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–6–
Business, Economic Development,
and Data Aggregator Uses
The final workshop session that was focused on a particular sector of the
American Community Survey (ACS) user base was designed to cover a range
of perspectives on business-related uses. This topic includes the direct use of
ACS analysis by private-sector firms for planning and implementation, the use
of data by economic development authorities to promote business expansion
and recruit new business opportunities, and the recoding and repackaging of
ACS data by “data aggregators” to make the data easier to use for downstream
users. Despite its origin in and widespread use in the public sector, the ACS has
become a heavily relied upon resource in the business community. As hinted at
in the discussion period for this session, the relationship between the private sec-
tor and the ACS is an increasingly important one because of two arguments that
have been stirred up on the debate over the future of the ACS: first, whether the
ACS is duplicative of information that could be obtained from other (including
business-generated) sources and, second, whether—if businesses benefit strongly
from the availability of ACS to make decisions—those businesses should pay
more for the ACS’s collection and maintenance.
Section 6–A summarizes the use of ACS data in the economic develop-
ment and workforce community, through the specific example of a develop-
ment group in Iowa. Section 6–B gives an overview of the experiences of one
data aggregator—the Integrated Public Use Microdata Series (IPUMS) project
housed at the University of Minnesota—and describes what IPUMS staff know
about the users of their versions of ACS files. Three specific business examples
99
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100 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
of ACS use are then discussed in turn—Acxiom’s use of ACS data in marketing
and business information management services (Section 6–C), the Conference
Board’s use of the data to study trends in teleworking and wage inequality (Sec-
tion 6–D), and AIR Worldwide’s incorporation of the data into their models
of risk exposure to natural and man-made catastrophes (Section 6–E). David
Crowe, chief economist for the National Association of Home Builders, mod-
erated the discussion block for the session, starting with brief remarks on the
ACS’s role in the housing community (Section 6–F).
6–A ACS DATA FOR ECONOMIC DEVELOPMENT AND
WORKFORCE PLANNING PROJECTS
Described by Andrew Conrad as having “a very academic name for a very
practical organization,” the Institute for Decision Making is an economic devel-
opment organization affiliated with the University of Northern Iowa (UNI). In
his remarks, Conrad briefly introduced five projects (or types of projects) con-
ducted by the institute and its partner organization that make particular use of
ACS data.
He said that the first of these—regional workforce analysis—had not really
been broached in the workshop’s previous sessions, but it relies “tremendously”
upon ACS data. He said that he assisted with workforce analyses completed by
and for three counties: the Texoma region of southeast Oklahoma and north-
east Texas; the Cedar Valley region of northern Iowa; and the Siouxland region
spanning parts of western Iowa, Nebraska, and South Dakota. These projects
provide businesses and employers in the region in question with a picture of the
characteristics of the area’s workforce and estimates supplies and demands for
specific worker types and talents, and they make extensive use of ACS data as
well as employment data maintained by the Bureau of Labor Statistics (BLS). In
fact, he said, he knew a few people in the workshop room had gone through
training that he conducted at the Census Bureau and that training called for
them to use these three regions. As a result, the training attendees “probably
know more about these regions than they want to” because the training makes
them work with the BLS and American FactFinder websites (“and see what it is
really like for us who are data users”).
Conrad said that these projects faced a major challenge in that a couple of
them started before the first ACS 5-year numbers were released. Consistent
with what Miller said about the availability of ACS data for rural areas, 64 of
Iowa’s 99 counties are under 20,000 population and so have no ACS estimates
except for the 5-year numbers; indeed, two-thirds (667 of 950) of Iowa’s incor-
porated places are under 1,000 population. In these projects, Conrad said that
they use ACS data for basic demographic (population by age, sex, and race and
Hispanic origin) and economic (household income and poverty status) informa-
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 101
tion. However, examinations of the prevailing workforce also depend critically
on ACS data on transportation and travel flows (commute time and means of
transportation to work), and on the ACS’s ability to profile employment earn-
ings by educational attainment. In rural areas, the “laborshed” from which em-
ployers may draw workers can be geographically large; hence, these studies typ-
ically work with data at the county (or groups of counties) and state level—with
some reference to the nation as a whole.
From the broad regional outlook to tract-level studies, Conrad said that the
second type of projects for which his organization uses ACS data are neigh-
borhood economic development technical assistance projects. In particular, he
referenced work done with the Waterloo Neighborhood Economic Develop-
ment Corporation (WNEDC) in his own home community (Waterloo being
a neighbor city of Cedar Falls, home of UNI). Waterloo contains some of the
most diverse neighborhoods in Iowa, defining “diverse” perhaps less stringently
than in very large urban centers as census tracts containing about one-fourth
(or greater) minority population. The new diversity of many of these neigh-
borhoods is a fairly recent phenomenon, based on an influx in immigrants since
2000. Accordingly, Conrad said that the arrival of ACS data has been welcomed
by groups like the WNEDC.
As with the general regional workforce projects, Conrad said that
neighborhood-focused economic development efforts rely on the ACS for basic
demographic, income/poverty, and industry/employment data at the census
tract and, depending on city size, block group. But ACS items of particular in-
terest for this analysis reflect the interests of the client audience. Neighborhood
economic development projects are used to inform prospective entrepreneurs,
so one very important metric derived from ACS data is an indicator of how
many entrepreneurs are already at work in each small area—that is, the number
and share of people who are self-employed. Because self-employment can occur
either by choice or by need, Conrad said that additional analysis is needed to
better understand the context of self-employment. These data are also used
to recruit prospective businesses, and those clients tend to be interested in
potential employment pools (daytime population in neighborhood areas) and
household characteristics by neighborhood (e.g., what type of income might be
tapped from a retail standpoint?).
The third project type Conrad described in which ACS data are used in
economic development studies are asset mapping studies, at the state or re-
gional level. He raised as an example a project done principally with two Iowa
state agencies—Iowa Workforce Development (the state employment agency)
and the Iowa Economic Development Authority—along with affiliated regional
and local economic development authorities. This project attempted to take
a high-level view of the regional economic development terrain in the state—
constructing, for each of 16 state-defined marketing regions, asset maps to help
assess the comparative strengths and weaknesses of the regions in terms of
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102 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
strategic workforce assets. He said that these asset maps made use of a host
of ACS data in addition to the standard demographic and income/poverty
breakdowns—data on travel/commute time, educational attainment, health in-
surance and costs, and prevailing housing values and rents. The asset map docu-
ments made use of these and other variables at the state and county level, along
with a U.S. total benchmark. Singling out the educational attainment variable,
Conrad said that ACS data have been useful in documenting and understanding
a phenomenon common to many rural areas: high school graduation rates that
can actually be very strong, but vastly smaller numbers and shares of individuals
with any post-secondary education (including 2-year as well as 4-year programs).
