American Community Survey 1999--2001 Three-Year Average and Census 2000
Sample

**Comparison Profile Technical Documentation**

# INTRODUCTION {#introduction .Memo-Level-3}

The data contained in these Profile Tables are based on the American
Community Survey (ACS) sample interviewed in 1999, 2000, and 2001, and
the census sample from Census 2000 for the same counties in the ACS
sample. The three years' worth of ACS data were combined to form
three-year average estimates. These multi-year average
estimates---centered on 2000---are being compared to the Census 2000
sample estimates. The purpose of this documentation is to provide data
users with a basic understanding of the sample design, estimation
methodology, and accuracy of the ACS data and census data used in these
comparisons.

In this data product, we include Profile Tables 1--4 for 1999--2001 ACS
three-year averages and data from the Census 2000 sample. Profile Table
1 provides ACS and Census 2000 sample estimates for 100% (\"short
form\") items. These Census item estimates are based only on the
response records in the Census sample and not on the full census count.
Profile Tables 2--4 provide ACS and Census 2000 estimates for sample
(\"long form\") items. Because the ACS is to replace the Census sample
and not the full count Census, comparisons of the ACS three-year
averages and the Census 2000 should concentrate on Profile Tables 2--4.
However, we are providing you with Profile Table 1 so that you have
complete information for each site. We know there are methodological
differences (e.g. edits and imputations, question wording, etc.) that
affect the comparison of ACS and Census 100% items. These differences
would also affect county and tract level comparisons of these items.

# SAMPLE DESIGN---ACS {#sample-designacs .Memo-Level-3}

**Number range CHAPTERLocations**---Thirty-six counties were selected
for inclusion in the 1999, 2000, and 2001 ACS. Number range CHAPTERThe
main criteria employed to select the 36 counties were factors which
could present special complications in data collection and estimation.
These factors include: the size of the county's population, the
proportion of the population in areas classified as hard to enumerate
(based on mail response rates in the 1990 census), and the speed of
growth or decline in population since 1990. In addition, the list
included counties that varied along important dimensions such as
dominant industry, classification as rural/urban/suburban, racial/ethnic
distribution, presence and number of mobile homes, large numbers of
non-city style addresses, seasonal populations, and presence of American
Indian reservations.

The primary sampling unit was the housing unit (HU), including all
occupants. Persons living in group quarters (GQ) were **NOT** included
in the sample. The 36 ACS counties are:

+---------------+--------------------------------+---------------------+
| ## FIPS       | **County**                     | **State**           |
+---------------+--------------------------------+---------------------+
| 04019         | Pima County                    | Arizona             |
+---------------+--------------------------------+---------------------+
| 05069         | Jefferson County               | Arkansas            |
+---------------+--------------------------------+---------------------+
| 06075         | San Francisco                  | California          |
+---------------+--------------------------------+---------------------+
| 06107         | Tulare County                  | California          |
+---------------+--------------------------------+---------------------+
| 12011         | Broward County                 | Florida             |
+---------------+--------------------------------+---------------------+
| 13293         | Upson County                   | Georgia             |
+---------------+--------------------------------+---------------------+
| 17097         | Lake County                    | Illinois            |
+---------------+--------------------------------+---------------------+
| 18103         | Miami County                   | Indiana             |
+---------------+--------------------------------+---------------------+
| 19013         | Black Hawk County              | Iowa                |
+---------------+--------------------------------+---------------------+
| 22031         | De Soto Parish                 | Louisiana           |
+---------------+--------------------------------+---------------------+
| 24009         | Calvert County                 | Maryland            |
+---------------+--------------------------------+---------------------+
| 25013         | Hampden County                 | Massachusetts       |
+---------------+--------------------------------+---------------------+
| 28089         | Madison County                 | Mississippi         |
+---------------+--------------------------------+---------------------+
| 29093         | Iron County                    | Missouri            |
+---------------+--------------------------------+---------------------+
| 29179         | Reynolds County                | Missouri            |
+---------------+--------------------------------+---------------------+
| 29221         | Washington County              | Missouri            |
+---------------+--------------------------------+---------------------+
| 30029         | Flathead County                | Montana             |
+---------------+--------------------------------+---------------------+
| 30047         | Lake County                    | Montana             |
+---------------+--------------------------------+---------------------+
| 31055         | Douglas County                 | Nebraska            |
+---------------+--------------------------------+---------------------+
| 35035         | Otero County                   | New Mexico          |
+---------------+--------------------------------+---------------------+
| 36005         | Bronx County                   | New York            |
+---------------+--------------------------------+---------------------+
| 36087         | Rockland County                | New York            |
+---------------+--------------------------------+---------------------+
| 39049         | Franklin County                | Ohio                |
+---------------+--------------------------------+---------------------+
| 41051         | Multnomah County               | Oregon              |
+---------------+--------------------------------+---------------------+
| 42057         | Fulton County                  | Pennsylvania        |
+---------------+--------------------------------+---------------------+
| 42107         | Schuylkill County              | Pennsylvania        |
+---------------+--------------------------------+---------------------+
| 47155         | Sevier County                  | Tennessee           |
+---------------+--------------------------------+---------------------+
| 48157         | Fort Bend County               | Texas               |
+---------------+--------------------------------+---------------------+
| 48201         | Harris County                  | Texas               |
+---------------+--------------------------------+---------------------+
| 48427         | Starr County                   | Texas               |
+---------------+--------------------------------+---------------------+
| 48505         | Zapata County                  | Texas               |
+---------------+--------------------------------+---------------------+
| 51730         | Petersburg City                | Virginia            |
+---------------+--------------------------------+---------------------+
| 53077         | Yakima County                  | Washington          |
+---------------+--------------------------------+---------------------+
| 54069         | Ohio County                    | West Virginia       |
+---------------+--------------------------------+---------------------+
| 55085         | Oneida County                  | Wisconsin           |
+---------------+--------------------------------+---------------------+
| 55125         | Vilas County                   | Wisconsin           |
+---------------+--------------------------------+---------------------+

