Additional Information About the Panel’s Analyses

This appendix provides additional information about and results from the analyses conducted by the panel, as described in Chapter 4. Included are three parts. The first complements the comparisons discussed in Chapter 4 with some additional tables concerning the differences between American Community Survey (ACS) estimates and administrative estimates based on the National Center for Education Statistics’(NCES) Common Core of Data (CCD). The second part describes the model used to assess stability over time and provides detailed model results. The third part describes the panel’s exploration of the use of global regression models for predicting differences between ACS and CCD estimates for the blended reimbursement rate (BRR) using a variety of covariates from the CCD.

**PART 1: COMPARISONS OF ACS ESTIMATES AND ESTIMATES BASED ON ADMINISTRATIVE DATA**

Tables F-1 and F-2 display the differences between ACS multiyear averages and CCD multiyear averages computed over roughly the same time periods. Table F-1 displays comparisons for 5-year estimates and Table F-2 for 3-year estimates. These tables present differences by district size (small, medium, and large), and free or reduced-price lunch (FRPL) category (very high, high, and low to moderate) for percentage eligible for free meals, percentage eligible for reduced-price meals, percentage eligible for free or reduced-price meals, and the BRR.

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Appendix F
Additional Information About
the Panel's Analyses
T
his appendix provides additional information about and results
from the analyses conducted by the panel, as described in Chapter 4.
Included are three parts. The first complements the comparisons
discussed in Chapter 4 with some additional tables concerning the differ-
ences between American Community Survey (ACS) estimates and admin-
istrative estimates based on the National Center for Education Statistics'
(NCES) Common Core of Data (CCD). The second part describes the
model used to assess stability over time and provides detailed model
results. The third part describes the panel's exploration of the use of
global regression models for predicting differences between ACS and
CCD estimates for the blended reimbursement rate (BRR) using a variety
of covariates from the CCD.
PART 1: COMPARISONS OF ACS ESTIMATES AND
ESTIMATES BASED ON ADMINISTRATIVE DATA
Tables F-1 and F-2 display the differences between ACS multiyear
averages and CCD multiyear averages computed over roughly the same
time periods. Table F-1 displays comparisons for 5-year estimates and
Table F-2 for 3-year estimates. These tables present differences by district
size (small, medium, and large), and free or reduced-price lunch (FRPL)
category (very high, high, and low to moderate) for percentage eligible
for free meals, percentage eligible for reduced-price meals, percentage
eligible for free or reduced-price meals, and the BRR.
316

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APPENDIX F 317
TABLE F-1 Average Differences Between ACS 5-Year Estimates and
5-Year Averages of CCD Estimates
All Large Medium Small
Estimand Districts Districts Districts Districts
Very High FRPL Districts (1,435) (113) (207) (1,115)
Percentage free 17.7 15.2 17.3 18.0
Percentage reduced price 3.2 3.6 4.1 3.0
Percentage free or reduced price 14.5 11.7 13.2 15.0
BRR, $ 0.35 0.29 0.32 0.36
High FRPL Districts (3,782) (280) (628) (2,874)
Percentage free 6.5 8.8 7.3 6.2
Percentage reduced price 1.9 2.2 2.3 1.8
Percentage free or reduced price 4.7 6.6 5.0 4.4
BRR, $ 0.12 0.16 0.13 0.11
Low to Moderate FRPL Districts (3,634) (263) (553) (2,818)
Percentage free 1.4 3.7 2.9 9.4
Percentage reduced price 2.3 2.0 1.9 2.4
Percentage free or reduced price 0.8 1.7 1.0 1.4
BRR, $ 0.01 0.05 0.03 0.02
NOTES: The ACS 5-year estimates (for 2005-2009) are compared with the average of CCD
estimates for 2005-2006, 2006-2007, 2007-2008, 2008-2009, and 2009-2010. ACS = American
Community Survey; BRR = blended reimbursement rate; CCD = Common Core of Data;
FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.
The purpose of this comparison is to illustrate the differences that
exist when the reference periods of the ACS and administrative estimates
are as similar as possible. These tables display the same patterns as those
observed in Chapter 4, where the administrative estimates pertain to the
most recent year of the reference period for the ACS estimates. Namely,
the ACS understates percentage free, percentage free or reduced price,
and the BRR and overstates percentage reduced price. The differences are
substantial in very high FRPL districts and are least pronounced in low
to moderate FRPL districts; high FRPL districts fall in between. Over all
districts, the BRR is understated by the 5-year ACS by 35 cents for very
high FRPL districts and 12 cents for high FRPL districts, and is overstated
by 1 cent in low to moderate FRPL districts.
Chapter 4 highlights the systematic differences between ACS and
CCD estimates for eligibility percentages and the BRR. The following
tables compare enrollment estimates from the two sources. Tables F-3 and
F-4 illustrate the differences between ACS multiyear estimates and CCD

