Scientific Data DocumentationSociographic Zipcode Information From The 1990 CensusSorry, no data are available. Only documentation was included in the archives. USER NOTE This file contains 1980 data and 1989-90 estimates based on 1988 data. Zip codes are not current. New intercensal estimates (for '80's) and post-censal estimates (for 1991-92) will be available around April 1st.METHODOLOGY 1988 Population and Household Estimates Donnelley Marketing maintains and continuously updates the nation's largest residential data base which describes the characteristics of nearly 80 million households, approximateiy 90 percent of all United States households. Through the application of Donnelley's Address Coding Guide, these households are geocoded and assigned to their appropriate small area Census geography. While not a complete census in themselves, longitudinal data from the Donnelley household universe provide a valid measure of household growth and decline, and erase the production of tract level household estimates on an individual basis. In a unique adaptation of the basic housing unit method, the Donnelley estimate method applies the 1980-1988 rates of change In Donnelley household counts to the 1980 Census household counts to produce the 1988 household estimates at the tract level. Since the Donnelley data constitute counts of actual households, the method is more direct than traditional housing unit methods which rely on separate estimates of housing units and vacancies to compute total households. In order to derive the population figure for each tract, an estimate of average household must ba applied to the estimate of 1988 households. The household size variable is critical to the development of accurate population figures since household sizes can shift dramatically as a resuit of changes in marriage patterns, divorces, increased longevity of the elderly, and housing Ability. Most estimating procedures compute a household size factor by assigning national level rates of change to the latest Census figures. However, the Donnelley method allows for household size variations specific to each place or county. Household sizes are determined from the relationship of the number of persons to the number of households. Donnelley uses the latest Census Bureau population figures for places and counties, adjusted to the estimate date and for the group quarters population, divided by the Donnelley household estimate. A household size rate of change is computed from the comparison of this estimated household size with the respective 1980 Census figure. This rate of change is used for all tracts within a specific place or county to produce household sizes that are unique for these areas. This procedure ensures that variations in household sizes due to the demographic composition of a particular locality are accurately measured. The household size estimates are multiplied by the corresponding household figures to calculate the estimated household population for each tract. The group quarters population is added back to its respective geographic entity resulting in an estimate of the total population. 1988 Age/Sex Starting with the tract level age/sex structure from the 1980 Census, age and sex specific survival rates from the National Center for Health Statistics are used to "age" the 1980 population ahead to 1988. The number of births during the 1980-1988 period is then estimated on the basis of 1980 child-woman ratios, the ratio of children under the age of five to women in their childbearing years, age 15-44. The resulting age/sex structures--expressed as a percent distribution--are applied to the tract ievel 1988 population estimates to produce estimates of 1988 population by age and sex. Care is taken in areas with large colleges or military population to maintain an accurate age structure, since these persons generally do not remain in these areas, but rather are continuously replaced by persons in the same age/sex categories. 1988 Race Population estimates by race are provided for three categories: White, Black and Other. Consistent with Census definitions, the White, Black and Other categories sum to the total population. A separate estimate of the Spanish population is made since these persons are an ethnic designation rather than a racial group. Census Bureau projections of Black population are used to estimate state level changes in the Black population between 1980 and 1988. The eight year changes are added to the 1980 Black population counts to produce 1988 state estimates of Black population. The non-Black (or White and Other) population for each year is the diiferance between total population and Black population. The 1980 proportion of the "White and Other" population