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APPLIED DEMOGAPHY: ITS BUSINESS AND PUBLIC SECTOR
COMPONENTS
D. A. Swanson
Department of Sociology, University of California Riverside, USA
Louis G. Pol
College of Business Administration, University of Nebraska at Omaha, USA
Contents
1. Introduction
2. Business and Public Sector Demography
3. Examples
4. Challenges and Opportunities
Summary
The development of data through estimation and projection (particularly for small areas) has and will
continue to be affected by continued improvements in methods and technology. Although we focus on
applied demography in the United States, the developments we described in methodology and
technology transcend national boundaries and, as such, serve to globalize its key features and practice.
These developments will, in turn, influence business demography not only in the United States, but
elsewhere. They suggest that even more skills and an expanded demographic perspective will be
needed by those with a desire to be successful applied demographers.
Keywords: estimates, projections, training, practice, current developments, trends, applied
demography, business demography, public sector demography
1. Introduction
Business demography is considered to be relatively new, as is its closely related counterpart, “public
sector demography.” Together, these two areas can be considered as comprising applied demography.
As such, we refer to them collectively as “applied demography” throughout our discussion. What is
applied demography? It is primarily concerned with solving exogenously-defined problems by
producing the information necessary to effect practical decision-making while minimizing the time and
resources needed to produce this information. Basic demography, in comparison, is primarily
concerned with solving endogenously-defined problems by offering convincing explanations of
demographic phenomena while viewing time and resources as barriers to surmount in order to
maximize precision and explanatory power. As is discussed in the Introduction to this volume,
demographers view basic demography as comprised of two distinct subsets, formal demography and
population studies.
It also is useful to distinguish business demography from public sector applications. Even though the
methods and materials utilized in both types of studies are the same, the unique, profit-oriented and
oftentimes proprietary nature of the work justifies separate consideration for business demography. It
also is worthwhile to note that much of the public sector applied work seen today has a longer history
and might have been classified as part of the more general rubric of “social demography” at an earlier
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time. As can be seen in Figure 1, the refinement and extension of census and survey data as well as
administrative records data (the foundation upon which estimates and projections are based) are
accomplished by the processes of estimation and projection, which overlap both sectors. Estimation
and projection are viewed as the heart and soul of applied demography.
Figure 1 Major Components of Applied Demography & Their Relationships.
Before turning to a discussion of business demography, it is useful to define not only the terms
“estimate” and “projection,” but also the term “forecast.” Because it is more inclusive, we generally
use the term “projection” rather than “forecast” in our discussion.
Estimate – A calculation of a current or past population, typically based on symptomatic indicators of
population change.
Projection-- The numerical outcome of a particular set of assumptions regarding future population
trends.
Forecast – The projection deemed most accurate for the purpose of predicting future population.
It also is useful to note here that there are two distinct traditions in regard to population estimates (1)
demographic; and (2) statistical – that is, the methods used by those who do sample surveys.
Demographic methods are used to develop estimates of a total population as well as the ascribed
characteristics – age, race, and sex - of a given population. Statistical methods are largely used to
estimate the achieved characteristics of a population – educational attainment, employment status,
income, and martial status, for example. Among survey statisticians, the demographer’s definition of
an estimate is generally termed an “indirect estimate” because unlike a sample survey, the data used to
construct a demographic estimate are symptomatic indicators of population change (e.g., K-12
enrollment data, births, deaths,) and do not directly represent the phenomenon of interest. Among
demographers, the term “indirect estimate” has a different meaning (See “Direct Estimate” and
“Indirect Estimate” in the Glossary). As is suggested in Figure 1, most national statistical agencies
produce estimates using both of these traditions, demographic and statistical. We return to this issue
later when we discuss small area demography. In regard to population projections, the distinction
between the demographic tradition and the statistical tradition is less pronounced, however.
It also is important to note that there are two different definitions of “population” used by national
statistical agencies. In the United States, for example, the Census Bureau uses a definition based on
place of “usual residence.” This also is known as the “De Jure” population. The other definition is
based on the concept of a “De Facto” population. Examples of de facto populations are many. They
include vacationers (of interest, for example, to the hotel owners in Phuket, Thailand, the casino
industry in Macao or Las Vegas, and the Hawaii Visitors Bureau), migratory workers (of interest, for
example, to health care, school, and other social service providers), and the people who work in the
central business district of a large city each day, but leave it largely vacant in the evenings (of interest
to the San Francisco City Planning Office, for example). While estimates of De Facto populations are
of great interest, they are difficult to make in countries that use the De Jure concept of residency for
their census counts. In addition, they are also difficult to evaluate in these countries because there is no
“gold standard” against which to measure them in terms of validity and reliability.
2. Business and Public Sector Demography
The development of an applied demographic focus has been driven by the interaction of several
elements that have shaped the nature of certain streams of research in demography. The first is the rise
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in the use of the demographic perspective, data and methods in addressing business opportunities and
problems. In the late 1950s, Donald Bogue introduced the concept of “micro-demography,” along with
a model of applied demography for small areas. The model presented applications in general planning
and was comprised of three components: transportation and facilities, urban renewal, and the market
analysis. Included within market analysis was the evaluation of new and existing sites with respect to
shopping centers and individual retail establishments. However, business demography as it is known
today is a phenomenon that only emerged in the late 1970s. Closely related is the rise in interest
regarding demographic applications in micro decision making (e.g., the decision of an individual
business in a specific location to expand its market reach). However, without improvements in
methods to produce good quality small area demographic estimates, some of the information needed to
drive micro decision-making would not be available. Encouraged in part by the need for small area
data, many demographers more recently have focused their efforts on the improvement of techniques
and technology needed to generate, display, and analyze small area data, both for the private and the
public sectors. Finally, while the vast majority of demographic research focuses on the demography of
geographic units, it is worthwhile to note that an interest in organizational demography and other nongeography-based applications has emerged in the last decade or so.
Both Business and Public Sector Demography focus on the use of demographic data, methods and
perspectives in decision-making. That is, managers, chief executive officers (CEOs), chief financial
officers (CFOs), elected officials and public planners are responsible for strategy development and
implementation. Demographic input is but one of several considerations as new possibilities in the
global marketplace are being evaluated and public works projects are being designed. Demographic
data and techniques have been used by businesses for over 100 years. However, the recognition of
business demography as a distinct field has only come about in the past 30 or so years. It now is
common for many businesses to make decisions on the basis of demographic criteria. To effect these
decisions, businesses rely upon the advice of experts who can assemble, analyze, and interpret
demographic data.
2.1 Basic Data Sources
In countries that have good census counts, supplemented by reasonable surveys, and maintain some
level of administrative records, such as vital events registration (e.g., Australia, Canada, France, India,
Japan, New Zealand, the United Kingdom, and the United States), most estimates rely on one or more
censuses and use administrative record systems on which different estimation methods for censusdefined populations rely (Of course, in countries that have accurate and comprehensive population
registers, estimates of this nature are not needed, as is the case in Finland, for example). In the United
States, these records include vital events, tax returns, housing permits, assessor parcel files, utility
hookups, licensed drivers, covered employment, K-12 enrollment, Medicare counts, and child support
payments, among others. It is important to note that there is some variation in availability and quality
of administrative records systems by state and by local jurisdictions in the U.S., as well as considerable
variation among countries. For example, Alaska makes annual payments to its residents, a unique
feature in the United Sates; while in Canada, these records include family allowance payments, as
would be the case in other countries with similar pro-natal policies. It also is important to note that the
U.S. Census Bureau maintains as much consistency in data sources and methods as it can because
among other desirable features it wants to have a consistent set of estimates for a given “vintage” year.
We also note here the emergence of an important resource directly collected by the U.S. Census Bureau
– a Master Address File (MAF) constructed for the 2000 census that is updated and maintained until
the next census. This is a new resource for the Census Bureau’s estimates program because in the
previous “mail-out/mail-back censuses, the MAF was constructed from scratch before each census.
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This housing unit inventory potentially serves as a key resource in the Census Bureau’s ability to
construct current population estimates. Similar data sets can be found in other countries that conduct
regular census counts. It also is useful to note that in these same countries, “record matching”
techniques (also known as dual system estimation) can be used to develop estimates. Similar
techniques, called “capture-recapture” methods, are used by biologists to estimate the sizes and
compositions of wildlife populations such as deer, eagles, and salmon.
In countries that have neither an accurate and comprehensive national population register nor good
census counts, surveys, and administrative record systems, the development of population information
for business demography is severely limited. In such cases, techniques that emerged from formal
demography are needed, such those described in The Demography of Tropical Africa by William Brass
et al. Another possible approach that could be used is the use of remote sensing (satellite imagery),
such as described by Qui, Woller, and Briggs and by Wicks, Vincent, de Almeida, and Swanson,
among others.
Demographic and related data of interest to the business demographer are also found in the private
sector. CLARITAS, for example, is a global vendor of demographic and related information in the
form of estimates and projections, as is PITNEY BOWES MAPINFO (formerly known as MAPINFO).
The large U.S.-based credit bureaus, Equifax, Experian (TRW) and TransUnion, for example, have vast
amounts of demographic and related data for households and individuals, as does R. L. Polk, a
company that sells city directories. At least some of these companies will sell data to other companies
and individuals for analytic and other uses related to business demography. While there are a few other
companies in the credit bureau business, there are many small companies in the business of selling
customized estimates, projections, and other types of analyses. These companies usually rely on a
singular feature that distinguishes business demography from public sector demography, namely the
interest in information for postal delivery areas (known as zip codes in the United States). Many
companies define their market areas by postal delivery areas and while some national statistical
agencies provide census data for these pieces of geography, they do not routinely produce estimates
and projections for them.
