The Rural Education Achievement Program: Title VI-B of the Elementary and Secondary Education Act
Congressional research reportJan 14, 2013
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The Rural Education Achievement Program:
Title VI-B of the Elementary and Secondary
Education Act
(name redacted)
Specialist in Education Policy
January 14, 2013
Congressional Research Service
7-....
www.crs.gov
R40853
CRS Report for Congress
Prepared for Members and Committees of Congress
The Rural Education Achievement Program
Summary
The No Child Left Behind Act of 2001 (NCLB, P.L. 107-110) established the Rural Education
Achievement Program (REAP) under Title VI, Part B of the Elementary and Secondary
Education Act of 1965 (ESEA). Congress created this program to address the unique needs of
rural schools that disadvantage them relative to nonrural schools.
To be eligible for REAP funds, a local education agency (LEA) must be designated rural and
must meet one of three additional requirements involving enrollment size, population density, and
poverty status. Currently, REAP provides awards to nearly 6,000 LEAs, out of a total of about
14,000 nationwide. REAP authorizes formula grants through two subprograms: the Small Rural
School Achievement (SRSA) program provides grants directly to LEAs and the Rural LowIncome School (RLIS) program provides grants to states, which then award subgrants to LEAs.
The amount of funds received by eligible LEAs is determined differently by the SRSA and RLIS
programs. Under the SRSA program formula, an initial amount is calculated for each eligible
LEA based on enrollment; these amounts are then reduced based on offsetting amounts received
from other ESEA programs. Under RLIS, formula grants are awarded to states based on the
state’s share of eligible students; states then subgrant funds to LEAs either on a formula or
competitive basis.
REAP funds may be used for a wide range of activities authorized throughout the ESEA,
including Titles I-A, II-A, II-D, III, IV-A, IV-B, and V-A. In addition, the so-called REAP-Flex
provision (ESEA, Sec. 6211) allows SRSA-eligible LEAs to use ESEA funds for certain activities
not authorized by the program through which the LEA received such funds. A Government
Accountability Office (GAO) study found that a large majority of LEAs use REAP funds to meet
the NCLB highly qualified teacher requirement as well as the district’s technology needs.
The authorization for REAP, along with the rest of the ESEA, expired at the end of FY2008.
However, these programs continue to operate as long as appropriations are provided. Congress is
expected to consider whether to amend and extend the ESEA programs, including REAP. This
report will conclude with a discussion of reauthorization issues related to REAP that may arise as
Congress takes up the ESEA.
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The Rural Education Achievement Program
Contents
Overview.......................................................................................................................................... 1
Challenges Facing Rural Schools .............................................................................................. 1
The Rural Education Achievement Program (REAP) ..................................................................... 2
Program Eligibility .................................................................................................................... 3
Small Rural School Achievement Program Eligibility........................................................ 4
Rural Low-Income School Program Eligibility .................................................................. 5
Grant Determination .................................................................................................................. 5
SRSA Grants ....................................................................................................................... 5
RLIS Grants ........................................................................................................................ 6
Grant Distribution...................................................................................................................... 7
Use of Funds .............................................................................................................................. 7
Reauthorization Issues ..................................................................................................................... 9
Rationale for Additional Support to Rural LEAs ...................................................................... 9
Impact of New Locale Codes on Program Eligibility ............................................................... 9
Allocating Excess Funds ......................................................................................................... 12
Increase Benefits to Small, Poor LEAs ................................................................................... 12
SRSA Formula Anomalies ....................................................................................................... 13
Poverty Data for RLIS Eligibility............................................................................................ 13
Tables
Table 1. Appropriations for REAP ................................................................................................... 3
Table 2. Estimated Number of LEAs Eligible for REAP Programs ................................................ 5
Table 3. Comparison of Schools Classified by Metro-Centric and by
Urban-Centric Locale Codes ...................................................................................................... 11
Table 4. Estimated Number of LEAs Eligible for REAP Under Old and New Locale
Codes, All Other Aspects of Current Law Unchanged ............................................................... 12
Table 5. Estimated Number of LEAs Eligible for RLIS Using Current and
Alternative Low-Income Indicators ............................................................................................ 14
Appendixes
Appendix. Analysis of FRPL Data for School District Poverty Determinations ........................... 16
Contacts
Author Contact Information........................................................................................................... 17
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The Rural Education Achievement Program
Overview
The No Child Left Behind Act of 2001 (NCLB, P.L. 107-110) established the Rural Education
Achievement Program (REAP) under Title VI-B of the Elementary and Secondary Education Act
of 1965 (ESEA). Congress created this program to address the unique needs of rural schools that
disadvantage them relative to nonrural schools. To compensate for the challenges facing rural
schools, REAP awards two types of formula grants; one goes directly to eligible school districts,
or local educational agencies (LEAs), and a second grant goes to states, which then award
subgrants to LEAs.
The authorization for REAP, along with the rest of the ESEA, expired at the end of FY2008.
However, these programs continue to operate as long as appropriations are provided. Congress is
expected to consider whether to amend and extend the ESEA programs, including REAP. This
report will discuss the challenges facing rural schools, the manner in which REAP addresses these
challenges, and reauthorization issues that may arise as Congress takes up the ESEA. Much of the
discussion of reauthorization considerations centers on allocation of funds, given that allocation
issues tend to factor prominently in deliberations about REAP.
Challenges Facing Rural Schools
According to their proponents, rural schools have some advantages over their urban and suburban
counterparts. Rural teachers are key members of the community and tend to know students and
their families well. Rural schools have less complex organizational structures with fewer layers
than nonrural school systems, and are able to adjust or adapt relatively quickly to change.
