The decision was, however, overruled on July 15, 1991,

by Secretary of Commerce Robert Mosbacher. 

In light of the 1990 census controversy, a broad range of stakeholders, including those in scientific

professions, the Bureau and Congress agreed that review and modification to the existing methodology was needed. The Decennial Census Improvement Act of 1991 required the Bureau to contract

with the National Academy of Sciences (NAS) to study ways in which to achieve a more accurate

count. Additionally, the Address List Improvement Act, enabled the Census Bureau to form partnerships with state, local, and Tribal governments to develop and update address lists to assist with identifying where people could be found. 

Numerous panels and advisory committees were organized including: The Task Force for Planning for

the Year 2000 Census and Census-Related Activities for 2000-2009, the NAS Panel to Evaluate

Alternative Census Methods, and The Panel on Census Requirements in the Year 2000 and Beyond.

By the mid 1990s it was evident that the Bureau and the NAS favored a 2000 census design that

included greater reliance on statistical methods. NAS, the General Accounting Office, and the

Commerce Department’s Office of the Inspector General, along with other stakeholders ultimately

recommended that the Bureau incorporate dual system estimation methods to achieve high quality statistical correction. 

The following are milestones in the Census 2000 operation and the history of the Census Monitoring

Board (CMB). 

Final Report to Congress

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SECTION 1

CENSUS 2000 AND CENSUS

MONITORING BOARD MILESTONES
1997 

Congress Addresses Funding for Statistical Adjustment and 

Establishes the Census Monitoring Board

The Census Bureau unveiled its Census 2000 strategy in February 1996, affirming the recommendations of the various groups and including plans for sampling. However, the plan generated heated congressional debate centered primarily on the issue of sampling. As the debate intensified, it consequently spilled onto the FY 1998 appropriation bills. The discussion broke primarily along partisan

lines, with Republicans opposing sampling as a method of increasing the rolls of Democratic voters,

and Democrats wanting to ensure that the growing net undercount from previous censuses be eliminated. In November of 1997 Congress reached a compromise with the passage of Public Law 105-

119. The law allowed the Census Bureau to design a two-track plan, one track was to include sampling, the other would not, which ultimately affected the 1998 Dress Rehearsal. It authorized

Congress to challenge the census plan in court, provided for expedited judicial review of any legal

challenges to the use of sampling in the census, setting the stage for the January 1999 Supreme Court

decision, and established the Census Monitoring Board as an oversight body to observe and monitor

all aspects of the preparation and implementation of the 2000 decennial census.

1998 

Census "Dress Rehearsal"

In 1998, the Census Bureau began testing its proposed 2000 plan in three sites: Columbia, South

Carolina (mainly rural); Sacramento, California (mainly urban); and Menominee, Wisconsin (an

American Indian reservation). The plan included a pre-census promotional campaign before mailing

out the forms to households; the non-response follow up; a new program called Sampling for NonResponse Follow-up, and the traditional post-enumeration statistical survey to assess accuracy. This

process would apply the results of the non-response follow up to the final ten percent of the non-respondent population. The rehearsal took place in as near a census-like environment as possible. 

Census Monitoring Board Organizes

In June 1998, the Census Monitoring Board was organized and held its first meeting in Washington,

D.C. By statute, the Board was divided evenly along partisan lines, with four members appointed by

President Bill Clinton and four by the Republican leaders in Congress:

◆ Presidentially-appointed members included Tony Coelho (co-chair), Gilbert F. Casellas, Dr.

Everett M. Ehrlich, and Lorraine A. Green; and 

◆ Congressionally-appointed members included J. Kenneth Blackwell (co-chair), Dr. David M.

Murray, A. Mark Neuman, and Joe D. Whitley. 

The authorizing statute gave CMB the responsibility for monitoring "all aspects" of Census 2000,

including operational planning, implementation, and post-censal analysis. The law mandated that

CMB report to Congress at least once every six months on its findings and issue a Final Report by

September 1, 2001, before ceasing operations on September 30, 2001. 

Congress appropriated $4 million for the Board for its first year – with the money to be shared equally between CMB-Presidential and CMB-Congressional. Each year thereafter, Congress appropriated

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Final Report to Congress
$3.5 million, also to be shared equally between the two groups. The Board was designed to operate

as two Boards within one, each with separate Executive Directors and staff. 

At its organizing meeting, the Board agreed that an affirmative vote of at least five members of the

Board would be required to issue any report or recommendation in the Board’s name. 

In July 1998, the Board held its first hearing at Census Headquarters in Suitland, Maryland. Acting

Census Bureau Director, James Holmes, testified and provided a detailed briefing on the Bureau’s

"dual track" preparations for Census 2000. One track was designed to include sampling while the

other would not. 

1999

Supreme Court Rules on Statistical Sampling

The discussion surrounding the use of sampled data intensified over time. It ultimately came to a head

when the Bureau announced their plan to use two forms of statistical sampling in the 2000 Decennial

Census in an effort to address the issue of the undercount. Two major lawsuits were filed in opposition, which were eventually incorporated into one, the Department of Commerce et al. v. United States

House of Representatives et al. The suit challenged the legality and constitutionality of using sampled data for the purposes of reapportionment. The U.S. Supreme Court heard the case and in

January, in a narrow 5 to 4 majority, the court ruled that current law prohibited the use of sampling

methods in deriving the state population totals used for reapportionment of the House of

Representatives. Writing for the majority, Justice Sandra Day O’Connor stated that the 1976 amendments to Title 13 U.S.C. (Census Act) changed the provision in law from one that "permitted" the use

of sampling for purposes other than apportionment into one that "required" that sampling be used for

such purposes if "feasible."

Following the decision, the Bureau issued a revised Census 2000 plan that included expanded efforts

to count the population directly for apportionment data and a smaller post enumeration survey to

measure the accuracy of that count. It also requested additional funds ($1.7 billion) to handle the

new operation. The post-enumeration survey, this time called the Accuracy and Coverage Evaluation

(A.C.E.), was designed to survey 300,000 households over 11,000 blocks or block-clusters around

the country.

This, however, did not lessen the debate on the issue. Opponents as well as supporters of sampling

disagreed regarding whether the Court’s interpretation of the Census Act allowed the use of sampling

methods to produce numbers to be used for redistricting. 

CMBP Report to Congress

On February 1, the CMBP issued its first report to Congress in which it provided information on the

six bipartisan public hearings held and discussed the results of the three dress rehearsals. Some conclusions reached were that no matter how sophisticated and well funded a traditional census might be,

it will still result in a substantial undercount of minorities and children, and only a timely PostEnumeration Survey can substantially reduce the differential undercount. The report also endorsed

the Local Update of Census Addresses (LUCA) program as a superior method for ensuring local

review and urged Congress to extend grants to state and local governments to develop standardized

address lists.

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Final Report to Congress
CMB Issues its First Joint Report

On April 1, the CMB issued its first joint report to Congress. The Report dealt with field office

staffing, complete count committees, partnership specialists, questionnaire assistance centers, the "Be

Counted" program, questionnaires and language assistance, and targeting how best to reach the hardto-enumerate population.

Casellas Replaces Coelho as CMB Co-Chair

In May, Co-Chair Tony Coelho stepped down from the Board and was replaced by CMBP member

Gilbert F. Casellas. California Lt. Governor Cruz M. Bustamante is later appointed by President

Clinton to fill the fourth slot. 

CMBP Report on State and Local Operational Plans 

On June 9, the Presidential Members provided Congress with a compilation of responses from state

and local officials on the Census Bureau’s operational plans for 2000. The information represented

strong bipartisan support for a full and accurate census utilizing modern statistical methods to adjust

for the undercount.

Second CMB Report Focuses on Advertising Campaign

The CMB’s second bipartisan report to Congress, issued October 1, focused on the $167 million paid

advertising campaign and "enthusiastically" endorsed the Bureau’s efforts. This marked the first time

that such a program had been instituted. The objectives outlined by Young & Rubicam, the agency

contracted to conduct the campaign, were:

◆ to raise public awareness about Census 2000; 

◆ to motivate Americans to return their census forms; 

◆ to create an environment that would facilitate the work of the enumerators during the nonresponse follow-up phase. 

The scope of the campaign was appropriately comprehensive for a decennial census and covered

diverse populations. 

CMBP Releases Public Attitudes Survey Results 

On October 26, CMBP published the results of a national nonpartisan opinion survey measuring

public attitudes toward the census and tested the effectiveness of the messages contained in the

Census Bureau’s advertising campaign. Belden Russonello & Stewart in collaboration with

Research/Strategy/Management, conducted a non-partisan survey of 1,885 participants in six separate focus groups and identified reasons for participation as well as those factors that inhibit participation in the census. 

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Final Report to Congress
2000

CMBP Report Details Potential Effects of Unadjusted Census

On March 9, the CMBP issued the first of its 15 research papers, detailing the effects of an unadjusted census. PricewaterhouseCoopers, a CMBP consultant, conducted a comprehensive analysis of how

the undercount could affect the allocation of Federal funds among the states, metropolitan areas and

center counties of metropolitan areas. The report “Effect of Census 2000 Undercount on Federal

Funding to States and Local Areas, 2002-2012,” noted that 26 states and the District of Columbia

would lose at least $9.1 billion in federal funds over the next decade if the 2000 census undercount

produced a similar undercount rate as that of the 1990 census.

Census Day 

April 1, Census Day, Americans are asked to identify where they lived on this day in the year 2000. 

April 19, Dr. Kenneth Prewitt, Director of the Census Bureau, announced that the mailback response

rate of 65 percent had exceeded expectations and reversed a trend of declining participation, which

started in 1980. 

Third CMB Report Focuses on Census Preparations

The CMB issued its third joint report to Congress on April 1, and concluded that the Bureau’s preparations for Census 2000 had "generally" proceeded well and included detailed findings from a series

of joint field observations. 

Census Bureau Issues Report on the Feasibility of Using Adjusted Data

In June, the Census Bureau in addressing the matter of "feasibility" (as raised by the January 1999

Supreme Court decision) issued its report “Accuracy and Coverage Evaluation, Statement on the

Feasibility of Using Statistical Methods to Improve the Accuracy of Census 2000.” The report concluded that traditional census methodologies would not effectively reduce the differential undercount

and that the most effective way to increase accuracy was through the use of scientific sampling applications. It also reported that completion of the A.C.E. was operationally feasible and that it would be

possible to produce statistically adjusted data prior to April 1, 2001. 

Fourth CMB Report Focuses on Partnership and Promotion 

The fourth CMB bipartisan report to Congress was presented on October 1. It detailed oversight visits to 51 Local Census Offices. The report focused on the Bureau’s partnership and promotion efforts,

especially targeting the hard-to-enumerate; recruitment and staffing; mailback response rates; field

operations; update/leave, update/enumerate, and list/enumerate operations; data capture, quality

assurance, and coverage improvement programs. 

On October 6, the Department of Commerce issued a final rule, which gave the Director of the Census

Bureau authority to make the final determination regarding the matter of sampling after receiving the

recommendations of the Executive Steering Committee for A.C.E. Policy (ESCAP). It further stipulated that the decision of the Director was not subject to review, reconsideration, or reversal by the

Secretary of Commerce. The rule was to become effective on November 6, 2000. 

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Final Report to Congress
Bureau Announces first Population Count Results 

On December 28, the Census Bureau announced that the national population of the United States on

April 1, 2000, was 281,421,906, up from 248.7 million in 1990. The 281.4 million figure was based

on the raw enumeration and did not include any statistical adjustment based on the A.C.E. State-bystate totals were also released at that time. 

2001

Census Director Kenneth Prewitt Departs

In January, following the change in Administration, Dr. Prewitt stepped down as Census Director.

William Barron was named Acting Director. 

Commerce Secretary Assumes Control Over Census Adjustment Decision

In February, shortly after being sworn in, the new Secretary of Commerce Don Evans announced that

he was rescinding the Federal regulation that gave the Census Bureau Director and a committee of senior, nonpartisan careerists the authority to decide whether the raw census count should be adjusted.

The Secretary of Commerce was restored as the ultimate decision-maker. 

Bureau Announces A.C.E. Results 

On March 1, the Bureau released the findings and recommendations of the ESCAP. The committee

recommended that unadjusted census data should be released to the states in March for redistricting

purposes based on the “apparent inconsistency in population growth over the decade as estimated by

the A.C.E. and demographic analysis.” Additionally, the committee determined that the differences

could not be resolved given the April 1, 2001, deadline for delivering intra-state population figures for

redistricting. 

The results of the A.C.E. revealed a significant error rate in the raw census count, although not as great

as in 1990. According to A.C.E.:

• The total population of the United States was 284.7 million, as compared to the census figure of 

281.4 million. 

• 6.4 million persons were missed and 3.1 million were counted twice. In other words, the 2000

raw census count produced a net undercount of 3.3 million persons, or 1.2 percent of the population, and a minimum of 9.5 million total errors, or miscounts. 

• The undercount continued to be “differential” in nature. Asians were missed nearly twice as

often as whites; African Americans missed nearly three times as often, Hispanics four times as

often as whites; Native Hawaiians and other Pacific Islanders over six times as often as whites,

and American Indians seven times as often as whites.

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Final Report to Congress
The following chart summarizes the national 2000 census results:

Raw count in Census 2000: 281.4 million

A.C.E. count: 284.7 million

Number counted twice: 3.1 million

Number missed: 6.4 million

Net undercount: 3.3 million

Net undercount rate: 1.2 %

Undercount rate by group

Non-Hispanic whites: .67 %

Blacks: 2.17 %

Hispanics: 2.85 %

Asians: .96 %

Native Americans and Alaskans: 4.74 %

On March 6, Secretary Evans, in agreement with the recommendations of the ESCAP and the Acting

Director, officially announced that the unadjusted numbers would be released to the states for their

use in redistricting. 

CMBP Analyses Compares 2000 and 1990 Census Errors 

On March 9, the CMBP released the first of a series of informational briefs analyzing census data and

reporting on the results obtained by CMBP experts. It dealt with the number of errors and uncertain

cases contained in the 2000 Census in comparison to those of the 1990 Census. The Bureau responded by providing information on their assessment of the errors, reporting that the 1990 Census contained 12.9 million gross errors, while Census 2000 contained 9.5 million gross errors.

On March 28, the CMBP produced estimates of the number of people missed in the 2000 Census in

all 50 states, the District of Columbia and five cities.

CMBP Issues Semi-Annual Report to Congress

In its semi-annual report to Congress, issued on April 1, the CMBP published summaries of nine

research projects commissioned to analyze and evaluate the results of the 1990 undercount on the populations most affected. Additionally, it provided in-depth information regarding the net undercount of

3.3 million people; levels of error which included the number of people missed, those counted twice,

or in the wrong place, as well as information on non-data defined people and re-instated possible

duplications. The report also provided a discussion on demographic analysis as one of the components used by the Bureau to determine the nation’s population. Dr. Jeffrey S. Passel, demographer and

researcher at the Urban Institute, noted that the discrepancy between the Bureau’s estimates of the

population were due primarily to incorrect assumptions about the level of legal and illegal immigration between 1990 and 2000. The A.C.E. and preliminary demographic analysis estimates of the total

population differed by about 5.2 million persons. 

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Experts Call for Release of Data 

On April 18, CMBP convened a panel of noted census experts to discuss their analysis of Census 2000

data and the factors that led the ESCAP and the Acting Director to recommend against adjustment. In

presenting their findings, they explained why they had arrived at a different conclusion from the one

reached by the Bureau. Dr. Jeffrey Passel, demographer and researcher, argued that “the inconsistency with DA should not be used as a basis for deciding against adjustment because the current estimate

underestimates immigration, particularly Hispanic and undocumented immigration.” On the matter

of synthetic error, Dr. Joseph Kadane, Professor of Statistics and Social Sciences at Carnegie Mellon

University, wondered “why the Bureau used synthetic error as a reason not to adjust when their materials make a case for just the opposite.” Dr. Stephen Fienberg, Professor at Carnegie Mellon

University, noted “that balancing error was even less a problem in 2000 than in 1990” and went on to

suggest “inconsistencies with the Bureau’s methodology when determining the accuracy of populations below 100,000.” The experts concluded by reiterating a call for the Bureau to release all data

for further scientific study and review. 

CMBP Study Details County-Level Undercounts

Continuing to meet the need for critical information and in the absence of the necessary data being

released by the Bureau, in July, the CMBP provided undercount estimates for the nation’s largest counties, those with populations over 500,000. 

CMBP Study Shows Fiscal Impact of Not Adjusting Census Count

On August 7, PricewaterhouseCoopers released a study, sponsored by the CMBP, projecting a loss of

federal funding of more than $4 billion in 31 states and the District of Columbia for the 8 programs

studied as a result of the 2000 Census undercount. The majority of the funds lost ($3.6 billion) would

be in 58 of the nation's largest counties. On a per capita basis, the funding loss translated into nearly

$3,000 per uncounted person in these counties. The eight programs were: Medicaid, Foster Care,

Social Services Block Grants, Rehabilitation Services, Basic Support, Substance Abuse Prevention,

and Treatment Block Grants, Vocational Education Basic Grants, Child Care and Development Block

Grants, and Adoption Assistance.

CMBP Issues Final Report

In September, CMBP issued their Final Report. The Board is scheduled to close its doors on September

30, 2001, having made every effort to fulfill its statutory responsibilities. 

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Final Report to Congress
Final Report to Congress

U.S. Census Monitoring Board

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SECTION 2

RECOMMENDATIONS

Throughout the course of our operation, our semi-annual reports to Congress have included recommendations based on our observation and oversight. Many of them were incorporated and ultimately

contributed to the operational success of Census 2000. Based on our overall experience, research and

field observations, the following recommendations are offered in an effort to improve future censuses. 

Overall Recommendations

All efforts should be made to remove partisan politics from the census process. The decennial census

is Constitutionally mandated as the method to re-distribute representation fairly. In recent times, the

census data has also been used as the most accurate basis for the allocation of federal funds. The perception of political manipulation of census results is an unfortunate by-product of making political

appointees the final arbiters of census policy. The decisions regarding accuracy of the census should be

based on sound science. While the National Academy of Sciences, the American Statistical Association,

the Council of Professional Associations on Federal Statistics, the American Sociological Association,

the National Association of Business Economists and other professional associations advocated the

positive use of sampling methods, the decisions made by the Census Bureau left the issue unresolved,

thereby co-opting it to the field of politics. 

The Census Bureau Director should have a 5 or 7 year term appointment. Other agencies charged

with developing critical statistical information, including the Bureau of Labor Statistics, have a fixed

term for their directors. This would serve to provide continuity and remove the challenge of political

pressure from the Office of the Director.

An early deadline should be set for resolving 2010 methodology. For the 2000 Census, the Bureau

had to prepare a “dual track” census plan, pending resolution of a lawsuit regarding the Bureau’s

methodology. One track of the plan would incorporate modern scientific methods, or sampling, while

the other track would not. Just one year before the 2000 Census, the Supreme Court decided that

based upon a 1976 statute, sampling could not be used for apportioning the House of Representatives.

Following the decision, the Bureau requested an additional $1.7 billion from Congress to adjust for

the consequences of the court ruling. While it is important to recognize the need for flexibility in the

planning and preparation stages, it is also important to note that operations used in the decennial census should be tested during the dress rehearsals. When an operation is employed during the decennial that has not been tested, it is necessary to understand that it may be subject to unknown error. 

Congress and other oversight bodies should be more sensitive to the consequences of redundant or

overlapping oversight. While a $6.5 billion dollar operation deserves full Congressional scrutiny and

oversight, the Census Bureau endured oversight and investigation by at least six different entities – the

Commerce Department’s Inspector General, the General Accounting Office, the U.S. House of

Representatives Census Subcommittee (which operates as two separate entities), the Census Bureau’s

own Race and Ethnic Advisory Committees, the Commerce Department’s Census 2000 Advisory

Committee and both sides of the U.S. Census Monitoring Board, which most often worked as two distinct bodies. Each entity was able to provide valuable information to Congress and to the public, but

top officials at the Bureau spent many hours answering repetitive and at times, unreasonable requests
for information (including a request to read all Census Bureau e-mail) just when the Bureau was at

the peak of its operation. 

The Census Bureau should strive to maintain a transparent census operation. For the 2000 Census,

most operational aspects were pre-specified and scrutinized by the public. During Acting Director

James Holmes’ and Director Ken Prewitt’s tenure, the Bureau maintained a high level of transparency and public communication. The Bureau outlined a plan for making decisions and worked with the

Commerce Department to establish regulations, which allowed for public comment. However, with

the change in Administration, extracting information from the Bureau became difficult. For example,

requests submitted by the CMBP went unanswered for months. In some cases the Bureau denied the

requests citing the possibility that the information would be misunderstood as the basis for its decision on adjustment.

Congress should continue to fund a post-enumeration survey. Regardless of the debate over sampling,

the post-enumeration survey serves as an invaluable quality check on the accuracy of the census. In

2000, the post-enumeration survey was called the Accuracy and Coverage Evaluation (A.C.E.) and

allowed the Bureau to measure the net and differential undercounts and to document increased

improvement.

A national paid advertising program should be included in future plans. Our own polling pointed to

strong public interest in the messages that the Census Bureau and the paid advertising agency of

Young & Rubicam employed to address the twin challenges of increasing awareness and commitment

to participate. Both sides of the Monitoring Board unanimously endorsed the Bureau’s decision to

pursue a paid advertising campaign as part of Census 2000. Increased participation of all communities provided solid evidence that the overall objectives of the program were achieved. 

Funding for the decennial Census must be judiciously considered. The actual and projected cycle cost

of the 2000 Census ($6.5 billion) nearly doubled the cycle cost of the 1990 Census. A national paid

advertising program, advancing technology and adequate pay for temporary employees create costs

but are an essential element of the operational success of the 2000 Census.1 As the country and the

associated census-taking costs continue to grow, Congress must continue to evaluate the swelling

expenditures associated with traditional census taking methods.

Demographic Analysis (DA) is a useful benchmark from which to evaluate census results, however, it

should not be used as a determining factor in the debate over adjustment. DA was designed to identify whether or not a racial differential undercount exists between the Black and Non-Black population primarily because when DA was developed in 1940, the largest minority was the Black population. Given the increased imprecision in measuring race and ethnicity caused by the changing nature

of American society, the race/ethnic-specific estimates from DA for future censuses should probably

not be held to the same standards as coverage measurement surveys. Inconsistencies between DA and

the surveys in terms of relative coverage of race/ethnic groups would need to be “explained” in demographic or sociological terms, but failure to agree should not be treated as prima facie evidence that

coverage has not been adequately measured or that adjustment is not warranted.

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Final Report to Congress

1 “2000 Census Review of Partnership Program Highlights Best Practices for Future Operations,” GAO-01-579, page 2 and “Bureau of the

Census Federal Funds,” Appendix to the Budget of the United States Government, submitted by the President of the United States, 1986

through 1996, adjusted for inflation.
Furthermore, measures of immigration have always been the weakest link in compiling a demographic estimate of the U.S. population. By its very definition, undocumented immigrants are the least likely to be included in an estimate of the U.S. population that relies largely on administrative records such

as birth and death records. Most disappointing is that as the economy expanded and the number of

immigrants dramatically increased over the past decade, the Census Bureau was unable to understand

how its own survey results showed increases in immigration. In 2000, the failure of DA to incorporate estimates of immigration based on the best available information — estimates of undocumented

immigration consistent with measures from the reweighted Current Population Survey, nonimmigrants from all categories based on INS data, and increased special agricultural worker (SAW) populations — changed the nature of the ESCAP decision on adjustment. Had the “base DA” estimate

included a better estimate of the immigrant population, there would have been more attention paid to

the A.C.E, more attention to the areas of agreement between the two measures, and more attention to

the potential deficiencies in Census 2000 itself rather than the inconsistency between DA and the

A.C.E. 

It is essential that the Census Bureau develop greater capacity to measure immigration. The Census

Bureau’s measure of undocumented immigration and nonimmigrants during the 1990s were drawn

entirely from outside the agency. Developing current estimates of undocumented and legal immigration and measuring changes in the flow of immigration should be done by the Bureau. Developing

the expertise within the agency is needed to enhance its ability to assess these changes. 

Gross error, rather than net error, should be the primary basis for evaluating the accuracy of the census. Some errors “cancel out,” leaving the impression that the results are more accurate than they really are. For example, the net undercount in the 2000 Census is 3.3 million but the number of people

missed is 6.4 million while the number of people counted twice is 3.1 million.2 Knowing where people are missed is also important because omissions do not necessarily occur in the same place as duplications. Errors are often geographically and racially differential. Indeed, the persistent racial differential undercount in Census 2000 illustrates this point.3

Imputations and removed potential duplicates should be included when discussing measures of gross

and net error in the census. Imputations make use of information not based upon direct observation

but rather, a computer is directed to “impute” people, based upon various clues, including how their

neighbors responded. Census 2000 included 5.7 million imputations – 2 percent of the nation’s population. Furthermore, imputations were applied differentially by race. The rate of imputation for Blacks

and Hispanics was twice that for Whites. Potential duplicates were identified late in the census

process but before delivering the apportionment number. The Bureau used a complex computer program to identify about 6 million potential duplicate persons in the census. The Bureau further

screened the 6 million people and added 2.4 million people back into the census, while taking out 3.6

million people. Neither the 5.7 million imputations nor the 2.4 million re-instated duplications were

included in the Accuracy and Coverage Evaluation. The effect of these uncertain cases should be thoroughly evaluated before they are extensively used in future censuses. 

The effects of the Local Update of Census Addresses (LUCA) program should be further analyzed.

The Census Bureau plans to conduct an ongoing LUCA program, which allows local governments and

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Final Report to Congress

2 “Statement by William C. Barron, Jr. on the Current Status of Results of Census 2000 Accuracy and Coverage Evaluation Survey,” July 13, 2001.

3 Executive Steering Committee on Accuracy and Coverage Evaluation Policy Report, March 1, 2001.
the Census Bureau to develop a more comprehensive address list over time.4 LUCA allows direct

input by local governments in the process of assembling address lists, a process, which should be

encouraged. However, there is much yet to understand about the effectiveness of LUCA. It is presumed that by improving the address lists for the 2000 Census, it should have decreased the number

of persons missed or allocated to the wrong locality. However, it may also have increased the variability by local area in the undercount or overcount, which in turn might have made the census estimates of shares (by state or local area) more accurate or less accurate. 

Federal funds should be made available to state and local governments to allow them to modernize

and standardize their own address list and mapping technologies in order to better contribute to

updating the Bureau’s national address list.

5

The Census Bureau should comply with its pre-determined data products release schedule. Following

its scheduled timeframe, the Bureau released the Congressionally mandated redistricting data summary file and additional Census 2000 demographic products. However, it failed to provide information regarding the number of people sleeping in shelters, living in cars, under bridges and on sidewalks, again out of fear that the public would misunderstand the numbers. Their action only added to

the degree of frustration, confusion and suspicion surrounding Bureau decisions.

Census Bureau Headquarters and the Regional Offices should continue partnerships with governmental, community-based, and religious organizations, as well as school systems, to encourage cooperation with the Census Bureau’s activities throughout the decade. It is widely believed that the over

140,000 partnerships contributed significantly to the reduced net undercount. The Census Bureau has

already expanded its Census Information Center program which partners the Bureau with community based organizations in an effort to distribute census information more widely in traditionally less

accessible communities. The Census Bureau would benefit from its continued outreach to traditionally undercounted communities through its Census in the Schools program. Scholastic, Inc. developed a census curriculum for teachers to use in the classroom during the 2000 Census, which proved

to be highly popular. Better-educated children lead to better enumerated households.

Census Bureau Headquarters should strive to provide the Regional Offices with materials and other

resources available for partnership efforts. Local and state governments often times communicated

directly with Census Bureau Headquarters rather than their Regional Offices for partnership information because information appeared not to filter down to the Regional Offices in a timely manner.

Congress should provide adequate funds to increase the number of qualified Partnership Specialists

who can serve as a bridge between the Bureau and local community leaders. While the more than 600

partnership specialists the Bureau hired often performed Herculean tasks, these census ambassadors

were often spread too thin. Partnership specialists were expected to attend public functions, schedule

meetings and organize census awareness events in order to ensure that every member of every community in the country was enumerated by the census. There are over 39,000 communities in the nation

and many of them have their own governments, several civic organizations, and numerous religious

institutions. More partnership specialists would allow the Bureau to achieve greater participation. 

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Final Report to Congress

4 LUCA was mandated by P.L. 103-430, to allow the Census Bureau to share their address list with local governments for a local check on the

address list’s accuracy. This public law also mandated that the Post Office share their address list with the Census Bureau. 5 Both sides of the Monitoring Board also agree that federal funds should be made available for cities and towns to form Complete Count

Committees, which allowed local governments to help raise awareness about participating in the 2000 Census. Joint Report to Congress by

U.S. Census Monitoring Board, April 1, 1999.
Operational Recommendations 

The following recommendations are the result of Monitoring Board staff meeting with 50 local census office (LCO) managers during peak decennial operations in the months of April, May, June and

July of 2000.6 The 50 offices visited cover a broad cross-section of the country – rural, urban and suburban, East Coast, West Coast and the Mid-West. Most of the offices were selected because they contained large numbers of historically undercounted populations, as documented by the Bureau’s

Planning Database.7 We selected two locations in each of the Bureau’s 12 Regions, attempting to

include a variety of operations – mailout/mailback, update/leave, and list/enumerate.8

Local Census Offices should be encouraged to contact building and gated community managers early,

in order to gain access to some areas with surprisingly low cooperation. Most of the attention to fixing the undercount in the years leading up to Census 2000 was focused on educating people who have

historically been undercounted – children and minorities. In many areas of the country, traditionally

undercounted communities worked closely with the Bureau to increase awareness such as in Chula

Vista, California and El Paso, Texas. However, during Census 2000, an unexpected challenge arose

in gaining access to large apartment buildings and gated communities in wealthier neighborhoods in

Chicago and New York. Contacting building and gated community managers early on could help prevent some of these unexpected and last minute problems that arose during field operations during

Census 2000.

A similar program to the Planning Database should be locally updated leading up to the next censuses. The planning database organized 1990 Census data to help the Bureau determine census tracts

that would need greater attention in 2000 to improve the census count. Factors such as mail response

rate, poverty rate and racial composition helped local census offices direct their resources. Both sides

of the Monitoring Board agree that the Planning Database was a useful tool in guiding local census

offices where to concentrate resources.9 For example, while cities such as New York, Chicago or Los

Angeles have always experienced a large population of immigrants, even remote and rural areas saw

more recent immigrants settle into their communities. Places like Burlington, Vermont, Dalton,

Georgia and Cheyenne, Wyoming saw an increased immigrant population since the 1990 Census and

the Bureau was able to both identify pockets of recent immigrants and to work with local community leaders to encourage increased inclusion in the census count.

The “notice of visit” cards – flyers left by enumerators after an unsuccessful non-response follow-up

visit – should include information in the 5 other major languages spoken in the U.S. (Spanish,

Chinese, Vietnamese, Tagalog, and Korean). The English-only flyers were successfully used in 2000

as a calling card when people were not at home. However, the practice could be improved by providing them in multiple languages.

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Final Report to Congress

6 Findings from these visits are included in the October 2000 semi-annual report to Congress which was authored jointly by both sides of the

U.S. Census Monitoring Board. 7 In preparation for the 2000 Census, Census Bureau Headquarters examined every local census office jurisdiction to determine factors that

might pose challenges to enumeration. This information gathered is called the Planning Database. 8 Mailout/mailback is the type of enumeration area where the Census Bureau relies on the U.S. Postal Service to deliver a form to an address

and expects a form returned in the mail. For those housing units that do not return their form in the mail by the deadline, the Bureau sends

an enumerator to the door. This type of enumeration is used in 80 percent of the nation’s housing units. Update/leave is when the Census

Bureau updates its address list as it delivers a questionnaire to housing units that did not have a city-style address. For those housing units

that do not return their form in the mail by the deadline, the Bureau sends an enumerator to the door. List/enumerate is when the Census

Bureau updates its address list at the same time as sending a person to the housing unit’s door for enumeration. This type of enumeration is

used in sparsely populated and remote areas. 9 Joint Report to Congress by U.S. Census Monitoring Board, April 1, 1999.
In-language forms should be continued. Findings from the Census 2000 Supplemental Survey indicated that one in five persons speaks a language other than English at home. This clearly identifies

the need for continued use of in-language forms. In 2000, the census forms were available in six languages – English, Spanish, Korean, Chinese, Vietnamese, and Tagalog. In areas along the Mexican

border where the update/leave type of ennumeration is used, both sides of the CMB agree that LCOs,

working in consultation with the Regional Offices, should be able to determine whether or not leaving a Spanish-language form is a better option than leaving an English-language form.10

More attention should be paid to recruiting and hiring bilingual enumerators and translators. Further,

LCOs should prioritize hiring to reflect the population of the community it serves. The 2000 Census

results document the need for greater diversity in the census workforce. With nearly a 60 percent

growth in the Asian and Hispanic population and a surprising growth of immigrants even in areas not

historically associated with large immigrant populations, the diverse staff helped the Bureau reach

those traditionally hard to enumerate.

The Bureau’s policy of adjusting its wage scales based on local labor market conditions should be

continued. The caliber and dedication of the temporary workforce was extremely impressive in the

2000 Census. With few exceptions, the temporary employees interviewed and observed performed

their duties professionally and competently. In areas where recruiting was difficult, adjusting the

wage scale proved to be an effective tool in generating a larger applicant pool. 

Enumerators would benefit from photo-identification badges, especially in urban areas where residents are most concerned about security. The badges used during the 2000 Census identified enumerators by signature only. Residents might be more comfortable with each enumerator’s photograph

on his or her badge.

Partnership specialists should report to the LCO Manager rather than a Regional Office Manager.

Partnership specialists played an important role in the operational success of the 2000 Census.

However, they were hired and directed by the Regional Offices rather than the LCO Manager that, at

times, created communication difficulties and animosity among LCO staff. 

Local facilitators should be used again. Local facilitators were not required to pass the enumerator

test or security background checks but served as invaluable “gatekeepers” for the Bureau to enter traditionally closed communities such as Alaska Native villages or some publicly assisted housing neighborhoods. Facilitators were used on an ad hoc basis to reach areas that resisted cooperation with the

census. LCOs identified ahead of time where facilitators would be most useful.

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Final Report to Congress

10 Joint Report to Congress by U.S. Census Monitoring Board, April 1, 2000.
Introduction

Census 2000 has been marked by controversy and debate, both political and academic. On one side

of the debate are those who favor the use of sampling and statistical models to adjust for the inevitable

undercount of the population. Opposed are those who believe that any estimation will create more

error than it removes, and that the goal of the census should be to reduce the undercount with better

procedures.

Census 2000 was unprecedented in terms of its budget – more money was spent on it than on any 

previous census. Its achievements are impressive. Not only did the Census Bureau reduce the net

undercount below the levels of previous censuses, but it also reduced the differentials between the

non-Hispanic White and minority undercounts.

As a statistical consultant to the Presidential Members of the Census Monitoring Board, I have

enjoyed the opportunity to observe the workings of Census 2000 and to analyze its results. In this

report, my goal is to evaluate the accuracy of the census. To do this, I must first present criteria for

defining accuracy, and discuss the merits of various alternatives. I will then provide my own analyses and draw conclusions. 

As with many things that are large and complicated, the answer to the question of whether Census

2000 was more accurate than its predecessors depends on the criteria you use to judge. Census 2000

appears to have been successful by the key standard of allocating the population among states, counties, and places. It did not accomplish this feat, however, by observing and counting a greater share

of the population than in 1990.

Goals of the Census

The Constitutional goal of the census is to allocate the population among states. This goal conceivably could be attained without a complete count of persons. For example, if every housing unit had

two people living in it, we could simply count the dwellings to get a good estimate of the population

distribution. This fanciful thought underlies a more serious point. When the census misses 5 percent

of minority populations, but less than 1 percent of non-Hispanic Whites, its results are biased against

the minorities.1 It is better to have a consistent undercount of 3 percent for all groups than undercounts

of 3 percent for minorities and 0.3 percent for Whites. The latter census is less fair, even though the

net undercount of the national population is lower than it would be for the alternative. The

Constitutional goal of the census is to fairly allocate the population distribution among local areas.

The differential, rather than the national net undercount, is the key statistic.

Final Report to Congress

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SECTION 3

AN EVALUATION OF THE 2000 CENSUS

Professor Eugene Ericksen

Temple University, Department of Sociology 

and Statistics

1 In 1990, the Bureau obtained the following estimates of undercount: American Indians – 12.2 percent; Hispanics – 5.0 percent; 

Blacks – 4.6 percent; Asians and Pacific Islanders – 2.4 percent; non-Hispanic Whites and others – 0.7 percent.
A second goal of the census is to describe the nature of the population. We need to know the racial

and ethnic composition of the population, as well as observe the distributions of age, gender, income,

and country of birth. For this, we need to obtain information directly from individuals, counting each

person once, and only once. 

Many of the methods used in Census 2000, such as whole person imputation, identifying duplicate

cases by computer and then deleting them, and improving the address register with local information

help to achieve the first goal. Some of these methods, however, detract from the second goal.

People Counted Directly and Correctly

In 1990, the official census count was 248.7 million, the adjusted estimate was 252.7 million, and the

implied net national undercount was 4.0 million. The comparable results for Census 2000 are 281.4

million counted, 284.7 million estimated to exist, and an implied net undercount of 3.3 million. The

net undercount was smaller in 2000 by 700,000 people, and in percentage terms the rate dropped from

1.6 to 1.2 percent.2

The Census Bureau estimated that 4.4 million of the counted people in the 1990 Census were “erroneous enumerations,” people who were fabricated by enumerators or counted twice at the same location.3 They also stated that 2.2 million were “whole person imputations,” or people created by a computer program rather than counted directly.4 Combining these two groups and dividing by the total

count, we see that 2.65 percent of the official population was not counted directly and correctly. If we

subtract the 6.6 million imputations and erroneous enumerations from the official count, we have

242.1 million people counted directly and correctly in 1990. Subtracting this figure from the estimated total, we estimate that (252.7 – 242.1 =) 10.6 million were omitted, or not counted directly.

They comprise 4.19 percent of the estimated total.

For Census 2000, the Bureau informs us that there were 3.1 million erroneous enumerations and 5.7

million imputations for a total of 8.8 million.5 They comprise 3.13 percent of the official count, an

increase over 1990. There were 272.6 million people directly and correctly counted, and (284.7 –

272.6 =) 12.1 million omissions. They comprise 4.25 percent of the estimated total, and this percentage is about the same as obtained for 1990. 

There was a shift in the nature of the undercount, though. In 1990, of the estimated 10.6 million persons not directly counted, 8.4 million were omissions and 2.2 million were imputations. In 2000, of

the estimated 12.1 million persons not directly counted, 6.4 million were omissions and 5.7 million

were imputations.

Omissions and Erroneous Enumerations

The Census Bureau’s definition of erroneous enumerations is controversial, as it omits a substantial

category of people counted at the wrong location. For example, if a person moved from New York to

California on April 15, 2000, but was counted in California, (s) he would create two errors. New York

would have one person too few and California would have one person too many. Because such an

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Final Report to Congress

2 Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy (ESCAP), March 1, 2001. 3 U. S. Bureau of the Census, Memorandum from Ruth Ann Killion to John Thompson, dated May 15, 1997, “Gross Errors and

Erroneous Enumerations in the 1990 Decennial Census.” 4 John H. Thompson, “Census 2000 – Decision on Release of Statistically Corrected Redistricting Data,” August 8, 2001 at the Joint

Statistical Meetings of the American Statistical Association, Atlanta, Georgia. 5 Letter from William Barron to Representative Carolyn Maloney, dated April 9, 2001.
error does not affect the national net undercount, the Census Bureau does not include it in its definition of erroneous enumeration. I believe that such an error should be counted as an omission in New

York and an erroneous enumeration in California.

Moreover, had the person been counted in both states, creating only one error, the Census Bureau

would still not consider it to be an erroneous enumeration. This seems especially incorrect to me.

Other examples of persons counted at locations other than their main residence, and not considered

erroneous by the Bureau are:

• College students living away from home, counted both at home and school,

• Families counted at their main and vacation homes, and

• One or both members of a commuter marriage counted at both addresses.

Adding these incorrectly located people to the count of erroneous enumerations increases the Census

Bureau estimate considerably. In 1990, 1.8 percent of the “E – sample” was a fictitious or duplicate

case; 2.2 percent of the sample was counted at the wrong location.6 Adding these in, the total estimate

of people erroneously enumerated rose from 4.4 to 9.9 million. The estimated total of imputations and

erroneous enumerations is 12.1 million, 4.87 percent of the official count. 

For Census 2000, the parallel calculation provides an estimate of 6.3 million erroneous enumerations

by the broader definition.7 Adding these to the 5.7 million imputations gives a total of 12.0 million,

4.26 percent of the official count. The percentage of persons not directly counted is lower than in

1990, but the number of such cases is substantial in both censuses.

The Census Bureau estimates the number of omissions in the Census as the sum of erroneous enumerations and the net undercount.8 In 1990, the net undercount was 4.0 million, and by the Bureau’s

definition 4.4 million were erroneously enumerated and 8.4 million were omitted. The comparable

total for Census 2000 is 6.4 million omissions.