Conrad also noted that ACS data involving health insurance information can be
useful as a proxy for employment benefits offered in a particular area.
A fourth type of economic development study using ACS data is the deriva-
tion and comparison of regional metrics—with a particular eye toward under-
standing change over time (or the lack thereof) rather than a pure snapshot at
a time period. He used as an example an analysis that his institute conducted
with the Iowa City Area Development Group. A public/private partnership,
this group wanted to better understand the drivers of economic growth in the
Iowa City area; Conrad said that the area has grown due to the presence of the
University of Iowa and its affiliated hospitals as well as expansion of private-
sector firms like ACT, Inc., Proctor and Gamble, and Rockwell Collins (the lat-
ter headquartered in nearby Cedar Rapids). In particular, they were interested
in using data both to establish a benchmark (current economic development)
and to project and estimate future economic activity and the possible impact
of new development strategies. ACS data used in developing this set of metrics
included earnings by educational attainment, travel/commute time and means
of transportation to work, and income/poverty status, as well as basic demo-
graphic splits. For use as a benchmark, this work involved use of ACS estimates
at the place (city/town), county, state, and national levels; as Conrad said, “Iowa
City wants to know what other metro markets are their competitors,” nearby
and nationwide, and regional economic development bodies generally have a
“hit list” of other localities that they compete against (and against which they
need to be compared).
As a final example, Conrad turned to a pure state-level measure—the devel-
opment of an Iowa Competitiveness Index. Unlike some other states, Iowa does
not have a statewide chamber of commerce; instead, the Iowa Business Council
brings together representatives from about 20 of the largest employers in Iowa
(including firms like John Deere and Company, Pella Corporation, Rockwell
Collins, and the major state universities). As Conrad put it, the Business Coun-
cil’s role is not to lobby but still to “push issues”—as it has for a number of years,
for instance, on the status of pre-kindergarten education and services. Like the
Iowa City example of regional metrics, the Business Council sought a way to
benchmark the state economy and track progress over the years. Accordingly,
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 103
in 2011, the Council published the first iteration of the Iowa Competitiveness
Index, a set of key indicators that are scored and combined to produce general
assessments of the state’s economic strength along five broad dimensions: eco-
nomic growth, education and workforce readiness, governance and fiscal mat-
ters, health and well-being, and workforce demographics and diversity. The
index has been updated for 2012, and the Council intends to maintain it as a
regular series. Several of the indicators—the levels of which are compared and
ranked across all the states and then scored by their “overall competitiveness
trend” (improving, no significant progress, or worsening)—are derived from a
variety of sources, including data from the Iowa Department of Education (by
way of the National Center for Education Statistics) and the Iowa-based ACT,
Inc., college testing service. However, several of the indicators are drawn from
the ACS, such as per capita income and measures of racial and ethnic diversity.
In closing, Conrad said that he wanted to emphasize that economic devel-
opment is a process, not an event. Some elected officials mistakenly think of it
as a discrete event—a particular ribbon-cutting or ground-breaking—when it is
a complicated (and sometimes lengthy) process. He said that he recalled work-
ing on a specific project for many months—working with Target Corporation
about 10 years ago on the placement of a 1.3 million square foot distribution
center. Conrad’s Institute for Decision Making was involved in early work on
the project for a long while before they even knew the specific company in-
terested in making the move, and it continued as Target “kicked the tires in
the community” before making its final decisions. But Conrad said that the
main point is that economic development is not only a process but one that in-
volves data-driven decisions at each step. Like other companies, Target had to
approach the decision from a market analysis perspective—where might they be
opening or expanding stores, how are they going to serve those markets, and
where would the workforce for both the stores and distribution center come
from. At even more detail, wanting to enter the community as an “employer
of choice,” Target wanted to understand the wages and benefits that it would
have to provide to be competitive. Conrad said that data analysis from the ACS
(including the use of health insurance as a proxy for benefits, as described above)
has proven useful in these kinds of projects.
Conrad also argued that ACS-based analysis is critical for entrepreneurs. He
recalled a specific example from Waterloo, of the WNEDC working with a lo-
cal Hispanic couple interested in opening a tienda in the area. That case in-
volved detailed analysis—possible only with the ACS—of 13 census tracts, using
the data to try to pick an ideal location based on the demographics of the area
and the access to transportation networks (anticipating that much of their clien-
tele would be foot or public transit traffic). As another example, Conrad said
that he had worked with Pella Corporation on numerous site location projects,
profiling small-area local workforces and trying to determine which areas have
workers with the educational attainment and skills needed for the facility and
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104 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
whether—if one built a 400-employee facility in a town of 5,000 population—
housing would be there for the new workforce. Unfortunately, he said, the
ACS data were not available at the time of this project.
During the discussion period for the session, Conrad was asked by his col-
league in economic development work—Patrick Jankowski (Greater Houston
Partnership and member of the workshop steering committee)—to comment
on particular uses of ACS data to profile and promote areas to foreign corpo-
rations and foreign investors. Conrad replied that he has worked in such cases
before and agreed that the ACS data are very helpful in building cases for foreign
investors—with two curious caveats. First, he said that he is struck that foreign
firms are “shocked at the level of detail that we are able to provide” through
public data resources. It is not only the ACS that yields this response—when
economic developers are attempting to weave a data-based story for investors,
data resources like the Bureau of Labor Statistics’ Quarterly Census of Employ-
ment of Wages and its Occupational Employment Statistics program also play
important roles. But the level of demographic detail from the ACS surprises
foreign firms—which occasions the second “caveat,” which is that foreign in-
vestors will commonly engage other consultant firms to review the data work
by economic developers as a check on whether the numbers are correct.
