Record Contents
NOTES
1. Data for each state are stored in separate files inside the zip file,
named by state abbreviation.
2. Data are in simple text files (ASCII) in fixed format layout.
ABSTRACT
Overview
Public-use microdata samples are computer tapes which contain records for a
sample of housing units, with information on the characteristics of each unit
and the people in it. In order to protect the confidentiality of
respondents, the Bureau excludes identifying information from the records.
Within the limits of the sample size and geographic detail provided, these
tapes permit users with special needs to prepare virtually any tabulations of
the data they may desire.
Three separate public-use microdata samples are available, each representing
five percent or one percent of the population and housing of the United
States:
A Sample, 5%, identifying all States and various subdivisions within
them, including most counties with 100,000 or more inhabitants;
B Sample, 1%, identifying all metropolitan territory and most SMSAs
individually, and groups of counties elsewhere;
C Sample, 1%, identifying regions, divisions, and most States by type
of area (urban/rural).
Three 1-in-1,000 samples are also prepared, one each extracted from the A, B,
and samples.
Comparison Of Summary Data and Microdata
Figure 1 illustrates the basic distinctions between summary data and
microdata. Summary data are the type of data found in census printed
reports, summary tape files, microfiche, and most special tabulations. In
summary data, the basic unit of analysis is a specific geographic area (for
example, a census tract, county, or State) for which counts of persons or
housing units in particular categories are provided. In microdata, the basic
unit is an individual housing unit and the persons who live in it.
There are two types of microdata: confidential microdata and public-use
microdata. Confidential microdata include the census basic record tapes,
computerized versions of the questionnaires collected from households, as
coded and edited during census processing. The Census Bureau tabulates these
confidential microdata in order to produce the summary data that go into the
various reports, summary tape files (STFs), and special tabulations. Public
-use microdata samples are extracts from the confidential microdata taken in
a manner that avoids disclosure of information about identifiable households
or individuals.
Figure 1. Comparison of Summary Data With Information on Microdata Files
SUMMARY DATA
Basic unit is an identified geographic area
Data summarized on people with housing in areas
Available for small areas
Illustrative Summary Data
Occupied Number of Renter Gross Rent
Total Housing Persons Occupied Under $80- $100-
City Pop. Units Per Unit Units $80 99 149
Weston City 110,938 49,426 2.2 31,447 858 3,967 13,282
Smithville 21,970 7,261 3.1 2,492 37 190 1,766
Junction 17,152 5,494 2.7 822 11 29 238
PUBLIC USE MICRODATA
Basic unit is an unidentified housing unit and its occupants
Unaggregated data to be summarized by the user
Allows detailed study of relationships among characteristics
Not available for small areas
Illustrative Microdata
State of Metro/ Persons in
Residence nonmetro household Telephone Plumbing
Housing Unit #1 Virginia Metro 3 Yes Yes
Rent Automobiles Household
type
$325 2 Married-couple family F
Place Years
of of
Relationship Sex Age Race Birth School Occupation Earnings
Person a Householder M 37 W Kansas 12 Plumber $22,100 B
Person b Spouse F 35 W Virginia 12
Person c Child M 6 W Virginia 1
State of Metro/ Persons in
Residence nonmetro household Telephone Plumbing
Housing Unit #2 Virginia Nonmetro 1 Yes Yes
Household
Rent Automobiles type
$150 1 Nonfamily householder
Place Years
of of
Relationship Sex Age Race Birth School Occupation Earnings
Person a Householder F 62 B Alabama 16 Elementary $15,300 1d
teacher
State of Metro/ Persons in
Residence nonmetro household Telephone Plumbing
Housing Unit #3 Virginia Metro 0 N/A Yes
Rent Automobiles Household
type
$205 N/A Vacant
*Public-use microdata samples do not actually contain alphabetic information.
Such information is converted to numeric codes; for example, the State of
Virginia has a numeric code of 51.
Protecting Confidential Information
Records on public-use microdata samples contain no names or addresses. Also,
the Bureau limits the detail on place of residence, place of work, high
incomes, and selected other items to further protect the confidentiality of
the records. Microdata records identify no geographic area with fewer than
100,000 inhabitants. Microdata samples include only a small fraction of the
population, drastically limiting the chance that the record of a given
individual is even contained in a microdata file, much less identifiable.
Uses of Microdata Files
Public-use microdata files essentially make possible "do-it-yourself" special
tabulations. The 1980 files furnish nearly all of the detail recorded on
long-form questionnaires in the census. Subject to the limitations on sample
size and geographic identification, it is possible for the user to construct
a seemingly infinite variety of tabulations interrelating any desired set of
variables. Users have the same freedom to manipulate the data that they
would have if they had collected the data in their own sample survey, yet
these files offer the precision of census data collection techniques and
sample sizes larger than would be feasible in most independent sample
surveys.
Microdata samples will be useful to users (1) who are doing research that
does not require the identification of specific small geographic areas or
detailed cross tabulations for small populations, and (2) who have access to
programming and computer time needed to process the samples. Microdata users
frequently study relationships among census variables not shown in existing
census tabulations, or concentrate on the characteristics of certain
specially defined populations, such as unemployed homeowners or families with
four or more children.
Sample Design and Size
Each microdata file is a stratified sample of the population, actually a sub-
sample of the full census sample (19.4% of all households) that received
census long-form questionnaires. Sampling was done household-by-household in
order to allow study of family relationships and housing unit
characteristics. Sampling of persons in institutions and other group
quarters was done on a person-by-person basis. Vacant units were also
sampled.
There are three independently drawn samples, designated "A," "B," and "C,"
each featuring a different geographic scheme, as discussed below. The B and
C Samples each contain 1 percent, i.e., one household for every one hundred
households in the Nation. Samples from the 1970 and 1960 censuses also
employed a 1-percent sample size. New for 1980 is a 5-percent sample,
designated the A Sample, which includes over one-fourth of the households
that received the census long-form questionnaire. Nationwide, the A Sample
gives the user records for over 11 million persons and over 4 million housing
units. (One could even use the A, B, and C Samples together, if there were
an advantage in having a 7-percent sample, since there is negligible overlap
among the samples). On the other hand, since processing a smaller sample is
less expensive, some users will be interested in one of the one-in-a-thousand
samples (extracts of the 1-percent and 5-percent samples) which are also
available from the Census Bureau. Sample design is discussed more thoroughly
in chapter 4.
The samples are self-weighting. The user can estimate the frequency of a
particular characteristic for the entire population by tallying records from
the microdata files and multiplying the result by the inverse of the sampling
rate, e.g., multiplying raw counts from the 5-percent A Sample by 20. A
section of chapter 2 discusses the preparation and verification of estimates
(see page 14).
Reliability improves with increases in sample size, so the choice of sample
size must represent a balance between the level of precision desired and the
resources available for working with microdata files. By using tables
provided in chapter 3 (see page 20), one can estimate the degree to which
sampling error will affect any specific number prepared from a microdata file
of a particular sample size. (It is also possible to estimate sampling error
using 100 "random groups" identified on sample records, see page 27). Users
of microdata files for State or SMSA estimates would normally use a 1- or 5-
percent sample, while users concerned only with national figures can
frequently get by with a 0.1 percent (one-in-a-thousand) sample. Even
national users may need a 1-percent or 5-percent sample if they contemplate
extremely detailed tabulations or are concerned with very small segments of
the population, for example, males 65 years old or over of Italian ancestry.
one of the examples in chapter 3 discusses the selection of appropriate
sample size for a particular study.
Subject Content
With only minor exceptions, microdata files contain the full range of
population and housing information collected in the 1980 census: 503
occupation categories, age by single years up to 90, income by $10 intervals
up to $75,000, and so forth. Because the samples provide data for all
persons living in a sampled household, users can study how characteristics of
household members are interrelated (for example, income and educational
attainment of husbands and wives).
Information for each housing unit in the sample appears on a 193-character
record with geographic and housing items, followed by a variable number of
193-character records with person information, one record for each member of
the household. Items on the housing record are listed beginning on page 52;
items on the population record are listed beginning on page 53. Each of the
items is further defined in the glossary (reprinted from the 1980 Census
Users' Guide), presented as Appendix K to this document.
Data users will frequently want to generate additional variables or otherwise
recode these items. For instance, a user desiring data on years of school
completed must construct this variable from the item included on highest
grade attended--reducing that value by one year for all persons who had not
finished that grade, as shown in another item on the record. Transformations
such as this, as well as corrections that apply to certain subjects, are
discussed in Appendix J.
There are no "missing data" categories in most items on these files.
Substitutions or allocations have been made for any missing data resulting
from incomplete questionnaires, inconsistent information or equipment
malfunction. "Allocation flags" appear at the end of each record indicating
each item which has been allocated. Thus, a user desiring to tabulate only
actually observed values can eliminate those cases with allocated values.
Allocation flags are discussed further on page 33.
Geographic Identification
The A, B, and C Samples each feature a different geographic scheme:
The A Sample, 5-percent size, identifies every State and most
individual counties with 100,000 or more inhabitants (350 in all, see
Appendix B.2). In many cases individual cities (see Appendix B.3) or
groups of places with 100,000 or more inhabitants are also identified.
Counties with populations under 100,000 have been grouped into vision
statement analytic units proposed by State Data Centers. These
frequently follow SMSA or State planning district boundaries. (Those
SMSA's shown on the A Sample are listed in Appendix B.1.) In New
England, areas are defined in terms of cities and towns rather than
counties.
The term "county group" is used loosely to apply to each of the areas
identified on these files. A 3-digit number, unique within State,
identifies each area.
The B Sample identifies 282 SMSAs of 100,000 or more inhabitants. The
remaining 36 SMSAs are paired together so that metropolitan and non-
metropolitan territory can be separately analyzed. (SMSAs not shown
separately are footnoted in Appendix B.1.) Thirty-one States are not
separately identified because they contain SMSAs which cross State
boundaries and have fewer than 100,000 persons within a State (See
Appendix C). Many large cities, groups of cites, and counties are
identified within large SMSAs. (See Appendixes B.2 and B.3.) Outside
SMSAs, counties are grouped according to State planning district or
into other reasonable analytic units with populations of 100,000 or
more.
The C Sample identifies 27 States and the District of Columbia. The
remaining States are shown in eight groups, none of which crosses a
census region or division boundary (see Appendix A). Four type-of-
area categories are shown throughout: central cities of urbanized
areas, urban fringe (i.e., the remainder of urbanized areas outside
central cities), other urban, and rural. Seventy-three individual
urbanized areas are shown (see Appendix D), all of which have at least
100,000 inhabitants in the central city and another 100,000 in the
urban fringe. This happens to include every urbanized area with a
total population over 800,000, and roughly half of the urbanized areas
between 200,000 and 800,000.
The characteristics of the three different geographic schemes are compared in
figure 2.
Figure 2. Comparison of Features on 1980 and 1970 Public-Use Microdata
Samples
-----1980 Samples----- -------1970 Samples-------
County Neigh.
A B C State Group Chars.
Sample Size 5% 1% 1% 1-2% 1-2% 1-2%
0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
Areas Identified
Divisions X - X X - X
States 51 20 28 51 4 -
SMSAs of 100,000+ 180 282 - - 125 -
Counties of 100,000+ 350 236 - - 104
Places of 100,000+ 123 135 58 - 12 5
County Groups 1154 1258 - - 409
Urbanized Areas - - 73 - - 6
Metro/Nonmetro - X - 23 States - -
Urban/Rural - - X 42 States - X
Neighborhood
Characteristics - - - - - X
Maps of county groups shown on the A and B Samples are included as appendixes
L and N, respectively. Where county group boundaries subdivide a county, as
in the identification of a city, lists of subcounty units in appendixes M and
O supplement the maps. The maps are also available as single nationwide
sheets, 30" x 42", with county group boundaries shown in color.
Comprehensive listings of county group components, illustrated in figure 3,
are available on microfiche. The components of each county group are also
derivable from a computerized County Group Equivalency File available
separately.
The 1-in-1,000 samples (0.1%) extracted from the A, B, and C Samples have the
same geography as the parent files.
Migration and Place-of-Work Data
The A and B Sample county groups apply not only to 1980 residence, but also
to place of work in 1980 and to place of residence in 1975. This makes
possible the detailed analysis of migration and commuting patterns in terms
of origin and destination. For instance, one can examine immigrants to an
area (i.e., people who lived in a different area in 1975) in terms of the
States or metropolitan areas from which they came. Further, if one purchases
data for the entire U.S., one can also examine the characteristics of
outmigrants (i.e., persons who lived in a particular county group in 1975 who
resided elsewhere in 1980). Similarly, one can look at the characteristics
of the work force in an area using the county group of work, irrespective of
whether workers reside in the same area.
Migration, place of work, and travel time to work appear on these files only
for one-half of the sampled households. (Due to budgetary limitations, only
part of the questionnaires could be coded.) Thus, the 5-percent sample
includes only a 2 1/2-percent sample for migration and place of work.
Therefore, the user must double the normal weights to derive estimates, as
discussed further in chapter 2.
Corresponding Microdata From Earlier Censuses
The Census Bureau created six separate 1-percent (one-in-a-hundred) samples
from the 1970 census, three based on the 15-percent versions of the 1970
questionnaire, and three based on the 5-percent version. Geographic areas
identified on 1970 and earlier microdata files were required to have at least
250,000 inhabitants. One geographic scheme employed in 1970 identified
States, a second identified SMSAs with 250,000 or more inhabitants and
similarly large county groups elsewhere, and the third identified only very
large areas but included records for "neighborhood characteristics." A
single one-in-a-hundred sample, identifying States, is available from the
1960 census. Characteristics of these 1970 geographic schemes are summarized
in figure 2.
The files from 1960 and 1970 use basically similar formats. The 1980
microdata record layouts differ from their 1960 and 1970 counterparts; but,
since most of the 1980 items were also included in the 1970 and 1960
censuses, these microdata files represent a rich resource for analysis of
trends. Items which were added, dropped, or substantially changed between
1970 and 1980 are listed in figure 4. The glossary, presented as Appendix K
to this document, discusses historical comparability of 1980 data items in
greater detail.
Geographic comparability is a larger problem. States can be identified on
microdata from each census (the 1960 and 1970 State samples and the 1980 A
Sample). Regions and divisions by type-of-area are derivable from the 1980 C
Sample and the 1970 "neighborhood characteristics" samples. Many, but far
from all, counties of 250,000 or more population in 1970 are identified on
the 1970 "County Group" Samples. These large counties typically are also
shown on the 1980 A Sample (and frequently also the B Sample). Counties
identified in both 1980 and 1970 are asterisked in Appendix B.2.
SMSAs are not always directly comparable between 1970 and 1980, however,
since roughly half of the SMSAs identified in 1970 files changed boundaries
prior to the 1980 census. In other words, a 1970 SMSA identified as a county
group may not match the corresponding 1980 SMSA shown on the B Sample. No
individual place or place group was shown prior to 1980. Outside identified
SMSAs there is almost no commonality between 1970 and 1980 county groups.
1970 county groups were based on "functional economic areas" defined by the
Bureau of Economic Analysis. These frequently crossed State lines and
involved grouping criteria unrelated to the administrative and local interest
factors that contributed to the definition of county groups in 1980.
Figure 4. 1970-1980 Subject Comparability
1980 Items Not on 1970 Files:
Ancestry
Boarded-up vacant unit
Carpooling
Condominium
Language spoken at home and ability to speak English
Hours usually worked per week last year
Public transportation disability
Selected monthly owner costs
Travel time to work (shown only for half of the samples)
Vans or light trucks available
Weeks unemployed last year
Concepts Substantially Changed:
Farm definition - old definition can be reconstructed
"Householder" places household head concept
Industry - many code changes; classification system changed somewhat
Occupation - classification system and codes substantially changed
Poverty definition - minor modifications; the old definition could be
reconstructed if necessary
Race - several categories added; revised classification rules affect White
and Other
Residence in 1975/Place of work - broad categories in 1970, but coded to
county group in 1980 on the A and B Samples; (shown only for half of the
sample)
Telephone in unit - narrower than telephone availability in 1970
Work disability - not counted for 1980 if it has lasted less than six months
1970 Items Not On 1980 Files:
Basement
Battery radio
Birthplace of parents (allowing identification of "foreign stock")
Clothes dryer
Clothes washing machine
Dishwasher
Duration of disability
Home food freezer
Industry and occupation 5 years ago
Mother tongue
Owner-occupied cooperative/condominium
Second home
Television
Vocational training
Year moved into unit (retained for householders, deleted for other persons)
Further information on the 1970 microdata files is contained in Public-Use
Samples of Basic Records From the 1970 Census: Description and Technical
Documentation and its three supplements, available for $5 from Customer
Services. A map, 22 by 32 inches, defining areas identified on the 1970
County Group Samples is included with the documentation. Documentation for
the 1960 microdata file is also available for $5.
Public-use microdata samples are being created from manuscript records of the
1940 and 1950 censuses, and will probably be available in late 1983.
PROCESSING THE DATA
Technical Conventions
A printout included with each tape copy purchased from the Census Bureau
includes the following information, some of which document options selected
by the purchaser:
Recording Language (ASCII or EBCDIC)
Density (1600 bpi or 6250 bpi)
Labelling (Label or no label)
Block Length (Any multiple of 193, up to 32617)
Record Length (193)
Record Count
Units of Issue
A total of 142 files are available in the A, B, and C Samples:
A Sample = 5 percent: 51 files
One file for each State and the District of Columbia
0.1 percent: 1 nationwide file
B Sample = 1 percent: 52 files
One file for each State and the District of Columbia,
exclusive of all county groups crossing State lines
One file for all county groups in the nation crossing State
lines (listed in Appendix C)
0.1 percent: 1 nationwide file
C Sample = 1 percent: 36 files
One file for each of the 27 States separately identified
and 1 for the District of Columbia
One file for each of the 8 groups of the remaining States
specified in Appendix A.
0.1 percent: 1 nationwide file
Each of these files may be purchased separately. Single file tapes are
priced at $140 per reel (as of January 1, 1983). Most files are sufficiently
small so that more than one can be accommodated on a single reel. Tapes with
more than one file copied onto them are priced at $165 per reel. Purchasers
of the B Sample for any of the 31 States which include area in a county group
crossing State lines may want to request that the "State Code 99" file be
stacked onto a tape being purchased. Estimates of the number of tapes
required for specified groups of files at a given density and blocking factor
are available on request from Customer Services.
File Structure
Each file consists of a series of 193-character logical records of two types
--housing and persons. Each housing unit record is followed by a variable
number of person records, one for each member of the household or none if
vacant, as illustrated in figure 1 on page 2. Each person in group quarters
has two records--a dummy "housing unit" record (most nongeographic fields are
not applicable), as well as a person record.
In the text of this document, the numeric identification of a particular data
item is the same as its character location within a record. Items on the
housing record are prefixed with an H, population items with a P. For
instance, Race, item P12-13, is a two-digit code beginning in character 12 of
the person record. (The data dictionary, or record layout, also introduces
mnemonic identifiers; see p. 54.)
Geographic identifiers, subsample identification and serial numbers appear
only on the housing unit record. Thus, most tabulations of person
characteristics require manipulation of both housing and person records. An
item on the housing record indicates the exact number of person records
following before the next housing record (H26-27). This feature allows a
program to anticipate what type of record will appear next, if necessary.
In order to use many software packages (e.g., BMD), users need to create
rectangular files , i.e., extract files with any desired household data
repeated with each person's record. While this imposes an intermediate
processing step on the user of such software, it will benefit those users who
are able to reduce significantly the size of the file. The Census Bureau's
CENSPAC system can be used to generate rectangular extract files.
Alternatively, users may obtain a software package capable of handling the
hierarchical structure or prepare their own software. The Census Bureau
offers such a software package called COCENTS, and others are available from
commercial sources (for example, SAS). Descriptions of CENSPAC and COCENTS
are available upon request from Customer Services.