The gross error is defined to be the sum of omissions and erroneous enumerations. By the Bureau’s

calculations, this quantity fell from (8.4 + 4.4 =) 12.8 million in 1990 to (6.4 + 3.1 =) 9.5 million in

2000. By the expanded definition, there were 13.9 million omissions in 1990 for a gross error of 23.8

million.9 There were 9.6 million omissions in 2000 for a gross error of 15.9 million.10 Making no

allowance for whole person imputations, the decline from 23.8 to 15.9 million indicates substantial

improvement in Census 2000, relative to 1990. Even so, the number of omissions is very large.

It can be argued that each computer imputation represents one omitted person whom the Bureau could

not directly count so the computer created his/her record. Adding these omissions to the previous

totals, we obtain gross error estimates of 25.0 million in 1990 and 21.6 million in 2000.11

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Final Report to Congress

6 The “E-sample” is the sample of census records that the Bureau matched against the A.C.E. survey data to determine the percentages

correctly and erroneously counted. The “P-sample” is the survey sample which is matched against the census records to determine the

percentages included and omitted from the count. 7 Obtained from the Census Bureau data file entitled “E-Sample Person Dual System Estimation Output File,” delivered to the Census

Monitoring Board on February 16, 2001. 8 In making these calculations, the Census Bureau did not estimate the number of omissions directly. Noting that the net undercount is

the difference between omissions and erroneous enumerations, it calculated the number of omissions as the sum of the net undercount

and erroneous enumerations, e.g., for 1990, 4.4 + 4.0 = 8.4 million. 9 We have 9.9 million erroneous enumerations, 4.0 million net undercount, and 13.9 million omissions for a total gross error of 23.8 million. 10 We have 6.3 million erroneous enumerations, 3.3 million net undercount, and 9.6 million omissions for a total gross error of 15.9 million. 11 For 1990, the number of omissions is 16.1 million, which when added to 9.9 erroneous enumerations provides a total of 25.0 million; for

2000 the comparable sum is 15.3 + 6.3 = 21.6 million.
To summarize, the Bureau’s achieved reduction in the net national and differential undercounts did not

necessarily occur because they “counted” many more people directly and correctly. Depending on

how one defines erroneous enumerations, omissions, and the gross error, the Bureau either did about

as well in Census 2000 as it did in 1990, or moderately better in 2000. Even by the definitions most

favorable to the Census Bureau, however, there was a substantial amount of indirect and erroneous

counting in 2000.

Geographic Considerations

If an omission and erroneous enumeration occur on the same block, but to two different people, they

cancel each other out at all meaningful levels of geography. To evaluate the effect of errors on population distributions, we need estimates of net undercount for each block. For example, if one block

had 100 counts, 10 omissions and 5 erroneous enumerations, the adjustment would add 5 people even

though the gross error was 15. An adjoining block might have 80 counts, 1 omission and 9 erroneous

enumerations, and the adjustment would subtract 8 people even though the gross error was 10. The

key statistic would be (5 + 8 =) 13 “changes.”

In 1990, when the Census Bureau calculated adjustments to individual blocks, it added 5.45 million

people and subtracted 1.46 million people for a total of 6.91 million changes.12 This statistic is much

smaller than the previously calculated estimates of gross error for two reasons: (1) many errors cancel out because they occur on the same blocks, and (2) the Bureau’s adjustment procedure does not

fully correct for the distribution of net errors across all blocks. I illustrate the point with two groups

of blocks included in the 1990 Post Enumeration Survey.

The 1990 survey, as did the 2000 Accuracy and Coverage Evaluation (A.C.E.) survey, sampled entire

blocks and then calculated direct dual systems estimates of each block’s population. In the examples

just given previously, the survey data would indicate census counts of 100 and 80 respectively, along

with dual systems estimates of 105 and 72. In a perfect world, the adjusted estimates for our two

blocks would equal the direct estimates, i.e., 105 and 72. The synthetic adjustment method used by

the Bureau, since it could not “explain” all the variation in net undercount rates across sample blocks,

considerably understated the block-level adjustments.13

In Table 1, I illustrate the point using estimates for 11 sample blocks in Manhattan and 8 sample

blocks in Ulster County, New York. I present the percentage undercount as estimated by the direct

and synthetic dual systems estimates for each block. For both Manhattan and Ulster, the direct estimates are more highly variable than are the synthetic estimates. This is demonstrated by the larger

standard deviations for the direct (13.88 for Manhattan and 5.89 for Ulster) than the synthetic (4.81

for Manhattan and 1.28 for Ulster) estimates. More to the point, the synthetic estimate is usually

between zero and the value of the direct estimate. For example, Block 1 in Manhattan has a direct

estimate of –24.20 percent and a synthetic estimate of –4.93 percent while Block 11 has estimates of

21.49 and 7.74 percent respectively. 

The synthetic adjustments are therefore smaller in absolute value than the direct estimates would be

if they were available for all blocks. For Manhattan and Ulster combined, the ratio of the average

direct to synthetic adjustment is about three. 

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12 Howard Hogan, “The 1990 Post-Enumeration Survey: Operations and Results,” Journal of the American Statistical Association,

September 1993, p.1054. 13 This is because the variables used to define post-strata in both 1990 and 2000 predict patterns of undercount in a general way for large

aggregates. Different variables would be needed to predict which particular blocks in a neighborhood would have larger and smaller

undercounts or overcounts. This point does not indicate errors on the part of Bureau; it merely points out the inevitable limitations of

any adjustment model.
14 The “imperfections” of the Bureau’s method are likely to mean that remaining errors, after adjustment, exist at the block level. These

block level errors largely cancel out within census tracts and legislative districts. The remaining errors for larger areas would be smaller

on a percentage basis than they are for blocks. 15 U. S. Census Bureau press release, “Statement by William G. Barron, Jr. on the Current Status of Results of Census 2000 Accuracy and

Coverage Evaluation Survey,” dated July 13, 2001. 16 The associations between the variables used to define the post-strata and the pattern of net undercount appear to be weak in 2000, suggesting that the factor of 2.3 might be too low, and the estimate of 12.1 million therefore biased downward.

I repeated this calculation for the entire nation, and found that on average, the direct adjustment was

2.3 times larger than the synthetic adjustment. Therefore, if the synthetic adjustment created 6.91 million changes, as indicated above, the number of changes that needed to be made was larger, i.e., 2.3 *

6.91 = 15.9 million. 

To explain it another way, had it been possible to sample all blocks in the United States, and calculate

dual systems estimates for each one, I estimate that there would be 12.5 million additions and 3.4 million deletions to be made. Because the factors included in the adjustment model cannot fully predict

the block-to-block variation in net undercount, the adjustments actually calculated only account for a

share, about 43 percent, of the adjustments that need to be made. The Bureau’s adjustments improve

the estimated distribution of population, but not perfectly.14 Moreover, the 15.9, rather than the 6.91

million, better indicate the extent of the undercount.

In Census 2000, the Bureau added 4.26 million and subtracted 1.00 million for a total of 5.26 million

changes.15 If we assume that the factor of 2.3 is appropriate for Census 2000, then the estimated number of changes that needed to be made would be 2.3 * 5.26 = 12.1 million. Looking at it another

way, I estimate that there need to be 9.8 million additions and 2.3 million deletions across all blocks. 

To summarize, when we define the gross error geographically, we see substantial progress in Census

2000, by (15.9 – 12.1 =) 3.8 million. To the extent, however, that the multiplier of 2.3 is too low, we

should revise the estimated number of changes for 2000 upward from 12.1 million, and the actual

improvement over 1990 would be smaller.16

Patterns of Undercount

A major story of Census 2000 is the reduction in the differential undercount (see Table 2). Both the

Hispanic and the non-Hispanic Black undercounts in Census 2000 are about half of what they were in

1990, the non-Hispanic White undercount remained constant, and the differentials were cut sharply.

This improvement in the estimated allocation of population among demographic groups reinforces the

apparent reduction in gross error just discussed.

In designing the survey and estimation procedure for Census 2000, the Bureau defined post-strata not

only by race and Hispanic origin, but also by tenure, metropolitan status, region (for White owners)

and the mail return rate. Only the first three of these factors had a consistent effect on the estimated

net undercount (Table 3). 

Looking first at 16 groups of non-Hispanic White owners defined by geographic location, the rates

cluster around zero. Six of the estimates are overcounts, seven of them are between 0 and 0.99 percent, and the remaining three are between 1.00 and 1.99 percent. Rates of undercount are slightly

higher in non-metropolitan areas than elsewhere.

Rates of undercount are somewhat higher for White non-owners than owners, but the differences are

not large. We also observe this pattern for other racial groups. For Hispanics, the undercounts were

higher in smaller and non-metropolitan areas, while the opposite was true for the non-Hispanic

Blacks. Indeed, the rates of net undercount for non-Hispanic Blacks and Whites living in smaller and

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non-metropolitan areas were similar to each other. Finally, for non-Hispanic Asians, Pacific Islanders,

and American Indians the rates of undercount were generally higher than average but substantially

below their comparable estimates for 1990.

In general, the rates of undercount among different post-strata within the same racial category are not

greatly different from each other, the one exception being owners versus non-owners. As different

states typically have similar percentages of owner-occupied housing units, we would not expect to see

large variations in undercount rates, once race has been taken into account. For smaller areas such as

counties and places, concentrations of owner or renter occupied housing may have a more substantial

impact.

Rates of Net Undercount for States

Relying on data provided by the Census Bureau, I have replicated their estimates of net undercount by

state (Table 4). I have also calculated “synthetic” estimates of undercount, or estimates of undercount

that you would get if you assumed that the national rates of undercount applied to each state. For

example, if a state’s population included 20 percent Hispanics, 30 percent non-Hispanic Blacks, and

50 percent non-Hispanic Whites, its synthetic estimate would be 

.20 * 2.85 + .3 * 2.17 + .5 * 0.67 = 1.56 percent.

Comparing the actual and synthetic estimates in Table 4, we see that they are quite similar. The estimates differ by more than a percentage point in only one state, Alaska. They differ by more than onehalf of one percentage point in only eight states.

The synthetic and A.C.E. estimates for states are close because states are large and diverse areas

including rich and poor, city, suburban and rural, and owner and non-owner areas. Moreover, the

A.C.E. estimates themselves do not vary greatly, as the range extends only from 0.29 percent

(Minnesota) to 2.67 percent (Alaska).

Variability on tenure and other indicators is greater for local areas within states. The Census Bureau

defined minority post-strata by putting large and medium metro areas into one group, and smaller and

non-metro areas into another group. This makes it possible to compare, within states, the minorityWhite differentials in more and less metropolitan districts. For example, in Georgia, is the BlackWhite differential in cities like Atlanta similar to the Black-White differential in more rural areas? In

Tables 5 and 6, I present comparisons of undercount differentials, first for non-Hispanic Blacks and

Whites and second for Hispanics and non-Hispanic Whites within more and less metropolitan areas

in the same state.

To illustrate the method, we see in Alabama, that the Black – White differential for large and medium

metro areas was (2.50 – 0.66 =) 1.84 percent. The corresponding differential for smaller and nonmetro areas was (0.94 – 1.09 =) –0.15 percent, indicating a greater racial disparity in urban areas like

Birmingham and Mobile than elsewhere.

Looking at the Black-White differentials in different states, they are consistently between 1.5 and 3 percent in the large/medium category. In these more metropolitan areas, the racial differential is consistently in the direction that we would expect from past censuses – the Black undercount is higher than

the White. The story changes in the small/non-metro category, where there is no consistent difference

in Black and White undercount rates, and they are usually close together. Indeed, the Black rate is lower

than the White rate, though not by very much, in the rural and small city areas of 14 of the 34 states.

This result has important implications for the South, as 85 percent of the non-Hispanic Black populaU.S. Census Monitoring Board

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tion living in smaller and non-metropolitan areas is located there. There appears to have been no racially differential undercount in the less metropolitan South. While the Census Bureau has not offered any

explanation for this result, my hypothesis would focus on the relative difficulties of building complete

address lists, without duplications, in minority compared to White areas. I suspect that good address

lists are most difficult to build in minority areas of large cities.

The pattern differs for the Hispanic – White comparison. As we would expect from past censuses,

Hispanic undercounts are consistently higher, by a few percentage points, than non-Hispanic White

undercounts. Both groups have higher undercounts in smaller and non-metropolitan areas, and the

differential between Hispanics and Whites is somewhat larger there as well. As a result, in many areas

of the West and Southwest the non-metropolitan rates of undercount are higher than those of large

cities.

Undercount Rates for Counties

The preceding discussion suggests that the variation in undercount rates among counties may not be

substantial. The racial differentials that we have just observed are typically less than three percentage

points and are sometimes much less than this amount. To study this question, I calculated undercount

rate estimates for approximately 1,500 counties located in 23 states. The states were selected by one

or both of two criteria: (a) at least 25 percent of the population was something other than non-Hispanic

White, or (b) it had a substantial share of its population located in large metropolitan areas as defined

by the Census 2000 post-stratification plan. States with large minority, or “big city” populations are

more likely to have variable rates of undercount among counties than the remaining less metropolitan

states with smaller minority populations.

To calculate the undercount estimates, I first divided the non-group quarters populations of each county into eight categories – owners and non-owners among Hispanics, non-Hispanic Blacks, nonHispanic Whites, and non-Hispanic others. I then obtained the ratios of adjusted to official populations for each state as provided by the Census Bureau. I display the individual county estimates in

Appendix A and summarize the results in Table 7.

The variation in county rates of undercount is not substantial. A full 87 percent of all counties studied have rates of undercount between 0.00 and 1.99 percent. There are only four counties, all with

small populations, that have rates of undercount above three percent. Six percent of counties, generally located in the Midwest, have overcounts but none of these is greater than one percentage point.

As a general pattern, county rates of undercount are higher in the West and lower in the Midwest. The

lack of a Black – White differential in less metropolitan areas had a substantial role in minimizing the

variation among counties located in Southern states. 

Effects of the Reduction in the Rate of Net Undercount

The ability of the Census Bureau to reduce both the national net and the racially differential undercounts is a major success story. Having counted a greater share of the minority population in Census

2000, we would expect to see the greatest improvement in areas with large minority populations. This

could make it difficult, though, to know how much of the measured 1990 – 2000 population growth

was real and how much was due to a reduced undercount. 

I conducted a study of those counties located in large metropolitan areas, as defined by the A.C.E. poststratification scheme. I selected these counties, whose collective Census 2000 official count is 86 million, because I believed these counties to be the ones where census-taking problems were most serious

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in 1990. I sorted them into four groups defined by the percentage minority, as (a) 50 percent or more,

(b) 25 to 49.9 percent, (c) 10 to 24.9 percent, and (d) less than 10 percent minority.17

I then obtained 1998 and 1999 population estimates from the Census Bureau website.18 Because these

estimates did not incorporate an adjustment for the undercount of the 1990 Census, they provided a

good benchmark to evaluate Census 2000. In other words, the 1999 estimate added the 1990 – 99

growth to the unadjusted 1990 count. I calculated a “2000 Census Projection” by adding the 1998 –

99 change to the 1999 estimate. For example, if the 1998 estimate was 180,000 and the 1999 estimate

was 185,000, I calculated the 2000 projection to be 190,000. This projection estimates what the

Census 2000 count would have been had the level of undercount been the same. It is subject to the

errors generally associated with population estimates, but there is no reason to expect these errors to

be consistently positive or consistently negative.

I compared the projection to the official 2000 count. If the count was 200,000, and the projection

190,000, then the projection was short by 10,000 or 5 percentage points. I summed the relevant population counts and projections by category, and calculated the overall shortfalls by county group

(Table 8).

Together, the counties with the largest minority populations had the largest shortfall. They were projected to grow by 5.24 percent, but actually “grew” by 9.25 percent. The shortfall of the population

projection was 3.67 percentage points, and this shortfall may well reflect the effects of improved

counting. It is larger than the shortfalls of 1.34, 1.10, and –0.55 percent found for the other three

groups of counties. In other words, those counties with higher minority shares had larger shortfalls

than did counties with lower shares.

New York City is an especially good example, as its projected growth rate was 1.73 percent compared

to actual growth of 9.36 percent. Washington DC, Philadelphia, and Hudson County, New Jersey had

similarly high and unexpected amounts of growth. The shortfall was positive in 15 of the 16 “high

minority” counties. It was three percentage points or more in 10 of the 16 counties. While there is

variation, we see consistently high and unexpected growth in urban areas with large minority populations. Some of this growth is undoubtedly due to improved counting.

It is tempting to believe that this improvement is due to the use of the Local Update of Census

Addresses (LUCA) program. In this program, local governments were allowed to submit lists of

addresses that they believed might not have been included in the master address file of the census. In

all, the Bureau added just over 4 million addresses through LUCA.19 One of the largest files of added

addresses came from New York City, where the Census Bureau accepted over 280,000 added addresses. These comprised 8.88 percent of the eventual city total of 3.2 million housing units. LUCA’s contribution compares to the 6.98 percent “shortfall” in New York City, suggesting that LUCA played no

small part in creating a large amount of measured growth.

There is, however, no consistent pattern in other cities, some of whom such as Washington DC and

Suffolk County, Massachusetts had large unexpected growth and a small LUCA contribution. Other

counties had the opposite experience, i.e., they made a big LUCA contribution but did not observe

unexpected growth. The overall correlation between the ratios of LUCA adds to all housing units and

the shortfall of the population projection, measured among counties, is -.014.

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17 Minority is here defined as persons who did not self-identify as only one race, non-Hispanic White. 18 See U. S. Bureau of the Census website, July 30, 2001 19 Data file received July 13, 2001 by the Census Monitoring Board, Presidential Members, from U. S. Bureau of the Census, entitled

“LUCA submissions and ‘adds’ by local government jurisdiction.”
The Nature of Census Error

My emphasis on the improvement in Census 2000 in terms of the national net and differential undercounts is not intended to minimize the importance of remaining error, both for political representation

and fund allocation. The Census Bureau decided not to adjust the results of Census 2000 on its

Redistricting file, but continues to consider whether or not it should adjust these results for use in fund

allocation and other purposes. In this section, I discuss some of the issues associated with that decision. 

Perhaps the major reason for the Bureau’s decision was the inconsistency between national population

totals calculated by the A.C.E. survey and demographic analysis. As the Bureau put it in the March

1 ESCAP report, 

“Initial D[emographic] A[nalysis] results, however, presented a major inconsistency with the

A.C.E. results – instead of confirming a net undercount, DA estimates that Census 2000 overcounted the national population by 1.8 million individuals . . .. substantially below the net

undercount of 3.3 million shown by the A.C.E. (page 3).”

The most likely culprit, from the perspective of the A.C.E., is the underestimation of erroneous enumerations. In other words, the official count includes more duplications, fabrications, and persons

counted in the wrong place than the A.C.E. indicated. Increasing the estimated number of erroneous

enumerations would reduce the net undercount, but also increase the gross error and indicate that the

quality of Census 2000 data was not as good as we originally thought.

There are good logical reasons to believe that the Census Bureau did underestimate erroneous enumerations. There were 16 million counted people excluded from the A.C.E., 8 million who lived in

group quarters, 5.7 million who were whole person imputations in households, and 2.3 million “late

adds” who were cases originally thought to be duplicates but who were added back into the count at

the end of the census counting period.20

The group quarters population could include overcounts, for example, among people included in outdated lists of residents at places such as hospitals, dormitories, and prisons. The number of “whole person” imputations, 5.7 million, may be too large, and to my knowledge the Bureau has never studied the

question of whether its computers created on average the correct number of records for addresses where

whole person imputation occurred. Finally, we already have reason to suspect that many of the 2.3 million “late census adds” were duplicated cases. The Bureau may be studying these possibilities, along

with their announced studies of subjects such as balancing error. We await its conclusions.

In general, problems of census taking arise due to the circumstances in which people live. There are

neighborhoods where poverty is high, education is low, use of foreign languages may be common,

housing is crowded or irregular, and crime rates high where it is especially difficult to count. Even

where some but not all of these conditions exist in extreme forms, census taking may still be difficult.

These difficulties lead not only to higher rates of omission, but also to higher rates of erroneous enumeration, whole person imputation, and records with incomplete and incorrect recording of characteristics such as race and Hispanic origin.

The focus on the racially differential undercount sometimes leads to a misplaced emphasis on racial

identity itself, rather than the conditions in which many minority group members live, as an explanation for why the undercount exists. Just as we would expect counting for non-Hispanic Whites to be

difficult when their living circumstances are difficult, we would expect the counting for Hispanics and

non-Hispanic Blacks to be easier when their conditions were better.

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20 John H. Thompson, “Census 2000 – Decision on Release of Statistically Corrected Redistricting Data,” August 8, 2001 at the Joint

Statistical Meetings of the American Statistical Association, Atlanta, Georgia. 
21 This file is called the Planning Database “CD-DSSD-comm-7 1990 data for Census 2000”and was delivered to the Census Monitoring

Board on November 14, 2000. 22 The Census Bureau uses the term “non-match” to refer to persons in the P-sample whose record could not be found in the census.

As part of its planning for Census 2000, the Bureau created a census tract planning file including

information on the demographic and economic circumstances of local populations and tract level

estimates of the undercount.21 This file afforded me the opportunity to study the effects of poverty, as

it combines with race, on the undercount. Because the poverty information is based on long-form

data, it was not available for use in calculating actual adjustments to either of the 1990 or 2000

Censuses.

Working with the 1990 census tract data, I created five categories defined by race. One included those

areas with Native American majorities. The second included that majority of tracts where the percentages non-Hispanic Black and Hispanic were each below 10 percent. I then identified tracts (a)

where each percentage was between 10 and 29.9, (b) where one or both were between 30 and 49.9 but

neither was as high as 50, and (c) where there was either a Black or Hispanic majority. I then subdivided the tracts a second way, depending on the poverty rate. My cross-classification made it possible to compare high and low poverty tracts where the racial composition was similar, as well as tracts

of different racial composition where the poverty level was similar. 

Table 9 shows that higher rates of poverty are associated with higher rates of undercount. Areas where

the poverty rate is low and the population predominantly non-Hispanic White, have very low rates of

undercount, 0.4 percent. Increases in this rate are associated both with increased percentages of

poverty and racial minorities. It should be noted that the rate of undercount for predominantly White

areas with a poverty rate over 50 percent is 3.6 percent, higher than the corresponding rate, 2.9 percent, for areas with concentrated minority populations but a low poverty rate. Many of the variables

actually used to define post-strata, such as tenure and the mail return rate, are attempts to create proxies for the difficult counting conditions created in part by poverty. It is important, though, not to consider these proxies to be the same as the conceptual variables that best explain the variation in rates

of undercount, but for which no data are available.

In the next step of my analysis, I attempted, for Census 2000, to demonstrate the manner in which the

various forms of census error congregate in similar locations. I compared rates of omission, erroneous enumeration, and imputation for groups of post-strata (see Table 10) defined by the key proxy

predictors – race, Hispanic origin, tenure, and metropolitan status.

There we see that those post-strata with higher rates of net undercount, generally those with minority

non-owner populations, also have higher rates of non-matching,22 erroneous enumeration, and imputation. Indeed the correlations between the net undercount and these three variables are, respectively,

.88, .51, and .67. The correlation between non-match and erroneous enumeration rates is .80. In

sum, conditions of poverty create difficult counting of all types. It is theoretically possible to imagine that the Bureau might solve the problem of differential undercount by increasing rates of erroneous

enumeration and imputation in poor neighborhoods. This would offset the higher rates of omission

and reduce the differential undercount. It would not mean, however, that a greater proportion of people were counted directly and correctly.

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The Limits of Improved Counting

The budget for Census 2000 is $6.55 billion, a large increase over the $2.6 billion budget for the 1990

Census even after inflation is taken into account.23 Using constant fiscal year 2000 dollars, the “per

household” cost rose from $36 in 1990 to $62 in 2000. There is little doubt that the added spending

improved census data quality. At the same time, there are important types of census error that are

impervious to budget size.

Better address lists are expensive to create, but they improve the count. While I was unable to demonstrate a direct link between LUCA investment and the improvement of the count in specific areas, it

is intuitively logical that it should exist. This is especially true in a place like New York City with a

concentration of older housing subdivided into apartments after originally being built for one family.

Moreover, among the counties I studied, areas with a larger minority share were more likely to participate in LUCA, increasing the chance of reducing the differential undercount.

Paying enumerators more money also seems like a good return on investment, because enumerator

mistakes are a major source both of omission and erroneous enumeration. Indeed, this investment

may even lead to savings since the count may be completed more quickly. Similarly, money spent on

advance publicity increases the mail return rate, and reduces the time and error of subsequent data collection. Finally, by investing in better and more extensive computer equipment, the Bureau can

improve its ability to manage the entire data collection and estimation process.

An increased Census Bureau budget is not likely, though, to reduce very much the frequency of errors

made by individuals filling out their census forms. The errors of people who enumerate themselves

at two locations, add inappropriate people to their census forms, and/or mistakenly leave others off are

usually honest mistakes. They occur frequently among people filling out and mailing back the forms.

Once such errors have been made, there is no feature of the census process that can correct them.

Within-household errors are probably the major component of omissions, and they are an important

component of erroneous enumerations.24

Problems of obtaining correct enumerations within households lead me to believe that census error is

inevitable, and is unresponsive to budget increases and design improvements. People will always be

left off census questionnaires. This sort of omission is so prevalent, and impervious to census method,

that we should always expect it to occur in the millions. The only way that the net undercount could

ever be zero, or close to it, is to have the numbers of omissions and erroneous enumerations offset

each other. The problem would then be that the geographic distributions of omissions and erroneous

enumerations would differ, and the differences cause distortions to the census results. This is why

some statistical adjustment is essential to correct the inevitable errors of the initial count. And,

throughout the 1990s until this year, the Census Bureau agreed. 

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23 U. S. General Accounting Office, “2000 Census: Review of Partnership Program Highlights Best Practices for Future Operations,”

August 2001. In fiscal year 2000 dollars, the cost of the 1990 Census was $3.275 billion. Based on fiscal year 2000 dollars, the per

officially counted person costs of the 1990 and 2000 Censuses were, respectively, $13.17 and $23.29. These calculations for 1990 are

based on data given in “Bureau of the Census Federal Funds,” Appendices to the Budgets of the United States Governments, submitted

by the President of the United States, 1986 – 1996. 24 Howard Hogan, “The 1990 Post-Enumeration Survey: Operations and Results,” Journal of the American Statistical Association,

September 1993, p.1056. See also Elizabeth Martin, “Who Knows Who Lives Here?” Public Opinion Quarterly, Summer 1999, pp.

220-36 and Eugene Ericksen, Leo Estrada, John Tukey and Kirk Wolter, “Report on the 1990 Decennial Census and Post-Enumeration

Survey,” submitted to the U. S. Secretary of Commerce, June 21, 1991, Appendix A, Table 6.
25 U. S. Census Monitoring Board, Congressional members, “A Guide to Statistical Adjustment: How it Really Works,” June 7, 2001. 26 U. S. Bureau of the Census, Memorandum from Howard Hogan to Susan Miskura, dated November 7, 2000, “Specification for

Reinstating Addresses Flagged as Deletes on the Hundred percent Census Unedited File (HCUF).” 27 U. S. Bureau of the Census, Memorandum from Susan Miskura to Preston J. Waite, dated November 21, 2000, “Results of

Reinstatement Rules for the Housing Unit Duplication Operations.” 

Imputing and Deleting Records

Statistical estimation to adjust the census has been a controversial issue, especially when it appeared

that the Bureau planned to adjust the results of Census 2000. Due in part to the political opposition

to adjustment, the Bureau received a substantial budget increase, for a stated goal of “counting,” as

opposed to “creating people by computer.” The Bureau did not adjust Census 2000, and it did reduce

both the national net and the differential undercount. Yet it did not do these things simply by “counting more people.” Moreover, if it had adjusted, it could have “eliminated” or corrected the remaining

undercount.

Had an adjustment taken place, about 4.3 million records would have been added to the count and 1.0

million deleted. These changes are what the political opposition to adjustment prevented. Yet, a computer imputed 5.7 million persons. This imputation makes use of information about people who live

in houses like those where the information was needed, but it is not based on direct observation.

Critics of adjustment point out that people living in places like Midland, Texas may be used to change

the populations of people living in New Haven, Connecticut.25 Yet they are silent about the fact that

donors and intended recipients of imputation are often very different. For example, it is very likely

that information about a White male age 35 could be used for a Black female age 57. Imputation, like

adjustment, improves the statistical estimate on average. For both methods, there are individual examples that appear to be incongruous.

The Bureau did not limit its use of the computer to imputation. Late in the census process, the Bureau

used a complex computer program to identify about 6 million duplications in their data file. As the

Bureau put it,

“[A]nalyses of the April 2000 and June 2000 MAF extracts still indicated that there was an

overcoverage problem. These concerns led the Census Bureau to identify and remove housing units (MAFIDs) from Census 2000. Housing units were identified as being included in

error with a relatively high likelihood based on a set of person and address matching rules.”26

It eliminated 3.64 million person records,27 i.e., it took records of “real people” out of the census The

Bureau returned the other 2.37 million people to the count, and they are referred to as “late census

adds.” Because the deletion and reinstatement operations took place late in the census process, the

Bureau was not able to include the late census adds in the A.C.E.

A review of these materials makes it clear that the Bureau monitored the level of the count throughout the census data collection period, and took the appropriate action that it deemed necessary. When

the count appeared to be too large, and therefore the rate of erroneous enumeration too high, the

Bureau eliminated 6 million person records. Most of the information on these records was received

from persons actually living in the affected households. Later, when it appeared that they might have

reduced the count by too great an amount, they put about 2.4 million of the records back in. The net

effect of these operations is that the eventual net undercount of 1.18 percent is substantially an artifact of the Bureau’s decisions about the apparently duplicated housing. 

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28 For the Bureau, the increase is from 3.1 to 6.74 million, and if we add persons counted at the wrong location to the count of erroneous

enumerations the increase is from 6.3 to 9.94 million.

Left unasked is the question of how these 6 million (now 3.64 million) duplications occurred in the

first place. Their inclusion in the census would have doubled the rate of erroneous enumeration by

the Bureau’s definition.28

Review of the Census Bureau procedure for removing duplicates reveals a complex method relying on the assumption that the census forms in question were filled out correctly. If a family

filled out two forms, but did so inconsistently, the Bureau may not have recognized it as a duplication. Like adjustment, “duplication removal by computer” will improve census distributions on

average, but make many individual mistakes. Duplication removal procedures are statistical in

that they rely on prespecified rules applied consistently to actual census data. A rational census

policy would apply the same criteria to duplication removal that they apply to statistical adjustment. The current policy, which depicts adjustment as a statistical procedure not to be used, but

duplication removal as a permissible procedure, makes a very fine distinction. I am not certain

that it is meaningful. 

Conclusion

In this report, I have attempted to summarize, discuss and analyze the issues of Census 2000 that

I considered to be most pertinent, with one exception. I have left the discussion of the possible

fit between the demographic estimate and the census count to my colleague, Dr. Jeffrey Passel.

As I have said repeatedly, the Bureau improved Census 2000 substantially over its 1990 counterpart, helped no doubt by a doubling of the “per person” census budget. We must understand the

limits to our ability to improve things by “better counting.” The Bureau, even though it did not

statistically adjust the census through the A.C.E. survey, did conduct an adjustment of sorts when

it deleted 3.64 million apparent duplicates from the count. I believe that we need to broaden the

discussion of census error and its possible remedies to include operations such as imputation and

the deletion of possibly duplicate records.

While we note the improvement of Census 2000 over its predecessors, we must keep in mind that

the differential undercount, especially in large cities, persists.

U.S. Census Monitoring Board

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Table 1: Comparison of Direct and Synthetic Dual Systems Estimates for 1990 

in Manhattan and Ulster County, New York

Manhattan

Subtractions Additions

Direct Synthetic Direct Synthetic Direct Synthetic

Block 1 -24.20% -4.93% 11.69 2.82 - -

Block 2 -17.11% -4.59% 15.05 4.52 - -

Block 3 -9.68% -5.56% 14.92 8.9 - -

Block 4 -6.89% 0.95% 7.09 - - 1.05 

Block 5 -5.10% 3.32% 1.99 - - 1.41 

Block 6 -4.14% -4.75% 4.77 5.44 - -

Block 7 1.34% -0.71% - 0.58 1.11 -

Block 8 3.39% 7.62% - - 2.74 6.43 

Block 9 15.43% 4.21% - - 39.04 9.4 

Block 10 18.68% 3.36% - - 8.73 1.32 

Block 11 21.49% 7.74% - - 20.8 6.38 

Standard 13.88% 4.81% Sum 55.51 22.26 72.42 25.99

Deviation

Ulster 

Subtractions Additions

Direct Synthetic Direct Synthetic Direct Synthetic

Block 1 -13.94% 2.15% 9.42 - - 1.69 

Block 2 -11.17% 0.10% 12.06 - - 0.12 

Block 3 -3.84% -0.99% 1.96 0.52 - -

Block 4 -0.83% -0.45% 0.48 0.26 - -

Block 5 0.00% -0.27% - 0.1 0 -

Block 6 0.35% -1.90% - 0.69 0.13 -

Block 7 1.11% 1.90% - - 0.73 1.26 

Block 8 3.99% -0.13% - 0.11 3.49 -

Standard 5.89% 1.28% Sum 23.92 1.68 4.35 3.07 

Deviation

Source: File of 5180 blocks from 1990 Census www.cmbc.gov/p/user/share/Census 2000/

FINAL REPORT/Tables/ Manhattan and Ulster

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Final Report to Congress

U.S. Census Monitoring Board

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Table 2: Comparisons of Undercount Rates and Differences, 1990 and 2000 Census

Racial Group 1990 2000 Change 

Hispanics ( A ) 4.99% 2.85% -2.14% 

Non-Hispanic Blacks ( B ) 4.57% 2.17% -2.40% 

Non-Hispanic Whites ( C ) 0.68% 0.67% -0.01% 

Hispanic- White Difference ( A - C) 4.31% 2.18% -2.13%

Black- White Difference ( B - C ) 3.89% 1.50% -2.39% 

Source: Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy, March 1, 2001, pages 3 and 4.
Table 3: Net Undercount Rates for Post-strata Groups1

GROUP AREA RATE

NH White Owners

Northeast Large -0.09% 

Northeast Medium -0.04% 

Northeast Small 0.06% 

Northeast Non-metro 1.52% 

Midwest Large -0.23% 

Midwest Medium -0.14% 

Midwest Small 0.33% 

Midwest Non-metro -0.98% 

South Large 0.78% 

South Medium 0.51% 

South Small 0.95% 

South Non-metro 0.51% 

West Large -0.18% 

West Medium 0.18% 

West Small 1.02% 

West Non-metro 1.26% 

NH White Renters 

Total US Large 1.58% 

Total US Medium 1.09% 

Total US Small 2.67% 

Total US Non-metro 2.46% 

NH Blacks 

Owners Large/Medium 0.91% 

Owners Small/Non-metro 0.17% 

Non-Owners Large/Medium 3.96% 

Non-Owners Small/Non-metro 2.32% 

Hispanics 

Owners Large/Medium 1.17% 

Owners Small/Non-metro 1.45% 

Non-Owners Large/Medium 3.90% 

Non-Owners Small/Non-metro 6.17% 

NH Asians 

Total US Owners 0.55% 

Total US Non-Owners 1.58% 

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Final Report to Congress
Table 3: Net Undercount Rates for Post-strata Groups1 (continued)

GROUP AREA RATE

Pacific Islanders 

Total US Owners 2.71% 

Total US Non-Owners 6.58%

AI on Reservations

Total US Owners 5.04% 

Total US Non-Owners 4.10% 

AI off Reservations 

Total US Owners 1.60% 

Total US Non-Owners 5.57% 

Total US Overall 1.18% 

1

High and low return rates have been combined for the post-strata groups. 

Poststrata are defined by the Census 2000 A.C.E. Methodology, vol. 3, tab 9, pg. 4. 

Source: FinalDSEUS.dat Census files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables p/user/share/Census2000/FINAL REPORT/Tables/Net Undercount Rates for Post-strata

U.S. Census Monitoring Board

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Final Report to Congress
Table 4: Comparisons of A.C.E. Undercount Rates with Synthetic Estimates

State A.C.E. Undercount Synthetic Undercount Difference 

Alaska 2.67% 1.15% 1.53% 

Vermont 1.51% 0.71% 0.80% 

Idaho 1.64% 0.90% 0.74% 

Wyoming 1.56% 0.91% 0.66% 

Maine 1.33% 0.72% 0.61% 

Montana 1.57% 0.96% 0.61% 

West Virginia 1.19% 0.74% 0.45% 

Washington 1.41% 0.98% 0.44% 

Delaware 1.50% 1.08% 0.42% 

Kentucky 1.23% 0.82% 0.41% 

New Hampshire 1.13% 0.73% 0.40% 

Nevada 1.68% 1.28% 0.39% 

Utah 1.35% 0.96% 0.39% 

Dist of Columbia 2.15% 1.78% 0.36% 

Oregon 1.27% 0.93% 0.34% 

Hawaii 2.16% 1.88% 0.28% 

Arkansas 1.28% 1.00% 0.28% 

Georgia 1.48% 1.23% 0.25% 

Tennessee 1.23% 0.98% 0.25% 

Virginia 1.33% 1.09% 0.24% 

North Carolina 1.36% 1.14% 0.22% 

Oklahoma 1.40% 1.19% 0.21% 

Maryland 1.40% 1.21% 0.19% 

Texas 1.76% 1.57% 0.19% 

Colorado 1.26% 1.14% 0.12% 

Louisiana 1.34% 1.23% 0.11% 

Alabama 1.19% 1.11% 0.07% 

South Carolina 1.20% 1.18% 0.02% 

Mississippi 1.24% 1.26% -0.02% 

New Mexico 1.94% 1.96% -0.02% 

Arizona 1.43% 1.46% -0.03% 

California 1.52% 1.55% -0.04% 

Florida 1.24% 1.28% -0.04% 

Connecticut 0.97% 1.03% -0.06% 

U.S. Census Monitoring Board

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Final Report to Congress
Table 4: Comparisons of A.C.E. Undercount Rates with Synthetic Estimates (continued)

State A.C.E. Undercount Synthetic Undercount Difference 

Pennsylvania 0.82% 0.90% -0.08% 

Rhode Island 0.85% 0.95% -0.10%

Indiana 0.77% 0.89% -0.12% 

Massachusetts 0.76% 0.92% -0.16% 

Wisconsin 0.70% 0.87% -0.17% 

New York 1.09% 1.26% -0.18% 

Michigan 0.71% 0.98% -0.27% 

Kansas 0.66% 0.95% -0.29% 

Iowa 0.48% 0.78% -0.30% 

Nebraska 0.56% 0.89% -0.32% 

Ohio 0.57% 0.90% -0.33% 

North Dakota 0.47% 0.89% -0.42% 

Missouri 0.46% 0.91% -0.44% 

South Dakota 0.56% 1.02% -0.46% 

Minnesota 0.29% 0.84% -0.55% 

New Jersey 1.15% 1.96% -0.81% 

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to Census Monitoring

Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

Note: Synthetic Estimates are based on race and Hispanic origin only.