Additionally, the schools within rural communities are very visible and strongly connected with
the community.1
However, rural schools also confront significant challenges. Many face the worst of local fiscal
limitations due to tax base constraints. Resource shortages produce various problems, including
limited range of curricular options (such as a lack of advanced placement course offerings) and
difficulties providing competitive salaries to attract and retain highly qualified teachers. Rural
schools tend to have declining enrollment due to net out-migration and an aging of the
population. Rural schools’ low population density results in other problems, such as high
transportation costs and limited access to cultural and educational resources.2
In addition to these general challenges, rural LEAs may face particular problems meeting NCLB
requirements, such as standards of adequate yearly progress (AYP). They may also find it difficult
to implement NCLB’s consequences for failure to make AYP (such as providing public-school
choice and supplementary educational services), and they often experience difficulty in attracting
and retaining qualified teachers of core academic subjects (such as math and science).3
1
Wisconsin Department of Public Instruction, Summary of the Official Proceedings Wisconsin Rural Policy Network
Forum, January 2004, pp. 2-3, http://www.dpi.state.wi.us/rural/pdf/ri_sum.pdf.
2
Ibid., pp. 3-5.
3
For more information, see CRS Report RL32495, Adequate Yearly Progress (AYP): Implementation of the No Child
Left Behind Act, by (name redacted); and CRS Report RL33333,
A Highly Qualified Teacher in Every Classroom:
Implementation of the No Child Left Behind Act and Reauthorization Issues for the 112th Congress, by (name redac
ted).
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A study by the Government Accountability Office (GAO) confirmed these problems. The GAO
study reached five main conclusions.
•
Achieving NCLB goals for large enrollments of economically disadvantaged
students presents more challenges for rural LEAs than for nonrural LEAs.
•
Some rural districts lack the community resources, such as libraries and
museums, that may support improved academic performance.
•
Compared with nonrural LEAs, rural LEAs are more likely to experience
problems recruiting teachers because of difficulties offering competitive salaries.
•
Small rural districts are more likely to report that factors related to school size
and geographic isolation, such as limited personnel, make it difficult to release
teachers and administrators for attending conferences and training, impeding
their ability to implement NCLB requirements.
•
Some rural districts indicated that limited numbers of staff created difficulties
completing NCLB requirements, such as reporting on school progress.4
The U.S. Department of Education (ED) has sought to address concerns of rural school districts.
In response to the GAO report, ED has attempted to provide additional flexibility for rural LEAs.
For example, ED allows teachers in rural LEAs “extra time—up to 3 years—to meet teacher
qualification requirements,” and permits states to “use a single state test for teachers to
demonstrate subject matter competency for core academic subjects.”5
The Rural Education Achievement Program (REAP)
Congress created REAP to meet many of the challenges identified in the subsequent GAO study.
According to the statute, REAP funds are to address “the unique needs of rural school districts
that frequently (1) lack the personnel and resources needed to compete effectively for Federal
competitive grants; and (2) receive formula grant allocations in amounts too small to be effective
in meeting their intended purposes.”6
REAP authorizes two rural education programs under ESEA Title VI-B. Subpart 1 authorizes the
Small, Rural School Achievement Program (SRSA), which focuses on LEAs with less than 600
students. Subpart 2 authorizes the Rural and Low-Income School Program (RLIS), which focuses
on larger rural LEAs with relatively high poverty rates (at least 20% of children from families
below the poverty line). Funds are to be divided equally between the SRSA and RLIS programs.
NCLB authorized REAP at $300 million for FY2002 and “such sums as necessary” for FY2003FY2007; however, the program continues to operate as long as appropriations are provided. In
FY2012, $179 million was appropriated for REAP. Table 1 shows the history of appropriations
4
U.S. Government Accountability Office (GAO), No Child Left Behind Act Additional Assistance and Research on
Effective Strategies Would Help Small Rural Districts, GAO-04-909, September 2004. (Cited hereafter as GAO
Effective Strategies.)
5
“Meeting Minutes of Secretary’s Rural Education Task Force,” October 14, 2005, p. 7, http://www.ed.gov/nclb/
freedom/local/rural/index.html#meetings. The Secretary’s March 31, 2004, policy letter announcing this flexibility is
available at http://www.ed.gov/policy/elsec/guid/secletter/040331.html.
6
ESEA, Section 6202.
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for the program. Appropriations have grown modestly, except for FY2006 and FY2011. Overall,
appropriations for FY2012 represent about a 10% increase over FY2002, the first year of program
funding.
Table 1. Appropriations for REAP
Fiscal Year
Appropriation
(rounded to nearest $000)
% Change from
Prior Year
2002
$162,500,000
2003
$167,653,000
3.2%
2004
$167,831,000
0.1%
2005
$170,624,000
1.7%
2006
$168,919,000
-1.0%
2007
$168,919,000
0.0%
2008
$171,854,000
1.7%
2009
$173,382,000
0.9%
2010
$174,882,000
0.9%
2011
$174,532,000
-0.2%
2012
$179,193,000
2.7%
Source: U.S. Department of Education, Budget Service.
Program Eligibility
To be eligible for REAP funds, LEAs must be designated rural by the ED. The National Center
for Education Statistics (NCES) has devised a typology to classify schools based on their
geographic location. Using Census Bureau geographic data, NCES assigns so-called “locale
codes” to each school. Locale codes are used to classify schools along an eight-point urban-torural scale that is based on their proximity to metropolitan areas. These so-called “metro-centric”
locale codes are defined as follows:
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1 = Large City: A central city of a core based statistical area (CBSA) or metropolitan
statistical area (MSA),7 with the city having a population greater than or equal to
250,000.
2 = Midsize City: A central city of a CBSA or MSA, with the city having a
population of less than 250,000.
3 = Urban Fringe of a Large City: Any territory within a CBSA or MSA of a Large
City and defined as urban by the Census Bureau.
