Census 2000 Testing,

Experimentation,

and Evaluation Program

October 21, 2003

Synthesis of Results from the Administrative Records Experiment\
in 2000 (AREX 2000)

Number range CHAPTERFINAL REPORT

This research paper reports the results of research and analysis
undertaken by the U.S. Census Bureau. It is part of a broad program, the
Census 2000 Testing, Experimentation, and Evaluation (TXE) Program,
designed to assess Census 2000 and to inform 2010 Census planning.
Findings from the Census 2000 TXE Program reports are integrated into
topic reports that provide context and background for broader
interpretation of results.

Dean H. Judson and Barry Bye

Planning, Research, and Evaluation Division

Intentionally Blank

**Acknowledgments**

The Administrative Records Experiment 2000 was conducted by the staff of
Administrative Records Research at the U.S. Census Bureau, led by
Charlene Leggieri. Questions and comments regarding this document can be
directed to Dean H. Judson at 301-763-2057.

Administrative Records Research Staff Members and Key Contributors to
AREX 2000:

  ----------------------- ----------------------- -----------------------
  Bashir Ahmed            Mikhail Batkhan         Mark Bauder

  Mike Berning            Harold Bobbitt          Barry Bye

  Benita Dawson           Joseph Conklin          Kathy Conklin

  Gary Chappell           Ralph Cook              Ann Daniele

  Matt Falkenstein        Eleni Franklin          James Farber

  Mark Gorsak             Harley Heimovitz        Fred Holloman

  David Hilnbrand         Dave Hubble             Robert Jeffrey

  Dean Judson             Norman Kaplan           Vickie Kee

  Francina Kerr           Jeong Kim               Myoung Ouk Kim

  Charlene Leggieri       John Long               John Lukasiewicz

  Mark Moran              Daniella Mungo          Esther Miller

  Tamany Mulder           Nancy Osbourn           Arona Pistiner

  Ron Prevost             Dean Resnick            Pamela Ricks

  Paul Riley              Douglas Sater           Doug Scheffler

  Kevin A. Shaw           Kevin M. Shaw           Larry Sink

  Diane Simmons           Amy Symens-Smith        Cotty Smith

  Herbert Thompson        Deborah Wagner          Phyllis Walton

  Signe Wetrogan          David Word              Mary Untch
  ----------------------- ----------------------- -----------------------

and

Members of the AREX 2000 Implementation Group

Intentionally Blank

# CONTENTS {#contents .unnumbered}

[CONTENTS iv](#contents)

[Executive Summary ix](#executive-summary)

[1. Background 1](#background)

[1.1 Introduction 1](#introduction)

[1.2 Administrative Record Census---Definition and Requirements
2](#administrative-record-censusdefinition-and-requirements)

[1.3 AREX Objectives 2](#arex-objectives)

[1.4 AREX Top-down and Bottom-up Methods
3](#arex-top-down-and-bottom-up-methods)

[1.5 Experimental Sites 5](#experimental-sites)

[1.6 AREX Source Files 6](#arex-source-files)

[1.7 AREX Evaluations 7](#arex-evaluations)

[1.8 Limitations of the Experiment 7](#limitations-of-the-experiment)

[2. THE AREX PROCESS EVALUATION 10](#the-arex-process-evaluation)

[2.1 Introduction 10](#introduction-1)

[2.2 Top-down enumeration 13](#top-down-enumeration)

[2.3 Bottom-up enumeration 19](#bottom-up-enumeration)

[3. AREX OUTCOMES AND HOUSEHOLD EVALUATIONS
34](#arex-outcomes-and-household-evaluations)

[3.1 Introduction 34](#introduction-2)

[3.2 AREX enumeration outcomes 35](#arex-enumeration-outcomes)

[3.3 Household-level analysis 58](#household-level-analysis)

[4. discussion and recommendations for 2010 Planning and other census
bureau programs
91](#discussion-and-recommendations-for-2010-planning-and-other-census-bureau-programs)

[4.1 Additional AREX 2000 evaluations
91](#additional-arex-2000-evaluations)

[4.2 Race and Hispanic Origin enhanced Numident
94](#race-and-hispanic-origin-enhanced-numident)

[4.3 Household substitution for NRFU/Unclassified households in 2010
95](#household-substitution-for-nrfuunclassified-households-in-2010)

[4.4 Person unduplication in the 2010 Census
97](#person-unduplication-in-the-2010-census)

[4.5 MAF improvement 98](#maf-improvement)

[4.6 Other Census Bureau programs 99](#other-census-bureau-programs)

[References 101](#number-range-chapterreferences)

[Attachment 1. AREX 2000 Implementation Flow Chart
104](#arex-2000-implementation-flow-chart)

[Attachment 2. StARS Process Steps -- Outline
105](#stars-process-steps-outline)

LIST OF TABLES

[Table 1. Source File Characteristics 11](#__RefHeading___Toc54420954)

[Table 2. Reference Dates of Source Files
12](#__RefHeading___Toc54420955)

[Table 3. Percent of cases with imputed Race or Hispanic Origin by age
and County 14](#__RefHeading___Toc54420956)

[Table 4. StARS 1999 and AREX Test Site Geocoding Tallies
15](#__RefHeading___Toc54420957)

[Table 5. AREX Administrative Record Address Geocoding Results
17](#__RefHeading___Toc54420958)

[Table 6. Top-down Population Tallies 19](#__RefHeading___Toc54420959)

[Table 7. Addresses eligible for the match to the DMAF
21](#__RefHeading___Toc54420960)

[Table 8. Computer Match Results \-- Administrative Records Counts
21](#__RefHeading___Toc54420961)

[Table 9. Clerical Review Match Results 22](#__RefHeading___Toc54420962)

[Table 10. Selection of FAV Addresses 23](#__RefHeading___Toc54420963)

[Table 11. FAV Sample Results 23](#__RefHeading___Toc54420964)

[Table 12. Bottom-up Administrative Records Address Processing
24](#__RefHeading___Toc54420965)

[Table 13. Census Pull Results 26](#__RefHeading___Toc54420966)

[Table 14. Bottom Up Method Population Tallies
28](#__RefHeading___Toc54420967)

[Table 15. Geographic Differences for Persons in both the Top-down and
Bottom-up methods 29](#__RefHeading___Toc54420968)

[Table 16. Bottom-up "best" address by FAV Status.
32](#__RefHeading___Toc54420969)

[Table 17. Top-down and Bottom-up Counts of Total Household Population
by County 38](#__RefHeading___Toc54420970)

[Table 18. Coverage by AREX of Census housing units.
62](#__RefHeading___Toc54420971)

[Table 19. Coverage by AREX of Census housing units, by NRFU status.
63](#__RefHeading___Toc54420972)

[Table 20. Coverage by AREX of Census housing units, by imputation
status. 63](#__RefHeading___Toc54420973)

[Table 21. Distributions of household size for Census and AREX for all
five AREX counties. Nonvacant housing units only.
64](#__RefHeading___Toc54420974)

[Table 22. Comparison of Census and AREX household size, by NRFU status,
and by imputation status. For linked housing units.
66](#__RefHeading___Toc54420975)

[Table 23. Comparisons between AREX and Census for demographic groups,
for linked households with the same number of people only.
68](#__RefHeading___Toc54420976)

[Table 24. Comparison of AREX and Census demographic composition of
households. For linked households with the same number of people only,
by size. 70](#__RefHeading___Toc54420977)

[Table 25. Comparison of AREX and Census demographic groups within
households. For linked households with the same number of people only,
by size. 71](#__RefHeading___Toc54420978)

[Table 26. Comparison of match rates and household comparisons between
occupied housing units at multi-unit BSAs and housing units at
single-unit BSAs. 72](#__RefHeading___Toc54420979)

[Table 27. Coverage by multi vs. single unit, and by household age
characteristics. 73](#__RefHeading___Toc54420980)

[Table 28. AREX to Census comparisons by size of housing unit and by
household age characteristics. 74](#__RefHeading___Toc54420981)

[Table 29. The effect of the presence of Persons other than White in a
household on household match rates and comparisons.
76](#__RefHeading___Toc54420982)

[Table 30. The effect of the presence of Hispanics on household match
rates. 77](#__RefHeading___Toc54420983)

[Table 31. The effect of AREX imputed race on household comparisons.
78](#__RefHeading___Toc54420984)

[Table 32. Overall Response Profile for the "Match" Variable
82](#__RefHeading___Toc54420985)

[Table 33. Goodness-of-Fit Measures for the Logistic Regression Model
82](#__RefHeading___Toc54420986)

[Table 34. Maximum Likelihood Parameter Estimates, Standard Errors, and
approximate tests 84](#__RefHeading___Toc54420987)

[Table 35. Classification Results for Predicted Probabilities.
86](#__RefHeading___Toc54420988)

[Table 36. Summary of match rates and household comparisons between AREX
and Census. 89](#__RefHeading___Toc54420989)

LIST OF FIGURES

[Figure 1. Summary Diagram of AREX 2000 Design
5](#__RefHeading___Toc58826859)

[Figure 2. Net Population Difference by Sex, County, and Collection
Method\--CO. 39](#__RefHeading___Toc58826860)

[Figure 3. Net Population Difference by Sex, County, and Collection
Method\--MD. 40](#__RefHeading___Toc58826861)

[Figure 5. Net Population Difference by Age, County and Collection
Method\--MD. 41](#__RefHeading___Toc58826862)

[Figure 6. Net Population Difference by Age, County and Collection
Method\--CO. 42](#__RefHeading___Toc58826863)

[Figure 7. Net Population Difference by Race, County and Collection
Method\--MD. 43](#__RefHeading___Toc58826864)

[Figure 8. Net Population Difference by Race, County and Collection
Method\--CO. 44](#__RefHeading___Toc58826865)

[Figure 9. Sex ALPE by County and Collection Method\--MD.
46](#__RefHeading___Toc58826866)

[Figure 10. Sex ALPE by County and Collection Method\--CO.
47](#__RefHeading___Toc58826867)

[Figure 11. Age ALPE by County and Collection Method\--MD.
48](#__RefHeading___Toc58826868)

[Figure 12. Age ALPE by County and Collection Method\--CO.
48](#__RefHeading___Toc58826869)

[Figure 13. Race ALPE by County and Collection Method\--MD.
50](#__RefHeading___Toc58826870)

[Figure 14. Race ALPE by County and Collection Method\--CO.
50](#__RefHeading___Toc58826871)

[Figure 15. Distribution of Tracts with Under- and Overcounts of Total
Population. 52](#__RefHeading___Toc58826872)

[Figure 16. Distribution of Blocks with Under- and Overcounts of Total
Population. 53](#__RefHeading___Toc58826873)

[Figure 17. AREX - Census Total Population ALPEs: Maryland Tracts.
55](#__RefHeading___Toc58826874)

[Figure 18. AREX - Census ALPEs for the Total Population: Colorado
Tracts. 56](#__RefHeading___Toc58826875)

[Figure 19. Goodness of Fit Diagnostic Plot.
87](#__RefHeading___Toc58826876)

#  {#section .unnumbered}

#  {#section-1 .unnumbered}

#  {#section-2 .unnumbered}

#  {#section-3 .unnumbered}

#  {#section-4 .unnumbered}

#  {#section-5 .unnumbered}

#  {#section-6 .unnumbered}

#  {#section-7 .unnumbered}

#  {#section-8 .unnumbered}

#  {#section-9 .unnumbered}

#  {#section-10 .unnumbered}

Intentionally Blank

# Executive Summary

The Administrative Records Experiment 2000 was an experiment in two
areas of the country designed to gain information regarding the
feasibility of conducting an administrative records census or the use of
administrative records in support of conventional decennial census
processes. The first experiment of its kind in the United States, the
Administrative Records Experiment 2000 was part of the Census 2000
Testing, Experimentation and Evaluation Program. The focus of this
program was to measure the effectiveness of new techniques for decennial
census enumeration. There were four evaluations: Process, Outcomes,
Household, and Request for Physical Address evaluations. The first three
are summarized here.

**Administrative Records Census definition and requirements**

In the Administrative Records Experiment, an administrative record
census was defined as a process that relies primarily, but not
necessarily exclusively, on administrative records to produce the
population count and content of the decennial census short form with a
strong focus on apportionment and redistricting requirements. In
addition to total population counts by state, the decennial census must
provide counts of the voting age (18 and over) population by race and
Hispanic origin for small geographic areas, currently in the form of
census blocks.

Demographically, the Administrative Records Experiment provided date of
birth, race, Hispanic origin, and sex. Geographically, the
Administrative Records Experiment operated at the level of basic street
address and corresponding Census block code. Unit numbers for multi-unit
dwellings were used in certain address matching operations and one of
the evaluations; but generally household and family composition were not
captured. The design did assume the existence of a Master Address File
and geographic coding capability similar to that available for the
Census 2000.

The principal objectives of Administrative Records Experiment 2000 were
twofold. The first objective was to develop and compare two methods for
conducting an administrative records census, one that used only
administrative records and a second that added some conventional support
to the process in order to complete the enumeration. The second
objective was to explore the potential use of administrative records
data for some nonresponding or unclassified households that occur in a
conventional census.

**Administrative Records Experiment Top-down and Bottom-up methods**

A two-phase process accomplished the Administrative Records Experiment
2000 enumeration. The first, or Top-down, phase involved the assembly of
records from a number of national administrative record systems and
unduplication of individuals within the combined systems. This was
followed by computer geocoding of street addresses to the level of
census block and two attempts to obtain and code physical addresses for
those that would not geocode by computer. Finally, there was a selection
of "best" demographic characteristics for each individual and "best"
street address within the experimental sites.

The second phase of the Administrative Records Experiment 2000 design
was an attempt to complete the administrative-records-only enumeration
by the correction of errors in administrative records addresses through
address verification (a coverage improvement analogue) and by adding
persons missed in the administrative records (a nonresponse followup
analogue). Considering the Top-down and Bottom-up processes as part of
one overall design, the Administrative Records Experiment can be thought
of as a prototype for a more or less conventional census with the
initial mailout replaced by a Top-down administrative records
enumeration.

**Limitations**

There were four principal limitations on the experiment.

-   The administrative records source files were limited to those used
    in the creation of the Statistical Administrative Records System
    1999, which relied primarily on files for tax year 1998 and other
    files extracted early in calendar year 1999. These files neither
    exhausted the national-level administrative records that might have
    been available for Administrative Records Experiment 2000 nor were
    they the timeliest with respect to April 1, 2000, Census Day for
    Census 2000.

-   The number of experimental sites was small. Although it would not
    have been reasonable or realistic to attempt to mount this first
    Administrative Records Experiment in a representative sample of
    geographic areas large enough to make national estimates, additional
    sites would have provided more confidence that the results were not
    idiosyncratic to the sites selected.

-   There was no experimental variation in key design parameters such as
    the clerical and field operations and the address selection
    algorithm. Without some factorial or fractional factorial structure,
    direct estimates of operational impacts of components, individually
    or in combination, were not possible.

-   The measurement of race and Hispanic origin in administrative
    records at the national level is deficient. Attempts were made to
    improve the measurement through the use of certain statistical
    models, but the results were not entirely satisfactory.

The limitations in the Administrative Records Experiment were largely
due to resource constraints and a short planning period for what was an
extremely complex and novel undertaking.

**Experimental sites**

Two sites were selected that were believed to have a total of
approximately one million housing units and a population of
approximately two million persons. One site included Baltimore City and
Baltimore County, Maryland. The other site included Douglas, El Paso and
Jefferson Counties, Colorado. The sites provided a mix of population and
housing characteristics needed to assess the difficulties that might
arise in conducting an administrative records census.

**Administrative Records Experiment Outcomes Evaluation**

As expected, the Bottom-up coverage is much improved compared to the
Top-down, and this is largely due to the completion of the Top-down
enumeration by using census data for nonmatched addresses, which
simulates a followup to the administrative records enumeration.
Specifically, the Bottom-up coverage of children (81 percent - 94
percent across the test sites) is substantially better than the Top-down
(72 percent - 83 percent). Coverage of children is a particular weakness
for administrative records used in Administrative Records Experiment
2000.

Adults in the Bottom-up are more or less uniformly overcounted (102
percent - 104 percent). The overcount of adults most likely is due to
unaccounted for deaths in the 12 months prior to Census Day, the lack of
special populations operations in the Administrative Records Experiment
(e.g., a group quarters enumeration), and failure to unduplicate persons
after adding census data for nonmatched addresses. Of course, the latter
means that there is some duplication of children as well.

Detailed enumeration results focused mainly on a comparison of the
Bottom-up enumeration with the Census 2000. The analysis did not include
group quarters and, due to limitations in the administrative records
sources, persons could not be reported with "multi" or "other" race. The
analysis progressed from large geographic areas to small geographic
areas, beginning with the five test site counties and ending with census
blocks within the sites. The evaluation incorporated a variety of
methods to accomplish its objectives, including univariate and
multivariate statistical analyses of the Administrative Records
Experiment/Census 2000 differences, and spatial/ecological maps that
examined the geographic distributions of key comparison measures. The
outcomes evaluation tried to disentangle the influence of demographic
change and Administrative Records Experiment processing, coverage and
data quality issues, while presenting basic enumeration statistics.

At the county level, the Bottom-up process undercounted total population
in all sites except Baltimore City. As with the total population, males
and females were undercounted in all sites except Baltimore City, but
the female undercounts were slightly greater than male undercounts. Age
groups showed more variability with most groups undercounted. Generally
the size of the undercounts increased with decreasing age, except for
the 20-24 age group. These patterns did not appear to be site-specific.
Overcounts for the oldest old and undercounts for the youngest persons
suggest that much more timely birth and death information must be
obtained. Also, the special enumeration requirements for populations
such as college students, the military and persons in nursing homes must
be incorporated into administrative records processes.

Administrative records are not currently a good source of data for race
and Hispanic origin, and the models were not sufficient to correct their
deficiencies. Blacks and Hispanics were undercounted when they were a
large minority group and overcounted when they were not. American
Indians and Alaskan natives were not well identified and the accuracy of
Asian/Pacific Islander counts was uncertain.

Bottom-up tract-level total population results indicated a good
correspondence between Administrative Records Experiment and the census.
The population counts of 70 percent of tracts were within 5 percent
points, and 95 percent of the tracts were within 25 percentage points,
though a sizable number of tracts had moderate and large undercounts. At
the block-level, population counts were the least accurate. For the
total population 38 percent of blocks met the 5 percent criterion and
about 85 percent of blocks met the 25 percent criterion.

A multivariate analysis of block differences showed that large
undercounts were associated with such block characteristics as high
population density, high rental rates, and large proportions of persons
age 20-24. Large overcounts were associated with high vacancy rates, low
population density, small proportions of persons under the age of 20 and
large proportions of persons age 20-24 and age 65 and over (Heimovitz,
2002).

**Administrative Records Experiment Household-level analysis**

The general goal of the household-level analysis was to assess how well
households formed from administrative records matched those from Census
2000 addresses. The evaluation focused, first, on the factors associated
with Administrative Records Experiment and Census 2000 addresses that
were (computer) linked. Then, demographic comparisons were made between
households at linked addresses. There was a special focus on Census 2000
households that required a nonresponse followup and Census 2000
unclassified (imputed) households.

The evaluation used both descriptive analyses and logistic regression
analysis to assess the coverage and accuracy of Administrative Records
Experiment households. Descriptive analyses were performed for
households in all five Administrative Records Experiment counties and
for the Census 2000 nonresponse followup and imputed households in the
test sites. A logistic regression model was developed to predict the
probability of an accurate household match using address and
Administrative Records Experiment processing characteristics as
predictors. Addresses with a high probability of correct demographic
match between occupants might be candidates for administrative records
substitution in the case of nonresponse followup in a conventional
census. In the following discussion the term "linked" is used to mean a
matched address. The term "matched" is reserved for household
demographic comparisons at linked addresses.

Administrative Records Experiment's coverage of the census nonresponse
followup universe was not as good as its coverage of the overall
universe. Administrative Records Experiment housing units were linked
with 70.9 percent of the census nonresponse followup housing units,
compared with 88.4 percent of the census responding housing units. For
occupied nonresponse followup housing units, the coverage rate was 76.7
percent. The Administrative Records Experiment housing units were linked
with 63.2 percent of households that were imputed to have people in
them, and 34.7 percent of those imputed to be vacant.

The Administrative Records Experiment and the census counted the same
number of people in the housing unit for 51.1 percent of the 889,638
linked households, and Administrative Records Experiment was within one
of the census for 79.4 percent of the units. The 51.1 percent is
effectively a ceiling on the percent of linked households that had
exactly the same persons from Administrative Records Experiment and
Census 2000. Although errors in address linkage would account for some
of the mismatched households, the deficiencies in administrative records
cited earlier in this report\--missing children, lack of special
population operations and the time gap between the administrative
records extracts and Census Day\--most likely account for the major
part.

For linked nonresponse followup housing units, Administrative Records
Experiment had the same numbers of persons for 37.0 percent of the units
and was within one 69.3 percent of the time. Census 2000 nonresponse
followup housing units were more susceptible to the Administrative
Records Experiment deficiencies than responding units. In addition,
enumeration errors in Census 2000 might have been higher for these
units.

The regression analysis demonstrated a number of factors associated with
greater probability of matched household demographics. These include:
single unit address rather than multi-unit, household with only one or
two members, all household occupants over the age of 65, at least one
White occupant, no occupant with imputed race in the Administrative
Records Experiment. The predictive power of the model was moderately
strong. At a predicted probability of 0.5 or higher, the probability of
a correct household match was about 72 percent. At a predicted
probability of 0.8 or higher, the probability of a correct match
increased to about 83 percent, but the proportion of addresses with
predicted probability this high was only about 4 percent of all
addresses. Evidently, the limitations in the data, particularly the
administrative records cutoffs and poor race and Hispanic origin
measurement, made household prediction quite difficult.

***Implications for 2010 Planning***

**Substitution for 2010 nonresponse followup households should continue
to be explored**

Although the results of the household-level analysis were not definitive
due to the limitations on Administrative Records Experiment 2000, they
were sufficiently strong that research into the substitution of
administrative records households for nonresponse followup or
unclassified households in a conventional census should continue. For
nonresponse followup households there is the potential for significant
cost savings, and for unclassified households, the potential for greater
accuracy than that provided by imputation.

The approach piloted in the Administrative Records Experiment 2000
should be tested as part of the 2004 Census Test using models developed
from a linkage of Statistical Administrative Records System 2000 data to
the Census 2000 files. The timing of the administrative records in the
Statistical Administrative Records System 2000 would be much closer to
Census Day than the Statistical Administrative Records System 1999 data
used in the Administrative Records Experiment 2000, and much more like
the data that could be acquired for 2010.

**Other 2010 impacts should be considered**

There are other aspects of 2010 Census development in which
administrative records might play a role. These include Master Address
File improvements, development and testing of unduplication methods for
2010, subnational Demographic Analysis, and coverage measurement
research.

**2010 data acquisition and research agenda**

Arrangements should be made to acquire administrative records on a
timelier basis and to obtain some data sets that might fill some of the
administrative records coverage gaps.

A research agenda for 2010 would include:

-   Additional evaluation of the impact of clerical and field operations
    in Administrative Records Experiment 2000.

-   Person unduplication in the Administrative Records Experiment
    Bottom-up process.

-   Repeating Administrative Records Experiment 2000 with Statistical
    Administrative Records System 2000 data.

-   Repeating the Household-level analysis using Statistical
    Administrative Records System 2000 data.

-   Analysis of administrative records coverage gaps, in particular gaps
    related to persons in group quarters.

-   Master Address File improvements using administrative records.

-   Improving address linkage techniques.

-   Enhancing Numident race and Hispanic origin data using Census 2000.

-   Contributing to subnational Demographic Analysis.

**Implications for other Census Bureau programs**

The research that went into the development of the Statistical
Administrative Records System and Administrative Records Experiment 2000
has had significant payoffs in Census programs other than the decennial
census, and the development of new uses for administrative records
should continue to benefit non-decennial programs in the future. There
have been huge gains in knowledge of the strengths and weaknesses of
national administrative records systems to support various Census Bureau
activities, in the capacity for large scale data processing, data
standardization, record linkage, file unduplicaton, and Social Security
Number search and verification that will have benefits throughout the
Census Bureau.

**Research agenda for other Census Bureau programs**

A research agenda for other Census Bureau programs could include:

-   Testing the use of Statistical Administrative Records System as a
    contributor to total population and age/race/sex/Hispanic origin
    intercensal estimates.

-   Testing the use of Statistical Administrative Records System data
    for improving noninterview weights in ongoing surveys.

-   Testing the use of Statistical Administrative Records System as a
    tool to support small area income and poverty estimates.

-   Continuing to test the use of Administrative Records databases for
    Social Security Number validation and search strategies.

-   Continuing to improve our record linkage capabilities (for example,
    linking Current Population Survey addresses and persons to
    comparable Decennial Census addresses and persons), both in terms of
    improvements and search strategy improvements.

Intentionally Blank

# Background

## Introduction

The Administrative Records Experiment 2000 (AREX 2000) was an experiment
in two areas of the country designed to gain information regarding the
feasibility of conducting an administrative records census (ARC), or the
use of administrative records in support of conventional decennial
census processes. The first experiment of its kind, AREX 2000 was part
of the Census 2000 Testing, Experimentation, and Evaluation Program. The
focus of this program was to measure the effectiveness of new
techniques, methodologies, and technologies for decennial census
enumeration.

Interest in taking a decennial census by administrative records dates
back at least as far as a proposal by Alvey and Scheuren (1982) wherein
records from the Internal Revenue Service (IRS) along with those of
several other agencies might form the core of an administrative records
census. Knott (1991) identified two basic ARC models: (1) the Top-down
model that assembles administrative records from a number of sources,
unduplicates them, assigns geographic codes, and counts the results; and
(2) the Bottom-up model that matches administrative records to a master
address file, fills the addresses with individuals, resolves gaps and
inconsistencies address by address, and counts the results. There have
been a number of other calls for ARC research --- see for example
Myrskyla 1991; Myrskyla, Taeuber and Knott 1996; Czajka, Moreno and
Shirm 1997; Bye 1997. All of the proposals fit either the Top-down or
Bottom-up model described here.

Knott also suggested a composite Top-down/Bottom-up model that would
unduplicate administrative records using the Social Security Number
(SSN), then match the address file, and proceed as in the Bottom-up
approach. In overall concept, AREX 2000 most closely resembles this
composite approach.

More recently, direct use of administrative records in support of
decennial applications was cited in several proposals during the Census
2000 debates on sampling for Nonresponse Followup (NRFU). The proposals
ranged from direct substitution of administrative data for
non-responding households (Zanutto, 1996; Zanutto and Zaslavsky, 1996;
1997; 2001) to augmenting the Master Address File development process
with U.S. Postal Service address lists (Edmonston and Schultze,
1995:103). AREX 2000 provided the opportunity to explore the possibility
of NRFU support.

The Administrative Records Research (ARR) staff of the Planning,
Research, and Evaluation Division (PRED) performed the majority of
coordination, design, file handling, and certain field operations of the
experiment. Various other divisions within the Census Bureau, including
Field Division, Decennial Systems and Contracts Management Office,
Population Division, and Geography Division supported the ARRS staff.

Throughout this report, rather than identifying individual workgroups or
teams, we shall refer to the operational decisions made in support of
AREX to be those of ARRS; that is, we shall say that "ARRS decided
to..." whenever a key operational decision is described, even though, of
course, ARRS were not the only decision makers.

