**An Evaluation of the Accuracy of U.S. Bureau of the Census County
Population Estimates\***

D. H. Judson

Carole L. Popoff

Michael J. Batutis, Jr.

\*Dean H. Judson (djudson@unr.edu), Nevada State Demographer's Office,
Mail Stop 024, University of Nevada, Reno, Reno, NV 89557, (775)
787-9308 Phone, (775) 787-8377 Phone/Fax. Carole L. Popoff
(demogecon@aol.com), President, Decision Analytics, Inc., Reno, NV
89507, (775) 787-9308 Phone, 775-787-8377 Phone/Fax. Michael J. Batutis
(mbatutis@census.gov), Population Division, U.S. Bureau of the Census.
An earlier version of this paper was presented at the 6th International
Conference on Applied and Business Demography, Bowling Green State
University, Bowling Green, OH, September 19-21, 1996. The views
expressed are attributable to the authors and do not necessarily reflect
the views of the U.S. Bureau of the Census, the Nevada State
Demographer's Office, or the University of Nevada, Reno.

Abstract

There has been much debate on discrepancies found between Census
generated and locally generated intercensal county estimates. Numerous
anecdotal speculations on the cause of the discrepancies at the state
level have been made. A more systematic study by Davis (1994) assessed
the accuracy of the Census Bureau's county population estimates by
examining the results of the Census Bureau's estimation methods against
the 1990 Census count. In this study, we examine the Census Bureau's
Administrative Records method and hypothesize that there are systematic
biases in the records used in the estimation method that are the source
of the discrepancies. We examine, in detail, the sources of data used in
the Census Bureau's methodology. Based on this examination, we develop a
theory explaining why there might be a systematic bias in the
administrative records themselves and in the data collection process
itself. We test for these biases by using county level indicative
economic and demographic data. The theory identifies causes of
discrepancies in estimates that are systematic to the methodology and
suggest the direction and likely magnitude of the discrepancy. In
virtually all cases, our results are completely consistent with a priori
hypotheses. Furthermore, they remain even when undercount adjustment is
made. We conclude by presenting several recommendations, including
incorporating an adjustment factor, improving vital statistics
geocoding, improving group quarters reporting, and testing for medicare
undercoverage.

INTRODUCTION

Each year, the U.S. Census Bureau produces July 1 estimates of the
population of U.S. counties. As a result of efforts to improve the
quality of and expand the types of these estimates, the Census Bureau
initiated an effort to evaluate the accuracy of county-level estimates
(Long, 1996). An estimates evaluation committee was formed and, as part
of this effort, Davis (1994) developed 1990 estimates for each U.S.
county. He compared the resulting estimates with the 1990 Census count
from which errors in the estimates relative to the count were developed.

In this paper, we examine the current estimation method, known as the
Administrative Records method. (Formerly it was called the Tax Return
method in honor of the means by which migration rates are calculated;
Batutis, 1994). Our approach consists of four steps:

-   First, we decompose the estimation method into its components,
    basing the decomposition on the sources of data used to estimate the
    component;

-   Second, we postulate reasons why those sources of data might
    generate [systematic]{.underline} biases, particularly, identifying
    the kinds of people who might be systematically missed by the data
    source, and we postulate the direction of the bias;

-   Third, we specify empirical indicators that indicate the presence of
    the kinds of people who would be systematically missed by the data
    source; and

-   Fourth, we test the predicted direction of error against the actual
    direction of error, by comparing the 1990 estimate to the 1990
    enumerated total population.

Davis' 1994 study is a key starting point for this analysis. Like other
studies (e.g., Schafer, Tayman, and Carter, 1995), he finds that:

-   Counties with smaller populations in 1980 uniformly have higher Mean
    Absolute Percent Error (MAPE), e.g., counties with less than 2500
    population in 1980 had a MAPE of 7.7%, while counties with greater
    than 100,000 population in 1980 had a MAPE of 2.0%;

-   Counties with 0-5% growth from 1980-1990 had the lowest MAPE (3.0%),
    while counties with growth of 25% or more over the decade had the
    highest MAPE (27.4%);

-   Counties with positive rates of growth tended to have negative
    algebraic percent errors (ALPEs), and conversely;

-   Using 1980 Census counts as estimates for 1990 results in higher
    MAPEs than using the 1990 Tax Return/Administrative Records method;
    and

-   When counties were judgementally grouped by the quality of the data
    going into the estimates, those counties rated most poorly had the
    highest MAPE, while counties rated best had the lowest MAPE.

Background: A brief history of intercensal population estimation methods

The Census Bureau has employed variations of two basic methods, stock
methods and flow methods, to produce intercensal estimates. Until 1960,
intercensal estimates at the national and state levels were done using a
version of a flow method called the Components Method II. Flow methods
start with a population base or "benchmark" (primarily from the
decennial Census count), and then sum the number of additions to and
subtractions from each component over a specific time period (Long,
1993). The sum of the each component's change, which represents the
total change in population, is added to the base. The two basic
components of population change are 1) natural increase (the net of
births minus deaths) and 2) net migration (international and internal
migration being estimated separately).

Births and deaths can be extracted from state and national Vital
Statistics reports. To a lesser extent, international migration can be
obtained from the Immigration and Naturalization Service. Internal
migration (among states and/or sub-state areas) presents a more
difficult problem. At one time a stock method had been used to measure
internal migration. This method employs the idea that a measureable
variable can be identified that correlates with population change and a
"ratio" of population to the measurable variable can be estimated. For
example, the change in school enrollment from the prior year, for which
comprehensive administrative records are kept, was used by estimating a
population-to-school-enrollment ratio.

In the 1960's the Census Bureau expanded the use of the
Ratio-Correlation Method, which ultimately replaced the Components
Method II. A regression equation was specified whose independent
variables included vital events, school enrollment, tax returns, number
of votes cast, motor vehicle registration, and building permits. Voter
registration and building permits were dropped in 1970. In the 1970's a
demand for population estimates at a lower-than-state level developed
and the Census Bureau responded by producing county level estimates. The
Component Method II and the Ratio-Correlation Method were used together
and a Housing Unit Method was added. At the same time, the Federal/State
Cooperative for Population Estimates (FSCPE) was formed to provide a
mechanism to involve the states in the process and to provide assistance
to the Census Bureau. A flexible but time consuming system was developed
wherein each state specified the data elements to be included in its own
regression model and provided its own data sets. The method was complex
and, of course, was not consistent across states.

The Census Bureau returned to a component-based method when the General
Revenue Sharing Act created demand for subcounty level estimates. Rather
than using school enrollment to estimate internal migration, income tax
records were used by matching returns in successive years using the
social security number (SSN) of the primary filer and then matching the
addresses to determine if a move had occurred. Internal migration was
presumed to be a function of total change in exemptions in an area. This
required coding mailing addresses to counties, incorporated places, and
minor civil divisions using "place of residence" reported on tax
returns.

The Current County Population Estimation Method

Since 1993, the Census Bureau has continued to use Federal tax returns
for an estimate of internal migration for the population sixty-four and
under. Other records are used to determine other population components.
For the population sixty-five and older, MEDICARE enrollment is used to
estimate base population and a net migration rate, and the rate of
MEDICARE enrollment or coverage acts as an adjustment. It is this
component change estimation procedure, called the Administrative Records
Method, that we chose to examine in this paper.

The key assumption underlying the Administrative Records approach is
that the components which constitute population change can be
represented by an administrative data series. Separate administrative
records series are selected to represent each aspect of population
change and used to estimate the change in population from July 1 through
June 30th of the prior year. This is then added to the base to arrive at
the population estimate as of July 1st of the current year. The current
method has several practical advantages:

1.  As a result of the method, several of the components of change are
    treated independently, which creates the opportunity for more
    disaggregated analysis of components;

2.  The method does not depend on individual states for specific
    information;

3.  The estimates are generated from data that are, in most cases,
    directly available to the Census Bureau; and

4.  Components represented by administrative records are more
    straightforward to describe to policy makers than regression-based
    methods.

Origins of Both Random and Systematic Error in Administrative Records

As described, the Administrative Records method relies on numerous
sources of data. However, not all sources of data are equally reliable
or useful. For example, while all data sets suffer from random error,
[systematic errors]{.underline} or [biases]{.underline} (as documented
in Judson and Sigmund, 1995; and Judson, 1999) will not be consistent
across administrative records. Therefore, they cannot simply be taken as
a perfect (or even equally reliable) representation of the population of
interest. While the danger in using administrative records to represent
a population may seem intuitively obvious to demographers, many users of
administrative records do not address the systematic errors that occur.

As an alternative, we propose that researchers can and do treat the
administrative record as a symptomatic indicator of the events of
interest. However, we expect that the records themselves are subject to
known or knowable biases (Myrskyla, 1991; Rosenbaum, 1995), and that
these biases noticably influence the direction of estimation errors in
census county estimates. Our position is that we must first find the
effects of these biases, then look for ways to correct them.

THEORY: THE SOURCE OF ERROR IN THE ADMINISTRATIVE RECORDS USED IN CENSUS
ESTIMATION METHODS

An "administrative record" comes to exist when 1) someone engages in a
behavior; 2) that behavior is recorded by an administrative recorder;
and 3) that record is shared with an analyst. At each stage in this
process, there is potential for bias: The target person may have a
motive to avoid detection or be unable to contact the administrative
recorder; the recorder may lack ability or propensity to record
accurately; and the data set itself may be handled or transmitted so as
to exclude some recorded events or persons.

Random and systematic components of the error can each be examined
separately, and we assert that systematic biases can also be examined in
terms of direction and possibly magnitude. It is from these postulated
biases that we form a theoretical basis for predicting error. To study
these biases, we turn the examination of error "on its head": [We do not
compare county characteristics with estimation error in an attempt to
find characteristics that correlate with error; instead, we first
develop a theory of bias in administrative records, and using that
theory, hypothesize what county characteristics should correlate with
bias]{.underline}.

As an example of the biases we wish to examine, consider the method by
which the Census Bureau calculates the net migration rate using matched
tax returns. The key variable here is that the returns are [matched
successively over two or more years]{.underline} on the SSN of the
primary filer. There are several problems with using SSN's. Studies of
the accuracy of SSN's (Department of Health and Human Services, 1990)
suggest that, on average, one in ten SSN's are erroneous, with higher
discrepancies in prisons and financial institutions, and the lowest
discrepancies in tax collecting organizations. (See, e.g., Jabine and
Scheuren, 1986, for a description of alternative methods of record
matching that do not rely solely on SSN's).

Thus, given that the matching process is on a [single]{.underline} field
provided by the [primary]{.underline} filer only, we should first ask:
"Is there any particular [reason]{.underline} a person is more likely to
not be matched over time?" Our argument is parallel to one made in
Lessler and Kalsbeek (1992:254): "In the absence of any hard data on
bias, the argument is often made that although the measurements may be
biased, we can assume that the bias is the same for each subgroup, so
that subgroup comparisons remain valid. This assumption can be very
wrong."

Matching on the SSN of the primary tax filer in successive years means
that a bias can occur in any situation in which either a) the primary
filer changes from year to year (e.g., if a divorce occurs); or b) the
household changes filing status (e.g., if the household income goes from
below to above the income-based filing cutoff). To illustrate this
point, if a county has a high proportion of non-filers who move to a
county with better economic conditions and become filers, they will not
be counted as a migrant in the year they migrate. The calculated net
migration rate of both the county of origin and the destination county
will be in error resulting in a [positively]{.underline} biased estimate
of net migration in the origin county, and a [negatively]{.underline}
biased estimate of net migration in the destination county.

The following two sections represent a summary of the Administrative
Records Method for county level population estimates followed by a
description of the source of each data element and how the source might
lead to under- or over-estimation of population. We examine the
components in some detail, although they have been presented elsewhere
(see Batutis, 1994), because they form the basis from which we develop
the expected biases. We have postulated many potential sources of error,
so we must necessarily be brief on each particular one.