Customer loyalty cards as well as credit cards can be used by the companies that offer them to build
purchasing records on the people that hold these cards. That is, these records can provide information
on the demand for goods and services. When these records can be statistically matched with
demographic, income and other information held on these same individuals from the large credit
bureaus, the supply side of the picture emerges. These data become a very important source for
analyzing patterns and trends when they are aggregated by characteristics on the demand and supply
side, as well as by geography and time.
2.2 Tools
The tools of the trade break down generally into those used for estimation and those used to make
projections, although there are important tools that do not neatly fall into either category, such as data
mining and GIS (Geographic Information Systems). Neither data mining nor GIS fall neatly into the
preceding discussion because they serve largely as precursors to developing either estimates or
projections.
The methods generally used to make estimates are generally found under the headings of component
methods, ratio methods, regression-based methods, housing unit methods, and combinations of one or
more of these methods, including ratio-correlation and synthetic estimation. These terms are defined in
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the Glossary and described in “Population Estimates,” by Tom Bryan, a chapter found in The Methods
and Materials of Demography, 2nd Edition that provides references to more detailed discussions. Some
or all of these methods are not only used by national statistical bureaus in such countries as Australia,
Canada, the United Kingdom, and the United States, but also by sub-national entities in these countries
such as states, provinces, counties, cities, and councils of government. Some of these same methods
can be combined with current sample survey data, to include the ratio methods and regression.
The basic projection methods found in business demography include simple and complex trend
extrapolation methods, simple and complex ratio methods, the cohort-component method and its
variants (e.g., the Hamilton/Perry Method), and structural models, which include
economic/demographic models and integrated land use and transportation models.
2.3 Training
Like many other scientific disciplines, training in applied demography has a formal education
component and a practical or on-the-job component. Applied demographers must be able to synthesize
data, methods and perspectives from a range of disciplines in order to explore the best opportunities
and to produce optimal solutions to problems. Critical to the mastery of these skills is the ability to use
the tools discussed in the preceding section. For example, a business demographer working on small
area real estate development such as a shopping mall would need to have knowledge of: (1) methods to
produce population estimates and projections for sub-county geographic units; (2) zoning regulations
for the area(s) of interest; (3) the location and characteristics of current and potential competitors; (4)
traffic flows; (5) local and regional government development plans and possible tax incentive
programs; (6) land costs; (7) construction costs; (8) an understanding of the markets from which
customers will be drawn; and (9) financing in order to be a full partner in the project. He or she would
have to determine if an adequate return on investment could be realized before the project was
advanced.
There are no programs of training that offer the broad-based education needed for a person to
participate at the level described in the scenario outlined above. For the most part, demographers
receive reasonably good methods training, but only a few programs offer several courses designed to
give students exposure to public and private sector applications. Programs and courses are offered at
the undergraduate and graduate levels of instruction and most are housed in departments of sociology.
Neither the close affiliation of demography with sociology nor its lack of interdisciplinary connections
serves the training needs of applied demography very well. In only a few instances are programs and
courses found within sociology that are well-designed for applied demography, much less business
demography. It also is the case that applied demography is neither often found in applied fields (e.g.,
urban planning departments) nor as part of interdisciplinary efforts (e.g., public policy departments).
Many colleges and universities offer individual courses in applied demography. These courses cover a
broad range of applied demography topics of study and several are taught in the context of a particular
line in inquiry. “Applied Demography” is a course in the undergraduate sociology curriculum at the
University of Mississippi designed both for majors in sociology and others. “Business Demography” is
taught to undergraduate business and MBA students at the University of Nebraska at Omaha. The
Helsinki School of Economics offers a course that is specific to business demography: “Demographic
Analysis and International Business,” while the Department of Business at Macquarie University offers
several undergraduate courses in demography, including “Business Demographics.” Moreover, the
program at Macquarie University is housed in the business department, thus providing some
advantages over programs that call Social Science or Arts and Science colleges their home. An
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undergraduate course titled “The Demography of Business/Policy” is offered at Duke University and
“Market Research in Public and Private Sector Organizations” is taught at Brown University. A
graduate course titled “Demographics for Planning and Policy” is taught at the University of California,
Irvine, which offers an entire Master’s Degree program in applied demography. “Applied demography”
is taught at the University of Calabria in Italy. Graduate level courses in applied demography also are
offered at: University of California at Berkeley, Marshall University, the University of Wisconsin at
Madison, the University of Connecticut, Lehman College, Concordia University, and the University of
Massachusetts at Amherst, among others. In terms of pedagogy, the teaching of these courses varies,
but some version of the case study method is often found in them. This list of institutions offering
courses and programs is meant to be illustrative, not exhaustive, and, as such, we note that new training
and education efforts are developing all over the world.
3. Examples.
3.1. The Need for a New Medical Facility
With the advent in the 1980s of intense competition among medical care providers in the U.S.,
organizations proposing medical facilities such as hospitals were required by local and state health
planning board to demonstrate a need for the proposed services by applying for a “certificate of need.”
In 2006, David Swanson was engaged by a client to evaluate population projections done using
geometric extrapolation to support the need for a medical facility in the urban fringe of a large city in
the southern U.S. As a basis for his evaluation, Swanson needed to develop three sets of cohortcomponent population projections comprised of high, medium, and low scenarios, for the set of zip
codes comprising the study area. Swanson found that the population forecasted for the study area on
behalf of the organization seeking to build the facility lacked face validity, was implausible, and failed
to give any indication of the uncertainty inherent in it. As Swanson discussed in his report, compound
growth, linear, and other mathematical extrapolation formulas have their place in the demographer’s
tool kit and are particularly useful when data series are incomplete and budgets are highly constrained.
However, in the case of the population of the study area, the data series were, in fact, quite complete,
and Swanson observed that was the organization should not have been so constrained by costs as to
require such a simplistic and inexpensive forecasting method, one that resulted in unrealistic results by
being carried too far into the future. A less damaging, but important defect in the organization’s
projection was that it provided no indication of the uncertainty inherent in it. A judge agreed with
these findings and found in favor of Swanson’s client.
3.2. Improving Cellular Market Valuation with Demographic Data
In the late 1980s, a client asked George Billings and Louis Pol to assist in determining the value of two
neighboring potential cellular phone markets in Florida. The client wanted an answer as to which of
four options were best: (1) buy one market; (2) buy the other market; (3) buy both markets; and (4)
pass on the opportunity. Billings and Pol used demographic and other data (e.g., cellular phone
penetration, household income) to develop a standard by which the markets could be compared and
analyzed. Once the client had the analysis produced by Billings and Pol, the initial asking prices of
both markets were deemed to be too high. After some rounds of negotiation and faced with objective,
demographically-based data for a valuation of these two markets, the seller offered a lower price to the
client of Billings and Pol.
3.3. Determining the Effect of Hurricane Katrina on Medical Practices
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In 2007, David Swanson was asked by a representative of two medical doctors whose practices had
been disrupted by Hurricane Katrina (which struck August 29th, 2005), to quantify the current and
future effects of the disruption on their patient populations in the zip codes that comprised their primary
and secondary service areas. Using the Hamilton-Perry method in conjunction with case data specific
to the area of practice of the two medial doctors, Swanson found that Katrina had a measurable effect
on the pool of patients who would present themselves for care by these two medical doctors in that the
primary service case load was reduced by more than ten percent and the secondary service area case
load was reduced by 55 percent. Swanson concluded that these declines represented a secular trend that
would further reduce the potential pool of patients well into the future. The medical doctors used these
results in negotiations over financial issues brought on by Hurricane Katrina.
4. Challenges and Opportunities
Applied demography, whether in Australia, Europe, Asia, South America or the United States, or in the
public or private sectors, does not take place in a vacuum. It is subject to broad economic forces that
manifest themselves in changes in working conditions. One may take the idea of “outsourcing” as an
example. Because of budget and related pressures, work that may once have been performed in-house
within many organizations is now outsourced via contract. For applied demographers this represents
both an opportunity and a challenge. It is an opportunity in that there is a great deal of part-time
consulting work of a project nature; it is a challenge in that this trend represents a decrease in job
security and access to important benefits (such as health insurance) that are normally provided by
employers in the United States.
Examples of the challenge provided by this outsourcing trend include the state of Minnesota, which
recently let go or reallocated staff in its long-standing demographic studies unit in the State Planning
Office. Similar challenges have appeared elsewhere, including Kansas, Michigan, and New York.
In a broad sense, the availability of consulting projects in applied demography represents a substantial
opportunity for demographers employed in academic institutions, where working conditions and
incentives encourage the pursuit of project contracts and consulting work. This is particularly true for
applied demographers within schools and colleges of business or public policy, where active hands-on
participation in projects outside of the academy is generally encouraged.
Because of these trends, two other phenomena can be observed. First, the number of occupations where
demographic skills can be used has grown. Many employed in both the public and private sectors now
count themselves as pseudo-demographers, even though they have little or no formal demographic
training. Second, the number of employment opportunities where a combination of demographic and
GIS/analytic skills are either required or give an applicant an edge over competitors is growing. In
turn, these two phenomena feed the demand for demographic products and expertise.
While there are challenges to the practice of applied demography, opportunities abound. The
opportunities are mainly in the form of the well-established understanding of the importance and
ubiquity of “demographics” over a broad range of endeavors found within the private and public
sectors. This serves as a strong source of demand for the products that applied demographers produce.
There are two major challenges in regard to training. The first is the close linkage between sociology
and demography. One problem with this linkage is that the normative practices within sociology
programs in the U.S. are strongly oriented toward basic (academic) science. As a consequence,
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teaching and research in applied areas generally is neither encouraged nor rewarded. The second issue
is the lack of cross disciplinary training. Both of these issues represent less than optimal conditions for
the training of applied demographers, largely because there are no strong ties to fields that can be
classified as decision-making sciences.