## Administrative Record Census---Definition and Requirements

In the AREX, an administrative record census was defined as a process
that relies primarily, but not necessarily exclusively, on
administrative records to produce the population counts and content of
the decennial census short form with a strong focus on apportionment and
redistricting requirements. Title 13, United States Code, directs the
Census Bureau to provide state population counts to the President for
the apportionment of Congressional seats within nine months of Census
Day. In addition to total population counts by state, the decennial
census must provide counts of the voting age population (18 and over) by
race and Hispanic origin for small geographic areas, currently in the
form of Census blocks, as prescribed by PL 94-171 (1975) and the Voting
Rights Act (1964). These data are used to construct and evaluate state
and local legislative districts.

Demographically, the AREX provided date of birth, race, Hispanic origin,
and sex, although the latter is not required for apportionment or
redistricting purposes. Geographically, the AREX operated at the level
of basic street address and corresponding Census block code. Unit
numbers for multi-unit dwellings were used in certain address matching
operations and one of the evaluations; but generally, household and
family composition were not captured. In addition, the design did not
provide for the collection of sample long form population or housing
data, needs that will presumably be met in the future by the American
Community Survey program. The design did assume the existence of a
Master Address File and geographic coding capability similar to that
available for Census 2000.

## AREX Objectives

The principal objectives of AREX 2000 were twofold. The first objective
was to develop and compare two methods for conducting an administrative
records census, one that used only administrative records and a second
that added some conventional support to the process in order to complete
the enumeration. The evaluation of the results also included a
comparison to Census 2000 results in the experimental sites.

The second objective was to test the potential use of administrative
records data for some part of the NRFU universe, or for the unclassified
universe. Addresses that fall into the unclassified status have very
limited information on them---so limited, in fact, that the address
occupancy status must be imputed, and, conditional on being imputed
"occupied," the entire household, including characteristics, must be
imputed. In order to effectively use administrative records databases
for substitution purposes; one must determine which kinds of
administrative record households are most likely to yield similar
demographic distributions to their corresponding census households.

Other more general objectives of the AREX included the collection of
relevant information, available only in 2000, to support ongoing
research and planning for administrative records use in the 2010 Census,
and the comparison of an administrative records census to other
potential 2010 methodologies. These evaluations and other data will
provide assistance in planning major components of future decennial
censuses, particularly those that have administrative records as their
primary source of data.

## AREX Top-down and Bottom-up Methods

### Top-down {#top-down .unnumbered}

A two-phase process accomplished the AREX 2000 enumeration. The first
phase involved the assembly and computer geocoding of records from a
number of national administrative record systems, and unduplication of
individuals within the combined systems. This was followed by two
attempts to obtain and code physical addresses (clerical geocoding and
request for physical address) for those that would not geocode by
computer. Finally, there was a selection of "best" demographic
characteristics for each individual and "best" street address within the
experimental sites. Much of the computer processing for this phase was
performed as part of the Statistical Administrative Records System
(StARS) 1999 processing (Judson, 2000; Farber and Leggieri, 2002). As
such, StARS 1999 was an integral part of AREX 2000 design.

One can think about the results of the Top-down process in two ways.
First, counting the population at this point provides, in effect, an
administrative-records-only census. That is, the enumeration includes
only those individuals found in the administrative records, and there is
no other support for the census outside of activities related to
geocoding. AREX 2000 provides population counts from the Top-down phase
so that the efficacy of an administrative-records-only census can be
assessed.

However, one might expect an enumeration that used only administrative
records to be substantially incomplete. Therefore, a second way to think
about the Top-down process is as a substitute for an initial mailout in
the context of a more conventional census that would include additional
support for the enumeration.

### Bottom-up {#bottom-up .unnumbered}

The fundamental difference between the Bottom-up method and the Top-down
method is the Bottom-up method matches administrative records addresses
to a separately developed "frame" of addresses, and based on this match,
performs additional operations. In this experiment, an extract of the
Census Bureau's Master Address File (MAF) served as the frame[^1].

The second phase of the AREX 2000 design was an attempt to complete the
administrative-records-only enumeration by the correction of errors in
administrative records addresses through address verification (a
coverage improvement analogue) and by adding persons missed in the
administrative records (a NRFU analogue). This phase began by matching
the addresses found in the Top-down process to the MAF in order to
assess their validity and to identify those MAF addresses for which no
administrative records were found. A field address review (FAV) was used
to verify non-matched administrative records addresses, and invalid
administrative records addresses were excluded from the Bottom-up
selection of best address. In design, non-matched MAF addresses would be
canvassed in order to enumerate persons at addresses not found in the
administrative records systems. In the AREX, such a canvassing was
simulated by adding those persons found in the Census 2000 at the
unmatched addresses to the adjusted administrative-records-only counts,
thus completing the enumeration. Accomplishing the AREX as part of the
Census 2000 obviated the need to mount a separate field operation to
canvass unmatched MAF addresses.

Considering the Top-down and Bottom-up processes as part of one overall
design, AREX can be thought of as a prototype for a more or less
conventional census with the initial mailout replaced by a Top-down
administrative records enumeration. Figure 1 below, provides a
conceptual overview of the experiment for enumerating the population
tested during the AREX. A more detailed description of data processing
flows can be found in Attachment 1. The graphical description presented
here is intended to convey the concept of both AREX methods when viewed
in terms of the Bottom-up method as a follow-on process to the Top-down
method.

[]{#__RefHeading___Toc58826859 .anchor}**Figure 1. Summary Diagram of
AREX 2000 Design**

![](media/image1.wmf){width="4.920833333333333in"
height="5.865972222222222in"}

## Experimental Sites

The experiment was set up to include geographic areas that include both
difficult and easy to enumerate populations. Two sites were selected
believed to have approximately one million housing units and a
population of approximately two million persons. One site included
Baltimore City and Baltimore County, Maryland. The other site included
Douglas, El Paso, and Jefferson Counties, Colorado. The sites provided a
mix of characteristics needed to assess the difficulties that might
arise in conducting an administrative records census. Approximately one
half of the test housing units was selected based on criteria assumed to
be easy-to-capture in an administrative records census (for example,
areas having a preponderance of city style addresses, single family
housing units, older and less mobile populations), and the other half
was selected based on criteria assumed to be hard to capture (the
converse).

## AREX Source Files

The administrative records for AREX were drawn from the StARS 1999
database. There were six national-level source files selected for
inclusion in StARS. A later section of this document describes the
source files in detail. The files were chosen to provide the broadest
coverage possible of the U.S. population, and to compensate for the
weaknesses or lack of coverage of a given segment of the population
inherent in any one-source file. See Section 2 for a description of the
source file characteristics.

### Timing {#timing .unnumbered}

An important limitation for the AREX is the gap between the reference
period for data contained in each source file and the point-in-time
reference of April 1, 2000 for the Census. The time lag has an impact on
both population coverage---births, deaths, immigration and
emigration---and geographic location---housing extant, and geographic
mobility. As an example, both IRS files include data for tax year 1998
with an expected current address as of tax filing time close to April
15, 1999. Note, however, that the IRS 1040 file only provided persons in
the tax unit as of December 31, 1998. The pertinent reference dates for
each of the files are provided in Section 2.

### State, Local and Commercial Files {#state-local-and-commercial-files .unnumbered}

ARRS decided not to use state and local files[^2] and commercially
available databases[^3] in the AREX 2000 experiment. Statistical
evidence is limited, but various reports from ARRS indicated that state
and local files come in an extremely diverse variety of forms, with
equally diverse record layouts and content (for historical information,
see Sweet, 1997; Buser, Huang, Kim, and Marquis, 1998; and other papers
in the Administrative Records Memorandum Series). Furthermore, ARRS
reported that it was quite time-consuming and intricate to develop the
interagency contractual arrangements necessary to use state and local
files. Public opinion results such as Singer and Miller (1992), Aguirre
International (1995), and Gellman (1997), convinced ARRS that public
sensitivity to the idea of linking commercial databases with government
databases (other than for address processing) would be too great, and
that such a linkage would be unwise.

###  {#section-11 .unnumbered}

### Census Numident {#census-numident .unnumbered}

An additional, and critical, file used in creation of the StARS database
was the Census Numident file. For the AREX, it was the source of most of
the demographic characteristics and some of the death data. Detailed
discussion regarding the creation and use of the Census Numident may
also be found in Section 2.

## AREX Evaluations

This report is a consolidation of four evaluations of AREX 2000 that
have been prepared by ARRS staff.

The **Process Evaluation** (Berning and Cook, 2002) documents and
analyzes selected components or processes of the Top-down and Bottom-up
methods in order to identify errors or deficiencies. It is designed to
catalogue the various processes by which raw administrative data became
final AREX counts and attempts to identify the relative contributions of
these various processes.

The **Request for Physical Address (RFPA) Evaluation** (Berning, 2002)
assesses the impact of noncity-style addresses. These addresses present
a significant hurdle to the use of an administrative records census on
either a supplemental or substitution basis. A particular problem is the
determination of residential addresses and their associated geographic
block level allocation for individuals whose administrative record
address is a P.O. Box or Rural Route.

The **Outcomes Evaluation** (Heimovitz, 2002) is a comparison of
Top-down and Bottom-up AREX counts by county, tract, and block level
counts of the total population by race, Hispanic origin, age groups and
gender, with comparable decennial census counts. This evaluation is
outcome rather than process oriented.

The **Household Evaluation** (Judson and Bauder, 2002) focuses on
household-level comparisons between administrative records and Census
2000. It assesses the potential for NRFU substitution and unclassified
imputations, and predictive capability.

## Limitations of the Experiment

In order to achieve a full understanding of the AREX processes and
outcomes, it is important to appreciate the context within which the
experiment was carried out. The AREX was the first attempt by the Census
Bureau to experiment with the use of administrative records as the
foundation of a short form decennial census. Planning for the experiment
did not begin until the end of 1997, which was quite late in the Census
2000 development cycle for an experiment of such complexity. The
resources for the experiment were limited to a part of the
Administrative Records Research Staff (ARRS) in the Planning, Research,
and Evaluation Division (PRED) with the help of other decennial census
staff.

###  {#section-12 .unnumbered}

### Administrative records source files {#administrative-records-source-files .unnumbered}

A consequence of the short planning time and limited resources was a
number of design and operational decisions that made the AREX 2000
enumeration process quite different from the way such an enumeration
might be carried out if administrative records were to be used in some
future decennial census. Chief among these differences was the decision
to use StARS 1999, the national administrative records database
developed by ARRS, as the source of the administrative records for the
experiment. The administrative records source files for StARS 1999
neither exhausted the national-level administrative records that might
have been available for AREX 2000 nor were they the timeliest. To cover
the population, StARS 1999 relied primarily on tax records for 1998
received by the Internal Revenue Service (IRS) in calendar year 1999.
While IRS tax records would have to be the core of any national
administrative records database, the coverage deficiencies are well
known\--adults without tax documents, children of taxpayers with more
than four dependents, and children of adults who did not have to file
1040 income tax returns. With additional time, more could have been done
to obtain administrative records from Social Security Administration
(SSA) and the Centers for Medicare and Medicaid Services (CMS) that
might have filled these coverage gaps. However, the acquisition of data
from Federal agencies is a difficult, time consuming, and sometimes
expensive process involving negotiations, interagency agreements, data
extract specifications, and testing and validation of the delivered
products before such data can be included in a national data base.

Obtaining timelier data for the AREX 2000 would have required, in some
cases, the receipt of data on a flow basis from the source agencies.
Receipt of tax forms filed in calendar year 2000, would have required
obtaining 1040 data from IRS on a flow basis and possibly 1099 and W-2
data from SSA as well. (See Section 2 for a discussion of the sources
and timing of tax data.) Also, more timely extracts might have been
obtained from other contributing agencies had there been sufficient time
to make the arrangements. As will be evident in this report, the fact
that the reference period for the administrative data was one or more
years behind census day (April 1, 2000) was the single most important
limitation to the AREX goal of testing the completeness and accuracy of
an administrative records census.

Using timely administrative records data in a decennial census implies
large administrative records data processing operations would be done
quickly as part of the decennial enumeration. One thing learned from
AREX 2000 is that such processing is technically feasible and could be
accomplished with the planning time and resources that would be
available for actual census operations as opposed to those typically
available for small experiments.

###  {#section-13 .unnumbered}

### Two experimental sites {#two-experimental-sites .unnumbered}

A second major limitation imposed by lack of planning time and resources
was the restriction of the experiment to five counties in two states.
Although it would not have been reasonable or realistic to attempt to
mount this first AREX in a representative sample of geographic areas
large enough to make national estimates, additional sites would have
provided more confidence that the results could be generalized beyond
the sites selected. While there is much to be learned from the AREX, it
is important to keep in mind that for the AREX results, descriptive
statistics are generally only representative of the test sites
themselves; and the modeling results, though suggestive of the
relationships between administrative records outcomes and their
covariates, are not definitive.

### Lack of experimental variation of key design parameters {#lack-of-experimental-variation-of-key-design-parameters .unnumbered}

There were several AREX operations relating to address processing that
could have been more thoroughly evaluated with some additional structure
in the experimental design. These operations involved clerical and field
attempts to validate addresses and obtain block-level geocodes, clerical
support for addressing matching of administrative records to the Master
Address File, and the "best address" selection algorithm for the
administrative records. In all cases, the objective of the evaluation
would have been to assess the impact of the particular operation or
algorithm on final address selection and ultimately whether the
operation contributed significantly to the accuracy of the AREX
enumeration.

Evaluation of the clerical and field operations, individually or in
combination, would be important because they represent potential costly
components were they to be implemented as part of a national
administrative records census. Evaluation of the address selection
algorithm would have revealed the impact of the preference of geocoded
addresses over others in the algorithm. Unfortunately, the experimental
design did not include factorial or fractional factorial structure
permitting direct estimates of the impact of operational components,
individually or in combination.

### Race and Hispanic origin models {#race-and-hispanic-origin-models .unnumbered}

Population tallies by race and Hispanic origin are a crucial product of
the short form census because of their use in drawing and evaluating
political districts at and below the state level. Measurement of race
and Hispanic origin is a major weakness of administrative records at the
national level and any attempt to use administrative records to
enumerate all or part of the population would have to find some way of
improving the information available in administrative records.

In his design proposal for an administrative record census in 2010, Bye
(1997) suggested building a list of SSNs annotated by race and Hispanic
origin by a series of operations that would begin by matching Census
2000 to SSA's Numident and continue during the years leading up to the
2010 Census. (See also Bye and Thompson (1999). Sections 4 and 5 of this
report describe activities currently underway at the Census Bureau.) Had
more planning time and resources been available to the AREX, it might
have been possible to incorporate race roster building into the
experiment by including one or more of the 1995 and 1996 census test
sites or Census 2000 Dress Rehearsal sites in the AREX (Bye, 1997).

However, such race roster building was not available to the AREX, and
ARRS decided to use Numident-based national-level models to augment the
race and Hispanic origin data (Bye 1998). Although using the models
generally worked in aggregate counts, the use of national-level models
to impute characteristics of small geographic areas has certain
well-known weaknesses in that the actual findings in the smaller areas
can vary substantially around the national predictions. Bye (1998)
provided tabulations for states and some substate areas showing the kind
of variation that could be expected when using the national models for
the AREX. Bye and Thompson (1999) provided a partial solution to this
problem, but an annotated Numident file is clearly a superior solution.

# THE AREX PROCESS EVALUATION

## Introduction

This section describes and evaluates the AREX enumeration processes. The
process description is taken largely from the process evaluation report
of Berning and Cook (2002). In this report, process descriptions have
been provided separately for the Top-down and Bottom-up enumerations.
The actual data processing flows were often intermingled and are
provided by Berning and Cook in great detail. Concerning process
evaluation, Berning and Cook focused mainly on data processing and
clerical operations.

### Administrative records {#administrative-records .unnumbered}

#### AREX source files {#arex-source-files-1 .unnumbered}

The administrative records for AREX were drawn from the StARS 1999
database. Number range CHAPTER The six national-level source files
selected for StARS were chosen to provide the broadest coverage possible
of the U.S. population. At a minimum, the files had to have for each
record, a name, Social Security Number (SSN), and street address.

The national level files that contributed to the StARS 1999 database and
therefore to AREX 2000, were:

Internal Revenue Service (IRS) Tax Year 1998 Individual Master File (IMF
1040),

IRS Tax Year 1998 Information Returns Master File (IRMF W-2 / 1099),

Department of Housing and Urban Development (HUD) 1999 Tenant Rental
Assistance Certification System (TRACS) File,

Center for Medicare and Medicaid Services (CMS) 1999 Medicare Enrollment
Database (MEDB) File,

Indian Health Services (IHS) 1999 Patient Registration System File, and

Selective Service System (SSS) 1999 Registration File.

The following table displays the primary reason each file was included
in the StARS database and the approximate number of input records
associated with each.

[]{#__RefHeading___Toc54420954 .anchor}Table 1. Source File
Characteristics

+---------+---------------------------------------+---------+---------+
| > File  | > Targeted Population Segment         | > \~ \# | > \~ \# |
|         |                                       | > of    | > of    |
|         |                                       | >       | >       |
|         |                                       | Address |  Person |
|         |                                       | >       | >       |
|         |                                       | Records | Records |
+---------+---------------------------------------+---------+---------+
| > IRS   | > Taxpayer and other members of the   | > 120   | > 243   |
| > 1040  | > reporting unit with current address | >       | >       |
|         |                                       | million | million |
+---------+---------------------------------------+---------+---------+
| > IRS   | > Persons with taxable income who     | > 598   | > 556   |
| >       | > might not have filed tax returns    | >       | >       |
| W2/1099 |                                       | million | million |
+---------+---------------------------------------+---------+---------+
| > HUD   | > Low income housing population       | > 3.3   | > 3.3   |
| > TRACS | > (possible non-taxpayers)            | >       | >       |
|         |                                       | million | million |
+---------+---------------------------------------+---------+---------+
| > M     | > Elderly population (possible        | > 57    | > 57    |
| edicare | > non-taxpayers)                      | >       | >       |
| > File  |                                       | million | million |
+---------+---------------------------------------+---------+---------+
| > IHS   | > Native American population          | > 3.1   | > 3.1   |
| > File  | > (possible non-taxpayers)            | >       | >       |
|         |                                       | million | million |
+---------+---------------------------------------+---------+---------+
| > SSS   | > Young male population (possible     | > 14.4  | > 13.1  |
| > File  | > non-taxpayers)                      | >       | >       |
|         |                                       | million | million |
+---------+---------------------------------------+---------+---------+
|         | > Total                               | > 795   | > 875   |
|         |                                       | >       | >       |
|         |                                       | million | million |
+---------+---------------------------------------+---------+---------+

Number range CHAPTERNotes: The variance between the number of address
records and person records within the input source files is a result of
the following source file characteristics:

1.  Each IRS 1040 input record may reflect up to six persons (primary
    filer, secondary, and dependents).

2.  Each SSS input record may reflect two addresses - defined as current
    and/or permanent address.

3.  The IRS W-2/1099 file undergoes a preliminary unduplication and
    clean-up process prior to the initial file edit process**.**

### Timing  {#timing-1 .unnumbered}

An important limitation for the AREX was the gap between the reference
period for data contained in each source file and the point-in-time
reference of April 1, 2000 for the Census. The gap had an impact on both
population coverage (births, deaths, immigration and emigration) and
geographic location (housing extant, and geographic mobility). As an
example, the IRS 1040 file included data for tax year 1998 with an
expected current address as of tax filing time close to April 15, 1999,
but provided only persons in the tax unit as of December 31, 1998.

The following table displays the reference periods of the files
available. Generally, the reference periods are about one year prior to
the day of Census 2000.

[]{#__RefHeading___Toc54420955 .anchor}Table 2. Reference Dates of
Source Files

  --------------- ---------- ----------- ---------------------------------
  Source File     Cut-off    Requested   Universe
                  Date       Cut Date    

  Indian Health   04/01/99   04/01/99    All persons alive at cut-off date
  Service                                

  Selective       Note 2     04/01/99    Males between the age of 18 -- 25
  Service                                

  HUD TRACS       04/01/99   04/01/99    All persons on file as of cut-off
                                         date

  Medicare        Note 3     04/01/99    All persons alive at cut-off date

  IRS 1040        12/98      Note 1      Individual tax returns for tax
                                         year 1998

  IRS W-2 / 1099  12/98      04/01/99    Forms W-2 and all 1099 forms tax
                                         year 1998
  --------------- ---------- ----------- ---------------------------------

Notes:

1\. File Cut date is for posting cycle weeks 1-39 only for IRS 1040, and
weeks 1-41 for IRS 1099 files. Weeks 40-52 (and 42-52 respectively) were
not included in StARS \'99. This file reflects the most current address
on file for the taxpayer. It could be an address that has been updated
since the 1998 tax return was posted.

Number range CHAPTER 2. Cut-off date is same as dates used to define
universe: persons born after April 2, 1972 and on (or before) April 1,
1980.

3\. Universe also defined as persons with a death date of 12/31/1989 or
later.

### Census Numident  {#census-numident-1 .unnumbered}

An additional, and critical, file used in creation of the StARS database
was the Census Numident file. For the AREX, it was the source of most of
the demographic characteristics and some of the death data.

The Census Numident was created by ARRS for the primary purpose of
validating Social Security Numbers (SSNs) used in the processing of
administrative records and supplying demographic variables missing from
source files. The Census Numident is an edited version of the Social
Security Administration's (SSA) Numerical Identification (Numident)
File. The SSA Numident file is the numerically ordered master file of
assigned Social Security Numbers (SSN) that may contain up to 300
entries for each SSN record, although on average contains two records
per SSN. Each entry represents an initial application for a SSN or an
addition or change (referred to as a transaction) to the information
pertaining to a given SSN. The SSA Numident contains all transactions
(and therefore, multiple entries) ever recorded against a single SSN.
The SSA Numident available for StARS 1999 reflected all transactions
through December 1998.

The Census Numident was designed to collapse the SSA Numident entries to
reflect "one best record" for each SSN containing the "best" demographic
data for each SSN on the file. Following edit, unduplication, and
selection of best demographics, the SSA Numident file of nearly 677
million records was reduced to just over 396 million records that
comprise the Census Numident file.

## Top-down enumeration

#### Dual stream process {#dual-stream-process .unnumbered}

The goal of the Top-down process was to use administrative records to
identify individuals residing at geocoded addresses in the AREX test
sites and to construct a data record for each individual that contained
demographic data (age, gender, race and Hispanic origin) corresponding
as closely as possible to census short form data. To achieve this goal,
a "dual-stream" processing approach was adopted. One processing stream
concerned the development of a unique record for each individual with
best demographics. The second stream involved development of an
unduplicated set of addresses, geocoded to the block level. In the end,
persons and addresses were brought together, and a best address was
selected for each person to complete the Top-down enumeration.

The following sections provide a brief description of the AREX Top-down
data processing steps. Much of the work was accomplished in the
development of StARS 1999 itself, but there were some differences in
demographic and address selection rules. More detail is given in Berning
and Cook (2002).

Top-down Person processing consisted of three main steps.

1.  File edits for person data,

```{=html}
<!-- -->
```
1.  SSN Verification of person records,

2.  Unduplication of person records, and creation of the Person
    Characteristics File (PCF) that contained the "best" demographic
    characteristics for each person record.

    Models were used to generate "best" demographic characteristics.
    Details about the models can be found in Bye (1998, race/Hispanic
    origin)[^4], and Thompson (1999, gender)[^5]. In general, a person's
    modeled race or gender was used only in the case where no race
    appeared on any administrative record, including the Numident**.**
    In the case of gender, the model was rarely used since the Numident
    reported gender more than 99 percent of the time. For Race, the
    model was used when the Numident race was shown as "Other" or
    "Unknown" or "Hispanic," and no other administrative record provided
    it. The vast majority of cases with unknown race were either
    children whose applications for SSNs were processed via SSA's
    enumeration-at-birth program, which was started in the mid-1980s, or
    older persons who had applied for Social Security benefits prior to
    SSA's development of the electronic Numident in the mid-1970s.

For Hispanic Origin, the model was used for all cases for which neither
the Numident nor any of the other administrative records indicated
Hispanic origin. Because the Numident did not capture Hispanic origin
prior to 1980, the model was used for well over 90 percent of the cases.
The following table shows the extent of Race and Hispanic Origin
imputation for the individuals included in the Top-down AREX
enumeration.

[]{#__RefHeading___Toc54420956 .anchor}Table 3. Percent of cases with
imputed Race or Hispanic Origin by age and County

  ---------------- ------------ ------------- ------------- ---------------
                   **Imputed                  **Imputed     
                   Race**                     Hispanic      
                                              Origin**      

  **County**       \<18         18 and over   \<18          18 and over

                                                            

  Baltimore City   40.7         2.8           99.4          96.7

  Baltimore County 49.3         4.0           99.2          98.5

  Douglas, CO      58.2         6.0           98.3          97.7

  El Paso, CO      52.2         9.0           93.9          93.9

  Jefferson, CO    54.8         8.0           94.7          95.2
  ---------------- ------------ ------------- ------------- ---------------

#### Top-down Address Processing {#top-down-address-processing .unnumbered}

Number range CHAPTERTop-down Address Processing consisted of four main
steps.

1.  File edits for address data.

2.  Code-1 processing and computer geocoding the address records.

3.  Manual geocoding for addresses not coded by computer.

4.  Creation of Master Housing File for administrative record addresses.

The creation of the Master Housing File for administrative record
addresses was the final step in the address processing before the
addresses were relinked with the person records. This step had two main
objectives. First there was an attempt to identify commercial addresses
in the files. Second, there was a final attempt to unduplicate the
addresses prior to the application of address selection rules.

[]{#__RefHeading___Toc54420957 .anchor}Table 4. StARS 1999 and AREX Test
Site Geocoding Tallies

+-------------------+----------------+--------------+----------------+
|                   | **\# Input     | **\# of      | **Percent**    |
|                   | Records to     | Records      |                |
|                   | Geocoding**    | Geocoded**   | **Geocoded**   |
+-------------------+----------------+--------------+----------------+
| StARS National    | 147,346,145    | 108,032,169  | 73.3           |
| Address File      |                |              |                |
+-------------------+----------------+--------------+----------------+
| Maryland subset   | 725,108        | 626,247      | 86.4           |
| of StARS National |                |              |                |
| File              |                |              |                |
+-------------------+----------------+--------------+----------------+
| Colorado subset   | 624,248        | 498,783      | 79.9           |
| of StARS National |                |              |                |
| File              |                |              |                |
+-------------------+----------------+--------------+----------------+

#### Clerical Geocoding and Request for Physical Address (RFPA) {#clerical-geocoding-and-request-for-physical-address-rfpa .unnumbered}

Addresses that cannot be geocoded by computer generally fall into three
categories: (1) city style addresses; (2) P.O. Box and non-city style
addresses (rural route/box number); or (3) addresses that are so
fragmented that they cannot be classified. Procedures for attempting to
obtain geocodes for the first two classes of addresses are described
below. Seriously fragmented addresses are discarded at this point.

[Master Address File Geocoding Office Resolution (MAFGOR)]{.underline}

MAFGOR was an existing operational capability within the Regional Census
Centers (RCC) to provide clerical geocoding for the Decennial Master
Address File as part of Census 2000. Addresses identified by ZIP code as
being potentially in the AREX sites but not geocoded by computer were
sent to the Philadelphia RCC (79,307) and the Denver RCC (83,841). These
two RCCs attempted to clerically geocode these addresses using trained
staff, reference materials, and maps. The clerical geocoding added about
3 percent to the total number of addresses coded.

