Education for the Disadvantaged: Analysis of Issues for the ESEA Title I-A Allocation Formulas

Title I, Part A, of the Elementary and Secondary Education Act (ESEA) authorizes federal aid to local educational agencies (LEAs) for the education of disadvantaged children. Title I-A grants provide supplementary educational and related services to low-achieving and other pupils attending pre-kindergarten through grade 12 schools with relatively high concentrations of pupils from low-income families. In recent years, they have also become a “vehicle” to which a number of requirements affecting broad aspects of public K-12 education for all pupils have been attached as a condition for receiving Title I-A grants. These include requirements for assessments of pupil achievement; adequate yearly progress (AYP) standards and determinations for schools, LEAs, and states; consequences for schools and LEAs that fail to make AYP for two consecutive years or more; plus teacher and paraprofessional qualifications.

The ESEA was initially adopted in 1965, and was most recently reauthorized and amended by the No Child Left Behind Act of 2001 (NCLB), P.L. 107-110. Currently, although the authorization for ESEA Title I-A has expired, appropriations have continued to be provided, and the program continues to be implemented under the policies established by the most recent authorization statute. The 111th Congress is expected to consider proposals to extend and amend the ESEA.

For the allocation of funds to states and LEAs, Title I-A has four separate formulas: the Basic, Concentration, Targeted, and Education Finance Incentive Grant (EFIG) formulas. Once these funds reach LEAs, they are no longer treated separately; they are combined and used without distinction for the same program purposes. While there are numerous complications and special features associated with the Title I-A allocation formulas, each has the same underlying structure. For each formula, a maximum grant is calculated by multiplying a “population factor,” consisting primarily of estimated numbers of school-age children in poor families, by an “expenditure factor” based on state average per pupil expenditures for public K-12 education. In some formulas, additional factors are multiplied by the population and expenditure factors, and/or the population factor is modified to direct increased funds to LEAs with concentrations of poverty.

Major Title I-A reauthorization issues regarding allocation formulas are likely to include the following: Should annual variations in the poverty estimates used to calculate Title I-A grants be reduced through multi-year averaging or other methods? Has the targeting of Title I-A funds on high poverty LEAs increased since 2001? Should the population weighting factors of the Targeted and Education Finance Incentive Grant (EFIG) formulas be modified to more equally favor LEAs with large numbers of school-age children in poor families and LEAs with high poverty rates? Should the expenditure factors continue to play a major role in the Title I-A formulas? Should there be some consolidation of the four different allocation formulas? Should the authorization level for Title I-A continue to be specified for future years, and if so, at what levels? Should the effort factor in the EFIG formula be modified? Should the equity factor in the EFIG formula be modified? Should the current provisions for intra-LEA allocation be reconsidered? Should the remaining special constraints on grants to Puerto Rico, the cap on aggregate population weights in the Targeted Grant formula, be removed? Should the Temporary Assistance to Needy Families (TANF) formula factor be eliminated? And finally, should each county portion of New York City and other multi-county LEAs continue to be treated as separate LEAs under the Title I-A allocation formulas? This report will not be updated.

Education for the Disadvantaged: Analysis of Issues for the ESEA Title I-A Allocation Formulas

September 10, 2009 (R40672)

Contents

Tables

Summary

Title I, Part A, of the Elementary and Secondary Education Act (ESEA) authorizes federal aid to local educational agencies (LEAs) for the education of disadvantaged children. Title I-A grants provide supplementary educational and related services to low-achieving and other pupils attending pre-kindergarten through grade 12 schools with relatively high concentrations of pupils from low-income families. In recent years, they have also become a "vehicle" to which a number of requirements affecting broad aspects of public K-12 education for all pupils have been attached as a condition for receiving Title I-A grants. These include requirements for assessments of pupil achievement; adequate yearly progress (AYP) standards and determinations for schools, LEAs, and states; consequences for schools and LEAs that fail to make AYP for two consecutive years or more; plus teacher and paraprofessional qualifications.

The ESEA was initially adopted in 1965, and was most recently reauthorized and amended by the No Child Left Behind Act of 2001 (NCLB), P.L. 107-110. Currently, although the authorization for ESEA Title I-A has expired, appropriations have continued to be provided, and the program continues to be implemented under the policies established by the most recent authorization statute. The 111th Congress is expected to consider proposals to extend and amend the ESEA.

For the allocation of funds to states and LEAs, Title I-A has four separate formulas: the Basic, Concentration, Targeted, and Education Finance Incentive Grant (EFIG) formulas. Once these funds reach LEAs, they are no longer treated separately; they are combined and used without distinction for the same program purposes. While there are numerous complications and special features associated with the Title I-A allocation formulas, each has the same underlying structure. For each formula, a maximum grant is calculated by multiplying a "population factor," consisting primarily of estimated numbers of school-age children in poor families, by an "expenditure factor" based on state average per pupil expenditures for public K-12 education. In some formulas, additional factors are multiplied by the population and expenditure factors, and/or the population factor is modified to direct increased funds to LEAs with concentrations of poverty.

Major Title I-A reauthorization issues regarding allocation formulas are likely to include the following: Should annual variations in the poverty estimates used to calculate Title I-A grants be reduced through multi-year averaging or other methods? Has the targeting of Title I-A funds on high poverty LEAs increased since 2001? Should the population weighting factors of the Targeted and Education Finance Incentive Grant (EFIG) formulas be modified to more equally favor LEAs with large numbers of school-age children in poor families and LEAs with high poverty rates? Should the expenditure factors continue to play a major role in the Title I-A formulas? Should there be some consolidation of the four different allocation formulas? Should the authorization level for Title I-A continue to be specified for future years, and if so, at what levels? Should the effort factor in the EFIG formula be modified? Should the equity factor in the EFIG formula be modified? Should the current provisions for intra-LEA allocation be reconsidered? Should the remaining special constraints on grants to Puerto Rico, the cap on aggregate population weights in the Targeted Grant formula, be removed? Should the Temporary Assistance to Needy Families (TANF) formula factor be eliminated? And finally, should each county portion of New York City and other multi-county LEAs continue to be treated as separate LEAs under the Title I-A allocation formulas? This report will not be updated.


Education for the Disadvantaged: Analysis of Issues for the ESEA Title I-A Allocation Formulas

Introduction

Title I, Part A, of the Elementary and Secondary Education Act (ESEA) authorizes federal aid to local educational agencies (LEAs) for the education of disadvantaged children. Title I-A grants provide supplementary educational and related services to low-achieving and other pupils attending pre-kindergarten through grade 12 schools with relatively high concentrations of pupils from low-income families. In recent years, they have also become a "vehicle" to which a number of requirements affecting broad aspects of public K-12 education for all pupils have been attached as a condition for receiving Title I-A grants. These include requirements for assessments of pupil achievement; adequate yearly progress (AYP) standards and determinations for schools, LEAs, and states; consequences for schools and LEAs that fail to make AYP for two consecutive years or more; plus teacher and paraprofessional qualifications.

Title I-A is the largest federal elementary and secondary education assistance program, with services provided to (1) more than 90% of all LEAs; (2) approximately 52,000 (54% of all) public schools; and (3) approximately 16.5 million (34% of all) pupils, including approximately 188,000 pupils attending private schools. Three-fourths of all pupils served are in pre-kindergarten through grade 6, while only 8% of pupils served are in grades 10-12.

The ESEA was initially adopted in 1965, and was most recently reauthorized and amended by the No Child Left Behind Act of 2001 (NCLB), P.L. 107-110. NCLB authorized Title I-A through FY2007, and an automatic extension, through FY2008 was provided under the General Education Provisions Act (Title IV of P.L. 90-247, as amended). Currently, although the authorization for ESEA Title I-A has expired, appropriations have continued to be provided, and the program continues to be implemented under the policies established by the most recent authorization statute. The 111th Congress is expected to consider proposals to extend and amend the ESEA.

The focus of this report is on the formulas used to allocate Title I-A funds to states, LEAs, and schools. These formulas are used to allocate funds not only under the largest federal K-12 education program, but also several other ESEA and non-ESEA programs under which grants are made in proportion to ESEA Title I-A allocations. This report will not be updated.

Another CRS report (CRS Report RL33731, Education for the Disadvantaged: Reauthorization Issues for ESEA Title I-A Under the No Child Left Behind Act, by [author name scrubbed] and [author name scrubbed]) discusses issues related to the accountability and other policies of ESEA Title I-A. Those interested in a more concise description of the ESEA Title I-A allocation formulas and review of reauthorization issues related to them than found in this report should refer to the final section of that report (RL33731).

Additional CRS reports provide more detailed discussions and analyses of selected major aspects of the Title I-A program, including pupil assessments,1 accountability,2 and qualifications for teachers and paraprofessionals.3 Also, see CRS Report RL34721, Elementary and Secondary Education Act: An Analytical Review of the Allocation Formulas, for a description and analysis of all of the ESEA's allocation formulas, as well as a discussion of general allocation formula concepts and procedures.

This report provides: (a) descriptions of the ESEA Title I-A allocation formulas; (b) a review of recent funding trends for Title I-A; and (c) analyses of major issues related to the Title I-A allocation formulas, divided into general categories of broad issues directly affecting all regions of the nation and issues that directly affect only a limited number of states or local educational agencies.

In summary, major Title I-A reauthorization issues regarding allocation formulas are likely to include the following:

Should annual variations in the poverty estimates used to calculate Title I-A grants be reduced through multi-year averaging or other methods?

  • Annual variations in estimates of school-age children in poor families have been exceptionally large for a number of states. Several options are available to reduce the more extreme variations, if desired.

Has the targeting of Title I-A funds on high poverty LEAs increased since 2001?

  • Targeting of Title I-A funds on the highest poverty LEAs has increased since adoption of the NCLB, although shifts have been gradual and relatively marginal.

Should the population weighting factors of the Targeted and Education Finance Incentive Grant (EFIG) formulas be modified to more equally favor LEAs with large numbers of school-age children in poor families and LEAs with high poverty rates?

  • In some respects, the formula population weighting factors of the Targeted and EFIG formulas favor LEAs with large numbers of formula children over those with high school-age child poverty rates.

Should the expenditure factor continue to play a major role in the Title I-A formulas?

  • The expenditure factor has a major impact on the distribution of all Title I-A funds, and the rationale for using this factor may be questioned. At best, it is a crude and indirect measure of variations in the costs of providing public K-12 education.

Should there be some consolidation of the four different allocation formulas?

  • The allocation of portions of each year's Title I-A appropriation under four different allocation formulas is a result of legislative compromise, not design.

Should the authorization level for Title I-A continue to be specified for future years, and if so, at what levels?

  • An authorized appropriation level was specified in the ESEA only through FY2007.

Should the effort factor in the EFIG formula be modified?

  • The current effort factor has very limited impact and favors states where school-age children are a relatively small share of the total population.

Should the equity factor in the EFIG formula be modified?

  • The current equity factor might be broadened to consider additional categories of "high cost" pupils.

Should the current provisions for intra-LEA allocation be reconsidered?

  • The participation of middle and, especially, high schools in Title I-A programs is very low, and might be increased through modification of the requirements for allocation of funds within LEAs.

Issues Affecting a Limited Number of States or LEAs:

Should the remaining special constraints on grants to Puerto Rico, the cap on aggregate population weights in the Targeted Grant formula, be removed?

  • Title I-A grants to Puerto Rico would be substantially higher if remaining special constraints were removed.

Should the Temporary Assistance to Needy Families (TANF) formula factor be eliminated?

  • This formula population factor is of little significance, and may remain primarily for historic and symbolic reasons.

Should each county portion of New York City and other multi-county LEAs continue to be treated as separate LEAs under the Title I-A allocation formulas?

  • This provision leads to substantially different treatment of Title I-A schools in different counties within New York City, and has mixed impact on total Title I-A grants to the City overall.

Finally, a general introductory note regarding funding levels and allocations: Most references to appropriation levels, and all discussions and analyses of allocation patterns, in this report refer to those for FY2008, the most recent year for which actual allocations were available at the time this report was prepared. Therefore, there will be only marginal reference to FY2009 appropriations or allocations for Title I-A, whether provided under the American Recovery and Reinvestment Act (P.L. 111-5) or regular FY2009 omnibus appropriations legislation (P.L. 111-8).

Description of the ESEA Title I-A Allocation Formulas

For the allocation of funds to states and LEAs, ESEA Title I-A has four separate formulas: the Basic, Concentration, Targeted, and Education Finance Incentive Grant (EFIG) formulas. Once these funds reach LEAs, they are no longer treated separately; they are combined and used without distinction for the same program purposes.

A primary rationale for using four different formulas to allocate a share of the funds for a single program is that the formulas have distinct allocation patterns, providing varying shares of allocated funds to different types of localities (e.g., LEAs with high poverty rates, or states with comparatively equal levels of spending per pupil among their LEAs), as is discussed later in this report. In addition, some of the formulas contain elements—such as the equity and effort factors in the EFIG formula—that are deemed to have important incentive effects or to be significant symbolically in addition to their impact on allocation patterns. There is also a historical explanation: the Targeted and EFIG formulas, in particular, were initially proposed as replacements for the Basic plus Concentration Grant formulas; that is, each of the Targeted and EFIG formulas was originally intended to be the Title I-A formula. But in subsequent deliberations, these formulas were ultimately authorized to supplement, but not replace, the Basic and Concentration Grant formulas, and implicitly to complement each other.

The discussion below describes the characteristics of the Title I-A allocation formulas as these have been amended by NCLB. The description immediately below is similar to that in a report on all of the ESEA program allocation formulas, CRS Report RL34721, Elementary and Secondary Education Act: An Analytical Review of the Allocation Formulas, by [author name scrubbed]. The formulas are described in three different formats:

  • First, the general characteristics of all four formulas are introduced in very brief, narrative form.
  • Second, selected characteristics of the four formulas are summarized in tabular format in Table 1.
  • Third, each of the four formulas is described individually, and in greater detail, including a mathematical expression of each formula.

General Overview of the Title I-A Allocation Formulas

While numerous complications and special features are associated with the Title I-A allocation formulas, each of them has the same underlying structure. For each formula, a maximum grant is calculated by multiplying a "population factor," consisting primarily of estimated numbers of school-age children in poor families, by an "expenditure factor" based on state average per pupil expenditures for public K-12 education. In some formulas, additional factors are multiplied by the population and expenditure factors. Then these maximum grants are reduced to equal the level of available appropriations for each formula, taking into account a variety of state and LEA minimum grant or "hold harmless" provisions. Only LEAs meeting minimum numbers and/or percentages of children counted in the population factor may receive grants.

Under Title I-A, funds are allocated to LEAs via state educational agencies (SEAs). Annual appropriations legislation specifies portions of each year's appropriation to be allocated under four different formulas; once funds reach LEAs, the amounts allocated under the four formulas are combined and used jointly. Under three of the formulas—Basic, Concentration, and Targeted Grants—funds are calculated initially at the LEA level, and state total grants are the total of allocations for LEAs in the state, adjusted to apply state minimum grant provisions. Under the fourth formula, Education Finance Incentive Grants, allocations are first calculated for each state overall, with state totals subsequently suballocated by LEA using a different formula.

The discussion below describes the characteristics of the Title I-A allocation formulas as these have been amended by NCLB. These characteristics are summarized in Table 1.

Table 1. Brief Summary of ESEA Title I-A Allocation Formula Characteristics

Formula Characteristic

Basic Grants

Concentration Grants

Targeted Grants

Education Finance Incentive Grants

Population factor (also referred to as formula children)

Children aged 5-17: (a) in poor families; (b) in institutions for neglected or delinquent children or in foster homes; and (c) in families receiving Temporary Assistance for Needy Families (TANF) payments above the poverty income level for a family of four

Same as Basic Grants

Same as Basic Grants

Same as Basic Grants

Population factor eligibility threshold for LEAs

10 or more formula children and a school-age child poverty rate of more than 2%

More than 6,500 formula children or a school-age child poverty rate of more than 15%

10 or more formula children and a school-age child poverty rate of 5% or more

10 or more formula children and a school-age child poverty rate of 5% or more

Weighting of population factor

None

None

At all stages of the allocation process, poor and other children counted in the formula are assigned weights on the basis of each LEA's school-age child poverty rate and number of poor school-age children

For allocation of funds within states only, poor and other children counted in the formula are assigned weights on the basis of each LEA's school-age child poverty rate and number of poor school-age children

Expenditure factor

State average expenditures per pupil for public K-12 education, subject to a minimum of 80% and maximum of 120% of the national average, further multiplied by 0.40

Same as Basic Grants

Same as Basic Grants

Same as Basic Grants, except that the minimum is 85% and the maximum is 115% of the national average

Minimum state grant

Up to 0.25% of total state grants, subject to a series of caps

Same as Basic Grants

Up to 0.35% of total state grants, subject to a series of caps

Same as Targeted Grants

LEA hold harmless

85%-95% of the previous year grant, depending on the LEA's school-age child poverty rate, applicable only to LEAs meeting the formula's eligibility thresholds

Same as Basic Grants except that LEAs are eligible for the hold harmless for up to four years after they no longer meet the eligibility threshold

Same as Basic Grants

Same as Basic Grants

Stages in the grant calculation process

Grants are calculated at the LEA level, subject to state minimum provisions

Same as Basic Grants

Same as Basic Grants

Grants are first calculated for states overall, then state total grants are allocated to LEAs in a separate process

Additional formula factors

None

None

None

State effort and equity factors are applied in the calculation of state total grants

Source: Table prepared by CRS.

In the discussion below, each of the four ESEA Title I-A allocation formulas is discussed separately.

Detailed Description of Each of the Title I-A Allocation Formulas

Basic Grants

Basic Grants are the original Title I-A formula, authorized and implemented each year since FY1966. It is also the formula under which the largest proportion of funds is allocated (47% of FY2008 appropriations), and under which the largest proportion of LEAs participate (approximately 94% in FY2008), largely due to its low LEA eligibility threshold (see below). However, since all post-FY2001 increases in Title I-A appropriations have been provided for the Targeted and Education Finance Incentive Grant formulas (see below), the proportion of Title I-A funds allocated under the Basic Grant formula has been declining steadily since FY2001, when it was 84% of FY2001 appropriations.

Compared to some of the other Title I-A formulas, the Basic Grant formula is relatively straightforward. Grants are based on each LEA's share, compared to the national total, of a population factor multiplied by an expenditure factor, subject to available appropriations, an LEA minimum or "hold harmless," and a state minimum. These formula factors are described below, followed by a mathematical expression of the formula.

Population factor—Children aged 5-17: (a) in poor families, according to the latest available estimates for LEAs from the Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program (these constitute approximately 96% of all formula children for FY2008); (b) in institutions for neglected or delinquent children or in foster homes (approximately 3.9% of all formula children for FY2008)4; and (c) in families receiving Temporary Assistance for Needy Families (TANF) payments above the poverty income level for a family of four (less than 0.1% of all formula children for FY2008). Each element of the population factor is updated annually.

Eligibility threshold—In order for an LEA to be eligible for a Basic Grant, the number of children counted in the population factor must constitute 10 or more such children and more than 2% of the total school-age population in the LEA.

Expenditure factor—State average per pupil expenditure for public K-12 education, subject to a minimum of 80% and a maximum of 120% of the national average, further multiplied by 0.40. The expenditure factor is the same for all LEAs in the same state.

LEA minimum grant or "hold harmless" level—If sufficient funds are appropriated, each LEA is to receive a minimum of 85%, 90%, or 95% of its previous year grant, depending on the LEA's school-age child poverty rate, assuming that the LEA continues to meet the Basic Grant formula's eligibility thresholds.5

Minimum state grantEach state is to receive a minimum of up to 0.25% of total Basic Grant appropriations if total Basic Grant funding is equal to or less than the FY2001 level (as has been the case each year since FY2001 thus far), and up to 0.35% of total Basic Grant appropriations in excess of the FY2001 amount, if any. A state may not, as a result of the state minimum provision, receive more than the average of: (1) 0.25% of the total FY2001 amount for state grants plus 0.35% of any amount above the FY2001 level, and (2) 150% of the national average grant per formula child, multiplied by the number of formula children in the state.

Ratable reductionAfter maximum grants are calculated, if appropriations are insufficient to pay the maximum amounts (as has been the case every year beginning with FY1967), these amounts are reduced by the same percentage for all LEAs, subject to LEA hold harmless and state minimum provisions, until they equal the aggregate level of appropriations.