**Number range CHAPTERSampling Rates**---Within a county, the base
sampling rates were the same for each of the three years. In Fort Bend
and Harris Counties, TX the base HU sampling rate was one percent. In
Broward, FL; Bronx, NY; Lake, IL; San Francisco, CA; and Franklin, OH
the base HU sampling rate was three percent. The remaining counties had
a base sampling rate of five percent. The sampling rate within the
county varied by the size of the governmental unit the housing unit was
in. A variable sampling rate was used for the purpose of providing
relatively more reliable estimates for small areas.

  ---------------------------------------------------------------------------
  Number range CHAPTERType of  Fort Bend    Broward, FL; Bronx,    All Other
  Area                         and Harris,  NY; Lake, IL; San      Counties
                               TX           Francisco, CA; and     
                                            Franklin, OH           
  ---------------------------- ------------ ---------------------- ----------
  Blocks in smallest           3%           9%                     15%
  governmental units (fewer                                        
  than 800 HUs)                                                    

  Blocks in small governmental 1.5%         4.5%                   7.5%
  units (between 800 and 1200                                      
  HUs)                                                             

  Blocks in large tracts (more 0.75%        2.25%                  3.75%
  than 2000 HUs)                                                   

  All other blocks (including  1%           3%                     5%
  ungeocoded records)                                              
  ---------------------------------------------------------------------------

Using the three-year average of the ACS estimates increases the
effective sample size, and therefore improves the sampling error of the
estimates.

Number range CHAPTERBecause of the smaller sample sizes in Harris and
Fort Bend Counties, tract level data was not produced. In order to have
local data below the county level, we produced data for groups of tracts
called user defined areas (UDAs).

# CONFIDENTIALITY---ACS {#confidentialityacs .Memo-Level-3}

No further confidentiality edits were applied to the data in this
product beyond those already applied to the single-year estimates. For
more information on the confidentiality edits for the single-year
estimates, see the individual year's Accuracy of the Data documentation
([1999](http://www.census.gov/acs/www/Downloads/ACS/Accuracy99.pdf),
[2000](http://www.census.gov/acs/www/Downloads/ACS/Accuracy00.pdf),
[2001](http://www.census.gov/acs/www/Downloads/ACS/Accuracy01.pdf)).

# ESTIMATION PROCEDURES---ACS {#estimation-proceduresacs .Memo-Level-3}

We include both three-year averages and single-year estimates in this
data product. The three-year averages are based on the 1999, 2000, and
2001 single-year estimates. For a detailed description of how the
single-year estimates were produced, the reader is referred to the
[Accuracy of the Data
(2001)](http://www.census.gov/acs/www/Downloads/ACS/Accuracy01.pdf)
documentation.