4 = Urban Fringe of a Midsize City: Any territory within a CBSA or MSA of a
Midsize City and defined as urban by the Census Bureau.
5 = Large Town: An incorporated place or Census-designated place with a
population greater than or equal to 25,000 and located outside a CBSA or MSA.
6 = Small Town: An incorporated place or Census-designated place with a
population less than 25,000 and greater than or equal to 2,500 and located outside
a CBSA or MSA.
7 = Rural, Outside MSA: Any territory designated as rural by the Census Bureau
that is outside a CBSA or MSA of a Large or Midsize City.
8 = Rural, Inside MSA: Any territory designated as rural by the Census Bureau that
is within a CBSA or MSA of a Large or Midsize City. 8
Small Rural School Achievement Program Eligibility
An LEA is eligible for the Small Rural School Achievement (SRSA) program if all schools
served by the LEA have a locale code of 7 or 89 and either its average daily attendance (ADA) is
less than 600 or the county or counties in which the LEA is located has a population density of
fewer than 10 people per square mile. The SRSA statute allows the Secretary of Education to
waive the locale code requirement (but not the ADA or population density requirements) based on
a state government agency’s determination that the LEA is located in a rural area.10
7
According to the Census Bureau website http://www.census.gov/population/www/estimates/aboutmetro.html:
The United States Office of Management and Budget (OMB) defines metropolitan [urban core area
with a population of 50,000 or more] and micropolitan [urban core area with a population between
10,000 and 50,000] statistical areas according to published standards that are applied to Census
Bureau data. The general concept of a metropolitan or micropolitan statistical area is that of a core
area containing a substantial population nucleus, together with adjacent communities having a high
degree of economic and social integration with that core…. The term “core based statistical area”
(CBSA) became effective in 2000 and refers collectively to metropolitan and micropolitan
statistical areas.
8
Source: NCES website at http://nces.ed.gov/ccd/rural_locales.asp.
9
NCES also classifies LEAs based on the code or codes assigned to schools within their boundaries; however, this
classification system is different than that required under NCLB. Under the NCES system, an LEA is assigned the
locale code of the schools enrolling 50% or more of the LEA’s students. If no single code accounts for 50% or more of
an LEA’s students, the LEA is assigned the code of schools accounting for the highest percentage of its students.
10
U.S. Department of Education, “Guidance on the Rural Education Achievement Program (REAP),” June 2003,
Appendix A-5 and Appendix A-6. (Cited hereafter as ED REAP Guidance.)
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Rural Low-Income School Program Eligibility
An LEA is eligible for the Rural Low-Income School (RLIS) program if all its schools have
locale codes of 6, 7, or 8 and at least 20% of the children the LEA serves are from families below
the poverty line. Unlike the SRSA program, the RLIS statute does not provide the Secretary with
waiver authority for the locale code requirement. Finally, an LEA that receives a grant under the
SRSA program is not eligible for RLIS funding.
Table 2 shows estimates of LEAs eligible for the SRSA and RLIS programs based on CRS
analysis of Common Core of Data (CCD).11 As the table illustrates, compared with determination
by locale alone, combining eligibility criteria significantly reduces the number of LEAs that are
eligible for assistance. In the case of the SRSA program (as noted below), actual grants for
eligible LEAs can be reduced or even eliminated depending on funds eligible LEAs receive under
offsetting ESEA formula grant programs.
Table 2. Estimated Number of LEAs Eligible for REAP Programs
Small Rural School Achievement Program Eligibility
All schools with a
locale code of 7 or 8
and enrollment less than 600 students
or in county with less than 10 persons per square mile
6,716
4,538
Rural Low-Income Schools Program Eligibility
All schools with a
locale code of 6, 7, or 8
and school-age poverty at least 20%
and not eligible for SRSA program
8,757
1,399
Source: CRS analysis of NCES, Common Core of Data, 2006-2007 school year.
Grant Determination
Amounts that LEAs receive and aggregate state amounts are determined differently under the
SRSA and RLIS programs. Under the SRSA program, an initial amount is calculated for each
eligible LEA and then funds are added based on enrollment and subtracted based on “offsetting”
amounts received from other ESEA programs. Under RLIS, grants are first made to states based
on a formula and then subgranted to LEAs either on a formula or competitive basis.
SRSA Grants
To the initial SRSA base grant of $20,000, an additional amount is added based on the number of
students in the LEA for LEAs with more than 50 students. The additional amount is equal to $100
11
These estimates use CCD data from the last year in which the metro-centric locale codes were updated by NCES,
2006-2007. CRS estimates may differ from the actual number of eligible LEAs as determined by the ED Budget
Service and REAP program office due to the waiver authority provided in the REAP statute as well as other
adjustments to program data made by ED.
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for each student over 50; however, no grant amount may exceed $60,000. The following are some
examples of initial amount calculations:
•
LEAs with 50 students or fewer have initial amounts of $20,000.
•
An LEA with 55 students has an initial amount of $20,500 (i.e., $20,000 plus
$500, which is $100 times the five students over 50).
•
An LEA with 449 students has an initial amount of $59,900 (i.e., $20,000 plus
$39,900, which is $100 times the 399 students over 50).
•
LEAs with between 450 and 599 students have initial grants of $60,000 (e.g., the
calculation based on 451 students would be $20,000 plus $40,100, which is $100
times the 401 students over 50; since this exceeds the maximum amount of
$60,000, the amount of the award would be $60,000).
Congress intended the SRSA program to be a supplement to certain other ESEA grant funds.