Fiscal requirements—There are three Title I-A fiscal accountability requirements, which are applicable to total LEA grants under all four formulas: (1) maintenance of effort: recipient LEAs must provide, from state and local sources, a level of funding (either aggregate or per pupil) in the preceding year that is at least 90% as high as in the second preceding year; (2) Title I-A funds must be used so as to supplement, and not supplant, state and local funds that would otherwise be available for the education of disadvantaged pupils in Title I-A participating schools; (3) comparability: services provided with state and local funds in schools participating in Title I-A must be comparable to those in non-Title I-A schools of the same LEA.6

Treatment of Puerto Rico, Outlying Areas, and the Bureau of Indian Affairs—With one possible exception,7 Puerto Rico is treated the same as a state under the Basic Grant formula. Grants to schools operated or supported by the Bureau of Indian Affairs, the Outlying Areas of Guam, American Samoa, the Virgin Islands, and the Commonwealth of the Northern Mariana Islands, as well as a competitive grant to the Outlying Areas plus certain Freely Associated States8 are provided via reservation of 1% of total Title I-A appropriations.

Further adjustments by SEAs of LEA grants as calculated by the U.S. Department of Education (ED)Among ESEA programs, a distinctive aspect of Title I-A is that after calculation of LEA grants by ED, applying the methods discussed herein, SEAs make a number of adjustments before determining the final amounts that LEAs actually receive. These adjustments are made to the total of Title I-A grants to LEAs under all four formulas combined. These adjustments include (1) reservation of 4% of state total allocations to be used for school improvement grants;9 (2) reservation of 1% of state total allocations under all formulas for ESEA Title I, Part A, plus Title I, Parts C and D (discussed below), or $400,000, whichever is greater, for state administration;10 (3) optional reservation of up to 5% of any statewide increase in total Part A grants over the previous year for academic achievement awards to participating schools that significantly reduce achievement gaps between disadvantaged and other pupil groups and/or exceed adequate yearly progress standards for two consecutive years or more; (4) adjustment of LEA grants to provide funds to eligible charter schools or to account for recent LEA boundary changes; and (5) optional use by states of alternative methods to reallocate all of the grants as calculated by ED among the state's small LEAs (defined as those serving an area with a total population of 20,000 or fewer persons).11

Basic Grant Allocation Formula

Step 1: Grant 1 = ( PF * EF ) or L_HH, whichever is greater

In Step 1, the population factor is multiplied by the expenditure factor for each eligible LEA. If this is less than the LEA's hold harmless level, the latter amount is used.

Step 2: Grant 2 = ( ( Grant 1 / ∑ Grant 1 ) * APP ) or L_HH, whichever is greater

In Step 2, the amount for each LEA in Step 1 is divided by the total of these amounts for all eligible LEAs in the nation, then multiplied by the available appropriation. Again, if this is less than the LEA's hold harmless level, the latter amount is used.

Step 3: Grant 3 = (Grant 2 * S_MIN_ADJ * L_HH_ADJ) or L_HH, whichever is greater

In Step 3, the amount for each LEA in Step 2 is adjusted through application of the state minimum grant provision and by a factor to account for the aggregate costs of raising affected LEAs to their hold harmless level, given a fixed total appropriation level. The state minimum grant adjustment is upward in the smallest states, where total grants are increased through application of the minimum, and downward in all other states, where funds are reduced in order to pay the costs of applying the minimum. The LEA hold harmless adjustment is downward for all LEAs except those at their hold harmless level. Again at this stage, if this is less than the LEA's hold harmless level, the latter amount is the LEA's grant.

Step 4: Final Grant = Grant 3 * SCH_IMP_ADJ * S_ADMIN_ADJ * AWD_ADJ * OTR_ADJ

In the final step of calculating LEA grants under all Title I-A allocation formulas, LEA grants as calculated in Step 3 are further adjusted for the school improvement and state administration reservations, possible state reservations for achievement awards, and other possible adjustments (such as for grants to charter schools) discussed above.

Where:

PF = Population factor

EF = Expenditure factor

L_HH = LEA minimum or "hold harmless" level

APP = Appropriation

S_MIN_ADJ = State minimum adjustment (proportional increase (in small states) or decrease (in other states) to apply the statewide minimum grant)

L_HH_ADJ = LEA minimum or "hold harmless" adjustment (proportional decrease, in LEAs not benefitting from the LEA "hold harmless," to apply the LEA minimum grant)

SCH_IMP_ADJ = Reservation by SEA for school improvement grants

S_ADMIN_ADJ = Reservation by SEA for state administration

AWD_ADJ = Possible reservation by SEA for achievement awards

OTR_ADJ = Other possible adjustments by the SEA

∑ = Sum (for all eligible LEAs in the nation)

Concentration Grants

The Concentration Grant formula is essentially the same as that for Basic Grants, with one major exception—it has a much higher LEA eligibility threshold. There are also differences regarding the LEA hold harmless and state minimum grant provisions. While the Title I-A statute has included Concentration Grant formulas (with varying provisions and sometimes under different names) since 1970, the current version dates from 1988 (P.L. 100-297). A relatively small (10% of FY2008 appropriations) and declining (from 14% in FY2001) proportion of Title I-A appropriations is allocated under the Concentration Grant formula. Approximately 50% of LEAs receive Concentration Grants (FY2008).

As with Basic Grants, Concentration Grants are based on each eligible LEA's share, compared to the national total, of a population factor multiplied by an expenditure factor, subject to available appropriations, an LEA minimum or "hold harmless," and a state minimum. These formula factors are described below, followed by a mathematical expression of the formula.

Population factor—Same as Basic Grants (see above).

Eligibility threshold—In order for an LEA to be eligible for a Concentration Grant, the number of children counted in the population factor must exceed either 6,500 such children or 15% of the total school-age population in the LEA.

Expenditure factor—Same as Basic Grants (see above).

LEA minimum grant or "hold harmless" level—The hold harmless rates for Concentration Grants are the same as those for Basic Grants. However, unlike Basic Grants and all of the other Title I-A formulas, the hold harmless applies to all LEAs that received grants for the previous year, even if they do not currently meet one of the Concentration Grant formula's eligibility thresholds, unless they fail to meet one of the thresholds for 4 consecutive years. That is, an LEA that is eligible to receive a Concentration Grant in one year can continue to receive a Concentration Grant for three succeeding years, even if it does not meet either of the eligibility thresholds in those succeeding years.12

Minimum state grantThe Concentration Grant state minimum is a modified version of the Basic Grant minimum. Each state is to receive a minimum of up to 0.25% of total Concentration Grant appropriations if total Concentration Grant funding is equal to or less than the FY2001 level (as has been the case each year since FY2001 thus far), and up to 0.35% of total Concentration Grant appropriations in excess of the FY2001 amount, if any. A state may not, as a result of the state minimum provision, receive more than the average of: (1) 0.25% of the total FY2001 amount for state grants plus 0.35% of the amount above this, and (2) the greater of (i) 150% of the national average grant per formula child, multiplied by the number of formula children in the state, or (ii) $340,000.

Ratable reductionSame as Basic Grants (see above).

Fiscal requirements—Same as Basic Grants (see above).

Treatment of Puerto Rico, Outlying Areas, and the Bureau of Indian Affairs—Same as Basic Grants (see above).

Further adjustments by SEAs of LEA grants as calculated by EDWith one exception, these are the same as for Basic Grants. The exception is that in states where the state total number of children counted in the population factor constituted less than 0.25% of the national total of such children as of the date of enactment of the NCLB,13 SEAs may allocate Concentration Grants among all LEAs with a number or percentage of children counted in the population factor that is greater than the state average for that year (not just LEAs meeting the 6,500 or 15% thresholds).

Concentration Grant Allocation FormulaThe mathematical expression of the Concentration Grant formula is the same as that for Basic Grants (above), with one exception. As discussed immediately above, in states where the number of children counted in the population factor constituted less than 0.25% of the national total of such children as of the date of enactment of the NCLB, the state total is to be allocated on the basis of the population factor among the LEAs that are to receive grants. These LEAs may include, at state discretion, either those LEAs in the state meeting the Concentration Grant eligibility criteria described above, or all LEAs in the state with a number or percentage of children counted in the population factor that is greater than the state average. In either case, for states where the number of children counted in the population factor constituted less than 0.25% of the national total of such children as of the date of enactment of the NCLB only (after state totals have been determined):

LEA Grant = ( PF / ∑ PF * ALL ) or L_HH, whichever is greater

Where:

PF = Population factor

ALL = State total allocation

L_HH = LEA minimum or "hold harmless" level

∑ = Sum (for all eligible LEAs in the state)

Targeted Grants

Targeted Grants were initially authorized in 1994,14 but no funds were appropriated for them until FY2002, after the formula was slightly modified by the NCLB. Beginning in FY2002, all increases in Title I-A appropriations have been allocated as either Targeted or Education Finance Incentive Grants (below). Thus, Targeted Grants constitute a substantial (21% of FY2008 appropriations) and growing portion of total Title I-A grants. They are allocated among a large majority of LEAs (87% in FY2008).

The allocation formula for Targeted Grants is essentially the same as that for Basic Grants, except for significant differences related to how children in the population factor are counted. For Targeted Grants, the poor and other children counted in the formula are assigned weights on the basis of each LEA's school-age child poverty rate and number of school-age children in poor families. As a result, LEAs receive higher grants per child counted in the formula, the higher their poverty rate and/or number. There is also a somewhat higher LEA eligibility threshold for Targeted Grants than for Basic Grants. Aside from these two differences, Targeted Grants are, like Basic Grants, based on each eligible LEA's share, compared to the national total, of a population factor multiplied by an expenditure factor, subject to available appropriations, an LEA minimum or "hold harmless," and a state minimum. These formula factors are described below, followed by a mathematical expression of the formula.

Population factor—The children counted for calculating Targeted Grants are the same as for Basic Grants (see above). However, for Targeted Grants, LEA-specific weights are applied to these child counts to produce a weighted child count that is used in the formula. Children counted in the formula are assigned weights on the basis of each LEA's number of school-age children in poor families and on the basis of each LEA's school-age child poverty rate. As a result, an LEA would receive higher grants per child counted in the formula, the higher its poverty rate or number. The weighting factors are applied in the same manner nationwide; formula children in LEAs with the highest poverty rates have a weight of up to four, and those in LEAs with the highest numbers of such children have a weight of up to three, compared to a weight of one for formula children in LEAs with the lowest poverty rate and number of such children (see Table 2, below). The higher of its two weighted child counts (on the basis of numbers and percentages) is actually used in the formula for calculating grants for each LEA.

Table 2. Weights Applied to Counts of Population Factor Children in the Calculation of ESEA Title I-A Targeted Grants

A. Weights Based on LEA Numbers of Children in the Population Factor

Population Factor Count Range

Weight Applied to Population Factor Children in This Range

0-691

1.0

692-2,262

1.5

2,263-7,851

2.0

7,852-35,514

2.5

35,515 or more

3.0

B. Weights Based on LEA Population Factor Children as a Percentage of Total School-Age Population

Population Factor Percentage Range

Weight Applied to Population Factor Children in This Range

Less than or equal to 15.58%

1.0

Above 15.58% but less than or equal to 22.11%

1.75

Above 22.11% but less than or equal to 30.16%

2.5

Above 30.16% but less than or equal to 38.24%

3.25

Above 38.24%

4.0

Source: Table prepared by CRS.

There are five ranges associated with each of the number and percentage weighting scales. These steps, or quintiles, were based on the actual distribution of Title I-A population factor children among the nation's LEAs, according to the latest available data in 2001 (at the time that the NCLB was being considered). Based upon those data, one-fifth of the national total of population factor children were in LEAs in each of the five numbers ranges and, separately, each of the five percentage ranges.

The Targeted Grant population factor weights are applied in a stepwise manner, rather than the highest relevant weight being applied to all population factor children in the LEA, and the greater of the two weighted child counts for each LEA is the number actually used to calculate the Targeted Grant. For example, assume an LEA has 2,000 population factor children, the total school-age population is 10,000, and therefore the population factor percentage is 20%. The population factor figure used to calculate Targeted Grants would be determined as follows:

Numbers Scale:

Step 1: 691 * 1.0 = 691

The first 691 population factor children are weighted at 1.0.

Step 2: (2,000 - 691) = 1,309 * 1.5 = 1,963.5

For an LEA with a total number of population factor children falling within the second step of the numbers scale, the number of population factor children above 691 (the maximum for the first step) is weighted at 1.5.

Total (Numbers Scale) = 2,654.5

The weighted population factor counts from Steps 1 and 2 are combined.

Percentage Scale:

Step 1: 15.58% * 10,000 = 1,558 * 1.0 = 1,558

A number of population factor children constituting up to 15.58% of the LEA's total school-age population is weighted at 1.0.

Step 2: (20% - 15.58%) = 4.42% * 10,000 = 442 * 1.75 = 773.5

For an LEA with a population factor percentage falling within the second step of the percentage scale, the number of population factor children above 15.58% of the LEA's total school-age population (the maximum for the first step) is weighted at 1.75.

Total (Percentage Scale) = 2,331.5

The weighted population factor counts from Steps 1 and 2 are combined.

Since the numbers scale weighted count of 2,654.5 exceeds the percentage scale weighted count of 2,331.5, the numbers scale count would be used as the population factor for this LEA in the calculation of Targeted Grants.

Eligibility threshold—In order for an LEA to be eligible for a Targeted Grant, the number of children counted in the population factor (with no weights applied) must constitute 10 or more such children and 5% or more of the total school-age population.

Expenditure factor—Same as Basic Grants (see above).

LEA minimum grant or "hold harmless" level—Same as Basic Grants (see above).

Minimum state grantEach state is to receive a minimum of up to 0.35% of all Targeted Grant appropriations. A state may not, as a result of the state minimum provision, receive more than the average of: (1) 0.35% of total state grants, and (2) 150% of the national average grant per formula child, multiplied by the number of formula children in the state. (In the latter calculation, population factor child counts are not weighted.)

Ratable reductionSame as Basic Grants (see above).

Fiscal requirements—Same as Basic Grants (see above).

Treatment of Puerto Rico, Outlying Areas, and the Bureau of Indian Affairs—Same as Basic Grants (see above), with the additional provision that for Puerto Rico (only), a cap of 1.82 is placed on the aggregate weight applied to the population factor under the Targeted Grant formula.15

Further adjustments by SEAs of LEA grants as calculated by ED: Same as Basic Grants (see above).

Targeted Grant Allocation FormulaSame as Basic Grants (see above), except that the population factor (PF) would be the weighted child count, as described above.

Education Finance Incentive Grants (EFIG)

The EFIG formula is in several ways significantly different from the other Title I-A allocation formulas. As with Targeted Grants, EFIG Grants were initially authorized in 1994,16 but no funds were appropriated for them until FY2002, after the formula was (in the case of EFIG) considerably modified by the NCLB. Beginning in FY2002, all increases in Title I-A appropriations have been allocated as either EFIG or Targeted Grants. Thus, as with Targeted Grants, EFIG Grants constitute a substantial (21% of FY2008 appropriations) and growing portion of total Title I-A grants. They are allocated among a large majority of LEAs (87% in FY2008).

The distinctive elements of the EFIG formula begin with the fact that the first stage in the process of calculating grants is based on data for states as a whole, not LEAs. LEA grants are determined in a separate, later stage of the allocation process.

A second major difference is that the EFIG formula includes not only a population factor and an expenditure factor, but also two unique factors. These are an effort factor, based on average per pupil expenditure for public K-12 education compared to personal income per capita for each state compared to the nation as a whole, and an equity factor, based on variations in average per pupil expenditure among the LEAs in each state.

A third distinctive feature of the EFIG formula is that while population factor child counts are not weighted when calculating state total grants, they are weighted in the separate process of suballocating state total grants among LEAs. This intra-state allocation process is based on the same number and percentage scales as used for Targeted Grants, although the weights attached to each point on those scales varies among states, based on the state's equity factor. A final difference between the EFIG Grant and other Title I-A formulas is that the expenditure factor is modified through application of slightly more narrow floor and ceiling constraints for EFIG Grants.

Thus, state total EFIG Grants are based on each state's share, compared to the national total, of a population factor multiplied by an expenditure factor, an effort factor, and an equity factor, adjusted by a state minimum. Then, each LEA's share of the state total EFIG Grant is based on a weighted population factor count for the LEA, compared to the total for all LEAs in the state, adjusted by an LEA hold harmless provision. These formula factors are described below, followed by a mathematical expression of the formula.

Population factor—In the first-stage calculation of state total EFIG Grants, this factor is the same as for Basic Grants (see above). In the second-stage suballocation of state total grants among LEAs, as under all stages of the allocation process for Targeted Grants, weights are applied to child counts before they are actually used in the formula. This process is the same as for Targeted Grants with respect to the number and percentage scales used, and use of the greater of the two weighted child counts to calculate LEA grants. However, for EFIG Grants only, the weights attached to each point on the number and percentage scales differs, depending on the state's equity factor (described below). This variation is illustrated in Table 3, below.

Table 3. Weights Applied to Counts of Population Factor Children in the Calculation of LEA Grants Under the ESEA Title I-A Education Finance Incentive Grant Formula

A. Weights Based on LEA Numbers of Children in the Population Factor

Population Factor Count Range

Weight Applied to Population Factor Children in This Range

State Equity Factor Below 0.10

State Equity Factor Above 0.10 But Below 0.20

State Equity Factor of 0.20 or Above

0-691

1.0

1.0

1.0

692-2,262

1.5

1.5

2.0

2,263-7,851

2.0

2.25

3.0

7,852-35,514

2.5

3.375

4.5

35,515 or more

3.0

4.5

6.0

B. Weights Based on LEA Population Factor Children as a Percentage of Total School-Age Population

Population Factor Percentage Range

Weight Applied to Population Factor Children in This Range

State Equity Factor Below 0.10

State Equity Factor Above 0.10 But Below 0.20

State Equity Factor of 0.20 or Above

Less than or equal to 15.58%

1.0

1.0

1.0

Above 15.58% but less than or equal to 22.11%

1.75

1.5

2.0

Above 22.11% but less than or equal to 30.16%

2.5

3.0

4.0

Above 30.16% but less than or equal to 38.24%

3.25

4.5

6.0

Above 38.24%

4.0

6.0

8.0

Source: Table prepared by CRS.

As indicated in Table 3, the weights rise more rapidly as the numbers and percentages of population factor children increase in states with higher equity factors. For states with an equity factor below 0.10, the weights are the same as for Targeted Grants. For states with equity factors between 0.10 and 0.20, or above 0.20, the maximum weights are 50% higher, and twice as high, respectively, as for Targeted Grants. As is discussed below, states with higher equity factors have relatively high degrees of variation in average per pupil expenditure among the state's LEAs.

Factors Not Found in Other ESEA Program Formulas—As noted above, the EFIG formula has two additional factors not found in any other ESEA program allocation formula.

Effort Factor—The effort factor is based on a comparison of state average per pupil expenditure (APPE) for public elementary and secondary education with state personal income per capita (PCI). More specifically, it is the ratio of APPE to PCI for each state divided by the ratio of APPE to PCI for the nation. The resulting index number is greater than 1.0 for states where the ratio of expenditures per pupil for public elementary and secondary education to personal income per capita is greater than average for the nation as a whole, and below 1.0 for states where the ratio is less than average for the nation as a whole. Narrow bounds of 0.95 and 1.05 are placed on the resulting multiplier, so that its influence on state grants is rather limited and its importance is largely symbolic.

Equity Factor—The equity factor is based upon a measure of the average disparity in average per pupil expenditure among the LEAs of a state called the coefficient of variation (CV). The CV is expressed as a decimal proportion of the state average per pupil expenditure. In the CV calculations for this formula, an extra weight (1.4 vs. 1.0) is applied to estimated counts of children from poor families. The effect is that grants would be maximized for a state where expenditures per pupil from a poor family are 40% higher than expenditures per pupil from a non-poor family.17 Typical state equity factors range from 0.0 (for the single-LEA jurisdictions of Hawaii, Puerto Rico, and the District of Columbia, where by definition there is no variation among LEAs), to approximately 0.25 for a state with high levels of variation in expenditures per pupil among its LEAs; the equity factors for most states fall into the 0.10 - 0.20 range.18 In calculating grants, the equity factor is subtracted from 1.30 to determine a multiplier to be used in calculating state grants. As a result, the lower a state's expenditure disparities among its LEAs, the lower is its CV and equity factor, the higher is its multiplier and its grant under the EFIG formula. Conversely, the greater a state's expenditure disparities among its LEAs, the higher is its CV and equity factor, and the lower is its multiplier and its grant under the EFIG formula.

Eligibility threshold—Same as Targeted Grants (see above).

Expenditure factor—State average per pupil expenditure for public K-12 education, subject to a minimum of 85% (not 80%, as in the other Title I-A formulas) and a maximum of 115% (not 120%, as in the other Title I-A formulas) of the national average, further multiplied by 0.40. The expenditure factor is the same for all LEAs in each state.

