Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 91
–5–
State, Local, Tribal, and
Urban/Rural Uses of ACS Data
Tasked, as the workshop was, to focus on nonfederal uses of American Com-
munity Survey (ACS) data, many of the speakers in several of the workshop
sessions touched on issues involved in using ACS data to inform state and lo-
cal government. Users at the state, local, and tribal levels—whether working
directly for governmental units and affiliated agencies or participating in the
Census Bureau’s State Data Center network to disseminate data broadly—are a
sufficiently large and important sector of the ACS user base to warrant a dedi-
cated workshop session. Included in this mix are users who focus on the general
issues of rural policy, an area of study for which the ACS presents special ben-
efits and burdens: While those interested in urban policy and large populated
areas face the “problem” of handling a wealth of ACS estimates of different vin-
tages (1-, 3-, and 5-year estimates all covering large population groups), workers
in rural policy must rely exclusively on the 5-year estimates.
Section 5–A summarizes work using the ACS to examine ancestry, migra-
tion, education, and—particularly—racial and ethnic diversity for the Minnesota
state government. The contrast in rural and urban uses of the data is spotlighted
in Sections 5–B and 5–C, the first of which describes work by the Rural Pol-
icy Research Institute and the second (complementing the applications by The
New York Times discussed in Section 4–C) illustrating the capability to map ACS
findings for small-area neighborhoods in New York City and State. Section 5–D
discusses the usability of ACS data to describe the unique geographic and demo-
graphic characteristics of individual chapters within the Navajo Nation. The
75
OCR for page 92
76 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
workshop steering committee asked Jacqueline Byers of the National Associ-
ation of Counties to serve as leader for the discussion of these presentations;
her own comments from the perspective of county government users, and the
general discussion, are summarized in Section 5–E.
5–A STATE GOVERNMENT USES: HIGHLIGHTING DIVERSITY
AND INFORMING POLICY IN MINNESOTA
On a continuous, daily basis, the Minnesota State Demographic Center
(SDC) fields questions and works on projects that depend critically on the ACS.
These include questions from other state agencies,1 nonprofit organizations, me-
dia outlets, or the general public—whether through the center’s dedicated email
“helpline” or other means. Not wanting to minimize those smaller, frequent re-
quests for information, Susan Brower (Minnesota state demographer) began her
comments by saying that she would focus on a few larger, longer-term projects
to suggest the substantive range of the center’s work with the ACS to inform
state policy.
She started by describing recent work making use of the ACS’s ability to
zero in on trends in very specific population groups. Over the past four decades,
Minnesota has become home to increasing numbers of Asian and Pacific Is-
landers, in large part reflecting arrivals of refugees fleeing repressive govern-
ments in southeast Asia. As Brower described, the mix of particular Asian
Pacific ethnic groups in Minnesota is considerably different from that of the
entire U.S. population, based on her analysis of 2008–2010 ACS data (via the
Integrated Public Use Microdata Series [IPUMS]). In the general U.S. popu-
lation, the proportions of southeast Asian groups (e.g., Cambodian, Hmong,
Vietnamese) and east Asian groups (e.g., Chinese, Japanese, Korean) are roughly
on par with each other, with east Asian groups holding a slight plurality and
a somewhat smaller percentage of south Asian groups (e.g., Asian Indian, Ti-
betan, Bangladeshi). By comparison, southeast Asian groups make up a clear
majority of Minnesota’s Asian population, with a markedly smaller share of
east Asian groups and slightly fewer from south Asian groups. In specific, the
largest single Asian Pacific ethnic group in Minnesota is the Hmong population;
the state’s approximately 3,000 Tibetans make Minnesota rank second among
U.S. jurisdictions in Tibetan-Americans; and the state’s concentration of Karen
(a Burmese–Thai minority group) is believed to be the largest outside of south-
east Asia (Council on Asian Pacific Minnesotans, 2012:i, 4).
In spring 2012, the Minnesota SDC collaborated with the state’s Council on
Asian Pacific Minnesotans—a state agency tasked to report to the governor and
legislature on issues important to Minnesota’s Asian community—on a broad
1 The Minnesota State Demographic Center is administratively housed in the state Department
of Administration.
OCR for page 93
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 77
report on the demographic and economic condition of Minnesota’s Asian Pa-
cific ethnic groups. The work resulted in an April 2012 report, State of the Asian
Pacific Minnesotans (Council on Asian Pacific Minnesotans, 2012), that made ex-
tensive use of ACS data as well as counts from the 2010 census. Brower said
that her center’s role in this report was to perform the analysis and (examining
the standard errors on the estimates) demonstrate to the Council that the results
were reliable and stable.
For the report, Brower chose to perform special tabulations from the
IPUMS 3-year microdata (2008–2010) from the ACS, profiling the nativity, an-
cestry, and socioeconomic and housing conditions of the state’s major Asian
subpopulations. She said that she found it necessary to build the special tabula-
tions, rather than use the detailed tables (which she made certain to describe as
“easily accessible” from the Census Bureau’s American FactFinder interface), to
provide reassurance that the analysis referenced all of the Asian population. In
particular, she said that she and the Council wanted to respect the very different
cultural and historical backgrounds of the different Asian Pacific subpopula-
tions and, therefore, carefully address each subpopulation separately. She said
that the American FactFinder tables tend to describe results for the Asian popu-
lation overall, and she and the Council needed finer resolution than that for this
project. To illustrate the point, Brower noted that the two largest segments of
Minnesota’s Asian population—Hmong (26 percent) and Indian (15 percent)—
are “at very different ends of the socioeconomic spectrum” and in background
in coming to Minnesota. The major influx of the Asian Indian population oc-
curred recently (over the past 10 years or so) and are more likely to be fluent in
English.
