# Testing Population Estimation Models in Virginia:

# Dealing with Independent Cities

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# Donna J. Tolson

# Weldon Cooper Center for Public Service

# University of Virginia

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# Presented at Census Bureau Estimates Methods Conference

# June 8, 1999

Dtolson@virginia.edu

804.982.5580

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# Testing Population Estimation Models in Virginia:

# Dealing with Independent Cities

Unlike most of the nation, Virginia has a comprehensive system of
independent cities and counties. Because our 40 independent cities are
estimated as though they were counties, county-level estimates fulfill
much of the same function in Virginia that subcounty estimates fulfill
in other states. Thus the importance of county estimates in Virginia is
enormous, and their accuracy is, of course, crucial. Independent cities
and the counties they border also have some unique characteristics that
affect the type and quality of data available to produce population
estimates, and the data series in turn affect the accuracy of different
methods. This paper discusses our past findings in testing estimation
models for counties and independent cities, and provides a blueprint of
our testing plans for the post-2000 estimates.

# Data characteristics of independent cities

From a jurisdictional perspective, independent cities and their
neighboring counties are separate but equal. Each locality maintains its
own local government and collects its own taxes. Because county
governments do not include cities, in many ways this system provides
cleaner data than more hierarchical arrangements. For instance, most
school divisions in the state correspond exactly to locality boundaries.
However, there is a major data problem: the U.S. Postal System does not
conform to the boundaries of counties and cities. In fact, the main post
offices that serve counties are usually centered in cities, resulting in
county residents that have city mailing addresses and zip codes that
often slice across jurisdictional lines. Consequently, data for
independent cities and their neighboring counties have a high potential
for geographic miscoding. Even self-reported data are often erroneous as
many residents give their mailing address rather than their locality of
residence when asked where they live.

As one might expect, most federally collected administrative data such
as Medicare and IRS tax records contain relatively high levels of
geographic inaccuracy for Virginia. In contrast, most data collected by
the state are screened for county-city coding errors. Vital statistics
records are put through a computerized program that checks the address
of the residence against the locality of residence. Many state tax
records are filed locally, where the Commissioner of Revenue has the
opportunity to check the address for correct residence coding. Through a
variety of measures, the state strives for and largely attains a greater
level of geographic accuracy in data coding than is generally present in
federal data where the independent city/county situation is a rarity.

Given these circumstances, the most accurate method for estimating
county population in the nation may not be the best method for
estimating counties in Virginia, especially if the method relies on
federal data. While we are the only state in the nation with so many
independent cities, much of our findings may relate to estimates for
independent cities in other parts of the country. There may also be
other areas of the nation where jurisdictional boundaries do not match
the geography of the data sets required by a given estimation method.
These states may also benefit from testing a variety of methods to
discover what will work best for them.

# Background to methods testing in Virginia

We will limit our testing to four basic methods:

-   Ratio-Correlation (RatCorr),

-   Tax Return method (TaxRet) and its precursor, Administrative Records
    (AdRec),

-   Component Method II (CMII), and

-   Housing Unit method (HU).

These methods were chosen because of data availability, past estimation
performance in Virginia, and in the case of TaxRet, the Bureau's
commitment to the method. None of these methods are new, but we will
test revisions to each method that take advantage of particular data
sets to determine which method or combination of methods will produce
the most accurate estimates.

Our purpose in testing is to determine which single method or
combination of methods generates estimates that most closely match the
population as measured by a decennial census enumeration. The basic
testing procedure we employ is:

1)  For each method tested, develop an equation based on the previous
    census year for estimating population. For example, in 2000, we will
    use methods based on 1990 Census data.

2)  Use the equation to generate population estimates for ten years
    hence, a "worst-case" scenario. Thus in 2000, we will test estimates
    that have been generated by models based on 1990 against the 2000
    census data.

3)  Compare the results of the individual methods. While mean absolute
    percent errors (MAPEs) are an important indicator of accuracy, it is
    also important to look at the frequency distribution of error to
    identify the number of outliers and the magnitude of the errors
    associated with specific localities.

4)  After analyzing the results of the single methods, average the
    results of the most promising methods in various combinations to
    develop the best overall method for all localities in the state.