LEA minimum grant or "hold harmless" level—Same as Basic Grants (see above), with one exception. The hold harmless is not taken into consideration in the initial calculation of state total grants. Therefore, it is possible (and has occurred in a small number of instances) that state total grants are insufficient to fully pay hold harmless amounts to all LEAs in the state. In that case, each LEA gets a proportional share of its hold harmless amount.

Minimum state grantSame as Targeted Grants (see above).

Ratable reductionSame as Basic Grants (see above).

Fiscal requirements—Same as Basic Grants (see above).

Treatment of Puerto Rico, Outlying Areas, and the Bureau of Indian Affairs—Same as Basic Grants (see above).

Further adjustments by SEAs of LEA grants as calculated by EDSame as Basic Grants (see above).

Education Finance Incentive Grant Allocation Formula

Stage 1: Calculation of State Total EFIG Allocations

Step 1: State Grant 1 = PF * EF * EFF * (1.30 - EQ)

In Step 1, the population factor is multiplied by the expenditure factor, the effort factor, and 1.30 minus the equity factor for each state.

Step 2: State Grant 2 = ( ( State Grant 1 / ∑ State Grant 1 ) * APP * S_MIN_ADJ ) or S_MIN, if greater

In Step 2, the amount for each state in Step 1 is divided by the total of these amounts for all eligible states in the nation, then multiplied by the available appropriation, adjusted through application of the state minimum grant provision. The state minimum grant adjustment is upward in the smallest states, where total grants are increased through application of the minimum, and downward in all other states, where funds are reduced in order to pay the costs of applying the minimum.

Stage 2: Calculation of LEA EFIG Allocations

Step 1: LEA Grant 1 = ( ( PF / ∑ PF ) * S_ALL ) or L_HH, whichever is greater

In Step 1, the population factor for each eligible LEA is divided by the total population factor for all eligible LEAs in the state. If this is less than the LEA's hold harmless level, the latter amount is used.

Step 2: LEA Grant 2 = ( LEA Grant 1 * L_HH_ADJ ) or L_HH, whichever is greater

In Step 2, the amount for each LEA in Step 1 is adjusted through application of a factor to account for the aggregate costs of raising affected LEAs in the state to their hold harmless level, given a fixed total state allocation level. The LEA hold harmless adjustment is downward for all LEAs except those at the hold harmless level.

Step 3: Final LEA Grant = LEA Grant 2 * SCH_IMP_ADJ * S_ADMIN_ADJ * AWD_ADJ * OTR_ADJ

In the final step of calculating LEA grants under all Title I-A allocation formulas, LEA grants as calculated in Step 2 are further adjusted for the school improvement and state administration reservations, possible state reservations for achievement awards, and other possible adjustments (such as for grants to charter schools) discussed above.

Where:

PF = Population factor

EF = Expenditure factor

EFF = Effort factor

EQ = Equity factor

APP = Appropriation

S_MIN_ADJ = State minimum adjustment (proportional increase (in small states) or decrease (in other states) to apply the statewide minimum grant)

S_MIN = State minimum

S_ALL = State total allocation

L_HH = LEA minimum or "hold harmless" level

L_HH_ADJ = LEA minimum or "hold harmless" adjustment (proportional decrease, in LEAs not benefitting from the LEA "hold harmless," to apply the LEA minimum grant)

SCH_IMP_ADJ = Reservation by SEA for school improvement grants

S_ADMIN_ADJ = Reservation by SEA for state administration

AWD_ADJ = Possible reservation by SEA for achievement awards

OTR_ADJ = Other possible adjustments by the SEA

∑ = Sum (for all states in the nation in Stage 1, and for all eligible LEAs in the state in Stage 2)

ESEA Title I-A School Improvement Grants

Under ESEA Title I-A, two different mechanisms are authorized for the generation of funds for School Improvement activities. Whatever the source, these funds are to be targeted on schools that are identified as being in need of improvement, corrective action, or restructuring because they have failed to make AYP for two consecutive years or more.19 First, states are to reserve 4% of their total Title I-A LEA grants, under the four formulas described above, for School Improvement activities.20

Second, the ESEA authorizes a separate appropriation for state School Improvement Grants. These funds are allocated to states in proportion to state total grants under ESEA Title I, Parts A, C (State Agency Migrant Program—see below), and D (State Agency Neglected, Delinquent, or At-Risk Program—see below). At least 95% of each state's funds from either source (the reservation or the separate appropriation) is to be allocated to LEAs for schools identified as being in need of improvement, corrective action, or restructuring. The funds are allocated at state discretion—there is no statutory intrastate allocation formula for School Improvement funds, beyond the general direction that they are to be directed to LEAs with schools identified as being in need of improvement, corrective action, or restructuring.

Title I grant factor—Funds are allocated to states in proportion to total grants under Title I, Parts A, C, and D.

School Improvement Grant Allocation Formula

State Grant = [ ( T1A + T1C + T1D ) / ∑ ( T1A + T1C + T1D ) ] * APP

Each state (including Outlying Areas and the Bureau of Indian Affairs) receives a School Improvement Grant equal to its proportional share of total grants under ESEA Title I, Parts A, C, and D.

Where:

T1A = State total grant under ESEA Title I, Part A

T1C = State total grant under ESEA Title I, Part C

T1D = State total grant under ESEA Title I, Part D

APP = Appropriation (separate) for School Improvement Grants

∑ = Sum (for all states)

Suballocation of LEA Grants to Schools

Unlike other federal elementary and secondary education programs, most Title I-A funds are allocated to individual schools, although LEAs retain substantial discretion to control the use of a significant share of Title I-A grants at a central district level.21 While there are several rules related to school selection, LEAs must generally rank their public schools by their percentage of pupils from low-income families, and serve them in rank order. All participating schools must generally have a percentage or number of children from low-income families that is higher than the LEA's average, or 35%, whichever of these two figures is lower,22 although LEAs have the option of setting school eligibility thresholds higher than the minimum in order to concentrate available funds on a smaller number of schools.23

Once schools are selected, Title I-A funds are allocated among them (and reserved for services to private school pupils) in proportion to their number of pupils from low-income families. In a large majority of cases, the data used to determine which pupils are from low-income families for the distribution of funds to schools are not the same as those used to identify school-age children in poor families for purposes of calculating allocations to states and LEAs. This is because data are not typically available on the number of school-age children enrolled in a school, or living in a residential school attendance zone, with income below the standard federal poverty threshold. Such "population in poverty" estimates, as used in the standard formulas for allocation of funds to states and LEAs (discussed above), are usually available only for LEAs, counties, and states.

Thus, LEAs must use available proxies for low-income status. The Title I-A statute allows LEAs to use the following low-income measures: (a) eligibility for free and reduced-price school lunches; (b) eligibility for Temporary Assistance to Needy Families (TANF); or (c) eligibility for Medicaid.24 At the level of individual schools, the most commonly used criterion for determining whether pupils are from low-income families is eligibility for free and reduced-price school lunches. According to the most recent relevant data, approximately 90% of LEAs receiving Title I-A funds use free/reduced-price school lunch data—sometimes alone, sometimes in combination with other authorized criteria—to select Title I-A schools and allocate funds among them.25 The income eligibility thresholds for free and reduced-price lunches are higher than the poverty levels used in the allocation formulas to states and LEAs: 130% of poverty for free lunches, 185% for reduced-price lunches.

After data have been compiled on the percentage or number of pupils from low-income families who are either enrolled in a LEA's public schools or residing in the attendance areas served by such schools, available Title I-A funds are allocated among these schools in rank order, beginning with the highest poverty schools, until no further funds are available. LEAs may choose to consider only schools of selected grade levels (e.g., only elementary schools) in determining eligibility for grants, as long as all schools with 75% or more of pupils from low-income families receive grants.

Funds are allocated among schools in proportion to their number of pupils from low-income families, although grants to eligible schools per pupil from a low-income family need not be equal for all schools. LEAs may choose to provide higher grants per child from a low-income family to schools with higher percentages of such pupils (e.g., higher grants per child to a school where 70% of pupils are from low-income families than to a school where 40% of pupils are from low-income families). If a LEA provides Title I-A funds to schools with low-income pupil percentages below 35%, then it must provide a minimum amount of funds per child from a low-income family—equal to at least 125% of the LEA's Title I-A grant per child from a low-income family—to each participating school.

In the 2004-2005 school year, an estimated 56% of all public schools in the nation received Title I-A grants. This included 82% of public schools in the highest quartile with respect to their percentage of pupils in low-income families, declining to 37% of schools in the lowest quartile. Elementary schools (70%) are much more likely than secondary schools (39%) to receive Title I-A grants. 26

Similarly, the share of funds to be used by each recipient LEA to serve educationally disadvantaged pupils attending private schools is determined on the basis of the number of children from low-income families living in the residential areas served by public schools selected to receive Title I-A grants. LEAs may use for this purpose either the same source of data used to select and allocate funds among public schools (i.e., usually free/reduced-price school lunch data) or one of a specified range of alternatives.27

Recent Funding Trends for Title I-A

Information on the Title I-A appropriations for FY2007-2009, plus the Administration budget request for FY2010 may be found in Table 4, below. The table is preceded by brief descriptions of appropriations for FY2008 and FY2009 plus the FY2010 request.

FY2008

The Administration's budget for FY2008 requested $13,909,900,000 for Title I-A LEA grants, an increase of $1,071,775,000 (8.3%) over the FY2007 appropriation, plus a separate appropriation of $500 million for school improvement grants, a fourfold increase over FY2007. All of the increase in LEA grants would have been devoted to Targeted Grants (along with a $62.5 million reduction in EFIG grants). P.L. 110-161, the Consolidated Appropriations Act for FY2008, provided a total of $13,898,875,000 for Title I-A grants to LEAs, plus a separate appropriation of $491,265,000 for school improvement grants. As in the recent past, the funding level for Concentration Grants was the same for FY2008 as for FY2007, and equal amounts were appropriated for the Targeted and EFIG formulas ($2,967,949,000 for each). All of an across-the-board reduction for Title I-A was applied to Basic Grants, reducing funds under that formula to $6,597,946,000.

FY2009

For FY2009, regular appropriations are provided under P.L. 111-8, an omnibus appropriations act. Under P.L. 111-8, total regular FY2009 appropriations for grants to LEAs are $14,492,401,000. The FY2009 funding for Basic and Concentration Grants is the same as for FY2008, while Targeted and EFIG grants each receive $3,264,712,000. In addition, $545,633,000 is separately appropriated for School Improvement Grants.

In addition to regular FY2009 appropriations legislation for ED, the American Recovery and Reinvestment Act of 2009 (ARRA), P.L. 111-5, provides a total of $13 billion in additional FY2009 appropriations for Title I-A—$10 billion for grants to LEAs and $3 billion for School Improvement Grants. These funds are in addition to amounts provided in regular FY2009 appropriations legislation. Half of the additional grants to LEAs will be allocated under the Targeted Grant formula and half under the Education Finance Incentive Grant formula.

FY2010

On May 7, 2009, the Obama Administration released its detailed budget recommendations for FY2010. For ESEA Title I-A, the Administration requested a total of $12,992,401,000 for grants to LEAs, a reduction of $1,500,000,000 (10.4%) from the FY2009 amount. All of this reduction would be applied to Basic Grants, which would decline from $6,597,946,000 for FY2009 to $5,097,946,000 for FY2010. At the same time, for School Improvement Grants under Title I-A, the Administration requested a $1,000,000,000 increase, from $545,633,000 for FY2009 to $1,545,633,000 for FY2010.

On July 24, 2009, the House passed H.R. 3293, to provide FY2010 appropriations for the Departments of Labor, Health and Human Services, and Education and Related Agencies. As passed by the House, H.R. 3293 would provide $14,492,401,000 for Title I-A grants to LEAs, $1,500,000,000 more than the Administration request for Basic Grants but the same as requested for all other formulas, and $545,633,000, the same as the Administration request, for School Improvement Grants. The Senate Committee on Appropriations reported its version of H.R. 3293 on July 30, 2009. As reported by the Senate Committee on Appropriations, H.R. 3293 would provide $13,792,401,000 for Title I-A grants to LEAs, $800,000,000 more than the Administration request for Basic Grants but the same as requested for all other formulas, and $545,633,000, the same as the Administration request for School Improvement Grants. Table 4, below, shows total Title I-A appropriations for FY2009-FY2010.

Table 4. FY2009-FY2010 Appropriations for ESEA Title I, Part A

Formula

FY2009 Regular Appropriations

FY2009 ARRA Additional Appropriations

FY2010 Administration Budget Request

FY2010 Under H.R. 3293 as Passed by House

FY2010 Under H.R. 3293 as Reported by Senate Committee

Basic Grantsa

$6,597,946,000

$5,097,946,000

$6,597,946,000

$5,897,946,000

Concentration Grants

1,365,031,000

1,365,031,000

1,365,031,000

1,365,031,000

Targeted Grants

3,264,712,000

$5,000,000,000

3,264,712,000

3,264,712,000

3,264,712,000

Education Finance Incentive Grants

3,264,712,000

5,000,000,000

3,264,712,000

3,264,712,000

3,264,712,000

Total ESEA Title I-A Grants to LEAs

14,492,401,000

10,000,000,000

12,992,401,000

14,492,401,000

13,792,401,000

School Improvement Grants
(separate authorization)

545,633,000

3,000,000,000

1,545,633,000

545,633,000

545,633,000

Source: Table prepared by CRS.

a. The amounts shown above for Basic Grants include approximately $3.5 million each year for census updates.

FY2008 Allocation Patterns

FY2008 (school year 2008-2009) grants are the latest available actual allocations under Title I-A. Overall, the FY2008 funding level for Title I-A is 8.3% above the FY2007 level. This contrasts with the period of FY2005-2007, when aggregate funding for Title I-A LEA grants was essentially constant.

Due largely to the comparatively large increase in Title I-A funding for FY2008, all states except one (Wisconsin, where grants declined by 1.3%) received higher total grants for FY2008 than for FY2007. At the LEA level, approximately 61% of all LEAs nationwide that received Title I-A grants for both FY2007 and FY2008 received larger grants for FY2008, while 39% received lower grants for FY2007. LEAs receiving lower Title I-A grants for FY2008 than in FY2007 have been experiencing reductions in their estimated number of school-age children in poor families; these include LEAs of all sizes and degrees of poverty concentration, in contrast to the FY2002-FY2006 period when a large majority of large or high-poverty LEAs experienced grant increases, while a majority of LEAs overall were losing funds.

Tables 5-8 provide a series of analyses of the distribution of Title I-A funds among the states, as well as different types or categories of LEAs. Each table is preceded by a brief description of the information provided in the table. Subsequently, these tables will be referred to in the course of a series of analyses of possible Title I-A formula reauthorization issues.

Table 5, below, shows state average FY2008 Title I-A grants per child counted in the Title I-A allocation formulas. Separate amounts are provided for each of the four formulas, plus a Title I-A total. The substantial variation in these amounts reflect a combination of factors, many of which are analyzed in detail in the final section of this report. These factors include:

  • State minimum grant provisions—Under all formulas, average grants per formula child are much higher for the smallest (in population) states.
  • Expenditure factor—Under all formulas, but especially with respect to Basic Grants, average grants per formula child are much higher for states with high expenditure factors (e.g., Connecticut, Massachusetts, New Jersey, or New York) than for states with low factors (e.g., Alabama, Arkansas, Mississippi, or Utah).
  • Targeting on LEAs with large numbers of school-age children in poor families—With the exception of the smallest states (where average grants per formula child are high regardless of poverty rates), average grants per child under the Concentration, Targeted, and Education Finance Incentive Grant (EFIG) formulas are higher for several states containing LEAs with very high numbers of school-age children in poor families (e.g., Illinois, Michigan, New York, or Pennsylvania) than for other states. In contrast, states with large numbers of LEAs with high poverty rates (e.g., Alabama, Arkansas, Mississippi, New Mexico) are below the national average, primarily due to low expenditure factors for these states.
  • Equity factor—Several states with especially favorable equity factors (e.g., the District of Columbia, Hawaii, West Virginia, and Wisconsin) receive relatively high average grants per formula child under the EFIG formula.

However, many key formula factors operate in opposite directions, largely cancelling each other out. For example, California has LEAs with very large numbers of school-age children in poor families, but also a relatively low expenditure factor, resulting in an average Targeted Grant per formula child that is approximately the same as the national average.

Table 5. ESEA Title I-A Grant Amount Per Child Counted in the Allocation
Formulas, FY2008

State

Title I-A Grant Amount Per Child Counted in the Allocation Formulas, FY2008

Basic Grant

Concentration Grant

Targeted Grant

Education Finance Incentive Grant

Total Title I-A Grant

United States

$684

$142

$308

$308

$1,441

 

 

 

 

 

 

Alabama

$566

$134

$241

$269

$1,211

Alaska

$979

$132

$518

$515

$2,143

Arizona

$624

$135

$264

$264

$1,287

Arkansas

$610

$143

$242

$307

$1,301

California

$641

$135

$303

$269

$1,347

Colorado

$624

$112

$259

$281

$1,275

Connecticut

$950

$142

$290

$371

$1,754

Delaware

$1,002

$123

$521

$521

$2,167

District of Columbia

$861

$212

$504

$438

$2,015

Florida

$576

$139

$336

$273

$1,324

Georgia

$658

$152

$295

$319

$1,424

Hawaii

$764

$178

$412

$421

$1,774

Idaho

$550

$90

$252

$252

$1,144

Illinois

$847

$165

$360

$321

$1,692

Indiana

$717

$120

$256

$330

$1,423

Iowa

$616

$78

$189

$287

$1,170

Kansas

$710

$123

$242

$353

$1,429

Kentucky

$642

$150

$272

$318

$1,382

Louisiana

$616

$153

$303

$244

$1,315

Maine

$822

$138

$337

$393

$1,690

Maryland

$872

$180

$419

$369

$1,840

Massachusetts

$869

$142

$319

$371

$1,702

Michigan

$773

$146

$342

$367

$1,629

Minnesota

$713

$80

$238

$305

$1,335

Mississippi

$584

$137

$254

$268

$1,243

Missouri

$628

$124

$242

$273

$1,266

Montana

$673

$148

$368

$368

$1,557

Nebraska

$695

$98

$267

$354

$1,414

Nevada

$548

$131

$331

$267

$1,277

New Hampshire

$939

$99

$481

$503

$2,022

New Jersey

$875

$138

$293

$375

$1,682

New Mexico

$605

$150

$276

$297

$1,328

New York

$917

$202

$481

$376

$1,975

North Carolina

$560

$133

$257

$271

$1,221

North Dakota

$1,198

$175

$631

$633

$2,636

Ohio

$740

$138

$294

$345

$1,518

Oklahoma

$559

$120

$225

$264

$1,169

Oregon

$650

$132

$246

$325

$1,352

Pennsylvania

$844

$155

$361

$372

$1,732

Puerto Rico

$548

$141

$269

$290

$1,248

Rhode Island

$908

$158

$354

$399

$1,818

South Carolina

$614

$146

$261

$303

$1,324

South Dakota

$807

$151

$467

$465

$1,888

Tennessee

$549

$129

$250

$266

$1,193

Texas

$593

$136

$290

$269

$1,287

Utah

$570

$72

$224

$261

$1,126

Vermont

$1,251

$204

$657

$665

$2,778

Virginia

$722

$127

$284

$300

$1,434

Washington

$631

$98

$225

$284

$1,238

West Virginia

$708

$168

$276

$361

$1,512

Wisconsin

$849

$125

$308

$379

$1,661

Wyoming

$1,345

$180

$716

$713

$2,954

Source: Table prepared by CRS.

Table 6, below, provides each state's percentage share of the funds allocated under each of the Title I-A formulas, as well as total Title I-A grants, for FY2008. The distinctive feature here is that while these shares are similar under all formulas for most states, some states receive substantially higher or lower shares under some formulas than under the other formulas. Focusing on those states where the highest share of grants under any formula is one-third or more above its lowest share, there are 19 states where the share of funds received under one of the four formulas is substantially different from the others. These include:

  • Seven small states where the share under the Targeted and/or EFIG formulas is much greater than under Basic or Concentration Grants, due to the higher state minimum under the former formulas (Alaska, Delaware, New Hampshire, North Dakota, South Dakota, Vermont, and Wyoming);
  • Four states with relatively low poverty rates where the share of Basic Grants is substantially higher than under any other formula (Connecticut, Minnesota, New Jersey, and Wisconsin);
  • Two states with many LEAs with relatively high poverty rates where shares are substantially higher under Concentration Grants than the other formulas (Louisiana and West Virginia);
  • One state where the share of Targeted Grants is substantially higher than under the other formulas, due to the impact of one very large LEA (Nevada); and
  • Five states where the share of EFIG Grants is substantially higher than under the other formulas, due primarily to relatively favorable equity factors (Iowa, Kansas, Nebraska, Utah, and Washington).