which was White, and the proportion which was Other, are computed and applied to produce the 1988 state level White and Other figures. Changes In racial ccharacteristics between the 1970 and 1980 Census are used to trend tract level race to 1988. The 1988 race estimates are then controlled to the sate level race distributions. Since by Census definition, persons of Spanish origin can be of any race, the 1988 estimates of Spanish origin population are computed using a separate procedure. The Census Bureau's Spanish surname file is matched against the 1980 and then the most recent Donnelley residential list. Comparisons of the 1980 match with with the 1980 Census data demonstrate the ability of this match to identify concentrations of Spanish origin population. The rates of change since 1980 in persent surname match are then applied to the percent Sanish from the 1980 Census to produce county specific estimates of the S panish origin population. Tract specific rates are based on percentages of the estimated "White and Other" population, and are adjusted to the surname-based county controls. All Spanish origin estimates are adjusted to the state and national level based on Spanish estimates from the Population Reference Bureau and the Census Bureau's Current Population Survey. 1988 Income Donnelley estimates are expressed in current dollars, and based on a money income concept to be consistent with data collected by the Census Bureau. This represents the total gross income received, before deductions for personal income taxes and Social Security, through: wage and salary income; net non farm self-employed Income; net farm self-employed income; Social Security and railroad retirement Income; public assistance income; and all other sources of money such as interest, dividends, veteran's payments, pensions, unemployment insurance, and alimony. 1980 Census Income distributions at the tract and minor civil division level are used as a basis for 1988 estimates. Estimation of a rate of change in county and sub-county level medians and distributions is the key process through which Donnelley Income estimates are derived. Tract level median household income is estimated through a regression model based on tract level demographics including the age, sex, race, and education of householders, tenure ofoccupied housing, and the area's occupational and industrial composition. The model Is updated based on changes to current year in the Census Bureau's Current Population Survey, and then applied to tract specific population and household compositions to estimate current dollar median household incomes. The tract level rates of change are then adjusted to inflation trends exhibited by the Consumer Price Index and county level changes in income reported by the internal Revenue Service. The 1980 income distributions are then advanceded to the estimated medians toproduce 1988 tract level income distributions. 1993 Population and Household Projections Donnelley population and household projections are produced by a graphic technique entitled Cohort Component Method. Tnis method is preferred because it projects the three components of demographic change separately: births, deaths and migration. Using 1988 population by age and sex as a base, age and sex specific five-year survival rates from the National Center for HaIth Statistics are used to "age" each age/sex cohort ahead to 1993, thus accounting for the City cmponent. Since the number of births in an area Is most closely related to the number of women in childbearing ages, births are accounted for through the application of 1980 child-woman ratios. This is the ratio of children under the age of five to the number of women ages 15-44. The advantage of using the children ratio is that projected births reflect any changes In the proportion and number of women in an area. In addition, this technique enables the measurement of fertility at an Individual tract level rather than applying state or national fertility rates that can be extremely misleading in smaller units of geography. Migration is the most important component as well as the most difficult to estimate. Donnelley's unique ability to continuously measure the net movement of households both into and out of specific Census tracts enables the forecasting of accurate migration trends. Other projection methods, however, rely on historical data such as the change from 1970-1980 and, therefore, tend to be less accurate the further from the Census date the projection is made. The Donnelley method estimates tract specific net migration to current year based on tract level changes In the Donnelley household file and the resulting estimates of total population. The estimated net migration rates