Administrative Records

Experiment in 2000 (AREX 2000)

Outcomes Evaluation

appendices

**Appendix 1: Profile of Test Sites**

## A1.1 County demographics

The ability to accurately measure the resident population using
administrative records is likely to vary by the age, race, sex, and
Hispanic composition of the AREX counties. These demographic groups are
likely to have distinct coverage rates within administrative records, as
well as mobility, fertility, and mortality rates. The latter rates are
also likely to interact with the record-keeping processes of the federal
agencies that collect and maintain the data. The sites were chosen for
their varying demographic characteristics to test the feasibility of
enumerating the population using administrative records. Table A1.1
provides a detailed breakdown of 2000 demographic characteristics for
the five counties in the AREX test sites. Some general comments on the
AREX test sites include:

-   Baltimore and Baltimore City have the largest populations, compared
    to the less populated CO counties.

-   Females exceed males in all five counties; the sex ratio is larger
    in the CO counties.

-   The MD counties are much older than the CO counties; the age 0-4 age
    group proportions are larger in the CO counties, while the older age
    groups are larger in the MD counties.

-   Baltimore City, and to a lesser extent, Baltimore County, have large
    Black populations; Hispanics are the largest minority population in
    CO, followed by APIs.

<table>
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<tbody>
<tr class="odd">
<td colspan="10"><p
id="table-a1-demographic-breakdown-of-the-census-2000-household-population-for-arex-counties"
class="heading">Table A1: Demographic Breakdown of the Census 2000
Household Population for AREX Counties</p></td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td colspan="2">Baltimore County</td>
<td colspan="2">Baltimore City</td>
<td colspan="2">Douglas County</td>
<td colspan="2">El Paso County</td>
<td colspan="2">Jefferson County</td>
</tr>
<tr class="odd">
<td>Total</td>
<td>736,652</td>
<td></td>
<td>625,401</td>
<td></td>
<td>175,300</td>
<td></td>
<td>501,533</td>
<td></td>
<td>519,326</td>
<td></td>
</tr>
<tr class="even">
<td>White</td>
<td>548,776</td>
<td>74.5%</td>
<td>196,427</td>
<td>31.4%</td>
<td>162,639</td>
<td>92.8%</td>
<td>408,167</td>
<td>81.4%</td>
<td>471,107</td>
<td>90.7%</td>
</tr>
<tr class="odd">
<td>Black</td>
<td>147,226</td>
<td>20.0%</td>
<td>404,198</td>
<td>64.6%</td>
<td>1,663</td>
<td>0.9%</td>
<td>31,875</td>
<td>6.4%</td>
<td>4,126</td>
<td>0.8%</td>
</tr>
<tr class="even">
<td>AI</td>
<td>1,923</td>
<td>0.3%</td>
<td>2,097</td>
<td>0.3%</td>
<td>716</td>
<td>0.4%</td>
<td>4,725</td>
<td>0.9%</td>
<td>3,971</td>
<td>0.8%</td>
</tr>
<tr class="odd">
<td>API</td>
<td>23,631</td>
<td>3.2%</td>
<td>9,168</td>
<td>1.5%</td>
<td>4,488</td>
<td>2.6%</td>
<td>13,954</td>
<td>2.8%</td>
<td>12,330</td>
<td>2.4%</td>
</tr>
<tr class="even">
<td>Hispanic</td>
<td>13,433</td>
<td>1.8%</td>
<td>10,712</td>
<td>1.7%</td>
<td>8,825</td>
<td>5.0%</td>
<td>56,677</td>
<td>11.3%</td>
<td>51,346</td>
<td>9.9%</td>
</tr>
<tr class="odd">
<td>Age 0-4</td>
<td>45,179</td>
<td>6.1%</td>
<td>41,593</td>
<td>6.7%</td>
<td>16,949</td>
<td>9.7%</td>
<td>39,006</td>
<td>7.8%</td>
<td>33,213</td>
<td>6.4%</td>
</tr>
<tr class="even">
<td>5-19</td>
<td>147,393</td>
<td>20.0%</td>
<td>135,558</td>
<td>21.7%</td>
<td>41,376</td>
<td>23.6%</td>
<td>115,404</td>
<td>23.0%</td>
<td>111,655</td>
<td>21.5%</td>
</tr>
<tr class="odd">
<td>20-24</td>
<td>41,740</td>
<td>5.7%</td>
<td>43,627</td>
<td>7.0%</td>
<td>5,478</td>
<td>3.1%</td>
<td>32,596</td>
<td>6.5%</td>
<td>28,901</td>
<td>5.6%</td>
</tr>
<tr class="even">
<td>25-34</td>
<td>100,363</td>
<td>13.6%</td>
<td>89,525</td>
<td>14.3%</td>
<td>28,552</td>
<td>16.3%</td>
<td>75,205</td>
<td>15.0%</td>
<td>70,672</td>
<td>13.6%</td>
</tr>
<tr class="odd">
<td>35-44</td>
<td>122,116</td>
<td>16.6%</td>
<td>97,983</td>
<td>15.7%</td>
<td>38,007</td>
<td>21.7%</td>
<td>90,039</td>
<td>18.0%</td>
<td>96,357</td>
<td>18.6%</td>
</tr>
<tr class="even">
<td>45-54</td>
<td>107,499</td>
<td>14.6%</td>
<td>81,691</td>
<td>13.1%</td>
<td>26,235</td>
<td>15.0%</td>
<td>68,878</td>
<td>13.7%</td>
<td>84,174</td>
<td>16.2%</td>
</tr>
<tr class="odd">
<td>55-64</td>
<td>67,187</td>
<td>9.1%</td>
<td>53,630</td>
<td>8.6%</td>
<td>11,597</td>
<td>6.6%</td>
<td>37,709</td>
<td>7.5%</td>
<td>46,190</td>
<td>8.9%</td>
</tr>
<tr class="even">
<td>65+</td>
<td>105,175</td>
<td>14.3%</td>
<td>81,794</td>
<td>13.1%</td>
<td>7,106</td>
<td>4.1%</td>
<td>42,696</td>
<td>8.5%</td>
<td>48,164</td>
<td>9.3%</td>
</tr>
<tr class="odd">
<td>65-74</td>
<td>54,768</td>
<td>7.4%</td>
<td>43,533</td>
<td>7.0%</td>
<td>4,784</td>
<td>2.7%</td>
<td>24,988</td>
<td>5.0%</td>
<td>28,025</td>
<td>5.4%</td>
</tr>
<tr class="even">
<td>75-84</td>
<td>40,114</td>
<td>5.4%</td>
<td>29,618</td>
<td>4.7%</td>
<td>1,959</td>
<td>1.1%</td>
<td>14,211</td>
<td>2.8%</td>
<td>15,900</td>
<td>3.1%</td>
</tr>
<tr class="odd">
<td>85+</td>
<td>10,293</td>
<td>1.4%</td>
<td>8,643</td>
<td>1.4%</td>
<td>363</td>
<td>0.2%</td>
<td>3,497</td>
<td>0.7%</td>
<td>4,239</td>
<td>0.8%</td>
</tr>
<tr class="even">
<td>Male</td>
<td>349,319</td>
<td>47.4%</td>
<td>288,070</td>
<td>46.1%</td>
<td>87,478</td>
<td>49.9%</td>
<td>248,764</td>
<td>49.6%</td>
<td>257,876</td>
<td>49.7%</td>
</tr>
<tr class="odd">
<td>Female</td>
<td>387,333</td>
<td>52.6%</td>
<td>337,3311</td>
<td>53.9%</td>
<td>87,822</td>
<td>50.1%</td>
<td>252,769</td>
<td>50.4%</td>
<td>261,450</td>
<td>50.3%</td>
</tr>
</tbody>
</table>

######### A1.2 Spatial and ecological issues affecting AREX tracts

*Summary: Though it appears that tracts with moderate/high population
density have more vacant and/or rental units, this is not true for all
tracts in the MD and CO AREX counties. Some higher density tracts may
have more desirable neighborhoods and fewer vacant units. Similarly,
there is evidence that suburban and rural tracts may have less stable
net migration of residents. In some cases, new home construction may be
related to vacant units, however, the spatial maps do not identify new
home subdivisions.*

![](media/image1.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

![](media/image2.wmf){width="5.9944444444444445in"
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height="3.816666666666667in"}

![](media/image4.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

Figures A1.1a-b, A1.2a-b show the ecological distribution of vacant
housing units and population density of MD and CO tracts from Census
2000 results. These basic ecological maps suggest that tracts with
larger proportions of vacant and/or rental (high-density) units are
heterogeneously distributed across the AREX counties. Some of the
highlights include:

-   Tracts with large numbers of vacant units coincide with high-density
    population tracts, though this is not true for all tracts,
    especially around Denver in Jefferson County.