The 1999 and 2000 data were reweighted to be consistent with the 2001
weighting methodology. The 2000 data had been reweighted in 2001 and
were used in the 2000 to 2001 American Community Survey Change profiles.
The reweighting for 1999 used 2000-based 1999 population and housing
unit controls. The original 1999 ACS data were released using 1990-based
population controls which differed from the 2000-based controls. In
addition, the original 1999 ACS data were released without housing unit
controls. Thus, the single-year estimates for 1999, 2000, and 2001 all
use 2000-based population and housing unit estimates or census data for
the controls and a consistent weighting methodology. Because of these
changes, the 1999 single-year estimates in this data product differ from
the 1999 data released in 2000.

To produce the three-year average, the data from all three years were
adjusted to provide a consistent reference year of 2000. This included
adjusting monetary related variables to constant 2000 dollars and using
the 2000 tabulation geography.

Three-year average estimates for counts are simply equal to the
arithmetic average of the three single-year estimates.

$$\text{AvgEst}_{\text{count}} = \frac{\text{Est}_{\text{1999}} + \text{Est}_{\text{2000}} + \text{Est}_{\text{2001}}}{3}$$

Three-year ratios were computed by taking the ratio of the aggregate
numerator over three years divided by the aggregate denominator.

$$\text{AvgEst}_{\text{ratio}} = \frac{\text{Est}_{n,\text{1999}} + \text{Est}_{n,\text{2000}} + \text{Est}_{n,\text{2001}}}{\text{Est}_{d,\text{1999}} + \text{Est}_{d,\text{2000}} + \text{Est}_{d,\text{2001}}}$$

The three-year estimates for the medians were obtained by pooling the
three years of data together and calculating the median of the combined
data.

Some caveats are important to note about this product compared to the
anticipated multi-year data products from the full ACS implementation.

-   Tract-level estimates are being produced from this special
    comparison study using a three-year average. Under the full ACS
    implementation, tract-level estimates will be produced only as
    five-year averages.

-   The pooled sampling rate for most of the counties in these
    three-year averages is 15 percent. Tract-level estimates produced
    under the full ACS implementation will have an average sampling rate
    of 12.5 percent pooled from five years of data. More densely
    populated counties could have lower sampling rates both in this
    sample and under the full implementation.

-   The data were centered on the middle year, 2000, for this data
    product in order to achieve comparability to Census 2000. In a
    normal multi-year average data product, the data will still produce
    an average which is centered on the middle year but the monetary and
    geography related variables will be updated for the last year of the
    multi-year average. For example, in the full implementation, a
    three-year average based on 2000--2002 data would have 2002
    geography and the monetary related variables would be translated
    into constant 2002 dollars in a standard data product.

**Number range CHAPTERERRORS IN THE DATA---ACS**

**Number range CHAPTERSampling Error**---The data in the ACS products
are estimates of the actual figures that would have been obtained by
interviewing the entire population using the same methodology. The
estimates from the chosen sample also differ from other samples of
housing units and persons within those housing units. Sampling error in
data arises due to the use of probability sampling, which is necessary
to insure the integrity and representativeness of sample survey results.
The implementation of statistical sampling procedures provides the basis
for the statistical analysis of sample data.

**Number range CHAPTERNonsampling Error**---In addition to sampling
error, data users should realize that other types of errors may be
introduced during any of the various complex operations used to collect
and process survey data. For example, operations such as editing,
reviewing, or keying data from questionnaires may introduce error into
the estimates. These and other sources of error contribute to the
nonsampling error component of the total error of survey estimates.
Nonsampling errors may affect the data in two ways. Errors that are
introduced randomly increase the variability of the data. Systematic
errors which are consistent in one direction introduce bias into the
results of a sample survey. The Census Bureau protects against the
effect of systematic errors on survey estimates by conducting extensive
research and evaluation programs on sampling techniques, questionnaire
design, and data collection and processing procedures. In addition, an
important goal of the ACS is to minimize the amount of nonsampling error
introduced through nonresponse for sample housing units.

