A PROTOTYPE

CONTINUOUS MEASUREMENT SYSTEM

FOR THE

U.S. CENSUS OF POPULATION AND HOUSING

Charles H. Alexander

U.S. Bureau of the Census

This paper reports the general results of research undertaken by Census
Bureau staff. The views expressed are attributable to the author and do
not necessarily reflect those of the Census Bureau.

This is document CM-17 in the Continuous Measurement Research Series.

INTRODUCTION

The Census Bureau is considering a proposal which might replace the
traditional long form content sample in the 2000 census with a
\"Continuous Measurement\" (CM) program which would collect the same
information throughout the decade. The Continuous Measurement system
would consist of:

i\) an ongoing field operation to locate and update a sample of
addresses from the Census Bureau\'s Master Address File (MAF), which is
linked to the TIGER geographic database;

ii\) a large Intercensal Long Form (ILF) survey;

iii\) a Program of Integrated Estimates (PIE) to combine data from the
ILF, other household surveys such as the Current Population Survey (CPS)
and the Survey of Income and Program Participation (SIPP), the previous
census short form, and demographic estimates derived from administrative
sources, to make small-area estimates.

Although the idea of \"spreading out the census\" has been suggested at
least since Eckler (1972), it began to be given more serious attention
after the 1990 census as discussed in Melnick (1991), Subcommittee on
Census and Population (1992), Sawyer (1993).

The proposal draws heavily on ideas of Kish (1981, 1990) and a previous
Census Bureau proposal by Herriot, Bateman, and McCarthy (1989), as well
as estimation ideas suggested by Herriot and Schneider (1990). The major
development since these earlier proposals is the availability of the
MAF, which is already being developed as a source of addresses for the
2000 Census.

This paper describes the prototype design being considered for the CM
system, the reasons for selecting it, and plans for testing and
evaluation of CM. Additional details of the design are given in
Alexander (1993), which includes additional references.

COMPONENTS OF THE CONTINUOUS MEASUREMENT DESIGN

**A sampling frame based on MAF/TIGER**

The Census Bureau is currently developing a system to build and keep up
to date a national MAF. This will largely be in place by 1996,
constructed by an ongoing computer match of U.S. Postal Service mail
delivery files with the 1990 census Address Control File. These
addresses will be linked to the TIGER geographical database. Addresses
not geocoded by the computer match will be resolved clerically when
possible, using resources such as commercial maps, assistance of local
governmental officials, and additional information from the Postal
Service and its letter carriers.

The CM prototype would add to these plans an ongoing field operation to
locate MAF addresses that are in the ILF sample, and check out any
situations that cannot be resolved by the computer and clerical
operations. The MAF/TIGER files would be updated to correct any errors
or duplications found in using the frame for the ILF and other current
surveys, or by special Quality Assurance samples. Additional updating
would be conducted by ILF interviewers when a block which needs updating
is near a housing unit being visited for the ILF. This new operation,
called the Sampling and Address-Correction Feedback Operation (SACFO) is
separate from the MAF/TIGER system, but interfaces with it.

The main uncertainty about this SACFO operation is the handling of
\"rural-style\" addresses, usually post office boxes or general
delivery. We hope that by 1999 most of these addresses can be linked to
a \"city-style\" address (house number, street name, apartment
designation) used for Emergency 911 service, even when this address is
not used for mail delivery. Respondents would be asked to write this
geocodable physical address on the ILF questionnaire sent to their
mailing address. The 2000 census form would also collect both addresses
whenever possible.

The updated MAF/TIGER will be linked to a file containing data from the
ILF, other household surveys, and the 2000 census. This will be used for
the Program of Integrated Estimates described below.

**The Intercensal Long Form (ILF) Survey**

The ILF will mail questionnaires to about 250,000 addresses per month.
The sample will be spread evenly across the MAF each month; i.e., the
sample housing units will be spread evenly across the country. Each
month\'s sample will be a separate set of housing units. Over five years
the cumulative mail out sample size will be about 15,000,000 housing
units.

Units that do not respond by mail, after several reminders, will be
interviewed by telephone whenever the telephone number can be obtained
from sources such as commercial lists or the previous census. Units that
cannot be interviewed by mail or telephone will be designated for
possible personal visit. Only a sample of these units will be sent out
to be interviewed. The sub-sampling rate for personal visit units,
including vacant units, will be 1 in 3 in most areas. A rate of about 1
in 5 will be used in remote areas. The total monthly interviewed sample
size is expected to be about 200,000 units, including vacant units for
which information is collected. This comes to about 12,000,000
interviews over a five-year period. (See Attachment A)

This compares to about 14,500,000 interviews for the 1990 long form.

For the years 1999-2001, the monthly mail out will be about 400,000 per
month, so that CM can start with small-area estimates based on three
years of data.