## Analysis of 1990 Testing

Many of our decisions about what methods to test for 2000 are based on
the results of our 1990 testing. Three methods were tested last decade:
Administrative Records, Component Method II, and Ratio Correlation. In
addition to the pure methods, tests were done on all possible
combinations of the three methods. Table 1 lists the MAPEs for each
method or combination for counties, cities, and total localities. City
MAPEs are higher in all cases than county MAPEs because all cities have
address coding difficulties, where only those counties adjacent to
independent cities have similar coding problems. Just as important as
MAPEs, however, is the frequency distribution of errors. Table 1 also
lists the number of localities for which each method or combination
produced excellent estimates (defined as estimates with an absolute
percent error under 2%), good (APE between 2 and 5%), fair (APE between
5 and 10%), or poor estimates (APE of 10% or greater).

## Administrative Records Method

The Census Bureau's AdRec method, as it existed in 1990, produced the
least accurate estimates among the three methods tested. It produced
excellent estimates in only 39 Virginia localities out of a possible 136
(we had one more independent city in 1990 than we do now). Its overall
MAPE of 5.15 was higher than any other method or combination tested in
Virginia. It also had the most outliers with the most extreme errors: 16
localities had errors of 10 percent or more with 3 of them above 25
percent. Not surprisingly, the worst errors were in localities where
postal codes overlapped neighboring jurisdictions. In its favor, AdRec's
MAPEs were not unreasonably high, and since it was clearly the Bureau's
method of choice, we knew it would be consistently available for the
next decade. Our conclusion was that AdRec might prove useful in
combination with another method.

## Component Method II

We tested CMII because our locality borders so closely match school
districts, the data series driving CMII's migration component. Although
it was already losing favor with the Bureau and FSCPE in 1990, CMII
produced the excellent estimates for 34 localities in Virginia. At 4.32,
its overall MAPE was much lower than the AdRec MAPE. Only 9 localities
had absolute percent errors over 10 percent, with the highest at just
over 16 percent. Areas it had trouble estimating tended to be small,
relatively fast-growing localities. While this method had promise in
Virginia, the Bureau was uncertain it would continue to provide the
various adjustment and exposure factors required for its production.

## Ratio Correlation Method

We had used RatCorr (averaged with AdRec) in the 1980s, so the first
step was to test the current model. It produced excellent estimates for
44 localities, more than either AdRec or CMII, and its MAPE for all
localities in the state was about 3.9 percent. We then recalibrated the
equation, including some new variables that were not available before. A
new equation emerged using state tax returns, housing stock (without
mobile homes), school enrollment in grades 1-8, and a three-year sum of
births. The new RatCorr equation yielded estimates far better than the
old method, with MAPEs for both counties and cities around 2.5 percent.
It also had the fewest outliers of any method, with the highest error
just under 12 percent. Although the Bureau was beginning to lose favor
with the RatCorr method, the solid performance of the method in a
worst-case scenario combined with the exceptional accuracy of the
recalibrated equation insured that we would continue to use this method
in some form.

## Averaged methods

Although the new RatCorr equation produced superior estimates for most
localities, we were committed to an averaged estimate in 1990 for all
the statistical reasons supporting such an approach. Averaging methods
should reduce outliers (which our testing supported), lend stability to
the estimates over time, and be less vulnerable to a loss of quality in
a single variable. We tested every possible combination of the three
methods (using the new RatCorr equation) and a scheme that averaged
different methods for counties than for the cities. Except for the
average of AdRec and CMII, the overall MAPEs for these different
combinations were acceptably low, varying from 3.2 to 2.8.

Although CMII had outperformed the AdRec method in Virginia, the Bureau
was no longer committed to producing the various inputs such as residual
net migration factors and civilian-military movement necessary to the
method. During the 1980s, our state-produced estimates and the Bureau's
estimates were identical and we had produced them cooperatively. Because
we wanted to avoid the confusion caused by the release of two different
estimate series, we decided to use the average of the RatCorr and AdRec
method, which both we and the Bureau had used in the previous decade. We
did not foresee the Bureau's decision to move to a single-method
approach in the early 1990s. We refer to the averaged estimates as the
Sumest series.

Parenthetically, we are not entirely convinced that an average of two or
more methods is necessarily better than choosing the best method for
each locality. In our 1990 testing, the recalibrated RatCorr equation
had very impressive error statistics to support using it alone. The
downfall of the best method approach has been the assumption that any
one method will tend to deteriorate over time. In 2000, we will be able
to test this assertion in Virginia, and depending on its outcome, may
choose a "best method" approach rather than an average of two or more
methods.