**Number range CHAPTERStandard Errors**---The standard error is a
measure of the deviation of a sample estimate from the average of all
possible samples. Sampling errors and some types of nonsampling errors
are estimated by the standard error. The sample estimate and its
estimated standard error permit the construction of interval estimates
with a prescribed confidence that the interval includes the average
result of all possible samples. The next section describes the method of
calculating standard errors and confidence intervals for the estimates
in this ACS product.

# CONTROL OF NONSAMPLING ERROR---ACS {#control-of-nonsampling-erroracs .Memo-Level-3}

For information on how nonsampling error was controlled in the three ACS
single-year estimates, please see the individual year's Accuracy of the
Data documentation
([1999](http://www.census.gov/acs/www/Downloads/ACS/Accuracy99.pdf),
[2000](http://www.census.gov/acs/www/Downloads/ACS/Accuracy00.pdf),
[2001](http://www.census.gov/acs/www/Downloads/ACS/Accuracy01.pdf)).

# CALCULATION OF STANDARD ERRORS---ACS {#calculation-of-standard-errorsacs .Memo-Level-3}

For information on how direct standard errors were calculated for the
three ACS single-year estimates, please see the [Accuracy of the Data
(2001)](http://www.census.gov/acs/www/Downloads/ACS/Accuracy01.pdf)
documentation.

The standard errors of the three-year average estimates were computed
using different methods based on the type of the estimate. For simple
counts of persons, housing units, etc., the standard error of the
three-year average estimate was the square root of the sum of the
squares of the standard errors for the three single years, divided by
three.

$$\text{SE}_{\text{count}} = \frac{\sqrt{\text{SE}_{\text{1999}}^{2} + \text{SE}_{\text{2000}}^{2} + \text{SE}_{\text{2001}}^{2}}}{3}$$

Some ACS estimates at the county level---including total population and
total number of housing units---are controlled to be equal to
independent population controls. These controlled estimates have no
sampling error. If the estimates for all three years of a particular
count are controlled, then its standard error is assigned a value of
'\*\*\*\*\*'. Some ACS estimates at the county level may also be nearly
controlled if some but not all of its constituent parts are controlled.
For example, sex may be nearly controlled if it is controlled for some
race groups but not all, or if it is controlled in some single-year
estimates but not all. Such estimates may have very small standard
errors. This is most likely to happen with estimates of sex, age, race,
and Hispanic origin.

For ratios, means, and percents, we used the standard approximation for
the standard error of the ratio of two estimates (as found in the
Accuracy of the Data documents), applied to the three-year average ratio
described in the Estimation section above.

$$\text{SE}_{\text{ratio}} = \frac{\text{Est}_{\text{n,1999}} + \text{Est}_{\text{n,2000}} + \text{Est}_{\text{n,2001}}}{\text{Est}_{\text{d,1999}} + \text{Est}_{\text{d,2000}} + \text{Est}_{\text{d,2001}}}\sqrt{\frac{\text{SE}_{n,\text{1999}}^{2} + \text{SE}_{n,\text{2000}}^{2} + \text{SE}_{n,\text{2001}}^{2}}{\left( \text{Est}_{\text{n,1999}} + \text{Est}_{\text{n,2000}} + \text{Est}_{\text{n,2001}} \right)^{2}} + \frac{\text{SE}_{d,\text{1999}}^{2} + \text{SE}_{d,\text{2000}}^{2} + \text{SE}_{d,\text{2001}}^{2}}{\left( \text{Est}_{\text{d,1999}} + \text{Est}_{\text{d,2000}} + \text{Est}_{\text{d,2001}} \right)^{2}}}$$

If the estimate of the denominator is zero for all three years, the
ratio is assigned a value of '-' with a standard error of '\*\*'.

If the estimate of the numerator is zero for all three years (and the
denominator is nonzero), then

$$\text{SE}_{\text{ratio}} = \sqrt{\frac{\text{SE}_{n,\text{1999}}^{2} + \text{SE}_{n,\text{2000}}^{2} + \text{SE}_{n,\text{2001}}^{2}}{\left( \text{Est}_{\text{d,1999}} + \text{Est}_{\text{d,2000}} + \text{Est}_{\text{d,2001}} \right)^{2}}}$$

If the standard error of any of the single-year numerator or denominator
components was given a value of '\*' (cannot be computed because the
sample size is too small), then the standard error of the ratio is also
assigned a value of '\*'.