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318 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
TABLE F-2 Average Differences Between ACS 3-Year Estimates and
3-Year Averages of CCD Estimates
Large and Medium Districts
Estimand 2005-2007 2006-2008 2007-2009
Very High FRPL Districts (327) (333) (329)
Percentage free 17.1 17.6 17.6
Percentage reduced price 3.5 2.9 3.2
Percentage free or reduced price 13.6 14.7 14.4
BRR, $ 0.33 0.35 0.35
High FRPL Districts (918) (964) (962)
Percentage free 7.5 8.7 9.5
Percentage reduced price 1.9 1.7 1.9
Percentage free or reduced price 5.6 7.0 7.6
BRR, $ 0.14 0.17 0.19
Low to Moderate FRPL Districts (830) (916) (973)
Percentage free 2.8 3.5 4.1
Percentage reduced price 1.6 1.3 1.3
Percentage free or reduced price 1.2 2.2 2.9
BRR, $ 0.03 0.06 0.07
NOTES: The ACS 3-year estimates are compared with 3-year averages of CCD estimates. For
example, the ACS estimates for 2005-2007 are compared with the average of CCD estimates
for 2005-2006, 2006-2007, and 2007-2008. ACS = American Community Survey; BRR = blend-
ed reimbursement rate; CCD = Common Core of Data; FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.
multiyear average estimates computed over the same time periods as the
ACS estimates, as well as the differences between the ACS multiyear esti-
mates and the CCD estimates for the most recent school year that overlaps
the ACS reference period. (For the latter, the ACS estimate for 2005-2009 is
compared with the CCD estimate for 2009-2010, and the ACS estimate for
2007-2009 is also compared with the CCD estimate for 2009-2010.)
In addition to sampling error in the ACS estimates and various other
errors in both the ACS and administrative estimates, enrollment estimates
may differ because school district boundaries are different in different
years. All of the ACS estimates are based on the school district boundar-
ies recorded in the Census Bureau's Topologically Integrated Geographic
Encoding and Referencing (TIGER) database for 2009-2010 and data
reflecting the number of students that resided within those boundaries
at some time during a calendar year. On the other hand, the CCD data
reflect the district's enrollment as of October of a school year based on the
boundaries for that year. School choice is another reason why enrollment
estimates may differ. Children who live in the catchment area of a school

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APPENDIX F 319
Large Districts Medium Districts
2005-2007 2006-2008 2007-2009 2005-2007 2006-2008 2007-2009
(118) (119) (116) (209) (214) (213)
15.1 16.4 16.8 18.2 18.3 18.1
3.6 2.8 3.0 3.4 3.0 3.4
11.5 13.6 13.8 14.7 15.3 14.7
0.28 0.33 0.33 0.36 0.37 0.36
(286) (293) (292) (632) (671) (670)
8.9 9.8 10.4 7.0 8.2 9.2
2.1 1.7 1.8 1.9 1.7 1.9
6.8 8.2 8.6 5.1 6.5 7.2
0.17 0.20 0.21 0.13 0.16 0.18
(270) (293) (303) (560) (623) (670)
3.3 4.3 4.7 2.6 3.1 3.9
1.8 1.3 1.4 1.6 1.3 1.2
1.4 3.0 3.2 1.0 1.8 2.7
0.04 0.08 0.08 0.03 0.08 0.07
district and attend public school may not attend a school associated with
the local public school district; some may attend an independent charter
school, for example. These differences are discussed more fully in Chap-
ter 4. Differences in the inclusion of prekindergarten students might also
contribute to differences in enrollment estimates.
The differences shown in Table F-3 for the 5-year ACS estimates tend
to be relatively small, but are largest (11 percent) for large very high FRPL
districts (when compared with CCD estimates for 2009-2010). Other cat-
egories of districts have differences of 4 percent or less. The 5-year ACS
estimates tend to overstate enrollment in very high FRPL districts and to
understate enrollment in low to moderate FRPL districts. Similar patterns
are illustrated in Table F-4, where small districts are not included because
there are no 3-year ACS estimates for them.
Table F-5 shows the average differences between ACS 1-year esti-
mates for enrollment and the CCD estimates for enrollment for each of
5 years. The ACS calendar-year estimates are compared with the CCD
school year estimates for the most recent school year that overlaps the

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320 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
TABLE F-3 Average Differences Between ACS 5-Year Estimates of
Enrollment and Various CCD Estimates
All Large Medium Small
Estimand Districts Districts Districts Districts
Very High FRPL Districts
Difference from CCD for 09-10 358 4,038 233 33
As percentage of 09-10 CCD 9 11 4 4
Difference from CCD 5-year average 248 2,787 175 5
As percentage of CCD 5-year average 6 7 3 1
High FRPL Districts
Difference from CCD for 09-10 25 19 27 26
As percentage of 09-10 CCD 1 0 0 3
Difference from CCD 5-year average 47 188 32 36
As percentage of CCD 5-year average 1 1 1 4
Low to Moderate FRPL Districts
Difference from CCD for 09-10 124 1,040 192 30
As percentage of 09-10 CCD 4 4 4 3
Difference from CCD 5-year average 112 647 161 53
As percentage of CCD 5-year average 3 3 3 5
NOTES: The ACS 5-year estimates are compared with (1) CCD estimates for the most recent
school year that overlaps the reference period of the ACS estimates (so the ACS estimates
for 2005-2009 are compared with CCD estimates for 2009-2010) and (2) 5-year averages of
CCD estimates (so the ACS estimates for 2005-2009 are compared with the average of CCD
estimates for 2005-2006, 2006-2007, 2007-2008, 2008-2009, and 2009-2010). ACS = American
Community Survey; CCD = Common Core of Data; FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.
calendaryear. (Hence, the ACS estimate for 2009 is compared with the
CCD estimate for 2009-2010.) These results are only for large districts that
have ACS 1-year estimates. The percentage differences are again largest
for the very high FRPL districts (averaging almost 10 percent) and low-
est for the low to moderate FRPL districts (averaging about 5 percent);
the high FRPL districts average .3 percent. Here the average differences
appear to be increasing in magnitude over time for both the very high and
low to moderate FRPL categories.
Tables F-6 through F-8 display the average differences between vari-
ous ACS estimates (5-year, 3-year, and 1-year) and the CCD estimate for
the most recent school year that overlaps the reference period of the ACS
estimate for low to moderate FRPL districts. These tables complement
Tables 4-1, 4-2, and 4-3 in Chapter 4, which present results for the very
high and high FRPL districts. Each table shows average differences for
percentage free, percentage reduced price, percentage free or reduced
price, and the BRR. Tables F-6, F-7, and F-8 show the same patterns of dif-