All fields are numeric, except for the Record Type which uses "H" and "P."
File Size
A printout included with each tape copy purchased from the Census Bureau
includes the total record count. A future supplement to this document will
contain record counts for each area identified. In the absence of those
counts, the number of 193-character records can be estimated as follows: Sum
the total number of persons, the total number of housing units and the number
of persons in group quarters (for whom there is a dummy housing unit record);
multiply that sum by the sampling rate. For example; the number of records
on the 5-percent A Sample nationwide can be estimated as 226,545,805
(persons) + 88,411,263 (housing units) + 5,714,931 (persons in group
quarters) = 320,671,999; x .05 = 16,033,600.
Record Sequence
Records on these files are sorted by geographic area. On the A and B
Samples, all households sampled within a particular county group appear
together. County groups are sequenced in ascending order within State. On
the 0.1% nationwide files, States are sequenced by State code. On the B
Sample, this means that all county groups with State code suppressed (i.e.,
shown as 99) appear at the end of the nationwide file.
On the C Sample, records are sequenced by urbanized area code by type of area
code within State or State group. Thus, households outside an identified
urbanized area (i.e., UA code 0000) appear first, further grouped by type-of-
area code, followed by data for each identified UA in ascending code
sequence, central city households preceding urban fringe households. On the
0.1% sample, States and State groups appear in code sequence.
The sequence of households within each identified geographic area has been
scrambled to avoid any implication of geographic information beyond that
which meets Census Bureau disclosure rules for public-use microdata.
Person records within household are sequenced by relationship code (P2).
Thus, for example; the record for the householder always immediately follows
the housing unit record for an occupied unit. This feature simplifies
tabulation of households or families by race of householder, ancestry of
householder, and even poverty status--characteristics not included on the
housing unit record--since the desired indicators are always on the first
person record. Where the household contains more than one person of a given
relationship, person records appear in sequence of decreasing age (P8-9).
Persons sampled from within the same group quarters are not identifiable as
such, since each has an independent dummy housing unit record.
Machine-Readable Documentation
The "data dictionary" or record layout which appears on pages 55 to 99 was
generated from a machine-readable file which is sold as part of the CENSPAC
system ($140) or may be obtained in conjunction with the County Group
Equivalency File (see below). Using that file it is possible to
automatically generate hard copy documentation for extract files or labels
for tabulations created with CENSPAC. With some adaptation, the data
dictionary file can also be used by other software packages or user programs
to automatically specify the layout of the microdata files.
Also available in machine-readable form is the County Group Equivalency File
($140), a list of counties (and places or MCDs where applicable) and their
assigned county group codes for the A and B samples. The printout in figure
3 was generated from a resorted version of this file.
Handling Invalid Codes
The data dictionary shows each category as having a unique representation.
It is possible, however, that certain variables may have a small number of
cases outside the intended range. Standard census practice is to assign
invalid codes to the next lower numbered valid category. For example, on an
allocation flag with valid codes 0, 2 and 3, a 1 would be counted with code
0, and a code of 4 or more would be counted with 3. Exceptions to this rule
occur in occupation and industry codes, where invalid codes are assigned to
the next higher valid category.
Preparing and Verifying Tabulations
Estimation of totals - Estimates of complete-count census figures may be made
from tabulations of public-use microdata samples by using a simple inflation
estimate - that is, by multiplying the sample tally by the reciprocal of the
sampling rate. For example, to estimate the total number of persons with a
certain population characteristic from a one-in-one-hundred sample, multiply
the sample total by 100; from a 5-percent sample, multiply by 20. To
estimate the number of persons who lived in a different county in 1975, data
for which only one-half of the sample is available, multiply an A Sample
table by 40 (i.e., the reciprocal of 1/2 of 5-percent). Persons in the
migration/place work/travel time sample carry a weight of 2 in character P46;
all others carry a weight of 0.
Estimation of percentages - Percentages are estimated by simply dividing the
weighted estimate of persons or housing units with a given characteristic by
the weighted sample estimate for the base. Normally, this yields the same as
would be obtained if one made the computation using sample tallies rather
than weighted estimates. For example, the percentage of housing units with
air conditioning in a one-in-one-hundred sample can be obtained by simply
dividing the tally of sample housing units with air conditioning by the total
number of sample housing units. When working with migration, place of work
or travel time to work figures, one must either take care to determine both
the numerator and base of the percentage from the 1/2 sample, or must use
weighted estimates in calculations rather than simply tallies.
Verifying tabulations - The 1980 public-use microdata samples have been
constructed so that it should not be difficult to obtain desired tabulations.
File structure and coding of items is straightforward. There are no missing
data (see the section on allocations, page 33). Records not applicable for
each item are assigned to specific "NA" categories, and it is frequently not
necessary to determine in a separate operation whether a record is in the
universe or not. A user must, however, anticipate the possibility of errors
in his or her own processing. Thus, user tabulations should be verified
against other available tallies. Two ways for the user to verify estimates
follow:
1. Using control tabulations from the samples
As each public-use microdata sample was produced, counts of persons,
housing units, vacant housing units, and group quarters persons
selected into the sample were tallied within each identified geographic
area. These control counts will be published as a supplement to this
documentation. (In the interim, counts for specific areas may be
requested from Customer Services.) A failure of user tallies to
replicate these exact counts would indicate an error in the user's data
processing.
2. Using published data from the 1980 census
Tabulations from the 1980 census data base are available in the printed
census publications and on summary tape files. The tabulations provide
an opportunity to check the reasonableness of statistics derived from
public-use microdata samples. A familiarity with summary data already
available may also facilitate planning of tabulations to be made from
microdata. Those publication series likely to be of greatest use for
this purpose are listed in figure 5.
In comparing sample tabulations with published data one must carefully
note the universe of the published tabulation. For instance, on
microdata records, Industry (P87-89) is reported for the civilian labor
force and for persons not in the labor force who reported having worked
1975 or later. Industry tabulations in 1980 census publications are
presented only for the employed population or the experienced civilian
labor force. Thus, a tally of industry for all persons for whom industry
is reported on microdata records would not correspond directly to any
published tabulation. A user should always pay particular attention to
concept definitions as presented in the glossary.
One cannot, of course, expect exact agreement between census publications
(which are based on the complete census count, full sample estimates, or
a subsample of the census sample) and user estimates based on tallies of
a 5-percent of smaller sample. They will inevitably differ to some
extent due to chance in selection of actual cases for public-use
microdata samples. Since the amount of likely chance variation for a
given statistic can be measured, any discrepancy beyond a certain level
can be identified as a likely error in programming. Chapter 3 discusses
sampling variability and its measurement.
User experience has indicated that careful verification of sample
tabulations is essential -- so important that it may frequently be
advisable to include additional cells in a tabulation for no other reason
than to provide counts or to yield marginal totals not otherwise
available, which may be verified against other available tabulations.
Figure 5. Selected 1980 Census Publications Useful in Verifying
Microdata Tabulations
PHC80-52 Advance Estimates of Social Economic and Housing
Characteristics
Basic distributions for most census items, for States,
counties, places of 25,000 or more inhabitants; issued
by State.
PC80-1-B General Population Characteristics
HC80-1-A General Housing Characteristics
PC80-1-C General Social and Economic Characteristics (mid 1983)
HC80-1-B Detailed Housing Characteristics (mid 1983)
Distributions in somewhat greater detail than PHC80-52,
many also shown for race and Spanish origin groups
(PC80-1-C also features characteristics for ancestry
groups); for States, SMSAs, urbanized areas, counties,
places of 2,500 or more inhabitants. Characteristics
by type of area (as in C Sample) are also shown at
the State level. All issued by State.
PC80-1-D Detailed Population Characteristics (late 1983)
HC80-2 Metropolitan Housing Characteristics (late 1983)
Crosstabulations of characteristics, some in
considerable detail.
PC80-1-D reports, issued by State, show the State
and SMSAs with 250,000 or more inhabitants. The
HC80-2 series includes a report for each SMSA
regardless of size, as well as a report for each
State.
PC80-2 Population Subject Reports (1983-1984)
HC80-3 Housing Subject Reports (1983-1984)
Very detailed cross tabulations, most shown only at
the national level. A series of nationwide reports
issued by Subject.
These publications are available for sale through the Superintendent of
Documents, Government Printing Office, Washington, D.C. 20402.
ACCURACY OF THE MICRODATA SAMPLE ESTIMATES
Introduction
The data summarized from a public-use microdata sample not only describe the
particular set of households in the sample, but are primarily used to
estimate what data would have been obtained if a complete census count of the
variables of interest were available. These estimates can be expected to
vary from the complete-count result, because they are subject to two basic
types of error --- sampling and nonsampling. The sampling error in the data
arises from the selection of persons and housing units to be included in both
the census sample and the microdata samples. The nonsampling error, which
affects both sample and complete count data, is the result of all other
errors that may occur during the collection and processing phases of the
census. A more detailed discussion of both sampling and nonsampling error is
given in this chapter. Chapter 4 describes the method used to select the
microdata samples.
Errors in the Data
Since the estimates that users produce are based on a sample, the data may
differ somewhat from complete-count figures that would have been obtained if
all housing units, persons within those housing units, and persons living in
group quarters had been enumerated using the same questionnaires,
instructions, enumerators, etc. In addition, if one were able to select all
possible samples, the estimates from each sample would differ, but the
average of these estimates would approximate the complete-count figure. The
deviation of a particular sample estimate from the average value obtainable
from all possible samples is called the sampling error. The standard error
of a survey estimate is a measure of the variation among the estimates from
the possible samples and thus is a measure of the precision with which an
estimate from a particular sample approximates the average result of all
possible samples. The sample estimate and its estimated standard error
permit the user to construct an interval estimate having prescribed
confidence that the interval includes the average result of all possible
samples. The method of calculating standard errors and confidence intervals
for the estimates produced from the microdata samples is given below.
In addition to the variability which arises from the sampling procedures,
both sample data and complete-count data are subject to nonsampling error.
Nonsampling error may be introduced during each of the many extensive and
complex operations used to collect and process census data. For example,
operations such as editing, reviewing, or handling questionnaires may
introduce error into the data. Nonsampling error may affect the data in two
ways. Errors that are introduced randomly will increase the variability of
the data, and should therefore be reflected in the standard error. Errors
that tend to be consistent in one direction will make both sample and
complete-count data biased in that direction. For example, if respondents
consistently tend to underreport their income, then the resulting counts of
households or families by income category will be skewed toward the low
income categories. Such biases are not reflected in the standard error. A
more detailed discussion of the sources of nonsampling error is given in the
section "Control of Nonsampling Error" in this chapter.
Calculation of Standard Errors Using Tables
Two methods are presented for calculating standard errors of estimated totals
and percentages. (The procedures for estimating totals and percentages
themselves were given in the previous chapter (page 14).) The first method,
described below, used tabled figures or simple formulas and produces an
approximate standard error quickly and inexpensively. The second method
(presented on page 27) requires extra tabulations by the user during the
processing of the microdata file, but it produces more precise standard
errors and is the preferred method. There are, of course, situations where
it is not feasible to do the extra tabulations required by the second method,
for instance, when one is trying to determine, prior to purchase, whether a
one-percent sample will yield estimates of adequate precision for a given
study or whether it is necessary to use the 5-percent sample instead. For
these purposes the method described in this section should produce an
acceptable approximation. On the other hand, for many statistics,
particularly from detailed crosstabulations, standard errors using the second
method should be substantially better. The second method is also applicable
to a wider variety of statistics, e.g., means and ratios.
Tables A through G in this chapter contain the information necessary to
calculate an approximate standard error of sample estimates. In order to
perform this calculation, one obtains (1) the unadjusted standard error for
the characteristic that would result under a simple random sample design (of
persons, families, or housing units) and estimation technique; and (2) an
adjustment factor, which partially reflects the effects of the actual sample
design and estimation procedure used for the 1980 census public-use microdata
samples, for the particular characteristic estimated. The adjustment factors
provided in this chapter are based on computations from the full census
sample and as such do not reflect the additional stratification used in the
selection of the public-use microdata samples (see chapter 4). Thus, in
general, these factors will provide conservative estimates of the standard
error. In addition, these factors only pertain to individual data items
(e.g., years of school completed, labor force status) and as such are not
entirely appropriate for use with detailed cross-tabulated data.
To calculate the approximate standard error of a 5-percent, 1-percent, or
0.1-percent sample estimate follow the steps given below:
a. Obtain the unadjusted standard error for the sampling rate to be used
from Table A, C, or E for estimated totals or from Table B, D, or F for
estimated percentages. Alternatively, the formula given at the bottom of
each table may be used to calculate the unadjusted standard error. (For
sample sizes, other than 5, 1, or 0.1 percent, see page 26).
In using Tables A, C, or E or corresponding formulas for estimated totals
use weighted figures rather than raw sample counts to select the
applicable row. To select the applicable column for person
characteristics, use the total population in the area being tabulated
(not just the total of the universe being examined), or use the total
count of housing units if the estimated total is a housing
characteristic. Similarly in using Tables B, D, or F or the
corresponding formula for estimated percentages, use inflated figures to
select the appropriate column.
b. Use Table G to obtain the factor for the characteristic (e.g., work
disability, years of school completed). If the estimate is a cross-
tabulation of more than one characteristic scan Table G for each
applicable factor and use the largest factor. Multiply the unadjusted
standard error from step a. by the factor obtained in step b.
Example 1: Standard error of a total - suppose we tally a 1% public-use
microdata sample for Alaska and find 358 persons in the sample who are 18
years and over and speak a language other than English at home. Therefore,
the weighted number of persons who are 18 years and over and speak a language
other than English at home is 358 x 100 = 35,800.
The unadjusted standard error for the estimated total is obtained from Table
C or from the formula below Table C. In order to avoid interpolation, the
use of the formula will be demonstrated here. The formula for the unadjusted
standard error, Se, is
Se(Y) = 99Y (1-Y/N)
35,800
Se (35,800) = 99 (35,800) (1 - 401,851) = 1,797 persons.
Note, in this example the complete census count of persons in Alaska of
401,851 was used. The standard error of the estimated 35,800 persons 18
years and over who speak a language other than English at home is found by
multiplying the unadjusted standard error, 1,797, by the appropriate
adjustment factor. The adjustment factor for "Language Usage and the Ability
to speak English" given in Table G is 1.5. Thus, the estimated standard
error is 1,797 x 1.5 or 2,696.
Example 2: Standard error of a percent - To illustrate the calculation of
the standard error of a percent, suppose the estimated percent of persons 18
years and over who speak a language other than English at home who speak
English "not well" or "not at all" is 12.7 (the estimated total persons 18
years and over who speak a language other than English at home used as the
base is 35,800). Using Table D, and interpolating among the nearest figures,
the unadjusted standard error is found to be approximately 0.53 and using the
same adjustment factor, the standard error for the estimated 12.7 percent is
0.53 x 1.5 = 0.795 percentage points.
A note of caution concerning numerical values is necessary. Standard errors
derived in this manner are approximate. Calculations could be expressed to
several decimal places, but to do so would suggest more precision in the data
then is justifiable. One useful rule of thumb is to round standard error
estimates to two significant digits. Thus, 2,696 would be rounded to 2,700
and 0.795 percentage points would be rounded to 0.80 percentage points.
Table A - Unadjusted Standard Errors for Estimated Totals, 5 Percent Sample
Estimated Size of Geographic Area Tabulated2
Total1 50,000 100,000 250,000 500,000 1 Million 5 Million
1,000 140 140 140 140 140 140
2,500 210 220 220 220 220 220
5,000 290 300 310 310 310 310
10,000 390 410 430 430 430 440
15,000 450 490 520 530 530 530
25,000 490 600 650 670 680 690
75,000 - 600 1,000 1,100 1,150 1,180
100,000 - - 1,070 1,230 1,310 1,360
250,000 - - - 1,540 1,890 2,120
500,000 - - - - 2,180 2,920
1,000,000 - - - - - 3,900
5,000,000 - - - - - -
10,000,000 - - - - - -
10 Million 25 Million
1,000 140 140
2,500 220 220
5,000 310 310
10,000 440 440
15,000 530 530
25,000 690 690
75,000 1,190 1,190
100,000 1,370 1,380
250,000 2,150 2,170
500,000 3,000 3,050
1,000,000 4,140 4,270
5,000,000 6,890 8,720
10,000,000 - 10,680
1 For estimated totals larger than 10,000,000, the standard error is somewhat
larger than the table values. The formula given below should be used to
calculate the standard error.
Where:
Se(Y) = 99Y (1-Y) N = Size of area
N
Y = Estimate of characteristic total
2 Total count of persons, housing units, or families in area if the estimated
total is a person, housing unit, or family characteristic, respectively.
Table B - Unadjusted Standard Error for Estimated Percentages, 5 Percent
Sample
(Standard errors expressed in percentage points)
Estimated Base (Weighted Total) of Percentage 1
Percent 1,000 1,500 2,500 5,000 7,500 10,000 25,000 50,000 100,000
2 or 98 1.9 1.6 1.2 0.9 0.7 0.6 0.4 0.3 0.2
5 or 95 3.0 2.4 1.9 1.3 1.1 1.0 0.6 0.4 0.3
10 or 90 4.1 3.4 2.6 1.8 1.5 1.3 0.8 0.6 0.4
15 or 85 4.9 4.0 3.1 2.2 1.8 1.6 1.0 0.7 0.5
20 or 80 5.5 4.5 3.5 2.5 2.0 1.7 1.1 0.8 0.6
25 or 75 6.0 4.9 3.8 2.7 2.2 1.9 1.2 0.8 0.6
30 or 70 6.3 5.2 4.0 2.8 2.3 2.0 1.3 0.9 0.6
35 or 65 6.6 5.4 4.2 2.9 2.4 2.1 1.3 0.9 0.7
50 6.9 5.6 4.4 3.1 2.5 2.2 1.4 1.0 0.7
250,000 500,000
2 or 98 0.1 0.1
5 or 95 0.2 0.1
10 or 90 0.3 0.2
15 or 85 0.3 0.2
20 or 80 0.3 0.2
25 or 75 0.4 0.3
30 or 70 0.4 0.3
35 or 65 0.4 0.3
50 1.4 0.3
1 For a percentage and/or base of percentage not shown in the Table, the
formula given below may be used to calculate the standard error.
Where:
Se(p) = 19 B = Base of estimated percentage
B p (100-p) (weighted total)
p = Estimated percentage
Table C - Unadjusted Standard Errors for Estimated Totals, 1 Percent Sample
Estimated Size of Geographic Area Tabulated2
Total1 50,000 100,000 250,000 500,000 1 Million 5 Million
1,000 310 310 310 310 310 310
2,500 480 490 500 500 500 500
5,000 670 690 700 700 700 700
10,000 890 940 970 980 990 990
15,000 1,020 1,120 1,180 1,200 1,210 1,220
25,000 1,110 1,360 1,490 1,530 1,550 1,570
75,000 - 1,360 2,280 2,510 2,620 2,700
100,000 - - 2,440 2,810 2,980 3,110
250,000 - - - 3,520 4,310 4,850
500,000 - - - - 4,970 6,670
1,000,000 - - - - - 8,900
5,000,000 - - - - - -
10,000,000 - - - - - -
10 Million 25 Million
1,000 310 310
2,500 500 500
5,000 700 700
10,000 990 990
15,000 1,220 1,220
25,000 1,570 1,570
75,000 2,710 2,720
100,000 3,130 3,140
250,000 4,910 4,950
500,000 6,860 6,960
1,000,000 9,440 9,750
5,000,000 15,730 19,900
10,000,000 - 24,370
1 For estimated totals larger than 10,000,000, the standard error is somewhat
larger than the table values. The formula given below should be used to
calculate the standard error.
Where:
Se(Y) = 99Y (1-Y) N = Size of area
N
Y = Estimate of characteristic total
2 Total count of persons, housing units, or families in area if the estimated
total is a person, housing unit, or family characteristic, respectively.
