Using the American Community Survey Benefits and Challenges (2007) / Chapter Skim
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3 Working with the ACS: Guidance for Users
3 Working with the ACS: Guidance for Users
Pages 77-138
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From page 77... ...
. Highlighted applications in clude the use of ACS 1-year, 3-year, and 5-year period estimates for fund allocation to states and localities (3-A.1)
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From page 78... ...
Many of the specific applications discussed -- each of which illustrates some but not all issues regarding use of ACS data products to replace long-formsample data products -- pertain to more than one category of user. Section 3-G discusses an issue that affects all users -- namely, the fact that new population and housing numbers from the decennial census every 10 years will likely interrupt the time series of ACS estimates.
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From page 79... ...
The Census Bureau should also support an ongoing education and outreach program for users who plan to work extensively with ACS data, including the staffs of state data centers and other groups whose mission is to assist the broad user community. As discussed in Chapter 4, the Census Bureau should consider the development of new data products that would help many users, such as 3-year period estimates for statistical areas that are larger than census tracts and smaller than public use microdata areas (PUMAs)
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From page 80... ...
The ACS should be able to serve all of these federal agency uses and more, providing more up-to-date information of higher quality than the long-form sample. Some of the issues that must be considered in using ACS estimates for federal applications are illustrated below in the discussion of two specific uses: formula fund allocation (3-A.1)
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From page 81... ...
data are used in two ways in allocation formulas: directly, in that longform-sample estimates provide one or more factors in a formula, or indirectly, in that the formula relies on estimates for which long-form-sample data are one input to an estimation process that also uses other data sources.2 Whether formulas use long-form-sample estimates directly or indirectly has implications for how proactive the responsible program agency needs to be in deciding how best to use ACS estimates in place of long-form-sample estimates. 3-A.1.a. Use of Long-Form-Sample Estimates in Fund Allocation Formulas Most federal allocation formulas that incorporate long-form-sample data use the long-form-sample estimates directly; see Box 3-2 for seven ex 2 Allocation formulas that use long-form-sample estimates (or estimates that incorporate long-form-sample data)
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From page 82... ...
One formula uses long-form-sample estimates of total population, poverty popula tion, and overcrowded housing units; the other formula uses long-form-sample estimates of total population, poverty population, and housing units built before 1940.
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From page 83... ...
In the past these estimates were obtained from the most recent census long-form sample; currently, more up-to-date estimates are obtained from statistical models developed by the Census Bureau in its Small Area Income and Poverty Estimates (SAIPE) program.3 The SAIPE state- and county-level models include long-form-sample poverty estimates as one input together with more up-to-date information from administrative records to predict school-age poverty from a 3-year average of data from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC)
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From page 84... ...
Thus, the Census Bureau SAIPE staff will presumably determine effective ways of including ACS data in their model-based estimates of poor school-age children that are used in the allocation of education funds to school districts under the No Child Left Behind Act. The Bureau of Economic Analysis (BEA)
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From page 85... ...
Geographic Area Considerations Yet another consideration in the selection of ACS estimates for fund allocation is the types and population sizes of geographic areas that are eligible for funding. Some formulas apply to a single type of geographic area, such as states, while others include several types of areas, such as states, cities, and counties, and still others have population size thresholds that may vary by type of area.
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From page 86... ...
Should it be deemed desirable to further smooth funding amounts, the Special Education Grants Program could average 2 years of 1-year period estimates or use 3-year period estimates, which should have very low sampling error for all 50 states and the District of Columbia. Programs like Community Development Block Grants and Home Investment Partnerships, however, provide funds to different types of governmental units, some of which are smaller in population size than the cutoff of 65,000 people or more for ACS 1-year period estimates.
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From page 87... ...
The incorporation of ACS data into the SAIPE county and school district models should make it possible to improve their timeliness and precision. Consistency of Period Estimates In trading off such considerations as currency and precision, in no instance should agencies use in their allocation formulas a mix of different periods of ACS estimates -- for example, 1-year (or 2-year)
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From page 88... ...
; and (3) the possible effects on the accuracy of ACS income estimates from the Census Bureau's procedure for adjusting income amounts for inflation.
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From page 89... ...
A reason to be conservative is that HUD is concerned not only with having estimates that are as up-to-date as possible, but also with reducing year-to-year fluctuations in median family income estimates that are due to sampling error. The previous discussion of using ACS estimates in fund allocation formulas concluded that estimates for different periods should not be used in the same formula because the resulting fund allocations could be inequitable.
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From page 90... ...