[Request for Physical Address (RFPA)]{.underline}

P.O. Box and rural route/box number addresses pose a special challenge
for geocoding. The P.O. Box address does not refer to a physical
location and the non-city style addresses often do not precisely
identify the housing unit location. The RFPA was an attempt Number range
CHAPTERto collect physical addresses (house number and street name) for
persons receiving mail at these potential test site addresses. Major
components of the operation were to:

-   Create an address file from administrative records where the mailing
    address was a Post Office Box or noncity-style address.

-   Design and mail a form requesting physical address information.

-   Have the RCCs attempt to clerically geocode the physical addresses
    of the returned forms to state, county and block.

-   Key addresses and geocode information to a file for further
    analysis.

The mailing was sent to 58,151 addresses associated with 138,653
individuals. For a number of reasons, the response rate to the mailout
was only about 20 percent of which about 86 percent (9,431 physical
addresses) were geocoded, 8,090 to an AREX test site county. The coded
addresses were to have been added to the address lists prior to AREX
address selection. However, because of the small number of persons that
would have been potentially added to the enumeration or for whom
addresses might have changed, these addresses were not incorporated into
the AREX address file. As indicated above, the RFPA was the subject of a
special evaluation. More can be found in Berning (2002).

Table 5 provides a summary of Top-down address coding. Note that only
about 3,000 addresses were too fragmented to be eligible for either
MAFGOR or RFPA.

[]{#__RefHeading___Toc54420958 .anchor}**Table 5**. AREX Administrative
Record Address Geocoding Results

  -------------- ------------- ------------ ------------ ------------ -----------------
                                                                      

  **State**      **Addresses   **TIGER**    **Not        **Eligible   **Coded by**
                 in**                       TIGER**      for**        

                 **Test        **Coded**    **Coded**    **MAFGOR**   **MAFGOR**
                 Sites**                                              

                                                                      

  **Maryland**   725,108       626,247      98,861       79,307       21,542

  **Colorado**   624,248       498,783      125,465      83,841       28,030

  **Total**      1,349,356     1,125,030    224,326      163,148      49,572

                                                                      

                 **Not         **Eligible   **Returned   **Coded to** **Not Eligible
                 Eligible**    for**        with**                    for**

                 **for         **RFPA**     **Useable    **AREX       **MAFGOR/RFPA**
                 MAFGOR**                   Info.**      Site**       

                                                                      

  **Maryland**   19,544        18,694       3,538        1,939        860

  **Colorado**   41,624        39,457       8,145        6,151        2,167

  **Total**      61,168        58,151       11,683       8,090        3,027
  -------------- ------------- ------------ ------------ ------------ -----------------

#### AREX Master Housing File {#arex-master-housing-file .unnumbered}

The AREX Master Housing File (MHF) contained an unduplicated set of
non-commercial address records that was linked with the person records
prior to the application of the best address selection algorithm.

#### AREX Top-down composite person records (CPR) {#arex-top-down-composite-person-records-cpr .unnumbered}

At this point in the AREX "dual stream" process, address and person data
were brought together in preparation for creation of the Composite
Person record. There were two principal tasks. First, individuals
potentially in the AREX test sites were identified. Then, the best
address was selected for these persons. If the best address was in the
test site, then the individual became part of the Top-down enumeration.

The development of the AREX person universe began with the national
databases of persons and addresses described in the previous sections.
First, all persons ever associated with an AREX address were included in
a file of potential AREX persons. Next, all of the addresses associated
with these persons\--addresses both in and outside of the test
site\--were assembled and subjected to the following selection
algorithm.

1.  Select geocoded addresses over non-geocoded addresses.

2.  Select the highest HUID category available.

3.  Select a non-proxy address over an address with a proxy.

4.  Select a non-commercial address over a commercial address.

5.  Select the address based on source file priority as follows:

6.  IRS 1040 record

7.  Medicare record

8.  Indian Health Service record

9.  IRS 1099 record

10. Selective Service record

11. HUD TRACs record

12. Select the most recent record based on the administrative record
    cycle dates.

13. Select the first record read-in to the processing array for output
    to the CPR.

    If the best address for any person record from among the AREX person
    universe file was determined not to be within the AREX test site,
    the person record was flagged "out of scope" to ensure the person
    was not counted in the population tallies for the AREX test site.

#### Top-down process results {#top-down-process-results .unnumbered}

The composite person record represents the completion of the Top-down
process for the AREX 2000 experiment. Prior to tabulation, a final match
of the AREX addresses was made to the Decennial Master Address File
(DMAF) for the purpose of transforming the collection geography to
tabulation geography[^6]^.^ Because the AREX addresses were initially
geocoded to collection geography, it was necessary to translate the
collection geographic codes into the tabulation geographic codes so that
the comparisons to Census 2000 tabulations could be made.

The tallies for the top down method are shown in the following table.

[]{#__RefHeading___Toc54420959 .anchor}Table 6. Top-down Population
Tallies

  ---------------------------- ------------- -------------- --------------
  Test Site County             AREX          Census 2000    Percent of
                               Population    Population     Census
                                                            Population

  Baltimore City Maryland      570,648       651,154        88%

  Under 18                     134,471       161,353        83%

  18 and over                  436,127       489,801        89%

  Baltimore County Maryland    696,183       754,292        92%

  Under 18                     146,012       178,363        82%

  18 and over                  550,086       575,929        96%

  Douglas County Colorado      148,270       175,766        84%

  Under 18                     40,085        55,477         72%

  18 and over                  108,165       120,289        90%

  El Paso County Colorado      456,891       516,929        88%

  Under 18                     110,504       142,480        78%

  18 and over                  346,322       374,449        92%

  Jefferson County Colorado    473,495       527,056        90%

  Under 18                     101,535       133,486        76%

  18 and over                  371,894       393,570        94%
  ---------------------------- ------------- -------------- --------------

The counts by age showed the expected results. Generally, administrative
records undercounted the population; but coverage of adults (89
percent - 96 percent) was much better than children (72 percent - 83
percent). There is an evaluation of the administrative records data
sources and Top-down processing tasks in Bye 2002.

## Bottom-up enumeration

The weaknesses of the Top-down process as exhibited above were not
unexpected. In fact, most historical proposals for an administrative
records census recognized that additional operations, beyond tallies of
administrative records, would have to be performed for a complete
enumeration to be obtained.

The Bottom-up phase of the AREX 2000 design was an attempt to complete
the administrative-records-only enumeration by adding persons missed in
the administrative records, a process analogous to a conventional
nonresponse followup (NRFU). There was also an attempt to correct
Top-down enumeration errors by removal of invalid administrative records
addresses prior to best address selection. A valid address was defined
as one that matched the DMAF or was deemed valid after a field address
review. There was no provision for correcting enumerations at households
with valid administrative records addresses. Non-matched DMAF addresses
were canvassed in order to enumerate persons at addresses not found
among the validated administrative records addresses. In the AREX, the
canvassing was simulated by adding those persons found in Census 2000 at
the unmatched addresses to the adjusted administrative-records-only
counts, thus completing the enumeration. This phase of the AREX was
designated as Bottom-up because it started with a known list of
residential addresses (in this case the DMAF), matched the
administrative records addresses to such a list, and reconciled any
non-matched cases.

The Bottom-upNumber range CHAPTER operational components of AREX were
conducted on records contained within the five test site counties. These
operations consisted of:

-   Computer matching AREX addresses to the DMAF.

-   Clerical review of unmatched administrative records addresses.

-   Field Address Verification of unmatched administrative record
    addresses

-   Address re-selection.

-   Census Pull, the simulated NRFU.

-   Bottom-up enumeration.

### Matching AREX records to the DMAF {#matching-arex-records-to-the-dmaf .unnumbered}

#### The DMAF Computer Match {#the-dmaf-computer-match .unnumbered}

Number range CHAPTERThe objective of the computer match operation was to
determine the extent and nature of agreement between addresses from
administrative records source files and eligible addresses from the
Census Bureau's Decennial Master Address File (DMAF). To most accurately
match the addresses, the AREX addresses were limited to those, which
were geocoded, or with a standardized street name, a standardized
property description or both. Excluded from the matching process were
non-standardized addresses, standardized post office or box addresses,
standardized post office and rural route addresses and undefined
addresses. Table 7 shows the administrative records addresses and the
DMAF addresses eligible for the computer match.

[]{#__RefHeading___Toc54420960 .anchor}Table 7. Addresses eligible for
the match to the DMAF

+-----------+---------+-----------------+----------+-----------------+
|           | **Ad    |                 |          | *               |
|           | dresses |                 |          | *Unduplicated** |
|           | from    |                 |          |                 |
|           | Adminis |                 |          | **DMAF          |
|           | trative |                 |          | Addresses**     |
|           | Re      |                 |          |                 |
|           | cords** |                 |          |                 |
+-----------+---------+-----------------+----------+-----------------+
|           |         | *               |          |                 |
|           |         | *TIGER/MAFGOR** |          |                 |
+-----------+---------+-----------------+----------+-----------------+
| **Test    | **      | **Coded**       | **Non    |                 |
| Site**    | Total** |                 | -coded** |                 |
+-----------+---------+-----------------+----------+-----------------+
| Maryland  | 656,073 | 647,789         | 8,284    | 650,109         |
+-----------+---------+-----------------+----------+-----------------+
| Colorado  | 531,382 | 526,813         | 4,569    | 526,018         |
+-----------+---------+-----------------+----------+-----------------+
| Total     | 1,      | 1,174,602       | 12,853   | 1,176,127       |
|           | 187,455 |                 |          |                 |
+-----------+---------+-----------------+----------+-----------------+

The matching process used AutoMatch, a commercial software package that
applies probabilistic record linkage techniques. The final results were
divided into matches; possible matches; non-matches and matches to
duplicate DMAF addresses. Number range CHAPTERTable 8 shows the results
of the computer match for the administrative records addresses.

[]{#__RefHeading___Toc54420961 .anchor}Table 8. Computer Match Results
\-- Administrative Records Counts

  ---------- ----------- ------------ ----------- ---------- ------------- -----------
  Test Site  \# Records  \# of        \% of       Possible   Non-Matched   Duplicate
             to Computer Addresses    Addresses   Matched    Records       Matches
             Match       Matched      Matched     Records                  

  Maryland   656,073     525,234      80%         2,134      128,286       419

  Colorado   531,382     432,140      81%         9,430      88,586        555

  Total      1,187,455   957,374      81%         11,564     216,872       974
  ---------- ----------- ------------ ----------- ---------- ------------- -----------

Number range CHAPTERSome administrative records matched to more than one
address in the DMAF, each of which might have had subtle differences.
When this occurred, addresses were flagged as having duplicate matches.
The duplicates were resolved later in the AREX operation where the best
address was determined based on pre-defined criteria.

###  {#section-14 .unnumbered}

#### Clerical Review of Unmatched Administrative Records Addresses {#clerical-review-of-unmatched-administrative-records-addresses .unnumbered}

**Number range CHAPTER**Following the computer match, the staff at the
National Processing Center conducted a clerical review.

The results of the clerical review are shown in Table 9.

[]{#__RefHeading___Toc54420962 .anchor}**Table 9**. Clerical Review
Match Results

  ---------- ------------ --------------- ---------------- ----------------
  Test Site  Records sent Matched Records Matched Records  \% of Matched
             to Computer  before Clerical after Clerical   Records matched
             Match        Review          Review           by Clerical
                                                           Review

  Maryland   656,073      525,234         543,811          3%

  Colorado   531,382      432,140         459,753          5%

  Total      1,187,455    957,374         1,003,564        4%
  ---------- ------------ --------------- ---------------- ----------------

###  {#section-15 .unnumbered}

### Field address verification (FAV) {#field-address-verification-fav .unnumbered}

Number range CHAPTERThe Field Address Verification operation was
implemented to check the validity of addresses that remained unmatched
to the DMAF following the computer matching and clerical review. The
purposes of the FAV were to:

-   Verify the physical existence or nonexistence of non-matched AREX
    2000 Test Site addresses.

-   Correct erroneous address field values.

-   Identify addresses meeting unique conditions such as being a
    duplicate of another address.

The original plan called for a review of 100 percent of the unmatched
addresses by census field staff, but the plan was changed to have only a
sample of addresses reviewed by Census Bureau Headquarters volunteers.
The results from the sample were used to estimate a regression equation
giving the probability of a valid address. The equation was then used to
impute validity or lack thereof to the non-sample addresses.

#### Sample design {#sample-design .unnumbered}

Number range CHAPTERAfter the computer phase of address matching, the
universe of addresses eligible for Field Address Verification was first
restricted to geocoded, city-style addresses within the AREX 2000 test
site counties. The universe was further restricted to exclude some AREX
2000 test site ZIP codes that belonged to three colleges, a medical
center, and an Air Force base in the belief that few or no residential
addresses existed in those areas.

With the redesign of the FAV operation, the addresses to be verified
were based on a stratified cluster (Census block) sample of unmatched,
city style addresses. The sample consisted of 112 blocks per AREX county
and resulted in 6,644 addresses being flagged as part of the FAV sample
(table 10).

[]{#__RefHeading___Toc54420963 .anchor}Table 10. Selection of FAV
Addresses

  ----------------------------- ------------------ ----------------------
  Test Site State               Number of FAV      Number of Addresses
                                Eligible Addresses Selected for FAV
                                                   Sample

  Maryland                      96,202             2,914

  Number range CHAPTERColorado  57,333             3,730

  Total                         153,535            6,644
  ----------------------------- ------------------ ----------------------

Number range CHAPTERAfter the fieldwork was completed and the results
keyed, PRED staff then reviewed each of the listing pages and annotated
a 5-digit status code on the page. The code categorized the type of
activity about the address that was shown on the listing page and the
validity of the address. In some instances, addresses were determined to
be valid as listed (without changes). In other cases, corrections were
made to the address to make the address valid. Table 11 provides the FAV
sample results.

[]{#__RefHeading___Toc54420964 .anchor}Table 11. FAV Sample Results

  -----------------------------------------------------------------------
  Test Site   Number of        Percent Valid   Percent     Percent Valid
              Addresses                        Valid as    After Lister
              Sampled                          Listed      Corrections
  ----------- ---------------- --------------- ----------- --------------
  Maryland    2,914            38%             13%         25%

  Colorado    3,730            41%             7%          34%

  Total       6,644            40%             10%         30%
  -----------------------------------------------------------------------

Of particular interest in this table are the percentages of addresses
determined to be valid as listed. Because these addresses did not match
the DMAF even after clerical review, it is possible that the DMAF was
incomplete. However, this may also reflect residual difficulties in the
matching process.

#### Imputing validity to non-sample addressesNumber range CHAPTER {#imputing-validity-to-non-sample-addressesnumber-range-chapter .unnumbered}

The FAV sample cases were used to estimate a logistic regression model,
logit(P(y=1\|x))= xβ. In this equation, the outcome measure y = 1 if the
address was valid, y = 0 otherwise. The predictor variables, x,
represented (1) characteristics of the administrative record addresses
as possibly modified by the FAV review, (2) DMAF block size of the DMAF
address to which the administrative record partially match, and (3) the
nature of the partial match. Generally, administrative records addresses
were found more likely to be valid if they were not commercial, were
found in multiple administrative record source files, had no unit
identifier**,** and matched a DMAF address or addresses (by state,
county, zip, street name, and street name suffix) for which there were
no unit identifiers (i.e., it or they appeared to be a single-family
dwelling or dwellings), and were located in blocks in which the DMAF
indicated a fairly large number of addresses. The overall probability of
misclassification\--the probability that an address was not valid times
the probability of a false positive plus the probability that an address
was valid times the probability of false negative\-- was estimated to be
0.32. (A detailed discussion of the model and regression results can be
found in Bye, 2002.)

Validity or lack thereof was imputed for all FAV eligible addresses that
were not part of the sample by using the regression equation to
calculate the probability that the address was valid, and comparing this
value to a random number drawn from a uniform distribution between 0
and 1. If the random number was less than or equal to the predicted
probability, the address was deemed to be valid for AREX Bottom-up
address selection purposes.

The net results of the Bottom-up administrative records addresses
processing \-- match of AREX addresses to the DMAF and the subsequent
FAV \-- are given in the following table.

[]{#__RefHeading___Toc54420965 .anchor}Table 12. Bottom-up
Administrative Records Address Processing

+---------+---------+-------------+----------+---------+-------------+
| Test    | Ad      | Matched     | Non      | FAV     | Number of   |
| Site    | dresses | Addresses   | -matched | E       | valid       |
|         | sent to | after DMAF  | A        | ligible | Addresses   |
|         | DMAF    | Computer    | ddresses | Ad      | of those    |
|         | C       | Match and   |          | dresses | eligible    |
|         | omputer | Clerical    |          |         | for FAV     |
|         | Match   | Review      |          |         |             |
|         |         |             |          |         | (FAV sample |
|         |         |             |          |         | or imputed) |
+---------+---------+-------------+----------+---------+-------------+
| M       | 656,273 | 543,881     | 112,392  | 96,202  | 36,661      |
| aryland |         |             |          |         |             |
+---------+---------+-------------+----------+---------+-------------+
| C       | 531,382 | 459,753     | 71,629   | 57,333  | 23,310      |
| olorado |         |             |          |         |             |
+---------+---------+-------------+----------+---------+-------------+
| Total   | 1,      | 1,003,634   | 184,021  | 153,535 | 59,971      |
|         | 187,655 |             |          |         |             |
+---------+---------+-------------+----------+---------+-------------+

As a result of the FAV operations, 93,382 (153,535 - 59,971) of the
FAV-eligible administrative records addresses were found to be invalid,
and were not eligible for the Bottom-up address selection. Note,
however, that unmatched addresses not eligible for the FAV remained in
the Bottom-up address pool as a possible Bottom-up address.

### Second DMAF match {#second-dmaf-match .unnumbered}

A second match of the AREX addresses was made to the DMAF for the
purpose of transforming the collection geography to tabulation
geography. Because the AREX addresses were initially geocoded to
collection geography, it was necessary to translate the collection
geographic codes into the tabulation geographic codes so that the
comparisons to Census 2000 tabulations could be made. In general, the
difference between collection blocks and tabulation blocks was that some
collection blocks were split in final decennial census tallies.

The contents of the DMAF were not stationary between the first and
second match. There were a number of problems with duplicate MAFIDs for
different addresses and multiple MAFIDs for the same address in both
DMAF matches. Sometimes administrative records that matched the first
time did not match the second. In these cases, if the original
collection block was split into more than one tabulation block, then the
address was statistically allocated to a tabulation block. (The second
DMAF match also had an impact on Top-down block assignments.)

### Bottom-up address selection and composite person records {#bottom-up-address-selection-and-composite-person-records .unnumbered}

For an address to be considered eligible for Bottom-up selection, the
following conditions had to be met after the rematch to the DMAF:

1.  The address had to have a Census tabulation block code.

2.  The address could not have been identified as a commercial address
during the FAV.

3.  The address had to be either non-FAV eligible, a FAV sample address
that was found to be valid during field review, deemed valid based on
FAV imputation, or valid based on matching during the rematch to the
DMAF.

Once the pool of eligible addresses was identified and linked to the
unduplicated list of AREX persons, the address selection operations were
similar to the top down selection and identification of persons in
administrative records who were eligible for the Bottom-up enumeration
were similar to those procedures used for Top-down selection. Generally,
all the addresses associated with an individual were assembled and
subjected to the address selection rules to obtain the "best" address
for each individual in the administrative record source files.

A possible outcome of the address selection process was that no persons
remained at valid addresses in the AREX test sites. That is, although
one or more persons were originally associated with the administrative
record address, best address selection resulted in all persons at the
address being assigned to another test site address or to an address
outside the test site. These addresses were designated as AREX vacant
addresses; there were 179,523 such addresses.

### Simulated NRFU \-- the Census Pull {#simulated-nrfu----the-census-pull .unnumbered}

A principle feature of the Bottom-up process was to complete the
enumeration by adding persons at test site addresses not found in
administrative records. Presumably this would have been accomplished by
some sort of mailout/mailback procedure or face-to-face interviews or
both. This can be considered as an analogue to a conventional
nonresponse followup; albeit in this case, the "nonresponse" is to the
initial administrative records enumeration.

For the AREX, the NRFU analogue was simulated by including persons found
in the Census 2000 Hundred Percent Detail File (HDF) at addresses that
were not found among the administrative records addresses, occupied or
vacant. These were persons enumerated in Census 2000, and the assumption
was that they would have been counted in the AREX had some sort of
followup been instituted. The process of including persons from the
Census 2000 HDF was referred to as the Census Pull.

Table 13 shows the number of Census Pull addresses and persons included
in the Bottom-up enumeration.

[]{#__RefHeading___Toc54420966 .anchor}Table 13. Census Pull Results

+---------------+-----------------+---------------------+-------------+
| State         | Census          | Census              | Census      |
|               |                 |                     |             |
|               | 2000 Addresses  | Pull Addresses      | Pull        |
|               |                 |                     | Persons     |
+---------------+-----------------+---------------------+-------------+
| Maryland      | 615,323         | 97,460              | 185,868     |
+---------------+-----------------+---------------------+-------------+
| Colorado      | 478,701         | 55,319              | 126,558     |
+---------------+-----------------+---------------------+-------------+
| Total         | 1,094,204       | 152,779             | 312,426     |
+---------------+-----------------+---------------------+-------------+

Of the Census Pull addresses, 35,591 were vacant Census 2000 addresses.

### Revised race imputation for children under 18  {#revised-race-imputation-for-children-under-18 .unnumbered}

Instead of using the race model to impute race for children under age 18
with unknown race in administrative records as was done in the Top-down
process, an alternative imputation method was used. The source of most
children in the AREX was the IRS 1040 file, which generally provided
primary and secondary tax payers and up to four dependents in each tax
unit. Children under 18 with unknown race, who could be associated with
a tax unit, were assigned the race of the primary taxpayer.

### Bottom-up results {#bottom-up-results .unnumbered}

#### Overall enumeration {#overall-enumeration .unnumbered}

The AREX Bottom-up enumeration results are shown in Table 14. As
expected, the coverage is much improved compared to the Top-down counts,
and is largely due to the completion of the Top-down enumeration by the
Census Pull. Specifically, the Bottom-up coverage of children (81
percent - 94 percent across the test sites) is substantially better than
the Top-down (72 percent - 83 percent). Adults in the Bottom-up are more
or less uniformly overcounted (102 percent - 104 percent). The overcount
of adults most likely is due to unaccounted for deaths in the previous
12 months, handling of special populations, and failure to unduplicate
persons after the Census Pull (discussed later in the report). Of
course, the latter means that there is some duplication for the children
as well.

[]{#__RefHeading___Toc54420967 .anchor}**Table 14**. Bottom Up Method
Population Tallies

  ---------------------------- -------------- -------------- --------------
  Test Site County             AREX           Census         \% of Census
                               Population     Population     Population

  Baltimore City Maryland      661,561        651,154        102%

  Under 18                     151,411        161,353        94%

  18 and over                  510,109        489,801        104%

  Baltimore County Maryland    745,893        754,292        99%

  Under 18                     154,500        178,363        87%

  18 and over                  591,313        575,929        103%

  Douglas County Colorado      170,102        175,766        97%

  Under 18                     46,394         55,477         84%

  18 and over                  123,689        120,289        103%

  El Paso County Colorado      509,597        516,929        99%

  Under 18                     121,647        142,480        85%

  18 and over                  387,888        374,449        104%

  Jefferson County Colorado    508,254        527,056        96%

  Under 18                     108,618        133,486        81%

  18 and over                  399,575        393,570        102%
  ---------------------------- -------------- -------------- --------------

#### Net effect of Bottom-up processes on administrative records tallies {#net-effect-of-bottom-up-processes-on-administrative-records-tallies .unnumbered}

Two of the Bottom-up operations entailed an attempt to improve the
administrative records addresses prior to Bottom-up "best" address
selection: (1) the initial match to the DMAF and its followup clerical
review, and (2) the FAV[^7]. The impact of these operations on the
administrative records part of the Bottom-up enumeration was threefold.
First, administrative records addresses were removed from consideration
if they did not match the DMAF, were FAV eligible but were not found or
deemed to be valid by the FAV. Second, addresses that were not geocoded
in the TIGER match or MAFGOR might have been coded through one or the
other of these operations. Third, some of the addresses that were
geocoded prior to the initial DMAF match might have received code
changes.

The impact of these operations on the addresses has been discussed in
the relevant sections. Table 15 provides some information on the net
impact of these operations on Bottom-up person tallies from
administrative records, and provides a comparison of the Top-down and
Bottom-up administrative records person tallies.

Of the 2.3 million persons tallied in the Top-down enumeration, 70,031
(about 3 percent) were excluded from the Bottom-up administrative record
counts. These exclusions occurred either because the only address that
the persons had was rejected by the Bottom-up processes or because the
only remaining addresses were outside of the AREX test sites.

For administrative records persons enumerated in both the Top-down and
the Bottom-up, Table 15 provides information on the change in geographic
location due to Bottom-up processes. Here, there seems to have been very
little impact; over 99 percent of these persons were at the same address
in both enumerations.

[]{#__RefHeading___Toc54420968 .anchor}Table 15. Geographic Differences
for Persons in both the Top-down and Bottom-up methods

  ------------ ------------- ----------- ----------- ------------ -----------
               Top-down                                           
               in...                                              

  Bottom-up    Same          Different   Different   Different    Different

  total        Address       Address     Block       Tract        County

  2,275,456    2,258,441     17,015      15,129      11,847       2,363

  100.0%       99.3%         0.8%        0.7%        0.5%         0.1%
  ------------ ------------- ----------- ----------- ------------ -----------

Overall, the net effect of the Bottom-up operations on the
administrative record tallies was quite modest.

### Bottom-up evaluation {#bottom-up-evaluation .unnumbered}

The Bottom-up evaluation focused on both operations and the goals of a
decennial short form census.

#### AREX Bottom -up processing operations {#arex-bottom--up-processing-operations .unnumbered}

*DMAF computer match*

The computer match rate between eligible AREX addresses and the DMAF was
only about 80 percent. A number of factors may have contributed to the
match rate level. First, there is the vintage of the administrative
record addresses. Most of the AREX addresses were of 1999 vintage, one
year or older than the DMAF. Destruction of housing units and changes in
official address components could account for some of the non-matches.

Second, although a number of adjustments were made to the AutoMatch
parameters to try to ensure optimum match rates, the clerical review
following the computer match resulted in a substantial number of
additional matches suggesting that there is still room for improvement
in the use of matching software. The fact that most of the unmatched
addresses were geocoded by TIGER or MAFGOR suggests just how difficult
address matching can be.

Third, a more consistent method of address standardization should
improve the overall match rate. Throughout the course of creating the
StARS database and subsequent iterations of the AREX address file, the
Geography Division's address standardizer was employed. The dynamic
nature of the standardizer software program and the flexibility of
operator control during its application most likely contributed to
inconsistencies and variances that led to erroneous non-matches (and
matches as well). Although difficult to quantify, the application of a
fixed version of the standardizer along with prescribed operator control
methodologies should improve the overall match rate during the computer
matching operations. Improving the computer match rate would, in turn,
reduce the number of address records requiring clerical review.

Finally, multiple MAFIDs assigned to a single address and duplicate
MAFIDs assigned to multiple addresses contributed to the difficulty in
classifying an address as matched, non-matched, or possibly matched.
These difficulties may be due to the Census Bureau's methodology and
audit trail for identification and retention of "surviving MAFIDs" on
the DMAF as the DMAF changes over time. Further research needs to be
done on the best formulation of DMAF extracts for administrative record
matching.

