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US Lifestyles Segments

Current Estimates and Five-Year Projections
Current-year forecasts and five-year projections provided by Percept are developed by Claritas using current and historical data, as well as economic modeling and data pooling statistical techniques. This process enhances demographic analysis in two important ways. First, it utilizes all current data and information to accurately estimate the current location of the population, households and income. Second, it defines the relationships between each demographic variable and the appropriate economic, cyclical, and migratory factors that cause their movements over time.
Population Characteristics
Population by Age/Sex
Population by age/sex composition is estimated and projected using cohort survival methods. Cohort survival is a major factor in changing age structures, and is driven by the reality that, for example, persons age 35 in 2000 who survive another five years, will be age 41 in 2006. Accordingly, a population with a large proportion of 35 year olds in 2000 can expect to have large proportions of 41 year olds in 2005. It is this process that has swelled the U.S. age structure at progressively older age categories as the baby boom generation (or birth cohort) has aged.
The Claritas cohort survival method is executed first at county level, then for tracts, and finally block groups, with each set of estimates controlled to the results at the next higher geographic level. To enhance consistency with Census Bureau age/sex estimates, the county estimates begin with the Census Bureau’s most recent county age/sex estimates.
Note: The Census Bureau county age estimates contain a known problem in some counties with large college populations living in households (not in dormitories). After consulting with the Census Bureau, Claritas completed a project to identify counties where this problem had the greatest impact, and effective with the 2006 Update, used the Census 2000 county age data as the starting point for estimates in these counties.
Tract and block group estimates begin with Census 2000 age/sex estimates. At all levels, the method starts with five-year age/sex categories—separating persons in households from those in group quarters. Because Census 2000 data (and the Census Bureau age/sex estimates) do not provide full age/sex detail for household versus group quarters populations, Claritas estimates the detail required to execute the cohort survival method. The cohort survival process is set into motion with the application of age/sex-specific five-year survival rates to the census age/sex data described above. Each round of cohort survival ages the population of each block group ahead five years.
For example, the process projects the number of 30-34 year olds in a block group who will survive to become 35-39 years old (and so on for all five-year age categories) by 2005. The initial survivals yield projections of age/sex composition for April 2005 (short of the January 1, 2006 estimate date), so a second survival is performed to 2010, and the results interpolated to January 2006. In the case of county estimates starting with July 2004 Census Bureau age/sex estimates, the single survival extends to July 2009, and the results are interpolated to January 2006.
Accounting for Births
As part of each round of cohort survival, the population less than age five is “survived” to age 5-9, so an estimate of births is required to fill the vacated 0-4 category. Births are estimated using the child/woman ratio—defined as the population age 0-4 divided by females age 15-44 (childbearing age).
The child/woman ratio is an indirect measure of fertility specific to each small area, but more important, it is sensitive to projected changes in the number of women of child bearing age—itself a byproduct of the cohort survival process. An increase in the number of child bearing women will result in an increased number of births even if fertility rates (or child-woman ratios) remain constant. The child/woman ratios applied in the Claritas age/sex estimates and projections are derived from the 2000 census, but reflect slight increases evident in the Census Bureau’s post-2000 estimates.
Exceptions to Cohort Survival
The cohort survival process is at work in all areas, but in some areas its effects are overridden by migration. In the absence of authoritative age-specific migration data for small areas, the Claritas method defaults to the assumption that the age/sex composition gained or lost through migration is similar to the area’s “survived” population.
However, because of migration, the cohort survival process is often not applicable to populations living in group quarters facilities such as dormitories, military quarters, prisons, and nursing homes. These populations have high turnover, and therefore age/sex compositions which tend to be stable, reflecting the nature of the facility. For this reason, cohort survivals are applied only to the population living in households. Group quarters populations are estimated separately and their age/sex compositions held constant with those measured in the census.