Table 6. State Shares of Funds Allocated Under Each of the ESEA
Title I-A Formulas, FY2008

ESEA Title I-A Grants to LEAs: State Shares of Grants by Formula

 

Basic Grants

Concentration Grants

Targeted Grants

Education Finance Incentive Grants

Total LEA Grants

United States

100.00%

100.00%

100.00%

100.00%

100.00%

 

 

 

 

 

 

Alabama

1.54%

1.77%

1.46%

1.63%

1.57%

Alaska

0.27%

0.18%

0.32%

0.32%

0.28%

Arizona

2.04%

2.14%

1.92%

1.92%

2.00%

Arkansas

1.04%

1.17%

0.91%

1.16%

1.05%

California

12.37%

12.62%

12.98%

11.54%

12.35%

Colorado

1.01%

0.88%

0.94%

1.01%

0.98%

Connecticut

0.96%

0.69%

0.65%

0.83%

0.84%

Delaware

0.27%

0.16%

0.31%

0.31%

0.28%

District of Columbia

0.31%

0.37%

0.40%

0.35%

0.34%

Florida

4.37%

5.10%

5.66%

4.61%

4.77%

Georgia

3.16%

3.52%

3.14%

3.41%

3.24%

Hawaii

0.29%

0.33%

0.35%

0.36%

0.32%

Idaho

0.34%

0.27%

0.35%

0.35%

0.34%

Illinois

4.55%

4.29%

4.30%

3.83%

4.32%

Indiana

1.91%

1.54%

1.51%

1.95%

1.80%

Iowa

0.59%

0.36%

0.40%

0.61%

0.53%

Kansas

0.73%

0.61%

0.55%

0.80%

0.69%

Kentucky

1.48%

1.67%

1.40%

1.63%

1.52%

Louisiana

2.11%

2.53%

2.31%

1.86%

2.14%

Maine

0.38%

0.31%

0.35%

0.41%

0.37%

Maryland

1.40%

1.39%

1.49%

1.31%

1.40%

Massachusetts

1.83%

1.45%

1.49%

1.74%

1.70%

Michigan

3.83%

3.50%

3.77%

4.04%

3.83%

Minnesota

1.04%

0.56%

0.77%

0.99%

0.92%

Mississippi

1.35%

1.53%

1.30%

1.37%

1.36%

Missouri

1.71%

1.63%

1.46%

1.65%

1.64%

Montana

0.29%

0.31%

0.35%

0.35%

0.32%

Nebraska

0.45%

0.31%

0.39%

0.51%

0.44%

Nevada

0.53%

0.61%

0.71%

0.58%

0.59%

New Hampshire

0.27%

0.14%

0.31%

0.32%

0.28%

New Jersey

2.29%

1.74%

1.70%

2.18%

2.09%

New Mexico

0.79%

0.95%

0.80%

0.86%

0.82%

New York

8.72%

9.27%

10.16%

7.96%

8.92%

North Carolina

2.52%

2.89%

2.57%

2.71%

2.61%

North Dakota

0.23%

0.17%

0.27%

0.28%

0.25%

Ohio

3.82%

3.44%

3.38%

3.96%

3.72%

Oklahoma

1.09%

1.13%

0.97%

1.14%

1.08%

Oregon

1.03%

1.01%

0.87%

1.14%

1.02%

Pennsylvania

4.22%

3.74%

4.01%

4.14%

4.11%

Puerto Rico

3.43%

4.28%

3.75%

4.04%

3.71%

Rhode Island

0.41%

0.34%

0.35%

0.40%

0.39%

South Carolina

1.46%

1.68%

1.38%

1.60%

1.49%

South Dakota

0.27%

0.25%

0.35%

0.35%

0.30%

Tennessee

1.69%

1.91%

1.70%

1.81%

1.74%

Texas

9.17%

10.14%

9.97%

9.23%

9.45%

Utah

0.47%

0.28%

0.41%

0.47%

0.44%

Vermont

0.23%

0.18%

0.26%

0.27%

0.24%

Virginia

1.74%

1.49%

1.52%

1.61%

1.64%

Washington

1.50%

1.13%

1.18%

1.50%

1.39%

West Virginia

0.71%

0.82%

0.62%

0.81%

0.72%

Wisconsin

1.56%

1.11%

1.26%

1.54%

1.45%

Wyoming

0.22%

0.14%

0.26%

0.26%

0.23%

Source: Table prepared by CRS.

Table 7, below, provides average Title I-A grants per formula child, by formula and total, for LEAs in five illustrative categories. It must be emphasized that these are limited numbers of LEAs in each category, selected to concretely illustrate certain patterns of Title I-A allocations. They are not necessarily representative of all LEAs in each category. (The following Table 8 provides summary data for all LEAs in each of 12 standard categories of localities.)

The illustrative categories for Table 7 are:

  • LEAs with very large numbers of formula children,
  • LEAs with very high percentages of formula children,
  • LEAs in minimum grant states,
  • LEAs with relatively large numbers, but relatively low percentages, of formula children, and
  • LEAs with low numbers and percentages of formula children.

Distinctive allocation patterns illustrated in Table 7, all of which will be discussed further in the issue analyses at the end of this report, include the following:

  • Grants per formula child are much higher than average under the Targeted and EFIG grant formulas for the selected LEAs with very large numbers of formula children;
  • The selected LEAs with very high percentages of formula children receive higher than average grants per formula child under the Targeted and EFIG formulas, but much lower than the LEAs with very large numbers of formula children, partially due to their treatment under these formulas but primarily because they are located in states with low expenditure factors;
  • The selected LEAs in minimum grant states receive higher grants per formula child than LEAs in any other category under all formulas except possibly Concentration Grants;28
  • The selected LEAs with relatively large numbers, but relatively low percentages, of formula children receive Concentration, Targeted, and EFIG grants per formula child that are above the national average, in spite of their low formula child percentages; and
  • The selected LEAs with low numbers and percentages of formula children receive grants per formula child that are well below average under all formulas except Basic Grants.

Table 7. ESEA Title I-A Grant Amount Per Child Counted in the Allocation Formulas for LEAs in Selected Categories, FY2008

State

LEA Code

LEA Name

Number of Formula Children, FY2008

Formula Child %, FY2008

Grant Amount Per Formula Child, FY2008

Basic Grants

Concentration Grants

Targeted Grants

Education Finance Incentive Grants

Category 1: LEAs with very large numbers of formula children

CA

622710

Los Angeles

245,840

28.8%

$641

$160

$463

$495

GA

1300120

Atlanta

24,617

32.0%

$761

$192

$391

$481

IL

1709930

Chicago

138,144

26.6%

$910

$230

$551

$581

NY

3682047

Kings County (Brooklyn)

137,262

29.5%

$902

$228

$628

$533

PA

4218990

Philadelphia

89,179

33.6%

$814

$210

$603

$728

 

 

Average for Category 1 LEAs Listed Above

 

 

$806

$204

$527

$564

Category 2: LEAs with Very High Percentages of Formula Children

AZ

401940

Chinle

3,461

61.7%

$548

$141

$401

$474

KY

2105970

Wolfe County

626

49.7%

$662

$161

$405

$474

MS

2801980

Holmes County

2,432

61.7%

$597

$141

$401

$502

TX

4823100

Hidalgo

1,574

63.0%

$559

$144

$413

$383

TX

4828290

Los Fresnos

4,494

62.1%

$559

$144

$411

$380

 

 

Average for Category 2 LEAs Listed Above

 

 

$585

$147

$406

$443

Category 3: LEAs in Minimum Grant States

DE

1000200

Christina

3,376

11.3%

$1,002

$0

$635

$660

NH

3304980

Nashua

1,424

9.1%

$929

$0

$616

$643

VT

5007050

Rutland City

487

19.0%

$1,273

$398

$717

$700

WY

5601980

Laramie County 01

1,465

10.4%

$1,341

$0

$815

$808

ND

3819260

Warwick 29

127

42.2%

$1,205

$483

$1,136

$1,343

 

 

Average for Category 3 LEAs Listed Above

 

 

$1,150

$176

$784

$831

Category 4: LEAs with Relatively Large Numbers, but Relatively Low Percentages, of Formula Children

FL

1200180

Broward County

42,837

13.9%

$576

$149

$383

$311

CO

804800

Jefferson County R-1

7,540

8.1%

$615

$159

$300

$322

GA

1301290

Cobb County

9,829

8.7%

$638

$164

$335

$372

MD

2400480

Montgomery County

9,244

5.6%

$1,085

$274

$444

$390

VA

5101260

Fairfax County

10,034

5.5%

$712

$184

$377

$436

 

 

Average for Category 4 LEAs Listed Above

 

 

$725

$186

$368

$366

Category 5: LEAs with Low Numbers and Percentages of Formula Children

IL

1731920

Pleasant Plains Community

62

4.3%

$721

$0

$0

$0

MA

2506900

Lincoln

50

3.7%

$822

$0

$0

$0

MN

2718810

Maple Lake

57

5.1%

$683

$0

$184

$217

NY

3606990

Chappaqua Central

74

1.8%

$0

$0

$0

$0

NY

3629850

Wantagh Union

71

2.1%

$822

$0

$0

$0

 

 

Average for Category 5 LEAs Listed Above

 

 

$609

$0

$37

$43

Source: Table prepared by CRS.

The last of the data tables in this report section, Table 8, displays the distribution of total school-age population, Title I-A formula children, and Title I-A grants (by formula and total) among LEAs in 12 standard locale categories. (Note that Puerto Rico is excluded from this analysis.) These categories are based on the "urban-centric" locale codes developed by the National Center for Education Statistics (NCES).29

The final column (Column J) in Table 8 shows the percentage difference between the share of total Title I-A grants going to LEAs in that category (Column I) and the share of Title I-A formula children (Column D). This figure indicates the aggregate size and direction of variations in the distribution of Title I-A formula children and the distribution of Title I-A grants. For example, if the amount in Column J were large and positive, this would indicate that LEAs in that category receive a substantially higher share of Title I-A funds than their share of the children counted in the Title I-A formulas. Conversely, if the amount in Column J were large and negative, this would indicate that LEAs in that category receive a substantially smaller share of Title I-A funds than their share of the children counted in the Title I-A formulas.

As shown in Table 8, applying an arbitrary threshold of +/- 10% or more to indicate substantial differences in shares of grants versus formula children, the following patterns are illustrated:

  • The urban group as a whole (locale codes 11-13) receives substantially higher shares of grants than their share of formula children (+13.8%) with virtually all of this differential occurring with respect to the large city groups of LEAs (code 11) with a difference of +25.3%. In addition, whether substantial or not, the direction of the difference is negative for all locale code groups except large city (11) and midsize city (12).
  • The town (codes 31-33) and rural (codes 41-43) LEA groups as a whole receive substantially lower shares of grants than their share of formula children.
  • The suburban (codes 21-23) LEA group receives lower shares of grants than its share of formula children, although the difference does not exceed the 10% threshold with respect to the large suburban group (code 21) or the suburban codes overall (codes 21-23).

Table 8. Distribution of School-Age Population, Title I-A Formula Children, and Title I-A Grants Among LEAs by Locale Type

LEA Percentage Shares of Title I-A Grants and Population by Locale Code, FY2008

A

B

C

D

E

F

G

H

I

J

Locale Code

Locale Type

Total School-Age Population, FY2008

Title I-A Formula Children, FY2008

Basic Grants

Concentration Grants

Targeted Grants

Education Finance Incentive Grants

Total Title I-A Grants

% Difference (Col. I – Col. D)

11

Large City

16.90%

24.93%

26.58%

32.20%

35.62%

36.75%

31.23%

25.3%

12

Midsize City

7.53%

9.25%

9.04%

10.13%

9.57%

9.73%

9.41%

1.8%

13

Small City

8.41%

9.11%

9.01%

9.06%

8.17%

8.04%

8.63%

-5.2%

11-13 Total

 

32.84%

43.28%

44.63%

51.39%

53.36%

54.52%

49.26%

13.8%

21

Large Suburb

32.32%

22.16%

22.23%

17.83%

19.91%

18.67%

20.55%

-7.3%

22

Midsize Suburb

3.38%

2.63%

2.54%

1.81%

2.14%

2.01%

2.27%

-13.8%

23

Small Suburb

2.08%

1.63%

1.61%

1.21%

1.23%

1.11%

1.38%

-15.1%

21-23 Total

 

37.79%

26.41%

26.37%

20.85%

23.28%

21.79%

24.20%

-8.4%

31

Fringe Town

3.48%

3.04%

3.03%

2.53%

2.20%

2.10%

2.61%

-14.3%

32

Distant Town

4.85%

5.50%

5.20%

5.37%

4.13%

4.07%

4.75%

-13.7%

33

Remote Town

3.79%

4.70%

4.45%

4.83%

3.87%

4.07%

4.28%

-8.9%

31-33 Total

 

12.12%

13.25%

12.68%

12.74%

10.20%

10.24%

11.64%

-12.2%

41

Fringe Rural

7.65%

6.69%

6.29%

5.14%

4.94%

4.99%

5.61%

-16.1%

42

Distant Rural

6.47%

6.56%

6.25%

5.80%

4.82%

4.82%

5.60%

-14.7%

43

Remote Rural

3.13%

3.81%

3.78%

4.09%

3.40%

3.64%

3.70%

-2.9%

41-43 Total

 

17.25%

17.06%

16.32%

15.03%

13.16%

13.45%

14.91%

-12.6%

Grand Total

 

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

0.0%

Source: Table prepared by CRS based on provisions of ESEA Title I-A.

ESEA Reauthorization Issues Related to the Title I-A Allocation Formulas

The remainder of this report describes and analyzes a number of issues that may arise in the context of efforts to amend and reauthorize the ESEA during the 111th Congress.

Should Annual Variations in the Poverty Estimates Used to Calculate Title I-A Grants Be Reduced Through Multi-year Averaging or Other Methods?

As noted earlier, all the factors used to calculate Title I-A grants are now updated each year. This includes the primary formula factor, estimated numbers of school-age children in poor families, which constitute approximately 96% of all children counted in the Title I-A allocation formulas.

The poverty estimates for Title I-A are from the Census Bureau's Small Area Income and Population Estimates (SAIPE) program, which provides estimates of poor and total children aged 5-17 for LEAs, counties, and states. Under the provisions of the Improving America's Schools Act (IASA) of 1994 (P.L. 103-382), use of SAIPE estimates replaced the previous practice of relying on data from the decennial Census surveys that were updated only once every 10 years.30 As amended by the IASA in 1994, the Title I-A statute provided that beginning in FY1997, the Secretary of Education "shall" use updated population data prepared by the Census Bureau "unless the Secretary [of Education] and the Secretary of Commerce determine that use of the updated population data would be inappropriate or unreliable, taking into consideration the recommendations" of a series of studies of the updating methodology to be conducted by the National Academy of Sciences (NAS).31 In March 1997, a NAS panel32 recommended use of a combination of 1990 census and income year (IY)199333 updated population estimates in allocating FY1997 (1997-1998) grants.34 In a later report, the panel recommended use of a slightly revised set of IY1993 SAIPE estimates as the sole basis for calculating FY1998 grants, and ED followed this recommendation as well. Finally, beginning with FY1999 grants, the NAS panel recommended that ED use the latest available SAIPE estimates of school-age children in poor families and that grants be calculated by ED on the basis of LEA, not county, population data,35 and ED has followed these recommendations.

The latest available poverty estimates from SAIPE are used to calculate each year's Title I-A grants. The SAIPE estimates are updated every year. As of this writing, the latest SAIPE data are for income year 2007; these estimates were initially published in December 2008, and will be used to calculate FY2009 Title I-A allocations. For IY1993 through IY1999 (FY1997-2002 Title I-A grants), these estimates were updated every two years. Since IY1999 (Title I-A grants from FY2003 through the present), they have been updated annually. There is a two-year gap in the income year for SAIPE estimates used to calculate FY2009 Title I-A grants (IY2007) versus FY2008 grants (IY2005) because Census has reduced the time required to develop the SAIPE estimates (previously the gap was three years).

SAIPE is not a survey of households separate from other federal surveys conducted by the Census Bureau or other agencies. SAIPE data are indirect estimates, produced through statistical modeling of data from the most recent decennial census, other Census Bureau household surveys, primarily the American Community Survey (ACS),36 and administrative records, such as federal income tax returns, Food Stamp and Supplemental Security Income program participation, and income data from the Bureau of Economic Analysis of the Department of Commerce.

The provision for use of population updates was added to Title I-A in an attempt to distribute funds on the basis of the latest available, reliable data on the distribution of school-age children in poor families among states and localities, and to try to minimize the considerable disruption that had occurred previously with the introduction of new population data only once every 10 years. However, somewhat unexpectedly, the updates themselves have caused significant shifts in allocation shares among states and regions. With the publication of each set of SAIPE estimates, shifts in the estimated number of school-age children in poor families have generally been relatively modest for most states, but there have always been a number of states (and LEAs) with quite substantial estimated shifts over a one- or two-year period.

Annual Shifts in Poverty Estimates

Table 9 provides data from the last four series of SAIPE estimates that have been, or will be, used to calculate Title I-A grants, those for income years 2003 (FY2006), 2004 (FY2007), 2005 (FY2008) and 2007 (FY2009). It provides each state's estimated number of school-age children in poor families, and the percentage change in this estimated number from the previous year.37

Table 9. State Estimated Number of School-Age Children in Poor Families, Income Years 2003-2007

State

Estimated Children Aged 5-17 in Poor Families, IY2003

Estimated Children Aged 5-17 in Poor Families, IY2004

Percentage Change in Estimated Number, IY2004 vs. IY2003

Estimated Children Aged 5-17 in Poor Families, IY2005

Percentage Change in Estimated Number, IY2005 vs. IY2004

Estimated Children Aged 5-17 in Poor Families, IY2007

Percentage Change in Estimated Number, IY2007 vs. IY2005

Alabama

165,225

160,787

-2.7%

172,197

7.1%

174,665

1.4%

Alaska

14,396

14,841

3.1%

16,841

13.5%

13,787

-18.1%

Arizona

214,052

214,962

0.4%

205,175

-4.6%

209,683

2.2%

Arkansas

104,883

95,393

-9.0%

108,273

13.5%

113,370

4.7%

California

1,292,920

1,225,762

-5.2%

1,197,835

-2.3%

1,062,605

-11.3%

Colorado

96,357

94,396

-2.0%

101,811

7.9%

114,762

12.7%

Connecticut

55,972

64,564

15.4%

62,095

-3.8%

58,597

-5.6%

Delaware

15,986

15,877

-0.7%

17,149

8.0%

18,289

6.6%

District of Columbia

21,775

19,536

-10.3%

19,634

0.5%

18,995

-3.3%

Florida

510,674

447,172

-12.4%

474,430

6.1%

437,055

-7.9%

Georgia

291,342

296,706

1.8%

304,220

2.5%

318,255

4.6%

Hawaii

26,812

19,121

-28.7%

23,729

24.1%

18,364

-22.6%

Idaho

36,037

33,487

-7.1%

39,106

16.8%

39,046

-0.2%

Illinois

333,218

369,244

10.8%

341,763

-7.4%

348,638

2.0%

Indiana

129,513

155,506

20.1%

166,214

6.9%

166,365

0.1%

Iowa

49,842

53,683

7.7%

57,449

7.0%

58,426

1.7%

Kansas

55,425

59,392

7.2%

60,203

1.4%

61,149

1.6%

Kentucky

137,877

135,287

-1.9%

146,404

8.2%

149,095

1.8%

Louisiana

207,713

201,957

-2.8%

220,555

9.2%

191,094

-13.4%

Maine

25,041

23,769

-5.1%

28,864

21.4%

27,487

-4.8%

Maryland

100,977

107,072

6.0%

98,407

-8.1%

92,601

-5.9%

Massachusetts

112,900

121,129

7.3%

129,183

6.6%

126,588

-2.0%

Michigan

250,954

276,639

10.2%

308,636

11.6%

308,714

0.0%

Minnesota

77,036

82,727

7.4%

87,697

6.0%

92,798

5.8%

Mississippi

139,285

142,059

2.0%

148,662

4.6%

144,806

-2.6%

Missouri

146,376

158,877

8.5%

170,436

7.3%

160,841

-5.6%

Montana

25,841

22,842

-11.6%

26,584

16.4%

25,988

-2.2%

Nebraska

32,497

32,859

1.1%

38,637

17.6%

39,327

1.8%

Nevada

59,385

60,862

2.5%

58,687

-3.6%

62,021

5.7%

New Hampshire

13,110

17,353

32.4%

17,856

2.9%

17,188

-3.7%

New Jersey

154,881

148,348

-4.2%

165,069

11.3%

154,235

-6.6%

New Mexico

85,364

75,513

-11.5%

82,630

9.4%

78,583

-4.9%

New York

639,014

638,113

-0.1%

594,230

-6.9%

576,131

-3.0%

North Carolina

247,890

252,410

1.8%

287,894

14.1%

275,164

-4.4%

North Dakota

11,284

10,712

-5.1%

11,872

10.8%

11,671

-1.7%

Ohio

258,469

288,329

11.6%

322,771

11.9%

323,397

0.2%

Oklahoma

116,879

103,472

-11.5%

120,814

16.8%

121,880

0.9%

Oregon

93,136

88,798

-4.7%

99,462

12.0%

91,925

-7.6%

Pennsylvania

274,501

289,566

5.5%

305,450

5.5%

293,616

-3.9%

Puerto Rico

400,212

399608

-0.2%

404,549

1.2%

389,831

-3.6%

Rhode Island

27,378

28,943

5.7%

28,272

-2.3%

25,763

-8.9%

South Carolina

138,184

143,249

3.7%

150,806

5.3%

142,963

-5.2%

South Dakota

19,212

20,000

4.1%

20,442

2.2%

20,625

0.9%

Tennessee

171,430

170,654

-0.5%

194,253

13.8%

206,308

6.2%

Texas

902,259

909,592

0.8%

983,654

8.1%

960,471

-2.4%

Utah

49,493

52,513

6.1%

51,517

-1.9%

55,841

8.4%

Vermont

9,667

8,424

-12.9%

10,753

27.6%

9,809

-8.8%

Virginia

148,985

143,376

-3.8%

153,431

7.0%

152,581

-0.6%

Washington

138,385

144,330

4.3%

145,368

0.7%

141,844

-2.4%

West Virginia

63,540

55,490

-12.7%

64,238

15.8%

56,406

-12.2%

Wisconsin

96,394

126,498

31.2%

114,754

-9.3%

120,571

5.1%

Wyoming

9,807

8,695

-11.3%

9,129

5.0%

9,461

3.6%

 

 

 

 

 

 

 

 

Total

8,799,785

8,830,494

0.3%

9,170,090

3.8%

8,889,675

-3.1%

Source: U.S. Census Bureau, Small Area Income and Poverty Estimates program.

