ESEA: Title I-A Poverty Measures and Grants to Local Education Agencies and Schools

November 9, 2020 R46600

ESEA: Title I-A Poverty Measures and Grants to November 9, 2020
Local Education Agencies and Schools
Rebecca R. Skinner
The primary source of federal aid to elementary and secondary education is the Elementary and
Specialist in Education
Secondary Education Act (ESEA)—particularly its Title I-A program, which authorizes federal
Policy
aid for the education of disadvantaged students. The ESEA was initially enacted in 1965 (P.L.

89-10) “to strengthen and improve educational quality and educational opportunities in the
Nation’s elementary and secondary schools.” The Title I-A program in particular provides

supplementary educational and related services to low-achieving and other students attending
elementary and secondary schools with relatively high concentrations of students from low-income families, as well as
eligible students who live in the areas served by these public schools but attend private schools. Title I-A is also a vehicle to
which a number of requirements affecting broad aspects of public elementary and secondary education for all students have
been attached as conditions for receiving grants.
Since the enactment of the ESEA, Title I-A grants have always been calculated based on one or more measures of a child’s
family financial situation (also referred to as poverty measures), with an emphasis on providing aid to schools serving
concentrations of children from low-income families. Currently, four individual formulas are used to determine Title I-A
grants to local educational agencies (LEAs). These formulas are based on a variety of factors, including data available from
the Small Area Income and Poverty Estimates (SAIPE) program, which is administered by the U.S. Census Bureau. SAIPE
includes estimates of the number of children ages 5-17 living in families in poverty.
Unlike other federal elementary and secondary education programs, Title I-A is unique in its requirement that funds be
provided to public schools based on a statutorily prescribed methodology. While the focus of this methodology continues to
be children from low-income families, SAIPE data generally are not available at the school-level, so LEAs must use available
proxies for low-income status to distribute Title I-A funds to schools. For decades, the proxy measure has generally been
students eligible for free and reduced-price lunch (FRPL) under the National School Lunch Program (NSLP). While using
this measure of the number of children from low-income families has always had caveats, it has become increasingly
complicated due to the implementation of the Community Eligibility Provision (CEP) under the NSLP. Schools participating
in CEP no longer identify children as being from low-income families in the same way as in past years. This has implications
for identifying the number of children from low-income families in a given school for grant distribution purposes as well as
for disaggregating data on student performance based on whether a student is from a low-income family or not.
Recognizing the need to continue to have a school-level poverty measure, the U.S. Department of Education (ED) has
undertaken several studies to identify a new measure or a socioeconomic status (SES) measure that could supplement
existing data. For example, ED is currently working with states that received a grant under the State Longitudinal Data
Systems program to test a new measure based on students’ addresses rather than FRPL eligibility. ED previously examined
the feasibility of creating a flexible neighborhood poverty indicator that could be used to identify schools in low-income
neighborhoods based on data from the American Community Survey (ACS) and estimation techniques developed for spatial
statistics. It also has examined the utility of creating school-level poverty estimates using data from the ACS, which are used
to develop the SAIPE data employed in determining Title I-A LEA grant amounts, but found the resulting data to be too
unreliable. ED also has examined the use of a new set of SES measures that would include a focus on poverty. ED asserts
that the development of a new school-level poverty measure that requires the collection of new data from all schools in the
United States would be cost prohibitive and would require new statutory authority and new funding. ED suggests that trying
to repurpose existing data would be a more effective solution. Until a new measure becomes av ailable, however, FRPL data
remain the standard measure for identifying low-income students at the school level, for making Title I-A grants to schools,
and for Title I-A accountability and reporting requirements.
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Introduction
The primary source of federal aid to elementary and secondary education is the Elementary and
Secondary Education Act (ESEA)—particularly its Title I-A program, which authorizes federal
aid for the education of disadvantaged students. The ESEA was initial y enacted in 1965 (P.L. 89-
10) “to strengthen and improve educational quality and educational opportunities in the Nation’s
elementary and secondary schools.” The Title I-A program in particular provides supplementary
educational and related services to low-achieving and other students attending elementary and
secondary schools with relatively high concentrations of students from low -income families, as
wel as eligible students who live in the areas served by these public schools but attend private
schools.1 Title I-A is also a vehicle to which a number of requirements affecting broad aspects of
public elementary and secondary education for al students have been attached as conditions for
receiving grants. The ESEA was most recently comprehensively amended and reauthorized by the
Every Student Succeeds Act (ESSA; P.L. 114-95). Appropriations for the ESEA for FY2020 were
$25.9 bil ion. Of this, $16.3 bil ion was appropriated for the Title I-A program.
Since the enactment of the ESEA, Title I-A grants always have been calculated based on one or
more measures of a child’s family financial situation (also referred to as poverty measures), with
an emphasis on providing aid to schools serving concentrations of children from low-income
families. Currently, four individual formulas are used to determine Title I-A grants to local
educational agencies (LEAs). These formulas are based on a variety of factors, including data
available from the Smal Area Income and Poverty Estimates (SAIPE) program, which is
administered by the U.S. Census Bureau. SAIPE includes estimates of the number of children
ages 5-17 living in families in poverty. These children account for about 97% of al children
included in the Title I-A formula calculations (commonly referred to as formula children).
However, SAIPE data are limited in their coverage of LEAs as SAIPE does not include LEAs that
do not have traditional geographic boundaries, such as charter schools that are their own LEAs.
Thus, adjustments for these and other LEAs (e.g., newly formed LEAs not included in SAIPE for
a given year) must be made by state educational agencies (SEAs) using other data sources.
Questions have been raised over the years about the measure of poverty used for SAIPE
population estimates that are a foundational element of the Title I-A al ocation formulas, and
whether these data are the most appropriate to use for determining LEA grant amounts. A
summary discussion of these issues may be found in Appendix B.
Among ESEA programs, Title I-A is unique in its requirement that funds be provided to public
schools based on a statutorily prescribed methodology. While the focus of this methodology
continues to be children from low-income families, SAIPE data general y are not available at the
school-level, so LEAs are required by law to use other sources of a family’s income status to
distribute Title I-A funds to schools.2 For decades, the primary source of data has general y been
students eligible for free and reduced-price lunch (FRPL) under the National School Lunch
Program (NSLP). While the use of this measure of the number of children from low-income
families always has had caveats,3 it has become increasingly complicated due to the
implementation of the Community Eligibility Provision (CEP) under the NSLP. Schools
participating in CEP no longer identify children as being from low -income families in the same