Details of the Current Method for Estimating County Population

For estimation purposes, total population is divided into two major
groups by age; those under sixty-five and those sixty-five and older.
The primary administrative record used to estimate the population
sixty-five and older is MEDICARE enrollment and because there is a
particularly strong motive to enroll to receive MEDICARE benefits,
enrollees should be a fairly complete representation of this group.
Unfortunately, no single, similarly inclusive, administrative record
currently exists for persons under sixty-five, so a series of
approximations is used.

In the equations that follow, please note that we suppress all ith and
tth subscripts referring to the ith county and the tth time period
respectively. Where a variable refers to the prior year we include the
t-1 subscript.

Total Population.

The basic estimation equation is:

TPOP = TPU + TPO

where:

TPOP = total county population in the ith county;

TPU = total population under 65 years of age in the ith county; and

TPO = total population over 64 years of age in the ith county.

Total Population under sixty-five.

Total population under sixty-five (TPU) is composed of total household
population plus current reports of persons in group quarters under
sixty-five times a rake factor to adjust to total U.S. group quarters
population. The National Rake Factor (NRFGQ) is used to insure
consistency between the sum of all county Group Quarters estimates and
the total US Group Quarters estimate. The following describes the
derivation of total population under sixty-five by showing each
component of household population and group quarters population under
sixty-five.

TPU = HHPOP + GQ \* NRFGQ

where:

HHPOP = household population under sixty-five in the ith county;

GQ = group quarters population under sixty-five in the ith county;

NRFGQ = sum of all county group quarters estimates divided by the U.S.
group quarters estimate.

Household population (HHPOP) is further broken down as:

HHPOP = P~t-1~ - GQ -- TPO - TPO \* AGADJ + B - D + I + NM

where:

P~t-1~ = the prior year's population (either the estimate or the Census
count updated to July 1);

TPO = total population over sixty-four years of age;

AGADJ = Adjustment for those aged 64 who will turn 65 during the
estimates cycle;

B = recorded births in the ith county;

D = recorded deaths in the ith county;

I = international immigration allocated to the ith county; and

NM = net internal migration for the ith county.

Total population sixty-five and over.

Total population sixty-five and over (TPO) is estimated using a
components method similar to the under sixty-five population. It
consists of household population sixty-five and older plus group
quarters population sixty-five and older. The basic components of
household population sixty-five and older are as follows.

HHP65 = HHP 65~t-1~ + NI65 + NM65 + NETMOVES65

where:

HHP65 = household population age 65 + in the current year;

HHP65~t-1~ = household population age 65 + for the prior year;

NI65 = natural increase, or those entering age 65 minus

deaths over the year since the last estimate;

NM65 = net domestic migration;

NETMOVES65= net movement from abroad during the year since the

last estimate.

The Origins of Error in Administrative Records Used for Intercensal
County Estimates

This section describes the way in which each component of TPU and TPO
originates as an administrative record. From this description we
postulate of the source of or the reason for systematic error. Since
components of change are estimated from many sources of data, there
exists the possibility for many sources of systematic error. We will
deal with components in the order of: Births/Deaths; International
Immigration; Group Quarters; Medicare (those 65 and over); and Net
Migration.

Births and deaths.

Technically, births should be those occurring from 7/1 through 6/30 and
allocated to the ith county by the most recent residence of the birth
mother. Since vital statistics are gathered by calendar year, 50% of
births from the current and prior years are summed for the estimate of
total births occurring from 7/1 through 6/30. If county-level birth data
are not available for the current period, total births are estimated by
using last year's totals to represent this year's totals, or by
multiplying the county's prior year's share by the new state total.
Sources of errors in the number of births are of two types 1)
misallocation of births across county lines, and 2) where no data exist,
the use of last year's figure or share.

1\) Misallocation of births - direction of error:

> Births in the county of residence will be understated for counties
> where there is no medical birth facility because the birth is
> erroneously placed in the county where the mother gave birth, rather
> than the residence county. Flotow and Burson (1996) proposed that such
> an effect occurs at the place and city level; Sink (1996) documents
> that it takes place distinctly in the D.C. area, and suggests further
> misallocations at the state level.

2\) Using last year's figure or share - direction of error:

> The number of births will be understated if inmigration is higher than
> the prior year and overstated if outmigration is higher than the prior
> year. Similarly, the number of births will be over- or understated if
> the change in the proportion of population in child-bearing years
> changes.

Deaths are attributed to the most recent residence of the deceased and
vital statistics records are used in precisely the same way they are
used for births. As with births, sources of errors in the number of
deaths are of two types 1) misallocation of current year's deaths and 2)
where no data exist, using last year's figure.

> 1\) Misallocation of deaths - direction of error:
>
> The number of deaths will be over-estimated in the county of residence
> when a death is recorded in an urban county adjacent to a rural county
> without hospitals or funeral facilities, or if the deceased moves a
> year before death but after the filing date for taxes (hence the
> migration is not recorded).
>
> 2\) Using last year's figures - direction of error:
>
> The number of deaths will be understated in the county of residence if
> inmigration is higher than the prior year and will be overstated in
> the county of prior residence if outmigration is higher than the prior
> year. Similarly, the number of deaths will be over- or understated
> when there is a change in the proportion of population at risk of
> dying.

International immigration.

Immigration is based on a national estimate of foreign migration which
includes emigration from the U.S. and the immigration of refugees, legal
immigrants, and undocumented immigrants. Estimates of the total for
national undocumented immigrants are allocated to states and counties
using the percentage of foreign born population who arrived between 1975
and 1980 and were enumerated as residents in the 1980 Census for each
area. Legal immigrants are allocated to counties on the basis of
intended residence reported to the Immigration and Naturalization
Service.

1\) Misallocations of immigration - direction of error:

> There will be an over-estimate in the reported county and an
> under-estimate in the county of actual residence when reported
> intended residence differs from actual residence. Similarly, over- or
> under-estimates can occur if 1) there is a change in percentage of
> undocumented immigrants from the 1975-80 percentage, or 2) the
> attractiveness of an area for undocumented migrants has changed since
> 1975.[^1]

Group quarters.

Group Quarters Population under 65 is composed of military personnel
living in barracks and on naval vessel crews; college students living in
dormitories; and populations of other group quarters such as penal
institutions, health care facilities, Job Corps Centers, etc., minus
anyone 65 or over. Data for military barracks residents and naval vessel
crews are from an annual survey of on-base housing facilities conducted
by Department of Defense in September except where it is collected by
the individual state and is deemed appropriate to use. Prior year's
survey results are used where no survey was conducted. Data for college
students living in dormitories are gathered from the states as of fall
of the preceding year. Data for inmates of correctional and juvenile
facilities, residents of health care facilities under 65, and residents
of job corps centers are institutional populations gathered from the
individual states as of July 1st of the estimate year or the average for
the estimate year. Other group quarters residents are from an annual
group quarters report submitted by state members of the FSCPE. Sources
of error in group quarters reports are from "double counting" or from
using the prior year's report when the current year is not available.

1\) "Double-counting" - direction of error.

> Over-estimates will result when persons in institutions are between
> the ages of sixty-four and sixty-five and are changing age at the time
> of the estimate; when college students are counted both at their
> parents' home and in dormitories; when people are counted in Job Corps
> Centers and place of residence; or when people are counted in health
> care facilities and place of residence because they have been included
> as exemptions on Federal income tax forms.

2\) Using prior year's reports - direction of error:

> Under-estimates occur when the group quarters population in the
> current year is actually greater than the group quarters population in
> the prior year but prior year data are used because the current year's
> report is unavailable. Batutis, 1996, indicates that, from 1990-95, 14
> states did not respond to requests for group quarters data. Similarly,
> Over-estimates occur when group quarters population for the current
> year is actually lower than the group quarters population in the prior
> year but prior year data are used because the current year report is
> unavailable.

The population 65 and older.

Total population age 65 years and older (TPO) represents the prior
year's population adjusted for components of change as described above.
The net migration factor is derived using MEDICARE records. Sources of
error for MEDICARE enrollees occur primarily because of individuals'
propensity to enroll in MEDICARE.

1\) Using MEDICARE enrollees - direction of error:

> MEDICARE underenrollments are likely to result when a county has a
> high proportion of 1) less educated (less likely to understand the
> process of signing up); 2) rural residents (fewer opportunities to
> sign up); 3) Indians on reservations (fewer opportunities to sign up);
> and 4) persons in poverty (fewer opportunities to sign up).

Net migration.

The final component of Total Population we discuss is net migration. Net
migration is calculated by multiplying the estimated migration base by
the estimated migration rate. The following describes each component of
this calculation: The migration base is the population considered at
risk of migrating. It equals the household base population under age 65
years plus 50% of the total of resident births minus resident deaths
plus international immigration. This, of course, assumes that only half
of the additions/deletions to the population would have taken place by
the midpoint of the year. The migration rate is calculated by comparing
residential addresses on individual Federal income tax returns, matched
by using the primary filer's SSN from the prior year and the estimate
year. Filers are categorized in each county as (1) inmigrants, (2)
outmigrants, and (3) nonmigrants. A net migration rate is derived based
on the difference between in- and outmigration of tax filers and their
dependents. As we have stated before, miscalculation of the rate of
migration is caused by non-matched tax returns in subsequent years. We
now demonstrate how this calculation leads to over- or under-estimates.

Let:

u~i~ = calculated net migration rate to/from the ith county(u~i~\>0

indicates net inmigration, while u~i~\<0 represents net

out migration, and u~i~=0 represents zero net migration);

T~ji~ = the number of exemptions in matched tax returns in the ith
county in year t and in the jth county in year t+1;

T~ij~ = the number of exemptions in matched tax returns in the jth
county in year t and in the ith county in year t+1;

T~i~ = the number of exemptions in tax returns in the ith county in year
t. Then u~i~ is calculated by:

$u_{i}$ = $\sum_{j = 1}^{n}\frac{T_{\text{ji}} - T_{\text{ij}}}{T_{i}}$

This equation illustrates that [the calculation of a migration rate is
inherently affected by characteristics of both origin county and
destination counties]{.underline}. We do not have specific data on
potential biases in each of these terms separately; but we can use these
terms to make a quantitative statement of the direction of error in the
migration rate calculation.

Consider the following thought experiment: For two counties, i and j,
assume there are 100 exemptions in county i and, in one year, ten
exemptions migrate from j to i and one exemption migrates from i to j.
The true [net]{.underline} migration rate [to]{.underline} county i is
(u~i~) = (10 - 1) / 100 or a positive net migration rate of 9/100.
However, realistically, in successive years the tax return is recorded
in i but not j, j but not i, or not in i at all. Readers should note the
[key point]{.underline}: Given the matching system used in the
Administrative Records method, if a return is identified in one county
but not in the other in successive years, it is [not
counted]{.underline} as "migrating" from the first to the second county.
Table 1 gives the possible cases and their effect on net migration.

\-\-- Table 1 about here \-\--

As can be seen in table 1, though the conclusion is not obvious on first
glance, it is clear in the calculation: If origin (county j) out
migrants (Case I) are more likely to be unmatched in successive years,
then the calculated net migration rate for county i will be [negatively
biased]{.underline}. If, as in Case II, the destination county (county
i) out migrants are more likely to be unmatched in successive years,
then the calculated net migration rate for county i will be [positively
biased]{.underline}.

What remains is to determine the characteristics of a county where
people are likely to not file in successive years thus generating
unmatched tax returns. As with the other symptomatic indicators, we use
social and economic characteristics of the county to indicate the
presence of people likely to not file a federal income tax return in
successive years. Thus we have our proposed effects:

1\) Misallocations of net migration - direction of error:

> Residence changes will result in an [over]{.underline}-estimate when
> someone moves into a group quarter but is also listed as an exemption
> on a federal income tax return in another county (e.g., college
> students moving into a dormitory; newly recruited military personnel;
> people moving into a long-term health care facility; or recently
> incarcerated prisoners). Residence changes will result in an
> [under]{.underline}-estimate when someone moves from a group quarter
> but doesn't file at the new residence and is not counted as an
> exemption (e.g., college students moving out of a dormitory or back
> home, recently released prisoners, persons leaving long-term health
> care facilities, or recently discharged military personnel).
> [Over]{.underline}-estimates in the county of origin and
> [under]{.underline}-estimates in the county of destination will result
> from persons who did not file in the previous year, moved, and filed
> in the current year, e.g., persons who move to seek employment.
> Persons who move in with elderly parents to care for them also result
> in misallocation to the county (Sater, 1995).

