Order Code RL33804
Rural Education and the Rural Education
Achievement Program (REAP):
Overview and Policy Issues
Updated January 29, 2008
Richard N. Apling and Jeffrey J. Kuenzi
Specialists in Social Legislation
Domestic Social Policy Division

---

Rural Education and the Rural Education Achievement
Program (REAP): Overview and Policy Issues
Summary
Advocates for rural local educational agencies (LEAs) maintain that these
school districts have many advantages — for example, that rural schools are more
likely to be closely connected to the community, parents, and students than is the case
in larger, urban and suburban LEAs. At the same time, rural schools face a variety
of challenges, both in general (such as lack of access to cultural and educational
resources) and more specifically regarding current federal requirements related to the
No Child Left Behind Act, or NCLBA, (such as special problems meeting the
requirement for “highly qualified” teachers under NCLBA).
There are many ways to define a rural school. The definition of a rural school
can be based on location (e.g., distance from metropolitan areas), by size, or by
population density. Targeting can also be based on how poor a rural school district
is. Depending on the definition used, the number of rural LEAs can vary from 11%
to more than 60% of all LEAs, and can be said to serve as few as 2% of all public
school students to as many as one-quarter of all students.
Rural school districts differ in important ways from their urban and suburban
counterparts. Rural districts tend to have fewer minority students: while large and
mid-size cities often have majority minority student populations, rural school districts
tend to be predominantly white. Rural districts tend to have smaller schools. For
example, high schools in rural areas have an average enrollment of about 200
students, while urban and suburban high schools average between 800 and 1,200
students. Similarly, rural schools have fewer teachers (for example, 20 teachers for
the average rural high school and nearly 60 teachers for the average urban high
school). Finally, rural districts are less likely to have special schools and programs.
For example, nearly 10% of urban schools are charter schools, while less than 2% of
rural schools are charters.
One way that Congress has aimed to aid rural schools is through the Rural
Education Achievement Program (REAP), which provides funds to small, rural LEAs
(an enrollment of less than 600) and relatively poor rural LEAs (a child poverty rate
of at least 20%). Approximately 4,000 LEAs receive funds under the Small, Rural
Schools Achievement program (SRSA), and an additional 1,200 LEAs receive Rural
Low-Income School (RLIS) grants.
The REAP program is part of the Elementary and Secondary Education Act
(ESEA), which the 110th Congress is expected to consider for reauthorization. One
possible policy question involves a potential change in how rural LEAs are identified
under the program. The statute specifies the use of locale codes to determine which
LEAs are located in rural areas. The U.S. Department of Education (ED) has
proposed changes to the determination of locale codes. If adopted, the new locale
code system could eliminate some LEAs from eligibility for REAP funds (perhaps
as many as 400 from the SRSA program) and add newly eligible LEAs (perhaps 35).
This report will not be updated.

---

Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is a Rural School District? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Metro-Centric Locale Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Urban-Centric Locale Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Population Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Characteristics of Rural School Districts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Ethnicity of Rural School Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Size Characteristics of Rural LEAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Special Programs in Rural LEAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
The Rural Education Achievement Program (REAP) . . . . . . . . . . . . . . . . . . . . . 17
REAP Eligibility, Grant Determination, and Use of Funds . . . . . . . . . . . . . 18
Eligibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Grant Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Use of Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Distribution of Certain ESEA Grants to Rural LEAs . . . . . . . . . . . . . . . . . . . . . 24
Does REAP Compensate Small, Rural LEAs for Small
Formula Grant Amounts? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Are Rural LEAs at a Disadvantage for Obtaining Competitive Grants? . . . 27
Urban-Centric Locale Codes: Possible Impacts on Eligibility . . . . . . . . . . . . . . . 29
Possible Policy Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Shift in Locale Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Allocating Excess Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Increase Benefits to Small, Poor LEAs . . . . . . . . . . . . . . . . . . . . . . . . 34
Adjust SRSA Formula to Reduce Anomalies . . . . . . . . . . . . . . . . . . . 35
Appendix: Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
CCD Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
ED Budget Service Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
ED Grants Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

---

List of Figures
Figure 1. Poverty Rate by Metro-Centric Locale Code . . . . . . . . . . . . . . . . . . . . . 9
Figure 2. Enrollment in LEAs in Cities, Towns, and Rural Areas
(based on metro-centric locale codes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Figure 3. Ethnic Make-Up of Urban, Suburban, Town, and Rural LEAs
(based on metro-centric locale codes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Figure 4. Where Children of Various Ethnic Groups Go to School . . . . . . . . . . 12
Figure 5. Average Size of Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 6. Average Class Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 7. Average Full-Time-Equivalent (FTE) Teachers per School . . . . . . . . 14
Figure 8. Percentage of Schools That Are Charters . . . . . . . . . . . . . . . . . . . . . . 15
Figure 9. Percentage of Title I Schools That Are Schoolwide Programs . . . . . . 16
Figure 10. Percentage of Schools That Are Magnets . . . . . . . . . . . . . . . . . . . . . 17
List of Tables
Table 1. Various Definitions of Rural School Districts . . . . . . . . . . . . . . . . . . . . . 5
Table 2. Comparisons of Metro-Centric and Urban-Centric Locale Codes . . . . . 7
Table 3. Appropriations for REAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Table 4. Estimating Numbers of LEAs Eligible for REAP Programs . . . . . . . . 19
Table 5. REAP State Amounts for FY2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Table 6. Comparison of Grants for SRSA-Eligible LEAs and Other LEAs . . . . 26
Table 7. Data on Competitive Grant Recipients (ED Office of Elementary
and Secondary Education, or OESE, FY2003) . . . . . . . . . . . . . . . . . . . . . . 28
Table 8. Comparison of Schools Classification by Metro-Centric and
by Urban-Centric Locale Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Table 9. Estimates of Amounts and Number of Grantees Under the SRSA
Formula Based on Metro-Centric and Urban-Centric Locale Codes . . . . . . 31

---

Rural Education and the Rural Education
Achievement Program (REAP):
Overview and Policy Issues
Introduction
Advocates for rural schools argue that these schools have many advantages. For
example, the Wisconsin Rural Policy Network Forum listed a number of advantages,
including the following:
! Rural teachers are key members of the community and tend to know
the students and their families.
! Rural schools have a flatter organizational structure with fewer
layers than non-rural school systems, and are able to adjust or adapt
relatively quickly to change.
! Students in rural schools tend to support one another with activities,
such as peer mentoring.
! The schools within rural communities are very visible and strongly
connected with the community.1
Rural school advocates also admit that rural schools face challenges. These
include the following:
! Rural schools face extreme fiscal limitations, which result in various
problems, such as limited range of curricular options and a lack of
advanced placement course offerings as well as difficulties
providing competitive salaries to attract and retain highly qualified
teachers.
! Rural schools tend to have declining enrollment.
! Many rural schools are in sparsely populated areas, which results in
several problems, such as high transportation costs and limited
access to cultural and educational resources.2
In addition to these general challenges, rural local educational agencies (LEAs)
may face particular problems meeting requirements of the Elementary and Secondary
Education Act (ESEA) as amended by the No Child Left Behind Act (NCLBA), such
as standards of adequate yearly progress (AYP), consequences of failure to make
1 Wisconsin Department of Public Instruction. Summary of the Official Proceedings
Wisconsin Rural Policy Network Forum, January 2004. Downloaded from [http://www.
dpi.state.wi.us/rural/pdf/ri_sum.pdf] on December 5, 2006, pp. 2-3.
2 Ibid., pp. 3-5.

---

CRS-2
AYP (such as providing public-school choice and supplementary educational
services), and ensuring that all teachers of core academic subjects (such as math and
science) are “highly qualified.”3 The Government Accountability Office (GAO) has
found that rural school districts may be more likely than other districts to face
problems in complying with NCLBA requirements. GAO findings include those
listed below.
! Achieving NCLBA goals for large enrollments of economically
disadvantaged students present more challenges for rural LEAs than
for nonrural LEAs.
! Some rural districts lack the community resources, such as libraries
and museums, which 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
NCLBA requirements.
! Some rural districts indicated limited numbers of staff created
difficulties completing NCLBA requirements, such as reporting on
school progress.4
Both the U.S. Department of Education (ED) and the Congress have sought to
address concerns of rural school districts. In response to the GAO report, ED pointed
out that it has attempted to provide additional flexibility to 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 Congress
has enacted and funded the Rural Education Achievement Program (REAP) to help
address challenges that rural LEAs face.
3 See, for example, the following CRS reports: CRS Report RL32495, Adequate Yearly
Progress (AYP): Implementation of the No Child Left Behind Act, by Wayne C. Riddle; CRS
Report RL30834, K-12 Teacher Quality: Issues and Legislative Action; by Jeffrey J.
Kuenzi; and CRS Report RL31329, Supplemental Educational Services for Children from
Low-Income Families Under ESEA Title I-A, by David P. Smole.
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.
Downloaded from [http://www.ed.gov/nclb/freedom/local/rural/index.html#meetings] on
December 5, 2006. (Cited hereafter as “Task Force Minutes”).