*Second DMAF match*

Prior to the AREX enumeration, a second match to the DMAF was required
to pick up "tabulation" block codes. The block codes obtained from the
original TIGER match and MAFGOR operation were "collection" block codes.
The difference between the codes is that some collection blocks were
split as part of a final decennial census-coding scheme. The AREX needed
to use the final block codes in order to facilitate comparisons between
AREX and Census 2000 results. Addresses that did not match the second
time and were in collection blocks that had been split by one or more
tabulation blocks were statistically allocated to one of the split
blocks.

*Clerical review of non-matched AREX addresses*

Number range CHAPTERThe original AREX plan called for PRED staff to do
the clerical review of the unmatched and possible-matched records after
the initial match of the administrative records addresses to the DMAF.
However, resource constraints due to changes in FAV plans required that
the review be shifted to the National Processing Center (NPC).
Accordingly, PRED trained approximately 25 reviewers to evaluate the
possible matches of AREX addresses against the DMAF and make a match/non
match determination for the address.

*Number range CHAPTERField Address Verification*

To minimize the impact of the lack of experience, the listers were not
used in the traditional role of assigning action codes but rather to
collect information about the address for later analysis and assignment
of the action code. The listers answered 11 questions about the property
from which the action code (called status code in this operation) was
later assigned. This modification worked well in minimizing the mistakes
made by inexperienced staff and created a collateral benefit of
collecting detailed information about the addresses for further research
and analysis.

One way to improve the list of addresses eligible for FAV is to improve
the identification and removal of commercial addresses from the AREX
address files. The product used was the American Business Information
(ABI), Inc. database file of commercial addresses (more than 10 million)
based on national telephone directories (both yellow and white pages).
Budgetary constraints precluded purchase of the ABI residential file.
The use of both files (commercial and residential) would have improved
the accuracy of commercial address identification and reduced the size
of the FAV eligible address list as well.

It is difficult to gauge the impact of the FAV because the actual review
was carried out on a small sample and because of the classification
error associated with the imputation based on the regression equation.
But there are some things that can be learned from the FAV sample.

The sample addresses were drawn from a list that did not match any
address in the initial DMAF match. About 25 percent of the sample
addresses found to be valid upon field review were found to be valid as
listed. They represent about 10 percent of all FAV eligible addresses.
The remaining 75 percent of valid sample addresses (30 percent of all
eligible addresses) were found to be valid after lister corrections. It
turned out that none of this group matched a Census 2000 address in the
second match to the DMAF nor, of course, did the uncorrected valid
group. It is not known whether any of these addresses truly represent
addresses not in the DMAF or are unmatched as a result of inaccuracies
in the address matching process.

Table 16 shows Bottom-up "best" administrative records addresses by FAV
status.

[]{#__RefHeading___Toc54420969 .anchor}Table 16. Bottom-up "best"
address by FAV Status.

  ---------- ------- --------- ----------- ------------ -------------- -----------
                     **FAV     **Imputed   **Not FAV    **Valid in**   
                     Valid**   Valid**     Eligible**                  

                                                        **Second       **Total**
                                                        DMAF**         

                                                        **Match,       
                                                        Only**         

  AREX               1,084     24,703      855,946      3,775          885,508
  Occupied                                                             

                     43%       47%         86%          33%            83%

  AREX               1,420     28,242      142,253      7,608          179,523
  Vacant                                                               

                     57%       53%         14%          67%            17%

  Total              2,504     52,945      998,199      11,383         1,065,031

                     100%      100%        100%         100%           100%
  ---------- ------- --------- ----------- ------------ -------------- -----------

It is interesting to note that FAV sample addresses selected as best
addresses were much more likely to be vacant than addresses that were
not FAV eligible. The FAV imputed addresses had occupancy rates that
were similar to the sample. The number of persons counted at FAV sample
addresses was 2,162 and at FAV imputed addresses, 44,912 for a total of
47,074. Inflating the FAV sample persons by the reciprocal of the
average selection probability (i.e. 2,162(1/. 0433)) yields 49,931, much
of the difference presumably due to address misclassification as a
result of the imputation. However, the closeness of the numbers suggests
that a 100 percent FAV would have yielded results similar to the
combined sample and imputation scheme.

*Including AREX vacant housing in the Census Pull*

The AREX address selection rules resulted in almost 180,000 vacant
addresses thought to be valid for the AREX test sites. Such addresses
that are actually found in the AREX sites through a match to the Census
2000 HDF would appear to be conceptually similar to the addresses
included in the Census Pull. Both kinds of addresses represent housing
units in the AREX sites for which no administrative records persons were
found to be resident. In both cases, it might have been that some
addresses were truly vacant on census day and others truly occupied. For
the latter, deficiencies in the administrative records or administrative
records processing resulted in the persons not being counted or counted
at the wrong address.

A match of the AREX vacant addresses to the Census 2000 HDF, in fact,
found about 76,000 matched addresses, and almost 67,000 were occupied in
Census 2000. (Refer to the analysis in Section 3 of this report.) Of
course, some of these persons may be the same as persons counted at
other administrative records addresses; but the same could be said for
the persons found at the Census Pull addresses. Therefore, in a
Bottom-up process, both types of addresses should have been canvassed;
and the AREX vacant addresses that matched addresses in the Census 2000
HDF should have been included in the Census Pull.

*Unduplication after the Census Pull*

There should have been an unduplication of individuals after the Census
Pull by matching persons obtained in the Pull with those from the
administrative records lists. The presence of duplicate individuals is
suggested not only by the overcounts of adults shown in various tables,
but also by a comparison of the total number of Bottom-up addresses with
the number of Census 2000 addresses in the test sites. The total number
of addresses in the Bottom-up was 1,217,810 \-- 1,065,031 administrative
record addresses and 152,779 from the Census Pull. The number of Census
2000 address in the test sites was 1,094,204. Thus, there were 123,606
more addresses in the Bottom-up enumeration than in Census 2000.

One way to accomplish the unduplication would be to search and verify
the SSNs for the individuals in the Census Pull and compare them with
the SSNs of the individuals in the administrative records lists. This
might not be completely effective because being part of the Census Pull
suggests that blocking on address will not facilitate the SSN search.
Alternatively, the Census Pull individuals could be matched directly
with the administrative record list blocking variously on such variables
as surname and date of birth.

When duplicate individuals were found, the Census Pull could be taken as
more accurate and the individuals would be removed from the
administrative records address. This approach could result in some
additional vacant addresses, so that the process might have to be
repeated several times in order to identify the "best" address for all
persons. In the end, there could be vacant administrative record
addresses that should have been filled by persons erroneously located
outside of the AREX sites in the administrative records systems. This
would imply that a national unduplication would be part of a full
Bottom-up census. Such an unduplication was out of scope for the
experiment.

# AREX OUTCOMES AND HOUSEHOLD EVALUATIONS

## Introduction

The evaluation of the numerical findings of the AREX was twofold. First
there was a comparison of the results of the Top-down and Bottom-up
enumerations with the Census 2000 enumeration in the experimental test
sites (Heimovitz 2002). This analysis progressed from large geographic
areas to small geographic areas, beginning with the five test site
counties and ending with Census 2000 blocks within the sites. The
outcomes evaluation tried to disentangle the influence of demographic
change and AREX processing, coverage and data quality issues, while
presenting basic enumeration statistics. Below the county level, the
analysis focused on the Bottom-up enumeration because the county-level
analysis was sufficient to show the evident weaknesses of the Top-down
process. Section 3.2 provides some of the highlights of portions of
Heimovitz's report; there was also a regression analysis that is omitted
here.

The primary goal of the second evaluation was to assess the accuracy of
households assembled from administrative records by comparing them to
Census 2000 enumeration results at the same addresses (Judson and
Bauder, 2002). This was a particularly important analysis for the type
of design that the AREX mounted because the completion of an
administrative records enumeration by canvassing addresses not found in
the records provides little opportunity to correct enumeration errors in
the administrative records themselves. Thus, it was important to learn
as much as possible about the strengths and weaknesses in the
administrative records households with an eye toward future
improvements.

In the course of the household-level analysis, some preliminary
information about a possible use of administrative records in a
conventional census was obtained. The question of interest was: Under
what conditions can administrative records households be substituted for
conventional Nonresponse Followup (NRFU) households, or households for
which occupancy status and household demographics were wholly imputed
("unclassified" households)? This assessment was carried out by matching
the demographic composition of AREX households to Census 2000 households
which were difficult to enumerate in Census 2000. In addition to a
descriptive analysis, there was a prediction-based approach to assess
the ability to predict when an AREX household is likely to
demographically match a census household. Section 3.3 provides a summary
of the results in the report by Judson and Bauder.

## AREX enumeration outcomes

### Methodology {#methodology .unnumbered}

#### Concept {#concept .unnumbered}

The enumeration outcomes analysis provides measures of how well AREX
replicates Census 2000 results at county and subcounty levels focusing
on key demographic characteristics that are important for decennial
census requirements but also relate to the possible use of
administrative records for intercensal and small-area estimation. A
series of research questions provides a conceptual outline of the basic
elements of the evaluation. General questions at larger geographies are
posed first:

-   How well does AREX measure total census population at the county
    level, and how do the results differ by whether the Top-down or
    Bottom-up approaches were used?

-   How do county-level differences between AREX and census differ by
    age, race, sex, and Hispanic origin, as well as between the Top-down
    or Bottom-up approaches?

A related question, how well does AREX measure the voting age population
(age 18+) of state legislative districts, is discussed in Heimovitz,
2002.

In a decennial census, total population counts are needed for
congressional apportionment. The voting age (18+) population by race and
Hispanic origin potentially meets the data requirements for legislative
redistricting. Population counts of persons under age 18 are needed by
states for planning purposes and estimating child poverty rates. Greater
differences between AREX and census counts are more likely at smaller
geographies. But focusing on smaller geographies allows more detailed
analyses of neighborhood characteristics and whether these attributes
are linked with AREX-Census 2000 differences: In particular, how does
the accuracy of tract and block counts compare to county results?

#### Outcome measures {#outcome-measures .unnumbered}

The terms 'undercount' and 'overcount' describe how well AREX counts
match Census 2000 results and have no further connotation. That is,
undercounts and overcounts reflect any of several problems, including
coverage issues, coding, and processing errors. Outcome and predictor
constructs are distinguished and used to highlight AREX-Census 2000
Bottom-up and Top-down differences. The outcome measures used in this
consolidated report are limited to the simple count differences between
AREX and Census 2000 counts and to the algebraic percent error (ALPE).
The full outcomes analysis (Heimovitz 2002) provides additional
measures.

#### Difference {#difference .unnumbered}

The simple difference between AREX and Census 2000 gauges the
county-level over and under-counts:

DIFF(A~i~,C~i~) = A~i~ -C~i~

> where:
>
> A~i~ = AREX tallies in county

C~i~ = Decennial census tallies in county

#### Algebraic percent error (ALPE) {#algebraic-percent-error-alpe .unnumbered}

AREX and Census 2000 counts are the inputs for calculating the algebraic
percent error for the i^th^ county, tract, or block:

$\text{ALPE}(A_{i},C_{i}) = \frac{A_{i} - C_{i}}{C_{i}}$

Where:

> A~i~ = AREX tallies in the i^th^ county, tract, or block; and

C~i~ = Decennial census tallies in the i^th^ county, tract, or block

Two problems can occur when computing ALPEs: zero blocks and inflated
ALPEs. Zero blocks occur when AREX reports in a particular block at
least one person having a particular characteristic but census does not.
Because Census 2000 is being used as the standard and is the
denominator, ALPEs for zero blocks are undefined. For the purpose of
block comparisons, zero blocks are omitted from the analyses. However,
county and tract-level counts and comparisons include these blocks
because they are aggregated at larger geographies.

Inflated ALPEs can sometimes occur when Census 2000 blocks have very
small counts and tend to produce large, positive ALPEs, despite small
differences between AREX and Census 2000 counts. For example, C=1 and
A=3 yields an ALPE=2. Such a large ALPE is quite unlikely when the size
of C\--the number of persons enumerated in the census area\--is large.
Small census counts are not unlikely, for example, for racial minorities
in sparsely populated areas. To reduce the impact of unusually large
ALPEs, ALPEs were trimmed (topcoded) by setting all values greater than
the 95^th^ percentile of the ALPEs across the areas in the analysis to
the value of the 95^th^ percentile. Still, care should be taken in
interpreting results for those analyses where the population is sparsely
populated within the geographic units of interest.

There is an additional problem when computing differences or ALPEs for
racial subpopulations. The problem stems from the differences between
AREX and Census 2000 classifications. Both AREX and Census 2000 have the
four traditional categories: White, Black, American Indian/Alaskan
Native, and Asian/Pacific Islander[^8]. But Census 2000 permits
respondents two additional options: "multiple race," and "Other race."
These additional groups were quite small for Maryland. For Colorado, the
multi and other race groups were much larger, encompassing more than 8
percent of the Census 2000 population for El Paso County. In the
following outcomes analysis, no attempt was made to distribute either
additional race category across the four common categories. Excluding
Census 2000 respondents with multi or other race could result in
positive differences and ALPEs for race subgroups, especially for
minority groups, that might not have occurred had the AREX and census
classifications been the same.

########  {#section-16 .unnumbered}

### Descriptive analyses {#descriptive-analyses .unnumbered}

This section is intended to be a top-level, descriptive summary of
AREX-Census 2000 differences, by county, tract, and block. County-level
counts and proportions are compared and display the raw, untransformed
numbers not shown in the multivariate analyses. The count differences
describe the aggregate under- and over-counts of age, race, sex, and
Hispanic origin categories, while the ALPEs show the contribution these
categories have on the under- and overcounts. One important aspect of
the bivariate analyses is the ecological variation within the AREX
counties. Thematic maps profile the heterogeneous AREX-Census 2000
differences in block-level total population counts.

AREX Top-down counts include persons later identified in Bottom-up as
group quarters residents; Bottom-up and Census 2000 counts exclude group
quarters residents and differ somewhat from counts in earlier tables for
which there were no exclusions.

#########  {#section-17 .unnumbered}

#### County-level count results {#county-level-count-results .unnumbered}

*Total population*

Total population results for the two Maryland counties and three
Colorado counties are reported in Table 17.

[]{#__RefHeading___Toc54420970 .anchor}Table 17. Top-down and Bottom-up
Counts of Total Household Population by County

<table style="width:100%;">
<colgroup>
<col style="width: 11%" />
<col style="width: 9%" />
<col style="width: 11%" />
<col style="width: 13%" />
<col style="width: 9%" />
<col style="width: 0%" />
<col style="width: 10%" />
<col style="width: 9%" />
<col style="width: 13%" />
<col style="width: 9%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td></td>
<td colspan="2"></td>
<td></td>
<td></td>
<td></td>
<td colspan="2"></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td colspan="2">Top-down Results</td>
<td></td>
<td></td>
<td></td>
<td colspan="2">Bottom-up Results</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>AREX</td>
<td>Census</td>
<td>Difference</td>
<td>ALPE</td>
<td></td>
<td>AREX</td>
<td>Census</td>
<td>Difference</td>
<td>ALPE</td>
</tr>
<tr class="even">
<td><p>Baltimore</p>
<p>County</p></td>
<td>696,183</td>
<td>736,652</td>
<td>-40,469</td>
<td>-5.5%</td>
<td></td>
<td>728,205</td>
<td>736,652</td>
<td>-8,447</td>
<td>-1.1%</td>
</tr>
<tr class="odd">
<td><p>Baltimore</p>
<p>City</p></td>
<td>570,648</td>
<td>625,401</td>
<td>-54,753</td>
<td>-8.8%</td>
<td></td>
<td>636,729</td>
<td>625,401</td>
<td>+11,328</td>
<td>+1.8%</td>
</tr>
<tr class="even">
<td><p>Douglas</p>
<p>County</p></td>
<td>148,270</td>
<td>175,300</td>
<td>-27,030</td>
<td>-15.4%</td>
<td></td>
<td>169,640</td>
<td>175,300</td>
<td>-5,660</td>
<td>-3.2%</td>
</tr>
<tr class="odd">
<td><p>El Paso</p>
<p>County</p></td>
<td>456,891</td>
<td>501,533</td>
<td>-44,642</td>
<td>-8.9%</td>
<td></td>
<td>494,253</td>
<td>501,533</td>
<td>-7,280</td>
<td>-1.5%</td>
</tr>
<tr class="even">
<td><p>Jefferson</p>
<p>County</p></td>
<td>473,495</td>
<td>519,326</td>
<td>-45,831</td>
<td>-8.8%</td>
<td></td>
<td>503,622</td>
<td>519,326</td>
<td>-15,704</td>
<td>-3.0%</td>
</tr>
</tbody>
</table>

AREX undercounted all five counties in the Top-down and four of five
counties in Bottom-up. The greatest Top-down differences were in
Baltimore City and Jefferson County. Bottom-up undercounts are much
smaller than Top-down undercounts in all five counties for total
population and demographic characteristics.

[]{#__RefHeading___Toc58826860 .anchor}Figure 2. Net Population
Difference by Sex, County, and Collection Method\--CO.

![](media/image2.png){width="6.636805555555555in"
height="3.7006944444444443in"}

*Sex*

[]{#__RefHeading___Toc58826861 .anchor}Figure 3. Net Population
Difference by Sex, County, and Collection Method\--MD.

![](media/image3.png){width="6.8805555555555555in"
height="3.3756944444444446in"}

Males and females are undercounted by the Top-down method in all five
counties. Bottom-up undercounts are much smaller for all counties, and
males are overcounted in Baltimore City. (Baltimore CTY is Baltimore
County.)

*Age*

[]{#__RefHeading___Toc58826862 .anchor}Figure 4. Net Population
Difference by Age, County and Collection Method\--MD.

![](media/image4.wmf)

[]{#__RefHeading___Toc58826863 .anchor}Figure 5. Net Population
Difference by Age, County and Collection Method\--CO.

![](media/image5.png){width="6.626388888888889in"
height="3.5381944444444446in"}

In the Maryland counties, Top-down overcounts the 75+ population and
undercounts other age groups; Bottom-up overcounts the 20-44, and 65+
age groups and undercounts all other age groups. In both Maryland and
Colorado, Top-down undercounts are greatest for the 0-19 age groups and
show the greatest improvements for Bottom-up counts relative to
Top-down. In the Colorado counties, generally, age 20-24 and 65+ age
groups are overcounted and other age groups are undercounted for both
Top-down and Bottom-up methods.

*Race*

In the Maryland counties, Hispanics were overcounted and other minority
race groups were generally undercounted in Top-down and Bottom-up. In
the Bottom-up method, Whites and Blacks were overcounted in Baltimore
City where Blacks are a majority. In the Colorado counties, Blacks and
APIs were generally overcounted while other race categories and
Hispanics were undercounted in Top-down and Bottom-up methods.

[]{#__RefHeading___Toc58826864 .anchor}Figure 6. Net Population
Difference by Race, County and Collection Method\--MD.

![](media/image6.png){width="6.31875in" height="3.1881944444444446in"}

[]{#__RefHeading___Toc58826865 .anchor}Figure 7. Net Population
Difference by Race, County and Collection Method\--CO.

![](media/image7.png){width="6.5159722222222225in"
height="3.7708333333333335in"}

One general pattern from tables and figures above is the relationship
between population shares and AREX under- and overcount. Race and
Hispanic origin groups with smaller shares tend to be overcounted, and
groups with larger shares tend to be undercounted. Examples of
overcounts are Hispanics in the Maryland counties, Whites in Baltimore
City, and Blacks and APIs in the CO counties.

The very large Top-down undercount of Blacks in Baltimore City is due
largely to the inappropriate use of the race model for children in the
Top-down process. The Black count changes dramatically in the Bottom-up
in which children with unknown race are generally assigned the race of
the primary taxpayer.

#### County-level ALPE results {#county-level-alpe-results .unnumbered}

The county-level analysis builds on the AREX-Census 2000 count results
by examining the algebraic percent error (ALPE). The ALPE measure
provides a different view of the county-level results because the
calculation method uses census group totals as bases and provides a
standardized gauge for comparing differences between Top-down and
Bottom-up, as well as between counties.

*Total Population*

All county Bottom-up ALPEs were smaller than Top-down ALPEs; Bottom-up
ALPE improvements were variable: both Douglas County and Baltimore City
had Top-down ALPEs of -8.8 percent, but Bottom-up for Douglas County was
-3.2 percent compared to +1.8 percent for Baltimore City. The smallest
total population Bottom-up ALPE was in Baltimore County (-1.1 percent);
the largest Bottom-up ALPE was in Douglas County

(-3.2 percent).

*Sex*

[]{#__RefHeading___Toc58826866 .anchor}Figure 8. Sex ALPE by County and
Collection Method\--MD.

![](media/image8.png){width="6.31875in" height="3.3756944444444446in"}

Male and female Bottom-up ALPEs were relatively small in all five
counties and ranged from -- 4.8 to + 4.2 percent.

Sex proportions were undercounted in all counties (except Baltimore City
males) and generally are unbiased, reflecting the magnitude of total
county-level proportions. Female undercounts were slightly worse than
male undercounts and generally had a marginal difference of less than 2
percent in Bottom-up. Some women may be less active within the
administrative records systems. For example, some studies indicate that
lifetime participation in the labor force varies by a woman's child
raising and care giving experiences, health status, and race/ethnicity
(Flippen and Tienda, 2000). However, lower mortality rates for women
might offset lower labor force participation with respect to AREX/Census
2000 comparisons.

[]{#__RefHeading___Toc58826867 .anchor}Figure 9. Sex ALPE by County and
Collection Method\--CO.

![](media/image9.png){width="6.763888888888889in"
height="3.513888888888889in"}

*Age*

Generally, younger age groups (especially the 0-4 age group) had the
largest negative ALPEs in all five counties. Bottom-up ALPEs for the 0-4
age group ranged from --33.9 percent in Jefferson County to --23.4
percent in Baltimore City. Older age groups (65-74, 75-84, and 85+)
tended to have positive ALPEs that increased by age. Bottom-up ALPEs
were generally smaller due to the Census-pull households that replaced
unmatched Census 2000 addresses.

[]{#__RefHeading___Toc58826868 .anchor}Figure 10. Age ALPE by County and
Collection Method\--MD.

![](media/image10.png){width="6.763888888888889in"
height="3.082638888888889in"}

[]{#__RefHeading___Toc58826869 .anchor}Figure 11. Age ALPE by County and
Collection Method\--CO.

![](media/image11.png){width="6.891666666666667in"
height="3.3756944444444446in"}

The large negative ALPEs for children and the large positive ALPES for
older groups are due mostly to the weaknesses in the administrative
records discussed in Section 2: missing births and deaths and migration
as a result of the cutoff dates of the administrative record files used
in the AREX, missing dependents on IRS 1040s, and missing children of
parents who did not have to file 1040s or were otherwise not found in
the administrative records. Persons aged 65+ were generally overcounted
in all five counties, and persons age 85+ displayed Bottom-up overcounts
ranging from about 2 percent to 36 percent\--77 percent in
less-populated Douglas County. Because the 85+ population is relatively
small, the denominators of the ALPE calculations are likely to be small
and potentially inflate ALPE measures.

The 20-24 year age group also has large positive ALPEs in some of the
AREX counties. This might be due to the handling of special populations
to which this age group belongs: college and university population, and
the military. College-age persons whose residence may have been reported
at a parent's IRS tax address may actually reside on a campus in a
different area. Removing group quarters from the Census 2000 counts but
not from the Top-down counts would bias Top-down ALPEs in the positive
direction. Removing group quarters from the Bottom-up counts would still
leave dependents claimed on IRS 1040 at the wrong location with respect
to decennial residency rules.

Douglas County appears to be a special case. The Census 2000 population
age 20-24 is 3.1 percent, less than half that of Colorado (7.1 percent)
and the national average (6.7 percent). But the Air Force Academy and
several other schools are located in Douglas County. The large Top-down
ALPE may be due to the fact that group quarters were not removed from
the administrative records. Although there was an attempt to remove
group quarters from Bottom-up enumeration, the large Bottom-up ALPE for
age 20-24 suggests this may not have been fully successful.

*Race*

It is difficult to interpret Top-down race ALPEs because of the
confounding effects of general undercounts, especially for children, and
the use of the race model for children under 15 with "other" or unknown
race in the administrative records. The following discussion will focus
on the Bottom-up results.

There are a number of reasons for the patterns of Bottom-up race and
Hispanic origin ALPEs. First is the use of the race model. As discussed
earlier, the race model was a national-level model and variation about
its predictions can be expected. The use of the model in small
geographic areas would tend to overstate the number of persons in those
race groups that are less than the national average and understate the
number of persons in groups that are above the national average. When
modeled race was assigned to children from an adult in the same
household, the result would be reinforced.

[]{#__RefHeading___Toc58826870 .anchor}Figure 12. Race ALPE by County
and Collection Method\--MD.

![](media/image12.png){width="6.997222222222222in"
height="3.2534722222222223in"}

[]{#__RefHeading___Toc58826871 .anchor}Figure 13. Race ALPE by County
and Collection Method\--CO.

![](media/image13.png){width="6.8493055555555555in" height="3.64375in"}

It is important to keep in mind that the race model was used for only
those adults whose administrative records did not provide a race other
than "other" or unknown. Table 3 in Section 2.2 shows the proportion of
adults and children with imputed race in each of the test sites. The
number of adults with imputed race ranged from about 3 to 9 percent and
was substantially lower in Maryland than Colorado. For Hispanic origin,
the imputation was used well over 90 percent of the time because, for
the most part, administrative records provide no direct measure of
ethnicity.

Other factors possibly affecting Bottom-up ALPE race patterns were: The
possible correlation between weaknesses in AREX population coverage and
race or Hispanic origin, unaccounted for migration and demographic
changes due to the age of the administrative records files, the possible
duplication of persons due to the Census Pull, the problem of comparing
AREX and Census 2000 race groups because the latter allows "multi" and
"other" and the former does not, and the positive ALPE bias for cells
with small denominators. Examining several of the race ALPE results
shows the complexity of the possible explanations.

For the Bottom-up, Black ALPEs were positive in all three Colorado
counties and Baltimore City and negative in Baltimore County (where
blacks are a large minority race group). The overcount of Blacks in
Colorado was most likely due to the race model because the proportion of
Blacks in Colorado was much smaller than the national average; and at
the same time, the proportion of adults in Colorado with imputed race
was relatively high, ranging from 6 to 9 percent. The undercount of
Blacks in Baltimore County might be due in part to the use of the race
model; but it might also be due in part to the migration of Blacks from
Baltimore City to the County in the period between the administrative
records cutoffs and April 1, 2000. The reasons for the overcount of
Blacks in Baltimore City are less clear but might also be due to
unaccounted for migration of Blacks from the city. An overcount is the
reverse of what would be expected if the race model were the main cause,
and the proportion of adults with modeled race was under 3 percent.

The ALPEs for APIs were positive in all three Colorado counties and
Baltimore City and negative in Baltimore County. This would appear again
to be a race model effect, except for Baltimore County, because
nationally, all five counties have API proportions below the national
average. Evidently, the net effect of these differences increased the
size of the Census 2000 API counts enough so that API ALPEs for all of
the AREX sites would have been negative had they been calculated from
these distributions. In any case, it is simply a matter that ALPE is
sensitive for small population subgroups.

Concerning APIs, the substantial negative ALPEs in all counties were not
unexpected. Identifying AIAN race is weak in the administrative records,
except, in areas around reservations, and AIAN prediction was the
weakest part of the race model as well.