RESEARCH HYPOTHESES FOR SOURCES OF ESTIMATION ERROR

In the previous section, we have identified factors present in the
administrative records themselves that we propose cause biases in their
coverage. Fortunately, the presence or absence of many of these factors
is identifiable through indicator data that we have available. That is,
for example, we have postulated above that the presence of high rates of
poverty in a county (and any amount of migration to and from the county)
would cause an artificial upward bias in net migration rates for that
county. Thus, the empirical hypothesis in this case is that counties
with high rates of poverty will be overestimated.

\-\-- Table 2 about here \-\--

Table 2 indicates the administrative records used in the Administrative
Records method, the proposed sources of bias in that administrative
record, the calculated direction of estimation error that bias creates,
and the empirical indicator we have chosen to indicate the presence,
absence, or level of that kind of bias for each county. Using the
reasoning described in detail in Table 2, we generate the following
empirical hypotheses:

**Birth/death misallocation**:

> H1: Counties that are rural and do not have hospital will be
> underestimated.
>
> H2: Counties that have a high rate of natural increase will be
> overestimated,
>
> H3: Counties that have a high positive (negative) rate of change will
> be under (over) estimated.

**Group quarters reporting biases:**

> H3: Counties that have high percentage of persons in institutional
> group quarters will be underestimated; this effect will be exacerbated
> in rapidly changing counties.
>
> H4: Counties that have a high percentage of college population will be
> underestimated; this effect will be exacerbated in rapidly changing
> counties.
>
> H5: Counties that have a high percentage of military population will
> be underestimated; this effect will be exacerbated in rapidly changing
> counties.
>
> H6: Counties that have a high percentage of prison population will be
> overestimated; this effect will be exacerbated in rapidly changing
> counties.

**Over 65 reporting biases:**

> H7: Counties with a high percentage of Social Security Insurance (SSI)
> enrollees will be overestimated.
>
> H8: Counties with a high percentage of over 65 who are note enrolled
> in SSI will be underestimated.
>
> H9: Counties that are rural will be underestimated.
>
> H10: Counties that have lower levels of educational attainment will be
> underestimated.

**International migration misallocation:**

> H11: Counties that have a high percentage of foreign born will be
> underestimated.

**Migration rate calculation biases:**

> H12: Counties with high poverty will be overestimated.
>
> H13: Counties with high percentage black will be overestimated; in the
> context of a poverty\*black interaction, the absolute direct effect of
> black population will decline toward zero while the effect of the
> poverty\*black interaction will increase.
>
> H14: Counties with high percentage Indian will be overestimated; in
> the context of a poverty\*Indian interaction, the absolute direct
> effect of Indian population will decline toward zero while the effect
> of the poverty\*Indian interaction will increase.
>
> H15: Counties with high percentage Hispanic will be overestimated; in
> the context of a poverty\*Hispanic interaction, the absolute direct
> effect of Hispanic population will decline toward zero while the
> effect of the poverty\*Hispanic interaction will increase.
>
> DATA AND METHODS
>
> The data sources for this examination are manyfold. The key source of
> data on estimation error was generated by the Census Bureau itself
> (Davis, 1994). For every county in the U.S., Davis' file contained a
> recorded population estimate for that county and a recorded
> date-adjusted Census count for that county. The estimates reported by
> Davis used only the information that would have been available if the
> analyst were producing an estimate for 1990.

The traditional error measure for analyses of this kind is the Absolute
Percent Error (APE). However, our hypotheses are sufficiently detailed
to allow us to predict [direction]{.underline} of error. Thus, we hold
that overestimation and underestimation are both important, and the APE
of course discards all information about the direction of error.
Therefore, we will examine algebraic percent errors rather than absolute
errors.

Using these two values, the estimate and the date-adjusted count, we
calculate Algebraic Percent Errors (ALPEs) for each county. The ALPEs
are defined as:

ALPE = 100 \* (EST - CC)/CC,

where:

EST = estimated county population; and

CC = Date adjusted Decennial Census count for that county.

Note that a positive number indicates that the estimate is [too
high]{.underline}, a negative number indicates that the estimate is [too
low]{.underline}, and an ALPE of zero occurs only when EST = CC.

A Note on Undercount

In addition to tests on unadjusted census counts, we also tested all the
models using the estimated undercount rates by state. We noted above
that ALPE is a biased error measure where the Census count itself is in
error (Das Gupta, 1995; Robinson and Kobilarcik, 1995). The key error in
the Census count is, of course, undercount, and the danger is that our
chosen variables are more accurately described as a measure of
susceptibility to undercount, rather than as an indicator of
administrative records biases (Simpson, 1996). In order to test this
possibility, we obtained from Post Enumeration Study materials (U.S.
Bureau of the Census, 1991) the Public Law 94-171 adjusted population
counts for all 3141 counties in the U.S. We used these adjusted
population counts to create a corrected ALPE measure (called CALPE).
Calculating the Pearson correlation between ALPE and CALPE yielded a
.968 correlation; we will address specific differences in the analyses
as part of the results section, later in the paper.

The second key data source was extracted from the USA Counties CD ROM
developed by the Bureau of the Census. This CD ROM includes a variety of
data at the county level, defined consistently across counties. Based on
the symptomatic indicators we have proposed, we extracted the variables
that most appropriately represented the symptomatic indicator. These two
data sources were matched using county FIPS codes.[^2]

We chose the symptomatic indicators from our 1994 USA Counties data set
to represent the presence of the people who would be motivated by the
proposed propensity to report or would indicate the presence of other
proposed biases. We recognize at this point that for some of our
indicators, the symptomatic indicator we have chosen is less than
perfect. Readers should particularly note the presence and importance of
specific interaction effects in the operational indicators. We have
separated indicators into groups based on the component of the
estimation method that they affect. In some cases (e.g., rural
counties), the effect may occur in more than one administrative record
source; in that case we have listed the indicator under both sources.

Obviously, ALPEs will differ over various places and these differences
were a focus of the 1994 Davis study. We group places in the same
categories as Davis to examine these patterns; however, where he
reported absolute percent errors, we used algebraic percent errors. We
find that ALPE error tends to decline toward zero as county population
increases as does the standard deviation in ALPE; however, this effect
is not uniform. For example, minimum and maximum ALPE values do not
appear to decline in absolute value as size increases until size reaches
the 100,000+ size group. Counties categorized by growth rate also vary
in ALPE which ranges from a high of 4.11% (for declining counties) to a
low of .18% for rapidly growing counties; however, the standard
deviations in ALPE do not decline over the growth classes. Likewise
minimum and maximum values exhibit little in the way of systematic
change over the growth classes. (Tables which depict mean algebraic
percent errors by region; by state; and by size and by growth rate of
county; and a map of algebraic percent errors can be obtained from the
authors.)

RESULTS

In this section, we construct two regression models designed to predict
algebraic percent error. We begin by defining variables in table 3. Our
first model is "Model 0," a "null" model based only on regional dummies
(REGION 1\--REGION 4) and control variables CHANGE1 and POP90, described
in table 4.

\-\-- Table 3 about here \-\--

\-\-- Table 4 about here \-\--

As can be seen in table 4, the control variables we have selected have
effects in the appropriate direction. As rate of change 1980-1990
(CHANGE1) increases by one percent, holding other effects constant, ALPE
declines by an expected .088 percent. Likewise, for a one thousand
person increase in population in 1990, holding other effects constant,
ALPE declines by an expected .000009 percent. Of course, since the
population variable is scaled in larger units than ALPE, the effect size
is scaled downward. The standardized regression coefficients (labeled
"Beta" in Table 3) illustrate relative effect sizes standardized in
standard deviation terms: For a one standard deviation increase in
population, ALPE declines by an expected .048 standard deviations,
holding other effects constant.

REGION1 (Northeast) was chosen as the comparison region, so coefficients
on REGION2-REGION4 should be interpreted as deviations from REGION1.
Consistent with the fact that other explanatory variables are not
present in the null model, the regional dummies have substantial
effects, with Midwest (REGION2) counties being an expected .12 percent
lower than Northeast (REGION1) on ALPE, and the South (REGION3) and West
(REGION4) regions being an expected 2.6 percent and 1.2 percent higher
than REGION1, respectively.

Model 1 adds to Model 0 the variables that would pick up estimation
errors through 1) birth/death misallocations; 2) group quarters
misallocations; 3) over-sixty-five misallocations; 4) international
migration misallocations and 5) misallocations from an inappropriately
high or low net migration rate. The results are found in Table 4.

\-\-- Table 4 about here \-\--

Because biases in administrative records are subtle, we expect that the
effect sizes (that is, regression coefficients) on the symptomatic
indicator variables will be small. However, we are in the happy
circumstance that we have the entire population of USA counties to
analyze[^3]. This means that we have no sampling variability; this also
means that standard error calculations are not important as a measure of
sampling variability, but are technically, in this case, zero. Under
this interpretation, t-statistics, etc., should not be treated as
measures of statistical significance[^4]. The importance of this claim
is that the coefficient we estimate for 1990 is not subject to sampling
variability; there is no uncertainty about that coefficient.

Let us consider at this point some of the larger effects in Model 1.
Examining standardized regression coefficients ("Beta's") is useful to
locate variables that are important indicators of estimation error even
though their unstandardized coefficients are small. We examine
standardized coefficients of absolute value .10 or greater for
discussion. For example, the coefficient on the dummy variable, NOHOSP,
(1 = county has a hospital; 0 = otherwise) is positive, as we would
expect, indicating that counties with hospitals are slightly
overestimated, consistent with the hypothesized misallocation of births.
The coefficient for RURALNOH (the interaction term between the indicator
variable for the presence of a hospital and a rural county) is negative
(-.035) indicating an expected .035% decrease in ALPE for a 1% increase
in rural population in counties having no hospital. This is consistent
with our hypothesis that rural counties with no hospitals will be
underestimated. The coefficients on institutional group quarters
(GROUPQI) and percent of population in college (PCTCOLL) are both
negative and quite large. For a 1% increase in institutional group
quarters population, we expect a .33% decline in ALPE, holding other
variables constant. Similarly, a 1% increase in percent of population in
college generates an expected .35% decrease in ALPE, holding other
variables constant. For GROUPQI, we strongly suspect that delays in
reporting from the states causes this negative coefficient (Batutis,
1996, as we have mentioned above, indicates that this is an ongoing
problem for estimation). Obviously, if the group quarters population has
grown but that growth is not reported, this generates an underestimate,
and vice versa for decline.

SSIPCT (the percentage of the 65 and older population who are MEDICARE
enrollees) has a positive standardized coefficient of .034 and an
unstandardized coefficient of .029. The model also includes DIFF (the
difference between the percentage of the population 65 or older and the
percentage of the population that are SSI enrollees; that is, the
percentage of the population not enrolled in SSI) and both the
unstandardized and the standardized coefficients are negative (-.200 and
-.117, respectively). To repeat, the coefficient on SSIPCT is positive,
consistent with the hypothesis of a strong propensity to enroll in
MEDICARE; and DIFF has the expected negative coefficient, because it is
picking up all persons 65 and older, who have not signed up for
MEDICARE. Thus, a 1% increase in the percentage of the population who
are eligible for MEDICARE but do not sign up generates an expected
-.117% decrease in ALPE, holding other effects constant.

As indicated in the arguments about net migration (and suggested in the
figure), poverty creates overestimates. For a 1% increase in the
population in poverty in 1989, we have an expected .072% increase in
ALPE, holding other effects constant. For example, a 10% increase in
persons in poverty generates an estimate that is almost 3/4 of a
percentage point too high (or, for a county whose population is 10,000
persons, a 10% increase in poverty generates an estimate about 720
persons high).