There are additional challenges facing business and public sector demographers. These include
concerns about confidentiality and privacy, the costs of data collection, and potential litigation. In the
United States, for example, a report released in 2005, the National Research Council’s Panel on Data
Access for Research Purposes has identified the lack of resources and structural incentives for making
data more readily available as major contributors to the difficulty of reconciling access to data by
researchers with the need to preserve confidentiality. The issue of confidentiality is not an insignificant
problem. As the U. S. Census Bureau learned from the experience of its tabulation of Arab-Americans
for the U. S. Department of Homeland Security in late 2001, even the perception of a breach of
confidentiality can lead to a major outcry.
Privacy is also a major concern to individuals. What may be ideal from a researcher’s point of view
may not be ideal from the perspective of others. For example, those concerned about the intrusion of
the federal government into private lives would not be pleased at the prospect of what amounts to a
national individual data base even no major outcry has been raised in regard to the three “lightly”
regulated, non-mandated, de facto private sector registration systems maintained by Equifax, Experian,
and TransUnion for purposes of determining credit worthiness.
Costs represent an important challenge. The major driving force in the decision of Statistics Finland to
discontinue “direct” census counts and to move to using administrative records as a basis for a regular
census was the cost of conducting a regular census. For example, Statistics Finland reported in 2003
that the cost of its last direct count census in 1980 was 35 million euros (in 2003 euros, or about 50
million U.S. dollars using current exchange rates) while the cost of its administrative records census of
2000 was only 1 million euros (in 2003 euros, or about 1.46 million U. S. dollars). This issue is a
challenge to all national statistical agencies, including the U.S Census Bureau, which continues to have
“close calls” in its effort to maintain funding for its new “rolling census,” the American Community
Survey, which was designed to be, among other things, a replacement for the long form in the 2010
census.
Litigation has increased in regard to the U.S. Census. With the arrival of 1980 census data there came a
flood of lawsuits. This was followed by more in 1990 and yet more again in 2000. These lawsuits
overwhelmingly are based on grounds that the census had undercounted some populations. A
successful action to have more people counted is not just an action that affects the census. It has a
ripple effect throughout the decade leading to the next census because the census is the starting point
for a set of annual estimates done by the Bureau that in themselves also distribute resources. There is
evidence to suggest that conflicts over census counts will not only continue but intensify. As they do, it
is likely that public confidence in the census will be eroded and with erosion of public confidence
comes higher levels of non-response, which, in turn, increases the need for the wider use of existing
statistical procedures and adjustments to compensate for those not responding. These additional
procedures will require more funding, forcing the U.S. Census Bureau to make choices about methods
that cannot provide optimal results for all populations. This could, in turn, lead to even more litigation
and other forms of conflict. While this situation is most evident in the United States, which extensively
uses population information for resource allocations in a zero-sum manner, it manifests itself wherever
population information is used for this purpose, with the extent of litigation and conflict being directly
proportional to the overall level of allocation.
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Glossary
ADMINISTRATIVE RECORDS. Data collected by governmental (and sometimes private)
organizations for taxation, registration, fee collection, and other administrative purposes that indirectly
provide demographic information. These data are used by demographers for analyses, estimates,
projections, and the evaluation of data specifically collected for demographic purposes.
ADMINISTRATIVE RECORDS METHOD. In the United States, a member of the family of
component methods for estimating population that relies on a past census, vital statistics data, and
migration data derived from tax returns.
AMERICAN COMMUNITY SURVEY (ACS). In the United States, an on-going household survey
conducted by the Census Bureau on a “rolling” geographic basis that is designed to provide
demographic characteristics for counties, places, and other small areas. It is destined to replace the
“long-form” sample survey in the 2010 U.S. census. The conceptual foundation for this survey was
largely developed by Leslie Kish and implemented by Charles Alexander.
APPLIED DEMOGRAPHY. A decision-making science in which substantive problems are defined
by those external to the discipline and which strives to provide the minimum level of demographic
information and precision required for a client to make an optimal decision while minimizing the time
and costs associated with generating the information. Business and public sector demography are the
two major components of applied demography
BASE PERIOD. In a population projection, this is the period between the initial year for which data
are used to generate the projection and the last year, which is known as the launch year.
BASIC DEMOGRAPHY. Also known as academic demography, this is a fundamental science in
which substantive problems are defined by those within the discipline and which strives to convincing
and precise explanations of demographic phenomena while viewing time and costs as obstacles to
overcome in order to provide these explanations. Formal demography and population studies are the
two major components of basic demography.
CAPTURE-RECAPTURE METHOD. A technique initially developed to estimate the size of a
given wildlife population in which two samples are taken. In the first sample, individuals are uniquely
marked, counted, and released. After a period of time to allow the marked individuals to redistribute
themselves among the unmarked population, a second sample is taken. From the distribution of marked
and unmarked individuals in the second sample, an estimate of population size can be obtained using
certain assumptions. For example, if one assumes that the population is “closed” (no migration) and the
two samples are independent, then one can develop an estimate by N = (n1*n2)/n12, where N is the
number estimated for the total population, n1 = the number marked in the first sample, n2 is the number
marked in the second sample and n12 is the number marked in both samples. This formula is an
algebraic re-arrangement of the formula used to determine expected cell size in a 2x2 table for purposes
of the Chi-squared test. Other assumptions lead to other estimation models. In a human setting, two sets
of administrative records usually take the place of the two samples. One exception to this is when
attempts are made to estimate or people with no usual place of residence (i.e., the homeless population)
in a given area.
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CENSAL RATIO METHOD. A set of population estimation techniques found within the “Change in
Stock Method” family that uses crude rates (e.g., birth and death) as measured at the most recent census
date(s) and post-censal administrative records. For example, a population estimate for 2002 can be
obtained by dividing reported deaths for 2002 by the crude death rate measured in 2000 or by a crude
death rate projected from 2000 to 2002. Often a series of Censal-Ratio estimates are averaged together.
Ron Prevost and David Swanson showed in 1986 that the Ratio-Correlation Method is algebraically
equivalent to a weighted average of censal-ratio estimates in which regression slope coefficients serve
as weights.
CENSUS. The count of a given population (or other phenomena of interest) and record its
characteristics, done at a specific point in time and usually at regular intervals by a governmental entity
for the geographic area or subareas under its domain.
COHORT-COMPONENT METHOD. A projection technique that takes into account the
components of population change, births, deaths, and migration, and a population’s age and sex
composition.
COMPONENT METHOD. In general, this refers to any technique for estimating population that
incorporates births, deaths, and migration. Also known as a “Flow Method.”
COMPONENT METHOD I. A component method of estimating population that uses the relationship
between local and national school enrollment data to estimate the net migration component.
COMPONENT METHOD II. A component method of estimating population that uses the
relationship between expected (survived) and actual local school enrollment data to estimate the net
migration component.
COMPONENTS OF CHANGE. There are four basic components of population change: births,
deaths, in-migration, and out-migration. The excess of births over deaths results in natural increase,
while the excess of deaths over births results in natural decrease. The difference between in- and outmigration is net migration. In an analysis of special characteristics or groups, the number of
components is broadened to include relevant additional factors.
COMPOSITE METHOD. A technique for estimating total population that is based upon independent
estimates of age or age-sex groups that are summed to obtain the total population.
DATA MINING. The process of sorting through extremely large data sets with the goal of extracting
“hidden” information that can be used to make decisions and guide actions.
DE FACTO POPULATION. A census concept that defines an enumerated person on the basis of his
or her actual location at the time of the census.
DE JURE POPULATION. A census concept that defines an enumerated person on a basis other than
his or her actual location at the time of the census. The most common basis is the person’s usual place
of residence at the time of a census.
DEMOGRAPHICS. A popular term for demography also used to represent demographic data and the
application of demographic data, methods, and perspectives to activities undertaken by non-profit
organizations, businesses, and governments.
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DIRECT ESTIMATE. In the field of demography this refers to the measurement of demographic
phenomena using data that directly represent the phenomena of interest. The term also is used by
survey statisticians to describe estimates obtained by survey sampling.
DUAL SYSTEM ESTIMATION. Estimation of the true number of events or persons by matching the
individual records in two data collections systems.
ECONOMIC/DEMOGRAPHIC MODEL. A computer and data-intensive approach to projecting
population and employment. It typically use economic theory and historical data to forecast
employment for a labor market area (e.g., a county, a set of counties, a state) and then use this as the
basis for the migration component of a cohort-component model that is linked to the economic model.
The demographic aspects then feed back into the economic aspects of the model.
EXTRAPOLATION. The process of determining (estimating or projecting) values that go beyond the
last known data point in a series (e.g., the most recent census or estimate). It is typically accomplished
by using a mathematical formula, a graphic procedure, or a combination of the two.
GEOGRAPHIC INFORMATION SYSTEM (GIS). A chain of operations involving the collection,
storage, manipulation, and display of data referenced by geographic or spatial coordinates (e.g., coded
by latitude and longitude).
HAMILTON-PERRY METHOD. A technique developed by H. Hamilton and J. Perry used in
population projections that refers to a type of survival rate calculated for a cohort from two censuses. It
includes not only the effects of mortality, but also the effects of net migration and relative census
enumeration error.
HOUSING UNIT METHOD. A population estimation technique found within the “Change in Stock
Method” family that uses current housing unit counts, vacancy estimates, and estimates of the number
of persons per household to estimate the total household population, to which can be added an estimate
of the group quarters population to obtain an estimate of the total population.