Hispanic ALPEs were positive in both MD counties where they are a small
minority group and negative in all three CO counties where Hispanics are
the largest minority group. The model for Hispanic origin was applied to
about 97 percent of adults in Maryland and is most likely the reasons
for the substantial overcounts there. Again, one might have also
expected small overcounts in Colorado were model use the main factor.
(See the discussion of Hispanics in Section 2) But the substantial
undercounts suggest that other factors may be at work such as high birth
rates and net in-migration of Hispanics in the period missed by the
administrative records used in the AREX.

#### Tract ALPE distributions {#tract-alpe-distributions .unnumbered}

Error: Reference source not found shows the ALPE distributions for the
five AREX counties. In all sites other than Baltimore City, more than 70
percent of tracts had AREX total population counts within +/-5 percent
of census results, and more than 95 percent of tracts had counts within
25 percent of census results. Baltimore City had less accurate results
with about 50 percent of tracts exceeding +/-5 percent of census
results. A larger proportion of tracts had moderate and large ALPE
undercounts (less than --5 percent) compared to overcounts.

[]{#__RefHeading___Toc58826872 .anchor}Figure 14. Distribution of Tracts
with Under- and Overcounts of Total Population.

![](media/image14.png){width="6.795833333333333in"
height="3.7090277777777776in"}

Though the tract-level ALPEs for the total population resemble
county-level results, the distributions indicate more Baltimore City
tracts were overcounted. It is unclear whether these overcounts are
related to persons who were actually uncounted in the census, or more
likely, weaknesses in AREX processing. Households may have been added
through the Census Pull process that replaced unmatched addresses that
existed in other tracts or addresses.

#### Block ALPE distributions  {#block-alpe-distributions .unnumbered}

The block-level ALPE results describe the accuracy of counts at the
smallest geographic level and relative to counties and tracts. The main
problem with this type of comparison is the ALPE denominator potentially
inflates block-level ALPEs for small population subgroups and especially
minorities. This inflation is likely to be greater than found in the
tract-county comparisons. A second issue affecting comparisons is the
exclusion of blocks where Census 2000 did not identify persons with a
particular attribute (zero blocks). County and tract ALPEs include
blocks with zero counts because these blocks were accumulated into
larger geographies. However, the block-level ALPEs used the reduced set
of blocks and the results may be quite different when comparing the
ALPEs at various geographies.

[]{#__RefHeading___Toc58826873 .anchor}Figure 15. Distribution of Blocks
with Under- and Overcounts of Total Population.

![](media/image15.png){width="6.499305555555556in"
height="4.221527777777778in"}

AREX was less accurate in estimating blocks than tracts in all counties.
Population totals for 18 to 39 percent of blocks were within 5 percent
of Census 2000, and about 85 percent were within 25 percent of the
census. Douglas County had the best results at the 5 percent criterion
and Baltimore County was best at the 25 percent criterion. In the
Maryland counties, slightly more blocks had moderate or large overcounts
(ALPEs exceeding 5 percent), compared to the Colorado counties where
more blocks had moderate undercounts (-5 to -24 percent).

The AREX counts were less accurate at the block-level. Population counts
are likely to be less accurate in smaller areas due to incorrect
assignment of households at tracts and blocks that average out for
county-level counts. This is demonstrated by the greater number of
moderate and large ALPEs and indicates how smaller denominators and AREX
processing weaknesses influenced the comparisons. Though zero blocks
were excluded and fewer blocks met the 5 percent criterion, a large
proportion of blocks met the 25 percent criterion in all five counties.

####  {#section-18 .unnumbered}

#### Geospatial Tract-Level Heterogeneity {#geospatial-tract-level-heterogeneity .unnumbered}

**Error: Reference source not found** and Error: Reference source not
found exhibit the geographic distribution of AREX-Census 2000 tract
ALPEs for total population counts. Baltimore City is the nucleus of the
MD AREX site and the most urban of all the sites. It has numerous tracts
with large under and overcounts. The tract-level total population was
clearly measured better in Baltimore County. There is also evidence of
tracts clustering by size of under and overcounts. Downtown Baltimore
and Towson include islands of moderate and large undercounts, while
clustered moderate overcounts are more frequent in other parts of the
City and County. Denver, in the north, and Colorado Springs are
metropolitan centers in the CO site (Map 2). Generally, the tract-level
CO population was counted more accurately in the suburbs of each city,
while urban and rural tracts tended to have moderate undercounts.

[]{#__RefHeading___Toc58826874 .anchor}Figure 16. AREX - Census Total
Population ALPEs: Maryland Tracts.

![](media/image16.wmf){width="8.191666666666666in"
height="5.195138888888889in"}

[]{#__RefHeading___Toc58826875 .anchor}Figure 17. AREX - Census ALPEs
for the Total Population: Colorado Tracts.

![](media/image17.wmf){width="8.714583333333334in"
height="5.527777777777778in"}

### Summary and conclusions  {#summary-and-conclusions .unnumbered}

The forgoing analysis provided measures of how well AREX replicated
Census 2000 results at several geographic levels focusing on key
demographic characteristics that are important for a decennial census.
As expected, the Bottom-up method performed better than the Top-down
method because of the simulated canvassing of households (the Census
Pull) at addresses not found in the administrative records. The
Bottom-up process undercounted total population in all sites except
Baltimore City. Algebraic percent errors for county-level population
totals were less than 5 percent though the results were not as good for
subcounty and demographic subgroups.

If the Bottom-up process were unbiased and counted all demographic
groups in the same way, ALPEs for all demographic categories would have
had the same relative size. As with the total population, males and
females were undercounted in all sites except Baltimore City, but the
female undercounts were slightly greater than male undercounts. Age
group ALPEs show more variability with most groups undercounted except
the 20-24 group and the oldest age groups. Generally the size of the
undercounts increased with decreasing age. These patterns did not appear
to be site-specific and are the result of the weaknesses of the
administrative records and certain AREX processing decisions as
discussed in Section 2. Overcounts for the oldest old and undercounts
for the youngest persons suggest that much more timely birth and death
information must be obtained. And the special enumeration requirements
for populations such as college students, the military and persons in
nursing homes must be incorporated into administrative records
processes.

Bottom-up tract-level total population ALPE results indicated a good
correspondence between AREX and Census 2000 (70 percent of tracts met
the 5 percent criterion; and 95 percent met the 25 percent criterion),
though a sizable number of tracts had moderate and large ALPE
undercounts. The block-level ALPE results provided the least accurate
measure of total population (38 percent of blocks met the 5 percent
criterion; and about 85 percent met the 25 percent criterion), compared
to tract and county results[^9].

The regression results confirm some of the key findings from the
univariate and bivariate analyses. Among the mobility variables, both
vacancy rate and rental rate were associated with under and overcounts.
Generally, rental rate had a greater association with undercounts and
vacancy rates had a greater association with overcounts in both AREX
sites. As observed in the bivariate analyses, large proportions of
persons under age 5 and 20-24 were associated with undercounts in both
sites. And in CO, large proportions of persons age 65+ were associated
with overcounts, other factors held constant (Heimovitz, 2002).

## Household-level analysis

### Methodology {#methodology-1 .unnumbered}

#### Concept {#concept-1 .unnumbered}

The general goal of the household-level analysis (Judson and Bauder,
2002) was to assess how well households formed from administrative
records matched those from Census 2000 at the same addresses in the
Hundred Percent Detail (HDF) file. The analysis did not include group
quarters or the households found at addresses not in the administrative
records files. An assessment of group quarters was beyond the scope of
this analysis because AREX did not mount the operations that would have
been needed to enumerate special populations in an administrative
records census context. And, the Bottom-up "NRFU" households could not
be evaluated because the canvassing was simulated by simply including
the Census 2000 households at the relevant addresses.

The household-level analysis assessed the ability of AREX administrative
records households to match the demographic composition of all
households, but there was a special focus on Census 2000 households that
required a nonresponse followup and Census 2000 unclassified households.
In Census 2000, addresses that did not respond to the mailout had to be
enumerated by nonresponse followup procedures. NRFU addresses are the
most expensive to enumerate and may represent the most vulnerable
segment of Americans. The household-level analysis provided a
preliminary look at the conditions under which households formed from
administrative records could be used for conventional NRFU households,
obviating the need for fieldwork in those cases.

Addresses that had the status "unclassified" in Census 2000 were those
for which so little information was available that occupancy status had
to be imputed, and, conditional on being imputed "occupied," the entire
household, including characteristics, had to be imputed as well. This
treatment of unclassified households was the subject of a lawsuit
reaching the U.S. Supreme Court (*Utah v. Evans*), in which the
plaintiffs objected to the imputation substituting for enumeration.
Although the census methodology prevailed, the possibility of
enumerating these types of addresses by administrative records might
provide a useful alternative to traditional imputation. This section
provides some information comparing administrative records enumeration
and Census 2000 imputations for the Census 2000 unclassified households
in the AREX test sites.

#### Special terminology {#special-terminology .unnumbered}

For this section, the term "census household" refers to the persons
enumerated at an address in Census 2000. The term "AREX household"
refers to persons at an administrative records address. "Household size"
refers to the number of people in the housing unit. For convenience,
these definitions are applied to vacant housing units, so that when a
Census or AREX address contains no people, the housing unit is assigned
a household size of zero. We use the term "imputed household" for
unclassified addresses whose occupancy status and household
characteristics have been imputed.

Pairs of addresses (AREX and Census) that were matched by computer or
clerical processes are referred to as "linked" housing units. The term
"linked households" is used when comparing the properties of people
within linked housing units. The term "demographic match" is used when
two households have the same age, race, sex, and Hispanic origin
distribution.

Finally, the term "AREX data" is used for administrative data obtained
from the Bottom-up operations (i.e., including DMAF linkage, clerical
review and FAV). The term "Census data" is used for data obtained from
the Census 2000 HDF file.

#### Descriptive analysis {#descriptive-analysis .unnumbered}

The household-level evaluation used both descriptive analyses and
multiple regression analysis to assess the coverage and accuracy of AREX
households. Descriptive analyses were performed for linked households in
all five AREX counties and for the Census 2000 NRFU and imputed
households in the test sites. These analyses provided the following
evaluations:

-   Coverage by AREX of its intended universe by determining the number
    and proportion of Census 2000 addresses that were matched by AREX
    addresses;

-   Characteristics of Census 2000 households associated with
    AREX/Census matched addresses;

-   Comparison of AREX and Census 2000 distributions of household size
    and household demographic characteristics;

-   Characteristics of AREX households associated with AREX/Census 2000
    household-to-household comparisons, including such properties as the
    presence of a person in the household of a particular race or
    ethnicity, and the presence of a person with a characteristic that
    was imputed in AREX.

#### Prediction model {#prediction-model .unnumbered}

To learn more about the characteristics of administrative records
households that match the census and to take a first look at the
potential uses of administrative records data to substitute for some
part of the nonresponse followup or unclassified households in a
conventional census, a logistic regression model was developed with the
AREX/Census 2000 linked households as the units of analysis. The
functional form of the model is Logit (Match=1\|x) = xβ where Match is a
dichotomous dependent variable, x is a vector of regressors, and β is a
vector of constants to be estimated. For each linked address, the
dependent variable was defined as follows:

$$\text{Match} = \left\{ \begin{matrix}
1 & \begin{matrix}
\text{if the fully crossed age } \times \text{race  } \times \text{sex } \times \text{ Hispanic  origin distributions in the} \\
\text{linked Census household match the AREX }\text{household;}
\end{matrix} \\
0 & \text{otherwise}\text{.}
\end{matrix} \right.\ $$

This measure was based on the distribution of personal characteristics
within an address and not on matches of individual persons. An address
in AREX and in the census that had exactly the same distributional
characteristics but were composed of entirely different persons would
still receive a match score of 1. The simpler dependent variable\--1 if
all persons were the same, 0 otherwise\--was not used because the AREX
operations did not provide for matching individuals from AREX and census
enumerations. Considering that the age distribution is in 5-year groups,
the match definition used would appear to provide a result very close to
an exact person match.

The regressors include characteristics of AREX households and
characteristics of the linked addresses, representing the kind of
information that would be available were data from administrative
records to be used in support of a conventional census.

#### Limitations {#limitations .unnumbered}

The principal limitations on the ability to link addresses and
demographically match households stemmed from the same deficiencies of
the AREX administrative records files discussed in previous sections.
First, the administrative data extracts were taken a year or more before
census day. This means that movers, births, deaths, immigration and
emigration, new housing, abandoned and demolished housing were
unaccounted for a period of 12 or more months prior to census day.
Second, many children are unaccounted for in administrative records at
the national level; and therefore, AREX 2000 had difficulty enumerating
children, generally, and, specifically, by virtue of the time lag
problem and the limited demographics available for children on the
Numident file (Miller, Judson, and Sater, 2000). Third, the race
measurement and reporting deficiencies of the administrative records and
differences in race measurement between AREX and the census presented
serious challenges to comparisons matching race and Hispanic origin
between members of AREX and census households. Finally, virtually all
persons identified as having Hispanic origin in the AREX were imputed as
such thus weakened the comparisons.

The AREX FAV had little impact on the household-level analysis; and a
100 percent FAV, if actually carried out, would have had little impact
as well. Persons at administrative records addresses that would have
been completely lost to the AREX as a result of the FAV would have had
no impact on the household-level analysis since none of their addresses
match the DMAF. And, as discussed in Section 2, there were would have
been very few persons who remained in the enumeration but at different
addresses as a result of the FAV.

Finally, deficiencies in administrative records and HDF addresses (for
example, address duplication) and address matching technology resulted
in a number of cases in which more than one administrative record
address matched the same HDF address and vice versa. All of the
administrative records addresses that matched the HDF but not on a
one-to-one basis were excluded from the analyses.

### Descriptive Analysis {#descriptive-analysis-1 .unnumbered}

#### AREX and Census address linkage {#arex-and-census-address-linkage .unnumbered}

In the five counties covered by the experiment, the Census 2000 HDF
contained 1,092,460 housing units (HUs) and 1744 group quarters (GQs),
the latter excluded from this analysis. 24,584 (2.3 percent) of census
households were "imputed households," and 360,914 (33.0 percent) were in
the Census 2000 NRFU universe.

Of the 1,065,031 AREX addresses 992,865 were linked with addresses that
existed in Census HDF; but 103,227 of the AREX addresses did not have a
one-to-one link and were also excluded. This left 889,638 linked AREX
addresses available for the household-level analysis. They represented
81.4 percent of census addresses.

Table 18 provides data on overall address linkage. AREX housing units
(i.e. addresses) were linked with 84.0 percent of the 1,017,273 occupied
census housing units. AREX housing units were linked with 46.4 percent
of the 75,187 vacant census housing units. About 88 percent of AREX
vacant units were found to be occupied by the census[^10]. This confirms
the discussion in Section 2 in which it was suggested that the AREX
vacant addresses should have been canvassed as part of the Bottom-up
process.

[]{#__RefHeading___Toc54420971 .anchor}Table 18. Coverage by AREX of
Census housing units.

+---------------+----------+--------------+--------------+------------+
|               | *        | **Linked     | **Linked     | **Linked   |
|               | *Total** | with AREX    | with AREX    | with AREX  |
|               |          | housing      | occupied     | vacant     |
|               |          | units**      | housing      | housing    |
|               |          |              | units**      | units (%   |
|               |          | **(% of      |              | of         |
|               |          | total)**     | **(% of      | total)**   |
|               |          |              | total)**     |            |
+---------------+----------+--------------+--------------+------------+
| Census        | 1        | 889,638      | 813,688      | 75,950     |
| housing units | ,092,460 |              |              |            |
|               |          | (81.4%)      | (74.5%)      | (7.0%)     |
+---------------+----------+--------------+--------------+------------+
| Occupied      | 1        | 854,741      | 787,802      | 66,939     |
| Census        | ,017,273 |              |              |            |
| housing units |          | (84.0%)      | (77.4%)      | (6.6%)     |
+---------------+----------+--------------+--------------+------------+
| Vacant Census | 75,187   | 34,897       | 25,886       | 9,011      |
| housing units |          |              |              |            |
|               |          | (46.4%)      | (34.4%)      | (12.0%)    |
+---------------+----------+--------------+--------------+------------+

AREX's coverage of the Census NRFU universe was not as good as its
coverage of the non-NRFU universe. AREX housing units were linked with
70.9 percent of the 360,914 Census NRFU housing units, compared with
88.4 percent of the Census non-NRFU housing units. For occupied NRFU
housing units, the coverage rate goes up to 76.7 percent. Table 19
contains more details about AREX's coverage of Census NRFU and non-NRFU
housing units.

[]{#__RefHeading___Toc54420972 .anchor}Table 19. Coverage by AREX of
Census housing units, by NRFU status.

  ------------------ ----------- ------------- ------------- -------------
  **Type of Census   **Total**   **Linked with **Linked with **Linked with
  housing unit**                 AREX housing  AREX occupied AREX vacant
                                 units**       housing       housing
                                               units**       units**

  NRFU               360914      70.9%         60.8%         10.1%

  non-NRFU           716450      88.4%         82.9%         5.5%

  Occupied NRFU      289224      76.7%         67.1%         9.6%

  Occupied non-NRFU  715115      88.5%         83.0%         5.5%

  Vacant NRFU        71690       47.6%         35.2%         12.3%

  Vacant non-NRFU    1335        58.7%         46.3%         12.4%
  ------------------ ----------- ------------- ------------- -------------

^\*^ Excludes 15,096 housing units in Census HDF with unknown NRFU
status.

There were 24,584 imputed census housing units in the AREX test sites.
AREX housing units were linked with 62.3 percent of them. AREX addresses
were linked with 63.2 percent of those that were imputed to have people
in them, and 34.7 percent of those imputed to be vacant. The linkage of
imputed occupied units was about twice that of imputed vacant units,
providing face validity for the Census 2000 imputation.

[]{#__RefHeading___Toc54420973 .anchor}[Table 20. Coverage by AREX of
Census housing units, by imputation status.]{.underline}

  ------------------- ----------- ------------- ------------- -------------
  **Type of Census    **Total**   **Linked with **Linked with **Linked with
  housing unit**                  AREX housing  occupied AREX vacant AREX
                                  units**       housing       housing
                                                units**       units**

  Imputed             24,584      62.3%         51.7%         10.5%

  Non-imputed         1,067,876   81.9%         75.0%         6.9%

  Imputed occupied    23,811      63.2%         52.6%         10.6%

  Non-imputed,        993,462     84.5%         78.0%         6.5%
  occupied                                                    

  Imputed vacant      773         34.7%         25.5%         9.2%

  Non-imputed, vacant 74,414      46.5%         34.5%         12.0%
  ------------------- ----------- ------------- ------------- -------------

The coverage by AREX of NRFU housing units and imputed housing units is
not as good as for non-NRFU and non-imputed housing units. This may be
due to several factors: (1) components of addresses from NRFU and/or
imputed housing units might be generally of lower quality, and thus
harder to match; (2) addresses of these housing units may be of types
that are harder to match, e.g., those in apartment buildings, those on
Rural Routes, or at P.O. boxes; and (3) people in these housing units
may be more likely not to show up on any of the administrative records
used for AREX.

#### AREX and Census household size {#arex-and-census-household-size .unnumbered}

*All occupied households*

*Table 21* shows the distributions of household size for linked and
non-linked occupied households in AREX and for Census. The AREX
distribution of household size was quite similar to the census
distribution.

[]{#__RefHeading___Toc54420974 .anchor}Table 21. Distributions of
household size for Census and AREX for all five AREX counties. Occupied
housing units only.

  ------------------ --------------- ---------- ------------------ ----------
                     **Census**                 **AREX**           

  **Household Size** **Total**       **%^1^**   **Total**          **%^2^**

  1                  276590          27.2%      246726             27.9%

  2                  331472          32.6%      262075             29.6%

  3                  171136          16.8%      155929             17.6%

  4                  142822          14.0%      127295             14.4%

  5                  60988           6.0%       56596              6.4%

  6                  21655           2.1%       22695              2.6%

  7-9                11275           1.1%       12481              1.4%

  10+                1335            0.1%       1625               0.2%

  **All Sizes**      **1,017,273**   **100%**   **885,422**        **100%**
  ------------------ --------------- ---------- ------------------ ----------

1.  Percent of all Census occupied housing units

2.  Percent of all AREX occupied housing units

One salient feature of the data was that among the unlinked housing
units in both Census and AREX, a very high percentage had one person.
One possible explanation of this fact is that a much higher percentage
of one-person households were at basic street addresses at which there
are multiple housing units, and addresses at such basic street addresses
(BSAs) were harder to link.

###  {#section-19 .unnumbered}

*Linked occupied and non-occupied households*

AREX and Census 2000 counted the same number of people in the housing
unit for 51.1 percent of the 889,638 linked households, and AREX was
within one of the census for 79.4 percent of the units. The 51.1 percent
is effectively a ceiling on the percent of linked households that had
exactly the same persons from AREX and Census 2000. Although errors in
address linkage would account for some of the mismatched households, the
deficiencies in administrative records cited earlier in this
report\--missing children, lack of special population operations and the
time gap between the administrative records extracts and census
day\--most likely account for the major part.

For linked NRFU housing units, AREX had the same numbers of persons for
37.0 percent of the units and was within one 69.3 percent of the time.
Evidently, Census 2000 NRFU housing units are more susceptible to AREX
deficiencies than non-NRFU units. In addition, enumeration errors (such
as "curbstoning") in Census 2000 may be higher for these units than for
units that responded to the initial mailout.

For the 15,043 linked imputed occupied households, AREX had the same
count for 31.8 percent, and was within one for 66.8 percent of these
addresses. The low percentage of household-by-household agreement
between AREX and the census for imputed households should be expected
from the error introduced by the imputation.

[]{#__RefHeading___Toc54420975 .anchor}**Table 22. Comparison of Census
and AREX household size, by NRFU status, and by imputation status. For
linked housing units.**

+-------------+--------+--------+--------+--------+--------+--------+
| AREX person | All    | Census | Census | Non-i  | I      | I      |
| count       | Census | no     | NRFU   | mputed | mputed | mputed |
| compared    | h      | n-NRFU | h      | Census | vacant | oc     |
| with Census | ousing | h      | ousing | h      | Census | cupied |
|             | units  | ousing | Units  | ousing | h      | Census |
|             |        | units  |        | units  | ousing | h      |
|             |        |        |        |        | units  | ousing |
|             |        |        |        |        |        | units  |
+-------------+--------+--------+--------+--------+--------+--------+
| Same count  | 4      | 359818 | 94619  | 4      | 71     | 4,784  |
|             | 54,437 |        |        | 49,582 |        |        |
|             |        | (      | (      |        | (      | (      |
|             | (51    | 56.8%) | 37.0%) | (      | 26.5%) | 31.8%) |
|             | .1%)\* |        |        | 51.4%) |        |        |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX one    | 1      | 84269  | 40437  | 1      | 95     | 2,092  |
| higher than | 24,706 |        |        | 22,519 |        |        |
| Census      |        | (      | (      |        | (      | (      |
|             | (      | 13.3%) | 15.8%) | (      | 35.5%) | 13.9%) |
|             | 14.0%) |        |        | 14.0%) |        |        |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX one    | 1      | 85178  | 42353  | 1      | 0      | 3,176  |
| lower       | 27,531 |        |        | 24,355 |        |        |
|             |        | (      | (      |        |        | (      |
|             | (      | 13.4%) | 16.5%) | (      |        | 21.1%) |
|             | 14.3%) |        |        | 14.2%) |        |        |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX 2 or 3 | 64,635 | 36769  | 27866  | 63,024 | 77     | 1,534  |
| higher      |        |        |        |        |        |        |
|             | (7.3%) | (5.8%) | (      | (7.2%) | (      | (      |
|             |        |        | 10.9%) |        | 28.7%) | 10.2%) |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX 2 or 3 | 79,848 | 47938  | 31910  | 77,463 | 0      | 2,385  |
| lower       |        |        |        |        |        |        |
|             | (9.0%) | (7.6%) | (      | (8.9%) |        | (      |
|             |        |        | 12.5%) |        |        | 15.9%) |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX 4 or   | 15,781 | 6486   | 9295   | 15,316 | 25     | 440    |
| more higher |        |        |        |        |        |        |
|             | (1.8%) | (1.0%) | (3.6%) | (1.8%) | (9.3%) | (2.9%) |
+-------------+--------+--------+--------+--------+--------+--------+
| AREX 4 or   | 22,700 | 13158  | 9542   | 22,068 | 0      | 632    |
| more lower  |        |        |        |        |        |        |
|             | (2.6%) | (2.1%) | (3.7%) | (2.5%) |        | (4.2%) |
+-------------+--------+--------+--------+--------+--------+--------+
| TOTAL       | 8      | 6      | 2      | 8      | 268    | 15,043 |
|             | 89,638 | 33,616 | 56,022 | 74,327 |        |        |
|             |        |        |        |        | (100%) | (100%) |
|             | (100%) | (100%) | (100%) | (100%) |        |        |
+-------------+--------+--------+--------+--------+--------+--------+

\* Percents are percents of column total

#### Demographic comparisons of occupied linked households of the same size {#demographic-comparisons-of-occupied-linked-households-of-the-same-size .unnumbered}

In this section, demographic characteristics of linked households are
compared. Because comparisons within households of different sizes are
difficult to interpret, only linked occupied housing units in which AREX
and Census 2000 have the same number of people are considered. There are
454,437 of these housing units representing 42.6 percent of all census
housing units, 42.7 percent of all AREX housing units, and 51.2 percent
of all linked housing units.

Tables 23-25 contain data only for linked households for which AREX and
the census had the same total count. The tables show the frequencies
with which AREX and the census agree for each:

Sex category;

Race category: White, Black, American Indian/Alaskan Native,
Asian/Pacific Islander;

Hispanic origin category, i.e. Hispanic/non-Hispanic;

Five-year age category: 0-4, 5-9, ..., 80-84, 85 and up;

Of the age categories: 0-17, 18-64, and 65 and up.

As expected, the agreements for racial composition and Hispanic origin
composition were good \-- in general, well above 90 percent. Generally
household members tend to be all of one race and Hispanic origin. Also
as expected, agreement rates did decline with household size because the
likelihood of missing or AREX imputed race and different Hispanic origin
imputations increases with number of persons in the household.

Agreement between AREX and the census across 5-year age groups provides
an estimate of the proportion of households with exactly the same
persons because it is improbable that two different households would
agree in age distributions in 5-year categories. About 81 percent of the
445,426 households had the same 5-year category distribution. This is
about 41 percent of all linked households.

The agreement rate for linked households of the same size is
substantially higher for the age group distribution with only three
categories, 0-17, 18-64 and 65 and up due to the increased tolerance for
reporting errors and the greater probability of chance agreement.

[]{#__RefHeading___Toc54420976 .anchor}Table 23. Comparisons between
AREX and Census for demographic groups, for linked households (HH) with
the same number of people only.