are then projected to 1993 to provide the migration component for individual tracts. The survived population, the population under age five (births during the projection period), and the migrant population are summed to compute the 1993 population projections. These 1993 projections are then adjusted to independently computed 1993 county, state, and national population projections. A projected household size is applied to the population figures to compute a projected number of households. These projected household sizes are based upon the assumption that household sizes will continue to decline as the result of certain demographic factors: postponement of marriage, rise in divorce, and an increasing elderly population. 1993 Age/Sex Projections of the 1993 population by age and sex are generated as part of the projection method previously described. In fact, the key to computing total 1993 population is anticipating changes in the age/sex structure at the tract level of geography. 1993 Race Population projections by race are computed In a manner similar to the race estimates, and the definitions and categories provided are identical. Census Bureau projections of Black population are used to project state level changes in the Black population between 1980 and 1993. The thirteen year changes are added to the 1980 Black population counts to produce 1993 state estimates of the dfflerence between total population and Black population. The 1980 proportion of the `White and Other" population which was White, and the proportion which was Other, are computed and applied to produce 1993 state level White and Other figures. 1970 and 1980 Census data are used to project tract level White, Black, and Other populations to 1993. The 1993 race projections are then controlled to the state level race distributions and summed to the estimated 1993 tract level projections of total population. The 1993 projections of the Spanish origin population reflect tract specific postcensal change to the estimate year, adjusted to conform to projected racial composition as well as national, state and county projections of the Spanish origin population. 1993 Income Household income distributions are projected to 1993 by computing five year rates of change in median household income for each tract and minor civil division based upon the changes exhibited by the 1980 Census and 1988 income estimates.RECORD LAYOUT Geographic Variables Variable Label Zipcode = 'zipcode' Poname = 'post office name' stateab = 'postal state abbreviation' statefip = 'postal state code' county = 'postal county code' pofinum = 'postal finance number (PFN)' poclcag = 'post office class and category code' mulzip = 'multi zip city indicator' pstab = 'primary state abbreviation' pstfip = 'primary state code' pcountyc = 'primary county code' pcountyn = 'primary county name' resident = 'residential indicator' msarea = 'metro stat area or prim. metro stat area' adicode = 'arbitron area of dominant influence code' smsarea = 'standard metro stat area code' ac_dma = 'A.C. Nielsen Designated Market Area Code' ac_cosiz = 'A. C. Nielsen County size code' ac_regcd = 'A. C. Nielsen Region Code' sami = 'Selling areas marketing Inc. Code' sesi_1 = 'Socioeconomic stat indicator(SESI)score' sesi_2 = 'SESI decile ranking, nationally' sesi_3 = 'SESI decile ranking, state' sesi_4 = 'SESI Decile Ranking, county' Population Variables Variable Label pop80 = '1980 population' pop90est = '1990 population estimate' Household Information Variables Variable Label hhl_80 = '1980 households' hhl90est = '1990 households estimate' a_hhl80i = '1980 mean household income' a_hhl90i = '1990 mean household income' m_hhl80i = '1980 median household income' m_hhl90i = '1990 median household income'; 1980 Income Distribution Variables (Contains One Implied Decimal) Variable Label hhl80i_1 = '1980 % of households $0-$7,499' hhl80i_2 = '1980 % of households $7,555-$9,999' hhl80i_3 = '1980 % of households $10,000-$14,999' hhl80i_4 = '1980 % of households $15,000-$24,999' hhl80i_5 = '1980 % of households $25,000-$34,999' hhl80i_6 = '1980 % of households $35,000-$49,999' hhl80i_7 = '1980 % of households $50,000-$74,999' hhl80i_8 = '1980 % of households $75,000 and above'; 1990 Income Distribution Variables (Contains One Implied Decimal) Variable Label hhl90i_1 = '1990 % of households $0-$7,499' hhl90i_2 = '1990 % of households $7,555-$9,999' hhl90i_3 = '1990 % of households $10,000-$14,999' hhl90i_4 = '1990 % of households $15,000-$24,999' hhl90i_5 = '1990 % of households $25,000-$34,999' hhl90i_6 = '1990 % of households $35,000-$49,999' hhl90i_7 = '1990 % of households $50,000-$74,999' hhl90i_8 = '1990 % of households $75,000 and above' hhlmo89 = '1989 % households moved out