In interpreting these sample sizes, it is necessary to take into account
the weighting of the survey. The personal-visit cases

will each be given a weight of 3 or 5 times the basic weight, according
to their subsampling rate. The weighted nonresponse rate for occupied
units, corresponding to the portion of the population not represented by
the survey because of nonresponse, is 7.5%. (See Attachment A)

The ILF will have larger standard errors than the 1990 long form for
comparable estimates. Partly this is due to the small sample size and
partly to the need to use weighted estimates with some units having much
higher weights than others. Differential weights increase the survey\'s
variance compared to an equally weighted sample of the same size. The
overall effect is that typical ILF standard errors will be 1.25 times as
large as the comparable 1990 long form standard error. Attachment B
illustrates this effect for estimates of the number of children in
poverty for various small areas.

This 25% increase in standard errors affects confidence intervals about
the same as going from a 95% to 90% level of confidence for a given
interval. This loss of precision would be worthwhile if there are
sufficient gains in data quality due to use of more recent data,
collected by better-trained interviewers.

The loss of precision would be greater for estimates of the
characteristics of vacant units or group quarters, which are sampled at
1/3 the regular rate, or 1/5 in remote areas.

There also may be a loss of precision compared to the 1990 census for
places of under 2500 population with their own governmental units. The
1990 census sampled such places, containing about 7.5% of the
population, at a rate of 1 in 2. To make up for this, the sample in
large areas\--tracts of over 4000 population\--had their sampling rate
reduced to 1 in 8 rather than the usual 1 in 6. The CM proposal
currently assumes a uniform sampling rate everywhere. If the 2000 census
content determination process establishes a need for extra sample in
certain areas, the CM design will be modified to meet the same need. The
legislative requirements for the oversampling have not yet been well
documented; certainly the sudden cutoff at 2500 needs to be evaluated.

The ILF sample size for individual tracts or other small areas would be
evaluated periodically. Areas with poor response rates, or low rates of
completion by mail and telephone, would have a higher-than-average mail
out sample size or personal-visit follow-up rate. This would avoid some
of the historical problems with insufficient long-form data in some
\"hard-to-enumerate\" areas.

Compared to a one-time census, the smaller, permanent ILF interviewer
staff would be more selectively recruited, more experienced, and more
extensively trained and observed. This seems likely to produce data of
better quality, although experimental evidence quantifying the effect is
lacking.

**The Program of Integrated Estimates**

The first CM estimates will be derived solely from the ILF, using
conventional weighting and tabulation methods along the lines of those
of the 1990 long form sample or CPS. The estimate for a specific block
or tract will be based almost exclusively on ILF sample data from that
block or tract, although some adjustments will be made based on
comparisons of the sample units to the entire MAF. There will also be
some form of adjustment of the estimates to agree with independently
derived demographic estimates for states or counties. For more details,
see Alexander and Wetrogan (1994).

After the 2000 census, the samples for the Census Bureau\'s current
household surveys, CPS, SIPP, the National Health Interview Survey
(NHIS), the National Crime Victimization Survey (NCVS), the American
Housing Survey (AHS), and the Consumer Expenditure Surveys (CES), would
use the MAF as a sampling frame. At this point, the linking of these
data and ILF data to the previous census short form will make it much
easier to get good synthetic estimates for characteristics measured by
these surveys for medium-sized areas such as cities and groups of
counties.

This methodology is particularly promising for estimates of income,
poverty, and housing quality. For these characteristics the ILF
questionnaire gives a crude measure of the phenomenon, which would be
highly correlated with the more valid measure given by the other,
smaller surveys. Information from the ILF could also be used to improve
substate labor force estimates from the CPS; here the CPS information
would dominate the estimates, and ILF data would be used to adjust for
differences between the CPS sample and the complete population.

The ILF would also serve as a useful screening sample for rare
subpopulations; this is especially important for NHIS. Using ILF this
way depends on legislative changes, which would permit some sharing of
addresses between the MAF system and other Federal activities.

The methods for publishing or releasing the CM estimates still need to
be worked out; this is a top priority for the Census Bureau\'s new CM
Development Staff. The general strategy will be to make available very
detailed general-purpose files, so that users can tabulate these to make
whatever estimates they need. The files will be compatible with one or
more standard statistical packages. Likely possibilities are 1) tallies
by block or block group for each month that can be summed to give
estimates for any geographic area and any time period; 2) a file of
individual household data, with identifying information and detailed
geography suppressed to preserve confidentiality. These data files would
be updated quarterly; we hope to have each quarter\'s processing
complete six months after the end of the quarter.

Although users can examine annual data for small areas, estimates for
areas as small as census tracts will be very imprecise unless at least
five years worth of sample (three years for 1999-2001) are used in the
estimates. For block groups, even five-year estimates will have large
standard errors; traditional long-form estimates for these areas also
have high standard errors (see Attachment B).