+----+-------+--------+--------+--------+--------+--------+----------+
| HH | Total | Equal  | Equal  | Equal  | Equal  | Equal  | Equal    |
| Si | li    | for    | for    | for    | for    | for    | for all  |
| ze | nked, | all    | all    | all    | all    | age    | dem      |
|    | of    | sex    | race   | Hisp.  |        | groups | ographic |
|    | equal | groups | groups | groups | 5-year | 0-17,  | group    |
|    | size  | ^1^    |        |        | age    | 18-64, | s**^3^** |
|    |       |        |        |        | groups | 65+    |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| A  | 44    | 91.2%* | 93.4%  | 94.8%  | 81.3%  | 93.1%  | 80.5%    |
| ll | 5,426 | *^2^** |        |        |        |        |          |
| s  |       |        |        |        |        |        |          |
| iz |       |        |        |        |        |        |          |
| es |       |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 1  | 13    | 92.2%  | 95.1%  | 97.5%  | 82.5%  | 96.1%  | 85.4%    |
|    | 9,292 |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 2  | 15    | 93.8%  | 94.8%  | 95.9%  | 83.9%  | 94.0%  | 84.3%    |
|    | 8,259 |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 3  | 6     | 87.1%  | 90.7%  | 92.3%  | 75.7%  | 88.4%  | 72.2%    |
|    | 0,641 |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 4  | 6     | 89.3%  | 90.7%  | 90.7%  | 80.8%  | 91.7%  | 74.0%    |
|    | 0,181 |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 5  | 2     | 86.8%  | 88.9%  | 89.3%  | 77.2%  | 89.0%  | 69.5%    |
|    | 0,723 |        |        |        |        |        |          |
+----+-------+--------+--------+--------+--------+--------+----------+
| 6  | 5,359 | 80.4%  | 86.0%  | 86.0%  | 68.0%  | 81.8%  | 59.2%    |
+----+-------+--------+--------+--------+--------+--------+----------+
| 7+ | 971   | 56.8%  | 80.8%  | 83.0%  | 28.7%  | 52.7%  | 28.7%    |
+----+-------+--------+--------+--------+--------+--------+----------+

^1\ I.e.,\ the\ AREX\ and\ Census\ households\ have\ the\ same\ number\ of\ males\ and\ the\ same\ number\ of\ females^

^2^ Percents are percents of the Total column

^3^ Both sex groups, all race groups, both Hispanic origin groups, and
age groups 0-17, 18-64, 65+

Table 24 shows that there was less AREX to Census 2000 agreement for
NRFU households than for other census households, overall and
controlling for size. Based on the 5-year age group match for Census
NRFU households, only about 19 percent of AREX households linked with
Census 2000 NRFU households seemed to have exactly the same persons. As
expected there is even less agreement in household characteristics
between AREX and Census imputed households (Table 25).

[]{#__RefHeading___Toc54420977 .anchor}Table 24. Comparison of AREX and
Census demographic composition of households. For linked households with
the same number of people only, by size.

  ------ ---------- --------- ------------- -------- -------- -------- ------------- --------------
  HH                Total     Equal for all Equal    Equal    Equal    Equal for age Equal for all
  Size                        sex           for all  for all  for all  groups        demo-graphic
                              groups^1,2^   race     Hisp.    5-year   0-17,18-64,   groups^3^
                                            groups   groups   age      65+           
                                                              groups                 

  All    NRFU       85,774    81.0%         87.7%    92.3%    58.1%    84.9%         63.4%

         non-NRFU   359,652   93.7%         94.7%    95.3%    86.9%    95.0%         84.6%

  1      NRFU       31,313    82.5%         89.3%    95.7%    57.5%    91.1%         68.9%

         non-NRFU   107,979   95.0%         96.8%    98.1%    89.7%    97.5%         90.2%

  2      NRFU       24,499    83.7%         88.5%    92.7%    58.6%    83.6%         64.9%

         non-NRFU   133,760   95.7%         96.0%    96.5%    88.6%    95.9%         87.9%

  3      NRFU       12,549    75.7%         85.6%    89.4%    54.3%    77.1%         54.8%

         non-NRFU   48,092    90.1%         92.1%    93.0%    81.4%    91.4%         76.8%

  4      NRFU       11,423    79.8%         86.3%    88.4%    63.2%    83.3%         60.2%

         non-NRFU   48,758    91.5%         91.7%    91.2%    84.9%    93.7%         77.3%

  5      NRFU       4,473     78.1%         84.9%    87.2%    60.4%    80.0%         56.8%

         non-NRFU   16,250    89.2%         90.1%    89.9%    81.8%    91.4%         73.0%

  6      NRFU       1,269     71.0%         80.4%    83.0%    54.0%    73.1%         46.8%

         non-NRFU   4,090     83.4%         87.8%    86.9%    72.4%    84.6%         63.0%

  7+     NRFU       248       53.6%         79.8%    81.2%    27.0%    47.6%         24.6%

         non-NRFU   723       58.0%         81.2%    80.0%    29.3%    54.5%         30.2%
  ------ ---------- --------- ------------- -------- -------- -------- ------------- --------------

1.  I.e., the AREX and Census households have the same number of males
    > and the same number of females.

2.  Percents are percents of Total.

3.  Both sex groups, all race groups, both Hispanic origin groups, and
    > age groups 0-17, 18-64, 65+.

[]{#__RefHeading___Toc54420978 .anchor}Table 25. Comparison of AREX and
Census demographic groups within households. For linked households with
the same number of people only, by size.

  ------ --------- --------- -------- -------- -------- -------- ------------- --------------
  HH               Total     Equal    Equal    Equal    Equal    Equal for age Equal for all
  Size                       for all  for all  for all  for all  groups        demo-graphic
                             sex      race     Hisp.    5-year   0-17,18-64,   groups
                             groups   groups   groups   age      65+           
                                                        groups                 

  All    Imputed   4,784     49.6%    74.9%    91.7%    7.0%     60.7%         23.0%

         Not       440,642   91.7%    93.6%    94.8%    82.1%    93.4%         81.2%
         imputed                                                               
  ------ --------- --------- -------- -------- -------- -------- ------------- --------------

#### Factors associated with demographic match rates {#factors-associated-with-demographic-match-rates .unnumbered}

*Single- and multi-unit BSAs*

***Table 26*** contains data regarding comparisons of coverage rates,
household size, and demographic characteristics for single- and
multi-unit BSAs.

For all census household sizes, AREX addresses were less likely to link
with census multi-unit addresses than with single-unit addresses. For
linked households of equal size, AREX differed from census in all
demographic groups more often for households at multi-unit addresses.
The difference in percentage of demographic agreement is about 12
percent for households of size 1 and in the neighborhood of 20 percent
for households of sizes greater than 1. Deficiencies in administrative
records coverage and timing of the extracts most likely explain the
differences in demographic agreement.

[]{#__RefHeading___Toc54420979 .anchor}**Table 26**. Comparison of match
rates and household comparisons between occupied housing units at
multi-unit BSAs and housing units at single-unit BSAs.

+-------+----------+---------+-------------+-------------+-------------+
| C     | Group    | Total   | Linked      | Equal size  | Equal in    |
| ensus |          |         |             |             | all         |
| HH    |          |         | ( % of      | (%)^1^      | demographic |
| Size  |          |         | Total)      |             | groups      |
|       |          |         |             |             |             |
|       |          |         |             |             | (%)^2^      |
+-------+----------+---------+-------------+-------------+-------------+
| All   | In       | 278,447 | 188,826     | 88,517      | 64,992      |
| siz   | mu       |         |             |             |             |
| es^3^ | lti-unit |         | (67.8%)     | (46.9%)     | (73.4%)     |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 738,826 | 665,915     | 356,909     | 293,720     |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (90.1%)     | (53.6%)     | (82.3%)     |
+-------+----------+---------+-------------+-------------+-------------+
| 1     | In       | 135,833 | 91,051      | 57,218      | 44,978      |
|       | mu       |         |             |             |             |
|       | lti-unit |         | (67.0%)     | (62.8%)     | (78.6%)     |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 140,757 | 125,568     | 82,074      | 74,034      |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (89.2%)     | (65.4%)     | (90.2%)     |
+-------+----------+---------+-------------+-------------+-------------+
| 2     | In       | 80,719  | 55,820      | 21,788      | 15,009      |
|       | mu       |         |             |             |             |
|       | lti-unit |         | (69.2%)     | (39.0%)     | (69.3%)     |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 250,753 | 226,676     | 136,471     | 118,386     |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (90.4%)     | (60.2%)     | (86.7%)     |
+-------+----------+---------+-------------+-------------+-------------+
| 3-4   | In       | 51,244  | 35,165      | 8,567       | 4,459       |
|       | mu       |         |             |             |             |
|       | lti-unit |         | (68.6%)     | (24.4%)     | (52.0%)     |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 237,644 | 237,644     | 112,255     | 83,906      |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (90.5%)     | (47.2%)     | (74.7%)     |
+-------+----------+---------+-------------+-------------+-------------+
| 5-6   | In       | 9390    | 6,063       | 926         | 456         |
|       | mu       |         |             |             |             |
|       | lti-unit |         | (64.6%)     | (15.3%)     | (49.2%)     |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 73,253  | 65,838      | 25,156      | 17115       |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (89.9%)     | (38.2%)     | (68.0%)     |
+-------+----------+---------+-------------+-------------+-------------+
| 7+    | In       | 1,261   | 727         | 18          | 0           |
|       | mu       |         |             |             |             |
|       | lti-unit |         | (57.7%)     | (2.5%)      |             |
+-------+----------+---------+-------------+-------------+-------------+
|       | In       | 11,349  | 10,189      | 953         | 279         |
|       | sin      |         |             |             |             |
|       | gle-unit |         | (89.8%)     | (9.4%)      | (29.3%)     |
+-------+----------+---------+-------------+-------------+-------------+

1.  Percent of linked

2.  Percent of linked of equal size

3.  Except size zero

*Age of household occupants*

The discrepancies between AREX and the census were due partly because
some households have moved out of, and others moved into, addresses
between the time of the administrative records cutoffs and the census.
It is possible that households containing only older people are less
likely to move, and may yield better AREX to the census comparisons.
Table 27 provides address linkage rates by whether the housing unit is
at multi-unit BSA, and by whether it has only people 50 and over. Table
28 provides comparisons of linkage rates, size, and demographics for
housing units containing only people 50 and over, and others. (Tables
B.16 and B.17A-B in Judson and Bauder (2002) contain similar comparisons
for ages 18 and over, and for 65 and over.)

The coverage by AREX of census households with everyone over 50 was
slightly, but consistently, higher. This was true whether controlling
for multi-units or controlling for size. The comparison for household
size and demographics were much better for one and two person households
with all members 50 and over. The demographic comparison was worse for
households of size 3 or more, but there were few of those where all
members were 50 and over.

[]{#__RefHeading___Toc54420980 .anchor}**Table 27. Coverage by multi vs.
single unit, and by household age characteristics**.

  ----------------- ---------------------- --------------- ---------------
  Type of housing   Census household age   Total           Percent linked
  unit              characteristic                         

  All HUs           All 50 or older        292,091         85.8%

                    Some under 50          639,088         79.9%

  In multi-unit     All 50 or older        81,480          69.8%

                    Some under 50          230,883         62.5%

  In single-unit    All 50 or older        210,661         91.8%

                    Some under 50          569,486         86.9%
  ----------------- ---------------------- --------------- ---------------

[]{#__RefHeading___Toc54420981 .anchor}Table 28. AREX to Census
comparisons by size of housing unit and by household age
characteristics.

+-------+------------+-----------+-----------+-----------+------------+
| Size  | Census     | Total     | Linked    | Equal     | Equal in   |
| of HH | household  |           | with AREX | size      | all        |
|       | age        |           | housing   |           | d          |
|       | char       |           | units ( % | (%)^1^    | emographic |
|       | acteristic |           | of Total) |           | groups^2^  |
|       |            |           |           |           |            |
|       |            |           |           |           | (%)^3^     |
+-------+------------+-----------+-----------+-----------+------------+
| 1     | All 50 or  | 148,335   | 121,781   | 86,518    | 78,500     |
|       | over       |           |           |           |            |
|       |            |           | (82.1%)   | (71.04%)  | (90.7%)    |
+-------+------------+-----------+-----------+-----------+------------+
|       | Some under | 128,235   | 94,838    | 52,774    | 40,512     |
|       | 50         |           |           |           |            |
|       |            |           | (74.0%)   | (55.7%)   | (76.8%)    |
+-------+------------+-----------+-----------+-----------+------------+
| 2     | All 50 or  | 137,758   | 123,412   | 83,662    | 76,685     |
|       | over       |           |           |           |            |
|       |            |           | (89.6%)   | (67.8%)   | (91.7%)    |
+-------+------------+-----------+-----------+-----------+------------+
|       | Some under | 193,714   | 159,084   | 74,597    | 56,800     |
|       | 50         |           |           |           |            |
|       |            |           | (82.1%)   | (46.9%)   | (76.1%)    |
+-------+------------+-----------+-----------+-----------+------------+
| 3+    | All 50 or  | 5878      | 5,357     | 2542      | 2072       |
|       | over       |           |           |           |            |
|       |            |           | (91.1%)   | (47.5%)   | (81.5%)    |
+-------+------------+-----------+-----------+-----------+------------+
|       | Some under | 403,233   | 350,269   | 145,313   | 136,147    |
|       | 50         |           |           |           |            |
|       |            |           | (86.9%)   | (41.5%)   | (93.7%)    |
+-------+------------+-----------+-----------+-----------+------------+

1.  Percent of linked households

2.  Equal in: both sex groups, all race groups, both Hispanic origin
    categories, and age groups 0-17, 18-64, 65+

3.  Percent of linked of equal size

*Race and Hispanic origin of household occupants*

Table 29 shows how coverage, size comparisons, and race comparisons,
vary with whether there was a person with race other than White in the
household according to Census 2000.

For census households with at least one person other than White, the
coverage by AREX is smaller, but not smaller by much, compared with
households all of whose members were White. On the other hand, the
household size comparisons and the racial composition comparisons
display more disagreement for households with at least one person other
than White. To some extent, this may be a consequence of race imputation
that would have affected comparison of households with one or more
persons other than White more often than all White households.

[]{#__RefHeading___Toc54420982 .anchor}Table 29. The effect of the
presence of Persons other than White in a household on household match
rates and comparisons.

+-------+-----------+--------+-------------+-------------+-------------+
| C     | Household | Total  | Linked with | Equal size  | Equal in    |
| ensus | type      |        | AREX        |             | all four    |
| HH    |           |        | housing     | (%)^1^      | race groups |
| Size  |           |        | units       |             |             |
|       |           |        |             |             | (%)^2^      |
|       |           |        | ( % of      |             |             |
|       |           |        | Total)      |             |             |
+-------+-----------+--------+-------------+-------------+-------------+
| All   | All White | 7      | 631,606     | 358,833     | 347,592     |
| sizes |           | 40,218 |             |             |             |
|       |           |        | (85.3%)     | (56.8%)     | (96.9%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 2      | 223,135     | 86,593      | 68,356      |
|       | one Other | 78,799 |             |             |             |
|       | race      |        | (80.0%)     | (38.8%)     | (78.9%)     |
+-------+-----------+--------+-------------+-------------+-------------+
| 1     | All White | 2      | 165,098     | 111,112     | 108,450     |
|       |           | 05,226 |             |             |             |
|       |           |        | (80.5%)     | (67.3%)     | (97.6%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 71,498 | 51,121      | 28,180      | 24,049      |
|       | one Other |        |             |             |             |
|       | race      |        | (72.1%)     | (54.7%)     | (85.3%)     |
+-------+-----------+--------+-------------+-------------+-------------+
| 2     | All White | 2      | 221,806     | 133,180     | 130,033     |
|       |           | 56,585 |             |             |             |
|       |           |        | (86.5%)     | (60.0%)     | (97.6%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 75,038 | 60,690      | 25,.079     | 19,995      |
|       | one Other |        |             |             |             |
|       | race      |        | (80.9%)     | (41.3%)     | (79.7%)     |
+-------+-----------+--------+-------------+-------------+-------------+
| 3-4   | All White | 2      | 192,772     | 93,694      | 89,491      |
|       |           | 19,030 |             |             |             |
|       |           |        | (88.0%)     | (48.6%)     | (95.5%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 95,207 | 80,037      | 27,128      | 20,105      |
|       | one Other |        |             |             |             |
|       | race      |        | (84.1%)     | (33.9%)     | (74.1%)     |
+-------+-----------+--------+-------------+-------------+-------------+
| 5-6   | All White | 53,202 | 46,707      | 20,319      | 19,144      |
|       |           |        |             |             |             |
|       |           |        | (87.8%)     | (43.5%)     | (94.2%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 29,635 | 25,194      | 5,763       | 3,896       |
|       | one Other |        |             |             |             |
|       | race      |        | (85.0%)     | (22.9%)     | (67.6%)     |
+-------+-----------+--------+-------------+-------------+-------------+
| 7+    | All White | 6,175  | 5,223       | 528         | 474         |
|       |           |        |             |             |             |
|       |           |        | (84.6%)     | (10.1%)     | (89.8%)     |
+-------+-----------+--------+-------------+-------------+-------------+
|       | At least  | 7,421  | 5,693       | 443         | 311         |
|       | one Other |        |             |             |             |
|       | race      |        | (76.7%)     | (7.8%)      | (70.2%)     |
+-------+-----------+--------+-------------+-------------+-------------+

1.  Percent of linked households

2.  Percent of linked households of equal size

AREX coverage of census addresses did not differ much between households
with and without Hispanics (Table 30). However, households with one or
more Hispanics in the census were much less likely to match
corresponding AREX households in size and Hispanic/non-Hispanic
composition. Differences in household sizes were most likely due to
deficiencies in administrative records coverage of Hispanics and the age
of the records vis-à-vis the Census. Differences in Hispanic composition
within households of equal size were most likely due to the fact that
Hispanic origin was model-based for virtually all AREX persons.

[]{#__RefHeading___Toc54420983 .anchor}Table 30. The effect of the
presence of Hispanics on household match rates.

+-------+------------+-------+------------+-------------+-------------+
| C     | Household  | Total | Linked     | Equal size  | Equal \# of |
| ensus | type       |       | with AREX  |             | Hispanics   |
| HH    |            |       | housing    | (% )^1^     |             |
| Size  |            |       | units      |             | (%)^2^      |
|       |            |       |            |             |             |
|       |            |       | ( % of     |             |             |
|       |            |       | Total)     |             |             |
+-------+------------+-------+------------+-------------+-------------+
| All   | All        | 95    | 803,272    | 424,867     | 411,698     |
| sizes | N          | 6,474 |            |             |             |
|       | onhispanic |       | (84.0%)    | (52.9%)     | (96.9%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 6     | 51,469     | 20,559      | 10,365      |
|       | one        | 2,533 |            |             |             |
|       | Hispanic   |       | (82.3%)    | (39.9%)     | (50.4%)     |
+-------+------------+-------+------------+-------------+-------------+
| 1     | All        | 26    | 210,745    | 136,114     | 134,063     |
|       | N          | 9,018 |            |             |             |
|       | onhispanic |       | (78.3%)    | (64.6%)     | (98.5%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 7,706 | 5,874      | 3,178       | 1,802       |
|       | one        |       |            |             |             |
|       | Hispanic   |       | (76.2%)    | (54.1%)     | (56.7%)     |
+-------+------------+-------+------------+-------------+-------------+
| 2     | All        | 31    | 268,371    | 151,588     | 147,697     |
|       | N          | 4,587 |            |             |             |
|       | onhispanic |       | (85.3%)    | (56.5%)     | (97.4%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 1     | 14,125     | 6,671       | 4,053       |
|       | one        | 7,036 |            |             |             |
|       | Hispanic   |       | (82.9%)    | (47.2%)     | (60.8%)     |
+-------+------------+-------+------------+-------------+-------------+
| 3-4   | All        | 28    | 250,589    | 112,467     | 106,922     |
|       | N          | 7,966 |            |             |             |
|       | onhispanic |       | (87.0%)    | (44.9%)     | (95.1%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 2     | 22,220     | 8,355       | 3,609       |
|       | one        | 6,271 |            |             |             |
|       | Hispanic   |       | (84.6%)    | (37.6%)     | (43.2%)     |
+-------+------------+-------+------------+-------------+-------------+
| 5-6   | All        | 7     | 64,212     | 23,831      | 22,235      |
|       | N          | 3,654 |            |             |             |
|       | onhispanic |       | (87.2%)    | (37.1%)     | (93.3%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 9,183 | 7,689      | 2,251       | 876         |
|       | one        |       |            |             |             |
|       | Hispanic   |       | (83.7%)    | (29.3%)     | (38.9%)     |
+-------+------------+-------+------------+-------------+-------------+
| 7+    | All        | 1     | 9,355      | 867         | 781         |
|       | N          | 1,249 |            |             |             |
|       | onhispanic |       | (83.2%)    | (9.3%)      | (90.1%)     |
+-------+------------+-------+------------+-------------+-------------+
|       | At least   | 2,347 | 1,561      | 104         | 25          |
|       | one        |       |            |             |             |
|       | Hispanic   |       | (66.5%)    | (6.7%)      | (24.0%)     |
+-------+------------+-------+------------+-------------+-------------+

1 Percent of linked

2 Percent of linked of equal size

*AREX race imputation*

Table 31 concerns linked households in which no person's AREX race was
imputed, and those in which at least one person's race was imputed. The
comparison was done with regard to the racial composition of the
household. As expected, households with imputed race were less likely to
agree on household race composition. Although the overall agreement rate
of 86 percent was quite high when one or more members had imputed race,
the agreement rate may have been much smaller when a member was imputed
to be of an other race.

[]{#__RefHeading___Toc54420984 .anchor}[Table 31. The effect of AREX
imputed race on household comparisons.]{.underline}

+--------+-------------+-----------+-----------+----------+-----------+
|        |             | HHs with  |           | HHs with |           |
|        |             | at least  |           | no       |           |
|        |             | one       |           | person   |           |
|        |             | person    |           | with     |           |
|        |             | with AREX |           | AREX     |           |
|        |             | imputed   |           | imputed  |           |
|        |             | race      |           | race     |           |
+--------+-------------+-----------+-----------+----------+-----------+
| Census | Total       | Number    | Equal in  | Number   | Equal in  |
| HH     | linked,     |           | all race  |          | all race  |
| Size   | with equal  | (% of     | c         | (% of    | c         |
|        | size        | \[1\])    | ategories | \[1\])   | ategories |
|        |             |           |           |          |           |
|        | \[1\]       | \[2\]     | (% of     | \[3\]    | (% of     |
|        |             |           | \[2\])    |          | \[3\])    |
+--------+-------------+-----------+-----------+----------+-----------+
| All    | 445,426     | 100,416   | 86,290    | 345,010  | 329,658   |
| s      |             |           |           |          |           |
| izes\* |             | (22.5%)   | (85.9%)   | (77.5%)  | (95.6%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 1      | 139,292     | 5,197     | 4,099     | 134,095  | 128,400   |
|        |             |           |           |          |           |
|        |             | (3.7%)    | (78.9%)   | (96.3%)  | (95.8%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 2      | 158,259     | 14,087    | 11,351    | 144,172  | 138,677   |
|        |             |           |           |          |           |
|        |             | (8.9%)    | (80.6%)   | (91.1%)  | (96.2%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 3-4    | 120,822     | 61,389    | 53,689    | 59,433   | 55,907    |
|        |             |           |           |          |           |
|        |             | (50.8%)   | (87.5%)   | (49.2%)  | (94.1%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 5+     | 27,053      | 19,743    | 17,151    | 7,310    | 6,674     |
|        |             |           |           |          |           |
|        |             | (73.0%)   | (86.9%)   | (27.0%)  | (91.3%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 5-6    | 26,082      | 18,991    | 16,558    | 7,091    | 6,482     |
|        |             |           |           |          |           |
|        |             | (72.8%)   | (87.2%)   | (27.2%)  | (91.4%)   |
+--------+-------------+-----------+-----------+----------+-----------+
| 7+     | 971         | 752       | 593       | 291      | 192       |
|        |             |           |           |          |           |
|        |             | (77.4%)   | (78.9%)   | (22.6%)  | (87.7%)   |
+--------+-------------+-----------+-----------+----------+-----------+

\* Not including zero

### Predicting AREX/Census household similarity {#predicting-arexcensus-household-similarity .unnumbered}

####  {#section-20 .unnumbered}

The purpose of the regression analysis was to try to understand more
about those circumstances under which AREX administrative records
households would match census households in both number and demographic
composition. This would also provide a first look at the potential uses
of administrative records data to substitute for some part of the
nonresponse followup or unclassified households in a conventional
census.

#### Model Specification {#model-specification .unnumbered}

For this initial model-building attempt, the units of analysis were all
889,638 one-to-one linked households. Separate equations for Census 2000
NRFU and unclassified households were not estimated, but dummy variables
were included in the equation for these two types of decennial census
household outcomes to see whether other predictor variables had
accounted for differences in AREX/Census 2000 household similarity
noticed in the descriptive analyses. This approach assumed that the
regression hyperplanes for the three types of census households were
parallel, an assumption that will be tested in future analyses.

Also, the analysis reported here attempted to account for household size
agreement and demographic composition simultaneously, providing in some
sense, the net association between predictors and outcomes. However, it
is possible that associations between predictors and household size
agreement are different from associations with demographic similarity
given agreement in size. This possibility will also be explored in
future work.

The functional form of the model is

$$$\text{y } = \text{ ln}\frac{P(\text{Match } = \text{ 1}|x)}{P(\text{Match } = \text{ 0}|x)} = \text{Logit}(P(\text{Match } = \text{ 1}|x) = x\beta$

where Match is a dichotomous dependent variable, x is a vector of
regressors, and β is a vector of constants to be estimated. For each
linked address, the dependent variable was defined as follows:

$$\text{Match} = \left\{ \begin{matrix}
1 & \begin{matrix}
\text{if the fully crossed age } \times \text{race  } \times \text{sex } \times \text{ Hispanic  origin distributions in the} \\
\text{linked Census household match the AREX }\text{household;}
\end{matrix} \\
0 & \text{otherwise}\text{.}
\end{matrix} \right.\ $$

#### Predictor variables {#predictor-variables .unnumbered}

The regressors, x, include characteristics of AREX addresses and
households that would be available were data from administrative records
to be used in support of a conventional census. The variable is
dichotomous, taking on the value 1 if the characteristic is present and
0, otherwise. The interaction terms are products of the individual
predictors. The predictors are numbered with the "Row num" in Table 34.

The predictors were chosen after examination of an extensive set of
bivariate crosstabulations with the dependent variable. The tabulations
and associated discussion can be found in Judson and Bauder (2002).