Analysis of the ACS data suggests great diversity by specific ethnicity group
in the broader Asian-ancestry population in Minnesota. Looking at “all Asian”
ancestry people in Minnesota in the 2008–2010 ACS data, 21 percent hold a
bachelor’s degree or higher. But that figure masks very different educational
attainment by specific groups; the majority Hmong population includes only
14 percent with a bachelor’s degree or higher; Asian Indians are second largest
in size in Minnesota’s population but tend to be better educated, leading other
Asian subpopulations at 82 percent with a bachelor’s degree or higher. The
state’s Chinese and Korean subpopulations count over 50 percent with at least
an undergraduate degree; only 9 percent of the state’s Cambodians and 6 per-
cent of Laotians have a degree. Additional interesting results were generated by
looking at nativity, comparing the ACS data with the 1990 and 2000 census long-
form samples. Brower found that the percent of native-born Asian Minnesotans
with at least a bachelor’s degree has steadily decreased over the decades—to the
extent that, as of the 2008–2010 ACS data, foreign-born Asians in Minnesota
were more likely to have an undergraduate degree than native-born Asians.
Brower briefly described two other recent projects in which the Minnesota
SDC used ACS data to inform specific policy uses. In April 2012, the Min-
OCR for page 94
78 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
nesota SDC performed analysis of 5-year (2006–2010) ACS PUMS data at the
request of the Minnesota Department of Human Rights. Specifically, the de-
partment wanted to focus on equal employment opportunity (EEO) compli-
ance for a very precise economic sector—the construction industry—to see if
state-mandated hiring goals were being met on construction projects funded by
the state. Recalling the earlier question from Census Bureau staff on the prac-
tical import of a few weeks or months in the timing of ACS data releases (Sec-
tion 2–F), Brower noted that the timing does matter—and that this EEO work
was one example where the ACS’s current release schedule gives it a unique ad-
vantage over other products. She said that the Census Bureau plans to release
special tabulations for EEO compliance at the end of calendar year 2012, and
that these special tabulations will facilitate much more detailed analysis than
SDC’s PUMS work. However, waiting until the end of 2012 was not a viable
option—the state human rights commissioner faced a deadline to get the data on
hiring goals during the state legislative session because 2012 is a revenue bond-
ing year. Major capital projects were in line for the session, and the state wanted
to make sure that hiring goals were in line with expectations. SDC plans to re-
peat the analysis when the special tabulations become available later this year,
to permit the human rights department to update its goals.
Just as ACS data are used at the federal level to allocate federal funds, so too
are they used by state governments to distribute funds to localities. Brower re-
counted that, in May 2012, SDC helped the Minnesota Department of Employ-
ment and Economic Development score applications under the Small Cities De-
velopment Grants Program. These grants—made from money allocated by the
U.S. Department of Housing and Urban Development—are intended to help
small, rural communities fund infrastructure and housing improvements. The
grant monies are distributed according to a formula based on the percentage of
persons in poverty at the city place level; SDC compiled data from the 5-year
2006–2010 ACS data to use in the formula.
Brower said that SDC is currently working on a longer-term planning
project, expected to culminate in a themed version of the center’s annual re-
port to the governor and legislature, tentatively titled The Time for Talent. The
basic goal of the report is to make use of the ACS’s detail on educational attain-
ment and occupation to document the levels of talented workers in the state,
and to use its data on place of birth to get a read on the movement of talented
workers in and out of the state. For this work, Brower said that the SDC took
the slightly unusual step of looking at four individual-year ACS PUMS files;
cognizant of the economic recession, they were concerned that some interest-
ing trends might be masked by looking at 3-year or 5-year data as a whole. She
said that the numbers that they obtained from the 1-year files were seemingly
too small to be stable, but they determined that the average of those single years
seemed to perform better. She emphasized that the work was still under way
at the time of the workshop, and that additional analysis would attempt to use
OCR for page 95
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 79
Table 5-1 Number of Movers to Minnesota
with at Least a Bachelor’s Degree by
State of Origin (or Abroad),
1995–2000 and 2007–2010
(annual averages)
State 1995–2000a 2007–2010 b
Wisconsin 16,207 19,764
Abroad 14,366 17,296
North Dakota 8,191 6,360
Florida 1,930 5,800
California 6,760 5,680
Illinois 7,936 5,526
Iowa 8,169 5,069
Texas 3,426 3,718
South Dakota 3,843 3,341
Colorado 2,574 2,996
aPerson Question 15a on the 2000 census long-form question-
naire asked “Did this person live in this house or apartment
5 years ago (on April 1, 1995)?” and included “No, outside
the United States” as a response. Question 15b asked “Where
did this person live 5 years ago?” and permitted specification
of a city, county, state, and ZIP Code.
b Person Question 15a on the 2012 ACS questionnaire (with
similar versions on earlier questionnaires) asks “Did this per-
son live in this house or apartment 1 year ago?” and in-
cludes “No, outside the United States and Puerto Rico” as
a response. Question 15b asks “Where did this person live
1 year ago?” and permits specification of an address, city,
county, state, and ZIP Code.
SOURCES: Tabulation from 2000 Census data and Integrated
Public Use Microdata Series, 2007–2010; adapted from
workshop presentation by Susan Brower.
the ACS data to zero in on the so-called STEM fields and occupations (science,
technology, engineering, and mathematics), on entrepreneurs, and on creative
talent.
Some illustrative high-level results from the work are shown in Tables 5-1
and 5-2. Table 5-1 suggests that Minnesota continues to draw large numbers
of workers with a bachelor’s degree or higher from the neighboring state of
Wisconsin—and nearly as many from outside the United States. Taking a longer-
term, life-course look by examining educational attainment and state of birth,
Table 5-2 suggests that Minnesota ranks fairly high in retaining degreed workers
who were born in the state; only the country-sized economies of Texas and
California, as well as North Carolina, have higher “staying power.”
OCR for page 96
80 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
Table 5-2 Percentage of
Degreed Workers
Living in State of
Birth, 2007–2010
State Percent
Texas 69.7
California 65.7
North Carolina 61.0
Minnesota 59.1
Georgia 58.7
Utah 57.4
Washington 56.0
Tennessee 55.4
South Carolina 55.1
Wisconsin 54.1
SOURCES: Tabulation from single-year
Integrated Public Use Microdata Series,
2007–2010; adapted from workshop
presentation by Susan Brower.