-   A large number of tracts have vacant housing units, especially in
    downtown Baltimore City, with several tracts having clusters of
    moderate and high numbers of vacancies in Baltimore County.

-   Despite the large land area of the CO tracts, there are few tracts
    with large numbers of vacant housing units; most of the vacant units
    are in El Paso county, within and around Colorado Springs.

Spatial and ecological issues impact how well administrative records
accurately measure the resident populations of sub-county regions and
their proximity to each other, and can have a variable affect on
demographic group counts. Counties with a large number of vacant housing
units are likely to provide poorer estimates because of the reporting
lag between a moving household and federal agencies recording of
population mobility. Residents of these areas may be less affluent and
potentially less-covered populations. Similarly, transient population
groups, like college students and military personnel, can flow into and
out of other residences and group quarters. Older residents, and
especially women, are more likely to enter or exit nursing homes,
compared to the general population. This group also experiences higher
mortality rates that may impact their coverage, due to reporting lag in
recording mobility or deaths.

A1.3 Demographic diversity of AREX tracts

*Summary: Age diversity is greater in urban and suburban tracts of MD,
while race/Hispanic diversity is greater in urban and suburban tracts of
CO. The Black population in Baltimore City is highly segregated and
appears to be as homogeneous as mostly White tracts in the other
counties. Some tract counts are harder to measure accurately,
particularly those where multi-race reporting occurs and large numbers
of non-relative household members live (not shown). These harder to
measure attributes tend to affect the same tracts.*

![](media/image5.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

![](media/image6.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

![](media/image7.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

![](media/image8.wmf){width="5.9944444444444445in"
height="3.816666666666667in"}

The demographic characteristics of tract residents and the type, price,
and availability of housing units are likely to attract or repel new
in-migrants and affect tract-level coverage rates. The Shannon-Wiener
diversity index measures the number of race/Hispanic groups and their
population proportions within a tract, but does not distinguish whether
a tract is predominantly White or Black. Tract-level diversity using
Census 2000 results is shown above in Figures A1.3a-b, A1.4a-b and can
be summarized:

-   In the MD counties, the most diverse tracts exist in the southern,
    more urban section of MD; the western portion of Baltimore City with
    a large proportion of Blacks appears to be as racially uniform as
    the White, northern portion of Baltimore County.

-   In the CO counties, diversity is concentrated in urban areas and
    several bordering tracts; this pattern may also reflect tracts with
    a large proportion of Hispanics and smaller White minority.

**Appendix 2: Race Imputation**

######### General description of the race imputation process

The race imputation process used logistic model results estimated from
linked CPS-SSA Numident files, as well as Hispanic and Asian surname
files and IHS records (see Bye, 1998 for complete details). The general
model algorithm used the Numident, IHS, and surname identifiers to
predict the matched CPS race codes. The type of Numident record,
frequency of consistent race reports, geographic identifiers, and
foreign birth indicators were also for calculating race probabilities.
The calculated probabilities were then processed through a hot deck
procedure for the final race assignment.

Persons under the age of 18 frequently lacked complete information and
had blank race assignments in their Numident records. More problematic
is that CPS did not include persons under age 15 years and the original
model results did not address this younger age group. Consequently, the
race information was incomplete and potentially inaccurate for minor
children and a second stage imputation process was applied. The derived
race assignment of the primary tax filer was applied to all children.
While this second stage may address problems with children's records, it
may also assign race from inaccurate race identifiers of some
householders.

Table A2 provides the results of the race assignment process and imputed
race codes by type of assignment:

+-------------------+------------+---------+---------+--------+--------+
| ######## Table    |            |         |         |        |        |
| A2: Race Assignme |            |         |         |        |        |
| nt and Imputation |            |         |         |        |        |
|  Rates by Method, |            |         |         |        |        |
|  Race, and County |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+
|                   |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+
| Imputation Method |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+
| Most Frequent     | Baltimore  | Ba      | Douglas | El     | Jef    |
| Report^1^         | County     | ltimore |         | Paso   | ferson |
+-------------------+------------+---------+---------+--------+--------+
| All Persons       | 81.0%      | 74.8%   | 69.0%   | 72.9%  | 75.8%  |
+-------------------+------------+---------+---------+--------+--------+
| White             | 82.5%      | 75.2%   | 40.2%   | 74.6%  | 77.6%  |
+-------------------+------------+---------+---------+--------+--------+
| Black             | 79.9%      | 75.8%   | 61.3%   | 77.2%  | 59.5%  |
+-------------------+------------+---------+---------+--------+--------+
| AI                | 55.7%      | 54.1%   | 35.5%   | 38.3%  | 36.3%  |
+-------------------+------------+---------+---------+--------+--------+
| API               | 64.4%      | 56.4%   | 56.7%   | 64.5%  | 61.2%  |
+-------------------+------------+---------+---------+--------+--------+
| Hispanic          | 1.2%       | 2.1%    | 1.8%    | 5.5%   | 4.4%   |
+-------------------+------------+---------+---------+--------+--------+
|                   |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+
| Imputed Primary   |            |         |         |        |        |
| Tax Filer Race    |            |         |         |        |        |
| (applied to       |            |         |         |        |        |
| persons under     |            |         |         |        |        |
| 18)^2^            |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+
| All Persons       | 9.4%       | 7.9%    | 13.1%   | 10.1%  | 9.7%   |
+-------------------+------------+---------+---------+--------+--------+
| White             | 9.1%       | 6.7%    | 13.3%   | 10.2%  | 9.8%   |
+-------------------+------------+---------+---------+--------+--------+
| Black             | 10.8%      | 8.6%    | 13.2%   | 12.0%  | 10.1%  |
+-------------------+------------+---------+---------+--------+--------+
| AI                | 8.3%       | 7.5%    | 11.2%   | 9.9%   | 9.0%   |
+-------------------+------------+---------+---------+--------+--------+
| API               | 8.8%       | 4.9%    | 10.3%   | 9.2%   | 9.7%   |
+-------------------+------------+---------+---------+--------+--------+
| Hispanic          | \-         | \-      | \-      | \-     | \-     |
+-------------------+------------+---------+---------+--------+--------+
|                   |            |         |         |        |        |
+-------------------+------------+---------+---------+--------+--------+

  ------------------- ------------ --------- --------- --------- ---------
  PCF Probability                                                
  Model (applied to                                              
  all adults)^2^                                                 

  All Persons         3.1%         1.8%      4.1%      6.8%      6.3%

  White               2.9%         4.3%      3.6%      6.7%      5.7%

  Black               0.9%         0.3%      13.6%     3.3%      21.5%

  AI                  20.5%        16.7%     15.1%     10.3%     12.1%

  API                 19.9%        15.8%     17.9%     18.1%     21.5%

  Hispanic            92.5%        82.6%     84.6%     85.3%     88.2%

  ^1^Most frequent                                               
  race report / total                                            
  AREX records                                                   

  ^2^Imputed records                                             
  / total AREX                                                   
  records                                                        
  ------------------- ------------ --------- --------- --------- ---------

**Appendix 3: Tract and Block Incongruities**

######### Technical factors affecting tract and block differences

The relationship between level of geography and the accuracy of AREX
counts is more complicated than it appears. For total population counts,
county-level results can be hypothesized as more accurate than
tract-level results, which in turn are expected to be more accurate than
block-level results. And this relationship was supported by total
population values across the geographic levels. However, statistical,
computational, and substantive issues affect this relationship when
looking at sparse populations that are likely to be distributed in a
heterogeneous fashion across counties.