For larger areas, annual estimates would be of interest. For areas of
250,000 persons or more, sample sizes would be large enough to support
analysis of annual data. Annual National estimates could be made with
considerable demographic detail.

However, annual estimates for the total population may not agree with
estimates from special-purpose surveys like CPS Supplements or SIPP,
because of differences in the questionnaires and interview mode.

RATIONALE FOR THE DESIGN

Our objective in selecting a CM design was to produce small-area (or
small domain) estimates that are better overall than the corresponding
small-area estimates from the traditional long-form design. The proposed
CM design would produce an estimate corresponding to any estimate which
can be produced from the traditional design, including estimates for
small areas such as tracts, block groups, school districts, traffic
analysis zones, etc., and small domains such as demographic subgroups
comprising 0.1% of the population or less. The fundamental differences
are:

i\) the CM estimate will be an average over a five-year period (three
years for 1999-2001);

ii\) the five-year average will be updated annually;

iii\) the CM estimates will typically have a 25% higher standard error.

The overall quality of these small-domain estimates is the major
uncertainty we must address in our research on CM. For large domains
where annual estimates have adequate standard errors for analysis, the
quality advantages of the CM design are much easier to demonstrate.

The case we intend to build for the overall better quality of CM
small-domain estimates depends on three main hypotheses:

A\) an annually updated five-year moving average is better for almost
all purposes than a once-a-decade point-in-time measurement;

B\) for the important uses of small-area data, the advantage in A is
sufficient to outweigh CM\'s increased standard errors;

C\) other differences in measurement error between CM and the long form
have relatively small impact and have an overall neutral effect on the
comparison.

Our proposed research is intended to support or refute these hypotheses.
The next few sections will discuss what we now know about these quality
issues, and present our general plans for tests, research, and
consultation with users about the research results.

Besides quality, the second major issue is cost. In addition to direct
savings from eliminating the long form, CM has the potential for savings
in other Federal data collection programs. These will be discussed in a
later section. Also, the improvements in MAF quality due to regular use
of the list for the ILF throughout the decade may lead to savings in the
address list operations prior to the 2010 census, beyond what MAF could
save without SACFO.

We need further research to estimate the magnitude of these costs and
savings with any degree of confidence. Some preliminary calculations for
design purposes did suggest that, for the prototype sample size, there
is some chance that the savings produced by CM over the entire Federal
system could equal or exceed the cost of the CM operation. This was
taken into account in proposing the design.

MEASUREMENT ERROR ISSUES

There are a whole range of detailed measurement error

comparisons between a continuous \"Intercensal Long Form\" and a
traditional once-a-decade long form. Each system has advantages and
disadvantages for small-area estimates. Right now there is not enough
information to draw a conclusion about the net impact

on \"total error\"; we hope to shed light on some components of the
error through research and testing over the next few years.

[Probable Measurement Error Advantages of the ILF compared to a
traditional long form]{.underline}

1\. Better training, observation, and evaluation of interviewers.

2\. Ability to conduct ongoing experiments to evaluate and improve
questions and procedures.

3\. More uniform actual interviewed sample sizes for small areas, since
problems in specific areas can be identified and corrected by increasing
sample sizes or assigning more effective interviewers.

4\. Greater opportunity to investigate and correct for errors in
estimates identified by independent local sources.

5\. More uniform treatment of seasonal effects. This is especially
important for places like seasonal resort areas. CM would be better for
areas where April characteristics are not representative of the whole
year, for example agricultural employment. However, it would be worse
for characteristics where April is \"representative\" and some other
months are not, such as educational activity.

6\. Use of variable reference period, eliminating the recall lag for
long-form units interviewed long after census day.

[Probable Measurement Error Disadvantages of the ILF Compared to a
traditional long form]{.underline}:

1\. Less complete coverage of housing units, compared to a survey done
at census time.

2\. Possible problems of within-household undercoverage of persons
compared to the number collected on census forms. Undercoverage relative
to the census is observed in CPS and other surveys. However, this
problem may not be as serious for the ILF, which will be a census-like
survey using census-like roster rules and interview modes.

3\. Lack of exact short-form counts for the same time period as the
survey, for use in controlling tract-level sample estimates to agree
with the full population.

4\. Worse measurement of income, for interviews, which take place late
in the year.

5\. Greater confusion about variable reference periods for questions,
compared to a census with a fixed census day.

6\. Greater confusion about residence rules.

Most likely there will not be a single conclusion about the measurement
quality that applies to all characteristics. We expect that CM will give
more uniform quality, eliminating very bad estimates for a few small
areas. However, for some important characteristics, such as income or
numbers of people by age-race-sex, the long form would give a more exact
estimate as of census day than the ILF does for any given time period.

DISCUSSION OF FIVE-YEAR MOVING AVERAGES

The critical uncertainty about the adequacy of CM small-area estimates
for small areas such as census tracts (or \"Block Numbering Areas\"
where tracts are not defined) is whether rolling five-year cumulated
estimates will meet the needs of data users. Our research and
consultations with users are at a very early stage, but some preliminary
conclusions can be drawn.