Thus, an LEA’s final grant is based on adjusting its initial amount by the total amount it received
from the following ESEA grant programs in the prior fiscal year:
•
LEA subgrants under the Teacher and Principal Training and Recruiting Fund
(Subpart 2 of Title II),
•
LEA technology grants (Section 2412(a)(2)(A) of Title II),
•
LEA grants under the Safe and Drug-Free Schools and Communities program
(Section 4114), and
•
Innovative Programs under the Promoting Informed Parental Choice and
Innovative Programs (Part A of Title V).
As a result of this “offset” provision, an LEA receiving a total of $60,000 or more from these four
ESEA programs would not receive any additional funds under the SRSA program.12 As SRSAeligible, these LEAs would also not receive funds under the RLIS program. State amounts for the
SRSA program are the sum of amounts allocated to LEAs in each state.
RLIS Grants
Unlike the SRSA program, the statute instructs the Secretary to reserve funds from the total RLIS
appropriation for Bureau of Indian Education (BIE) schools (0.5%) and for outlying areas
(0.5%).13 The remainder is allotted to states based on each state’s share of students attending
schools in eligible LEAs nationwide. Thus, for example, a state with 2% of the national
enrollment in RLIS-eligible LEAs would receive 2% of funds remaining after reserving BIE and
outlying area funds. States then award subgrants to eligible LEAs either competitively or based
12
In FY2008, approximately 450 SRSA-eligible LEAs received no SRSA funding because the amount of funding they
received from the offsetting ESEA programs equaled or exceeded their initial SRSA grant amounts. However, as noted
below, these LEAs are eligible for some flexibility in using funds under these four offsetting programs; see the
discussion of uses of funds below.
13
The outlying areas receiving RLIS grants are American Samoa, Guam, the Northern Mariana Islands, and the Virgin
Islands.
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on a formula selected by the state, and approved by the Secretary.14 Note that this procedure
makes it impossible to estimate individual LEA grants.
Grant Distribution
In a number of cases, states receive funds under one program but not under the other. For
example, Alabama receives no SRSA grants but does receive RLIS funding. This is because none
of Alabama’s 132 LEAs have enrollments less than 600. This is also true for other southeastern
states, which tend to have larger consolidated or countywide LEAs and few or no small LEAs. On
the other hand, Alabama has about 60 LEAs for which all schools have metro-centric locale codes
of 6, 7, or 8 and poverty rates of at least 20%. Thus, Alabama receives a substantial grant under
the RLIS program, as do other southeastern states.
On the other end of the spectrum, some states receive little, if any, RLIS funding and
comparatively large SRSA awards. One reason is that some states have very few high-poverty
LEAs. For example, Connecticut, which receives no RLIS funding, has only six of its 193 LEAs
with poverty rates of 20% or more and none of them are rural. Minnesota, which receives
relatively little RLIS funding, has 159 of its 168 LEAs eligible for SRSA grants, which results in
a relatively large amount of SRSA funding.
Some states have many LEAs that are eligible for both programs but can only be eligible for
SRSA grants, as required under the statute. For example, South Dakota, which has about 30 of its
179 LEAs eligible under RLIS, has only four that receive RLIS funds because the rest are eligible
under both the RLIS and SRSA programs. Finally, there are several states that receive little or no
funds from either program. In FY2010, Hawaii, Maryland, Vermont, and the District of Columbia
receive no REAP funding.
Use of Funds
Recipients of SRSA grants may use funds for activities authorized by several ESEA programs:
•
Improving Basic Programs Operated by Local Educational Agencies (Part A of
Title I),
•
Teacher and Principal Training and Recruiting Fund and Enhancing Education
Through Technology (Part A or D of Title II),
•
Language Instruction for Limited English Proficient and Immigrant Students
(Title III),
•
Safe and Drug-Free Schools and Communities and 21st Century Community
Learning Centers (Part A or B of Title IV), and
•
Innovative Programs (Part A of Title V).
14
A state may use a formula based on the proportion of students in average daily attendance in eligible LEAs or an
alternative formula, as approved by the Secretary, that results in serving “equal or greater concentrations of children
from families with incomes below the poverty line, relative to the concentrations that would be served” if the ADA
formula were used (§6221(b)((2)(C)).
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In addition, under the so-called REAP-Flex provision, all LEAs that are eligible for SRSA grants
(whether or not they receive grants because offsetting ESEA funding exceeds initial grant
calculations) have the flexibility to use “offsetting funds” from other ESEA programs for any
activities authorized by the above ESEA programs.15 ED provides the following example of use
of funds under REAP-Flex: “[A]n LEA may use funds under the Safe and Drug-Free Schools
Program (Title IV, Part A) to incorporate technology into its early reading program—an
authorized local activity under the Educational Technology State Grant (Title II, Part D).”16
The GAO found that flexibility under the SRSA program allowed small, rural LEAs to redirect
funds to crucial NCLB needs. “[I]n one rural state contacted, officials reported that many of their
districts used Safe and Drug-Free School Program funds to support their technology initiatives,
which, in turn, helped with implementing some of the provisions of NCLB.”17
RLIS grant recipients may use funds for the following purposes:
•
teacher recruitment and retention, including the use of signing bonuses and other
financial incentives;
•
teacher professional development, including programs that train teachers to
utilize technology to improve teaching and to train special needs teachers;
•
educational technology, including software and hardware, as described in Part D
of Title II (Enhancing Education Through Technology);
•
parental involvement activities;
•
activities authorized under the Safe and Drug-Free Schools program under Part A
of Title IV;
•
activities authorized under Part A of Title I; and
•
activities authorized under Title III (Language Instruction for Limited English
Proficient and Immigrant Students).18
The GAO reported other uses of REAP funds to help meet costs associated with NCLB
requirements, including
•
86% of responding rural superintendents reported spending REAP funds on
student and teacher technology needs;
•
66% reported using REAP funds for NCLB supplementary services for students;
15
In its guidance on REAP, ED refers to alternative use of funds as “REAP-Flex” and differentiates this flexibility
from other ESEA flexibility as follows:
REAP-Flex does not involve a transfer of funds from one program to another. Rather, REAP-Flex
gives an LEA broader authority in spending “applicable funding” for alternative uses under
selected federal programs. On the other hand, when an LEA transfers funds from one program to
another under the transferability authority in section 6123, the transferred funds increase the
allocation of the receiving program and are subject to all of the rules and requirements of the
receiving program. ED REAP Guidance, (section II-B-1).