Table A3.1 (next page) is a listing of blocks for a single tract that
focuses on AI residents and indicated AREX overcounted Census by 250
percent.[^1] Each record shows the block level Algebraic Percent Error
(ALPE) and AREX and census counts and difference for that block. This
single tract covers 34 blocks, but only three have AI residents, based
on Census results, while AREX indicates one block has AI residents.
However, there are four blocks with AI residents, according to AREX, but
three are zero-blocks for Census. Because of the computational problems,
the block level results have two blocks each with 100 percent
undercounts of census. But the five AREX persons who were not counted at
the block-level contributed to a 267 percent overcount at the
tract-level (11-3)/3.[^2]

There is reason to be skeptical about the validity of the AREX
overcounts for Census zero blocks. AREX overcounts may indicate a single
person in a block is an AI but one would expect at least two or three
AIs in a block, reflecting family members and neighbors with similar
backgrounds living in the same neighborhood. The validity of these
overcounts is important when considering the accuracy of the various
geographic levels. One would expect the greatest accuracy at the
county-level, because AREX overcounts could be 'absorbed' by the larger
population counts. At the tract level, AREX overcounts are included in
calculations, but tract-level denominators are sometimes small,
resulting in inflated ALPE overcounts and highly skewed distributions
that are sometimes U-shaped. At the block-level, AREX overcounts are not
included in the distributions and calculations because the zero-blocks
render these as undefined. This is problematic for small populations and
sparse distributions, especially AIs and persons 75+ or 85+.

**Table A3. Block Counts of American Indians for a Sample Tract**

> Tract Block \*\*\*\*AI Block counts\*\*\*\*\*
>
> Block Blks/tract ALPE ALPE AREX Census Difference
>
> 1234501.47 34 2.5 . 0 0 0
>
> 1234502.47 34 2.5 -1 0 1 -1
>
> 1234503.47 34 2.5 . 0 0 0
>
> 1234504.47 34 2.5 . 0 0 0
>
> 1234505.47 34 2.5 . 0 0 0
>
> 1234506.47 34 2.5 . 0 0 0
>
> 1234507.47 34 2.5 -1 0 1 -1
>
> 1234508.47 34 2.5 . 0 0 0
>
> 1234509.47 34 2.5 . 0 0 0
>
> 1234510.47 34 2.5 . . 0 .
>
> 1234511.47 34 2.5 . 0 0 0
>
> 1234512.47 34 2.5 . 0 0 0
>
> 1234513.47 34 2.5 . 1 0 1
>
> 1234514.47 34 2.5 . 0 0 0
>
> 1234515.47 34 2.5 . 0 0 0
>
> 1234516.47 34 2.5 . 0 0 0
>
> 1234517.47 34 2.5 . 0 0 0
>
> 1234518.47 34 2.5 . 0 0 0
>
> 1234519.47 34 2.5 . 0 0 0
>
> 1234520.47 34 2.5 . 0 0 0
>
> 1234521.47 34 2.5 . 0 0 0
>
> 1234522.47 34 2.5 . 0 0 0
>
> 1234523.47 34 2.5 . 0 0 0
>
> 1234524.47 34 2.5 . 0 0 0
>
> 1234525.47 34 2.5 . 0 0 0
>
> 1234526.47 34 2.5 . 0 0 0
>
> 1234527.47 34 2.5 . 0 0 0
>
> 1234528.47 34 2.5 . 0 0 0
>
> 1234529.47 34 2.5 . 0 0 0
>
> 1234530.47 34 2.5 . 0 0 0
>
> 1234531.47 34 2.5 . 4 0 4
>
> 1234532.47 34 2.5 0.000 1 1 0
>
> 1234533.47 34 2.5 . 5 0 5
>
> 1234534.47 34 2.5 . 0 0 0
>
> **Tract Total 11 3 8**

**Appendix 4: Block-Level Analyses**

######### Block-level demographic ALPEs

Summary of Results: The block-level ALPE results provided the least
accurate measure of total population (26 to 38 percent of blocks met the
five percent criterion and about 85 percent met the 25 percent
criterion), compared to tract and county results. But block results were
better than tract ALPEs for sex and selected age groups (0-4, 20-24,
65+, older age groups). Race groups with larger populations provided
better estimates of Census counts at the five percent criterion, but all
block-level ALPEs were worse using the 25 percent criterion. The
block-level results exclude zero blocks and mean county ALPEs are
affected by smaller denominators, an especially important issue for
small population groups that reside in few blocks.

![](media/image9.wmf){width="4.995138888888889in" height="3.5in"}

The block 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 did not identify persons with a
particular attribute (zero blocks). Tract and block ALPEs include blocks
with zero counts because these blocks were collapsed into larger
geographies. However, the block-level ALPEs use the reduced sample of
blocks and the results may be quite different when comparing the ALPEs
at various geographies.

######### TOTAL POPULATION

-   AREX was more accurate in estimating tracts than blocks in all
    counties; from 26 to 38 percent of blocks were within the five
    percent criterion, and about 85 percent were within the 25 percent
    criterion in the five counties; Douglas County had the best results
    at the five percent criterion and Baltimore County was best at the
    25 percent criterion.

-   In the MD counties, slightly more blocks had moderate or large
    overcounts (ALPEs exceeding five percent, compared to the CO
    counties where more blocks had moderate undercounts (minus five
    percent to --24 percent; distributions not shown).

The AREX counts were less accurate at the block-level. Total population
proportions are likely to be less accurate at 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 flaws influenced the results. Though zero blocks were
excluded and fewer blocks met the five percent criterion, a surprisingly
large proportion of blocks met the 25 percent criterion in all five
counties.

######### SEX

![](media/image10.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image11.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image12.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image13.wmf){width="4.995138888888889in" height="3.5in"}

-   The accuracy of AREX sex results at the five percent criterion was
    better for blocks than tracts.

-   From 39 to 55 percent of male and female ALPEs were within the five
    percent criterion in the five counties; from 91 to 94 percent of
    blocks were within the 25 percent criterion.

Male and female undercounts were similar at all geographic levels and
reflected the total population results. This similarity suggests that
AREX processing was neutral towards whether individuals were male or
female. However, males and females have different demographic rates
(migration and mortality) at different points in the life-cycle, which
may account for the small differences in the male and female AREX
results.

######### AGE

![](media/image15.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image16.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image18.wmf){width="4.995138888888889in" height="3.5in"}

-   Age ALPE results support previous findings from tract and county
    results: AREX counts were within five percent of Census counts more
    often for the age 25-74 groups than younger age groups.

-   The age ALPE results for age 25-64 age groups were much worse for
    blocks than tracts in all counties at both five percent and 25
    percent criteria; however, block-level results were better for the
    age 0-4, 20-24, and 65+ age groups at the five percent criterion.

-   Old age ALPEs at the five percent criterion were much better for
    blocks than tracts; though a smaller proportion of blocks had ALPEs
    of less than five percent, compared to tracts; results for the 75-84
    and 85+ age groups were as good or better than for the 65-74 age
    group.

In general, the block-level results for age were less accurate than the
tract-level ALPE results. Besides having smaller denominators for ALPE
calculations, blocks with zero population counts are excluded from the
analyses. But if AREX performs poorly in some blocks and those blocks
are contiguous, it suggests that some block-level ALPE results may be
better than corresponding tract ALPEs. That is, errors may be smaller in
blocks but cumulated into larger ALPEs within tracts. This may be the
case for the 0-4, 20-24, and 65+ age groups because a larger proportion
of blocks (compared to tracts) met the five percent criterion.

######### RACE / ETHNICITY

![](media/image20.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image21.wmf){width="4.995138888888889in" height="3.5in"}

![](media/image23.wmf){width="4.995138888888889in" height="3.5in"}

-   In general, ALPE results at the five percent criterion were better
    for blocks than tracts; but race groups with smaller populations
    were less accurately counted by AREX.

-   All race groups had fewer blocks meeting the 25 percent criterion,
    compared to tract results.

-   In the MD counties, a smaller proportion of blocks were within the
    five percent criterion for Whites and Blacks, compared to tracts;
    but a larger proportion of each of the other race groups was within
    the five percent criterion.

The expected pattern of smaller geography and less accurate AREX counts
is supported by the AREX results at the 25 percent criterion. But there
is a general tendency for some race groups to be counted more accurately
at the block rather than tract-level. This again suggests that
cumulative errors may be occurring at tract and county levels, and is
especially evident for AIs and APIs.