.8 Income To put income amounts that are reported for differing 12-month reference periods on a comparable calendar-year basis, the Census Bureau expresses them in constant dollar terms by using the national consumer price index for urban consumers-research series (CPI-U-RS) for the latest calendar year covered by an estimate.9 For 1-year period income estimates for 2005, for example, each reported amount on a person record is adjusted by the ratio of the annual average CPI for 2005 divided by the average of the monthly CPIs for the particular 12-month reporting period for that person.
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From page 91... ...
(4) Adjustment Factors for 2005–2007 ACS 3-Year Period Person Income x = 214.5/195.3 = 1.09, x is multiplied by the adjusted 2005 a.
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From page 92... ...
illustrate the problems of using price change as a proxy for income change when comparing survey estimates. For 1998 -- a period of strong economic growth -- they estimated that the Census Bureau's inflation adjustment would make up only 22 percent of the difference between average person total income from a simulated 1998 ACS sample compared with average person total income reported for calendar year 1998.
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From page 93... ...
Housing For housing amounts, such as value, rent, utilities, property taxes, and others, the Census Bureau makes no inflation adjustments for the 1year period estimates. When, however, the 1-year period estimates for housing amounts are cumulated over 3 or 5 years, the Census Bureau adjusts them for inflation by using the ratio of the annual average CPI value for the latest year of the period to the annual average CPI value for the year for which the amounts were reported.
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From page 94... ...
3. HUD could decide to use ACS 3-year or 5-year period estimates for counties and ask the Census Bureau to develop an alternative method for adjusting income responses in the ACS to reflect HUD's need for current-year estimates.
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From page 95... ...
For example, HUD requires states and localities to have a Comprehensive Housing Affordability Strategy. This plan includes an assessment of the housing needs of families residing in a jurisdiction that is developed, in part, from long-form-sample data on demographic and housing unit characteristics for individual census tracts in the area.
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From page 96... ...
Such estimation requires additional data from administrative records or other sources, similarly to the way that the Census Bureau's SAIPE program uses food stamp and federal income tax data to generate updated county estimates of poor school-age children. Before deciding to use any type of updating procedure, simple or complex, it is essential to carefully examine the procedure's underlying assumptions.
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From page 97... ...
NOTE: See text on the need to understand and evaluate the assumptions that underlie any modeling procedure, even the simplest, before using a particular procedure to update ACS 5-year (or 3-year) period estimates to 1-year period estimates.
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From page 98... ...
period estimates available for most counties, the procedure may not be widely useful when the goal is to adjust 5year period estimates for smaller counties to the latest year. The Census Bureau's SAIPE program currently uses this type of simple procedure to produce updated estimates of poor school-age children for school districts within counties.
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From page 99... ...
The Census Bureau provides updated estimates of total population throughout the decade for counties and places (the updates include age, sex, and race/ethnicity detail for counties) , as well as updated estimates of school-age poverty for counties and school districts.
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From page 100... ...
Another strategy is to request special tabulations from the Census Bureau of 1-year or, more likely, 3-year period estimates for user-defined subcity areas that meet the Census Bureau's population thresholds of at least 65,000 people for 1-year period estimates and at least 20,000 people for 3-year period estimates. Cities should give early attention to their possible need for such custom estimates and work with the Census Bureau 10 Research on sampling error by the Census Bureau (Starsinic, 2005)
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From page 101... ...
• School-age poverty increased in BIG CITY/COUNTY, so the 5-year period estimate (2010–2014) of 17.2 percent is lower than the latest 3-year period estimate (2012–2014)
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From page 102... ...
(See Section 4-D.4 for a recommendation that the Census Bureau consider producing 3-year and even 1-year period estimates for areas smaller than PUMAs in large cities.) 3-C.1.b Analyzing Change over Time In addition to comparative analyses among subcity areas, users will likely want to analyze trends over time for BIG CITY as a whole and for its subareas.
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From page 103... ...
Only if BIG CITY experiences a large real change is the estimate of the difference between two successive 1-year period ACS estimates likely to be statistically significant. Yet BIG CITY will benefit greatly once a time series of 1-year period ACS estimates is available, because the patterns of yearly change will be informative regarding the existence (or not)
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From page 104... ...
• The year-to-year differences (line 2) for BIG CITY are not statistically significant, even though the example purposefully accelerates the increases in school-age poverty compared with Table 3-3; the only significant 1-year difference for VERY BIG CITY is the 3 percentage point increase in school-age poverty between 2013 and 2014.
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From page 105... ...