Put briefly, Conrad said that professionals in the economic development
community “use the ACS data on a daily basis”—and communicate impressions
and findings from those data to their end clients. To be sure, he said, ACS
data are not the only important data resource; they rely on other government-
produced data series such as those produced by the Bureau of Labor Statistics.
But, he said, the ACS is becoming increasingly critical to making the kind of
“informed decisions” that help communities and local governments cultivate
entrepreneurs and businesses, and continued availability of the data is critical.
He noted that other speakers had “wish lists” to make the ACS more useful
and relevant; from the economic development perspective, Conrad said that the
addition of a question on multiple jobs—how many jobs each person actually
holds—would be invaluable in sorting out dynamics that are presently murky.
Above all, though, Conrad stressed the importance of the 5-year ACS estimates
to a state like Iowa and its roughly 3.1 million population—a state in which two-
thirds of counties have less than 20,000 population and only 22 communities
have populations sufficient to obtain 3-year average estimates.
6–B IPUMS: COMPILING AND DISSEMINATING ACS DATA
PRODUCTS
Funded by the National Science Foundation and the National Institutes of
Health and administered by the Minnesota Population Center (MPC) at the
University of Minnesota, the IPUMS project produces (and makes available for
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 105
free) microdata series for research use that are recoded from the originals to use
consistent labels and concepts across time (past iterations) and space (other coun-
tries).1 For the purposes of this workshop—and among U.S. data users—the
term “IPUMS” is generally used to refer to products from the IPUMS-USA sub-
project, including harmonized, consistently coded microdata samples from all
U.S. censuses since 18502 and—since 2001—the ACS.3 The other two branches
under the IPUMS label are the IPUMS International subproject that enables
linkages across multiple iterations of several foreign censuses and IPUMS-CPS,
which applies consistent coding to multiple microdata series from the Current
Population Survey (CPS) samples. Across all its data sets, MPC currently main-
tains on the order of 800–850 million individual person records—and it dis-
tributes them to a steadily growing user base. Katie Genadek of the IPUMS
user support team said that MPC averaged slightly less than 100 gigabytes of
data downloaded by users from its servers in 2001, around the time that its web
distribution strategy came into place; as of 2010, the data volume moved by
MPC had grown 10-fold to about 1 terabyte per week, on average. The IPUMS-
USA samples, including the 1-percent microdata samples from the ACS (yearly
and multiyear samples), are the most widely used of MPC’s data resources, and
currently have about 30,000 registered users on MPC’s website.
Speaking for the IPUMS user support team, Genadek remarked that her
workshop presentation would be different from the rest of the workshop be-
cause she has used ACS data extensively but is not actively in the business of
producing anything from the ACS—“no map, no table, nothing.” But the per-
spective she said she would bring to the session was that of a data aggregator—
repackaging and adding value to the base ACS products and brokering their use
by a broader class of data users. Significantly, she noted that she can also speak
to the ways in which users manipulate and analyze the IPUMS version of ACS
data files.
Genadek said that the “value added” to the IPUMS version of the public ACS
files runs along four basic lines—three related to data content. The first is the
database’s titular integration. Even in the relatively short lifespan of the ACS,
there have been changes to the exact coding of responses to particular questions,
to the labels associated with specific responses on the questionnaires, and to un-
1 Additional information on IPUMS is available from the project’s website, http://www.ipums.
org.
2 There is one exception to IPUMS’ census coverage; most of the original schedules (the ledger-
type “questionnaires” on which personal information were recorded) from the 1890 census, from
which microdata would be drawn, were destroyed by fire in 1921.
3 Though IPUMS is well known (and a widely used term) among data users, the preferred way
that the term should be pronounced is less clear. As an aside to her presentation, Genadek remarked
that the preferred in-house pronunciation is ih-pums (soft I)—consistent with the pronunciation of
the word (“integrated”) providing the initial—although eye-pums (hard I) is commonly used. As she
said, “we are not related to the Apple products”—and try to avoid association with them—making
the pronunciation distinction more important in recent years.
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106 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
derlying geographic and other codes. To the greatest extent possible, IPUMS
recodes the public files to be able to facilitate more direct comparisons across
years—or to make pooling of different years’ samples possible. The second com-
ponent of value added is the detail of the online documentation of the data sets.
The samples themselves are described with care, as are individual variables; the
history of individual variables, including the comparability of data across sam-
ples and changes in the underlying questionnaire texts, is recounted in detail.
The third content-related value-added component is “new” variables derived by
MPC and included on the IPUMS files. These include “crosswalk” occupation
and industry variables (for which consistency across year and beween samples is
particularly hard to maintain because of changing definitions—and the changing
nature of industries). The IPUMS-coded ACS data also include derived variables
to reconstruct family and subfamily interrelationships, to permit analyses using
families as the basis for analysis rather than household/housing unit. The fam-
ily relationship variables are derived from various patterns in responses as well
as the general census/ACS question on relationship to household head—an item
that technically asks about the relationship of each person in a housing unit to
“Person 1” (the one who completes the questionnaire or who is interviewed),
who is intended to be but is not necessarily the effective head of household—or,
for that matter, a family member of some people in the housing unit4 ).
Genadek said that the fourth value-added component is the online interface
and data extraction system, which gives users great flexibility in selecting as
many or few variables as they might like from as many samples (census or ACS)
as desired. The IPUMS site has also taken care over the years to provide results in
many output forms, in whichever way is most convenient for the user to access
and analyze. User-specified extracts can be output in formats for common sta-
tistical analysis packages (SAS, Stata, SPSS); recently, facility was added to gen-
erate results in basic comma-separated value (CSV) format, for easy input into
Microsoft Excel or other spreadsheet software. For users without access to sta-
tistical software, the MPC platform has built-in access to the Survey Documen-
tation and Analysis (SDA) package developed by the University of California,
Berkeley, Computer-Assisted Survey Methods Program. Hence, it is possible
for users to directly analyze all of the IPUMS-USA (census and ACS microdata
samples) through online queries. Genadek said that she is part of the IPUMS
user support team that has proven to be a popular aspect of using the ACS data;
she and her small team of colleagues field email and telephone inquiries about
the data and using the data, with the goal of responding to all questions within
three days (and, more often, within a few hours). At the workshop, she said that
4 Similar to earlier versions, the main body of the 2012 ACS questionnaire begins with the
parenthetical instruction: “(Person 1 is the person living or staying here in whose name this house
or apartment is owned, being bought, or rented. If there is no such person, start with the name of
any adult living or staying here.)”