**Appendix 5: Multivariate Model Parameter Estimates**

  ------------------- ------------ ------ ------- ------------ ------ ------- ----------- ------ ------- ----------- ------ ------- ---
  **Table A5.1a:                                                                                                                    
  Categorical                                                                                                                       
  Logistic Regression                                                                                                               
  Results Predicting                                                                                                                
  Total Block-Level                                                                                                                 
  ALPEs-MD                                                                                                                          
  (n=13731)^1^**                                                                                                                    

                                                                                                                                    

                      Large                       Moderate                    Moderate                   Large                      
                      undercount                  undercount                  overcount                  overcount                  

                      β                   se      β                   se      β                  se      β                  se      

  intercept           -1.932       \*\*   0.183   -0.964       \*\*   0.147   -0.923      \*\*   0.153   -2.146      \*\*   0.189   

  Baltimore City      0.106        \*\*   0.081   -0.165       \*\*   0.064   0.107              0.066   0.722       \*\*   0.086   

  vacancy rate (\>    -0.288       \*\*   0.073   -0.060              0.058   0.444       \*\*   0.058   0.811       \*\*   0.073   
  median)                                                                                                                           

  rental rate (\>     1.011        \*\*   0.088   0.390        \*\*   0.071   0.050              0.072   0.438       \*\*   0.087   
  median)                                                                                                                           

  nonrelatives in HH  0.332        \*\*   0.073   0.174        \*\*   0.058   -0.030             0.057   -0.160      \*\*   0.071   
  (\> median)                                                                                                                       

  imputed race %-tax  -0.791       \*\*   0.071   -0.201       \*\*   0.057   0.206       \*\*   0.056   0.173       \*\*   0.070   
  (\> median)                                                                                                                       

  imputed race %-pcf  -0.305       \*\*   0.068   -0.010              0.054   0.027              0.053   -0.036             0.067   
  (\> median)                                                                                                                       

  imputed ethnicity   0.851        \*\*   0.124   0.706        \*\*   0.109   0.898       \*\*   0.119   0.880       \*\*   0.133   
  %(\> median)                                                                                                                      

  censpull % (\>      -0.092              0.077   0.124        \*\*   0.062   0.176       \*\*   0.061   0.080              0.076   
  median)                                                                                                                           

  multi-race-any      -0.268       \*\*   0.073   -0.037              0.057   -0.169      \*\*   0.058   -0.510      \*\*   0.079   
  mention                                                                                                                           

  some other race-any -0.003              0.091   0.001               0.071   -0.266      \*\*   0.077   -0.215      \*\*   0.113   
  mention                                                                                                                           

  population          0.099               0.108   -0.652       \*\*   0.089   -0.273      \*\*   0.083   0.526       \*\*   0.101   
  density-Q1                                                                                                                        

  population          0.088               0.098   -0.244       \*\*   0.075   -0.175      \*\*   0.074   0.255       \*\*   0.094   
  density-Q2                                                                                                                        

  population          0.162        \*\*   0.082   0.117               0.065   -0.012             0.067   -0.414      \*\*   0.085   
  density-Q5                                                                                                                        

  neighborhood-1      -0.044              0.138   -0.139              0.109   -0.108             0.105   0.117              0.124   

  neighborhood-3      0.446        \*\*   0.123   -0.002              0.094   -0.261      \*\*   0.095   -0.033             0.125   

  neighborhood-4      0.421        \*\*   0.117   -0.020              0.088   -0.130             0.085   -0.039             0.111   

  White %-Q1          0.243               0.212   -0.714       \*\*   0.199   -0.294             0.207   0.920       \*\*   0.225   

  White %-Q2          0.096               0.192   -0.619       \*\*   0.181   -0.343             0.194   0.658       \*\*   0.215   

  White %-Q5          -0.217       \*\*   0.094   -0.175       \*\*   0.074   0.036              0.072   0.422       \*\*   0.094   

  Blacks-any          -0.358              0.200   0.422        \*\*   0.186   0.633       \*\*   0.198   0.031              0.217   

  Hispanics-any       -0.316       \*\*   0.080   -0.002              0.061   -0.108             0.061   -0.599      \*\*   0.085   

  age \< 5 (\>        0.466        \*\*   0.068   0.342        \*\*   0.054   -0.312      \*\*   0.055   -0.596      \*\*   0.071   
  median)                                                                                                                           

  age 5-19 (\>        0.149               0.078   0.211        \*\*   0.062   -0.230      \*\*   0.062   -0.564      \*\*   0.078   
  median)                                                                                                                           

  age 20-24 (\>       0.398        \*\*   0.070   0.194        \*\*   0.057   0.089              0.057   -0.036             0.071   
  median)                                                                                                                           

  age 25-44 (\>       -0.042              0.074   0.173        \*\*   0.059   -0.064             0.059   -0.076             0.073   
  median)                                                                                                                           

  age 65+ (\> median) -0.265       \*\*   0.077   -0.012              0.062   0.071              0.062   -0.071             0.076   

                                                                                                                                    

  \*\* p \< .05; used                                                                                                               
  to distinguish                                                                                                                    
  important                                                                                                                         
  predictors for                                                                                                                    
  these non-sample                                                                                                                  
  data.                                                                                                                             

  ^1^Reference range                                                                                                                
  is best quartile                                                                                                                  
  estimate, where                                                                                                                   
  AREX is -2.3% to                                                                                                                  
  +5.5% of Census                                                                                                                   
  total population                                                                                                                  

                                                                                                                                    

  *Large undercount                                                                                                                 
  \< -14.4%; Moderate                                                                                                               
  undercount=-14.4%                                                                                                                 
  to -2.3%; Moderate                                                                                                                
  overcount=5.5% to                                                                                                                 
  19.8%; Large                                                                                                                      
  overcount \> 19.8%*                                                                                                               
  ------------------- ------------ ------ ------- ------------ ------ ------- ----------- ------ ------- ----------- ------ ------- ---

  ----------------- ------------ ------ ------- ------------ ------ ------- ----------- ------ ------- ----------- ------ -------
  **Table A5.1b:                                                                                                          
  Categorical                                                                                                             
  Logistic                                                                                                                
  Regression                                                                                                              
  Results                                                                                                                 
  Predicting Total                                                                                                        
  Block-Level                                                                                                             
  ALPEs-CO                                                                                                                
  (n=16948)^1^**                                                                                                          

                                                                                                                          

                    Large                       Moderate                    Moderate                   Large              
                    undercount                  undercount                  overcount                  overcount          

                    β                   se      β                   se      β                  se      β                  se

  intercept         -2.346       \*\*   0.162   -1.707       \*\*   0.139   -1.363      \*\*   0.149   -2.352      \*\*   0.168

  Douglas County    -0.126              0.091   0.180        \*\*   0.071   -0.002             0.075   -0.105             0.093

  El Paso County    -0.191       \*\*   0.066   -0.253       \*\*   0.055   0.063              0.054   0.043              0.065

  vacancy rate (\>  -0.057              0.088   0.133               0.074   0.550       \*\*   0.074   0.740       \*\*   0.084
  median)                                                                                                                 

  rental rate (\>   0.527        \*\*   0.065   0.238        \*\*   0.054   0.011              0.053   0.193       \*\*   0.065
  median)                                                                                                                 

  nonrelatives in   0.456        \*\*   0.064   0.305        \*\*   0.052   0.159       \*\*   0.051   -0.074             0.064
  HH (\> median)                                                                                                          

  imputed race      -0.651       \*\*   0.068   -0.007              0.055   0.355       \*\*   0.054   0.331       \*\*   0.067
  %-tax (\> median)                                                                                                       

  imputed race      -0.065              0.064   0.178        \*\*   0.051   0.248       \*\*   0.051   0.521       \*\*   0.064
  %-pcf (\> median)                                                                                                       

  imputed ethnicity 0.936        \*\*   0.099   1.260        \*\*   0.095   1.559       \*\*   0.113   1.393       \*\*   0.113
  %(\> median)                                                                                                            

  censpull % (\>    0.133               0.070   0.342        \*\*   0.058   0.028              0.059   -0.020             0.071
  median)                                                                                                                 

  multi-race-any    -0.530       \*\*   0.074   -0.219       \*\*   0.056   -0.315      \*\*   0.056   -0.772      \*\*   0.077
  mention                                                                                                                 

  some other        -0.214       \*\*   0.079   -0.119       \*\*   0.059   -0.182      \*\*   0.059   -0.222      \*\*   0.084
  race-any mention                                                                                                        

  population        0.134               0.085   -0.503       \*\*   0.073   -0.318      \*\*   0.070   0.693       \*\*   0.084
  density-Q1                                                                                                              

  population        0.172        \*\*   0.077   -0.107              0.061   0.029              0.059   0.468       \*\*   0.078
  density-Q2                                                                                                              

  population        0.496        \*\*   0.110   0.299        \*\*   0.089   -0.349      \*\*   0.106   -0.024             0.148
  density-Q5                                                                                                              

  neighborhood-1    0.106               0.111   0.125               0.086   -0.104             0.083   -0.138             0.110

  neighborhood-2    0.653        \*\*   0.104   0.170        \*\*   0.086   -0.115             0.086   0.130              0.107

  neighborhood-4    0.278        \*\*   0.111   0.030               0.096   -0.302      \*\*   0.092   -0.011             0.102