1 Although T itle I-A funds are used to serve eligible private school students, funds remain under the control of public
school authorities (i.e., they are not transferred to private schools).
2 ESEA, Section 1113(a)(5).
3 As is discussed later in this report, these include variations in the rate at which the families of different types of
students apply for FRPL and errors in eligibility determinations among those who do apply.
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way as in past years. This has implications for identifying the number of children from low -
income families in a given school for grant distribution purposes as wel as for disaggregating
data on student performance based on whether a student is from a low-income family. As a result,
consideration is being given to whether there is a more effective and reliable way to measure low -
income status at the school level, as wel as questions about whether relying only on whether a
child is from a low-income family is the most appropriate way to identify disadvantaged children
for the purposes of the Title I-A program.
This report begins with an overview of the Title I-A formulas used by the U.S. Department of
Education (ED) to make grants to LEAs, with a focus on the numerous ways in which measures
of a child’s family income are employed. This is followed by an historical overview of the Title I-
A formulas and how the measures of poverty included in them have changed over time. The
discussion then focuses on measures of school-level poverty that are used to make Title I-A grants
from LEAs to the school level. This includes a focus on al ocation methods for the past 20 years
and complications that have arisen related to continuing to use these al ocation methods to make
grants to schools and for Title I-A accountability and reporting requirements. The last section of
the report discusses some alternative school-level poverty measures that ED has considered or is
currently examining. The report also includes two appendices. Appendix A provides an overview
of the key factors included in each of the Title I-A formulas. Appendix B provides a detailed
discussion about the standard federal poverty measure included in the SAIPE data used to
determine Title I-A LEA grants.
Allocation of Title I-A Funds by ED to States and
LEAs Under Current Law
This section provides an overview of the four formulas used to make Title I-A grants to LEAs and
how measures of a child’s family income are incorporated in multiple ways throughout these
formulas. As discussed below, the number of children living in families in poverty is the primary
income measure included in al four formulas and accounts for nearly al of the children included
in the formula child count that is used across the formulas. Al of the formulas include hold
harmless provisions that are based on the percentage of formula children in a given LEA. Each
formula also has minimum threshold criteria related to the formula child count or percentage of
formula children that an LEA must meet to be considered eligible to receive a grant under that
formula. Two of the four formulas apply additional weighting to the formula child count or
percentage of formula children in an LEA to adjust grant amounts further.
The four formulas Title I-A uses for the al ocation of funds to states and LEAs are the Basic
Grant, Concentration Grant, Targeted Grant, and Education Finance Incentive Grant (EFIG)
formulas. Individual LEAs may be eligible to receive grants under one, two, three, or al four of
the formulas. Although portions of each year’s appropriation are al ocated separately under each
of these formulas, when the funds reach LEAs they are combined and used jointly.
Under the current ESEA, appropriations for Basic Grants and Concentration Grants are limited to
the amount provided for each grant in FY2001, while appropriations in excess of this amount are
to be al ocated under the Targeted Grant and EFIG formulas.4 In practice, under annual