*Address administrative records source files*

Generally, it was assumed that addresses appearing in more than one
administrative record source file would be less likely to represent a
moving household than addresses found in only one file. Additionally
households with addresses in Medicare files would largely represent
older persons and represent stable households. The following variables
pertain to the source of the "best" administrative records address.

\[10\] **In IRS file** \-- In the IRS 1040 file.

\[11\] **In IRMF file** \-- In the IRS Information Returns Master File
(i.e. the 1099 file).

\[12\] **In Medicare file** \-- In the Medicare eligibility file

\[13\] **In IRS & IRMF** \--\[10\]\*\[11\]

\[14\] **In IRS & MED** \-- \[10\]\*\[12\]

\[15\] **In MED and IRMF** \-- \[11\]\*\[12\]

*AREX Household characteristics*

The following are characteristics of AREX households thought to be
associated with same size and demographic similarity with the linked
census households. To a certain extent, these variable are suggested by
the descriptive analysis of the previous section, keeping in mind that
the descriptive analysis often used census household characteristics
rather than AREX household characteristics.

\[5\] **One or two persons** \-- Household contains only 1 or 2 persons.

\[6\] **No imputed race** \-- No household member has imputed race.

\[7\] **Hhold has children** \-- Household has one or more children
under the age of 18.

\[8\] **Hhold has 1+ White** \-- Household has one or more White
members.

\[9\] **Hhold all age 65+** \-- All members of the household are age 65
or over.

\[16\] **Age 65+ & One/two** \-- \[5\]\*\[9\]

\[17\] **Age 65+ & 1+ White** \-- \[8\]\*\[9\]

\[18\] **One/two & 1+ White** \-- \[5\]\*\[8\]

\[19\] **65+ & 1 or 2 & 1+ White** \-- \[5\]\*\[8\]\*\[9\]

\[21\] **65+ and no imputed race** \-- \[6\]\*\[9\]

*AREX/ DMAF address characteristics*

Single unit addresses are assumed to be more predictable than multi-unit
addresses.

\[4\] **Not multi unit** -- AREX indicates that the address is single
unit.

\[20\] **65+ and Not multiunit** \-- \[4\]\*\[9\]

\[22**\] No imputed Race and not multi** \-- \[4\]\*\[6\]

\[23\] **65+ & No imp. & not multi** \-- \[4\]\*\[6\]\*\[9\]

\[1\] **Colorado effect** \-- AREX address is in Colorado

*Census 2000 response type*

Including variables representing Census 2000 response type provides a
first indication of whether separate models might be needed for each
type. (The reference group is mailback respondents.)

\[2\] **Enumerator return** \-- NRFU respondent household

\[3\] **Imputed return** \-- Census 2000 whole house imputation

#### Regression results {#regression-results .unnumbered}

Although the regression results are useful in obtaining an initial
understanding the relationships between AREX address and household
characteristics and AREX/Census Match status, it is important to keep in
mind that these matched households are not a nationally representative
sample, that the analysis is exploratory in nature, and that
improvements in administrative records processing in the future will be
substantial. Therefore, these results should be considered illustrative
in nature.

[]{#__RefHeading___Toc54420985 .anchor}Table 32. Overall Response
Profile for the "Match" Variable

  --------------------------------------------------- -------------------
  **Response Profile and Overall Model Fit            
  Statistics**                                        

  Match Status                                        Total Frequency

  Demographics Match                                  342294 (38.5%)

  Demographics Do Not Match                           547344 (61.5%)
  --------------------------------------------------- -------------------

About 38.5 percent of all linked addresses also matched on demographics.

[]{#__RefHeading___Toc54420986 .anchor}Table 33. Goodness-of-Fit
Measures for the Logistic Regression Model

  ---------------- -------------------------- ---------------------------
  Criterion        Intercept Only             Intercept and Covariates

  AIC              1,185,613.2                1,001,550.2

  SC               1,185,624.9                1,001,831.0

  -2 Log L         1,185,611.2                1,001,502.2

  Pseudo R-Square  0.1869                     
  ---------------- -------------------------- ---------------------------

Test Chi-Square DF Pr \> ChiSq

Likelihood Ratio 184,108.945 23 \<.0001

(full model versus null model of intercept only)

Note: N=889,638 households in two AREX test sites in Colorado and
Maryland whose addresses were computer linked; A household is declared
"matched" if it's age, race, sex and Hispanic origin composition is the
same across the AREX household and the equivalent census household. AIC
is the Akaike Information Criterion; SC is the Schwarz criterion. --2
Log L is --2 times the log likelihood (LL) of the model, evaluated at
its maximum; R-square is the pseudo R-square value, consisting of
(LL(model) -- LL(intercept only))/LL(model). The Likelihood Ratio test
tests the null hypothesis that all coefficients except the intercept are
zero in the population; Pr\>ChiSq is the (nominal) probability of
obtaining that Chi-Square value by chance; Because observations may not
be I.I.D., standard errors may be understated and significance levels
overstated. (Note is also applicable to Table 34 ).

All of the variables taken together significantly improved the
prediction of Match status. The Pseudo R-Square value indicates that the
model results in a 19 percent improvement in the log-likelihood over the
null model of an intercept only.

*Coefficient estimates*

Table 34 provides maximum likelihood estimates of the coefficients for
the full model. The rightmost column indicates exponentiated
coefficients, and can be interpreted as the (multiplicative) change in
the odds of being a match given the corresponding characteristic,
holding all other variables constant. An exponentiated coefficient of
"1" indicates no effect, greater than 1 indicates positive effect, and
less than 1 indicates negative effect.

The presence of interaction terms makes the interpretation of individual
coefficients somewhat difficult. Still, the results for AREX address and
household characteristics seem generally as expected. AREX households
that are smaller (one or two members), have only members aged 65+, have
one or more Whites, and have no members with imputed race tend to be
more likely to match the corresponding Census 2000 household. AREX
households at single-unit addresses are more likely to match the census
than those at multi-unit addresses.

The negative coefficients on the Enumerator Return and Imputed Return
indicate that these households remain less predictable other factors
held constant. Separate equations for Census NRFU households might be
required. Households where the census return was imputed are very
unlikely to have the same demographics as their AREX counterparts and
have added some noise to the coefficient estimates.

[]{#__RefHeading___Toc54420987 .anchor}Table 34. Maximum Likelihood
Parameter Estimates, Standard Errors, and approximate tests

+---------+----------------+---+------+-------+-------+------+--------+
| Row     | Variable       | d | Esti | Sta   | Wald  | PR   | Exp    |
|         |                | f | mate | ndard |       | \>C  | (Est)  |
| Number  |                |   |      | Error | Chi-S | hiSq |        |
|         |                |   |      |       | quare |      |        |
+=========+================+===+======+=======+=======+======+========+
| \[0\]   | Intercept      | 1 | -2   | 0.050 | 29    | \<.  | 0.064  |
|         |                |   | .756 |       | 77.43 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[1\]   | Colorado       | 1 | -0   | 0.005 | 3     | \<.  | 0.903  |
|         | Effect         |   | .102 |       | 79.62 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[2\]   | Enumerator     | 1 | -1   | 0.006 | 266   | \<.  | 0.334  |
|         | Return         |   | .096 |       | 48.72 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[3\]   | Imputed Return | 1 | -3   | 0.110 | 8     | \<.  | 0.044  |
|         |                |   | .133 |       | 09.52 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[4\]   | Not Multi-unit | 1 | 0    | 0.018 | 26    | \<.  | 2.525  |
|         |                |   | .926 |       | 56.05 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[5\]   | One or Two     | 1 | 0    | 0.011 | 70    | \<.  | 2.672  |
|         | Persons        |   | .982 |       | 13.33 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[6\]   | No Imputed     | 1 | 0    | 0.018 | 17    | \<.  | 2.205  |
|         | Race           |   | .790 |       | 78.60 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[7\]   | Hhold has      | 1 | 0    | 0.007 | 12    | \<.  | 1.317  |
|         | Children       |   | .275 |       | 39.27 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[8\]   | Hhold has      | 1 | 0    | 0.009 | 41    | \<.  | 1.819  |
|         | 1+White        |   | .598 |       | 68.03 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[9\]   | Hhold all age  | 1 | 0    | 0.187 | 2.25  | 0.   | 1.325  |
|         | 65+            |   | .281 |       |       | 1334 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[10\]  | In IRS File    | 1 | -0   | 0.047 | 1.04  | \<0. | 0.953  |
|         |                |   | .048 |       |       | 3075 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[11\]  | In IRMF File   | 1 | -0   | 0.047 | 52.61 | \<.  | 0.710  |
|         |                |   | .341 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[12\]  | In Medicare    | 1 | -0   | 0.048 | 2.50  | \<0. | 0.927  |
|         | File           |   | .076 |       |       | 1136 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[13\]  | In IRS & IRMF  | 1 | 0    | 0.047 | 3     | \<.  | 2.462  |
|         |                |   | .901 |       | 63.32 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[14\]  | In IRS &       | 1 | -0   | 0.015 | 9     | \<.  | 0.614  |
|         | Medicare       |   | .488 |       | 96.77 | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[15\]  | In Medicare    | 1 | 0    | 0.047 | 68.23 | \<.  | 1.478  |
|         | and IRMF       |   | .390 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[16\]  | Age 65+ &      | 1 | 0    | 0.156 | 30.81 | 0.   | 2.389  |
|         | One/Two        |   | .870 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[17\]  | Age 65+ & 1 +  | 1 | -1   | 0.167 | 38.63 | \<.  | 0.353  |
|         | White          |   | .042 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[18\]  | One/Two & 1 +  | 1 | -0   | 0.013 | 8.001 | \<0. | 1.037  |
|         | White          |   | .036 |       |       | 0047 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[19\]  | 65+ & 1 or 2 & | 1 | 0    | 0.168 | 33.25 | \<.  | 2.649  |
|         | 1+ White       |   | .974 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[20\]  | 65+ and not    | 1 | -1   | 0.119 | 73.41 | \<.  | 0.360  |
|         | Multi-unit     |   | .021 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[21\]  | 65+ and no     | 1 | 0    | 0.105 | 16.23 | \<.  | 1.531  |
|         | Imputed Race   |   | .425 |       |       | 0001 |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[22\]  | No imputed     | 1 | -0   | 0.019 | 10    | \<.  | 0.532  |
|         | race and not   |   | .630 |       | 57.22 | 0001 |        |
|         | Multi          |   |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[23\]  | 65+ & no       | 1 | 0    | 0.120 | 29.90 | \<.  | 1.931  |
|         | imputed Race & |   | .657 |       |       | 0001 |        |
|         | not Multi      |   |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[1     | Total Effect   | ( |      |       |       |      | 1.666  |
| 0\]\*\[ | of Capture in  | g |      |       |       |      |        |
| 11\]\*\ | IRS and IRMF   | i |      |       |       |      |        |
| \[13\]  |                | v |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | n |      |       |       |      |        |
|         |                | n |      |       |       |      |        |
|         |                | o |      |       |       |      |        |
|         |                | t |      |       |       |      |        |
|         |                | i |      |       |       |      |        |
|         |                | n |      |       |       |      |        |
|         |                | M |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | d |      |       |       |      |        |
|         |                | i |      |       |       |      |        |
|         |                | c |      |       |       |      |        |
|         |                | a |      |       |       |      |        |
|         |                | r |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | ) |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[10    | Total Effect   | ( |      |       |       |      | 1.401  |
| \]\*... | of Capture in  | w |      |       |       |      |        |
|         | all Three      | / |      |       |       |      |        |
| ...\    | Files          | o |      |       |       |      |        |
| *\[15\] |                | t |      |       |       |      |        |
|         |                | h |      |       |       |      |        |
|         |                | r |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | - |      |       |       |      |        |
|         |                | w |      |       |       |      |        |
|         |                | a |      |       |       |      |        |
|         |                | y |      |       |       |      |        |
|         |                | i |      |       |       |      |        |
|         |                | n |      |       |       |      |        |
|         |                | t |      |       |       |      |        |
|         |                | e |      |       |       |      |        |
|         |                | r |      |       |       |      |        |
|         |                | a |      |       |       |      |        |
|         |                | c |      |       |       |      |        |
|         |                | t |      |       |       |      |        |
|         |                | i |      |       |       |      |        |
|         |                | o |      |       |       |      |        |
|         |                | n |      |       |       |      |        |
|         |                | ) |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[5\]\* | Total Effect   |   |      |       |       |      | 14.92  |
| \[8\]\* | of all of 65+, |   |      |       |       |      |        |
|         | White, and 1/2 |   |      |       |       |      |        |
| \[9     | Person Hhold   |   |      |       |       |      |        |
| \]\*\[1 |                |   |      |       |       |      |        |
| 6\]...\ |                |   |      |       |       |      |        |
| \       |                |   |      |       |       |      |        |
| *\[19\] |                |   |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+
| \[4\]\* | Total Effect   |   |      |       |       |      | 4.177  |
| \[6\]\* | of all of 65+, |   |      |       |       |      |        |
|         | Nonmulti-unit, |   |      |       |       |      |        |
| \[9\]\  | nonimputed     |   |      |       |       |      |        |
| *\[20\] | race           |   |      |       |       |      |        |
|         |                |   |      |       |       |      |        |
| ...\    |                |   |      |       |       |      |        |
| *\[23\] |                |   |      |       |       |      |        |
+---------+----------------+---+------+-------+-------+------+--------+

The last four rows of the table indicate the net effect of some
combinations of variables, calculated by multiplying their exponentiated
coefficients. For example, the total effect of being captured in IRS,
IRMF, and Medicare however, is effectively that the household is about
1.6 times more likely to demographically match.

The effect of having all persons 65 or older, at least one White person,
and consisting only of a one or two person household (given that the
household is multi-unit and has at least one member with imputed race)
is dramatically positive, 14.92. Similarly, a household having all
persons 65 or older, not being a multiunit address, and having no
imputation from the administrative records (but also other than white
and more than two persons) is about four times more likely to match
census demographics, holding other effects constant.

*Goodness of Fit*

One way to evaluate the ability of the model to correctly predict
household match status is to establish a decision rule that first
chooses a probability level, c, and then deems an observation to be
demographically matched if the probability that Match = 1 for that
observation, calculated from the model, is greater than c. More
succinctly, for a given level of c, 0 ≤ c ≤ 1, if P(Match=1\|xβ)≥c,
predict "AREX household is demographically matched." Otherwise predict
that the household is not demographically matched. For a given
probability level, there are several measures that can be used to
evaluate the decision rule.

**Accuracy**: Proportion of all cases correctly classified.

**False positive**: Proportion of cases where the true match status is 0
given that the prediction is 1.

**False negative**: Proportion of cases where the true match status is 1
given that the prediction is 0.

**Sensitivity**: Proportion of cases where the prediction is 1 given
that the true match status is 1.

**Specificity**: Proportion of cases where the prediction is 0 given
that the true match status is 0.

Table 35 shows the estimates of these quantities for decision rule
probability levels between .5 and .9.

[]{#__RefHeading___Toc54420988 .anchor}Table 35. Classification Results
for Predicted Probabilities.

  ------------------ ------------- ----------- --------------- --------- ----------------- -------------- -------------- ------- -------
  **Classification                                                                                                               
  Table**                                                                                                                        

  Prob. Level        **Correct**               **Incorrect**             **Percentages**                                         

                     Event         Non-Event   Non-Event       Event     Correct           Sensit-ivity   Specif-icity   False   False
                                                                                                                         POS     NEG

  0.5                184,230       457,943     89,401          158,064   72.2              53.8           83.7           32.7    25.7

  0.6                110,701       506,699     40,645          231,593   69.4              32.3           92.6           26.9    31.4

  0.7                72,335        530,307     17,037          269,959   67.7              21.1           96.9           19.1    33.7

  0.8                32,373        540,798     6,546           309,921   64.4              9.5            98.8           16.8    36.4
  ------------------ ------------- ----------- --------------- --------- ----------------- -------------- -------------- ------- -------

As can be seen, if we choose the cutoff of 0.5 (so that we predict a
"match" when P\[Match=1\|xβ\] is greater than or equal to .5), we obtain
about 184,000 correct match predictions, and about 458,000 correct
nonmatch predictions. Dividing the sum of the correct predictions by the
total number of cases above the 0.5 cutoff (about 889,000) gives 72.2
percent correct predictions (accuracy). Similarly, 54 percent of the
matches were correctly predicted to be matches (sensitivity); 83.7
percent of the nonmatches were correctly predicted to be nonmatches
(specificity); there was a 32.7 percent false positive rate and a 25.7
percent false negative rate.

As the cutoff level, c, increases (i.e., becomes more stringent), the
probability of making an error in deeming a match status of 1 above the
cutoff (probability of a false positive) declines. For example at c=0.8,
the number of correct predictions is 32,373 and the number of incorrect
predictions is 6,546 for a total of 38,919. Thus the probability of a
false positive is 6,546/38,919 = 0.168. As shown in the table, as c
increases, the sensitivity and overall correctness decline, and
specificity and the probability of a false negative increase.

In order to evaluate cutoffs and their implications for goodness of fit,
sensitivity and specificity, we present the following evaluative
figures. Error: Reference source not found provides an assessment of the
goodness of fit of the obtained logit function against "jittered"
outcomes.

In this figure, the ordinate is the value of the logit function
ln(p/1-p). A 10 percent sample of the 889,638 observations are plotted
here. Each individual observation (a linked pair of addresses) is
plotted as a point near zero or one. The points have been "jittered"
slightly to simulate density and avoid overplotting. The abscissa is the
predicted probability that an observation will be a match. If we choose
0.5 as our cutoff (so that we declare an observation a predicted match
is P\[match\|XB\]\>0.5), then this corresponds to a logit value of zero,
and the vertical line. The horizontal line at 0.5 is for reference.
Points in the upper right hand quadrant are "hits"---correct predictions
that the demographics of the households match. Points in the lower left
hand quadrant are also "hits"---correct predictions that the
demographics of the households will not match. Points in the upper left
hand and lower right hand quadrants are misses---incorrect predictions.
Comparing the predicted logit function to the density of the obtained
match outcomes assesses goodness of fit. (For more on the development
and interpretation of this graph, see Judson, 1992. For more goodness of
fit measures and graphics, see Judson and Bauder, 2002.)

[]{#__RefHeading___Toc58826876 .anchor}Figure 18. Goodness of Fit
Diagnostic Plot.

![](media/image18.png){width="6.3125in" height="5.028472222222222in"}

#########  {#section-21 .unnumbered}

In thinking about using a regression model approach in deciding when to
substitute an administrative records household for a nonresponse
household in a conventional census, the probability of false match would
have to be small, providing confidence that the household substitution
was accurate and obviating the need for further enumeration. But the
proportion of households in scope for substitution, that is, the
proportion of households above the decision cutoff level would have to
be large enough to provide substantial savings over face-to-face
enumerations. For example, from Table 35, a cutoff of 0.8 would provide
a relatively low probability of false negative, 0.168; but the
proportion of households in scope for substitution at that cutoff would
be about 4 percent (38,919/889,638).

#########  {#section-22 .unnumbered}

### Summary and conclusions {#summary-and-conclusions-1 .unnumbered}

####  {#section-23 .unnumbered}

#### General similarity between AREX data and Census data {#general-similarity-between-arex-data-and-census-data .unnumbered}

A summary of the AREX to Census 2000 comparisons is given in Table 36.

The overall coverage of occupied census housing units by AREX was about
84 percent (81 percent of occupied and vacant units). The coverage of
census addresses by administrative records addresses could be raised
substantially by resolving matches that were not one-to-one, by filling
coverage gaps in administrative records, and by obtaining administrative
records extracts at points in time closer to census day. Proposals for
accomplishing all of these tasks are provided in Section 5 of this
report.

Similarity in size and demographic composition of linked AREX and census
households was rather low. Of the occupied census linked households,
AREX and the census had the same number of people in 52.1 percent of the
cases (51.4 percent of all linked households). In 41.9 percent of
occupied linked households, AREX and the census had the same number of
people, and the same demographic distributions using the three broad age
categories.

About 81 percent of households had the same 5-year age distribution and
about 93 percent had the same age distribution in the three broad
groups. This suggests that the proportion of households of the same size
that had exactly the same persons was somewhere in between. The
relatively low percentage of households (80.5 percent) with similarity
along all demographic dimensions was due in large part to race and
Hispanic origin imputation and the difference in race categories between
AREX and the census. It is unlikely that even improved race and Hispanic
origin models will be sufficient, in themselves, for decennial census
enumeration. Another approach is currently being developed. (See the
discussion in Sections 4 and 5.)

In summary, the key deficiency of the AREX administrative records
processing was the failure to get the right number of people (and,
therefore, the right people) at many of the addresses. Dissimilarity of
households is of special concern for an AREX-type of design because of
the limited opportunities to correct that part of the enumeration
obtained from the administrative records. This will be the biggest
challenge for future administrative records development.

[]{#__RefHeading___Toc54420989 .anchor}Table 36. Summary of match rates
and household comparisons between AREX and Census.

+------------------+---------+---------+---------+---------+---------+
| Type of Housing  | All of  | NRFU    | n       | Imputed | non-    |
| Unit             | Census  |         | on-NRFU |         | Imputed |
|                  |         |         |         | HHs     |         |
|                  |         |         |         |         | HHs     |
+------------------+---------+---------+---------+---------+---------+
| Total Occupied   | 1,      | 289,224 | 728,049 | 23,811  | 993,462 |
| Census Housing   | 017,273 |         |         |         |         |
| Units            |         |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| Census Occupied, | 854,741 | 221,909 | 632,832 | 15,043  | 839,698 |
| linked           |         |         |         |         |         |
|                  | (84     | (76.7%) | (86.9%) | (63.2%) | (84.5%) |
|                  | .0%)^1^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| Linked occupied  | 455,426 | 85,774  | 359,652 | 4,784   | 440,642 |
| with equal       |         |         |         |         |         |
| number           | (52     | (38.7%) | (56.8%) | (31.8%) | (52.5%) |
|                  | .1%)^2^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 406,349 | 69,488  | 336,861 | 2,373   | 403,976 |
| counts both sex  |         |         |         |         |         |
| categories       | (91     | (81.0%) | (93.7%) | (49.6%) | (91.7%) |
|                  | .2%)^3^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 415,948 | 75,262  | 340,686 | 3,583   | 412,365 |
| counts equal in  |         |         |         |         |         |
| all race         | (93     | (87.7%) | (94.%)  | (74.9%) | (93.6%) |
| categories       | .4%)^3^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 422,063 | 79,146  | 342,917 | 4,388   | 417,675 |
| counts equal in  |         |         |         |         |         |
| both Hispanic    | (94     | (92.3%) | (95.4%) | (91.7%) | (94.8%) |
| origin           | .8%)^3^ |         |         |         |         |
| categories       |         |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 362,202 | 49,833  | 312,369 | 335     | 361,867 |
| counts equal in  |         |         |         |         |         |
| all 5-year age   | (81     | (58.1%) | (86.9%) | (7.0%)  | (82.1%) |
| categories       | .3%)^3^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| Equal in age     | 414,668 | 72,835  | 341,833 | 2,905   | 411,763 |
| groups 0-17,     |         |         |         |         |         |
| 18-64, 65+       | (93     | (84.9%) | (95.1%) | (60.7%) | (93.5%) |
|                  | .1%)^3^ |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 333,577 | 43,210  | 290,367 | 138     | 333,439 |
| counts equal in  |         |         |         |         |         |
| sex, race,       | (74     | (50.4%) | (80.7%) | (2.9%)  | (75.7%) |
| Hispanic origin, | .9%)^3^ |         |         |         |         |
| and 5-year age   |         |         |         |         |         |
| groups           |         |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+
| AREX and Census  | 358,712 | 54,400  | 304,312 | 1,099   | 357,613 |
| equal in         |         |         |         |         |         |
| demographic      | (80     | (63.4%) | (84.6%) | (23.0%) | (81.2%) |
| composition:     | .5%)^3^ |         |         |         |         |
| sex, race,       |         |         |         |         |         |
| Hispanic origin, |         |         |         |         |         |
| and age groups   |         |         |         |         |         |
| 0-17, 18-64, 65+ |         |         |         |         |         |
+------------------+---------+---------+---------+---------+---------+

*^1^* Percent of Census occupied housing units

*^2^* Percent of Census linked housing units

*^3^* Percent of linked housing units with equal numbers of people

#### Similarity between AREX and Census NRFU and imputed households {#similarity-between-arex-and-census-nrfu-and-imputed-households .unnumbered}

There was less similarity between AREX and Census NRFU households than
non-NRFU households across all outcome measures. The address linkage
rate for Census NRFU households was about 77 percent compared to 84
percent for non-NRFU households. For NRFU households, AREX and census
agreement on the household size was 39 percent (57 percent for
non-NRFU), and agreement on all demographic groups given agreement on
size was 63 percent (85 percent for non-NRFU).

These results suggest that substituting AREX households for NRFU
households in a conventional census will be more difficult than matching
households in general. It may be that households for which
administrative records are weak overlap disproportionately with the
Census NRFU population. However, it may also be the case that the
characteristics of Census NRFU were more likely to be affected by AREX
source file cutoffs and other AREX processing decisions than non-NRFU
households. Also the Census 2000 enumeration of NRFU households may be
less reliable.

Similarity between AREX administrative records households and Census
2000 unclassified households was substantially weaker than with the NRFU
households. This is not surprising since the census was least sure of
the status of these addresses and the persons placed at them were
imputed by the Census. AREX 2000 did not provide the information needed
to assess whether using administrative records to enumerate census
unclassified households would be more accurate than the imputation.

#### Predicting household similarity {#predicting-household-similarity .unnumbered}

The logistic regression model predicted modestly well when AREX and
census households matched demographically. Factors that predicted
demographic matches included: one or two person households, households
with exclusively older persons, households where members are captured by
more than one administrative record system, households with no race
imputation, and households that were at single-unit addresses.

A decision rule that deemed matches if predicted probability was above
50 percent resulted in correct match status in 72 percent of the cases
but with a false positive rate of about 33 percent. The most stringent
cutoff of 80 percent reduced the false positive rate to 16.8 percent,
entailed only about 4 percent of the household population.

# discussion and recommendations for 2010 Planning and other census bureau programs

## Additional AREX 2000 evaluations 

### Assess the net impact of clerical and field Processes on the Bottom-up enumeration {#assess-the-net-impact-of-clerical-and-field-processes-on-the-bottom-up-enumeration .unnumbered}

There were originally four operations in AREX 2000 that were designed to
improve administrative records addresses. MAFGOR was used to geocode
city-style addresses that were not coded by computer. The RFPA was
designed to obtain physical addresses for persons whose mailing
addresses were non-city style or P.O. Box and geocode them. The clerical
reviews following the initial match to the MAF and the FAV were designed
to validate administrative record addresses that did not match the MAF.
All of these operations were complex and labor intensive.

It is not possible to evaluate each of the clerical and field operations
separately because the AREX design did not vary these factors
experimentally, and the RFPA results were not included in the AREX.
Still it may be possible to gauge the net effect of the three operations
on the Bottom-up results by stepping through the Bottom-up process
excluding address information from any of the clerical or field
operations and comparing the results to the Bottom-up enumeration.

The impact of eliminating the MAFGOR, clerical review of the first DMAF
match, and the FAV would be threefold. First, the effect of eliminating
the three operations would be to decrease the number of persons
enumerated from administrative records. Those persons at addresses that
did not computer geocode, did not match the MAF through computer
operations, or were found to be valid only by the FAV would be
eliminated if their addresses were all of these types. Second, selected
addresses for some individuals enumerated from administrative records
would change because their current Bottom-up address would be eliminated
leaving some other address still acceptable to the Bottom-up process.
Finally, because the total number of acceptable administrative record
addresses would be smaller, there would be more non-matched DMAF records
to canvass in order to complete the enumeration. That is, the number of
addresses brought in by the Census Pull, simulating the canvassing,
would be larger.

In broad terms, eliminating the results of the three address improvement
operations would require the following steps:

-   Recreate the address lists available to the Bottom-up by removing
    all addresses that were in the original list due to any of the three
    operations. (Because the new address list is a subset of the
    original list, an additional match to the DMAF for tabulation block
    codes should not be required. This ignores the possibility that a
    MAFGOR coded address might have picked up a block code from the
    DMAF);

-   Recreate the Bottom-up composite person records by matching the
    smaller set of addresses to the individuals and reapplying the
    address selection rules; and,

-   Rematch to the Census 2000 HDF in the AREX sites and include persons
    at unmatched HDF addresses.

There are a number of ways that this alternative process could be
evaluated. First, the alternative Bottom-up counts could be compared
with the Census 2000 using methodology similar to that of Section 3. The
purpose would be to test if basic enumeration results were different
from those of the original Bottom-up process.