---

CRS-3
What Is a Rural School District?
Despite the interest and concern of Congress and others about rural school
districts, determining which LEAs are rural is complex and sometimes controversial.
Complexity and controversy can result because different definitions of “rural” can
result in significant changes in the number of such LEAs and on the targeting of any
program aimed at assisting them.
Estimates of the number of rural districts vary widely. For example, according
to the Secretary of Education’s task force on rural education, “forty three percent of
the nation’s public schools are in rural areas” and “nearly one-third of America’s
school-aged children attend public schools in these communities.”6 Presumably
using a more stringent definition, the GAO found that “in the 2001-02 school year,
rural districts comprised 25 percent of all school districts in the country.”7
Among possible characteristics that might be used to identify rural LEAs are
location, size, population density, and poverty level. Table 1 shows numbers and
characteristics of LEAs identified as rural based on these characteristics.
Location
A rural LEA might be defined as one located in a rural area. But what is a rural
area? ED’s National Center for Education Statistics (NCES), in cooperation with the
Census Bureau, has devised a typology to classify the location of LEAs and
individual schools. NCES uses these “locale codes” to classify each school in an
LEA based on its geographic location. NCES than categorizes each LEA based on
the code or codes assigned to each school.8 Since the 1980s, NCES has used the so-
called “metro-centric” locale codes, which have 8 classifications. As discussed
below, NCES and the Census Bureau have recently changed codes to an “urban-
centric” system with 12 classifications.
Metro-Centric Locale Codes. Metro-centric locale codes are based on the
physical location represented by an address that is matched against a geographic
database maintained by the Census Bureau. This database is the Topographically
Integrated and Geographically Encoded Referencing system, or “TIGER.”9 Metro-
centric locale codes are used to classify schools and LEAs according to the following
typology:10
6 Task Force Minutes, p. 5.
7 GAO Effective Strategies, p. 2.
8 The general rubric is that 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.
9 Source: NCES website at [http://nces.ed.gov/ccd/rural_locales.asp].
10 Ibid.

---

CRS-4
1.
Large City: A central city of a core based statistical area (CMSA) or
metropolitan statistical area (MSA),11 with the city having a population greater
than or equal to 250,000.
2.
Mid-Size City: A central city of a CMSA or MSA, with the city having a
population less than 250,000.
3.
Urban Fringe of a Large City: Any territory within a CMSA or MSA of a
Large City and defined as urban by the Census Bureau.
4.
Urban Fringe of a Mid-size City: Any territory within a CMSA or MSA of a
Mid-size 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 CMSA 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 CMSA or MSA.
7.
Rural, Outside MSA: Any territory designated as rural by the Census Bureau
that is outside a CMSA or MSA of a Large or Mid-size City.
8.
Rural, Inside MSA: Any territory designated as rural by the Census Bureau
that is within a CMSA or MSA of a Large or Mid-size City.
As Table 1 shows, LEAs categorized by NCES as rural (locale codes of 7 and
8) account for over 50% of all school districts and nearly 18% of all students.
Including LEAs categorized as locale 6 (small towns) accounts for over 60% of all
school districts and 25% of all students.
As noted above (footnote 8) NCES classifies LEA status based on the code of
a majority or plurality of its schools. A more stringent definition is to classify an
LEA as rural only if all of its schools are classified as rural. Table 1 shows that this
definition makes a significant difference: reducing the number of LEAs defined as
rural from 8,200 to 7,200 and cutting the number of students served by rural LEAs
by 4 million. Reductions are also seen when this more stringent definition is applied
to the 6, 7, and 8 classification.
11 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.

---

CRS-5
Table 1. Various Definitions of Rural School Districts
Number of Percentage
school
of school
districts
districts
meeting
meeting
Estimated
Estimated
criterion
criterion
number of
enrollment in
with
with
school
Percentage
school
poverty
poverty
districts
of all
districts
Percentage
rate of
rate of
Rural school district
meeting
school
meeting
of total
20% or
20% or
criterion
criterion
districts
criterion
enrollment
more
more
LEA categorized as locale
code of 7 or 8
8,213
51.5%
8,502,709
17.7%
2,061
25.1%
All schools in LEA
categorized as locale code of
7 or 8
7,222
45.3%
4,486,772
9.3%
1,858
25.7%
LEA categorized as locale
code of 6, 7, or 8
9,964
62.5%
12,183,312
25.3%
2,618
26.3%
All schools in LEA
categorized as locale code of
6, 7, or 8a
9,322
58.4%
9,398,303
19.5%
2,533
27.2%
All schools in LEA meeting
new locale codes of rural (41,
42, 43)
5,957
37.3%
3,206,824
6.7%
1,641
27.5%
LEA with enrollment less than
600
6,579
41.0%
1,612,972
3.4%
1,336
19.5%
LEA in counties with fewer
than 10 persons per square
mile
1,757
11.0%
844,233
1.8%
550
31.3%
Total enrollment PK to 12
48,093,461
LEAs with enrollment > 0
15,955
3,382
24.3%a
Source: CRS analysis of CCD data for school year 2003-2004.
a. Percentage based on number of LEAs for which poverty data are available (approximately 14,000)
Urban-Centric Locale Codes. Over the last two years, NCES has worked
with the Census Bureau to revise location classifications for schools and LEAs. To
differentiate this new system from the old locale codes, these are termed “urban-
centric” locale codes.12 NCES provides two reasons for these changes: First,
improvement in geocoding permits precise location of most schools based on
longitude and latitude. The second reason is changes made by the Office of
12 According to NCES, the metro-centric codes are based on metropolitan statistical areas
and are coterminous with counties. The urban-centric codes rely on urbanized areas, which
are densely settled geographic cores with densely settled areas surrounding them. “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.

---

CRS-6
Management and Budget (OMB) in the definition of metropolitan and non-
metropolitan areas.
NCES maintains that improved geocoding technology makes the new codes
more accurate. In addition, the new codes overcome some shortcomings of the
metro-centric locale codes, such as the lack of a classification for suburbs, a
significant undercounting of school districts in towns, and imprecision in
distinguishing rural schools in remote, isolated areas from those nearer to urban
cores.13
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 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 less than 100,000.
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 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 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.
13 See [http://nces.ed.gov/ccd/rural_locales.asp].

---

CRS-7
Table 2 shows comparable codes under the 2 classification systems.
Table 2. Comparisons of Metro-Centric and
Urban-Centric Locale Codes
Corresponding Categories
Metro-Centric
Urban-Centric
City
1, 2
11, 12, 13
Suburb
3, 4
21, 22, 23
Town
5, 6
31, 32, 33
Rural
7, 8
41, 42, 43
Source: NCES website [http://nces.ed.gov/ccd/rural_locales.asp].
Table 1 shows that the new codes result in significantly fewer LEAs classified
as rural, assuming that the stricter “all schools” rule is used.14 A comparison of LEAs
with all schools classified as rural under the metro-centric system (7 and 8) with
those with all rural schools classified as rural under the urban-centric system (41,
42, and 43) shows a reduction of more than 1,200 LEAs classified as rural, and a
reduction in the number of students served in rural LEAs of over 1 million.
Size
Another criterion for identifying rural school districts is enrollment size. There
are many small LEAs in the United States. The median LEA size (i.e., the
enrollment encompassing 50% of all LEAs) is 880 students.15 Ten percent of all
LEAs have 100 students or fewer. As discussed below, Congress has defined a small
LEA as one having fewer than 600 students. As Table 1 shows, approximately 6,500
LEAs (or more than 40% of LEAs reporting some enrollment in 2003-2004) fit this
criterion, and these LEAs enroll about 1.6 million students (about 3.4% of all public
school students in 2003-2004). On average, these LEAs have 1 or 2 schools and
about 20 teachers.
Many of these small LEAs are in midwest and southwestern states as well as in
California. For example, Texas alone has over 500 LEAs with fewer than 600
students, about 45% of all its LEAs and (nearly 9% of the total of such districts in the
nation). On the other hand, several states, such as South Carolina, West Virginia,
Florida, Alabama, and Delaware, have only a handful of LEAs that meet the 600
student criterion.
14 The current CCD data provide urban-centric codes for individual schools but not for
LEAs.
15 Percentages are based on LEAs reporting some enrollment in school year 2003-2004
(approximately 16,000 LEAs).