INDIRECT ESTIMATE. In demography, this refers to the measurement of demographic phenomena
using data that do not directly represent the phenomena of interest. Among survey statisticians, this
term refers to an estimate not based on a sample survey.
INTEGRATED LAND USE & TRANSPORTATION MODEL. A computer-based, data-intensive
method for estimation and projection that focuses on sub-county areas, using a wide range of input data
(e.g., demographic, land use, land cost, and travel costs and time).
ITERATIVE PROPORTIONAL FITTING. A method for adjusting a multi-way distribution to a set
of independently derived total values that approximates a least-squares approach.
LAUNCH YEAR. The year in which a population projection is launched, typically the year of the
most recent census. Sometimes referred to as the Jump-Off year, it is the starting point of the projection
horizon.
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MASTER ADDRESS FILE (MAF). In the United States, the set of records maintained by the
Census Bureau for purposes of conducting the decennial census. It is intended to represent the
geographic location of every housing unit.
ORGANIZATIONAL DEMOGRAPHY. The study of the size, distribution, and composition of
organizations (e.g., corporations, book clubs, university alumni groups, farm cooperatives and the
military), to include the organizations themselves, their members, dependents, or even combinations
thereof, along with their components of change, and the determinants and consequences of change.
POPULATION. In the demographic sense, the “inhabitants” of a given area at a given time, where
inhabitants could be defined either on the De Facto or De Jure basis (but not a mixture of both). Note
that the concept of “area” can be generalized beyond the geographical sense to include, for example,
formal organizations. In the statistical sense, the term “population” refers to the entire set of persons
(or phenomenon) of interest in a particular study, as compared to a sample, which refers to a subset of
the whole.
POPULATION ESTIMATE. An approximation of a current or past population of a given area at a
given time, or its distribution and composition, in the absence of a complete enumeration, ideally done
in accordance with one of two standards for defining a population, De Facto or De Jure.
POPULATION FORECAST. An approximation of the future size of the population for a given area,
often including its composition and distribution. A forecast usually is one of a set of projections
selected as the most likely representation of the future.
POPULATION PROJECTION. The numerical outcome of a particular set of implicit and explicit
assumptions regarding future values of the components of population change for a given area in
combination with an algorithm. Strictly speaking, it is a conditional statement about the size of a future
population (often along with its composition and distribution), ideally made in accordance with one of
the two standards used in defining a population, De Facto or De Jure.
POPULATION REGISTER. An administrative record system used by many countries (e.g., China,
Finland, Japan, and Germany) that requires residents to register their place of residence, usually at a
local police station. By itself, such a system provides limited demographic information (e.g., total
population), but where it can be matched to other administrative record systems (e.g., tax, social and
health care services), the result is often a system that provides a wide range of longitudinal and crosssectional demographic information.
PROJECTION HORIZON. In a population projection, the period between the launch year and the
target year.
RATIO-CORRELATION METHOD. A regression-based sub-national population estimation
technique found within the “Change in Stock Method” family. Introduced by Robert Schmitt and Al
Crosetti in the early 1950s: (1) the dependent variable consists of the ratio formed by dividing the most
recent population proportion for a set of sub-areas (e.g., proportion of a state population in each of its
counties at the most recent census) by the population proportion for the same sub-areas at an earlier
time (i.e., the previous census); and (2) the independent variables consist of corresponding ratios of
proportions for symptomatic indicators of population (e.g., school enrollment, automobile registrations,
births, deaths) available from administrative records. Variations of the Ratio-Correlation Method
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include the Difference-Correlation Method introduced by Robert Schmitt and John Gier in 1966 and
the Rate-Correlation Method introduced by David Swanson and Lucky Tedrow in1984.
RATIO ESTIMATION. A set of techniques used to estimate population based on ratios across
geographic areas, variables, or both.
RECORD MATCHING. Assembly of data in a common format from different sources but pertaining
to the same unit of observation, (e.g., a person, household, or an event such as death).
SMALL AREA. The subdivisions of the primary political subdivisions of a country. In the United
States, counties and their subdivisions are usually considered small areas, although some limit the term
to subcounty areas such as census tracts, block groups, and blocks and the areas that can be aggregated
from them.
SYNTHETIC METHOD. A member of the family of ratio estimation methods that is used to estimate
characteristics of a population in a sub-area (e.g., a county) by re-weighting ratios (e.g., prevalence
rates or incidence rates) obtained from survey or other data available at a higher level of geography
(e.g., a state) that includes the sub-area in question.
TARGET YEAR. In a population projection, the final year for which a population is projected, the
end point of the projection horizon.
VITAL RATES METHOD. A censal-ratio method of population estimation introduced by Don
Bogue in the 1950s that uses crude birth and crude death rates.
ZIP CODE. Administrative areas set up by the U.S. Postal Service as postal delivery areas and used
for marketing and related purposes in the United States. They have fluid boundaries that do not
correspond to any established political area or statistical area of the decennial census but may
approximate some small areas defined by the census. Zip codes are widely used in business
demography, much less so in public sector demography, and virtually not at all in basic demography.
Bibliography
Akkerman, A. 1980. “On the Relationship between Household Composition and Population Age
Distribution.” Population Studies 34 (3): 525-534. The author develops a household
composition from which fertility can be derived such that the matrix can be used to project
household populations and household.
Alho, J. 1994. “Analysis of Sample-based Capture-Recapture Experiments.” Journal of Official
Statistics 10: 245-256. The author considers the estimation of population totals in sample based
capture-recapture experiments under heterogeneous capture probabilities modeled by logistic
regression.
Alho, J. 1990. “Logistic Regression in Capture-Recapture Models.” Biometrics 46: 623-635. The
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Alvey, W. and F. Scheuren. 1982. “Background for an Administrative Record Census.” pp. 137-146 in
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“administrative records” census in the United States.
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Bogue, D. 1957. “Micro-Demography,” pp. 46-52 in Applications of Demography: The Population
Situation in the U.S. edited by D. Bogue. University of Chicago, Chicago. The author provides
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Statistical Association 45: 191-193. The author introduces the “vital statistics” technique for
making population estimates.
Bogue, D., and B. Duncan. 1959. "A Composite Method for Estimating Postcensal Population of Small
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conjunction with census and survey data to develop estimates of a population’s characteristics.
Bracken, I. 1991. “A Surface Model Approach to Small Area Population Estimation.” Town Planning
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The Demography of Tropical Africa. Princeton, NJ: Princeton University Press. A classic in
which techniques are described that can be used estimating the size and composition of a
population along with its components of change in the absence of good census and
administrative records data. The techniques are largely based on formal demography and
require a good understanding of the assumptions underlying them to successfully implement.
Brown, H.1955. '"A Technique for Estimating the Population of Counties." Journal of the American
Statistical Association 50: 323-343. The author describes different administrative data sets
which the censal ratio method can used to develop population estimates and provides examples.
Bryan, T. 2004a. “Basic Sources of Statistics.” pp. 9-41 in J. Siegel and D. Swanson (Eds.) The
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generally by country, along with the strengths and weaknesses of these data.
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along with examples.
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demographic research is collected and how they can be processed to generate information.
Burch, T. 1970. “Some Demographic Determinants of Average Household Size: An Analytic
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use with a stationary population to the case of a stable population and finds under all family
systems, average household size is positively correlated with fertility, life expectancy, and
average age at marriage. The author suggests that a number of modifications in the model
would make for greater fit between model and real family systems.
Burghardt, J. and V. Geraci. 1980. “State and Local Annual Population Estimation Methods Employed
by the Bureau of the Census.” Review of Public Data Use 8 (4): 339-354. the authors provide an
overview of population estimation used by the U.S. Bureau of the Census in the 1970s.
Cai, Q. 2007. “New Techniques in Small Area Population Estimates by Demographic Characteristics.”
Population Research and Policy Review 26 (2): 203-218. Using Multnomah County, Oregon as
a case study, the author explores methods for population estimates by age, sex, race, and
Hispanic origin at the census tract level. She suggests the use of building permits to indirectly
estimate migration and the use of the American Community Survey to examine changes in
age/sex structure using the American Community Survey.
Casparis, J. 1969. “Shopping Center Location and Retail Store Mix in Metropolitan Areas,”
Demography 6:125-131. The author finds that as metropolitan areas grow, the proportion of
retail sales in major retail centers grows while those in the Central Business District decrease,
but that the ratio remains constant.
Cavanaugh, F. 1981. "The Census Bureau's 1980 Census Test of Population Estimates," in Small-Area
Population Estimates -Methods and Their Accuracy and New Metropolitan Area Definitions
and Their Impact on the Private and Public Sector, Series GE-41, No. 7.Washington, DC,
Government Printing Office. The author describes the results of comparative tests done by the
Census Bureau on the population estimation methods it used in the 1970s.
Clemen, R. 1989. “Combining Forecasts: A Review and Annotated Bibliography.” International
Journal of Forecasting 5:559-583. The author provides a review and annotated bibliography of
the literature on combining forecasts as a way to improve accuracy. He offers suggestions for
how to combine forecasts and recommends that this technique become part of the mainstream
of forecasting practice.
Congdon, P. 1989. “An Analysis of Population and Social Change in London Wards in the 1980s.”
Transactions of the Institute of British Geographers N.S. 14: 478-491. The author discusses the
estimation and projection of small area populations in London and considers trends and indices
that can be calculated from these estimates. He also provides a typology that can be used to
indicate population distribution and processes such as gentrification and ethnic concentration.
Courgeau, D, V. Nedellec, and P. Empereur-Bissonnet. 2000. “Duration of Residence in the Same
Dwelling. A Test of Measurement Using Electricity Utility Company Records.” Population 12:
335-342. The authors use records from the national utility company of France to develop
estimates of residential duration.
