Scientific Data Documentation
Sociographic Zipcode Information From The 1990 Census
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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.'