4 Section 1121 of the ESEA specifies that Basic Grants and Concen tration Grants are each to be appropriated the
amount they were appropriated in FY2001 and any funds in excess of this amount are to be appropriated equally to
T argeted Grants and EFIG. In practice, appropriations for Basic and Concentration Grants have be en below their
FY2001 appropriations levels for several years. All T itle I -A appropriations that are not provided for Basic Grants and
Concentration Grants are divided evenly between T argeted Grants and EFIG.
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appropriations legislation for each year since FY2001, the amount al ocated annual y under the
Concentration Grant formula has remained constant, the amount al ocated as Basic Grants has
declined somewhat (as across-the-board budget cuts for Title I-A overal in some years have been
applied only to Basic Grants), and remaining funds have been equal y split between the Targeted
Grant and EFIG formulas. For the latest fiscal year (FY2020), 40% of Title I-A appropriations
were al ocated under the Basic Grant formula, 8% under the Concentration Grant formula, and
26% each under the Targeted Grant and EFIG formulas.
Common Formula Elements Related to a Child’s Family Income
There are several elements included in al four of the Title I-A al ocation formulas that are related
to a child’s family income. Each of these factors is discussed below. A table summarizing the
Title I-A formula factors may be found in Appendix A.
Population Factor (also referred to as the formula child count): Each formula has a population
factor, which is the same in al four formulas. Currently, this factor comprises children ages 5–17
 in poor families, as estimated annual y by the SAIPE program and based on the
Census Bureau’s standard poverty income thresholds (these constitute 96.7% of
al formula children for FY2020);
 in certain institutions for neglected or delinquent children and youth, or in certain
foster homes (these constitute 3.3% of al formula children for FY2020); and
 in families receiving Temporary Assistance for Needy Families (TANF)
payments with incomes above the poverty income level for a family of four
(these constitute less than 0.1% of al formula children for FY2020).
The total number of children across these three groups wil be referred to in this report as formula
children. The report focuses primarily on the first category of Title I-A formula children—those
ages 5-17 in poor families, as estimated under the SAIPE program. As indicated above, almost al
children considered in the al ocation of Title I-A grants are included in this category. The
evolution of this formula population category over time, in terms of both the definition of low
family income/poverty as wel as sources for population estimates, are discussed in more detail
below.
The second category of children above—those in certain institutions for neglected or delinquent
children and youth or in certain foster homes—are included as a separate group because of their
special needs and because they are general y not included in the estimated poverty counts, even
though their income level may be low. They wil not be discussed further in this report because
they are a relatively smal and comparatively constant portion of the Title I-A al ocation formula
population.
The third category of children above—those in families receiving TANF payments above the
poverty level for a family of four—are discussed briefly in historical context. In any recent year
they have constituted a smal portion of the children considered in the al ocation of Title I-A
funds.
LEA Minimum Eligibility Threshold: Each formula has an eligibility threshold for LEAs, which is
a minimum number formula children, or a minimum formula child rate (formula children as a
percentage of total school-age children), that must be met to be eligible for grants in most cases.
The LEA minimum eligibility threshold varies by formula: it is 10 formula children and a school-
age child poverty rate of either 2% for Basic Grants, or 5% for the Targeted Grant and EFIG
formulas. For Concentration Grants, the LEA eligibility threshold is 6,500 formula children or a
15% school-age child poverty rate. With the partial exception of Concentration Grants, if an LEA
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ESEA: Title I-A Poverty Measures and Grants to Local Education Agencies and Schools

does not meet the eligibility threshold for a given year, the LEA hold-harmless provision (see
below) does not apply.
LEA Hold Harmless: Each of the formulas has a hold-harmless provision—a minimum annual
grant level for LEAs. The hold harmless is a percentage of the previous year’s grant under each
formula that ranges from 85%-95% based on an LEA’s formula child rate. Thus, hold-harmless
provisions preserve 85%-95% of the previous year’s funding levels for individual LEAs
regardless of changes in the students counted under the al ocation formulas, so long as LEA
minimum eligibility thresholds are met.5
There also are two additional factors that are used in each of the Title I-A formulas that are not
related to a child’s family income. They are discussed briefly below.
Expenditure Factor: Under each of the formulas, the population factor is multiplied by an
expenditure factor, which is based on state average expenditures per pupil (AEPP) for public K–
12 education, subject to minimum and maximum levels. For al except the EFIG formula, the
minimum AEPP is 80% and the maximum is 120% of the national average. For the EFIG
formula, the minimum is 85% and the maximum is 115% of the national average. These amounts
are further multiplied by a federal share of 0.4 to determine maximum authorized grants.
State Minimum Grant: In general, no state is to receive less than approximately 0.25% of total
al ocated Title I-A funds in amounts up to the FY2001 appropriation level, and approximately
0.35% of funds above that level, applied separately to each formula.
Under al four formulas, amounts determined on the basis of the formula factors described above
are reduced proportional y to the aggregate level of available funds, subject to LEA hold-harmless
and state minimum grant provisions.
Distinctive Elements of the Targeted Grant and EFIG Formulas
Related to a Child’s Family Income
In addition to these common elements, the Targeted Grant and EFIG formulas include other
factors focused on formula child counts or rates.
For al stages in the al ocation of funds under the Targeted Grant formula, as wel as the al ocation
of state total grants to LEAs under the EFIG formula, the formula children are assigned weights
on the basis of each LEA’s number of formula children or formula child rate. As a result, the
higher an LEA’s formula child number or rate is, the higher grants per child counted in the
formula it wil receive. Under the Targeted Grant formula, the weighting factors are applied in the
same manner nationwide; formula children in LEAs with the highest formula child numbers have
a weight of up to 3.0, and those in LEAs with the highest rates of such children have a weight of
up to 4.0, compared to a weight of 1.0 for formula children in LEAs with the lowest formula child
number and rate of such children. In contrast, under the EFIG formula the degree of targeting (in
terms of the ratio of the highest to the lowest weight) varies depending on the value of each
state’s equity factor (described below). Under both formulas, the higher of the two weighted child
counts (on the basis of numbers or rates) is used in calculating grants for each LEA.6

5 Under the Concentration Grant formula, LEAs are eligible for the hold harmless for up to four years after they no
longer meet the minimum eligibility threshold.
6 In calculating grants for Puerto Rico, a cap of 1.82 is placed on the net aggregate weight applied to the population factor
under the T argeted Grant formula. T his cap was intended to provide that the share of T argeted Grants allocated to Puerto
Rico would be approximately equal to its share of grants under the Basic Grant and Concentration Grant formulas for
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ESEA: Title I-A Poverty Measures and Grants to Local Education Agencies and Schools