Crosetti, A., and R. Schmitt. 1956. "A Method of Estimating the Intercensal Population of Counties."
Journal of the American Statistical Association 51: 587-590. The authors examine the accuracy
of the ratio-correlation method of estimating population against four other methods using 1940
census data for the 39 counties in Washington State.
De Gans, H. 1999. Population Forecasting, 1895-1945: The Transition to Modernity. Dordrecht,
Netherlands: Kluwer Academic Publishers. Focusing on the uses of population projections by
Dutch urban planners, the author describes the move from qualitative forecasting (prophesy) to
mathematical extrapolation during the 19th century and then the move to using the cohortcomponent method in the 20th century. He discusses philosophical issues underlying
forecasting, particularly those described by George Hubert Mead in “the philosophy of the
present.”
Devine, J. and C. Coleman. 2003. “People Might Move but Housing Units Don’t: An Evaluation of the
State and County Housing Unit Estimates.” Population Division Working Paper Series No. 71.
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Washington, DC: U.S. Census Bureau. The authors find that the 2000 state and county level
housing unit estimates developed from building permits, mobile home shipments, and
demolitions not only performed with a degree of accuracy similar to the state and county April
1, 2000 population estimates produced by the Population Division of the Census Bureau, but
that they follow a pattern similar to other estimates produced by Population Division in that
they tended to be more accurate for larger states and counties.
Duke-Williams, O. and P. Rees. 1998. “Can Census Offices Publish Statistics for More than One
Small Area Geography? An Analysis of the Differencing Problem in Statistical Disclosure.”
International Journal of Geographic Information Science 12:579-605. The authors consider a
case whereby confidentiality might be violated in the production of small area population data.
They find that publishing statistics is not safe in some cases unless the areas have been
carefully designed.
Ericksen, E. P. 1973. “A Method for Combining Sample Survey Data and Symptomatic Indicators to
Obtain Population Estimates for Local Areas.” Demography 10 (2): 137-160. The author
introduces a refinement to the ratio-correlation method in which current survey data are
combined with symptomatic indicators. He tests the refinement using data from 75 counties in
which special census counts were conducted between 1964 and 1967 and finds the results
promising, but not conclusive.
Erickson, E. 1974. “A Regression Method for Estimating Population Changes of Local Areas.”
Journal of the American Statistical Association 69: 867-875. The method introduced by the
author in 1973 is tested using 1970 census data for states and counties. He finds that it offers
improvements in accuracy.
Espenshade, T. and J. Tayman. 1982. “Confidence Intervals for Postcensal State Population Estimates.”
Demography 19 (2): 191-210. The authors describe a transformation in which forecast intervals
associated with regression equations used to estimate age-specific death rates can be applied to
state population estimates.
Espenshade, T., F. Hobbs, and L. Pol. 1981. “An Experiment in Estimating Postcensal Age
Distributions of State Populations from Death Registration Data.” Review of Public Data Use
9: 97-114. The authors describe a method of disaggregating postcensal estimates of total state
population into five-year age categories. They find that the technique produces good results for
Florida when tested against 1970 census results, but suggest that more extensive testing is
required before a definitive evaluation can be rendered.
Fay, R. and R. Herriot. 1979. “Estimates of Income for Small Places: An Application of James Stein
Procedures to Census Data.” Journal of the American Statistical Association 74: 269-277. The
authors apply a regression-based adaptation of the James Stein estimator to sample income
data for small places from the 1970 census and find that the resulting estimates have smaller
average error than either the sample estimates or a set using county averages.
Goldberg, D., V. R. Rao, and N. K. Namboodiri. 1964. "A Test of the Accuracy of Ratio-Correlation
Population Estimates." Land Economics 40 (1): 100-102. The authors test the ratio-correlation
method introduced by Schmitt and Crosetti and find that it performs well relative to other
methods.
Gonzalez, M. and C. Hoza. 1978. “Small Area Estimation with Applications to Unemployment and
Housing Estimates.” Journal of the American Statistical Association 79: 7-15. The authors
evaluate the accuracy of regression models and synthetic estimation in regard to unemployment
and dilapidated housing estimates and discuss the results.
Griffin, D. and J. Waite. 2006. “American Community Survey overview and the role of external
evaluations.” Population Research and Policy Review 25 (3): 201-223. The authors provide an
overview of the reasons that led the U. S. Census Bureau to implement the American
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Community Survey and the evolution of this survey. They describe the importance of external
reviews in the ongoing refinement of this survey.
Hamilton, C. and J. Perry. 1962. “A Short Method for Projecting Population by Age from One
Decennial Census to Another.” Social Forces 41 (December): 163-170. The authors introduce
the “Cohort Change Ratio” approach to population projections, one that has become known as
the Hamilton-Perry method. The method refers to a type of survival rate calculated for a cohort
from two censuses. It includes not only the effects of mortality, but also the effects of net
migration and relative census enumeration error.
Happel, S., and T. Hogan. 1987. "Estimating the Winter Resident Population of the Phoenix Area"
Applied Demography 3: 7-8. The authors report on annual survey of mobile home/RV/travel
trailer parks that is used to estimate the number of “snowbirds” who temporarily reside in the
area of Phoenix, Arizona over the winter months.
Heer, D. M., and P. Herman. 1990. “Estimating the population of Los Angeles County census tracts by
ethnicity. pp. 83-88 in 1990 Proceedings of the Social Statistics Section. Alexandria, VA:
American Statistical Association. The authors derive postcensal estimates of the population of
Los Angeles County, California by census tract and within each census tract by detailed
ethnicity." A variation of the censal ratio method is used, which "consists in using as
symptomatic indicators the expected values from a linear regression equation relating the
number of births (or deaths) in each year from 1980 through 1986 to time as the independent
variable." Results are compared with U.S. Bureau of the Census figures.
Hepburn, G., T. Mayor, and J. Stafford. 1976. “Estimation of Market Area Population from Residential
Electrical Utility Data.” Journal of Marketing Research 13 (3): 230-236. The authors evaluate
the use of electrical utility data to estimate population for census tracts in the area of Houston,
Texas.
Howe, A. 1999. “Assessing the Accuracy of Australia's Small-Area Population Estimates.” Journal of
the Australian Population Association 16(1-2): 47-63. The author finds that it is difficult to
evaluate the accuracy of annual estimates released by the Australian Bureau of Statistics. Both
broad and specific actors affect the quality of these estimates including inherent characteristics
of the region, such as population size and growth rate; changes in the geographic boundaries;
quality of input data; estimation method; and adjustments to control totals.
Keilman, N. 1988. "Dynamic household models" pp. 123-138 in: N. Keilman, A. Kuijsten, and A.
Vossen. (eds.) Modelling Household Formation and Dissolution Oxford, England: Clarendon
Press. The author compares five different macro demographic models and finds that the
modeling of individual behavior rather than household behavior is a good strategy. He states
that the multidimensional approach holds considerable promise for modeling household
behavior and observes that there are three difficulties common to most models (1) the
availability of data necessary to run the models; (2) inconsistencies that arise when individuals
of different sex are modeled separately; and (3) the exponential increase in the number of states
when a detailed household breakdown is considered.
Kimpel, T. and T. J. Lowe. 2007. “Estimating Household Size for use in Population Estimates.”
Population Estimates and Projections Research Brief No. 47. Olympia, WA: Washington State
Office of Financial Management. The authors evaluate he accuracy of estimates of household
size against results from the 200 census for counties in Washington State and then compare the
results of three different regression-based approaches for estimating average household size in
2005. They conclude that additional research in this area is needed as there is presently no
single model specification or set of variables that population analysts/demographers agree upon.
Kintner, H. and L. Pol. 1996. “Demography and Decision Making.” Population Research and Policy
Review 15 (5/6): 579-584. The authors describe how demographic input is but one of several
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components needed to make decisions by businesses and argue that to some extent the future of
applied demography will be determined by non-demographers.
Kintner, H. and D. Swanson. 1994. “Estimating Vital Rates from Corporate Databases: How Long will
GM’s Salaried Retirees Live?” pp. 265 – 295 in H. Kintner, T. Merrick, P. Morrison, and P.
Voss (Eds.) Demographics: A Casebook for Business and Government. Boulder, CO:
Westview Press. The authors described how they used internal company records and standard
demographic techniques to develop estimates of the longevity of General Motors’ “white
collar” retirees. These estimates where then used to generate expected future “white collar”
retiree populations.
Kintner, H. and D. Swanson. 1996. “Ties that Bind: A Case Study of the Link Between Employers,
Families, and Health Benefits.” Population Research and Policy Review 15 (5-6): 509-526.
The authors examine the linkages among employer policies, employee turnover, and family
dynamics. They find that employers face limits to the control that they can exert over the size of
the health benefits group associated with their active workforce. Demographic processes
unrelated to employee turnover or transfers to layoff or retirement accounted for a large portion
of the population change in the case study
Kintner, H., T. Merrick, P. Morrison, and P. Voss. 1994. Demographics: A Casebook for Business and
Government. Boulder, Colorado: Westview Press. This is a collection of 20 case studies
prepared by a group of demographers; professors of economics, sociology, population studies,
business, and marketing; and research analysts. It is written in non-technical language and
designed for instructional use. It covers applications of applied demography in government
planning, long term corporate strategy, forecasting, human resource management, and
marketing.
Kitagawa, E. et al. 1980. Estimating Population and Income of Small Areas. Washington, D.C.:
National Academy Press. In this book, the authors examine the population estimation program
of the U. S. Bureau of the Census it provides guidance for improving the Census Bureau's
program and for policy makers who use such estimates for allocating funds.