Table D - Unadjusted Standard Error for Estimated Percentages, 1 Percent
Sample
(Standard errors expressed in percentage points)
Estimated Base (Weighted Total) of Percentage1
Percent 1,000 1,500 2,500 5,000 7,500 10,000 25,000 50,000 100,000
2 or 98 4.4 3.6 2.8 2.0 1.6 1.4 0.9 0.6 0.4
5 or 95 6.9 5.6 4.3 3.1 2.5 2.2 1.4 1.0 0.7
10 or 90 9.4 7.7 6.0 4.2 3.4 3.0 1.9 1.3 0.9
15 or 85 11.2 9.2 7.1 5.0 4.1 3.6 2.2 1.6 1.1
20 or 80 12.6 10.3 8.0 5.6 4.6 4.0 2.5 1.8 1.3
25 or 75 13.6 11.1 8.6 6.1 5.0 4.3 2.7 1.9 1.4
30 or 70 14.4 11.8 9.1 6.4 5.3 4.6 2.9 2.0 1.4
35 or 65 15.0 12.8 9.5 6.7 5.5 4.7 3.0 2.1 1.5
50 15.8 12.8 9.9 7.0 5.7 5.0 3.1 2.2 1.6
250,000 500,000
2 or 98 0.3 0.2
5 or 95 0.4 0.3
10 or 90 0.6 0.4
15 or 85 0.7 0.5
20 or 80 0.8 0.6
25 or 75 0.9 0.6
30 or 70 0.9 0.6
35 or 65 0.9 0.7
50 1.0 0.7
1 For a percentage and/or base of percentage not shown in the Table, the
formula given below may be used to calculate the standard error.
Where:
Se(p) = 99 B = Base (weighted total) of estimated
B p (100-p) percentage
p = Estimated percentage
Table E - Unadjusted Standard Errors for Estimated Totals, 0.1 Percent Sample
Estimated Size of Geographic Area Tabulated2
Total1 50,000 100,000 250,000 500,000 1 Million 5 Million
1,000 990 990 1,000 1,000 1,000 1,000
2,500 1,540 1,560 1,570 1,580 1,580 1,580
5,000 2,120 2,180 2,210 2,220 2,230 2,230
10,000 2,830 3,000 3,100 3,130 3,140 3,160
15,000 3,240 3,570 3,750 3,810 3,840 3,870
25,000 3,530 4,330 4,740 4,870 4,930 4,980
75,000 - 4,330 7,240 7,980 8,330 8,590
100,000 - - 7,740 8,940 9,480 9,980
250,000 - - - 11,170 13,690 15,400
500,000 - - - - 15,800 21,200
1,000,000 - - - - - 28,270
5,000,000 - - - - - -
10,000,000 - - - - - -
10 Million 25 Million
1,000 1,000 1,100
2,500 1,580 1,580
5,000 2,230 2,230
10,000 3,160 3,160
15,000 3,870 3,870
25,000 4,990 5,000
75,000 8,620 8,640
100,000 9,940 9,970
250,000 15,600 15,720
500,000 21,780 22,120
1,000,000 29,980 30,970
5,000,000 49,970 63,210
10,000,000 - 77,420
1 For estimated totals larger than 10,000,000, the standard error is somewhat
larger than the table values. The formula given below should be used to
calculate the standard error.
Where:
Se(Y) = 999Y (1-Y) N = Size of area
N
Y = Estimate of characteristic total
2 Total count of persons, housing units, or families in area if the estimated
total is a person, housing unit, or family characteristic, respectively.
Table F - Unadjusted Standard Error for Estimated Percentages, 0.1 Percent
Sample
(Standard errors expressed in percentage points)
Estimated Base (Weighted Total) of Percentage1
Percent 1,500 2,500 5,000 7,500 10,000 25,000 50,000 100,000
2 or 98 11.4 8.8 6.3 5.1 4.4 2.8 2.0 1.4
5 or 95 17.8 13.8 9.7 8.0 6.9 4.4 3.1 2.2
10 or 90 24.5 19.0 13.4 10.9 9.5 6.0 4.2 3.0
15 or 85 29.1 22.6 16.0 13.0 11.3 7.1 5.0 3.6
20 or 80 32.6 25.3 17.9 14.6 12.6 8.0 5.7 4.0
25 or 75 35.3 27.4 19.4 15.8 13.7 8.7 6.1 4.3
30 or 70 37.4 29.0 20.5 16.7 14.5 9.2 6.5 4.5
35 or 65 38.9 31.2 21.3 17.4 15.1 9.5 6.7 4.8
50 40.8 31.6 22.3 18.2 15.8 10.0 7.1 5.0
250,000 500,000
2 or 98 0.9 0.6
5 or 95 1.4 1.0
10 or 90 1.9 1.3
15 or 85 2.3 1.6
20 or 80 2.5 1.8
25 or 75 2.7 1.9
30 or 70 2.9 2.0
35 or 65 3.0 2.1
50 3.2 2.2
1 For a percentage and/or base of percentage not shown in the Table, the
formula given below may be used to calculate the standard error.
Where:
Se(p) = 999 B = Base (weighted total) of
B p (100-p) estimated percentage
p = Estimated percentage
Table G: Standard Error Adjustment Factors
Characteristic Factor
POPULATION
Urban and Rural 1.0
Age, Sex, Race, and spanish Origin 1.2
Household Type 1.1
Household Relationship 1.3
Household Size 1.1
Marital Status 1.0
Language Usage and Ability to Speak English 1.5
Ancestry 1.7
Type of Group Quarters 0.9
Citizenship, Place of Birth, and Year of Immigration 2.1
Residence in 1975 3.6
Place of Work 2.2
Travel Time to Work 1.8
Means of Transportation to Work and Private Vehicle Occupancy 1.3
School Enrollment 1.5
Years of School Completed 1.2
Veteran Status and Period of Service 1.1
Work & Public Transportation Disability 1.2
Labor Force Status 1.3
Hours Worked Per Week and Weeks Worked in 1979 1.2
Unemployment in 1979 1.2
Industry and Occupation 1.2
Class of Worker 1.3
Household Income 1.1
Income Type 1.3
Family Income 1.1
Unrelated Individual Income 1.2
Workers in Family 1.3
Poverty Status - Family 1.1
Poverty Status - Persons 2.0
Poverty Status - Unrelated Individuals 1.2
HOUSING
Occupancy and Vacancy Status 1.1
Tenure 1.1
Units in Structure 1.1
Stories in Structure 1.0
Passenger Elevator 1.0
Source of Water 1.1
Sewage Disposal 1.1
Year Structure Built 1.1
Year Householder Moved Into Housing Unit 1.1
Heating Equipment and Fuels 1.2
Kitchen or Plumbing Facilities 1.1
Number of Rooms, Bedrooms, or Bathrooms 1.1
Telephone in Housing Unit 1.1
Air Conditioning 1.1
Vehicles Available 1.1
Gross Rent, Contract Rent, or Value 1.1
Inclusion of Utilities in Rent 1.1
Mortgage Status and Selected Monthly Owner Costs 1.1
Confidence Intervals and Inferences Based on the Sample
A sample estimate and its estimated standard error may be used to construct
confidence intervals about the estimate. These intervals are ranges that
will contain, with a known probability, the value of the estimated
characteristic that would be obtained by averaging the estimates from all
possible samples. For example, if all possible samples that could result
under the 1980 public-use microdata sample design were independently selected
and surveyed under the same conditions, and if the estimate and its estimated
standard error were calculated for each of these samples, then:
(1) Approximately 68 percent of the intervals from one estimated standard
error below the estimate to one estimated standard error above the
estimate would contain the average result from all possible samples; and
(2) Approximately 95 percent of the intervals from two estimated standard
errors below the estimate to two estimated standard errors above the
estimate would contain the average result from all possible samples.
The intervals are referred to as 68-percent and 95-percent confidence
intervals, respectively.
One may be tempted to think of a confidence interval in terms such as these:
that the number we are trying to estimate, the average value calculated over
all possible samples, has a given probability of falling between the upper
and lower limits of that interval. Actually, this is not technically
correct, since the average estimate from all possible samples already exists,
though its value is unknown, and it remains the same regardless of which of
the possible samples we select. Rather, it is the confidence interval which
varies from sample to sample. Thus, one can say, with a specified
probability or level of confidence, that the confidence interval, as
calculated from the particular sample selected, includes the average estimate
from all possible samples.
Confidence intervals may also be constructed for the difference between two
sample figures. This is done by computing the difference between these
figures, obtaining the standard error of the difference and then forming a
confidence interval for this estimated difference as above. For the
difference between two sample estimates (totals or percentages), the standard
error is approximately the square root of the sum of the standard errors for
each estimate squared; that is for standard errors Se(x) and Se( ) of
estimated tools x and , the standard error of the differences between x and
is:
2 2
Se(x - y) = (Se(x)) + (Se(y))
The formula for the standard error between two percentages is similarly
defined.
This method will, however, overestimate the standard error if the two
estimates (x and ) are positively correlated, or underestimate the standard
error if they are negatively correlated.
Example 3: Confidence interval for a total - To illustrate the calculation
of a confidence interval consider the previous example, where the standard
error of the estimated 35,800 persons in Alaska who were 18 years and over
who speak a language other than English was found to be 2,700. An
approximate 95-percent confidence interval for this estimated total is
obtained by adding and subtracting twice the standard error from the
estimated total. In this example, the 95-percent confidence interval is:
35,800 - 2 (2,700) to 35,800 + 2 (2,700)
-or-
30,400 to 41,200
One can say with about 95-percent confidence that this interval includes the
value that would have been obtained by averaging the estimates obtainable
from all possible samples.
Selecting an appropriate sample size - One virtue in the use of Tables A to G
for calculating standard errors and confidence intervals is that this method
can be employed prior to making any sample tabulations, and thus can help the
user decide prior to purchase whether a 5-percent, 1-percent or 0.1-percent
sample size is most appropriate for a proposed study. Suppose that, in the
foregoing example, the 35,800 figure was a guess, perhaps based on published
data. The confidence interval could be calculated as above. In this case,
it is apparent that tabulating a 1-percent sample for this particular
characteristic would result in a rather broad confidence interval: 30,400 to
41,200. On the other hand, if one assumed that tabulations will be made
using a 5-percent sample instead, the confidence interval could be
recalculated using Table A, and found to be to 33,000 to 38,200, a much
narrower range. There is no particular rule of thumb that dictates how large
a confidence interval is acceptable: this depends on the relative precision
necessary for a particular application as balanced against the relative cost
of tabulating microdata samples of the various sizes.
Example 4: Confidence interval for a difference - The use of standard errors
and confidence intervals can also be illustrated for a difference of two
estimated percentages. Suppose that, from a 1% microdata sample for Hawaii,
we tally 1,997 persons 18 years and over who speak a language other than
English at home, of whom 395 speak English "not well" or "not at all". Thus,
the percentage of persons 18 years and over who speak a language other than
English at home who speak English "not well" or "not at all" is 19.8 percent.
The unadjusted standard error interpolated from Table D (using 199,700 as the
base of the percentage) is 0.96 percent. The adjustment factor is 1.5 for
"Language Usage and the Ability to Speak English" and the approximate
standard error of the percentage (19.8 percent) is 0.96 x 1.5 = 1.4
percentage points.
The difference between the percentages of persons in Alaska and Hawaii who
are 18 years and over who speak a language other than English at home, who
speak English "not well" or "not at all" is
19.8 - 12.7 = 7.1 percent.
The standard error of the difference, Se(7.1), is
2 2
Se(7.1) = (Se(19.8)) + (Se(12.7))
2 2
= (1.4) + (0.80)
= 1.6 percent.
The 95-percent confidence interval for the difference is formed as before and
is
7.1 - 2 (1.6) to 7.1 + 2 (1.6)
-or-
3.9 to 10.3.
One can say with 95-percent confidence that the interval includes the
difference that would have been obtained by averaging the results from all
possible samples. When, as in this example, the interval does not include
zero, one can conclude, again with 95-percent confidence, that the difference
observed between the two States on this characteristic is greater than can be
attributed to sampling error.
Adjusting Tables A Through F for Other Sample Sizes
Tables A through F may also be used to approximate the unadjusted standard
errors for other sample sizes by adjusting for the sample size desired. The
adjustment for sample size is obtained as follows:
let:
f1 be the sampling rate shown in any of Tables A through F.
f2 be the sampling rate for the sample size to be used.
Then the adjustment for sample size can be read from the following table.
f2 Sample Size Adjustment Factor
0.07 0.84
0.06 0.91
Multiply the standard
0.04 1.12 errors in tables A or B
(where f1 = 0.05)
0.03 1.30 by this factor.
0.02 1.61
0.009 1.06
0.007 1.20 Multiply the standard
errors in tables C or D
0.005 1.42 (where f1 = 0.01)
by this factor.
0.003 1.83
0.002 2.25
0.0009 1.05 Multiply the standard
errors in tables E or F
0.0005 1.41 (where f1 = 0.001)
by this factor.
0.0001 3.16
For example, if the user were to select a subsample of one half of a one-
percent sample, i.e., f2 = 0.005, then the standard errors shown in Table C
or D for a one-percent sample must be multiplied by 1.42 to obtain the
standard errors for a 0.005 sample. The factor of 1.42 shows that the
standard errors increase by 42 percent when the sample size is halved.
(Although migration, place of work and travel time data are available only
for one half of the sample, it is not necessary to multiply standard errors
for those figures by 1.42, since the sample reduction is already reflected in
the factors in Table G).
The principle is also applicable when combining microdata samples to achieve
a sample size larger than five percent. If, for instance, all three samples
are combined for the same area, the standard errors for this sample size
(i.e., seven percent) can be obtained by multiplying those shown in Tables A
and B by 0.84. Thus, the increase from a 5-percent to a 7-percent sample
reduces the standard error by approximately sixteen percent.
Alternatively, the user may wish to use the following formulas to directly
calculate the unadjusted standard errors.
For estimated totals, calculate
Where:
Se(Y) = (1 - 1) Y (1-Y/N) N = size of area tabulated
f2
Y = estimate (weighted) of
characteristic total
For estimated percentages, calculate
Where:
Se(p) = (1 - 1) p(100-p)
f2 B p = estimated percentage
B = base of estimated
percentage (weighted
estimate)
Estimation of Standard Errors Directly From the Microdata Samples
Use of tables or formulas to derive approximate standard errors as discussed
above is simple, inexpensive, and does not complicate processing.
nonetheless, a more accurate estimate of the standard error can be obtained
from the samples themselves using the random group method. Using this method
it is also possible to compute standard errors for means, ratios, indexes,
correlation coefficients, or other statistics for which the tables or
formulas presented earlier do not apply.
The random group method does increase processing costs somewhat since it
requires that the statistic of interest, for example a total, be computed
separately for each of up to 100 random groups. The variability of that
statistic for the sample as a whole is estimated from the variability of the
statistic among the various random groups within the sample. The procedure
for calculating a standard error by the random group method for various
statistics is given below.
Totals - to obtain the standard errors of estimated totals, the following
method should be used.
1
Let x = f (x) be the estimated total
Where:
f = the sampling fraction for the sample size used
(e.g., f = .05 for a 5 percent sample); and
(x) = the unweighted microdata sample total of the characteristic
of interest.
Then the random groups estimate of the variance of x is given by
2 t t 2
t 1 +xg-1/t( xg)+
Var(x) = (t-1) (f) g=1 g=1
Where:
t = the number of random groups selected; and
xg = the unweighted microdata sample total of the characteristic
of interest from the gth random group.
The standard error of the estimated total = the square root of the estimated
variance.
It is suggested that t=100 for estimating the standard error of a total
since, as discussed in chapter 4, each of the A, B and C Sample records was
assigned a two-digit subsample number consecutively from 00 to 99. This two-
digit number can be used to form the 100 random groups. For example, all
sample cases with 01 as the two-digit number will be in random group 1, all
sample cases with 02 as the two-digit number will be in random group 2, etc.,
up to 00 as the one-hundredth random group. Use of t = 100 will also provide
maximum reliability of the estimated standard errors.
Percentages, Ratios and Means
Percentages, Ratios and Means - To obtain the estimated standard error of a
percent, ratio, or mean, the following method should be used.
Let r = x/y be the estimated percent, ratio, or mean
Where:
x and y = the estimated totals as defined above for the x and y
characteristics.
For the case where both numerator and denominator are obtained from the full
microdata sample (i.e., all data items except place of work, travel time to
work and migration) or from the migration/place of work half sample, the
random groups estimate of the variance of is given by
r
2 t 2
t 1 (xg - ryg)
Var(r) = (t-1) (y) g=1
Where:
t and xg are as defined above,
y = the unweighted full microdata sample total for the y characteristic;
and
yg = the corresponding unweighted total for the gth random group.
If the percentage, ratio, or mean is estimated by using the migration/place
of work half sample for the numerator characteristic, and the full microdata
sample for the denominator characteristic, then the random group variance
estimator is as follows:
2 t 2
t 1 (2xg - ryg)
Var(r) = (t-1) (y) g=1
Where:
All terms are as previously defined--again, use of t = 100 is recommended.
Correlation Coefficients, and Regression Coefficients
Correlation Coefficients, and Regression Coefficients and Other Complex
Statistics - The random group method for computing the variance of
correlation coefficients, regression coefficients, and other complex
nonlinear statistics can be expressed as:
t 2
1 (0g - 0)
Var(0) = t(t-1) g=1
Where:
0g = the weighted estimate (at the tabulation area level) of the statistic
of interest computed from the gth random group; and
0 = corresponding weighted estimate computed from the full microdata
sample.
Care must be exercised when using this variance estimator for complex
nonlinear statistics as its properties have not been fully explored for such
statistics. In particular, the choice of the number of random groups to be
used must be considered more carefully. When using the 5 percent sample, use
of t = 100 for all areas tabulated is recommended. When using the 1-percent
sample or samples having a smaller sampling fraction, the user should
consider using a smaller number of random groups to insure that each random
group contains at least 25 records. Fewer than 100 random groups can be
formed by appropriate combination of the two digit subsample numbers. For
example, to construct fifty random groups assign all records in which the
subsample number is 01 or 51 to the first random group; all records in which
the subsample number is 02 or 52 to the second random group, etc. Finally,
assign all records in which the subsample number is 00 or 50 to random group
50. Ten random groups can be constructed by including all records having
subsample numbers with the same "units" digit in a particular random group.
For example, subsample numbers 00,10,20,..., 90 would form one random group;
subsample numbers 01,11,...,91 would form a second random group, etc. A more
extensive discussion of the considerations leading to the proper choice of
the number and size of the random groups is given in Hansen, Hurwitz and
Madow, Sample Surveys Methods and Theory, Vol. 1, Chapter 10, section 16,
page 440 ff.
Medians
Medians - The random group method given above is not applicable to a sample
median. Assuming the user has calculated the median from the individual
sample observations, an approximate 95-percent confidence interval can be
obtained by counting n observations to the left and right of the sample
median value,
Where:
n = the raw sample count of the number of observations on which the median
is based; and,
median = the value of the n/2 observation.
Thus, the upper and lower limits of the confidence interval are the values
for the (n/2 + n) observation and (n/2 - n) observation.
Additional Notes On Standard Errors
Two additional points concerning the standard errors calculated by the random
group method presented in this section are important. First, the estimated
standard errors obtained from this procedure do not include all portions of
the variability due to nonsampling error that may be present in the data.
Thus, the calculated standard errors represent a lower bound of the total
error. As a result, confidence intervals formed using these estimated
standard errors may not meet the stated levels of confidence (e.g., 68 or 95-
percent). Thus, some care must be exercised in the analysis of the microdata
sample data based on the estimated standard errors from the random groups
procedure.