Our initial discussions with data users suggest that the idea of
cumulative estimates takes some getting used to. The first reaction is
inevitably to compare the five-year average to good annual estimates;
clearly good annual estimates would be ideal. However, when these the
comparison is made to a once a decade snapshot, we have so far not found
many situations that obviously favor once a decade.

At a very simplest level, the situation is this. When small areas are
very stable over time, a five-year average is as good a single number to
describe a small area as an estimate at a single arbitrary point in
time. When the characteristics of the area are changing dramatically, an
estimate at a single point in time is very misleading. In this case, a
single five-year average can also be misleading, but a time series of
moving averages gives some information about the change.

The five-year average estimate needs to be supplemented by:

i\) some numerical measure of variability within the five years, which
will signal that the estimate should not be accepted at face value;

ii\) the ability to display the five single-year estimates, with their
standard errors, so that the nature of any extreme variation can be
noted;

iii\) the ability to display seasonal patterns so that these can be
noted.

Example 1:

(Assume constant population size for simplicity)

Poverty Rate

Tract #1 Tract #2

Year 1 25 5

Year 2 20 10

Year 3 15 15

Year 4 10 20

Year 5 [5]{.underline} [25]{.underline}

Average 15 15

This kind of example has been cited by several critics as an unfavorable
example for the five-year cumulation. The five-year average says the two
tracts have equal poverty rates, but tract #2 currently has a much
higher poverty rate. A supplemental display of annual estimates might
reveal the trend, but the individual annual estimates are based on too
small a sample to be trustworthy. The official measures, used for such
purposes as allocating funds according to need, would be the five-year
averages.

However, a one-time snapshot would give worse results overall. If Year 1
were the census year, then Tract #2 would be identified as having a very
low poverty rate, with no indication that any change has occurred. If
year 3 were the census year, the results would be the same as the
five-year average, but with no indication of uncertainty. If year 4 or 5
were the census year, the data would not yet be published since it takes
about two years to complete processing for the large one-time long form
sample attached to the census. We expect the smaller, ongoing ILF to
have about a six months processing lag; this expectation does need to be
tested.

The big advantage of the moving average is that after year 6 there will
be an update that will gradually reveal the high poverty rate in Tract
#2, if it persists.

Example 1 (Continued)

Poverty Rate

Tract #1 Tract #2

Year 2 20 10

Year 3 15 15

Year 4 10 20

Year 5 5 25

Year 6 [5]{.underline} [25]{.underline}

Average 11 19

There are technical problems with cumulations that must be solved, and
at best will have only imperfect solutions. How are dollar amounts to be
adjusted for inflation? How do we handle situations where blocks are
split and it is hard to determine the correct block for units
interviewed before the split? Changes in the boundaries of cities are
simpler; past years\' values for the current city boundaries can be
calculated retroactively, but this is still a complication.

Another issue is whether the five-year averages would be
population-weighted. For CM, population-weighted averages will be much
more convenient computationally. For a rate, the population-weighted
average would be

where R~i~ = rate in month i

N~i~ = population in month i

X~i~ = numerator of rate in month i, and

The alternative unweighted rate is

To illustrate the difference, consider a small area where a large
increase in population (families) in the middle of the five-year period
dramatically increases the poverty rate

Example 2:

N~i~ R~i~

months i = 1..,30 100 0

months i = 31,\...,60 1000 .90

R^u^ = .45 and R^W^ = .818

The larger rate R^W^ in effect looks at the total number of
\"family-months\" and determines what proportion were spent in poverty.

PLANS FOR CM RESEARCH

AND TESTING

The timing and objectives of proposed testing and development are
described in Attachment C. Our plan is to use the test results to
address the research issues as follows.

[The 1994-95 Cumulative Estimates Simulation Project]{.underline}

For a few test areas we plan to create a simulated population on the
computer for the years 1980-1992. Housing units for the 1980 and 1990
census will be linked when possible. Simulated values for intermediate
years, and non-long-form households, will be generated using probability
distributions consistent with the observed values. Transition
probabilities for intermediate years can be estimated from American
Housing Survey sample households, for which 1980 long form data are also
available. For blocks with large numbers of new units, we will try to
determine when the actual units were built.

With the simulated population, the sampling and estimation for the ILF
can be implemented. Also the 1980 and 1990 long form sampling and
weighting can be implemented and checked against actual census
estimates. It will then be possible to examine various uses of long form
estimates, see how these uses would be affected by using CM estimates
instead, and compare the results to the \"actual\" population values at
the time the data are used.

If funding permits, the simulation files will be made available to
interested users who wish to compare the CM and long form estimates.