Kliss, B. and W. Alvey 1984. (Eds). Statistical Uses of Administrative Records: Recent Research and
Present Prospects, Volumes I and II. Washington, D.C.: Department of the Treasury, Internal
Revenue Division, Statistics of Income Division. The authors describe research into the use of
administrative records for purposes of determining population numbers and the characteristics
of these populations. Suggestions on offered in regard to the strengths of weaknesses of using
administrative records for these purposes.
Krotki, K. 1978. (Ed.) Developments in Dual System Estimation of Population Size and Growth.
Edmonton, Alberta, Canada: University of Alberta Press. The authors provide an assessment of
costs and benefits using two independent systems for estimating both vital events and
population and describe the state of the art for them. Suggestions are made for improving these
systems.
Lee, E. S. and H. F. Goldsmith. 1982. (Eds.), Population Estimates: Methods for Small Area Analysis.
Beverly Hill, CA: Sage Press. These are the proceedings of the 1978 Small Area Estimation
Conference sponsored by the U.S. National Institute of Mental Health. Ten papers by various
authors are included, together with a series of evaluations and synopses by the editors. The
overall theme of the papers is exploring new ways to estimate population numbers and
characteristics.
Lee, R. and S. Tuljapurkar, 1994. “Stochastic Population Forecasts for the United States: Beyond
High, Medium, and Low.” Journal of the American Statistical Association 89: 1175-1189.
The authors blend mathematical demography and statistical time series methods to estimate
stochastic models of mortality and fertility using historical data. They then use random-matrix
products to forecast various demographic measures.
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Mandell, M. and J. Tayman. 1982. “Measuring Temporal Stability in Regression Models of Population
Estimation.” Demography 19: 135-146. The authors introduce an empirical indicator designed
to measure the temporal stability of regression models used to estimate sub-national
populations. In a case study using the 67 counties of Florida, they find that variable
measurement and type are important determinants of estimation accuracy and that the temporal
stability of the regression models’ coefficients is not a determining influence in accuracy.
Marks, A. G. Thrall, and M. Arno. 1992. “Siting Hospitals to Provide Cost-Effective Health Care. Geo
Info Systems 2:58-66. The authors describe how a Geographic Information System (GIS) can
be used both to create a spatial data base and to perform a market analysis of the need for
additional hospitals. The robustness of a GIS in market analysis is shown by including satellite
data for land use-land cover and slope, incorporating data about distance to population decay
factors, creating buffers around existing hospitals, and identifying a unique service area for
each hospital by applying a weighted Voronoi polygon technique.
McKibben, J. and D. Swanson, 1997. “Linking Substance and Practice: A Case Study of the
Relationship Between Socio-Economic Structure and Population Estimation.” Journal of
Economic & Social Measurement 24: 135-147. The authors argue that some ofthe
shortcomings in small area population estimation methods would be better understood by
linking these methods with the substantive socio-economic and demographic dynamics that
underlie them. Using a case study of Indiana over two periods, 1970-1980 and 1980-1990, the
authors find that changes in the coefficients of a regression-based estimation model are linked
to Indiana's transition to a post-industrial economy. They then describe how this transition
operated through demographic dynamics that ultimately affected the estimation model.
Morrison, P. and A. Abrahamse. 1996. “Applying Demographic Analysis to Store Site Selection.”
Population Research and Policy Review 15: 479-489. This case study illustrates how applied
demographic analysis can help structure business decision making. The authors screened every
one of several thousand square miles within metropolitan Southern California to identify the 10
best locations for a large supermarket catering to one-stop shoppers. The analytic framework
for comparing high-potential locations played a central role in structuring the client's thinking.
Morrison, P., D. Swanson, J. Tayman, I. Sharkova, and C. Popoff. 2002. “Meeting Local Information
Needs: A Case Study in Team Applied Demography.” Applied Demography 14 (Spring): 1-3.
The authors describe how a set of different skills can be used to develop a small area population
estimation system.
Murdock, S. and D. Ellis. 1991. Applied Demography: An Introduction to Basic concepts, Methods,
and Data.
Boulder, Colorado: Westview Press. This book shows the application of
demographic techniques in a wide range of areas, including real estate, marketing, and regional
and services planning. It includes chapters on demographic concepts and trends, data sources
and the principles of data use, basic methods and measures of applied demography, and
methods for estimating and projecting populations.
Myers, D (ed.). 1990. Housing Demography: Linking Demographic Structure and Housing Markets.
Madison, WI: University of Wisconsin Press. This book consists of 13 papers selected to
bridge the gap between the separate analysis of population data and housing data in the United
States. The approach is interdisciplinary, involving demography, economics, geography,
sociology, and urban planning. A major objective of the book is theory building. The papers are
grouped under three main headings: linking housing characteristics with household
composition, life course and cohort models of housing choice, and housing consumption among
the elderly.
Norcross-Bryan, K. and R. George. 2004. “Geographic Information Systems.” pp. 733-750 in J. Siegel
and D. Swanson (Eds.) The Methods and Materials of Demography, 2nd Edition. New York,
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NY: Elsevier/Academic Press. The authors provide an overview of how Geographic
Information Systems can be used by demographers.
Pitkin, J.R. 1992. “A Comparison of Vendor Estimates of Population and Households With 1990
Census Counts in California.” Applied Demography 7(1):5-8. The author compares the 1990
population estimates of four national demographic data vendors with the actual counts of the
1990 U.S. Census. Differences in accuracy are found among the vendors and between
variables.
Plane, D. and P. Rogerson. 1994. The Geographical Analysis of Population, with Applications to
Planning and Business. New York, NY: John Wiley and Sons. This book focuses on both
applied demographic and planning techniques that rely upon geographical aspects of
population data. It is designed to be used for instructional purposes and describes methods used
to assess the impact of population change on facility demand, school enrollment, changes in
product market, transportation and recreation demand forecasting.
Platek, R., J. Rao, C. Sarndal, and M. Singh (Eds.) 1987. Small Area Statistics: An International
Symposium. New York, NY: John Wiley. This book covers a range of demographic and
statistical techniques used to develop small area and small domain population information.
Pol, L. and R. Thomas. 1997. Demography for Business Decision Making. Westport, CT. Quorum
Books. The authors provide practitioners and students alike with an introduction to the
concepts and methods of business demography. They also present an overview of recent and
future demographic trends and provide many examples of real world situations in which
demographic methods, data, perspective, and theory are actively applied.
Pol, L. 1998. “Demographic Methods in Applied Demography: An American Perspective.” Genus 52
(1-2): 159-176. The author explores the usage of demographic methods in applied demography
and documents the range of methods being used to help address a wide range of public and
private sector opportunities and problems in the United States.
Popoff, C. and D. Judson, 2004. “Some Methods of Estimation for Statistically Underdeveloped
Areas.” pp. 603-641 in J. Siegel and D. Swanson (Eds.) The Methods and Materials of
Demography, 2nd Edition. New York, NY: Elsevier Academic Press. The authors describe a
wide range of techniques, largely based on formal demography, for making population
estimates in countries lacking good census counts and administrative records. They provide
illustrations of these techniques.
Pozzi, F., Small, C., and G. Yetman. 2003. “Modeling the distribution of human population with nighttime satellite imagery and gridded population of the world.” Earth Observation Magazine
12(4): 24–30. The authors examine the “World Stable Lights” dataset as a potential means to
refine the spatial detail of the population dataset. They found a consistent relationship between
population density and light frequency and developed a transfer function to relate light
frequency to population density and a mass-conserving algorithm that relocates fractions of
populations within large administrative units to locations of lighted settlements.
Prevost, R. and D. Swanson. 1986. “A New Technique for Assessing Error in Ratio-correlation
Estimates of Population: A Preliminary Note.” Applied Demography 1(November): 1-4. The
authors demonstrate the algebraic equivalency of the Ratio-Correlation Method to a weighted
average of censal-ratio estimates in which regression slope coefficients serve as weights.
Purcell, N. & Kish, L. 1980. “Postcensal Estimates for Local Areas (or domains).” International
Statistical Review 48: 3-18. The authors demonstrate how iterative proportional fitting can be
used to fit sample data to a flexible nonlinear model to develop estimates that preserve
specified relationships found in a preceding census.
Qiu, F., K. Woller, and R. Briggs. 2003. “Modeling Urban Population Growth from Remotely Sensed
Imagery and TIGER GIS Road Data.” Photogrammetric Engineering and Remote Sensing 69:
1031–1042. Rao, J. 2002. Small Area Estimation. San Francisco, CA: Jossey-Bass. The
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authors modeled population growth from 1990 to 2000 in the north Dallas-Fort Worth
Metroplex using two different methods: These methods were applied at both city and censustract levels and were evaluated against the actual population growth.
Raymondo, J.C. 1992. Population Estimation and Projection: Methods for Marketing, Demographic,
and Planning Personnel. New York: Quorum Books. The author describes the major methods
of population estimation and population projection as a guide for the marketing and planning
professionals who are the frequent users of population estimates and projections.
Rees, P., P. Norman, and D. Brown. 2004. “Improving Small Area Population Estimates.” Journal of
the Royal Statistical Society, Series A 167: 5–36. Using data from England, the authors present
a framework for small area population estimation that enables users to select a method that is fit
for the purpose. They concludes with a discussion of how data, on stream since 1998, might be
included in future small area estimates.
Rives, N., W. Serow, A. Lee, H. Goldsmith, and P. Voss. 1995. (Eds.) Madison. Basic Methods for
Preparing Small-Area Population Estimates. Madison, WI: Applied Population Laboratory,
Department of Rural Sociology, University of Wisconsin. This book covers the standard
demographic techniques used to develop small area population estimates.