  White %-Q1        0.379               0.332   -2.721       \*\*   1.017   n/a                n/a     0.628       \*\*   0.298

  White %-Q2        0.651        \*\*   0.084   -0.026              0.067   -0.095             0.069   0.844       \*\*   0.089

  White %-Q5        0.132               0.087   -0.445       \*\*   0.074   -0.445      \*\*   0.072   0.214       \*\*   0.086

  Blacks-any        -0.355       \*\*   0.102   -0.130              0.079   0.056              0.080   -0.093             0.105

  Hispanics-any     -0.270       \*\*   0.077   0.110               0.065   0.096              0.064   -0.448      \*\*   0.077

  age \< 5 (\>      0.537        \*\*   0.067   0.326        \*\*   0.054   -0.291      \*\*   0.055   -0.603      \*\*   0.074
  median)                                                                                                                 

  age 5-19 (\>      0.487        \*\*   0.065   0.271        \*\*   0.055   -0.033             0.055   -0.569      \*\*   0.069
  median)                                                                                                                 

  age 20-24 (\>     0.334        \*\*   0.066   0.149        \*\*   0.055   0.139       \*\*   0.055   0.151       \*\*   0.068
  median)                                                                                                                 

  age 25-44 (\>     -0.145       \*\*   0.065   -0.016              0.054   -0.263      \*\*   0.054   -0.102             0.067
  median)                                                                                                                 

  age 65+ (\>       0.148        \*\*   0.072   0.174        \*\*   0.061   0.183       \*\*   0.059   0.244       \*\*   0.070
  median)                                                                                                                 

                                                                                                                          

  \*\* p \< .05;                                                                                                          
  used to                                                                                                                 
  distinguish                                                                                                             
  important                                                                                                               
  predictors for                                                                                                          
  these non-sample                                                                                                        
  data.                                                                                                                   

  ^1^Reference                                                                                                            
  range is best                                                                                                           
  quartile                                                                                                                
  estimate, where                                                                                                         
  AREX is -4.2% to                                                                                                        
  +2.0% of Census                                                                                                         
  total population                                                                                                        

                                                                                                                          

  *Large undercount                                                                                                       
  \< -16.7%;                                                                                                              
  Moderate                                                                                                                
  undercount=                                                                                                             
  -16.7% to -4.2%;                                                                                                        
  Moderate                                                                                                                
  overcount=2.0% to                                                                                                       
  16.2%; Large                                                                                                            
  overcount \>                                                                                                            
  16.2%*                                                                                                                  
  ----------------- ------------ ------ ------- ------------ ------ ------- ----------- ------ ------- ----------- ------ -------

  ----------------- ----------- -------- ------------- -------- --------- -------------------- ----------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------
  **Table A5.2a:                                                                                                                                                                                                  
  Piecewise                                                                                                                                                                                                       
  Logistic                                                                                                                                                                                                        
  Regression                                                                                                                                                                                                      
  Results                                                                                                                                                                                                         
  Predicting Total                                                                                                                                                                                                
  Block-Level                                                                                                                                                                                                     
  ALPEs-MD                                                                                                                                                                                                        
  (n=13731)^1^**                                                                                                                                                                                                  

                                                                                                                                                                                                                  

                                         Mobility                         Imputation/Process                          Other                     Population                              Neighborhoods             
                                         Variables                        variables                                   Race                      Density                                                           
                                                                                                                                                Quintile                                                          

  Q-grp             Intercept   vac50    rent50        nrel50   imp-tax   imp-pcf              imp-hisp    censpull           mrace    sor                   Q1       Q2       Q5                       neigh-1   neigh-3

  1                 -0.289      0.014    -0.016                 0.043     0.032                            0.026                                             -0.063   -0.018                            -0.027    -0.027

  2                 -0.068      0.005    -0.004        -0.003   0.004                                                                                                          -0.003                             

  3                 0.110       0.004                                                                                                                                          -0.005                             

  4                 0.278       0.006                           0.005     -0.005                                              -0.007                         0.020    0.012    -0.012                             

  5                 0.010                                                 0.003                0.007       0.003                                             -0.003                                     -0.004    -0.005

                                                                                                                                                                                                                  

  **Table A5.2a                                                                                                                                                                                                   
  (cont'd)**                                                                                                                                                                                                      

                                         White                  Race                           County      Age Groups                                                                                             
                                         Quintiles                                                                                                                                                                

  Q-grp             neigh-4     Q1       Q2            Q5       Black     Hispanic             Baltimore   \< 5               5-19     20-24                 25-44    65+                                         

  1                 -0.029      -0.040   -0.035                 0.036                          -0.016                                                                 0.032                                       

  2                                                                       0.003                                                        -0.003                         0.003                                       

  3                             0.013                                     -0.005               0.006                                                                                                              

  4                             0.021                                                          0.009       -0.010             -0.006                                                                              

  5                 -0.003      -0.006                 -0.003   0.007                          -0.002                                                                                                             

  note: p \< .05                                                                                                                                                                                                  
  for all parameter                                                                                                                                                                                               
  estimates shown;                                                                                                                                                                                                
  non-significant                                                                                                                                                                                                 
  estimates have                                                                                                                                                                                                  
  been omitted to                                                                                                                                                                                                 
  simplify display;                                                                                                                                                                                               
  used to                                                                                                                                                                                                         
  distinguish                                                                                                                                                                                                     
  important                                                                                                                                                                                                       
  predictors for                                                                                                                                                                                                  
  these non-sample                                                                                                                                                                                                
  data.                                                                                                                                                                                                           

                                                                                                                                                                                                                  

  Q-grps relative                                                                                                                                                                                                 
  to reference                                                                                                                                                                                                    
  group: 1=large                                                                                                                                                                                                  
  undercount,                                                                                                                                                                                                     
  2=moderate                                                                                                                                                                                                      
  undercount,                                                                                                                                                                                                     
  3=moderate                                                                                                                                                                                                      
  overcount,                                                                                                                                                                                                      
  4=large overcount                                                                                                                                                                                               

  ^1^See notes at                                                                                                                                                                                                 
  end of Table                                                                                                                                                                                                    
  A5.2f for                                                                                                                                                                                                       
  additional                                                                                                                                                                                                      
  information.                                                                                                                                                                                                    
  ----------------- ----------- -------- ------------- -------- --------- -------------------- ----------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------

  ----------------- ----------- -------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------
  **Table A5.2b:                                                                                                                                                                                                 
  Piecewise                                                                                                                                                                                                      
  Logistic                                                                                                                                                                                                       
  Regression                                                                                                                                                                                                     
  Results                                                                                                                                                                                                        
  Predicting Total                                                                                                                                                                                               
  Block-Level                                                                                                                                                                                                    
  ALPEs-CO                                                                                                                                                                                                       
  (n=16948)^1^**                                                                                                                                                                                                 

                                                                                                                                                                                                                 

                                         Mobility                         Imputation/Process                         Other                     Population                              Neighborhoods             
                                         Variables                        variables                                  Race                      Density                                                           
                                                                                                                                               Quintile                                                          

  Q-grp             Intercept   vac50    rent50        nrel50   imp-tax   imp-pcf              imp-hisp   censpull           mrace    sor                   Q1       Q2       Q5                       neigh-1   neigh-2