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 107
she had specifically checked her email for queries from IPUMS ACS users and
found about 2,500 such email messages.
Of all the user-specified extracts generated from IPUMS-USA—samples from
the 1850–2000 decennial censuses and the 2001–2010 iterations of the ACS—
some version of the ACS was included in 55 percent of the queries. Of those
IPUMS requests asking for ACS samples, 137,029 have asked for one or more 1-
year PUMS files; smaller but sizable numbers of extracts have asked for the more
recently available 3-year (5,726 extracts) and 5-year (2,141) samples. Also a fairly
recent development, the SDA-based online analysis component of the IPUMS
interface has generated 250,000 user-requested tables using ACS data—13,000 of
those in the month preceding the workshop alone.
Turning to the question of who uses the IPUMS-produced files, Genadek
noted that academic scholars are the largest segment of IPUMS’ user base.
As evidence, she displayed a graph of the number of citations to three MPC
databases—including, and with trends driven principally by, IPUMS—each year
from 1993 to 2011, as found in Google Scholar searches. Similar to the graph
of MPC data downloads, references to IPUMS and MPC resources in the aca-
demic literature began to grow precipitously around 2002 and have increased
by roughly 50 percent over the last 3 years, hitting a level of just over 1,100
citations in 2011. As Genadek indicated, the graph includes publications from
non-IPUMS data maintained by MPC; however, Genadek said that she believed
that the IPUMS versions of ACS products account for a large part of this growth
and that the ACS “is driving a lot of this research right now.”
Using basic information from the user profiles defined on the MPC site,
Genadek briefly displayed a pie chart showing the rough breakdown of IPUMS
users’ backgrounds. Indeed, users with academic affiliations make up about 77
percent of the known users; the largest share of these are users affiliated with
an economics department (31 percent), followed by demography/sociology (16
percent). Genadek says that MPC and IPUMS staff are aware of IPUMS data be-
ing the source for “tons of dissertations”—graduate students may be “our num-
ber one user”—and an increasing number of class assignments and projects. But
other blocs of IPUMS users are significant—state and local government agencies,
private industry, journalists. Genadek said that, at the federal level, IPUMS data
had been accessed and used by—among others—the U.S. Government Account-
ability Office, the Bureau of Labor Statistics, and the Federal Reserve Board
(and its member banks).
IPUMS’ online extract system is such that IPUMS staff can determine how
frequently users check specific ACS variables for inclusion in their extract data
sets. Genadek reported that the data item on race is “by far number one” in
terms of included variables. But some ACS-specific variables (or, at least, vari-
ables that are consistently reported across small areas by the ACS) are inter-
spersed with the standard demographic variables, suggesting that users are not
just using the ACS for general population estimates. To wit, the second-most-
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108 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
included variable in IPUMS extract is employment status—a major driver in the
workforce and employment studies described by Conrad (Section 6–A). Person
age ranks third, but place of birth and educational attainment both slip into the
rankings ahead of sex and Hispanic origin. Rounding out the list of the most
commonly accessed variables in IPUMS ACS, in descending order, are relation-
ship to household head, metropolitan area, state, marital status, income, and
language spoken at home.
Genadek said that she also combed through the emails to the IPUMS sup-
port team and characterized the most frequent questions in terms of both topic
and technical concern. In terms of topics, she said that the IPUMS staff are most
often contacted for clarity on poverty estimates and the relationship of poverty
levels to the ACS microdata. Next most frequent are questions of a geographic
coverage nature—why, in the microdata sample, do people appear from one spe-
cific county and not another? Questions about migration and mobility of peo-
ple within the ACS sampling framework are common, as are questions about the
derived variables on family and subfamily composition. Questions about occu-
pation coding, measures of physical or mental disability, and income round out
the topic-based questions. On more technical matters, IPUMS staff commonly
field questions about how income variables are adjusted based on the rolling,
multiyear sample design; they also receive and address questions about weight-
ing, allocation flags, and variance estimation, as well as more general questions
on the interpretation and communication of estimates from multiyear samples.
In communication with IPUMS users, Genadek commented that she also
has a sense of common wishes—things that IPUMS users would really like to
have included in the ACS. In her assessment, the biggest thing that she hears
from users is desire for the month of the survey’s administration to be coded
in the microdata. In addition to possibly permitting some glimpses at seasonal
populations and very precise construction of age variables, the month of inter-
view could also allow advanced users to apply their own income adjustments to
the finance and income questions. She conceded that she was not sure exactly
how much of a concern the identification of survey month would be from a
confidentiality standpoint, but it remains a major part of users’ wish lists for
the survey. Another common request is inclusion of a clearer and more detailed
relationship question. The IPUMS coding tries to reconstruct family structures
as much as possible, but the form of the relationship question and the way in
which the question is phrased can obscure some family relationships depending
on who answers the question.5 Also high on IPUMS users’ wish lists is the re-
5 A previous National Research Council (2006:133) report, following Schwede (2003), cited a
simple example of this masking phenomenon: “Consider a case where a man and woman live to-
gether, unmarried, along with the woman’s child from a previous relationship. If the man is the
census respondent, the woman may be reported as an unmarried partner or an ‘other nonrelative,’
while the child would likely be ‘other nonrelative’; the biological link between woman and child is
obscured. If the woman is the respondent, the biological link between her and her child would be
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 109
instatement of two variables that were available in previous census long-form
samples but were dropped over time. The lifetime fertility question—number
of children ever born to a mother—was dropped from the 2000 census long-
form sample and has not resurfaced in the ACS. Questions on the place of birth
of mother and father have been absent even longer, having been dropped from
the 1980 census long form; the Census Bureau announced plans to add parental
place of birth questions to the ACS questionnaire in 2013, but withdrew those
plans in May 2012.6 But, Genadek said, much though IPUMS ACS users might
appreciate such small revisions, her sense is that the users’ main concern and
interest is for minimal change in the future. IPUMS users tend to be interested
in long, historical time sequences—for that, one wants continuity in content,
form of question, and sample size. Hints or rumors of content being changed in
major ways or dropped together—periodically, for example, the ACS question
on ancestry is thought to be vulnerable—are disturbing prospects for policy-
relevant analyses of specific ancestry groups such as Brower’s profile of specific
Asian subpopulations in Minnesota (Section 5–A).