# Testing Plans for 2000 

## Based on what we learned from last decade's testing, we plan to test several methods again. We will test the current RatCorr equation and recalibrate a new one that will likely use new variables. We will test the TaxRet estimates currently produced by the Bureau. We would also like to test CMII, assuming we can develop our own factors, and a version of the Housing Unit Method, which we have not tested in Virginia before. The methods and the data issues associated with each one are summarized in the handout entitled Plans for Testing County Estimation Methods in Virginia 2000.

## Ratio-Correlation Method

As discussed earlier, a regression model using double ratios of state
tax returns, estimated housing stock, a three-year sum of births, and
public and private school enrollment in grades 1-8 produced very
accurate estimates for most of Virginia's counties and independent
cities in 1990. We have used this method averaged with the AdRec and
TaxRet method to produce the Sumest series this decade. We will test
this model again to see how it has held up over time.

### Current Variables

### Three-year births 

#### This decade, we used a 3-year sum of births by residence. The three-year sum smoothed out the random fluctuations inherent in birth data and was easier to estimate than single-year births for provisional estimates. Data come from the Center for Health Statistics at the Virginia Department of Health.  {#this-decade-we-used-a-3-year-sum-of-births-by-residence.-the-three-year-sum-smoothed-out-the-random-fluctuations-inherent-in-birth-data-and-was-easier-to-estimate-than-single-year-births-for-provisional-estimates.-data-come-from-the-center-for-health-statistics-at-the-virginia-department-of-health. .unnumbered}

### School enrollment (grades 1-8)

The existing school enrollment variable includes public and private fall
enrollment in grades 1-8. These grades were chosen to limit the data to
years of compulsory attendance. Public data are collected from annual
Superintendents' Reports published by the Virginia Department of
Education. We collect private school data in an annual survey of private
schools with students in grades 1-8. We ask each school to tell us how
many children they have in these grades and their locality of residence.

### State tax returns 

#### The state tax data series includes all returns filed for Virginia. State tax return data are provided annually from the Virginia Department of Taxation.  {#the-state-tax-data-series-includes-all-returns-filed-for-virginia.-state-tax-return-data-are-provided-annually-from-the-virginia-department-of-taxation. .unnumbered}

### Estimated housing stock without mobile homes

The current housing variable is constructed with 1990 Census housing
stock brought forward with cumulative building permit data. A lag time
of three months is factored into the permit data. Our source for the
permit data is *Permit Authorized Construction in Permit-Issuing Places
by State and County*, released annually by the Manufacturing and
Construction Division (MCD) of the Census Bureau. Unfortunately, several
localities in Virginia are inconsistent in their reporting, and although
the Bureau imputes missing data, we have found the imputations to be
fairly inaccurate. We prefer to directly contact those local governments
who have not reported and request the missing data. If we cannot get
documented local data, we use the Bureau's imputed figures.

Just as in the 1990 testing, we will recalibrate the regression
equation. At that time, we will introduce refinements to existing
variables, and a few variables based on new data that we have not had
before:

### Estimate year births 

#### We now have the capability to construct a July 1-June 30 birth variable. {#we-now-have-the-capability-to-construct-a-july-1-june-30-birth-variable. .unnumbered}

### More complete school enrollment 

We now have division-level data on home-schooled children and children
not attending school due to religious exemption, both growing trends in
Virginia. We will also survey public school superintendents for counts
of transfer students who attend public schools in one locality but live
in another. We will test school enrollment variables that take these
additional data sources into account. While we would like to test K-8
and K-12 enrollment, we do not have the historic private school data for
those additional grades, so we would either have to use separate
variables for public and private enrollment, or limit the variable to
public school enrollment.

### State tax exemptions 

#### The major change we will test in the tax data is an alternate variable of personal and dependent exemptions rather than returns. Surprisingly, exemptions did not remain in the model last decade, but we will to test it again as it intuitively seems that it should be a more powerful predictor of variance than returns.  {#the-major-change-we-will-test-in-the-tax-data-is-an-alternate-variable-of-personal-and-dependent-exemptions-rather-than-returns.-surprisingly-exemptions-did-not-remain-in-the-model-last-decade-but-we-will-to-test-it-again-as-it-intuitively-seems-that-it-should-be-a-more-powerful-predictor-of-variance-than-returns. .unnumbered}