She concluded by reiterating that Minnesota’s state government makes
heavy use of ACS data for three principal purposes: developing population pro-
files of Minnesota-specific groups, conducting research to support development
of state policy and distribution of funds, and crafting special reports to help with
long-term planning. She also commented that, shortly before the workshop, the
SDC hosted a new forum for boosting public awareness of ACS data: its first
“Minnesota Data Opener” (a takeoff on the opening of fishing season, which
began at roughly the same time), in partnership with Minnesota Public Radio.
The data opener took the form of an online chat. SDC published a “workbook”
of data on education (derived principally from the ACS) in advance, and invited
the public to look at the data themselves and comment on what they found most
interesting. In particular, the “data opener” included a contest, asking people to
produce some kind of visualization from the data. They were very encouraged
by the results—it was an “engagement project using data,” and the results were
numerous, interesting ways of presenting data for the SDC to consider moving
forward.
5–B PLANNING HUMAN SERVICES IN RURAL AMERICA
Kathleen Miller—program director of the Rural Policy Research Institute
(RUPRI) at the University of Missouri—described RUPRI’s recently completed
Geography of Need project, an effort to compare the needs for various human
OCR for page 97
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 81
services in rural America compared to urban areas. RUPRI undertook the Ge-
ography of Need project in collaboration with Colleen Heflin (also from the
University of Missouri) and with funding from the Kresge Foundation. The
work grew out of Kresge’s interest in recent years in redesigning their human
services portfolio. Particularly in light of the economic recession and funding
pressures created by federal and state budget deficits, Kresge tasked RUPRI to
develop a national overview of how needs for services vary by geography and
urbanization, and what types of needs for services occur in different combina-
tions in different places. That regions of the country—many of them poor—are
characterized by persistent poverty is well known, but the way in which needs
for services vary within those areas is much less clear.
As RUPRI set about conceptualizing and planning this study, Miller said
they did so by following three basic rationales:
• Human service needs depend on the characteristics of the population in need:
For instance, military veterans have unique needs to services, and also
have existing access to an array of services not open to other populations
(e.g., U.S. Department of Veterans Affairs health centers). Needs for hu-
man services can also vary across the life course—the elderly have very
different needs than do young children—and in combination with geog-
raphy or other factors (e.g., a town whose composition changes due to
either a large outmigration of young people or a rapid inmigration of re-
tirees relative to one with very young populations).
• Specific local conditions affect the demand for and provision of human ser-
vices: These local conditions could include cultural differences or language
barriers (linguistic isolation). In rural settings, transportation barriers are
of particular concern. As Miller put it, “all the services in the world are
not going to help if you can’t get to them”; if someone lives in a rural
area an hour or more away from any service—“everything from a gallon
of milk to your pediatrician”—and their car breaks down, they are stuck.
• Human service needs are also a function of the economic structure of an area:
Again, as mentioned above, some rural areas are characterized by persis-
tent poverty and limited employment opportunities, and that will affect
the range of needed services.
Starting work, Miller and RUPRI faced the task of deciding what level of
geographic resolution to pursue in mapping the landscape of need. State-level
analysis was simple to rule out as being too coarse, but going down to finer levels
of geography like Public Use Microdata Areas (PUMAs) or tracts raised issues
of “poor data and smaller sample size.” The basic task to construct a national-
level portrait put additional constraints on the problem: The researchers had
to think in terms of a set of indicators that could be calculated for all units at
the chosen resolution (and not just cities in metropolitan areas), and the idea of
OCR for page 98
82 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
simply dropping smaller geographic units from consideration ran counter to the
project’s purpose.
Ultimately, RUPRI settled on counties as the appropriate unit—a choice
with certain undeniable benefits. The number of county-level equivalents in
the United States (3,141) is large but not overwhelming, and their boundaries
are generally well understood and stable over time. More pertinent, counties
are also the natural operational geography for many human services providers
(e.g., county health and welfare agencies), so counties seemed to mesh well with
the project’s goals. To be sure, though, counties also raised some disadvantages
because they range widely in both population and land area, their size along ei-
ther dimension can obscure important urban/rural distinctions within county
borders, and whole counties might be less likely to stand out with respect to
some needs/risk criteria than a purely rural community.
Cinching the choice of county as the level of resolution was what Miller
called a “fortunate” bit of timing—active work on the Geography of Need study
began around December 2010, coinciding with the release of the first 5-year
ACS estimates. The release of the 2005–2009 5-year ACS estimates was the first
exposure to ACS data and first update of socioeconomic data since the 2000
census for about 40 percent of counties—and, for purposes of rural data analysis,
“this was early Christmas.”
The approach taken by Miller and RUPRI was to construct a data set of 12
demographic and economic indicators for all counties. While most of these in-
dicators could be calculated directly from the 5-year ACS data, RUPRI favored
data resources from the U.S. Bureau of Economic Analysis and the U.S. Depart-
ment of Veterans Affairs over the self-report data in the ACS, on income from
government transfer payments and veterans status, respectively. RUPRI also
turned to the Census Bureau’s annual population estimates program to derive
the population denominators used in its calculations and to its annual model-
based Small Area Income and Poverty Estimates for the county poverty rate.
RUPRI’s final set of county-level indicators included:
• Percent of the population age 65 and over;
• Working-age dependency ratio;2
• Fertility rate;
• Foreign-born population;
• Percent of population in racial or ethnic minority groups;
• Percent of population living in subfamily arrangements;3
2 Segmenting the population into “working-age” (e.g., from ages 15–64) and “dependents” (the
complement, e.g., children under age 15 and seniors over age 64), the working age dependency ratio
is simply the ratio of dependents to working-age.
3 Subfamilies are families that live in the household of some other person—for instance, an un-
married mother living with her children in the house of her parents or a married couple (with or
OCR for page 99
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 83
• Percent of population with less than a high school education;
• Veterans as a percent of total population;
• Percent of households receiving Supplemental Nutrition Assistance Pro-
gram (SNAP/Food Stamp) benefits;4
• (County) poverty rate;
• Percent of households without a vehicle available; and
• Share of personal income from government transfer payments.