Part A of Table 3-5 compares pairs of successive 5-year period estimates for the rates of school-age poverty in SMALL CITY or BIG CITY SUBAREA (population 50,000, including 10,000 school-age children)
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From page 106... ...
The formula for calculating standard errors for estimates of change has been adjusted in the case of overlapping pairs of estimates to take account of the data shared in common; see Table 6-4. • To create BIG CITY subareas, the user must aggregate 5-year period estimates for census tracts.
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From page 107... ...
C Estimating Change by Comparing Pairs of 5-Year Period Estimates That Overlap Less and Less, Assuming a Jump in School-Age Poverty in 2015 Percent Poor Difference from School-Age Children Prior Period Estimate 90% MOE Estimate 90% MOE (o)
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From page 108... ...
Part B of Table 3-5 compares pairs of 5-year period estimates for school-age poverty in SMALL CITY or BIG CITY SUBAREA that overlap less and less, in which the underlying trend is also a steady increase of 1.2 percent in the percentage poor school-age children from one year to the next. The differences between 5-year estimates that have 3 years' overlap
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From page 109... ...
are significant. The reason is that the decreasing extent of overlap between pairs of 5-year period estimates adds more new data to the comparison, thereby increasing the precision of the estimated difference.
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From page 110... ...
The user must use other information, however, to differentiate between the linear upward trend in poverty in Parts A and B and the jump in poverty in Part C Examining 1-year or 3-year period estimates for larger geographic areas, such as counties or PUMAs, may help assess the underlying dynamics of change for SMALL CITY or BIG CITY SUBAREA.
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From page 111... ...
In 2000, Area X included 33,000 occupied housing units and 80,000 residents. However, the 2000 census data did not capture the rapid social and economic change that had occurred in the area in recent years, and no post-2000 data were available to evaluate trends.
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From page 112... ...
Whichever strategy the planners ultimately select, the availability of ACS estimates would be a vast improvement over the current situation in which indirect or partial measures of change had to suffice. The ACS data
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From page 113... ...
, compared with the average ACS initial sampling rate of 1 in 9 housing units (refer back to Table 2-3, Part A)
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From page 114... ...
The precision of the 5-year period ACS estimates can be improved by aggregating small areas into larger units. Indeed, this is the recommended strategy for large jurisdictions -- namely, to aggregate census tracts and block groups into larger subcity or subcounty areas for such purposes as planning the location of governmental service sites and services.
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From page 115... ...
Of course, ACS estimates for larger population groups will be more precise than those for small groups, and the 5-year period estimates for some large groups in small jurisdictions may reach acceptable precision, particularly if the jurisdiction's housing units are oversampled. For example, a 5-year period estimate of 15 percent total poor people in an oversampled jurisdiction of 1,500 people will have a 90 percent confidence interval of 11.4 to 18.6 percent, which is much narrower than the interval of 7.0 to 23.0 percent for poor school-age children.
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From page 116... ...
100,000 15,000 15.0 (based on 12-month average data with July population control) ACS 1-year period estimate, 2010 (not controlled)
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From page 117... ...
in population are a small percentage of the year-round population, or the characteristics of seasonal and year-round residents do not differ appreciably. In areas for which users believe that seasonal differences may be significant, they may wish to make a case to the Census Bureau of the need for tabulations of their population at different times of the year (see Section 7-D.2)
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From page 118... ...
The U.S. Department of Transportation has worked closely with the Census Bureau and with state transportation departments and metropolitan planning organizations to improve the quality of the data on place of work (by, for example, encouraging large employers to inform workers of the addresses to report for particular workplaces)
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From page 119... ...
14 The current scheme for selecting the ACS PUMS files draws an equal-probability sys tematic sample of all ACS housing unit records and their household members in each state, with the records sorted by several characteristics (see the 2005 PUMS accuracy statement at http://factfinder.census.gov/home/en/acs_pums_2005.html)
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From page 120... ...
procedures that the Census Bureau uses for imputing missing responses and (2) decisions it makes regarding the data that can be provided while protecting confidentiality.
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From page 121... ...
The 5-year estimates can provide intercensal checks on local-area transportation patterns that would not be possible with the decennial long-form sample, although estimates for traffic analysis zones will often need to be combined to attain an acceptable level of precision. The ACS PUMS can be used in a variety of ways, and it is issued more frequently than the long-form-sample PUMS.
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From page 122... ...