prev. year' hhlmi89 = '1989 % households moved in prev. year' hhlmil5y = '1989 % households moved in prev 5 yrs'; HHLYRS and HHLIN Variables (Include One Implied Decimal) Variable Label hhlyrs_1 = 'lived at current address < or = 2 yrs' hhlyrs_2 = 'lived at current address 3-5 years' hhlyrs_3 = 'lived at current address 6-9 years' hhlyrs_4 = 'lived at current address > or = 10 yrs' hhlinsin = '1990 % households living in 1 fam. unts' hhlinmul = '1990 % households in >1 fam. units'; Banking Data Variables Based upon information from the federal deposit insurance corporation, the federal home loan bank board and the national credit union administration. The term private includes individuals, partnerships, corporations, and mutual savings banks; Variable Label totbanko = 'total banking offices' rtpopbak = 'ratio 1989 pop per banking office' rthhlbak = 'ratio 1989 households to banking office' Commercial Banking Summary Variables Variable Label ctddipcm = 'total comm demand deposits, private' ctsdipcm = 'total comm savings deposits, private' codipcms = 'total comm other deposits, non-govt' cdtdfsog = 'comm demand/time/savings deposit, govt' cdtsdoic = 'comm deposits official/commercial' totcommd = 'total deposits in commercial banks' Thrift Banking Summary Variables Variable Label ttddipcm = 'total thrift demand dep, private' ttsdipcm = 'total thrift savings dep, private' todipcms = 'total thrift deposits, private sources' tdtdfsog = 'total thrift deposits by govt' tdtsdoic = 'tot. thrift dep. by official/comm banks' tottdep = 'total deposits in thrift institutions' Summary of Deposits Variables Both Commercial Banks & Thrift Institutions Variable Label sctdipcm = 'total private demand dep, comm & thrift' sctsipcm = 'total private savings dep, comm & thrift' sctoipcm = 'total private other dep, comm and thrift' sdtsfsog = 'total deposit, comm & thrift, all govt' sdtsdoic = 'comm & thrift dep. official & comm banks' stdinc_t = 'total deposits in commercial and thrift' Social Security Data Variables From the Social Security Administration Varaible Label tnoasdib = 'total number oasdi beneficiaries' tmoasdib = 'total monthly oasdi benefits in thou' oasdi65o = 'number oasdi beneficiaries 65 or older' oasdi65u = '# adult oasdi beneficiaries under 65' oasdiamb = '# of adult male oasdi beneficiaries' oasdiafb = '# of adult female oasdi beneficiaries' oasdichi = 'total children oasdi beneficiaries' tssi = 'total supp. sec. income beneficiaries' Percent Characteristics of Population Variables Variable Label popsex_1 = '% male in population' popsex_2 = '% female in population' poprac_1 = '% white in population' poprac_2 = '% black in population' poprac_3 = '% other race in population' Percent of Persons Variables (By Age) Variable Label popage_1 = '% persons 0-5 years' popage_2 = '% persons 6-17 years' popage_3 = '% persons 18 - 24 years' popage_4 = '% persons 25 - 34 years' popage_5 = '% persons 35 - 44 years' popage_6 = '% persons 45- 54 years' popage_7 = '% persons 55 - 64 years' popage_8 = '% persons 65 years and older' medage_m = 'median age: male population' medage_f = 'median age: female population' medagetp = 'median age: total population' mage18_o = 'median age: 18+ population' pspanish = '% population which is Spanish' Percent Households - By Type By Type Variable Label p_m1phhl = '% male 1 person household' p_f1phhl = '% female 1 person household' p_hwhhl = '% husband-wife households' p_mhead = '% male head households' p_fhead = '% female head households' Percent Households by Presence of Children By Presence of Children Variable Label p_hhlu18 = '% households including under 18' hhlnochi = '% households w/o children under 18' phead65o = '% head/household 65 yrs and older' meanhhls = 'mean household size' Percent of Owner Occupied Units By Value of the Unit Variable Label p_oouvu1 = '% owner dwelling val $0-$29,999' p_oouvu2 = '% owner dwelling val $30,000-$49,999' p_oouvu3 = '% owner dwelling val $50,000-$79,999' p_oouvu4 = '% owner dwelling val $80,000-$99,999' p_oouvu5 = '% owner dwelling val $100,000-$149,999' p_oouvu6 = '% owner dwelling val $150,000-$199,999' p_oouvu7 = '% owner dwelling val $200,000+' medvoou = 'median value, owner occupied units' p_ooallo = '% owner occupied of all occupied' Percent of Renter Occupied Units By Monthly Rent Variable Label p_rentm1 = '% renter occupied, rent > $100/month' p_rentm2 = '% renter occupied, rent $100-$199' p_rentm3 = '% renter occupied, rent $200-$200' p_rentm4 = '% renter occupied, rent $300-$399' p_rentm5 = '% renter occupied, rent $400-$499' p_rentm6 = '% renter occupied, rent $500 or more' medrentr = 'mean rent/renter occupied units' preoallo = '% renter occupied of all occupied' Percent of Rental Units in Structure; Variable