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APPENDIX F 321
ferences as the tables in Chapter 4, but the magnitudes of the differences
are much smaller.
PART 2: MODELING OF VARIATION
Let Adt denote the 1-year ACS estimate of the true BRR, Cdt, for school
district d in year t, where Cdt is the BRR value as computed from the CCD.1
We write
Cdt = µt + Md + mdt
where µt is a common time trend across districts, Md is a district-specific
deviation that is constant over time, and mdt is the district- and time-
specific deviation from the common time trend and constant district
deviation. We write
Adt = Cdt + bt + Bd + bdt + edt
where edt is sampling error with known variance s2dt, and bt + Bd + bdt rep-
resents the difference between the CCD and ACS estimates after sampling
error is removed. Because the CCD is treated as the gold standard in this
discussion, we refer to bt + Bd + bdt as "bias," with bt representing a common
time trend in the bias across districts, Bd representing a district-specific bias
that is constant over time, and bdt representing the district- and time- specific
deviation from the common time trend and constant district-specific bias.
Biases here are due primarily to measurement error from the use of differ-
ent concepts and measurements between the ACS and the CCD.
We treat µt and bt as fixed effects (nonrandom) and the remaining
terms as random effects. Hence, Md, mdt, Bd, bdt, and edt are assumed to
be zero mean random processes, with the following conditions on the
theoretical variances and covariances:
· Md and Bd are correlated with each other but uncorrelated with
mdt and gdt = bdt + edt.
· Both mdt and gdt are first-order autoregressive (AR(1)) processes,
and their correlation with each other also has AR(1) form. All
three AR(1) models have the same autoregressive coefficient.2
1As discussed in Chapters 2-4, administrative estimates are also subject to error.
2In SAS, this is called the UN@AR(1) covariance structure. Although preliminary investi-
gations did indicate similar, weak correlations for mdt and gdt and weak cross-correlations,
the assumption of common autoregressive parameters is primarily for simplicity. In particu-
lar, it allows use of a built-in covariance structure, UN@AR(1), in SAS Proc Mixed.

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322 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
TABLE F-4 Average Differences Between ACS 3-Year Estimates of
Enrollment and Various CCD Estimates
Large and Medium Districts
Estimand 2005-2007 2006-2008 2007-2009
Very High FRPL
Difference from CCD for 1 SY 1,438 1,529 1,276
As percentage of 1-year CCD 7 8 8
Difference from CCD 3-year average 1,183 1,290 1,013
As percentage of CCD 3-year average 6 7 6
High FRPL Districts
Difference from CCD for 1 SY 133 13 13
As percentage of 1-year CCD 1 0 0
Difference from CCD 3-year average 118 85 80
As percentage of CCD 3-year average 1 1 1
Low to Moderate FRPL Districts
Difference from CCD for 1 SY 484 383 439
As percentage of 1-year CCD 4 3 4
Difference from CCD 3-year average 347 371 428
As percentage of CCD 3-year average 3 3 4
NOTES: The ACS 3-year estimates are compared with (1) CCD estimates for the most recent
school year that overlaps the reference period of the ACS estimates (so ACS estimates for
2005-2007 are compared with CCD estimates for 2007-2008) and (2) 3-year averages of CCD
estimates (so ACS estimates for 2005-2007 are compared with the average of CCD estimates
TABLE F-5 Average Differences Between ACS 1-Year Estimates of
Enrollment and CCD Estimates
Estimand 2005 2006 2007 2008 2009
Very High FRPL Districts
Difference from CCD 3,149 3,941 4,628 5,057 4,418
As percentage of CCD 7 9 10 11 12
High FRPL Districts
Difference from CCD 184 211 297 111 131
As percentage of CCD 1 1 1 0 0
Low to Moderate FRPL
Districts
Difference from CCD 767 1,295 1,554 1,650 1,839
As percentage of CCD 3 5 6 6 7
NOTES: Calendar year ACS estimates are compared with the CCD estimates for the most re-
cent school year that overlaps the calendar year of the ACS. For example, the ACS estimates
for 2009 are compared with the CCD estimates for 2009-2010. ACS = American Community
Survey; CCD = Common Core of Data; FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.