Note: No data are shown above for IY2006 because SAIPE data for that year were not used in the calculation of ESEA Title I-A grants. Estimates for IY2005 were used to calculate FY2008 Title I-A grants while IY2007 estimates will be used to calculate FY2009 grants. Thus, the change from IY2005 to IY2007 estimates represents a one year change in terms of Title I-A allocations.

As seen in Table 9, for many states there is a substantial degree of year-to-year variation in these poverty estimates. For IY2004 compared to IY2003, the national aggregate poverty estimate increased by 0.3%, virtually no change at all, while the estimates for individual states ranged from -28.7% to +32.4%. Comparing IY2005 with IY2004, while the national aggregate poverty estimate increased by 3.8%, the estimates for individual states ranged from -9.3% to +27.6%. Finally, comparing IY2007 with IY2005, the national poverty estimate declined by 3.1%, while the estimates for individual states varied from -22.6% to +12.7%.

Not only is the range in annual shifts in SAIPE poverty estimates for the states overall quite large; these estimates also fluctuate quite substantially from year to year for a number of individual states. As is illustrated in Table 10, below, the estimates for several states have fluctuated widely in recent years, at a time when the estimated aggregate change was relatively small. While several of these are states with relatively small populations (e.g., Alaska, Montana, New Hampshire, and Vermont), this group also includes such relatively large states as Florida and Wisconsin, as well as the moderate size states of Hawaii and West Virginia.

Table 10. Estimated Annual Changes in Estimated Number of School-Age Children in Poor Families, Income Years 2003-2007, Selected States

State

Percentage Change in Estimated Number of School-Age Children in Poor Families, IY2004 vs. IY2003

Percentage Change in Estimated Number of School-Age Children in Poor Families, IY2005 vs. IY2004

Percentage Change in Estimated Number of School-Age Children in Poor Families, IY2007 vs. IY2005

Alaska

3.1%

13.5%

-18.1%

Florida

-12.4%

6.1%

-7.9%

Hawaii

-28.7%

24.1%

-22.6%

Louisiana

-2.8%

9.2%

-13.4%

Montana

-11.6%

16.4%

-2.2%

New Hampshire

32.4%

2.9%

-3.7%

Vermont

-12.9%

27.6%

-8.8%

West Virginia

-12.7%

15.8%

-12.2%

Wisconsin

31.2%

-9.3%

5.1%

Source: Table prepared by CRS.

During the initial period of use of the SAIPE poverty estimates, special provisions were added to FY1997-2001 appropriations legislation for Title I-A to limit the impact of the updates. As was discussed above, in all years, the Title I-A authorizing statute provides for "hold harmless" rates of 85-95% of the previous year grant, applied at the LEA level. The FY1997-2001 appropriations acts for ED provided for higher 100% hold harmless rates, applied either at the state or the state plus LEA levels.38

These high hold harmless rates were applied during a period when there was little or no growth in aggregate Title I-A funding and substantial shifts in the estimated number of school-age children in poor families for many states and LEAs. As a result of 100% hold harmless rates and little growth in total appropriations, state and LEA funding shares remained quite static during this FY1997-2001 period.

Beginning with FY2002, the 100% hold harmless rates were dropped from annual appropriations acts for Title I-A. This shift was facilitated by a combination of significantly increased total funding (an increase of 18% for FY2002 compared to FY2001), and initial funding of two formulas (the Targeted Grant and EFIG formulas) that resulted in each state (although not each LEA) receiving an increase in Title I-A funds for FY2002. As the rate of annual appropriations increases declined over the period of FY2003-2007, not only a substantial number of LEAs, but also (beginning with FY200439) a number of states experienced annual reductions in Title I-A grants. Finally, for FY2008, Title I-A appropriations rose by 8.3%, and only one state received a smaller allocation for FY2008 than for FY2007.

Selected Alternatives to Use of Only the Most Current Poverty Estimates

Concern about the variability of the SAIPE poverty estimates for many states may lead to proposals to limit resulting decreases in Title I-A grants, beyond the effects of the statutory hold harmless provisions for LEAs. The remainder of this section of the report provides a discussion and analysis of four possible options for limiting year-to-year reductions in Title I-A grants resulting from fluctuations in poverty estimates.

Option 1: Higher Hold Harmless Rates

One option might be a return to higher (100%) hold harmless rates in annual appropriations legislation, applied at either the LEA or state level, as occurred between FY1997 and 2001. This would have the effect of eliminating reductions in Title I-A grants overall, while limiting increases to states or LEAs with rising estimated numbers of school-age children in poor families. If there were little or no increase in total Title I-A appropriations, this would result in a static geographic distribution of funds, as occurred between FY1997 and FY2001.

A variation of this option would address the particular problems of LEAs that have experienced dramatic shifts in funding from one year to the next as their school-age child poverty rate varies by small amounts around the Targeted Grant and EFIG formula child eligibility threshold of 5.0%. Large swings in funding make it exceptionally difficult to use Title I-A funds efficiently. The four-year phase-out of hold-harmless provisions, now applied only to Concentration Grants, might be extended to Targeted and EFIG grants.

Option 2: Use of the Average of the Latest and Second Latest Poverty Estimates for All LEAs

A second alternative for limiting the impact of large variations in annual poverty estimates would be to combine the most recent poverty estimates with the estimates for one or more immediately preceding years, in order to make the transition to the most recent estimates more gradual for states or LEAs where estimated changes are relatively large. This could be accomplished by using the average of the poverty estimates for the last two or even three years in the Title I-A allocation formulas. Table 11, below, illustrates the estimated impact of this approach on grants for FY2008. Actual FY2008 Title I-A grants under current law (i.e., based on IY2005 poverty estimates) are compared to estimates under an alternative formula using the average of the latest and the second most recent poverty estimates (i.e., those for IY2005 and IY2004) as the poverty population factor. The estimated FY2008 grants based on the average (two-year) poverty estimates are compared to actual grants for FY2008 (Column E) and FY2007 (Column G), along with a comparison of actual grants for FY2008 compared to FY2007 (Col. F).

As mentioned earlier, with a relatively substantial (8.3%) funding increase for FY2008 over FY2007, under current law only one state (Wisconsin) received a lower grant for FY2008 than for FY2007 (a reduction of 1.3%). For the other states, the rate of increase for FY2008 over FY2007 ranged from 0.1% to 20.8% (Column F). As seen in Table 11 (Column G), under the alternative formula, all states are estimated to have received higher grants for FY2008 compared to FY2007, with increases ranging from 1.8% to 16.1%. Thus, as expected, the range in variation from previous year (FY2007) grants is somewhat less under the alternative formula (1.8% to 16.1%) than under current law (-1.3% to 20.8%). Comparing estimated FY2008 grants under the alternative formula to actual FY2008 grants (Column E of Table 11), estimated differences range from -4.3% (Maine) to 5.9% (Nevada).

Option 3: Use of the Greater of the Latest or the Average of the Latest and Second Latest Poverty Estimates for Each LEA

A third approach, illustrated in Table 12, would be to use the greater of: (i) the latest poverty estimate, or (ii) the average of the latest and the previous year estimate for each LEA. Under this alternative, if the latest poverty estimate is higher than the one for the previous year for an LEA, then only the latest estimate is used in calculating grants. Alternatively, if the latest poverty estimate for an LEA is lower than the one for the previous year, then the average of the latest and the immediately preceding estimate is used. As a result, areas with estimated increases in poverty receive "credit" for that increase, while losses are cushioned for LEAs with estimated decreases in poverty. This would have a more limited impact on grants than the use of the average of the poverty estimates for the last two years for all LEAs.

As seen in Table 12 (Column G), under this alternative formula, all states are estimated to have received higher grants for FY2008 compared to FY2007, with increases ranging from 0.5% to 19.9%. This range of variation falls in between the wider range under current law (-1.3% to 20.8%—Column F) and the somewhat more narrow range under the alternative formula discussed in option 2 (1.8% to 16.1%). Comparing estimated FY2008 grants under the alternative formula to actual FY2008 grants (Column E of Table 12), estimated differences fall within the relatively narrow range of -1.5% (Puerto Rico) to 2.5% (Connecticut).

Table 11. State Total Grants under Title I-A, ESEA: Actual Grants for FY2007 and FY2008 Compared to Estimated Grants Based on the Average of IY2004 and IY2005 Estimates of School-Age Children in Poor Families

A

B

C

D

E

F

G

State

FY2007 Actual Grant

FY2008 Actual Grant

FY2008 Estimated Grant Using Averaged Poverty Estimates

Col. D - Col. C, %

Col. C-
Col. B, %

Col. D - Col. B, %

United States

$12,706,341,000

$13,755,995,000

$13,755,995,000

0.0%

8.3%

8.3%

 

 

 

 

 

 

 

Alabama

$194,251,000

$215,192,000

$212,462,000

-1.3%

10.8%

9.4%

Alaska

$34,025,000

$38,846,000

$38,452,000

-1.0%

14.2%

13.0%

Arizona

$263,204,000

$274,777,000

$278,525,000

1.3%

4.4%

5.8%

Arkansas

$122,031,000

$144,268,000

$140,185,000

-2.8%

18.2%

14.9%

California

$1,643,496,000

$1,698,808,000

$1,724,902,000

1.6%

3.4%

5.0%

Colorado

$123,928,000

$135,392,000

$134,102,000

-0.9%

9.2%

8.2%

Connecticut

$111,879,000

$115,562,000

$116,648,000

0.9%

3.3%

4.3%

Delaware

$34,110,000

$38,380,000

$38,173,000

-0.5%

12.5%

11.9%

District of Columbia

$46,026,000

$47,295,000

$47,981,000

1.4%

2.8%

4.2%

Florida

$589,157,000

$656,255,000

$662,416,000

1.0%

11.4%

12.4%

Georgia

$410,011,000

$446,271,000

$450,508,000

1.0%

8.8%

9.9%

Hawaii

$39,639,000

$44,337,000

$42,678,000

-3.7%

11.9%

7.7%

Idaho

$41,327,000

$46,662,000

$45,938,000

-1.6%

12.9%

11.2%

Illinois

$593,136,000

$593,980,000

$604,850,000

1.9%

0.1%

2.0%

Indiana

$230,085,000

$247,109,000

$243,299,000

-1.5%

7.4%

5.7%

Iowa

$69,214,000

$72,717,000

$72,075,000

-0.8%

5.1%

4.1%

Kansas

$88,061,000

$95,359,000

$98,042,000

2.7%

8.3%

11.3%

Kentucky

$185,854,000

$208,551,000

$205,444,000

-1.5%

12.2%

10.5%

Louisiana

$277,650,000

$294,843,000

$291,151,000

-1.2%

6.2%

4.9%

Maine

$43,870,000

$51,525,000

$49,360,000

-4.3%

17.4%

12.5%

Maryland

$188,034,000

$192,239,000

$201,638,000

4.8%

2.2%

7.2%

Massachusetts

$211,607,000

$233,354,000

$229,240,000

-1.9%

10.3%

8.3%

Michigan

$460,302,000

$527,255,000

$510,710,000

-3.1%

14.5%

11.0%

Minnesota

$114,583,000

$126,936,000

$125,733,000

-0.9%

10.8%

9.7%

Mississippi

$174,679,000

$187,346,000

$187,871,000

0.2%

7.3%

7.6%

Missouri

$201,452,000

$225,205,000

$223,161,000

-0.9%

11.8%

10.8%

Montana

$38,635,000

$43,555,000

$42,545,000

-2.3%

12.7%

10.1%

Nebraska

$50,662,000

$60,246,000

$57,667,000

-4.2%

18.9%

13.8%

Nevada

$80,299,000

$80,755,000

$85,544,000

5.9%

0.6%

6.5%

New Hampshire

$34,248,000

$38,198,000

$38,196,000

-0.0%

11.5%

11.5%

New Jersey

$252,409,000

$286,765,000

$275,943,000

-3.8%

13.6%

9.3%

New Mexico

$103,847,000

$113,156,000

$111,672,000

-1.3%

9.0%

7.5%

New York

$1,210,071,000

$1,226,786,000

$1,267,983,000

3.3%

1.4%

4.8%

North Carolina

$301,104,000

$358,570,000

$347,188,000

-3.2%

19.1%

15.3%

North Dakota

$29,825,000

$33,742,000

$33,306,000

-1.3%

13.1%

11.7%

Ohio

$449,255,000

$511,797,000

$496,022,000

-3.1%

13.9%

10.4%

Oklahoma

$128,266,000

$148,406,000

$142,748,000

-3.9%

15.7%

11.3%

Oregon

$121,425,000

$139,987,000

$136,136,000

-2.7%

15.3%

12.1%

Pennsylvania

$516,459,000

$565,518,000

$561,609,000

-0.7%

9.5%

8.7%

Puerto Rico

$455,589,000

$510,525,000

$528,108,000

3.4%

12.1%

15.9%

Rhode Island

$50,390,000

$52,978,000

$53,402,000

0.8%

5.1%

6.0%

South Carolina

$187,902,000

$205,597,000

$206,275,000

0.4%

9.4%

9.8%

South Dakota

$37,274,000

$41,539,000

$41,607,000

0.2%

11.4%

11.6%

Tennessee

$205,728,000

$239,071,000

$232,300,000

-2.9%

16.2%

12.9%

Texas

$1,169,500,000

$1,299,356,000

$1,276,395,000

-1.8%

11.1%

9.1%

Utah

$58,197,000

$60,019,000

$61,594,000

2.6%

3.1%

5.8%

Vermont

$27,199,000

$32,862,000

$31,571,000

-3.9%

20.8%

16.1%

Virginia

$204,733,000

$226,096,000

$225,253,000

-0.3%

10.4%

10.0%

Washington

$182,795,000

$191,853,000

$194,708,000

1.5%

5.0%

6.5%

West Virginia

$89,221,000

$99,607,000

$96,055,000

-3.5%

11.6%

7.7%

Wisconsin

$201,601,000

$199,030,000

$205,173,000

3.1%

-1.3%

1.8%

Wyoming

$28,094,000

$31,516,000

$31,453,000

-0.2%

12.2%

12.0%

Source: Actual FY2007 and FY2008 grants under current law are provided by the U.S. Department of Education. Estimated FY2008 grants based on the average of IY2004 and IY2005 poverty estimates were prepared by CRS. Table prepared by CRS.

Note: The estimated FY2008 grants are provided solely to assist in comparisons of the relative impact of alternative formulas and funding levels in the legislative process. They are not intended to predict specific amounts that states will receive.

Table 12. ESEA Title I, Part A, Actual Grants for FY2007 and FY2008 Compared to Estimated FY2008 Grants Using the Greater of: (i) Poverty Estimates for Income Year 2005 (FY2008) or (ii) the Average of Poverty Estimates for Income Years 2004 (FY2007) and 2005 (FY2008) for Each LEA

A

B

C

D

E

F

G

State

FY2007 Actual Grant

FY2008 Actual Grant

FY2008 Estimated Grant Using Modified Poverty Estimates

Col. D - Col. C, %

Col. C - Col. B, %

Col. D - Col. B, %

United States

$12,706,341,000

$13,755,995,000

$13,755,995,000

0.0%

8.3%

8.3%

 

 

 

 

 

 

 

Alabama

$194,251,000

$215,192,000

$213,686,000

-0.8%

10.8%

10.0%

Alaska

$34,025,000

$38,846,000

$38,648,000

-0.5%

14.2%

13.6%

Arizona

$263,204,000

$274,777,000

$278,205,000

1.2%

4.4%

5.7%

Arkansas

$122,031,000

$144,268,000

$142,369,000

-1.3%

18.2%

16.7%

California

$1,643,496,000

$1,698,808,000

$1,704,364,000

0.4%

3.4%

3.7%

Colorado

$123,928,000

$135,392,000

$133,860,000

-1.1%

9.2%

8.0%

Connecticut

$111,879,000

$115,562,000

$118,434,000

2.5%

3.3%

5.9%

Delaware

$34,110,000

$38,380,000

$38,188,000

-0.5%

12.5%

12.0%

District of Columbia

$46,026,000

$47,295,000

$46,971,000

-0.7%

2.8%

2.1%

Florida

$589,157,000

$656,255,000

$650,338,000

-0.9%

11.4%

10.4%

Georgia

$410,011,000

$446,271,000

$446,194,000

0.0%

8.8%

8.8%

Hawaii

$39,639,000

$44,337,000

$43,935,000

-0.9%

11.9%

10.8%

Idaho

$41,327,000

$46,663,000

$46,569,000

-0.2%

12.9%

12.7%

Illinois

$593,136,000

$593,980,000

$606,656,000

2.2%

0.1%

2.3%

Indiana

$230,085,000

$247,109,000

$247,784,000

0.3%

7.4%

7.7%

Iowa

$69,214,000

$72,717,000

$72,487,000

-0.3%

5.1%

4.7%

Kansas

$88,061,000

$95,359,000

$96,331,000

0.9%

8.3%

9.4%

Kentucky

$185,854,000

$208,551,000

$207,191,000

-0.6%

12.2%

11.5%

Louisiana

$277,650,000

$294,843,000

$291,772,000

-1.0%

6.2%

5.1%

Maine

$43,870,000

$51,525,000

$51,186,000

-0.7%

17.4%

16.7%

Maryland

$188,034,000

$192,239,000

$195,258,000

1.5%

2.2%

3.8%

Massachusetts

$211,607,000

$233,354,000

$234,399,000

0.3%

10.3%

10.8%

Michigan

$460,302,000

$527,255,000

$522,951,000

-0.8%

14.5%

13.6%

Minnesota

$114,583,000

$126,936,000

$126,864,000

-0.0%

10.8%

10.7%

Mississippi

$174,679,000

$187,346,000

$187,220,000

-0.1%

7.3%

7.2%

Missouri

$201,452,000

$225,205,000

$224,749,000

-0.2%

11.8%

11.6%

Montana

$38,635,000

$43,555,000

$43,468,000

-0.2%

12.7%

12.5%

Nebraska

$50,662,000

$60,246,000

$59,471,000

-1.3%

18.9%

17.4%

Nevada

$80,299,000

$80,755,000

$82,146,000

1.7%

0.6%

2.3%

New Hampshire

$34,248,000

$38,198,000

$38,255,000

0.1%

11.5%

11.7%

New Jersey

$252,409,000

$286,765,000

$286,417,000

-0.2%

13.6%

13.5%

New Mexico

$103,847,000

$113,156,000

$111,977,000

-1.0%

9.0%

7.8%

New York

$1,210,071,000

$1,226,786,000

$1,248,212,000

1.7%

1.4%

3.2%

North Carolina

$301,104,000

$358,570,000

$353,723,000

-1.3%

19.1%

17.5%

North Dakota

$29,825,000

$33,742,000

$33,530,000

-0.6%

13.1%

12.4%

Ohio

$449,255,000

$511,797,000

$510,172,000

-0.3%

13.9%

13.6%

Oklahoma

$128,266,000

$148,406,000

$146,612,000

-1.3%

15.7%

14.3%

Oregon

$121,425,000

$139,987,000

$138,357,000

-1.1%

15.3%

13.9%

Pennsylvania

$516,459,000

$565,518,000

$562,552,000

-0.5%

9.5%

8.9%

Puerto Rico

$455,589,000

$510,525,000

$503,311,000

-1.5%

12.1%

10.5%

Rhode Island

$50,390,000

$52,978,000

$53,371,000

0.7%

5.1%

5.9%

South Carolina

$187,902,000

$205,598,000

$203,574,000

-1.0%

9.4%

8.3%

South Dakota

$37,274,000

$41,539,000

$41,559,000

0.0%

11.4%

11.5%

Tennessee

$205,728,000

$239,072,000

$235,834,000

-1.4%

16.2%

14.6%

Texas

$1,169,500,000

$1,299,356,000

$1,294,962,000

-0.3%

11.1%

10.7%

Utah

$58,197,000

$60,019,000

$60,150,000

0.2%

3.1%

3.4%

Vermont

$27,199,000

$32,862,000

$32,609,000

-0.8%

20.8%

19.9%

Virginia

$204,733,000

$226,096,000

$224,376,000

-0.7%

10.4%

9.6%

Washington

$182,795,000

$191,853,000

$192,495,000

0.4%

5.0%

5.3%

West Virginia

$89,221,000

$99,607,000

$98,225,000

-1.4%

11.6%

10.1%

Wisconsin

$201,601,000

$199,030,000

$202,654,000

1.8%

-1.3%

0.5%

Wyoming

$28,094,000

$31,516,000

$31,375,000

-0.4%

12.2%

11.7%

Source: Actual FY2007 and FY2008 grants under current law are provided by the U.S. Department of Education. Estimated FY2008 grants based on the greater of IY2004 and IY2005 poverty estimates were prepared by CRS. Table prepared by CRS.

