The Rural Education Achievement Program: Title VI-B of the Elementary and Secondary Education Act
Released: 2013-01-14T00:00:00

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.

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.

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).

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.

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."

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."

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 FY2003-FY2007; 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 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.

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-to-rural scale that is based on their proximity to metropolitan areas. These so-called "metro-centric" locale codes are defined as follows:

1 = Large City : A central city of a core based statistical area (CBSA) or metropolitan statistical area (MSA), 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.

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 8 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.

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). 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.

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 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. As SRSA-eligible, 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%). 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 on a formula selected by the state, and approved by the Secretary. 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 21 st Century Community Learning Centers (Part A or B of Title IV), and Innovative Programs (Part A of Title V).

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. 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)."

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."

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).

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; 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.

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. 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. 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 hold-harmless 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. 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. 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. 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 urban-centric 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."

Based on these data, some have proposed replacing the metro-centric rural codes with the urban-centric 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.

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" 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 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. This results because they have only one or a few students. One approach for reducing this result would be to limit LEA participation to LEAs with a minimum total enrollment. 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, 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. The measure used for RLIS eligibility is the same as that used for many federal programs (including ESEA Title I-A); that is, the Census Bureau's Small Area Income and Poverty Estimates (SAIPE).

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. 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, 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). One proposal to use FRPL data is contained in S. 1052 , the Rural Education Achievement Program Reauthorization Act of 2009. 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.

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.

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.

Despite noting several reporting and enrollment problems with these data (documented both by the Department of Agriculture and NCES ), 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.

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."

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.

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 reduced-price 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.

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].

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 low-income children.