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APPENDIX F 323
Large Districts Medium Districts
2005-2007 2006-2008 2007-2009 2005-2007 2006-2008 2007-2009
3,816 4,078 3,376 106 147 148
8 9 9 2 3 3
3,122 3,428 2,667 88 101 113
7 8 7 2 2 2
208 60 46 100 44 38
1 0 0 2 1 1
239 115 103 64 71 69
1 0 0 1 1 1
1,054 811 1,005 225 204 206
4 3 4 4 4 4
673 736 901 190 199 214
3 3 3 4 4 4
for 2005-2006, 2006-2007, and 2007-2008). ACS = American Community Survey; CCD = Com-
mon Core of Data; FPRL = free or reduced-price lunch; SY = school year.
SOURCE: Prepared by the panel.
TABLE F-6 Average Differences Between ACS 5-Year Estimates and
CCD Estimates for Low to Moderate FRPL Districts
All Large Medium Small
Districts Districts Districts Districts
Estimand (5,255) (354) (859) (4,042)
Percentage free 4.7 7.1 6.1 4.1
Percentage reduced price 2.3 2.1 1.8 2.4
Percentage free or reduced price 2.4 5.0 4.3 1.7
BRR, $ 0.06 0.12 0.11 0.05
NOTES: The ACS estimates for 2005-2009 are compared with CCD estimates for the most
recent school year that overlaps the reference period of the ACS estimates, namely school
year 2009-2010. ACS = American Community Survey; BRR = blended reimbursement rate;
CCD = Common Core of Data; FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.

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324 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
TABLE F-7 Average Differences Between ACS 3-Year Estimates and
CCD Estimates for Low to Moderate FRPL Districts
Large and Medium Districts
2005-2007 2006-2008 2007-2009
Estimand (1,001) (1,117) (1,213)
Percentage free 3.2 4.4 6.2
Percentage reduced price 1.5 1.2 1.4
Percentage free or reduced price 1.7 3.2 4.8
BRR, $ 0.05 0.08 0.12
NOTES: The ACS estimates for a 3-year period are compared with CCD estimates for
the most recent school year that overlaps the reference period of the ACS estimates. For
example, ACS estimates for 2005-2007 are compared with CCD estimates for school year
2007-2008. ACS = American Community Survey; BRR = blended reimbursement rate; CCD
= Common Core of Data; FPRL = free or reduced-price lunch.
SOURCE: Prepared by the panel.
TABLE F-8 Average Differences Between ACS 1-Year Estimates and
CCD Estimates for Low to Moderate FRPL Districts
2005 2006 2007 2008 2009
Estimand (295) (311) (313) (330) (354)
Percentage free 3.3 3.4 4.8 5.3 5.3
Percentage reduced price 1.5 0.9 1.2 1.0 1.1
Percentage free or reduced price 1.8 2.5 3.6 4.3 4.2
BRR, $ 0.05 0.06 0.09 0.10 0.10
NOTES: The ACS estimates are compared with the CCD estimates for the most recent school
year that overlaps the reference period of the ACS estimates. For example, ACS estimates
for 2005 are compared with CCD estimates for 2005-2006. ACS = American Community
Survey; BRR = blended reimbursement rate; CCD = Common Core of Data; FPRL = free or
reduced-price lunch.
SOURCE: Prepared by the panel.
We constructed a data set with four variables: Y (either Cdt or Adt Cdt);
Method (0 for Cdt and 1 for Adt i); District (1-393); and Time (1-5). The
model is fitted in SAS using Proc Mixed.3 Box F-1 displays the SAS code,
and Boxes F-2 through F-7 display the SAS output.
3Although fitting with Proc Mixed maximizes a Gaussian likelihood, this does not require
that the error processes be jointly normally distributed. The residuals--CCD (estimated dis-
trict effect) and ACSCCD (estimated district effect)--do tend to be symmetric and strongly
unimodal, but with evidence of heavier tails than normal. Without normality of the error
processes, Proc Mixed still produces sensible estimates of mean, variance, and covariance
parameters, comparable to method-of-moments estimates. This is why the fitted model is
able to reproduce empirical variances, such as variances of 1-year changes.

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APPENDIX F 325
Large Districts Medium Districts
2005-2007 2006-2008 2007-2009 2005-2007 2006-2008 2007-2009
(313) (330) (354) (688) (787) (859)
3.9 5.2 6.8 2.9 4.0 5.9
1.7 1.4 1.7 1.4 1.2 1.2
2.2 3.9 5.1 1.5 2.9 4.7
0.06 0.10 0.13 0.04 0.07 0.12
BOX F-1
SAS Code for Analysis of Variability
Proc mixed data = school;
class District Method Time;
model Y = Method Time Method * Time;
random Method /subject = District type = un ggcorr;
repeated Method Time /subject = District type = UN@AR(1) rrcorr;
lsmeans Method * Time;
SOURCE: Prepared by the panel.
Box F-7 displays the least-squares means for Method*Time. These
are the estimates of µt for the 5 years, followed by estimates of bt for the
5 years. The 2 × 2 estimated G matrix in Box F-5 is the covariance matrix
of (Md ,Bd). The estimated autocovariance function for mdt is given by
0.01032 * (0.1704)|h|. The estimated autocovariance function for gdt is
given by 0.02878 * (0.1704)|h|, and the estimated cross-covariance function
between mdt and gdt is given by 0.00944 * (0.1704)|h|. These are the values
that fill out the 10 × 10 covariance matrix R shown in Box F-3. The vari-
ance of gdt includes the design variance, but this is not used in building
the model. Assumptions about the sampling error and its design variance
are introduced below to extrapolate results from large districts to medium
and small districts.
Table F-9 shows variances of 1-year changes computed in the
absence of a global (independent of district) time trend for large districts
only. Model variances come from the SAS fit of the mixed model with
UN@AR(1) covariance structure. Empirical variances are computed using