Label p_unust1 = '% units with 1 unit in structure' p_unust2 = '% units with 2-9 units in structure' p_unust3 = '% units w/10 or more units in structure' p_mohotr = '% mobile home, trailer' p_boallo = '% black occupied of all occupied' p_pfornb = '% persons foreign born' Percent Adults 25 Years or Older By Educational Attainment By Educational Attainment Variable Label p_25oea1 = '% adults >24 with elementary education' p_25oea2 = '% adults >24 with some high school' p_25oea3 = '% adults >24, high school grad' p_25oea4 = '% adults >24 some college' p_25oea5 = '% adults >24 college grad/post grad' msyca250 = 'median school yrs of adults >24' Percent Employed Persons 16 Years and Older by Occupation By Occupation Variable Label pep16000 = '% employed as professionals' pep16001 = '% employed as managers/admin, non-farm' pep16002 = '% employed as sales workers' pep16003 = '% employed as clerical workers' pep16004 = '% employed as craftsmen and foremen' pep16005 = '% emp. machine operator/assemb/inspect' pep16006 = '% employed in transporter/mover occupat' pep16007 = '% emp-handlers/cleaners/helpers/laborrs' pep16008 = '% employed in farm/forestry/fishing' pep16009 = '% emp in protective/nonprot service' pep1600a = '% employed as private household workers' pep1600b = '% emp as technicians/related support' Percent of Employed Persons 16 Years and Over By Industry Variable Label pep160i0 = '% employed by ag/forestry/fish/mine' pep160i1 = '% employed by construction' pep160i2 = '% employed by manufacturing' pep160i3 = '% employed by transportation' pep16014 = '% employed by community/public utility' pep160i5 = '% employed by wholesale/retail trade' pep160i6 = '% employed by finance/insur/real estate' pep160i7 = '% employed by professional and related' pep160I8 = '% employed in public administration' Percent of Persons 16 Years and Older by Labor Force By Labor Force Variable Label p_160lf1 = '% 16 or older in armed forces' p_160lf2 = '% 16 or older in civ. labor force' p_160lf3 = '% 16 or older unemployed' p_160lf4 = '% 16 or older not in labor force' p_160lf5 = '% 16 or older fem. in civ. labor force' Percent of Working Persons By Means of Transportation to Work Varaible Label pwpmtwk1 = '% workers who drive to work' pwpmtwk2 = '% workers public transport to work' pwpmtwk3 = '% workers, all other transport to work' Percent Distribution of Dates Housing Units Were Built Variable Label p_thubi1 = '% total house units built 1975-mar 1980' p_thubi2 = '% total house units built 1970-1974' p_thubi3 = '% total house units built 1960-1969' p_thubi4 = '% total house units built 1950-1959' p_thubi5 = '% total house units built pre-1950' Percent Distribution of Date Housing Units Were Moved Into Variable Label p_thumi1 = '% units moved into 1975-mar 1980' p_thumi2 = '% units moved into 1970-1974' p_thumi3 = '% units moved into 1960-1969' p_thum14 = '% units moved into 1959 or earlier' Percent Occupied Units by Number of Bedrooms By Number of Bedreooms Variable Label p_ounbr0 = '% occupied units, 0 bedroom' p_ounbr1 = '% occupied units, 1 bedroom' p_ounbr2 = '% occupied units, 2 bedroom' p_ounbr3 = '% occupied units, 3 bedroom' p_ounbr4 = '% occupied units, 4 bedroom' p_ounbr5 = '% occupied units, 5 or more bedroom' Percent Occupied Units by Number of Automobiles By Number of Automobiles Variable Label p_ounau0 = '% occupied units, no automobile' p_ounau1 = '% occupied units, 1 automobile' p_ounau2 = '% occupied units, 2 automobile' p_ounau3 = '% occupied units, >2 automobile' Percent Occupied Units by Availability of Telephone By Availability of Telephone Variable Label p_oatel1 = '% occupied units with phone avail.' p_oatel2 = '% occupied units no phone available' Percent Occupied Units by Type of Heating Equipment By Type of Heating Equipment Variable Label p_outhe1 = '% occ. units heated by steam' p_outhe2 = '% occ. units heated by central' p_outhe3 = '% occ. units heated by electric' Percent Occupied Units by Type of Heating Fuel By Type of Heating Fuel Variable Label p_outhf1 = '% occ units, heat fueled by utility gas' p_outhf2 = '% occ units, heat fueled by bottled gas' p_outhf3 = '% occ units, heat fueled by electricity' p_outhf4 = '% occ units heat fueled by oil, kerosene' p_outhf5 = '% occ units, heat fueled by coal, coke' p_outhf6 = '% occ units, heat fueled by wood' p_outhf7 = '% occ units, heat fueled by other' Percent Occupied Units by Type of Cooking Fuel By Type of Cokking Fuel Variable Label p_outcf1 = '% occ units utility gas cooking fuel' p_outcf2 = '% occ units bottled gas cooking fuel' p_outcf3 = '% occ units electricity cooking fuel' p_outcf4 = '% occ units other cooking fuel' p_ouwair = '% occupied units w/Air Cond.'
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