All race/ethnicity by poverty effects are small with the exception of
Indian population and Indians in poverty. The direct effect of Indian
population is negative; a 1% increase in Indian population generates an
expected .10% decrease in the ALPE, holding other effects constant.
However, the effect of Indians in poverty is dramatically positive; a 1%
increase in poverty in the Indian population generates an expected .398%
increase in ALPE, holding other effects constant. We expect that the
latter effect is the tax filing/migration problem described in our
hypotheses.

We can summarize our substantive conclusions from the results as
follows:

1)  Consistent with other studies, counties with rapid change tend to
    have estimation errors opposite the direction of change;

2)  Counties with a high rate of natural increase tend to be
    overestimated;

3)  Rural counties with no hospital tend to be underestimated;

4)  Counties with a large proportion of the population in group quarters
    tend to be underestimated, and this effect is strongly exacerbated
    if the county is also growing;

5)  A notable exception to #4 is counties with a high percentage of its
    population in prison;

6)  Counties with a high proportion of SSI enrollees tend to be
    overestimated, but if the county has a high proportion of the 65 and
    over population that is not enrolled, the county tends to be
    underestimated;

7)  Counties with a high percentage foreign born tend to be
    underestimated;

8)  Counties with a high percentage of the population in poverty tend to
    be overestimated;

9)  Counties with a high percentage of native american indian population
    tend to be underestimated; and

10) Counties with a high percentage of native american indians in
    poverty tend to be overestimated.

Analyses using undercount-corrected population counts

Analyses using CALPE, the undercount-corrected measure of error, had
similar results as those using ALPE[^5]. In virtually all cases, the
magnitude varied only slightly in the CALPE model from the ALPE model.
For example, the variable "percent in poverty" had a standardized
regression coefficient of .117 in the ALPE model; this variable had a
standardized regression coefficient of .102 in the ALPE model. In some
cases, coefficents very close to zero in the ALPE model changed sign in
the CALPE model, but no substantive conclusion was at risk, with the
exception of one. The variable "indians in poverty" had a standardized
regression coefficient of .22 in the ALPE model, and had a standardized
regression coefficient of .011 in the CALPE model. Thus, we conclude
that for this particular variable, the effect of undercount is creating
the results presented here, and conclusion #10 above is a result of
undercount. Given that native american indians living on reservations
were the most undercounted racial group, this is not surprising.

However, we would like to emphasize that even with the
undercount-corrected population count used as a standard, [no other
substantive conclusion changes]{.underline}. Thus, we feel confident
that our hypotheses for bias are independent of the undercount
correction.

DISCUSSION AND IMPLICATIONS FOR FUTURE ESTIMATION METHODS

Biases in administrative records are subtle; Judson (1996; 1999) has
proposed that biases are created by 1) improper record matching (i.e.
SSN errors); 2) response errors in the propensity of individuals to fill
out the pertinent administrative record; 3) data management and
transaction management errors; 4) data volatility (i.e., changes in the
database that occur [after]{.underline} an extraction takes place); 5)
coding errors; and 6) program effects or the propensity of the
particular record keeper to over- or under-state program participation.
Furthermore, in this study we have postulated that people or
institutions in certain circumstances are more likely to generate these
kinds of biases, and we have attempted to record the presence of these
situations at the county level using symptomatic county data indicative
of the underlying cause.

In almost all cases, we have found the direction of estimation error to
be consistent with our proposed administrative records biases. In only
one case (prison population) have we found a coefficient opposite to our
[a priori]{.underline} expectations. Thus, we find these results very
encouraging; in fact, we were surprised at how consistent the outcome of
the analysis was with our [a priori]{.underline} expectations. However,
we must indicate that these results are only [indicative]{.underline} of
biases in administrative records, not definitive. While it is true that
the direction of error is consistent with our postulated biases, without
a direct study of the administrative records themselves, we cannot rule
out the possibility of alternative explanations for these results.

What are the implications of these results for future estimation? We
summarize our recommendations:

1)  When using data series from administrative records for any
    estimation method, careful consideration should be given to the
    potential for bias caused by "program effects," mismatching of
    personal identifiers, the unequal probability that different kinds
    of people will exist in the administrative record, and similar
    effects.

2)  The Census Bureau should consider, perhaps as part of continuous
    measurement initiatives, a special sampling study of the
    administrative records themselves. Such a study should include an
    on-site verification of the veracity of the administrative record.
    Essentially, this procedure treats the administrative record [file
    itself]{.underline} as a population to be examined and from which a
    sample may be drawn and tested (Judson, 1999).

3)  Given that we have demonstrated that the direction and magnitude of
    bias can be appropriately estimated, a correction factor should be
    constructed for administrative non-coverage. A reliable correction
    factor thus could be used to adjust intercensal estimates. For
    example, we take our estimated model $\frac{E - C}{C} = \text{XB}$,
    where E=Estimate, C=Census Count, X is the matrix of independent
    variables and B is the matrix of estimated coefficients. After some
    algebraic simplification, this model implies that
    $\frac{E}{1 + \text{XB}} = C\text{.}$ Obviously, if all of the
    estimated coefficients were zero, then E=C and all is well; there is
    no bias. If XB results in a positve number, then the correction
    factor deflates E to make it match C. If XB results in a negative
    number, then the correction factor inflates E to make it match C.
    Thus, 1+XB is our proposed candidate for a correction factor.

4)  Our results imply additional, smaller recommendations: That the
    estimates would be well served if vital statistics geocoding were to
    be improved at all levels; that Group Quarters update and reporting
    is very important, particularly in changing counties; and that a
    medicare coverage study would be useful for assessing undercoverage
    and the effects of undercoverage on the estimates process.

5)  Last, but perhaps most important, in the latter half of the 20^th^
    century much is being done using administrative data as the
    indicator of some underlying activity and as an aid to estimation
    and enumeration efforts (see, for example, the AREX 2000
    Administrative Records Experiment and the American Community Survey
    efforts). Heretofore, administrative records data have been
    collected only to serve the narrow purposes of a particular entity.
    As data bases are being linked to provide a less expensive analytic
    tool, data users must be motivated to collectively work together to
    find ways to make these data more reliable and universal. We
    recommend that Federal statistical agencies continue to take the
    lead because Federal data are used extensively in all areas of
    business and government, and, in fact, set the standard for
    competent use of such data (Long, 1996; Prevost, 1996).

REFERENCES

Batutis, Michael J., Jr. (1994). [Subnational Estimates of Total
Population by the Tax Return Methodology]{.underline}. Unpublished
technical paper. Washington, D.C.: Bureau of the Census.

Batutis, Michael J., Jr. (1996). [Response to Request for Group Quarters
Data]{.underline}. Unpublished memorandum, U.S. Bureau of the Census,
May 30, 1996.

Bureau of the Census (1991). [Census of Population and Housing, 1990:
Public Law (P.L.) 94-171 Data (Adjusted) Machine Readable data
Files]{.underline}. Prepared by the Bureau of the Census. Washinton,
D.C.: Bureau of the Census.

Bureau of the Census (1992). [Assessment of Accuracy of Adjusted Versus
Unadjusted 1990 Census Base for Use in Intercensal
Estimates]{.underline}. Unpublished technical report of the committee on
adjustment of postcensal estimates. Washington, D.C.: Bureau of the
Census.

Das Gupta, Prithwis (1995). [Evaluation Measures for Subnational
Postcensal Estimates]{.underline}. Unpublished Memorandum, March 15,
1995. Washington, D.C.: Bureau of the Census.

Davis, Sam T. (1994). [Evaluation of the Postcensal County Estimates for
the 1980s]{.underline}. Technical Working Paper No. Washington, D.C.:
Bureau of the Census.

Department of Health and Human Services, Office of the Inspector General
(1990). [Extent of Social Security Number Discrepancies]{.underline}.
Washington, DC: Department of Health and Human Services.

Flotow, Mark and Burson, Ryan (1996). [Allocation Errors of Birth and
Death Records to Subcounty Geography.]{.underline} Paper presented at
the 1996 Annual meeting of the Population Association of America. New
Orleans, LA, May 9-11, 1996.

Jabine, Thomas B., and Scheuren, Fritz J. (1986). Record linkage for
statistical purposes: Methodological issues. [Journal of Official
Statistics]{.underline}, [2]{.underline}:255-277.

Judson, Dean H., and Sigmund, Charles L. (1995). [On the Use of
Unemployment Insurance Wage Records for Program Follow-up and
Evaluation]{.underline}. Reno, NV: Decision Analytics.

Judson, D.H. (1999). [Research Use of Administrative
Records]{.underline}. Reno, NV: Nevada State Demographer's Office.

Lessler, Judith T., and Kalsbeek, William D. (1992). [Nonsampling Error
in Surveys]{.underline}. New York, NY: John Wiley and Sons, Inc.

Long, John F. (1993). [Postcensal Population Estimates: States,
Counties, and Places]{.underline}. Technical Working Paper No. 3.
Washington, D.C.: Bureau of the Census.

Long, John F. (1996). [A Theoretical Perspective on Administrative
Records and Demographic Accounting.]{.underline} Paper presented at the
1996 Annual meeting of the Population Association of America. New
Orleans, LA, May 9-11, 1996.

Myrskyla, Pekka (1991). Census by questionnaire\--Census by registers
and administrative records: The experience of Finland. [Journal of
Official Statistics]{.underline}, [7]{.underline}:457-474.

Prevost, Ron (1996). [Administrative Records and the New Statistical
Era]{.underline}. Paper presented at the 1996 Annual meeting of the
Population Association of America. New Orleans, LA, May 9-11, 1996.

Robinson, J. Gregory, and Kobilarcik, Edward L. (1995). [Identifying
differential undercounts at Local Geographic Levels: A Targeting
Database Approach]{.underline}. Paper presented at the 1995 Annual
meeting of the Population Association of America, San Francisco, CA.
April 8th, 1995.

Rosenbaum, Paul R. (1995). [Observational Studies]{.underline}. New
York, NY: Springer-Verlag.

Sater, Douglas (1995). [Differences in Location of Households and Tax
Filing Units]{.underline}. Paper presented at the 1995 Annual meeting of
the Population Association of America, San Francisco, CA. April 8th,
1995.

Schafer, Edward, Tayman, Jeff, and Carter, Lawrence (1995). [Forecasting
Errors and Confidence Intervals for Small Area Population Forecasts: The
Relationship Among Population Size, Rate of Growth, and Precision and
Bias]{.underline}. Paper presented at the 1995 Annual meeting of the
Population Association of America, San Francisco, CA. April 8th, 1995.

Sink, Larry (1996) [Race Classification Consistency Between the Census
Bureau and the National Center for Health Statistics]{.underline}. Paper
presented at the 1996 Annual meeting of the Population Association of
America. New Orleans, LA, May 9-11, 1996.