Note: The estimated FY2008 grants are provided solely to assist in comparisons of the relative impact of alternative formulas and funding levels in the legislative process. They are not intended to predict specific amounts that states will receive.

Those concerned about the impact of frequent, sometimes quite large, variations in poverty estimates and subsequent Title I-A grants on program operations might support the use of two-year averages for poverty data, as reflected in the alternative formulas of Tables 11 and 12. Given the underlying reliance of the SAIPE estimation process on sample survey data, the reliability of the estimates should be increased through combination of estimates for multiple years. The most recent poverty estimates would still be used, but introduced more gradually. States and LEAs would be allowed more time to adjust to either increases or decreases in allocations, and program stability would be enhanced.

However, opponents of a shift from the current practice of always using the latest available poverty estimates would argue that averaging poverty estimates over two years, or choosing the greater of the latest estimates or a two-year average for each LEA, would delay implementation of updates. Under current practice, estimates based on income for calendar year 2007 will be applied to grants for FY2009, the 2009-2010 school year; thus, there is a two- to three-year lag between the income year and the program year. Use of one of the alternatives discussed above under Options 2 and 3 would add, in part, another year to this time lag. Further, the most severe negative impacts of reductions in poverty estimates are already limited by the LEA hold harmless provisions, under which grants may not fall below 85-95% of the previous year amount, with high poverty LEAs offered the greatest degree of protection. Finally, if the SAIPE process is deemed to provide reliable poverty estimates, that are preferable to those from other sources, some ask why should not the latest available estimates be used in calculating Title I-A grants?

Option 4: Limit the Degree of Annual Decreases in Poverty Estimates

Another option, given that annual shifts in poverty estimates have thus far been especially large for a small number of states, would be to place a limit on the size of these shifts. Either a floor, or a floor and ceiling, might be placed on the annual percentage change in either each state's estimated number, or on each state's share of the national total estimated number, of school-age children in poor families. For example, it might be provided that no state's percentage share of the national total estimated number of school-age children in poor families could decline by more than 10% compared to the previous year. If the estimated decline were greater than 10%, the estimate used in the Title I-A allocation formulas would be set at the level representing a 10% percentage share reduction. This example is based on state percentage shares, rather than the estimated number, of school-age children in poor families in order to adjust for nationwide increases or decreases in these estimates. Also, as mentioned earlier (footnote 37), given a fixed annual total appropriation for Title I-A, changes in each state's percentage share of the total estimated number of school-age children in poor families are more closely related to trends in allocations than are changes in the estimated number of such children.

Such a provision—a 10% limit on reductions in state share of poverty estimates—would have affected one state for FY2005, four states for FY2006, nine states for FY2007, and four states for FY2008. Grants to those states would have increased, while those to most other states would have declined.

Has the Targeting of Title I-A Funds on High Poverty LEAs Increased Since 2001?

For many years, a primary issue regarding the Title I-A allocation formulas has been the extent to which funds are targeted on high-poverty LEAs. Over 90% of the nation's LEAs receive grants under ESEA Title I-A, largely because the eligibility thresholds for three of the four allocation formulas, as described above, are relatively low. In general, all LEAs receive Title I-A grants except those that have extraordinarily low school-age poverty rates or have extremely few pupils.40 A few LEAs (including certain charter schools that are treated as separate LEAs under state law) are eligible for relatively small Title I-A grants, but choose not to participate in the program, at least in part because the responsibilities accompanying participation are perceived to exceed the value of the prospective grants.

Table 13, below, presents the distribution of Title I-A grants among LEAs grouped by poverty rate quintile.41 Each quintile contains LEAs with one-fifth of the nation's total estimated number of school-age children in poor families, on the basis of the Census Bureau IY2005 population estimates used in calculating FY2008 grants. Table 13 lists the percentage share (of the national total) of Title I-A grants that are allocated to LEAs in each poverty quintile. These data are provided separately for each of the four Title I-A allocation formulas, as well as for total grants for FY2008.42

As illustrated in Table 13 and Figure 1, below, the share of Title I-A funds allocated to LEAs in various poverty rate ranges varies significantly among the four allocation formulas. For Basic Grants, the share is similar for each quintile of LEAs, varying only within the narrow range of 19.2%-21.1%. For Concentration Grants, the share of funds allocated to LEAs in each poverty rate range is again similar, with the exception of the lowest-poverty quintile, which receives a much lower share (4.0% of total grants vs. 23.1%-25.2% for the other four quintiles). This reflects the eligibility threshold for Concentration Grants (formula child rate of at least 15% or 6,500 formula children). Overall, the primary pattern for both Basic and Concentration Grants is relatively constant shares of funds for all quintiles of LEAs meeting minimum eligibility thresholds. In other words, grants per poor and other child counted in the Title I-A allocation formulas are approximately the same for all LEAs meeting the initial eligibility criteria for Basic and Concentration Grants, whether those LEAs have high, average, or somewhat below average school-age child poverty rates.

The pattern of distribution of grants under the Targeted and EFIG formulas is somewhat different. Under each of these formulas, the share of total grants increases steadily from the lowest to the second-highest poverty rate quintile, then is approximately constant for the 4th and 5th quintiles. While this partly reflects the slightly higher eligibility threshold for these formulas in comparison to Basic Grants (5% vs. 2% formula child rate), it primarily results from the structure of these formulas. Under both the Targeted and EFIG (within-state) formulas, the grant per formula child continuously increases as either the LEA's school-age child poverty rate, or its total number of children counted in the Title I-A formulas, increases. The share of funds going to LEAs in the 5th quintile (highest poverty rates) under each of these formulas is not substantially higher than the share going to LEAs with the second highest poverty rates (4th quintile) primarily because of the strong influence of high numbers of formula children on the allocation of funds,43 the influence of the expenditure factor,44 and the cap placed on Targeted Grant formula population weights for Puerto Rico.45

Table 13. Share of ESEA Title I-A Funds Allocated to LEAs by LEA Poverty Rate Quintile, FY2008

 

Poverty Rate Quintile

 

1

2

3

4

5

 

Title I-A Formula

(Poverty Rates of
13.59% or Below)

(Poverty Rates At or Above 13.59% But Below 18.64%)

(Poverty Rates At or Above 18.64% But Below 25.73%)

(Poverty Rates At or Above 25.73% But Below 31.37%)

(Poverty Rates At or Above 31.37%)

All LEAs

Percentage Share of Total Grants

Total Title I-A Grants, FY2008

16.5%

19.3%

19.0%

22.9%

22.3%

100.0%

Basic Grants (48% of FY2008 appropriations)

21.1%

19.7%

19.2%

20.5%

19.5%

100.0%

Concentration Grants (10% of FY2008 appropriations)

4.0%

23.1%

23.9%

25.2%

23.9%

100.0%

Targeted Grants (21% of FY2008 appropriations)

14.5%

18.4%

17.9%

24.8%

24.3%

100.0%

Education Finance Incentive Grants (21% of FY2008 appropriations)

14.1%

17.4%

17.6%

25.4%

25.6%

100.0%

Source: Table prepared by CRS.

Notes: Table reads (for example): The quintile of LEAs with the highest school-age child poverty rates received 22.3% of total FY2008 ESEA Title I-A grants, 19.5% of all funds allocated as Basic Grants for FY2008, 23.9% of Concentration Grants, 24.3% of Targeted Grants, and 25.6% of Education Finance Incentive Grants.

Figure 1. Share of ESEA Title I-A Funds Allocated to LEAs by Poverty Rate Quintile, FY2008

Source: Figure prepared by CRS.

Overall, the share of funds allocated to LEAs in the top two poverty rate quintiles is substantially higher under the Concentration (49.1%), Targeted (49.1%), and especially the EFIG (51.0%) Grant formulas than under the Basic Grant formula (40.0%). As a result, as long as all additional funds (i.e., amounts in excess of the previous year appropriation) continue to be allocated under the Targeted and EFIG Grant formulas, as has been the case each year from FY2002-2009, the degree of targeting on high poverty LEAs for total Title I-A grants would increase. Thus, overall targeting on high poverty LEAs has increased since the enactment of the NCLB.

While noteworthy, at least by historical standards, these shifts are nevertheless relatively marginal. For example, the share of total Title I-A funds allocated to LEAs in the two highest poverty rate quintiles rose from 42.3% for FY2002 (when Targeted and EFIG Grants were first funded and Basic Grants constituted 69% of total Title I-A LEA grant appropriations) to 45.2% for FY2008 (when Basic Grants constitute 48% of total Title I-A LEA grant appropriations). Another way to evaluate trends in targeting is to compare the share of grants actually allocated to LEAs in the top two poverty rate quintiles for FY2008 with an estimate of this share if FY2008 funds were allocated in the same manner as in the last pre-NCLB year of FY2001, when 84% of Title I-A appropriations was allocated under the Basic Grant formula and 16% under Concentration Grants. Applying that fund distribution from FY2001, 40.6% of FY2008 funds would have gone to LEAs in the top two poverty rate quintiles versus 45.2% for FY2008 actual grants.

A partial reason why increases in targeting, measured as above, are relatively marginal is that allocations under the Targeted and EFIG Grant formulas are highly influenced by the number, as well as the percentage, of formula children in each LEA, while this sort of targeting analysis identifies high poverty LEAs only in terms of their percentage of formula children. If "high poverty" LEAs were defined as those with either high percentages or high numbers of Title I-A formula children, the estimated increase in targeting would be slightly greater. For example, defining "high poverty" LEAs as those in one of the top two quintiles in the statutory Targeted and EFIG Grant formulas (i.e., 7,852 or more formula children, or a formula child percentage of 30.16% or higher), 53.4% of actual FY2008 grants went to such "high poverty" LEAs compared to an estimated 47.9% under the FY2001 distribution (84% Basic Grants and 16% Concentration Grants). This difference, of 5.5 percentage points, is slightly higher than the 4.6 percentage point differential based on poverty rates alone.

Finally, while debates regarding the targeting of Title I-A funds have primarily focused on shifting fund distribution toward areas with the greatest concentrations of poverty, some have been concerned about declines in the share of funds going to relatively low poverty LEAs. While low poverty LEAs may be assumed to have less need for Title I-A assistance in general, they are experiencing declines in funding at a time when they are subject to substantial and increasing requirements applicable to all LEAs that participate in Title I-A. In particular, LEAs with a school-age child poverty rate of between 2.0% and 5.0% are generally eligible only for Basic Grants, funding for which has declined by 8.0% in nominal terms since enactment of the NCLB, from $7,169,471,000 for FY2001 to $6,597,946,000 for FY2008.

Should the Population Weighting Factors of the Targeted and EFIG Formulas Be Modified to More Equally Favor LEAs With Large Numbers of School-Age Children in Poor Families and LEAs With High Poverty Rates?

As is discussed above, both the Targeted and the EFIG Grant formulas are designed to allocate to LEAs increased amounts of aid per formula child as either their school-age child poverty rate or their total number of formula children rises. The scales of steadily increasing weights applied to LEA formula child counts would appear to favor LEAs with high poverty rates, because relatively higher weights are assigned to LEAs with high poverty rates than to those with high numbers of formula children. For example, in the Targeted Grant formula, the highest weight assigned on the basis of numbers is 3.0 while the maximum weight assigned on the basis of poverty rates is 4.0.

However, in practice, these formulas tend to favor LEAs with either high numbers of formula children as well as those with high poverty rates, and in some respects may seem to favor LEAs with moderately large numbers of formula children over those with moderately high poverty rates. The major reasons for this effect are that (a) a very large LEA will have a much larger share of its formula children weighted at the highest point in the scale than will a LEA with a very high school-age child poverty rate, and (b) LEAs with moderately large numbers of formula children are treated at least as favorably as LEAs with marginally lower numbers of formula children but much higher school-age child poverty rates.

One way to view the level of targeting provided under the Targeted and EFIG Grant formulas is to examine the average grant per formula child for high poverty LEAs versus state averages. Table 14, below, provides the Title I-A grant per formula child, by formula, for the 15 LEAs in the nation with the largest number of formula children for FY2008. The table also provides these statistics for the LEA with the highest poverty rate in these states plus the state average grants per formula child, by formula. LEAs are compared with others in the same state to adjust for variations in grants per child arising from statewide factors including the expenditure factor used in all formulas plus the effort and equity factors of the EFIG formula.

As seen in Table 14, Basic and Concentration Grants per formula child are approximately the same for all LEAs in the same state (assuming minimum LEA eligibility criteria are met); variations in grants per formula child result primarily from hold harmless effects. However, LEAs that are among the 15 largest in the nation receive much higher grants per formula child than other LEAs in the same state under the Targeted and EFIG Grant formulas. Targeted Grants per formula child are in many cases more than twice as high as the average for all other LEAs in the state for the largest LEA in the states shown in Table 14, while EFIG Grants per formula child are in some cases more than three times as high. In most cases, the Targeted and EFIG Grants per formula child are higher than the state average for LEAs in each state having the highest poverty rate, although not as high as for the LEA with the largest number of formula children. This reflects a general pattern whereby the Targeted and EFIG Grant formulas are favorable to LEAs with both high numbers of formula children and high poverty rates, but generally somewhat more favorable to the former.

Table 14. Title I-A: Illustration of the Allocation of Funds to the Nation's 15 Largest LEAs
Compared to Other LEAs in the Same State

State

LEA

Total Number of Formula Children, FY08

Formula Child Percentage, FY08

Total Title I-A Grant Per Child

Basic Grant Per Child

Concentration Grant Per Child

Targeted Grant Per Child

Education Finance Incentive Grant Per Child

CA

Los Angeles Unified

245,840

28.84%

$1,760

$641

$160

$463

$495

CA

West Fresno Elementary

611

53.60%

$1,738

$773

$182

$401

$383

CA

State Average

 

 

$1,347

$641

$135

$303

$269

CA

Average for All Other LEAs in the State

 

 

$1,248

$640

$129

$264

$214

 

 

 

 

 

 

 

 

 

FL

Broward County School District

42,837

13.87%

$1,419

$576

$149

$383

$311

FL

Dade County School District

89,990

21.97%

$1,499

$576

$149

$427

$347

FL

Holmes County

977

33.10%

$1,208

$576

$149

$266

$217

FL

State Average

 

 

$1,324

$576

$139

$336

$273

FL

Average for All Other LEAs in the State

 

 

$1,269

$576

$136

$307

$250

 

 

 

 

 

 

 

 

 

IL

City of Chicago School District 299

138,144

26.56%

$2,273

$910

$230

$551

$581

IL

Cairo Community Unit School District

443

61.61%

$2,080

$857

$188

$527

$507

IL

State Average

 

 

$1,692

$847

$165

$360

$321

IL

Average for All Other LEAs in the State

 

 

$1,315

$805

$123

$235

$152

 

 

 

 

 

 

 

 

 

MI

Detroit City School District

81,330

38.64%

$2,186

$749

$193

$549

$694

MI

Covert Public Schools

409

54.24%

$2,149

$836

$200

$513

$600

MI

State Average

 

 

$1,629

$773

$146

$342

$367

MI

Average for All Other LEAs in the State

 

 

$1,442

$781

$130

$273

$257

 

 

 

 

 

 

 

 

 

NV

Clark County School District

46,446

14.94%

$1,370

$548

$141

$370

$310

NV

Mineral County

181

21.19%

$970

$548

$141

$177

$103

NV

State Average

 

 

$1,277

$548

$131

$331

$267

NV

Average for All Other LEAs in the State

 

 

$1,021

$549

$103

$222

$147

 

 

 

 

 

 

 

 

 

NY

Bronx County

110,726

38.65%

$2,244

$880

$223

$619

$522

NY

Kings County

137,262

29.52%

$2,292

$902

$228

$628

$533

NY

New York County

57,676

32.89%

$2,184

$910

$230

$577

$467

NY

Queens County

58,060

16.24%

$2,364

$1,053

$266

$577

$468

NY

Kiryas Joel Village Union

2,620

52.12%

$2,043

$858

$217

$549

$419

NY

State Average

 

 

$1,975

$917

$202

$481

$376

NY

Average for All Other LEAs in the State

 

 

$1,556

$911

$158

$299

$189

 

 

 

 

 

 

 

 

 

PA

Philadelphia City School District

89,179

33.55%

$2,355

$814

$210

$603

$728

PA

Farrell Area School District

506

43.66%

$2,135

$921

$233

$479

$503

PA

State Average

 

 

$1,732

$844

$155

$361

$372

PA

Average for All Other LEAs in the State

 

 

$1,498

$855

$134

$270

$239

 

 

 

 

 

 

 

 

 

TN

Memphis City School District

37,567

27.70%

$1,475

$548

$141

$353

$433

TN

Oneida Special School District

522

49.62%

$1,471

$548

$141

$355

$427

TN

State Average

 

 

$1,193

$549

$129

$250

$266

TN

Average for All Other LEAs in the State

 

 

$1,128

$549

$126

$226

$227

 

 

 

 

 

 

 

 

 

TX

Dallas Independent School District

50,609

27.17%

$1,623

$705

$178

$384

$355

TX

Houston Independent School District

77,606

31.34%

$1,539

$601

$152

$408

$378

TX

Santa Maria Independent School District

388

72.12%

$1,546

$559

$144

$438

$405

TX

State Average

 

 

$1,287

$593

$136

$290

$269

TX

Average for All Other LEAs in the State

 

 

$1,246

$586

$132

$274

$254

 

 

 

 

 

 

 

 

 

WI

Milwaukee

40,118

34.04%

$2,192

$889

$225

$484

$595

WI

Augusta

327

35.12%

$1,858

$843

$213

$360

$442

WI

State Average

 

 

$1,661

$849

$125

$308

$379

WI

Average for All Other LEAs in the State

 

 

$1,393

$829

$75

$220

$270

Source: Table prepared by CRS.