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Final Report to Congress

Table 5: State Undercount Rates

Large/ Medium1 Small/ Non Metro2 

State Undercount Difference Undercount Difference 

Alabama NH - Black 2.50% 

1.84% 

NH - Black 0.94% 

-0.16% NH - White 0.66% NH - White 1.09% 

Arizona NH - Black 2.84% 

2.43% 

NH - Black 1.26% 

-0.09% NH - White 0.41% NH - White 1.35% 

Arkansas NH - Black 2.82% 

2.18%

NH - Black 1.21% 

0.09% NH - White 0.64% NH - White 1.12% 

California NH - Black 2.96% 

2.53% 

NH - Black 1.57% 

0.10% NH - White 0.43% NH - White 1.48% 

Colorado NH - Black 2.99% 

2.62% 

NH - Black 1.40% 

-0.23% NH - White 0.36% NH - White 1.64% 

Connecticut NH - Black 2.57% 

2.18%

NH - Black 1.74% 

0.58% NH - White 0.39% NH - White 1.15% 

Delaware NH - Black 2.58% 

1.49%

NH - Black 1.20% 

-0.25% NH - White 1.10% NH - White 1.44% 

Florida NH - Black 2.38% 

1.80%

NH - Black 0.97% 

-0.04% NH - White 0.58% NH - White 1.01% 

Georgia NH - Black 2.96% 

2.25% 

NH - Black 1.18% 

0.00% NH - White 0.71% NH - White 1.19% 

Illinois NH - Black 2.29% 

2.25%

NH - Black 1.32% 

0.82% NH - White 0.04% NH - White 0.51% 

Indiana NH - Black 2.74% 

2.61%

NH - Black 1.53% 

0.77% NH - White 0.13% NH - White 0.76% 

Kansas NH - Black 2.76% 

2.62% 

NH - Black 1.57% 

1.22% NH - White 0.14% NH - White 0.35% 

Kentucky NH - Black 2.80% 

2.14%

NH - Black 1.39% 

0.15% NH - White 0.65% NH - White 1.24% 

Louisiana NH - Black 2.48% 

1.70% 

NH - Black 1.09% 

-0.16% NH - White 0.78% NH - White 1.25% 

Maryland NH - Black 2.44% 

1.62%

NH - Black 1.04% 

-0.22% NH - White 0.82% NH - White 1.26% 

Massachusetts NH - Black 2.56% 

2.18% 

NH - Black 1.52% 

0.63% NH - White 0.38% NH - White 0.89%

Michigan NH - Black 2.70% 

2.49%

NH - Black 1.27% 

0.92% NH - White 0.21% NH - White 0.35% 
U.S. Census Monitoring Board

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Final Report to Congress

Table 5: State Undercount Rates (continued)

Large/ Medium1 Small/ Non Metro2 

State Undercount Difference Undercount Difference 

Minnesota NH - Black 3.25% 

3.19% 

NH - Black 2.38% 

2.45% NH - White 0.06% NH - White -0.07% 

Mississippi NH - Black 2.46% 

1.71%

NH - Black 1.12% 

-0.13% NH - White 0.76% NH - White 1.24% 

Missouri NH - Black 2.48% 

2.41%

NH - Black 1.77% 

1.63% NH - White 0.07% NH - White 0.14% 

Nevada NH - Black 2.73% 

1.96%

NH - Black 1.41% 

-0.11% NH - White 0.78% NH - White 1.52% 

New Jersey NH - Black 2.27% 

1.72%

NH - Black 0.52% 

-1.25% NH - White 0.55% NH - White 1.77% 

New York NH - Black 2.17% 

2.26%

NH - Black 1.56% 

0.12% NH - White -0.09% NH - White 1.44% 

North Carolina NH - Black 2.86% 

2.12%

NH - Black 0.97% 

-0.16% NH - White 0.73% NH - White 1.13% 

Ohio NH - Black 2.66% 

2.54%

NH - Black 1.41% 

0.98% NH - White 0.12% NH - White 0.42% 

Oklahoma NH - Black 3.17% 

2.43%

NH - Black 1.29% 

0.24% NH - White 0.74% NH - White 1.05% 

Pennsylvania NH - Black 1.99% 

1.77%

NH - Black 1.42% 

0.36% NH - White 0.22% NH - White 1.06% 

South Carolina NH - Black 2.52 % 

1.76%

NH - Black 0.79% 

-0.32% NH - White 0.76% NH - White 1.10% 

Tennessee NH - Black 2.40% 

1.66%

NH - Black 1.05% 

-0.08% NH - White 0.74% NH - White 1.13% 

Texas NH - Black 2.67% 

1.57%

NH - Black 1.14% 

0.02% NH - White 1.10% NH - White 1.13% 

Virginia NH - Black 2.91% 

2.11%

NH - Black 0.82% 

-0.35% NH - White 0.80% NH - White 1.17% 

Washington NH - Black 3.20% 

2.46%

NH - Black 1.83% 

0.15% NH - White 0.75% NH - White 1.68% 

Wisconsin NH - Black 2.93% 

2.84%

NH - Black 2.30% 

1.74% NH - White 0.09% NH - White 0.56% 

1 Large/ Medium MSA’s as defined by Census poststrata found in the Census 2000 ACE methodology vol. 3 tab 9 pg. 4

2 Small/ Non Metro MSA’s and all other TEA’s as defined by the Census poststrata found in the Census 2000 ACE methodology vol. 3 tab 9 pg. 4

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to 

Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/C200 Census 2000/Final Report/tables
Final Report to Congress

Table 6: State Undercount Rates

Large/ Medium1 Small/ Non Metro2 

State Undercount Difference Undercount Difference 

Arizona Hispanic 2.54%

2.14%

Hispanic 3.12%

1.77% NH - White 0.41% NH - White 1.35%

California Hispanic 2.57%

2.14%

Hispanic 3.60%

2.13% NH - White 0.43% NH - White 1.48%

Colorado Hispanic 2.59%

2.23%

Hispanic 2.92%

1.28% NH - White 0.36% NH - White 1.64%

Connecticut Hispanic 3.08%

2.69%

Hispanic 3.84%

2.69% NH - White 0.39% NH - White 1.15%

Florida Hispanic 2.18%

1.61%

Hispanic 4.25%

3.23% NH - White 0.58% NH - White 1.01%

Georgia Hispanic 3.82%

3.11%

Hispanic 5.05%

3.86% NH - White 0.71% NH - White 1.19%

Illinois Hispanic 2.63%

2.59%

Hispanic 3.67%

3.16% NH - White 0.04% NH - White 0.51%

Indiana Hispanic 2.66%

2.54%

Hispanic 4.33%

3.56% NH - White 0.13% NH - White 0.76%

Kansas Hispanic 2.76%

2.62%

Hispanic 3.85%

3.51% NH - White 0.14% NH - White 0.35%

Louisiana Hispanic 2.51%

1.73%

Hispanic 3.41%

2.16% NH - White 0.78% NH - White 1.25%

Maryland Hispanic 2.79%

1.97%

Hispanic 3.83%

2.57% NH - White 0.82% NH - White 1.26%

Massachusetts Hispanic 3.11%

2.73%

Hispanic 3.37%

2.48% NH - White 0.38% NH - White 0.89%

Michigan Hispanic 2.57%

2.36%

Hispanic 3.06%

2.71% NH - White 0.21% NH - White 0.35%

Minnesota Hispanic 3.15%

3.09%

Hispanic 3.45%

3.52% NH - White 0.06% NH - White -0.07%

Missouri Hispanic 2.56%

2.49%

Hispanic 3.84%

3.70% NH - White 0.07% NH - White 0.14%

Nevada Hispanic 2.90%

2.13%

Hispanic 3.75%

2.22% NH - White 0.78% NH - White 1.52%

New Jersey Hispanic 2.86%

2.31%

Hispanic 2.84%

1.07% NH - White 0.55% NH - White 1.77%

New Mexico Hispanic 1.98%

1.72%

Hispanic 2.41%

0.85% NH - White 0.26% NH - White 1.56%

U.S. Census Monitoring Board

Page 36 of 174
Table 6: State Undercount Rates (continued)

Large/ Medium1 Small/ Non Metro2 

State Undercount Difference Undercount Difference 

New York Hispanic 3.09%

3.18%

Hispanic 3.62%

2.18% NH - White -0.09% NH - White 1.44%

North Carolina Hispanic 4.19%

3.46%

Hispanic 5.34%

4.21% NH - White 0.73% NH - White 1.13%

Ohio Hispanic 2.63%

2.51%

Hispanic 2.88%

2.45% NH - White 0.12% NH - White 0.42%

Oklahoma Hispanic 2.99%

2.25%

Hispanic 3.45%

2.40% NH - White 0.74% NH - White 1.05%

Oregon Hispanic 3.23%

2.85%

Hispanic 3.90%

2.24% NH - White 0.38% NH - White 1.67%

Pennsylvania Hispanic 2.39%

2.17%

Hispanic 4.49%

3.43% NH - White 0.22% NH - White 1.06%

Tennessee Hispanic 4.02%

3.28%

Hispanic 4.57%

3.45% NH - White 0.74% NH - White 1.13%

Texas Hispanic 2.60%

1.50%

Hispanic 2.85%

1.73% NH - White 1.10% NH - White 1.13%

Utah Hispanic 2.61%

1.96%

Hispanic 3.77%

2.10% NH - White 0.65% NH - White 1.67%

Virginia Hispanic 2.85%

2.06%

Hispanic 4.31%

3.15% NH - White 0.80% NH - White 1.17%

Washington Hispanic 2.96%

2.21%

Hispanic 4.35%

2.66% NH - White 0.75% NH - White 1.68%

Wisconsin Hispanic 2.99%

2.90%

Hispanic 4.14%

3.58% NH - White 0.09% NH - White 0.56%

1 Large/ Medium MSA's as defined by Census poststrata found in the Census 2000 ACE methodology vol. 3 tab 9 pg. 4

2 Small/ Non Metro MSA's and all other TEA's as defined by the Census poststrata found in the Census 2000 ACE methodology vol. 3 tab 9 pg. 4

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to 

Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/C200 Census 2000/Final Report/tables

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Final Report to Congress

Table 7: Undercount Numbers and Rates by Region

Northeast 0

Connecticut 0 5 3 0 0 8

Massachusetts 0 11 3 0 0 14

New Jersey 0 11 9 1 0 21

New York 3 20 38 1 0 62

Pennsylvania 0 33 34 0 0 67

Subtotal 3 80 87 2 0 172

Shares 1.7% 46.5% 50.6% 1.2% 0.0% 100.0%

Midwest

Illinois 49 43 10 0 0 102

Michigan 38 41 4 0 0 83

Subtotal 87 84 14 0 0 185

Shares 47.0% 45.4% 7.6% 0.0% 0.0% 100.0%

South

Alabama 0 40 27 0 0 67

Delaware 0 0 3 0 0 3

Florida 0 35 30 2 0 67

Georgia 0 59 93 7 0 159

Louisiana 0 23 40 1 0 64

Maryland 0 5 18 1 0 24

Mississippi 0 40 42 0 0 82

North Carolina 0 33 67 0 0 100

South Carolina 0 23 23 0 0 46

Texas 0 26 186 41 1 254

Virginia 0 31 99 5 0 135

Subtotal 0 315 628 57 1 1,001

Shares 0.0% 31.5% 62.7% 5.7% 0.1% 100.0%

West

Arizona 0 1 6 7 1 15

California 0 5 41 12 0 58

Hawaii 0 0 0 4 1 5

U.S. Census Monitoring Board

Page 38 of 174

Region

and State

Less than

0.00%

0.00 to 

0.99%

1.00 to 

1.99%

2.00 to 

2.99%

3.00% or

Higher

Total
Table 7: Undercount Numbers and Rates by Region (continued)

Nevada 0 1 16 0 0 17

New Mexico 0 0 10 22 1 33

Subtotal 0 7 73 45 3 128

Shares 0.0% 5.5% 57.0% 35.2% 2.3% 100.0%

All States 90 486 802 104 4 1,486

Shares 6.1% 32.7% 54.0% 7.0% 0.3% 100.0%

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to 

Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/C200 Census 2000/Final Report/tables

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Final Report to Congress

Region

and State

Less than

0.00%

0.00 to 

0.99%

1.00 to 

1.99%

2.00 to 

2.99%

3.00% or

Higher

Total
Table 8: Projected and Actual Growth in Large Metropolitan Counties, 1999-2000

1990-2000 Percentage of Growth

50 or more 36,095,387 37,986,821 39,435,665 5.24% 9.25% 3.67%

25 to 49 22,725,781 25,224,826 25,568,405 11.00% 12.51% 1.34%

10 to 24.9 15,620,043 17,510,324 17,704,654 12.10% 13.35% 1.10%

0 to 9.9 2,807,202 3,242,678 3,225,047 15.51% 14.88% -0.55%

Total 77,248,473 83,964,649 85,933,771 8.69% 11.24% 2.29%

1 Defined as 1 minus the projected 2000 population over the official 2000 population.

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Final Report to Congress

Percent

Minority

Official 1990

Population

Projected 2000

Population

Official 2000

Population

Projected Official Shortfall1
Final Report to Congress

U.S. Census Monitoring Board

Page 41 of 174

Table 9: Percent of Population Living in Poverty in 1990

Percent of Population Living in Poverty

30 50 Size of 

Racial Composition1 0 to 9.9 10 to 29.9 to 49.9 or more Total Population2 

(millions)

Percentages NonHispanic Black 

and Hispanic each

below 10

Percentages NonHispanic Black and

Hispanic each between

10 and 29.9

One or both of the

percentages NonHispanic Black and

Hispanic between 30

and 49.9

One or both of the

percentages NonHispanic Black and

Hispanic above 50

Native American

Majority

Total 

Size of population 

(millions) 

1 Except where specified, census tracts with Native American majorities are excluded from the calculations.

2 An additional 2.2 million people live in census tracts with no poverty rate available.

Source: Census Bureau planning database issued September 20, 2000, CD-DSS D-COMM 7

0.4% 1.1% 2.2% 3.6% 0.7% 146.6

1.8% 2.2% 2.7% 3.9% 2.1% 49.3 

2.3% 3.0% 3.3% 3.7% 2.9% 18.4 

2.9% 3.8% 4.3% 4.7% 4.0% 31.7

0.0% 3.7% 9.1% 11.5% 8.9% 0.5

0.8% 2.0% 3.8% 4.9% 1.6% 246.5 

127.0 96.9 18.7 3.9 246.5 
U.S. Census Monitoring Board

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Final Report to Congress

Table 10: Undercount Rankings

Net Erroneous

Race1 Location Tenure Undercount Nonmatch Enumeration2 Imputations3

Pacific Is. All Areas Non-owner 6.58% 17.62% 7.67% 4.70%

Hispanic Small MSA/ Non-owner 6.17% 18.22% 7.74% 5.59%

Non-metro

American Indian Off Reservation Non-owner 5.57% 15.21% 6.84% 4.12%

American Indian On Reservation Owner 5.04% 14.57% 4.35% 6.00%

American Indian On Reservation Non-owner 4.10% 12.92% 3.85% 5.59%

NH Black Lg/Med MSA Non-owner 3.96% 17.05% 9.04% 5.07%

Hispanic Lg/Med MSA Non-owner 3.90% 14.91% 7.09% 4.81%

Pacific Is. All Areas Owner 2.71% 12.17% 6.21% 4.49%

NH White Small MSA Non-owner 2.67% 11.01% 6.09% 2.73%

NH White Non-metro Non-owner 2.46% 13.38% 6.73% 4.99%

NH Black Small MSA/ Non-owner 2.32% 14.04% 8.21% 4.26%

Non-metro

American Indian Off Reservation Owner 1.60% 10.33% 5.45% 3.51%

NH Asian All Areas Non-owner 1.58% 12.67% 7.55% 4.10%

NH White Large Non-owner 1.58% 11.77% 6.92% 3.76%

Hispanic Small/ Owner 1.45% 10.19% 3.88% 5.34%

Non-metro

Hispanic Lg/Med MSA Owner 1.17% 8.88% 3.71% 4.34%

NH White Medium MSA Non-owner 1.09% 11.08% 7.32% 3.04%

NH Black Lg/Med MSA Owner 0.91% 9.89% 5.68% 3.72%

NH White Small MSA Owner 0.63% 5.10% 3.16% 1.38%

NH Asian All Areas Owner 0.55% 7.66% 4.16% 3.13%

NH White Non-metro Owner 0.42% 7.44% 3.85% 3.32%

NH White Medium MSA Owner 0.17% 4.49% 3.01% 1.41%

NH Black Small/ Owner 0.17% 9.82% 5.90% 3.99%

Non-metro

NH White Large MSA Owner 0.03% 4.99% 3.30% 1.73%

Total 1.18% 8.42% 4.72% 2.93%

1 Whites and Blacks are non-Hispanic. 2 Erroneous enumerations include people who are counted in the wrong area or people who are counted multiple times. 3 Housing units that assumed to be occupied that the Census Bureau imputes.

Source: FinalDSEUS.dat Census files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/ C200 Census 2000/Final

Report/tables p/user/share/Census2000/FINAL REPORT/Tables/Net Undercount Rates for Post-strata.
Final Report to Congress

U.S. Census Monitoring Board

Page 43 of 174

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Autauga 43,411 43,952 1.23% 541

Baldwin 138,141 139,291 0.83% 1,150

Barbour 26,320 26,628 1.16% 308

Bibb 19,587 19,745 0.80% 158

Blount 50,414 50,871 0.90% 457

Bullock 10,203 10,306 1.00% 103

Butler 21,134 21,305 0.80% 171

Calhoun 109,821 111,332 1.36% 1,511

Chambers 36,065 36,377 0.86% 312

Cherokee 23,628 23,831 0.85% 203

Chilton 39,242 39,586 0.87% 344

Choctaw 15,789 15,883 0.59% 94

Clarke 27,480 27,677 0.71% 197

Clay 13,988 14,114 0.89% 126

Cleburne 14,010 14,135 0.88% 125

Coffee 42,835 43,308 1.09% 473

Colbert 54,392 55,101 1.29% 709

Conecuh 14,010 14,111 0.71% 101

Coosa 11,810 11,892 0.69% 82

Covington 37,108 37,450 0.91% 342

Crenshaw 13,505 13,623 0.86% 118

Cullman 76,485 77,244 0.98% 759

Dale 47,464 48,224 1.58% 760

Dallas 45,826 46,312 1.05% 486

DeKalb 63,588 64,279 1.08% 691

Elmore 60,533 61,267 1.20% 734

Escambia 35,435 35,751 0.88% 316

Etowah 101,416 102,695 1.25% 1,279

Fayette 18,155 18,316 0.88% 161

Franklin 30,821 31,222 1.29% 401

Geneva 25,490 25,718 0.88% 228

Greene 9,896 9,969 0.73% 73

Hale 16,895 17,012 0.69% 117

Henry 16,131 16,258 0.78% 127

Houston 87,639 88,836 1.35% 1,197

Estimated Rates of Undercount and Numbers of Persons Missed by County-- Alabama

APPENDIX A

Extimated Rates of Undercount and Numbers of Persons 

Missed by County
U.S. Census Monitoring Board

Page 44 of 174

Final Report to Congress

Jackson 53,347 53,856 0.94% 509

Jefferson 645,934 655,360 1.44% 9,426

Lamar 15,705 15,852 0.93% 147

Lauderdale 86,259 87,396 1.30% 1,137

Lawrence 34,572 34,990 1.19% 418

Lee 110,625 112,087 1.30% 1,462

Limestone 63,033 63,902 1.36% 869

Lowndes 13,426 13,505 0.58% 79

Macon 21,820 22,010 0.86% 190

Madison 269,442 273,122 1.35% 3,680

Marengo 22,346 22,510 0.73% 164
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Arizona

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate 

Persons 

Missed

Apache 68,150 70,350 3.13% 2,200

Cochise 112,088 114,436 2.05% 2,348

Coconino 113,230 116,076 2.45% 2,846

Gila 50,404 51,335 1.81% 931

Graham 30,257 30,892 2.05% 635

Greenlee 8,520 8,762 2.76% 242

La Paz 19,435 19,829 1.99% 394

Maricopa 3,027,366 3,066,626 1.28% 39,260

Mohave 153,801 155,076 0.82% 1,275

Navajo 95,230 97,695 2.52% 2,465

Pima 821,712 831,642 1.19% 9,930

Pinal 164,497 167,401 1.73% 2,904

Santa Cruz 38,176 39,244 2.72% 1,068

Yavapai 163,753 166,520 1.66% 2,767

Yuma 154,163 157,716 2.25% 3,553

Total 5,020,782 5,093,601 1.43% 72,819

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to

Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 45 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- California

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Alameda 1,416,006 1,436,499 1.43% 20,493

Alpine 1,207 1,226 1.54% 19

Amador 30,519 30,960 1.43% 441

Butte 197,327 200,888 1.77% 3,561

Calaveras 40,129 40,734 1.49% 605

Colusa 18,357 18,815 2.44% 458

Contra Costa 937,479 946,778 0.98% 9,299

Del Norte 23,674 24,099 1.76% 425

El Dorado 155,247 155,991 0.48% 744

Fresno 781,740 794,136 1.56% 12,396

Glenn 26,065 26,634 2.14% 569

Humboldt 122,445 124,624 1.75% 2,179

Imperial 131,317 135,201 2.87% 3,884

Inyo 17,788 18,109 1.77% 321

Kern 631,675 641,460 1.53% 9,785

Kings 109,332 112,095 2.46% 2,763

Lake 57,220 58,218 1.71% 998

Lassen 24,918 25,341 1.67% 423

Los Angeles 9,344,086 9,520,496 1.85% 176,410

Madera 115,009 116,768 1.51% 1,759

Marin 235,803 237,663 0.78% 1,860

Mariposa 15,704 15,961 1.61% 257

Mendocino 84,124 85,762 1.91% 1,638

Merced 207,699 212,884 2.44% 5,185

Modoc 9,037 9,197 1.74% 160

Mono 12,495 12,761 2.08% 266

Monterey 380,786 390,997 2.61% 10,211

Napa 119,046 120,273 1.02% 1,227

Nevada 91,167 92,463 1.40% 1,296

Orange 2,803,924 2,839,802 1.26% 35,878

Placer 245,511 246,717 0.49% 1,206

Plumas 20,636 20,972 1.60% 336

Riverside 1,511,034 1,529,341 1.20% 18,307

Sacramento 1,198,004 1,211,008 1.07% 13,004

San Benito 52,727 53,975 2.31% 1,248

San Bernardino 1,664,402 1,690,236 1.53% 25,834

San Diego 2,716,820 2,750,616 1.23% 33,796

San Francisco 756,976 768,991 1.56% 12,015

San Joaquin 544,827 551,879 1.28% 7,052

San Luis Obispo 231,110 235,532 1.88% 4,422

San Mateo 696,711 704,799 1.15% 8,088

Santa Barbara 382,730 392,031 2.37% 9,301

Santa Clara 1,652,871 1,673,275 1.22% 20,404

Santa Cruz 246,574 249,407 1.14% 2,833

Shasta 159,897 162,462 1.58% 2,565

Sierra 3,519 3,575 1.56% 56

U.S. Census Monitoring Board

Page 46 of 174

Final Report to Congress
Siskiyou 43,611 44,323 1.61% 712

Solano 378,568 383,097 1.18% 4,529

Sonoma 447,512 451,551 0.89% 4,039

Stanislaus 439,508 448,880 2.09% 9,372

Sutter 77,547 79,091 1.95% 1,544

Tehama 55,034 56,022 1.76% 988

Trinity 12,780 12,983 1.56% 203

Tulare 361,980 371,291 2.51% 9,311

Tuolumne 49,665 50,468 1.59% 803

Ventura 739,985 748,417 1.13% 8,432

Yolo 161,145 163,018 1.15% 1,873

Yuba 58,885 60,112 2.04% 1,227

Total 33,051,894 33,560,906 1.52% 509,012

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered

to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 47 of 174

Final Report to Congress

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Estimated Rates of Undercount and Numbers of Persons Missed by County-- California (continued)
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Connecticut

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Fairfield 864,591 874,000 1.08% 9,409

Hartford 830,338 837,435 0.85% 7,097

Litchfield 179,682 181,400 0.95% 1,718

Middlesex 148,844 149,829 0.66% 985

New Haven 796,334 805,399 1.13% 9,065

New London 247,208 249,524 0.93% 2,316

Tolland 125,311 125,669 0.29% 358

Windham 105,318 106,687 1.28% 1,369

Total 3,297,626 3,329,944 0.97% 32,318

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 48 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Delaware

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Kent 123,067 125,365 1.83% 2,298

New Castle 482,751 490,220 1.52% 7,469

Sussex 153,199 155,019 1.17% 1,820

Total 759,017 770,604 1.50% 11,587

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat 

Census files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 

02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 49 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Florida

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Alachua 205,107 208,536 1.64% 3,429

Baker 20,154 20,326 0.85% 172

Bay 144,873 146,656 1.22% 1,783

Bradford 21,933 22,133 0.90% 200

Brevard 466,535 471,980 1.15% 5,445

Broward 1,603,094 1,622,540 1.20% 19,446

Calhoun 11,297 11,410 0.99% 113

Charlotte 139,035 140,453 1.01% 1,418

Citrus 115,924 117,037 0.95% 1,113

Clay 139,247 140,463 0.87% 1,216

Collier 246,587 250,917 1.73% 4,330

Columbia 53,586 54,107 0.96% 521

DeSoto 28,980 29,560 1.96% 580

Dixie 12,720 12,815 0.74% 95

Duval 763,204 772,533 1.21% 9,329

Escambia 272,444 275,729 1.19% 3,285

Flagler 49,370 49,865 0.99% 495

Franklin 9,339 9,424 0.90% 85

Gadsden 42,665 43,074 0.95% 409

Gilchrist 13,116 13,217 0.77% 101

Glades 9,666 9,789 1.26% 123

Gulf 11,935 12,036 0.84% 101

Hamilton 10,826 10,931 0.96% 105

Hardee 25,019 25,567 2.14% 548

Hendry 33,488 34,192 2.06% 704

Hernando 128,693 129,498 0.62% 805

Highlands 86,047 87,244 1.37% 1,197

Hillsborough 981,521 993,328 1.19% 11,807

Holmes 16,845 17,005 0.94% 160

Indian River 110,650 111,987 1.19% 1,337

Jackson 40,561 40,941 0.93% 380

Jefferson 11,868 11,955 0.73% 87

Lafayette 5,700 5,777 1.33% 77

Lake 206,761 208,465 0.82% 1,704

Lee 435,271 441,195 1.34% 5,924

Leon 225,858 228,894 1.33% 3,036

Levy 33,833 34,125 0.86% 292

Liberty 5,578 5,629 0.90% 51

Madison 17,032 17,176 0.84% 144

Manatee 257,997 260,350 0.90% 2,353

Marion 252,035 254,851 1.11% 2,816

Martin 123,375 124,946 1.26% 1,571

Miami-Dade 2,207,391 2,250,053 1.90% 42,662

Monroe 78,227 79,672 1.81% 1,445

Nassau 56,976 57,533 0.97% 557

Okaloosa 164,991 167,226 1.34% 2,235

U.S. Census Monitoring Board

Page 50 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Florida

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Okeechobee 33,815 34,405 1.72% 590

Orange 877,513 889,008 1.29% 11,495

Osceola 170,093 172,557 1.43% 2,464

Palm Beach 1,111,856 1,123,433 1.03% 11,577

Pasco 339,393 341,589 0.64% 2,196

Pinellas 898,571 906,278 0.85% 7,707

Polk 471,378 477,758 1.34% 6,380

Putnam 69,025 69,720 1.00% 695

St. Johns 120,942 121,777 0.69% 835

St. Lucie 189,983 192,338 1.22% 2,355

Santa Rosa 115,191 116,352 1.00% 1,161

Sarasota 319,484 321,743 0.70% 2,259

Seminole 361,590 364,983 0.93% 3,393

Sumter 47,078 47,449 0.78% 371

Suwannee 34,179 34,524 1.00% 345

Taylor 18,039 18,191 0.83% 152

Union 9,294 9,397 1.09% 103

Volusia 428,606 433,694 1.17% 5,088

Wakulla 21,719 21,887 0.77% 168

Walton 38,806 39,199 1.00% 393

Washington 19,524 19,696 0.87% 172

Total 15,593,433 15,789,117 1.24% 195,684

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census 

files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 51 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Georgia

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Appling 17,177 17,368 1.10% 191

Atkinson 7,563 7,681 1.53% 118

Bacon 9,948 10,065 1.16% 117

Baker 4,062 4,100 0.94% 38

Baldwin 36,889 37,313 1.14% 424

Banks 14,422 14,571 1.02% 149

Barrow 45,687 46,203 1.12% 516

Bartow 75,118 75,883 1.01% 765

Ben Hill 17,117 17,347 1.33% 230

Berrien 16,082 16,263 1.11% 181

Bibb 148,577 150,850 1.51% 2,273

Bleckley 11,029 11,139 0.99% 110

Brantley 14,548 14,661 0.77% 113

Brooks 16,051 16,209 0.98% 158

Bryan 23,287 23,514 0.97% 227

Bulloch 52,393 53,242 1.59% 849

Burke 21,963 22,213 1.12% 250

Butts 17,607 17,776 0.95% 169

Calhoun 5,005 5,055 0.99% 50

Camden 41,814 42,409 1.40% 595

Candler 9,165 9,295 1.40% 130

Carroll 84,039 85,041 1.18% 1,002

Catoosa 52,867 53,421 1.04% 554

Charlton 9,156 9,234 0.84% 78

Chatham 223,998 227,498 1.54% 3,500

Chattahoochee 10,006 10,280 2.67% 274

Chattooga 23,800 24,072 1.13% 272

Cherokee 140,920 142,161 0.87% 1,241

Clarke 93,309 95,318 2.11% 2,009

Clay 3,300 3,331 0.92% 31

Clayton 233,259 238,216 2.08% 4,957

Clinch 6,519 6,587 1.03% 68

Cobb 600,457 609,046 1.41% 8,589

Coffee 35,954 36,383 1.18% 429

Colquitt 40,788 41,545 1.82% 757

Columbia 88,597 89,785 1.32% 1,188

Cook 15,531 15,703 1.10% 172

Coweta 88,428 89,469 1.16% 1,041

Crawford 12,381 12,486 0.84% 105

Crisp 21,533 21,881 1.59% 348

Dade 14,384 14,520 0.94% 136

Dawson 15,902 16,056 0.96% 154

Decatur 27,539 27,922 1.37% 383

DeKalb 652,194 666,810 2.19% 14,616

Dodge 17,482 17,665 1.04% 183

Dooly 10,254 10,376 1.18% 122

U.S. Census Monitoring Board

Page 52 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Georgia

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Dougherty 91,584 93,048 1.57% 1,464

Douglas 91,325 92,378 1.14% 1,053

Early 12,096 12,220 1.01% 124

Echols 3,754 3,824 1.83% 70

Effingham 37,287 37,614 0.87% 327

Elbert 20,243 20,451 1.02% 208

Emanuel 21,029 21,286 1.21% 257

Evans 9,911 10,041 1.29% 130

Fannin 19,654 19,833 0.90% 179

Fayette 90,685 91,494 0.88% 809

Floyd 86,809 88,342 1.74% 1,533

Forsyth 97,678 98,478 0.81% 800

Franklin 19,695 19,885 0.95% 190

Fulton 784,622 801,518 2.11% 16,896

Gilmer 23,290 23,612 1.36% 322

Glascock 2,452 2,475 0.91% 23

Glynn 66,306 67,187 1.31% 881

Gordon 43,675 44,327 1.47% 652

Grady 23,381 23,672 1.23% 291

Greene 14,209 14,353 1.00% 144

Gwinnett 582,063 589,901 1.33% 7,838

Habersham 34,035 34,486 1.31% 451

Hall 136,980 140,030 2.18% 3,050

Hancock 8,613 8,680 0.77% 67

Haralson 25,352 25,628 1.08% 276

Harris 23,486 23,664 0.75% 178

Hart 22,463 22,755 1.28% 292

Heard 10,903 11,015 1.02% 112

Henry 118,586 119,705 0.93% 1,119

Houston 108,372 109,704 1.21% 1,332

Irwin 9,562 9,659 1.01% 97

Jackson 40,780 41,262 1.17% 482

Jasper 11,343 11,453 0.96% 110

Jeff Davis 12,588 12,731 1.13% 143

Jefferson 16,806 17,007 1.18% 201

Jenkins 8,449 8,544 1.11% 95

Johnson 7,933 8,001 0.85% 68

Jones 23,287 23,461 0.74% 174

Lamar 15,082 15,242 1.05% 160

Lanier 6,963 7,033 1.00% 70

Laurens 43,630 44,101 1.07% 471

Lee 23,959 24,195 0.97% 236

Liberty 56,826 57,891 1.84% 1,065

Lincoln 8,276 8,343 0.80% 67

Long 10,304 10,454 1.43% 150

Lowndes 85,291 86,667 1.59% 1,376

(continued)

U.S. Census Monitoring Board

Page 53 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Georgia

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Lumpkin 19,664 19,987 1.61% 323

McDuffie 20,899 21,183 1.34% 284

McIntosh 10,683 10,765 0.76% 82

Macon 13,091 13,219 0.97% 128

Madison 25,572 25,823 0.97% 251

Marion 7,059 7,136 1.08% 77

Meriwether 22,105 22,323 0.98% 218

Miller 6,250 6,310 0.96% 60

Mitchell 21,960 22,193 1.05% 233

Monroe 21,131 21,322 0.89% 191

Montgomery 7,516 7,592 1.01% 76

Morgan 15,288 15,436 0.96% 148

Murray 36,302 36,772 1.28% 470

Muscogee 177,184 180,190 1.67% 3,006

Newton 60,931 61,672 1.20% 741

Oconee 25,985 26,245 0.99% 260

Oglethorpe 12,519 12,623 0.82% 104

Paulding 81,166 81,800 0.78% 634

Peach 22,594 22,871 1.21% 277

Pickens 22,771 22,992 0.96% 221

Pierce 15,521 15,670 0.95% 149

Pike 13,373 13,490 0.87% 117

Polk 37,287 37,834 1.45% 547

Pulaski 8,482 8,579 1.13% 97

Putnam 18,542 18,722 0.96% 180

Quitman 2,598 2,619 0.79% 21

Rabun 14,766 14,938 1.15% 172

Randolph 7,488 7,566 1.03% 78

Richmond 188,864 191,778 1.52% 2,914

Rockdale 69,009 69,910 1.29% 901

Schley 3,755 3,795 1.05% 40

Screven 15,065 15,194 0.85% 129

Seminole 9,065 9,148 0.91% 83

Spalding 57,544 58,340 1.36% 796

Stephens 24,494 24,768 1.10% 274

Stewart 4,983 5,031 0.95% 48

Sumter 31,688 32,120 1.35% 432

Talbot 6,481 6,528 0.72% 47

Taliaferro 2,054 2,072 0.87% 18

Tattnall 18,367 18,640 1.47% 273

Taylor 8,397 8,472 0.89% 75

Telfair 10,247 10,344 0.94% 97

Terrell 10,762 10,882 1.10% 120

Thomas 41,588 42,169 1.38% 581

Tift 36,898 37,412 1.37% 514

Toombs 25,593 25,990 1.53% 397

(continued)

U.S. Census Monitoring Board

Page 54 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Georgia

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Towns 8,812 8,888 0.85% 76

Treutlen 6,466 6,529 0.97% 63

Troup 57,262 58,162 1.55% 900

Turner 9,343 9,446 1.10% 103

Twiggs 10,450 10,533 0.79% 83

Union 16,846 17,004 0.93% 158

Upson 27,145 27,458 1.14% 313

Walker 60,048 60,941 1.47% 893

Walton 60,145 60,774 1.04% 629

Ware 33,232 33,731 1.48% 499

Warren 6,219 6,270 0.82% 51

Washington 19,724 19,903 0.90% 179

Wayne 24,417 24,779 1.46% 362

Webster 2,388 2,409 0.87% 21

Wheeler 5,099 5,154 1.07% 55

White 19,429 19,629 1.02% 200

Whitfield 82,757 84,753 2.35% 1,996

Wilcox 7,102 7,165 0.88% 63

Wilkes 10,552 10,683 1.23% 131

Wilkinson 10,137 10,217 0.78% 80

Worth 21,759 21,974 0.98% 215

Total 7,952,631 8,072,483 1.48% 119,852

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census 

files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 

02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 55 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Hawaii

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Hawaii 145,873 149,445 2.39% 3,572

Honolulu 845,211 862,954 2.06% 17,743

Kalawao 147 153 4.13% 6

Kauai 57,831 59,211 2.33% 1,380

Maui 126,693 129,967 2.52% 3,274

Total 1,175,755 1,201,729 2.16% 25,974

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 56 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Illinois

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate1

Persons 

Missed1

Adams 65,527 65,355 -0.26% -172

Alexander 8,995 9,003 0.08% 8

Bond 15,219 15,162 -0.38% -57

Boone 41,467 41,933 1.11% 466

Brown 4,978 4,963 -0.30% -15

Bureau 35,003 34,949 -0.16% -54

Calhoun 5,029 5,008 -0.42% -21

Carroll 16,426 16,576 0.90% 150

Cass 13,483 13,501 0.13% 18

Champaign 164,831 166,947 1.27% 2,116

Christian 33,562 33,860 0.88% 298

Clark 16,761 16,703 -0.35% -58

Clay 14,083 14,024 -0.42% -59

Clinton 33,169 33,431 0.78% 262

Coles 48,642 48,678 0.07% 36

Cook 5,283,124 5,354,117 1.33% 70,993

Crawford 18,884 19,038 0.81% 154

Cumberland 11,118 11,067 -0.46% -51

De Witt 16,532 16,682 0.90% 150

DeKalb 81,184 81,690 0.62% 506

Douglas 19,622 19,579 -0.22% -43

DuPage 889,048 892,169 0.35% 3,121

Edgar 18,916 18,871 -0.24% -45

Edwards 6,899 6,867 -0.46% -32

Effingham 33,797 33,882 0.25% 85

Fayette 20,028 19,949 -0.40% -79

Ford 13,838 13,801 -0.27% -37

Franklin 38,453 38,787 0.86% 334

Fulton 35,688 36,005 0.88% 317

Gallatin 6,374 6,348 -0.41% -26

Greene 14,436 14,567 0.90% 131

Grundy 37,188 37,123 -0.18% -65

Hamilton 8,416 8,380 -0.43% -36

Hancock 19,761 19,919 0.79% 158

Hardin 4,563 4,545 -0.39% -18

Henderson 8,136 8,109 -0.34% -27

Henry 50,433 50,869 0.86% 436

Iroquois 30,648 30,784 0.44% 136

Jackson 53,404 54,103 1.29% 699

Jasper 10,039 9,986 -0.53% -53

Jefferson 37,475 37,383 -0.25% -92

Jersey 20,771 20,695 -0.37% -76

Jo Daviess 22,086 22,283 0.88% 197

U.S. Census Monitoring Board

Page 57 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Illinois

Johnson 10,154 10,102 -0.52% -52

Kane 397,616 400,191 0.64% 2,575

Kankakee 99,737 100,334 0.60% 597

Kendall 54,352 54,398 0.08% 46

Knox 51,466 51,416 -0.10% -50

La Salle 108,301 109,375 0.98% 1,074

Lake 623,378 626,394 0.48% 3,016

Lawrence 14,864 14,998 0.90% 134

Lee 33,056 32,989 -0.20% -67

Livingston 36,055 35,980 -0.21% -75

Logan 26,908 26,865 -0.16% -43

Macon 111,133 111,866 0.66% 733

Macoupin 47,829 47,655 -0.37% -174

Madison 253,162 253,818 0.26% 656

Marion 40,722 40,610 -0.28% -112

Marshall 12,903 12,851 -0.40% -52

Mason 15,819 15,963 0.90% 144

Massac 14,827 14,950 0.82% 123

McDonough 28,137 28,153 0.06% 16

McHenry 258,558 258,871 0.12% 313

McLean 139,140 140,416 0.91% 1,276

Menard 12,304 12,254 -0.40% -50

Mercer 16,744 16,681 -0.38% -63

Monroe 27,235 27,103 -0.49% -132

Montgomery 28,093 28,111 0.06% 18

Morgan 33,246 33,571 0.97% 325

Moultrie 13,818 13,933 0.83% 115

Ogle 50,418 50,940 1.02% 522

Peoria 176,553 178,338 1.00% 1,785

Perry 20,651 20,575 -0.37% -76

Piatt 16,173 16,102 -0.44% -71

Pike 16,662 16,609 -0.32% -53

Pope 4,126 4,107 -0.47% -19

Pulaski 7,057 7,056 -0.01% -1

Putnam 6,075 6,048 -0.45% -27

Randolph 29,778 29,667 -0.38% -111

Richland 15,999 15,950 -0.31% -49

Rock Island 144,729 146,408 1.15% 1,679

Saline 25,550 25,776 0.88% 226

Sangamon 185,617 187,396 0.95% 1,779

Schuyler 7,084 7,058 -0.37% -26

Scott 5,481 5,463 -0.32% -18

Shelby 22,600 22,502 -0.44% -98

St. Clair 251,213 253,187 0.78% 1,974

Stark 6,215 6,195 -0.32% -20

Stephenson 48,159 48,588 0.88% 429

Tazewell 125,183 125,953 0.61% 770

(continued)

U.S. Census Monitoring Board

Page 58 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Illinois

Union 17,378 17,537 0.91% 159

Vermilion 80,810 81,630 1.00% 820

Wabash 12,760 12,722 -0.30% -38

Warren 17,512 17,481 -0.18% -31

Washington 14,886 14,821 -0.44% -65

Wayne 16,944 16,877 -0.40% -67

White 14,944 14,894 -0.34% -50

Whiteside 59,399 60,050 1.08% 651

Will 492,605 493,886 0.26% 1,281

Williamson 59,643 60,195 0.92% 552

Winnebago 273,344 276,341 1.08% 2,997

Woodford 34,371 34,620 0.72% 249

Total 12,097,512 12,202,511 0.86% 104,999

 Negative undercount rates indicate over counts.