### Estimated housing stock with mobile/manufactured homes

Because the Census Bureau's permit data did not include mobile or
manufactured homes in 1990, we tested the variable without them and it
still proved to be a good predictor of population change (in fact, the
housing variable carries the largest coefficient in the current model).
However, since 1990, many of the rural localities in Virginia have
experienced substantial growth in mobile and manufactured housing, while
many urban localities have restricted this type of housing to a few
trailer parks or outlawed them altogether. We are concerned that the
differential growth of mobile and manufactured housing may have affected
the quality of the housing stock variable over this decade, and will be
anxious to see how it tests out in 2000. We are currently trying to
collect mobile home permits from local governments for the decade and
plan to test a housing stock variable that includes mobile and
manufactured housing. If only a subset of localities can provide
historic data on these permits, we will test a stratification scheme
that creates one equation for localities with such data and another
equation for those without.

Demolitions were also excluded because the quality of the data was so
poor (they were dropped completely from the MCD data in 1993). If the
local data support it, we will also test a housing stock variable that
includes demolition and conversion information as well.

### Deaths

We tested deaths in the RatCorr model last decade, but it did not remain
in the model. We will test a few death variables again, most likely an
estimate year variable and a three-year sum, as we are doing with birth
data. Historic and current data will be provided by the Center for
Health Statistics at the Virginia Department of Health.

### Driver's Licenses

Driver's licenses were not tested in Virginia in 1990 because the 1980
data required for the double ratios were not available. The Virginia
Department of Motor Vehicles can now provide us with annual reports of
the total number of licensed drivers in the state by county or city of
residence back to 1990. We plan to test a variable based on the number
of licensed drivers as of July 1. Intuitively, this variable should have
a fairly powerful correlation with the variance of the 18 and over
population.

### Adjusted Medicare data

Medicare data in Virginia has traditionally been very poor, subject to
the same types of geocoding problems inherent in the federal tax data.
However, we have had some success this decade by adjusting Medicare
data. The adjustment aggregates data from localities with overlapping
zip codes into a cluster and distributes the cluster total back to the
localities based on their portion of the cluster's 65 and over
population in 1990. This adjusted variable is already used in the TaxRet
model, and may contribute to the accuracy of a RatCorr equation.
However, it will be important to test it in a worst-case scenario to
make sure that the adjustment based on 65+ population ten years earlier
holds up over time.

In the past, we have always used RatCorr to produce estimates of the
non-group quarter's population. In addition to this traditional product,
we also want to investigate RatCorr's power to estimate other dependent
variables.

### Using RatCorr to estimate total and noninstitutional population 

Virginia has experienced deterioration in the quality of military group
quarters data this decade. The Navy stopped providing us with counts of
personnel in housing or on ships afloat in 1994. The Census Bureau still
receives naval data and shares the barracks data with us, but will not
share afloat data due to confidentiality restrictions imposed by the
Department of Defense. A few years ago, housing reports from the Army
were discontinued, and we have been told that 1998 was the last year
that the Marines would collect barracks data. Due to the decline in
availability of military data, we would like to test ratio correlation's
power to predict both the total population and the noninstitutional
population (defined for our purposes as the household and military
population). While we do not have high hopes for the accuracy of such a
model, we think it is worth trying in light of the current problems
securing military housing data.

### Stratification and other techniques

Finally, we plan to try stratification of the RatCorr estimates. Over
the years, we have done some very interesting things while testing the
RatCorr method, including ridge regression, using single ratios rather
than double ratios, and using variables in non-ratio form. None of these
techniques, including stratification, have ever yielded satisfying
results. However, we have come closest to a good solution with
stratification. In the past, we have tested stratification on growth,
size, metropolitan/nonmetropolitan, urban/rural, SES proxies such as
income and race, and military presence. Repeatedly, the stratification
scheme worked exceptionally well for all but one group, whose estimates
were so poor that they were unusable. However, we persist in the belief
that if we could get it to work, stratified results might yield better
estimates than anything we have now. Given that, we expect to test
several stratification schemes again this decade.

### RatCorr and the state control

Unlike other estimation methods, the product of the RatCorr method is
not an actual population estimate; it is the proportion of the state
total that should be applied to each locality. Consequently, it is by
definition a "top-down" model that requires a state estimate not just
for controlling, but for the actual calculation of the estimate. Before
the Bureau's change to the Tax Return method, we received a state
control well in advance of the release of the county-level estimates.
This gave us plenty of time to produce the RatCorr estimates before the
AdRec estimates were available to average in the Sumest procedure. With
the Bureau's change to a "bottom-up" approach, the amount of time we
have to produce the averaged estimates is greatly reduced. Additionally,
the state estimate released by the Bureau changes in response to
challenges and special censuses from other counties in the nation,
making it a less stable figure over time. As a consequence, if our
testing supports the continued use of RatCorr in the next decade, we may
need to develop a way to estimate a state control on our own.