The EFIG equity factor is based on a measure of the average disparity in expenditures per pupil
among each of the LEAs of a state and the state average per pupil expenditure.7 This measure is
referred to as the coefficient of variation (CV). In the CV calculations for this formula, an extra
weight (1.4 vs. 1.0) is applied to estimated counts of formula children. Thus, the CV for a state
would be minimized if it spent, from state and local funds, exactly 40% more per formula child
compared to spending on other students, on average. In calculating grants, the equity factor is
subtracted from 1.30. Typical state equity factors range from 0.0 (for the single-LEA jurisdictions
of Hawai , 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 fal into the 0.10 to 0.20 range.8 Thus, the
multiplier (1.30 minus equity factor) typical y ranges from 1.05 to 1.30. As a result, the lower a
state’s weighted expenditure disparities among its LEAs are, the lower its CV and equity factor,
the higher its multiplier, and the higher its state total grant wil be. Conversely, the greater a
state’s weighted expenditure disparities among its LEAs are, the higher its CV and equity factor,
the lower its multiplier, and the lower its state total grant wil be.
The EFIG effort factor, while not related to a child’s family income, is based on a comparison of
state average expenditures per pupil for public K-12 education with state personal income per
capita. This ratio for each state is further compared to the national average ratio, resulting in an
index number that is greater than 1.0 for states where the ratio of expenditures per pupil for public
K-12 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 the average for the nation as a whole. Narrow
bounds of 0.95 and 1.05 are placed on the resulting multiplier, so that its effect on state grants is
limited.
The EFIG formula also differs from the other three formulas in terms of being a two-stage
formula. Under the Basic Grant, Concentration Grant, and Targeted Grant formulas, maximum
grants are calculated for LEAs by multiplying the population factor by the expenditure factor for
al LEAs meeting the minimum eligibility thresholds. For the EFIG formula only, in the first
stage, state total grants are calculated in proportion to each state’s total population factor
(unweighted) multiplied by its expenditure factor, by 1.3 minus its equity factor, and by its effort
factor. In the second stage, these state total grants are al ocated to LEAs on the basis of a
modified version of the formula child weighting scheme of the Targeted Grant formula, with the
degree of targeting (the ratio of the weight applied to formula children in the highest poverty
ranges compared to the weight for such children in the lowest poverty ranges) varying in three
stages. The stage, or degree of targeting, used for substate al ocation varies depending on each
state’s equity factor: the higher the equity factor (and therefore the greater the disparities in

FY2001.
7 According to ED, the equity factor is calculated based on current expenditures per pupil (U.S. Department of
Education, National Center for Education Statistics, Allocating Grants for Title I, January 2016, p. 9,
https://www.google.com/url?client =internal-element -cse&cx=011774183035190766908:u7ygjkz8dry&q=https://
nces.ed.gov/surveys/annualreports/pdf/titlei20160111.pdf&sa=U&ved=
2ahUKEwjizLaigNrsAhWSlnIEHYqGBPcQFjAAegQIAxAC& usg=AOv Va w2dBPQFvRT nyMGPO7rST A8a). For a
definition of current expenditures, see U.S. Department of Education, National Center for Education Statistics,
Revenues and Expenditures for Public Elem entary and Secondary Education: School Year 2015 –16 (Fiscal Year
2016), December 2018, p. B-1, https://nces.ed.gov/pubs2019/2019301.pdf.
8 T here is a special provision for states meeting the expenditure disparity standard established in regulations for the
Impact Aid program (ESEA T itle VIII), for which the equity f actor is capped at a maximum of 0.10. For an explanation
of the Impact Aid equalization provision, see CRS Report R45400, Im pact Aid, Title VII of the Elem entary and
Secondary Education Act: A Prim er.
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expenditures per pupil among a state’s LEAs) is, the greater the degree of targeting on high-
poverty LEAs in the intrastate al ocation of EFIG funds wil be.
One Program, Four Formulas
One reason for using four different formulas to al ocate shares of the funds for a single program is
that the formulas have distinct al ocation patterns, which are intended to provide varying portions
of funds to states and localities with differing indicators of need for assistance. In addition, some
of the formulas contain elements that are deemed to have desirable incentive effects or to be
significant symbolical y in addition to their impact on al ocation patterns (e.g., the equity and
effort factors in the EFIG formula). Final y, there is an explanation for the use of four different
formulas based on legislative history: the Targeted Grant and EFIG formulas were initial y
proposed as replacements for the Basic Grant plus Concentration Grant formulas (i.e., the
Targeted Grant and EFIG formulas were each original y intended to be the sole Title I-A
formula). However, as proposals were debated and compromised in the development of the
Improving America’s Schools Act (P.L. 103-382), which reauthorized the ESEA in 1994, both of
these formulas were ultimately established to complement, but not replace, the Basic Grant and
Concentration Grant formulas.
SAIPE Data
As previously discussed, the primary factor used to determine an LEA’s formula child count is
the number of children ages 5-17 in an LEA living in families in poverty. This number is
estimated annual y by the Census Bureau based on the number of children ages 5-17 living in an
LEA, regardless of whether they attend a public or private school, or no school at al , and whether
or not the public school they attend is operated by a traditional, geographical y based LEA, a
regional LEA providing certain types of education (e.g., vocational-technical education) to
students in multiple traditional LEAs, a school in a different LEA under a multiple-LEA or
statewide choice program, or a charter school that is treated as a separate LEA under state law and
enrolls students who reside within the boundaries of one or more traditional LEAs. In making
these estimates, however, the Census Bureau is only able to include LEAs that have traditional
geographic boundaries. If the Census Bureau tried to include other entities, such as charter
schools, which are considered to be LEAs under the laws of many states, the same children could
be counted multiple times. For example, for the District of Columbia, the SAIPE data includes an
estimate of the number of children ages 5-17 living in families in poverty for the District of
Columbia Public Schools (DCPS). The District of Columbia also includes dozens of charter
schools that are considered their own LEAs. If the Census Bureau tried to estimate the number of
such children living in the boundaries of each charter school, each child ages 5-17 living in
families in the District of Columbia would be counted dozens of times as the geographic
boundaries for the charter schools that are their own LEAs are the same as the geographic
boundaries for DCPS.
Due to these limitations, regulations require state educational agencies (SEAs) to adjust the LEA
al ocations calculated by ED on the basis of traditional LEAs to shift shares of those grants to
LEAs that are not included in ED’s al ocation procedures, including charter school LEAs.9 As
SAIPE data are not available to make these determinations, SEAs must use other measures of a