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered to

Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 59 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by Parish-- Louisiana

Parish 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Acadia 57,879 58,769 1.52% 890

Allen 21,258 21,486 1.06% 228

Ascension 75,981 76,695 0.93% 714

Assumption 23,188 23,377 0.81% 189

Avoyelles 38,379 38,787 1.05% 408

Beauregard 31,796 32,115 0.99% 319

Bienville 15,379 15,522 0.92% 143

Bossier 96,225 97,592 1.40% 1,367

Caddo 245,764 249,366 1.44% 3,602

Calcasieu 179,030 181,687 1.46% 2,657

Caldwell 9,852 9,952 1.01% 100

Cameron 9,926 10,018 0.92% 92

Catahoula 10,412 10,502 0.86% 90

Claiborne 15,686 15,840 0.97% 154

Concordia 19,558 19,756 1.00% 198

De Soto 25,172 25,414 0.95% 242

East Baton Rouge 398,268 404,168 1.46% 5,900

East Carroll 8,364 8,466 1.21% 102

East Feliciana 18,508 18,656 0.79% 148

Evangeline 33,662 34,056 1.16% 394

Franklin 20,474 20,686 1.03% 212

Grant 18,479 18,662 0.98% 183

Iberia 71,651 72,528 1.21% 877

Iberville 29,966 30,231 0.88% 265

Jackson 15,119 15,272 1.00% 153

Jefferson 451,109 457,132 1.32% 6,023

Jefferson Davis 31,020 31,363 1.09% 343

Lafayette 185,654 188,338 1.43% 2,684

Lafourche 88,258 89,555 1.45% 1,297

La Salle 13,358 13,485 0.94% 127

Lincoln 37,200 37,686 1.29% 486

Livingston 91,230 91,941 0.77% 711

Madison 12,261 12,417 1.25% 156

Morehouse 30,066 30,392 1.07% 326

Natchitoches 36,476 36,934 1.24% 458

Orleans 467,033 477,332 2.16% 10,299

Ouachita 142,285 144,507 1.54% 2,222

Plaquemines 26,029 26,295 1.01% 266

Pointe Coupee 22,426 22,635 0.92% 209

Rapides 120,833 122,661 1.49% 1,828

Red River 9,339 9,432 0.98% 93

Richland 19,812 20,032 1.10% 220

Sabine 23,042 23,275 1.00% 233

St. Bernard 66,441 67,241 1.19% 800

St. Charles 47,642 48,073 0.90% 431

St. Helena 10,453 10,528 0.72% 75

U.S. Census Monitoring Board

Page 60 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by Parish-- Louisiana

Parish 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

St. James 20,951 21,097 0.69% 146

St. John the Baptist 42,601 42,960 0.84% 359

St. Landry 86,172 87,339 1.34% 1,167

St. Martin 47,791 48,379 1.22% 588

St. Mary 52,871 53,441 1.07% 570

St. Tammany 188,922 190,584 0.87% 1,662

Tangipahoa 97,313 98,352 1.06% 1,039

Tensas 6,127 6,196 1.12% 69

Terrebonne 103,090 104,344 1.20% 1,254

Union 22,353 22,561 0.92% 208

Vermilion 53,040 53,655 1.15% 615

Vernon 49,161 50,033 1.74% 872

Washington 42,139 42,563 1.00% 424

Webster 40,895 41,460 1.36% 565

West Baton Rouge 21,031 21,258 1.07% 227

West Carroll 11,524 11,644 1.03% 120

West Feliciana 9,965 10,065 1.00% 100

Winn 15,122 15,285 1.06% 163

Total 4,333,011 4,392,074 1.34% 59,063

 Note: Calculations apply to the non-group quarters population only.

files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 

02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census 

U.S. Census Monitoring Board

Page 61 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Maryland

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Allegany 68,772 69,823 1.51% 1,051

Anne Arundel 473,666 478,763 1.06% 5,097

Baltimore City 625,401 638,809 2.10% 13,408

Baltimore 736,652 745,774 1.22% 9,122

Calvert 73,982 74,861 1.17% 879

Caroline 29,319 29,697 1.27% 378

Carroll 147,316 148,614 0.87% 1,298

Cecil 84,730 85,535 0.94% 805

Charles 119,177 120,555 1.14% 1,378

Dorchester 30,004 30,381 1.24% 377

Frederick 190,622 192,457 0.95% 1,835

Garrett 29,229 29,585 1.20% 356

Harford 217,028 219,153 0.97% 2,125

Howard 244,224 247,163 1.19% 2,939

Kent 17,849 18,074 1.25% 225

Montgomery 863,910 875,540 1.33% 11,630

Prince George's 784,158 799,379 1.90% 15,221

Queen Anne's 40,054 40,409 0.88% 355

St. Mary's 83,429 84,472 1.23% 1,043

Somerset 19,843 20,087 1.22% 244

Talbot 33,200 33,635 1.29% 435

Washington 122,503 123,934 1.15% 1,431

Wicomico 81,500 82,602 1.33% 1,102

Worcester 45,862 46,505 1.38% 643

Total 5,162,430 5,235,806 1.40% 73,376

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census 

files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 62 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Massachusetts

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Barnstable 216,553 218,510 0.90% 1,957

Berkshire 128,821 130,147 1.02% 1,326

Bristol 521,004 523,639 0.50% 2,635

Dukes 14,794 14,938 0.96% 144

Essex 706,530 711,954 0.76% 5,424

Franklin 70,129 70,825 0.98% 696

Hampden 441,799 445,402 0.81% 3,603

Hampshire 134,006 134,495 0.36% 489

Middlesex 1,412,506 1,422,014 0.67% 9,508

Nantucket 8,760 8,858 1.11% 98

Norfolk 632,876 635,792 0.46% 2,916

Plymouth 461,098 463,119 0.44% 2,021

Suffolk 653,214 664,410 1.69% 11,196

Worcester 725,791 730,782 0.68% 4,991

Total 6,127,881 6,174,884 0.76% 47,003

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 63 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Michigan

County 

Name

Official 

Count

Adjusted 

Count 

Undercount 

Rate1

Persons 

Missed1

Alcona 11,520 11,450 -0.61% -70

Alger 8,876 8,840 -0.41% -36

Allegan 103,748 103,884 0.13% 136

Alpena 30,747 30,974 0.73% 227

Antrim 22,824 22,718 -0.47% -106

Arenac 16,465 16,389 -0.46% -76

Baraga 7,963 7,954 -0.12% -9

Barry 56,320 56,670 0.62% 350

Bay 108,384 109,221 0.77% 837

Benzie 15,734 15,658 -0.49% -76

Berrien 158,086 158,923 0.53% 837

Branch 42,661 43,012 0.82% 351

Calhoun 133,853 135,086 0.91% 1,233

Cass 50,360 50,738 0.74% 378

Charlevoix 25,802 25,941 0.53% 139

Cheboygan 26,087 25,985 -0.39% -102

Chippewa 32,577 32,583 0.02% 6

Clare 30,751 30,624 -0.41% -127

Clinton 63,948 63,634 -0.49% -314

Crawford 13,806 13,748 -0.42% -58

Delta 37,951 38,224 0.71% 273

Dickinson 27,005 27,196 0.70% 191

Eaton 101,921 102,783 0.84% 862

Emmet 30,725 30,638 -0.28% -87

Genesee 430,794 434,485 0.85% 3,691

Gladwin 25,685 25,851 0.64% 166

Gogebic 16,353 16,298 -0.34% -55

Grand Traverse 75,831 76,435 0.79% 604

Gratiot 37,228 37,553 0.86% 325

Hillsdale 45,046 45,121 0.17% 75

Houghton 32,910 32,848 -0.19% -62

Huron 35,397 35,234 -0.46% -163

Ingham 262,493 265,751 1.23% 3,258

Ionia 55,729 56,150 0.75% 421

Iosco 26,944 27,016 0.27% 72

Iron 12,563 12,507 -0.45% -56

Isabella 57,248 57,926 1.17% 678

Jackson 148,383 149,618 0.83% 1,235

Kalamazoo 227,463 229,830 1.03% 2,367

Kalkaska 16,377 16,299 -0.48% -78

Kent 561,546 564,909 0.60% 3,363

Keweenaw 2,123 2,109 -0.67% -14

Lake 10,730 10,698 -0.30% -32

Lapeer 86,035 86,234 0.23% 199

Leelanau 20,899 20,825 -0.35% -74

Lenawee 93,744 94,159 0.44% 415

U.S. Census Monitoring Board

Page 64 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Michigan

County 

Name

Official 

Count

Adjusted 

Count 

Undercount 

Rate1

Persons 

Missed1

Livingston 155,270 155,475 0.13% 205

Luce 5,965 5,948 -0.29% -17

Mackinac 11,746 11,735 -0.09% -11

Macomb 779,288 782,065 0.36% 2,777

Manistee 23,363 23,280 -0.35% -83

Marquette 60,598 60,485 -0.19% -113

Mason 27,739 27,964 0.80% 225

Mecosta 37,170 37,110 -0.16% -60

Menominee 24,891 24,799 -0.37% -92

Midland 81,416 81,298 -0.15% -118

Missaukee 14,272 14,210 -0.44% -62

Monroe 144,402 144,850 0.31% 448

Montcalm 58,549 58,689 0.24% 140

Montmorency 10,188 10,134 -0.54% -54

Muskegon 164,141 164,981 0.51% 840

Newaygo 47,193 47,004 -0.40% -189

Oakland 1,180,408 1,187,435 0.59% 7,027

Oceana 26,108 26,079 -0.11% -29

Ogemaw 21,341 21,243 -0.46% -98

Ontonagon 7,641 7,602 -0.52% -39

Osceola 22,837 22,745 -0.41% -92

Oscoda 9,358 9,311 -0.50% -47

Otsego 23,070 22,967 -0.45% -103

Ottawa 229,536 230,167 0.27% 631

Presque Isle 14,190 14,113 -0.54% -77

Roscommon 25,136 25,013 -0.49% -123

Saginaw 204,439 206,366 0.93% 1,927

St. Clair 162,558 163,125 0.35% 567

St. Joseph 61,426 61,753 0.53% 327

Sanilac 43,791 43,880 0.20% 89

Schoolcraft 8,522 8,492 -0.35% -30

Shiawassee 70,966 71,493 0.74% 527

Tuscola 56,805 57,192 0.68% 387

Van Buren 74,347 74,985 0.85% 638

Washtenaw 301,593 304,482 0.95% 2,889

Wayne 2,028,544 2,058,051 1.43% 29,507

Wexford 30,113 30,344 0.76% 231

Total 9,688,555 9,757,591 0.71% 69,036

Negative undecount rates indicates over counts.

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered

to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 65 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Mississippi

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Adams 33,853 34,222 1.08% 369

Alcorn 34,044 34,423 1.10% 379

Amite 13,575 13,674 0.72% 99

Attala 19,279 19,468 0.97% 189

Benton 7,910 7,974 0.81% 64

Bolivar 38,441 39,004 1.44% 563

Calhoun 14,821 14,978 1.05% 157

Carroll 10,467 10,547 0.76% 80

Chickasaw 19,232 19,485 1.30% 253

Choctaw 9,442 9,524 0.86% 82

Claiborne 10,027 10,106 0.79% 79

Clarke 17,776 17,917 0.79% 141

Clay 21,553 21,766 0.98% 213

Coahoma 29,848 30,256 1.35% 408

Copiah 27,511 27,836 1.17% 325

Covington 19,113 19,259 0.76% 146

DeSoto 106,621 107,747 1.05% 1,126

Forrest 67,194 68,304 1.62% 1,110

Franklin 8,355 8,416 0.73% 61

George 18,741 19,001 1.37% 260

Greene 11,070 11,150 0.72% 80

Grenada 22,737 22,996 1.12% 259

Hancock 42,515 43,161 1.50% 646

Harrison 182,320 185,503 1.72% 3,183

Hinds 240,607 243,955 1.37% 3,348

Holmes 20,937 21,140 0.96% 203

Humphreys 11,111 11,257 1.29% 146

Issaquena 2,011 2,035 1.16% 24

Itawamba 22,002 22,202 0.90% 200

Jackson 129,454 131,447 1.52% 1,993

Jasper 17,987 18,111 0.68% 124

Jefferson 9,104 9,173 0.75% 69

Jefferson Davis 13,858 13,959 0.72% 101

Jones 63,250 63,920 1.05% 670

Kemper 10,056 10,133 0.76% 77

Lafayette 33,922 34,390 1.36% 468

Lamar 38,605 39,186 1.48% 581

Lauderdale 74,749 75,873 1.48% 1,124

Lawrence 13,164 13,270 0.80% 106

Leake 20,185 20,382 0.97% 197

Lee 74,405 75,368 1.28% 963

Leflore 34,987 35,509 1.47% 522

Lincoln 32,521 32,822 0.92% 301

Lowndes 59,677 60,571 1.48% 894

Madison 72,615 73,632 1.38% 1,017

Marion 24,665 24,988 1.29% 323

U.S. Census Monitoring Board

Page 66 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Mississippi

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Marshall 33,294 33,580 0.85% 286

Monroe 37,599 38,102 1.32% 503

Montgomery 12,039 12,155 0.95% 116

Neshoba 28,076 28,378 1.06% 302

Newton 21,102 21,290 0.88% 188

Noxubee 12,370 12,473 0.82% 103

Oktibbeha 38,522 39,156 1.62% 634

Panola 33,677 33,996 0.94% 319

Pearl River 47,833 48,295 0.96% 462

Perry 12,020 12,114 0.78% 94

Pike 38,081 38,464 1.00% 383

Pontotoc 26,483 26,755 1.02% 272

Prentiss 24,703 24,944 0.96% 241

Quitman 9,988 10,097 1.08% 109

Rankin 110,458 112,080 1.45% 1,622

Scott 28,099 28,420 1.13% 321

Sharkey 6,475 6,555 1.23% 80

Simpson 26,675 26,911 0.88% 236

Smith 16,051 16,168 0.73% 117

Stone 12,918 13,035 0.90% 117

Sunflower 29,029 29,450 1.43% 421

Tallahatchie 14,800 14,939 0.93% 139

Tate 24,217 24,448 0.94% 231

Tippah 20,446 20,658 1.03% 212

Tishomingo 18,887 19,187 1.56% 300

Tunica 9,117 9,261 1.56% 144

Union 25,117 25,372 1.01% 255

Walthall 14,978 15,100 0.81% 122

Warren 49,043 49,608 1.14% 565

Washington 62,117 63,029 1.45% 912

Wayne 21,015 21,172 0.74% 157

Webster 10,100 10,200 0.98% 100

Wilkinson 9,273 9,342 0.74% 69

Winston 19,599 19,783 0.93% 184

Yalobusha 12,952 13,071 0.91% 119

Yazoo 25,774 26,141 1.40% 367

Total 2,749,244 2,783,770 1.24% 34,157

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 67 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Nevada

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons

Missed

Carson City 49,234 50,218 1.96% 984

Churchill 23,568 24,001 1.80% 433

Clark 1,356,350 1,378,606 1.61% 22,256

Douglas 41,023 41,713 1.65% 690

Elko 44,497 45,318 1.81% 821

Esmeralda 965 983 1.81% 18

Eureka 1,642 1,671 1.75% 29

Humboldt 15,883 16,167 1.76% 284

Lander 5,708 5,803 1.64% 95

Lincoln 3,814 3,874 1.54% 60

Lyon 33,992 34,576 1.69% 584

Mineral 4,962 5,046 1.66% 84

Nye 32,254 32,505 0.77% 251

Pershing 5,279 5,379 1.85% 100

Storey 3,395 3,446 1.48% 51

Washoe 334,076 340,694 1.94% 6,618

White Pine 7,940 8,068 1.59% 128

Total 1,964,582 1,998,067 1.68% 33,485

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat 

Census files delivered to Census Monitoring Board by Census Bureau 02/26/01 and 

02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 68 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- New Jersey

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Atlantic 246,058 248,854 1.12% 2,797

Bergen 872,769 880,663 0.90% 7,898

Burlington 409,222 412,191 0.72% 2,971

Camden 498,526 503,406 0.97% 4,883

Cape May 99,670 100,389 0.72% 720

Cumberland 134,173 135,864 1.25% 1,692

Essex 770,844 785,159 1.82% 14,319

Gloucester 249,472 251,764 0.91% 2,294

Hudson 599,525 612,758 2.16% 13,236

Hunterdon 117,643 118,245 0.51% 603

Mercer 329,669 333,307 1.09% 3,640

Middlesex 729,342 737,009 1.04% 7,671

Monmouth 605,265 609,939 0.77% 4,678

Morris 461,026 465,032 0.86% 4,008

Ocean 503,025 506,212 0.63% 3,190

Passaic 479,073 486,840 1.60% 7,770

Salem 63,063 63,601 0.85% 539

Somerset 292,981 296,411 1.16% 3,432

Sussex 142,479 144,539 1.43% 2,061

Union 514,733 521,685 1.33% 6,954

Warren 100,971 101,640 0.66% 670

Total 8,219,529 8,315,510 1.15% 96,026

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 69 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- New Mexico

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Bernalillo 546,051 553,613 1.37% 7,562

Catron 3,533 3,601 1.90% 68

Chaves 60,086 61,462 2.24% 1,376

Cibola 24,529 25,262 2.90% 733

Colfax 13,768 14,083 2.24% 315

Curry 43,863 44,856 2.21% 993

De Baca 2,169 2,215 2.08% 46

Dona Ana 169,663 173,582 2.26% 3,919

Eddy 50,908 51,856 1.83% 948

Grant 30,398 31,071 2.17% 673

Guadalupe 4,155 4,251 2.25% 96

Harding 810 826 1.88% 16

Hidalgo 5,847 5,995 2.46% 148

Lea 53,711 54,740 1.88% 1,029

Lincoln 19,192 19,584 2.00% 392

Los Alamos 18,251 18,515 1.43% 264

Luna 24,763 25,328 2.23% 565

McKinley 73,939 76,779 3.70% 2,840

Mora 5,124 5,219 1.82% 95

Otero 61,070 62,337 2.03% 1,267

Quay 9,967 10,200 2.28% 233

Rio Arriba 40,725 41,620 2.15% 895

Roosevelt 17,253 17,688 2.46% 435

Sandoval 89,213 90,484 1.40% 1,271

San Juan 112,587 115,495 2.52% 2,908

San Miguel 28,735 29,377 2.18% 642

Santa Fe 126,916 129,602 2.07% 2,686

Sierra 13,004 13,280 2.08% 276

Socorro 17,504 17,942 2.44% 438

Taos 29,679 30,322 2.12% 643

Torrance 16,387 16,692 1.83% 305

Union 4,158 4,251 2.19% 93

Valencia 64,781 65,886 1.68% 1,105

Total 1,782,739 1,818,015 1.94% 35,276

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 70 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- New York

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate1

Persons 

Missed1

Albany 279,008 282,700 1.31% 3,692

Allegany 45,623 46,408 1.69% 785

Bronx 1,285,415 1,320,464 2.65% 35,049

Broome 191,421 193,520 1.08% 2,099

Cattaraugus 80,719 82,091 1.67% 1,372

Cayuga 77,162 77,853 0.89% 691

Chautauqua 133,363 135,049 1.25% 1,686

Chemung 85,557 86,562 1.16% 1,005

Chenango 50,143 50,999 1.68% 856

Clinton 72,797 74,059 1.70% 1,262

Columbia 60,371 61,441 1.74% 1,070

Cortland 45,479 45,890 0.90% 411

Delaware 46,037 46,828 1.69% 791

Dutchess 261,987 261,883 -0.04% -104

Erie 919,474 924,381 0.53% 4,907

Essex 35,943 36,558 1.68% 615

Franklin 44,155 44,897 1.65% 742

Fulton 53,146 53,530 0.72% 384

Genesee 58,931 59,943 1.69% 1,012

Greene 44,252 44,920 1.49% 668

Hamilton 5,284 5,374 1.67% 90

Herkimer 63,319 63,777 0.72% 458

Jefferson 103,536 104,714 1.13% 1,178

Kings 2,426,027 2,470,205 1.79% 44,178

Lewis 26,680 27,134 1.67% 454

Livingston 57,579 57,715 0.24% 136

Madison 64,767 65,868 1.67% 1,101

Monroe 708,834 713,116 0.60% 4,282

Montgomery 48,498 49,371 1.77% 873

Nassau 1,312,886 1,311,387 -0.11% -1,499

New York 1,477,358 1,505,582 1.87% 28,224

Niagara 215,628 216,396 0.36% 768

Oneida 220,055 222,709 1.19% 2,654

Onondaga 444,830 447,040 0.49% 2,210

Ontario 97,133 97,401 0.27% 268

Orange 327,675 333,466 1.74% 5,791

Orleans 40,685 40,805 0.29% 120

Oswego 118,372 119,828 1.21% 1,456

Otsego 56,664 57,645 1.70% 981

Putnam 93,581 95,132 1.63% 1,551

Queens 2,202,506 2,232,299 1.33% 29,793

Rensselaer 147,346 149,104 1.18% 1,758

Richmond 434,542 435,730 0.27% 1,188

Rockland 279,104 279,440 0.12% 336

Saratoga 196,326 196,802 0.24% 476

Schenectady 141,893 142,549 0.46% 656

U.S. Census Monitoring Board

Page 71 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- New York

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate1

Persons 

Missed1

Schoharie 29,841 30,347 1.67% 506

Schuyler 18,602 18,919 1.68% 317

Seneca 31,717 32,048 1.03% 331

St. Lawrence 100,765 102,026 1.24% 1,261

Steuben 97,197 98,862 1.68% 1,665

Suffolk 1,390,791 1,388,294 -0.18% -2,497

Sullivan 69,141 70,464 1.88% 1,323

Tioga 51,273 52,138 1.66% 865

Tompkins 84,546 85,507 1.12% 961

Ulster 166,455 168,240 1.06% 1,785

Warren 62,010 62,167 0.25% 157

Washington 57,294 58,274 1.68% 980

Wayne 92,036 92,579 0.59% 543

Westchester 899,806 904,806 0.55% 5,000

Wyoming 39,003 39,237 0.60% 234

Yates 23,428 23,570 0.60% 142

Total 18,395,996 18,598,045 1.09% 202,049

 1 Negative undercount rates indicate over counts.

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 72 of 174

Final Report to Congress
Final Report to Congress

Estimated Rates of Undercount and Numbers of Persons Missed by County-- North Carolina

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Alamance 127,049 128,725 1.30% 1,676

Alexander 33,368 33,889 1.54% 521

Alleghany 10,468 10,587 1.12% 119

Anson 23,848 24,051 0.84% 203

Ashe 24,083 24,318 0.97% 235

Avery 15,293 15,439 0.94% 146

Beaufort 44,381 44,834 1.01% 453

Bertie 19,575 19,732 0.80% 157

Bladen 31,580 31,876 0.93% 296

Brunswick 72,419 73,109 0.94% 690

Buncombe 199,565 202,834 1.61% 3,269

Burke 85,538 87,011 1.69% 1,473

Cabarrus 128,894 130,333 1.10% 1,439

Caldwell 76,266 77,243 1.26% 977

Camden 6,881 6,935 0.78% 54

Carteret 58,299 58,894 1.01% 595

Caswell 22,202 22,386 0.82% 184

Catawba 139,543 142,056 1.77% 2,513

Chatham 48,726 49,368 1.30% 642

Cherokee 24,014 24,230 0.89% 216

Chowan 13,827 13,964 0.98% 137

Clay 8,651 8,721 0.81% 70

Cleveland 93,807 95,222 1.49% 1,415

Columbus 53,370 53,882 0.95% 512

Craven 86,299 87,749 1.65% 1,450

Cumberland 284,497 289,571 1.75% 5,074

Currituck 18,027 18,198 0.94% 171

Dare 29,735 30,065 1.10% 330

Davidson 145,350 146,863 1.03% 1,513

Davie 34,459 34,791 0.95% 332

Duplin 48,081 48,824 1.52% 743

Durham 213,504 217,750 1.95% 4,246

Edgecombe 54,484 55,121 1.15% 637

Forsyth 296,118 300,552 1.48% 4,434

Franklin 46,009 46,496 1.05% 487

Gaston 187,274 189,770 1.32% 2,496

Gates 10,359 10,438 0.76% 79

Graham 7,876 7,946 0.88% 70

Granville 43,007 43,447 1.01% 440

Greene 17,751 17,962 1.17% 211

Guilford 407,071 413,358 1.52% 6,287

Halifax 55,610 56,341 1.30% 731

Harnett 88,138 89,248 1.24% 1,110

Haywood 53,072 53,616 1.01% 544

Henderson 87,332 88,329 1.13% 997

Hertford 22,181 22,397 0.96% 216

U.S. Census Monitoring Board

Page 73 of 174
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Pennsylvania

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Missed 

Persons

Adams 87,681 88,193 0.62% 549

Allegheny 1,241,049 1,246,401 0.47% 5,869

Armstrong 71,217 72,424 1.71% 1,237

Beaver 177,005 177,423 0.28% 492

Bedford 49,516 50,356 1.71% 861

Berks 361,219 365,488 1.21% 4,421

Blair 125,037 126,087 0.87% 1,103

Bradford 61,721 62,769 1.71% 1,074

Bucks 588,611 592,090 0.63% 3,725

Butler 167,668 170,511 1.71% 2,914

Cambria 144,290 144,974 0.51% 744

Cameron 5,893 5,993 1.71% 103

Carbon 57,884 58,105 0.42% 245

Centre 120,981 122,684 1.43% 1,754

Chester 418,757 420,835 0.53% 2,252

Clarion 39,448 40,123 1.72% 691

Clearfield 80,147 80,866 0.93% 752

Clinton 35,719 35,910 0.57% 206

Columbia 60,393 61,223 1.40% 855

Crawford 86,664 88,017 1.58% 1,389

Cumberland 199,722 200,084 0.22% 445

Dauphin 245,011 246,492 0.64% 1,583

Delaware 529,123 532,446 0.67% 3,544

Elk 34,646 35,234 1.71% 603

Erie 266,890 268,694 0.71% 1,916

Fayette 145,633 145,919 0.24% 347

Forest 4,570 4,647 1.70% 79

Franklin 126,284 127,021 0.62% 790

Fulton 14,159 14,399 1.71% 246

Greene 37,405 37,606 0.58% 217

Huntingdon 40,903 41,597 1.71% 711

Indiana 84,325 85,764 1.72% 1,475

Jefferson 45,003 45,649 1.46% 665

Juniata 22,334 22,726 1.77% 402

Lackawanna 205,460 206,267 0.43% 893

Lancaster 456,302 459,841 0.81% 3,730

Lawrence 91,671 92,067 0.47% 435

Lebanon 115,964 116,277 0.31% 361

Lehigh 301,839 303,261 0.51% 1,548

Luzerne 306,387 306,909 0.21% 649

Lycoming 114,531 115,651 1.01% 1,168

McKean 43,296 43,510 0.53% 232

Mercer 113,931 114,462 0.50% 578

Mifflin 45,796 46,577 1.72% 800

Monroe 134,847 136,618 1.34% 1,827

Montgomery 726,840 730,552 0.55% 4,015

U.S. Census Monitoring Board

Page 74 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Pennsylvania

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Missed 

Persons

Montour 17,233 17,528 1.72% 302

Northampton 256,529 257,865 0.56% 1,443

Northumberland 90,837 91,319 0.57% 520

Perry 43,100 43,832 1.71% 750

Philadelphia 1,462,819 1,481,482 1.30% 19,278

Pike 45,910 46,687 1.70% 796

Potter 17,781 18,082 1.71% 309

Schuylkill 143,110 145,100 1.41% 2,051

Snyder 35,257 35,864 1.73% 622

Somerset 76,529 76,844 0.45% 347

Sullivan 6,113 6,217 1.71% 106

Susquehanna 41,804 42,310 1.24% 523

Tioga 39,486 40,157 1.71% 688

Union 32,931 33,102 0.56% 185

Venango 55,735 56,682 1.71% 971

Warren 42,794 42,963 0.43% 187

Washington 197,556 198,446 0.49% 972

Wayne 45,821 46,601 1.71% 799

Westmoreland 361,497 363,601 0.62% 2,255

Wyoming 27,390 27,855 1.71% 477

York 373,749 377,473 1.03% 3,881

Total 11,847,753 11,940,753 0.82% 97,954

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

(continued)

U.S. Census Monitoring Board

Page 75 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County-- South Carolina

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Persons 

Missed

Abbeville 25,457 25,654 0.77% 197

Aiken 140,479 142,296 1.28% 1,817

Allendale 10,033 10,123 0.89% 90

Anderson 163,064 164,761 1.03% 1,697

Bamberg 15,593 15,719 0.80% 126

Barnwell 23,201 23,403 0.86% 202

Beaufort 114,328 116,268 1.67% 1,940

Berkeley 137,209 138,995 1.28% 1,786

Calhoun 15,024 15,125 0.67% 101

Charleston 298,571 303,072 1.49% 4,501

Cherokee 51,808 52,596 1.50% 788

Chester 33,752 34,101 1.02% 349

Chesterfield 42,109 42,517 0.96% 408

Clarendon 30,976 31,219 0.78% 243

Colleton 37,931 38,240 0.81% 309

Darlington 66,163 66,718 0.83% 555

Dillon 30,295 30,603 1.01% 308

Dorchester 94,324 95,677 1.41% 1,353

Edgefield 22,002 22,271 1.21% 269

Fairfield 23,033 23,221 0.81% 188

Florence 121,895 123,249 1.10% 1,354

Georgetown 55,216 55,825 1.09% 609

Greenville 368,791 373,455 1.25% 4,664

Greenwood 63,960 64,714 1.16% 754

Hampton 19,674 19,832 0.79% 158

Horry 194,124 197,111 1.52% 2,987

Jasper 19,331 19,557 1.15% 226

Kershaw 52,127 52,539 0.78% 412

Lancaster 59,367 59,954 0.98% 587

Laurens 66,939 67,566 0.93% 627

Lee 18,433 18,576 0.77% 143

Lexington 213,131 215,291 1.00% 2,160

McCormick 8,512 8,572 0.70% 60

Marion 35,132 35,466 0.94% 334

Marlboro 27,183 27,441 0.94% 258

Newberry 35,079 35,458 1.07% 379

Oconee 65,575 66,507 1.40% 932

Orangeburg 87,925 88,638 0.80% 713

Pickens 103,348 104,325 0.94% 977

Richland 292,665 297,519 1.63% 4,854

Saluda 18,919 19,156 1.24% 237

Spartanburg 246,352 249,458 1.25% 3,106

Sumter 101,201 102,464 1.23% 1,263

Union 29,455 29,707 0.85% 252

Williamsburg 36,882 37,136 0.69% 254

York 160,407 161,842 0.89% 1,435

Total 3,876,975 3,923,934 1.20% 46,959

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

U.S. Census Monitoring Board

Page 76 of 174

Final Report to Congress
Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas

County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Anderson 40,481 40,949 1.14% 468

Andrews 12,925 13,152 1.73% 227

Angelina 77,423 78,705 1.63% 1,282

Aransas 22,229 22,538 1.37% 309

Archer 8,799 8,881 0.93% 82

Armstrong 2,071 2,093 1.04% 22

Atascosa 38,273 39,053 2.00% 780

Austin 23,314 23,695 1.61% 381

Bailey 6,534 6,667 2.00% 133

Bandera 17,464 17,640 1.00% 176

Bastrop 55,728 56,344 1.09% 616

Baylor 4,044 4,094 1.22% 50

Bee 24,803 25,440 2.50% 637

Bell 228,837 233,366 1.94% 4,529

Bexar 1,358,420 1,383,073 1.78% 24,653

Blanco 8,265 8,363 1.17% 98

Borden 729 739 1.41% 10

Bosque 16,670 16,870 1.19% 200

Bowie 82,527 83,564 1.24% 1,037

Brazoria 230,806 234,142 1.42% 3,336

Brazos 139,070 142,180 2.19% 3,110

Brewster 8,466 8,653 2.16% 187

Briscoe 1,790 1,814 1.35% 24

Brooks 7,904 8,115 2.60% 211

Brown 35,451 35,926 1.32% 475

Burleson 16,365 16,552 1.13% 187

Burnet 33,255 33,650 1.17% 395

Caldwell 30,513 31,082 1.83% 569

Calhoun 20,473 20,841 1.77% 368

Callahan 12,783 12,910 0.99% 127

Cameron 331,138 340,294 2.69% 9,156

Camp 11,374 11,567 1.67% 193

Carson 6,417 6,476 0.91% 59

Cass 29,959 30,228 0.89% 269

Castro 8,220 8,395 2.09% 175

Chambers 25,797 26,034 0.91% 237

Cherokee 43,835 44,565 1.64% 730

Childress 5,934 6,019 1.41% 85

Clay 10,912 11,008 0.87% 96

Cochran 3,655 3,721 1.78% 66

Coke 3,571 3,612 1.14% 41

Coleman 9,072 9,188 1.26% 116

Collin 488,343 494,530 1.25% 6,187

Collingsworth 3,153 3,192 1.23% 39

Colorado 19,541 19,836 1.49% 295

Comal 76,754 77,592 1.08% 838

Undercount

U.S. Census Monitoring Board

Page 77 of 174

Final Report to Congress
Comanche 13,684 13,879 1.41% 195

Concho 2,596 2,637 1.56% 41

Cooke 35,521 35,985 1.29% 464

Coryell 58,046 59,102 1.79% 1,056

Cottle 1,867 1,895 1.50% 28

Crane 3,951 4,016 1.62% 65

Crockett 4,044 4,123 1.92% 79

Crosby 6,980 7,142 2.26% 162

Culberson 2,966 3,032 2.19% 66

Dallam 6,206 6,323 1.85% 117

Dallas 2,185,429 2,231,188 2.05% 45,759

Dawson 12,713 12,956 1.87% 243

Deaf Smith 18,307 18,735 2.28% 428

Delta 5,212 5,263 0.96% 51

Denton 423,826 429,769 1.38% 5,943

DeWitt 18,229 18,492 1.42% 263

Dickens 2,243 2,274 1.38% 31

Dimmit 10,126 10,377 2.42% 251

Donley 3,625 3,665 1.10% 40

Duval 12,536 12,807 2.12% 271

Eastland 17,510 17,711 1.13% 201

Ector 119,412 121,862 2.01% 2,450

Edwards 2,134 2,168 1.57% 34

Ellis 109,427 110,884 1.31% 1,457

El Paso 666,878 680,784 2.04% 13,906

Erath 31,193 31,810 1.94% 617

Falls 16,490 16,703 1.27% 213

Fannin 27,865 28,169 1.08% 304

Fayette 21,244 21,479 1.09% 235

Fisher 4,268 4,327 1.37% 59

Floyd 7,630 7,781 1.95% 151

Foard 1,580 1,600 1.28% 20

Fort Bend 348,154 352,868 1.34% 4,714

Franklin 9,295 9,393 1.05% 98

Freestone 16,324 16,490 1.01% 166

Frio 14,112 14,475 2.51% 363

Gaines 14,372 14,587 1.48% 215

Galveston 246,002 250,024 1.61% 4,022

Garza 4,415 4,505 2.00% 90

Gillespie 20,257 20,511 1.24% 254

Glasscock 1,406 1,434 1.95% 28

Goliad 6,805 6,904 1.43% 99

Gonzales 18,265 18,631 1.97% 366

Gray 21,033 21,268 1.10% 235

Grayson 107,622 109,170 1.42% 1,548

Gregg 108,285 109,776 1.36% 1,491

Grimes 20,821 21,060 1.13% 239

Guadalupe 87,411 88,508 1.24% 1,097

Hale 34,198 35,036 2.39% 838

U.S. Census Monitoring Board

Page 78 of 174

Final Report to Congress

County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Undercount

Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas (continued)
Hall 3,741 3,797 1.47% 56

Hamilton 7,994 8,084 1.11% 90

Hansford 5,283 5,371 1.64% 88

Hardeman 4,657 4,720 1.34% 63

Hardin 47,647 48,287 1.33% 640

Harris 3,358,444 3,426,843 2.00% 68,399

Harrison 60,431 61,259 1.35% 828

Hartley 4,112 4,158 1.10% 46

Haskell 5,989 6,068 1.30% 79

Hays 89,950 91,453 1.64% 1,503

Hemphill 3,206 3,244 1.18% 38

Henderson 72,137 72,857 0.99% 720

Hidalgo 563,801 576,049 2.13% 12,248

Hill 31,501 31,909 1.28% 408

Hockley 22,106 22,518 1.83% 412

Hood 40,499 40,983 1.18% 484

Hopkins 31,488 31,901 1.29% 413

Houston 20,186 20,391 1.01% 205

Howard 28,840 29,345 1.72% 505

Hudspeth 3,312 3,374 1.85% 62

Hunt 74,700 75,592 1.18% 892

Hutchinson 23,560 23,934 1.56% 374

Irion 1,771 1,793 1.24% 22

Jack 7,682 7,769 1.12% 87

Jackson 14,136 14,353 1.51% 217

Jasper 34,667 34,968 0.86% 301

Jeff Davis 2,139 2,178 1.81% 39

Jefferson 236,952 240,588 1.51% 3,636

Jim Hogg 5,237 5,359 2.28% 122

Jim Wells 38,818 39,645 2.09% 827

Johnson 124,321 125,703 1.10% 1,382

Jones 15,820 16,010 1.19% 190

Karnes 11,832 12,060 1.89% 228

Kaufman 69,875 70,824 1.34% 949

Kendall 23,264 23,581 1.34% 317

Kenedy 410 428 4.25% 18

Kent 821 830 1.05% 9

Kerr 41,882 42,581 1.64% 699

Kimble 4,417 4,481 1.44% 64

King 299 307 2.52% 8

Kinney 3,356 3,415 1.73% 59

Kleberg 30,238 31,033 2.56% 795

Knox 4,121 4,181 1.44% 60

Lamar 47,382 48,111 1.51% 729

Lamb 14,422 14,680 1.76% 258

Lampasas 17,405 17,639 1.32% 234

La Salle 5,263 5,381 2.19% 118

Lavaca 18,738 18,953 1.13% 215

Lee 15,035 15,210 1.15% 175

U.S. Census Monitoring Board

Page 79 of 174

Final Report to Congress

County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Undercount

Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas (continued)
Leon 15,224 15,364 0.91% 140

Liberty 65,113 65,936 1.25% 823

Limestone 20,196 20,436 1.18% 240

Lipscomb 3,011 3,050 1.27% 39

Live Oak 10,711 10,864 1.41% 153

Llano 16,796 16,955 0.94% 159

Loving 67 68 1.21% 1

Lubbock 233,022 238,068 2.12% 5,046

Lynn 6,506 6,630 1.88% 124

McCulloch 8,083 8,239 1.89% 156

McLennan 204,292 208,005 1.79% 3,713

McMullen 851 864 1.48% 13

Madison 10,069 10,185 1.14% 116

Marion 10,852 10,986 1.22% 134

Martin 4,661 4,742 1.71% 81

Mason 3,705 3,750 1.20% 45

Matagorda 37,482 38,159 1.77% 677

Maverick 47,182 48,440 2.60% 1,258

Medina 37,522 38,117 1.56% 595

Menard 2,319 2,356 1.58% 37

Midland 114,415 116,456 1.75% 2,041

Milam 23,806 24,211 1.67% 405

Mills 4,860 4,916 1.13% 56

Mitchell 7,027 7,133 1.48% 106

Montague 18,713 18,903 1.00% 190

Montgomery 292,077 295,423 1.13% 3,346

Moore 19,911 20,318 2.01% 407

Morris 12,882 13,004 0.93% 122

Motley 1,426 1,444 1.26% 18

Nacogdoches 54,691 55,680 1.78% 989

Navarro 43,694 44,465 1.73% 771

Newton 14,472 14,578 0.72% 106

Nolan 15,302 15,568 1.71% 266

Nueces 307,884 315,714 2.48% 7,830

Ochiltree 8,933 9,114 1.99% 181

Oldham 1,915 1,945 1.55% 30

Orange 83,985 84,956 1.14% 971

Palo Pinto 26,714 27,083 1.36% 369

Panola 22,304 22,495 0.85% 191

Parker 85,574 86,579 1.16% 1,005

Parmer 9,863 10,065 2.01% 202

Pecos 14,742 15,038 1.97% 296

Polk 37,784 38,139 0.93% 355

Potter 106,522 108,821 2.11% 2,299

Presidio 7,208 7,383 2.37% 175

Rains 9,067 9,148 0.89% 81

Randall 102,546 103,976 1.38% 1,430

Reagan 3,281 3,333 1.55% 52

Real 2,969 3,012 1.42% 43

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County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Undercount

Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas (continued)
Red River 14,041 14,192 1.07% 151

Reeves 11,982 12,235 2.07% 253

Refugio 7,717 7,857 1.78% 140

Roberts 887 896 1.05% 9

Robertson 15,728 15,939 1.32% 211

Rockwall 42,463 42,871 0.95% 408

Runnels 11,202 11,365 1.44% 163

Rusk 44,641 45,136 1.10% 495

Sabine 10,345 10,420 0.72% 75

San Augustine 8,679 8,751 0.82% 72

San Jacinto 22,103 22,259 0.70% 156

San Patricio 65,583 67,009 2.13% 1,426

San Saba 5,617 5,697 1.40% 80

Schleicher 2,888 2,940 1.77% 52

Scurry 14,699 14,922 1.49% 223

Shackelford 3,240 3,274 1.05% 34

Shelby 24,844 25,119 1.10% 275

Sherman 3,107 3,157 1.60% 50

Smith 170,032 172,756 1.58% 2,724

Somervell 6,651 6,734 1.24% 83

Starr 53,200 54,437 2.27% 1,237

Stephens 9,025 9,144 1.30% 119

Sterling 1,368 1,389 1.49% 21

Stonewall 1,652 1,670 1.10% 18

Sutton 4,046 4,127 1.97% 81

Swisher 7,738 7,886 1.88% 148

Tarrant 1,423,249 1,447,269 1.66% 24,020

Taylor 120,201 122,564 1.93% 2,363

Terrell 1,081 1,100 1.73% 19

Terry 11,789 12,021 1.93% 232

Throckmorton 1,828 1,851 1.24% 23

Titus 27,466 27,912 1.60% 446

Tom Green 99,742 101,812 2.03% 2,070

Travis 791,574 804,911 1.66% 13,337

Trinity 13,599 13,725 0.92% 126

Tyler 19,287 19,437 0.77% 150

Upshur 34,842 35,142 0.85% 300

Upton 3,371 3,429 1.70% 58

Uvalde 25,307 25,869 2.17% 562

Val Verde 44,051 45,202 2.55% 1,151

Van Zandt 47,194 47,668 0.99% 474

Victoria 82,581 84,284 2.02% 1,703

Walker 44,667 45,335 1.47% 668

Waller 29,454 29,859 1.36% 405

Ward 10,540 10,703 1.52% 163

Washington 28,640 28,971 1.14% 331

Webb 190,039 195,486 2.79% 5,447

Wharton 40,441 41,244 1.95% 803

Wheeler 5,150 5,214 1.23% 64

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County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Undercount

Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas (continued)
Wichita 120,544 122,753 1.80% 2,209

Wilbarger 13,755 13,975 1.58% 220

Willacy 19,010 19,458 2.30% 448

Williamson 245,041 247,764 1.10% 2,723

Wilson 31,890 32,294 1.25% 404

Winkler 7,038 7,133 1.34% 95

Wise 47,640 48,124 1.01% 484

Wood 35,293 35,630 0.95% 337

Yoakum 7,274 7,396 1.64% 122

Young 17,541 17,841 1.68% 300

Zapata 12,157 12,408 2.02% 251

Zavala 11,237 11,533 2.57% 296

Total 20,290,711 20,654,743 1.76% 364,032

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files 

delivered to Census Monitoring Board by Census Bureau 02/16/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