---

CRS-8
Not all small school districts are located in rural areas. Of the 6,579 LEAs with
fewer than 600 students, 4,700 have locale codes of 7 or 8. At the same time, nearly
900 have locale codes of 1 (i.e., are classified as within an MSA or CMSA).16
Population Density
Population density is another way to define rural school districts. One measure
of population density is total population divided by total land area in square miles
to produce data on persons per square mile. Unfortunately, these data are readily
available only by county. Since many states have multiple LEAs per county17 as well
as LEAs that are located in multiple counties, merging LEA data with county data on
people per square mile gives only a rough approximation of how densely or sparsely
populated LEAs are.
These county-level data suggest that there are many LEAs in relatively sparsely
populated areas. For example, 50% of all LEAs are in counties with fewer than 90
persons per square mile; 25% are in counties with fewer than 30 persons per square
mile; and 10% are in counties with fewer than 10 persons per square mile, which is
the criterion used in the SRSA program (as discussed below). As one would expect,
many rural LEAs are in sparsely populated counties. Ninety percent of rural LEAs
outside metropolitan areas (locale code 7) are in counties with fewer than 90 people
per square mile. Nearly 25% of these rural LEAs are in counties with less than 10
persons per square mile.18 Table 1 shows that LEAs in counties with less than 10
persons per square mile account for about 10% of all LEAs and about 2% of all
students.
As one would expect, most of the LEAs in low-density counties are in western
states. For example, nearly 90% of Alaska’s LEAs are in counties with fewer than
10 people per square mile. This is true for 85% of Wyoming’s LEAs and for
two-thirds of North Dakota’s and Nevada’s LEAs. On the other hand, most states
east of the Mississippi River have less than 2% of their LEAs in counties that are this
sparsely populated, and 19 states have no LEAs in counties with population density
less than 10 persons per square mile.
Poverty
Although measures of poverty are not indicators of whether an LEA is rural, as
we shall see, Congress has used a measure of child poverty as a means of targeting
funds to some rural LEAs. The only source of data on school-age poverty (ages 5 to
16 Presumably, many of these are charter schools classified as LEAs. The CCD LEA data
base does not provide information on whether an LEA is a charter school.
17 For example, the website for Lake County, Illinois lists 44 school districts; see
[http://www.epodunk.com/cgi-bin/localList.php?local=6328&locTGroup=School_
districts&direction=down&sec=0&qty=44].
18 Not all LEAs classified as rural are in sparsely populated counties. For example, nearly
one-fourth of rural LEAs within metropolitan areas (locale code 8) are in counties with 600
people or more per square mile.

---

CRS-9
17) for LEAs is the Small Area Income and Poverty Estimates (SAIPE) from the
Census Bureau.19 The average poverty rate20 is 14.2% for all LEAs in the CCD that
are also included in the SAIPE data base. About 25% of all LEAs have poverty rates
of 20% or more (the standard Congress used to target funds to LEAs with relatively
high poverty rates). This finding — that about 25% of LEAs have poverty rates of
at least 20% — holds for most categorizations of rural LEAs discussed above and
included in Table 1 (see last column). The exceptions are LEAs in sparsely
populated counties, which tend to have higher poverty rates (31.3% are at or above
this criterion), and small LEAs, which tend to have lower poverty rates (19.5% are
at or above the 20% poverty level).21
Figure 1 shows how poverty rates vary by locale code. Large city school
districts tend to have the highest poverty rates. LEAs in mid-size cities, towns, and
rural areas outside urban areas have similar rates. LEAs in fringe cities and in rural
areas within urban areas tend to have lower rates.
Figure 1. Poverty Rate by Metro-Centric
Locale Code
25%
20%
15%
10%
5%
0%
1. large
2. mid-
3. fringe
4. fringe
5. large
6. smal
7. rural
8. rural
city
size city large city
mid-city
tow n
tow n
outside
inside
urban
urban
areas
areas
Source: CRS analysis of ED data.
There are, of course, regional differences in poverty rates. Schools in the South
and West tend to have higher rates than LEAs in New England and Mid-Atlantic
states. For example, the overall poverty rate among LEAs in New England states is
8.4%, and the average rate for rural LEAs outside urban areas in New England is
19 See [http://www.census.gov/hhes/www/saipe/index.html]. These estimates for income
year 2003 have been merged into the CCD LEA data set. There are about 14,000 LEAs that
are in both data bases.
20 Poverty rate is calculated by dividing estimated number of children 5 to 17 years old in
poverty by total number of children 5 to 17 years old.
21 The average poverty rate for LEAs in counties with fewer than 10 persons per square mile
is 17.3%. The average poverty rate for LEAs with fewer than 600 students is 15.3%.

---

CRS-10
10.3%. Overall school poverty in southern states is 21.0%, and the average rate for
southern rural LEAs outside urban areas in 22.5%.
Characteristics of Rural School Districts
This section compares selected characteristics of rural LEAs to LEAs in other
categories based on one definition of rural and non-rural status: LEAs’ metro-centric
locale codes (codes 7 and 8 for rural LEAs). As noted above and as shown in Figure
2, rural school districts — as determined based on locale codes — enrolled about 8.5
million children in school year 2003-2004. This represents about 18% of the 48
million public school children in that school year. At the same time, nearly 75% of
all public school children attend schools in large, mid-size, and fringe cities.22
Figure 2. Enrollment in LEAs in Cities, Towns, and Rural Areas
(based on metro-centric locale codes)
25,000,000
20,000,000
15,000,000
10,000,000
5,000,000
-
Large and Mid-
Fringe cities
Towns
Rural
Size Cities
Source: CRS analysis of ED data.
22 These data are based on the total enrollment variable in the CCD LEA data base. Recall
that NCES uses a decision rule based on the plurality of schools’ locale codes to assign
locale codes to LEAs. Tabulation of enrollments from the CCD school data base for the
same school year (2003-2004) results in higher rural school enrollments (about 10 million
or about 21% of the 48 million public school children. Much of the redistribution comes
from schools and LEAs with the locale code of 8 (rural within an urban area). Presumably,
a number of LEAs have individual schools located in these rural areas, but the plurality of
these schools receive a non-rural code. The point to remember, as discussed earlier in this
report, is that analyses may differ significantly, depending on how one defines rural LEAs.

---

CRS-11
Ethnicity of Rural School Children
The ethnicity of school districts differ substantially, as Figure 3 shows. Large
and mid-size LEAs tend to be majority-minority. For example, in school year 2003-
2004 whites made up only about 20% of the enrollment in large city districts and less
than 50% of enrollment in mid-size school districts. On the other hand, whites made
up large proportions of the enrollment in small towns (about 70%) and in rural school
districts (about 80%).
Figure 3. Ethnic Make-Up of Urban, Suburban, Town,
and Rural LEAs (based on metro-centric locale codes)
100%
90%
80%
70%
Indian
60%
As ian
50%
Hispanic
40%
Black
White
30%
20%
10%
0%
1. large
2. mid-
3. fringe
4. fringe
5. large
6. s mall
7. rural
8. rural
city
size city large city mid-city
town
town
outside
inside
urban
urban
areas
areas
Source: CRS analysis of ED data.
Another view of ethnic make-up of school districts is to ask where children from
various groups go to school (see Figure 4). Whites tend to be enrolled in suburban
and rural school districts (45% and 27%). Black, Hispanic, and Asian-American
students are much more likely to be enrolled in cities and suburbs and less likely to
be enrolled in rural LEAs (11% of Blacks, 8% of Hispanics, and 5% of Asian-
Americans are enrolled in rural school districts). Indian students are more likely to
attend schools in rural areas (40% enroll in such schools) and less likely to enroll in
urban or suburban districts.