Second, percentage estimates of zero and estimated totals of zero are subject
to both sampling and nonsampling error. While the magnitude of the error is
difficult to quantify, the user should be aware that such estimates are
nevertheless subject to both sampling and nonsampling error even though in
the case of zero estimates the corresponding random groups estimate of the
standard error will be zero.
Control of Nonsampling Error
As mentioned above, nonsampling error is present in both sample and complete-
count data. If left unchecked, this error could introduce serious bias into
the data, the variability of which could increase dramatically over that
which would result purely from sampling. While it is impossible to
completely eliminate nonsampling error from an operation as large and complex
as the 1980 census, the Bureau of the Census attempted to control the sources
of such error during the collection and processing operations. The primary
sources of nonsampling error and the programs instituted for control of this
error are described below. The success of these programs, however, was
contingent upon how well the instructions were actually carried out during
the census. To the extent possible, both the effects of these programs and
the amount of error remaining after their application will be evaluated.
Undercoverage--It is possible for some housing units or persons to be
entirely missed by the census. This undercoverage of persons and housing
units can introduce biases into the data. Several extensive programs were
developed to focus on this important problem.
The Postal Service reviewed mailing lists and reported housing unit
addresses which were missing, undeliverable, or duplicated in the
listings.
The purchased commercial mailing list was updated and corrected by a
complete field review of the list of housing units during a precanvass
operation.
A record check was performed to reduce the undercoverage of individual
persons in selected areas. Independent lists of persons, such as
driver's license holders, were matched with the household rosters in
the census listings. Persons not matched to the census rosters were
followed up and added to the census counts if they were found to have
been missed.
A recheck of housing units initially classified as vacant or non-
existent was utilized to further reduce the undercoverage of persons.
More extensive discussions of programs developed to reduce undercoverage will
be published as the analyses of those programs are completed.
Respondent and Enumerator Error - The person answering the questionnaire or
responding to the questions posed by an enumerator could serve as a source of
error by offering incorrect or incomplete information. To reduce this source
of error, questions were phrased as clearly as possible based on precensus
tests, and detailed instructions for completing the questionnaire were
provided to each housing unit. In addition, respondents' answers were edited
for completeness and consistency and followed up as necessary. For example,
if labor force items were incomplete for a person 15 years and over, long-
form field edit procedures would recognize the situation and a follow-up
attempt to obtain the information would be made.
The enumerator may misinterpret or otherwise incorrectly record information
given by a respondent; may fail to collect some of the information for a
person or housing unit; or may collect data for housing units that were not
designated as part of the sample. To control these problems, the work of
enumerators was carefully monitored. Field staff were prepared for their
tasks by using standardized training packages which included experience in
using census materials. A sample of the housing units interviewed by
enumerators for nonresponse were reinterviewed to control for the possibility
of data for fabricated persons being submitted by enumerators. Also, the
estimation procedure was designed to control for biases that would result
from the collection of data from housing units not designated for the sample.
Processing Error - The many phases of processing the census represent
potential sources for the introduction of nonsampling error. The processing
of the census questionnaires includes the field editing, follow-up, and
transmittal of completed questionnaires; the manual coding of write-in
responses; and the electronic data processing. The various field, coding and
computer operations undergo a number of quality control checks to insure
their accurate application.
Nonresponse - Nonresponse to particular questions on the census questionnaire
allows for the introduction of bias into the data, since the characteristics
of the nonrespondents have not been observed and may differ from those
reported by respondents. As a result, any allocation procedure using
respondent data may not completely reflect this difference either at the
element level (individual person or housing unit) or on the average. Some
protection against the introduction of large biases is afforded by minimizing
nonresponse. In the census, nonresponse was substantially reduced during the
field operations by the various edit and follow-up operations aimed at
obtaining a response for every question. Characteristics of the nonresponses
remaining after this operation were allocated by computer as discussed below.
Editing of Unacceptable Data
The objective of the processing operation is to produce a set of statistics
that describes the population as accurately and clearly as possible. To meet
this objective, certain unacceptable entries were edited.
In the field, questionnaires were reviewed for omissions and certain
inconsistencies by a census clerk or an enumerator and, if necessary, as
follow-up was made to obtain missing information. In addition, a similar
review of questionnaires was done in the central processing offices. As a
rule, however, editing was performed by hand only when it could not be done
effectively by machine.
As one of the first steps in editing, the configuration of marks on the
questionnaire column was scanned electronically to determine whether it
contained information for a person or merely spurious marks. If the column
contained entries for at least two of the basic characteristics
(relationship, sex, race, age, marital status, Spanish origin), the inference
was made that the marks represented a person. In cases in which two or more
basic characteristics were available for only a portion of the people in the
unit, other information on the questionnaire provided by an enumerator was
used to determine the total number of persons. Names were not used as a
criterion of the presence of a person because the electronic scanning did not
distinguish any entry in the name space.
If any characteristics for a person were still missing when the
questionnaires reached the central processing offices, they were supplied by
allocation. Allocations, or assignments of acceptable codes in place of
unacceptable entries, were needed most often when the entry for the given
item is lacking or when the information reported for a person on that item
was inconsistent with entries for other persons with similar characteristics.
Thus, a person who was reported as a 20-year-old son of the householder, but
for whom marital status was not reported, was assigned the same marital
status as that of the last son processed in the same age group. The
assignment of acceptable codes in place of blanks or unacceptable entries, it
is believed, enhances the usefulness of the data.
The 1980 census data on the economic questions such as industry, occupation,
class of worker, work experience, and income were processed using an
allocation system which assigned values to missing entries in these
questions, as necessary, from a single respondent with similar socioeconomic
characteristics. In the 1970 census, allocation of each of the economic
items was conducted separately; thus, assigned values could come from more
than one respondent.
Prior to the allocation of all economic variables, the computer records were
sorted according to such characteristics as sex, race and ethnicity,
household relationship, years of school completed, and geographic area. The
actual allocation operation was implemented in the following manner:
1. The computer stored, in a series of matrices, reported economic
information of persons by selected characteristics such as age,
disability status, presence of children, veteran's status, employment
status, occupation, industry, class of worker status, work experience
in 1979, level of earnings in 1979, and value of property or monthly
rent.
2. The stored entries in the various matrices were retained in the
computer only until a succeeding person having the same set of
characteristics was processed through the computer. Then the
economic question responses of the succeeding person were stored in
place of those previously stored.
3. When one or more of the economic questions was not reported, or the
entry was unacceptable, the variables assigned to this person were
those stored in the appropriate matrix for the last person who
otherwise had the same set of characteristics.
The use of this single allocation system ensured that the distribution of
economic variable assignments would correspond closely to the entries of
persons who had actually reported in the census.
Specific tolerances were established for the number of computer allocations
and substitutions that would be permitted. If the number of corrections was
beyond tolerance, the questionnaires in which the errors occurred were
clerically reviewed. If it was found that the errors resulted from damaged
questionnaires, from improper microfilming, from faulty reading by FOSDIC of
undamaged questionnaires, or from other types of machine failure, the
questionnaires were reprocessed.
The impact of the editing performed on 1980 census data can be gauged by
reviewing allocation tables in selected reports based on the complete count-
-PC80-1-B tables B-1 to B-4 and HC80-1-A tables A-1 and A-2-- and from the
full census sample--PC80-1-C tables C-1 to C-5 and HC80-1-B tables B-2 and B-
2. Most of these tables provide rates of allocation for the various items.
Two tables (PC80-1-B:B-1 and PC80-1-C:C-2) allow comparison of simple
distributions as published (i.e., "after allocation") with corresponding
distributions where missing values were not imputed (i.e., "before
allocation"). An additional editing process, substitution, referred to in
the complete-count reports, is not applicable to data from the full census
sample or public-use microdata samples.
Use of Allocation Flags in These Files
As a result of the editing there are no blank fields or missing data in
public-use microdata sample files. Each field contains a data value or a
"not applicable" indicator, except for the few items where allocation was not
appropriate and a "not reported" indicator is included. For every subject
item it is possible for the user to differentiate between entries which were
actually reported by the respondent and entries which were allocated, by
means of "allocation flags" in terms H117 through H162 and P140 through P193
in the microdata files. For all items it is possible to compute the
allocation rate and, if the rate is appreciable, to compute the distribution
of actually observed values (with allocated data omitted) and compare it with
the overall distribution including allocated values.
Descriptions of many of the allocation flags indicate more than one possible
type of allocation. "Consistency edits" or "assignments" imputed missing
characteristics based on other information recorded for the person or housing
unit; for example, if the marital status was missing for a person with a
reported relationship of husband/wife, the imputation of "married" was termed
a consistency edit. "Hot deck" allocation supplied the missing information
from the record of another person or housing unit with similar
characteristics. "Cold deck" allocation, employed for only a few items,
supplied missing information from a predetermined distribution; for example,
a missing quarter of birth was assigned at random with equal probability
given to each of the possible 4 categories. Those flags designated "pre-
edit" indicate that the original entry was rejected because it fell outside
the range of acceptable values.
In general, the allocation procedures provide better data than could be
obtained by simply weighting up the observed distribution to account for
missing values. The procedures reflect local variations in characteristics
as well as variation among the strata used in imputation. There are,
however, certain circumstances where allocated data may introduce undesirable
bias. It may be particularly important to analyze allocations of data in
detailed studies of subpopulations or in statistics derived from cross-
classification of variables, such as correlation coefficients or measures of
regression. The degree of editing required was greater for some subjects
than for others. While the allocation procedure was designed to yield
appropriate statistics for the overall distribution or for specific
subpopulations (the strata used in the allocation process), allocated
characteristics will not necessarily preserve a valid relationship with other
observed variables for the same individual. For example, consider a
tabulation of persons 80 years old and over by income. Income allocations
were made separately for different age groupings, including the category 65
years old and over, but not separately for persons 80 years old and over.
Since persons 65 to 70 or 75 are more likely to have significant earnings
than persons 80 or over, allocated income data for the latter group would be
biased upward. Thus, if the rate of allocations for the group is appreciably
large and a bias in the allocated values is evident, it may be desirable to
exclude allocated data from the analysis.
It should also be apparent from this illustration that knowledge of the
specific allocation procedures is valuable in detailed subject analysis.
Descriptions of the editing and allocation procedures for each item are
being incorporated in the History of the 1980 Census of Population and
Housing to be published in 1985. An advance copy of the procedural
descriptions for desired items can be requested from the Census History
Staff, Data User Services Division, Bureau of the Census, Washington, D.C.
20233. A user may contact either Population Division or Housing Division,
Bureau of the Census, if more information is desired on the allocation scheme
for a specific subject item.
SAMPLE DESIGN FOR THE PUBLIC USE MICRODATA SAMPLES
General Information
This chapter discusses the selection procedure for the public-use microdata
samples in terms of three major operations (1) the selection of the full 1980
census sample, (2) the estimation procedure for the full census sample, and
(3) the selection of the public-use microdata samples from the persons and
housing units included in the full 1980 census sample, using weights derived
from the full sample estimation procedure.
1980 Census Sample Design and Estimation Procedure
While every person and housing unit in the United States was enumerated on a
questionnaire that requested certain basic demographic information (e.g.,
age, race, relationship), a sample of persons and housing units was
enumerated on a questionnaire that requested additional information. The
basic sampling unit for the 1980 census was the housing unit, including allz
occupants. For persons living in group quarters, the sampling unit was the
person. Two sampling rates were employed. In counties, incorporated places
and minor civil divisions estimated to have fewer than 2,500 persons (based
on precensus estimates), one-half of all housing units and persons in group
quarters were to be included in the sample. In all other places, one-sixth
of the housing units or persons in group quarters were sampled. The purpose
of this scheme was to provide relatively more reliable estimates for small
places. When both sampling rates were taken into account across the Nation,
approximately 19 percent of the Nation's housing units were included in the
census sample.
The sample designation method depended on the data collection procedures. In
about ninety-five percent of the country the census was taken by the
mailout/mailback procedure. For these areas, the Bureau of the Census either
purchased a commercial mailing list which was updated and corrected by Census
Bureau field staff, or prepared a mailing list by canvassing and listing each
address in the area prior to Census Day. These lists were computerized, and
every sixth unit (for 1-in-6 areas) or every second unit (for 1-in-2 areas)
was designated as a sample unit by computer. Both of these lists were also
corrected by the Post Office.
In non-mailout/mailback areas, a blank listing book with designated sample
lines (every sixth or every second line) was prepared for the enumerator.
Beginning about Census Day, the enumerator systematically canvassed the areas
and listed all housing units in the listing book in the order they were
encountered. Completed questionnaires, including sample information for any
housing unit which was listed on a designated sample line, were collected.
In both types of data collection procedure areas, an enumerator was
responsible for a small geographic area known as an enumeration district, or
ED. An ED usually represented the average workload area for one enumerator.
In order to reduce the cost of processing the full census sample, a scheme
was designed, while the sample questionnaires were being processed, to select
a sample of questionnaires on which the travel time to work, place of work
and migration data items would be coded (hereafter referred to as POW/MIG
items). The sample questionnaires were processed by work units consisting of
1980 census EDs. In work units (EDs) where these data items had not yet been
coded, every second sample questionnaire within the work unit was selected
for these coding operations. In work units where the POW/MIG data items
already had been coded, all sample questionnaires were included in
tabulations.
Estimation Procedure For Published Sample Data
The estimates which appear in census sample publications were obtained from
an iterative ratio estimation procedure which resulted in the assignment of a
weight to each sample person or housing unit record. For any given
tabulation area, a characteristic total was estimated by summing the weights
assigned to the persons or housing units in the tabulation area which
possessed the characteristic. Estimates of family characteristics were based
on the weights assigned to the family members designated as householders.
Each sample person or housing unit record was assigned one weight to be used
to produce estimates of all characteristics. (Persons with the migration,
travel time to work, and place of work characteristic received an additional
weight.) For example, if the weight given to a sample person or housing unit
had the value five, all characteristics of that person or housing unit would
be tabulated with a weight of five. The estimation procedure, however, did
assign weights which vary from person to person or housing unit to housing
unit.
The estimation procedure used to assign the weights was performed in
geographically defined "weighting areas." Weighting areas were generally
formed of adjoining portions of geography, which closely agreed with census
tabulation areas within counties. Weighting areas were required to have a
minimum sample of 400 persons. Weighting areas were never allowed to cross
state or county boundaries. In small counties with a sample count of less
than 400 persons, the minimum required sample condition was relaxed to permit
the entire county to become a weighting area.
Within a weighting area, the ratio estimation procedure for persons was
performed in three states. For persons the first stage employed seventeen
household-type groups. The second stage used two groups: householders and
non-householders. The third stage could potentially use 160 age-sex-race
-Spanish origin groups. The stages were as follows:
Stage I - Type of Household
Group Persons in Housing Units with a Family with Own Children under 18.
1 2 persons in housing unit
2 3 persons in housing unit
3 4 persons in housing unit
4 5 to 7 persons in housing unit
5 8-or-more persons in housing unit
Persons in Housing Units with a Family without Own Children under 18.
6-10 2 persons in housing unit through 8-or-more persons in
in housing unit
Persons in All Other Housing Units.
11 1 person in housing unit
12-16 2 persons in housing unit through 8-or-more persons in
in housing unit
17 Persons in Group Quarters.
Stage II - Householder/Nonhouseholder
Group
1 Householder
2 Nonhouseholder (including persons in group quarters)
Stage III - Age/Sex/Race/Spanish Origin
Group
White Race
Persons of Spanish Origin
Male
1 0 to 4 years of age
2 5 to 14 years of age
3 15 to 19 years of age
4 20 to 24 years of age
5 25 to 34 years of age
6 35 to 44 years of age
7 45 to 64 years of age
8 65 years of age or older
Female
9-16 Same age categories as groups 1 to 8
Persons Not of Spanish Origin
17-32 Same age and sex categories as groups 1 to 16
Black Race
33-64 Same age/sex/Spanish Origin categories as groups 1 to 32
Asian, Pacific Islander Race
65-96 Same age/sex/Spanish Origin categories as groups 1 to 32
Indian (American) or Eskimo or Aleut Race
97-128 Same age/sex/Spanish Origin categories as groups 1 to 32
Other Race (includes those races not listed above)
129-160 Same age/sex/Spanish Origin categories as groups 1 to 32
Within a weighting area, the first step in the estimation procedure was to
assign each sample person record an initial weight. This weight was
approximately equal to the inverse of the probability of selecting a person
for the census sample, for example 6 in a 1-in-6 area.
The next step in the estimation procedure was to combine, if necessary, the
groups within each of the three stages prior to the repeated ratio estimation
in order to increase the reliability of the ratio estimation procedure. For
the first and second stages, any group that did not meet certain criteria
concerning the unweighted sample count or the ratio of the complete count to
the initially weighted sample count, was combined, or collapsed, with another
group in the same stage according to a specified collapsing pattern. At the
third stage, the "Other" race category was collapsed with the "White" race
category before the application of the above collapsing criteria as well as
an additional criterion concerning the number of complete count persons in
each category.
As a final step, the initial weights underwent three stages of ratio
adjustment which used the groups listed above. At the first stage, the ratio
of the complete census count to the sum of the initial weights for each
sample person was computed for each stage I group. The initial weight
assigned to each person in a group was then multiplied by the stage I group
ratio to produce an adjusted weight. In stage II, the stage I adjusted
weights were again adjusted by the ratio of the complete census count to the
sum of the stage I weights for sample persons in each stage II group.
Finally, the stage II weights were adjusted at stage III by the ratio of the
complete census count and the sum of the stage II weights for sample persons
in each stage III group. The three stages of adjustment were performed twice
(two iterations) in the order given above. The weights obtained from the
second iteration for stage III were assigned to the sample person records.
However, to avoid complications in rounding for tabulated data, only whole
number weights were assigned. For example, if the final weight for the
persons in a particular group was 7.2, then one-fifth of the sample persons
in this group were randomly assigned a weight of 8 and the remaining four-
fifths received a weight of 7.
Separate weights were derived for tabulating the travel time to work, place
of work, and migration data items. The weights were obtained by adjusting
the weight derived above for persons on questionnaires selected for coding by
the reciprocal of the ED coding rate and a ratio adjustment to ensure that
the sum of the weights and the complete-count total population figure would
agree.
The ratio estimation procedure for housing units was essentially the same as
that for persons. The major difference was that the occupied housing unit
ratio estimation procedure was done in two stages and the vacant housing unit
ratio estimation procedure was done in one stage. The first stage for
occupied housing units employed sixteen household type categories and the
second stage could potentially use 190 tenure-race-Spanish origin-value/rent
groups. For vacant housing units three groups were utilized. The stages for
the ratio estimation for housing units were as follows:
Occupied housing units
Stage I - Type of Household
Group
1 Housing Units with a Family with Own Children under 18
2 2 persons in housing unit
3 3 persons in housing unit
4 4 persons in housing unit
5 5 to 7 persons in housing unit
8-or-more persons in housing unit
Housing Units with a Family Without Own Children under 18
6-10 2 persons in housing unit through 8-or-more persons in
in housing unit
All Other Housing Units
11 1 person in housing unit
12-16 2 persons in housing unit through 8-or-more persons in
in housing unit
Stage II - Tenure/Race and Origin of Householder/Value or Rent
Owner
White race (Householder)
Group Persons of Spanish origin (Householder)
Value of house
1 $ 0 - $ 9,999
2 $ 10,000 - $ 19,999
3 $ 20,000 - $ 24,999
4 $ 25,000 - $ 49,999
5 $ 50,000 - $ 99,999
6 $100,000 - $149,999
7 $150,000 +
8 Other Owners
Persons not of Spanish Origin
9-16 Same value categories as groups 1 to 8
Black race
17-32 Same value - Spanish origin categories as groups 1 to 16
Asian, Pacific Islander Race
33-48 Same value - Spanish origin categories as groups 1 to 16
Indian (American) or Eskimo or Aleut Race
49-64 Same value - Spanish origin categories as groups 1 to 16
Other Race (includes those races not listed above)
65-80 Same value - Spanish origin categories as groups 1 to 16
Renter
White Race
Persons of Spanish origin
Rent categories
81 $ 1 - $ 59
82 $ 60 - $ 99
83 $100 - $149
84 $150 - $199
85 $200 - $249
86 $250 - $299
87 $300 - $399
88 $400 - $499
89 $500 +
90 Other Renter
91 No Cash Rent
Persons not of Spanish origin
92-102 Same rent categories as groups 81 to 91
Black Race
103-124 Same rent - Spanish origin categories as groups 81 to 102
Asian, Pacific Islander Race
125-146 Same rent - Spanish origin categories as groups 81 to 102
Indian (American) or Eskimo or Aleut Race
147-168 Same rent - Spanish origin categories as groups 81 to 102
Other Race (includes those races not listed above)
169-190 Same rent - Spanish origin categories as groups 81 to 102
Vacant housing units
1 Vacant for Rent
2 Vacant for Sale
3 Other Vacant
The estimates produced by this procedure realize some of the gains in
sampling efficiency that would have resulted if the population had been
stratified into the ratio estimation groups before sampling, and the sampling
rate had been applied independently to each group. The net effect is a
reduction in both the standard error and the possible bias of most estimated
characteristics to levels below what would have resulted from simply using
the initial (unadjusted) weight. A by-product of this estimation procedure
is that the estimates from the sample will, for the most part, be consistent
with the complete-count figures for the population and housing unit groups
used in the estimation procedure.