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County 

Name

Official 

Count

Adjusted 

Count Rate

Persons 

Missed

Undercount

Estimated Rates of Undercount and Numbers of Persons Missed by County- Texas (continued)
Estimated Rates of Undercount and Numbers of Persons Missed by County-- Virigina

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Missed 

Persons

Accomack 37,441 37,897 1.22% 461

Albemarle 77,701 78,699 1.28% 1,008

Alleghany 12,677 12,812 1.06% 136

Amelia 11,294 11,397 0.92% 104

Amherst 29,922 30,228 1.02% 310

Appomattox 13,570 13,703 0.98% 135

Arlington 185,295 188,839 1.89% 3,568

Augusta 63,616 64,306 1.09% 698

Bath 4,814 4,867 1.10% 54

Bedford 59,995 60,599 1.01% 611

Bland 6,233 6,298 1.04% 66

Botetourt 29,968 30,273 1.02% 309

Brunswick 15,518 15,650 0.86% 134

Buchanan 25,773 26,053 1.09% 283

Buckingham 13,424 13,548 0.93% 126

Campbell 50,574 51,112 1.06% 544

Caroline 21,543 21,732 0.88% 192

Carroll 28,736 29,064 1.14% 332

Charles City 6,926 6,969 0.63% 44

Charlotte 12,241 12,361 0.98% 121

Chesterfield 255,664 258,181 0.99% 2,550

Clarke 12,340 12,482 1.15% 144

Craig 5,049 5,104 1.09% 56

Culpeper 32,547 32,968 1.29% 425

Cumberland 8,981 9,064 0.93% 84

Dickenson 16,258 16,435 1.09% 179

Dinwiddie 23,535 23,758 0.95% 226

Essex 9,829 9,919 0.92% 91

Fairfax 959,452 971,772 1.28% 12,444

Fauquier 54,571 55,195 1.14% 631

Floyd 13,819 13,974 1.12% 156

Fluvanna 19,116 19,302 0.97% 188

Franklin 46,240 46,738 1.08% 504

Frederick 58,423 59,089 1.14% 674

Giles 16,562 16,748 1.12% 188

Gloucester 34,446 34,809 1.05% 367

Goochland 15,475 15,612 0.89% 139

Grayson 16,754 16,944 1.14% 192

Greene 15,097 15,262 1.10% 167

Greensville 8,485 8,552 0.79% 68

Halifax 36,431 36,782 0.97% 355

Hanover 84,197 84,914 0.86% 728

Henrico 258,488 261,799 1.28% 3,345

Henry 57,493 58,148 1.14% 662

Highland 2,536 2,563 1.08% 28

Isle of Wight 29,548 29,829 0.96% 285

James City 46,857 47,290 0.93% 439

King and Queen 6,630 6,686 0.86% 57

King George 16,448 16,659 1.28% 213

King William 13,047 13,165 0.91% 120

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Estimated Rates of Undercount and Numbers of Persons Missed by County-- Virginia

Lancaster 11,146 11,248 0.92% 103

Lee 23,354 23,631 1.19% 280

Loudoun 168,748 170,706 1.16% 1,980

Louisa 25,442 25,688 0.97% 249

Lunenburg 11,955 12,075 1.00% 121

Madison 12,301 12,436 1.10% 137

Mathews 9,138 9,231 1.02% 94

Mecklenburg 30,811 31,131 1.04% 324

Middlesex 9,673 9,767 0.98% 96

Montgomery 74,469 75,518 1.40% 1,059

Nelson 14,239 14,388 1.05% 151

New Kent 13,066 13,186 0.92% 122

Northampton 12,726 12,876 1.18% 152

Northumberland 12,247 12,354 0.88% 109

Nottoway 14,069 14,223 1.10% 156

Orange 25,394 25,673 1.10% 282

Page 22,917 23,194 1.21% 280

Patrick 19,178 19,394 1.13% 219

Pittsylvania 61,350 61,934 0.96% 592

Powhatan 19,898 20,051 0.77% 155

Prince Edward 15,967 16,139 1.08% 174

Prince George 28,079 28,464 1.37% 389

Prince William 278,424 282,259 1.37% 3,871

Pulaski 34,015 34,416 1.18% 405

Rappahannock 6,973 7,054 1.16% 82

Richmond 7,057 7,130 1.03% 74

Roanoke 83,522 84,424 1.08% 913

Rockbridge 20,604 20,837 1.13% 236

Rockingham 66,162 66,955 1.20% 801

Russell 28,818 29,136 1.10% 322

Scott 23,060 23,323 1.14% 266

Shenandoah 34,562 35,013 1.30% 456

Smyth 31,996 32,378 1.19% 386

Southampton 15,917 16,074 0.99% 159

Spotsylvania 89,841 90,774 1.04% 945

Stafford 90,952 91,892 1.04% 952

Surry 6,829 6,886 0.84% 58

Sussex 9,961 10,059 0.99% 100

Tazewell 43,779 44,278 1.14% 505

Warren 31,038 31,405 1.18% 371

Washington 49,761 50,327 1.14% 572

Westmoreland 16,613 16,784 1.03% 174

Wise 39,012 39,468 1.17% 461

Wythe 27,189 27,499 1.14% 313

York 55,670 56,234 1.02% 572

Alexandria city 126,382 129,185 2.18% 2,819

Bedford city 5,690 5,763 1.28% 74

Bristol city 16,735 16,966 1.38% 234

Buena Vista city 6,058 6,131 1.21% 74

Charlottesville city 38,217 38,936 1.86% 724

Chesapeake city 195,070 197,477 1.23% 2,432

Clifton Forge city 4,083 4,133 1.22% 50

Colonial Heights city 16,663 16,822 0.96% 161

(continued)

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Missed 

Persons

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Estimated Rates of Undercount and Numbers of Persons Missed by County-- Virigina

Covington city 6,292 6,368 1.20% 76

Danville city 46,732 47,363 1.35% 638

Emporia city 5,407 5,484 1.42% 78

Fairfax city 20,970 21,246 1.31% 279

Falls Church city 10,306 10,435 1.25% 130

Franklin city 8,072 8,179 1.32% 108

Fredericksburg city 16,960 17,305 2.01% 347

Galax city 6,698 6,807 1.61% 109

Hampton city 133,969 136,369 1.77% 2,417

Harrisonburg city 33,274 33,918 1.91% 648

Hopewell city 22,026 22,364 1.52% 341

Lexington city 4,598 4,659 1.32% 61

Lynchburg city 58,718 59,503 1.33% 793

Manassas city 34,274 34,761 1.41% 491

Manassas Park city 10,288 10,416 1.25% 130

Martinsville city 14,728 14,917 1.28% 191

Newport News city 174,317 177,559 1.84% 3,264

Norfolk city 211,114 215,518 2.06% 4,432

Norton city 3,855 3,907 1.35% 53

Petersburg city 32,834 33,684 2.54% 855

Poquoson city 11,468 11,593 1.09% 127

Portsmouth city 95,751 97,594 1.90% 1,856

Radford city 13,046 13,284 1.80% 239

Richmond city 186,554 190,777 2.23% 4,247

Roanoke city 92,373 93,709 1.44% 1,348

Salem city 23,051 23,353 1.30% 305

Staunton city 21,216 21,491 1.29% 278

Suffolk city 62,698 63,691 1.57% 1,001

Virginia Beach city 417,574 422,701 1.23% 5,181

Waynesboro city 19,255 19,526 1.40% 273

Williamsburg city 7,500 7,606 1.40% 106

Winchester city 22,800 23,251 1.95% 454

Total 6,847,117 6,938,813 1.33% 92,582

 Note: Calculations apply to the non-group quarters population only.

Source: Census 2000 Summary File 100 Percent Data/CurrentStateSum.dat and FinalDSEUS.dat Census files delivered

to Census Monitoring Board by Census Bureau 02/26/01 and 02/16/01/neraphl/user/share/

C200 Census 2000/Final Report/tables

County 

Name

Official 

Count

Adjusted 

Count

Undercount 

Rate

Missed 

Persons

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Final Report to Congress

U.S. Census Monitoring Board

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SECTION 4

DEMOGRAPHIC ANALYSIS: AN EVALUATION

Jeffrey S. Passel, Ph.d.

Urban Institute1

The Census Bureau used three methods to determine the nation’s population in 2000: the traditional

headcount, the post-enumeration survey (Accuracy and Coverage Evaluation or A.C.E.), and

Demographic Analysis (DA). The three estimates produced significantly different and confounding

results. The divergence among the three — especially, the discrepancy between DA and the A.C.E. —

is considered the primary reason the Census Bureau recommended against adjusting the 2000 Census.

What is Demographic Analysis?

DA is one of the two techniques used by the Census Bureau to measure coverage of

Census 2000. DA involves first constructing an estimate of the population using demographic techniques applied to data from sources essentially independent of Census 2000. As described by the

Census Bureau, the 2000 DA estimate is computed as:

P2000 = P1990 + B - D + I - E

or,

Estimated population at the Census 2000 date (P2000) equals

Population at the 1990 Census date (P1990) plus

Births during the interval (B) minus

Deaths during the interval (D) plus

Immigrants during the interval (I) minus

Emigrants during the interval (E).

For the 2000 adjustment decision, the Census Bureau constructed DA estimates for the Black and the

non-Black populations by sex for four age groups. Then, the estimated net undercount (or overcount)

from DA for a group is the difference between the DA estimate and the census count:

Ui = Pi, 2000 - Ci, 2000

where,

Undercount for group i (Ui ) equals

Estimated population in group i at the Census 2000 (Pi, 2000) minus

Census 2000 count for group i (Ci, 2000).

1 The views and opinions expressed are the authors’ and do not necessarily represent nor should they be attributed to the Urban Institute, its

staff, officers or trustees, or any organizations providing financial support.
Unlike survey-based measures of census coverage, DA does not measure components of census

undercount, such as gross omissions and erroneous enumerations, but only the net undercount. In

addition, when comparisons are made for subgroups of the population, the DA measure includes not

only coverage errors, but also reporting and classification errors. Thus, the reported DA undercounts

for race groups include, as part of the census “error,” differences in race reporting between

Census 2000 and the historical data used to construct the DA estimate. Similarly, for age groups, the

reported DA undercounts include age misreporting as part of the error.

DA Estimation Methods for 2000. The DA estimates for 2000 consist of two main “pieces” —

the population under age 65 and the population aged 65 and over. The estimates for the older group

were constructed with data on the population enrolled in Medicare with a correction for underenrollment. For the population under age 65, the DA estimates begin with the DA estimates for the

population under age 55 in 1990 and update the estimates with the following demographic components of population change between April 1, 1990 and April 1, 2000:

Births, adjusted for underregistration (+);

Deaths (-);

Legal immigration, except refugees (+);

Refugee arrivals (+);

Emigration of legal foreign-born residents (-);

Emigration of U.S. natives (-);

Puerto Rican migration (+);

Net change in temporary residents, i.e. nonimmigrants (+);

Net undocumented immigration (+);

Net civilian citizen migration, mainly government (+);

Net change in Armed Forces overseas (-).

Most of these components employ the same data used for the Census Bureau’s on-going national estimates program with updates and refinements. (See Robinson 2001b for a description of the DA

methods and Robinson 2001c for estimates of the components.) For the preliminary DA estimates initially supplied by the Census Bureau (Robinson 2001a), the population aged 65 and over was estimated with the same component method as the younger group rather than adjusted Medicare data.

Although this description of methods focuses on the change since 1990, the underlying method is

actually a good deal more complicated because the 1990 DA estimate is based on a considerable

amount of historic demographic data. The DA estimate for ages 65 and over in 1990 is based on

Medicare enrollments corrected for underregistration. For ages under 55 in 1990, the DA estimates

begin with registered births for 1935–1990 corrected for underregistration using factors derived from

birth registration tests conducted for 1940, 1950, and 1964–68. The corrected births are carried forward to 1990 with estimates of the same demographic components noted above covering the period

from 1935 through 1990. The intermediate group, ages 55–64 in 1990 begins with corrected births

from 1925–1935 for whites, the estimated 1960 population aged 25–34 for Blacks, and an interpolated population estimate in 1990 for the other races. Finally, for undocumented immigrants, an estimate

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of the number of residing in the country in 1990 is added, rather than estimating this component for

each time interval. (See Robinson et al. 1993.)

Historical Development of DA. Demographic estimates of net census undercount were first developed by the Census Bureau following the 1970 Census, for both the 1960 and 1970 Censuses (Siegel

1974). The methods paralleled those described above: (1) births corrected for underregistration from

1935 to 1970, carried forward with demographic components of change; (2) Medicare enrollments

corrected for underregistration in 1970 for ages 65 and over, with “backward survival” to 1960 for

ages 55 and over; and (3) estimates of the intermediate group aged 35–64 in 1970 using specialized

demographic techniques. 

Whereas the results from the birth registration tests of 1940 and 1950 had been used to evaluate census coverage for infants, there had not been a full demographic evaluation of census coverage prior to

the 1970 Census. Several important developments aided Siegel’s pioneering effort. First, the implementation of the Medicare program in the mid-1960s provided a means for measuring coverage of the

population aged 65 and over without relying on historical demographic data from the 19th century and

early 20th century, periods when the official U.S. data were incomplete in terms of both geographic

and population coverage. Second, newly developed techniques of mathematical population modeling

had been applied to historical data for whites (Coale and Zelnik 1963) and for Blacks (Coale and

Rives 1973) to provide estimates of population and undercount through 1960 for persons born

between 1905 and 1935 (ages 35–64 in 1970). Third, a new birth registration test (U.S. Bureau of the

Census 1973) provided evidence to complete the series of births through 1970.

Several gaps remained in the demographic series, but these were deemed to be sufficiently small as to

not affect the overall results. Specifically, there were no available measures of undocumented immigration nor were there reliable measures of emigration since the 1950s. However, both of these components were thought to be small and, further, were thought to approximately offset one another.

Subsequent research proved those assumptions to be essentially correct.

Following the 1970 Census, Census Bureau demographers worked within the framework of the estimates developed by Siegel and introduced a number of improvements and refinements. In developing the estimates for the 1980 Census, Passel and Robinson were able to “backwards survive” the DA

estimates to 1950 and 1940 (Fay et al. 1988). This work provided a consistent, integrated set of demographic population and undercount estimates for every census since 1940. With the complete series

of estimates, Passel (1991) and Robinson et al. (1990) introduced some further corrections to estimates for Blacks born between 1935 and 1945. 

In comparison with the estimates for the intermediate cohorts (i.e., born before 1935), the

Medicare-based estimates for the elderly and the birth-based estimates for the younger age groups

(born since 1935) are thought to be more reliable because they draw on more recent data and fewer

mathematical models. The simple passage of time has lessened the importance of these middle age

groups, limiting them to ages 45–64 in 1980, ages 55–64 in 1990, and eliminating the need for them

entirely in 2000. In addition, further research and comparisons with the Medicare-based estimates

permitted the substitution of estimates based on births for whites between 1925 and 1935 (Whelpton

1950) into the estimates for 1990.

The emergence of large-scale undocumented immigration in the 1970s greatly complicated DA

because the assumption of offsetting emigration and undocumented immigration was no longer satisfactory. This component remains the most elusive and, with emigration, is at the heart of much of the

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uncertainty surrounding DA estimates of coverage for recent censuses. The initial DA estimates

undercount for 1980 showed no “measured” undercount in large part because DA assumed that there

were no undocumented immigrants in the country or in the 1980 Census (Fay et al. 1988). Subsequent

work by Warren and Passel (1987) showed that about 2 million undocumented immigrants were

included in the 1980 Census. Ultimately, the DA estimates for 1980 included about 3 million undocumented immigrants (Fay et al. 1988). As will be discussed below, the uncertainty in the estimate of

this population for 1990 at 3.3 million (Robinson et al. 1993) and for 2000 remain the major issue in

resolving the discrepancy between the DA and A.C.E. estimates for Census 2000.

Demographic Analysis Results

Historical Results for 1940–1990. Table 1 displays the DA estimates of census coverage for 1940

through 1990. The DA estimates show a steady pattern of improvement in census coverage from 1940

(5.4 percent net undercount) through 1980 (1.2 percent) but a worsening of coverage in 1990 (1.8 percent). The same pattern of change is apparent for both the minority Black population and the balance

of the U.S. population (largely the majority white population). However, the difference in undercoverage between the Black and non-Black population shows no such trend; in fact, the 4.4 percentage

point difference in coverage in the 1990 Census is the highest shown.

The series of undercount estimates from DA show other strong and persistent patterns over the series

of six censuses. Undercount rates for males have been generally higher than for females. For Blacks,

the male-female difference has been in the range of 4-6 percentage points; for non-Blacks, the difference was less than 1 percentage point through 1960 and has gradually widened to about 1.4 percentage points (Robinson et al. 1993: Table 2). For both groups, the principal source of the sex differentials is significantly higher undercount rates for adult males (roughly ages 25–64). Undercount rates

for the youngest children (under age 5) tend to be high and did not experience any reduction from

1970 through 1990. For older children, each successive age group through 15–19 years has a lower

undercount rate than the next younger one.

Preliminary Results for 2000. The initial DA estimate for 2000 showed a total population for the

United States of 279.6 million. This figure was about 1.8 million lower than the census count of

281.4 million, implying an overcount of 0.65 percent. In other words, according to the DA estimate,

Census 2000 counted more people than there were in the country. This result was inconsistent with

the A.C.E. results which showed a total population of 284.7 million or an undercount of 3.3 million

or 1.13 percent (Table 2). Both the A.C.E. and preliminary DA suggested a reduction in the undercount rate for the Black population. Further, according to both measures the difference in undercount

rate between the Black and the non-Black populations was in the 1–2 percentage point range, a difference considerably smaller than in previous censuses.

The nature of the inconsistency between the A.C.E. and DA differs considerably from previous censuses. The Census Bureau has conducted coverage measurement surveys in conjunction with the censuses of 1950 through 1990 although none was as extensive as the A.C.E. Historically, the difference

in measured undercount between DA and the coverage measurement surveys was not nearly as large

as the difference between the preliminary DA results and A.C.E., generally only a few tenths of a percentage point, not almost 2 percentage points, and never approaching 5 million people. Further, the

coverage measurement surveys tended to produce lower undercount estimates than DA, a pattern usually attributed to correlation bias in the surveys and/or their inability to reach all segments of the population. One pattern from previous studies did occur in 2000. The coverage measurement surveys in

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the past have not found a significantly higher undercount rate for adult males than adult females, especially among the Black population — a persistent pattern in the DA estimates that is strongly supported by sex ratio analyses (Robinson et al. 1993). In the preliminary DA estimates for 2000, Black

males aged 18–49 had an undercount rate almost 8 percentage points higher than Black females. In

the A.C.E., the difference was only about 1 percentage point.

The Census Bureau’s investigation of the A.C.E.-DA discrepancy has included studies of both DA

and A.C.E.. Several indications in analysis of the DA results pointed toward problems in measuring some components of immigration, particularly undocumented immigration (Robinson 2001b).

In addition, a smaller share of the difference may also be related to the adjustments for undercoverage in the Medicare data. Ultimately, the Census Bureau was unable to reconcile the difference

of 5.1 million between the two estimates to its satisfaction. The low DA estimate proved to be a

significant factor, perhaps the most significant factor, in the recommendation not to employ an

adjustment based on the A.C.E.. Below are possible modifications to the DA estimates based on

current research on measuring immigration. The results suggest that DA and A.C.E. are considerably more consistent with one another than the initial analyses suggest. Finally, some recommendations for future work at the Census Bureau are presented.

Strengths and Weaknesses of Demographic Analysis

Demographic Analysis does not merely measure census coverage. Rather, it provides a structured estimate of the U.S. population classified by age, sex, and race. Further, it provides this information at

different points in time, not just the census date. Thus, if a discrepancy arises between the DA estimate and other measures or between DA and expected patterns, the inherent demographic structure

both across time and across the population provides a means for assessing the source of the anomaly

and either explaining or resolving it.

In contrast, coverage measurement surveys provide considerably more detail about census coverage,

especially in terms of geographic variation, components of under- and overcounts, and for groups

defined by social or economic rather than basic demographic characteristics. However, each coverage measurement survey is an entity unto itself. There is no necessity that results are consistent across

space and time. (In fact, they tend not to be because of variations in survey operations and improvements in survey and matching methods.)

The demographic structure inherent in DA also means that some measures from DA are considerably

more robust than equivalent measures from the census or A.C.E.. For example, the sex ratio in an age

group (i.e., the ratio of males to females or males per 100 females), is a function of the sex ratio of

the group at birth, changes since birth attributable to sex differences in mortality, and changes driven

by migration. The sex ratio at birth varies little over time (and somewhat more across race groups).

For young cohorts especially, mortality-induced changes in sex composition tend to be small and predictable in size and direction; the latter is true in general for all cohorts. Similarly, migration changes

are limited by the magnitude and sex balance of migration. Thus, sex ratios from DA are relatively

robust with respect to estimation problems. Further, differences between adjacent age groups are constrained to be small by the continuous nature of demographic change. These structural limitations

make sex ratio analyses extremely useful in identifying both coverage differences in the census and

potential measurement problems in the A.C.E. (See Passel 1993 for further discussion of these

issues.)

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The limitations of DA generally have to do with the ability to produce precise estimates, that is estimates with relatively small bands of error, rather than the ability to produce accurate or unbiased estimates. Because the ultimate purpose of DA is the assessment of census coverage, the DA estimates

must conform to categories available in the census. Further, because it is the difference between the

DA estimate and the census that is important, the potential error in the DA estimate must be smaller

than this difference.

Demographers are generally able to produce estimates for any demographic group or any level of

geography. For example, the Census Bureau produces monthly estimates of the U.S. population by

age and sex for four major race groups (white, Black, American Indian and Alaska Native, and Asian

or Pacific Islander) and for the Hispanic and non-Hispanic population. In addition, it produces annual estimates for states with the same demographic detail as well as annual population estimates for

every county and almost all places in the country. Nonetheless, in producing estimates of census coverage with demographic techniques, the Census Bureau uses only two race groups — Black and

non-Black — and produces only national estimates. This limitation is related to the ultimate purpose

of the measurement, comparison with the census. The Census Bureau, thus, recognizes that estimating population for subnational levels of geography requires measuring internal migration and locating the destinations of immigrants, factors that generally have larger ranges of variation (and larger

errors) than the potential undercounts. For alternative race groupings (beyond Black and non-Black),

the estimates suffer from other measurement limitations (discussed below).

The Census Bureau has attempted to assess the degree of potential error in the DA estimates and to

provide measures somewhat analogous to statistical confidence intervals. (See Robinson et al. 1993.)

These measures have focused on the problems in measuring each of the various components of population change and the ability to reduce errors in measurement. As such, they have produced some

useful results. However, the measures are limited. The lack of fully appropriate models of estimation

error tends to produce intervals that are too narrow, thus, implying more precision in the DA estimates

than may actually exist. On the other hand, none of the current models fully incorporate the robustness implied by demographic structures relating to age and sex composition. This factor works in the

opposite direction and leads to intervals that are too wide, thus understating the precision in the demographic estimates.

Current and Future Limitations. Changes in the demographic composition of the U.S. population over

the last generation have made the development of DA estimates of census coverage more difficult and

introduced new limitations. Since the 1950s, the racial/ethnic composition of the country’s population has changed considerably. In 1950, the United States was basically a “Black and white” country

with the “majority” population (i.e. white non-Hispanic population) representing just under 90 percent of the population. Blacks were about 10 percent of the population and represented almost 90 percent of the racial/ethnic “minority” population (Passel and Edmonston 1994). By 2000, the white

non-Hispanic population accounted for less than 70 percent of the population and Blacks represented

only about 40 percent of the minority population. Aside from the measurement difficulties induced

by these changes (discussed below), the compositional factors mean that the difference between Black

and non-Black undercount rates, the central comparison available from DA, is no longer a comparison of the undercount of virtually all of the racial/ethnic minority population with the undercount of

the majority population. In fact, according to the results from Census 2000, between 20 and 25 percent of the DA non-Black population consists of racial/ethnic minorities.

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Changing definitions and conceptions of race and ethnicity together with increasing prevalence of

marriage and childbearing across racial/ethnic lines has further complicated the construction and

interpretation of DA estimates. Because DA uses historical data on births, deaths, and immigration,

the categories of the estimates are limited by the available data. Further, comparisons with census figures must be for groupings that are the same or nearly the same; if the comparison groups are defined

differently, then one component of the difference, and possibly a major component, is definitional

change. Complicating such comparisons even more is the new method of collecting race data in

Census 2000 which allows respondents to select more than one race, an option that was not available

when the historical series of birth and death data were collected.

Intermarriage rates are particularly high for the Hispanic and American Indian populations with moderate levels in the Asian population (Edmonston et al. 2000). Further, intermarriage rates and multiracial childbearing for Blacks have increased considerably in the last 30 years. As recently as 1970,

only about 1–2 percent of births with one Black parent had a non-Black parent; during the 1990s, this

figure was in the 10–15 percent range. In Census 2000, almost 5 percent of respondents identifying

as Black also gave another racial response. For DA, these patterns introduce a considerable amount

of uncertainty as to whether individuals classified as Black in the historical statistics on births are

identifying themselves as Black in Census 2000 and, conversely, whether the Black population as

measured by Census 2000 is accurately represented by the historical time series. As difficult as these

issues are for the Black population, they loom considerably larger in trying to measure census coverage of the American Indian and Asian populations with demographic techniques. In fact, application

of DA to the American Indian population shows that most of the increase in the American Indian population between 1960 and 1990 was attributable to “non-demographic” factors such as individual

changes in racial identity and classification (Passel 1996). In response to measurement problems such

as these, the Census Bureau groups the American Indian and Asian populations together with whites

in the DA estimates even though all of the requisite data on demographic components are produced

for all of the race groups.

Measurement of the Hispanic population encounters similar difficulties but is further complicated by

the fact that the definition of this group is separate from racial classifications. National data on the

components of population change for Hispanics (e.g., births and deaths) are not complete until the

1980s. Consequently, a full demographic estimate is not possible for this group using the same methods as for race groups. An additional complication for DA is introduced by the response patterns of

Hispanics to the race item in Census 2000. Almost 15 million Hispanics or more than 40 percent of

the Hispanic population did not select one of the specific races listed but chose the “other” category.

In historical vital statistics data, there is no such category. Thus, to make the census data consistent

with the DA categories, it is necessary to “re-cast” or reclassify the census data. This reclassification

involves not only choosing how to assign persons who gave more than one race response, but also how

to assign specific races to the persons choosing “other” race. The choice of reclassification method

can obviously have an impact on the measured undercount. For the DA comparison, the Census 2000

Black population is 37.1 million or about 700,000 persons more than the figure used for the A.C.E.

comparison (Table 2). From a measurement perspective, none of the race classification issues is fatal.

In particular, they have no impact on the measured population total (or the age-sex totals). They do,

however, affect the race group comparisons by introducing more imprecision into the population estimates, the modified census totals, and the undercount measures. Further, by using reclassified census populations for DA, the undercount estimates from DA do not relate to any of the populations

appearing in standard tabulations or publications.

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Immigration in Demographic Analysis. The generational changes in the U.S. population noted

above are related to substantial increases in the level of international migration and the origins of the

migrants over the last three decades. Measuring immigration flows accurately and completely has

become a critical factor in demographic analysis where it was not in 1970. According to the

1970 Census, there were fewer than 10 million immigrants living in the country, accounting for less

than 5 percent of the U.S. population (Passel and Fix 2001); further, the foreign-born population had

changed little since 1940, decreasing slightly. By 2000, the immigrant population had reached 30 million and represented about 11 percent of the population. Even more important for DA, the role of

immigration in population change had grown substantially. The total number of births during the

1960s and the 1990s was about the same — just under 39 million registered in the 1960s versus a little over 40 million in the 1990s. However, the magnitude of immigration flows differed considerably.

The 1970 Census included about 3 million immigrants who had arrived in the 1960s. In contrast, the

Census 2000 Supplementary Sample (C2SS) has more than 13 million immigrants who arrived in the

previous 10 years. Obviously, accurate measurement of immigration flows is much more critical to

assessing the size (and composition) of the U.S. population in 2000 than it was when DA was first

developed.

Further complications are introduced into DA by the nature of the immigration flows and the data

available. By the 1990s, a significant portion of immigration was occurring outside of the normal

process of admitting legal immigrants and refugees for permanent residence in the United States. The

best known and most important aspect of this problem is undocumented immigration. According to

the Census Bureau’s estimates used in the preliminary DA, more than 25 percent of immigrants arriving during the 1990s were undocumented. This figure is clearly too low; from the data presented

below, a better estimate would be 40 percent. Regardless of the exact figure, it is clear that this one

component introduces a considerable amount of uncertainty and imprecision into the DA estimate. In

contrast, the best available evidence suggests that virtually none of the immigrants enumerated in the

1970 Census were undocumented (Passel 1999). 

Another less well-known measurement problem concerning immigration surrounds aliens admitted on

a temporary basis for residence in the United States, known as “nonimmigrants” in the terminology

of the Immigration and Naturalization Service (INS). The nonimmigrant population includes foreign

students and various kinds of “guest workers” such as specialized technical workers (H-1B visas),

intracompany transfers (L-1), and agricultural workers (H-2A). The number of nonimmigrant admissions and the resident nonimmigrant population have grown considerably during the last decade.

Unfortunately, there is only a small amount of data on these groups and there are no “official” estimates of the number of residents made by any agency (Passel 1997). In the preliminary DA estimates,

the Census Bureau allows for increases during the 1990s in the number of H-1B workers and the number of foreign students (F-1 visas), but omits a considerable number of legal temporary aliens, at least

hundreds of thousands and possibly more.

Yet another complication relates to the assignment of immigrants to race groups. Most of the data on

immigration comes from INS information on the number of legal immigrants admitted. These data

are classified by country of birth, but not by race. The Census Bureau assigns the legal immigrants

to race groups based on responses from the previous census or more recent data, if available. For other

immigrant groups, similar procedures are used. Thus, not only are some of the immigration statistics

quite old, they all suffer because the race classification is not based on responses from the immigrants

themselves.

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The difficulties surrounding measurement of immigration obviously have seriously affected the quality of the DA estimates. All of these issues affect the precision of the DA estimates because they introduce potential sources of error in classification. In addition, unlike many of the limitations noted

above, problems related to measuring immigration can bias the DA estimates. In the case of the preliminary estimates, underestimation of immigration has led to underestimating the overall level of the

undercount. Below, we assess the potential impact of this understatement.

Alternative Measures and Impact on Demographic Analysis 

In its “Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy

(ESCAP)” and the supporting Memorandum B-4 on “Accuracy and Coverage Evaluation:

Demographic Analysis Results” (Robinson 2001b), the Census Bureau focused its primary attention

on the measure of undocumented immigration during the 1990s. Comparison of the DA components

with research results on immigration flows strongly supports the view that the Census Bureau underestimated undocumented immigration during the decade, possibly by a substantial amount. In addition, there is direct as well as indirect evidence that the DA assumptions understated other immigration components, but to a lesser degree than undocumented immigration.

This section includes a discussion of the various immigration components and suggests corrections

based on available research. In addition, some estimation issues on other components are presented.

Correcting the estimate of net undocumented immigration during the 1990s increases the DA estimate

by 2.6 million. Two other changes to the immigration components add another 750,000 to the DA

estimate from the legal nonimmigrant or temporary immigration flow (500,000) and the entry of

legalized farmworkers (250,000). In addition, there are indications that the estimate for the elderly

population based on Medicare data adjusted for underregistration could be too low by 500,000. Taken

together, these corrections add 3.9 million or possibly more to the DA estimate of the total population,

bringing it to 283.5 million (Table 5). With these assumptions, DA shows an undercount of 2.1 million or 0.7 percent (Table 6). This estimate differs by about 1.2 million or only 0.4 percent from the

A.C.E. estimate, a figure only slightly greater than the DA-PES difference in 1990.

Undocumented Immigration. The base DA estimate implicitly assumes that there are 6.1 million

undocumented immigrants living in the United States as of April 1, 2000 — 3.3 million from the

1990 DA estimate plus a net increase during the 1990s of 2.8 million. The net increase, based on the

work of Warren (1997), is extrapolated from trends for 1992–1996 and assumes an annual net increase

of 281,000 for 1990-1992 and 275,000 for the rest of the decade (Robinson 2001b). In its report on

DA, the Census Bureau produced an “alternative” DA estimate that arbitrarily doubled the increase in

undocumented immigration during the 1990s (Robinson 2001b). This alternative figure produced

population estimates that were more consistent with the A.C.E. results, but the Census Bureau did not

adopt the alternative because it had not developed empirical evidence to support the higher figure.

Although the Bureau’s lower estimate of undocumented migration comported well with what had been

a widely-accepted consensus on the “conventional wisdom” regarding the numbers of undocumented

immigrants, at least through September or October 2000, data available from several sources since

then supports a significantly higher estimate.

Residual Estimates of the Undocumented Population. Virtually all of the estimates of the size of the

undocumented immigrant population currently available use some form of residual estimation. (See

Passel 1999 for a discussion of the method and some examples. See also Bean et al. 2001 and Warren

2000 for variants.) This method basically involves making an estimate of legal foreign-born residents

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and subtracting it from a measure of the total foreign-born population, usually measured with a survey

or census. The difference (or residual) is treated as an estimate of the undocumented population.

Variations on the method involve different definitions and computations for the legal population and

assumptions regarding the coverage of the legal and undocumented populations in the census or survey.

The residual method employs data on the foreign-born population classified by country of birth and

period of entry, and ideally by age and sex. Generally, the most detailed and best data for such estimates come from the decennial census itself. Since the sample data from Census 2000 will not be

available until 2002, alternative data sources are necessary. The principal data now available for measuring undocumented migration come from the March Supplements to the Current Population Survey

(CPS). For evaluating Census 2000, the March 2000 CPS Supplement has been available since

October 2000. However, the March 2000 CPS is weighted to population estimates very similar to the

preliminary DA estimates. As such, the survey does not adequately represent the population enumerated in Census 2000, particularly for the Hispanic and Asian populations that are immigrant-dominated. For purposes of measuring undocumented immigration, the population weights for the

March 2000 CPS have been adjusted to agree with the appropriate population figures from

Census 2000.2

Another useful data source is the Census 2000 Supplementary Survey (C2SS). C2SS is a

700,000 household survey that was conducted during 2000 using essentially the same questionnaire

as Census 2000. This survey is weighted to agree with the household population from Census 2000

on a state-by-state basis. Because the C2SS sample is almost 15 times the size of the CPS, the

age-sex-country detail from this survey is potentially a much better representation of the population

than the CPS. Unfortunately, the C2SS data currently available outside the Census Bureau are quite

limited — the foreign-born population by citizenship for states is cross-classified separately by period of entry and by region of birth, but not by both and not by country of birth. For the analysis presented here, the publicly-available data from C2SS are combined with tabulations from the re-weighted March 2000 CPS to provide more detailed data.

Estimates of Legal Residents. The legally-resident immigrant population for April 2000 is estimated by carrying forward the legally-resident population of March 1995 with demographic components

of change.3 This initial population is subdivided by 35 countries and regions of birth, six periods of

entry (1990-1995, 1985–1989, 1980–1984, 1975–1979, 1970–1974, pre-1970), five-year age groups,

and sex for the six largest immigrant states (California, New York, Texas, Florida, Illinois, New Jersey,

and the balance of the country). New immigrants are added each year; the total is the number of

refugee and parolee arrivals derived from Office of Refugee Resettlement (ORR) data plus the number of INS admissions of legal permanent residents, excluding those who have already been counted

as refugees or parolees. INS data on date of arrival, state of intended residence, age, and sex by counU.S. Census Monitoring Board

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2 The Census Bureau reweighted the March 2000 CPS using Census 2000 data for its ESCAP evaluation (Robinson 2001a), but did not use

it in re-estimating undocumented immigration flows. The Bureau did not release either the weights or the necessary tabulations until

August 20, 2001 — too late to be used for this report. The analysis in this report is based on reweighting done according to Census

Bureau specifications using approximations to the required population totals based on available data from Census 2000 and other Census

Bureau estimates (Passel 2001a). Comparison of the approximation to the Census Bureau’s weights shows that the two data sets are

extremely close. The foreign-born populations differ by only 5,000 persons or about .02 percent% — 30.081 million from the Census

Bureau versus the approximation of 30.087 million. Differences for specific countries and periods of entry also tend to be small in 3 See Passel 1999 for a detailed description of the 1995 estimate and data used to develop it. Passel 2001b contains a more detailed description of the estimation techniques and components of change for 1995–2000.magnitude, but percentage differences are larger; none of the

differences are substantively important for the estimates presented here.
try of birth are used to distribute the new arrivals across the demographic-geographic categories.

Deaths are estimated for each year with race-specific life tables and emigration is estimated by applying a set of age-specific emigration rates derived from the work of Ahmed and Robinson (1994).

Finally, movement of legal immigrants between states is estimated using in- and out-migration rates

and the age distribution of interstate migrants derived from annual CPS data (Passel and Zimmermann

2001). The estimated legally-resident immigrant population in April 2000 is about 22 million

(Table 3, row d).4

The next step in the estimation procedure is to determine the number of legal foreign-born residents

who are represented in the surveys (March 2000 CPS, reweighted March 2000 CPS, C2SS). This step

requires measuring or estimating the coverage of legal immigrants. Because there are no direct measures of coverage of immigrants, legal or otherwise, it is necessary to introduce some assumptions. The

basic premise is that recently-arrived legal immigrants are more likely to be missed than the general

population because of language difficulties, fear or discomfort of governmental contact, and less regular living conditions. (See also Bean et al. 2001) Accordingly, the undercount rates assumed for

legal immigrants are a multiple of the appropriate age-sex-race specific undercount rates estimated

from the 2000 A.C.E. for Hispanics, Blacks, whites, and Asian/Pacific Islanders combined.5

Immigrants arriving in the 1990s are assumed to have undercount rates equal to 1.5 times the A.C.E.

rates; those arriving in the 1980s are assigned the A.C.E. undercount rates. With these assumptions,

immigrants arriving after the mid-1970s have an undercount of about 1.5 percent in the data systems

(Table 3, row e).

Undocumented Immigrants Enumerated. The foreign-born population in the CPS or census

includes three different populations: (1) enumerated legal residents, estimated with the methods

described above; (2) enumerated legal temporary foreign-born residents or legal nonimmigrants;

and (3) enumerated undocumented immigrants. The number of legal temporary residents enumerated is estimated by matching the characteristics of CPS respondents, such as date of arrival, occupation, and living arrangements with the legal requirements for various nonimmigrant visa categories

(Passel and Clark 1998). Individuals in the CPS who appear to meet the admission criteria are

assumed to be legal nonimmigrants. Based on these procedures, the March 2000 CPS is estimated to

contain about 842,000 legal nonimmigrants and the reweighted March 2000 CPS, about 900,000

(Table 3, row b). These detailed methods cannot be applied to the C2SS yet, because the requisite

public-use data on individuals have not been released. For the estimates presented here, the C2SS is

assumed to contain somewhat more legal nonimmigrants than the CPS because of better outreach and

housing unit coverage, or just over 1.0 million (Table 3).6

The number of undocumented immigrants represented in the CPS or C2SS is computed as the

total foreign-born population from the survey minus the estimate of legal immigrants enumerated minus the estimate of legal temporary residents enumerated. For the March 2000 CPS, the

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4 The estimates of legal foreign-born residents in 2000 differ for comparison with each data source. The component estimation procedure

described in the text is used only for immigrants arriving after the mid-1970s. For earlier arrival cohorts, the number of legal residents in

2000 is assumed to be equal to the number in the survey. In other words, the procedure assumes that of immigrants who came to the

country before the mid-1970s, none remains in an undocumented status in 2000. 5 Rates for race groups are for non-Hispanics. Each country or region of birth is assigned rates for a race/Hispanic group based on the predominant race/Hispanic group in the CPS data. 6 The C2SS figures for legal nonimmigrants are assumed to be 20 percent higher than the reweighted CPS for those entering from 1995–2000

and 10 percent higher for those entering before 1995. It is important to stress that these estimates are for legal nonimmigrants represented

in the survey, not the total number in the country. For DA estimates of census coverage, the latter figures are required, however.
method yields an estimate of 5.9 million undocumented immigrants included in the survey. For

the reweighted CPS, the estimate is considerably larger at 7.3 million. The reweighted estimate

is 1.4 million larger because the overall foreign-born population is almost 1.7 million. Finally, the

still larger foreign-born population in the C2SS yields an even larger estimate of undocumented

immigrants enumerated in the survey — 7.9 million. In all of the various estimates, more than

50 percent of the enumerated undocumented immigrants are from Mexico (Table 3).

Total Undocumented Population and Change Since 1990. The procedures outlined to this point

yield an estimate of the undocumented population in the CPS or C2SS, but the DA estimates require

an estimate of the total number of undocumented immigrants in the United States, not just the number in the survey. To arrive at such an estimate, it is necessary to know what proportion of the undocumented population appears in the survey. Again, without direct measures of coverage for this group

it is necessary to use indirect measures or assumptions based on other evidence. Recent studies

(reported in Bean et al. 2001, Van Hook and Bean 1997) using data from Mexico and other sources,

suggest that omission rates for undocumented immigrants are approximately three times that of legal

immigrants. The estimates shown in Table 3 assume that undercount rates for undocumented immigrants entering in the 1990s are three times the corresponding rates for legal immigrants; for those

entering in the 1980s, twice the rate of legal immigrants. For the CPS-based estimates, age-sex specific rates are computed with these assumptions; for the C2SS estimates, the period-specific rates

from the CPS estimates are used because detailed information by age and sex is not available. These

assumptions imply that 8–12 percent of undocumented immigrants are missed by the CPS and C2SS

(Table 3, row i). 