---

CRS-12
Figure 4. Where Children of Various Ethnic Groups Go to School
60%
Cities
Suburbs
Towns
Rural Areas
50%
40%
30%
20%
10%
0%
White
Black
His panic
Asian
Indian
Ethnic Group
Source: CRS analysis of ED data.
Size Characteristics of Rural LEAs
As noted earlier, one characteristic of many rural LEAs is small size. For
example, many have fewer than 600 students. As Figure 5 shows, not only are many
LEAs small but their schools are also relatively small. For example, while high
schools in large and mid-size cities average more than 1,000 students, rural high
schools outside urban areas (locale code 7) average about 200 students. Similarly,
urban and suburban middle schools and primary schools tend to be substantially
larger than rural schools — especially rural schools outside urban areas.

---

CRS-13
Figure 5. Average Size of Schools
1400
primary
middle
high
other
1200
1000
800
600
400
200
0
1. large city 2. mid-size
3. fringe
4. fringe
5. large
6. small
7. rural
8. rural
city
large city
mid-city
town
town
outs ide
inside
urban
urban
areas
areas
Source: CRS analysis of ED data.
Class size, however, does not appear to vary substantially across LEA location
(Figure 6). Class size in urban and suburban LEAs tends to be a bit over 15 children
per class; rural classes tend to be at or a bit below 15 children per class.
Figure 6. Average Class Size
25
primary
middle
high
20
15
10
5
0
1. large city 2. mid-size
3. fringe
4. fringe
5. large
6. small
7. rural
8. rural
city
large city
mid-city
town
town
outside
inside
urban
urban
areas
areas
Source: CRS analysis of ED data.

---

CRS-14
At least part of the reason for this is that city and suburban LEAs tend to have
more teachers (Figure 7). For example, high schools in large and mid-size cities
have an average of about 60 full-time equivalent (FTE) teachers. Rural schools,
being smaller, tend to have few teachers. For example, high schools in rural school
districts outside urban areas average about 20 teachers. At least some of these
differences in numbers of teachers and similarities in class size probably reflect
school funding formulas based to some degree on enrollments.
Figure 7. Average Full-Time-Equivalent (FTE)
Teachers per School
80
primary
middle
high
70
60
50
40
30
20
10
0
1. large city
2. mid-size
3. f ringe
4. fringe mid- 5. large tow n 6. small tow n
7. rural
8. rural inside
city
large city
city
outside urban urban areas
areas
Source: CRS analysis of ED data.
Special Programs in Rural LEAs
In recent years, Congress has supported a number of reforms aimed at increasing
flexibility and improving education. The CCD schools data base provides
information on three of these reforms by identifying which schools are charter
schools, which are schoolwide projects under the ESEA Title I-A program, and
which are magnet schools. CCD data indicate that all 3 of these reforms tend to be
concentrated in urban and suburban school districts.
Public charter schools are public elementary or secondary schools that are
“exempted from certain rules and regulations otherwise applicable to public schools,
in exchange for a commitment toward attaining positive results in meeting state
content and performance standards in accordance with the terms and conditions of
a charter granted by an authorized public chartering agency.”23 Figure 8 shows that,
in school year 2003-2004, charter schools accounted for 9% of schools in large cities
and 5% of schools in mid-size cities while accounting for less than 2% of schools in
23 Summary, CRS Report RL31128, Funding for Public Charter School Facilities: Federal
Policy Under the ESEA, by David P. Smole.

---

CRS-15
rural districts outside urban areas and about 2% of rural schools within urban areas.
Similarly, in 2003-2004, nearly 80% of all charter schools were located in LEAs in
large or mid-size cities or fringe cities. Only 15% were located in rural LEAs.
Figure 8. Percentage of Schools That Are Charters
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
1. large city
2. mid-size
3. fringe
4. fringe mid- 5. large tow n 6. small tow n
7. rural
8. rural
city
large city
city
outside
inside urban
urban areas
areas
Source: CRS analysis of ED data.
The schoolwide programs provision under the Title I-A of ESEA permits a
school with a relatively high percentage of pupils from low-income families (40%)
to use funds from Title I-A and from certain other ESEA programs to serve all
children in the school. (Schools with lower rates of pupils from low-income families
must target Title I-A funds on services for Title I-A eligible children.)24 Figure 9
shows that schoolwide programs are more prevalent in large urban LEAs (where 80%
of Title I schools are schoolwide programs) and LEAs in mid-size cities (where about
70% are schoolwide programs) than in rural LEAs (less than 50% of Title I schools
are schoolwide programs).
24 For further information, see CRS Report RL31487, Education for the Disadvantaged:
Overview of ESEA Title I-A Amendments Under the No Child Left Behind Act, by Wayne
C. Riddle.

---

CRS-16
Figure 9. Percentage of Title I Schools
That Are Schoolwide Programs
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1. large city
2. mid-size
3. fringe
4. fringe mid- 5. large tow n 6. small tow n
7. rural
8. rural
city
large city
city
outside
inside urban
urban areas
areas
Source: CRS analysis of ED data.
Magnet schools can be “defined as public elementary or secondary schools or
education centers that offer special curricula capable of attracting substantial
numbers of students with different racial backgrounds.”25 For example, a magnet
school might feature certain academic subjects, such as a science magnet school or
a fine arts magnet or might concentrate on particular careers, such as a health careers
magnet school or a magnet focusing on aero-space careers.
As Figure 10 shows, magnet schools tend to be more common in urban areas
(at least in part because of the role these schools have played in desegregation plans).
Magnets make up about 12% of schools in large urban school districts but only about
1% of rural schools. Similarly, nearly 90% of all magnet schools are in large and
mid-size cities and fringe cities.
25 CRS Report RL33506, School Choice Under the ESEA: Programs and Requirements, by
David P. Smole, pp. 15-16.

---

CRS-17
Figure 10. Percentage of Schools That Are Magnets
14%
12%
10%
8%
6%
4%
2%
0%
1. large city 2. mid-size
3. f ringe
4. fringe
5. large
6. small
7. rural
8. rural
city
large city
mid-city
tow n
tow n
outside
inside
urban
urban
areas
areas
Source: CRS analysis of ED data.
The Rural Education Achievement Program (REAP)
As part of the No Child Left Behind Act (NCLBA), Congress created the Rural
Education Achievement Program (REAP-Title VI-B of the ESEA) to address “the
unique needs of rural school districts” (§6202). These needs, according to the statute,
include the lack of “personnel and resources needed to compete effectively for
Federal competitive grants” and “formula grant allocations in amounts too small to
be effective in meeting their intended purposes.”26
REAP authorizes 2 programs: the Small, Rural School Achievement Program
(SRSA-subpart 1), which focuses on LEAs with less than 600 students and the Rural
and Low-Income School Program (RLIS-subpart 2), which focuses on larger rural
LEAs with relatively high poverty rates (at least 20% of children from families below
the poverty line).
REAP authorized $300 million for FY2002 and “such sums” for FY2003-
FY2007. Funds are to be divided equally between the two programs. Table 3 shows
the appropriations for the program. Appropriations have grown modestly, except for
the FY2006 amount, which was subject to the 1 percent across-the-board cut for most
domestic discretionary programs required by P.L. 109-148. Overall, appropriations
for FY2008 represent about a 6% increase over FY2002, the first year of program
funding.
26 20 U.S.C. §7341a(1) and (2).

---

CRS-18
Table 3. Appropriations for REAP
Appropriation
% Change
(rounded to nearest
from Prior
Fiscal Year
$000)
Year
2002
$162,500,000
2003
$167,653,00
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%
REAP Eligibility, Grant Determination, and Use of Funds
Eligibility. As noted above, rural LEAs can be defined in various ways, and
Congress has chosen combinations of rural definitions to determine LEA eligibility,
which differ for the two programs. An LEA is eligible for the SRSA program if all
schools served by the LEA have a metro-centric locale code of 7 or 827 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.
An LEA is eligible for the RLIS program if all its schools have locale codes of 6, 7,
or 828 and at least 20% of the children the LEA serves are from families below the
poverty line. Finally, an LEA that receives a grant under the SRSA program is not
eligible for RLIS funding. Table 4 shows how these criteria interact to produce
estimates of LEAs eligible for the SRSA and RLIS programs. 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 off-
setting ESEA formula grant programs.
27 The Secretary of Education may waive the locale code requirement (but not the
ADA/density requirement) based on a state government agency’s determination that the
LEA is located in a rural area. (§6211(b)(2)) See 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”.)
28 The statute does not provide the Secretary with waiver authority of the locale code
requirement for the RLIS program.