A major reason for this pattern is that LEAs with large numbers of formula children are often able to apply relatively high weights to higher proportions of their formula children than are smaller LEAs with relatively high percentages of formula children. This can be illustrated by the following comparison of Los Angeles, California, a LEA with a very high number of formula children, with the Hidalgo Independent LEA in Texas, that has one of the highest school-age child poverty rates in the nation. The following table shows the number and percentage share of each LEA's formula children for FY2008 to which various weights are applied under the number and percentage scales for Targeted Grants. After each calculation is made for each LEA, the greater of the two is selected for use in the formula; therefore, the numbers scale is ultimately applicable to Los Angeles and the percentage scale to Hidalgo. As indicated below, a large majority of the total formula child count for Los Angeles (85.6%) falls in the highest weight category (3.0) on the numbers scale, while a much smaller percentage of Hidalgo's formula children (39.3%) falls in the highest weight category (4.0) on the percentage scale.

Table 15. Weighted Formula Child Counts for FY2008

 

 

Local Educational Agency

 

 

Los Angeles

Hidalgo

 

Weight applied to formula children in this range

Number of Formula Children Falling Within the Range

Percentage
of Total Formula Children Falling Within this Range

Number of Formula Children Falling Within the Range

Percentage of Total Formula Children Falling Within this Range

Formula child number range

0-691

1.00

691

0.3%

691

43.9%

692-2,262

1.50

1,571

0.6%

883

56.1%

2,263-7,851

2.00

5,589

2.3%

0

0.0%

7,852-35,514

2.50

27,663

11.3%

0

0.0%

35,515 and above

3.00

210,326

85.6%

0

0.0%

Total weighted child count based on numbers of formula children

 

714,361

100.0%

2,016

100.0%

Formula child percentage range

Less than or equal to 15.58%

1.00

132,810

54.0%

389

24.7%

Above 15.58% but less than or equal to 22.11%

1.75

55,664

22.6%

163

10.4%

Above 22.11% but less than or equal to 30.16%

2.50

57,369

23.3%

201

12.8%

Above 30.16% but less than or equal to 38.24%

3.25

0

0.0%

202

12.8%

Above 38.24%

4.00

0

0.0%

619

39.3%

Total weighted child count based on school-age child poverty rates

 

373,647

100.0%

4,308

100.0%

Source: Table prepared by CRS.

Note: Table reads (in part)—The total weighted formula child count for Los Angeles is 714,361 on the numbers weighting scale and 373,647 on the percentage weighting scale; therefore, the numbers scale weighted child count is used to calculate Targeted Grants for Los Angeles. In contrast, the total weighted formula child count for Hidalgo is 2,016 on the numbers weighting scale and 4,308 on the percentage weighting scale; therefore, the percentage scale weighted child count is used to calculate Targeted Grants for Hidalgo. Further, 85.6% of all of the Los Angeles formula children are weighted at the highest level (3.0) on the scale used to calculate its Targeted Grants (the numbers scale), while only 39.3% of all of the Hidalgo formula children are weighted at the highest level (4.0) on the scale used to calculate its Targeted Grants (the percentage scale).

Another perspective on the impact of the Targeted and EFIG Grant formulas on different types of LEAs is provided in Table 16, below. It again focuses on different types of LEAs in the same state, to eliminate the influence of statewide formula factors, especially the expenditure factor.46 The table compares state average grants per formula child, by formula, with grants per formula child to three types of LEAs: (1) LEAs with relatively large numbers, but relatively low percentages, of formula children, (2) LEAs with relatively large numbers and above-average percentages of formula children, and (3) LEAs with percentages of formula children that are among the highest in the state. LEAs in these categories are compared in three states—Florida, Maryland, and Virginia—that have a number of LEAs in each of these categories. The general pattern seen in Table 16 for these states is that LEAs in both of the first two categories—LEAs with relatively large numbers, but relatively low percentages, of formula children and LEAs with relatively large numbers and above-average percentages of formula children—invariably receive Targeted, EFIG, and total grants per formula child that are significantly above the state average. LEAs that have the state's highest percentages of formula children also receive higher than average grants in Virginia, but not in Florida or Maryland. This is another reflection of the way in which even moderately high numbers of formula children can substantially influence the distribution of Targeted and EFIG Grants, even for LEAs that have very low school-age child poverty rates by national standards.

Table 16. Illustration of the Effect of the Title I-A Formulas on LEAs with High Numbers but Low Percentages of Formula Children Comparing LEAs in the Same State

 

 

 

Grants Per Formula Child, FY2008

LEA

Total Number of Formula Children, FY2008

Formula Child Rate, FY2008

Total Title I-A

Basic Grants

Concentration Grants

Targeted Grants

Education Finance Incentive Grants

A. Florida

LEAs with relatively large numbers, but relatively low percentages, of formula children

Broward County School District

42,837

13.9%

$1,419

$576

$149

$383

$311

Brevard County School District

10,414

12.8%

$1,284

$576

$149

$308

$251

LEAs with Relatively Large Numbers and Above-average Percentages of Formula Children

Dade County School District

89,990

22.0%

$1,499

$576

$149

$427

$347

LEAs with Very High Percentages of Formula Children

Hardee County School District

1,692

32.8%

$1,204

$576

$149

$264

$215

Holmes County School District

977

33.1%

$1,208

$576

$149

$266

$217

State Total/average

495,604

17.27%

$1,324

$576

$139

$336

$273

B. Maryland

LEAs with Relatively Large Numbers, but Relatively Low Percentages, of Formula Children

Baltimore County Public Schools

10,549

8.0%

$1,960

$911

$230

$441

$378

Prince George's County Public Schools

15,105

9.6%

$1,937

$822

$212

$475

$428

LEAs with Relatively Large Numbers and Above-average Percentages of Formula Children

Baltimore City Public School System

34,984

31.1%

$2,048

$822

$212

$521

$494

LEAs with Very High Percentages of Formula Children

Dorchester County Public Schools

1,035

20.4%

$1,483

$822

$212

$261

$188

Somerset County Public Schools

856

26.0%

$1,577

$822

$212

$313

$229

State Total/average

104,513

10.4%

$1,840

$872

$180

$419

$369

C. Virginia

LEAs with Relatively Large Numbers, but Relatively Low Percentages, of Formula Children

Virginia Beach City Public Schools

8,187

9.7%

$1,639

$712

$184

$354

$389

Fairfax County Public Schools

10,034

5.5%

$1,709

$712

$184

$377

$436

LEAs with Relatively Large Numbers and Above-average Percentages of Formula Children

Richmond City Public Schools

8,597

29.8%

$1,657

$712

$184

$360

$401

Norfolk City Public Schools

10,553

26.6%

$1,724

$712

$184

$382

$446

LEAs with Very High Percentages of Formula Children

Town of Colonial Beach Public Schools

208

42.8%

$1,846

$748

$184

$412

$503

Franklin City Public Schools

474

33.1%

$1,586

$712

$184

$329

$361

State total/average

157,663

12.2%

$1,434

$722

$127

$284

$300

Source: Table prepared by CRS.

Thus, while the Targeted and EFIG Grant formulas cannot be said to be sharply biased against relatively small LEAs with high poverty rates, LEA size (in terms of numbers of formula children) can be said to have somewhat greater influence on allocation patterns than poverty rates. If desired, an adjustment for this pattern could be accomplished through changes in the weights associated with different ranges of LEA formula child numbers and rates. For example, the formula child percentage scale could be left as is under the Targeted Grant formula, while revising the formula child number scale to top out at 2.0 or 2.5, instead of 3.0, with comparable changes made to the three sets of weighting scales used for EFIG Grants. Alternatively, LEAs with relatively large numbers of formula children could be required to have minimum poverty rates in order to benefit from the full formula child weight on the numbers scale. For example, if an LEA has a formula child rate of less than 20% (approximately the national average), its numbers scale weight could be multiplied by its formula child percentage divided by 20%.

Should the Expenditure Factors Continue to Play a Major Role in the Title I-A Formulas?

  • The state expenditure factors, while little noticed, have a major impact on the distribution of Title I-A grants. As discussed above, they are the same statewide, with no consideration of local variations; they likely provide little incentive to increase state spending on K-12 education; and they reflect differences in ability to raise revenues at least as much as differences in costs. Perhaps the best argument for continuing them is that they partially, roughly, and indirectly compensate for the lack of a geographical cost adjustment for the poverty population factor income thresholds.47

As was discussed earlier, the Title I-A expenditure factor for a given fiscal year is equal to state48 current49 expenditures for public elementary and secondary education per pupil in average daily attendance50 in the third preceding fiscal year. This amount is multiplied by a "federal share" of 0.4. Floor and ceiling constraints are placed on this calculation. For three of the four formulas—Basic, Concentration, and Targeted Grants—these are 80% and 120%, respectively, of the national average. For the fourth formula—Education Finance Incentive Grants—the floor and ceiling are 85% and 115%, respectively, of the national average. Until recently, the expenditure factor floor was adjusted for one jurisdiction, Puerto Rico, that is otherwise treated as a state in the Title I-A formulas.51 Table 17, below, presents state expenditure factors that were used in calculating Title I-A grants for FY2008 (school year 2008-2009).

As seen in Table 17, among the 50 states and the District of Columbia (i.e., excluding Puerto Rico), the FY2008 expenditure factor ranges from $2,956 to $4,435, a ratio of 1.5 to 1, for all formulas except EFIG Grants, and from $3,141 to $4,250, a ratio of 1.35 to 1, for the EFIG formula. In effect, the expenditure factor acts as a "weight" applied to each formula's population factor, resulting in grants per formula child that are 50% higher in states at the expenditure factor ceiling than for states at the floor under Basic, Concentration, and Targeted Grants, and 35% higher under EFIG Grants, when all other relevant factors are held constant.

Table 17. ESEA Title I-A State Expenditure Factors for FY2008

State

Expenditures Per Pupil

Expenditure Factor for all but EFIG Grants

Expenditure Factor for EFIG Grants

United States

$9,239

$3,696

$3,696

 

 

 

 

Alabama

$7,299

$2,956

$3,141

Alaska

$12,004

$4,435

$4,250

Arizona

$7,110

$2,956

$3,141

Arkansas

$8,143

$3,257

$3,257

California

$7,969

$3,188

$3,188

Colorado

$8,303

$3,321

$3,321

Connecticut

$13,014

$4,435

$4,250

Delaware

$11,871

$4,435

$4,250

District of Columbia

$16,416

$4,435

$4,250

Florida

$7,779

$3,112

$3,141

Georgia

$8,581

$3,432

$3,432

Hawaii

$10,322

$4,129

$4,129

Idaho

$6,524

$2,956

$3,141

Illinois

$9,734

$3,894

$3,894

Indiana

$9,072

$3,629

$3,629

Iowa

$8,041

$3,216

$3,216

Kansas

$9,381

$3,752

$3,752

Kentucky

$8,439

$3,376

$3,376

Louisiana

$8,115

$3,246

$3,246

Maine

$11,175

$4,435

$4,250

Maryland

$11,130

$4,435

$4,250

Massachusetts

$12,158

$4,435

$4,250

Michigan

$10,119

$4,048

$4,048

Minnesota

$9,223

$3,689

$3,689

Mississippi

$6,999

$2,956

$3,141

Missouri

$8,219

$3,288

$3,288

Montana

$8,865

$3,546

$3,546

Nebraska

$9,447

$3,779

$3,779

Nevada

$7,202

$2,956

$3,141

New Hampshire

$10,304

$4,122

$4,122

New Jersey

$14,842

$4,435

$4,250

New Mexico

$7,882

$3,153

$3,153

New York

$15,498

$4,435

$4,250

North Carolina

$7,505

$3,002

$3,141

North Dakota

$8,309

$3,324

$3,324

Ohio

$9,634

$3,854

$3,854

Oklahoma

$6,786

$2,956

$3,141

Oregon

$8,694

$3,478

$3,478

Pennsylvania

$10,997

$4,399

$4,250

Puerto Rico

$5,093

$2,956

$3,141

Rhode Island

$13,410

$4,435

$4,250

South Carolina

$8,166

$3,266

$3,266

South Dakota

$7,678

$3,071

$3,141

Tennessee

$6,754

$2,956

$3,141

Texas

$7,554

$3,022

$3,141

Utah

$5,521

$2,956

$3,141

Vermont

$12,739

$4,435

$4,250

Virginia

$9,618

$3,847

$3,847

Washington

$8,170

$3,268

$3,268

West Virginia

$9,232

$3,693

$3,693

Wisconsin

$9,981

$3,992

$3,992

Wyoming

$11,820

$4,435

$4,250

Source: CRS calculations based on source data from the U.S. Department of Education, National Center for Education Statistics.

Rationale for the Current Expenditure Factor

The Title I-A allocation formulas have included an expenditure factor since the program was initiated in 1965 (P.L. 89-10).52 Proponents of inclusion of the expenditure factor in the Title I-A allocation formulas have argued that this serves four policy goals:

  • It recognizes and compensates for differences in the costs of providing public elementary and secondary education in different areas of the Nation, by providing higher grants per child to areas with higher educational costs.
  • It provides an incentive for states and LEAs to increase spending for public elementary and secondary education, since Title I-A grants would be increased in response to increases in state and local spending.
  • It rewards states that spend relatively high amounts per pupil for public K-12 education.
  • Since no geographic cost of living adjustment is applied to the income thresholds used to calculate estimated numbers of school-age children in poor families, and since (it is argued) such costs of living tend to be correlated with variations in average expenditures per pupil, the expenditure factor helps to treat different regions of the nation more fairly than a reliance on poverty data alone.

There are limits associated with each of these rationales. A difficulty with the first rationale is that the expenditure factor is based on levels of state and local spending, and is not a measure or index of the costs of providing public elementary and secondary education in the various states. A cost index would measure the relative costs of providing a standardized service in different states or localities. In contrast, the Title I-A expenditure factor measures the average level of expenditures per pupil in each state for public elementary and secondary education services which may vary substantially in nature and quality. State average differences in expenditures per pupil are likely to vary in part due to underlying differences in the costs of providing similar educational services, but also in response to such factors as differences in ability to pay for educational services; the relative priority that a state's population places on elementary and secondary education (as opposed to other public services or tax limitation); the nature of the services provided; and even the relative share of a state's total population that consists of school-age children and youth.53 Further, since the Title I-A expenditure factor is the same for all LEAs in each state, it could not account for the potentially large differences in the costs of providing public education among the LEAs within states.

With respect to the second rationale for inclusion of the expenditure factor in the ESEA Title I-A allocation formulas, it is true that for many states, future Title I-A grants would increase in response to an increase in the level of state and local expenditures per pupil for public elementary and secondary education. However, several considerations are likely to significantly limit the impact of the Title I-A expenditure factor as a potential incentive to raise state and local public education spending, including the following.

  • An increase in state and local expenditures per pupil would lead to an increase in Title I-A grants only if the rate of increase in expenditures per pupil were greater than the national average increase in expenditures per pupil over the relevant time period. For example, if the national average level of expenditures per pupil increased by 4% from one year to the next, a state would have to increase its level of expenditures per pupil by more than 4% in order to experience any increase in Title I-A grants.
  • If a state's expenditure factor were already significantly more than 120%, or significantly below 80%, of the national average (115% and 85% for EFIG Grants), then an increase in state and local spending might have no effect on the state's Title I-A expenditure factor. For example, if a substantial increase in state and local spending were to increase a state's expenditure factor from 70% to 75%, or from 125% to 130%, of the national average, there would be no significant effect on Title I-A grants to the state.54
  • Since the expenditure factor is the same for all LEAs in each state, and is based on aggregate state and local source spending within the state, it is likely to provide little incentive for individual LEAs to increase their level of spending, since such increases might have very little impact on aggregate state expenditures per pupil.
  • Even if the relative level (in comparison to other states) of a state's expenditure factor were to increase, and the state fell between the floor and ceiling parameters, so an increase would have a direct impact on the state's expenditure factor as used in the Title I-A formulas, the amount of the consequent increase in Title I-A grants would likely be very small in comparison to the size of the increase in state and local spending. For example, if California (a state at 86% of the national average for FY2008) were to have increased its average expenditure per pupil for 2005-2006 (the year upon which the expenditure factor for FY2008 grants was based) by $100, and if all other relevant factors (such as the expenditure factors for other states) remained constant, then estimated total Part A grants to California for FY2008 would increase by $10.7 million. This is equivalent to only 1.7% of the $635 million in additional spending of state and local funds that would have been necessary to increase the expenditure factor for California by $100 per pupil for that year. It seems unlikely that such a relatively small "bonus," with a three-year time lag, would provide substantial motivation to states and LEAs in deciding whether to increase their level of spending for public elementary and secondary education.

Regarding the third rationale noted above, the current expenditure factor certainly does reward states with relatively high levels of spending for public K-12 education per pupil. However, it does not reward effort—expenditures relative to each state's fiscal capacity, i.e., its ability to raise revenues.

Finally, with respect to the fourth rationale for the current expenditure factor, it is true that the income thresholds used to determine whether or not a family is poor vary only by family size, not by the state or locality in which the family resides. As a result, it can be reasonably argued that the number of school-age children in poor families is underestimated in areas with high living costs, and possibly overestimated in areas with relatively low living costs. Proposals have been made to incorporate a geographic cost of living adjustment to the poverty thresholds,55 but none of these has yet been adopted by the Federal Government. Nevertheless, there is no widely-accepted measure of variation in state or local costs of living,56 and no definitive evidence that such costs are closely associated with variations in state average expenditures per pupil.

Alternatives to the Title I-A Expenditure Factor

Some policy analysts have expressed concerns about the Title I-A expenditure factor, partly because of the limits on the factor's rationale discussed above, and partly because of its "disequalizing" effect, since on average it provides higher grants per formula child to states with relatively high average levels of income than to states with relatively lower income levels.57

Use the National Average as the Expenditure Factor for All States

The simplest alternative to the current Title I-A expenditure factor provision would be to eliminate the factor completely—i.e, use the national average expenditure per pupil as the expenditure factor for all states.58 This would follow the example of the other very large federal K-12 education program—the state grant program under Part B of the Individuals with Disabilities Education Act (IDEA)—under which the national average is used as an expenditure factor for all states. Table 18, below, shows the estimated impact on FY2008 Title I-A grants of totally eliminating a (varying) expenditure factor—i.e., using the national average as the expenditure factor for all states, the District of Columbia, and Puerto Rico. This allocation formula change would significantly affect the estimated level of grants to most states. While there would be no estimated change in grants to states receiving the minimum grant amount under all four formulas (Alaska, Delaware, New Hampshire, North Dakota, South Dakota, Vermont and Wyoming), grants to many states would change substantially. Estimated grants would rise by 10% or more for such relatively low expenditure factor states as Alabama, Nevada, North Carolina, Oklahoma, and Tennessee ; while they would decline by 10% or more for relatively high expenditure factor states such as Connecticut, Maine, Maryland, Massachusetts, Nebraska, New Jersey, and Pennsylvania.

It is worth noting that the "dividing line" between gaining and losing states as a result of eliminating the expenditure factor would not actually be the national average expenditure factor, but rather would be somewhat below that, typically approximately 93-95% of the national average. In other words, losing states would include not only all those with average expenditures per pupil above the national average, but also those with expenditures slightly below the average. This is because the national average is, in effect, an average weighted on the basis of the total school-age population in each state, whereas it would be the national average weighted by each state's number of poor and other children counted in the Title I-A formulas that would be relevant to Part A grants. Since many of the states with the greatest concentrations of poor school-age children also tend to have relatively low expenditure factors, the average expenditure factor weighted according to poor children is below the national average weighted according to total school-age children.