This part of the research will address the utility of five-year moving
averages, and the impact on estimates of not having tract-level controls
from the short form. We do not expect to address measurement bias with
this study, although some information may be collected on the
variability of small-area nonresponse rates in the long form.

The simulated CM estimates give us a good opportunity to illustrate the
CM data delivery system. Some mock output files will be produced and
distributed to interested data users for comments on their utility. This
will not be a realistic test of our ability to produce real data from
the system quickly. The data delivery system for the 1995 Questionnaire
Test will be more complete and realistic.

[The 1995 Questionnaire Test]{.underline}

We plan to collect ILF information by telephone from November 1994
through December 1995, using a variable reference period. We have not
yet decided whether the test will be National or be conducted in only a
few test sites. The test will use a questionnaire based on the 1990 long
form, with any changes needed because of the moving reference period.
The questionnaire would e revised once the 2000 census content
determination is complete.

A control group will be interviewed around April 1995 using a fixed
reference day. This will address our most serious questionnaire concern,
possible recall error in the reporting of income late in the year. To
help interpret any differences, a comparative study is being considered
of income estimates from various existing household surveys using
different reference periods and interview times (CPS March Supplement,
1990 long form, SIPP, CES, NCVS, etc., as well as some non-Census-Bureau
surveys).

This test will also be used as a trial of the data processing and data
delivery system. An initial version of the public data files is
tentatively scheduled for August 1995. Depending on funding and staffing
of the CM Development Staff, the initial files may be fairly complete,
or may be restricted to an illustrative set of variables. These files
will be made widely available to interested data users; the mechanism
for distributing the files has not been worked out. The data delivery
system will be revised based on user comments, and improved versions
will be released during 1995 and 1996 as necessary as response to the
comments.

Additional test components to get at the effects of alternative
reference periods and seasonal variability will be considered. This test
will not address coverage. Some experience with cumulative estimates as
compared to a March or April long form might be gained if some test
areas overlap with the 1995 Census Test areas; the merits of this are
being discussed.

Some cost information relevant to the telephone interviewing and data
processing activities of the full CM system would be collected.

[The 1996 CM Operational Test]{.underline}

Starting in FY 1996, there will be a full-scale implementation of CM in
a few test areas, including at least one site with many non-city-style
addresses. The test sample will probably use a sampling rate at least as
large as the proposed 1999 system. We are considering a larger sample
rate to get more precise estimates quickly. This test will give us more
information on cost parameters, by monitoring the workflow and cost of
the test.

The test will also be used to evaluate CM\'s coverage of households and
persons within households. Our likely approach to this is to apply
coverage measurement procedures being developed for the 1995 census
test. We might actually be able to use 1995 census test final address
lists to evaluate the CM list, if the CM test uses some of the same
sites.

The CM estimates will be studied to try to evaluate some measurement
errors, to confirm or further investigate findings of the 1995
Questionnaire Test. This will involve looking at variations in the CM
monthly estimates, and comparing estimates with other data sources.

USES OF CM TO ENHANCE OTHER

PARTS OF THE FEDERAL STATISTICAL SYSTEM

Even without any ILF, SACFO\'s coordination of address sampling and
updating activities and PIE\'s linking of data census for use in
estimation would have important benefits for the operations of Current
Household Surveys conducted by the Census Bureau. A sampling operation
based on MAF would be more flexible than the current system based on
listing building permits throughout the decade in sample areas. Linking
survey data to census data and information from administrative records
systems holds great promise for small-area estimation and intercensal
demographic estimates. A small \"mini-ILF\", aimed only at collecting
information on areas which would be \"outliers\" in small-area
estimation models could dramatically improve the performance of the
estimation methods at relatively low cost.

The full-scale ILF would increase these benefits by providing a larger
number of address corrections and more direct information for small-area
modeling.

The ILF could also be used to screen for rare subpopulations or
characteristics, allowing programs that need to collect data for small
groups to reach these groups affordably. The ILF sample is sufficiently
large that the screening sample could be confined to the same areas as
the other surveys and still yield plenty of cases.

For CM to be used as a screening survey for other Federal programs, some
changes in legislation are needed to allow sharing of addresses among
agencies. Most of the cost advantages of screening for rare
subpopulations could be obtained if the Census Bureau could supply other
statistical programs with a list of addresses containing an oversample
of units in the rare subpopulation, even without supplying any data on
the units.

We have just begun to contact Federal agencies to see how CM might help
them to achieve their mission more efficiently. Some ideas, that at
first glance seem technically realistic, are listed below as examples of
applications of CM besides direct updating of long-form data.

[Uses of small-area estimation methods]{.underline}

1\. Use ILF or PIE data as covariates in ratio estimates to reduce the
variance of CPS state or substate (large counties or cities) labor force
estimates.

2\. Use ILF or PIE small area data in combination with global income and
poverty data from SIPP or the CPS March Supplement to produce synthetic
estimates for small areas such as school districts.