---

CRS-19
Table 4. Estimating Numbers of LEAs
Eligible for REAP Programs
Small Rural School Achievement Program Eligibility
and
or
enrollment less than 600
in county with less than 10
Locale Code of 7 or 8
students
persons per square mile
7,222
4,653
5,088
Rural Low-Income Schools Program Eligibility
and
and
school-age poverty at least
not eligible for SRSA
Locale Code of 6, 7, or 8
20%
program
9,322
2,533
1,292
Source: CRS analysis of CCD Data.
Grant Determination. Amounts that LEAs receive and aggregate state
amounts are determined differently under the two programs. Under the SRSA
program, an initial amount is calculated for each eligible LEA as follows: To a base
grant of $20,000 an additional amount is added based on the number of students over
50 times $100; however, no initial 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 51 students has an initial amount of $20,100 ($20,000
plus $100 times 51-50).
! An LEA with 449 students has an initial amount of $59,900
($20,000 plus $100 times 449-50).
! LEAs with 450 to 599 students have initial grants of $60,000 (for
example, the calculation based on 451 students would be $20,000
plus $100 times 451-50=$60,100, which exceeds the maximum, so
the initial amount is $60,000).
As noted above, the SRSA program aims to supplement ESEA grant programs.
As a result, 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).

---

CRS-20
As a result of this “off-set” provision, an LEA receiving a total of $60,000 or more
from these 4 ESEA programs would not receive any additional funds under the SRSA
program.29 State amounts for the SRSA program are the sum of amounts allocated
to LEAs. (See the first column of grants in Table 5.)
The current SRSA statutory formula does not provide a mechanism under which
all currently appropriated funds could be allocated. For example in FY2004, the
formula alone (with the $20,000 minimum and $60,000 maximum together with the
off-set provision) would have allotted about $67 million; while the appropriation for
that fiscal year was about $83 million for the SRSA program. The statute does not
provide details on how to deal with this situation, i.e., how to ratably increase
grants.30 ED does allocate all funds appropriated and has implemented a procedure
to do this, which, among other things, appears to maintain the minimum and
maximum formula amounts. For example, ED does not ratably increase an initial
grant above $60,000.
Unlike the SRSA program, grants are first made to states under the RLIS
program. Also unlike the SRSA program, the statute instructs the Secretary to
reserve funds from the total RLIS appropriation for Bureau of Indian Affairs (BIA)
schools (½%) and for outlying areas (½%).31 The remainder is allotted to states
based on each state’s share of students attending schools in eligible LEAs. Thus, for
example, a state with 1% of students attending schools in eligible LEAs in that state
would receive 1% of funds remaining after reserving BIA and outlying area funds.
(See the second column of grants in Table 5 for FY2008 estimated state amounts.)
States then award grants to eligible LEAs either competitively or based on a
formula.32 Note that this procedure makes it impossible to estimate individual LEA
grants at the national level (unlike the SRSA program).
29 Approximately 200 SRSA-eligible LEAs receive no SRSA funding because the amount
of funding they receive from the off-setting ESEA programs equals or exceeds their initial
grant amounts. However, as noted below, these LEAs are eligible for some flexibility in
using funds under these 4 off-setting programs. See the discussion of uses of funds below.
30 The statute does generally provide for the situation in which funds are initially
insufficient, and subsequently additional funds become available (20 U.S.C. §7345a(b)(3)).
31 The outlying areas receiving RLIS grants are American Samoa, Guam, the Northern
Mariana Islands, and the Virgin Islands.
32 A state may use a formula based on the proportion of students in average daily attendance
(ADA) 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).

---

CRS-21
Table 5. REAP State Amounts for FY2008
(FY2008 estimates, rounded to the nearest $000)
Small Rural
Rural,
School
State or Entity
Low-Income
Achievement
Schools
Program
Alabama
0
5,870,000
Alaska
128,000
0
Arizona
2,158,000
1,188,000
Arkansas
1,236,000
3,703,000
California
6,116,000
1,266,000
Colorado
2,054,000
223,000
Connecticut
1,194,000
0
Delaware
0
107,000
District of Columbia
0
0
Florida
0
1,474,000
Georgia
30,000
7,385,000
Hawaii
0
0
Idaho
906,000
21,000
Illinois
5,942,000
834,000
Indiana
276,000
302,000
Iowa
4,532,000
0
Kansas
3,830,000
122,000
Kentucky
175,000
5,815,000
Louisiana
66,000
6,005,000
Maine
1,685,000
2,164,000
Maryland
0
0
Massachusetts
1,405,000
124,000
Michigan
2,770,000
946,000
Minnesota
2,985,000
117,000
Mississippi
55,000
7,257,000
Missouri
5,333,000
3,047,000
Montana
4,996,000
152,000
Nebraska
4,339,000
87,000
Nevada
86,000
0
New Hampshire
1,279,000
816,000
New Jersey
1,973,000
0
New Mexico
589,000
2,139,000
New York
1,859,000
1,571,000
North Carolina
813,000
4,718,000
North Dakota
661,000
50,000
Ohio
2,259,000
1,870,000
Oklahoma
7,093,000
4,793,000
Oregon
1,487,000
542,000
Pennsylvania
312,000
493,000
Rhode Island
63,000
0
South Carolina
0
3,799,000
South Dakota
907,000
46,000

---

CRS-22
Small Rural
Rural,
School
State or Entity
Low-Income
Achievement
Schools
Program
Tennessee
130,000
2,807,000
Texas
8,565,000
7,643,000
Utah
264,000
0
Vermont
0
0
Virginia
50,000
773,000
Washington
2,103,000
1,019,000
West Virginia
0
3,607,000
Wisconsin
3,213,000
73,000
Wyoming
10,000
0
American Samoa
0
81,000
Guam
0
175,000
Northern Mariana Islands
0
55,000
Puerto Rico
0
0
Virgin Islands
0
118,000
Freely Associated States
0
0
Indian set-aside
0
430,000
Other (non-State allocations)
0
100,000
Source: ED Budget Service.
Note: Totals may differ slightly due to rounding.
Table 5 shows a number of patterns in the distribution of grants under the two
REAP programs. In a number of cases, states receive funds under one program but
not under the other. For example, Alabama receives no SRSA funding but does
receive RLIS grants. This is because practically none of Alabama’s 130 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 hand, some states receive little, if any, RLIS funding. One reason
is that some states have very few high poverty LEAs. For example, Iowa, which
receives no RLIS funding, has only 2 LEAs with poverty rates of 20% or more.
Another reason is that 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 received more than 10 times the funding under the
SRSA than under RLIS, has nearly 85% of its LEAs that are eligible for both
programs and thus can receive grants only under the SRSA program. Finally, there
are several states that receive little or no funds from either program. In FY2008,

---

CRS-23
Hawaii, Maryland, and Vermont receive no REAP funding.33 Hawaii is a single
statewide LEA. Maryland’s LEAs do not appear to meet eligibility criteria for either
program. Vermont LEAs may not apply for the SRSA program because grants are
considered too small.34
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).
In addition, all LEAs that are eligible for SRSA grants (whether or not they receive
grants because off-setting ESEA funding exceeds initial grant calculations) have the
flexibility to use funds from the off-setting ESEA programs for any activities
authorized by the above ESEA programs.35 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).”36
The GAO also found that flexibility under the SRSA program allowed small,
rural LEAs to redirect funds to crucial NCLBA needs. “[I]n one rural state contacted,
officials reported that many of their districts used Safe and Drug-Free School
33 This was also the case for the District of Columbia (all schools in an urban area).
34 The GAO study, which selected the most rural states for its study “based on the
percentage of their school districts in rural communities, the percentage of their students
attending schools in rural communities, and the average distance between the school district
in the state and the nearest metropolitan statistical area as a measure of geographic
isolation” (GAO Effective Strategies, p. 3). Vermont met these criteria and was included
in GAO’s study.
35 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).
36 “ED REAP Guidance,” section II-B-5.