16
ED REAP Guidance, section II-B-5.
17
GAO Effective Strategies, p. 35.
18
States may reserve no more than 5% of RLIS funds for state administration and technical assistance (§6222(b)).
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•
94% said they used these funds for professional development related to helping
teachers meet NCLB highly qualified teacher requirements; and
•
60% used REAP funds for student remedial services to prepare them for annual
assessments.19
Reauthorization Issues
Rationale for Additional Support to Rural LEAs
According to statute, REAP aims to compensate rural school districts because they often “receive
formula grant allocations in amounts too small to be effective in meeting the intended purposes”
of these grant programs.20 CRS analysis of ED Budget Service data reveal that SRSA-eligible
LEAs indeed receive substantially smaller formula grant amounts than SRSA-ineligible LEAs
due to their substantially smaller enrollments.21 On the other hand, hold harmless provisions in
programs like Title II-A mean that SRSA-eligible LEAs receive substantially higher awards than
SRSA-ineligible LEAs on a per-pupil basis. Whether Congress chooses to reauthorize this holdharmless provision could determine whether SRSA-eligible LEAs continue to receive a higher
per-pupil share of these federal funds, in addition to funds awarded under REAP. In addition,
Congress may consider whether the supplemental funds provided under REAP are spread too
thinly to make a difference. While the average award per student for SRSA grants is $81, the
average award per pupil for RLIS grants is only $28.
Impact of New Locale Codes on Program Eligibility
Since the 1980s, NCES has used the “metro-centric” locale codes described earlier in this report
as having eight urban-to-rural classifications. In recent years, NCES and the Census Bureau have
devised a new “urban-centric” locale code system with 12 classifications. NCES contends that the
new codes more accurately depict a school’s geographic context for three reasons: (1) improved
geocoding technology, (2) reflection of recent residential developments and population shifts, and
(3) additional classifications allow for finer distinctions between the edges of suburb land and the
beginnings of rural territory.22 The new urban-centric locale codes are as follows:
11 = Large City: Territory inside an urbanized area and inside a principal city with
population of 250,000 or more.
12 = Midsize City: Territory inside an urbanized area and inside a principal city with
population of less than 250,000 and greater than or equal to 100,000.
13 = Small City: Territory inside an urbanized area and inside a principal city with
population of less than 100,000.
19
GAO Effective Strategies, p. 34.
§6202(2).
21
LEA grants are not available at the national level for the RLIS programs because funds are allocated to states by
formula, and no national data are available on states’ distribution of RLIS grants to LEAs.
22
“Meeting Minutes of Secretary’s Rural Education Task Force,” April 27, 2006, p. 9, downloaded from
http://www.ed.gov/nclb/freedom/local/rural/index.html#meetings on December 5, 2006.
20
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21 = Large Suburb: Territory outside a principal city and inside an urbanized area
with population of 250,000 or more.
22 = Midsize Suburb: Territory outside a principal city and inside an urbanized area
with population of less than 250,000 and greater than or equal to 100,000.
23 = Small Suburb: Territory outside a principal city and inside an urbanized area
with population of less than 100,000.
31 = Fringe Town: Territory inside an urban cluster that is less than or equal to 10
miles from an urbanized area.
32 = Distant Town: Territory inside an urban cluster that is more than 10 miles and
less than or equal to 35 miles from an urbanized area.
33 = Remote Town: Territory inside an urban cluster that is more than 35 miles from
an urbanized area.
41 = Fringe Rural: Census-defined rural territory that is less than or equal to 5 miles
from an urbanized area, as well as rural territory that is less than or equal to 2.5
miles from an urban cluster.
42 = Distant Rural: Census-defined rural territory that is more than 5 miles but less
than or equal to 25 miles from an urbanized area, as well as rural territory that is
more than 2.5 miles but less than or equal to 10 miles from an urban cluster.
43 = Remote Rural: Census-defined rural territory that is more than 25 miles from
an urbanized area and is also more than 10 miles from an urban cluster.
NCES has planned on phasing out the metro-centric codes, but continues to make them available
solely for operation of REAP. Should Congress reauthorize the program, it will likely consider
moving to the new locale codes. In doing so, policy makers may want to consider which of the
new codes are comparable to the old codes and the impact that switching codes may have on
REAP eligibility.
NCES contends that the old metro-centric locale codes for rural locations correspond closely with
the new urban-centric rural locale codes.23 CRS analysis of CCD data confirms this position.
Table 3 reveals a great deal of overlap between rural schools identified under the old and new
systems. Of the schools classified as rural under the metro-centric system (i.e., coded as 7 or 8),
over nine-in-ten (92.5%) were also classified as rural under the urban-centric system (i.e., coded
as either 41, 42, or 43). Similarly, nearly nine-in-ten (89.5%) schools given city or urban fringe
codes under the metro-centric system (i.e., codes 1 through 4) were given city or suburban codes
under the urban-centric system (i.e., codes 11-23).
Correspondence among the old and new town categories is not quite as straightforward; however,
it appears the best match for the old “small town” code (6) is the new codes for “distant” and
“remote” towns (32 and 33, respectively). Of the schools coded “small town” under the urbancentric system, three-quarters were given a metro-centric code of “distant town” (38.5%) or
“remote town” (38.3%) and an additional 12.0% were coded “rural.”