For medians, the single-year methodology was applied to the three years'
pooled data. If the estimate fell into the upper or lowest interval in
an open-ended distribution, its standard error was assigned a value of
'\*\*\*'.

# SAMPLE DESIGN---CENSUS SAMPLE {#sample-designcensus-sample .Memo-Level-3}

For information on the sample design of the census sample, please see
the Accuracy of the Data section of the [SF-3 Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

**CONFIDENTIALITY---CENSUS SAMPLE**

Because group quarters persons were excluded from the census estimates
used in this study, the housing unit and population census estimates
were rounded, to avoid possible disclosure issues. Housing unit
estimates were rounded to the nearest five. Population estimates were
rounded to the nearest 10, except for estimates between one and seven
which were rounded to four. All computations in the product were made
using these rounded census values. Due to this rounding, tract-level
census estimates may not add to the county level estimates.

For information on other confidentiality procedures used in the census
sample, please see the Accuracy of the Data section of the [SF-3
Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

# ESTIMATION PROCEDURES---CENSUS SAMPLE {#estimation-procedurescensus-sample .Memo-Level-3}

For basic information on estimation procedures used to produce the
census sample estimates, please see the Accuracy of the Data section of
the [SF-3 Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

Special issues for the data produced for the ACS/Census Comparison
profiles are:

-   All census data in this data product exclude the group quarters
    population. Since the ACS data do not include the group quarters
    population, the Census data were made to be directly comparable to
    the ACS data.

-   The set of demographic profile tables (Profile Table 1) uses the
    census sample data. Usually these tables are produced using the
    Census hundred percent counts. Since we are interested in comparing
    the ACS three-year average data to the Census sample data, we used
    the Census sample data to produce these numbers.

**Number range CHAPTERERRORS IN THE DATA---CENSUS SAMPLE**

For information on errors in the data of the census sample, please see
the Accuracy of the Data section of the [SF-3 Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

# CONTROL OF NONSAMPLING ERROR---CENSUS SAMPLE {#control-of-nonsampling-errorcensus-sample .Memo-Level-3}

For information on control of nonsampling error in the census sample,
please see the Accuracy of the Data section of the [SF-3 Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

# CALCULATION OF STANDARD ERRORS---CENSUS SAMPLE {#calculation-of-standard-errorscensus-sample .Memo-Level-3}

For basic information on the calculation of standard errors for census
sample estimates, please see the Accuracy of the Data section of the
[SF-3 Technical
Documentation](http://www.census.gov/prod/cen2000/doc/sf3.pdf).

The Census standard errors in this product were generally calculated as
specified in the SF-3 technical documentation using a simple random
sample formula multiplied by a design factor. However, for some means,
ratios and medians, complications prevented us from calculating the
standard errors. Consequently, for these cases, we based the Census
standard error on the ACS standard error. The Census standard error was
approximated as

$$\text{SE}_{\text{census}} = \sqrt{\frac{n_{\text{ACS}\text{1999}} + n_{\text{ACS}\text{2000}} + n_{\text{ACS}\text{2001}}}{n_{\text{census}}}} \times \text{SE}_{\text{ACS}}$$

For these cases, if the ACS standard error cannot be calculated, then
the Census standard error is coded as missing.

Note that the total population and total housing units are assumed to
have zero standard error at the county and tract levels. The Census
sample weighting controls these numbers to the census hundred percent
counts at the county level and the weighting group level (roughly a
tract, but not necessarily the whole tract) and therefore these numbers
have zero standard error at these levels.

# COMPUTATION OF THE ACS-CENSUS CHANGE ESTIMATES AND STANDARD ERRORS {#computation-of-the-acs-census-change-estimates-and-standard-errors .Memo-Level-3}

We used two methodologies to compute the difference between the ACS
three-year average estimate and the Census sample estimate, based on the
profile line compared. The first methodology was applied to most counts.
We initially identified a "universe" that the estimate belongs to. For
example, the number of persons in the "Asian alone" profile line was
considered to be a subset of the total population. We computed the
percentage of the universe that the group "Asian alone" represents for
both the ACS and Census, and then used the percentages to compute the
estimate of change and its standard error.

