Scientific Data DocumentationPublic Use Microdata Sample, 1980
Notes Abstract Summary Data Public Use Microdata Proccessing the Data Accuracy of the Microdata Sample Estimates Sample Design for the Public Use Microdata Samples Record ContentsNOTES 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 FilesSUMMARY 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 238PUBLIC 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 ItemsHow 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 fillPUMSP 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)
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