## Tax Return Method

We are anxious to test the TaxRet method in 2000, since we have no
statistical information about how this method performs under worst-case
scenario conditions in Virginia. However, because it is methodologically
so similar to AdRec, we expect the TaxRet method to have many of the
same weaknesses that AdRec did in Virginia. Based on our 1990 test
results and the differences between RatCorr and TaxRet estimates this
decade, Bureau staff has agreed that the TaxRet errors for some Virginia
localities are likely to be egregious. To remedy the situation, the
Bureau implemented an adjustment procedure mid-decade informally called
the "Virginia Fix." For localities with address coding problems, this
procedure aggregates the TaxRet estimates into clusters and
redistributes the aggregated total according to the proportions
indicated by the RatCorr estimates we produce at the Cooper Center.
However, this procedure has developed its own problems and needs
refinements to deal with issues like group quarters populations.

We do not have the data to construct TaxRet estimates ourselves, so our
testing procedure will be limited to a comparison of the Bureau\'
estimates with 2000 census counts. We will test TaxRet estimates
unadjusted by the Virginia Fix to get a true reading on the accuracy of
the method, contingent on the Bureau's ability to provide us with
unadjusted TaxRet estimates.

### Using RatCorr to produce domestic net migration

If the TaxRet method tests indicate inferior results, we may test one
hybrid of the method that would substitute a RatCorr estimate of the
domestic migration component of the TaxRet method. Since the major
downfall of the AdRec method for Virginia was in the domestic migration
component that depends on federal tax data, its estimates might be
substantially improved if we could replace the domestic migration rate
with an improved one. The form of the independent variables in such an
equation would most likely be differences over time, e.g. numeric change
in state tax returns, births, school enrollment, housing, driver's
licenses, etc.

## Component Method II

Because it tested out so well in 1990, we want to look at CMII again for
Virginia. The Bureau is no longer calculating the mortality rates,
migration exposure factors, and residual migration adjustment factors
used by the method, but they have given us the methodology to calculate
them ourselves. Assuming that we are successful in developing these
factors, we plan to test the method with the following adjustments:

### Replace life table deaths with actual deaths

> Instead of using national mortality rates to estimate deaths to the
> school-age cohort, we will use county-specific deaths by single year
> of age.

### Omit or adjust civilian to military movement variable

> Unless a military draft is reinstated, we will omit or adjust the net
> movement between the military and civilian populations.

### Investigate different enrollment variables

> As discussed in the RatCorr section above, we now have more data on
> school age children that will allow us to test different types of
> enrollment variables. In addition to including private 1-8 enrollment
> in the variable, which we did in 1990, we will test the model using
> 1-8 enrollment including home-schooled children and children not
> enrolled in school due to religious exemption. We may also test a K-8
> enrollment variable.

### Investigate 65+ migration factor: use adjusted Medicare data 

> Because Medicare data have been poor in Virginia in the past, we will
> construct an adjusted Medicare series (see Adjusted Medicare under
> Ratio Correlation above) if the Medicare data vary substantially from
> the 65 and over population in 2000.

## Housing Unit Method

Because so little housing data are collected centrally, we have never
tested a housing unit method in Virginia. However, because the state
requires permits for all types of residential construction, the building
permit databases are generally of high quality. The current housing
variable in this decade's RatCorr model is based on local building
permits, consequently, we now have a historic data series that will
allow us to test a worst-case scenario of the model.

The Housing Unit Distributive Method (HUDM) developed at the Census
Bureau for subcounty population estimates will serve as the basic model
for this method. However, we hope to assemble the local data necessary
to use actual permit data for mobile/manufactured homes, demolitions,
and conversions rather than allocated data based on state or national
figures. Since we have not collected these data in the past, the first
step was to conduct a survey of data availability.

### Survey to assess local housing data availability

In the spring of 1999, we developed a survey to be sent to every local
government in Virginia. Before designing the survey, we interviewed
officials in four representative localities: an independent city, a
large suburban county, a fast-growing rural county, and a small rural
county with relatively slow growth. Based on their comments, we
developed a two-part questionnaire (see Survey attachment).