---

CRS-24
Program funds to support their technology initiatives, which, in turn, helped with
implementing some of the provisions of NCLBA.”37
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).38
The GAO reported other uses of REAP funds to help meet costs associated with
NCLBA requirements, including
! 86% of responding rural superintendents reported spending REAP
funds on student and teacher technology needs;
! 66% reported using REAP funds for NCLBA supplementary
services for students;
! 94% said they used these funds for professional development related
to helping teachers meet NCLBA highly qualified teacher
requirements; and
! 60% used REAP funds for student remedial services to prepare them
for annual assessments.39
Distribution of Certain ESEA Grants to Rural LEAs
As noted above, two purposes of the REAP program are to compensate rural
school districts because they are at a disadvantage in obtaining competitive grants
from the Department of Education, and ED formula grants are often too small to have
an impact. Certainly conventional wisdom would support these contentions. With
respect to competitive grants, rural schools — especially small rural schools — tend
to have fewer administrative staff, who are generally thought to be key to writing
grant proposals and obtaining competitive grants. Regarding formula grants, since
37 GAO Effective Strategies, p. 35.
38 States may reserve no more than 5% of RLIS funds for state administration and technical
assistance (§6222(b)).
39 Ibid., p. 34

---

CRS-25
many grants are distributed to states and to LEAs based on factors related to size
(e.g., school-age population and school-age poverty), smaller LEAs receive smaller
grants. The grant application process and the subsequent federal reporting
requirements can reduce or eliminate the value of formula grants for some smaller
LEAs.
Data on formula and competitive grants do not completely support these
conventional notions, however.
Does REAP Compensate Small, Rural LEAs
for Small Formula Grant Amounts?
As noted above, the REAP aims to compensate rural school districts because
they often receive formula grants that are “too small to be effective in meeting” the
intended goals of the formula grant programs.40 Table 6 examines this proposition
for LEAs eligible for the SRSA program.41 The table presents median grants for the
four ESEA programs that off-set the final SRSA grants (see discussion above on
SRSA grant determination). Clearly SRSA-targeted LEAs tend to receive smaller
grants than do other LEAs. For example, the median grants for ESEA Title II-A
(Teacher and Principal Training and Recruiting Fund and Enhancing Education
Through Technology) for SRSA-eligible LEAs is less than 50% of the median grant
for other LEAs ($14,300 vs. $30,600). Similarly, the median total of all four grants
for SRSA-eligible LEAs is $19,700 compared with $42,300 for other LEAs. At the
same time, SRSA-eligible grants are somewhat larger on a per student basis. For
example, the combined median per-student grant for the four ESEA programs is $89
compared with a combined median per-student grant of $60 for all other LEAs.
40 §6202(2).
41 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.

---

CRS-26
Table 6. Comparison of Grants
for SRSA-Eligible LEAs and Other LEAs
(grants rounded to nearest $100)
All but SRSA-
SRSA-eligible LEAs
eligible LEAs
Grant
Median
Median
grant
grant
Median
per
Median
per
grant
student
grant
student
ESEA Title II-A
14,300
63
30,600
44
ESEA Title III-D
1,500
6
2,100
4
ESEA Title IV-A
1,800
8
4,100
5
ESEA Title V
2,100
8
3,900
6
Total of 4 ESEA
grants 19,700
89
42,300
60
Final SRSA grant
19,000
92
NA
NA
Final SRSA + ESEA
grants total
42,400
181
NA
NA
Source: CRS analysis of ED Budget Service data.
Note: Median.
Table 6 shows that the median SRSA grant (for FY2004) was $19,000 for the
nearly 4,000 LEAs that received grants.42 The median per-pupil grant was $92. In
part, because of the SRSA off-setting requirement, SRSA grant totals and per-pupil
amounts can vary widely. Maximum grants were, of course $60,000. However,
grants ranged as low as $39. Per student grants also ranged widely, from less than
$1.00 per student to $19,000 per student. Per-pupil amounts for some western LEAs
were substantial because these LEAs qualified for the minimum grant of $20,000 and
are very small, in a few cases having less than 10 students. Finally, Table 6 shows
that the SRSA program does indeed compensate small, rural school districts for
relatively small ESEA grants. The median combination of the four ESEA grants and
the SRSA grant was $42,400, roughly the same as the median combined ESEA grants
for other LEAs. On a per-pupil basis, SRSA grantees fared better: $181 vs. $60. In
addition, the SRSA flexibility provision permits small, rural LEAs to concentrate
funds from the off-setting programs and the SRSA grant on one or a few activities
authorized by these programs; whereas, other LEAs must confine their use of these
ESEA funds to activities authorized by the individual programs.
42 Although eligible for SRSA grants, 223 LEAs received no grant because the sum of the
four off-setting ESEA formula grant programs equaled or exceeded their initial SRSA
amount.

---

CRS-27
Are Rural LEAs at a Disadvantage for
Obtaining Competitive Grants?
In FY2003, according to the ED database on discretionary and formula grant
awards,43 the Office of Elementary and Secondary Education (OESE) awarded 500
discretionary (i.e., competitive) grants.44 Of these, about 60% went to LEAs for
which data are available from the CCD. The remainder went to other entities, such
as institutions of higher education. Overall, OESE competitive grants for FY2003
totaled approximately $78 million, of which about $51 million went to LEAs
represented in the CCD database.
Three grant competitions accounted for nearly 80% of the grants and 67% of the
funds:
! 21st Century Community Learning Centers/After School Learning
Centers,
! Fund for the Improvement of Education/Smaller Learning
Communities, and
! Improving Literacy Through School Libraries.
Table 7 shows that, on several measures, rural LEAs (locale codes 7 and 8)
fared well in obtaining OESE grants. These LEAs received slightly more than one-
third of the LEA grants (34.6%) and of the overall LEA funding (34.2%). In
addition, average grants were similar for large urban LEAs ($158,000) and for rural
LEAs (locale 7: $161,000 and locale 8: $149,000). However, when comparing the
number of LEAs that received grants, disparities are evident. About 2% of all LEAs
and about 4% of large urban LEAs received OESE grants in FY2003 but only about
1% of rural LEAs received grants.
43 See [http://www.ed.gov/fund/data/award/grntawd.html].
44 The database contains grants authorized under other statutes; however, this analysis was
limited to OESE competitive grants, which are generally authorized under ESEA.

---

CRS-28
Table 7. Data on Competitive Grant Recipients
(ED Office of Elementary and Secondary Education, or OESE, FY2003)
Totals
4. Urban
(grants that
3. Urban
Fringe of a
7. Rural,
8. Rural,
can be
1. Large
2. Mid-Size Fringe of a
Mid-Size
5. Large
6. Small
Outside
Inside
classified by
City
City
Large City
City
Town
Town
MSA
MSA
locale code)
Total OESE
discretionary
grants
36
46
57
36
3
42
80
31
331
Percentage of
discretionary
grants
10.9%
13.9%
17.2%
10.9%
0.9%
12.7%
24.2%
9.4%
100.0%
Average grant
$158,000
$142,000
$136,000
$149,000 $164,000
$188,000
$161,000
$149,000
$155,000
Total grant
amount
$5,702,000 $6,532,000 $7,754,000 $5,355,000 $491,000 $7,887,000 $12,909,000 $4,610,000 $51,240,000
Percent of
total grant
amount
11.1%
12.7%
15.1%
10.5%
1.0%
15.4%
25.2%
9.0%
100.0%
Percent of
all LEAs
receiving
grants
4.4%
4.8%
2.2%
2.3%
2.3%
2.4%
1.4%
1.1%
2.1%
Source: CRS analysis of ED grants database at [http://www.ed.gov/fund/data/award/grntawd.html].
Note: Average and total grants rounded to the nearest $000.