Genadek closed by referencing two additional MPC products, including the
National Historical Geographic Information System (NHGIS)—essentially, a
mapping analogue to the extended time series across censuses and data collec-
tions that can be calculated from IPUMS data. Small-area data (down to the
finest level of resolution possible in earlier years) can be extracted in mappable
form from the 1790–2010 censuses and, now, from ACS 1-year and multiyear
products. Like the IPUMS extraction tool, users can pull multiple tables and
variables from multiple years at once, and shapefiles for use in geographic in-
formation systems software for the different time periods can be downloaded as
well. The NHGIS interface also gives users access to a summary file not avail-
able from interactive tables on the Census Bureau’s American FactFinder site: a
5-year summary file from the 2006–2010 ACS that can be queried down to the
block group level. She said that MPC plans to disseminate all of the ACS files
as they become available and are processed—not as a replacement or competi-
tor to FactFinder, but as an alternative for people who find FactFinder hard to
navigate.
preserved, but it would be ambiguous to family and household researchers whether the male is the
child’s biological father or not.”
6 The Bureau posted notice of new ACS questions in the Federal Register (77 FR 18203–18205)
in advance of its filing a request for three additional years of clearance from the U.S. Office of
Management and Budget (OMB) under the Paperwork Reduction Act. The decision to withdraw
the parental place of birth questions was communicated in a memo updating that clearance package,
viewable at http://www.reginfo.gov under Information Collection Review number 201202-0607-
003.
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116 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
ferences in teleworking propensity by region. In their models of teleworking
behavior, they included dummy variables to capture state-level effects (control-
ling for individual characteristics); classifying and mapping these state-level ef-
fects, he said the regional differences are striking. In general, teleworking rates
are considerably higher in the West—likely because “hubs of technology” and
places with high concentrations of the industries most likely to enable telework
are on the West Coast; high levels of telework are also found in the extremes
of the Eastern Seaboard (New England, Georgia, and Florida), while the lowest
rates are in the Deep South and the Rust Belt.
Of this examination of telework behaviors, Levanon made clear that this is
not especially complex work—indeed, he said, this is a quite “simple usage of
the microdata.” But he said that he thinks that it speaks to an important base
of potential data users—namely, people in the human resources field interested
in trends in the workforce. Levanon said that, as best he knows, the Confer-
ence Board’s specific analysis of teleworking using the ACS data had not been
done before, and he said that the range of interesting kinds of things that can be
learned from ACS microdata might spur additional work—and interest in using
the microdata—among parties new to the data.
The second ACS-related Conference Board project that Levanon described
is one that is still in progress, but one that again makes use of the ACS’s strength
in providing consistent measures across a broad range of geography. Specifically,
the Conference Board is studying wage inequality, by any number of factors. As
an example of the work and the ACS’s utility in it, he displayed the graph shown
in Figure 6-1. The graph provides a general sense of geographic differences in
wage inequality through a relatively straightforward metric; making use of ACS
microdata, he calculated the ratio of the 90th percentile of total wages for full-
time workers in each state to the 10th percentile. Printed at a small size, he
noted that it is hard to read, but some of the states at the top—with the largest
ratios and hence the greatest spread in wages—include California, Texas, New
Jersey, the District of Columbia, Georgia, and Virginia. At the bottom—smaller
ratios and less spread across wages—are South Dakota, Maine, Vermont, North
Dakota, Wisconsin, and Iowa. The dark vertical bar shows the ratio for the
nation as a whole. He said that this fairly simple univariate slice from ACS
microdata spurs questions and areas for future probing; based on the states on
the high and low ends of the spectrum, “one can speculate that ethnic diversity”
might be an important determinant of the inequality, and that is something
that can be examined at finer levels of aggregation. The national-level vertical
line—and the fact that so many states are below the line—suggests interesting
trends as well; in early looks at the change in the ratios over time, Levanon
said that shifts in the national-level ratio are more pronounced than are shifts
in the distribution of inequality across the states. Again, he observed, these are
interesting phenomena that “I don’t think can be done [using] any other data
source” with such a level of confidence.
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 117
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Figure 6-1 Ratio of 90th percentile household income to 10th percentile,
by state
SOURCES: Calculated from American Community Survey data; adapted from workshop
presentation by Gad Levanon.
Finally, Levanon described work that he had done on housing characteristics
and demand in support of the Demand Institute, a new joint venture sponsored
by the Conference Board and Nielsen. Given the current economic climate,
one factor that the Demand Institute was interested in learning about is the phe-
nomenon of “doubling up”—multiple families or individuals sharing the same
housing unit. Within that “doubling up” population, an important subgroup is
young adults continuing to live in the parental home. Levanon said that the ACS
data were extremely useful to illuminate some points about this population. He
briefly displayed a chart using data from 2006 and 2010 ACS files examining
the percentage change in home ownership rates, which varies inversely with age
group; home ownership rates dropped by almost 18 percent among 20–24-year-
olds and almost 14 percent for 25–29-year-olds, compared to only a 0.5 percent
change among 65–69-year-olds. Part of this drop among young age categories
seems attributable to “doubling up”; as shown in Figure 6-2, almost half of 20–
24-year-olds and a quarter of 25–29-year-olds postponed independent household
formation and continued to live with their parents, as measured in 2010 ACS
data. Even among 35–39-year-olds, 8 percent continued to live with parents—a
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118 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
47%
22%
12%
8%
20-24 25-29 30-34 35-39
Percent
6% 16% 24% 23%
change
since 2006:
Figure 6-2 Percent of people living with parent by age, United States,
2010
SOURCES: Calculated from American Community Survey data; adapted from workshop
presentation by Gad Levanon.
23 percent increase over 2006 estimates. The contrast with pre-recession num-
bers (2006) is particularly interesting and suggests the recession’s role in driving
housing decisions. Like the other analyses, these general observations raise in-
teresting follow-up questions that can be addressed at the state level (or other
aggregations) using the ACS, and this work permits one to see whether “dou-
bling up” is particularly concentrated in states hardest hit by the recession.