Table 18. Actual State Total Allocations under Title I, Part A of the Elementary and Secondary Education Act (ESEA) for FY2008 under Current Law and Estimated FY2008 Grants Under an Alternative Formula Under Which the National Average Expenditure Factor Is Used for All States

State

Estimated FY2008 Grant Using National Average Expenditure Factor for Each State

Actual FY2008 Grant

Percentage Difference, Estimated Grant vs. Actual

Alabama

$238,061,000

$215,192,000

10.6

Alaska

$38,846,000

$38,846,000

0.0

Arizona

$295,440,000

$274,777,000

7.5

Arkansas

$148,647,000

$144,268,000

3.0

California

$1,767,463,000

$1,698,808,000

4.0

Colorado

$137,091,000

$135,392,000

1.3

Connecticut

$104,022,000

$115,562,000

-10.0

Delaware

$38,380,000

$38,380,000

0.0

District of Columbia

$45,747,000

$47,295,000

-3.3

Florida

$707,349,000

$656,255,000

7.8

Georgia

$441,793,000

$446,271,000

-1.0

Hawaii

$42,060,000

$44,337,000

-5.1

Idaho

$50,229,000

$46,663,000

7.6

Illinois

$561,663,000

$593,980,000

-5.4

Indiana

$237,859,000

$247,109,000

-3.7

Iowa

$75,600,000

$72,717,000

4.0

Kansas

$89,079,000

$95,359,000

-6.6

Kentucky

$208,498,000

$208,551,000

0.0

Louisiana

$303,792,000

$294,843,000

3.0

Maine

$45,388,000

$51,525,000

-11.9

Maryland

$171,446,000

$192,239,000

-10.8

Massachusetts

$202,869,000

$233,354,000

-13.1

Michigan

$475,441,000

$527,255,000

-9.8

Minnesota

$119,428,000

$126,936,000

-5.9

Mississippi

$204,985,000

$187,346,000

9.4

Missouri

$229,829,000

$225,205,000

2.1

Montana

$42,728,000

$43,555,000

-1.9

Nebraska

$54,244,000

$60,246,000

-10.0

Nevada

$90,651,000

$80,755,000

12.3

New Hampshire

$38,201,000

$38,198,000

0.0

New Jersey

$248,351,000

$286,765,000

-13.4

New Mexico

$119,596,000

$113,156,000

5.7

New York

$1,117,112,000

$1,226,786,000

-8.9

North Carolina

$396,320,000

$358,570,000

10.5

North Dakota

$33,747,000

$33,742,000

0.0

Ohio

$473,890,000

$511,797,000

-7.4

Oklahoma

$164,870,000

$148,406,000

11.1

Oregon

$136,247,000

$139,987,000

-2.7

Pennsylvania

$498,612,000

$565,518,000

-11.8

Puerto Rico

$572,073,000

$510,525,000

12.1

Rhode Island

$50,223,000

$52,978,000

-5.2

South Carolina

$211,199,000

$205,597,000

2.7

South Dakota

$41,542,000

$41,539,000

0.0

Tennessee

$267,630,000

$239,072,000

11.9

Texas

$1,406,168,000

$1,299,356,000

8.2

Utah

$65,921,000

$60,019,000

9.8

Vermont

$32,867,000

$32,862,000

0.0

Virginia

$204,003,000

$226,096,000

-9.8

Washington

$196,828,000

$191,853,000

2.6

West Virginia

$93,647,000

$99,607,000

-6.0

Wisconsin

$186,805,000

$199,030,000

-6.1

Wyoming

$31,516,000

$31,516,000

0.0

Total

$13,755,995,000

$13,755,995,000

0.0

Source: Actual grants for FY2008—U.S. Department of Education. Estimated grants for FY2008 using the national average expenditure factor for all states—CRS estimates based on data provided by the U.S. Department of Education.

Note: These estimates are provided solely to assist in comparisons of the relative impact of alternative formulas and funding levels in the legislative process. They are not intended to predict specific amounts that states will receive.

Replace the Expenditure Factor With a Cost Index

A second alternative to the current Title I-A expenditure factor would involve substituting a cost index for it. This would be conceptually appropriate, assuming that the primary purpose of the expenditure factor is to reflect differences in costs. The major obstacle is the lack of an official, or otherwise widely accepted, index for state or local variations in the costs of providing public elementary and secondary education. Nevertheless, serious efforts have been made to develop such an index, and the results of one such recent project are shown in Table 19, below. Table 19 compares the current Title I-A expenditure factor (with both sets of floors and ceilings, 80-120% and 85-115%) with a Comparable Wage Index (CWI) that has been developed as a measure for comparing educational costs across states and localities. Both the expenditure factor and the CWI are expressed as index numbers with a national average value of 1.00. No floor or ceiling has been applied to the CWI (as it falls beyond the bounds of 0.80-1.20 in extremely few cases), while the two current combinations of floor and ceiling have been applied to the Title I-A expenditure factor.

The CWI attempts to measure state and local differences in the costs of providing public K-12 education by focusing on the primary source of such costs, salaries for teachers and other professional staff.59 Further, in order to minimize the influence of factors that can be influenced by SEA and LEA policies, the CWI is based on wages paid to individuals in professions with similar educational requirements outside of K-12 education. Thus, the focus is on the competitive labor market conditions faced by local school systems in hiring and retaining staff.

As can be seen in columns E and F of Table 19, there are numerous substantial differences in relative state values (i.e., state value compared to the national average) for the Title I-A expenditure factors versus the CWI. This has at least two implications: (a) use of the CWI as a substitute for the current expenditure factor would result in very substantial shifts in Title I-A grants among the states; and (b) to the extent that the CWI may be considered to be an appropriate measure of the relative costs of providing public K-12 education, the correlation between this measure and the expenditure factor is very limited. There are several states where the expenditure factor is above the national average but the CWI is below average (Alaska, Hawaii, Kansas, Maine, Michigan, Nebraska, New Hampshire, Ohio, Pennsylvania, Vermont, Wisconsin, and Wyoming), and a smaller number of other states with a relatively low expenditure factor but a CWI that is at or above the national average (California and Washington). It is also clear that variations among the states with respect to the CWI are less than variations in the expenditure factor, even with the application of floors or ceilings to the latter but not the former. Only two states (Montana and South Dakota) have a CWI index for 2005 that is below .80, and only the District of Columbia has a CWI index above 1.20.

In order to reflect not only interstate, but also intrastate, differences in costs, the CWI has been calculated for individual LEAs, as well as states. However, the CWI is the same for all LEAs in the same metropolitan area. The current Title I-A expenditure factor could also be calculated for and applied to all individual LEAs, rather than being applied only at the state level.60

Table 19. Comparison of State Expenditure Factor and the Comparable Wage Index (CWI)

A

B

C

D

E

F

State

Expenditure Index FY2008, 80-120 Limits

Expenditure Index FY2008, 85-115 Limits

CWI Index

Percentage Difference, Col. D - Col. B

Percentage Diff, Col. D - Col. C

United States

1.00

1.00

1.00

0%

0%

Alabama

0.80

0.85

0.88

10%

3%

Alaska

1.20

1.15

0.95

-21%

-17%

Arizona

0.80

0.85

0.92

15%

8%

Arkansas

0.88

0.88

0.82

-7%

-7%

California

0.86

0.86

1.10

27%

27%

Colorado

0.90

0.90

0.96

7%

7%

Connecticut

1.20

1.15

1.10

-8%

-4%

Delaware

1.20

1.15

1.02

-15%

-12%

District of Columbia

1.20

1.15

1.23

2%

7%

Florida

0.84

0.85

0.93

10%

9%

Georgia

0.93

0.93

0.98

6%

6%

Hawaii

1.12

1.12

0.95

-15%

-15%

Idaho

0.80

0.85

0.81

1%

-5%

Illinois

1.05

1.05

1.03

-2%

-2%

Indiana

0.98

0.98

0.89

-10%

-10%

Iowa

0.87

0.87

0.84

-4%

-4%

Kansas

1.02

1.02

0.85

-17%

-17%

Kentucky

0.91

0.91

0.88

-3%

-3%

Louisiana

0.88

0.88

0.87

-1%

-1%

Maine

1.20

1.15

0.83

-30%

-27%

Maryland

1.20

1.15

1.08

-10%

-6%

Massachusetts

1.20

1.15

1.09

-9%

-5%

Michigan

1.10

1.10

0.98

-11%

-11%

Minnesota

1.00

1.00

0.97

-3%

-3%

Mississippi

0.80

0.85

0.83

4%

-2%

Missouri

0.89

0.89

0.90

2%

2%

Montana

0.96

0.96

0.74

-23%

-23%

Nebraska

1.02

1.02

0.85

-17%

-17%

Nevada

0.80

0.85

0.99

24%

17%

New Hampshire

1.12

1.12

0.93

-17%

-17%

New Jersey

1.20

1.15

1.13

-6%

-2%

New Mexico

0.85

0.85

0.88

3%

3%

New York

1.20

1.15

1.12

-7%

-3%

North Carolina

0.81

0.85

0.94

16%

11%

North Dakota

0.90

0.90

0.80

-11%

-11%

Ohio

1.04

1.04

0.96

-8%

-8%

Oklahoma

0.80

0.85

0.84

5%

-1%

Oregon

0.94

0.94

0.91

-3%

-3%

Pennsylvania

1.19

1.15

0.95

-20%

-17%

Puerto Rico

0.80

0.85

na

na

na

Rhode Island

1.20

1.15

1.01

-16%

-12%

South Carolina

0.88

0.88

0.90

2%

2%

South Dakota

0.83

0.85

0.76

-8%

-11%

Tennessee

0.80

0.85

0.92

15%

8%

Texas

0.82

0.85

0.99

21%

16%

Utah

0.80

0.85

0.92

15%

8%

Vermont

1.20

1.15

0.85

-30%

-26%

Virginia

1.04

1.04

1.08

4%

4%

Washington

0.88

0.88

1.04

17%

17%

West Virginia

1.00

1.00

0.85

-15%

-15%

Wisconsin

1.08

1.08

0.95

-12%

-12%

Wyoming

1.20

1.15

0.81

-33%

-30%

Source: CRS calculations based on data from the U.S. Department of Education, National Center for Education Statistics.

Note: The CWI data are for 2005.

Replace the Expenditure Factor With an Effort Index

Since major, if secondary, rationales for the current Title I-A expenditure factor are to provide an incentive for increased spending for public K-12 education or to reward high levels of expenditures, a third alternative to the current factor might be an effort index. This would reward high levels of expenditures for public K-12 education relative to each state's fiscal capacity—i.e., its ability to pay.

An effort index is included in the Title I-A Education Finance Incentive Grant formula. It is based on a three-year average of expenditures per pupil relative to personal income per capita for the state compared to the national average. However, in addition to affecting only one of the four Title I-A formulas, this factor is quite limited in its impact, as a floor of 0.95 and a ceiling of 1.05 are placed on the effort index (compared to a national average of 1.0).

A K-12 education expenditure effort index that is more broad than the one used in the EFIG formula is illustrated in Table 20, below. It is based upon total state and local expenditures for public K-12 education relative to a measure of Total Taxable Resources (TTR), not just personal income, compiled by the U.S. Department of Commerce. The TTR index is a comprehensive measure of the relative ability of state and local governments to raise revenues.61

Since, unlike the CWI-based cost index, the TTR-based effort index varies among the states approximately as widely as the Title I-A expenditure factor if left unconstrained (from 0.68 for Delaware to 1.43 for Vermont), separate comparisons are made between the two current versions of the expenditure factor and versions of the effort factor using the same limits (80%/120% and 85%/115% of the national average). Thus, Column E represents the percentage difference between the TTR-based effort index with 80% and 120% limits (Col. C of Table 20) and the expenditure factor with the same limits (Col. B of Table 19), and Column F represents the percentage difference between the TTR-based effort index with 85% and 115% limits (Col. D of Table 20) and the expenditure factor with those limits (Col. C of Table 19).

Table 20. Comparison of State Expenditure Factor and Education Expenditure Effort Index Based on Total K-12 Education Expenditures Relative to Total Taxable Resources (TTR)

A

B

C

D

E

F

State

TTR-Based Effort Index

TTR-Based Effort Index, 80-120 Limits

TTR-Based Effort Index, 85-115 Limits

Percentage Difference, 80-120 Limits

Percentage Difference, 85-115 Limits

United States

1.00

1.00

1.00

0%

0%

Alabama

0.94

0.94

0.94

17%

10%

Alaska

1.16

1.16

1.15

-3%

0%

Arizona

0.88

0.88

0.88

10%

3%

Arkansas

1.14

1.14

1.14

29%

29%

California

0.92

0.92

0.92

7%

7%

Colorado

0.80

0.80

0.85

-11%

-5%

Connecticut

1.01

1.01

1.01

-16%

-12%

Delaware

0.68

0.80

0.85

-33%

-26%

District of Columbia

0.71

0.80

0.85

-33%

-26%

Florida

0.75

0.80

0.85

-5%

0%

Georgia

1.02

1.02

1.02

10%

10%

Hawaii

0.91

0.91

0.91

-18%

-18%

Idaho

0.98

0.98

0.98

22%

15%

Illinois

0.95

0.95

0.95

-10%

-10%

Indiana

1.08

1.08

1.08

10%

10%

Iowa

0.93

0.93

0.93

7%

7%

Kansas

0.98

0.98

0.98

-3%

-3%

Kentucky

0.99

0.99

0.99

8%

8%

Louisiana

0.98

0.98

0.98

12%

12%

Maine

1.27

1.20

1.15

0%

0%

Maryland

0.90

0.90

0.90

-25%

-22%

Massachusetts

1.00

1.00

1.00

-17%

-13%

Michigan

1.25

1.20

1.15

10%

5%

Minnesota

0.89

0.89

0.89

-11%

-11%

Mississippi

1.08

1.08

1.08

34%

27%

Missouri

0.91

0.91

0.91

2%

2%

Montana

1.09

1.09

1.09

14%

14%

Nebraska

0.98

0.98

0.98

-4%

-4%

Nevada

0.69

0.80

0.85

0%

0%

New Hampshire

0.97

0.97

0.97

-13%

-13%

New Jersey

1.23

1.20

1.15

0%

0%

New Mexico

1.08

1.08

1.08

27%

27%

New York

1.18

1.18

1.15

-2%

0%

North Carolina

0.82

0.82

0.85

1%

0%

North Dakota

0.89

0.89

0.89

-1%

-1%

Ohio

1.09

1.09

1.09

5%

5%

Oklahoma

0.96

0.96

0.96

20%

13%

Oregon

0.88

0.88

0.88

-6%

-6%

Pennsylvania

1.09

1.09

1.09

-9%

-6%

Puerto Rico

na

na

na

na

na

Rhode Island

1.11

1.11

1.11

-8%

-4%

South Carolina

1.07

1.07

1.07

21%

21%

South Dakota

0.78

0.80

0.85

-4%

0%

Tennessee

0.78

0.80

0.85

0%

0%

Texas

0.99

0.99

0.99

21%

17%

Utah

0.90

0.90

0.90

12%

6%

Vermont

1.43

1.20

1.15

0%

0%

Virginia

0.87

0.87

0.87

-17%

-17%

Washington

0.83

0.83

0.85

-6%

-4%

West Virginia

1.34

1.20

1.15

20%

15%

Wisconsin

1.10

1.10

1.10

2%

2%

Wyoming

0.93

0.93

0.93

-23%

-19%

Source: CRS calculations based on data from the U.S. Department of Commerce and the U.S. Department of Education, National Center for Education Statistics.

Again, as indicated in Columns E and F of Table 20, there are in many cases notable differences between the relative level of state effort, according to this measure, and of the state expenditure factor (which is not repeated in Table 20—see Table 19). States where the effort index is much higher (at least 10% in one or both comparisons) than the expenditure index include Alabama, Arizona, Arkansas, Georgia, Idaho, Indiana, Louisiana, Michigan, Mississippi, Montana, New Mexico, Oklahoma, South Carolina, Texas, Utah, and West Virginia. Many of these are Southern and Western states with relatively low expenditure factors but also with relatively low total taxable resources. In contrast, states where the effort index is much lower (again, at least 10% in one or both comparisons) than the expenditure index include Colorado, Connecticut, Delaware, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Hampshire, Virginia, and Wyoming, plus the District of Columbia. Many of these states have expenditure factors that are above the national average, but also have relative levels of total taxable resources that exceed the national average to a substantially greater degree.

Should There Be Some Consolidation of the Four Different Allocation Formulas?

Although there are differences among the four Title I-A allocation formulas, and each of them has a somewhat distinctive distributional pattern, it may be questioned whether each formula serves a sufficiently distinct role and purpose as to justify its continued use. This potential issue will be noted only briefly here, but there may be interest in some degree of consolidation of the four Title I-A LEA allocation formulas. It has never been directly intended that portions of Title I-A appropriations be allocated under four different formulas. The four-formula strategy has resulted from compromises over proposals to replace previous proposals with a single new formula. At the least, the use of four different allocation formulas for portions of each year's Title I-A appropriations leads to complication and occasional confusion.

Historically, the Tile I-A program has tended to have one formula under which almost all LEAs qualify, Basic Grants, and one or more formulas that are targeted more on LEAs relatively high percentages and/or numbers of school age children in poor families. This pattern could be resumed through either selection of one of the remaining three formulas—Concentration, Targeted, and EFIG Grants—or some new formula combining elements of these, as the second, more targeted, formula. For example, the Concentration, Targeted and EFIG Grant formulas be combined into a single formula through application of the effort and equity factors to the Targeted Grant formula, incorporating the Targeted Grant formula's child weighting factors, along with a higher eligibility threshold, as under Concentration Grants.

Should the Authorization Level for Title I-A Continue to Be Specified for Future Years, and If So, at What Levels?

Since the enactment of the NCLB, a great deal of attention has been paid to the level of funding appropriated for Title I-A in comparison to the amount authorized. Over the decades since enactment of the original ESEA in 1965, the typical pattern of ESEA authorizing statutes has been to specify an authorized level of appropriations only for the first year of the authorization period (if at all) for Title I-A, and to simply authorize "such sums as may be necessary" for the remaining years. The NCLB broke with this pattern, specifying authorization amounts for Title I-A for each of FY2002-2007.

The question of authorized funding levels is closely linked to concepts of "full funding" for this program. For Title I-A, many program advocates have argued that the "full funding" level should be based on maximum payment calculations under the Basic Grant allocation formula, even in years when no authorization level was explicitly specified. As discussed earlier in this report, the Title I-A Basic Grant formula establishes a maximum payment based on poor and other "formula children" multiplied by a state expenditure factor. The total of these maximum payments is understood by a number of analysts to represent the "full funding" level for Part A. In contrast, at least during periods when Title I-A authorizations are explicitly specified in statute (e.g., FY2002-2007), many argue that these authorization amounts are the only meaningful concepts of "full funding" for Title I-A.

There is a link between the two alternative "full funding" concepts for Title I-A. In the NCLB, Title I-A appropriations authorization levels were specified for each of FY2002-FY2007. Under the automatic extension provisions of the General Education Provisions Act, the FY2007 authorization applied to FY2008 as well. The FY2007 amount was set at a level approximately equal to the level of maximum Basic Grants as of FY2001 (the year preceding enactment of NCLB). Thus, the implicit goal was for funding to increase from the then-current appropriation level to the then-current maximum payment level for Basic Grants over the period of FY2002-2007. In practice, over the FY2002-FY2007 period, the appropriation for Title I-A was below the authorized amount each year, with the gap between authorization and appropriation increasing each year. In addition, by the end of the period to which the NCLB authorizations applied (FY2008), maximum Basic Grants had grown from approximately $25 billion to an estimated $33.2 billion. Thus, the practical impact of specifying authorization amounts for each year may be questioned. In addition, any authorization levels for the Title I-A program thus far may be deemed to be somewhat arbitrary, since there is no precise way to specify the level of additional spending necessary to raise the achievement of low-achieving pupils to a proficient level.

At the same time, specified authorizations do provide a goal for those seeking increased funding, and express the judgment of those involved in the authorizing process of an appropriate level of funding. Finally, if authorization issues are to be specified for future years, there may be proposals to link implementation of certain Title I-A requirements to the provision of authorized (or some other specified) levels of appropriations, or even to appropriate the authorized amounts in reauthorization legislation.

Table 21, below, provides appropriations for FY2001-FY2009 compared to authorization levels for Title I-A overall, where applicable. Under the automatic extension provisions for the General Education Provisions Act, the Title I-A authorization level for FY2008 was the same as the FY2007 level. For fiscal years beyond FY2008, there is no specific authorization level for Title I-A, until new authorization legislation is enacted.

For FY2002, the Title I-A authorization was $13.5 billion, and the appropriation was $10.35 billion.62 This FY2002 appropriation level represented a substantial increase of 17.0% over the FY2001 level for Title I-A. Appropriations also increased significantly, by 14.0%, for FY2003 compared to FY2002. Appropriations continued to increase, but at a declining rate, for FY2004 compared to FY2003 (5.6%). Funding was essentially flat over the period of FY2004-FY2007 for Title I-A. Finally, for FY2008, funding for Title I-A increased by 8.3% over FY2007. For FY2009, regular appropriations are 4.3% over those for FY2008. However, the additional amounts provided under the American Recovery and Reinvestment Act (ARRA) represent a much larger increase, the specific size of which depends on the extent to which these additional funds are considered as FY2009 or FY2010 amounts.

Table 21. Authorizations and Appropriations for ESEA Title I-A Grants to LEAs, FY2001-FY2009

(in millions of dollars)

Fiscal Year

ESEA Title I-A

Appropriation

Authorization

Appropriation as a Percentage of Authorization

2001

$8,763

na

na

2002

10,250

$13,500

76%

2003

11,689

16,000

73%

2004

12,342

18,500

67%

2005

12,740

20,500

62%

2006