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326 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
BOX F-2
SAS Proc Mixed Output:
The Mixed Procedure
Model Information
Data Set WORK.SCHOOL
Dependent Variable Y
Covariance Structures Unstructured,
Unstructured @
Autoregressive
Subject Effects District, District
Estimation Method REML
Residual Variance Method None
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Containment
Class Level Information
Class Levels Values
District 393 1 2 3 4 5 6 7 8 9 10 11 12 13
14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33
.
.
.
383 384 385 386 387 388 389
390 391 392 393
Method 2 01
Time 5 12345
Dimensions
Covariance Parameters 7
Columns in X 18
Columns in Z Per Subject 2
Subjects 393
Max Obs per Subject 10
Number of Observations
Number of Observations Read 3930
Number of Observations Used 3930
Number of Observations Not Used 0
Iteration Evaluations 2 Res Log Like Criterion
0 1 825.49213626
1 2 3590.79275559 0.00012752
2 1 3591.50965749 0.00000058
3 1 3591.51280315 0.00000000
Convergence criteria met.
SOURCE: Prepared by the panel.

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330 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
BOX F-6
SAS Proc Mixed Output, Fit Statistics
2 Res Log Likelihood 3591.5
AIC (smaller is better) 3577.5
AICC (smaller is better) 3577.5
BIC (smaller is better) 3549.7
Null Model Likelihood Ratio Test
DFChi-Square Pr>ChiSq
6 4417.00 <.0001
Type 3 Tests of Fixed Effects
Effect
DFDFF ValuePr> F
Method 1 784 8932.18<.0001
Time 4 3136 43.37<.0001
Method*Time 4 3136 62.50<.0001
SOURCE: Prepared by the panel.
BOX F-7
SAS Proc Mixed Output, Least Squares Means
Least Squares Means
Effect Method Time Estimate Error DF t Value Pr> |t|
Method*Time 0 1 1.5894 0.01521 3136 104.49<.0001
Method*Time 0 2 1.6047 0.01521 3136 105.50<.0001
Method*Time 0 3 1.6334 0.01521 3136 107.38<.0001
Method*Time 0 4 1.6826 0.01521 3136 110.62<.0001
Method*Time 0 5 1.7617 0.01521 3136 115.82<.0001
Method*Time 1 1 0.2054 0.01144 3136 17.96<.0001
Method*Time 1 2 0.2216 0.01144 3136 19.37<.0001
Method*Time 1 3 0.2681 0.01144 3136 23.44<.0001
Method*Time 1 4 0.2940 0.01144 3136 25.70<.0001
Method*Time 1 5 0.2787 0.01144 3136 24.37<.0001

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APPENDIX F 331
TABLE F-9 Model Versus Empirical Estimates for Variances of Year-
to-Year Changes, Large Districts Only
Standard Deviation
Standard Relative to Average
Large Districts Variance ($2) Deviation ($) CCD BRR (%)
CCD Empirical 0.016 0.125 7.6
CCD Model 0.017 0.131 7.9
ACS1 Empirical 0.035 0.187 11.3
ACS1 Model 0.049 0.222 13.4
ACS3 Empirical 0.005 0.071 4.3
ACS3 Model 0.006 0.081 4.9
ACS5 Empirical NA NA NA
ACS5 Model 0.002 0.049 2.9
Model Empirical 0.014 0.12 7.3
Model Model NA NA NA
NOTES: The average value of the BRR computed from CCD data for large districts was
$1.65. The ratio of the standard deviation to this value is a coefficient of variation. ACS =
American Community Survey; ACS1 = ACS 1-year estimates; ACS3 = ACS 3-year estimates;
ACS5 = ACS 5-year estimates; BRR = blended reimbursement rate; CCD = Common Core
of Data; NA = not applicable.
SOURCE: Prepared by the panel.
the following sequence of steps. First, for each available pair of consecu-
tive years, compute the year-to-year difference for each district. Second,
for each available pair of consecutive years, compute the empirical vari-
ance (across all 393 large districts) using the set of differences computed
in the first step. Finally, average the empirical variances across all avail-
able pairs of years. This analysis is not affected by any time trend in
the data because any trend appears in the difference for each district as
trend(t + 1) trend(t), which is constant across districts for a given con-
secutive pair of years. That constant does not affect the empirical variance
for each consecutive pair of years in the second step, so it does not affect
the average empirical variance across all pairs of years in the final step.
Comparison of empirical and model variances shows that the model
does a fairly good job of capturing the variance of 1-year change in CCD
and of 1-year change in ACS-CCD. There are, however, some discrep-
ancies between the empirical and model variances for the 1-year ACS
estimates. Nonetheless, the standard deviations (19 cents empirical vs.