---

CRS-29
Urban-Centric Locale Codes:
Possible Impacts on Eligibility
As noted above, eligibility for the two REAP programs is based, in part, on the
locale codes of LEAs’ schools. In addition to other requirements, all schools in an
LEA must have metro-centric locale codes of 7 or 8 for the LEA to be eligible for the
SRSA program and 6, 7, or 8 for an LEA to be eligible for the RLIS program. As
discussed above, NCES and the Census Bureau have devised a new set of urban-
centric codes, which are said to more accurately depict a school’s geographic
location. Currently, both sets of locale codes are available and will be available for
perhaps two years. Thereafter NCES will only make available the more recent urban-
centric locale codes.45 Therefore the Congress may wish to consider how the use of
the new codes might impact REAP eligibility and funds distribution.
Table 8 compares the number of schools classified according to metro-centric
locale codes as located in cities, urban fringes, towns, and rural areas with those
classified according to urban-centric locale codes as located in these areas. Clearly
there is a great deal of overlap. For example, nearly 92% of all schools classified as
rural under the metro-centric system are also classified as rural under the urban-
centric system. At the same time, some schools are classified differently under the
two systems. For example, about 8% of schools classified as rural under the metro-
centric system would not be rural under the urban-centric system.
Table 8. Comparison of Schools Classification
by Metro-Centric and by Urban-Centric Locale Codes
Urban-centric locale codes (new codes)
City
Suburb
Towns
Rural
Estimated
(codes 11- (codes 21- (codes 31- (codes 41-
number of
13)
23)
33)
43)
Totals
schools
Cities
(codes 1
and 2)
96.0%
2.4%
0.4%
1.2%
100.0%
24,895
Urban
Metro-
fringe
centric
(codes 3
locale
and 4)
1.4%
80.7%
13.4%
4.5%
100.0%
31,268
codes (old Towns
codes)
(codes 5
and 6)
0.2%
0.4%
87.0%
12.4%
100.0%
9,628
Rural
(codes 7
and 8)
0.5%
2.0%
5.7%
91.8%
100.0%
29,497
Source: CRS analysis of CCD data.
45 Telephone conversation with John Sietsema of NCES, November 14, 2006.

---

CRS-30
What impact would a change to the urban-centric locale codes have on the
distribution of REAP funds? Table 9 shows estimated SRSA state totals and
estimated numbers of LEA grant recipients based on metro-centric and urban-centric
locale codes.46 All other formula factors (e.g., enrollment and county density) were
the same for both sets of estimates. All formula factors (except for the urban-centric
codes, which came from the CCD database) came from an ED Budget Service data
base used to determine LEA SRSA grants.47 The total allocated under both scenarios
($83.2 million) is the total of FY2004 SRSA grants for LEAs for which complete
data were available.48
Table 9 shows some substantial changes, in both the estimated total funds states
would receive and the number of LEAs receiving SRSA grants. Estimated dollar
differences range from an increase of $1.3 million (Oklahoma) to a loss of $1.2
million (California). Estimated percentage changes range from a gain of nearly 40%
(North Dakota and South Dakota) to a loss of more than 60% (Massachusetts).49
Overall, an estimate of 386 fewer LEAs would receive grants based on the
urban-centric locale code criterion. This estimate includes 421 LEAs estimated to
lose grants based on the urban-centric codes and 35 LEAs that would receive grants
if the urban-centric codes were used. Virtually all of the LEAs estimated to lose
funding are classified as metro-centric code 7 (a reduction of 186 LEAs or 6% in this
category) or metro-centric code 8 (a reduction of 208 LEAs or 23% in this
category).50 Recall that the latter code designated rural LEAs within metropolitan
areas. The urban-centric locale coding systems, which takes into account distance
from metropolitan areas, apparently does not classify some of these LEAs as rural.
46 RLIS allocations are not estimated because it is uncertain which urban-centric code or
codes should be used to substitute for metro-centric code 6.
47 This approach may underestimate the impact of the new codes because it does not allow
for LEAs that were not eligible based on the Budget Service data to be deemed eligible. It
only permits estimating numbers that would no longer be eligible and resulting reallocation.
In some cases, states gain in the estimated number of LEAs receiving grants. This is
because there are additional funds to allocate (because some LEAs are no longer eligible).
As a result, some LEAs now receive funding when they would not under the implementation
of current law.
48 The amount allocated to LEAs for FY2004 was $83.5 million. This total is slightly higher
than the amount allocated in Table 9 because complete data were not available for 15 LEAs
that received FY2004 grants.
49 Larger estimated percentage changes occur for Wyoming (more than a 100% increase in
the state total) and for Delaware (a 100% loss), although these changes are from very small
original totals.
50 The remainder are LEAs that were eligible because of state alternative rural definitions.
These LEAs perhaps would remain eligible if their alternative definitions were accepted by
ED.

---

CRS-31
Table 9. Estimates of Amounts and Number of Grantees Under the SRSA Formula
Based on Metro-Centric and Urban-Centric Locale Codes
(estimated grants rounded to nearest $000; calculations may differ slightly due to rounding)
Estimated
Estimated
Estimated total
Estimated total
number of LEA number of LEA
grants based on grants based on
grantees based
grantees based
Change in
current law
current law
Percentage
on current law
on current law
estimated
(metro-centric
(urban-centric
Dollar
dollar
(metro-centric
(urban-centric
number of
State
locale codes)
locale codes)
difference
difference
locale codes)
locale codes)
grantees
Alabama
$0
$0
$0
0%
0
0
0
Alaska
171,000
216,000
45,000
26%
13
15
2
Arizona
1,693,000
1,665,000
-28,000
-2%
78
69
-9
Arkansas
1,058,000
1,069,000
11,000
1%
51
49
-2
California
5,198,000
3,985,000
-1,213,000
-23%
285
210
-75
Colorado
2,020,000
2,280,000
259,000
13%
86
84
-2
Connecticut
1,124,000
951,000
-172,000
-15%
35
27
-8
Delaware
59,000
0
-59,000
-100%
2
0
-2
District of Columbia
0
0
0
0%
0
0
0
Florida
0
0
0
0%
0
0
0
Georgia
21,000
23,000
3,000
13%
2
2
0
Hawaii
0
0
0
0%
0
0
0
Idaho
799,000
918,000
119,000
15%
44
42
-2
Illinois
5,882,000
5,392,000
-490,000
-8%
249
206
-43
Indiana
264,000
218,000
-47,000
-18%
11
8
-3
Iowa
4,743,000
4,991,000
248,000
5%
168
163
-5
Kansas
3,641,000
4,130,000
489,000
13%
159
156
-3
Kentucky
165,000
84,000
-81,000
-49%
7
4
-3
Louisiana
44,000
27,000
-17,000
-39%
2
1
-1
Maine
1,797,000
1,913,000
116,000
6%
114
105
-9
Maryland
0
0
0
0%
0
0
0
Massachusetts
1,170,000
432,000
-738,000
-63%
37
14
-23

---

CRS-32
Estimated
Estimated
Estimated total
Estimated total
number of LEA number of LEA
grants based on grants based on
grantees based
grantees based
Change in
current law
current law
Percentage
on current law
on current law
estimated
(metro-centric
(urban-centric
Dollar
dollar
(metro-centric
(urban-centric
number of
State
locale codes)
locale codes)
difference
difference
locale codes)
locale codes)
grantees
Michigan
2,319,000
2,129,000
-190,000
-8%
119
102
-17
Minnesota
2,750,000
3,051,000
301,000
11%
122
120
-2
Mississippi
57,000
56,000
-1,000
-1%
4
3
-1
Missouri
5,152,000
5,620,000
468,000
9%
243
227
-16
Montana
5,342,000
5,783,000
441,000
8%
332
320
-12
Nebraska
7,257,000
7,376,000
119,000
2%
320
295
-25
Nevada
200,000
209,000
9,000
4%
8
8
0
New Hampshire
969,000
730,000
-239,000
-25%
59
44
-15
New Jersey
1,991,000
958,000
-1,033,000
-52%
61
28
-33
New Mexico
300,000
391,000
91,000
30%
27
28
1
New York
1,304,000
1,278,000
-26,000
-2%
77
69
-8
North Carolina
606,000
294,000
-312,000
-51%
20
11
-9
North Dakota
683,000
944,000
260,000
38%
86
92
6
Ohio
1,589,000
941,000
-648,000
-41%
44
28
-16
Oklahoma
6,945,000
8,274,000
1,329,000
19%
346
346
0
Oregon
1,242,000
1,333,000
91,000
7%
71
64
-7
Pennsylvania
164,000
183,000
19,000
12%
9
9
0
Rhode Island
83,000
93,000
10,000
12%
3
3
0
South Carolina
0
0
0
0%
0
0
0
South Dakota
881,000
1,205,000
324,000
37%
70
73
3
Tennessee
126,000
131,000
5,000
4%
4
4
0
Texas
8,100,000
8,826,000
725,000
9%
375
357
-18
Utah
148,000
103,000
-45,000
-30%
7
5
-2
Vermont
0
0
0
0%
0
0
0
Virginia
39,000
45,000
6,000
16%
2
2
0
Washington
2,017,000
2,106,000
89,000
4%
110
98
-12

---

CRS-33
Estimated
Estimated
Estimated total
Estimated total
number of LEA number of LEA
grants based on grants based on
grantees based
grantees based
Change in
current law
current law
Percentage
on current law
on current law
estimated
(metro-centric
(urban-centric
Dollar
dollar
(metro-centric
(urban-centric
number of
State
locale codes)
locale codes)
difference
difference
locale codes)
locale codes)
grantees
West Virginia
0
0
0
0%
0
0
0
Wisconsin
3,101,000
2,853,000
-248,000
-8%
118
103
-15
Wyoming
7,000
16,000
9,000
137%
2
2
0
Totals
83,221,000
83,221,000
0
0%
3,982
3,596
-386
Source: CRS analysis based on CCD data and ED Budget data.
Note: These are estimated grant totals only. In addition to other limitations, much of the data that would be used to calculate actual grants are not
yet available. These estimates are provided solely to assist in comparisons of the relative impact of alternative formulas in the legislative process.
They are not intended to predict specific amounts states will receive.