  1                 -0.301                             0.015    0.056     0.032                           0.019                                             -0.053   -0.030   -0.020                             -0.034

  2                 -0.092               -0.003                 0.004                                                                                                         -0.006                             -0.005

  3                 0.089                                                                                                    -0.004   -0.003                0.008                                      -0.005    

  4                 0.258                              -0.007                                  -0.013                        -0.007                         0.022                                                0.011

  5                                                    -0.002                                  -0.006     -0.002                                            0.002                                                

                                                                                                                                                                                                                 

  **Table A5.2b                                                                                                                                                                                                  
  (cont'd)**                                                                                                                                                                                                     

                                         White                  Race                           County     Age Groups                                                                                             
                                         Quintiles                                                                                                                                                               

  Q-grp             neigh-4     Q1       Q2            Q5       Black     Hispanic             Douglas    El Paso            \< 5     5-19                  20-24    25-44    65+                                

  1                 -0.026      -0.100   -0.032                 0.024     0.015                                              -0.014   -0.016                         0.013                                       

  2                 -0.005               -0.006                                                                              -0.003                                                                              

  3                                      0.008                            -0.005                                             -0.004   -0.005                                                                     

  4                 0.014       0.052    0.026         0.020              -0.012                                             -0.007   -0.012                -0.009                                               

  5                                                    0.003              -0.003                                             -0.002   -0.002                                                                     

  note: p \< .05                                                                                                                                                                                                 
  for all parameter                                                                                                                                                                                              
  estimates shown;                                                                                                                                                                                               
  non-significant                                                                                                                                                                                                
  estimates have                                                                                                                                                                                                 
  been omitted to                                                                                                                                                                                                
  simplify display;                                                                                                                                                                                              
  used to                                                                                                                                                                                                        
  distinguish                                                                                                                                                                                                    
  important                                                                                                                                                                                                      
  predictors for                                                                                                                                                                                                 
  these non-sample                                                                                                                                                                                               
  data.                                                                                                                                                                                                          

                                                                                                                                                                                                                 

  Q-grps relative                                                                                                                                                                                                
  to reference                                                                                                                                                                                                   
  group: 1=large                                                                                                                                                                                                 
  undercount,                                                                                                                                                                                                    
  2=moderate                                                                                                                                                                                                     
  undercount,                                                                                                                                                                                                    
  3=moderate                                                                                                                                                                                                     
  overcount,                                                                                                                                                                                                     
  4=large overcount                                                                                                                                                                                              

  ^1^See notes at                                                                                                                                                                                                
  end of Table                                                                                                                                                                                                   
  A5.2f for                                                                                                                                                                                                      
  additional                                                                                                                                                                                                     
  information.                                                                                                                                                                                                   
  ----------------- ----------- -------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------

  ----------------- ----------- ------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ ------- ------- ------- --------------- --------- ---------
  **Table A5.2c:                                                                                                                                                                                             
  Piecewise                                                                                                                                                                                                  
  Logistic                                                                                                                                                                                                   
  Regression                                                                                                                                                                                                 
  Results                                                                                                                                                                                                    
  Predicting Age                                                                                                                                                                                             
  0-4 Block-Level                                                                                                                                                                                            
  ALPEs-CO (n=12603                                                                                                                                                                                          
  for all 5 models)                                                                                                                                                                                          
  ^1^**                                                                                                                                                                                                      

                                                                                                                                                                                                             

                                        Mobility                         Imputation/Process                         Other                     Population                           Neighborhoods             
                                        Variables                        variables                                  Race                      Density                                                        
                                                                                                                                              Quintile                                                       

  Q-grp             Intercept   vac50   rent50        nrel50   imp-tax   imp-pcf              imp-hisp   censpull           mrace    sor                   Q1      Q2      Q5                      neigh-1   neigh-2

  1                 n/a         n/a     n/a           n/a      n/a       n/a                  n/a        n/a                n/a      n/a                   n/a     n/a     n/a                     n/a       n/a

  2                 -0.543              -0.011                 0.026                          -0.034     0.010                                                                                               

  3                 -0.319                                     0.013                          -0.017     0.013                                                                                               

  4                 0.754                                                                                                   -0.163                                                                           

  5                 0.037                                      -0.011                         -0.031     -0.013             -0.022   -0.013                0.008   0.006                                     

                                                                                                                                                                                                             

  **Table A5.2c                                                                                                                                                                                              
  (cont'd)**                                                                                                                                                                                                 

                                        White                  Race                           County     Age Groups                                                                                          
                                        Quintiles                                                                                                                                                            

  Q-grp             neigh-4     Q1      Q2            Q5       Black     Hispanic             Douglas    El Paso            \< 5     5-19                  20-24   25-44   65+                               

  1                 n/a         n/a     n/a           n/a      n/a       n/a                  n/a        n/a                n/a      n/a                   n/a     n/a     n/a                               

  2                                                   0.013              -0.017                                                                                            0.014                             

  3                                                   0.010                                                                 -0.014   -0.006                                                                  

  4                                     0.112                  -0.131                                                       -0.430                                         0.172                             

  5                                     0.023         0.006    -0.015    -0.018               -0.012                        -0.039                                         0.008                             

  note: p \< .05                                                                                                                                                                                             
  for all parameter                                                                                                                                                                                          
  estimates shown;                                                                                                                                                                                           
  non-significant                                                                                                                                                                                            
  estimates have                                                                                                                                                                                             
  been omitted to                                                                                                                                                                                            
  simplify display;                                                                                                                                                                                          
  used to                                                                                                                                                                                                    
  distinguish                                                                                                                                                                                                
  important                                                                                                                                                                                                  
  predictors for                                                                                                                                                                                             
  these non-sample                                                                                                                                                                                           
  data.                                                                                                                                                                                                      

                                                                                                                                                                                                             

  Q-grps relative                                                                                                                                                                                            
  to reference                                                                                                                                                                                               
  group: 1=large                                                                                                                                                                                             
  undercount,                                                                                                                                                                                                
  2=moderate                                                                                                                                                                                                 
  undercount,                                                                                                                                                                                                
  3=moderate                                                                                                                                                                                                 
  overcount,                                                                                                                                                                                                 
  4=large overcount                                                                                                                                                                                          

  ^1^See notes at                                                                                                                                                                                            
  end of Table                                                                                                                                                                                               
  A5.2f for                                                                                                                                                                                                  
  additional                                                                                                                                                                                                 
  information.                                                                                                                                                                                               
  ----------------- ----------- ------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ ------- ------- ------- --------------- --------- ---------

  ----------------- ----------- -------- ------------- -------- --------- -------------------- ----------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------
  **Table A5.2d:                                                                                                                                                                                                  
  Piecewise                                                                                                                                                                                                       
  Logistic                                                                                                                                                                                                        
  Regression                                                                                                                                                                                                      
  Results                                                                                                                                                                                                         
  Predicting Age                                                                                                                                                                                                  
  65+ Block-Level                                                                                                                                                                                                 
  ALPEs-MD (n=12688                                                                                                                                                                                               
  for all 5 models)                                                                                                                                                                                               
  ^1^**                                                                                                                                                                                                           

                                                                                                                                                                                                                  

                                         Mobility                         Imputation/Process                          Other                     Population                              Neighborhoods             
                                         Variables                        variables                                   Race                      Density                                                           
                                                                                                                                                Quintile                                                          

  Q-grp             Intercept   vac50    rent50        nrel50   imp-tax   imp-pcf              imp-hisp    censpull           mrace    sor                   Q1       Q2       Q5                       neigh-1   neigh-3

  1                 -0.534      0.039                           0.029     0.067                0.063       0.048              0.044                          -0.155   -0.076                            -0.054    -0.091

  2                 0.098                -0.007                                                                               -0.006                                           -0.005                             

  3                 0.275       0.008                                                                                                                                                                             

  4                 0.741                                                                                                                                    0.289    0.190                                       0.193

  5                                                                                            -0.008                         -0.005   -0.007                0.010             -0.007                             0.007

                                                                                                                                                                                                                  

  **Table A5.2d                                                                                                                                                                                                   
  (cont'd)**                                                                                                                                                                                                      