Selection of the Public-Use-Microdata Samples
A stratified systematic selection procedure with probability proportional to
a measure of size was used to select each public-use microdata sample. The
sampling elements were the occupied housing unit including all occupants, the
person in group quarters or the vacant housing unit. The measure of size was
the full sample weight that resulted from the 1980 census ratio estimation
procedure described above.
It was also necessary to employ a subsampling scheme to yield microdata
samples with a consistent proportion of cases, from area to area, for which
place of work, travel time and migration were coded. The subsampling scheme
resulted in the occasional designation of selected microdata sample elements
for which the place of work, travel time and migration information was
blanked. This subsampling scheme was instituted so that the POW/MIG data
would be uniformly available for one-half of all microdata cases, not half in
most areas but more than half in other areas. Thus, each 1-percent microdata
sample gives a 0.5-percent sample of records containing POW/MIG data, and the
5-percent microdata sample gives a 2.5-percent sample for POW/MIG data. The
subsampling scheme was also based on a probability-proportional-to-size
sampling scheme which utilized measures of size based on both the POW/MIG
half-sample and full sample weights.
The sample selection procedures were as follows. First, the sample units
were stratified during the selection process. This stratification was
intended to improve the reliability of the 5-percent, 1-percent, and 0.1-
percent samples by defining strata within which there is an appreciable
degree of homogeneity among the census sample households with respect to
characteristics of major interest.
A total of 102 strata were defined: 72 strata for persons living in occupied
housing units; 24 strata for persons in group quarters (GQ); and 6 strata for
vacant housing units. The strata are shown on Figures 6, 7, and 8.
The sample selection procedures were applied on a state-by-state basis to
obtain the microdata sample. Briefly, for any particular state, the
procedure to accomplish the sample selection consisted of creating a number
of cells in the computer which correspond to each of the strata defined
above. A random value was assigned to each cell and the sample edited detail
file (i.e., the internal-use microdata from the full census sample) was then
passed and the appropriate weight from each sample housing unit/GQ person was
cumulated into the cell corresponding to the appropriate stratum for each
unit/person. For occupied housing units, the full sample person weight
assigned to the householder of the unit was used. For GQ persons, the full
sample person weight was used, while for vacant housing units, the full
sample housing unit weight was used.
For a given 1-percent sample, when a unit/person caused the cumulation to
exceed 100, that unit/person was designated for the sample, and the value of
the cell was reset. The procedure was then repeated. For the 5-percent
sample selection, the procedure was the same except that the cumulation cut-
off was 20 instead of 100. The starting value of each cell was set so as to
minimize the likelihood that any one case would be selected into more than
one public-use microdata sample, and the overlap among the samples may be
considered negligible. There is a small probability that a given individual
unit (one with a high census weight) may have been selected into the 5-
percent sample more than once, but this duplication should not have any
particularly undesirable consequences.
The POW/MIG subsampling operation was performed by first assigning each
selected microdata unit, from the POW/MIG coded strata, a measure equal to
the ratio of the POW/MIG half-sample weight to the full sample weight for the
selected microdata unit. These measures were cumulated from the selected
microdata sample units until the cumulation exceeded 2. The POW/MIG data for
the units which caused the cumulation to exceed 2 was retained; otherwise,
the POW/MIG information was blanked.
Selection of One-in-One-Thousand and Other Subsamples
During the sample selection operation, consecutive two-digit subsample
numbers from 00 to 99 were assigned to each sample case in the five-percent
and one-percent samples to allow for the designation of various size
subsamples and as discussed in chapter 3 to allow for the calculation of
standard errors. As an example, for a B or C one-percent public-use
microdata sample, the choice of records having subsample numbers with the
same "units" digit (e.g., the ones "units" digit includes subsample numbers
01, 11, 21, ... 91) will provide a one-in-one-thousand subsample.
The Bureau has chosen one one-in-one thousand subsample from each of the A,
B, and C public-use microdata samples. The one-in-one-thousand subsample
from the A Sample was obtained by selecting those records with a subsample
number of 13 or 63. The one-in-one-thousand subsamples from the B and C
Samples were obtained by selecting those records with subsample number having
a units digit of 4 on the B Sample, or a units digit of 9 on the C Sample,
ignoring the tens digit of the two-digit subsample number.
Samples of any size between 1/20 and 1/10,000 may be selected in a similar
manner by using appropriate two-digit subsample numbers assigned to the A, B,
or C microdata samples. Care must be exercised when selecting such samples.
If only one "units" digit is required, the "units" digit should be randomly
selected. If two "units" digits are required, the first should be randomly
selected. If two "units" digits are required, the first should be randomly
selected and the second should be either five more or five less than the
first. Failure to use this procedure, e.g., selection of records with the
same "tens" digit instead of records with the same "units" digit, would
provide a one-in-ten subsample but one that would be somewhat more clustered
and as a result subject to larger sampling error.
RECORD CONTENTS
General Information
This chapter, in conjunction with several appendices, defines the record
layout and applicable codes for the public-use microdata samples. the
detailed data dictionary begins on page 55 for the housing record and page 75
for the person record, with explanatory notes on page 54.
An index to the basic data items begins below, followed on page 50, by an
index to allocation items. Compact lists in numerical sequence of the items
on the housing and person records appear on pages 52 and 53. In these
introductory pages, data fields are specified in the form "H9" or "P12-13,"
where the letter indicates the Housing or Person record and the numbers
indicate the character positions occupied on that record. For example, "P12-
13" is a two-character field beginning in character 12 of the person record.
In the data dictionary itself, the "P" or "H" designation appears only at the
top of the page, and location is expressed in terms of two separate elements,
the beginning location and the size.
Index To Items
Location Mnemonic Description
P31 ENGLISH Ability to Speak English
P86 ABLE Able to Take Job Last Week
P84 ABSENT Absent From Work Last Week
H40 ACCESS Access
H60 ACREAGE1 Acreage of Property (H10a on questionnaire)
H61 ACREAGE2 Acreage of Property (H15a on questionnaire)
P44 COLL75 Activity Status in 1975: Attending College
P43 AF75 Activity Status in 1975: In Armed Forces
P45 WORK75 Activity Status in 1975: Working at a job or
Business
P8-9 AGE Age
P10 QTRBIRTH Age: Quarter of Birth
P35-36 AGEMAR Age at First Marriage
P37 QTRMAR Age at First Marriage: Quarter
H41 YRBUILT Age of Structure
H28 GQTYPE Aged, Inmate of Home for
H51 AIRCOND Air Conditioning
H117-H162 Allocation Flags for Housing Items (For detail,
see page 50.)
P140-P193 Allocation Flags for Population Items (For
detail, see page 50.)
P16-18 ANCSTRY1 Ancestry--1st entry
P19-21 ANCSTRY2 Ancestry--2nd entry
H36-37 UNITS1 Apartments
H9 AREATYPE Area, Type of
P81 LABOR Armed Forces Status
P43 AF75 Armed Forces Status in 1975
H56 AUTOS Automobiles Available
H48 BATHROOM Bathrooms
H45 BEDROOMS Bedrooms
P22-24 BIRTHPL Birth, Place of
H32 VACANCY3 Boarded Up Status
P106-110 INCOME2 Business Income in 1979
P87-89 INDUSTRY Business, Type of (Industry)
P68 RIDERS Carpool Occupancy
P67 CARPOOL Carpooling
H9 AREATYPE Central City Residence
P61 POWCC Central City Recode: Place of Work
(C Sample only
P2-3 RELAT1 Children
H105 CHILDREN Children, Own, Presence and Age of
P32-33 FERTILITY Children Ever Born
P25 CITIZEN Citizenship
P93 CLASS Class of Worker
P39, 40-41 SCHOOL College Attendance
P44 COLL75 College Attendance in 1975
H28 GQTYPE College Dormitory, Person in
H63 COMMUSE Commercial Establishment or Medical Office on
Property
P65-66 MEANS Commuting to Work
H35 CONDO Condominium Status
H99-100 RENT1 Contract Rent
H55 FUELCOOK Cooking Fuel
H28 GQTYPE Correctional Institution, Inmate of
P22-24 BIRTHPL Country of Birth
H6-8 COGRP County Group (A and B Samples only)
P57-59 POWCOGRP County Group: Place of Work (A and B Samples)
P49-51 COGRP75 County Group: Residence in 1975
(A and B Samples)
P52 MIG75 County-State Recode: Residence in 1975
P4 RELAT2 Detailed Relationship
P69 DISABIL1 Disability Which Limits Work
P71 DISABIL3 Disability Which Limits or Prevents Use of
Public Transportation
P70 DISABIL2 Disability Which Prevents Work
P116-120 INCOME4 Dividend, Interest, or Net Rental Income in 1979
H3 DIVISION Division
H33 VACANCY4 Duration of Vacancy
P111-115 INCOME3 Earnings in 1979: From Farm Self-Employment
P106-110 INCOME2 Earnings in 1979: From Nonfarm Self-Employment
P101-105 INCOME1 Earnings in 1979: From Wage or Salary
P42 FINGRADE Education: Finished Highest Grade
P40-41 GRADE Education: Highest Year of School Attended
P39 SCHOOL Education: School Enrollment and Type of School
H67-69 ELECCOST Electricity, Monthly Cost of
H66 ELECPAID Electricity, Payment of
H43 ELEVATOR Elevator, Passenger
Employment in 1979 - See Work in 1979
P81 LABOR Employment Status
P31 ENGLISH English, Ability to Speak
H112-116 FAMINCOM Family Income in 1979
P2-3 RELAT1 Family Membership
H104 HHTYPE Family Type
P111-115 INCOME3 Farm Self-Employment Income in 1979
H62 FARM Farm Status and Sales of Farm Products
Farm Workers - See: Occupation
P32-33 FERTILITY Fertility: Children Ever Born
P42 FINGRADE Finished Highest Grade
P22-24 BIRTHPL Foreign Country of Birth
H55 FUELCOOK Fuel, Cooking
H53 FUELHEAT Fuel, House Heating
H54 FUELWTR Fuel, Water Heating
P82-83 HOURS Full Time-Part Time Work: Hours Worked Last Week
P79-98 HOURS79 Full Time-Part Time Work in 1979: Usual Hours
Worked Per Week
H71-73 GASCOST Gas, Monthly Cost of
H70 GASPAID Gas, Payment of
Government Workers - See: Class of Worker
H101-103 RENT2 Gross Rent
P2-3 RELAT1 Group Quarters Status
H28 GQTYPE Group Quarters, Type of
H52 HEATING Heating Equipment
P40-41 GRADE Highest Year of School Attended
H29 TENURE Home Ownership
P82-83 HOURS Hours Worked Last Week
P97-98 HOURS79 Hours Worked Per Week in 1979, Usual
H53 FUELHEAT House Heating Fuel
H107-111 HHINCOME Household Income in 1979
P2-3 RELAT1 Household Relationship
P4 RELAT2 Household Relationship, Detailed
H26-27 PERSONS Household Size
H104 HHTYPE Household Type
H20-25 SERIALNO Housing Unit/GQ Person Serial Number
P26 IMMIGR Immigration, Year of
H94 INSINCL Inclusion of Insurance Premiums in Payment to
Lender
H93 TAXINCL Inclusion of Real Estate Taxes in Payment to
Lender
P134-138 INCOME8 Income From All Sources in 1979
P129-133 INCOME7 Income in 1979: All Other
H112-116 FAMINCOM Income in 1979: Family
P111-115 INCOME3 Income in 1979: Farm Self-Employment
H107-111 HHINCOME Income in 1979: Household
P116-120 INCOME4 Income in 1979: Interest, Dividend and Net
Rental
P106-110 NSUBFAM Income in 1979: Nonfarm Self-Employment
P125-128 INCOME6 Income in 1979: Public Assistance
P121-124 INCOME5 Income in 1979: Social Security
P101-105 INCOME1 Income in 1979: Wage or Salary
P139 POVERTY Income Ratio to Poverty Level
P87-89 INDUSTRY Industry
P2-3, H28 RELAT1 Inmate Status
H28 GQTYPE Institution, Type of
H83-86 TAXINSUR Insurance Premiums and Real Estate Taxes
Combined
H94 INSINCL Insurance Premiums, Inclusion in Payment to
Lender
P116-120 INCOME4 Interest, Dividend, and Net Rental Income in 1979
H47 KITCHEN Kitchen Facilities
P81 LABOR Labor Force Status
P27 LANG1 Language Usage
P28-30 LANG2 Language Spoken at Home
P31 ENGLISH Language: Ability to Speak English
P85 LOOKING Looking for Work
P35-36 AGEMAR Marital History: Age at First Marriage
P37 QTRMAR Marital History: Quarter of First Marriage
P34 TIMESMAR Marital History: Times Married
P38 WIDOWED Marital History: Widowed
P11 MARITAL Marital Status
H104 HHTYPE Married-Couple Families
P65-66 MEANS Means of Transportation to Work
H28 GQTYPE Mental Hospital, Inmate of
H9 AREATYPE Metropolitan Residence (A and B Samples)
Migration - See: Residence in 1975; Year
Householder Moved Into Unit
P46 MIGWGT Migration/Place of Work/Travel Time Weight
Military - See Labor Force Status;
Armed Forces Status in 1975; Veteran Status;
Period of Service
H28 GQTYPE Military Quarters
H36-37 UNITS1 Mobile Homes
H67-69 ELECCOST Monthly Cost of Electricity
H71-73 GASCOST Monthly Cost of Gas
H87 MORTGAG1 Mortgage Status
H88 MORTGAG2 Mortgage: Second or Junior
H89-92 MORTGAG3 Mortgage: Total Monthly Payment to Lender
P25 CITIZEN Nativity
P106-110 INCOME2 Nonfarm Self-Employment Income in 1979
H26-27 PERSONS Number of Person Records Following This Housing
Unit Record
H106 NSUBFAM Number of Subfamilies in Family
P90-92 OCCUP Occupation
H78 FUELPAID Oil, Coal, Kerosene, Wood, etc., Payment of
H79-82 FUELCOST Oil, Coal, Kerosene, Wood, etc., Yearly Cost of
H105 CHILDREN Own Children, Presence and Age of
H95-98 OWNERCST Owner Costs, Selected Monthly
H29 TENURE Owner/Renter Status
H43 ELEVATOR Passenger Elevator
H66 ELECPAID Payment of Electricity
H70 GASPAID Payment of Gas
H78 FUELPAID Payment of Oil, Coal, Kerosene, Wood, etc.
H74 WTRPAID Payment of Water
P75 VETERAN4 Period of Service Between February 1955 and
July 1964
P79 VETERAN8 Period of Service During Any Other Time
P76 VETERAN5 Period of Service During Korean Conflict
(June 1950-January 1955)
P74 VETERAN3 Period of Service During Vietnam Era
(August 1964-April 1975)
P78 VETERAN7 Period of Service During World War I
(April 1917-November 1918)
P77 VETERAN6 Period of Service During World War II
(September 1940-July 1947)
P73 VETERAN2 Period of Service May 1975 or later
H26-27 PERSONS Persons in Household
P22-24 BIRTHPL Place of Birth
P46 MIGWGT Place of Work/Migration/Travel Time Weight
P61 POWCC Place of Work: Central City Recode (C
Sample only)
P57-59 POWCOGRP Place of Work: County Group (A and B Samples)
P62 POWPLSIZ Place of Work: Place Size (C Sample only)
P60 POWMETRO Place of Work: SMSA Recode (A and B Samples)
P55-56 POWSTATE Place of Work: State
P62 POWPLSIZ Place Size: Place of Work (C Sample only)
H46 PLUMBING Plumbing Facilities
P139 POVERTY Poverty Status in 1979
H105 CHILDREN Presence and Age of Own Children
P68 RIDERS Private Vehicle Occupancy
P125-128 INCOME6 Public Assistance Income in 1979
P71 DISABIL3 Public Transportation Disability Status
P65-66 MEANS Public Transportation to Work
P10 QTRBIRTH Quarter of Birth
P37 QTRMAR Quarter of First Marriage
P12-13 RACE Race
H18-H19 SUBSAMPL Random Group Subsample Number
P139 POVERTY Ratio of Family or Unrelated Individual Income
to Poverty Cutoff in 1979
H83-86 TAXINSUR Real Estate Taxes and Insurance Premiums Combined
H93 TAXINCL Real Estate Taxes: Inclusion in Payment to
Lender
H1/P1 RECTYPE Record Type
H3 DIVISION Region/Division
P2-3 RELAT1 Relationship
P4 RELAT2 Relationship, Detailed
H99-100 RENT1 Rent, Contract
H101-103 RENT2 Rent, Gross
P116-120 INCOME4 Rental, Net, Dividend, and Interest Income
in 1979
H29 TENURE Renter/Owner Status
P49-51 COGRP75 Residence in 1975: County Group
(A and B Samples)
P53-54 METRO75 Residence in 1975: SMSA Recode
(A and B Samples)
P47-48 STATE75 Residence in 1975: State
P52 MIG75 Residence in 1975: State-County Recode
H28 GQTYPE Rooming House, Person in
H44 ROOMS Rooms
H9 AREATYPE Rural Residence (C Sample only)
H62 FARM Sales of Farm Products
H2 SAMPLE Sample Identifier
P39 SCHOOL School Enrollment and Type of School
P42 FINGRADE School: Finished Highest Grade
P40-41 GRADE School: Highest Year Attended
H30 VACANCY1 Seasonal and Migratory Vacancy Status
H88 MORTGAG2 Second or Junior Mortgage
H95-98 OWNERCST Selected Monthly Owner Costs
Self-Employed Workers - See: Class of Worker
Self-Employed Earnings - See: Income in 1979
H20-25 SERIALNO Serial Number for Housing Unit/GQ Person
H50 SEWAGE Sewage Disposal
P7 SEX Sex
H26-27 PERSONS Size of Household
H10-13 SMSA SMSA (A and B Samples only)
P53-54 METRO75 SMSA Recode: Residence in 1975 (A and B Samples)
P60 POWMETRO SMSA Recode: Place of Work (A and B Samples)
P121-124 INCOME5 Social Security Income in 1979
H49 WATER Source of Water
P14 SPANISH Spanish Origin
P15 SURNAME Spanish Surname
P22-24 BIRTHPL State of Birth
P52 MIG75 State-County Recode: Residence in 1975
H4-5 STATE State
P55-56 FUELCOOK State: Place of Work
P47-48 KITCHEN State: Residence in 1975
H42 STORIES Stories in Structure
H106 NSUBFAM Subfamilies in Family, Number of
P6 SUBFAM2 Subfamily Number
P5 SUBFAM1 Subfamily Relationship
H18-19 SUBSAMPL Subsample Number
H83-86 TAXINSUR Taxes, Real Estate and Insurance Premiums
Combined
H58 TELEPHON Telephone in Housing Unit
H29 TENURE Tenure
P34 TIMESMAR Times Married
H89-92 MORTGAG3 Total Monthly Payment to Lender
P65-66 MEANS Transportation to Work, Means of
P63-64 TIME Travel Time to Work
P46 MIGQGT Travel Time/Place of Work/Migration Weight
H57 TRUCKS Trucks and Vans Available
H9 AREATYPE Type of Area
H28 GQTYPE Type of Group Quarters
P39 SCHOOL Type of School
P81 LABOR Unemployment
P99-100 WEEKSU79 Unemployment in 1979: Weeks Unemployed
P86 ABLE Unemployment: Able to Take Job Last Week
P84 ABSENT Unemployment: Absent From Work Last Week
P85 LOOKING Unemployment: Looking for Work
H38-39 UNITS2 Units at Address
H36-37 UNITS1 Units in Structure
H9 AREATYPE Urban/Rural Status (C Sample Only)
H14-17 UA Urbanized Area (C Sample Only)
H34 UHE Usual Home Elsewhere
P97-98 HOURS79 Usual Hours Worked Per Week in 1979
H33 VACANCY4 Vacancy, Duration of
H32 VACANCY3 Vacancy Status, Boarded Up
H30 VACANCY1 Vacancy Status, Seasonal and Migratory
H31 VACANCY2 Vacancy Type
H64-65 VALUE Value
H57 TRUCKS Vans and Trucks Available
P68 RIDERS Vehicle Occupancy: Carpool
H56 AUTOS Vehicles Available: Automobiles
H57 TRUCKS Vehicles Available: Trucks and Vans
P72 VETERAN1 Veteran Status
P75 VETERAN4 Veteran's Period of Service: Between
February 1955 and July 1964
P79 VETERAN8 Veteran's Period of Service: During Any Other
Time
P76 VETERAN5 Veteran's Period of Service: During Korean
Conflict (June 1950-January 1955)
P74 VETERAN3 Veteran's Period of Service: During Vietnam Era
(August 1964-April 1975)
P78 VETERAN7 Veteran's Period of Service: During World War I
(April 1917-November 1918)
Wage and Salary Workers - See Class of Worker
P101-105 INCOME1 Wage and Salary Income in 1979
H54 FUELWTR Water Heating Fuel
H74 WTRPAID Water, Payment of
H49 WATER Water, Source of
P99-100 WEEKSU79 Weeks Unemployed in 1979
P95-96 WEEKSW79 Weeks Worked in 1979
P125-128 INCOME6 Welfare Income in 1979
P11 MARITAL Widowed (Current Status)
P38 WIDOWED Widowed (from First Marriage)
P69 DISABIL1 Work Disability Status: Limited
P70 DISABIL2 Work Disability Status: Prevented from Working
Work in 1979 - See: Work Last Year, Weeks Worked
in 1979, Usual Hours Worked Per Week in 1979
P94 WORK79 Work Last Year
P45 WORK75 Work Status in 1975
H59 YRMOVED Year Householder Moved into Unit
P80 YEARWORK Year Last Worked
P26 IMMIGR Year of Immigration
P40-41 GRADE Year of School, Highest Attended
H41 YRBUILT Year Structure Built
H79-82 FUELCOST Yearly Cost of Oil, Coal, Kerosene, Wood, Etc.