Having constructed a set of indicators, RUPRI derived a very simple index
measure: Each indicator was separately sorted and ranked, and a county was as-
signed a 1-point risk factor if it fell in the bottom 10 percent (and 0 otherwise).
Summing the scores across counties produced a Human Services Need Index
score between 0 and 12; these scores are mapped in Figure 5-1. Miller said that
the red-shaded areas (those with more than 3 or even up to 9) on the map high-
light some areas where RUPRI expected to see hardship high levels of human
services need: Appalachia, the “Black Belt” of the South, the Mississippi Delta,
the Texas–Mexico border region, the California Central Valley, and American
Indian lands in the Great Plains and the Four Corners. What also struck RUPRI
staff was the vast swath of the country with 0 risk factors and the remote nature
of many of the highest-scoring counties, suggesting the possibility of significant
transportation barriers and difficulty in service delivery.
RUPRI’s full analysis of these data tries to probe what is behind the red-
shaded areas on the map—what combinations of the 12 indicators/risk factors
seem to show up with greatest frequency, and where. Miller said that it was not
surprising to see one frequent combination—low educational attainment and
high poverty. But, for some parts of the country, a less-expected combination
(high elderly population and veterans’ status) proved most prevalent.
Table 5-3 breaks down the index scores by whether the county is or is not
part of a metropolitan area. Strikingly, over half of metropolitan counties ex-
hibit 0 risk factors, compared to only about one-third among nonmetropolitan
counties. On the high end of the scale—between 3 and 9 risk factors—the dif-
ference is even more stark, covering only about 9 percent of metropolitan coun-
ties but just over one-quarter of nonmetropolitan counties. Subsequent analysis
suggested that noncore counties (those containing no urban cluster with at least
10,000 residents) are “very overrepresented” among the nonmetropolitan coun-
ties with high risk factors.
without children) living with some other relative. The concept is described in greater detail on the
IPUMS project website at http://usa.ipums.org/usa/volii/subfamilies.shtml.
4 Housing Question 12 on the 2012 version of the ACS questionnaire asks “IN THE PAST 12
MONTHS, did anyone in this household receive Food Stamps or a Food Stamp benefit card? Include
government benefits from the Supplemental Nutrition Assistance (SNAP). Do NOT include WIC or the
National School Lunch Program.” Only a yes/no checkbox response is permitted.
OCR for page 100
84
Number of Risk Factors
0
1
2
3 to 5
6 to 9
Figure 5-1 Calculated Human Services Need Index for U.S. counties, 2009
SOURCES: Calculated using 2009 Population Estimates, 2005–2009 American Community Survey, and 2009 SAIPE estimates (U.S. Census Bureau) and data
from the U.S. Department of Veterans Affairs and the U.S. Bureau of Economic Analysis Regional Economic Information System. Analysis and mapping by
Rural Policy Research Institute. Adapted from workshop presentation by Kathleen Miller.
BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
OCR for page 101
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 85
Table 5-3 Rural/Urban Differences in Human
Services Need Index, 2009
Number of Metropolitan Area Non-Metropolitan Area
Risk Factors Counties Counties
0 585 (53.2%) 690 (33.8%)
1 267 (24.3%) 464 (22.7%)
2 149 (13.5%) 346 (16.9%)
3–5 91 (8.3%) 459 (22.5%)
6–9 8 (0.7%) 84 (4.1%)
SOURCE: Calculations by Rural Policy Research Institute,
University of Missouri; adapted from workshop presentation by
Kathleen Miller.
Wrapping up, Miller commented that the ACS is hardly perfect for studying
and understanding rural America. Revisiting the point that a large fraction of
U.S. counties fall below the 20,000 population cutoff—and so have to wait for
5-year estimates from the ACS—she conceded that there are grounds to be con-
cerned about the sample size for rural counties, about large margins of error, and
less timely estimates (only two nonoverlapping 5-year periods per decade for the
purest indication of change over time). But—in short—the ACS “is the best data
and often the only data we have” for rural analysis. RUPRI’s particular anal-
ysis of human services risk factors would not have been possible without the
5-year estimates; the work has helped RUPRI develop hypotheses for further
research and helped the sponsoring Kresge Foundation retool its portfolio of
grants dealing with human services.
5–C MAPPING ACS DETAIL IN NEW YORK CITY
Shifting from ACS analysis of intensely rural areas to the survey’s use in
explaining trends in the nation’s largest city, Steven Romalewski described re-
cent work in the mapping of ACS estimates and—in particular—first steps in
reflecting margins of error in the plots. Romalewski directs the Mapping Ser-
vice in the City University of New York’s (CUNY) Center for Urban Research,
which uses geographic information systems (GIS) software to design a variety
of online applications using Census Bureau data. Of particular note, and in
support of the 2010 census, Romalewski helped develop an easy-to-use mapping
application for the Census Bureau’s tract-level planning database—a tabulation
of 2000 census long-form samples used to calculate an index score (not unlike
Miller’s additive Human Services Risk Index in the previous section) that indi-
cated the “hard-to-count” nature of each tract. Other applications developed by
the CUNY Mapping Service made use of the long-form-sample data from the
OCR for page 104
88 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
of City Planning, and Ford Fessenden and colleagues at The New York Times (see
Section 4–C).
Again, Romalewski emphasized, the problem is very much open and he has
not yet reached any conclusions about preferred approaches; he moved swiftly
through a series of approaches, with their attendant pros and cons. One ap-
proach is to visually mask areas with high levels of uncertainty:
• For a map of Asians and Pacific Islanders as a percentage of total popula-
tion at the census tract level within New York City—2000 census data in
one pane and 5-year 2005–2009 ACS estimates in the other—Romalewski
sought to flag “tracts with unreliable estimates.” Different percentages of
the Asian population were portrayed in shades of green (or a light yellow
tint for 0–10 percent Asian population) but—for the 5-year ACS pane—
tracts with a coefficient of variation of 25 percent or more were shaded
grey. Major trends register strongly in the maps—the extreme growth
of the Asian population in parts of Queens (see Sections 4–C and 7–A)
and historic concentrations in lower Manhattan and parts of Brooklyn.
