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Scientific Data Documentation
Sociographic Zipcode Information From The 1990 Census
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 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.

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

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

 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

 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

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.'

This page last reviewed: Thursday, January 28, 2016
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