Rives, N. W., W. Serow, A. Lee, and H. F. Goldsmith. 1989. Small Area Analysis: Estimating Total
Population. Rockville, MD: U.S. Department of Health and Human Services. This monograph
covers the standard demographic techniques used to develop small area population estimates of
the total population.
Rives, N. W. and W. Serow. 1984. Introduction to Applied Demography. Beverly Hills, California:
Sage Publications. This was the first book on applied demography to be published. It covers
basi techniques for estimation and projection and the sources of data that provide inputs to these
techniques.
Roe, L., J. Carlson, and D. Swanson. 1992. “A Variation of the Housing Unit Method for Estimating
the Population of Small, Rural Areas: A Case Study of the Local Expert Procedure.” Survey
Methodology 18(1):155-163. the authors describe a variation of the housing unit method that
can be used to develop confidence intervals around population estimates.
Russell, C. 2006. Best Customers: Demographics of Consumer Demand. New York, NY: New
Strategist Publications. This book identifies the best and biggest customers for hundreds of
individual products and services, alerting marketers to potential booms and busts in the years
ahead. The author analyzes household spending on more than 300 products and services by age
of householder, household income, household type, race and Hispanic origin of householder,
region of residence, and new to this edition, educational attainment of householder. She
identifies which households spend the most on a product or service and which control the
largest share of spending.
Schmitt, Robert C. and Albert H. Crosetti. 1954. “Accuracy of Ratio-Correlation Method for
Estimating Postcensal Population,” Land Economics 30(3): 279- 280, August. The authors
introduce the ratio-correlation method by applying it to the 39 counties of Washington State
Shahidullah, M. and M. Flotow. 2005. “Criteria for selecting a suitable method for producing post2000 county population estimates: A case study of population estimates in Illinois.” Population
Research and Policy Review 24 (3) 215-229. The authors compared 2000 county population
estimates using the administrative records method, the ratio correlation method, and an average
of the two methods for Illinois against 2000 census counts. The administrative records method
performed best.
Siegel, J. 2002. Applied Demography: Applications to Business, Government, Law and Public Policy,
San Diego, Academic Press. This book provides a comprehensive portrait of applied
demography. It covers methods, sources of data and offers many examples.
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Siegel, J. and D. A. Swanson.2004. The Methods and Materials of Demography, 2nd Edition. San
Diego/Amsterdam, Academic Press/Elsevier. This book is a fundamental resource that covers
the methods and materials of demography. It includes chapters on population estimation and
population projection.
Simpson, S., I. Diamond, P. Tonkin, and R. Tye. 1996. “Updating Small Area Population Estimates in
England and Wales.” Journal of the Royal Statistical Society, Series A, 159 (part 2): 235–247.
The author describes how small area population estimates (postal delivery areas and election
wards) in England and Wales are not as systematically done as are those for larger areas. Using
the type of method along with characteristics of the small area in question as explanatory
variables, he finds that the method used is of great importance in determining accuracy.
Smith, S. 1994. “Estimating Temporary Populations.” Applied Demography 9 (1): 4-7. The author
describes the difficulty of tracking temporary short-term population movements (commuting,
seasonal visitation, convention and business travel) with a focus on Hawaiian statistician
Robert Schmitt's work. The author finds that "Schmitt's contributions toward a methodology for
estimating daytime populations were important because this approach utilized data sources that
were widely available for small areas on at least an annual basis. Consequently, this approach
could be used for frequent updates of the estimates, for many areas and at relatively little cost.
Smith, S. 1989. “Toward a Methodology for Estimating Temporary Residents." Journal of the
American Statistical Association 84: 430-436. The author discusses the problems of defining
and estimating temporary residents, focusing on the strengths and weaknesses of a number of
data sources and estimation techniques. He concludes with an assessment of the potential
usefulness of developing methods to estimate temporary residents.
Smith, S. and S. Cody.1994. “Evaluating the Housing Unit Method: A Case Study of 1990 Population
Estimates in Florida.” Journal of the American Planning Association. 60: 209-221. The authors
evaluate the accuracy and bias of HU population estimates produced for counties and
subcounty areas in Florida for April 1, 1990 and find, among other things, that population size
has a negative effect on estimation errors (disregarding sign) but no effect on bias. The authors
argue that the HU method offers a number of advantages over other population estimation
methods and provides planners and demographers with a powerful tool for small-area analysis.
Smith, S. and M. Mandell. 1984. “A Comparison of Population Estimation Methods: Housing Unit
versus Component II, Ratio-Correlation, and Administrative Records.” Journal of the American
Statistical Association 79: 282-289. Using data from Florida, the authors find that the Housing
Unit Method performs as well as Component Method II, ration-correlation, and the
administrative records method.
Smith, S. and P. Morrison. 2005. “Small-Area and Business Demography.” pp. 761-786 in M. Micklin
and D. Poston, Jr. (Eds.) Handbook of Population. New York, NY: Kluwer Academic/Plenum.
The authors provide an overview of business and small area demography.
Smith, S. and T. Sincich. 1988. “Stability over Time in the Distribution of Population Projection
Errors.” Demography 25:461-474. The authors evaluate the assumption that forecast errors
remain stable over time by making population projections for [U.S.] states for a number of time
periods during the 20th century, comparing these projections with census enumerations to
determine forecast errors, and analyzing the stability of the resulting error distributions over
time. They find that in the distribution of absolute percentage errors remained relatively stable
over time and data on past forecast errors provided very useful predictions of future forecast
errors."
Smith, S. K., J. Nogle and S. Cody. 2002. “A Regression Approach to Estimating the Average Number
of Persons per Household.” Demography 39(4): 697-712. The authors develop regression-based
estimates of the average number of persons per household and compare the accuracy of these
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estimates to those generated by other methods. They find that the regressions-based estimates
are more precise and less biased than those produced by the other methods.
Smith, S., J. Tayman, and D. Swanson. 2001. State and Local Population Projections: Methodology
and Analysis. New York: Kluwer/Plenum Academic Press. This book focuses on the
methodology and analysis of state and local population projections. It considers the strengths
and weaknesses of various projection methods, paying special attention to the unique problems
of making projections for small areas, and concludes with an examination of technological and
methodological changes affecting the production of small-area population projections.
Statistics Canada. 1987. Population Estimation Methods, Canada. Ottawa, ON: Statistics Canada. This
monograph describes the methods used to produce official population estimates for Canada are
discussed and evaluated. Individual chapters are organized around three recurring themes: (1)
method; (2) data sources, and; (3) quality evaluation.
Statistics Finland. 2004. Use of Register and Administrative Data Sources for Statistical Purposes:
Best Practices of Statistics Finland. Handbook Series, No. 45. Helsinki, Finland: Statistics
Finland. This monograph provides a comprehensive look at Finland’s population register
system and its related components. It includes a history of the development of the population
register and gives examples of how it is used.
Swanson, D. 2004. “Advancing Methodological Knowledge within State and Local Demography: A
Case Study. Population Research and Policy Review 23 (4): 379-398. The author critically
evaluates a “fugitive” method used to generate population estimates in the state of Nevada. The
evaluation reveals statistical and methodological shortcomings in this model that lead to an
alternative model not subject to these shortcomings. The author suggests that states in which
estimates are used to allocate resources would be well-served by subjecting new methods being
considered for use to academic peer review before they are adopted.
Swanson, D. 1989. “Confidence Intervals for Postcensal Population Estimates: A Case Study for Local
Areas.” Survey Methodology 15 (2): 271-280. The author demonstrates how confidence
intervals can be developed for population estimates of small areas
Swanson, D. 1981. “Allocation Accuracy in Population Estimates: An Overlooked Criterion With
Fiscal Implications.” pp. 13-21 in Small Area Population Estimates and Their Accuracy, Series
GE-41, No. 7. U.S. Bureau of the Census. Washington, DC: US Government Printing Office.
The author argues that population estimates used for purposes of allocating resources in a zero
sum environment should be evaluated in terms of mis-allocation error. He proposes the use of
the index of dissimilarity for this purpose and provides an example of its uses.
Swanson, D. 1980. “Improving Accuracy in Multiple Regression Estimates of Population using
Principles from Causal Modeling.” Demography 17: 413-428. The author shows how
relationships based on matrix algebra can be used to assess coefficient stability and to modify
coefficients in the ration-correlation regression model to be consistent with post-censal
population changes, thereby providing more accurate estimates.
Swanson, D. and D. Beck. 1994. “A New Short-Term County Population Projection Method.”
Journal of Economic and Social Measurement 20:25-50. The authors introduce the lagged
ration-correlation method as a means of producing short term population projections and
provide tests of its accuracy. They conclude that the method shows promise.
Swanson, D., and L. Pol. 2005. “Contemporary Developments in Applied Demography within the
United States.” Journal of Applied Sociology 21 (2): 26-56. The authors discuss current
developments in the field of applied demography and relate them to broader issues.
Swanson, D., and G. E. Stephan. 2004. “Glossary.” pp. 751-778 in J. Siegel and D. Swanson, (Eds.)
The Methods and Materials of Demography, 2nd Edition. New York, NY: Elsevier/Academic
Press. The authors provide a comprehensive glossary of demographic terms.
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Swanson, D., and J. Tayman. 1995. “Between a Rock and a Hard Place: The Evaluation of
Demographic Forecasts.” Population Research and Policy Review 14 (2): 233-249. The
authors argue that there is a high level of cognitive dissonance found the process of forecasting,
which can be reduced by refocusing forecast evaluations to include utility. They propose for
this purpose the Proportionate Reduction in Error (PRE) measure and conclude that including
PRE as an evaluation criterion can reduce stress by reducing cognitive dissonance without, at
the same time, either trivializing the evaluation process or substantively altering how forecasts
are done and presented.