But the “problem” is that there are many such “unreliable” tracts—the
ACS-based map is overwhelmingly grey; the map avoids the false preci-
sion of the 2000 census pane where many of the ACS-grey tracts show up
in the yellow-tinted 0–10 percent Asian category, but the presentation of
so many “unreliable estimate” tracts might lead viewers to question the
reliability of the data as a whole.
• For a map of tract-level percentages of population living in poverty (in-
come under 150 percent of the poverty line) in the Chicago metropolitan
region,7 Romalewski demonstrated a more overt “mask.” The poverty
levels were mapped in red-orange shades, and he displayed a version with
and without pale blue dots marking tracts with coefficients of variation of
25 percent or more. Again, the attempt at an honest representation of the
variability inherent in the estimates winds up calling the reliability of the
whole map into question because there are so many blue dots. Perhaps
worse, the blue dots very literally mask the picture of poverty where cen-
sus tracts are smaller and most dense: The dots make it physically difficult
to see the shades of poverty in the city of Chicago itself.
Another approach is to confront the margins of error directly—to plot the
lower and upper bounds of a confidence interval estimate for a location rather
than a single point estimate. To illustrate the point, Romalewski revisited the
view of block groups in Suffolk County, shaded based on the percentage of
housing units with no access to a vehicle, using 5-year 2006–2010 ACS estimates.
7 The maps were clipped to make a vertical, portrait-orientation presentation and so focus on
the core of the Chicago area in Illinois; the map coverages spill over into northern Wisconsin and
northwest Indiana but the view is truncated, as it is for outer-ring Illinois counties like DeKalb and
LaSalle.
OCR for page 105
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 89
Figure 5-2 Lower bound estimate of percentage of housing units with no
vehicle, Long Island, New York, by block group, 2006–2010
SOURCES: Calculated from 2006–2010 American Community Survey, Table B25044; adapted
from workshop presentation by Steven Romalewski.
Figure 5-2 shows the map based on the lower bound of an interval estimate
for the no-car percentage, subtracting the margin of error from the estimated
number of no-car households and dividing by the total number of occupied
housing units. He said that this might be considered a “worst case” map for
no vehicle access. Most people on Long Island have cars, so low percentages
dominate the picture, but Romalewski said that the pattern of block groups
with high percentages is fairly similar to the pattern based on 2000 census long-
form data. But when the view is switched to the upper bound on the estimates
(Figure 5-3), the volatility in the data becomes clear; the map is saturated in the
red and orange tones connoting the highest levels of no vehicle access because
large standard errors drive up the upper bound. It is a “world of difference”
between the two maps and—for purposes of envisioning specific transportation
routes and policies—it suggests that the ACS data exhibit extreme uncertainty
at the fine, block-group level.
Romalewski’s next series of maps looked at the same geographic area—
Suffolk County, this time looking at census tracts rather than block groups—
but a slightly different data source. As discussed in greater detail in Chapter 7,
OCR for page 106
90 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
Figure 5-3 Upper bound estimate of percentage of housing units with no
vehicle, Long Island, New York, by block group, 2006–2010
SOURCES: Calculated from 2006–2010 American Community Survey, Table B25044; adapted
from workshop presentation by Steven Romalewski.
the Census Bureau issued a special tabulation from 5-year 2006–2010 ACS data
for use in legislative redistricting and more easily assessing the equity of newly
drawn districts. Known as the Voting Age Population by Citizenship and Race
tabulation—or CVAP for short—the file contains counts of voting-age U.S. cit-
izens (and their associated margins of error) for geographic units from the na-
tion as a whole down to the census block group level. The maps Romalewski
displayed using CVAP data grew out of work that the Mapping Center has been
doing with a group involved with boosting civic engagement among the Long
Island population; in particular, this set of maps examined the percentage of
the total CVAP population in each tract that is of Hispanic origin. Given this
application area, one instinctive “answer” to the problem of volatility in the
ACS estimates—switching to a more aggregate level of resolution like county
subdivisions or defined villages—is not really viable; higher-level geographic ag-
gregates are far from optimal for purposes of planning specific, targeted voter
registration drives or activities affiliated with schools.
Using the CVAP data, Romalewski began by repeating the approach of gen-
erating separate maps based on lower-bound and upper-bound estimates for per-
OCR for page 107
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 91
cent Hispanic. The results were similar to, but more muted than, those for
the no-car variable by block group; a cluster of high-Hispanic voting-age pop-
ulation in the northwest section of the town of Islip stands out, but numerous
tracts flare up to higher levels (and deeper shading on the map) in the upper
bound map due to large margins of error. He displayed the results from a few
other approaches:
• “Triangulation”/corroboration with related, auxiliary data: In this specific
setting, he displayed a map that looked at the Hispanic voting-age (18 and
older) population from the 2010 census summary data file. This is not the
same population because it is not restricted to citizens, but it can serve as
an intuitive check—or an effective, true upper bound—on the CVAP data.
The high-Hispanic-concentration areas in Suffolk County that registered
strongly in the CVAP maps show up on the 2010 summary file map—
northwest Islip, Brentwood, Huntington. And one could construct a
“corrected” map through this triangulation—for instance, an area in East
Shoreham shows up in the 11–25 percent Hispanic shading level on the
upper-bound CVAP map but that percentage is logically capped at the 6–
10 percent category because that is the 2010 census percentage Hispanic-
and-voting-age for the tract (without regard to citizen status).
• Omission of unreliable-estimate areas: A variant of the masking approach,
another take would be to visually omit tracts with highly unreliable es-
timates (coefficient of variation 25 percent or greater) rather than call at-
tention to them. In practice, this took the form of leaving the unreliable-
estimate tracts unshaded (white). Though he repeated that he has made
no conclusions, he did suggest that this approach is undesirable because—
in general—“you want to try to avoid losing information from the map,”
and this feels like going out of the way to not present the whole picture.