From the C2SS, these methods yield an estimate of 8.6 million undocumented immigrants in the

United States. From the CPS, the estimates are lower — 6.6 million from the March 2000 CPS and

8.3 million from the reweighted CPS. Both the reweighted CPS and C2SS yield estimates that are

considerably higher than the estimate implied by DA of 6.1 million. 

The component actually used in the DA estimates is “change in the undocumented population since

1990,” so the estimated undocumented population in 1990 of 3.3 million (Woodrow 1991) must be

subtracted from the estimates for 2000. For the “base DA” estimates, the Census Bureau incorporated estimated change of just less than 2.8 million people. The C2SS estimates imply almost twice

as much change in the undocumented population during the decade or 5.3 million and the reweighted

CPS implies 5.0 million. 

Refinement of the DA estimates for the undocumented immigration component also requires changing the age-sex structure of “net undocumented immigration.” The figures used in DA assume that

the age-sex distribution of undocumented immigrants is the same as “persons who acquired legal status under the provisions of the Immigration Reform and Control Act (IRCA)” of 1986 (Robinson

2001b). While this is a reasonable assumption for the characteristics of undocumented immigrants

entering the country, it fails to recognize the demographic properties of the actual DA component.

The DA component should represent change in the undocumented population, not the number of new

undocumented immigrants. As undocumented immigrants get older, many either leave the United

States, die, or transition to the legal immigrant population. All of these demographic changes should

be included in the DA component conceptually, but are omitted from the actual DA component. The

correct basis for estimating the DA component is to take the difference in the estimates undocumented immigrant population at two points in time on an age-by-age basis. Table 4 shows the results of

such a calculation based on the estimated undocumented immigrant population for 2000. Even if the

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level of change incorporated in DA were maintained, the appropriate estimation methodology yields

a substantially larger change for the groups aged 18–29 years in 2000 and much smaller changes for

ages 30–49 years and 50 years and over; in fact, for the oldest age group, the change in the undocumented population should be negative. These changes bring both the DA estimates and the Census

Bureau’s postcensal estimates more in line with the Census 2000 and A.C.E. results by reducing the

extremely large shortfall in the estimates at young adult ages for Hispanics (Robinson 2001b).

While it is likely that a full analysis of the long-form data from Census 2000 will yield better estimates of the undocumented population than either the CPS or the C2SS, there is no reason not to

incorporate these new results into DA since they are based on empirical analysis of data through the

year 2000 whereas DA assumptions are extrapolations based on data through 1996 only.

Incorporation of the C2SS-based estimate into DA reduces the discrepancy between DA and A.C.E.

by about half.

Temporary Migrants. There is a large, but unknown number of foreign-born persons living in the

United States legally with temporary visas. According to the residence rules used in the census, many

of these individuals should be enumerated in the census and, in fact, many are. For DA, it is necessary to determine which categories of nonimmigrants are to be considered U.S. residents and estimate

the number in the country for each category. The DA estimates for 2000 incorporate 1990–2000

change for two large groups of nonimmigrants — foreign students (F-1 visa holders) and the “hi-tech

guest workers” (or H1-B visa holders). Both groups grew substantially during the 1990s and the DA

estimates incorporate estimated change of 374,000.7 

While these estimates seem accurate, they omit a number of potentially significant categories.

The major categories of nonimmigrants which could possibly account for a significant number of

U.S. residents include:

A (foreign diplomatic personnel)

E (treaty traders & investors)

G (international organizations or foreign governments)

H-1A (registered nurses)

H-2 (temporary workers)

J-1 (exchange visitors, including)

Physicians

Scholars

Au pairs

Exchange students

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7 The estimates for foreign students are based on data from the Institute of International Education (2001) and for H-1B visa holders on the

work of Lowell (2000).
L-1 (intracompany transfers)

N (NATO workers)

O-1 (workers with extraordinary ability or achievement)

P-1 (internationally recognized athletes or entertainers) 

Q-1 (cultural exchange)

R-1 (religious workers)

T (NAFTA workers)

Dependents of principals in these and other various visa categories

One indicator of growth is the increasing number of admissions of nonimmigrants. Between 1990 and

1998, annual admissions other than tourists, students, and H-1B’s grew by 75 percent from 750,000

to 1.3 million. A basic problem in estimating the nonimmigrant population is that while INS counts

admissions by visa category, it does not keep track of departures from the country, transitions to other

categories, or multiple arrivals of the same individual. 

There are no widely-accepted estimates of nonimmigrants or techniques for developing them. One

possible approach uses INS information on duration of stay of departing nonimmigrants (INS 1996)

to translate the arrival data into estimates of the number of nonimmigrants residing in the U.S. long

enough to be considered U.S. residents for census purposes (generally 6 months or more). Applying

the proportions of these longer-term stays from the INS study to admissions for 1990 and 1998 and

then extrapolating to 2000 yields an estimate of approximately 500,000 additional U.S. residents in

2000 beyond the DA assumptions. More up-to-date and more detailed data might yield different estimates, but the base DA assumption of no change in the nonimmigrant population since 1990 other

than students and H-1B workers is clearly too low and larger numbers should be incorporated into the

DA estimate.

In addition to the proposed revisions for additional change in the nonimmigrant population between

1990 and 2000, it is possible that the estimate of the resident nonimmigrant population in 1990, the

base for the 2000 DA estimate, is too low. To derive the total number of nonimmigrants residing in

the country in 1990, the Bureau apparently used the work of Word (1995) who estimated that the

1990 Census included approximately 450,000 legal nonimmigrants. As an estimate of the total resident nonimmigrant population, this figure could be low by several hundred thousand, because it represents those counted in the census. If this is indeed the source of the DA estimate for 1990, then both

the 1990 and 2000 DA estimates would need to be increased. The alternative DA estimates presented below do not deal with this component because the evidence supporting a revision is not compelling enough to warrant an increase in the estimate at this.

Legal Immigration and Emigration. Every one of the components of immigration poses measurement problems for DA, but net undocumented immigration and legal temporary residents appear

to be the ones with the largest measurement problems during the 1990s. There are two others that bear

mentioning, however: legal emigration and another that the Census Bureau has not included in its estimates at all, movement of persons admitted for residence but not living in the United States. The latU.S. Census Monitoring Board

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ter is particularly problematic for base DA estimates. Research conducted by Mexican and U.S.

demographers for the Mexico-United States Binational Migration Study showed that the number of

Mexicans living in the United States as legal residents increased during the 1990s more than indicated by data on legal admissions. (See Bean et al. 2001.) This increase occurred because many

Mexicans who were granted the right to become legal U.S. residents as a result of the Immigration

Reform and Control Act of 1986 actually lived in Mexico when they acquired legal U.S. residency.

This phenomenon was especially prevalent among the special agricultural workers (SAWs) who did

not have to prove that they had lived in the United States, but merely had to show that they had worked

in U.S. agriculture for 90 days to acquire temporary legal status. Approximately 900,000 of the

1.1 million SAWs who eventually acquired legal status were from Mexico.8

For the residual estimates, what is required is an estimate of the number of SAWs residing legally in

the United States at the time of the estimate. Without firm data, many analysts employed a range of

assumptions for this group and most assumed that a significant fraction of the Mexican SAWs, possibly as many as half of them, did not live in the United States in the late 1980s or early 1990s (e.g.,

Woodrow 1991; Woodrow and Passel 1990; Clark et al. 1994). More recently, studies conducted in

Mexico have supplied more definitive information on SAWs, specifically the number living in Mexico

and, by extension, the number living in the United States. Bean et al. (2001) place the number of

Mexican SAWs in the U.S. in the mid-1990s at 580,000–690,000. These figures imply larger numbers than were included in the DA estimates for 1990 (Woodrow 1991). This increase is not accounted for in any of the other components of DA. Thus, the DA estimates for 2000 are too low by the

increase in the number of Mexican SAWs residing in the United States since 1990; there should

include an allowance for this component in DA. The available evidence puts this increase at about

250,000 for the decade of the 1990s.9

The DA estimates of legal emigration are computed by multiplying a set of age-specific emigration

rates times the estimated legal foreign-born population. The emigration rates used are derived from

the work of Ahmed and Robinson (1994) for the 1980s. This method, then, assumes implicitly that

the rate of emigration from the U.S. for legal immigrants remained the same during the 1980s and the

1990s. Evidence to validate this assumption is essentially lacking. However, given the very strong

U.S. economy of the mid-to-late 1990s and the indications of increased attractiveness of the U.S., as

evidenced by the greatly increased level of undocumented immigration, it would seem reasonable that

emigration rates could have decreased during the 1990s. A decrease in the estimate of emigration

leads to an increase in the DA estimate of the population. If emigration rates during the 1990s were

only 10 percent less than in the 1980s, then the DA estimate in 2000 would increase by 270,000.

Because of the more speculative nature of potential changes in the emigration component, they are not

incorporated in the illustrative revised DA estimates shown below.

Medicare. The base DA estimate for the population aged 65 and over in 2000 (34.5 million) is almost

600,000 or 1.7 percent less than the estimate the Census Bureau obtained by carrying forward the

1990 population aged 55 and over with demographic components of change (Robinson 2001a, 2001c).

This demographic method is used for all groups below age 65 in 2000, but the elderly population is

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8 As an indicator that many SAWs were not U.S. residents when they applied, more than 100,000 of the Mexican SAWs applied for legal

status at the U.S.-Mexico border after coming from the interior of Mexico. 9 This component was also included in the new residual estimates of undocumented immigrants. If fewer SAWs are assumed to be in the

United States, then the revised estimates of undocumented immigrants would be larger by roughly the same amount.
ment. The available data and information supplied by the Census Bureau do not permit a detailed,

thorough review of this adjustment. However, part of the underenrollment is the newly-arrived immigrants who are not eligible for Medicare. It seems very possible that the adjustment for Medicare

underenrollment could be slightly higher than the Census Bureau employs. This component clearly

warrants further investigation. In the illustrative estimates shown in the next section, the previous

Census Bureau estimates based on components (Robinson 2001a) are used, in part, because more

demographic detail on race and ethnic origin is available for them.

In terms of size, the most important demographic components of change for the 1990s are births and

deaths; for the full DA estimates, these same two components prior to 1990 are also the largest.

Rather small percentage revisions in these components could thus translate into larger changes than

those discussed above. However, the historical data on births and deaths have been analyzed in detail

several times and are less problematic. Accordingly, no changes to these components are incorporated into our illustrative estimates, but continuing attention to them is clearly warranted.

Comparison with A.C.E. Results. It is very difficult to compare DA with the A.C.E. results under

the best of circumstances. The two sets of data use different definitions of race groups and refer to

different populations — A.C.E. to the household population and DA to the total population. Further,

some of the data needed to try to align the two datasets have not been released by the Census Bureau.10

Nonetheless, it is possible to piece together some comparisons from the available data. DA estimates

for the Hispanic population and the non-Hispanic Black population were computed by updating the

1990 DA population to 2000 using the base DA components supplied by the Census Bureau in

detailed age, sex, detailed race, and Hispanic categories (Robinson 2001c).11 The A.C.E. estimates of

both the adjusted and unadjusted household population were added to figures from Census 2000 for

the group quarters population12 to develop population and undercount figures by age, sex, and

race/ethnic group using the A.C.E. definitions. For these versions of DA, A.C.E., and Census 2000,

the definitions of the Hispanic population, the non-Hispanic Black population, and the balance of the

population are sufficiently close that the results can be readily compared.

From the base DA population (incorporating the component estimate for ages 65 and over), a “revised

DA” figure was developed by adding the additional undocumented immigration estimated using the

C2SS data, the additional change in legal nonimmigrants, and the extra population from migration of

SAWs into the United States. With the revisions, the new DA estimate for the total population in 2000

is 283.5 million, implying an undercount of 2.1 million (Tables 5 and 6). The new DA population is

substantially closer to the A.C.E. population of 284.6 million than the Base DA population — only

1.2 million less rather than 4.5 million less.13 The revised DA undercount rate of 0.7 percent is only

0.4 percentage points less than the A.C.E. rate of 1.1 percent.

As can be seen in the table below and in Table 6, the revised DA estimates agree fairly closely with

the level and pattern of the A.C.E. estimates. The highest undercount rates in both are for the Hispanic

population with non-Hispanic Blacks significantly higher than the balance of the population but lower

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10 Examples include the group quarters population classified according to the A.C.E. race/ethnic groups and fully detailed demographic estimates of the Hispanic population consistent with the DA estimates 11 The 1990 population carried forward to 2000 for these modified DA estimates incorporates the results for race/Hispanic groups from the

1990 PES while retaining the population totals from the basic 1990 DA estimates for Blacks and non-Blacks by age and sex. 12 Some approximations were required to convert the race and Hispanic groups as tabulated into the groups used in A.C.E., but these were small. 13 The true "base DA" from Robinson (2001b) is 5 million less than the A.C.E. because it uses the adjusted Medicare estimates for the elderly.
than the Hispanic population. The differences across groups are larger in the revised DA, with the DA

undercount rate (and population estimate) for Hispanics exceeding the A.C.E. estimate.

Percent Undercount

Base Revised Revised

Group DA DA A.C.E. Diff.

Total -0.4 0.7 1.1 0.4

Black, not Hispanic 1.9 1.9 2.1 0.1

Hispanic -3.8 3.6 2.8 -0.8

Balance of population -0.3 0.0 0.7 0.7

Source: Table 6

The major effect of the suggested revisions to DA is a substantial increase in the estimated Hispanic

population. The addition of 2.6 million Hispanics to the DA estimate converts the base DA estimated

overcount of 3.8 percent into an estimated undercount of 3.6 percent. In addition, the Base DA versus

A.C.E. difference for Hispanic males is reduced from 9.3 percentage points to 0.2 percentage points.

While the revised DA estimates are much closer to the A.C.E. than were the original DA estimates for

the total population and for race/ethnic totals, the two sets of estimates still do not give the same picture of sex differences in coverage. The DA estimates tend to show much greater percentage point

differences in male and female coverage rates within each race/ethnic group than do the A.C.E. estimates. For non-Hispanic Blacks, the revised DA estimates show a 4.9 percentage point difference in

coverage for the sexes (4.5 percent undercount for males versus 0.4 percent overcount for females),

whereas the A.C.E. estimates show only a 0.6 percentage point difference. For the non-Black

non-Hispanic population, the revised DA shows a sex differential of 1.9 percentage points and the

A.C.E., a 0.6 percentage point difference. For Hispanics, the direction is reversed with DA showing

a 0.5 percentage point difference and the A.C.E., 1.7 percentage points.

The revised DA estimates bring the A.C.E. and DA figures into approximate agreement as to the

amount of undercount and the race/ethnic differences. The two systems should not be expected to give

exactly the same estimates of coverage and, indeed, demographic analysis and survey estimates of coverage have always shown some difference in the United States. The principal area of disagreement

remaining between the two estimates is the sex difference in undercount. This pattern is quite similar

to the results for 1990 comparisons of DA with the 1990 post-enumeration survey (PES) and for 1980

comparisons. A more detailed assessment of the DA components, including more refined estimates

of undocumented immigration, a fuller assessment of the nonimmigrant population, and further study

of foreign-born emigration would probably bring the two sets of estimates into even closer alignment

for 2000, but such work is beyond the scope of this study.

Conclusion and Recommendations for the Future of Demographic Analysis

Two broad, related trends in American society over the past generation or so have made the application of demographic analysis to measuring census coverage considerably more difficult and severely

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limited its utility. The rise of large-scale immigration and the emergence of undocumented immigration as a significant factor in population change have greatly increased the problems in accurately

measuring the size of the U.S. population. Related to the first trend is the substantial increase in the

size of the racial/ethnic minority population, particularly the portion that is not Black. With the current DA methods limited to producing estimates of coverage for the Black and non-Black populations,

the utility of DA is severely circumscribed.

Individual identification with a single race/ethnic group in the United States has lessened due to

increasing rates of intermarriage. The racial/ethnic changes brought about by immigration and intermarriage have blurred the linkage of historical data (from both census and non-census sources) with

contemporary census race/ethnic categories. This blurring is further exacerbated by changes in collection methods permitting multiple responses to the census race question. Accordingly, the interpretation of even the existing DA estimates has become more problematic as the reference population

requires the construction of race categories increasingly more removed from the collection and tabulation categories used in census data.

As a consequence of these trends, even improved DA estimates (along the lines recommended below)

will suffer from serious limitations. Nonetheless, DA should remain an essential part of any census

evaluation program. The age and sex structure of the population can be specified with high precision

using DA. Thus, DA can be used to judge the quality of both the census data and results from coverage measurement studies, such as A.C.E. However, race estimates from DA are likely to become even

more imprecise in the future as immigration, intermarriage, and changing individual identification

continue. But, DA estimates can be used as guides to the changes that are occurring and to the quality of census and survey data. 

Expansion of DA to groups other than Blacks would enhance the utility of the data. However, given

the increased imprecision in measuring race and ethnicity caused by the changing nature of American

society, the race/ethnic-specific estimates from DA for future censuses should probably not be held to

the same standards as coverage measurement surveys. Disagreements between DA and the surveys in

terms of relative coverage of race/ethnic groups would need to be “explained” in demographic or sociological terms, but failure to agree should not be treated as prima facie evidence that coverage has not

been adequately measured or that adjustment is not warranted.

Measurement of Immigration. The failure of DA to incorporate estimates of immigration based

on the best available information — estimates of undocumented immigration consistent with measures from the reweighted CPS, nonimmigrants from all categories based on INS data, and increased

SAW populations — changed the nature of the ESCAP decision on adjustment. Had the “base DA”

estimate been 2.1 million greater than the census rather than 1.8 million less and had it been only 1.2

million below the A.C.E. rather than 5 million, there would have been more attention to the A.C.E,

more attention to the areas of agreement rather than disagreement between the two measures, and

more attention to the potential deficiencies in Census 2000 itself. 

The problems with measuring immigration in DA relate directly to the Census Bureau’s national population estimates. The Census Bureau’s measures of undocumented immigration and nonimmigrants

during the 1990s were drawn entirely from outside the agency. It is essential that the Census Bureau

develop greater capacity to measure immigration. More attention needs to be paid to developing current estimates of undocumented immigration and to measuring changes in the flow. Although data

and methods have improved recently, the available methods remain somewhat limited, but promising

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approaches are under investigation. However, virtually none of the current research is being done at

the Census Bureau. Greater focus on this topic from the Census Bureau with its own staff and in cooperative efforts with outside researchers could greatly improve the Bureau’s ability to measure undocumented immigration and various other components of immigration.

The nonimmigrant population has also increased in numerical importance. It has proved difficult to

measure, but the Census Bureau has devoted almost no attention to this component. Here again, more

research is required. Increasingly, changes in immigration laws and regulations have affected the

flows of people into the country and changed the size and composition of the U.S. resident population. During the 1990s, modifications to the Census Bureau’s estimates resulting from such changes

were serendipitous rather than the result of careful planning and investigation of data and methods. It

is essential that the Census Bureau develop the capacity to monitor changes in immigration laws and

regulations for their potential effect on the U.S. population. Attention to all aspects of immigration

should become a regular part of the estimates program together with an on-going research program to

incorporate regulatory and legislative changes national and subnational population estimates.

Race and Hispanic Origin. The measurement of race in Census 2000 and other data systems, as

directed by the Office of Management and Budget, represents a historic disjuncture for the country.

The explicit recognition of persons identifying with more than one race group is a reflection of the

current demographic reality in the United States and permits the census to better capture the characteristics of the population. However, the new categories and data do not fit neatly into the categories

used in historical data, both from previous censuses and non-census sources. As a consequence, the

DA estimates for race/ethnic groups should be considered only as approximations to the contemporary census data, at best. The current approach of DA has been to re-cast the census data into historical categories. This approach is certainly reasonable and is probably the simplest to implement.

However, it limits the interpretation of DA results because they do not relate clearly to categories of

data as collected and published.

The Census Bureau needs to devote more research to developing methods to “map” historical data for

race/ethnic categories into the current ones. This approach will require developing more historical

data on intermarriage patterns than currently exists. Further, it also requires study of response patterns to the race and Hispanic origin items on the part of persons with multiple backgrounds. The

Bureau is currently pursuing such research, but it must be integrated into the population estimates and

DA programs.

The Census Bureau should also devote more attention to developing DA estimates for more detailed

race/ethnic categories (i.e., other than Black). Such estimates are likely to be considerably less precise than the estimates for Blacks, probably too imprecise to be used for census adjustment.

Nonetheless, such estimates should be an integral part of the demographic assessment of every census and should enter adjustment decisions in at least an advisory role.

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Table 1. Demographic Analysis Estimates of Percent Net Census

Undercount, by Race, for the United States: 1940–1990

Race 1990 1980 1970 1960 1950 1940

Total 1.8 1.2 2.7 3.1 4.1 5.4

Black 5.7 4.5 6.5 6.6 7.5 8.4

Not Black 1.3 0.8 2.2 2.7 3.8 5.0

Black-Not Black

Difference 4.4 3.7 4.3 3.9 3.6 3.4

Source: Table 2, Robinson et al.1993.

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Table 2. Population and Percent Net Census Undercount, by Race According to 

Alternative Measures, for the United States: 2000

Race and Census Preliminary

Measure 2000 DA (Base) A.C.E.*

Total population** 281.4 279.6 284.7

Undercount — -1.8 3.3

Pct. Undercount — -0.65% 1.15%

Black (tabulated) 36.4 — 37.2

Black (modified) 37.1 37.5 —

Undercount — 0.3 0.8

Pct. Undercount — 0.93% 2.08%

Not Black (tabulated) 245.0 — 247.5

Not Black (modified) 244.3 242.1 —

Undercount — -2.2 2.5

Pct. Undercount — -0.90% 1.01%

Black-Not Black

Difference — 1.83% 1.07%

Source: Appendix Table 2, Robinson 2001b.

Notes: 

**All populations in millions. 

* Figures for A.C.E. include groups quarters population from Census 2000 and assume, in effect, that the group quarters population was enumerated without error. Census populations by race differ because of the treatment of persons who marked the “other race” category. For the

DA comparison, these persons are reassigned to a specific race category (including Black) for consistency with the historical demographic

data used to construct the DA estimates. For the A.C.E. comparison, these persons are grouped with the non-Hispanic white category, part of

the “Not Black” category in the table.Table 3. Components of estimates of undocumented immigrant population based on different data

sources, for the all undocumented immigrants and those born in Mexico: 2000

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Table 3. Components of Estimates of Undocumented Immigrant Population Based

on Different Data Sources, For All Undocumented Immigrants and Those Born in

Mexico: 2000

All countries of birth Born in Mexico

Population March Rewgtd. March Rewgtd.

(in thousands) CPS CPS C2SS CPS CPS C2SS

Total Foreign-born 

in census/survey 28,381 30,088 30,523 7,841 8,388 8,774 a

Legal Nonimmigrants

in census/survey 842 898 1,026 23 27 32 b

Total Immigrants

in census/survey 27,539 29,191 29,497 7,818 8,361 8,743 c=a-b

Estimated Total

Legal Immigrants 21,878 22,081 21,791 4,642 4,648 4,511 d

Undercount for Post-1970s

Percent 1.5% 1.5% 1.4% 2.1% 2.1% 1.9% e

Amount 229 229 214 68 68 61 f=e*d

Estimated Legal Immigrants

in census/survey 21,649 21,852 21,577 4,574 4,581 4,451 g=d-f

Enumerated Undocumented

in census/survey 5,890 7,339 7,920 3,244 3,780 4,292 h=c-g

Percent Undercount for

Undocumented 10.5% 11.8% 8.4% 10.1% 15.3% 10.4% i

Total Undocumented

Immigrants 6,581 8,323 8,650 3,608 4,462 4,789 j=h/(1-i)

Notes:

See text and Passel 2001b for methods. All computations carried out for detailed groups — 35 countries and regions by age, sex, and period

for legal immigrants and CPS data; 8 regions and Mexico by period for C2SS.

b. Based on methods from Passel and Clark (1998). C2SS assumed to have 20% better coverage for 1995–2000 entrants and 10% for

1990–1995 entrants.

d. Estimates of immigrants entering before the mid-1970s come from census or survey.

e. Assumed undercount rates: 150% of A.C.E. rates for 1990s entrants; 100% for 1980s entrants; 50% for late 1970s; none for earlier.

Rates from reweighted CPS applied to C2SS.

i. Assumed undercount rates: 1990s entrants — 3 times rates for corresponding legals; 1980s entrants — 2 times; 1970s entrants — 1

times. Rates from reweighted CPS applied to C2SS.

Source: Passel 2001b.

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Table 4. Alternative Estimates of Change in the Undocumented Population, for the

United States: 1990 to 2000 (in thousands)

Sex and Change DA Total Difference:

Age in Based on Preliminary with C2SS Adjusted DA

2000 C2SS DA (Base) Distribution minus Base

Both sexes, total 5,317 2,765 2,765 0

Under 18 years 1,231 576 640 64

18-29 years 3,318 1,207 1,726 519

30-49 years 1,065 844 554 -290

50 years and over -297 139 -154 -293

Males, total 3,171 1,453 1,649 196

Under 18 years 707 287 368 80

18-29 years 2,062 675 1,072 397

30-49 years 479 434 249 -185

50 years and over -77 56 -40 -96

Females, total 2,147 1,312 1,116 -196

Under 18 years 524 289 272 -16

18-29 years 1,256 531 653 122

30-49 years 586 410 305 -105

50 years and over -219 83 -114 -197

Source: Demographic estimates — data supplied by Census Bureau (Robinson 2001c);

C2SS estimates — derived from Passel 2001b.

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Table 5. Suggested Modifications to "Base" Demographic Analysis Estimate,

A.C.E., and Census 2000, by Sex and Race/Ethnicity (in millions)

Sex and Base Revised Revised

Race/ DA Undoc. Nonimm. Revised Census

Hispanic (Mod.) Change & SAWs DA A.C.E. (unadj.)

Total 280.2 2.6 0.7 283.5 284.6 281.4

Male 138.2 1.7 0.5 140.5 140.1 138.0

Female 141.9 0.8 0.2 143.0 144.5 143.4

Black* 35.8 0.0 0.0 35.8 35.9 35.1

Male 17.4 0.0 0.0 17.4 17.1 16.7

Female 18.4 0.0 0.0 18.4 18.8 18.5

Hispanic 34.0 2.3 0.3 36.6 36.3 35.3

Male 17.2 1.4 0.3 18.9 18.8 18.2

Female 16.8 0.8 0.1 17.7 17.4 17.1

Balance** 210.4 0.3 0.4 211.1 212.5 211.0

Male 103.6 0.3 0.3 104.2 104.2 103.2

Female 106.8 0.0 0.1 106.9 108.3 107.8

Notes and sources:

* Not Hispanic.

* Non-Hispanic, non-Black population.

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"Base DA" population uses component estimates for all ages. Under 65 is identical to "Base DA" from ESCAP supporting report (Robinson

2001b); 65 and over uses preliminary components from Robinson 2001a.

"Revised Undocumented Change" is the difference between the estimates of change in the undocumented population from 1990 to 2000

based on the C2SS analysis and the "Base DA" estimate of change. See Table 4 and text.

"Revised Nonimmigrants and SAWs" includes the additional 491,000 estimates change in legal temporary residents other than students and

H-1B’s plus the additional 250,000 movement of SAWs into the country. See text for explanation.

"Revised DA" equals the Base DA plus the two revised components. The Hispanic estimate from DA uses the adjusted 1990 population plus

components of change from Robinson (2001c).

"A.C.E." is the estimated (adjusted) household population from A.C.E. plus the unadjusted group quarters population. The Black, not

Hispanic population includes persons responding to the race question as Black either alone or in combination with other race groups. 

The Hispanic population excludes American Indians, as defined by A.C.E.

"Census 2000 (unadjusted)" is the unadjusted A.C.E. household population plus the unadjusted group quarters population.
Table 6. Amount and Percent Net Census Undercount based on "Base" 

Demographic Analysis, A.C.E., and Modified Demographic Analysis, 

by Sex and Race/Ethnicity: Census 2000

Amount of Net Undercount (millions) Percent Net Undercount 

Sex and

Race/ Base Rev. A.C.E DA Base Rev. A.C.E DA

Hispanic DA A.C.E DA Base Rev. DA A.C.E DA Base Rev.

Total -1.2 3.3 2.1 4.5 1.2 -0.4% 1.1% 0.7% 1.6% 0.4%

Male 0.3 2.1 2.5 1.9 -0.4 0.2% 1.5% 1.8% 1.3% -0.3%

Female -1.5 1.1 -0.4 2.6 1.6 -1.0% 0.8% -0.3% 1.8% 1.1%

Black* 0.7 0.7 0.7 0.1 0.0 1.9% 2.1% 1.9% 0.2% 0.1%

Male 0.8 0.4 0.8 -0.4 -0.4 4.4% 2.4% 4.5% -2.0% -2.1%

Female -0.1 0.3 -0.1 0.4 0.4 -0.5% 1.8% -0.4% 2.3% 2.2%

Hispanic -1.3 1.0 1.3 2.3 -0.3 -3.8% 2.8% 3.6% 6.6% -0.8%

Male -1.0 0.7 0.7 1.7 0.0 -5.7% 3.6% 3.8% 9.3% -0.2%

Female -0.3 0.3 0.6 0.6 -0.2 -1.9% 1.9% 3.3% 3.8% -1.4%

Balance** -0.6 1.5 0.1 2.1 1.4 -0.3% 0.7% 0.0% 1.0% 0.7%

Male 0.5 1.0 1.0 0.6 0.0 0.5% 1.0% 1.0% 0.5% 0.0%

Female -1.1 0.5 -0.9 1.5 1.4 -1.0% 0.4% -0.9% 1.4% 1.3%

Notes and sources:

* Not Hispanic.

* Non-Hispanic, non-Black population.

Computed from populations in Table 5. Net undercount is estimate minus census; negative sign indicates estimated net overcount. Base of percent is estimated population. See Table 5 and text for definitions and sources.

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minus minus
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DSSD Census Procedures and Operations Memorandum Series B-4. Washington, DC: U.S.

Census Bureau. March 2.

Robinson, J. Gregory. 2001c. "Description of the Components of Change (1990 to 2000) Used to

Produce the National Postcensal Population Estimates during the 1990’s." Unpublished handout.

Washington, DC: U.S. Census Bureau. March 19.

Robinson, J. Gregory, Bashir Ahmed, Prithwis Das Gupta, and Karen A. Woodrow. 1993. "Estimating

Coverage in the 1990 United States Census Based on Demographic Analysis." Journal of the

American Statistical Association 88 (423, September): 1061–1071.

Robinson, J. Gregory, Prithwis Das Gupta, and Bashir Ahmed. 1990. "A Case Study in the

Investigation of Errors in Estimates of Coverage Based on Demographic Analysis: Black Adults

Aged 35 to 54 in 1980." Proceedings of the Social Statistics Section of the American Statistical

Association, 1990: 187–192.

Siegel, Jacob S. 1974. Estimates of Coverage of Population by Sex, Race, and Age: Demographic

Analysis. Evaluation and Research Program, 1970 Census of Population and Housing, Report

PHE(E)-4. Washington, DC: U.S. Bureau of the Census. February.

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U.S. Bureau of the Census. 1973. Test of Birth Registration Completeness, 1964 to 1968. Evaluation

and Research Program, 1970 Census of Population and Housing, Report PHE(E)-2. Washington,

DC.

U.S. Immigration and Naturalization Service. 1996. Duration of Stay of Nonimmigrants Departing

the United States. Office of Policy and Planning, Statistics Branch. Washington, DC.

Van Hook, Jennifer and Frank D. Bean. 1997. "Estimating Underenumeration among Unauthorized

Mexican Migrants to the United States: Applications of Mortality Analyses." Technical Appendix

in Binational Study of Migration between Mexico and the United States. Washington, DC:

Commission on Immigration Reform. July.

Warren, Robert E. 1997. Estimates of the Undocumented Immigrant Population Residing in the

United States: October 1996. Washington, DC: U.S. Immigration and Naturalization Service.

August.

Warren, Robert E. 2000. Annual Estimates of the Unauthorized Immigrant Population Residing in

the United States and Components of Change: 1987 to 1997. Unpublished document released by

Hon. Lamar Smith (D-TX). Washington, DC: U.S. Immigration and Naturalization Service.

Warren, Robert E. and Jeffrey S. Passel. 1987. "A Count of the Uncountable: Estimates of

Undocumented Aliens Counted in the 1980 United States Census." Demography 24 (3, August

1987): 375–393.

Woodrow, Karen A. 1991. "Preliminary Estimates of Undocumented Residents in 1990."

Demographic Analysis Evaluation Project D2, Population Division, U.S. Bureau of the Census

Woodrow and Passel. 1990. "Post-IRCA Undocumented Immigration to the United States: An

Assessment Based on the June 1988 CPS." In Bean, Edmonston, and Passel (eds.),

Undocumented Migration to the United States: IRCA and the Experience of the 1980s.

Washington, D.C.: The Urban Institute Press.

Word, David L. 1995. Estimates of nonimmigrants in the United States in the 1990 Census.

Unpublished, personal communication. Washington, DC: U.S. Bureau of the Census.

Whelpton, Pascal K. 1950. "Births and Birth Rates in the Entire United States, 1909 to 1948." Vital

Statistics, Special Reports 33: 137–162.

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SECTION 5

EFFECT OF CENSUS 2000 UNDERCOUNT

ON FEDERAL FUNDING TO STATES AND

SELECTED COUNTIES, 2002-2012

prepared by

ABSTRACT

Congress relies on the census for purposes of allocating funds under various federal grant programs

to state governments. Inaccuracies in the census count can cause federal funds to be distributed in a

way that is not fully consistent with congressional intent. Many state-funded grant programs to localities also rely on census counts, compounding the misallocation of grant money. For those jurisdictions that are counted relatively poorly by the census, this translates into fewer services for families

with the greatest needs. Analysis by the Census Bureau estimates that Census 2000 undercounted the

actual U.S. population by a net of over three million individuals, representing an undercount rate of

1.18 percent.

This study focuses on eight programs with a combined total of $145 billion in federal spending in FY

2001 that would be most affected by the undercount. Because this study does not consider all programs affected by census population figures, the total effect of the Census 2000 undercount on the

allocation of federal funds is likely to exceed the estimates in this report.

For the eight federal grant programs included in this study, the Census 2000 undercount is estimated

to cause the District of Columbia and the 31 states adversely affected by the undercount to lose $4.1

billion in federal funding over the 2002-2012 fiscal year period. The shift in federal funds due to the

undercount is most pronounced in metropolitan counties. These areas not only share in state losses

from the undercount but also lose funds to other localities within the state because of the relatively

high undercounts of urban areas.

The federal funding loss to the 58 largest counties adversely affected by the undercount is estimated

to reach $3.6 billion over the ten year period, or $2,913 per uncounted person in these jurisdictions.

The census undercount not only redistributes funds among jurisdictions, it also causes a net loss to the

states of funds from federal entitlement programs, such as Medicaid and Foster Care. For the programs

included in this study, the Census 2000 undercount is estimated to reduce net federal funds to the

states by $478 million over the 2002-2012 period.

Previous Research 

In March 2000, PricewaterhouseCoopers prepared a study 4 for the Presidential Members of the U.S.

Census Monitoring Board that estimated the impact of the projected Census 2000 undercount on the

allocation of federal funds. This March 2000 report assumed similar undercount rates by demograph-
ic group as were estimated following the 1990 census and used Census population projections for

2000. The study projected that the 2000 census undercount rate would be 1.75 percent. This was considered a conservative estimate since the Census Bureau predicted an undercount rate of 1.9 percent.

Now that Census 2000 is complete, the data indicate that the Census Bureau counted a higher percentage of the population in 2000 than in 1990. The Census Bureau estimates that the Census 2000

net undercount rate was 1.18 percent. This report updates PricewaterhouseCooper’s previous study by

using Dr. Ericksen’s analysis and extension of the information the Census Bureau has made public

about the Census 2000 undercount rate rather than projections based on the 1990 Census experience.

Methodology

This study generally follows the same methodology for estimating funding effects as the March

2000 PricewaterhouseCoopers report.

The eight programs studied accounted for $145 billion in federal grant spending in fiscal ear 2001

(see Table A). These programs represent 87 percent of the funding of major rograms identified by

the General Accounting Office (GAO) as being affected by the undercount.1 The effect of the undercount on smaller federal programs has been excluded. State programs that rely on census data to

distribute funds to localities also have been excluded. Because all federal and state grant programs

affected by the undercount were not analyzed in this study, the shift in funds due to the Census 2000

undercount is likely to be larger than is estimated in this report.

The methodology used in this report can be summarized as follows:

1. Based on the Census Bureau’s and Dr. Ericksen’s estimates of the Census 2000 undercount rate

by state and selected county, derive adjusted state and county population levels for comparison

with Census 2000 population counts.

2. Determine the formulae for allocating the eight federal grant programs included in this study.

3. Project national funding levels for these federal programs through 2012.

4. Project the effect of the Census 2000 undercount on the allocation of federal funds to states and

selected counties over the period affected by Census 2000 (generally, fiscal years 2002-2012).

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1 General Accounting Office, Formula Grants: Effects of Adjusted Population Counts on Federal Funding to States, GAO/HEHS-99-69,

February 1999.
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2 The Census Bureau only provided undercount rates for the non-group-quarters population. In order to evaluate the funding effects, we require an

undercount estimate for the entire population. We assumed that the undercount rate for the group-quarters population equals the undercount rate

for the non-group-quarters population. The alternative assumption of a perfect count of the group-quarters population would not materially affect

our results. 3 The Census Bureau excluded the group-quarters population (7.8 million persons) from its undercount estimates. Assuming that the group-quarters

population is undercounted at the same rate as the non-group-quarters population implies a national undercount of 3.4 million persons and an overall national undercount rate of 1.18 percent. Source: Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy,

March 1, 2001 and Dr. Eugene Ericksen, Estimates of State and County Undercount Rates, May 1, 2001. 4 Because of statutory provisions that guarantee minimum reimbursement rates, Medicaid funding for certain states would remain the same using

either adjusted or unadjusted population counts. Some states, like New York, receive the minimum reimbursement of 50 percent of state expenditures under adjusted or unadjusted figures. The District of Columbia has a reimbursement rate set by statute at 70 percent. These areas experience

significant undercounts, but the Medicaid minimum reimbursement provisions limit the federal funding losses from the undercount.

Several key assumptions underlie the results in this report. First, Dr. Ericksen’s extension of the

Census Bureau’s methods is assumed to be accurate. Second, the undercount rate is assumed to not

vary substantially between group-quarters and non-group-quarters persons.2 Third, current formulae

for allocating federal grant programs are assumed to remain unchanged over the 2002-12 period.

Fourth, the national funding level for these programs over the FY 2002-2012 period is based on the

Administration’s fiscal year 2001 Current Services Budget. Last, states are assumed to allocate federal funding among local governments in proportion to their respective populations, as enumerated in

the decennial census. To the extent possible, the results in this study are based on federal data, estimates, and methodology.

Effect of Census 2000 Undercount on Federal Funding to States

The Census Bureau has estimated a national net undercount rate for the non-group-quarters population in Census 2000 of 1.18 percent, totaling nearly 3.3 million persons missed. Assuming the same

undercount rate for the group-quarters population, Dr. Ericksen estimates a total net undercount of 3.4

million.3 Over the 2002-2012 fiscal year period, for the eight programs analyzed,

PricewaterhouseCoopers estimates that this Census 2000 undercount will result in a loss of $4.1 billion in federal funding among the 31 states adversely affected by the undercount and the District of

Columbia. Medicaid accounts for the largest shift in federal funds, representing 92 percent of all reallocated funds (see Figure A).4

The estimated 2000 undercount is expected to cause the biggest dollar losses in California, Texas and

Georgia (see Figure B). These are large states that have relatively large undercount rates. 

Even in states that are relatively well counted by the census, certain portions of the state may have

high undercount rates. For example, while Massachusetts is counted relatively well, Suffolk County

(containing Boston, MA) is estimated to lose $58 million in federal funds over the 2002-2012 period

as a result of its high undercount. Similarly, while Illinois is counted relatively well, Cook County

(containing part of Chicago, IL) is estimated to lose $193 million in federal funds over the 2002-2012

period.

Note that the funding effects of the Census 2000 undercount are not a "zero-sum game." The shift in

federal funds away from states that are counted relatively poorly is greater than the shift in funds to

states that are counted relatively well. The Census 2000 undercount is expected to result in a net loss

of $478 million in federal funds to the states as a whole. This overall loss in federal funding is due to

federal entitlement programs such as Medicaid, under which the national level of funding depends on

population measures and is not a fixed sum.

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Effect of Census 2000 Undercount on Federal Funding to Selected Counties 

The Census 2000 undercount also will affect counties receiving a portion of federal grants allotted to

states. The net impact on county funding depends on the effect of the undercount on both the allocation of federal funds between states (the "between-state" effect) and the allocation of funds among

jurisdictions within a state (the "within-state" effect). The net impact of the Census 2000 undercount

on the allocation of federal funds to counties is the sum of the between-state and within-state effects.