3\. Share sample units with the American Housing Survey, which has many
questions in common with the long form housing questions.

[Uses of ILF or MAF as a screening sample]{.underline}

1\. Use ILF to screen for rare populations needed by NHIS to provide the
detail needed to understand the causes of health patterns seen in
aggregate data.

2\. Use brief ILF supplemental questions to screen for units likely to
have rare characteristics such as health conditions, residential
alterations or, for NCVS, rare crimes such as abduction of children.
Further information would be collected by followup interviews.

3\. The MAF could be used to supply addresses of newly constructed
units, for surveys of energy use or expenditures.

Besides benefits for existing programs, the full CM system provides the
opportunity to meet new needs for data quickly and efficiently.

[Uses for new topics]{.underline}

1\. The MAF provides a ready sample to concentrate interviewing in any
local area. With ILF in place, there would always be a current baseline
of long form \"background variables\" for any geographical area defined
in terms of whole blocks. This would allow a focused local survey to
measure needs and rate of recovery for areas affected by natural
disasters, such as floods, earthquakes, or hurricanes, or unusual
economic or environmental events.

2\. The ILF supplement could be used to provide National and subnational
information on a variety of topics within the planned limits of 5-10
minutes worth of supplemental questions.

3\. There may be efficiencies or opportunities to improve quality by
coordinating MAF/TIGER and SACFO with the Census Bureau\'s systems for
collecting information on building permits. Information on permits is
used as a Leading Economic Indicator.

CONCLUSION: PROSPECTS FOR

CONTINUOUS MEASUREMENT

The testing plan outlined above has two purposes:

i\) begin the implementation of SACFO and PIE;

ii\) provide the users of Federal statistics with the information to
determine whether the benefits of the CM system are sufficiently
compelling to justify a change from the time-tested long form design
starting in 2000.

Some amount of work to develop SACFO and PIE is clearly worthwhile.
Census Bureau staff currently devote considerable effort on disjoint
systems for i) maintaining an address frame for household surveys, ii)
constructing a list of addresses for the decennial census, iii) using
administrative records for demographic estimates. Using MAF for all
three programs in a

coordinated way requires planning and coordination, but so far seems to
require very few additional operations. Instead, there is an opportunity
to eliminate redundant operations.

It is an ambitions research task to prove the feasibility and value of
CM in time for a decision about the 2000 census. If users of long form
data strongly prefer updated cumulative estimates for the ILF system,
this would be a reason to pursue replacing the long form with the ILF in
2000. This is by no means a foregone conclusion. Alternatively, if this
comparison is about even, but new uses of ILF as a source of screening
sample or modeling covariates are sufficiently compelling, that would
give us reason to go forward. Once the research has determined the
benefits of CM relative to a long form, the cost of the system and the
additional response burden of collecting the additional information must
be weighed against the benefits. An important goal of our research is to
develop the details of the CM operation and, through testing, to measure
the cost.

Our general research timing would be to provide evidence about whether
CM is a superior source of data by the end of 1995. If the results are
positive, this would justify a tentative decision to proceed with the
system for 2000. Evidence on cost and feasibility would be available by
the end of 1996, at which time a final decision on \"census\" content is
needed.

The remaining argument in favor of an early implementation of ILF is
that eliminating the long form would simplify decennial census
operations. However, there is no evidence that the long form is a
distraction that interferes with the census count operation, so
simplification by itself does not seem to justify a 2000 implementation
of CM unless we can demonstrate benefits for data users. Our immediate
goal is therefore to contact data users and professional organizations
familiar with uses of census data, to obtain their assistance in
evaluating our research plans and research results.

References

Alexander, C.H. (1993). \"A Continuous Measurement Alternative for the
U.S. Census.\" Internal Census Bureau Report #CM-10, dated October 28,
1993.

Alexander, C.H. and S.I. Wetrogan (1994). \"Small Area Estimation with
Continuous Measurements: What We Have and What We Want.\" Internal
Census Bureau Report #CM-14. To appear in [Proceedings of the 1994
Census Bureau Annual Research Conference]{.underline}.

Eckler, A.R. (1972). [The Bureau of the Census]{.underline}. Praeger
Publishers

Herriot, R., D.B. Bateman, and W.F. McCarthy (1989). \"The Decade Census
Program\--New Approach for Meeting the Nation\'s Needs for Sub-National
Data.\" [Proceedings of the American Statistical Association Social
Statistics Section]{.underline}, pp 351-355

Herriot, R.A. and P.J. Schneider (1990). \"Improved Intercensal
Demographic Estimates for Small Areas\--An Interim Approach.\"

[Proceedings of the American Statistical Association]{.underline}.

Kish, L (1981). \"Population Counts from Cumulated Samples\". In [Using
Cumulated Roll Samples to Integrate Census and Survey Operations of the
Census Bureau]{.underline}, U.S. Government Printing Office, Washington,
D.C., June 26, 1981, pp 5-50.