Swanson, D., and L. Tedrow. 1984. “Improving the Measurement of Temporal Change in Regression
Models used for County Population Estimates.” Demography 21: 361-372. The authors
introduce the rate-correlation method and provide a test of its accuracy. They conclude that it
can provide better accuracy in situations where spatial auto-correlation may be present.
Swanson, D., G. Hough, J. Rodriguez, and C. Clemens. 1998. “K-12 Enrollment Forecasting:
Merging Methods and Judgment.” ERS Spectrum 16:24-31. The authors describe how the
cohort-component method can informed by the use of GIS and informed judgment to develop
K-12 enrollment forecasts. They provide a test of accuracy and conclude that the method shows
promise.
Swanson, D., D. Beck, and J. Tayman. 1995 “On the Utility of Lagged Ratio-Correlation as a Shortterm County Population Estimation Method: A Case Study of Washington State.” Journal of
Economic and Social Measurement 21:1-16. The authors evaluate the lagged ratio-correlation
method for short term population forecasts by using a context that goes beyond accuracy to
include utility. They find that the method performs well.
Swanson, D., T. Burch, and L. Tedrow. 1996. “What is Applied Demography?” Population Research
and Policy Review 15 (5/6): 403-418. The authors argue that applied demography is
intrinsically distinct from basic demography because it exhibits the value-orientation and
empirical characteristics of a decision-making science while the latter exhibits the valueorientation and empirical hallmarks of a basic science. They examine this conceptualization of
applied demography in terms of the methods and materials that fall within its purview and
discuss some important consequences, including research agendas and training programs..
Swanson, D., J. Tayman, and C. Barr. 2000. “A Note on the Measurement of Accuracy for Subnational
Demographic Estimates.” Demography 37: 193-201. The authors argue that the mean absolute
percent error (MAPE), a standard measure of accuracy for population estimates overstates error
because it is right-skewed. They introduce a revised MAPE called MAPE-R and show how it
can be used. The authors argues that MAPE-R should be part of the standard set of evaluation
measures.
Swanson, D., R. Forgette, M. Van Boening, C. Holley, and A. Kinnell. 2007. “Assessing Katrina’s
Demographic and Social Impacts on the Mississippi Gulf Coast.” Journal of the Mississippi
Academy of Sciences 54 (2): 228-242. The authors provide estimates of the demographic and
social effects of Hurricane Katrina on the Mississippi Gulf Coast using data collected in a
special survey.
Tayman, J. and Pol. L. 1995. Retail Site Selection and Geographic Information Systems. The Journal
of Applied Business Research, 11(2): 46-54.A guide to using GIS in conjunction with
demography and related information to assist in selecting sites for retail outlets.
Tayman, J. 1996. “Forecasting, Growth Management, and Public Policy Decision Making.” Population
Research and Policy Review 15 (5-6): 491-508. The author describes the interplay between
forecasting and decision making. He shows how a forecast helped shape public policy and, in
turn, how public policy influenced a forecast. He concludes that normative forecasting best
describes the relationship between the forecast and these public policy decisions.
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Tayman, J., E. Schafer, and L. Carter. 1998. “The Role of Population Size in the Determination and
Prediction of Population Forecast Errors: An Evaluation Using Confidence Intervals for
subcounty areas.” Population Research and Policy Review 17: 1-20. The authors describe a
technique for making subcounty population forecasts and for generating confidence intervals
around their forecast error. They find a non-linear, inverse relationship between population size
and forecast accuracy and demonstrate the ability to accurately predict average forecast error
and confidence intervals based on this relationship.
Tayman, J., S. Smith, and L. Lin. 2007. “Precision, Bias, and Uncertainty for State Population
Forecasts: An Exploratory Analysis of Time Series Models.” Population Research and Policy
Review 26 (6): 347-369. The authors develop and evaluate six ARIMA time series models for
states in the United States. Using annual population estimates from 1900 to 2000 and a variety
of launch years, base periods, and forecast horizons, they construct population forecasts for four
states chosen to reflect a range of population size and growth rate characteristics. They find that
prediction intervals based on some ARIMA models provide relatively accurate forecasts of the
distribution of future population counts but prediction intervals based on other models do not.
Verma, R., K. Basavarajappa, and R. Bender. 1984. “Estimation of Local Area Population: An
International Comparison.” pp. 324-329 in 1984 Proceedings of the Social Statistics Section,
Alexandria, VA: American Statistical Association. The authors discuss the current status of
methodology on local area population estimation in five developed countries, Australia,
Canada, England and Wales, New Zealand, and the U.S.A.
Verma, R., K. Basavarajappa, and R. Bender. 1983. “The Regression Estimates of Population for subprovincial Areas in Canada.” pp. 512-517 in 1983 Proceedings of the Social Statistics Section,
Alexandria, VA: American Statistical Association. The authors describe two sets of post-censal
population estimates that are published yearly by the government of Canada. The two sets were
found to be statistically similar, though the first set is more timely, and the second providing
more details on the components of population change."
Walashek, P. and D. Swanson. 2006 “The Roots of Conflict over U.S. Census Counts in the Late 20th
Century and Prospects for the 21st Century.” Journal of Economic and Social Measurement 31
(3-4): 185-205. The authors described the modern U.S. Census as a “commons,” in which
private benefits are gained at public expense. They identify the role that historical actions
played in making the census into a Commons, thereby setting the stage for modern day census
litigation and other forms of conflict. The authors observe that as a Commons, the census is
facing a potential collapse that cannot be prevented by methodological developments and
conclude by noting that a course of political action may be the best course for preventing such a
collapse.
Webster, C. J. 1996. “Population and Dwelling Unit Estimates from Space.” Third World Planning
Review 18(2):155-76. The author reports on attempts to measure the morphological patterns in
an urban satellite scene and to use these for image interpretation. The interpretation task
addressed is the estimation of residential dwelling units from the patterns discernible in high
resolution satellite images of cities. The practical results include dwelling estimates that can be
aggregated to any geographical unit of analysis, population estimates for cities and a dwelling
density surface that can be categorized into any number of residential land-use classes."
Wicks, J. and D. Swanson (eds.). 1987. Issues in Applied Demography: Proceedings of the 1986
National Conference. PSRC Press: Bowling Green, Ohio. The focus of the conference was on
ways in which demographic data and concepts can be used to produce better and more
informed business and public policy decisions. Subject headings include applied demography
and government policy; technical issues in business demography; relations between public
sector and academic demographers; demography and management issues in business; and
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linking the demographic information needs of business, state and local governments, and
universities.
Wicks, J., R. Vincent, L. P. De Almeida, and D. Swanson. 1999. "Population Estimates from Remotely
Sensed Data: A Discussion of Recent Technological Developments and Future Research
Plans." Paper presented at the Annual Meeting of the Canadian Population Society,
Lennoxville, Quebec, Canada. The authors discuss developments that have created
opportunities for the advancement of using remotely sensed data to produce accurate and timely
population and housing estimates. They outline plans for the design and testing of procedures
capable of producing current population estimates from remotely sensed data.
Wolter, K. M. and B. Causey. 1991. “Evaluation of Procedures for Improving Population Estimates for
Small Areas.” Journal of the American Statistical Association 86: 278-284. The authors
provide and illustrate methods for evaluating across-the-board ratio estimation and synthetic
estimation. They observe that these two techniques have the potential to improve the accuracy
of estimates for small areas.
Zeng, Y. K. Land, Z. Wang, and D. Gu. 2006. “U.S. family household momentum and dynamics: an
extension and application of the ProFamy Method.” Population Research and Policy Review
25: 1–41. The authors introduce a household projection method that uses demographic rates as
input and provides more detailed household types, sizes, and living arrangements for all
members of the population. Tests of projections from 1990 to 2000 show that discrepancies
between projections done using this method and census observations in 2000 are reasonably
small, validating the new method.
Biographical Sketches
David A. Swanson is Professor of Sociology, University of California Riverside and a Research
Associate with the Blakely Center for Sustainable Suburban Development. His Ph.D. is from the
University of Hawaii. Dr. Swanson’s teaching interests include demographic methods, population
forecasting, and business demographics. He regularly returns each summer to the Helsinki School of
Economics, where he served as Dean of the English-language bachelor’s program from 1999 to 2003,
to teach Demographic Analysis and International Business. Dr. Swanson’s research focuses on applied
demography, especially small area population estimation and population forecasting He has published
more than 65 sole- and co-authored refereed journal articles and hundreds of reports. With Stan Smith
and Jeff Tayman, he wrote State and Local Population Projections: Methodology and Analysis (2001)
and with Jay Siegel, edited The Methods and Materials of Demography 2nd Edition (2004). He serves
as a member of the U.S. Census Bureau’s Advisory Committee for Professional Associations and is a
past editor of the journal, Population Research & Policy Review.
Louis G. Pol is John Becker Dean of the College of Business Administration, University of Nebraska,
Omaha. His Ph.D. is from Florida State University. Dr. Pol’s teaching interests include business and
health demography, marketing research, and statistics. His research interests include demographic
applications, research methodology and statistics, and health care policy. Dr. Pol is the author of six
books, including Demography for Business Decision Making (1997), with Richard Thomas, and The
Demography of Health and Healthcare, 2nd Edition (2001), also with Richard Thomas. In addition, he
has published over 70 articles, book chapters, and research notes. His most recent work focuses on the
determinants and consequences of being without health insurance, and developing a methodology for
identifying rural areas at risk to not providing adequate health services to their populations. He serves
or has served on the editorial boards of Journal of Hospital Marketing, Journal of Professional Service
Marketing, Journal of Marketing Theory and Practice, Health Marketing Quarterly, and Population
Research and Policy Review. He is past president of Southern Demographic Association
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