9 34 C.F.R. 200.72. More detailed information about the process by which SEAs make these adjustments is included in
U.S. Department of Education, State Educational Agency Procedures for Adjusting Basic, Concentration, Targeted,
and Education Finance Incentive Grant Allocations Determ ined by the U.S. Departm ent of Education , May 23, 2003,
https://www2.ed.gov/programs/titleiparta/seaguidanceforadjustingallocations.doc.
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child’s family income to determine the number of relevant children in each of the LEAs for which
ED was unable to calculate grants. This topic is discussed further below.
Measures of Family Income Used as the Primary
Population Factor in Allocation of Title I-A Funds to
LEAs Since Enactment of the ESEA10
When the ESEA was initial y adopted in 1965, there was no official or standard measure of
poverty in use by the federal government. As is discussed further below, the poverty measure now
used by the Census Bureau and other federal agencies began to be developed in the early 1960s,
but was adopted as the official federal measure of poverty in 1969. Largely as a result of this, the
initial versions of Title I-A relied on single, fixed thresholds of income to define school-age
children in low-income families. Until adoption of the standard federal poverty measure for Title
I-A al ocations in the ESEA amendments of 1974,11 the income thresholds used in Title I-A did
not vary by family size, nor were they effectively updated at any time between 1965 and 1974.
After adoption of the 1974 amendments to the ESEA, subsequent ESEA amendments made major
changes to some of the measures of family income used in the Title I-A al ocation formulas, as
wel as the geographic level at which the grants were calculated by ED and the frequency with
which al ocation population data were updated. The Title I-A al ocation population factors
required under the original ESEA and subsequent amendments are discussed below.
The Original ESEA of 1965
The original ESEA of 1965 (P.L. 89-10) established a low-income threshold of $2,000. Thus,
counts of children ages 5-17 from low-income families used to calculate Title I-A grants were
those in families with income below $2,000, according to the 1960 Census.
At the same time, it was understood that many families with school-age children had incomes
above $2,000 per year due primarily or solely to financial assistance received under the income
support program authorized by Title IV, Part A of the Social Security Act (SSA), then known as
Aid to Families with Dependent Children (AFDC).12 There was also interest in incorporating a
population factor that would be updated more frequently than the Decennial Census; the AFDC
counts were to be updated annual y, based on data collected by what was then the Department of
Health, Education, and Welfare (DHEW).13 Thus, it was decided that estimates of the number of
school-age children in families with income below $2,000 would be supplemented by counts of
such children in families receiving AFDC payments above $2,000.14

10 For additional information on this topic, see CRS Report R44898, History of the ESEA Title I-A Formulas.
11 A discussion of the development of the standard federal poverty measure, and issues related to it, may be found in
Appe ndix B.
12 T he 1996 welfare reform law (T he Personal Responsibility and Work Opportunity Reconciliation Act; P.L. 104-193)
replaced the AFDC program with T emporary Assistance for Needy Families (T ANF). T hus, for the purposes of this
report, descriptions of the T itle I-A program before 1996 reference AFDC. For more information on AFDC and T ANF,
see CRS Report R40946, The Tem porary Assistance for Needy Fam ilies Block Grant: An Overview.
13 T his is now the Department of Health and Human Services (HHS).
14 See H.Rept. 93-805, House Committee on Education and Labor report on H.R. 69, the Elementary and Secondary
Education Amendments of 1974, pp. 8-12. See also Stephen K. Bailey and Edith K. Mosher, ESEA—The Office of
Education Adm inisters a Law (Syracuse University Press, 1968); and P.L. 89 -10, Section 203, especially Subsection (c)
and Subsection (d).
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The initial ESEA (as wel as every subsequent revision of it) provided that al ocation formula data
should be compiled, and grants be calculated by the federal government, on the basis of LEAs, if
satisfactory LEA-level population data were available. In 1965, and for many years thereafter—
until FY1999, as discussed below—such satisfactory LEA-level population data were not
available, and grants were calculated by the U.S. Office of Education (until 1979)/Department of
Education (subsequently) on a county basis, with sub-county grants to LEAs calculated and
distributed by SEAs.
Reauthorizations of the ESEA: 1966-1988
Subsequent to the adoption of the ESEA in 1965, Title I-A was revised with respect to the
al ocation formula population requirements on numerous occasions. These revisions are briefly
outlined below.
1966—Elementary and Secondary Education Amendments of 1966 (P.L. 89-750)
Counts of children ages 5-17 who are neglected, delinquent, and in foster care were first added to
those from families with income below $2,000 and in families receiving AFDC payments above
$2,000.15 This amendment also provided that the low-income threshold be raised from $2,000 to
$3,000 for the al ocation of funds appropriated for FY1968 and beyond, but this increase was
never implemented.16
1967/1968—Elementary and Secondary Education Act Amendments of 1967
(P.L. 90-247)
The provision for increasing the low-income threshold from $2,000 to $3,000 was delayed until
maximum authorized payments based on estimates of school-age children in families with income
below the $2,000 level were provided, which never occurred.17
1969/1970—Elementary and Secondary Education Act Amendments of 1969
(P.L. 91-230)
These amendments provided for increases in the low-income threshold—to $3,000 for FY1972,
and to $4,000 thereafter.18 Again, these increases would only take effect after maximum
authorized payments based on the $2,000 level were provided, which did not occur.
1974—Education Amendments of 1974 (P.L. 93-380)
In the period leading up to consideration and adoption of the 1974 ESEA amendments, major
shifts were projected to occur in the al ocation of funds among regions, states, and counties
nationwide. The primary cause of the changes in al ocation patterns was replacement of estimates
from the 1960 Census with estimates from the 1970 Census on children ages 5-17 in families with
income below $2,000. The 1970 Census estimates of the number of such children were much
lower, while the number of children in families receiving AFDC payments above $2,000, updated