Work with the housing data also occasioned Levanon to comment favorably
on the variety of questions in the ACS. Even seemingly obscure ones like the
number of rooms in the home9 can yield interesting insights from the ACS data.
In this case, Levanon observed that home sizes decreased during the recent hous-
ing crisis. Detached single family homes10 with 5 or less rooms ticked up slightly
from about 23 percent to about 29 percent of the stock of detached single fam-
ily homes; apartments with 3 rooms or less rose from about 38 percent to just
short of 50 percent between 2006–2009, though the percentage dropped (and so
apartment sizes grew) between 2009 and 2010. He concluded that these kinds
9 Housing Question 7 on the 2012 questionnaire; part a asks about the total number of rooms
and part b asks for the number of bedrooms, “count[ing] as bedrooms those rooms you would list
if this house, apartment, or mobile home were for sale or rent.”
10 Housing Question 1 on the 2012 ACS questionnaire asks about the general nature of the home,
asking “Which best describes this building?”; responses include “a one-family house detached from
any other house,” “a building with 5 to 9 apartments,” and “boat, RV, van, etc.”
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 119
of shifts in housing are important to businesses and other parties interested in
understanding the demand for housing in the nation or in specific regions.
6–E ACS IN BUSINESS: RISK ASSESSMENT AND INSURANCE
Headquartered in Boston, AIR Worldwide has been modeling the risks of
natural catastrophes since 1987, and today does so in more than 90 countries. It
is a member of Verisk Insurance Solutions at Verisk Analytics; as that label im-
plies, many of AIR’s clients are insurance and reinsurance companies seeking to
understand and manage their risks, but AIR’s client base also includes financial
institutions and government entities. Cheryl Hayes, senior research manager at
AIR, commented at the workshop about the ACS variables that AIR uses in its
research projects and about the general importance of the ACS to the field of
catastrophe modeling.
Hayes said that AIR began its work by developing models of hurricanes, tor-
nadoes, and earthquakes and their attendant impacts and risks in 1987, but that
catastrophe modeling did not come to the forefront until two extremely costly
natural disasters, 1992’s Hurricane Andrew and 1994’s Northridge earthquake.
The magnitude of damage and costs in those two disasters sparked awareness of
the major impacts that could come from catastrophes, and companies began to
realize that catastrophe modeling could help them better manage their financial
and personnel risks. The industry, its models, and its methods evolved over the
next decade, to be updated and expanded yet again after a major disaster, this
time the terrorist attacks of September 11, 2001; models for terrorist behavior
and workers’ compensation were first introduced by AIR in 2002. By at least
one metric, Hayes said, AIR and its clients have begun to see the benefits of
catastrophe modeling; while 11 companies became insolvent as a direct result of
Hurricane Andrew in 1992, only three such insolvencies was recorded following
the record-breaking 2004 and 2005 hurricane seasons (punctuated by Hurricane
Katrina in 2005; see Section 3–C).
The way in which ACS data enter this process is that they are crucial for
developing the Industry Exposure Database that is a core component of a catas-
trophe model. An Industry Exposure Database is essentially a representation of
the built environment that may be impacted by a catastrophe; it includes counts
of buildings in a particular geographic area and information on characteristics
of those buildings, such as their occupancy type, date of construction, and floor
area. These variables contribute to another key feature of the database, which is
the corresponding replacement values of those buildings. Hayes said that AIR
built its first Industry Exposure Database for the United States when the com-
pany started in 1987 and has been updating it annually ever since—and, all along,
the primary sources of information used to build and update the database are the
decennial population censuses, the economic censuses, and, now, the ACS.
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120 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
Elaborating on that point, Hayes sketched the general processes involved in
constructing the database, beginning with the generation of risk counts, which
are the numbers of dwellings and establishments. Information that they try
to assemble for properties include the manner and type of construction—e.g.,
whether it is a high- or low-rise structure, and made of wood or concrete—
because that information affects the potential vulnerability of the structure. In
addition to occupancy type, the floor area of properties is a particularly im-
portant variable because it is essential to valuing the property. In addition to
the censuses, this information (along with data on construction costs) is de-
rived from housing surveys and from property costing and construction re-
ports. The replacement/rebuild costs are calculated from the data on square
footage, using different multipliers based on construction type and height; costs
are also adjusted based on local and regional variation in costs of materials and
labor. When these elements are combined and then benchmarked against other
sources—data from clients and reports from the insurance industry—the result
is a robust Industry Exposure Database.
Hayes said that the decennial census provides requisite information on the
number of housing units—total housing counts. Displaying a map showing the
percentage change in housing counts between the 2000 and 2010 censuses, Hayes
noted that many of the states showing the highest rate of change in housing
counts are the southeastern states—Florida through North Carolina—that are
also prone to the specific disaster of hurricanes, which means that they have an
increasing number of properties at risk of damage.11 While the census sheds
light on the contours and gross change in housing, the ACS is essential to gen-
erate detail on the characteristics of the housing. The specific housing items
included in the ACS questionnaire are fairly blunt, but Hayes noted that many
of them provide key clues for modeling:
• The type of structure, number of units in the building, and the year the
structure was built all hint at the vulnerability of the structure; mobile
homes are more likely to be damaged or destroyed by events like torna-
does than large complexes.
• Combined with other information from building permits or industry
analyses, the year the structure was built can be used to infer things about
the likely type of materials (e.g., whether masonry or wood was more
likely to be used in particular states at a certain time).
• The ACS variable on the number of rooms in the house can be used as a
rough proxy for the floor area of the housing unit (and structure), which
in turn is a key part of the replacement value calculation.
11 Texas, also on the Gulf Coast, also ranked in the high growth in housing rate category on
Hayes’ map; much of the Mountain West also experienced major growth in the number of housing
units, and Nevada stands alone as the most extreme housing growth rate—and in the impact from
the housing crisis and recession.
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 121
• Other variables like per capita income and employment status have bear-
ing on the valuation (replacement cost) of a property.