TABLE 1:

Effect of Matching Problem on Calculation of Net Migration Rate

  ----------------- ----------------- ----------------- -----------------
  Migrant with      Matching problem  Calculation of    Effect on net
  matching problems                   net migration     migration rate
  moves from:                         rate              

  county j to       Not recorded in   (9-1)/100         too low
  county i          i, recorded in j                    

                    Not recorded in   (9-1)/100         too low
                    j, recorded in i                    

                    Recorded in j,    (9-1)/100         too low
                    not recorded in i                   

                    Recorded in i and (10-1)/100        Correct
                    in j                                

  county i to       Not recorded in   (10-0)/100        too high
  county j          j, recorded in i                    

                    Not recorded in   (10-0)/99         too high
                    j, not recorded                     
                    in i                                

                    Recorded in j,    (10-0)/99         too high
                    not recorded in i                   

                    Recorded in i and (10-1)/100        Correct
                    in j                                
  ----------------- ----------------- ----------------- -----------------

TABLE 2

Proposed Administrative Records Errors and Biases and Empirical
Indicators

+-----------+---+--------------------------+---+---------------+---+--------------+
| *         | * |                          | * |               | * |              |
| *Record** | * |                          | * |               | * |              |
|           | S |                          | D |               | E |              |
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|           | u |                          | r |               | p |              |
|           | r |                          | e |               | i |              |
|           | c |                          | c |               | r |              |
|           | e |                          | t |               | i |              |
|           | o |                          | i |               | c |              |
|           | f |                          | o |               | a |              |
|           | E |                          | n |               | l |              |
|           | r |                          | o |               | * |              |
|           | r |                          | f |               | * |              |
|           | o |                          | E |               |   |              |
|           | r |                          | r |               | * |              |
|           | o |                          | r |               | * |              |
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|           | * |                          | B |               | c |              |
|           | * |                          | i |               | a |              |
|           | B |                          | a |               | t |              |
|           | i |                          | s |               | o |              |
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|           | s |                          | * |               | * |              |
|           | * |                          |   |               | * |              |
|           | * |                          |   |               |   |              |
+===========+===+==========================+===+===============+===+==============+
| **        |   |                          |   |               |   |              |
| Migration |   |                          |   |               |   |              |
| rate      |   |                          |   |               |   |              |
| calcu     |   |                          |   |               |   |              |
| lation:** |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Tax       | P |                          | U |               | p |              |
| Return    | e |                          | n |               | o |              |
|           | r |                          | d |               | v |              |
|           | s |                          | e |               | e |              |
|           | o |                          | r |               | r |              |
|           | n |                          | - |               | t |              |
|           | / |                          | e |               | y |              |
|           | f |                          | s |               | ; |              |
|           | a |                          | t |               |   |              |
|           | m |                          | i |               | p |              |
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|           | m |                          | e |               | r |              |
|           | o |                          | i |               | t |              |
|           | v |                          | n |               | y |              |
|           | e |                          | c |               | \ |              |
|           | d |                          | o |               | * |              |
|           | f |                          | u |               | e |              |
|           | o |                          | n |               | t |              |
|           | r |                          | t |               | h |              |
|           | j |                          | y |               | n |              |
|           | o |                          | o |               | i |              |
|           | b |                          | f |               | c |              |
|           |   |                          | d |               | p |              |
|           | - |                          | e |               | e |              |
|           | f |                          | s |               | r |              |
|           | i |                          | t |               | c |              |
|           | l |                          | i |               | e |              |
|           | e |                          | n |               | n |              |
|           | d |                          | a |               | t |              |
|           | l |                          | t |               | ; |              |
|           | a |                          | i |               |   |              |
|           | s |                          | o |               | p |              |
|           | t |                          | n |               | o |              |
|           | y |                          | ; |               | v |              |
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|           | a |                          | v |               | r |              |
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|           | , |                          | r |               | y |              |
|           | d |                          | - |               | \ |              |
|           | i |                          | e |               | * |              |
|           | d |                          | s |               | r |              |
|           | n |                          | t |               | u |              |
|           | o |                          | i |               | r |              |
|           | t |                          | m |               | a |              |
|           | f |                          | a |               | l |              |
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|           | e |                          | i |               | r |              |
|           | i |                          | n |               | c |              |
|           | n |                          | c |               | e |              |
|           | c |                          | o |               | n |              |
|           | u |                          | u |               | t |              |
|           | r |                          | n |               |   |              |
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|           | a |                          | i |               |   |              |
|           | r |                          | g |               |   |              |
|           | , |                          | i |               |   |              |
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|           | o |                          | . |               |   |              |
|           | m |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Tax       | P |                          | U |               | p |              |
| Return    | e |                          | n |               | o |              |
|           | r |                          | d |               | v |              |
|           | s |                          | e |               | e |              |
|           | o |                          | r |               | r |              |
|           | n |                          | - |               | t |              |
|           | / |                          | e |               | y |              |
|           | f |                          | s |               | ; |              |
|           | a |                          | t |               |   |              |
|           | m |                          | i |               | p |              |
|           | i |                          | m |               | o |              |
|           | l |                          | a |               | v |              |
|           | y |                          | t |               | e |              |
|           | m |                          | e |               | r |              |
|           | o |                          | i |               | t |              |
|           | v |                          | n |               | y |              |
|           | e |                          | c |               | \ |              |
|           | d |                          | o |               | * |              |
|           | f |                          | u |               | e |              |
|           | o |                          | n |               | t |              |
|           | r |                          | t |               | h |              |
|           | j |                          | y |               | n |              |
|           | o |                          | o |               | i |              |
|           | b |                          | f |               | c |              |
|           |   |                          | d |               | p |              |
|           | - |                          | e |               | e |              |
|           | f |                          | s |               | r |              |
|           | i |                          | t |               | c |              |
|           | l |                          | i |               | e |              |
|           | e |                          | n |               | n |              |
|           | d |                          | a |               | t |              |
|           | i |                          | t |               | ; |              |
|           | n |                          | i |               |   |              |
|           | c |                          | o |               | p |              |
|           | u |                          | n |               | o |              |
|           | r |                          | ; |               | v |              |
|           | r |                          | o |               | e |              |
|           | e |                          | v |               | r |              |
|           | n |                          | e |               | t |              |
|           | t |                          | r |               | y |              |
|           | y |                          | - |               | \ |              |
|           | e |                          | e |               | * |              |
|           | a |                          | s |               | r |              |
|           | r |                          | t |               | u |              |
|           | , |                          | i |               | r |              |
|           | d |                          | m |               | a |              |
|           | i |                          | a |               | l |              |
|           | d |                          | t |               | p |              |
|           | n |                          | e |               | e |              |
|           | \ |                          | i |               | r |              |
|           | ' |                          | n |               | c |              |
|           | t |                          | c |               | e |              |
|           | f |                          | o |               | n |              |
|           | i |                          | u |               | t |              |
|           | l |                          | n |               |   |              |
|           | e |                          | t |               |   |              |
|           | l |                          | y |               |   |              |
|           | a |                          | o |               |   |              |
|           | s |                          | f |               |   |              |
|           | t |                          | o |               |   |              |
|           | y |                          | r |               |   |              |
|           | e |                          | i |               |   |              |
|           | a |                          | g |               |   |              |
|           | r |                          | i |               |   |              |
|           | , |                          | n |               |   |              |
|           | n |                          | . |               |   |              |
|           | o |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Tax       | C |                          | O |               | C |              |
| Return    | o |                          | v |               | o |              |
|           | l |                          | e |               | l |              |
|           | l |                          | r |               | l |              |
|           | e |                          | - |               | e |              |
|           | g |                          | e |               | g |              |
|           | e |                          | s |               | e |              |
|           | s |                          | t |               | p |              |
|           | t |                          | i |               | o |              |
|           | u |                          | m |               | p |              |
|           | d |                          | a |               | u |              |
|           | e |                          | t |               | l |              |
|           | n |                          | e |               | a |              |
|           | t |                          | i |               | t |              |
|           | l |                          | n |               | i |              |
|           | i |                          | c |               | o |              |
|           | s |                          | o |               | n |              |
|           | t |                          | u |               |   |              |
|           | e |                          | n |               |   |              |
|           | d |                          | t |               |   |              |
|           | a |                          | y |               |   |              |
|           | s |                          | w |               |   |              |
|           | e |                          | h |               |   |              |
|           | x |                          | e |               |   |              |
|           | e |                          | r |               |   |              |
|           | m |                          | e |               |   |              |
|           | p |                          | p |               |   |              |
|           | t |                          | a |               |   |              |
|           | i |                          | r |               |   |              |
|           | o |                          | e |               |   |              |
|           | n |                          | n |               |   |              |
|           | o |                          | t |               |   |              |
|           | n |                          | s |               |   |              |
|           | p |                          | l |               |   |              |
|           | a |                          | i |               |   |              |
|           | r |                          | v |               |   |              |
|           | e |                          | e |               |   |              |
|           | n |                          | ; |               |   |              |
|           | t |                          | u |               |   |              |
|           | s |                          | n |               |   |              |
|           | \ |                          | d |               |   |              |
|           | ' |                          | e |               |   |              |
|           | r |                          | r |               |   |              |
|           | e |                          | - |               |   |              |
|           | t |                          | e |               |   |              |
|           | u |                          | s |               |   |              |
|           | r |                          | t |               |   |              |
|           | n |                          | i |               |   |              |
|           | , |                          | m |               |   |              |
|           | n |                          | a |               |   |              |
|           | o |                          | t |               |   |              |
|           | t |                          | e |               |   |              |
|           | n |                          | i |               |   |              |
|           | e |                          | n |               |   |              |
|           | c |                          | c |               |   |              |
|           | e |                          | o |               |   |              |
|           | s |                          | u |               |   |              |
|           | s |                          | n |               |   |              |
|           | a |                          | t |               |   |              |
|           | r |                          | y |               |   |              |
|           | i |                          | o |               |   |              |
|           | l |                          | f |               |   |              |
|           | y |                          | c |               |   |              |
|           | c |                          | o |               |   |              |
|           | o |                          | l |               |   |              |
|           | u |                          | l |               |   |              |
|           | n |                          | e |               |   |              |
|           | t |                          | g |               |   |              |
|           | e |                          | e |               |   |              |
|           | d |                          | . |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | q |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| **Group   |   |                          |   |               |   |              |
| Quarters  |   |                          |   |               |   |              |
| :**       |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| College   | G |                          | U |               | C |              |
| Dorm      | r |                          | n |               | o |              |
|           | o |                          | d |               | l |              |
|           | u |                          | e |               | l |              |
|           | p |                          | r |               | e |              |
|           | q |                          | - |               | g |              |
|           | u |                          | e |               | e |              |
|           | a |                          | s |               | p |              |
|           | r |                          | t |               | o |              |
|           | t |                          | i |               | p |              |
|           | e |                          | m |               | ; |              |
|           | r |                          | a |               |   |              |
|           | s |                          | t |               | C |              |
|           | r |                          | e |               | o |              |
|           | e |                          | i |               | l |              |
|           | p |                          | n |               | l |              |
|           | o |                          | c |               | e |              |
|           | r |                          | o |               | g |              |
|           | t |                          | u |               | e |              |
|           | u |                          | n |               | p |              |
|           | s |                          | t |               | o |              |
|           | e |                          | y |               | p |              |
|           | d |                          | o |               | \ |              |
|           | f |                          | f |               | * |              |
|           | r |                          | c |               | p |              |
|           | o |                          | o |               | e |              |
|           | m |                          | l |               | r |              |
|           | l |                          | l |               | c |              |
|           | a |                          | e |               | e |              |
|           | s |                          | g |               | n |              |
|           | t |                          | e |               | t |              |
|           | y |                          | . |               | c |              |
|           | e |                          |   |               | h |              |
|           | a |                          |   |               | a |              |
|           | r |                          |   |               | n |              |
|           | , |                          |   |               | g |              |
|           | b |                          |   |               | e |              |
|           | u |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
|           | A |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | y |                          |   |               |   |              |
|           | , |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | x |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | x |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
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|           | t |                          |   |               |   |              |
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|           | t |                          |   |               |   |              |
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|           | s |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
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|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
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|           | n |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | , |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
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|           | d |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
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|           | - |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | w |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | x |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | y |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | f |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Military  | G |                          | U |               | M |              |
| base      | r |                          | n |               | i |              |
|           | o |                          | d |               | l |              |
|           | u |                          | e |               | i |              |
|           | p |                          | r |               | t |              |
|           | q |                          | - |               | a |              |