Matching the two sets of Bottom-up results person by person could make a
more extensive analysis. This would permit an analysis of (1) persons
lost completely to the Bottom-up as a result of dropping the address
operations, (2) persons whose address changed within the site, (3)
persons omitted from administrative records counts who were picked up at
additional Census Pull addresses, and (4) new Census Pull persons not in
the first AREX Bottom-up results. Of course, a person-to-person match
would require substantial work. The methodology used by Wagner (2002) in
matching the Numident to the Census 2000 HCUF may provide a useful
approach.

Finally, there could be analyses that focus on the addresses rather than
the persons. Address analyses could address the administrative records
sources of rejected addresses, the impact of rejected addresses on
address selection, and a comparison of addresses for persons omitted
from the administrative records counts but who showed up in the Census
Pull. In the latter, it might be important to understand why the Census
Pull address was not obtained from administrative records address
selection.

###  {#section-24 .unnumbered}

### Adding AREX vacants and unduplicating Bottom-up results {#adding-arex-vacants-and-unduplicating-bottom-up-results .unnumbered}

###  {#section-25 .unnumbered}

In addition to comparing the two Bottom-up processes described above,
consideration should be given to creating two additional Bottom-up
enumerations based on proposals offered in the Bottom-up evaluation in
Section 3: (1) adding the vacant AREX addresses to the Census Pull if
they matched the HDF, and (2) unduplicating individuals between the
Census Pull and the administrative records. This pair of Bottom-up
enumerations would appear to be more correct than the corresponding pair
without these additional operations.

###  {#section-26 .unnumbered}

### Repeating AREX 2000 with StARS 2000 without clerical or field operations {#repeating-arex-2000-with-stars-2000-without-clerical-or-field-operations .unnumbered}

###  {#section-27 .unnumbered}

If eliminating the clerical address operations, as discussed in Section
2, turns out to be relatively inconsequential, then there would be great
value in \"repeating\" the AREX Bottom-up process (without the clerical
address operations) and the statistical evaluations with StARS 2000. The
reason is that the administrative records data sets used in StARS 2000
are much closer to those that might be available in an actual
administrative records census than those of StARS 1999 (putting aside
the possibility of additional administrative records sources). Having
the results from this administrative records baseline will be important
in planning for AREX 2010 because the test items will not be confounded
with the timing problems due to the administrative records extract dates
in StARS 1999.

###  {#section-28 .unnumbered}

Since the purpose of redoing the Bottom-up would not be primarily to
compare with the StARS 1999 results, operational improvements, such as
improved SSN validation for IRS files that have been incorporated into
StARS 2000 would be appropriate. The StARS 2000 AREX should include DMAF
matched AREX vacant addresses in the Census Pull, and there should be
unduplication after it. Also, the more timely administrative records
would provide an opportunity to take a new look at the address selection
algorithm, redirecting the emphasis to choosing the address that best
reflects residence as of Census Day and away from the somewhat
artificial focus on the presence of block codes.

###  {#section-29 .unnumbered}

Using the StARS 2000 files does not resolve two of the major limitations
on the AREX: the handling of special populations, and race and Hispanic
origin measurement. For the latter, consideration should be given to
using Wagner\'s race and Hispanic origin data (2002). Although this is
somewhat circular; again, it might provide results that are much closer
to what would have been achieved in an actual census. Correct handling
of special populations may need to await future experiments.

### Analysis of administrative records coverage gaps[^11] {#analysis-of-administrative-records-coverage-gaps .unnumbered}

In this report, a number of coverage gaps in administrative records for
both adults and children have been identified; but the population sizes
and characteristics of the missing persons is not known precisely. A
linkage of StARS to a Survey of Income and Program Participation (SIPP)
or Current Population Survey (CPS) sample for a corresponding time
period would provide some information about the characteristics of
persons in the survey who were not found in the administrative records.
The linkage could be easily accomplished using the SSNs developed for
those surveys, and using probabilistic methods for those without SSNs.
The subsequent analysis would not provide a complete look at the
non-covered population because of coverage deficiencies in the survey,
itself, both in terms of segments of the population underrepresented and
persons in the survey sample for whom SSNs are not available. Still,
much could be learned about the adults and children not found in the
administrative record systems.

A SIPP linkage might be particularly useful for missing adults because
it would identify the government programs in which they are
participating and provide details about social and economic
circumstances. For households in which adults have been matched but not
all of the children, a key focus might be to identify the administrative
records characteristics of the adults who then might become an
additional special population for administrative records census
purposes. That is, these households might receive a special mailing in
order to obtain a more complete enumeration of the household in AREX
2010.

###  {#section-30 .unnumbered}

##  Race and Hispanic Origin enhanced Numident

Improved modeling of race and Hispanic origin for administrative records
will not provide a general solution to decennial census measurement for
two reasons. First, it is unlikely that the models can provide adequate
fit for the most difficult to measure groups such as American Indians
and Alaskan Natives, Hawaii Islanders, and persons classifying
themselves as multi-race. Second, even the best models will always
suffer from a lack of fit in small geographic areas due to variation in
local responses about model predicted averages.

Looking ahead to 2010, it is important to continue to annotate the
Numident with survey reported race and Hispanic origin in order to make
the annotated Numident as complete as possible. The American Community
Survey (ACS) would be a major source of ongoing updates, under full
implementation.

Because there will always be a residual subset of the Numident for which
survey responses to race and Hispanic origin are not available, there
will continue to be a need for race and Hispanic origin models. The
methods proposed here make that subset smaller and smaller.

The initial task of annotating the Census Numident with race and
Hispanic origin from Census 2000 was described briefly in Section 4, and
an ongoing process to fill in the remaining gaps in the Numident was
proposed. There are some research activities that should be considered
in connection with this process.

###  {#section-31 .unnumbered}

### Evaluation of the initial match {#evaluation-of-the-initial-match .unnumbered}

###  {#section-32 .unnumbered}

First, the accuracy of the Numident/Census match should be evaluated,
perhaps by manual examination of a sample of matched cases. Bye (1999)
estimated very high accuracy for a similar matching process between the
SSA Numident and a pair of ACS test sites. His results suggest that an
evaluation sample need not be large and should be stratified by strong
and weak stages of the matching operation with the largest part of the
sample coming disproportionately from the weakest areas.

Second, the extent to which the initial match covers a typical
administrative records population should be explored.

Finally, the possibility of augmenting the initial match between the
Numident and the Census should be explored.

### Analysis of response variance over time in reported race and Hispanic origin {#analysis-of-response-variance-over-time-in-reported-race-and-hispanic-origin .unnumbered}

One purpose of continuing to annotate the Numident with race and
Hispanic origin after the initial match to Census 2000 is to make the
annotated Numident as complete as possible and to have race reports as
current as possible for use in 2010 and the intervening years. A second
purpose would be to study the response variation in self reports over
time comparing the Census 2000 measures to those obtained in later years
from other surveys. Although the reason for observed changes would be
confounded somewhat by differences in mode of administration of the
questions (hopefully the categories would remain unchanged), an analysis
of the frequency and nature of reported differences would permit an
assessment of the efficacy of using reported race or Hispanic origin
reported at previous times. It would also be useful to be able to
compare change over time with simple response variance if the latter
measurement is available from reinterviews in past censuses or surveys.

### New race and Hispanic origin models {#new-race-and-hispanic-origin-models .unnumbered}

However the enhancement of the Numident is carried out, there will
always be a need for models to impute race and Hispanic origin to the
residual of persons enumerated from administrative records whose
Numident record was not enhanced. In such cases, the models developed by
Bye (1998), and Bye and Thompson (1999) should be discarded, and new
models should be estimated from the enhanced Numident itself. The
enhanced Numident would provide very large samples with race measurement
in the correct format, a situation nonexistent prior to its creation.

## Household substitution for NRFU/Unclassified households in 2010

Although the results were not convincingly strong, due largely to the
limitations on AREX 2000, the idea of substituting administrative
records households for NRFU or unclassified households in a conventional
census merits further consideration. For NRFU households there is the
potential for significant cost savings, and for unclassified households,
the potential for greater accuracy than that provided by imputation.

The general methodology of household substitution reported in Section 3
was a two-step approach: Address linkage followed by household
substitution in cases with a high probability of correct household
membership. This approach should be tested as part of the 2004 Census
Test using models developed from a linkage of StARS 2000 data to the
Census 2000 HDF. The timing of the administrative records in StARS 2000
would be much closer to Census Day than the StARS 1999 data used in AREX
2000, and much more like the data that would be available in 2010. Of
course, similar administrative data would have to be available in 2004,
the year of the Census Test.

### Household level research {#household-level-research .unnumbered}

The immediate focus of household-level research would be in connection
with the repeat of the AREX Bottom-up using StARS 2000. For this test
enumeration, it will be important to assess the accuracy of the
households formed from the more current administrative records using the
same kinds of descriptive and regression analyses that were applied to
the original AREX data set.

Redoing the household-level analysis using AREX results based on StARS
2000 would give a more accurate assessment of the ability of
administrative records to recreate Census households without the
handicap of the time lag resulting from the use of StARS 1999. With
these more current data, it would be more reasonable to focus the
analysis on exact person matches between households and not demographic
matches. There are several advantages to using exact person matches.
First, it is the most simple in concept: How often and under what
circumstances can households be obtained from administrative records
that are the same as those in the census? Simply put, when do we get the
same people?

Second, the person match would remove the emphasis on race and Hispanic
origin and the problems that imputation introduced into the previous
analysis. Although determining when two groups of persons are matched is
more difficult than matching demographic patterns, exact person matching
would rely primarily on name and date of birth; and race and Hispanic
origin would be minor match keys if used at all. ARRS now has much
experience in person matching.

Third, with exact person matching, analyses can be done of \"true\" near
misses by the administrative records. For example, for households with
different numbers of persons, dependent variables could be constructed
that indicate that all of the AREX persons were contained in the Census
household except for 1 (or 2) and vice versa. Not only might these kinds
of misses be acceptable in certain future applications, but also
studying the characteristics of the missing persons may suggest
improvements for administrative records sources or processing.

### NRFU and/or imputed households substitution {#nrfu-andor-imputed-households-substitution .unnumbered}

The possibility of substituting administrative records households for
NRFU households should be explored using StARS 2000 data matched to
Census 2000. The data would come naturally from a StARS 2000 AREX as
discussed above or could be developed separately if the StARS 2000 AREX
is not done. The StARS 2000 dual process would supply the unduplicated
individuals and a set of addresses for each individual to which an
address selection rule would be applied. Administrative record persons
assembled by final addresses would then be matched to Census 2000 in
more or less the same way as it was done in the AREX to produce a data
set for analysis. The one major difference between a data processing
operation focused on substitution and a full enumeration is that block
tabulations would not be required in the substitution approach. The
sample could be limited to the AREX test sites; but if resources permit,
representative samples of the full population of administrative records
should be used.

The dependent variable could be analogous to that used above\--a
dichotomous variable indicating an exact household match or not. But
other more lenient variables such as those suggested above (e.g., all
persons are the same except one) might be used if they represent
alternatives that might be used in 2010.

If an adequate model for NRFU or imputed household substitution can be
obtained from the StARS 2000 records, the results could be used in a
2010 Census test, scheduled for 2004 and 2006. Assuming that the 2004
test is successful, it raises the question of what data would be used to
construct the model that would actually be used in 2010.

## Person unduplication in the 2010 Census

It is known that developing unduplication techniques that have a solid
operational and statistical foundation is no small task.

There are two difficult parts of person unduplication, in particular
long-range unduplication. The first is determining that in fact the two
enumeration records are a duplicate. The second is determining which
duplicate record should be \"preferred\" with respect to geographical
location (and, implicitly, which is an erroneous enumeration).

For the first problem (duplicate detection), methods have been developed
by the Census Bureau for finding candidate duplicate pairs. By adding
the administrative records data from the NUMIDENT file onto the source
data files, we gain a powerful field (the Protected Identification Key,
or PIK) for confirming that the candidate duplicate pairs are indeed
duplicates. This confirmation function has the direct effect of reducing
followup workload. Results from Bean and Bauder (2002) clearly
demonstrate this effect: 86.7 percent of the duplicates proposed by an
enhanced \"Further Study of Person Duplication\" operation were
confirmed using administrative records data.

The second difficult problem (geographical location) continues to be a
challenge. While we expect only a modest benefit in using administrative
records data to make the geographic placement decision, we really do not
have any hard data to address the question. Research should determine
just how big this \"modest\" benefit is, and its cost-saving
implications.

## MAF improvement

Many of the administrative records obtained by the Census Bureau include
addresses for the people on those records. One result of building the
Statistical Administrative Records System (StARS) database each year is
a MAF-like list of these addresses, many of which are geocoded to census
blocks. This list, the StARS Master Housing File (MHF), is, except for
geocoding, constructed independently of the MAF and can thus help to
evaluate and improve the MAF by:

-   Providing small area tallies to compute MAF quality metrics

-   Providing a comprehensive, accurate and timely source of data for
    change detection

-   Assisting with targeting of counties or other areas for updating
    purposes

Other administrative records could also be useful to identify newly
constructed housing units or areas where new construction is occurring
but not yet complete. The national scope of StARS combined with its
precise geography make it very flexible in assisting with the completion
of the objectives of the MAF/TIGER Enhancement program and meeting the
needs of other programs.

### Duplicate and multiple MAF IDs {#duplicate-and-multiple-maf-ids .unnumbered}

Multiple MAFIDs assigned to a single address and duplicate MAFIDs
assigned to multiple addresses contributed substantially to the
difficulty in matching administrative records addresses to the DMAF and
in classifying addresses as matched, non-matched, or possibly matched
for subsequent address operations. These problems were compounded in the
experiment because of the need for a second match to the DMAF to
transform \"collection\" geographic codes to \"tabulation\" geographic
codes.

### Address record linkage techniques {#address-record-linkage-techniques .unnumbered}

The 81 percent link rate between administrative records addresses and
the Census 2000 HDF, reported in the Household-level analysis, was
somewhat lower than expected. In particular, as many as 10 percent of
administrative records addresses that matched the HDF did not match on a
one-to-one basis. Improvements in address editing and standardization
and in developing tools for address record linkage across databases have
the potential to yield significant benefits in increasing linkage rates.
At the same time, the AREX did not provide an assessment of falsely
linked addresses and their characteristics. Thus research needs to be
done on both sides of the linkage issue in order to insure improved
linkage of administrative records addresses in the future.

## Other Census Bureau programs

### SSN verification and search {#ssn-verification-and-search .unnumbered}

The successful development of SSN verification and search methodology by
ARRS is one of the most valuable results of administrative record
research. Unduplication of persons in administrative records is a
crucial step in the development of StARS/AREX enumerations. The
unduplication is based largely on the SSN; and therefore, availability
and correctness of the SSNs in administrative records is crucial to the
process. Bye (1997) provided a discussion of SSN verification approaches
in the context of an administrative records census.

In addition to decennial census applications, there are a variety of
applications in connection with administrative records linkage with
Census surveys. These include SSN verification and search for surveys
that collect SSNs from respondents such as the Survey of Income and
Program Participation and the Current Population Survey. Additionally,
the applications include SSN search for respondents in surveys in which
the SSN is not requested, such as the American Community Survey.

SSN verification and search methodology consists of direct searches of
the Census Numident matching name and date of birth and indirect
searches that use address information in administrative records to
identify possible SSNs for persons at survey addresses. Probabilistic
record linkage software is used to associate data reported in surveys
with Numident data in order to establish ownership of the SSNs. More
information about these methods and applications can be found in Bye
(1999).

### Intercensal estimates program {#intercensal-estimates-program .unnumbered}

In demographic applications, early evaluations of the StARS 1999 and
2000 files versus Census 2000 and existing estimates strongly suggest
that StARS data have the potential to be a useful "check" on existing
cohort-component, ratio, and the so-called "administrative records"
estimation method (not to be confused with StARS itself). Because StARS
has the potential to be updated on a year-by-year basis, this "check" is
likely to be particularly important in the later years of the decade.

Two lines of research appear promising: StARS contributing to total
population estimates at the county level, and contributing to
Age/Race/Sex/Hispanic Origin Estimates at the county level:
Age/Race/Sex/Hispanic origin estimates are particularly important
component of the total estimates program, because they serve as
important control totals to ongoing surveys.

As the decade past a decennial census proceeds, population estimates
based on the decennial census and proceeding forward begin to degrade in
quality as local population changes deviate from that expected. The
administrative records databases, however, provide an annual
\"snapshot\" for county and possibly incorporated city level estimates.
While this snapshot might be slightly inaccurate in level, the year over
year change in the snapshot could provide an important \"check\" on
existing population estimates.

###  {#section-33 .unnumbered}

One significant strength of the StARS system is that many of its
addresses have been geocoded to Census Blocks, even specific Master
Address File (MAF) identifiers. Using StARS data in a synthetic fashion,
and using the explosion in techniques for small area estimation, we can
consider generating total population estimates at tract, block group or
even block levels. These estimates can then feed back into survey
frames, ACS controls, and the like.

### Improving Current Surveys  {#improving-current-surveys .unnumbered}

A related use is to use administrative records data to improve
noninterview weighting for nonresponse in surveys; this also requires
matching and substitution or modeling.

Currently, for ongoing survey noninterviews, noninterview adjustment
"cells" are constructed by identifying limited aspects of the
noninterview household. For these cells, a noninterview adjustment
factor is calculated. However, with the administrative records data
bases (StARS) covering the entire country, perhaps improvements can be
made. Two different approaches should be tested: Noninterview adjustment
cell construction, and direct imputation modeling, each using
administrative records data.

# Number range CHAPTERReferences

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Administrative Records*. Unpublished document available from the U.S.
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Alvey, Wendy and Scheuren, Fritz (1982). Background for an
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Bean, S. L. and Bauder, D. M. (2002) *Census and Administrative Records
Duplication Study*. DSSD A.C.E. Revision II Memorandum Series #PP-44.
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Berning, Michael A., and Cook, Ralph H. (2002). *Administrative Records
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Buser, Pascal, Huang, Elizabeth, Kim, Jay K., and Marquis, Kent (1998).
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Census Bureau.

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Bye, Barry V. (1998). *Race and Ethnicity Modeling with SSA Numident
Data*. Administrative Records Research Memorandum Series #19, U.S.
Census Bureau.

Bye, Barry V. (1999). *Social Security Number Search And Verification At
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Applications*. Administrative Records Research Memorandum Series #31,
U.S. Census Bureau.

Bye, Barry V., and Thompson, Herbert (1999). *Race & Ethnicity Modeling
w/SSA Numident Data: Two Level Regression Model*. Administrative Records
Research Memorandum Series #22, U.S. Census Bureau.

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## AREX 2000 Implementation Flow Chart {#arex-2000-implementation-flow-chart .Heading-Attachment}

![](media/image19.wmf){width="5.999305555555556in" height="7.40625in"}

## StARS Process Steps -- Outline {#stars-process-steps-outline .Heading-Attachment}

The process steps outline that follows is a synthesized extract from
pertinent StARS 1999 programming specifications. The outline is
presented here to assist in understanding the complex nature (at a high
level) of the operations required to build the StARS database. For a
more detailed description of the processes, refer to the StARS
specifications available from the Administrative Records Research Staff.
In outline format, the "dual-stream" processing steps in the creation of
the StARS 1999 database are as follows:

1.  **Edit and standardize address data** from the national-level source
    files.

    a.  Combine all records and split resulting file into 1000 ZIP Code
        cuts in preparation for the Code-1 process.

    b.  Pass records through Code-1 to standardize and "clean" the
        address data.

    c.  Unduplicate the address records and create the GEO Extract File.

        1.  Unduplicate on exact match of all address fields (full
            9-digit ZIP Code).

        2.  Extract file contains minimum number of data fields for
            TIGER coding.

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3.  **Edit and standardize person** **demographic** **data** from
    national-level files.

    a.  Name edits and standardization designed to enable record
        matching, linking, and unduplication within the database once
        SSNs are verified.

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    ```
    a.  Split and sort records into Census Numident segments by Social
        Security Number (SSN) in preparation for SSN Search and
        Verification (S&V) phase of StARS.

4.  **Verify and validate SSNs** by matching and comparing name data,
    date-of-birth data, and gender information against the Census
    Numident using AutoMatch.

    a.  Pass unverified SSNs through "name/date-of birth search" phase
        using AutoMatch.

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    ```
    a.  Differing match cut-off scores and weights established for each
        source file.

    b.  Use Census Numident data to fill missing demographic input data.
        Demographic data (other than name fields) for all IRS records
        derived from Census Numident.

    c.  Person records now ready for re-link to the geocoded address
        records.

5.  **Create** the **Master Housing File (MHF)** as follows:

    a.  Pass the ABI commercial file through Code-1 and the address
        standardizer to format and "clean" commercial addresses.

    ```{=html}
    <!-- -->
    ```
    a.  Unduplicate ABI file (exact match of parsed fields), and assign
        address type.

    b.  Pass Geocoded files through the address standardizer to obtain
        parsed address fields in preparation for record unduplication.

        1.  Assign address type based on standardized return fields.

        2.  Unduplicate GEO files based on exact match of parsed fields
            within type.

    c.  Merge unduplicated Geocoded file with unduplicated ABI file to
        identify and flag commercial addresses within each 3-digit ZIP
        Code file.

        1.  Assign a Housing Unit Identification Number (HUID).

        2.  HUID provides a numeric variable indicator to assist in
            selection of the best address for output to the final StARS
            database (the CPR).

    d.  Update the Master Pointer File (MPF) to enable address linkage
        back to original source files. MPF also reflects number of
        duplicate addresses associated with each address selected for
        retention on the MHF.

    e.  Merge the MHF and MPF and split resulting file back to original
        source cuts.

        1.  Select only the "current" address from Selective Service
            Records

        2.  Merge split files with source Proxy Files to append proxy
            addresses and create Enhanced Master Pointer File.

6.  **Create Linked Person Files**

    a.  Use "direct access" method to link person records with Enhanced
        Master Pointer File.

    ```{=html}
    <!-- -->
    ```
    a.  UID variable identifies the correct EMPF source file to access
        for selecting required geographic data for inclusion on Linked
        Person File.

    b.  Link unverified SSN records in the same fashion.

7.  **Create** the **Composite Person Record** (**CPR**) by selecting
    the "best record" from the Linked Person Files as follows:

    a.  Invoke address selection rules to **determine** the **best
        address** for the person records. Address selection rules
        follow:

        1.  Select the highest HUID category available.

        2.  Select a non-proxy address over an address with a proxy.

        3.  Select a non-commercial address over a commercial address.

        4.  Select the address based on source file priority as follows:

            a.  IRS 1040 record

            b.  Medicare record

            c.  Indian Health Service record

            d.  IRS 1099 record

            e.  Selective Service record

            f.  HUD TRACs record

        5.  Select most recent record based on the administrative record
            cycle dates.

        6.  Select first record read-in to the processing array for
            output to the CPR.

    ```{=html}
    <!-- -->
    ```
    a.  **Select** the **best race** based on the following rules:

        1.  If American Indian or Alaska Native is reflected on the IHS
            record, accept the value.

        2.  If an input value is blank or unknown -- defer to the PCF.

        3.  Select the most frequent occurrence.

        4.  If tied among occurrences, defer to the PCF.

        5.  If record is from the "New SSN List," defer to the PCF.

        6.  If ties still occur, select first record read-in.

    b.  **Select** the **best** indicator of **Hispanic origin** based
        on the following rules:

        1.  Most frequent non-blank observation (Numident value counted
            once).

        2.  If ties occur, defer to the PCF.

        3.  If the input value is blank, defer to the PCF.

        4.  If record is from "New SSN List" and non-blank, output a
            positive Hispanic origin; if blank; output a blank value
            (SSN not on PCF).

    c.  **Select** the **best gender** based on the following rules:

        1.  If a Selective Service record available, select "male"
            gender.

        2.  Select most frequent occurrence, if no Selective Service
            record available.

        3.  If ties occur among the observations, defer to the PCF
            (using random number probabilities).

        4.  If record from "New SSN List" and reflects a blank value,
            output a blank value to the CPR; if ties exist among the
            records, output "female" gender.

    d.  **Select Date of Death** (DOD) based on the following rules:

        1.  If Medicare record reflects DOD, output the value.

        2.  If more than one Medicare record reflects DOD, select the
            value from the most recent record (based on transaction
            cycle date).

        3.  If no Medicare record available, output the value present on
            the Numident.

        4.  If no reported DOD, defer to the PCF using random number
            probability after calculating gender.

        5.  If input is blank and the PCF indicates "alive," output a
            blank DOD value.

    e.  **Select** the **date of birth** (DOB) based on the following
        rules:

        1.  Select the highest DOB score within the following source
            file priority:

            a.  Medicare

            b.  Selective Service

            c.  Census Numident

            d.  HUD TRACS

            e.  Indian Health Service

        2.  If input is blank, output a blank value to the CPR.

    f.  **Select** the **best "name** fields" based on the following
        criteria:

        1.  Highest name score with an exact match of last name.

        2.  Exclude all IRS records and records from the "New SSN List."

        3.  If only excluded names are in the processing array, select
            the first record read-in.

        4.  If ties occur, select the first record read-in.

8.  Each variable is flagged to reflect the decision rule invoked and
    the source of the data. Decision rules are established to account
    for the characteristics of each input source date.

[^1]: In this report, we use the term "MAF" generically. Our operations
    were based on extracts from the Decennial Master Address File
    (DMAF).

[^2]: Such as state and local tax returns, drivers license files, local
    utilities, assessor's records, and the like.

[^3]: Such as commercially available mailing lists, credit card
    databases, and the like.

[^4]: The Race and Hispanic Origin models were developed using Numident
    data and Spanish and Asian name lists. The principal variables in
    the prediction equations were: (1) race or Hispanic origin as it
    appeared in the Numident, (2) place of birth, (3) Spanish and Asian
    surname indicators for the SSN holder and parents' surnames, and (4)
    indicator field in the Indian Health Service file. The Race and
    Hispanic Origin models were originally developed to augment race and
    Hispanic origin information in the Numident.

[^5]: The gender model was based on the strength of association between
    first and middle names and reported sex. Look-up tables created for
    common names, uncommon names, name-gender proportions, and gender
    model parameters were created and a final gender probability
    assigned after the four look-up tables were created and run against
    each input record.

[^6]: The taking of the census spans approximately a two year period,
    including the address list building phase. The geographic framework
    going into the census is called collection geography. Prior to
    tabulation of the final Census counts, changes must be incorporated
    to reflect boundaries in effect on January 1, 1999. This final
    geographic framework is called "tabulation" geography.

[^7]: The second match to the DMAF had an impact on address selection
    for both the Top-down and Bottom-up and should not be considered
    solely a Bottom-up operation.

[^8]: Multiple Census 2000 categories were combined for Asian/Pacific
    Islander.

[^9]: From data not shown in this report (but available from Heimovitz,
    2002) ALPE results for sex and age were similar for tract and county
    analyses. Baltimore City had the worst results for total and
    demographic ALPE measures but the most accurate results for blacks.
    However, Baltimore City also had the largest proportion of census
    pull records and smallest proportion of imputed black race codes.
    For the race/Hispanic minority groups, the relative size of the
    minority population in the tract was associated with how well AREX
    simulated Census results. Tracts with small minority proportions
    were more likely to have moderate or large positive ALPEs than other
    tracts.

[^10]: Recall that AREX vacant housing units are those with an address
    that was linked to the HDF but for which no persons remained after
    best address selection.

[^11]: Suggestions for additional administrative records acquisitions
    are given in Bye, 2002, section 5.