---

CRS-34
Possible Policy Issues
Shift in Locale Codes. One policy issue is the possible shift to the urban-
centric locale codes in determining eligibility for REAP grants. As discussed above,
replacing metro-centric codes with the newer, arguably more accurate urban-centric
codes will remove some LEAs from eligibility and add others. As a result, some
LEAs and states will lose funding, others will gain. Unless there are significant
increases in REAP funding (unlikely given current budget constraints), any formula
change will be controversial because there will be “winners” and “losers.” This, in
turn, means that there are no easy policy alternatives.
One possible option would be to mandate the continued use of the metro-centric
codes. This approach has the obvious advantage of ensuring that LEAs are not
eliminated from the program and that funds are not shifted from state to state. It has
the disadvantage of continuing the use of rural definitions that may be inferior to
other, available definitions, and, as a result, allocating funds to LEAs that may not
need as much assistance as “truly rural” LEAs that are in greater need of assistance.
Another possible option would be to hold harmless those eliminated LEAs for
a period of time (perhaps at a decreasing percentage of their prior year grants) so they
can adjust to the funding loss. Although softening the blow to these LEAs, it would
result in lower grants (assuming level or near-level funding) to other, remaining
LEAs as funds are distributed among the 2 groups already served and the newly
eligible LEAs.
Allocating Excess Funds. As discussed above, the current SRSA formula
does not permit all currently 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 this situation. 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”51 procedure maintains both the $60,000 cap and $20,000 floor for the
SRSA grants and ratably increases grants falling between these two requirements.
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.
Increase Benefits to Small, Poor LEAs. As discussed above, 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
51 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.

---

CRS-35
about 1,000 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 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 less than 200 LEAs to the
RLIS-eligibility pool.
Adjust SRSA Formula to Reduce Anomalies. The SRSA formula has
resulted in some quirks, 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 median per-pupil grant is about $90, a few LEAs receive per-pupil grants
as high as $19,000. This results because they have only one or a few students.52 One
approach for reducing this result would be to limit LEA participation to LEAs with
a minimum total enrollment. If minimum enrollment were set at 10,53 about 100
LEAs would be eliminated.
Another seeming anomaly occurs when LEAs have off-setting 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 problem would be to eliminate final
grants that are deemed to be below a size to be effective. Alternatively, grants
deemed too small on a per-pupil basis could be eliminated. (Presumably some LEAs
take this into account by not applying for grants after a year in which they receive a
minimal amount). For example, about 350 LEAs have per-pupil grants of less than
$30, nearly 200 LEAs have per-pupil grants for less than $20, and about 75 LEAs
have per-pupil grants of less than $10. These funds could then be distributed to other
LEAs to enhance their grants.
Another problem occurs when LEAs eligible for the SRSA program have off-
setting grants larger than their initial grant. While these LEAs can still use the REAP
Flex provision, they receive no additional REAP funds. 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 off-setting grant amounts, then
apply the minimum and maximum grant amounts. This would reduce the number of
LEAs that are eligible but receive no funding. (About 200 LEAs currently experience
this.)
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 off-setting program
grant amounts). This could be addressed by reserving 2% (or some other percent) of
the appropriation for the SRSA program for state administration. These funds could
be distributed to states based on their proportion of students enrolled in SRSA
eligible LEAs for the prior year. Of course, this would reduce funds going to small,
rural LEAs by the percentage reserved for state administration.
52 According the CCD data, 5 states (Arizona, Maine, Minnesota, Montana, and Nebraska)
report at least one LEA with one student.
53 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.

---

CRS-36
Appendix: Data Sources
Data for this report came from a variety of sources: the Common Core of Data
(CCD) collected and made available by the National Center for Education Statistics
(NCES) at the U.S. Department of Education (ED), REAP allocation spreadsheets
from the ED Budget Service, the ED database on discretionary and formula grant
awards54, and the Small Area Income and Poverty Estimates (SAIPE) and county
population density data both from the U.S. Census Bureau.55
CCD Data. NCES annually collects data on all public schools and public
school districts, which it provides through the CCD database. Among the CCD’s
purposes is”to provide basic information and descriptive statistics on public
elementary and secondary schools and schooling in general.”56 State educational
agencies (SEA) are mainly responsibility for providing CCD data on schools and
school districts to NCES. Two CCD data files were used in this report: the Public
School Universe and the Local Educational Agency (School District) Universe. Both
data files were pared down to include only open schools and operating school
districts. In addition, only schools and school districts in the 50 United States and
the District of Columbia were included. (The CCD includes other schools and school
districts, which are not eligible for REAP funding, such as Department of Defense
Schools.)
The Public School Universe file was used to determine locale code eligibility
for school districts. Although the Local Educational Agency Universe file classified
school districts by locale code, the algorithm NCES used to attribute codes differed
from the requirement in the REAP program. REAP requires that all schools have
certain locale codes (7 or 8 or 6, 7, or 8). This files was also used to determine ethnic
and racial characteristics of rural and non-rural schools. In addition, the Public
School Universe file was used to determine which schools had special characteristics;
for example, how many charter schools and magnet schools are in rural areas.
The Local Educational Agency Universe file was used to determine the number
of school districts that would be considered as rural under various definitions. To do
this, this file was merged with other databases. For example, it was merged with the
SAIPE database to determine LEA poverty rates, which are necessary to determine
eligibility for the RLIS program. The LEA Universe file was merged with data on
ED grants to analyze how many rural and non-rural school districts received ED
competitive grants. The file was also merged with Census Bureau data on county-
level population density.
54 See [http://www.ed.gov/fund/data/award/grntawd.html].
55 See [http://www.census.gov/population/www/censusdata/density.html].
56 See [http://nces.ed.gov/ccd/aboutCCD.asp]. Files for the 2003-2004 school year were
used because they are the only files containing school data on both the metro-centric and
urban-centric locale codes.

---

CRS-37
ED Budget Service Data. The requirements of REAP eligibility necessitate
that ED to collect data from the states that are not otherwise available. Most notably,
for the SRSA program, ED must collect data on local ESEA grants for the off-setting
programs and information necessary to evaluate states’ requests for waivers of the
locale code criterion. These formula factors cannot be obtained from any other
source. As a result, Budget Service data57 had to be used to estimate the impacts of
changing locale codes from metro-centric to urban-centric (while holding constant
all other eligibility criterion). The Budget Service data had to be merged with CCD
data to include the urban-centric codes.
ED Grants Data. ED provides information on grants awarded by fiscal year.
Data on competitive grants made by the Office of Elementary and Secondary
Education (OESE) for FY2003 were merged with the CCD Local Educational
Agency Universe file. The grants awards data base contains data on 500 OESE
awards for that fiscal year. Of those awards, 337 were made to LEAs in the CCD
file. Other grantees included institutions of higher education and state educational
agencies.
Merging data sets reduces the number of cases for which there are useable data.
For example, the CCD Local Educational Agency Universe file has data on about
17,800 school districts; however, only about 16,000 appeared to have enrolled
children in school year 2003-2004. When the CCD data are merged with the Budget
Service REAP data set, about 15,600 school districts have useable data.
57 Excel spreadsheet obtained from the ED Budget Service, March 2005.

---