• Overlay some visual marker on areas where margins of error could shift esti-
mate to a different color category: In this sample map, Romalewski shaded
the Suffolk County tracts in some tint of green based on Hispanics as a
percentage of total CVAP population, but overlaid a dot-mesh layer on
tracts where the interval estimate could place the estimate in one of the
other green-tint ranges. He said that his basic reaction to this picture is
that it is “okay to an extent,” but it seems to put a very high burden on
the map’s readers to sort out what the map is really telling them.
He closed by noting that the Cornell Applied Demographics program is doing
particular work on techniques to flag potentially unreliable areas in an interac-
tive, online mapping application, focusing the viewer’s attention on the possible
pitfalls and permitting a more detailed look at the reason for the flags by clicking
on them.
OCR for page 108
92 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
5–D STUDYING DEMOGRAPHIC AND ECONOMIC
CONDITIONS IN THE NAVAJO NATION
Using ACS data to craft a demographic and economic portrait of the Navajo
Nation is a very different proposition from using it to explore patterns in New
York City. The Navajo Nation lands cover a land area in the Four Corners re-
gion of the Southwest that is roughly comparable to the state of West Virginia—
across which is spread a resident population of roughly the same size as the single
neighborhood of Flushing, Queens. However, the ACS is proving just as useful
a resource, as Lester Tsosie (from the Navajo Nation’s Division of Economic
Development) described in his workshop presentation. His workshop slides
were actually a direct copy of a briefing that Tsosie’s division had prepared for
tribal leaders in April 2012 as a briefing on the “economic landscape” of the
Navajo Nation, so the core of his remarks at the workshop was actually a pure
demonstration of the way in which ACS data are used to address the questions
of policy makers.
For the benefit of the workshop presentation, though, he began with some
basic overview of structure. The Navajo are one of roughly 550 American In-
dian tribes in the United States and—in both its membership population and
the area of its tribal land holdings—is the largest of those tribes. The tribe main-
tains its own membership rolls, but respondents to the decennial census and the
ACS can self-identify as Navajo in answering the question on race. Per a graph
in his slides, the 2010 census reported just over 308,000 self-identified Navajo
in the total U.S. population; of these, the majority (about 56 percent) lived “on
reservation,” within the Navajo Nation’s tribal lands. That figure represents
a decreased share of Navajo living on tribal land compared to the 2000 census
(about 61 percent); Tsosie said that much of this migration from living on tribal
land to living elsewhere is principally about Navajo people going to large urban
centers for employment (and educational) opportunities.
Functionally, the Navajo Nation is a semiautonomous territory with its
own “national” (tribe-level) executive, legislative, and judicial branches. It is
divided into 110 chapters that perform some local government functions; these
chapters vary greatly in size and population—and 110 chapters partition the
on-reservation population of about 173,000 quite finely—but the chapters are a
natural and meaningful level of resolution for looking at demographic data.
Tsosie’s economic and demographic briefing for tribal leaders centered
around four choropleth maps, working with four variables calculated from 5-
year 2006–2010 ACS data:
• Families in poverty: Per Tsosie’s calculations, on the order of 13.8 per-
cent of the total U.S. population has income levels below the poverty line;
within the Navajo Nation, that proportion is nearly tripled (37.7 percent).
Based on the map, the Division of Economic Development superimposed
black circles to draw tribal leaders’ attention to 10 particularly prominent
OCR for page 109
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 93
clusters of poverty within the Navajo Nation—some single-chapter and
geographically compact (e.g., the Alamo and Tóhajiilee chapters in New
Mexico) and others more sprawling (e.g., a cluster of roughly 15 chap-
ters centered around the Manuelito and Tsayatoh chapters and spreading
across the Arizona–New Mexico border).
• Percent of civilian labor force unemployed: Again, viewed as a whole, the
Navajo Nation’s unemployment rate (estimated for the presentation at
15.6 percent) exceeds—and is nearly double—the national level. For this
map, deriving the percent unemployed by chapter from the ACS data, the
Division of Economic Development did not superimpose any graphic de-
vice to call attention to particular clusters because the areas with the high-
est unemployment rates (higher than 36 percent) stand out very promi-
nently on the map: a north-south line of New Mexico chapters running
from Whiterock to Smith Lake and four chapters in Arizona near the
eastern border of the Hopi Indian Reservation, which is geographically
an enclave in the main Navajo Nation landmass.
• Median household income: Tsosie’s department’s calculations suggest that
median household income in the Navajo Nation ($26,232 per year) is just
under half of the median income for the United States as a whole. Mapped
by chapter, the higher median-income chapters tend to be on the edges of
the Navajo Nation’s land area—most strikingly, in a line along the western
border of the nation from Lechee and Coppermine in the north to Leupp
in the south (or, roughly, from Page, Arizona, to just east of Flagstaff,
Arizona). In particular, Tsosie said that his division is using this map (in
combination with the unemployment map) to reevaluate the division’s
strategic development plans, seeing how the maps coincide (or not) with
developing commerce centers within the Navajo Nation.
• Percentage of people whose spoken language is something other than English:
As a crude measure of speakers of the Navajo language, the Division of
Economic Development calculated the percentage of each chapter’s popu-
lation that reported speaking a language other than English; presumably,
this mainly identifies speakers of the native Navajo language and its di-
alects. About 70.7 percent of the entire on-reservation Navajo Nation
population speak a non-English language, per the presentation’s calcula-
tions. Though there are pockets of non-English concentrations on the
northern and eastern edges of the Navajo Nation’s lands, the general im-
pression from the map is a radial pattern—strongest concentration in the
chapters in the northeast corner of Arizona and spreading (to lower con-
centrations) away from that center. This map, and the underlying data,
are now being examined by education authorities within the Nation, be-
cause it does have implications for the service populations of educational
institutions. As an aside, Tsosie also noted that this map also serves as a
OCR for page 110
94 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
resource in helping several counties in Arizona, New Mexico, and Utah
meet their obligation to provide bilingual voting materials (see Box 7-1
and Chapter 7 generally).