Claritas also identifies segments of the household population (such as concentrations of college students in off-campus housing) for which cohort survival is not applicable. Concentrations of these “hidden group quarters” populations are identified through their distinctive imprint on small area age compositions, and are similarly exempted from the cohort survival process.
Five Year Projections
Five year projections of age/sex composition are produced with the same method used for the current year estimates. For example, in the 2006 Update, the 2006 estimates of population by age/sex were the starting point for five year survivals to 2011. As with the current year estimates, age/sex projections are produced first for counties, followed by tracts and block groups, with adjustments ensuring consistency between geographic levels.
Population by Race and Ethnicity
There are no universally accepted definitions of race and Hispanic ethnicity. The census currently defines “Hispanic or Latino” as an ethnicity, not a race. Race and Hispanic ethnicity are separate census questions, so in census tabulations, persons of Hispanic ethnicity can be of any race. Hispanics are included in each race
category, possible combinations of two or more races. When cross-tabulated by Hispanic/non-Hispanic, there are 126 race-by-Hispanic categories.
Short of presenting data for all 63 race categories, there are two basic tabulation options—single classification and all-inclusive.
The single classification options are:
White Alone  
Black or African American alone  
American Indian and
Alaska Native alone
Asian alone  
Native Hawaiian and
Other Pacific Islander alone
Some other race alone  
Two or more races  
This option identifies the number of persons marking each race category by itself, and then provides a seventh category identifying the number marking two or more races. The tabulation is similar to those used in the past, and sums to total population. However, it
provides no information about the race of persons in the “two or more” category, so it is not possible to determine the total number of persons identifying with a given race. The total number of persons marking a given race category is revealed by the following all-inclusive categories:
White Alone or in combination  
Black or African American alone
or in combination
American Indian and Alaska Native alone or in combination  
Asian alone or in combination  
Native Hawaiian and Other Pacific Islander alone or in combination  
Some other race alone
or in combination
This option identifies the total number of persons marking each race category—either by itself or as part of a combination of two or more races. However, because persons marking two or more races are counted two or more times, the table sums to totals larger than total population.
The Claritas Update provides estimates and projections for both the single-classification and all-inclusive tabulations. Estimates for the seven single-classification categories (by Hispanic/not-Hispanic ethnicity) are produced first, and all-inclusive estimates are then derived from the single-classification numbers.
Estimates and Projections of Race and Hispanic Ethnicity
At the county level and above, estimates of race and Hispanic ethnicity are based on the Census Bureau’s estimates of population by race and ethnicity at the county level.
The application is not straightforward, since the Census Bureau’s race estimates reflect a modified definition, in which persons marking “Some other race” were re-assigned (with imputation techniques) to a specified race category. This reassignment increases the numbers in the specified categories, making them inconsistent with the census definition race counts reported in
standard Census 2000 products.
For this reason, the Claritas method applies the Census Bureau’s estimated rates of change from the most applicable modified race category to the relevant Census 2000 race counts. For example, the census estimates might suggest a 4.2 percent increase in the percent of a county’s population that is (modified) “Asian not Hispanic.” The Claritas estimate is established by applying this rate of change to “percent Asian not-Hispanic” from the 2000 census. Estimates are produced for the seven not-Hispanic race categories. Percent Hispanic or Latino population is estimated separately based on the rate of change in percent Hispanic population suggested by the Census Bureau estimates. The Hispanic or Latino estimates are then distributed to race based on county specific percentages from the 2000 census. The estimates for the 14 race/ethnicity categories are then finalized by applying estimated percent race/ethnicity to the previously completed estimates of total population for each county.
Race/ethnicity estimates below the county level are based on 1990-2000 census trends in the percent of population in each race/ethnicity category. Again, the method focuses on the percent of population in each category. Estimates are produced first for tract level (with adjustments to county level), then for block groups (with adjustments to tract level). The projection of inter-censal trends is not a preferred method, but the approach was an achievement made possible by the conversion of 1990 data to 2000 geography, and the bridging of 1990 race to 2000 race definitions.