For a profile line, and for both the ACS three-year average estimate and
the Census sample estimate:

$$P_{\text{Line}} = \text{100}\text{\%} \times \frac{\text{Est}_{\text{Line}}}{\text{Est}_{\text{Univ}}}$$

For the Census sample estimate,

$$\text{SE}(P_{\text{Line}}) = \text{DF} \times \sqrt{\frac{5}{\text{Est}_{\text{Univ}}} \times P_{\text{Line}} \times (\text{100}\text{\%} - P_{\text{Line}})}$$

where DF is the Census 2000 long form design factor for the appropriate
characteristic.

For the ACS estimate,

$$\text{SE}(P_{\text{Line}}) = \text{100}\text{\%} \times \frac{1}{\text{Est}_{\text{Univ}}}\sqrt{\text{SE}_{\text{Line}}^{2} - \left( \frac{\text{Est}_{\text{Line}}}{\text{Est}_{\text{Univ}}} \right)^{2} \times \text{SE}_{\text{Univ}}^{2}}$$

If the universe estimate is zero, then the estimate of the percentage is
assigned a value of '-' and its standard error a value of '\*\*'.

If the estimate of the line is equal to the universe, then

$$\text{SE}(P_{\text{Line}}) = \text{100}\text{\%} \times \frac{\text{SE}_{\text{Line}}}{\text{Est}_{\text{Univ}}}$$

If the standard error of the line or the universe cannot be computed
(was assigned a value of some number of asterisks), we assign that value
to the standard error of the proportion as well.

If the computed standard error of the ACS proportion was greater than
70%, the value was capped at 70%. At that level, no estimate between 0%
and 100% is statsistically significantly different from another, so
reporting a standard error of 70% instead of, say, 300% would not affect
the interpretation of the results.

If the value under the square root sign is negative, we use an
approximation based on the 2000 census data.

$$\text{SE}(P_{\text{Line}}) = \text{DF} \times \sqrt{\frac{\text{ACSAVWGT} - 1}{\text{Est}_{\text{Univ}}} \times P_{\text{Line}} \times (\text{100}\text{\%} - P_{\text{Line}})}$$

DF is the Census 2000 long form design factor for the appropriate
characteristic, and ACSAVWGT is the average of the three single-year
county-level average weights.

Once the proportion and its standard error have been calculated for both
the ACS three-year average and the Census sample,

*Change = P~ACS~ -- P~CEN~*

*SE(Change) **=***
$\sqrt{\text{SE}(P_{\text{ACS}})^{2} + \text{SE}(P_{\text{CEN}})^{2}}$

If the standard error of the ACS estimate or the Census estimate cannot
be computed (was assigned a value of some number of asterisks), we
assign that value to the standard error of the change as well.

The second methodology was used for many of the universe lines, as well
as for estimates which are not counts (medians, ratios, and estimates
that were already percentages). Here, the change estimate is simply the
difference between the two estimates.

*Change = Est~ACS~ -- Est~CEN~*

*SE(Change) =*
$\sqrt{\text{SE}_{\text{ACS}}^{2} + \text{SE}_{\text{CEN}}^{2}}$

Again, if the standard error of the ACS estimate or the Census estimate
cannot be computed (was assigned a value of some number of asterisks),
we assign that value to the standard error of the change as well.

# DETERMINATION OF STATISTICAL SIGNIFICANCE {#determination-of-statistical-significance .Memo-Level-3}

For profile lines where the change estimate and standard error have been
computed, we can make a determination whether the difference is
statistically significant. The profiles provide that answer at the at
the 90% confidence level.

A Z-score is computed for each (eligible) line:

$$Z = \frac{\text{Change}}{\text{SE}(\text{Change})}$$

If the Z-score is greater than 1.65 or less than -1.65, then the
difference is significant at the 90% confidence level.

The p-value is also provided whenever a Z-score can be calculated. This
is the probability, under the null hypothesis of no change, that the
estimate of change would be as extreme or more extreme than was
observed. A p-value of less than 0.10 indicates a significant difference
at the 90% confidence level.

# NOTE ON COMPUTATIONS OF ESTIMATES IN THIS PRODUCT {#note-on-computations-of-estimates-in-this-product .Memo-Level-3}

The computation of three-year averages and their standard errors, the
change estimates and their standard errors, Z-scores, and p-values were
based on unrounded data. The estimates on the files have been rounded,
so the results users obtain may differ slightly from estimates that we
provide.