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332 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
22 cents model) are not all that different from a practical point of view.
Therefore, the panel believes the model can provide sensible quantita-
tive guidance, particularly for comparing estimators, even if the specific
model predictions should be treated with caution. Further research could
develop and validate more refined models.
Table F-10 shows the same results on variances of 1-year changes for
medium districts only. Empirical variances are computed as described
above. Model variances are computed from the model fitted to the
large districts only, extrapolated to medium districts using the extrapo-
lated design variance, as described below, at the median enrollment for
medium districts. There are 835 medium districts used in this analysis,
with median enrollment of 4,797 students. For medium districts, the CCD
empirical variance is very similar, but not identical, to that for large
districts. The CCD model variance is derived from the model fitted for
large districts, which does not depend on enrollment. Therefore, the CCD
model row is exactly the same for medium and large districts.
TABLE F-10 Model Versus Empirical Estimates for Variances of
Year-to-Year Changes, Medium Districts Only
Standard Deviation
Standard Relative to Average
Medium Districts Variance ($2) Deviation ($) CCD BRR (%)
CCD Empirical 0.017 0.130 7.9
CCD Model 0.017 0.131 7.9
ACS1 Empirical NA NA NA
ACS1 Model 0.110 0.332 20.1
ACS3 Empirical 0.017 0.130 7.9
ACS3 Model 0.013 0.115 7.0
ACS5 Empirical NA NA NA
ACS5 Model 0.005 0.069 4.2
Model Empirical 0.026 0.160 9.7
Model Model NA NA NA
NOTES: The average value of the BRR computed from CCD data for medium districts was
$1.65. The ratio of the standard deviation to this value is a coefficient of variation. ACS =
American Community Survey; BRR = blended reimbursement rate; CCD = Common Core
of Data; NA = not applicable.
SOURCE: Prepared by the panel.

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APPENDIX F 333
Table F-11 shows the same results on variances of 1-year changes for
small districts only. Empirical variances are computed as for large and
medium districts. Model variances are computed from the model fitted to
the large districts only, extrapolated to small districts using the extrapo-
lated design variance at the median enrollment for small districts. There
are 3,989 small districts used in this analysis, with median enrollment of
627 students.
As expected, the CCD empirical variance is much larger for small dis-
tricts than for medium or large districts. The CCD model line again does
not depend on enrollment, so it looks the same as for medium or large
districts, except that the average CCD BRR has changed very slightly; thus
the percentage changes slightly.
The panel considered fitting a model for 3-year estimates for either
large or medium districts (or both combined) but decided that it would
be difficult to fit such a model given time constraints. This is because the
3-year estimates are correlated across years because of not only the tem-
TABLE F-11 Model Versus Empirical Estimates for Variances of
Year-to-Year Changes, Small Districts Only
Standard Deviation
Standard Relative to Average
Small Districts Variance ($2) Deviation ($) CCD BRR (%)
CCD Empirical 0.028 0.168 10.3
CCD Model 0.017 0.131 8.0
ACS1 Empirical NA NA NA
ACS1 Model 0.569 0.755 46.1
ACS3 Empirical NA NA NA
ACS3 Model 0.064 0.254 15.5
ACS5 Empirical NA NA NA
ACS5 Model 0.023 0.152 9.3
Model Empirical 0.017 0.132 8.0
Model Model NA NA NA
NOTES: The average value of the BRR computed from CCD data for small districts was
$1.64. The ratio of the standard deviation to this value is a coefficient of variation. ACS =
American Community Survey; BRR = blended reimbursement rate; CCD = Common Core
of Data; NA = not applicable.
SOURCE: Prepared by the panel.

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334 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
poral correlation of the BRR values but also the moving average of the
sampling error. Further research could be undertaken to fit such a model.
The analysis above for medium and small districts relies on extrap-
olating results from the model fitted to data for large districts only.
Extrapolating the fitted model as a function of enrollment requires
model ing the design variance for 1-year ACS estimates in medium and
small districts (which could be derived at the Census Bureau but may
not be able to be released under current disclosure rules). Suppose that
ACS sample sizes are constant from year to year within a district, and
the design variance s2dt s2d depends on the district but is constant from
year to year. Given the design of the ACS, it is reasonable to assume that:
· Sampling error autocovariances are zero:
d
2
if h = 0
Cov ( edt , ed ,t + h = Cov ( ed ,t + h , edt =
) )
0 if h 0
where s2d is the sampling variance of the 1-year ACS estimator
for district d.
· All cross-covariances with sampling error are zero.
· The design variance for 3-year ACS estimates is one-third of the
design variance for 1-year ACS estimates, and the design variance
for 5-year ACS estimates is one-fifth of the design variance for
1-year ACS estimates.
The design variance within a district is determined largely by sample size,
which is, in turn, highly correlated with enrollment. Figure F-1 displays a
scatter plot of data and the regression model fit for log(design variance)
as a function of log(enrollment) for the 1-year ACS estimates in large
districts. The fitted linear relationship is given by log(design variance) =
4.5 0.9 log(enrollment).
We choose log(enrollment) = 9.8 as a typical value for a large district
because it is close to log(median(enrollment)) = 9.84. If we plug this value
into the linear relationship above and transform to the design standard
deviation, we get 0.1153, which is very close to the average design stan-
dard deviation across districts and years, 0.1146. Next, we take the SAS
fit, which models gdt = bdt + edt as AR(1), and approximate the fitted AR(1)
by AR(1) + uncorrelated noise, where the noise has variance equal to the
"typical value" .0133 = (0.1153)2. The resulting model for bdt ~ AR(1) has
process variance 0.01548 and autoregressive parameter 0.3168. Finally,
taking the model for bdt as fixed, let the variance for edt depend on enroll-
ment through the above linear relationship. Tables F-10 and F-11, dis-
cussed above, were constructed using this analysis, with enrollment taken