Kish, L. (1990). \"Rolling Samples and Censuses\". [Survey
Methodology]{.underline}, 16, 1, pp. 63-79. 22

Melnick, D. (1991). \"The Census of 2000 A.D. and Beyond\". In [Review
Major Alternatives for the Census in the Year 2000]{.underline}, U.S.
Government Printing Office, Washington, D.C., August 1, 1991, pp 60-74.

Sawyer, T.C. (1993). \"Rethinking the Census: Reconciling the Demands
for Accuracy and Precision in the 21st Century\". Presented at the
Research Conference on Undercounted Ethnic Populations, Bureau of the
Census, May 7, 1993.

Subcommittee on Census and Population (1992). [2000 Census Planning:
Decennial Census Questionnaire Content]{.underline}. Hearing before the
subcommittee, October 1, 1992. U.S. Government Printing Office,
Washington, D.C., especially pp 1-51.

Attachment A

Anticipated Breakdown

of Monthly ILF Sample Size

Occupied Units: 225,000

Completed by mail 135,000

Completed by telephone 50,400

Eligible for personal visit (P.V.) 39,600

Subsampled out 27,600

Designated for P.V. 12,000

P.V. Interview 6,886

P.V. Noninterview 5,114

Vacant Units: 25,000

Subsampled out 17,424

Data collected by P.V. 7,576

Total Mailouts: 250,000

Total Interviews: Occupied = 135,000 + 50,400 + 6886 = 192,286

Vacant = 7576

Total = 199,862

Average subsampling rate = .85 x 3 + .15 x 5 = 3.3

(assumes \"remote areas\" have 15% of population)

Weighted noninterview rate for occupied units =

Attachment B

Illustrative Comparison of Reliability Between Decennial Census and
Continuous Measurement Data

Collection Systems for Areas in Maryland Percent of Children 5-17 in
Poverty

+-------------------+----------+-------+------+----------+-----------+
| Areas             | D        |       |      | Int      |           |
|                   | ecennial |       |      | ercensal |           |
|                   | Census   |       |      | Long     |           |
|                   |          |       |      | Form     |           |
|                   |          |       |      |          |           |
|                   |          |       |      | (ILF)\*  |           |
+-------------------+----------+-------+------+----------+-----------+
|                   | Po       | Est   | CV   | CV       |           |
|                   | pulation | imate | \*\* |          |           |
|                   |          |       |      |          |           |
|                   | Size     |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
|                   |          |       |      | 12-mon   | 60-mont   |
|                   |          |       |      | th\*\*\* | h\*\*\*\* |
+-------------------+----------+-------+------+----------+-----------+
| **Maryland**      | 4        | 10.5  | 1.1% | 3.2%     | 1.45%     |
|                   | ,781,468 |       |      |          |           |
| State Total       |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Baltimore City    | 736,014  | 31.3  | 1.5% | 4.1%     | 1.8%      |
+-------------------+----------+-------+------+----------+-----------+
| Anne Arundel      | 427,239  | 5.3   | 5.6% | 15.7%    | 7.0%      |
| County            |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Carroll County    | 123,372  | 3.6   | 1    | 32.3%    | 14.7%     |
|                   |          |       | 0.0% |          |           |
+-------------------+----------+-------+------+----------+-----------+
|                   |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| St. Mary\'s       | 75,974   | 9.4   | 9.2% | NA       | 11.9%     |
| County            |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Gaithersburg      | 39,542   | 7.4   | 1    | NA       | 21.2%     |
|                   |          |       | 6.2% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Somerset County   | 23,440   | 15.6  | 1    | NA       | 18.3%     |
|                   |          |       | 4.0% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Kent County       | 17,842   | 12.9  | 1    | NA       | 22.4%     |
|                   |          |       | 4.9% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Hyattsville       | 13,864   | 5.6   | 2    | NA       | 35.1%     |
|                   |          |       | 5.8% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Havre de Grace    | 8,952    | 23.5  | 1    | NA       | 19.6%     |
|                   |          |       | 4.2% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Capitol Heights   | 3,633    | 7.2   | 4    | NA       | 61.7%     |
|                   |          |       | 6.5% |          |           |
+-------------------+----------+-------+------+----------+-----------+
| Cottage City Town | 1,236    | 5.0   | 4    | NA       | 103.8%    |
|                   |          |       | 6.0% |          |           |
+-------------------+----------+-------+------+----------+-----------+
|                   |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| **Congressional   |          |       |      |          |           |
| Districts**       |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
|                   |          |       |      |          |           |
+-------------------+----------+-------+------+----------+-----------+
| District 1        | 597,684  | 10.2  | 3.2% | 9.1%     | 4.1%      |
+-------------------+----------+-------+------+----------+-----------+
| District 2        | 597,683  | 6.3   | 4.2% | 11.8%    | 5.3%      |
+-------------------+----------+-------+------+----------+-----------+
| District 3        | 597,680  | 11.9  | 3.0% | 8.4%     | 3.7%      |
+-------------------+----------+-------+------+----------+-----------+
| District 4        | 597,690  | 8.0   | 3.6% | 10.4%    | 4.6%      |
+-------------------+----------+-------+------+----------+-----------+
| District 5        | 597,681  | 4.7   | 4.7% | 13.9%    | 6.2%      |
+-------------------+----------+-------+------+----------+-----------+
| District 6        | 597,688  | 8.3   | 3.5% | 10.2%    | 4.6%      |
+-------------------+----------+-------+------+----------+-----------+
| District 7        | 597,680  | 30.2  | 1.6% | 4.7%     | 2.1%      |
+-------------------+----------+-------+------+----------+-----------+
| District 8        | 597,682  | 4.1   | 5.2% | 14.8%    | 6.6%      |
+-------------------+----------+-------+------+----------+-----------+