23
http://nces.ed.gov/ccd/rural_locales.asp
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Table 3. Comparison of Schools Classified by Metro-Centric and by
Urban-Centric Locale Codes
Urban-Centric (New) Locale Codes
MetroCentric
(Old)
Locale
Codes
City /
Suburb
(11-23)
Fringe
Town
(31)
Distant
Town
(32)
Remote
Town
(33)
Rural
(41-43)
Totals
Estimated
Number
of Schools
City / Urban
Fringe (1-4)
89.5%
5.0%
2.4%
0.2%
3.0%
100.0%
57,780
Large Town (5)
2.4%
13.0%
28.8%
52.0%
3.8%
100.0%
998
Small Town (6)
0.7%
10.6%
38.5%
38.3%
12.0%
100.0%
8,009
Rural (7-8)
2.4%
1.3%
2.2%
1.6%
92.5%
100.0%
29,391
Source: CRS analysis of NCES, Common Core of Data from the last year in which the metro-centric locale
codes were updated by NCES, 2006-2007.
Based on these data, some have proposed replacing the metro-centric rural codes with the urbancentric rural codes in the following manner: (1) to be SRSA-eligible, all schools in an LEA must
have an urban-centric code between 41 and 43, and (2) to be RLIS-eligible, all schools in an LEA
must have an urban-centric code between 32 and 43. Table 4 presents the estimated number of
LEAs that would be eligible using the old and new locale codes while retaining all other aspects
of current law (i.e., the ADA, population density, and poverty requirements remain unchanged).
According to the CRS analysis presented in Table 4, the proposed switch to the new locale codes
would increase the number of eligible LEAs in both the SRSA and RLIS programs from 4,538 to
4,611 and 1,399 to 1,563, respectively.
Although there is a very large amount of overlap among these LEAs, replacing metro-centric
codes with the newer and arguably more accurate urban-centric codes will remove hundreds of
LEAs from eligibility and add hundreds of others. As a result, some LEAs and states will lose
funding, others will gain funding. Unless there are significant increases in REAP funding, any
formula change will produce “winners” and “losers.” Since the Census Bureau is eliminating the
metro-centric codes, continued use of these data is not an option unless legislation specifically
mandates their continued production. To mitigate the impact of the new codes, Congress could
choose to hold harmless those eliminated LEAs indefinitely or for a period of time (perhaps at a
decreasing percentage of their prior year grants) so they can adjust to the funding loss. While
hold-harmless provisions would soften the blow to these LEAs, a formula change based on the
new locale codes would result in lower grants overall (assuming level or near-level funding) to
other remaining LEAs as funds are distributed among the two groups already served and the
newly eligible LEAs.
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Table 4. Estimated Number of LEAs Eligible for REAP Under Old and New Locale
Codes, All Other Aspects of Current Law Unchanged
Old Locale Codes
New Locale Codes
Small Rural School Achievement Program Eligibility
All schools with metro-centric locale code 7 or 8
and enrollment less than 600 students
or in county with less than 10 persons per square mile
All schools with urban-centric locale code 41-43
and enrollment less than 600 students
or in county with less than 10 persons per square mile
4,538
4,611
Rural Low-Income Schools Program Eligibility
All schools with metro-centric locale code 6-8
and school-age poverty at least 20%
and not eligible for SRSA program
All schools with urban-centric locale code 32-43
and school-age poverty at least 20%
and not eligible for SRSA program
1,399
1,563
Source: CRS analysis of NCES, Common Core of Data from the last year in which the metro-centric locale
codes were updated by NCES, 2006-2007.
Allocating Excess Funds
The current SRSA formula often does not permit all appropriated funds to be allocated to LEAs.
In part, this is because SRSA grants are capped at $60,000. The act does not specify how to deal
with the allocation of excess funds. As a result, ED has had to make policy on how these excess
funds should be distributed. Apparently to adhere to the statute, the ED “ratable increase”24
procedure maintains both the $60,000 cap and $20,000 floor for the SRSA grants and ratably
increases grants falling between these two amounts. The statute could be amended to reflect ED’s
current procedures. This would ensure that ED continues to follow this procedure in the future.
Alternatively, the statute could be amended to provide a different policy for dealing with
additional appropriations. For example, the statute could specify a ratable increase procedure
under which the minimum and maximum grants could be ratably increased along with all other
grants. Presumably, this approach would slightly reduce LEAs’ grants that fall between the
minimum and maximum grants. Another option would be to increase the cap in current law from
$60,000 to some higher amount.
Increase Benefits to Small, Poor LEAs
LEAs that are eligible for the SRSA program (based, in part, on enrollment below 600) are not
eligible for grants under the RLIS program (which targets rural LEAs with relatively high poverty
rates). Since it can be argued that these LEAs are triply disadvantaged—being rural, small, and
poor—a possible change in the statute could recognize this by allowing small, poor rural LEAs to
benefit from both programs. This would add hundreds of LEAs to the RLIS eligibility list and
redistribute RLIS state grants by increasing grants to states with large numbers of small, poor
LEAs and reducing grants to states with few small LEAs (mostly states in the Southeast). If
24
Ratably increasing grants means increasing grants in proportion to the relationship between each LEA’s initial grant
and the total excess funds to be distributed.
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further targeting were desired, a higher poverty threshold could be set for small, poor LEAs. For
example, a poverty rate of 30% or greater would add far fewer LEAs to the RLIS eligibility pool.