Race Bridging
The current race definitions make it impossible to identify definitive race trends between the 1990 and 2000 censuses. However, to estimate 1990-2000 trends, Claritas “bridged” 1990 census race data to the 2000 definitions. Specifically, Claritas estimated what the 1990 census race data might have looked like had it been collected using 2000 categories, and the option of marking two or more races.
All race bridging was accomplished separately for the Hispanic or Latino and not-Hispanic populations (preserving race by Hispanic cross-tabulation options) for all block groups nationwide. The first step was the bridging of 2000 race to 1990 definitions. After
combining the Asian and Native Hawaiian and Other Pacific Islander categories (whether alone or part of combinations) to the 1990 Asian or Pacific Islander Category, counts from the remaining multiple-race categories were distributed to single 1990 race categories. This distribution was accomplished with equal fractions assignments in most cases (combinations of two races distributed half to one category and half to the other, combinations of three races distributed by thirds, and so forth), but National Health Interview Survey proportions were used for selected combinations. These include:
White or Black or African American
White and American Indian or Alaska Native
White and Asian
Black or African American and American Indian or Alaska Native
The bridged 2000 race data suggests how many persons would have been added to each “race alone” category had multiple-race response not been an option in 2000. For example, bridging 2000 data to 1990 definitions added some persons from multi-race categories to “Black or African American alone” to estimate the 1990 “Black” category. From the reverse perspective, the data suggests the proportion of the bridged “Black” population that would be lost to race combinations when transitioning back to the 2000 “Black or African American alone” definition. The 2000 bridged data suggests such percentages for all 1990 race categories, and these percentages were applied to the 1990 census race data (converted to 2000 block groups) to estimate the number that would have been lost from each category to multiple race responses in 1990. Census 2000 patterns then were used to distribute the estimated 1990 “two or more races” population to the 57 categories reflecting combinations of two or more 2000 census race categories.
The bridging project produced a set of 1990 census population data distributed to the 126 Census 2000 race by Hispanic categories, and converted to 2000 census block groups. This data—collapsed to single-assignment race—provided a basis for estimating race/Hispanic population trends for census tracts and block groups.
Five-Year Projections
Five year projections of race/ethnicity are produced with similar methods—projecting the current year estimates (of percent race/ethnicity) to the five-year projection date. Again, projections are made for percent race/ethnicity distributions, and applied to previously completed projections of population. Counties are projected first, followed by tracts and block groups, with adjustments ensuring consistency between geographic levels.
All-Inclusive Race
Estimates and projections for all-inclusive race/ethnicity are produced as derivatives of the single-classification estimates and projections. For each race/ethnicity category, the 2000 census ratio of all-inclusive race/single-classification race is identified, and applied to the estimate or projection of single-classification race—with adjustments made in some areas to ensure consistency with the number of persons estimated (or projected) to be of two or more races. Because the all-inclusive estimates and projections are derived from data already adjusted to county controls, the all-inclusive estimates and projections are produced only at the block group level, and summed to higher levels.
Population by Age/Sex by Race/Ethnicity
Estimates and projections also are provided for the cross-tabulation of population by age/sex/race/ethnicity. These estimates start with the completed estimates of population by age/sex and population by race/ethnicity at the block group level. Census 2000 does not provide age/sex/race/ethnicity detail at the block group level. For this reason, age/sex/race/ethnicity distributions for census tracts are used as “seed values” for component block groups, and iteratively adjusted to conform with the previously completed estimates of population by age/sex and population by race/ethnicity. This application of IPF produces block group estimates consistent with estimated age/sex and race/ethnicity, as well as the statistical relationship between these characteristics observed for the census tract in the 2000 census.