After constructing these maps, Tsosie paid credit to the consistency of the
ACS as a data source. The Navajo Nation lands spread across three U.S. states
and cover (or otherwise interact with) a host of county and local governments.
Touching on so many other jurisdictions, Tsosie said that he knows from expe-
rience that it is very difficult for an organization like his Division of Economic
Development to coordinate, work with, and obtain information from all those
other sources. Information is also not necessarily coded or recorded in the same
way in the various jurisdictions; data available in (and obtainable from) the state
of New Mexico may be markedly different from that in Arizona. By compar-
ison, he said that the ACS at least provides a stable, consistently-applied-and-
coded source for the kinds of data products and tools that he wants to develop
for tribal leaders.
Tsosie then walked through a quick demonstration of his divison’s website,
http://www.navajobusiness.com, from which a variety of additional data anal-
yses and reports are available.8 The Division of Economic Development fields
some data collection activities on its own—for instance, the Navajo Shopping
Preference Survey described in one issue of their Navajo Economic Data Bulletin
in 2012 and a visitor survey—but Tsosie said that they are increasingly turning to
the ACS population data and related sources. On one dedicated subpage of the
site,9 Tsosie and colleagues performed a service for the individual Navajo chap-
ters by extracting chapter-specific data from the most recent 5-year 2006–2010
ACS data as well as the 2010 census Summary File 1 tabulation. Admittedly,
Tsosie said, some of these extracts are simply PDF files of tables constructed
using the American FactFinder interface while others reformat the data into
easier-to-work-with Excel files. However, the division’s work in putting the doc-
uments in one place means that the chapters have a single source and do not need
to learn the nuances of American FactFinder on their own. Tsosie said that this
repository of reports—and the ACS’s coverage in Navajo Nation—has been very
well received by the chapters, and has been particularly appreciated by officials
from the tribal government and individual chapters involved in preparing grant
proposals.
Tsosie also lauded the topic coverage of the ACS as being useful in tribal de-
cision making and resource allocation. One of the tabulations presented on the
site—and one that Tsosie asked to be included in the case study/agenda book
for the workshop—are chapter-level estimates to the question of whether the
housing unit has complete plumbing facilities or not. He said that the questions
8 See in particular the postings page at http://navajobusiness.com/pdf/Ads/Annoucemts.htm,
under the “Doing Business” heading but not, apparently, readily linked from top-level pages.
9 See http://navajobusiness.com/pdf/Ads/CensusRep.html.
OCR for page 111
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 95
of whether the housing unit contains a flush toilet or plumbing facilities are
often mentioned in discussions of whether the ACS is too intrusive, and they
are sometimes lampooned. But he said that the questions are important to ask
because—in many parts of the Navajo Nation, in other American Indian reser-
vations, and in other rural communities—the answer to them is “no.” Indeed,
paging through the chapter-by-chapter listings, quite a few of the 110 chapters
have a majority of housing units lacking complete plumbing facilities. If one
takes plumbing for granted, the questions might seem frivolous, but in poor,
rural areas the information is essential for documenting current conditions and
working on changed policies.
Similar to Miller’s observation that ACS data are certainly not perfect for
rural analysis—but that they are the best and sometimes the only data available
(Section 5–B)—Tsosie noted that he has seen some of the same quirks as others
when working with small-area, small-population data. Looking at detailed data
on household income, he said that he has seen one or two pockets of unusually
high values. Like Plyer’s anomalous high-wealth section in New Orleans’ Lower
Ninth Ward (see Section 3–C), the ACS estimates seem to show that “there are
supposedly some very rich Navajos out there—we haven’t found them yet.” But,
in his assessment, the benefits of the ACS data outweigh these quirks.
That said, Tsosie said that he needed to mention one special kind of “bur-
den” associated with the ACS and, with it, one direction in which additional
information from the Census Bureau could be a credibility boost. That “bur-
den” is the obligation to not only detail the statistical and survey methodology
of the ACS, but also explain the precise mechanics of how the data are col-
lected. Though tribal and chapter leaders appreciate the value of the numbers
they are beginning to receive from the ACS, there is a great deal of confusion
over exactly how the data are collected—on and off reservation lands—on an
ongoing basis. The data collection process is much more visible in the decen-
nial census, with its high-profile publicity push and its structured interactions
with tribal authorities on how to conduct the enumeration.10 But exactly how
the Census Bureau operates on tribal lands in off-census years, and how it reg-
ularly collects ACS information in on-reservation lands—while respecting the
reservation’s sovereignty—remains a great mystery to outside observers. Based
on the comments that Tsosie and colleagues have received, speculation on how
the ACS data are actually collected in the Navajo Nation ranges from “put[ting]
out a table outside a Bashas’ [Grocery Store] to get the information” by inter-
view to “just get[ting] the data off of the different [web] sites out there on the
Navajo Nation.” Tsosie conceded that he and his colleagues are not really able
10 In the 2010 census, tribes were given options on the degree to which in-person field visits
would be used relative to mail (incoming or outgoing) methods. Most American Indian lands—
including the Navajo Nation—were counted via Update/Enumerate methods—field staff knocking
on every door, updating their address lists as necessary, and completing the questionnaire through
personal interview (rather than simply leaving them to be returned by mail).
OCR for page 112
96 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
to dispel or correct these speculations because there does not seem to be good
documentation of the true procedures in areas like the Navajo Nation without
a great deal of conventional, city-style, mailable addresses. He also remarked
that his efforts to “get some clarification” through the Census Bureau’s Denver
regional office has been met with “very limited success.”
Revisiting that comment during the session’s discussion period, Tsosie added
that part of the concern over how the ACS data are collected on Navajo lands
stems from longstanding issues of trust. As he put it, “our historical legacy is
not always on good grounds with the federal government,” and he conceded
that it can be “very difficult to extract data from our people sometimes because
of what the federal government has done to our people” in the past. But he
closed by commending a great benefit of the ACS: its continual presence and
its coverage of demographic information within the Navajo Nation serves to
“validate our existence”; it “validates our sovereignty” by chronicling that the
Navajo are and “will continue to be there on our home lands,” and so the ACS
is extremely valuable to the tribe.