Of course, Hayes said, the ACS is not AIR’s sole source of information,
but the critical variable of floor area is a good example of the ways in which
AIR finds the ACS essential to fill in gaps. She displayed a graph depicting the
average residential structure for small areas across the country, based on data
that AIR is able to acquire directly through various means. More to the point,
the map also showed—shaded in dull grey—the areas in which the direct data are
not available. The coverage of the direct data sources is spotty—weak in rural
areas in the West but also surprisingly low for some midwestern and eastern
states. But, Hayes said, the ACS data provide great assistance—using the ACS
variable on the number of rooms in housing units as proxy for floor area, the
same map drawn with ACS data has vastly fewer “no data” holes, and the direct
measures can be compared with the ACS-based proxies for areas where they
overlap in order to judge the quality of the proxy measurement.
In general, Hayes concluded, the ACS is vital to AIR’s annual update and
maintenance of a robust Industry Exposure Database and, once updated, that
database drives a number of important analyses and modeling efforts. From an
ongoing research standpoint, Hayes said that the exposure database is continu-
ally used along with historic loss estimates to develop, validate, and recalibrate
its core catastrophe models. The models and the exposure database are used
for the main thrust of AIR’s work, part of which is to generate real-time esti-
mates of losses—before events occur and as they are unfolding—to enable better
planning and management of reserves. The data and methods are also used to
validate the losses reported by individual companies—and so assess the quality
of their own data—so that they can better manage their financial risk.
Hayes wrapped up her discussion by displaying a table showing AIR’s es-
timates of the total insured value of properties in each state that borders the
Atlantic Ocean or the Gulf of Mexico, along with the percentage of that value
corresponding to coastal counties—and so at greater risk of hurricane damage.12
Hayes said that table speaks to the importance of catastrophe modeling to com-
panies writing insurance in areas like Florida—where AIR estimates that almost
80 percent of the insured property value is in coastal areas. Moreover, the devel-
opment of these models over time has suggested rapid growth in this value—an
annual increase in coastal counties’ property valuations of roughly 7 percent.
She ended by commending the ACS as an integral part of Industry Exposure
Databases, and so argued that the continuance of the ACS is vital to the clients
who rely on AIR’s models to manage their risks. She conceded that many of
AIR’s clients “might not know it”—might not fully appreciate how big a role
that Census Bureau and ACS data play in the modeling and their resulting abil-
12 The same table is available at http://www.air-worldwide.com/_public/images/pdf/AIR2008_
Coastline_at_Risk.pdf, which also spells out which counties are defined as “coastal.”
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122 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
ity to manage catastrophe-exposure risk—but that the survey’s value is not lost
on AIR and the insurance industry generally.
6–F ACS FROM THE CONSTRUCTION AND HOME BUILDING
PERSPECTIVE, AND DISCUSSION
Moderating the discussion session, David Crowe (chief economist, National
Association of Home Builders [NAHB]) said that he wanted to take the op-
portunity to make clear that the clientele and users in the housing industry
value the ACS in the same way as other business sectors. He said that housing
is one of the most distinctly different and variable commodities from place to
place—the old saying stressing the importance of “location, location, location”
has never been truer. As suggested in Hayes’ presentation, Nevada experienced
a staggering home construction boom—and has been hit accordingly hard by
the subsequent bust—while other states (e.g., Texas) were not as hard hit. The
way we know such things, Crowe said, is through data sources like the ACS. He
observed that NAHB has made extensive use of ACS data—in analyses for its
client builder firms to understand the differentiation in housing trends across
space.
In addition to documenting the effects of the recent economic trends, Crowe
said that the ACS has made possible analyses of important aspects of the broader
real estate industry, and he offered two specific examples. The first is home
remodeling, which Crowe noted has become a much more productive compo-
nent of the real estate industry than new construction. ACS estimates were
instrumental in NAHB’s construction of county-level estimates of remodeling
expenditures, which in turn permit NAHB clients to understand the strengths
and weaknesses in this increasingly important segment of the marketplace. As
another example, Crowe said that NAHB has used ACS data to derive “afford-
ability indexes”—factors to understand how housing affordability varies across
locations and by demographic groups.
Listening to the presentations in this session, Crowe said that the com-
mon thread is “the use of data to make appropriate and intelligent decisions”—
nothing more elegant or sophisticated than that—but that each presentation is
also “a good demonstration that [there] isn’t an immediate transfer from the
Census Bureau to the client.” Intermediaries are needed to compile the infor-
mation, to develop it further, to construct products of the necessary level of
detail, and to provide assistance in handling and interpreting the numbers—all
of which helps the clients make informed decisions.
Opening the question period, Crowe asked the speakers a form of the same
overview question that had been raised in previous sessions: What would each
speaker do, and what (if any) data could serve as a backstop, if the ACS were no
more? Conrad answered that he and other economic development organizers
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 123
would have to turn to private data vendors—and hope that “they are somewhere
in the ballpark with their numbers” relative to what is available in the ACS.
However, he emphasized that he works with small communities, which are par-
ticularly limited in their budgets and so would sharply constrain their ability
to acquire the data; the availability of ACS data as a public good is greatly im-
portant to users. Genadek offered that IPUMS users would likely turn to the
CPS but—in the interest of continuing research across time—would have to hold
out hope that the ACS content would return in the form of a decennial census
long-form sample. Hayes—from the perspective of a company reliant on sta-
tistical modeling—said that AIR would have to do a lot more modeling on its
own, pulling together historical data and doing whatever it could to model and
project from that base. But, she emphasized, the loss of the ACS “would def-
initely put us at a big disadvantage.” Levanon concurred with Genadek that,
for the Conference Board’s labor market research, the alternative would almost
surely be the CPS—but, as noted in other presentations, the sample size limita-
tions on the CPS are such that the CPS is a “far second-best” alternative in many
applications. For detailed analysis of occupations like those that are now pos-
sible with the ACS, researchers like the Conference Board would face the stark
options of either using larger aggregation groups or not doing projects at all, and
that would be significantly damaging. The unique nature of Acxiom’s data re-
sources is such that Christenson said that one reaction would be to position the
company’s private records-data-based holdings as an alternative for some users’
needs. But, as Christenson emphasized in his presentation, Acxiom depends
on the ACS data as well; were the ACS to cease to exist, he said that Acxiom
would have to treat the last release of ACS data like a decennial census data file:
freeze it and continue to use it in its models going forward, but recognize that
it will be less reliable over time. Crowe summarized, adding his and NAHB’s
viewpoint that what would result is less definitive decisions—or at least poorer
information feeding decisions.