NA - Not Applicable

\* Calculations of reliability for ILF estimates are based on: 1) a
sample size 64% of that needed to provide reliability comparable with
that from the decennial census and 2) no oversampling of governmental
units under 2,500.

\*\* The CV or coefficient of variation is a measure of sampling
variability. The CV is the ratio of the standard error of a sample
estimate to its expected value. There is no specific rule to determine
if a given CV is good or not. This determination is based on
considerations, such as use of the data, consequences of making the
wrong decision, and so forth. In practice, a CV of 10% less is often
considered to be adequate, between 10 and 50% to be acceptable, and 50%
or more to be undesirable.

\*\*\* Estimates are based on weighted observations from 12 months of
interviews.

\*\*\*\* Estimates are based on weighted observations from 60 months of
interviews.

Attachment C

ACCELERATED MAF-BASED

CONTINUOUS MEASUREMENT

Data Collection Activities

+-----+-------------------------------+--------------------------------+
| FY  | Data Collection Activity      | Objectives                     |
+-----+-------------------------------+--------------------------------+
| 1   | Cumulative Estimates          | o Demonstrate properties of    |
| 994 | Simulation Project            | Cumulative                     |
|     |                               |                                |
|     |                               | estimates.                     |
|     |                               |                                |
|     |                               | o Measure effect of population |
|     |                               | controls                       |
|     |                               |                                |
|     |                               | on estimates                   |
|     |                               |                                |
|     |                               | o Illustrate data delivery     |
|     |                               | system.                        |
+-----+-------------------------------+--------------------------------+
| 1   | RDD Test with 2000/month      | o Test alternative versions of |
| 995 | total in 3-4 sites,           | questionnaire                  |
|     |                               |                                |
|     | starting November 1994.       | o Measure effect on time of    |
|     | Convert to split-             | year and moving                |
|     |                               |                                |
|     | sample questionnaire test in  | reference period on income     |
|     | July 1995.                    | data, etc.                     |
|     |                               |                                |
|     | Small mail pretest.           | o Demonstrate ability to       |
|     |                               | deliver timely data            |
|     |                               |                                |
|     |                               | o Tentative decision regarding |
|     |                               | 2000 long form                 |
+-----+-------------------------------+--------------------------------+
| 1   | MAF-based test with at least  | o Develop/test field           |
| 996 | 4000/month total              | procedures                     |
|     |                               |                                |
|     | in 4 sites, starting October  | o Measure coverage of          |
|     | 1995.                         | MAF/SACFO                      |
|     |                               |                                |
|     |                               | o Decision regarding 2000 long |
|     |                               | form                           |
+-----+-------------------------------+--------------------------------+
| 1   | MAF-based \"development       | o Refine actual procedures     |
| 997 | survey\" for                  |                                |
|     |                               | o Produce annual estimates for |
|     | Congressional-District-level  | areas of                       |
|     | estimates,                    |                                |
|     |                               | 500,000 or more                |
|     | full speed in January 1997.   |                                |
|     | Rural sample                  | o Final content determination  |
|     |                               |                                |
|     | clustered in PSUs.            |                                |
+-----+-------------------------------+--------------------------------+
| 1   | Expand MAF-based sample size; | o Further evaluation of        |
| 998 | change                        | quality                        |
|     |                               |                                |
|     | procedures and questionnaire  | o More annual estimates for    |
|     | to fix                        | areas of                       |
|     |                               |                                |
|     | problems found in FY 1997.    | 500,000 or more                |
|     | Better rural                  |                                |
|     |                               | o Phase-in full system         |
|     | spread.                       |                                |
+-----+-------------------------------+--------------------------------+
| 1   | Full MAF-based system.        | o Collect small-area data to   |
| 999 | Complete rural                | replace                        |
|     |                               |                                |
|     | spread.                       | 2000 long form                 |
+-----+-------------------------------+--------------------------------+