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APPENDIX F 335
2
3
4
log(design variance)
5
6
7
8
9 10 11 12 13
log(enrollment)
FIGURE F-1 Regression fit of log(design variance) versus log(enrollment).
SOURCE: Prepared by the panel.
FIGF-1.eps
to be the observed median enrollment for medium districts and for small
districts, respectively.
The standard deviation (SD) and coefficient of variation (CV) (relative
to mean (CCD) = $1.65) of 1-year change in 5-year estimates for various
enrollments are shown in Table F-12.
There are real differences in the amount of noise under which districts
normally operate with traditional application and certification proce-
dures. Small districts combined have a percentage standard deviation
(CV) of 10.3 percent for CCD 1-year changes, but those with less than
the median enrollment have a CV of 11.6 percent, while those with more
than the median enrollment have a CV of 8.7 percent. These are compa-
rable to the ACS5 (modeled) CVs at enrollments of 400-800, according to
Table F-12, which is the same as Table 4-8 in Chapter 4. Figure F-2, which
is the same as Figure 4-5 in Chapter 4, displays a transformation of the
data in Table F-12. For a given district, the point (1/enrollment, CV2) can
be plotted on the figure. If the plotted point is above the curve, the dis-
trict currently experiences more variability in its administrative estimates

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336 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
TABLE F-12 Intertemporal Variability of ACS 5-Year Estimates, by
Enrollment
Variability of 1-Year Change in ACS 5-Year Estimates of
Blended Reimbursement Rates
Coefficient of Variation (%)
Enrollment Standard Deviation ($) (relative to BRR of $1.65)
100 0.34 20.5
200 0.25 15.1
400 0.18 11.2
800 0.14 8.3
1,600 0.10 6.3
3,200 0.08 4.8
6,400 0.06 3.8
12,800 0.05 3.2
SOURCE: Prepared by the panel.
400
300
CV2
200
100
0
0.000 0.002 0.004 0.006 0.008 0.010
1/enrollment
FIGURE F-2 Squared coefficient of variation of year-to-year change in ACS 5-year
estimate of BRR versus inverse of enrollment.
NOTES: ACS = American Community Survey; BRR = blended reimbursement
FIGF-2.eps
rate; CV = coefficient of variation.
SOURCE: Prepared by the panel.

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APPENDIX F 337
than it would if it used ACS 5-year estimates (at least according to the
model and ignoring timeliness bias). In this situation, the district might
find use of the ACS 5-year estimates to be acceptable. On the other hand,
if its plotted point is below the curve, the district currently experiences
less variability in its administrative estimates than it would with the ACS
5-year estimates, and might find the latter unacceptably variable for use
in determining reimbursements under the ACS Eligibility Option (AEO).
Table F-13 shows standard deviations, biases, and root mean squared
errors (RMSEs) for ACS 1-year, 3-year, and 5-year estimators, with and
without a 2-year lag (reflecting the lag in the availability of ACS estimates
for use in establishing claiming rates). For large districts, these values are
computed in two ways: (1) using the AR(1) model originally fitted via
SAS for gdt and (2) using the AR(1) + noise model for gdt. The latter model
makes results consistent with the analysis for medium and small districts,
all of which use the AR(1) + noise model. The other difference in the
AR(1) analysis for the large districts is that bt is estimated from the data
(see Box F-7) and incorporated in the bias computations, while in the
AR(1) + noise analysis, it is assumed to be constant over time (or zero,
without loss of generality). Again this is done to maintain consistency
with the analysis for medium and small districts, for which estimation of
bt from the data is not possible.
TABLE F-13 Standard Deviation, Bias, and RMSE for ACS 1-Year,
3-Year, and 5-Year Estimates at Lags of 0 and 2 Years
District ACS1, ACS1, ACS3, ACS3, ACS5, ACS5,
Size no lag lag 2 no lag lag 2 no lag lag 2
Large SD (2) 0.170 0.221 0.135 0.137 0.124 0.126
SD (1) 0.169 0.221 0.134 0.137 0.123 0.125
Bias (2) 0.000 0.128 0.069 0.153 0.107 NA
Bias (1) 0.025 0.143 0.096 0.131 0.107
RMSE (2) 0.170 0.256 0.152 0.205 0.164 NA
RMSE (1) 0.172 0.263 0.165 0.189 0.163
Medium SD 0.243 0.282 0.168 0.170 0.147 0.148
Bias 0.000 0.115 0.062 0.130 0.092 NA
RMSE 0.243 0.304 0.179 0.214 0.173 NA
Small SD 0.537 0.556 0.324 0.325 0.260 0.261
Bias 0.000 0.104 0.059 0.107 0.079 NA
RMSE 0.537 0.565 0.329 0.342 0.271 NA
NOTES: The results for large districts were obtained using two methods: (1) using the AR(1)
model for gdt and (2) using the AR(1) plus noise model for gdt. ACS = American Community
Survey; NA = not applicable; RMSE = root mean squared error; SD = standard deviation.
SOURCE: Prepared by the panel.