5–E ACS VIEWS FROM THE COUNTIES, AND DISCUSSION
Asked to moderate the discussion session, National Association of Counties
(NACo) Research Director Jacqueline Byers began by noting that the 2010 cen-
sus was the fourth with which she has worked with the decennial census in a
professional capacity, whether in her current role at NACo or when working
in state government. Now, working with NACo members and helping them
use ACS data, she said that—“finally”—she doesn’t need to “apologize that the
data is ten years old anymore . . . and it is a wonderful thing.” Byers also said
that she wanted to follow up on Tsosie’s comments on the complete plumbing
and flush toilet questions—and remark specifically on the discussion of privacy
issues in the workshop session dedicated to ACS burden (summarized in Chap-
ter 8, but a session that immediately preceded this state/local/tribal session dur-
ing the workshop). She said that she finds it an “interesting contradiction in
how people approach privacy” that “people tell everything about themselves
on Facebook”—that you can barely slip and fall on the street (or anything else
embarrassing) without it “going up on YouTube within half an hour”—yet will
“have a problem with telling [the Census Bureau] how many toilets they have
or what time they leave for work in the morning.”
Turning to counties’ perspectives on the ACS, Byers said that counties are
an often-underestimated force in public policy; within the states, counties (and
county-level equivalents) are typically the administrative units that “are charged
with the responsibilities of making things work.” Counties’ work involves a
great deal of planning—developing services and then ensuring that they are de-
livered to those who need them. Accordingly, “counties do everything with
OCR for page 113
STATE, LOCAL, TRIBAL, AND URBAN/RURAL USES OF ACS DATA 97
data,” and so are making extensive use of the ACS. Particularly in small, rural
counties, the 5-year ACS data are playing a major role in counties’ economic de-
velopment plans—documenting the education and skill sets in the areas in order
to attract new business (and jobs). As mentioned in the previous sessions, coun-
ties also make use of the ACS for targeting services for children and for foreign
language assistance; for making transportation decisions like road repair pro-
grams and development of mass transit routes; and for developing public health
and safety plans (and prepare for disasters). She noted seeing a recent news story
talking about the millions of dollars being spent by counties to deal with un-
expected consequences of tough economic conditions—additional funds to help
the homeless and stock food banks to keep up with demand and funds spent to
maintain foreclosed properties so that values of surrounding properties do not
completely collapse. Increasingly, she said, counties are turning to the ACS to
inform these kinds of interventions because they can get the information easily
and frequently, especially in large urban counties.
She then asked each of the presenters for their responses to two questions—
questions she admitted were “not particularly provocative” but ones important
to the current debate on the nature and future of the ACS. First, she asked sim-
ply: How would the state governments, tribal authorities, and other client agen-
cies described in the presentations do what they need to do without the ACS?
• Miller said that for her applications—trying to look at measures covering
the whole country and trends in rural America in particular—“we would
be very limited in what we could do and what we could say,” to the extent
that it is hard to imagine other alternatives. There are some other county-
level data resources that might be brought to bear, but none has the same
level of detail as the ACS.
• Brower answered that her center would most likely have to turn to state
administrative records sources, but that there would be “no comparison”
with the range of analysis that can be done with the ACS.
• Romalewski followed up on that comment, saying that he imagined that
administrative records from state and local governments would be used
more frequently, but he said that these would likely be less consistent and
reliable than systematic collection of ACS data. He said that some of this
spottiness in records data would arise from inconsistency in collection
and coding at the most local levels; even in a large metropolitan county
like Suffolk County on Long Island, the county “sometimes doesn’t have
much say over how the individual towns collect the information,” and
that’s “not a good situation.”
• Tsosie said that tribal governments like the Navajo Nation would likely
have to turn to the federal Bureau of Indian Affairs (BIA) and try to ac-
cess the records that BIA maintains. However, he said that he suspected
that much of that data “would be outdated and probably not very useful.”
OCR for page 114
98 BENEFITS, BURDENS, AND PROSPECTS OF THE AMERICAN COMMUNITY SURVEY
For more detailed survey-type data, he said that the recourse could be to
hire consultants or survey research firms, but that those costs could be
prohibitive.
Byers’ second question for the panel of speakers—and the rest of the work-
shop attendees—was what they specifically think would happen to their data
projects and their ability to use ACS data if response to the survey were made
purely voluntary rather than mandatory.
• Miller said that there are already difficult-to-reach and low-response-rate
populations in the mandatory ACS—the most rural areas in the United
States counting as one of those. She said that she suspected that a volun-
tary ACS would impair the effective sample size of the survey, and that
the groups that are already most difficult to reach would be more likely
to be missed in a voluntary ACS.
• Brower agreed, saying that “we wouldn’t have the confidence that we have
[now] in the representativeness” of the population. Given her center’s
analyses, she said that she would particularly worry about a distorted view
of low-income or linguistically isolated populations.
• Romalewski said that, not being a statistician, he could not really judge
the specific effects of what happens—but he expected that his mapping
applications would be worse as a result.
• Tsosie recalled his concern about exactly how ACS data are collected on
American Indian lands at present under the mandatory collection; if it is
made voluntary, “forget it, [tribal areas] won’t get anything.”
Byers closed with some personal comments about the legislative debate on
the future of the ACS. She said that this workshop—and its assemblage of ACS
stakeholders—is an important effort. However, she said, a critical perspective
not represented in the room is one that is critical to consider going forward:
that perspective is “the average citizen who is filing the complaints” with the
Census Bureau and their congressman about the ACS. Byers says that she sees
this as a major educational challenge and opportunity—boosting awareness in
the mind of the average citizen about how data from the census and ACS affect
their lives. For example, the average person putting children on the school bus
in the morning might not fully realize that—very often—it is ACS and census
data that play major roles in planning the school bus service (e.g., ensuring that
the children have a seat in the bus), in making sure that they have a classroom to
go to at the end of the ride (school siting to meet demand), that there is a teacher
standing in front of the room (school staffing decisions), and so forth.