                                         White                  Race                           County      Age Groups                                                                                             
                                         Quintiles                                                                                                                                                                

  Q-grp             neigh-4     Q1       Q2            Q5       Black     Hispanic             Baltimore   \< 5               5-19     20-24                 25-44    65+                                         

  1                 -0.081      -0.164   -0.137                 0.110     0.056                                               0.031                                   0.107                                       

  2                                                                       -0.007               0.008                                                                  -0.012                                      

  3                                                                       -0.007                           0.007                                                      -0.020                                      

  4                             0.271                                                                                                                                 -0.281                                      

  5                 0.006                              0.004              -0.007                                                                             0.004    -0.015                                      

  note: p \< .05                                                                                                                                                                                                  
  for all parameter                                                                                                                                                                                               
  estimates shown;                                                                                                                                                                                                
  non-significant                                                                                                                                                                                                 
  estimates have                                                                                                                                                                                                  
  been omitted to                                                                                                                                                                                                 
  simplify display;                                                                                                                                                                                               
  used to                                                                                                                                                                                                         
  distinguish                                                                                                                                                                                                     
  important                                                                                                                                                                                                       
  predictors for                                                                                                                                                                                                  
  these non-sample                                                                                                                                                                                                
  data.                                                                                                                                                                                                           

                                                                                                                                                                                                                  

  Q-grps relative                                                                                                                                                                                                 
  to reference                                                                                                                                                                                                    
  group: 1=large                                                                                                                                                                                                  
  undercount,                                                                                                                                                                                                     
  2=moderate                                                                                                                                                                                                      
  undercount,                                                                                                                                                                                                     
  3=moderate                                                                                                                                                                                                      
  overcount,                                                                                                                                                                                                      
  4=large overcount                                                                                                                                                                                               

  ^1^See notes at                                                                                                                                                                                                 
  end of Table                                                                                                                                                                                                    
  A5.2f for                                                                                                                                                                                                       
  additional                                                                                                                                                                                                      
  information.                                                                                                                                                                                                    
  ----------------- ----------- -------- ------------- -------- --------- -------------------- ----------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- ---------

  ----------------- ----------- ------- ------------- -------- --------- -------------------- ----------- ---------- ------- ------- ------- ------------ -------- -------- -------- --------------- --------- ---------
  **Table A5.2e:                                                                                                                                                                                               
  Piecewise                                                                                                                                                                                                    
  Logistic                                                                                                                                                                                                     
  Regression                                                                                                                                                                                                   
  Results                                                                                                                                                                                                      
  Predicting                                                                                                                                                                                                   
  Block-Level Black                                                                                                                                                                                            
  ALPEs-MD (n=11238                                                                                                                                                                                            
  for all 5 models)                                                                                                                                                                                            
  ^1^**                                                                                                                                                                                                        

                                                                                                                                                                                                               

                                        Mobility                         Imputation/Process                          Other                   Population                              Neighborhoods             
                                        Variables                        variables                                   Race                    Density                                                           
                                                                                                                                             Quintile                                                          

  Q-grp             Intercept   vac50   rent50        nrel50   imp-tax   imp-pcf              imp-hisp    censpull           mrace   sor                  Q1       Q2       Q5                       neigh-1   neigh-3

  1                 -0.778                                               0.048                            0.045                                           -0.069                                               

  2                 -0.254              -0.018                 0.012                                                         0.021   0.012                -0.020                                               

  3                 0.173                                                                                 -0.009                                          0.017             -0.013                             

  4                 0.952                             0.330    -0.303                                     -0.323                                          0.910                                                

  5                                                                                                                                                                                                            

                                                                                                                                                                                                               

  **Table A5.2e                                                                                                                                                                                                
  (cont'd)**                                                                                                                                                                                                   

                                        White                  Race                           County      Age Groups                                                                                           
                                        Quintiles                                                                                                                                                              

  Q-grp             neigh-4     Q1      Q2            Q5       Black     Hispanic             Baltimore   \< 5               5-19    20-24                25-44    65+                                         

  1                 -0.046                            -0.140             0.042                            0.029                      0.031                         -0.031                                      

  2                             0.047   0.038         -0.123   0.027     0.011                            -0.008                                                                                               

  3                                                            -0.030    -0.011               0.017       -0.007                                                                                               

  4                                                                                                                                                                                                            

  5                                                                                                                                                                                                            

  note: p \< .05                                                                                                                                                                                               
  for all parameter                                                                                                                                                                                            
  estimates shown;                                                                                                                                                                                             
  non-significant                                                                                                                                                                                              
  estimates have                                                                                                                                                                                               
  been omitted to                                                                                                                                                                                              
  simplify display;                                                                                                                                                                                            
  used to                                                                                                                                                                                                      
  distinguish                                                                                                                                                                                                  
  important                                                                                                                                                                                                    
  predictors for                                                                                                                                                                                               
  these non-sample                                                                                                                                                                                             
  data.                                                                                                                                                                                                        

                                                                                                                                                                                                               

  Q-grps relative                                                                                                                                                                                              
  to reference                                                                                                                                                                                                 
  group: 1=large                                                                                                                                                                                               
  undercount,                                                                                                                                                                                                  
  2=moderate                                                                                                                                                                                                   
  undercount,                                                                                                                                                                                                  
  3=moderate                                                                                                                                                                                                   
  overcount,                                                                                                                                                                                                   
  4=large overcount                                                                                                                                                                                            

  ^1^See notes at                                                                                                                                                                                              
  end of Table                                                                                                                                                                                                 
  A5.2f for                                                                                                                                                                                                    
  additional                                                                                                                                                                                                   
  information.                                                                                                                                                                                                 
  ----------------- ----------- ------- ------------- -------- --------- -------------------- ----------- ---------- ------- ------- ------- ------------ -------- -------- -------- --------------- --------- ---------

  --------------------- ----------- ------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- --------- --
  **Table A5.2f:                                                                                                                                                                                                              
  Piecewise Logistic                                                                                                                                                                                                          
  Regression Results                                                                                                                                                                                                          
  Predicting                                                                                                                                                                                                                  
  Block-Level Hispanic                                                                                                                                                                                                        
  ALPEs-CO (n=11289 for                                                                                                                                                                                                       
  all 5 models) ^1^**                                                                                                                                                                                                         

                                                                                                                                                                                                                              

                                            Mobility                         Imputation/Process                         Other                     Population                              Neighborhoods                       
                                            Variables                        variables                                  Race                      Density                                                                     
                                                                                                                                                  Quintile                                                                    

  Q-grp                 Intercept   vac50   rent50        nrel50   imp-tax   imp-pcf              imp-hisp   censpull           mrace    sor                   Q1       Q2       Q5                       neigh-1   neigh-2   

  1                     -0.989              0.025         0.013    0.013     0.047                                              0.030    0.041                 -0.026   -0.020   -0.028                                       

  2                     -0.472                                     0.016     0.054                           0.014              0.022    0.012                 -0.025   -0.016   -0.023                   0.019               

  3                     0.434                                                                                                   -0.047   -0.071                                  -0.039                                       

  4                     1.494                                      0.336     0.210                                                       -0.303                                                                               

  5                     0.017               -0.009                 -0.012    -0.021               -0.020                        -0.019   -0.029                0.009                                                          

                                                                                                                                                                                                                              

  **Table A5.2f                                                                                                                                                                                                               
  (cont'd)**                                                                                                                                                                                                                  

                                            White                  Race                           County     Age Groups                                                                                                       
                                            Quintiles                                                                                                                                                                         

  Q-grp                 neigh-4     Q1      Q2            Q5       Black     Hispanic             Douglas    El Paso            \< 5     5-19                  20-24    25-44    65+                                          

  1                                                                0.026                                                                 0.017                                                                                

  2                                         -0.017                 0.022                                     -0.014                                                                                                           

  3                                                                                                                                                                                                                           

  4                                                       -0.388                                                                                                                                                              

  5                                                                                               0.009      0.006                                             -0.006                                                         

  note: p \< .05 for                                                                                                                                                                                                          
  all parameter                                                                                                                                                                                                               
  estimates shown;                                                                                                                                                                                                            
  non-significant                                                                                                                                                                                                             
  estimates have been                                                                                                                                                                                                         
  omitted to simplify                                                                                                                                                                                                         
  display; used to                                                                                                                                                                                                            
  distinguish important                                                                                                                                                                                                       
  predictors for these                                                                                                                                                                                                        
  non-sample data.                                                                                                                                                                                                            