|           | u |                          | e |               | r |              |
|           | a |                          | s |               | y |              |
|           | r |                          | t |               | p |              |
|           | t |                          | i |               | o |              |
|           | e |                          | m |               | p |              |
|           | r |                          | a |               | u |              |
|           | s |                          | t |               | l |              |
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+-----------+---+--------------------------+---+---------------+---+--------------+
| Prison    | G |                          | o |               | P |              |
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|           | d |                          |   |               |   |              |
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|           | d |                          |   |               |   |              |
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|           | t |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | ' |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
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|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | b |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
|           | [ |                          |   |               |   |              |
|           | ^ |                          |   |               |   |              |
|           | 6 |                          |   |               |   |              |
|           | ] |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Long Term |   | Group quarters report    |   | u             |   | I            |
| Health    |   | obtained from last       |   | nder-estimate |   | nstitutional |
| Care      |   | years, but GQ residence  |   | in county     |   | GQ           |
| In        |   | up.                      |   | where         |   | population;  |
| stitution |   |                          |   | institution   |   |              |
|           |   |                          |   | is located    |   | I            |
|           |   |                          |   |               |   | nstitutional |
|           |   |                          |   |               |   | GQ           |
|           |   |                          |   |               |   | population   |
|           |   |                          |   |               |   | \*           |
|           |   |                          |   |               |   |              |
|           |   |                          |   |               |   | percent      |
|           |   |                          |   |               |   | change       |
+-----------+---+--------------------------+---+---------------+---+--------------+
| **P       |   |                          |   |               |   |              |
| opulation |   |                          |   |               |   |              |
| 65 and    |   |                          |   |               |   |              |
| older:**  |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| MEDICARE  | E |                          | O |               | S |              |
| Enrollees | n |                          | v |               | S |              |
|           | r |                          | e |               | I |              |
|           | o |                          | r |               | p |              |
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|           | m |                          | s |               | c |              |
|           | e |                          | t |               | e |              |
|           | n |                          | i |               | n |              |
|           | t |                          | m |               | t |              |
|           | c |                          | a |               | a |              |
|           | o |                          | t |               | g |              |
|           | v |                          | e |               | e |              |
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|           | a |                          | c |               | p |              |
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|           | s |                          | t |               | l |              |
|           | h |                          | y |               | a |              |
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|           | - |                          | s |               |   |              |
|           | - |                          | i |               |   |              |
|           | t |                          | d |               |   |              |
|           | h |                          | e |               |   |              |
|           | i |                          | n |               |   |              |
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|           | o |                          | ( |               |   |              |
|           | p |                          | i |               |   |              |
|           | u |                          | . |               |   |              |
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|           | i |                          | a |               |   |              |
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|           | n |                          | o |               |   |              |
|           | s |                          | i |               |   |              |
|           | h |                          | d |               |   |              |
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|           | u |                          | n |               |   |              |
|           | l |                          | d |               |   |              |
|           | d |                          | e |               |   |              |
|           | b |                          | r |               |   |              |
|           | e |                          | e |               |   |              |
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|           | s |                          | t |               |   |              |
|           | t |                          | i |               |   |              |
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|           | a |                          | t |               |   |              |
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|           | d |                          | ) |               |   |              |
|           | w |                          |   |               |   |              |
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|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| MEDICARE  | N |                          | U |               | E |              |
| Enrollees | o |                          | n |               | d |              |
|           | t |                          | d |               | u |              |
|           | a |                          | e |               | c |              |
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|           | l |                          | m |               | e |              |
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|           | i |                          | i |               | ; |              |
|           | g |                          | n |               |   |              |
|           | i |                          | c |               | R |              |
|           | b |                          | o |               | u |              |
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|           | n |                          | y |               | p |              |
|           | r |                          | o |               | o |              |
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|           | l |                          | r |               | u |              |
|           | l |                          | e |               | l |              |
|           | u |                          | s |               | a |              |
|           | n |                          | i |               | t |              |
|           | d |                          | d |               | i |              |
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|           | r |                          | n |               | n |              |
|           | H |                          | c |               | ; |              |
|           | C |                          | e |               |   |              |
|           | F |                          |   |               | P |              |
|           | A |                          |   |               | e |              |
|           | . |                          |   |               | r |              |
|           | W |                          |   |               | c |              |
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|           | i |                          |   |               | 5 |              |
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|           | l |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | k |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           |   |                          |   |               |   |              |
|           | \ |                          |   |               |   |              |
|           | - |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | w |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| **Birth   |   |                          |   |               |   |              |
| and death |   |                          |   |               |   |              |
| r         |   |                          |   |               |   |              |
| ecords:** |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Birth     | B |                          | O |               | R |              |
| record    | i |                          | v |               | u |              |
|           | r |                          | e |               | r |              |
|           | t |                          | r |               | a |              |
|           | h |                          | - |               | l |              |
|           | r |                          | e |               | p |              |
|           | e |                          | s |               | o |              |
|           | c |                          | t |               | p |              |
|           | o |                          | i |               | u |              |
|           | r |                          | m |               | l |              |
|           | d |                          | a |               | a |              |
|           | e |                          | t |               | t |              |
|           | d |                          | e |               | i |              |
|           | i |                          | i |               | o |              |
|           | n |                          | n |               | n |              |
|           | c |                          | c |               | ; |              |
|           | o |                          | o |               |   |              |
|           | u |                          | u |               | C |              |
|           | n |                          | n |               | o |              |
|           | t |                          | t |               | u |              |
|           | y |                          | y |               | n |              |
|           | w |                          | w |               | t |              |
|           | h |                          | h |               | y |              |
|           | e |                          | e |               | w |              |
|           | r |                          | r |               | i |              |
|           | e |                          | e |               | t |              |
|           | h |                          | h |               | h |              |
|           | o |                          | o |               | o |              |
|           | s |                          | s |               | u |              |
|           | p |                          | p |               | t |              |
|           | i |                          | i |               | h |              |
|           | t |                          | t |               | o |              |
|           | a |                          | a |               | s |              |
|           | l |                          | l |               | p |              |
|           | l |                          | i |               | i |              |
|           | o |                          | s |               | t |              |
|           | c |                          | l |               | a |              |
|           | a |                          | o |               | l |              |
|           | t |                          | c |               | ; |              |
|           | e |                          | a |               |   |              |
|           | d |                          | t |               | C |              |
|           | , |                          | e |               | o |              |
|           | n |                          | d |               | u |              |
|           | o |                          | ; |               | n |              |
|           | t |                          | u |               | t |              |
|           | a |                          | n |               | y |              |
|           | t |                          | d |               | w |              |
|           | m |                          | e |               | i |              |
|           | o |                          | r |               | t |              |
|           | t |                          | - |               | h |              |
|           | h |                          | e |               | o |              |
|           | e |                          | s |               | u |              |
|           | r |                          | t |               | t |              |
|           | \ |                          | i |               | h |              |
|           | ' |                          | m |               | o |              |
|           | s |                          | a |               | s |              |
|           | a |                          | t |               | p |              |
|           | d |                          | e |               | i |              |
|           | d |                          | i |               | t |              |
|           | r |                          | n |               | a |              |
|           | e |                          | c |               | l |              |
|           | s |                          | o |               | \ |              |
|           | s |                          | u |               | * |              |
|           | ( |                          | n |               |   |              |
|           | m |                          | t |               | r |              |
|           | o |                          | y |               | u |              |
|           | t |                          | w |               | r |              |
|           | h |                          | h |               | a |              |
|           | e |                          | e |               | l |              |
|           | r |                          | r |               | p |              |
|           | \ |                          | e |               | o |              |
|           | ' |                          | m |               | p |              |
|           | s |                          | o |               | u |              |
|           | a |                          | t |               | l |              |
|           | d |                          | h |               | a |              |
|           | d |                          | e |               | t |              |
|           | r |                          | r |               | i |              |
|           | e |                          | a |               | o |              |
|           | s |                          | n |               | n |              |
|           | s |                          | d |               | ; |              |
|           | m |                          | b |               |   |              |
|           | a |                          | a |               | \ |              |
|           | y |                          | b |               | [ |              |
|           | b |                          | y |               | B |              |
|           | e |                          | r |               | i |              |
|           | a |                          | e |               | r |              |
|           | P |                          | s |               | t |              |
|           | O |                          | i |               | h |              |
|           | B |                          | d |               | - |              |
|           | o |                          | e |               | d |              |
|           | x |                          | . |               | e |              |
|           | o |                          |   |               | a |              |
|           | r |                          |   |               | t |              |
|           | m |                          |   |               | h |              |
|           | a |                          |   |               | \ |              |
|           | y |                          |   |               | ] |              |
|           | b |                          |   |               | a |              |
|           | e |                          |   |               | s |              |
|           | m |                          |   |               | p |              |
|           | i |                          |   |               | e |              |
|           | s |                          |   |               | r |              |
|           | s |                          |   |               | c |              |
|           | t |                          |   |               | e |              |
|           | a |                          |   |               | n |              |
|           | t |                          |   |               | t |              |
|           | e |                          |   |               | o |              |
|           | d |                          |   |               | f |              |
|           | ) |                          |   |               | p |              |
|           | . |                          |   |               | o |              |
|           |   |                          |   |               | p |              |
|           |   |                          |   |               | u |              |
|           |   |                          |   |               | l |              |
|           |   |                          |   |               | a |              |
|           |   |                          |   |               | t |              |
|           |   |                          |   |               | i |              |
|           |   |                          |   |               | o |              |
|           |   |                          |   |               | n |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Birth     |   | Increase (decline) of    |   | Under (over)  |   | birth-death  |
| record    |   | births to mothers in     |   | -estimate,    |   | as percent   |
|           |   | resident county\--prior  |   | opposite to   |   | of           |
|           |   | year birth report used   |   | direction of  |   | population   |
|           |   | in lieu of current year  |   | change.       |   | \* percent   |
|           |   | records.                 |   |               |   | change       |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Death     | D |                          | O |               | R |              |
| record    | e |                          | v |               | u |              |
|           | a |                          | e |               | r |              |
|           | t |                          | r |               | a |              |
|           | h |                          | - |               | l |              |
|           | r |                          | e |               | p |              |
|           | e |                          | s |               | o |              |
|           | c |                          | t |               | p |              |
|           | o |                          | i |               | u |              |
|           | r |                          | m |               | l |              |
|           | d |                          | a |               | a |              |
|           | e |                          | t |               | t |              |
|           | d |                          | e |               | i |              |
|           | a |                          | i |               | o |              |
|           | t |                          | n |               | n |              |
|           | a |                          | r |               | ; |              |
|           | d |                          | e |               |   |              |
|           | d |                          | s |               | C |              |
|           | r |                          | i |               | o |              |
|           | e |                          | d |               | u |              |
|           | s |                          | e |               | n |              |
|           | s |                          | n |               | t |              |
|           | w |                          | t |               | y |              |
|           | h |                          | c |               | w |              |
|           | e |                          | o |               | i |              |
|           | r |                          | u |               | t |              |
|           | e |                          | n |               | h |              |
|           | d |                          | t |               | o |              |
|           | e |                          | y |               | u |              |
|           | a |                          | o |               | t |              |
|           | t |                          | f |               | h |              |
|           | h |                          | d |               | o |              |
|           | o |                          | e |               | s |              |
|           | r |                          | c |               | p |              |
|           | f |                          | e |               | i |              |
|           | u |                          | a |               | t |              |
|           | n |                          | s |               | a |              |
|           | e |                          | e |               | l |              |
|           | r |                          | d |               | ; |              |
|           | a |                          | , |               |   |              |
|           | l |                          | u |               | C |              |
|           | p |                          | n |               | o |              |
|           | r |                          | d |               | u |              |
|           | e |                          | e |               | n |              |
|           | p |                          | r |               | t |              |
|           | a |                          | - |               | y |              |
|           | r |                          | e |               | w |              |
|           | a |                          | s |               | i |              |
|           | t |                          | t |               | t |              |
|           | i |                          | i |               | h |              |
|           | o |                          | m |               | o |              |
|           | n |                          | a |               | u |              |
|           | t |                          | t |               | t |              |
|           | o |                          | e |               | h |              |
|           | o |                          | i |               | o |              |
|           | k |                          | n |               | s |              |
|           | p |                          | c |               | p |              |
|           | l |                          | o |               | i |              |
|           | a |                          | u |               | t |              |
|           | c |                          | n |               | a |              |
|           | e |                          | t |               | l |              |
|           |   |                          | y |               | \ |              |
|           | - |                          | t |               | * |              |
|           | n |                          | o |               |   |              |