Users have been invited by the Census Bureau to comment on the prototype summary file.15 Researchers who work with the new product will need to be cognizant of the larger sampling errors of the ACS tables compared with the 2000 long-form-sample tables and develop strategies for effective use of the ACS. Such strategies include combining data for census tracts and block groups into larger areas, collapsing data categories, and combining ACS summary files for nonoverlapping periods.
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From page 123... ...
Researchers will also need to grapple with the different reference periods for different respondents in the ACS PUMS files and develop appropriate analytical strategies. For income amounts for the previous 12 months, the Census Bureau will provide the reported amount, not adjusted for inflation.
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From page 124... ...
Although sampling error affects such uses of the ACS data as trend analysis and comparative rankings, the regularly updated ACS estimates will be more helpful to users than the once-a-decade estimates from the 16 Multiyear profiles will be published for geographic areas defined according to the latest known boundaries for all years shown. 17 The Census Bureau provides 90 percent margins of error; for agreement with standard statistical practice, it should provide 95 percent margins of error instead (refer back to Box 2-5)
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From page 125... ...
The Census Bureau has also published state poverty estimates from the C2SS and the 2001–2004 ACS test surveys and, now, the 2005 ACS. Comparisons of trends from the CPS ASEC state poverty estimates averaged over 2 years with those from the C2SS and the ACS 2001–2004 test surveys revealed instances in which the two data sources did not agree on the poverty rate or the direction of change (increase or decrease in poverty)
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From page 126... ...
• Imputation and weighting procedures: The CPS ASEC procedures for imputing an amount for unreported income are carried out on a national basis, whereas the ACS imputation procedures are carried out state by state, thereby capturing state differences in income patterns. The CPS ASEC population controls are applied for demographic population groups at the national level, and there are no housing unit controls, whereas the ACS population (and housing unit)
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From page 127... ...
. The use of control totals is important to reduce sampling error in the estimates and to adjust the ACS estimates for possible undercoverage of housing and of the population, which may be particularly pronounced for some demographic groups.
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From page 128... ...
, the Census Bureau plans to use an average of controls in which the population estimates for precensus years are adjusted to be consistent with the census counts. One might consider that ACS estimates of percentages, as opposed to levels, would not be affected by the problem of differences in precensus and postcensus population controls.
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From page 129... ...
• The 2010 census results indicate that the Hispanic population and, consequently, the poverty population grew faster prior to 2010 than previously estimated, so that the ACS estimates of the number of poor Hispanics and total poor were too low for the years 2008–2010. • Users will not know until the 2020 census is taken the extent of error that may occur in the 2010 census-based population estimates that are used as controls for the ACS in the period 2011–2020.
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From page 130... ...
. 3-H.1 General Guidelines for ACS Use Abstracting from the specific applications discussed above, this section provides the panel's basic general guidelines for appropriate use of ACS estimates.
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From page 131... ...
However, with due care they may be able to work with 5-year pe riod estimates for large population groups in their jurisdiction and 5-year period estimates for smaller groups for a larger area, such as their county, to assess changes in the composition of their own area. Small governmental units might also ask the Census Bureau to develop ACS estimates for their area for periods longer than 5 years.
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From page 132... ...
for all geographic areas or population groups that are being compared. Do not use a mixture of different period estimates. • For example, when determining the share of federal or state pro gram funds that is to be allocated to each county in a state, the ACS estimates that are used will most likely need to be 5-year period estimates.
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From page 133... ...
• As one example, 5-year period estimates for small areas (census tracts in a city, towns in a county, small counties in a state) could be updated by adjusting their 5-year period estimates to the latest 1-year (or 3-year)
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From page 134... ...
Otherwise, readers may draw an incorrect inference -- for example, assuming that a 5-year period estimate of 15 percent poverty is the rate for the end year, when the end-year rate could be considerably higher or lower. • ACS 1-year period estimates are also an average over 12 months (except for the special estimates released in June 2006 for Janu ary–August 2005 and September–December 2005 for areas affected by Hurricanes Katrina and Rita)
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From page 135... ...
b. Steps to determine which data and methods to use for particular applications: • Make use of information from the Census Bureau about the likely sampling error for different size areas to determine the most useful ACS estimates for the agency's application(s)
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From page 136... ...
. c. Steps to work with public officials, the media, and other constituents: • Develop templates for appropriate interpretative language to use in press releases and talking points about each summer's issuance of the latest ACS estimates from the Census Bureau.
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From page 137... ...
. • Similarly, work with the Census Bureau and stakeholders to adjust geographic boundaries for census tracts and block groups in ways that reflect population change but minimize discontinuities in local geographic boundaries over time.
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