Allocation of:
P155 ALANG3 Ability to Speak English
H126 AACCESS Access
H146 AACRE1 Acreage of Poverty (H10a on questionnaire)
H147 AACRE2 Acreage of Poverty (H15a on questionnaire)
P164 ACOLL75 Activity in 1975: Attending College
P163 AAF75 Activity in 1975: In Armed Forces
P165 AWORK75 Activity in 1975: Working at a Job or Business
P143 AAGE Age
P158 AAGEMR Age at First Marriage and Quarter of First
Marriage
H137 AAIRCOND Air Conditioning
P193 AINCOME7 All Other Income in 1979
H142 AAUTOS Automobiles Available
H134 ABATHROO Bathrooms
H131 ABEDROOM Bedrooms
H121 AVAC3 Boarded Up Status
P171 ARIDERS Carpool Occupancy
P170 ACARPOOL Carpooling
P156 AFERTIL Children Ever Born
P151 ACITIZEN Citizenship
P182 ACLASS Class of Worker
H149 ACOMMERC Commercial Establishment or Medical Office
H123 ACONDO Condominium Status
H162 ARENT1 Contract Rent
H141 AFUELCOO Cooking Fuel
P141 ARELAT2 Detailed Relationship
H122 AVAC4 Duration of Vacancy
P189 AINCOME3 Farm Self-Employment Income in 1979
H148 AFARM Farm Status and Sales of Farm Products
P162 AFINGRAD Finished Grade
H138 AHEATING Heating Equipment
P161 AYEARSCH Highest Year of School Attended
P179 AHOURS Hours Worked Last Week
H139 AFUELHEA House Heating Fuel
P140 ARELAT1 Household Relationship
H161 AINSINCL Inclusion of Insurance Premiums in Payment to
Lender
H160 ATAXINCL Inclusion of Taxes in Payment to Lender
P180 AINDUSTR Industry
P190 AINCOME4 Interest, Dividend or Net Rental Income in 1979
H133 AKITCHEN Kitchen Facilities
P178 ALABOR Labor Force Status
P153 ALANG1 Language Usage
P154 ALANG2 Language Spoken at Home
P145 AMARITAL Marital Status
P169 AMEANS Means of Transportation to Work
H151 AELECCOS Monthly Cost of Electricity
H152 AGASCOST Monthly Cost of Gas
H157 AMORTG1 Mortgage Status
P188 AOCCUP Occupation
H129 AELEVATO Passenger Elevator
P150 ABIRTHPL Place of Birth
H132 APLUMBIN Plumbing Facilities
P149 AANCSTRY Pre-edit of Ancestry (both 1st and 2nd entry)
P147 ARACE2 Pre-edit of Detailed Race and American Indian
P192 AINCOME6 Public Assistance Income in 1979
P174 DISABL3 Public Transportation Disability Status
P144 AQTRBRTH Quarter of Birth
P146 ARACE1 Race
H155 ATAX Real Estate Taxes
P166 AMIG751 Residence in 1975: Same House/Different House
P167 AMIG752 Residence in 1975: Specific Area
H130 AROOMS Rooms
P160 ASCHOOL School Enrollment and Type of School
H119 AVAC1 Seasonal and Migratory Vacancy Status
H158 AMORTG2 Second or Junior Mortgage
H136 ASEWAGE Sewage Disposal
P142 ASEX Sex
P191 AINCOME5 Social Security Income in 1979
H135 AWATER Source of Water
P148 ASPANISH Spanish Origin
H128 ASTORIES Stories in Structure
H144 ATELEPHO Telephone in Housing Unit
H118 ATENURE Tenure
P157 ATIMESMA Times Married
H159 AMORTG3 Total Monthly Payment to Lender
P168 ATIME Travel Time to Work
H143 ATRUCKS Trucks and Vans Available
H117 AGQTYPE Type of Group Quarters
H125 AUNITS2 Units at Address
H124 AUNITS1 Units in Structure
P185 AHOUR79 Usual Hours Worked per Week in 1979
H120 AVAC2 Vacancy Type
H150 AVALUE Value
P175 AVET1 Veteran Status
P176 AVET2 Veteran's Period of Service
P187 AINCOME1 Wage or Salary Income in 1979
H140 AFUELWTR Water Heating Fuel
P186 AWEEKU79 Weeks Unemployed in 1979
P184 AWEEKW79 Weeks Worked in 1979
P159 AWIDOWED Widowed
P172 ADISABL1 Work Disability Status: Limited
P173 ADISABL2 Work Disability Status: Prevented from Working
P183 AWORK79 Work Last Year
H145 AYRMOVED Year Householder Moved Into Unit
P177 AYEARWRK Year Last Worked
P152 AIMMIGR Year of Immigration
H127 AYRBUILT Year Structure Built
H154 AFUELCOS Yearly Cost of Oil, Coal, Kerosene, Wood, etc.
H153 AWTRCOST Yearly Cost of Water
H156 AINSUR Yearly Insurance Premium
Items On Housing Record
Character
Location Description
H1 Record Type (Housing Record)
H2 Sample Identifier
H3 Region/Division
H4-5 State
H6-8 County Group (A and B Samples only)
H9 Type of Area
H10-13 SMSA (A and B Samples only)
H14-17 Urbanized Area (C Sample only)
H18-19 Subsample Number
H20-25 Housing Unit/CQ Person Serial Number
H26-27 Number of Person Records Following This Housing Unit Record
H28 Type of Group Quarters
H29 Tenure
H30 Seasonal and Migratory Vacancy Status
H31 Vacancy Type
H32 Boarded Up Status
H33 Duration of Vacancy
H34 Usual Home Elsewhere
H35 Condominium Status
H36-37 Units in Structure
H38-39 Units at Address
H40 Access
H41 Year Structure Built
H42 Stories in Structure
H43 Passenger Elevator
H44 Rooms
H45 Bedrooms
H46 Plumbing Facilities
H47 Kitchen Facilities
H48 Bathrooms
H49 Source of Water
H50 Sewage Disposal
H51 Air Conditioning
H52 Heating Equipment
H53 Fuels
H54 Water Heating Fuel
H55 Cooking Fuel
H56 Automobiles Available
H57 Trucks and Vans Available
H58 Telephone in Housing Unit
H59 Year Householder Moved into Unit
H60 Acreage of Property (H10a on questionnaire)
H61 Acreage of Property (H15a on questionnaire)
H62 Farm Status and Sales of Farm Products
H63 Commercial Establishment or Medical Office on Property
H64-65 Value
H66 Payment of Electricity
H67-69 Monthly Cost of Electricity
H70 Payment of Gas
H71-73 Monthly Cost of Gas
H74 Payment of Water
H75-77 Yearly Cost of Water
H78 Payment of Oil, Coal, Kerosene, Wood, Etc.
H79-82 Yearly Cost of Oil, Coal, Kerosene, Wood, Etc.
H83-86 Real Estate Taxes Last Year and Yearly Insurance Premiums
Combined
H87 Mortgage Status
H88 Second or Junior Mortgage
H89-92 Total Monthly Payment to Lender
H93 Inclusion of Real Estate Taxes in Payment to Lender
H94 Inclusion of Insurance Premiums in Payment to Lender
P95-98 Selected Monthly Owner Costs
H99-100 Contract Rent
H101-103 Gross Rent
H104 Household Type
H105 Presence and Age of Own Children
H106 Number of Subfamilies in Family
H107-111 Household Income in 1979
H112-116 Family Income in 1979
H117 to Allocation Flags for Housing Items
H162
H163-193 Filler (zeroes)
Items On Person Record
Character
Location Description
P1 Record Type (Person Record)
P2-3 Relationship
P4 Detailed Relationship
P5 Subfamily Relationship
P6 Subfamily Number
P7 Sex
P8-9 Age
P10 Quarter of Birth
P11 Marital Status
P12-13 Race
P14 Spanish Origin
P15 Spanish Surname
P16-18 Ancestry--1st entry
P19-21 Ancestry--2nd entry
P22-24 Place of Birth
P25 Citizenship
P26 Year of Immigration
P27 Language Spoken at Home Other Than English
P28-30 Language Spoken at Home
P31 Ability to Speak English
P32-33 Children Ever Born
P34 Times Married
P35-36 Age at First Marriage
P37 Quarter of First Marriage
P38 Widowed
P39 School Enrollment and Type of School
P40-41 Highest Year of School Attended
P42 Finished Highest Grade
P43 Activity in 1975: In Armed Forces
P44 Activity in 1975: Attending College
P45 Activity in 1975: Working
P46 Migration/Place of Work/Travel Time Weight
P47-48 Residence in 1975
P49-51 Residence in 1975: County Group
P52 Residence in 1975: State-County Recode
P53-54 Residence in 1975: SMSA Recode
P55-56 Place of Work: State
P57-59 Place of Work: County Group (A and B Sample)
P60 Place of Work: SMSA Recode (A and B Sample)
P61 Place of Work: Central City Recode (C Sample only)
P62 Place of Work: Place Size
P63-64 Travel Time to Work
P65-66 Means of Transportation to Work
P67 Carpooling
P68 Carpool Occupancy
P69 Work Disability Status
P71 Public Transportation Disability Status
P72 Veteran Status
P73 Period of Service May 1975 or later
P74 Period of Service During Vietnam Era
P75 Period of Service Between
P76 Period of Service During Korean Conflict
P77 Period of Service During World War II
P78 Period of Service During World War I
P79 Period of Service During Any Other Time
P80 Year Last Worked
P81 Labor Force Status
P82-83 Hours Worked Last Week
P84 Absent From Work Last Week
P85 Looking for Work
P86 Able to Take Job Last Week
P87-89 Industry
P90-92 Occupation
P93 Class of Worker
P94 Work Last year
P95-96 Weeks Worked in 1979
P97-98 Usual Hours Worked Per Week in 1979
P99-100 Weeks Unemployed in 1979
P101-105 Wage or Salary Income in 1979
P106-110 Nonfarm Self-Employment Income in 1979
P111-115 Farm Self-Employment Income in 1979
P116-120 Interest, Dividend, or Net Rental Income in 1979
P121-124 Social Security Income in 1979
P125-128 Public Assistance Income in 1979
P129-133 All Other Income in 1979
P134-138 Income From All Sources in 1979
P139 Poverty Status in 1979
P140 to Allocation Flags for Population
P193 Items
How To Use The Data Dictionary
The following computer-generated pages document the data contents and record
layout of the file. Below is a brief description of the information provided
for each data item.
The first line of each data item descriptive gives the name, size/scale,
begin position, and item title. On subsequent lines are value codes and any
applicable notes. Each of these elements is defined below.
1. Name. This is an arbitrarily assigned 8-character mnemonic
identifier, e.g., "STATE," "INCOME8."
2. Size/Scale. The size is the number of characters occupied by the
item. The reference to scale is not applicable since none of the
data are scaled.
3. Begin. This is the location within the 193-character data record of
the first character of the data item.
4. Description. (Not labeled on the data dictionary.)
Title. This is a single 40-character line or a set of 40-character
lines which provide the title for the data item.
Value Codes and Notes. Lines after the title describe individual
categories of the data item. Each code for which a separate label is
provided is listed at the left. Codes for which the values are self-
explanatory (e.g., dollar amounts in an income field), are not listed
individually, but are defined in a range listed in the right-hand
column.
The file used to generate the following printout is available as part of any
CENSPAC tape sold after February 1983, or in conjunction with the County
Group Equivalency File.
PUMSH Data Dictionary
Record H
Positions 1-20
SIZE/
NAME SCALE BEGIN
RECTYPE 1 1
Record Type
H Housing Record
SAMPLE 1 2
Sample Identifier
1 A Sample
2 B Sample
3 C Sample
DIVISION 1 3
Region/Division
0 Region/division not identifiable (selected SMSA's
on B Sample, See App. C)
Northeast region:
1 New England division
2 Middle Atlantic division
North Central region:
3 East North Central division
4 West North Central division
South region:
5 South Atlantic division
6 East South Central division
7 West South Central division
West region:
8 Mountain division
9 Pacific division
STATE 2 4
State
01-56 FIPS state code (See App. A)
61-68 State group code (selected states on C Sample-
See App. A)
99 State not identified (selected county groups on
B Sample-See App. C)
COGRP 3 6
County Group (A and B Samples only)
000 N/A (C Sample)
001-998 County group code (unique within state)
AREATYPE 1 9
Type of Area
1 Central city of SMSA (selected central cities on
A and B Samples)
2 SMSA, outside central city (selected areas on
A and B Samples)
3 SMSA, central city/remainder not separately
identified (selected SMSAs or SMSA parts on
A and B Samples for which codes 1 and 2 cannot
be shown)
4 Mixed SMSA/non SMSA area (A Sample only)
5 Outside SMSAs (A and B Samples)
6 Central city of urbanized area (C Sample only)
7 Urban fringe (C Sample only)
8 Urban, outside urbanized areas (C Sample only)
9 Rural (C Sample only)
SMSA 4 10
SMSA (A and B Samples only)
0000 N/A (C Sample, area outside SMSAs)
0040-9340 FIPS SMSA code, selected SMSAs (See app. B)
9999 County group consisting of 2 or more SMSAs or mixed
SMSA/nonSMSA area)
UA 4 14
Urbanized Area (C Sample only)
0000 N/A (A and B Samples, area outside identified UAs)
0080-9320 Census urbanized area code (selected UAs-See
App. D)
SUBSAMPL 2 18
Subsample Number
00-99 See text, pp. 29 and 43
SERIALNO 6 20
Housing Unit/GQ Person Serial Number
000000-999999 Unique identifier assigned within State or
State group
Positions 26-40
PERSONS 2 26
Number of Person Records Following This Housing
Unit Record
00 Vacant Unit
01 One person record (one person in household or any
person in group quarters)
02-31 Number of persons in household
GQTYPE 1 28
Type of Group Quarters
0 N/A (not in group quarters)
1 Inmate of mental hospital
2 Inmate of home for the aged
3 Inmate of correctional institution
4 Inmate of other institution
5 In military quarters
6 In college dormitory
7 In rooming house
8 Other in group quarters, including noninmate living
in institution
TENURE 1 29
Tenure
0 N/A (vacant unit or group quarters)
1 Owner occupied
Renter occupied:
2 With cash rent
3 No cash rent
VACANCY1 1 30
Seasonal and Migratory Vacancy Status
0 N/A (occupied or group quarters)
1 Vacant, year round
2 Vacant, seasonal or migratory
VACANCY2 1 31
Vacancy Type
0 N/A (occupied, group quarters or vacant seasonal
or migratory)
1 For rent
2 For sale only
3 Rented or sold, awaiting occupancy
4 Held for occasional use
5 Other vacant
VACANCY3 1 32
Boarded Up Status
0 N/A (occupied, group quarters or vacant seasonal
or migratory)
1 Boarded up
2 Not boarded up
VACANCY4 1 33
Duration of Vacancy
0 N/A (occupied, group quarters or vacant seasonal
or migratory)