|           | o |                          | w |               | r |              |
|           | t |                          | h |               | u |              |
|           | i |                          | i |               | r |              |
|           | n |                          | c |               | a |              |
|           | c |                          | h |               | l |              |
|           | o |                          | d |               | p |              |
|           | u |                          | e |               | o |              |
|           | n |                          | a |               | p |              |
|           | t |                          | t |               | u |              |
|           | y |                          | h |               | l |              |
|           | o |                          | i |               | a |              |
|           | f |                          | s |               | t |              |
|           | r |                          | a |               | i |              |
|           | e |                          | t |               | o |              |
|           | s |                          | t |               | n |              |
|           | i |                          | r |               | ; |              |
|           | d |                          | i |               |   |              |
|           | e |                          | b |               | B |              |
|           | n |                          | u |               | i |              |
|           | c |                          | t |               | r |              |
|           | e |                          | e |               | t |              |
|           | . |                          | d |               | h |              |
|           |   |                          | . |               | - |              |
|           |   |                          |   |               | d |              |
|           |   |                          |   |               | e |              |
|           |   |                          |   |               | a |              |
|           |   |                          |   |               | t |              |
|           |   |                          |   |               | h |              |
|           |   |                          |   |               | a |              |
|           |   |                          |   |               | s |              |
|           |   |                          |   |               | p |              |
|           |   |                          |   |               | e |              |
|           |   |                          |   |               | r |              |
|           |   |                          |   |               | c |              |
|           |   |                          |   |               | e |              |
|           |   |                          |   |               | n |              |
|           |   |                          |   |               | t |              |
|           |   |                          |   |               | o |              |
|           |   |                          |   |               | f |              |
|           |   |                          |   |               | p |              |
|           |   |                          |   |               | o |              |
|           |   |                          |   |               | p |              |
|           |   |                          |   |               | u |              |
|           |   |                          |   |               | l |              |
|           |   |                          |   |               | a |              |
|           |   |                          |   |               | t |              |
|           |   |                          |   |               | i |              |
|           |   |                          |   |               | o |              |
|           |   |                          |   |               | n |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Death     | I |                          | O |               | B |              |
| record    | n |                          | v |               | i |              |
|           | c |                          | e |               | r |              |
|           | r |                          | r |               | t |              |
|           | e |                          | ( |               | h |              |
|           | a |                          | u |               | - |              |
|           | s |                          | n |               | d |              |
|           | e |                          | d |               | e |              |
|           | ( |                          | e |               | a |              |
|           | d |                          | r |               | t |              |
|           | e |                          | ) |               | h |              |
|           | c |                          | e |               | a |              |
|           | l |                          | s |               | s |              |
|           | i |                          | t |               | p |              |
|           | n |                          | i |               | e |              |
|           | e |                          | m |               | r |              |
|           | ) |                          | a |               | c |              |
|           | i |                          | t |               | e |              |
|           | n |                          | e |               | n |              |
|           | d |                          | , |               | t |              |
|           | e |                          | o |               | o |              |
|           | a |                          | p |               | f |              |
|           | t |                          | p |               | p |              |
|           | h |                          | o |               | o |              |
|           | s |                          | s |               | p |              |
|           | \ |                          | i |               | u |              |
|           | - |                          | t |               | l |              |
|           | - |                          | e |               | a |              |
|           | p |                          | t |               | t |              |
|           | r |                          | o |               | i |              |
|           | i |                          | d |               | o |              |
|           | o |                          | i |               | n |              |
|           | r |                          | r |               | \ |              |
|           | y |                          | e |               | * |              |
|           | e |                          | c |               | p |              |
|           | a |                          | t |               | e |              |
|           | r |                          | i |               | r |              |
|           | r |                          | o |               | c |              |
|           | e |                          | n |               | e |              |
|           | p |                          | o |               | n |              |
|           | o |                          | f |               | t |              |
|           | r |                          | c |               | c |              |
|           | t |                          | h |               | h |              |
|           | u |                          | a |               | a |              |
|           | s |                          | n |               | n |              |
|           | e |                          | g |               | g |              |
|           | d |                          | e |               | e |              |
|           | i |                          | . |               |   |              |
|           | n |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | f |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | y |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| **Foreign |   |                          |   |               |   |              |
| immig     |   |                          |   |               |   |              |
| ration:** |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| INS       | U |                          | U |               | P |              |
| A         | n |                          | n |               | e |              |
| llocation | d |                          | d |               | r |              |
| of        | o |                          | e |               | c |              |
| m         | c |                          | r |               | e |              |
| igrants + | u |                          | e |               | n |              |
| a         | m |                          | s |               | t |              |
| llocation | e |                          | t |               | f |              |
| of        | n |                          | i |               | o |              |
| und       | t |                          | m |               | r |              |
| ocumented | e |                          | a |               | e |              |
| i         | d |                          | t |               | i |              |
| mmigrants | a |                          | e |               | g |              |
|           | l |                          | i |               | n |              |
|           | i |                          | n |               | b |              |
|           | e |                          | c |               | o |              |
|           | n |                          | o |               | r |              |
|           | s |                          | u |               | n |              |
|           | m |                          | n |               |   |              |
|           | i |                          | t |               |   |              |
|           | g |                          | y |               |   |              |
|           | r |                          | o |               |   |              |
|           | a |                          | f |               |   |              |
|           | t |                          | u |               |   |              |
|           | e |                          | n |               |   |              |
|           | t |                          | d |               |   |              |
|           | o |                          | o |               |   |              |
|           | c |                          | c |               |   |              |
|           | o |                          | u |               |   |              |
|           | u |                          | m |               |   |              |
|           | n |                          | e |               |   |              |
|           | t |                          | n |               |   |              |
|           | i |                          | t |               |   |              |
|           | e |                          | e |               |   |              |
|           | s |                          | d |               |   |              |
|           | w |                          | a |               |   |              |
|           | h |                          | l |               |   |              |
|           | e |                          | i |               |   |              |
|           | r |                          | e |               |   |              |
|           | e |                          | n |               |   |              |
|           | o |                          | ' |               |   |              |
|           | t |                          | s |               |   |              |
|           | h |                          | d |               |   |              |
|           | e |                          | e |               |   |              |
|           | r |                          | s |               |   |              |
|           | u |                          | t |               |   |              |
|           | n |                          | i |               |   |              |
|           | d |                          | n |               |   |              |
|           | o |                          | a |               |   |              |
|           | c |                          | t |               |   |              |
|           | u |                          | i |               |   |              |
|           | m |                          | o |               |   |              |
|           | e |                          | n |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | , |                          |   |               |   |              |
|           | b |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| *         |   |                          |   |               |   |              |
| *Regional |   |                          |   |               |   |              |
| effects   |   |                          |   |               |   |              |
| and       |   |                          |   |               |   |              |
| c         |   |                          |   |               |   |              |
| ontrols** |   |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| Other     |   |                          | N |               | R |              |
| effects   |   |                          | o |               | e |              |
| not       |   |                          | p |               | g |              |
| captured  |   |                          | a |               | i |              |
| here      |   |                          | r |               | o |              |
|           |   |                          | t |               | n |              |
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|           |   |                          | e |               | . |              |
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|           |   |                          | s |               | C |              |
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|           |   |                          |   |               | s |              |
|           |   |                          |   |               | u |              |
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|           |   |                          |   |               | B |              |
|           |   |                          |   |               | u |              |
|           |   |                          |   |               | r |              |
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|           |   |                          |   |               | u |              |
|           |   |                          |   |               | R |              |
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|           |   |                          |   |               | i |              |
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|           |   |                          |   |               | n |              |
|           |   |                          |   |               | 1 |              |
|           |   |                          |   |               | \ |              |
|           |   |                          |   |               | . |              |
|           |   |                          |   |               | . |              |
|           |   |                          |   |               | . |              |
|           |   |                          |   |               | R |              |
|           |   |                          |   |               | e |              |
|           |   |                          |   |               | g |              |
|           |   |                          |   |               | i |              |
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|           |   |                          |   |               | n |              |
|           |   |                          |   |               | 4 |              |
|           |   |                          |   |               | ) |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| P         | T |                          | G |               | \ |              |
| opulation | h |                          | r |               | % |              |
| growth    | e |                          | o |               | c |              |
|           | p |                          | w |               | h |              |
|           | e |                          | t |               | a |              |
|           | r |                          | h |               | n |              |
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|           | s |                          | n |               | f |              |
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|           | t |                          | t |               | 1 |              |
|           | t |                          | e |               | 9 |              |
|           | e |                          | s |               | 8 |              |
|           | n |                          | u |               | 0 |              |
|           | d |                          | n |               | - |              |
|           | e |                          | d |               | 1 |              |
|           | n |                          | e |               | 9 |              |
|           | c |                          | r |               | 9 |              |
|           | y |                          | e |               | 0 |              |
|           | o |                          | s |               |   |              |
|           | f |                          | t |               |   |              |
|           | a |                          | i |               |   |              |
|           | n |                          | m |               |   |              |
|           | y |                          | a |               |   |              |
|           | e |                          | t |               |   |              |
|           | s |                          | e |               |   |              |
|           | t |                          | ; |               |   |              |
|           | i |                          | d |               |   |              |
|           | m |                          | e |               |   |              |
|           | a |                          | c |               |   |              |
|           | t |                          | l |               |   |              |
|           | i |                          | i |               |   |              |
|           | o |                          | n |               |   |              |
|           | n |                          | e |               |   |              |
|           | m |                          | g |               |   |              |
|           | e |                          | e |               |   |              |
|           | t |                          | n |               |   |              |
|           | h |                          | e |               |   |              |
|           | o |                          | r |               |   |              |
|           | d |                          | a |               |   |              |
|           | t |                          | t |               |   |              |
|           | o |                          | e |               |   |              |
|           | f |                          | s |               |   |              |
|           | a |                          | o |               |   |              |
|           | i |                          | v |               |   |              |
|           | l |                          | e |               |   |              |
|           | t |                          | r |               |   |              |
|           | o |                          | e |               |   |              |
|           | f |                          | s |               |   |              |
|           | u |                          | t |               |   |              |
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|           | c |                          | t |               |   |              |
|           | a |                          | e |               |   |              |
|           | p |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | u |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | g |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | w |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | h |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | d |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | i |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | . |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+
| P         | T |                          | L |               | P |              |
| opulation | h |                          | a |               | o |              |
| of county | e |                          | r |               | p |              |
|           | e |                          | g |               | u |              |
|           | m |                          | e |               | l |              |
|           | p |                          | r |               | a |              |
|           | i |                          | c |               | t |              |
|           | r |                          | o |               | i |              |
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|           | c |                          | n |               | n |              |
|           | a |                          | t |               | i |              |
|           | l |                          | i |               | n |              |
|           | r |                          | e |               | 1 |              |
|           | e |                          | s |               | 9 |              |
|           | l |                          | s |               | 9 |              |
|           | a |                          | h |               | 0 |              |
|           | t |                          | o |               | . |              |
|           | i |                          | u |               |   |              |
|           | o |                          | l |               |   |              |
|           | n |                          | d |               |   |              |
|           | s |                          | h |               |   |              |
|           | h |                          | a |               |   |              |
|           | i |                          | v |               |   |              |
|           | p |                          | e |               |   |              |
|           | t |                          | e |               |   |              |
|           | h |                          | r |               |   |              |
|           | a |                          | r |               |   |              |
|           | t |                          | o |               |   |              |
|           | l |                          | r |               |   |              |
|           | a |                          | s |               |   |              |
|           | r |                          | c |               |   |              |
|           | g |                          | l |               |   |              |
|           | e |                          | o |               |   |              |
|           | r |                          | s |               |   |              |
|           | c |                          | e |               |   |              |
|           | o |                          | r |               |   |              |
|           | u |                          | t |               |   |              |
|           | n |                          | o |               |   |              |
|           | t |                          | z |               |   |              |
|           | i |                          | e |               |   |              |
|           | e |                          | r |               |   |              |
|           | s |                          | o |               |   |              |
|           | h |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | v |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
|           | m |                          |   |               |   |              |
|           | a |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | l |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | p |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | c |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | n |                          |   |               |   |              |
|           | t |                          |   |               |   |              |
|           | e |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | o |                          |   |               |   |              |
|           | r |                          |   |               |   |              |
|           | s |                          |   |               |   |              |
+-----------+---+--------------------------+---+---------------+---+--------------+