Household Characteristics
Households by Income
All Claritas income estimates are expressed in current year dollars using the money income definition reported in the 2000 census. The estimates and projections reflect household income, which includes the income earned by all persons living in a housing unit (i.e., all household members). In contrast to the 2000 census, which reported income for the previous calendar year(1999), Claritas income estimates are for the calendar year relevant to each set of estimates and projections. For example, the 2006 estimates would reflect 2006 income for 2006 households.
The method starts by estimating rates of change in median household income for each area. Based on this rate of change, household income distributions from the 2000 census are advanced to current (or projection) year. As with the population estimates and projections, data was first produced for large areas, then for progressively smaller areas, with successive ratio adjustments ensuring consistency between levels. Aggregate and per capita income numbers were derived from the resulting income distributions. Claritas estimates household income for all 16 income categories reported by the 2000 census in Summary File 3 (SF3).
Income Estimation Method
Income change at the national level is estimated based on national estimates of income change from the Current Population Survey and the American Community Survey. The final estimates reflect an average of estimates based on the two sources. The national income distributions serve as a target for the state estimates, rather than a control total.
State income estimates are based on IRS wage and salary data, and BEA estimates of per capita income. Because national IRS and BEA income data tends to reflect more rapid income growth than the national estimate, these sources are used to monitor each state’s income growth relative to the national level—change in the ratio of state income to national income. The final rates of change reflect the average of such ratios based on IRS and BEA data, as well as a projection of the ratio based on 1990-2000 census trends.
County income rates of change are estimated with similar
procedures—this time applying county/state ratios of IRS and BEA income data to 2000 census county/state income ratios. Again, the final estimated rates of change reflect the average of ratios based on IRS and BEA data, and the projection of 1990-2000 census trends.
Income change at the tract level also is estimated with alternative sources, with the final estimated rate reflecting the average of these rates. The first estimate is based on historical performance. Specifically, tracts were estimated to outpace or lag behind county income growth in proportion to their performance relative to county during the 1990 to 2000 census period. The second is based on post-2000 trends in income estimates aggregated from the Equifax TotalSource consumer household database. The TotalSource income estimates are modeled for all individual household records on the database. The third is based on trends in the Equifax ACE-Geosummary database, which provides tract level summaries of consumer financial information from the Equifax Consumer Marketing Database (ECMD). Although not a direct measure of income, the ECMD data item “Average sum of credit limits for bank, national credit card, savings & loan, and credit union revolving accounts” is strongly associated with income at the tract level, so change in this variable is used to track change in income at the tract level.
The approach with all three sources is to track change in the tract/county ratios—or the performance of tract income relative to county level. Income change at the block group level is estimated with sources and methods similar to those described for census tracts above.
  For all geographic levels, the estimated rate of income change is used to advance, or shift, the 2000 census distribution of households by income forward to current year. This procedure involves the estimation of the number of households advancing from one income category to another—based on the area’s estimated rate of income growth.  
  The resulting current year distribution is adjusted to conform with that estimated for the next higher geographic level. For example, the estimated household income distribution for states is adjusted to the national distribution, the county estimates are adjusted
to the final state distributions, and so forth.
Five Year Projections
Five year projections of income begin with the projection of current year median household income to the projection year, and the advancing of the household income distribution to reflect the projected change. Median incomes for sub-national areas are produced by projecting estimated income change to the projection
year, and advancing the current year estimated income distribution to reflect that rate of change. As with the current year estimates, the initial income distributions are adjusted to the final distributions for the next higher geographic level. State projections are adjusted to national, county to state, and so forth.
Family Household Income
A family household is one in which the householder is related to one or more persons living in the household. Family households also include any other non-related persons living in the same housing unit. Family household income includes the income of all persons living in a family household.