Over the 2002-2012 period, the federal funding loss to the 58 largest counties adversely affected by

the undercount is estimated to reach $3.6 billion, or $2,913 per uncounted person in these jurisdictions. Because counties with large populations generally experience undercount rates that are higher

than the state average, we assume that they will fail to receive their proportionate share of any funds

distributed by the state based on unadjusted population counts. These "within-state" effects cause the

funding losses of metropolitan areas to exceed the funding losses at the state level.

Eight counties are estimated to lose over $100 million each in federal funds: Los Angeles County, CA;

Bronx County, NY; Kings County, NY (which comprises the borough of Brooklyn, NY); Harris

County, TX (which contains the city of Houston, TX); New York County, NY (which comprises the

borough of Manhattan, NY); Cook County, IL (Chicago), Dallas County, TX, and Miami-Dade

County, FL (see Figure C). In New York City, the funding loss across the five boroughs is estimated

to reach $847 million. Because some state-funded grant programs also rely on the decennial census

for purposes of allocating funds among localities, the impact of the Census 2000 undercount on metropolitan areas will be larger than the federal funding effect.

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I. INTRODUCTION

The Presidential Members of the United States Census Monitoring Board retained

PricewaterhouseCoopers LLP (PwC) to conduct an independent estimate of the funding effects of the

Census 2000 undercount, based on undercount rate estimated by decennial census expert and Temple

University statistics professor Dr. Eugene P. Ericksen. PwC was asked to project the undercount’s

effects on the allocation of federal funds among states and selected counties over the next decade.

This report updates the results of the March 2000 PwC report1 which was based on projections of the

Census 2000 undercount rate made before Census 2000 was completed.

Estimates of the Census 2000 undercount at the state and selected county levels are presented in this

report. These undercounts are derived from undercount rates estimated by the Census Bureau and

extended by Dr. Eugene P. Ericksen of Temple University. Using these undercount estimates, we calculate adjusted population counts for the states and selected counties for comparison with the Census

2000 counts.

Additionally, the impact of the Census 2000 undercount on the allocation of federal funds to states

and selected counties is estimated in this report. Formula allocations under federal grant programs that

depend on population counts were calculated with unadjusted and then adjusted population figures to

estimate the change in federal funds flowing to each state. Changes in funding levels at the state level

were then translated into changes at the county level.

The main findings of the report are summarized in the final section.

Six appendices accompany this report:

1. Appendix A reports Census 2000 state population totals (adjusted and unadjusted) along with

estimated undercounts and undercount rates of persons over and under 18 years of age.

2. Appendix B shows 2000 population totals by selected county with and without adjustments for

the estimated undercount along with number of persons missed and the undercount rate.

3. Appendix C describes the federal programs analyzed in this report.

4. Appendix D provides detailed information on the estimated funding effects of the Census 2000

undercount by state by program.

5. Appendix E provides details on the funding effects for selected counties.

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1 “Effect of Census 2000 Undercount on Federal Funding to States and Local Areas,” 2002-2012 (March 2000).
II. ESTIMATE OF CENSUS 2000 UNDERCOUNT

A. Methodology Used by the Census Bureau and Dr. Ericksen

For the 2000 Census, the Census Bureau conducted the Accuracy and Coverage Evaluation (A.C.E.)

survey, the successor to the Census 1990 Post-Enumeration Survey (PES), to determine the accuracy

of the census count. Historically the census has not achieved an exact count of the population because

it has missed certain individuals and incorrectly enumerated others.2 For the A.C.E. survey, the Bureau

conducted detailed interviews with a sample of households. The results of this intensive interview

process can be compared to the official 2000 census enumeration to assess the accuracy of the census. This information can be used to estimate the net undercount (persons missed less persons incorrectly enumerated) by geographic region or demographic group, and to prepare an adjusted 2000 population count (i.e., the official count plus an estimate of net uncounted persons).

The A.C.E. survey established undercount adjustment factors for 448 post-strata (e.g., Black renters

in small Metropolitan Statistical Areas or White owners in large Metropolitan Statistical Areas in the

North). From the results of the A.C.E. survey, the Census Bureau developed undercount rates for the

50 states, and the District of Columbia. Dr. Eugene P. Ericksen, a census expert and professor of statistics at Temple University, working on behalf of the Presidential Members of the U.S. Census

Monitoring Board, has reviewed the estimates of the state undercount rates and extended the analysis

for counties with population in excess of 500,000 plus Richmond County (Staten Island), NY.3

For the states and the District of Columbia, Dr. Ericksen obtained the undercount adjustment factors

from a file that the Bureau provided. The file contains adjustment factors for 448 post-strata for each

of the 50 states plus the District of Columbia.4 For each state-level post-stratum, Dr. Ericksen divided

the dual system undercount estimate by the census count to calculate the adjustment factor, or ratio. Dr.

Ericksen then created a weighted average of the adjustment factors, where the population shares in the

post-strata were the weights. For the large county undercount rate estimates, Dr. Ericksen did not have

the exact distributions of post-strata populations by county, but he approximated them with 2000

Census state totals by racial group and 1990 census data sorted by racial group and housing tenure.

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2 Incorrect enumerations would arise from the inclusion of a child born after April 1, a person who died before April 1, or a college student

living away from home but counted in the parents’ house instead of his or her usual place of residence. 3 Dr. Ericksen’s estimates, like the Census Bureau rate upon which they are based, are for non-group-quarters residents. For this study we

will be assuming that the undercount rate for group-quarters residents is comparable by state and post-strata. 4 Access to this file was given to the Census Subcommittee, the National Academy of Sciences, and the Census Monitoring Board in

February 2001.
B. Estimated 2000 Undercount by State

Based on the Census Bureau’s methodology, the undercount rate for the non-group-quarters population in Census 2000 is estimated to be 1.18 percent or nearly 3.3 million persons. Assuming the same

undercount rate for the group-quarters population, Dr. Ericksen estimates a total national undercount

of 3.4 million (see Table 1).5 Table A-2 in Appendix A shows net undercount rates by state for populations over and under 18 years of age. Children have undercount rates that exceed the national average. Nationally, persons under the age of 18 are estimated by Dr. Ericksen to have an undercount rate

of 1.56 percent6 of the actual population, resulting in over 1.1 million uncounted children.

Consequently, funding programs targeting children, such as the Child Care and Development Block

Grant, are especially vulnerable to the undercount.7

Four states account for nearly 40 percent of the estimated Census 2000 undercount: California

(522,796), Texas (373,567), New York (209,123), and Florida (200,670). States (plus the District of

Columbia) with the highest percentage undercounts are Alaska (2.67 percent), Hawaii (2.16 percent),

the District of Columbia (2.15 percent), New Mexico (1.94 percent), and Texas (1.76 percent). States

with the lowest undercount rates are Minnesota (0.29 percent), Missouri (0.46 percent), North Dakota

(0.47 percent), Iowa (0.48 percent), Nebraska (0.56 percent), and South Dakota (0.56 percent).

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5 The Census Bureau excluded the group-quarters population (7.8 million persons) from its undercount estimates. In order to evaluate the

funding effects, we require an undercount estimate for the entire population. We assumed that the undercount rate for the group-quarters

population equals the undercount rate for the non-group-quarters population. Assuming that the group-quarters population is undercounted

at the same rate as the non-group-quarters population implies a national undercount of 3.4 million persons and an overall national undercount rate of 1.18 percent. The alternative assumption of a perfect count of the group-quarters population would not materially affect our

results. Source: Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy, March 1, 2001 and Dr. Eugene

Ericksen, Estimates of State and County Undercount Rates, May 1, 2001. 6 In the Report of the Executive Steering Committee for Accuracy and Coverage Evaluation Policy, March 1, 2001, the Census Bureau

reports a national undercount for the under 18 population of 1.54 percent. 7 See the GAO report for a detailed description of the funding formulas. General Accounting Office, Formula Grants: Effects of Adjusted

Population Counts on Federal Funding to States, GAO/HEHS-99-69, February 1999.
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II. FUNDING EFFECT OF CENSUS 2000 UNDERCOUNT

A. Federal Programs Analyzed

This study examines the effect of the Census 2000 undercount on the allocation of funds under eight

federal grant programs: (1) Medicaid; (2) Foster Care; (3) Rehabilitation Services Basic Support; (4)

Social Services Block Grant; (5) Substance Abuse Prevention and Treatment Block Grant; (6)

Adoption Assistance; (7) ChildCare and Development Block Grant; and (8) Vocational Education

Basic Grants.These eight programs account for all of the funding shifts identified in the

GeneralAccounting Office (GAO) study of the effects of the 1990 census undercount onfederal funding to states in fiscal year 1998. 8

The GAO study focused on 25 large formula grant programs, whose funding represented 90 percent

of the total federal grants affected by the census undercount. Of the 25 programs analyzed in the GAO

study, ten programs (amounting to $21 billion in 2001) were excluded because their funding formulae depended on population variables for which undercount rates are not available (e.g., the population below the poverty line). Of the remaining 15 programs, five of the programs (amounting to $43

billion) were not affected by the undercount because the formulae had components which made the

undercount immaterial. Two programs (amounting to $2 million) used population figures adjusted for

the undercount.9

The remaining eight programs (listed in Table 4) were affected by the undercount. These programs

represent over 87 percent of the funding under major programs that depend on unadjusted census

counts.

Table 4: Federal Grant Programs and FY 2001 Obligations

[Obligations in billions of dollars; Major programs affected by census undercount]

Program Obligations

Medicaid $130.0

Foster Care 5.1

Rehabilitation Services Basic Support 2.4

Child Care and Development Block Grant 2.0

Social Services Block Grant 1.7

Substance Abuse Prevention and Treatment Block Grant 1.7

Adoption Assistance 1.2

Vocational Education Basic Grants 1.1

Subtotal, eight programs included in study 145.1

Total for major grant programs affected by undercount $166.6

Source: Budget of the United States, FY 2002, GAO, and PricewaterhouseCoopers calculations.

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8 General Accounting Office, Formula Grants: Effects of Adjusted Population Counts on Federal Funding to States, GAO/HEHS-99-69,

February 1999. 9 These two programs, administered by the Department of Labor, rely on estimates of the civilian labor force. If the Department of Labor

does not adjust its estimates of the labor force, these programs would also be affected by the undercount.
B. Current Services Funding Levels over FY 2002-2012 Period

Depending on the first year of impact, Census 2000 will affect federal grant allocations over the 2002-

2011 or the 2003-2012 period.10

For each of the eight federal grant programs analyzed in this report, the Administration’s FY 2002

budget projects Current Services funding levels through 2011. The Current Services Budget estimates

funding levels necessary to continue programs at a level equal to the most recently funded year (i.e.,

2001 for the 2002 budget). In essence, it is a prediction of the funding necessary to support current

law expenditures over the budget period.

The Current Services Budget projects that funding of discretionary programs will grow with inflation.

Unlike entitlement programs, the funding of discretionary programs is dependent on the annual

Congressional appropriations process. Three of the eight federal grant programs included in this study

are classified as discretionary: (1) Substance Abuse Block Grant, (2) Vocational Education, and (3)

Child Care and Development Block Grant.

The Current Services Budget projects that funding for entitlement programs will grow with the underlying eligible population and inflation. Three of the federal programs included in this study are classified as entitlement programs: (1) Medicaid, (2) Foster Care, and (3) Adoption Assistance.

The remaining two programs included in this study, Social Services Block Grant and Rehabilitation

Services, are mandatory programs that are projected to grow at rates consistent with their enacting

legislation.

The fiscal year 2002 budget includes Current Services funding levels through 2011. Funding levels

for four programs included in this study were extrapolated through 2012 based on the growth rates

projected by the Office of Management and Budget over the FY 2002-2011 budget period (see Table

5).

Current Services funding levels for the Substance Abuse Block Grant are extrapolated through 2012

using the annual Office of Management and Budget general budget inflator for the 2003-2011 period

of 2.2 percent. The Current Services Budget projects slowing growth for the entitlement programs,

and this trend is assumed to continue through 2012. No extrapolations were necessary for the mandatory programs because the 2000 Census will affect their funding allocations over 2002-2011, the current budget period.

Assuming the Current Services spending levels, census population counts from Census 2000 ultimately will be used to distribute $2.5 trillion over the 2002-2012 fiscal year period.

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10 This report assumes that the effects of Census 2000 are not incorporated until 2000 population figures are used in allocation formulas. If

population estimates from earlier years, such as 1999, are adjusted consistent with Census 2000, allocations could be affected before 2002.
C. Funding Effect of Census 2000 Undercount on States

State allocation shares under federal grant programs are determined before the onset of the funding

year; thus, state allocations for the current year are based on population estimates from several years

earlier. The Census Bureau publishes population estimates for the years between decennial censuses.

These estimates are based on the decennial population enumeration and are updated using administrative records (e.g., birth and death certificates). Consequently, errors in the decennial population

count persist for ten years, until the next census enumeration. Consequently, the Census 2000 undercount will affect federal grant allocations over a ten-year period.

For example, the funding formula for the Social Services Block Grant program depends on population estimates from the second prior year. Thus, Census 2000 will affect Social Services Block Grant

allocations over the 2002-2011 period. For the eight programs included in this report, Census 2000

will first affect grant allocations in either 2002 or 2003, and the effect will persist over the 2002-2011

or 2003-2012 period, depending on the program.

The effect of the Census 2000 undercount on the allocation of federal funds to states initially was calculated for a base year and then extrapolated over the 2002-2012 period. The base year for each grant

program was determined as: the first year affected by the 2000 census figures or the most recent year

for which data were available for all of the variables (other than population) in the funding formula.

For most programs, 2002 was the base year used in the calculations. Because data for some of the formulae were not available to calculate the 2002 allocation, the base year for the corresponding programs is 2001. For example, the formula for Vocational Education depends on per capita personal

income by state as released by the Bureau of Economic Analysis (BEA) for the second preceding year.

Final per capita personal income figures are available for 1999; consequently, the base year for the

Vocational Education program is 2001.

Once a base year was established for each program, we calculated state funding allocations using both

official and adjusted 2000 state population projections. These calculations take into account all elements of the current funding formulae, including hold harmless and minimum share provisions. Each

state’s share of national program funding in the base year was then determined under both the official

and adjusted 2000 population projections. The difference between these two shares of national program funding is an estimate of the impact of the Census 2000 undercount on the state’s allocation of

federal funds. For example, suppose that a state’s share of federal program funds increases from 3.0

percent to 3.1 percent, in the base year, as a result of using adjusted versus official 2000 population

projections. For this state, the effect of the Census 2000 undercount is estimated to be a loss of 0.1

percentage points (3.1 percent minus 3.0 percent) of national program funding.

For the eight federal grant programs analyzed in this study, the Census 2000 undercount is estimated

to reduce federal funding in 31 states and the District of Columbia by $4.l billion over the 2002-2012

period (see Table 6). In 2003 alone, the undercount is estimated to reduce federal funds allocated to

these states by $277 million. By comparison, the General Accounting Office estimated that the effect

of the 1990 census undercount on these federal programs was to shift $449 million among states in

1998. Because the estimated 2000 undercount is both smaller and more uniform across jurisdictions

than the estimated 1990 undercount, the total amount of federal funds reallocated is smaller. 

States that are counted relatively well in the census are estimated to receive higher levels of federal

funding as a result of the undercount; however, the additional federal funds received by these states

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are less than the loss of federal funds in the other states. The effect of census undercounts on the federal funding of entitlement programs is not a “zero-sum game” among the states because an increase

in funding to one state does not require a reduction in funding to other states. For the federal programs

analyzed in this study, federal funds allocated to all 50 states and the District of Columbia are estimated to be $478 million less over the 2002-2012 fiscal year period as a result of the Census 2000

undercount.

The loss of funds over the 2002-2012 period for the eight analyzed programs ranges from $26 per

undercounted person in Colorado to over $6,300 per person missed by the census in Alaska (see Table

7).11 In 2003, the first year fully impacted by the undercount, the funding loss in 31 undercounted

states and the District of Columbia averages $114 per uncounted individual. This figure is less than

GAO’s 1998 estimate of $145 per uncounted individual, which was based on the higher 1990 undercount rate.

Of the eight federal programs analyzed in this report, Medicaid accounts for 92 percent of the federal funds that would be shifted as a result of the Census 2000 undercount. As a percent of total program funding, the programs most affected by theCensus 2000 undercount are Vocational Education

(0.28 percent) and Rehabilitation Services (0.27 percent).12 Table 8 summarizes the impact of the

Census 2000 undercount by program.

D. Funding Effect of Census 2000 Undercount on Counties

This section analyzes the effect of the Census 2000 undercount on counties. The county effects are

estimated under the assumption that states allocate federal funds among county in proportion to their

official census population counts. 

The Census 2000 undercount can affect federal funding to counties in two ways. First, the undercount

at the state level affects the allocation of funds among the states, which alters the amount of funds that

states have available to pass through to local governments (the “between-state” funding effect). For

example, the Census 2000 undercount is estimated to cause the state of Illinois to receive a larger

share of the federal funds under the programs analyzed than it would with an accurate census count

(other states, therefore, receive a smaller share because of the undercount). Counties in the state, such

as Cook County (Chicago), benefit from the fact that the state receives these additional funds. The

between-state effect measures the effect on metropolitan areas of the funding shifts among the states

due to the census undercount.

Second, the undercount at the local level may affect a state’s allocation of federal funds among its

counties (the “within-state” funding effect). Assuming the state allocates funds to local areas within

the state using population counts, any undercount would distort the flow of funds within the state.

Because Cook County is estimated to experience a high undercount rate relative to the other areas in

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11 Because of statutory provisions that guarantee minimum reimbursement rates, Medicaid funding for certain states would remain the same

using either adjusted or unadjusted population counts. Some states, like New York, receive the minimum reimbursement of 50 percent of

state expenditures under adjusted or unadjusted figures. The District of Columbia has a reimbursement rate set by statute at 70 percent.

These areas experience significant undercounts, but the Medicaid minimum reimbursement provisions limit the federal funding losses from

the undercount. Table D-5 in Appendix D lists the effect of the census undercount on state funding levels under the Medicaid program. 12 These percentages translate into $33 million for Vocational Education and $72 million for Rehabilitation Services.
Illinois, it receives a smaller share of the state funds than it would have gotten under an accurate census count. Therefore, it experiences a negative within-state effect. The within-state effect measures the

impact of the undercount on funding allocations within states.

The “net” funding effect of the census undercount on a county is the sum of the between-state and within-state funding effects. Because the between-state and within-state effects could have the same or different signs, the net effect could be larger or smaller than the between-state or within-state effects alone.

1. Between-State Funding Effect

For the counties within each state, the between-state funding effect was estimated in two steps. The

effect of the Census 2000 undercount on the state’s level of federal funding was first calculated for the

2002-2012 period (see section III.C., above). The funding effect at the state level was then apportioned

among the counties in proportion to their unadjusted population counts. Thus, counties in states that

lose federal funding as a result of the Census 2000 undercount are each estimated to share proportionately in this funding loss.

2. Within-State Funding Effect

For the counties within each state, the within-state funding effect was estimated in four steps. First,

the state’s share of federal funding over the 2002-2012 period was determined based on adjusted 2000

population counts (as described in section III.C., above). Second, state funding was apportioned

among the counties in proportion to their estimated 2000 adjusted census counts. Third, state funding

was apportioned among the counties in proportion to their 2000 official (unadjusted) census counts.

Finally, the within-state funding effect was estimated by subtracting the county funding levels determined in step two (based on adjusted population counts) from step three (based on official population

counts).

Counties with an undercount rate higher than the overall state average have a negative within-state

funding effect, while relatively well counted areas have a positive within-state funding effect.

3. Net Funding Effect

For the counties within each state, the net funding effect of the Census 2000 undercount over the 2002-

2012 period was calculated as the sum of the between-state and within-state funding effects. For any

county, these two funding effects can work in the same or opposite directions. For example, Cook

County is estimated to have a positive $9 million between-state funding effect, because the State of

Illinois is relatively well counted by the census. However, Cook County is estimated to have a negative $202 million within-state funding effect because it is relatively poorly counted by the census compared to other jurisdictions within the state. Thus, the netfederal funding effect in Cook County of the

Census 2000 undercount is negative $193 million ($9 million less $202 million) over the 2002-2012

period, because the funding loss from the within-state effect is larger than the funding gain from the

between-state effect. The federal funding loss to the 58 largest counties adversely affected by the

undercount is estimated to reach $3.6 billion over the period, or an average of $2,913 per uncounted

person in these jurisdictions.

Table 9 shows the net funding effect of the Census 2000 undercount on the 25 counties that are estimated to experience the largest loss in federal funding over the 2002-2012 period. The five counties

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expecting the largest funding loss from the Census 2000 undercount are Los Angeles County, CA

($636 million), Bronx County, NY ($362 million), Kings County, NY ($269 million), Harris County,

TX ($234 million), and New York County, NY ($212 million). Results for all 112 selected counties

are shown in Appendix E.

This analysis only considers the effect of the Census 2000 undercount on federal funds allocated to

local governments. Because a variety of state grant programs are also distributed to local governments

on the basis of official population counts, the total shift in funds from federal and state grant programs

will likely be larger than the estimates in this report.

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APPENDIX A

2000 Population Counts and Estimated Undercounts of

Persons Over and Under 18 Years of Age by State
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APPENDIX B

2000 Population Undercount by Selected County
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APPENDIX C

Federal Program Descriptions

Federal Program Descriptions

The federal programs analyzed in the report are summarized below. Additional information, such as

the formulas used to allocate funds to states, is available from the General Accounting Office report.1

The total effect on the eight federal programs analyzed appears in Table D-1 in Appendix D.

1. Adoption Assistance

The Adoption Assistance program supports the adoption of children with special needs. Specifically,

the program provides maintenance payments to the families adopting the qualifying children, payments

to state agencies for the administrative costs involved with placing the children in adoptive homes, and

payments for training professional staff and parents involved in the adoptions. States determine which

children qualify for the assistance; in general, children with special circumstances that make their adoption less likely, such as a mental or physical handicap, are eligible for the program.

The federal government provides a specified percentage of the payments made to the qualifying families, and states provide the remainder. Administrative and training expenses are matched at the same

rate in all states (50 percent and 75 percent, respectively). The federal government reimburses maintenance payments based on a state-specific percentage that depends on each state’s per capita income.

This percentage, the Federal medical assistance percentage (FMAP), ranges from 50 percent to 83 percent and also determines reimbursement rates under the Medicaid program.

To calculate the effect of the 2000 undercount on the Adoption Assistance funding received by each

state, the FMAP for each state was calculated using adjusted and unadjusted per capita income, which

relied on adjusted and unadjusted population counts. Adjusted and unadjusted funding levels by state

were produced by calculating the product of the FMAP (adjusted or unadjusted) and the maintenance

payments.2 Table D-2 in Appendix D summarizes the estimated effect of the Census 2000 undercount

on this program. 

2. Child Care and Development Fund Discretionary Funds (formerly the Child Care and

Development Block Grant)

This program provides funding to assist low-income families with child care and to improve the availability and quality of child care. States establish programs with the funds subject to certain Federal

restrictions. For instance, to qualify for services under the program, children must be from families

that earn less than 85 percent of the state median income.

The program allocates funding amounts to states based on a formula that includes the state population

under 5 years old, the number of children qualifying for the School Lunch program, and the state per

capita income. To calculate the effect of the 2000 undercount on the funding received by each state,

adjusted and unadjusted population (under 5 and overall) figures were used in the formula to calculate adjusted and unadjusted state shares. Multiplying these shares by the total funding level for the

program yielded the adjusted and unadjusted state funding levels. Table D-3 in Appendix D summarizes the estimated effect of the Census 2000 undercount on this program.
3. Foster Care

The Foster Care program supports families and facilities that provide homes to needy foster children.

The program provides funding for maintenance payments to the homes hosting the children, payments

to the state agencies for administrative costs, and payments to state and local agencies for training

expenses. Any foster child that would have qualified for the Aid to Families with Dependent Children

(AFDC) program, as in effect in 1995, qualifies for Foster Care payments, which are made to the foster care family home, private child care facility, or public child care institution (with more than 25

people). The maintenance payments are intended to cover the costs associated with raising a child,

such as expenses for food, shelter, and supervision.

Similar to the Adoption Assistance program, administrative and training expenses are matched at the

same rate in all states (50 percent and 75 percent, respectively). The federal government reimburses

maintenance payments based on the FMAP. 

To calculate the effect of the 2000 undercount on the Foster Care funding received by each state, the

FMAP for each state was calculated using adjusted and unadjusted per capita income, which relied on

adjusted and unadjusted population counts. Adjusted and unadjusted funding levels by state were produced by calculating the product of the FMAP (adjusted or unadjusted) and the maintenance payments. Table D-4 in Appendix D summarizes the estimated effect of the Census 2000 undercount on

this program.

4. Medicaid

The Medicaid program provides medical assistance to certain low-income individuals. States design

and administer their own programs, subject to Federal regulations, and receive reimbursements from

the Federal government for their expenses. In general, low-income children and pregnant women,

adults in families with dependent children, low-income persons with disabilities, and low-income elderly persons qualify for the program. The program covers expenses for medical assistance such as

inpatient and outpatient hospital care, laboratory and x-ray services, and physician services.

Administrative expenses, amounts for family planning, and amounts paid to Indian Health Services

facilities are matched at the same rate in all states (50 percent, 90 percent, and 75 percent, respectively). Medical assistance payments (i.e., payments for care) are matched based on the FMAP.

To calculate the effect of the 2000 undercount on the Foster Care funding received by each state, the

FMAP for each state was calculated using adjusted and unadjusted per capita income, which relied on

adjusted and unadjusted population counts. Adjusted and unadjusted funding levels by state were produced by calculating the product of the FMAP (adjusted or unadjusted) and the medical assistance

payments. Table D-5 in Appendix D summarizes the estimated effect of the Census 2000 undercount

on this program.

5. Rehabilitation Services, Basic Grants

This program provides vocational rehabilitation to disabled individuals and their families.

Specifically, individuals with physical or mental impairments receive services such as reader services for the blind, interpreter services for the deaf, prosthetic devices, job placement, and transportation

to vocational rehabilitation facilities. States administer independent programs, subject to Federal

guidelines, and receive grants annually from the Federal government.

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The program allocates funding to states based on a formula that considers the amount received by the

state in 1978, state population, and per capita income. To calculate the effect of the 2000 undercount,

state shares were calculated using adjusted and unadjusted state and national population figures.

Adjusted and unadjusted state funding levels were calculated by multiplying the state shares (adjusted and unadjusted) by the total funding for the program. States are guaranteed to receive at least onethird of one percent of the total appropriation; state funding levels (adjusted and unadjusted) were

adjusted to conform to this restriction. Table D-6 in Appendix D summarizes the estimated effect of

the Census 2000 undercount on this program.

6. Social Services Block Grant

This program provides grants to states for providing social services. States determine the use of the

funds at their own discretion but must use the funds towards one of five goals: (1) to prevent, reduce,

or eliminate dependency; (2) to achieve or maintain self-sufficiency; (3) to prevent neglect, abuse, or

exploitation of children and adults; (4) to prevent or reduce inappropriate institutional care; and (5) to

secure admission or referral for institutional care when other forms of care are inappropriate.3 In the

past, states have used the funds for child day care, protective and emergency services for children and

adults, adoption, foster care, and counseling.

States receive allotments under the program based on a formula that relies on the state’s share of the

national population. To calculate the effect of the 2000 undercount, state shares were calculated using

adjusted and unadjusted state and national population figures. Adjusted and unadjusted state funding

levels were calculated by multiplying the state shares (adjusted and unadjusted) by the total funding

for the program. Table D-7 in Appendix D summarizes the estimated effect of the Census 2000 undercount on this program.

7. Substance Abuse Prevention and Treatment Block Grant

This program provides grants to states for the prevention and treatment of drug and alcohol abuse.

Subject to certain federal restrictions, states design and implement programs to reduce drug and alcohol abuse and provide rehabilitation to individuals with drug and alcohol problems.

States receive allocations under the program based on a formula that depends on the population aged

18 to 24, population aged 25 to 64, urban population aged 18 to 24, per capita income, and a cost

index. The cost index, which is recalculated every three years, consists of a wage component and a

measure of average rental prices for housing.

To calculate the effect of the 2000 undercount, state shares were calculated using adjusted and unadjusted state and national population figures (the cost index and urban share of population were

assumed to remain constant). Adjusted and unadjusted state funding levels were calculated by multiplying the state shares (adjusted and unadjusted) by the total funding for the program. After the calculation of these funding levels, additional adjustments were made to guarantee that each state

received a minimum share of the increase in the national funding level and a minimum share of the

national funding level, as is standard practice under current law. Table D-8 in Appendix D summarizes

the estimated effect of the Census 2000 undercount on this program.

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8. Vocational Education

This program provides grants to states for vocational education programs for youths and adults. State

programs offer courses to prepare individuals for employment in occupations not requiring a baccalaureate or an advanced degree. States use the funds provided by this program for a variety of activities, including purchasing occupationally-relevant equipment and curriculum materials, providing

career counseling and guidance, hiring staff, and offering remedial classes.

The formula used to allot the funding amount to states depends on the population aged 15 to 19, population aged 20 to 24, population aged 25 to 65, and per capita income. To calculate the effect of the

2000 undercount, state shares were calculated using adjusted and unadjusted state and national population figures. Adjusted and unadjusted state funding levels were calculated by multiplying the state

shares (adjusted and unadjusted) by the total funding for the program. Current law contains a “holdharmless” provision to guarantee that the amount a state receives in the current year always exceeds

the amount received in the prior year (assuming the national funding level rises). State funding levels

were adjusted to ensure that this provision was satisfied. Table D-9 in Appendix D summarizes the

estimated effect of the Census 2000 undercount on this program.

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APPENDIX D

Estimated Funding Effect by State Program
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APPENDIX E

Estimated Funding Effect by Selected County
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SECTION 6

CENSUS 2000 PARTNERSHIP PROGRAM

Overview

For the 2000 Census, the U.S. Census Bureau embarked on a comprehensive Partnership program with

state, local, and tribal governments as well as community-based organizations and the private sector.

The intended purpose of the program was to improve census response rates.

The Partnership Program involved as many as 140,000 organizations. The focus was to mobilize grass

roots efforts to boost participation in the census. Organizations were asked to place the census on their

meeting agendas and participate in public awareness initiatives. The Program involved promotional

activities with national/umbrella community, religious and educational organizations; trade associations; labor unions; foundations; and Fortune 500 companies. The 2000 effort also included a Colonias

Initiative, Rural Initiative, Minority College and University Initiative, Large City and State Initiative,

Special Language Initiative, Natural Disaster Initiative and the African and Caribbean Immigrants

Initiative. 1 

Partnership activities were conducted nationwide during every major phase of Census 2000 operations: Master Address File (MAF) Development, Recruitment, Identification of Hard to Enumerate

Areas, Service Based Enumeration, Multiple Language Mailing, and Non-Response Follow-Up

(NRFU). More than 600 Partnership specialists were hired in the 2000 Census to coordinate activities between headquarters, regional offices and the local census operations. The Bureau spent $142.9

million on Partnership activities between October 1997 and September 2000.2 These funds were complemented by state and local resources, as well as creative in-kind contributions from Non

Government Organizations. 

Given the size and scope of the program, this report reviews the program as conducted in two fastgrowing and diversely populated states, Georgia and California, and seeks to evaluate its effectiveness.

The Monitoring Board held field hearings in Atlanta, Los Angeles and San Diego specifically on the

partnership program; our analysis draws upon testimony presented in those hearings. 

GEORGIA

Regional/State Profile

Georgia falls within the Bureau’s Atlanta Region, which in addition to Georgia, comprises Florida and

Alabama, 12,895,935 housing units, 168,751 square miles, 41 congressional districts and 55 local

census offices. 

The Regional Office identified a common set of challenges including extensive areas of Non English

speaking Asian and Hispanic populations, the growing homeless population, residents who live in

recreational vehicles and move frequently, isolated communities reachable only by boat, multiple families living in public housing projects, “snowbirds,” people who move south for the winter, and pockets of communities with low literacy rates.

1 Partnership Report, Volume I, U.S. Census Bureau, March 2000.

2 2000 Census: Review of Partnership Program Highlights Best Practices for Future Operations, GAO August 2001.
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3 Partnership Report, Volume I, U.S. Census Bureau, March 2000.

4 Ibid. 

Goals set by the Atlanta Regional Office were uniform for Georgia, Florida and Alabama and were:

using partners to help set priorities; managing expectations; offering a wide-ranging menu of partnership opportunities; developing an approach based on the interests, needs and benefits of individuals and organizations; seeking to help where it was needed the most; and providing assistance to customers.3

Regional Office’s Partnership Efforts For Georgia

While the Atlanta Office developed a set of common goals to motivate census participation and developed a regional strategy based on shared enumeration challenges, the office clearly had a tailor-made

strategy for Georgia. It took into account state demographic trends including an increase of about one

million residents since 1990 and a rapidly increasing Spanish-speaking population.

The Regional Office actively sought partnerships with state and local officials as well as numerous

business representatives and community leaders. The Regional Office offered program ideas, material support and partnership specialists dedicated to community outreach, especially in hard to count

areas in the metropolitan Atlanta area and throughout the state. The Office provided a structured

framework in carrying out the partnerships, though a principal objective for the office was to allow

local groups autonomy in performing their work. 4

More than 3,000 partnerships were officially recorded statewide; however, census observers in the

state estimate that the number is a mere fraction of the total number of partnerships that were actually generated. Many of the census partnerships were formed during the 1990 Census and have been

ongoing; some had roots in other collaborative efforts and re-emerged in Census 2000; and others

were virtual start-ups that proved highly effective. 

The following are examples of Best Practices involving collaborations at every level and were the

focus of a Monitoring Board hearing held in Atlanta on September 11, 2000. 

Best Practices in Georgia

Governor Roy Barnes’ Outreach Plan

In preparing for Census 2000, Georgia Governor Barnes appointed a 40-member Complete Count

Committee comprised of a broad cross section of business and community leaders. Approximately

$3.5 million of state funding was used in developing a marketing and media campaign aimed at raising awareness of the census among different population groups.

The States’ outreach efforts proved to be successful, in large part because of a “three tiered” outreach

strategy involving the State, 16 Regional Development Committees (RDCs) and local community

groups. The RDCs, especially helpful in the development of address lists and the Local Update of the

Census Addresses (LUCA) program, played a pivotal role in the Partnership effort from beginning to

end. Working to enhance public participation in the census, the State sought to build on pre-existing

relationships with major organizations operating within the State, i.e. the Georgia Homebuilders

Association and the Poultry Association (both of which have close ties to the Hispanic community in

Georgia), the Bankers Association and the Public Service Commission. 
Local Grant Program

In recognition that the Bureau would publicly announce the mail back response rates for the state and

each county in the state, a local grant program was launched that offered a $10,000 grant to the county in each of the RDC’s with the highest percentage increase from 1990. The program was developed

on the basis that it would enhance community interest in the census and that the grant money would

ultimately be used for the equipment and recreational facilities benefiting children in the local area. 

Census in the Schools

Georgia, like other states, deployed a Census in the Schools Initiative developed by the Bureau. As a

first step in implementing the program in Georgia, a former employee of the State Department of

Education worked to ensure that the program was able to be formally included in the state’s school

curriculum. Secondly, the generic census information provided by the Census Bureau was modified

to include information more applicable to Georgians. A leading state official said the 8-page packets

students were provided in the classroom made their way home at high rates and that the program succeeded like no other program in getting census information to hard to count households.

Alpha Kappa Alpha Sorority

In October 1998, the Phi Alpha Omega Chapter of the Alpha Kappa Alpha Sorority partnered with the

local census office in Atlanta in “spreading the word about Census 2000.” Committing itself to a twoyear outreach plan, the sorority worked to enhance African American participation in the census by

focusing its attention on four areas: the church, schools, the metro Atlanta community and the sorority itself. Speaker training offered by the Census Bureau helped in communicating their message. 

The Omega Chapter completed a total of twenty-five projects over a two-year period. Activities

included sharing census information in local schools, PTA’s and churches, hosting information booths

and distributing fliers and promotional items at a local housing project. In addition, information was

provided to the homeless community.

The Census Bureau recognized the chapter as an “extraordinary” partner for its census outreach

efforts and awarded them two grants which were used to produce and disseminate Census 2000 memorabilia such as personalized Phi Alpha Omega cups, T-shirts and mugs.

Mexican American Legal Defense and Education Fund (MALDEF), Regional Office

MALDEF focused its outreach efforts in five population groups identified as hard to enumerate.

These included apartment dwellers and transients, migrant workers, people who worked at poultry

plants and factories, the elderly and children. Activities included distributing brochures, posters and

videos to 12,000 employees in the poultry industry, most of whom were Hispanic. MALDEF worked

cooperatively with state officials and the Poultry Association to maximize available networks to the

Spanish-speaking community.

Georgia Power Company

The Census Bureau contacted Georgia Power Company in early 1999 to coordinate efforts in educating minority communities of the value in census participation. Georgia Power embraced the proposed

partnership, and recognizing that outreach efforts in the African American and Hispanic communities

were relatively well advanced, chose to concentrate its minority outreach to Asian Americans. The

company’s goal was to add value to the census campaign where it could.

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5 Partnership Report, Volume I, U.S. Census Bureau, March 2000.

In concert with the Census Bureau, Georgia Power identified a few key leaders in the Asian American

community and worked closely with them in organizing an Asian American Census Summit. More

than 150 leaders participated in the event. The event drew widespread media attention and led to a

number of local initiatives within the Asian American community.

Georgia Power also advertised census jobs and the importance of the census in people’s everyday lives

through bill inserts to their customers. This initiative contributed significantly to the state’s public

awareness efforts given the company’s large customer base.

CALIFORNIA

Regional/State Profile

The Bureau’s Los Angeles Region covers California’s 39 southern counties (and Hawaii) and the

Seattle Region covers California’s 39 northern counties (and Alaska, Idaho, Oregon and Washington).

In this report we limit our examination to Partnership efforts in two of the largest and most diversely

populated southern counties—Los Angeles County and San Diego County. 

The Regional Office cited numerous challenges in motivating people to participate in the census. They

included the inflow of immigrants who often fear and mistrust government, communities that are

either non-English speaking or have low literacy rates, large homeless populations, and multiple families living in single-dwelling housing units.

The over-arching goal set by the Regional Office was to meet these challenges and “acquire an accurate count of all residents including previously undercounted population groups—African Americans,

Asians and Pacific Islanders, Hispanics and American Indians through extensive partnerships.5

Regional Office Partnership Strategy For Southern California

The Bureau’s Los Angeles Regional Office began reaching out to local jurisdictions in March 1997,

thus enabling the establishment of strong local Complete Count Committees early in the census

process. Booklets offering useful recommendations on how the committees could be structured and

on types of activities that could be undertaken were offered. In addition, with support from local community groups, the Regional Office aggressively advertised local employment opportunities relating

to the census. In Los Angeles County alone, the applicant pool reached 315,000; more than 63,000

candidates were appointed.

According to Regional Director John Reeder, the following three census programs led to the high

response rates in Los Angeles and San Diego Counties: the first-ever national advertising campaign

bolstered by complementary advertising efforts carried out at the state and local level; the nationallyrun Census in the Schools initiative which was especially welcome in the Los Angeles Unified School

District; and finally, targeted outreach efforts by the Local Census Offices (The Santa Ana Office in

particular) including “neighborhood walks” timed with the March 2000 mass mailing of census questionnaires to residents. Reeder acknowledged that community outreach efforts at the local level are

indispensable in an undertaking as large and personal as the census. 

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6 CMB-P Board Member, Lt. Governor Cruz M. Bustamante, served on the California Complete Count Committee.

The following are Best Practices as carried out in California. They were highlighted in Monitoring

Board hearings held in Los Angeles (June 26, 2000) and in San Diego (June 27, 2000).

Best Practices in California

Governor Gray Davis’ Outreach Plan

The State of California spent $24.7 million to increase the Census 2000 response rate among its citizens. The funds were distributed to local groups demonstrating an ability to carry out outreach programs in an effective manner. The State’s Complete Count Committee, created in the Fall of 1999, met

regularly to ensure census promotion efforts were being implemented and that funds were allocated

appropriately. 6

L.A. County Efforts

Beginning in 1998, Los Angeles County worked closely with the Census Bureau in updating local

addresses for Census 2000. In that first year, the County provided 78,000 address updates (more than

half the addresses were accepted by the Bureau). In 1999, the County provided about 3,000 new rural

addresses and in 2000, 77,000 new addresses were added to the lists. These technical efforts were

made possible through partnerships with all 88 cities falling within the County’s jurisdiction.

In addition, L.A. County supported other Bureau address list programs including the Consolidated

Boundary Initiative designed to ensure that those counted in the census were attributed to the appropriate city or unincorporated community in the County. More than 2,000 boundary adjustments were

made in L.A. County because of the program. The County’s participation in the Bureau’s Special

Places Survey for the homeless led to the addition of approximately 3,000 address sites as well. These

efforts are said to have been successful due to the dedication of the County’s diverse Complete Count

Committee, as well as the commitment of leading county agencies including the Department of Social

Services.

In terms of promoting the census, the County trained upwards of 800 employees to become “Census

Ambassadors.” The employees were trained to speak with authority about the census and emphasized

the confidentiality aspect of the undertaking to allay fears people may have had about personal information being misused by Government. Moreover, the County set up a web site, listed toll free phone

numbers so that people could have questions answered, posted links to other census web sites including the Bureau’s, and enlisted “Walking Man,” a local organization that distributed census fliers to

doorsteps. The fliers, printed in English and Spanish, informed residents that enumerators would be

visiting their homes and that they should be welcomed.