1 Less than 1 month
2 1 up to 2 months
3 2 up to 6 months
4 6 up to 12 months
5 1 year up to 2 years
6 2 years or more
UHE 1 34
Usual Home Elsewhere
0 Not originally reported as usual home elsewhere, or
elsewhere, or group quarters
1 Originally reported as usual home elsewhere
CONDO 1 35
Condominium Status
0 N/A (group quarters)
1 Not a condominium unit
2 Condominium unit
UNITS1 2 36
Units in Structure
00 N/A (group quarters)
01 Mobile home or trailer
02 One-family house detached from any other house
03 One-family house attached to one or more houses
04 Building for 2 families
05 Building for 3 or 4 families
06 Building for 5 to 9 families
07 Building for 10 to 19 families
08 Building for 20 to 49 families
09 Building for 50 or more families
10 Boat, tent, van, etc.
UNITS2 2 38
Units at Address
00 N/A (group quarters)
01 One
02 Two
03 Three
04 Four
05 Five
06 Six
07 Seven
08 Eight
09 Nine
10 Ten or more
11 Mobile home or trailer
ACCESS 1 40
Access
0 N/A (group quarters)
1 Access to unit directly from the outside
or through a common or public hall
2 Access to unit through someone else's living
quarters
Positions 41-60
YRBUILT 1 41
Year Structure Built
0 N/A (group quarters)
1 1979 to March 1980
2 1975 to 1978
3 1970 to 1974
4 1960 to 1969
5 1950 to 1959
6 1940 to 1949
7 1939 or earlier
STORIES 1 42
Stories in Structure
0 N/A (group quarters)
1 1 to 3 stories
2 4 to 6 stories
3 7 to 12 stories
4 13 or more stories
ELEVATOR 1 43
Passenger Elevator
0 N/A (group quarters or structure with less than
4 stories)
1 With passenger elevator in structure
2 No passenger elevator in structure
ROOMS 1 44
Rooms
0 N/A (group quarters)
1 One room
2 Two rooms
3 Three rooms
4 Four rooms
5 Five rooms
6 Six rooms
7 Seven rooms
8 Eight rooms
9 Nine or more rooms
BEDROOMS 1 45
Rooms
0 N/A (group quarters)
1 None
2 One bedroom
3 Two bedrooms
4 Three bedrooms
5 Four bedrooms
6 Five or more bedrooms
PLUMBING 1 46
Plumbing Facilities
0 N/A (group quarters)
1 Complete plumbing for exclusive use
Lacking complete plumbing for exclusive use:
2 Complete plumbing but used by another household
3 Some but not all plumbing facilities
4 No plumbing facilities
KITCHEN 1 47
Kitchen Facilities
0 N/A (group quarters)
1 Complete kitchen facilities
2 No complete kitchen facilities
BATHROOM 1 48
Bathrooms
0 N/A (group quarters)
1 No bathroom or only a half bath
2 One complete bathroom
3 One complete bathroom plus half bath(s)
4 Two or more complete bathrooms
WATER 1 49
Source of Water
0 N/A (group quarters)
1 Public system or private company
2 Individual drilled well
3 Individual dug well
4 Some other source
SEWAGE 1 50
Sewage Disposal
0 N/A (group quarters)
1 Public sewer
2 Septic tank or cesspool
3 Other means
AIRCOND 1 51
Air Conditioning
0 N/A (group quarters)
1 Central system
2 One individual room unit
3 Two or more individual room units
4 None
HEATING 1 52
Heating Equipment
0 N/A (group quarters)
1 Steam or hot water system
2 Central warm-air furnace
3 Electric heat pump
4 Other built-in electric units
5 Floor, wall, or pipeless furnace
6 Room heaters with flue
7 Room heaters without flue
8 Fireplaces, stoves, or portable room heaters
9 None
FUELHEAT 1 53
House Heating Fuel
0 N/A (vacant unit or group quarters)
1 Utility gas from underground pipes serving the
neighborhood
2 Bottle, tank or LP gas
3 Electricity
4 Fuel oil, kerosene, etc.
5 Coal or coke
6 Wood
7 Other fuel
8 No fuel used
FUELWTR 1 54
Water Heating Fuel
0 N/A (vacant unit or group quarters)
1 Utility gas from underground pipes serving the
neighborhood
2 Bottle, tank or LP gas
3 Electricity
4 Fuel oil, kerosene, etc.
5 Coal or coke
6 Wood
7 Other fuel
8 No fuel used
FUELCOOK 1 55
Cooking Fuel
0 N/A (vacant unit or group quarters)
1 Utility gas from underground pipes serving the
neighborhood
2 Bottle, tank or LP gas
3 Electricity
4 Fuel oil, kerosene, etc.
5 Coal or coke
6 Wood
7 Other fuel
8 No fuel used
AUTOS 1 56
Automobiles Available
0 N/A (vacant unit or group quarters)
1 None
2 One
3 Two
4 Three or more
TRUCKS 1 57
Trucks and Vans Available
0 N/A (vacant unit or group quarters)
1 None
2 One
3 Two
4 Three or more
TELEPHON 1 58
Telephone in Housing Unit
0 N/A (vacant unit or group quarters)
1 With telephone
2 No telephone
YRMOVED 1 59
Year Householder Moved into Unit
0 N/A (group quarters or vacant)
1 1979 to March 1980
2 1975 to 1978
3 1970 to 1974
4 1960 to 1969
5 1950 to 1959
6 1949 or earlier
ACREAGE1 1 60
Acreage of Property (H10a on questionnaire-used
in determining universe for value and rent)
0 N/A (group quarters, or two or more units
at address)
1 House on a property of 10 or more acres
2 House on a property of less than 10 acres
Positions 61-87
ACREAGE2 1 61
Acreage of Property (H15a on questionnaire-used
in determining universe for farm status)
0 N/A (group quarters)
1 City or suburban lot, or a place of less than
1 acre
2 1 to 9 acres
3 10 or more acres
FARM 1 62
Farm Status and Sales of Farm Products
0 N/A (urban, city or suburban lot, place of less
than 1 acre, vacant unit or group quarters)
Rural nonfarm (not all rural nonfarm is included):
1 $0 to $49
2 $50 to $249
3 $250 to $599
4 $600 to $999
Rural farm:
5 $1000 to $2499
6 $2500 or more
COMMUSE 1 63
Commercial Establishment or Medical Office on
Property
0 N/A (group quarters, or two or more units in
structure, mobile home or trailer)
1 Yes
2 No
VALUE 2 64
Value
00 N/A (group quarters; vacant unit except vacant for
sale; renter-occupied unit; mobile home or
trailer; or noncondominium unit with two or more
units at address on 10 or more acres, or with a
commercial establishment or medical office on
property)
01 Less than $10,000
02 $10,000 to $14,999
03 $15,000 to $17,499
04 $17,500 to $19,999
05 $20,000 to $22,499
06 $22,500 to $24,999
07 $25,000 to $27,499
08 $27,500 to $29,000
09 $30,000 to $34,999
10 $35,000 to $39,999
11 $40,000 to $44,999
12 $45,000 to $49,999
13 $50,000 to $54,999
14 $55,000 to $59,999
15 $60,000 to $64,999
16 $65,000 to $69,999
17 $70,000 to $74,999
18 $75,000 to $79,999
19 $80,000 to $89,999
20 $90,000 to $99,999
21 $100,000 to $124,999
22 $125,000 to $149,999
23 $150,000 to $199,999
24 $200,000 or more
ELECPAID 2 66
Payment of Electricity
0 N/A (vacant unit or group quarters)
1 Electricity paid, amount shown in ELECOST
2 Included in rent or no charge
3 Electricity not used
ELECOST 3 67
Monthly Cost of Electricity
000 N/A (vacant unit, group quarters, or no payment
for electricity)
001-199 Cost in dollars
200 $200 or more
GASPAID 1 70
Payment of Gas
0 N/A (vacant unit or group quarters)
1 Gas paid, amount shown in GASCOST
2 Included in rent or no charge
3 Gas not used
GASCOST 3 71
Monthly Cost of Gas
000 N/A (vacant unit, group quarters, or no payment
for gas)
001-149 Cost in dollars
150 $150 or more
WTRPAID 1 74
Payment of Water
0 N/A (vacant unit or group quarters)
1 Water paid, amount shown in WTRCOST
2 Included in rent or no charge
WTRCOST 3 75
Monthly Cost of Water
000 N/A (vacant unit, group quarters, or no payment
for water)
001-499 Cost in dollars
500 $500 or more
FUELPAID 1 78
Payment of Oil, Coal, Kerosene, Wood, etc.
0 N/A (vacant unit or group quarters)
1 Fuels paid, amount shown in FUELCOST
2 Included in rent or no charge
3 These fuels not used
FUELCOST 4 79
Yearly Cost of Oil, Coal, Kerosene, Wood, etc.
0000 N/A (vacant unit, group quarters, or no payment
for these fuels)
0001-1999 Cost in dollars
2000 $2000 or more
TAXINSUR 4 83
Real Estate Taxes Last Year and Yearly
Property Insurance Premiums, Combined
0000 No tax or insurance payments, or N/A (renter-
occupied unit, vacant unit, unit on 10 or more
acres, unit with a commercial establishment or
medical office on property, two or more units in
structure, mobile home or trailer, condominium,
or group quarters)
0001-2999 Taxes plus insurance premiums in dollars
3000 $3000 or more
MORTGAG1 1 87
Mortgage Status
0 N/A (see TAXINSUR)
1 Yes, mortgage, deed of trust or similar debt
2 Yes, contract to purchase
3 No
Positions 88-107
MORTGAG2 1 88
Second or Junior Mortgage
0 N/A (no mortgage, deed of trust, contract to
purchase or similar debt on this property, and
others in TAXINSUR)
1 Yes
2 No
MORTGAG3 4 89
Total Monthly Payment to Lender
0000 No regular payments required or N/A
(units with no mortgage and others in TAXINSUR)
0001-1499 Payment in dollars
1500 $1500 or more
TAXINCL 1 93
Inclusion of Real Estate Taxes in Payment to Lender
0 N/A (no regular payment required and
others in MORTGAG2)
1 Yes, payment includes taxes
2 No
INSINCL 1 94
Inclusion of Insurance Premiums in Payment to
Lender
0 N/A (see TAXINCL)
1 Yes, payment includes insurance premiums
2 No
OWNERCST 4 95
Selected Monthly Owner Costs
0000 N/A (renter-occupied unit, vacant unit, unit on
10 or more acres, unit with a commercial
establishment or medical office on property,
two or more units in structure, mobile home or
trailer, condominium, or group quarters)
0001-1999 Cost in dollars
2000 $2000 or more
RENT1 2 99
Contract Rent
00 N/A (owner-occupied unit, vacant unit except vacant
for rent, unit on 10 or more acres with one unit
at address, or group quarters)
01 Less than $50
02 $50 to $59
03 $60 to $69
04 $70 to $79
05 $80 to $89
06 $90 to $99
07 $100 to $109
08 $110 to $119
09 $120 to $129
10 $130 to $139
11 $140 to $149
12 $150 to $159
13 $160 to $169
14 $170 to $179
15 $180 to $189
16 $190 to $199
17 $200 to $224
18 $225 to $249
19 $250 to $274
20 $275 to $299
21 $300 to $349
22 $350 to $399
23 $400 to $499
24 $500 or more
25 No cash rent
RENT2 3 101
Gross Rent
000 N/A (owner-occupied unit, unit rented without
payment of cash rent, vacant unit, unit on 10 or
more acres with one unit at address, or group
quarters)
001-998 Gross rent in dollars
999 $999 or more
HHTYPE 1 104
Household Type
0 N/A (vacant unit or group quarters)
1 Married-couple family household
2 Family household with male householder, no wife
present
3 Family household with female householder, no
husband present
4 Nonfamily household
CHILDREN 1 105
Presence and Age of Own Children
0 N/A (nonfamily household, vacant unit or group
quarters)
1 Family with own children under 6 years only
2 Family with own children 6 to 17 years only
3 Family with own children, some 6 to 17 years, and
some under 6 years
4 Family without own children
NSUBFAM 1 106
Number of Subfamilies in Family
0 None or N/A (vacant unit or group quarters)
1 One subfamily
2 Two subfamilies
3 Three subfamilies
4 Four subfamilies
HHINCOME 1 107
Household Income in 1979
00000 No income/loss or N/A (vacant unit or group
quarters)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars
75000 Income of $75000 or more
Positions 112-140
FAMINCOM 5 112
Family Income in 1979
00000 No income/loss or N/A (nonfamily household, vacant
unit or group quarters)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars
75000 Income of $75000 or more
AGQTYPE 1 117
Allocation of Type of Group Quarters
0 Not allocated or N/A
1 Allocated, assigned
ATENURE 1 118
Allocation of Tenure
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AVAC1 1 119
Allocation of Seasonal and Migratory
Vacancy Status
0 Not allocated or N/A
1 Allocated, assigned
AVAC2 1 120
Allocation of Vacancy Type
0 Not allocated or N/A
1 Allocated, assigned
AVAC3 1 121
Allocation of Boarded Up Status
0 Not allocated or N/A
1 Allocated, assigned
AVAC4 1 122
Allocation of Duration of Vacancy
0 Not allocated or N/A
1 Allocated, hot deck
ACONDO 1 123
Allocation of Condominium Status
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
3 Allocated, structure edit
AUNITS1 1 124
Allocation of Units in Structure
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AUNITS2 1 125
Allocation of Units at Address
0 Not allocated or N/A
1 Allocated, hot deck
AACCESS 1 126
Allocation of Access
0 Not allocated or N/A
1 Allocated, assigned
AYRBUILT 1 127
Allocation of Year Structure Built
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
ASTORIES 1 128
Allocation of Stories in Structure
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AELEVATO 1 129
Allocation of Passenger Elevator
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AROOMS 1 130
Allocation of Rooms
0 Not allocated or N/A
1 Allocated, hot deck
ABEDROOM 1 131
Allocation of Bedrooms
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
APLUMBIN 1 132
Allocation of Plumbing Facilities
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AKITCHEN 1 133
Allocation of Kitchen Facilities
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
ABATHROO 1 134
Allocation of Bathrooms
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AWATER 1 135
Allocation of Source of Water
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
ASEWAGE 1 136
Allocation of Sewage Disposal
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AAIRCOND 1 137
Allocation of Air Conditioning
0 Not allocated or N/A
1 Allocated, hot deck
AHEATING 1 138
Allocation of Heating Equipment
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AFUELHEA 1 139
Allocation of House Heating Fuel
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AFUELWTR 1 140
Allocation of Water Heating Fuel
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
Positions 141-163
AFUELCOO 1 141
Allocation of Cooking Fuel
0 Not allocated or N/A
1 Allocated, hot deck
AAUTOS 1 142
Allocation of Automobiles Available
0 Not allocated or N/A
1 Allocated, hot deck
ATRUCKS 1 143
Allocation of Trucks and Vans
0 Not allocated or N/A
1 Allocated, hot deck
ATELEPHO 1 144
Allocation of Telephone in Housing Unit
0 Not allocated or N/A
1 Allocated, hot deck
AYRMOVED 1 145
Allocation of Year Householder Moved Into Unit
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AACRE1 1 146
Allocation of Acreage of Property (H10a on
questionnaire)
0 Not allocated or N/A
1 Allocated, hot deck
AACRE2 1 147
Allocation of Acreage of Property (H15a on
questionnaire)
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AFARM 1 148
Allocation of Farm Status and Sales of Farm
Products
0 Not allocated or N/A
1 Allocated, hot deck
ACOMMERC 1 149
Allocation of Commercial Establishment or
Medical Office
0 Not allocated or N/A
1 Allocated, hot deck
AVALUE 1 150
Allocation of Value
0 Not allocated or N/A
1 Allocated, hot deck
AELECCOS 1 151
Allocation of Monthly Cost of Electricity
0 Not allocated or N/A
1 Allocated, hot deck
AGASCOST 1 152
Allocation of Monthly Cost of Gas
0 Not allocated or N/A
1 Allocated, hot deck
AWTRCOST 1 153
Allocation of Yearly Cost of Water
0 Not allocated or N/A
1 Allocated, hot deck
AFUELCOS 1 154
Allocation of Yearly Cost of Oil, Coal, Kerosene,
Wood, etc.
0 Not allocated or N/A
1 Allocated, hot deck
ATAX 1 155
Allocation of Real Estate Taxes
0 Not allocated or N/A
1 Allocated, hot deck
AINSUR 1 156
Allocation of Yearly Insurance Premium
0 Not allocated or N/A
1 Allocated, hot deck
AMORTG1 1 157
Allocation of Mortgage Status
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AMORTG2 1 158
Allocation of Second or Junior Mortgage
0 Not allocated or N/A
1 Allocated, hot deck
AMORTG3 1 159
Allocation of Total Monthly Payment to Lender
0 Not allocated or N/A
1 Allocated, hot deck
ATAXINCL 1 160
Allocation of Inclusion of Taxes in Payment
to Lender
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
AINSINCL 1 161
Allocation of Inclusion of Insurance Premiums in
Payment to Lender
0 Not allocated or N/A
1 Allocated, hot deck
2 Allocated, assigned
ARENT1 1 162
Allocation of Contract Rent
0 Not allocated or N/A
1 Allocated, hot deck
FILLER 31 163
Zero fill
PUMSP Data Dictionary
Record P
Positions 1-40
SIZE/
NAME SCALE BEGIN
RECTYPE 1 1
Record Type
P Person Record
RELAT1 2 2
Relationship
00 Householder
Family member other than householder:
01 Spouse
02 Child
03 Brother or sister
04 Parent
05 Other relative (See RELAT2)
Person not related to householder:
06 Roomer or boarder
07 Partner or roommate
08 Paid employee
09 Other nonrelative
In group quarters:
10 Inmate
11 Noninmate
RELAT2 1 4
Detailed Relationship
0 N/A (person not listed as "other relative" of
householder)
1 Son-in-law or daughter-in-law
2 Grandchild
3 Father-in-law or mother-in-law
4 Brother-in-law or sister-in-law
5 Nephew or niece
6 Grandparent
7 Uncle or aunt
8 Cousin
9 Other person related by blood or marriage
SUBFAM1 1 5
Subfamily Relationship
0 N/A (group quarters or not in a subfamily)
1 Husband/wife in married-couple subfamily
2 Parent in parent-child subfamily
3 Child in subfamily
SUBFAM2 1 6
Subfamily Number
0 N/A (group quarters or not in a subfamily)
1 In subfamily #1
2 In subfamily #2
3 In subfamily #3
4 In subfamily #4
SEX 1 7
Sex
0 Male
1 Female
AGE 2 8
Age
00-89 Age in years
90 90 years or more
QTRBIRTH 1 10
Quarter of Birth
0 January-March
1 April-June
2 July-September
3 October-December
MARITAL 1 11
Marital Status
0 Now married, except separated
1 Widowed
2 Divorced
3 Separated
4 Single or N/A (under 15 years of age)
RACE 2 12
Race
01 White
02 Black
03 American Indian, Eskimo, Aleut
Asian and Pacific Islander:
04 Japanese
05 Chinese
06 Filipino
07 Korean
08 Asian Indian
09 Vietnamese
10 Hawaiian
11 Other Asian and Pacific Islander, including
Guamanian and Samoan
Other (Race n.e.c.):
12 Spanish write-in entry
13 Other
SPANISH 1 14
Spanish Origin
0 N/A (not of Spanish origin)
1 Mexican
2 Puerto Rican
3 Cuban
4 Other Spanish
SURNAME 1 15
Spanish Surname
0 N/A (not in Arizona, California, Colorado, New
Mexico, or Texas; in Bowie County, Texas on
B sample)
1 Spanish surname
2 Not Spanish surname
3 Not reported
ANCSTRY1 3 16
Ancestry-First Entry
001-999 See App. E
ANCSTRY2 3 19
Ancestry-Second Entry
001-919 See App. E
BIRTHPL 3 22
Place of Birth
001-056 FIPS state code (See App. A)
060-997 Foreign country or outlying area of the U.S.