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338 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
The bias and RMSE results reflect the specific mt estimated for the
particular 5-year time window covered by the estimates available to the
panel, separately for each district size class. For any size class, the esti-
mate of mt is simply the year t average CCD BRR across all districts. For
large districts, these estimated mt values are given in Box F-7.
PART 3: MODEL-BASED PREDICTION OF
SYSTEMATIC DIFFERENCES BETWEEN ACS
ESTIMATES AND CCD ESTIMATES FOR BRR
This part of the appendix describes the results of the panel's modeling
of the differences between ACS estimates and CCD estimates for the BRR.
The analysis was limited to very high FRPL districts with both 5-year ACS
estimates and CCD estimates for 2009-2010 in the panel's evaluation data
set prog09.merged.fns. To eliminate outliers that could adversely impact
regression results, we excluded any districts that had either a percentage
certified for free meals of less than 10 percent or a percentage certified for
free or reduced-price meals of less than 20 percent. Districts with missing
data for potential predictor variables were also excluded.
The ACS estimate used in the analysis is the 5-year ACS estimate for
the BRR (denoted ACS5 BRR below). The CCD estimate used is the BRR
based on certification data in the 2009-2010 CCD (denoted CCD0910 BRR
below). The dependent variable used in the analysis is the difference
between ACS5 BRR and CCD0910 BRR divided by the standard error of
ACS5 BRR. This variable is regressed on a variety of predictor variables
from the 2009-2010 CCD as described below. Table F-14 provides regres-
sion results for a variety of alternative models.
In the table, p is the number of covariates in a model, and FOI stands
for "first-order interactions." The "Additive" model is the most basic
model, with no interactions or quadratic terms, and the "FOI, No Factor
Interaction" model includes interactions among continuous covariates but
not with or among the categorical covariates. Box F-8 lists the covariates
used in the modeling. The results of our exploratory analyses of whether
a global predictive model could be used for adjusting for differences
between ACS and administrative estimates are discussed in Chapter 4.

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TABLE F-14 Results for Various Models Predicting Differences Between ACS 5-Year Estimates for 2005-2009
and CCD Estimates for 2009-2010
Without FRPL Covariates With FRPL Covariates
(1,433 districts) (1,366 districts)
Model Covariates p R2 Adj. R2 RMSE AIC p R2 Adj. R2 RMSE AIC
Additive 73 0.420 0.389 1.068 6009 81 0.628 0.604 0.806 6408
FOI, No Factor 159 0.572 0.519 0.917 6273 258 0.779 0.727 0.621 6765
Interactions
FOI, No State/Locale 172 0.579 0.522 0.910 6271 334 0.804 0.740 0.585 6777
Interactions
FOI, No State 317 0.650 0.550 0.830 6245 542 0.855 0.760 0.503 6774
Interaction
FOI, All Variables 717 0.782 0.563 0.655 6124 981 0.923 0.728 0.366 6768
FOI and Quadratic 726 0.784 0.561 0.652 6116 996 0.926 0.726 0.359 6784
ACS5 BRR - CCD0910 BRR
NOTES: The basic model is ~ covariates. AIC = Akaike Information Criterion; ACS = American Community Survey;
SE ( ACS5 BRR )
FOI = first-order interactions; NA = not applicable; RMSE = root mean squared error.
SOURCE: Prepared by the panel.
339

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340 USING ACS DATA TO EXPAND ACCESS TO THE SCHOOL MEALS PROGRAMS
BOX F-8
Covariates Used in Regression Analysis
The covariates in the "Without FRPL" models are as follows:
1.C0910_Num_Enroll (number of enrolled students)
2.C0910_Pct_inNonRegSch (percentage of students in nonregular--special
education, vocational education, or alternative--schools)
3.C0910_Pct_inChartSch (percentage of students in charter schools)
4.C0910_Pct_inChartNonRegSch (percentage of students in charter or non-
regular schools)
5.C0910_Pct_inChartMagSch (percentage of students in charter or magnet
schools)
6. C0910_Pct_inChartMagNonRegSch (percentage of students in charter,
magnet, or nonregular schools)
7. C0910_Pct_AIAN (percentage of students who are American Indian or
Alaska Native)
8.C0910_Pct_AsianHNPI (percentage of students who are Asian, Hawaiian
Native, or Pacific Islander)
9.C0910_Pct_Hispanic (percentage of students who are Hispanic)
10.C0910_Pct_Black (percentage of students who are black)
11.C0910_Pct_White (percentage of students who are white)
12.C0910_ChartDistance (index measuring distance to nearby charter-only
districts)
13.C0910_ChartDistance_Enroll (index measuring distance to nearby charter-
only districts, weighted by charter enrollment)
14.C0910_ChartDistance_Enroll_Rel (index measuring distance to nearby
charter-only districts, weighted by charter enrollment relative to district's
enrollment)
15.C_State (state)
16.C_Locale_Type (type of locale as defined in CCD)
The "With FRPL" models add the following covariates:
17.C0910_Pct_Free (percentage of students certified for free meals)
18.C0910_Pct_Reduced (percentage of students certified for reduced-price
meals)
19.C0910_Num_Free (number of students certified for free meals)
20.C0910_Num_Reduced (number of students certified for reduced-price
meals)
21.C0910_ChartDistance_FRPL (index measuring distance to nearby charter-
only districts, weighted by number of charter students certified for free or
reduced-price meals)
22.C0910_ChartDistance_FRPL_Rel (index measuring distance to nearby
charter-only districts, weighted by number of charter students certified for
free or reduced-price meals relative to number in district)
23.C0910_Need (categorical variable for whether percentage of students certi-
fied for free or reduced-price meals is < 50, 50-74, or 75)
24.C0910_CCDSchools_CharterCode (categorical variable for whether all,
some, or none of the schools in district are charter schools)
SOURCE: Prepared by the panel.