Family household income is estimated by applying the final estimated household income growth rates (1999 to current year) to the 2000 census distribution of family households by income—advancing the family household income distribution to reflect the relevant rate of income growth. Five year projections were produced by trending the estimated rate of family income change out five years, and advancing the current year distribution to reflect the projected change. Because the estimates and projections of family household income are derivatives of the completed household income estimates—which already reflect control totals for large areas—they are estimated and projected at block group
level only, and summed directly to higher levels.
Income by Age of Householder
The cross-tabulation of household income by age of householder is valuable because income and life cycle stage, when combined, are so strongly associated with consumer needs and behavior. The Claritas income by age updates are produced after the estimates of population by age and households by income have been completed. The data constitutes a 198 cell table defined by 18 categories of household income and 11 categories of householder age. The row and column totals from these tables (the income and age totals) are commonly referred to as the marginal totals.
The estimates of households by income serve as the income marginals, but population by age estimates must be converted to householder by age for use as the age marginals. For each area estimated, 2000 census data is used to determine age-specific headship rates, or the percent of persons in specific age categories who are householders. These headship rates are then applied to estimated population by age to produce estimated
householders by age. A final adjustment to total households ensures consistency with that critical base count.
With the income and age (row and column) marginal totals estimated, the final step is to estimate the full cross-tabulation of income by age of householder. In other words, values must be determined for each of the 198 income by age categories, or cells. Block group level income by age cell values from the 2000 census
provide the initial input (or seed values). Within each age category, the 2000 census income distributions are advanced to reflect the block group’s (previously) estimated rate of income growth. This adjustment expresses the 2000 census income by age distribution in current dollar values. The resulting table is then adjusted to conform with both the income and age of householder totals estimated for current year. These adjustments are accomplished through iterative proportional fitting, which adjusts the 2000 table to conform simultaneously with the household income and
householder by age estimates, while preserving the block group specific statistical relationship between income and age reflected in the 2000 census income by age data.
The income by age estimates are produced at the county, tract, and block group levels, with adjustments ensuring consistency between levels.
Five year projections are produced using similar methods. Projected households by income serve as the income marginal totals, and Census 2000 headship rates again are used to convert projected population by age to projected householders by age. The income by age table is then advanced to projection year dollar values, and iteratively adjusted to the projected income and age marginal totals.
Income by Race and Ethnicity of Householder
Estimates and projections of households by the race and ethnicity of the householder are produced by applying the estimated/projected rates of change in income for each area to the income distribution for each race/ethnicity group in the area. The rates of change are used to project each distribution forward to the current (or projected) year, and the resulting distributions are adjusted to conform with the householder by race/ethnicity estimates and projections described above.
Householders by Race and Ethnicity
Estimates and projections of householders by (single assignment) race and Hispanic ethnicity are based on the estimates and projections of population by race/ethnicity.
For each block group, the 2000 census ratio of householders by race/Hispanic to population by race/ethnicity is identified, and applied to the current year estimate of population by race/ethnicity. This ratio indicates the percent of persons in each race/ethnicity category who were householders in the 2000 census. The final ratio is modified somewhat through the adjustment of householders by race to total households for each area, and it is the final current year ratio that is applied to the five-year projections.
Households by Size
Working at the block group level, estimates of households by size (number of persons) are produced for the following categories:
1 person
2 persons
3 persons
4 persons
5 persons
6 persons
7 or more persons
The distribution of households by size from the 2000 census serves as the base from which the current year estimates are derived. The 2000 distribution is advanced to current year based on estimated change in persons per household (average household size). Iterative proportional fitting is then used to ensure consistency with estimated household totals and average household size.
Projections of households by size are based on the 2000 census and current year estimated distribution of households by size. The current year distribution is shifted to reflect the growth or decline in average household size during the projection interval. Iterative
proportional fitting is then used to ensure consistency with projected household totals and average household size.
Households by Year Moved Into Unit
Year moved in survival probabilities are computed from 1990 and 2000 census data (in this case reflecting the loss of residents of specific lengths of residence). These national level probabilities are applied to the 2000 census distribution of households by “Year Moved In” to establish estimated and projected distributions.