15 T hese comparatively small T itle I-A allocation formula population groups, constituting 3.2% of all formula children
for the 2019-2020 school year, will not be discussed further in this report.
16 P.L. 89-750, §§104 and 106.
17 P.L. 90-247, §107.
18 P.L. 91-230, §113.
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annual y, continued to increase. As a result, the number of children counted under the AFDC
factor was, for the first time, substantial y higher than the number counted under the low-income
factor.19 At the same time, a standard federal definition of poverty for statistical purposes had
been adopted in 1969, as is discussed further below.20
After extended congressional debate, much of it focused on estimates of the effects of possible
al ocation formula revisions on states and counties that were developed by the Congressional
Research Service (CRS)21 and ED, the Title I-A population factor was changed to the total
estimated number of children ages 5-17 in families with income below the standard federal
poverty measure, specifical y applying the poverty thresholds as used for the 1970 Census,22 plus
the number of children ages 5-17 in families receiving AFDC payments above the standard
federal poverty level for a non-farm family of four persons, multiplied by two-thirds.23 A
discussion of the development of the standard federal poverty measure, and issues related to it, is
in Appendix B.
1978—Education Amendments of 1978 (P.L. 95-561)
The AFDC child count was changed from two-thirds of children ages 5-17 in families receiving
AFDC payments with incomes above the standard federal poverty level for a non-farm family of
four persons to 100% of such children.24 The AFDC program was replaced by the TANF program
in 1996.25 This provision remains unchanged to the present. However, in years subsequent to the
adoption of the 1978 ESEA amendments—as poverty income thresholds rose faster than AFDC,
then TANF, payments—the number of children counted in the Title I-A formulas because their
families receive AFDC/TANF payments yet have incomes above the poverty level for a family of
four has steadily declined so as to become virtual y nonexistent. For FY2020 grants, the number
of such children constituted less than 0.1% of al children counted in the Title I-A al ocation
formulas.
The 1978 amendments provided that estimates on school-age children in poor families from the
1970 Census be replaced with more recent Census estimates when those became available.26 In

19 For FY1966, the first year of implementation of the original T itle I-A formula, the national total number of children
counted under the $2,000 low-income factor was 4.9 million, and the number counted under the AFDC factor was 0.6
million. By FY1974, the national total number of children counted under the $2,000 low-income factor was 2.6 million,
with the initial implementation of data from the 1970 Census, while the number counted under the AFDC factor had
grown steadily to become 3.6 million. See House Report 93 -805, House Committee on Education and Labor report on
H.R. 69, the Elementary and Secondary Education Amendments of 1974, p. 9.
20 Bureau of the Budget, Definition of Poverty for Statistical Purposes, Budget Circular No. A-46, August 29, 1969.
Also see Gordon M. Fisher, T he Development of the Orshansky Poverty T hresholds and T heir Subsequent History as
the Official U.S. Poverty Measure, U.S. Census Bureau, https://www.census.gov/library/working-papers/1997/demo/
fisher-02.html.
21 See, for example, H.Rept. 93-805, House Committee on Education and Labor report on H.R. 69, the Elementary and
Secondary Education Amendment s of 1974, pp. 14 and 226.
22 T he requirement to specifically apply the poverty thresholds as used in compiling the 1970 Census remained in effect
until the adoption of T itle I-A revisions in 1988 (P.L. 100-297), even though data from the 1980 Census were made
available in the meantime. T hus, for several years, T itle I-A grants were based on data from the 1980 Census that were
compiled applying the poverty thresholds from th e 1970 Census.
23 P.L. 93-30, §101(a)(2)(B) and (C).
24 P.L. 95-561, §101(a).
25 See the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 ( P.L. 104-193). Also see CRS
Report R44668, The Tem porary Assistance for Needy Fam ilies (TANF) Block Grant: A Legislative History .
26 Beginning on a partial basis for FY1982, and fully for FY1983, 1980 Decennial Census data replaced 1970
Decennial Census data on children from families with incom e below the poverty line. While the 1978 amendments
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addition, the 1978 amendments provided that a portion of future appropriation increases would be
al ocated at the state level (only) using a different measure of low-income and a different source
of population data. This change was made primarily in order to utilize population data more
current than that available from the 1970 Census.27 One-half of appropriation increases over the
FY1979 level was to be al ocated to states on the basis of children ages 5-17 in families with
income below 50% of the (national) median income for four-person families. The source of these
estimates was not to be the Decennial Census but rather a one-time Survey of Income and
Education (SIE) conducted by the Census Bureau in 1976 (based on 1975 income).28 These state
total grants were then to be al ocated within states in proportion to the remaining Basic Grants
(based on Census and AFDC population data).29
1988—Augustus F. Hawkins-Robert T. Stafford Elementary and Secondary
School Improvement Amendments of 1988 (P.L. 100-297)
The provision for al ocation of a portion of Title I-A grants to states on the basis of population
estimates from the 1976 Survey of Income and Education was dropped. Also eliminated were
references to 1970 poverty thresholds, so the most recent Census poverty income thresholds could
be applied to 1980 and subsequent Census data.30
The Improving America's Schools Act of 1994: Grants by LEA and
Poverty Population Updates
The 1994 amendments to the ESEA—the Improving America’s Schools Act (IASA; P.L. 103-
382)—provided for major changes in the source of estimates for children ages 5-17 in poor
families, and in the geographic level at which grants would be calculated by the federal
government.
As discussed earlier, from the beginning of Title I-A in 1965, grants were calculated by the
federal government on a county basis, with sub-county al ocations to LEAs calculated by SEAs.
From 1965 onward, the primary population factor data on school-age children in low-
income/poor families for counties were updated only once every 10 years,31 when data from the
Decennial Census became available.