(See App. F)
CITIZEN 1 25
Citizenship
0 Born in the United States or outlying areas
1 Naturalized citizen
2 Not a citizen
3 Born abroad of American parents
IMMIGR 1 26
Year of Immigration
0 N/A (born in the United States of outlying areas
or born abroad of American parents)
1 1975 to 1980
2 1970 to 1974
3 1965 to 1969
4 1960 to 1964
5 1950 to 1959
6 Before 1950
LANG1 1 26
Language Usage
0 N/A (under 3 years of age)
1 Speak a language other than English at home
2 Speak only English at home
LANG2 3 28
Language Spoken at Home
000 N/A (under 3 years of age or speaks only English)
011-997 Language code (See App. G)
998 Language not reported
ENGLISH 1 31
Ability to Speak english
0 N/A (Speaks only English or under 3 years of age)
1 Very well
2 Well
3 Not well
4 Not at all
FERTILTY 2 32
Children Ever Born
00 N/A (under 15 years of age or male)
01 None
02 One
03 Two
04 Three
05 Four
06 Five
07 Six
08 Seven
09 Eight
10 Nine
11 Ten
12 Eleven
13 Twelve or more
TIMESMAR 1 34
Times Married
0 N/A (under 15 years of age or never married)
1 Once
2 More than once
AGEMAR 2 35
Age at First Marriage
00 N/A (under 15 years of age or never married)
12-89 Age at first marriage in years
90 90 years or over at first marriage
QTRMAR 1 37
Quarter of First Marriage
0 N/A (under 15 years of age or never married)
1 January-March
2 April-June
3 July-September
4 October-December
WIDOWED 1 38
Widowed
0 N/A (under 15 years of age, never married, or
not married more than once)
1 First marriage ended because of death of spouse
2 Not widowed
SCHOOL 1 39
School Enrollment and Type of School
0 Not enrolled in school or N/A (under 3 years
of age)
Enrolled in school:
1 Public
2 Church-related
3 Other private
GRADE 2 40
Highest Year of School Attended
00 Never attended school or N/A (under 3 years
of age)
01 Nursery school
02 Kindergarten
Elementary:
03 First grade
04 Second grade
05 Third grade
06 Fourth grade
07 Fifth grade
08 Sixth grade
09 Seventh grade
10 Eighth grade
High school:
11 Ninth grade
12 Tenth grade
13 Eleventh grade
14 Twelfth grade
College:
15 First year
16 Second year
17 Third year
18 Fourth year
19 Fifth year
20 Sixth year
21 Seventh year
22 Eighth year or more
Positions 42-80
FINGRADE 1 42
Finished Highest Grade
0 Never attended school or N/A (under 3 years
of age)
1 Now attending this grade
2 Finished this grade
3 Did not finish this grade
AF75 1 43
Activity in 1975: in Armed Forces
0 N/A (under 16 years of age)
1 Yes
2 No (includes all persons 16-20)
COLL75 1 44
Activity in 1975: Attending College
0 N/A (under 16 years of age)
1 Yes
2 No (includes all persons 16-20)
WORK75 1 45
Activity in 1975: Working at a Job or
Business
0 N/A (under 16 years of age)
1 Yes, full time
2 Yes, part time
3 No (includes all persons 16-20)
MIGWGT 1 46
Migration/Place of Work/Travel Time Weight
0 N/A (not included in migration/place of work/
travel time sample, i.e., no data for
following 10 items)
2 In migration/place of work/travel time sample
STATE75 2 47
Residence in 1975: State
00 N/A (not included in migration/place of work/
travel time sample, or born April 1975 or
later)
01-56 FIPS state code (See App. A)
61-68 State group code (selected states on C Sample-
See App. A)
72 Puerto Rico
73 U.S. outlying area
97 Abroad
98 Same house
99 State not identified (selected county groups on
B Sample-See App. C)
COGRP75 3 49
Residence in 1975: County Group (A and B Samples)
000 N/A (C Sample, not included in migration/place of
work/travel time sample, born April 1975 or
later, or living abroad in 1975)
001-998 County group code
999 Same house
MIG75 1 52
Residence in 1975: State-County Recode
0 N/A (not included in migration/place of
work/travel time sample, born April 1975 or
later)
1 Same house
Different house:
2 Same county
Different county:
3 Same state
Different state:
4 Region not specified (B Sample only)
5 Northeast (A, C Samples only)
6 North Central (A, C Samples only)
7 South (A, C Samples only)
8 West (A, C Samples only)
9 Abroad
METRO75 2 53
Residence in 1975: SMSA Recode
00 N/A (C Sample, not included in migration/place of
work/travel time sample, born April 1975 or
later)
01 Living in same house in 1975
Living in SMSA in 1980 (not applicable if
AREATYPE=4, mixed metro/nonmetro areas on
A Sample):
Different house in same SMSA:
02 In central city(s)
03 Outside central city(s)
Different house in different SMSA:
04 In central city(s)
05 Outside central city(s)
06 Outside any SMSA, or abroad
Living outside SMSA in 1980 (not applicable if
AREATYPE=4, mixed metro/nonmetro areas on
A Sample):
07 Different house in central city of an SMSA
08 Different house in SMSA, outside central city
09 Outside an SMSA, or abroad
10 Living in a mixed metro/nonmetro area in 1980,
different house (A Sample only)
POWSTATE 2 55
Place of Work: State
00 N/A (not included in migration/place of work/
travel time sample, under 16 years of age,
not at work)
1-56 FIPS state code (See App. A)
61-68 State group code (selected states on C Sample-
See App. A)
72 Puerto Rico
73 U.S. outlying area
97 Abroad
98 State and/or county not reported
99 State not identified (selected county groups on
B Sample-See App. C)
POWCOGRP 3 57
Place of Work: County Group (A and B samples)
000 N/A (C Sample; not included in migration/place of
work/travel time sample, under 16 years of age;
not at work; place of work in outlying area or
foreign country, abroad, at sea, or state and/
or county not reported)
001-998 County group code
Note: In New York (state code = 36) county group
code 099 indicates "New York City, county
not specified" on both A and B samples.
This applies only to place of work.
POWMETRO 1 60
Place of Work: SMSA Recode (A and B samples)
0 N/A (C Sample; not included in migration/place of
work/travel time sample; under 16 years of age;
not at work; place of work in outlying area or
foreign country, abroad, at sea, or state and/
or county not reported)
Living in SMSA (not applicable if
AREATYPE=4, mixed metro/nonmetro area on
A Sample):
Working in same SMSA:
1 In CBD
2 In remainder of central city
3 Outside central city
Working in different SMSA:
4 In central city
5 Outside central city
6 Working outside any SMSA
Living outside SMSA or in a mixed metro/nonmetro
area (AREATYPE=4, A Sample only):
7 Working in central city of an SMSA
8 Working in an SMSA, outside central city
9 Working outside any SMSA
POWCC 1 61
Place of Work: Central City Recode (C Sample only)
0 N/A (A and B Samples; not in migration/place of
work/travel time sample; under 16 years of age;
not at work; place of work in outlying area or
foreign country, abroad, at sea, or state and/
or county not reported)
1 Working in the CBD of a UA central city
2 Working in the remainder of a central city of a
UA (or anywhere in a central city with no CBD)
3 Working elsewhere
POWPLSIZ 1 62
Place of Work: Place Size (C Sample only)
0 N/A (A or B Sample; not in migration/place of
work/travel time sample; under 16 years of age;
not at work; place of work in outlying area or
foreign country, abroad, at sea, or state and/
or county not reported)
1 2,500 to 9,999
2 10,000 to 24,999
3 25,000 to 49,999
4 50,000 or more
5 Not in an identified place of 2,500 or more, or
not reported at the place level
TIME 2 63
Travel Time to Work
00 N/A (not included in migration/place of
work/travel time sample; under 16 years of age;
not at work; or worked at home)
1-98 Time in minutes
99 99 minutes or more
MEANS 2 65
Means of Transportation to Work
00 N/A (under 16 years of age or not at work)
Private vehicle:
01 Car
02 Truck
03 Van
Public transportation:
04 Bus or streetcar
05 Railroad
06 Subway or elevated
07 Taxicab
08 Motorcycle
09 Bicycle
10 Walked only
11 Worked at home
12 Other means
CARPOOL 1 67
Carpooling
0 N/A (under 16 years of age, not at work, means
of transportation to work other than car,
truck, or van)
1 Drive alone
Carpool:
2 Share driving
3 Drive others only
4 Ride as passenger only
RIDERS 1 68
Carpool Occupancy
0 N/A (under 16 years of age, not at work, drives
along to work, means of transportation to work
other than car, truck, or van)
1 Two
2 Three
3 Four
4 Five
5 Six
6 Seven or more
DISABIL1 1 69
Work Disability Status: Limited
0 N/A (under 16 years of age)
1 With a work disability
2 No work disability
DISABIL2 1 70
Work Disability Status: Prevented from Working
0 N/A (under 16 years of age)
1 Prevented from working
2 Not prevented from working
DISABIL3 1 71
Public Transportation Disability Status
0 N/A (under 16 years of age)
1 With a public transportation disability
2 No public transportation disability
VETERAN1 1 72
Veteran Status
0 Veteran of active-duty military service
1 Not a veteran or N/A (under 16 years of age)
VETERAN2 1 73
Period of Service May 1975 or Later
0 No or N/A (under 16 years of age)
1 Yes
VETERAN3 1 74
Period of Service During Vietnam Era (August
1964-April 1975)
0 No or N/A (under 16 years of age)
1 Yes
VETERAN4 1 75
Period of Service Between February 1955 and
July 1964)
0 No or N/A (under 16 years of age)
1 Yes
VETERAN5 1 76
Period of Service During Korean Conflict
(June 1950-January 1955)
0 No or N/A (under 16 years of age)
1 Yes
VETERAN6 1 77
Period of Service During World War II
(September 1940-July 1947)
0 No or N/A (under 16 years of age)
1 Yes
VETERAN7 1 78
Period of Service During World War I
(April 1917-November 1918)
0 No or N/A (under 16 years of age)
1 Yes
VETERAN8 1 79
Period of Service During Any Other Time
0 No or N/A (under 16 years of age)
1 Yes
YEARWORK 1 80
Year Last Worked
0 N/A (under 16 years of age)
1 1980
2 1979
3 1978
4 1975-1977
5 1970-1974
6 1969 or earlier
7 Never worked
Positions 81-134
LABOR 1 81
Labor Force Status
0 N/A (under 16 years of age)
In labor force:
Civilian labor force:
Employed:
1 At work
2 With a job but not at work
3 Unemployed
Armed Forces:
4 At work
5 With a job but not at work
6 Not in labor force
HOURS 2 82
Hours Worked Last Week
00 N/A (under 16 years of age or not at work)
1-98 Hours worked last week
99 99 or more hours worked last week
ABSENT 1 84
Absent from Work Last Week
0 N/A (under 16 years of age or at work)
1 Yes, on layoff
2 Yes, on vacation, temporary illness, labor
dispute, etc.
3 No
4 Not reported
LOOKING 1 85
Looking For Work
0 N/A (under 16 years of age or at work)
1 Yes
2 No
3 Not reported
ABLE 1 86
Able to Take Job Last Week
0 N/A (under 16 years of age, at work or not
looking for work)
1 No, already had a job
2 No, temporarily ill
3 No, other reasons (in school, etc.)
4 Yes, could have taken a job
5 Not reported
INDUSTRY 3 87
Industry
000 N/A (under 16 years of age, in Armed Forces,
last worked before 1975 and not in labor force,
or never worked)
010-992 Industry code (See App. G)
OCCUP 3 90
Occupation
000 N/A (under 16 years of age, in Armed Forces,
last worked before 1975 and not in labor force,
or never worked)
003-909 Occupation code (See App. H)
CLASS 1 93
Class of Worker
0 N/A (under 16 years of age, last worked before
1975, or never worked)
1 Private wage and salary worker:
Employee of private company
2 Federal government worker
3 State government worker
4 Local government worker
5 Self-employed worker--business not incorporated
6 Employee of own corporation
7 Unpaid family worker
WORK79 1 94
Work Last Year
0 N/A (under 16 years of age)
1 Worked in 1979
2 Did not work in 1979
WEEKSW79 2 95
Weeks Worked in 1979
00 N/A (under 16 years of age or did not work
in 1979)
00-52 Weeks worked
HOURS79 2 97
Usual Hours Worked Per Week in 1979
00 N/A (under 16 years of age or did not work
in 1979)
01-98 Usual number of hours
99 99 or more hours per week
WEEKSU79 2 99
Weeks Unemployed in 1979
00 N/A (under 16 years of age or with no
unemployment in 1979)
01-52 Weeks looking for work or on layoff
INCOME1 5 101
Wage or Salary Income in 1979
00000 N/A (under 16 years of age or no
income from this source)
00005-74995 Income in dollars (midpoint of $10 interval)
75000 Income of $75000 or more
INCOME2 5 106
Nonfarm Self-Employment Income in 1979
00000 N/A (under 16 years of age or no
income/loss from this source)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars (midpoint of $10
interval)
75000 Income of $75000 or more
INCOME3 5 111
Farm Self-Employment Income in 1979
00000 N/A (under 16 years of age or no
income/loss from this source)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars (midpoint of $10
interval)
75000 Income of $75000 or more
INCOME4 5 116
Interest, Dividend or Net Rental Income in 1979
00000 N/A (under 15 years of age or no
income/loss from this source)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars (midpoint of $10
interval)
75000 Income of $75000 or more
INCOME5 4 121
Social Security Income in 1979
0000 N/A (under 15 years of age or no
income from this source)
0005-9995 Income in dollars (midpoint of $10 interval)
INCOME6 4 125
Public Assistance Income in 1979
0000 N/A (under 15 years of age or no
income from this source)
0005-9995 Income in dollars (midpoint of $10 interval)
INCOME7 5 129
All Other Income in 1979
00000 N/A (under 15 years of age or no
income from sources other than those
separately identified)
00005-74995 Income in dollars (midpoint of $10 interval)
75000 Income of $75000 or more
INCOME8 5 134
Income From All Sources in 1979
00000 N/A (under 15 years of age or no
income/loss from any source)
-9995 Loss of $9990 or more
-9985 to 74995 Income (or loss) in dollars
75000 Income of $75000 or more
Positions 139-170
POVERTY 1 139
Poverty Status in 1979 (Ratio of Family or
Unrelated Individual Income in 1979 to
Poverty Cutoff)
0 N/A (inmate of institution, person in military
group quarters or in college dormitory, or
unrelated individual under 15 years of age)
Below poverty level:
1 Below .75 of poverty cutoff (including no income
or net loss)
2 .75 to .99
Above poverty level:
3 1.00 to 1.24
4 1.25 to 1.49
5 1.50 to 1.74
6 1.75 to 1.99
7 2.00 or more
ARELAT1 1 140
Allocation of Household Relationship
0 Not allocated
1 Allocated, consistency edit (in GQ: cold deck)
2 Allocated, hot deck (in households only)
ARELAT2 1 141
Allocation of Detailed Relationship
0 Not allocated
1 Allocated
ASEX 1 142
Allocation of Sex
0 Not allocated
1 Allocated, consistency edit (in GQ: allocated)
2 Allocated, hot deck (in households only)
AAGE 1 143
Allocation of Age
0 Not allocated
1 Allocated, hot deck (in GQ: cold deck)
2 Allocated, hot deck (in GQ only)
AQTRBRTH 1 144
Allocation of Quarter of Birth
0 Not allocated
1 Allocated, cold deck
AMARITAL 1 145
Allocation of Marital Status
0 Not allocated or N/A
1 Yes, consistency edit
2 Yes, hot deck
ARACE1 1 146
Allocation of Race
0 Not allocated
1 Allocated from relative, this household
(in GQ: cold deck)
2 Allocated from nonrelative, this household
(in GQ: hot deck)
3 Allocated, hot deck (in households only)
ARACE2 1 147
Pre-edit of Detailed Race and American Indian
0 Not allocated
1 Allocated: pre-edit
ASPANISH 1 148
Allocation of Spanish Origin
0 Not allocated
1 Allocated from information for this person or
from relative, this household
(in GQ: allocation)
2 Allocated from nonrelative, this household
(in household only)
3 Allocated from information for this person or from
hot deck, different household (in
households only)
AANCSTRY 1 149
Pre-edit of Ancestry (both 1st and 2nd entry)
0 Not allocated
1 Allocated: pre-edit
ABIRTHPL 1 150
Allocation of Place of Birth
0 Not allocated
1 Allocated: pre-edit
2 Allocated, consistency edit
3 Allocated, hot deck
ACITIZEN 1 151
Allocation of Citizenship
0 Not allocated
2 Yes, consistency edit
3 Yes, hot deck
AIMMIGR 1 152
Allocation of Year of Immigration
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
ALANG1 1 153
Allocation of Language Usage
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
ALANG2 1 154
Allocation of Language Spoken at Home
0 Not allocated or N/A
1 Allocated: pre-edit
2 Allocated, consistency edit
3 Allocated, hot deck
ALANG3 1 155
Allocation of Ability to Speak English
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AFERTIL 1 156
Allocation of Children Ever Born
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
ATIMESMA 1 157
Allocation of Times Married
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AAGEMR 1 158
Allocation of Age at First Marriage and
Quarter of First Marriage
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AWIDOWED 1 159
Allocation of Widowed
0 Not allocated or N/A
3 Allocated, hot deck
ASCHOOL 1 160
Allocation of School Enrollment and Type
of School
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AYEARSCH 1 161
Allocation of Highest Year of School Attended
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AFINGRAD 1 162
Allocation of Finished Grade
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AAF75 1 163
Allocation of Activity in 1975: in Armed Forces
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
ACOLL75 1 164
Allocation of Activity in 1975: Attending College
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AWORK75 1 165
Allocation of Activity in 1975: Working at a Job
or Business
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AMIG751 1 166
Allocation of Residence in 1975: Same House/
Different House
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AMIG752 1 167
Allocation of Residence in 1975: Specific Area
0 Not allocated or N/A
1 Allocated: pre-edit
2 Allocated, consistency edit
3 Allocated, hot deck
ATIME 1 168
Allocation of Travel Time to Work
0 Not allocated or N/A
3 Allocated, hot deck
AMEANS 1 169
Allocation of Means of Transportation to Work
0 Not allocated or N/A
3 Allocated, hot deck
ACARPOOL 1 170
Allocation of Carpooling
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
Positions 171-193
ARIDERS 1 171
Allocation of Carpool Occupancy
0 Not allocated or N/A
3 Allocated, hot deck
ADISABL1 1 172
Allocation of Work Disability Status: Limited
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
ADISABL2 1 173
Allocation of Work Disability Status: Prevented
from Working
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
ADISABL3 1 174
Allocation of Public Transportation Disability
Status
0 Not allocated or N/A
3 Yes, hot deck
AVET1 1 175
Allocation of Veteran Status
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
AVET2 1 176
Allocation of Veterans' Period of Service
0 Not allocated or N/A
3 Allocated, hot deck
AYEARWRK 1 177
Allocation of Year Last Worked
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
ALABOR 1 178
Allocation of Labor Force Status
0 Not allocated or N/A
3 Allocated, hot deck
AHOURS 1 179
Allocation of Hours Worked Last Week
0 Not allocated or N/A
3 Allocated, hot deck
AINDUSTR 1 180
Allocation of Industry
0 Not allocated or N/A
1 Allocated: pre-edit
2 Allocated, consistency edit*
3 Allocated, hot deck
AOCCUP 1 181
Allocation of Occupation
0 Not allocated or N/A
1 Allocated: pre-edit
2 Allocated, consistency edit*
3 Allocated, hot deck
ACLASS 1 182
Allocation of Class of Worker
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
AWORK79 1 183
Allocation of Work Last Year
0 Not allocated or N/A
2 Yes, consistency edit
3 Yes, hot deck
AWEEKW79 1 184
Allocation of Weeks Worked in 1979
0 Not allocated or N/A
2 Yes, consistency edit*
3 Yes, hot deck
AHOUR79 1 185
Allocation of Usual Hours Worked Per Week in 1979
0 Not allocated or N/A
3 Allocated, hot deck
AWEEKU79 1 186
Allocation of Weeks Unemployed in 1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME1 1 187
Allocation of Wage or Salary Income in 1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME2 1 188
Allocation of Nonfarm Self-Employment Income in
1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME3 1 189
Allocation of Farm Self-Employment Income in
1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME4 1 190
Allocation of Interest, Dividend or Net Rental
Income in 1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME5 1 191
Allocation of Social Security Income in 1979
0 Not allocated or N/A
1 Allocated: pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME6 1 192
Allocation of Public Assistance Income in 1979
0 Not allocated or N/A
1 Allocated, pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
AINCOME7 1 193
Allocation of All Other Income in 1979
0 Not allocated or N/A
1 Allocated, pre-edit*
2 Allocated, consistency edit*
3 Allocated, hot deck
*Not counted as an allocation in census publications (PC80-1-C)