Households in excess of those surviving (staying in place) to longer lengths of residence are those estimated to have moved in following the 2000 census. Thus, areas with rapid household growth will show the greatest concentrations of new movers.
Housing Unit Characteristics
Housing Value
Housing value (often referred to as home value) is estimated and projected for all owner-occupied housing units, and is based on the 2000 census measure, which reflects census respondents’ estimates of how much their dwellings would sell for, or the asking price of units currently for sale. Median value is estimated and
projected as well as the distribution of units among the 24 categories of value reported by the 2000 census.
The total number of owner-occupied housing units is estimated by applying the relevant 2000 census percentage to the completed estimate of total occupied housing units. The basic rate of change in value is estimated first, and is used to advance the 2000 census distribution of units by value to reflect this rate of change.
At the national and state levels, the rate of change in home value is monitored through the Census Bureau’s American Community Survey (ACS), and House Price Index data from the OFHEO. Even in its test phase, the ACS was collecting data on home value from a nationwide sample of 700,000 households. And the OFHEO House Price Index is a measure of post-2000 changes in housing value derived from Fannie Mae and Freddie Mac mortgage transaction data.
County rates of change in home value are derived from two sources at the metropolitan area level. The first is data indicating the change in median sales price from the NAR. Changes in sales price reflect only units sold during the time in question, but are strongly associated with overall change in home value. The second source is change in the OFHEO House Price Index described above.
At the census tract level, change in home value is tracked with
ACE-Geosummary data from the Equifax Consumer Marketing database. The Equifax files do not measure home value directly, but the variable “Average original balance on mortgage accounts” is strongly associated with home value. Claritas has compiled tract summaries of this variable for all census tracts for years back to 2000, and uses trends to track small area changes in home value. The greater the increase in mortgage amounts, the greater the increase in home values.
Five Year Projections
Five year projections of value are based on rates of change derived form change in median value from 2000 census to the current year estimate.
For each geographic level, the estimated rates of change are used to advance the 2000 census home value distribution to current year. Estimates and projections are produced first at state and national levels, but these estimates serve as targets for the county
estimates and projections, rather than control totals. Starting at the county level, the estimates and projections serve as control totals for small areas.
Housing Units by Year Built
Estimates and projections of housing units by year built start with the distributions from the 2000 census. These distributions are advanced to current year (and five year) targets based on housing loss patterns exhibited in the 1990 and 2000 censuses.
Additional Terminology
Block Group Parts
  Many Claritas methods are executed at what is technically the block group and block group part level of geography. Block group parts are defined where block groups are split by place and/or MCD boundaries, and census data reported for block groups is reported for these block group parts. Thus, block group parts function as a geographic level between block group and block. Because it is more familiar, the term block group level is used throughout this document. However, it is worth keeping in mind that Claritas block group level applications usually refer to data and methodologies executed for block groups and block group parts.  
  Consistency of Complete Count and Sample Census Total  
  Because much census information was collected on a sample basis using the census long form, the Census Bureau used weighting techniques to present such data in complete count form. The weighted sample totals presented in SF 3 often differ from the SF 1 complete count totals by small amounts. For example, a census tract with 1,200 (SF 1) households might have an income
table (from SF 3) summing to 1,206 or 1,197 households. The differences are statistically inconsequential.
  Claritas products provide 2000 census data as published by the Census Bureau. The 1990 census data also is provided as published, but has been converted to 2000 census geography. Thus, for both 1990 and 2000 census, the usually minor discrepancies between sample and complete count totals are preserved.  
  Adjustment Techniques  
  The adjustment process is essential to the production of estimates that use input data at various geographic levels, and are consistent across all levels of geography. The Claritas updates are geographically consistent, meaning that for each data item, block group data always sums to tract totals, which always sums in
turn to county, state, and national totals. Adjustment techniques also ensure that characteristic distributions sum to base count totals (e.g., households by income always sums to total households). The simultaneous adjustment of characteristics to higher level control totals and to total persons or households within each smaller area is achieved with IPF. The basic techniques
are described below.
