The Future of Federal Household Surveys Summary of a Workshop (2011) / Chapter Skim
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6 Small-Area Estimation
6 Small-Area Estimation
Pages 61-76
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From page 61... ...
What constitutes a sufficiently large sample size depends on the intended use of the data, and this is an important question because it determines whether direct estimation is adequate for a specific task. Fay recalled the 1976 Survey of Income and Education (SIE)
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From page 62... ...
Even when reliability targets were negotiated in advance, however, the impact of sampling error on the face validity of the estimates was more pronounced than survey designers anticipated. Although the SIE met the target reliability requirements, the state of Alabama protested the large decrease in its poverty rate since the preceding census.
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From page 63... ...
The ACS pools data over 1-, 3-, and 5-year periods to produce estimates. Although the 5-year estimates produce data for the smallest publishable geographic areas, the SAIPE program currently models the 1-year ACS estimates, most of which
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From page 64... ...
Looking ahead, Fay predicted that the area on the boundary between traditional design-based survey estimates and small-area estimates will probably grow in importance because there is an increasing demand for subnational esti mates, surveys costs are rising, and modeling tools represent a possible route for incorporating existing administrative records into the estimates. Review of the case studies presented and similar ones can help guide the evolution of policy on the use of small-area estimation at federal statistical agencies.
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From page 65... ...
. In particular, the IRS tax data leave out many low-income people who do not need TABLE 6-1 Typical Sources of Error for Different Data Sources Error Data Source Sampling Nonsampling Target Sample survey X X Census Maybe X X Administrative records X X SOURCE: Workshop presentation by William Bell.
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From page 66... ...
+ e i where: yi = direct survey estimate of population target Yi for area i ei = sampling errors that are assumed to be independently distributed with a normal N(0, vi) distribution, with vi assumed known xi = vector of regression variables for area i b = vector of regression parameters ui = area i random effects (model errors)
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From page 67... ...
2 It would then be possible to combine the direct survey estimates and the regression estimates using the best linear unbiased prediction (BLUP) as follows: ˆ ˆ Y = h y + (1 – h )
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From page 68... ...
TABLE 6-2 Prediction Mean Squared Errors (MSE) for 2004 Poverty Rates for Children Ages 5-17 Based on the Current Population Survey Target and the Fay-Herriot Model with One Regressor (FH1)
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From page 69... ...
Table 6-3 compares the MSEs of the one-regressor Fay-Herriot model to the MSEs for the full SAIPE production model. The mean squared errors are lower with the full model, and, again, the difference is bigger in the case of smaller states, where the predictions are less able to rely on the direct estimates.
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From page 70... ...
Second, the time was right for this initiative when the Improving America's Schools Act was passed in 1994, requiring the allocation of Title 1 education funds according to updated poverty estimates for school districts for the 5-17 age group, unless the modelbased estimates were deemed "inappropriate or unreliable." In addition, a panel of the Committee on National Statistics that reviewed SAIPE methods and initial results also recommended that the model-based estimates be used (National Research Council, 2000)
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From page 71... ...
Raghunathan and his colleagues combined the BRFSS data with data from the NHIS, which covers both telephone and nontelephone households and has higher response rates. The technique selected for this study was a hierarchical model, treating NHIS data as unbiased estimates and BRFSS data as potentially biased estimates.
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From page 72... ...
SR = ? If Claim = 0 Health Covariates Self-report NHANES Condition FIGURE 6-1 Data layout for the Medicare Current Beneficiary Survey (MCBS)
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From page 73... ...
Little argued that hierarchical models are the right way to think about this conceptually. The advantage of hierarchical models is that it is not necessary to use either the direct estimates or the model-based estimates, because they provide a compromise between the direct estimate from the saturated model and the model-based estimate from the unsaturated model.
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From page 74... ...
Graham Kalton asked Raghunathan whether using Medicare administrative records was considered when producing estimates about the population ages 65 and older. Raghunathan responded that he is a "scavenger for information," using as much data as he can find, and he did explore the Medicare claims information, which is now part of the administrative data used for the fourth project he discussed.
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From page 75... ...
There were also a lot of challenges based on the 2000 census data, using the census numbers to estimate the school district to county shares of poverty and making reference to what the previous estimates were. Generally, data users compare the current estimates to something else, and they tend to react when they see a large discrepancy, even though it is clearly possible that the other estimate was incorrect.
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From page 76... ...
Zaslavsky added that if the general feeling is that there are not enough people who can do this type of analysis, then it is important to think about the implications for new directions in training. Fay said that this debate has been going on for many years, and the concern about model-based estimation has always been that data users cannot understand the complex techniques and are suspicious of what is going on "behind the curtain." But if data users really understood what is involved with designbased estimation, for example, postsurvey adjustment and variance estimation, they would be concerned about that as well.
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