specified that the Secretary use the most recent satisfactory data available in determining the number of children in
poor families, the amendments retained references to the 1970 Census poverty thresholds. T hus, beginning in FY1982
the 1970 Census poverty threshold was applied to the 1980 Census data to allocate T itle I -A grants. T his limitation was
removed in 1988 amendments to the ESEA.
27 H.Rept. 95-1137, pp. 8-11.
28 T he Education Amendments of 1974 included a provision directing the Secretary of the Department of Health,
Education and Welfare (HEW) to arrange with the Bureau of the Census for a survey providing state -level estimates of
the number of children living in poor families and related data. At that time, the Census Bureau's annual Current
Population Survey provided reliable estimates for such children only at the national level, not for states. T he resulting
Survey of Income and Education (SIE) was conducted in 1976, based on income in 1975 (see
https://www.icpsr.umich.edu/icpsrweb/ICPSR/studie s/7634). After considering several alternatives, Congress decided
to allocate a portion of T itle I-A funds on the basis of data from the SIE, and to use an income threshold of 50% of the
(national) median income for a four-person family, rather than the standard federal poverty measure. T his was
originally intended to provide a partial update and transition toward subsequent implemen tation of population data
from the 1980 Census, but in fact remained in effect until adoption of the Education Amendments of 1988.
29 P.L. 95-561, §101(a).
30 P.L. 100-297, §1001.
31 As discussed earlier, the sole, partial exception was the period of FY1980 -FY1988, when a portion of T itle I-A funds
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No data from the 1960 Census were compiled by the Census Bureau by LEA. Data were
compiled by LEA from the 1970 Census and 1980 Census, but these were not deemed to be
sufficiently reliable to be used for the purpose of al ocating Title I-A grants. However, in the
1990s, the Census Bureau initiated efforts both to compile selected population data for LEAs and
to update these data more frequently than once per decade, through what became known as the
SAIPE program.32 The provision for use of these 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
from the Decennial Census.
As amended by the IASA in 1994,33 the Title I-A statute provided that beginning in FY1997, the
Secretary of Education “shal ” 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 and process to be
conducted by the National Academy of Sciences (NAS).34 In March 1997, an NAS panel35
recommended use of a combination of 1990 Census and income year (IY)1993 updated
population estimates in al ocating FY1997 (1997-1998) Title I-A grants.36 Subsequently, the NAS
panel recommended the use of a revised set of IY1993 SAIPE estimates as the sole basis for
calculating FY1998 grants, and ED followed this recommendation as wel .37
These grants continued to be calculated by ED on a county level. However, beginning with
FY1999 grants, the NAS panel recommended38 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 estimates from SAIPE, and ED has since followed these
recommendations.
Use of SAIPE Data to Calculate Title I-A Grants to LEAs
Initial y, SAIPE provided estimates of population data used to calculate Title I-A grants—total
children ages 5-17, related children ages 5-17 in poor families, and total population (al ages)—