SRSA Formula Anomalies
The SRSA formula has resulted in some distributional anomalies, which might be addressed by
formula modifications. For example, the minimum grant of $20,000 results in some very large
per-pupil grants. While the average per-pupil grant is about $80, a few LEAs receive per-pupil
grants as high as $19,000.25 This results because they have only one or a few students.26 One
approach for reducing this result would be to limit LEA participation to LEAs with a minimum
total enrollment.27 Another anomaly occurs when LEAs have offsetting program amounts that are
just a few dollars less than their final SRSA grant. For example, some LEAs receive grants as low
as $39. A solution to this would be to eliminate final grants that are deemed to be below a size to
be effective. These funds could then be distributed to other LEAs to enhance their grants.
Some have considered the circumstance in which LEAs eligible for the SRSA program have
offsetting grants larger than their initial grant to be problematic. While such LEAs can still use
the REAP Flex provision, they receive no additional REAP funds. Although this is in keeping
with the intent of the REAP purposes, some argue that such LEAs are still in need of additional
assistance. One alternative to this situation would be to calculate the SRSA initial grants without
the minimum and maximum grants of $20,000 and $60,000, subtract the offsetting grant amounts,
then apply the minimum and maximum grant amounts. This would reduce the number of LEAs
that are eligible but receive no funding.
A final concern that some states have is that, unlike the RLIS program, states receive no state
administration funding under the SRSA program, despite having to provide ED with much of the
data used to allocate funds (such as offsetting program grant amounts). This could be addressed
by reserving 2% (or some other percent) of the appropriation for the SRSA program for state
administration. Of course, this would reduce funds going to small, rural LEAs by the percentage
reserved for state administration.
Poverty Data for RLIS Eligibility
Although some argue that national poverty thresholds overstate poverty in rural areas compared
to cities,28 others have suggested that the measure used to identify “low-income” LEAs for RLIS
eligibility does not adequately reflect poverty in very small, rural locations.29 The measure used
for RLIS eligibility is the same as that used for many federal programs (including ESEA Title IA); that is, the Census Bureau’s Small Area Income and Poverty Estimates (SAIPE).
25
U.S. Department of Education, Fiscal Year 2010 Justifications of Appropriation Estimates to the Congress, vol. 1.
According to the CCD data, five states (Arizona, Maine, Minnesota, Montana, and Nebraska) report at least one LEA
with one student.
27
This is a standard used in the ESEA Title I-A program, which has an eligibility threshold of 10 children living in
poor families in order for LEAs to receive Title I-A funds.
28
Nancy K. Cauthen and Sarah Fass, Measuring Poverty in the United States, National Center for Children in Poverty,
New York, NY, June 2008, http://www.nccp.org/publications/pub_825.html.
29
Jerry Johnson and Marty Strange, Why Rural Matters 2007: The Realities of Rural Education Growth, Rural School
and Community Trust, Arlington, VA, October 2007, http://files.ruraledu.org/wrm07/WRM07.pdf.
26
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The SAIPE program provides annual estimates of income and poverty statistics for all states,
counties, and school districts. For states and counties, these estimates combine survey data with
population estimates and administrative records. For school districts, the county estimates are
combined with data from the decennial census and federal tax information to produce estimates of
the number of related children ages five to 17 in families in poverty.30 For purposes of the REAP
program, what is important to know about the SAIPE school district poverty estimates is that they
are generated through a process in which data at broad levels of aggregation (i.e., from national,
regional, or state sources) are progressively distributed to more narrow geographic levels (i.e., to
counties and school districts). This process inevitably involves some degree of distributional error
as it moves from large, populous areas to smaller, sparsely populated areas.
Some rural program proponents contend that in small, sparsely populated school districts, child
poverty could be better estimated using data from the National School Lunch Program,31
commonly referred to as Free and Reduced-Price Lunch (FRPL) data. State agencies that
administer the program submit data on the number of FRPL-eligible students to NCES through its
Public Elementary/Secondary School Universe Survey (part of the CCD).32 One proposal to use
FRPL data is contained in S. 1052, the Rural Education Achievement Program Reauthorization
Act of 2009.33 This bill proposes replacing the 20% poverty threshold using SAIPE data with a
40% low-income threshold using FRPL data for RLIS eligibility. Table 5 presents the estimated
number of LEAs which would be eligible using the SAIPE and FRPL data under both the old and
new locale codes.
Table 5. Estimated Number of LEAs Eligible for RLIS Using Current and
Alternative Low-Income Indicators
Old Locale Codes
All schools with metro-centric locale code 6-8
and SAIPE low-income at least 20%
and not eligible for SRSA program
All schools with metro-centric locale code 6-8
and FRPL low-income at least 40%
and not eligible for SRSA program
1,399
2,178
New Locale Codes
All schools with urban-centric locale code 32-43
and SAIPE low-income at least 20%
and not eligible for SRSA program
All schools with urban-centric locale code 32-43
and FRPL low-income at least 40%
and not eligible for SRSA program
1,563
2,475
Source: CRS analysis of NCES, Common Core of Data from the last year in which the metro-centric locale
codes were updated by NCES, 2006-2007.
30
More information on SAIPE can be found at http://www.census.gov/did/www/saipe/index.html.
The National School Lunch Program provides free meals to eligible children in households with income at or below
130% of the federal poverty guidelines, and reduced-price meals to eligible children in households with income above
130% and at or below 185% of these guidelines. For more information on this program, see http://www.fns.usda.gov/
cnd/Lunch/.
32
For more information on this survey, see http://nces.ed.gov/ccd/pubschuniv.asp.
33
A companion bill has also been introduced in the House as H.R. 2446.