With the floor opened to questions, Lester Tsosie (Division of Economic
Development, Navajo Nation) asked about using ACS data to study labor
migration—whether it is possible to extract information on between-county mi-
gration or, ideally, between tribal reservation areas and surrounding areas, from
the PUMS files or other tabulations. Conrad answered that he does not typi-
cally use PUMS data much due to the nature of his projects. Reiterating that the
basic nature of economic development projects is telling a story with a variety
of data sources, he said that he commonly uses the county-to-county migration
data developed by the Internal Revenue Service based on annual tax returns
and—specific to mobility within labor market areas—data from the Census Bu-
reau’s Longitudinal Employer-Household Dynamics (LEHD) program. Scott
Boggess (U.S. Census Bureau) commented that county-to-county migration tab-
ulations from the ACS (based on the question on place of residence 1 year ago)
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124 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
are available on the Census Bureau website—albeit not directly from the Amer-
ican FactFinder interface, where those tabulations do not naturally fit.13
Crowe asked another question of the set of presenters, asking them whether
they think that their clients ultimately have a good sense of where the infor-
mation comes from—do they know that the products being delivered to them
are based on ACS data, or just general “census data” from the Census Bureau?
Christenson answered first: “the term ‘American Community Survey’ confuses
every single person I talk to, internally and externally.” He sees very little aware-
ness of exactly what the ACS is and that, historically, Acxiom and others wind
up using “Census Bureau data” as a descriptor because the ACS label simply
does not ring a bell. Hayes agreed, recalling that references to “ACS” were un-
clear to many, if not most, of the participants of an AIR client conference in
April 2012; they, too, turned to general descriptions of “census data,” though
they also tried to introduce the concepts and terminology of the ACS. Conrad
admitted to using “census data” as a descriptor, but more as a convenience; in
the economic development arena, so many data sources and so many acronyms
can crop up in products that “census data” clears some of the acronym clutter.
However, he argued that—if users like himself and his fellow presenters rely on
the data so much—it is incumbent on them to try to inform and educate clients.
Another workshop participant added that “ACS is a brand name that nobody
knows” on Capitol Hill; in conversations with constituents, congressional staff
find that “census data” is less confusing. In short, Crowe summarized, what
he concluded from this exchange is that—“to use an Acxiom phrase”—the ACS
“needs branding.” Christenson acknowledged this, and added that Acxiom itself
is starting to label new releases of its Market Indices product as “Market Indices
ACS,” to start to build awareness.
Ken Hodges (Nielsen, and co-chair of the workshop steering committee)
said that he wanted to interject a point that perhaps runs to the contrary. He
recalled a session about 8 years ago, when his company was still known as Clar-
itas, where he ran a session on the ACS at a well-attended client conference.
At that time, awareness of the ACS and its basic features seemed to be quite
high. Genadek added that academic researchers in general—such as make up a
large share of the IPUMS user base—seem to have high awareness of the ACS
nomenclature; in academic circles, at least, the “branding” of the ACS has gone
fairly well. Crowe agreed that intensive data users know the ACS very well but
that he is not surprised that end-stream clients of business users or data aggre-
gators are not fully aware of the ACS. He said that the challenge, and the point
that he wanted to raise in starting the line of questioning, is that if people do
not know that “the ACS” is a valuable piece of information, “they will never
come to the defense of it” if the survey is vulnerable. Christenson commented
that the calls, emails, and questions he has received concerning the ACS have
13 These migration data are available at http://www.census.gov/hhes/migration/data/acs.html.
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BUSINESS, ECONOMIC DEVELOPMENT, AND DATA AGGREGATOR USES 125
probably increased 10-fold with the legislative moves challenging the ACS (see
Section 1–B); a possible “silver lining” of the recent increased scrutiny of the
ACS is that awareness of the survey may grow as well.
Constance Citro (Commitee on National Statistics) asked Crowe and the
presenters about the housing content of the ACS—what use is made of the ques-
tions on the ACS about mortgages and utility bills, because those questions are
among the most difficult for respondents to answer. Conrad replied that he
regularly makes use of the ACS data on average monthly rents and mortgage
payments in characterizing the housing markets in smaller, rural areas. In those
rural areas, realtor data—if they exist—-can be particularly sensitive to the role
of realtors as sellers; the ACS measures might be less direct but may better re-
flect prevailing conditions than volatile under- or overstatement of values that
might creep into realtor data. Crowe added that these ACS housing variables
are important to NAHB’s calculation of affordability indexes because they get
at payment burdens and loan-to-value ratios. He added that NAHB has also
made use of the ACS’s data item on property taxes paid. The property tax rate
in a particular locality is often stated as percent of the value of the home or
property, but is often based on something other than the true market value of
the property. He said that the ACS variables may provide a better measure of
the actual value of the home and, thus, a better basis for comparison across areas.
NAHB has published county and even subcounty maps to illustrate particularly
high and low property tax rates, based on ACS data. Christenson added that,
historically, his clients would most commonly request information on median
income and median home value; now, his users are growing much more inter-
ested in the more-recently-added ACS questions on first and second mortgages
and on health insurance questions.
Roderick Little (U.S. Census Bureau) asked the presenters for their views on
the issue of voluntary versus mandatory response to the ACS, and whether they
would see a voluntary ACS as having a major impact on their work. Genadek
said that she agreed with the comments from presenters in other sessions—that
mandatory is better than voluntary if only to stave off declines in the response
rate (and effective sample size).14 Conrad echoed Miller’s comment earlier in
the workshop (see Section 5–B) about the impacts being potentially worse for
rural areas; repeating that Iowa has only 22 counties with populations 20,000
and larger, his bottom-line concern is that the loss of mandatory response could
start the slide to exacerbating the divide between “data haves” and “data have-
nots.” In sum, his big concern is approaching a situation where, for Iowa, there
will be a lot of information on Des Moines but nothing about the rest of the
state. Citro concluded the session by summarizing the brief, hypothesized re-
14 There followed a brief colloquy on exactly what the response rate to the ACS is—the response
to the initial mail sample, the weighted response rates including follow-up collection by phone or
personal interview, and so forth.
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126 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
sults: that voluntary response would cause mail response to decrease consider-
ably, meaning that either additional funding would be needed to get back to the
current effective sample size or data might have serious issues concerning bias
and representativeness.