  Ratio Adjustment  
  Ratio adjustment is used to bring small area data into conformity with large area totals. For example, if preliminary block group population estimates sum to a tract total of 552, but the independent tract estimate is 561, the preliminary block group estimates are adjusted upward by 1.63 percent (561/552) to achieve the target tract total. Similar adjustments are made to bring preliminary distributions (such as age and race) into conformity with population totals for each geographic unit.  
  Iterative Proportional Fitting  
  Iterative Proportional Fitting (IPF) methods are an elaborate form of ratio-adjustment, and are used when estimates must be adjusted to conform simultaneously with two sets of marginal control totals—often referred to as the dimensions of a two-dimensional table. Income by age of householder is a good example. The estimates must sum to both households by income and householders by age.  
  IPF methods begin with a table with target row and column totals, referred to as the row and column marginal totals. For example, one might have 12 categories of households by income as the row totals and 11 categories of householders by age as the column totals established for a 132 cell (12 by 11) table. The objective is to produce estimates for the table’s 132 cells that sum to both the row and column marginals.  
  The execution of IPF methods requires initial or seed cell values. In the case of income by age of householder, seed values are obtained from the 1990 census. This matrix of cell values reflects an intricate set of probabilities defining the relationship between income and age—as measured for the specific geography in the census. However, these 1990 census figures sum to neither estimated households by income nor estimated householders by age.  
  Iterative proportional fitting achieves this conformity through a series of ratio adjustments to the row and column marginal totals. Each round (or iteration) of row and column adjustments brings the seed values closer to conformity with the marginal totals. The number of iterations required varies by area, but the values eventually converge on a result that sums, within rounding error, to the marginal totals. The resulting estimates not only sum to the desired marginal totals, but preserve the statistical relationship between the two variables (income and age) measured for the area by the census.  
  Income Distributions  
  A source of occasional confusion is the fact that the 2000 census reported income earned during calendar year 1999. This is the case whether the data are described as 1999 income or 2000 census income. The one year census lag is logical, since no one had yet received their 2000 income in April 2000 when the census was taken. The Claritas update is not constrained by this reporting
limitation, and therefore presents income for the calendar year corresponding to the household estimate or projections. For example, the 2005 update provided estimates of 2005 households by income earned in 2005. When comparing such estimates against the census, note that total households represent a five year change since 2000, while income represents a six year change since 1999.
  Inflation and Income  
  A common question is how the effect of inflation is accounted for in the Claritas income estimates. Inflation, as commonly measured by the Consumer Price Index, reflects changing prices, and a corresponding change in the value of a dollar. For example, items that would have cost $100 in 1983, would have cost about $147 by 1993—a 47 percent inflation in prices. Thus, $100 was not the same in 1993 as it was in 1983.  
  Inflation is not a measure of income change, but the two are related. Some income sources (such as Social Security and some union contracts) are indexed by inflation, and workers typically require and demand more pay to cover the increased costs of living. Although income tends to follow inflation, it does not move at the same rate. There are periods when income growth outpaces inflation, and periods when it lags behind. These income changes relative to inflation are referred to as real income growth.  
  The Claritas income estimates and projections are expressed in current dollar values, which reflect how many dollars are being received at the relevant year. As such, they reflect both real income growth (or decline) and the change due to the effect of inflation. Rather than estimating the effects separately, Claritas measures the combined or net effect through input sources (such as the Bureau of Economic Analysis income estimates), which themselves estimate income change in current dollars. The inflation effect built into these estimates is implicitly incorporated into the Claritas estimates. Note that accounting for inflation in this manner is different from controlling for inflation, which requires removing the effect of inflation, to produce estimates in constant dollar values.