31
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According to CRS analysis, the proposed switch to the FRPL data would substantially increase
the number of eligible LEAs using either the old or new locale codes. Using the old locale codes,
a switch from SAIPE data to FRPL data would add nearly 800 LEAs. Using the new locale codes,
this switch would add over 900 LEAs. Should Congress decide to switch to FRPL data, it could
set a threshold higher than 40% to reduce the number of newly eligible LEAs. Given the well
documented problems with the quality of these data (see the Appendix at the end of this report
for a discussion of these issues), Congress may wish to analyze the distributional impacts of using
FRPL data more closely. Even if the threshold were set high enough to keep the number eligible
LEAs about the same as the current number, there would undoubtedly be significant shifts in the
distribution of RLIS funds across regions and states.
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Appendix. Analysis of FRPL Data for School
District Poverty Determinations
Through the 1994 ESEA amendments (P.L. 103-382), Congress directed the National Research
Council (NRC) to examine the use of SAIPE data for Title I allocations and for other purposes.
The NRC concluded that SAIPE data were the best currently available; however, it recommended
that research be conducted into possible improvements that may result from incorporating
additional income-related information, including FRPL data, into the SAIPE procedures.34
Despite noting several reporting and enrollment problems with these data (documented both by
the Department of Agriculture and NCES35), analysts at the Census Bureau undertook research to
determine whether FRPL data may improve SAIPE data. The researchers concluded:
Through regression analysis we estimate a positive relationship between FRPL data and
Census 2000 poverty estimates with a median prediction error of 30 percent. The high degree
of prediction error suggests the FRPL data are not sufficiently precise for formal use in
producing school district poverty estimates at this time.36
Since some have proposed that FRPL data replace SAIPE data for determining LEA poverty and
RLIS eligibility, it is worth taking a closer look at the limitations of FRPL data. In its CCD
documentation, NCES notes the following with respect to the counts of students eligible for the
free school lunch program: “These counts of students [eligible for the free school lunch program]
may be taken by the schools at a different time than the membership counts [a measure of
enrollment], therefore the count of free lunch and membership students may not be comparable in
a given school.”37
In a 2005 Federal Register notice, the Secretary of Education described additional problems with
FRPL data as follows:
First, the family income threshold needed to qualify for the FRPL program is 185 percent of
the poverty level used by the Census Bureau. Hence, many more children qualify for the
FRPL program than are considered poor under the census definition, which makes FRPL
eligibility too expansive a measure of poverty.
34
Panel on Estimates of Poverty for Small Geographic Areas, Constance F. Citro and Graham Kalton, eds., Small-Area
Income and Poverty Estimates: Priorities for 2000 and Beyond, Committee on National Statistics, National Research
Council, National Academy Press, ISBN 0-309-07146-1, Washington, DC, 2000, http://www.nap.edu.
35
U.S. Department of Agriculture, Food and Nutrition Service, Accuracy of SFA Processing of School Lunch
Applications—RORA 2005, December, 2005. U.S. Department of Education, National Center for Education Statistics,
Documentation to the NCES Common Core of Data Public Elementary/Secondary School Universe Survey: School
Year 2002–03, NCES 2004–333, by Julia Naum and Jennifer Sable, Project Officer: John Sietsema, Washington, DC,
2004.
36
Craig Cruse and David Powers, Estimating School District Poverty with Free and Reduced-Price Lunch Data, U.S.
Census Bureau, Small Area Estimates Branch, Washington, DC, 2006, p. 1, http://www.census.gov//did/www/saipe/
publications/conference.html.
37
U.S. Department of Education, National Center for Education Statistics. Documentation to the NCES Common Core
of Data Public Elementary/Secondary School Universe Survey: School Year 2002–03, NCES 2004–333, by Julia Naum
and Jennifer Sable, Project Officer: John Sietsema, Washington, DC, 2004, p. 9.
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Second, FRPL data tend to undercount children in middle and high schools, because children
in the upper grades tend to participate in the school lunch program in significantly lower
numbers. Therefore, the number of poor children in high school districts are typically not
accurately represented by FRPL counts.
Third, FRPL data are self-reported data. The number of children included in the FRPL count
depends on how many families apply for the program. The extent to which school districts
and schools reach out and recruit families to apply for the program will affect the number.
Because of this factor, the USDA, which administers the school meals programs, has raised
concerns about the accuracy of these data. Several data sources, including the eligibility
verifications performed by school districts, indicate that a significant number of ineligible
children appear to have been certified for free and reduced meals and, therefore, that these
data may not be an adequate measure for poverty for other program uses. USDA believes
that the authority for school officials to use counts of children eligible for free and reducedprice meals in determining Title I within-district allocations may provide an incentive for
those officials to inflate those counts.
Finally, because FRPL are self-reported data, the relationship between census poverty and
FRPL is not consistent across geographic areas. Nationally, for example, the number of
children eligible for the FRPL in school year 2000–01 among the States ranges from 1.5 to
41 times the number of children who meet the census criteria for poverty.38
Census Bureau analysis put the discrepancy between official poverty estimates and FRPL
estimates this way:
Between 1994 and 2004, the ratio of school-age children receiving free or reduced-price
lunch increased from 28.6 to 32.2 percent, using state-level data from the [Food and
Nutrition Service]. For the same time period, the estimated poverty rate for related children
ages 5 to 17 decreased from 19.8 to 16.2 percent, using data from the [Current Population
Survey].39
As official estimates of poverty declined during the economic expansion of the late 1990s, the
FRPL data collected during that time suggest that there was an increase in the number of lowincome children.
Author Contact Information
(name redacted)
Specialist in Education Policy
[redacted]@crs.loc.gov, 7-....
38
U.S. Department of Education, “Innovation for Teacher Quality,” 70 Federal Register 38020, July 1, 2005.
Craig Cruse and David Powers, Estimating School District Poverty with Free and Reduced-Price Lunch Data, U.S.
Census Bureau, Small Area Estimates Branch, Washington, DC, 2006, p. 2, http://www.census.gov//did/www/saipe/
publications/conference.html.
39
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