TABLE 3

Variable Names and Brief Definitions

  -----------------------------------------------------------------------
  Variable     Definition
  name         
  ------------ ----------------------------------------------------------
  ALPE         ALPEC is the dependent variable, Algebraic percent error:
               100\* (EST -- CC)/ CC.

  REGION2      region2 is an indicator variable taking on the value one
               for counties in region 2 (Midwest), and zero otherwise.

  REGION3      region3 is an indicator variable taking on the value one
               for counties in region 3 (South), and zero otherwise.

  REGION4      region4 is an indicator variable taking on the value one
               for counties in region 4 (West), and zero otherwise.

  POP90        Pop90 is the Census population for 1990.

  CHANGE1      Change1 is the percent change in population from 1980 to
               1990 (expressed as a decimal, i.e .01 = 1%).

  BDPCT        bdpct is the natural increase divided by 1990 population.

  RURAL        rural is the percent of the population living in areas
               classified as "rural".

  NOHOSP       nohosp is an indicator variable taking on the value one if
               the county had a hospital in 1990, zero otherwise.

  RURALNOH     ruralnoh is the interaction between rural and nohosp
               (rural\*nohosp).

  GROUPQI      Groupqi is the percentage of the population living in
               institutional group quarters.

  PCTCOLL      Pctcoll is the percentage of the population living in
               college dormitories.

  MILPCT       Milpct is the percentage of the population living in
               military quarters.

  PRISPCT      Prispct is the percentage of the population living in
               prison.

  GQICHG       GQICHG is the interaction between GROUPQI and percentage
               change.

  CCHNG        cchg is the interaction between college dorm population
               and percentage change (pctcoll\*change1).

  MPCH         mpch is the interaction between percent military and
               population change (milpct\*change1).

  PPCHG        ppchg is the interaction between prison population and
               population change (prispct\*change1).

  SSIPCT       Ssipct is the percentage of the population receiving
               Social Security Insurance benefits.

  DIFF         DIFF is the difference of the percentage 65 and over and
               the percentage of the population who are SSI enrollees,
               (thus indicating the percent of 65 and over who are not
               SSI enrollees).

  EDRATE       Edrate is the percentage of the population who have
               attained high school degrees or greater in 1990.

  FORPCT       Forpct is the percentage of the 1990 population
               foreign-born.

  POV          Pov is the percentage of the population living below
               poverty in 1989.

  BLACKPOP     Blackpop is the percentage of the population that is
               black.

  INDPOP       Indpop is the percentage of the population that is native
               American Indian.

  HISPPOP      hisppop is the percentage of the population that is of
               Hispanic origin.

  RURALPOV     RURALPOV is the interaction effect between rural and
               poverty (rural\*pov).

  BLACKPOV     Blackpov is the percentage of the black population living
               below poverty.

  INDPOV       Indpov is the percentage of the Indian population living
               below poverty.

  HISPPOV      hisppov is the percentage of the Hispanic population
               living below poverty.

  \_CONS       The constant; because REGION1 is the omitted category
               dummy variable, the constant [de facto]{.underline}
               estimates the average algebraic percent error for counties
               in Region 1 (northeast).
  -----------------------------------------------------------------------

TABLE 4

MODEL 0: Regression of ALPE on Control Variables Only

Source \| SS df MS Number of obs = 3141

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
F( 5, 3135) = 97.34

Model \| 1.01579593 5 .203159186 Prob \> F = 0.0000

Residual \| 6.54332072 3135 .002087184 R-squared = 0.1344

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
Adj R-squared = 0.1330

Total \| 7.55911665 3140 .002407362 Root MSE = .04569

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

alpe \| Coef. Std. Err. t P\>\|t\| Beta

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

region2 \| -.0012286 .0034672 -0.354 0.723 -.0118283

region3 \| .0263425 .0033761 7.803 0.000 .2673338

region4 \| .0120128 .0038188 3.146 0.002 .085311

pop90 \| -.000009 .00000317 -2.841 0.005 -.0483721

Change1 \| -.0881782 .0050891 -17.327 0.000 -.304311

\_cons \| .014323 .0031934 4.485 0.000 .

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

Note: The number of observations is the 3141 US counties in 1990; \_CONS
is the estimated regression constant; region2 is an indicator variable
taking on the value one for counties in region 2 (Midwest), and zero
otherwise; region3 is an indicator variable taking on the value one for
counties in region 3 (South), and zero otherwise; region4 is an
indicator variable taking on the value one for counties in region 4
(West), and zero otherwise; REGION1 (Northeast) is the omitted category;
Pop90 is the Census population for 1990; Change1 is the percent change
in population from 1980 to 1990 (expressed as a decimal, i.e .01 = 1%);
Coef. is the regression coefficient; Std. Err. is the population
standard error; t is the coefficient divided by the standard error, and
Beta is the standardized regression coefficient. Under the
interpretation that these results represent the total population of
counties, F-statistics, t-statistics and p-values are illustrative only.

TABLE 5

Regression of ALPE on Complete Set of Indicator Variables

Source \| SS df MS Number of obs = 3141

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
F( 29, 3111) = 34.85

Model \| 1.85364153 29 .063918673 Prob \> F = 0.0000

Residual \| 5.70547512 3111 .001833968 R-squared = 0.2452

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
Adj R-squared = 0.2382

Total \| 7.55911665 3140 .002407362 Root MSE = .04282

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

alpe \| Coef. Std. Err. t P\>\|t\| Beta

\-\-\-\-\-\-\-\--+\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

region2 \| -.0072795 .0033978 -2.142 0.032 -.0700832

region3 \| .0141413 .0036426 3.882 0.000 .1435115

region4 \| .0025681 .0039113 0.657 0.511 .0182377

pop90 \| .0000039 .00000357 1.101 0.271 .0211462

Change1 \| -.0612704 .0073152 -8.376 0.000 -.2114498

BIRTH/DEATH INDICATORS ESTIMATION ERROR INDICATORS

bdpct \| .9224643 .3173705 2.907 0.004 .0905089

rural \| .009145 .0066715 1.371 0.171 .0559835

nohosp \| .0296158 .0085617 3.459 0.001 .2255863

ruralnoh \| -.033575 .0095049 -3.532 0.000 -.2369856

GROUP QUARTERS ESTIMATION ERROR INDICATORS

groupqi \| -.3408452 .1015814 -3.355 0.001 -.1487647

pctcoll \| -.3029027 .0469092 -6.457 0.000 -.1232623

milpct \| -.3547109 .0703749 -5.040 0.000 -.086852

prispct \| .4131984 .1153268 3.583 0.000 .1640343

gqichg \| -.6409098 .5980738 -1.072 0.284 -.0594903

cchg \| -.0151471 .0037422 -4.048 0.000 -.0740519

mpch \| -.0040686 .0021307 -1.910 0.056 -.0326152

ppchg \| .8490982 .6973883 1.218 0.223 .0628466

OVER 64/MEDICARE ESTIMATION ERRORINDICATORS

ssipct \| .0294205 .0327273 0.899 0.369 .0339869

diff \| -.2001848 .0455054 -4.399 0.000 -.1164724

edrate \| -.0272872 .0179069 -1.524 0.128 -.0407128

INTERNATIONAL IMMIGRATION ESTIMATION ERROR INDICATORS

forpct \| -.13502 .0339946 -3.972 0.000 -.0999809

NET MIGRATION ESTIMATION ERROR INDICATORS

pov \| .072446 .0325804 2.224 0.026 .1174622

blackpop \| .0001519 .0076704 0.020 0.984 .0004438

indpop \| -.1039429 .0285915 -3.635 0.000 -.1538393

hisppop \| -.0058464 .0223936 -0.261 0.794 -.0131631

ruralpov \| .0108678 .0364221 0.298 0.765 .0182611

blackpov \| -.0038511 .003271 -1.177 0.239 -.0185331

indpov \| .4072148 .0792336 5.139 0.000 .2208309

hisppov \| -.0165445 .0661579 -0.250 0.803 -.0120251

\_cons \| -.0000111 .0076936 -0.001 0.999 .

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

Note: The number of observations is 3141 USA counties in 1990; \_cons is
the estimated regression constant; region2 is an indicator variable
taking on the value one for counties in region 2 (Midwest), and zero
otherwise; region3 is an indicator variable taking on the value one for
counties in region 3 (South), and zero otherwise; region4 is an
indicator variable taking on the value one for counties in region 4
(West), and zero otherwise; REGION1 (Northeast) is the omitted category;
Pop90 is the Census population for 1990; Change1 is the percent change
in population from 1980 to 1990 (expressed as a decimal, i.e .01 = 1%);
bdpct is the natural increase divided by 1990 population; population;
bdchg is bdpct interacted with Change1 (bdpct\*Change1); rural is the
percent of the population living in areas classified as "rural"; nohosp
is an indicator variable taking on the value one if the county had a
hospital in 1990, zero otherwise; ruralnoh is the interaction between
rural and nohosp (rural\*nohosp); Groupqi is the percentage of the
population living in institutional group quarters; Pctcoll is the
percentage of the population living in college dormitories; Milpct is
the percentage of the population living in military quarters; Prispct is
the percentage of the population living in prison; GQICHG is the
interaction between GROUPQI and percentage change; cchg is the
interaction between college dorm population and percentage change
(pctcoll\*change1); mpch is the interaction between percent military and
population change (milpct\*change1); ppchg is the interaction between
prison population and population change (prispct\*change1); Ssipct is
the percentage of the population receiving Social Security Insurance
benefits; DIFF is the difference of the percentage 65 and over and the
percentage of the population who are SSI enrollees, (thus indicating the
percent of 65 and over who are not SSI enrollees); Edrate is the
percentage of the population who have attained high school degrees or
greater in 1990; Forpct is the percentage of the 1990 population
foreign-born; Pov is the percentage of the population living below
poverty in 1989; Blackpop is the percentage of the population black;
Indpop is the percentage of the population native American Indian;
hisppop is the percentage of the population of Hispanic origin; Blackpov
is the percentage of the black population living below poverty; Indpov
is the percentage of the Indian population living below poverty; hisppov
is the percentage of the Hispanic population living below poverty;
RURALPOV is the interaction effect between rural and poverty
(rural\*pov); Coef. is the regression coefficient; Std. Err. is the
population standard error; t is the coefficient divided by the standard
error, and Beta is the standardized regression coefficient. Under the
interpretation that these results represent the total population of
counties, F-statistics, t-statistics and p-values are illustrative only.

[^1]:
    > We consider it likely that destination preferences for
    > undocumented immigrants have changed since 1975.

[^2]: Three counties came to exist subsequent to the 1990 Census: These
    three counties were special boroughs in Alaska. Since 1990 counts
    for these counties were not available they were deleted from the
    completed file prior to analytic work.

[^3]: Alternatively, one could argue that the 3141 counties we have
    represent a sample of one year's worth of estimation from an ongoing
    process of estimates production, hence they do indeed have sampling
    variability as samples from a population of such estimates. In order
    to allow the reader to make his/her own interpretation, we present
    results to aid either interpretation.

[^4]: t-statistics are presented in Tables 3 and 4 as illustrative
    calculations for reference and/or sampling interpretation. We have
    omitted confidence intervals and presented instead the standardized
    regression coefficients.

[^5]: A copy of analyses performed using CALPE instead of ALPE is
    available from the first author.

[^6]: We note for the record that our a priori expectation was in the
    opposite direction; this post-explanation for the effect of prison
    population is constructed based on our understanding of "program
    effects" in administrative records and the probability that a
    prisoner has avoided detection by administrative sources prior to
    his/her incarceration. Both of these result in an underestimate
    prior to incarceration and an overestimate after. This is
    corroborated by the study cited above that discusses findings that
    prisons and financial institutions have the highest discrepancies in
    social security numbers (DHHS, 1990).