Los Angeles County received $1.75 million from the State of California for outreach. 

Los Angeles City Efforts

The City of Los Angeles established close ties with a broad network of groups to account for those

typically missed in the census. Using state and city funds, the L.A. Outreach Project developed carefully crafted messages concerning the census. The city also prepared an educational video in six different languages that saw wide circulation.

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7 Local officials in Los Angeles and the city’s homeless advocates regret the Bureau ultimately opted not to post the homeless count at the

block level. 8 The overall mail response rate for Long Beach was a high 66 percent.

9 The mail response rate reached a robust 71 percent in 2000 as compared to the 1990 response rate of 67 percent.

The City played a lead role in updating local address lists, and considerable time and effort was spent

to ensure that the city’s homeless encampments were properly identified. The city allocated approximately $330,000 to implement the Bureau’s Non-Sheltered Outdoor (TNSOL) program. In doing so

numerous homeless sites were identified for the Bureau to visit.7 In a complementary effort, more than

300 homeless Outreach Workers were hired, ultimately serving as “cultural agents” who not only

sought to encourage census participation among the homeless but also accompanied census enumerators during NRFU to increase the response rate. City employees themselves were encouraged to work

part-time as enumerators, as well. 

Long Beach City Efforts

The City of Long Beach, falling within the Los Angeles County jurisdiction, partnered with a wide

range of representatives of predominantly minority neighborhoods, community groups and organizations throughout the city in an effort to reduce the undercount rate in the area. As in Los Angeles, city

agencies and employees were tasked to use their positions in promoting the census. 

Long Beach enhanced momentum for the Bureau’s Census in the Schools program and reached out to

local students in many other ways, as well. For instance, to capture the public’s attention, the city

launched an “It’s Cool to be Counted” campaign that involved “Census Guy,” who became a recognizable icon appearing on census materials distributed throughout the Long Beach area. Moreover,

special efforts were made to ensure that the Bureau’s scholastic materials were distributed to all

schools in the Long Beach Unified School District rather than merely to schools with 50 percent or

more of their school children in public-free lunch programs, as the Bureau originally planned. 

Other innovative activities carried out by the city included the development and placement of banners,

theater slides, and bus advertisements. The local Complete Count Committee paved the way for census promotion as well, by making $500,000 in grant money available to soccer teams, neighborhood

associations and churches for the purpose of disseminating educational census materials. 

Preparations for Census 2000 began in 1997 with the city’s development of a well-formed work plan.

A prominent city planner credited the federal, state and local partnership for the high response rate in

traditionally hard to count communities.8 

County and City of San Diego Efforts

In Fall 1998, the San Diego Association of Governments (SANDAG) formed a Complete Count

Committee involving elected officials, city and county staff, educators, the media and a host of other

community leaders. The committee met regularly over a two-year period for the purpose of developing and implementing “creative activities” as a means to achieve the greatest possible mail back

response rate in the diverse community of about 2.8 million people. 9

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10 LA kept its homeless shelters open an extra month to ensure the homeless the opportunity to be counted on Census Day.

The County received approximately $400,000 in State funding that was distributed to 24 city, county

and community-based organizations in the area, for the purpose of census education. Local groups

and agencies dedicated an additional $200,000 for public outreach activities, a majority of which were

aimed at already identified hard-to enumerate neighborhoods.

The City of San Diego established an independent Complete Count Committee and made a conscientious effort not to duplicate efforts of the Regional Committee. The outreach efforts of the committee

were geared toward three groups: the general public, city staff and contractors and select, targeted

populations. City-sponsored activities included the development of a web site, production and distribution of bookmarks at city libraries, as well as posting banners at libraries, recreation centers, fire

stations and many public places. In addition, the city promoted the census by sending out an educational mailing to 11,000 families receiving housing assistance, using its cable-access station for public service announcements and distributing census wash cloths for the homeless. 10 

Community Efforts in San Diego

Community efforts to raise awareness and motivate people to participate in the undertaking were

extensive. 

For example, The Black Health Network organized a number of census activities in recognition of the

fact that the African American community has historically been undercounted at a disproportionate

rate compared to non-Hispanic Whites. Such activities included producing and distributing census

videos to clinics and physicians’ offices, collaborations with African American churches, as well as

distribution of census materials to beauty salons and other places where residents typically gather. 

The Union of Pan Asian Communities (UPAC), which serves Asian Pacific Islanders and new immigrants and refugees, worked with the National Asian Pacific Legal Center in Washington, D.C. to

maximize census participation. Through the network, UPAC was instrumental in translating census

materials into 16 different Asian and Pacific Islander languages. The Lao, Cambodian, Hmong,

Vietnamese, Samoan and Tongan communities received priority attention based on 1990 results indicating a high undercount rate. The California endowment helped finance UPAC’s census efforts.

The San Diego Police Department’s Gang Unit worked closely with the Bureau’s Local Census Office

in Chula Vista whose jurisdiction is home to several gangs. The unit provided enumerators with safety tips including advice on appropriate clothing to wear (some gangs claim certain colors) and which

blocks required back-up support. The Gang Unit supplied enumerators with protective vests to be

worn in case of shootings. The Unit also offered training on how to diffuse tense situations in the event

of confrontation.

Finally, San Diego’s neighboring City of Chula Vista worked to promote the census through Census

2000 Street Theatre, which became a popular local attraction. Using grant money provided by

SANDAG, the city hired a producer/director who wrote two plays concerning the census, in Spanish

and English. The five-to-ten minute skits were carried out in parks, restaurants, at the local trolley station and at other public meeting places. The theatre group made about 70 presentations addressing

concerns about confidentiality and other issues that sometimes cause people not to participate in the

census.

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Conclusion

The Census 2000 Partnership programs in Georgia and California serve as good examples of how

Government and Non-Government organizations can work together for the public good. The programs

achieved success stressing local operations and grass roots initiatives. However, the Partnership program was not perfect. 

Concerns aired in Georgia were that the Bureau could have committed more resources to advertising

buys in non-English media and that there were sometimes problems in filtering materials from Bureau

headquarters to the Regional Office in a timely manner. In addition, cooperation between the Bureau

and the U.S. Postal Service would have yielded an even higher response rate in the State; the LUCA

program (while successful) should have been launched earlier in the process; and the Regional office

did not always provide local partners the autonomy they needed.

Concerns aired in California were that more cooperation in translating materials at the national level

would have been helpful; State funds could have been distributed earlier, and in large, diverse population areas, the number of partnership specialists should have been allocated accordingly.

Many of the above issues were raised in interviews held with stakeholders in other parts of the country. While we address some of the principal issues in 2010 Recommendations, ongoing study of the

2000 Partnership program and its efficacy are needed so as to ensure the program is further enhanced

ten years forward. 

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Final Report to Congress

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SECTION 7

CENSUS MONITORING BOARD

ACTIVITIES BY CATEGORY

Congress appropriated $4 million to create the U.S. Census Monitoring Board in 1998, and $3.5 million for every year thereafter, and charged it with the responsibility of monitoring and reporting on

“all aspects of the 2000 decennial census.” The office opened in June 1998 and is scheduled to close,

as mandated, September 30, 2001. The following represents our efforts to raise public awareness, to

study, and to report about Census 2000. Further information about each of these listings is available

on our website, www.cmbp.gov.

HEARINGS

July 8, 1998 – Hearing. Subject: General Status of 2000 Census. Witnesses: James Holmes, Acting

Director, Census Bureau. Location: Suitland Federal Center, Suitland, Maryland.

August 5, 1998 – Hearing. Subject: Census 2000 Dress Rehearsal in Columbia, South Carolina.

Census Bureau Witnesses: James Holmes, Acting Director; Susan Hardy, Regional Director; Diane

Bennett, Community Partnership Coordinator for the Charlotte Region. State and Local Government

Witnesses: Richard Semon, Director of Community Development, City of Columbia; Ken Knudsen,

Land Development Administrator, Richland County; Bobby Bowers, Director, Office of Research and

Statistics, State of South Carolina; Jack Maguire, Geographic Information Specialist, Office of

Research and Statistics. Community Witnesses: Anita Floyd, Director, Women’s Shelter in Columbia;

Reggie Alexander, Director, Alston Wilkes Society; Karen Rutherford, Complete Count Committee

Liaison, Benedict College; Virginia Saunders, Management Analyst, Fort Jackson Military Base.

Location: Strom Thurmond Federal Center, Columbia, South Carolina.

November 6, 1998 – Hearing. Subject: Status of 2000 Census. Department of Commerce Witnesses:

Jonnie Frazier, Acting Inspector General, Department of Commerce; Dr. Kenneth Prewitt, Director,

Census Bureau; James Holmes, Former Acting Director, Census Bureau; John Thompson, Associate

Director for the Decennial Census, Census Bureau. County Government Witnesses: Shoreh Elhami,

GIS Director for Delaware County, Ohio Auditor’s Office; Jon Peterson, Auditor, Delaware County,

Ohio. Location: Rayburn House Office Building, Washington, D.C.

November 23, 1998 – Hearing. Subject: Administrative Records. Census Bureau Witnesses: Dr.

Kenneth Prewitt, Director; Cynthia Clark, Associate Director for Standards and Methodology;

Howard Hogan, Acting Chief of the Decennial Statistical Studies Division; Paula Schneider, Principal

Associate Director for Programs. Independent Expert Witness: Dr. John Czajka, Mathematica Policy

Research, Inc. Location: Suitland Federal Center, Suitland, Maryland.

December 16, 1998 – Field Hearing. Subject: Census 2000 Dress Rehearsal in Sacramento,

California. Census Bureau Witnesses: James Holmes, Atlanta Regional Director and Special Advisor

to the Director; Moises Carrascos, Seattle Regional Director; Ed Salazar, Sacramento Community
Partnership Specialist. Local Government Witnesses: Joe Serna Jr., Mayor of Sacramento; Lauren

Hammond, City Councilperson, City of Sacramento; David Martinez, Deputy City Manager, City of

Sacramento; Kelly Grieve, Analyst, Sacramento Area Council of Governments. Community

Witnesses: Israel Valdez, Member, Census 2000 Complete Count Committee; Tom Buruss, Member,

Census 2000 Complete Count Committee; Steve Ybarra, Member, Census 2000 Complete Count

Committee. Location: City Hall, Sacramento, California.

March 8, 1999 – Hearing. Subject: Census 2000 Operations Update. Census Bureau Witnesses:

Kenneth Prewitt, Director; John Thompson, Associate Director for Decennial Census. Statistician

Witnesses: Stephen Fienberg, Carnegie Mellon University; Barbara Bailar, National Opinion

Research Center. Community Witnesses: Wade Henderson, Leadership Conference on Civil Rights;

Marisa Demeo, Mexican American Legal Defense and Education Fund. Additional Witness: William

Hill, Retired Bureau Regional Director. Location: Suitland Federal Center, Suitland, Maryland.

August 3, 1999 – Hearing. Subject: Young and Rubicam Advertising Campaign. Briefed by: John

Thompson, Associate Director for the Decennial Census; Terry Dukes, Account Managing Director,

Young and Rubicam. Location: Government Printing Office Conference Room, Washington, DC.

November 18, 1999 – Hearing. Subject: Census 2000 field operations update from Regional

Directors. Participants: All 12 Regional Directors; Dr. Kenneth Prewitt, Director of Bureau of the

Census. Location: Suitland Federal Center, Suitland, Maryland.

June 26, 2000 – Hearing. Subject: Census partnership efforts in Los Angeles. Census Bureau

Witness: John Reeder, Los Angeles Regional Director. Local Government Witnesses: John

Humphrey, Census 200 Coordinator, City of Long Beach; Jan Perry, City of Los Angeles Census 2000

Project; Lari Sheehan, Chair, Los Angeles Complete Count Committee; Jessica Heinz, City Attorney’s

office of Los Angeles; Natalie Profant Komuro, Los Angeles Homeless Services Authority.

Community Witnesses: Antonia Hernandez, Mexican American Legal Defense and Education Fund;

John Mack, Los Angeles Urban League; Bonnie Tang, Equal Access Unit, Asian Pacific American

Legal Center. Location: University of Southern California, Los Angeles, CA.

June 27, 2000 – Hearing. Subject: Census partnership efforts in San Diego. Local Government

Witnesses: Jeri Gulbransen, Office of the City Manager of Chula Vista; Karen Lamphere, San Diego

Association of Governments; Joey Perry, City Planning Department of San Diego. Community

Witnesses: Grover Diemert, Bayside Settlement House; Augie Bereno, Chicano Federation

Questionnaire Assistance Program; William Rowel, Black Health Network; Tanya Farley, Union of

Pan Asian Communities; Peter Martinez, Gang Suppression Unit. Location: San Diego Association

of Governments (SANDAG), San Diego, CA.

September 11, 2000 – Hearing. Subject: Census partnership efforts in Atlanta area. Local

Government Witnesses: Roy E. Barnes, Governor of Georgia; Cathy Cox, Georgia Secretary of State;

Bill Campbell, Mayor of Atlanta; Tracey-Ann Nelson, State of Georgia; Linda Meggers, Georgia

Legislative Redistricting Services; John Heath, Altanta City Planning Department. Community

Witnesses: Lani Wong, National Association of Chinese Americans, Stephanie Tan, Georgia Power

and Light Company; Jacqueline Thomas-Rosier, Mexican-American Legal Defense and Education

Fund; Gloria Carithers, Alpha Kappa Alpha Sorority; Myrna Ballard, Valdosa Chamber of Commerce;

Phyllis Stephens, Dalton Whitfield Chamber of Commerce; Rafael Sanhueza-Bazaes, Centro Latino,

Inc.; Luz Rodman, Fulton-Atlanta Community Action Authority; Rev. Timothy McDonald, Concerned

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Black Clergy. Census Bureau Participant: Jim Holmes, Atlanta Region Director. Location: Georgia

Capitol Education Center, Atlanta Georgia.

RESEARCH REPORTS

December 28, 2000 – Findings Issued. Subject: Demographic Analysis Estimate of U.S. Population.

December 28, 2000 – Findings Issued. Subject: Evaluation of the Census Net and Differential

Undercounts. Researched and authored by Dr. Eugene P. Ericksen.

January 18, 2001 – Report. “Implications for Minority Voters in 2001: An Analysis from the 1990

Census.” Researched and authored by Dr. Allan Lichtman.

January 24, 2001 – Report. “An Analysis of the 1990 Infant and Children Undercount: Implications

for Census 2000.” Researched and authored by Dr. Beth Osborne Daponte and Amelia Haviland.

February 1, 2001 – Findings Issued. “Will Poor Children Be Left Behind? Research Showing

500,000 to 2,000,000 Poor Children Missed in 1990 Census Demonstrates Need for Accurate Data in

2001.” Researched by Drs. Don Hernandez and Nancy Denton.

February 9, 2001 – Report. “Profiling the Native American Community in Albuquerque: Assessing

the Impacts of Census Undercounts and Adjustments.” Researched and authored by Dr. Theodore

Jojola.

February 13, 2001 – Report. “Effects of Census Undercoverage on Analyses of School Enrollments:

A Case Study of Portland Public Schools.” Researched and authored by Dr. Barry Edmonston.

February 15, 2001 – Report. “Long Beach: A Case Study of the Effects of Uncorrected vs. Corrected

Data on Funding and Planning.” Researched and authored by Dr. Chris Williamson

February 20, 2001 – Report. “The Effects of Using Unadjusted Data on Measures of Access to

Health Care.” Researched and authored by Dr. Darrell Gaskin.

February 20, 2001 – Report. “Undercounting Commuters.” Researched and authored by Dr. Paul

Ong.

February 20, 2001 – Report. “Examining the Effects of Census Adjustments on Estimates of

Working-Age Uninsured Minorities in the United States.” Researched and authored by Dr. Lee

Cornelius.

March 6, 2001 – Findings Released. Subject: Demographic Analysis should not be used as a reason

against adjustment Census 2000. Researched and authored by Dr. Jeffrey S. Passel.

March 9, 2001 – Findings Released. Subject: Errors in Census 2000. Researched and authored by

Dr. Eugene P. Ericksen.

March 28, 2001 – Findings Released. Subject: Estimates of State Undercounts in Census 2000.

Researched by Dr. Eugene P. Ericksen.

July 2, 2001 – Findings Released. Subject: Census 2000 undercount estimates for the nation’s largest

counties. Estimates based on Census Bureau’s state level post-strata by analyzed by Dr. Eugene P.

Ericksen.

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August 7, 2001 – Findings Released. “Effect of Census 2000 Undercount on Federal Funding to States

and Selected Counties, 2002-2012.” Researched and authored by PriceWaterhouseCoopers.

BRIEFINGS

July 21, 1998 – Briefing. Subject: Dress Rehearsals. Conducted by: Christopher Mihm, Acting

Associate Director for Federal Management and Workforce Issues, General Accounting Office.

Location: General Accounting Office, Washington, D.C.

October 20, 1998 – Briefing. Subject: Methodology behind the Integrated Coverage Measurement

(ICM). Conducted by: Howard Hogan, Acting Chief of Decennial Statistical Studies Division, Census

Bureau. Location: Suitland Federal Center, Suitland, Maryland.

October 21, 1998 – Briefing. Subject: Local Update of Census Addresses (LUCA) and re-engineering of the Master Address File (MAF). Conducted by: Bob Marx, Geography Division Chief, Census

Bureau. Location: Suitland Federal Center, Suitland, Maryland.

October 27, 1998 – Briefing. Subject: Budget for the 2000 Decennial Census. Conducted by:

Preston Jay Waite, Assistant Director for the Decennial Census, Census Bureau; Carol Vanhorn,

Assistant to the Associate Director for Field Programs, Census Bureau; Mike Perez, Assistant Division

Chief, Decennial Census Management Division, Census Bureau. Location: Suitland Federal Center,

Suitland, Maryland.

October 28, 1998 – Briefing. Subject: 2000 Census Advertising. Conducted by: Census Bureau; Ken

Meyer, Chief of Census 2000 Publicity Office, Census Bureau; Jennifer Marks, Assistant Chief of

Census 2000 Publicity Office, Census Bureau. Location: Suitland Federal Center, Suitland, Maryland.

October 28, 1998 – Briefing. Subject: Administrative Records, Plans and Research. Conducted by:

Cynthia Clark, Associate Director for Standards and Methodology, Census Bureau; John Thompson,

Principal Associate Director for Programs, Census Bureau. Location: Suitland Federal Center,

Suitland, Maryland.

January 20, 1999 – Briefing. Subject: Dual Track Preparations. Conducted by: John Thompson,

Principal Associate Director for Programs, Census Bureau. Location: Suitland Federal Center,

Suitland, Maryland.

February 12, 1999 – U.S. Commission on Civil Rights Briefing. Subject: Evaluation of January 1999

Supreme Court decision on Census 2000. Participants: Lorraine A. Green, U.S. Census Monitoring

Board, Presidential Members; A. Mark Neuman, U.S. Census Monitoring Board, Congressional

Members; Matt Glavin, President of the Southeastern Legal Foundation; Edward Still, Lawyers’

Committee for Civil Rights Under Law; Wade Henderson, Executive Director, Leadership Conference

on Civil Rights; Marisa Demeo, Regional Counsel, Mexican American Legal Defense and Education

Fund; Karen Narasaki, Executive Director, National Asian Pacific American Legal Consortium;

Charles Schultze, Senior Fellow Emeritus, Brookings Institution; Andrew Pincus, General Counsel

U.S. Commerce Department; Jack C. Jackson, Jr., Government Affairs Director, National Congress of

American Indians; and Kenneth Darga, Senior Demographer, State of Michigan. Location:

Washington, D.C.

July 8, 1999 – Board Briefing. Subject: Young and Rubicam Advertising Campaign. Conducted by:

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Terry Dukes, EVP, Account Managing Director, Young and Rubicam; various subcontractors.

Location: Young and Rubicam Headquarters, New York City.

August 5, 1999 – Briefing. Subject: Census 2000 in New York City. Conducted by: Sara Vidal, Citywide Census 2000 Coordinator; Dr. Joseph Salvo, New York City Department of Planning; Pat Lally,

Legislative Representative, Mayor Rudolph Giuliani. Location: Office of the Mayor of New York

City.

June 15, 2000 – Briefing. Subject: Census 2000 in New York City. Conducted by: Sara Vidal, Citywide Census 2000 Coordinator; Dr. Joseph Salvo, New York City Department of Planning; Pat Lally,

Legislative Representative, Mayor Rudolph Giuliani. Location: Office of the Mayor of New York

City.

June 19, 2000 – Briefing. Subject: Hard To Enumerate Action Plan. Conducted By: Marvin Raines,

Associate Director for Field Operations, Rick Blitzer, Field Operations; John Gidneck, Field

Operations. Location: Suitland Federal Center, Suitland, Maryland.

February 22-24, 2000 – Regional Census Center Briefing and Two Local Census Office Visits.

Subject: Status of Operations in the New York Region. Location: New York City.

February 28-29, 2000 – Regional Census Center Briefing and Two Local Census Office Visits.

Subject: Status of Operations in the Dallas Region. Locations: Dallas and El Paso, Texas.

September 14, 2000 – Staff Briefing. Subject: Count Question Resolution program. Conducted by:

Susan M. Miskura, Decennial Management Division Chief. Location: Suitland Federal Center,

Suitland, Maryland.

December 19, 2000 – Staff Briefing. Subject: Demographic Analysis. Conducted by: John

Thompson, Associate Director for Decennial Census; J. Gregory Robinson, Population Analysis and

Evaluation Division. Location: Suitland Federal Center, Suitland, Maryland.

January 9, 2001 – Staff Briefing. Subject: Address duplications in the Master Address File.

Conducted by: John Thompson, Associate Director for Decennial Census; Howard Hogan, Chief of

Decennial Statistical Studies Division; Susan Miskura, Chief of Decennial Management Division.

Location: Suitland Federal Center, Suitland, Maryland.

March 21, 2001 – Briefing. Subject: Executive Steering Committee on Accuracy and Coverage

Evaluation Policy recommendation not to adjust 2000 Census. Participants: Dr. Everett M. Ehrlich,

U.S. Census Monitoring Board, Presidential Members; J. Kenneth Blackwell, Co-Chair, U.S. Census

Monitoring Board, Congressional Members; John Thompson, Associate Director for Decennial

Census, Howard Hogan, Chief, Decennial Statistical Studies Division, U.S. House of Representatives

Census Subcommitee staff; U.S. Census Monitoring Board staff; National Academy of Sciences staff.

Location: Suitland Federal Center, Suitland, Maryland.

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REPORTS TO CONGRESS

February 1, 1999 – First Report to Congress.

April 1, 1999 – Joint Report to Congress.

June 9, 1999 – Report to Congress.

October 1, 1999 – Joint Report to Congress.

October 26, 1999 – Report to Congress. Researched and authored by Belden, Russenello and Stewart.

March 9, 2000 – Report to Congress. Researched and authored by PricewaterhouseCoopers..

April 1, 2000 – Joint Report to Congress.

October 1, 2000 – Joint Report to Congress.

April 1, 2001 – Report to Congress.

September 1, 2001 – Final Report to Congress.

NEWSLETTERS

May 17, 2000 – “Countdown” Newsletter.

December 31, 2000 – “Countdown” Newsletter.

May 31, 2001 – “Countdown” Newsletter.

OFFICIAL LETTERS

December 20, 2000 – Letter to President-elect George W. Bush regarding media reports that a Bush

Administration would block release of statistically corrected census results.

May 31, 2001 – Letter to Commerce Secretary Don Evans expressing shock over a proposed

Memorandum of Understanding that plainly violated the U.S. Census Monitoring Board’s enabling

legislation, threatened the ability to obtain information from the Census Bureau, and severely

impeded statutory reporting requirements.

July 2, 2001 – Letter to U.S. Senators Ernest Hollings, Joseph Lieberman, and Richard Durbin alerting them that the Bureau’s refusal to provide necessary data would limit the scope of a final report to

Congress.

FIELD OBSERVATION

(Listing does not include observations at 50 local census offices as reported in our October 2000

Report to Congress. Listing also does not include site visits to all 12 Accuracy and Coverage

Evaluation Regional Offices and field observations made during the Summer and Fall of 2000.)

October 23, 1998 – Field Observation. Objective: Assess Operations at the Census Bureau’s Data

Capture Center in Jeffersonville, Indiana. Location: Jeffersonville, Indiana.

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November 4, 1998 – Field Observation. Objective: Assess Operations at Menominee Dress Rehearsal

Site. Board Attendence: Lorraine Green. Location: Menominee, Wisconsin.

COMMUNITY FORUMS

February 24, 1999 – Community Forum. Subject: Census 2000 Participation Promotion.

Participants: Tony Coelho, Co-Chair, U.S. Census Monitoring Board; Mario Salas, City Councilman;

Ray Lopez, President of the Board of Trustees, North Side Independent School District; Ms. Pilar

Oates, Methodist Healthcare Ministries; Dr. William Vega, Metropolitan Research, University of

Texas at San Antonio; Aurora Ortega-Geis, Director, San Antonio Partnership Office, Fannie Mae.

Location: San Antonio, Texas.

February 25, 1999 – Community Forum. Subject: Census 2000 Participation Promotion. Participants:

Gilbert F. Casellas, U.S. Census Monitoring Board, Presidential Members; Dr. Everett M. Ehrlich,

U.S. Census Monitoring Board, Presidential Members; Annie Neasman, Executive Administrator for

the Florida Department of Health, Miami-Dade County, Roger Cuevas, Superintendent, Miami-Dade

County Public Schools; Sara Herald, Acting District 11 Administrator for the Department of Children

and Families. Location: Miami-Dade Public Library, Miami, Florida.

June 14, 1999 – Community Forum. Subject: Community Participation in the Census. Participants:

Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board; Dr. Everett M. Ehrlich, U.S. Census

Monitoring Board, Presidential Members; Thomas Scott, County Commissioner; Jan Platt, County

Commissioner; Jim Norman, County Commissioner; State Representative Bob Henriquez, Bob

Buckhorn, City Councilmember; Gwen Miller, City Councilmember; Jim Hosler, Director of

Research and Economic Development; Carmen Ledo, Supervisor, Hillsborough County Health and

Social Services Department; Charlie Reese, Chamber of Commerce; Jerry Harvey, African-American

Liaison to County Administrator. Location: Hillsborough County Commissioner Board Room,

Tampa, Florida.

November 10, 1999 – Community Forum. Subject: Census partnership efforts in New York City.

Board Participants: Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board, Presidential

Members; Dr. Everett M. Ehrlich, U.S. Census Monitoring Board, Presidential Member; Ken

Blackwell, Co-Chair, U.S. Census Monitoring Board, Congressional Members. Census Bureau

Witness: Tony Farthing, New York Regional Director. Local Government Witnessses: Roberto

Ramirez, State Assembly of New York; Virginia Fields, Manhattan Borough President; Herbert

Berman, City Council Member; Helen Marshall, City Council Member. Community Witnesses:

Margaret Chin, Asian Americans for Equality; Margie McHugh, New York Immigration Coalition; Fr.

Kevin Sullivan, Catholic Charities; John Flateau, Medgar Evers College of CUNY; Michael

Amezquita, Northern Manhattan Coalition for Immigrant’s Rights. Location: Hispanic Federation,

New York City.

April 13, 2000 – Community Forum/Town Hall Meeting. Subject: Census 2000 in Ward 8,

Washington, D.C. Participants: Lorraine Green, U.S. Census Monitoring Board, Presidential

Members; A. Mark Neuman, U.S. Census Monitoring Board, Congressional Members; U.S. House

Representative Eleanor Holmes Norton; Sandra Allen, Member of City Council; Vincent Gray,

Covenant House; Reverend Allan Lipscombe, Allen Chapel AME Church; William Lockeridge,

School Board Member; Winston Robinson, Jr., 7th District Police Commander. Location: Covenant

House, Washington, D.C.

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January 28, 2001 – Community Forum. Subject: Effects of census undercounts on children.

Participants: U.S. House Representative Charles Gonzalez; Margarita Roque, Executive Director of

the U.S. Census Monitoring Board, Presidential Members; Rebecca Maria Barrera, President of the

National Latino Children’s Institute; Felix Sanchez, President and CEO of TerraCom. Location: San

Antonio, Texas.

February 9, 2001 – Community Roundtable. Subject: Effects of Census Undercount on American

Indians. Participants: Margarita Roque, Executive Director of the U.S. Census Monitoring Board,

Presidential Members; Jim Baca, Mayor of the City of Albuquerque; LaDonna Harris, Founder and

President of Americans for Indian Opportunity; Theodore Jojola, University of New Mexico; Joyce

Knows His Gun, Morning Star Center; Louis Tutt, Navajo Nation. Location: Albuquerque, New

Mexico.

February 20, 2001 – Research Summit. Subject: Effects of Census Undercount. Participants: Gilbert

F. Casellas, Presidential Co-Chair, U.S. Census Monitoring Board, Presidential Members; Dr. Everett

M. Ehrlich, U.S. Census Monitoring Board, Presidential Members; Lorraine A. Green, U.S. Census

Monitoring Board, Presidential Members; Dr. Lee Cornelius, University of Maryland; Dr. Beth

Osborne Daponte, Carnegie Mellon University; Dr. Darrell Gaskin, Georgetown University Medical

Center; Dr. Ted Jojola, University of New Mexico; Dr. Allan Lichtman, American University; Dr. Paul

Ong, University of California Los Angeles; Dr. Chris Williamson, University of Southern California.

Location: National Press Club, Washington, DC.

February 23, 2001 – Community Forum. Subject: Leave No Child Behind: Census Data & Its Impact

on Children. Participants: Mary Rose Wilcox, Maricopa County Board Supervisor; U.S. House

Representative Ed Pastor; Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board, Presidential

Members; Carol Kamin, Executive Director, Children’s Advocate Alliance; John Lewis, Executive

Director, Arizona Intertribal Council; Domingo Rodriguez, Vice President for Health and Human

Services, Chicanos Por La Causa. Location: Maricopa County Board of Supervisors Auditorium,

Phoenix, Arizona.

March 2, 2001 – Community Forum. Subject: Effects of Census Undercount in Chicago. Participants:

U.S. House Representative Danny K. Davis; Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring

Board; Al Pritchett, Chair, Cook County Complete Count Committee; Dr. John Lumpkin, Director,

Illinois Department of Public Health; Dr. Linda Murray, Co-Chief Medical Director of the

Ambulatory and Community Health Network of Cook County; Javette C. Orgain, M.D., Assistant

Professor of Clinical Family Medicine, University of Illinois College of Medicine and Past President

of the National Medical Association; Joanna Su, Executive Director, Asian Health Coalition of

Illinois; Carmen Valezquez, Executive Director, Alivio Medical Center; Dr. Lee Cornelius, University

of Maryland. Location: Cook County Board of Supervisors Auditorium, Chicago, Illinois.

June 21, 2001 – Community Forum. Subject: Latinos & the Census Undercount. Participants:

Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board; John Gutierrez, Center for Latin

American, Caribbean, & Latino Studies, City University of New York Graduate Center; Lorraine

Cortés-Vázquez, President, Hispanic Federation; Fernando Ferrer, Bronx Borough President; Jeffrey

Passel, Urban Institute; Darrell Gaskin, Institute for Health Care Research and Policy at Georgetown

University Medical Center; Guillermo Linares, Member of New York City Council; Ken Fisher,

Member of New York City Council; Rosa Gil, University Dean for Health Sciences, City University

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of New York; Beth Osborne Daponte, Carnegie Mellon University; Nancy Denton, State University of

New York at Albany; Gladys Padró-Soler, Executive Director, Casita María. Location: Martin E.

Segal Theatre, City University of New York Graduate Center, New York City.

CONFERENCES/MEETINGS/PANEL DISCUSSIONS

June 3, 1998 – Organizing Meeting. Subject: Census Monitoring Board rules and procedures.

Location: Suitland Federal Center, Suitland, Maryland.

December 3, 1998 – Workshop. Subject: National League of Cities on Census 2000. Participants:

Jeff Wice, consultant to U.S. Census Monitoring Board, Presidential Members; J. Kenneth Blackwell,

Co-Chair, U.S. Census Monitoring Board, Congressional Members; Mike Morrison, Mayor, Waco,

Texas. Location: Kansas City, Missouri.

January 27, 1999 – Presentation at U.S. Conference of Mayors Winter Meeting. Subject: Census

2000 Preparations. Participants: Gilbert F. Casellas, U.S. Census Monitoring Board, Presidential

Members; J. Kenneth Blackwell, Co-Chair, U.S. Census Monitoring Board, Congressional Members;

U.S. Representative Dan Miller, Chairman of Census Subcommittee; U.S. Representative Carolyn

Maloney, Ranking Member of Census Subcommittee; Robert Shapiro, Undersecretary for Economic

Affairs, U.S. Department of Commerce. Location: Washington, DC.

April 9, 1999 – Presentation at National Conference of State Legislatures Task Force Meetings.

Subject: Census 2000 Preparations. Participants: Tony Coelho, Co-Chair of U.S. Census Monitoring

Board, Presidential Members; J. Kenneth Blackwell, Co-Chair of U.S. Census Monitoring Board,

Congressional Members; State Representative Wes Marsh, Arizona. Location: Jacksonville, Florida. 

April 24, 1999 – Keynote address at American Federation of State, County and Municipal Employees

Conference. Subject: Census 2000 Participation and Promotion. Participants: Tony Coelho, Co-Chair,

U.S. Census Monitoring Board, Presidential Members. Location: Las Vegas, Nevada.

May 8, 1999 – Keynote address for National Conference of State Legislatures Assembly on Federal

Issues. Subject: Census 2000 Preparations. Participants: Tony Coelho, Co-Chair of U.S. Census

Monitoring Board, Presidential Members; A. Mark Neuman, U.S. Census Monitoring Board,

Congressional Members; State Representative Brian Flaherty, Connecticut; State Representative Jim

Costa, California. Location: Washington, DC.

May 21, 1999 – Keynote address. Subject: Census 2000 Activities Update for Asian Pacific American

Institute for Congressional Studies. Participants: Dr. Everett M. Ehrlich, U.S. Census Monitoring

Board, Presidential Members. Location: J.W. Marriott Hotel, Washington, DC.

June 18, 1999 – Meeting. Subject: Census 2000 and Latinos at National Association of Latino Elected

and Appointed Officials (NALEO). Participants: Gilbert F. Casellas, Co-Chair, U.S. Census

Monitoring Board; Ray Sanchez, Speaker of the State House, New Mexico; State Representative Rene

Olivera, Chair of the House Mexican American Legislative Caucus, Texas; State Senator Efrain

Gonzalez, New York; Councilmember Jenny Oropeza, Long Beach City Council; Larry Gonzalez,

Director of the NALEO Washinton, DC office. Location: Philadelphia, Pennsylvania. 

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July 30, 1999 – Workshop at Organization of Chinese Americans Annual Conference. Subject:

Census 2000 Participation. Participants: Carrie Pugh, U.S. Census Monitoring Board staff. Location:

Dallas, Texas. 

September 9, 1999 – Meeting. Subject: Update of Census 2000 Activities in Washington, DC.

Participants: Lorraine A. Green, U.S. Census Monitoring Board, Presidential Members; Mayor

Anthony Williams, Mayor of Washington, DC; Wanda Alston, City Census 2000 Coordinator.

Location: Office of the Mayor, Washington, DC.

November 9, 1999 – Navajo Nation Meeting. Subject: Encourage Participation in Census 2000.

Participants: Regena Thomas, consultant to the U.S. Census Monitoring Board, Presidential Members;

David Murray, U.S. Census Monitoring Board, Congressional Members; Dr. Kenneth Prewitt,

Director, U.S. Census Bureau; Arbin Mitchell, Navajo Nation Local Census Office Manager; Edward

T. Begay, Speaker, Navajo Nation Council; Larry Rodgers, Administrator, Utah Navajo Trust Fund

Office; Belva Morrison, Tribal Partnership Team Leader, U.S. Census Bureau, Denver Region; Leila

Help-Tulley, Tribal Government Partnership Specialist, U.S. Census Bureau, Denver Region.

Location: Gallup, New Mexico.

November 15, 1999 – Presentation. Subject: Texas State Mexican American Legislative Caucus

Census 2000 Update. Participants: Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board,

Presidential Members; Marisela Lopez, Partnership Staff, U.S. Census Bureau Dallas Regional

Census Office; Steve Murdock, Texas State Data Center; Viviana Lopez-Davis, Mexican American

Legal Defense and Education Fund; Bill Hobby, former Lt. Governor of Texas. Location: Texas State

House, Austin, Texas.

November 30, 1999 – Maryland Statewide Latino Census 2000 Summit. Subject: Latinos and Census

2000 Preparations. Participants: Maryland Lt. Governor Kathleen Kennedy Townsend; Douglas M.

Duncan, Montgomery County Executive; Gilbert F. Casellas, U.S. Census Monitoring Board,

Presidential Members; Gilberto de Jesus, Secretary of Juvenile Justice; Council Member Peter

Shapiro; U.S. Representative Connie Morella; Fernando Armstrong, U.S. Census Bureau, Philadelphia

Regional Director. Location: Long Branch Community Center, Silver Spring, MD.

December 2, 1999 – National Conference of State Legislatures Panel Discussion. Participants: Dr.

Everett M. Ehrlich, U.S. Census Monitoring Board, Presidential Members; David Murray, U.S. Census

Monitoring Board, Congressional Members; Rudy Garcia, Florida State House of Representatives

Location: Hyatt Regency on Capitol Hill, Washington, DC.

December 3, 1999 – Meeting. Subject: Promote Census 2000 Participation at National Black Caucus

of State Legislators. Participants: Lorraine Green, U.S. Census Monitoring Board, Presidential

Members; Gloria Griffin, Baltimore City’s Complete Count Committee Coordinator. Location:

Baltimore, Maryland.

February 11, 2000 – Empowerment Summit. Subject: Encourage African-American Census

Participation in Baltimore, Prince George’s County, Maryland and Washington, D.C. Participants:

Lorraine A. Green, U.S. Census Monitoring Board, Presidential Members; Earl Shinhoster, co-host,

NAACP Census 2000 Coordinator; Deputy Mayor of Baltimore; Wanda Alston, co-host, Washington,

DC Office of Public Advocate; Dorothy Bailey, Chair, Prince George’s County Council; Iris T. Metts,

Superintendent of Schools for Prince George’s County; Jerusa Williams, Baltimore City Complete

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Count Committee; Kent Amos, Chair of the District of Columbia Complete Count Committee;

Elizabeth Hewlett, Chair of Prince George’s County Complete Count Committee; Mondrea Jacobs,

Evangelist, Never Say Never Foundation; Robert Hill, Vice Chair of the Census Bureau’s Race and

Ethnic Advisory Committee; John Tschechtelin, President, Baltimore City Community College

Location: Baltimore City Community College, Baltimore, MD. 

July 18, 2000 – Meeting. Subject: Census 2000 Activities in Chicago, Illinois. Participants: Dr.

Everett M. Ehrlich, U.S. Census Monitoring Board, Presidential Members; Don Davis, Director of

Special Projects, Office of Budget and Management. Location: Office of the Mayor, Chicago, Illinois.

February 15, 2001 – Press Conference. Subject: Release of report on effects of census undercounts

on city services in Long Beach. Participants: Beverly O’Neill, Mayor of City of Long Beach; Jack

Humphrey, Advance Planning Officer; Chris Williamson, Research Associate Professor of Geography

at the University of Southern California. Location: Long Beach, California.

April 18, 2001 – Press Briefing. Subject: Census Bureau recommendation not to adjust Census 2000

count. Participants: Dr. Everett M. Ehrlich, U.S. Census Monitoring Board, Presidential Members;

David Murray, U.S. Census Monitoring Board, Congressional Members; Dr. Jeffrey S. Passel, Urban

Institute; Dr. Joseph B. Kadane, Carnegie Mellon University; Dr. Stephen Fienberg, Carnegie Mellon

University. Location: National Press Club, Washington, D.C.

June 20, 2001 – Meeting. Subject: Proposed Memorandum of Understanding from Census Bureau.

Participants: Gilbert F. Casellas, Co-Chair, U.S. Census Monitoring Board, Presidential Members;

Stan Brand, Counsel to U.S. Census Monitoring Board, Presidential Members; William G. Barron,

Acting Director, Census Bureau; John Thompson, Principal Associate Director for Programs; Carol

Vanhorn, Assistant to the Associate Director for Field Programs. Location: Suitland Federal Center,

Suitland, Maryland. 

August 8, 2001 – Presentation at Joint Statistical Meetings. Subject: Census 2000-Decision on

Release of Statistically Corrected Redistricting Data. Participants: Dr. Everett Ehrlich, U.S. Census

Monitoring Board, Presidential Members; David Murray, U.S. Census Monitoring Board,

Congressional Members; William Barron, Jr., Acting Director, U.S. Bureau of the Census; John

Thompson, Principal Associate Director for Programs, U.S. Bureau of the Census; Alan Zaslavsky,

Harvard University. Location: Atlanta, Georgia.

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STAFF FOR THE U.S. CENSUS 

MONITORING BOARD, 

Presidential Members

Margarita Roque

Robert Cunningham

Mark Johnson

POLICY

Frances Bourne

Alexander Boyle

Courtney Lattimore

Carrie Pugh

Donna Smith

Chris Strobel

COMMUNICATIONS

John Chambers

Noemi Lugo

Estela Mendoza

Alyssa Pfleging

OPERATIONS

Nathan Austin

Hank Hardesty

Angela Johnson

Natalie Walker

Justine Whitney