                                                                                                                                                                                                                              

  Q-grps relative to                                                                                                                                                                                                          
  reference group:                                                                                                                                                                                                            
  1=large undercount,                                                                                                                                                                                                         
  2=moderate                                                                                                                                                                                                                  
  undercount,                                                                                                                                                                                                                 
  3=moderate overcount,                                                                                                                                                                                                       
  4=large overcount                                                                                                                                                                                                           

                                                                                                                                                                                                                              

  vac50=vacant                                                                                                                                                                                                                
  proportion of block                                                                                                                                                                                                         
  housing units: binary                                                                                                                                                                                                       
  indicator of top 50%                                                                                                                                                                                                        

  rent50=rental                                                                                                                                                                                                               
  proportion of block                                                                                                                                                                                                         
  housing units: binary                                                                                                                                                                                                       
  indicator of top 50%                                                                                                                                                                                                        

  nrel50=non-relative                                                                                                                                                                                                         
  household member                                                                                                                                                                                                            
  proportion of block                                                                                                                                                                                                         
  households: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  imp-tax=proportion of                                                                                                                                                                                                       
  cases imputed using                                                                                                                                                                                                         
  tax method: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  imp-pcf=proportion of                                                                                                                                                                                                       
  cases imputed using                                                                                                                                                                                                         
  pcf method: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  imp-hisp=proportion                                                                                                                                                                                                         
  of cases imputed for                                                                                                                                                                                                        
  hispanic origin:                                                                                                                                                                                                            
  binary indicator of                                                                                                                                                                                                         
  top 50%                                                                                                                                                                                                                     

  censpull=proportion                                                                                                                                                                                                         
  of census pull cases:                                                                                                                                                                                                       
  binary indicator of                                                                                                                                                                                                         
  top 50%                                                                                                                                                                                                                     

  mrace=presence of                                                                                                                                                                                                           
  multi-race reports                                                                                                                                                                                                          

  sor=presence of some                                                                                                                                                                                                        
  other race reports                                                                                                                                                                                                          

  popd Q1, Q2,                                                                                                                                                                                                                
  Q5=population density                                                                                                                                                                                                       
  quintiles of block:                                                                                                                                                                                                         
  Q1 (low), Q2, Q5 with                                                                                                                                                                                                       
  Q3 and Q4 as the                                                                                                                                                                                                            
  reference group                                                                                                                                                                                                             

  neigh-1, 2, 3, 4=type                                                                                                                                                                                                       
  of neighborhood from                                                                                                                                                                                                        
  factor analyses                                                                                                                                                                                                             

  wht Q1, Q2, Q5=White                                                                                                                                                                                                        
  quintile groups from                                                                                                                                                                                                        
  White proportion of                                                                                                                                                                                                         
  block population: Q1                                                                                                                                                                                                        
  (low), Q2, Q5 with Q3                                                                                                                                                                                                       
  and Q4 as the                                                                                                                                                                                                               
  reference group                                                                                                                                                                                                             

  Black=Black                                                                                                                                                                                                                 
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  hispanic=presence of                                                                                                                                                                                                        
  any Hispanic                                                                                                                                                                                                                
  residents on block                                                                                                                                                                                                          

  baltimore=indicator                                                                                                                                                                                                         
  for Baltimore City:                                                                                                                                                                                                         
  Baltimore County as                                                                                                                                                                                                         
  reference group;                                                                                                                                                                                                            
  douglas,                                                                                                                                                                                                                    
  elpaso=indicators for                                                                                                                                                                                                       
  counties: Jefferson                                                                                                                                                                                                         
  County as reference                                                                                                                                                                                                         
  group                                                                                                                                                                                                                       

  age \< 5=age 0-4                                                                                                                                                                                                            
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  age 5-19=age 5-19                                                                                                                                                                                                           
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  age 20-24=age 0-4                                                                                                                                                                                                           
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  age 25-44=age 25-44                                                                                                                                                                                                         
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        

  age 65+=age 65+                                                                                                                                                                                                             
  proportion of block                                                                                                                                                                                                         
  population: binary                                                                                                                                                                                                          
  indicator of top 50%                                                                                                                                                                                                        
  --------------------- ----------- ------- ------------- -------- --------- -------------------- ---------- ---------- ------- -------- -------- ------------ -------- -------- -------- --------------- --------- --------- --

**Appendix 6: Glossary of Terms and Definitions**

  ---------------------- ------------------------------------------------
  ALPE                   Algebraic Percent Error, formed from Census and
                         AREX counts using Census results as the
                         standard.

  ABI                    American Business Information; ABI is a
                         commercially available list of residential and
                         business addresses covering the entire U.S.

  AI                     American Indians.

  API                    Asian and Pacific Islanders.

  AREX 2000              Administrative Records Experiment in 2000.

  Bottom-up              Bottom-up method of processing AREX counts that
                         includes MAF address verification and variable
                         imputation.

  Census-pull            For addresses that failed to match the MAF, the
                         bottom-up process replaced some of these
                         addresses with actual Census 2000 records.

  Code-1                 Code-1 is a commercially available software
                         product used to standardize and match addresses
                         to other address lists.

  FAV estimation         For addresses that failed to match the MAF, the
                         bottom-up process replaced some of these
                         addresses using estimated counts derived from a
                         sample of households that were authenticated by
                         a field address verification (FAV) process.

  GIS                    Geographic information system.

  Hispanic origin        Hispanic origin of any type, based on
                         administrative reports, surname processing,
                         country of origin, and Hispanic origin of
                         householder.

  Hot deck assignment    The race imputation process used statistical
                         models to calculate expected race probabilities
                         for each person. The hot deck assignment was
                         based on an algorithm that compared the
                         calculated probability with a randomly drawn
                         number to determine whether a calculated
                         probability was large enough to be assigned to a
                         particular race category.

  Index of Dissimilarity Index of summed differences between AREX and
                         Census counts based on either race/ethnicity or
                         age groups.

  MAF or Master Address  The master list of verified household addresses
  File                   used to conduct Census-related activities.

  Multi-race rate        Derived from Census: based on reported number of
                         race responses.

  Neighborhood           Estimated from factor analyses that distinguish
  characteristics        four types of AREX neighborhoods in each AREX
                         state; derived from demographic, housing unit,
                         and population density variables.

  Non-relative rate      Derived from Census: proportion of households
                         with non-relative members.

  NRFU                   Nonresponse follow-up; households that could not
                         be enumerated through usual Census enumeration
                         methods.

  Numident               The electronic roster of participants in any of
                         the social programs maintained by the Social
                         Security Administration, compiled from SSN
                         applications, name changes, and corrections.

  Overcount              AREX counts that are greater than Census counts,
                         expressed as differences or ALPEs.

  PCF probability model  The personal characteristics file (PCF) used a
                         probabilistic race imputation methodology based
                         on logistic regression models and hot deck
                         assignment.

  Population density     Population per unit area, expressed as persons
                         per square mile.

  PRED                   Planning, Research, and Evaluation Division.

  Race                   AREX race values are based on 'generally
                         accepted' race categories that are derived from
                         complex AREX processing rules; Census race
                         measures use self-reported race from Census
                         forms and exclude persons claiming some other
                         race or multi-race.

  Rental rate            Derived from Census: proportion of housing units
                         identified as rental units.

  Shannon-Wiener Index   Summed index of age or race components using
  of Diversity           AREX-only measures to distinguish regions with
                         more or less diverse populations.

  StARS                  Statistical Administrative Records System.

  Top-down               Top-Down Administrative Records counts that
                         includes block-coding but no further
                         enhancements.

  TIGER                  Topologically Integrated Geographic and
                         Cartographic Encoding and Referencing database
                         of all U.S. regions and Puerto Rico.

  Undercount             AREX counts that are less than Census counts,
                         expressed as differences or ALPEs.

  Vacancy rate           Derived from Census: proportion of housing units
                         identified as vacant.

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

[^1]: Actual tract numbers have been dummied to ensure confidentiality.

[^2]: This ALPE exceeds the 95^th^ percentile and was topcoded to 2.5.