One part of the survey is directed to a building permit official. The
survey asks for details on the availability of permit data by type,
information on Certificates of Occupancy, permit compliance, and lag
time. Preliminary responses from the survey indicate that virtually all
localities in Virginia have permit records for all new residential
construction, including mobile and manufactured homes, and that most
have records of demolitions. However, not all localities can provide
these data back as far as 1990.

The other part of the survey was sent to staff members in planning
departments. We ask these contacts whether their locality already has or
is developing a comprehensive housing unit database for a system such as
E911. Preliminary responses to this part of the survey indicate that
most localities have such systems, but few can currently identify the
data by type of unit except to distinguish residential from
nonresidential buildings.

### Testing the traditional method

To test the traditional housing unit method, we will develop estimated
housing stock for 2000 by applying cumulative data to the 1990 total
housing stock. From *Permit Authorized Construction in Permit-Issuing
Places by State and County,* we already have a historic series of
permits for new residential units not including mobile or manufactured
housing. We will use cumulative local data for mobile/manufactured
units, demolitions, and conversions to develop the estimated housing
stock variable. Where we cannot get local data, we will use the Bureau's
allocation method for the component. We will test the method using the
1990 vacancy rates and persons per household weight by type of unit, to
test a worst-case scenario. Based on the responses to our survey, we
will develop an average lag time for permits by type of structure, as
well as weights to apply for nonpermitted construction and permitted
construction that is never completed.

### Look at availability of CO's instead of building permits

All localities in Virginia issue Certificates of Occupancy (CO) at the
completion of a building project. CO's potentially could be used in
place of building permits, eliminating the need to introduce lag time
factors and adjustments for uncompleted building projects. However, the
coverage of CO's may vary widely across localities, and until we have
more information, we cannot say whether it will be promising enough to
test.

### Testing a method using snapshot data

### Depending on the prevalence of the data, we would like to test the accuracy of estimates produced by a housing unit method that makes use of the "snapshot" housing unit counts. The availability of snapshot counts of total housing stock introduces a potentially superior alternative to permit data. First, depending on the completeness of the data, it may eliminate the need to estimate certain components to the method such as mobile homes, nonpermitted or demolished structures, or other types of housing loss. Second, units permitted but never completed will not be counted, as they might be in the traditional method. Third, no lag time will be necessary if the data can be obtained for June 30 or July 1. Finally, snapshot data do not require cumulating data for all years between the most recent decennial census and the estimating year, or the risk of compounding errors over time. 

### Investigate estimating PPH

While state estimates of PPH indicate that it has changed little in
Virginia over the current decade, we would like to investigate ways of
estimating a county-level PPH or housing type-specific PPH figure if we
have time.

# Conclusion

There are several reasons to support a "home-grown" model for
county-level population estimates for Virginia. First, we know that
Virginia's system of independent cities and counties presents geocoding
challenges for many data series. At the very least, one cannot assume
that the quality of federal data for Virginia will match the quality of
the same data nationally. At best, data collected by the state is
substantially superior to comparable federal data series.

Second, there are several data series that have potential use for
estimates that are available within the state that may not be available
in other states. This decade, we have made use of state-produced school
enrollment, tax return, and vital statistics data and we are currently
developing a system to collect local housing data.

Third, we know from experience that the method chosen by the Census
Bureau for county-level estimates may not produce the most accurate
estimates in Virginia. While the accuracy of the Tax Return method has
not yet been tested in a worst-case scenario, our 1990 testing results
for AdRec indicate that TaxRet estimates are not likely to outperform
those produced by a RatCorr model.

As the state-mandated agency for producing population estimates for the
distribution of state funds, the Virginia FSCPE will test the results of
many methods for counties and independent cities in Virginia in 2000.
Some methods, such as Ratio Correlation, may not be feasible to test
nationally. Obviously, we will use the method or combination that
produces the most accurate estimates for Virginia, regardless of the
Bureau's choice of method. We can well appreciate the Bureau's need to
invest its staff time in the method that performs best for counties
throughout the nation, and that Virginia's idiosyncratic system makes
many of its localities anomalies. However, in the interest of accuracy
as well as simplification, the Bureau's estimates for Virginia could be
much improved if the Bureau accepted the estimates produced by the most
accurate method, regardless of where and by whom they are produced.