(one-half of the increase over appropriations for FY1979) was allocated at the state level based on estimates of the
number of school-age children in families with income below 50% of the (national) median income for a four -person
family, according to the 1976 Survey of Income and Education.
32 For information about the origins of the SAIPE program, see https://www.census.gov/programs-surveys/saipe/about/
origins.html.
33 P.L. 103-382, §101.
34 Section 1124(c)(3) and (4) of the ESEA text in effect between 1994 and 2001.
35 Panel on Estimates of Poverty for Small Geographic Areas, Committee on National Statistics, National Research
Council. T he most recent of the reports on SAIPE by this panel is National Academy of Sciences, Sm all-Area Incom e
and Poverty Estim ates: Priorities for 2000 and Beyond , 2000.
36 Specifically, the panel recommended that each county’s school-age child poverty rates based on 1990 Census and
IY1993 SAIPE estimates should be averaged, and those average poverty rates be multiplied by the IY1993 estimate of
total school-age children in the county. T he resulting combined estimate of school-age children in poor families was
used in calculating T itle I-A grants for FY1997.
37 National Research Council, Panel on Estimates of Poverty for Small Geographic Areas, Small-Area Estimates of
School- Age Children in Poverty: Evaluation of Current Methodology, ed. Constance F. Citro and Gr aham Kalton
(National Academy Press, 2000), p. 5 (hereinafter referred to as “ NRC, Small-Area Estimates of School-Age Children
in Poverty: Evaluation of Current Methodology”).
38 Ibid., p. 7.
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every second year, starting with estimates for IY1993. Beginning with data for IY1999, however,
SAIPE estimates have been prepared annual y. As of the cover date of this report, the latest
published SAIPE estimates are for IY2018 and were published in December 2019.39
SAIPE estimates are available at the state, county, and LEA levels. SAIPE is not a survey of
households separate from other federal surveys by the Census Bureau or other agencies.40 Rather,
SAIPE estimates are produced through statistical modeling and adjustment of administrative data
along with survey data. The adjustments and modeling are intended to produce estimates for
smal areas such as LEAs that are more reliable and accurate than estimates based only on survey
data. The administrative data used for SAIPE include tax returns from the Internal Revenue
Service (IRS), and participation data for the Supplemental Nutrition Assistance Program
(SNAP)41 and the Supplemental Security Income (SSI) program.42 The survey data include
personal income data from the Bureau of Economic Analysis, Decennial Census and annual
population estimates, and data from the American Community Survey (ACS).43 LEA boundaries
are provided by the Census Bureau’s School District Review Program (SDRP).44
More specifical y, SAIPE estimates for states and counties are based on ACS population samples,
supplemented by administrative data (IRS data and SNAP participation data). SAIPE estimates
for LEAs are based on ACS population samples, IRS data, and population and poverty estimates
for the counties in which LEAs are located. The total number of children ages 5-17, and the
number of such children in poor families, are estimated for counties using a weighted
combination of regression model ing (based on administrative data from IRS and SNAP) and
direct estimates from sample surveys. LEA estimates are based on shares of the populations
within counties.45

39 For more information, see https://www.census.gov/programs-surveys/saipe.html.
40 See CRS Report R44780, An Introduction to Poverty Measurement.
41 T he Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program, assists low-
income households obtain a nutritionally adequate diet.
42 T he SSI program, administered by the Social Security Administration, provides financial benefits to certain adults
and children with disabilit ies, as well as certain low-income persons aged 65 or older; see https://www.ssa.gov/benefits/
ssi/.
43 For more information, see https://www.census.gov/programs-surveys/saipe/guidance/model-input-data.html. For
several decades through the year 2000, the Decennial Census included a short fo rm of basic information sent to all
households, and a long form to collect more detailed information from a sample of households. After the 2000 Census,
the long form has been replaced by the American Community Survey. T he ACS is an ongoing survey collecti ng a
variety of information on income, housing, education, and related demographic factors from a representative sample of
approximately 3.5 million households each month. T he SAIPE program began to integrate ACS data into its population
estimates for IY2005. (See https://www.census.gov/programs-surveys/acs/about.html.) Prior to IY2005, SAIPE used
data from the Annual Social and Economic Supplement to the Current Population Survey (CP S) as its source of
population survey data.
44 For more information, see https://www.census.gov/programs-surveys/sdrp.html.
45 For more information, see https://www.census.gov/programs-surveys/saipe/technical-documentation/methodology/
school-districts/overview-school-district.html. While some LEAs are responsible for an entire county (or a county -
equivalent entity, such as an independent city), the more typical pattern nationwide is to have multiple LEAs per
county. Further, while most LEAs are located within a single county, there are cases whe re portions of an LEA are
located within two or more counties. Finally, there are a small number of cases of LEAs that cover two or more
counties (or independent cities treated as counties in the T itle I-A allocation process) in their entirety.
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Subsequent Reauthorizations of the ESEA
Subsequent reauthorizations of the ESEA by the No Child Left Behind Act (NCLB; P.L. 107-110)
and the Every Student Succeeds Act (ESSA; P.L. 114-95) did not make changes to the poverty
measures included in the Title I-A formulas.
ESEA Title I-A Grants to Schools
Unlike other federal elementary and secondary education programs, under which grants are made
to LEAs or states, most Title I-A funds have always been al ocated to individual schools, although
LEAs retain substantial discretion to control the use of a share of Title I-A grants at a central
district level. In almost al cases, the data used to determine which students are from low-income
families for the selection of participating schools and distribution of funds among them are not
the same as those used to estimate the number of children ages 5-17 living in families in poverty
for purposes of calculating al ocations to states and LEAs. This is because Census data are
general y not 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.
Thus, LEAs use available proxies for low-income status.
History of Title I-A Provisions for School Selection, Identification
of Low-Income Students within Schools, and Allocation of Funds
The original version of the ESEA, enacted in 1965, contained only broad and unspecific
provisions regarding the selection of schools to participate in Title I-A. The statute provided that
“payments under this part wil be used for programs and projects .. which are designed to meet
the special educational needs of educational y deprived children in school attendance areas
having high concentrations of children from low-income families.”46
This statutory language was supplemented by policy guidance from the U.S. Office of Education
(USOE). By the mid-1970s, the USOE guidance47 provided that LEAs could, with approval by
their SEA, use any of the following measures of low family income (or a combination of such
measures) to select schools to participate in Title I-A:
 number of children in families receiving AFDC payments,
 number of children in families with income below the poverty level as used by
the Census Bureau,
 number of children eligible for free or reduced-price school lunch;
 number of children in low-income families according to school surveys,
 “health statistics,”
 “housing statistics,”
 “employment statistics,” or

46 Section 205(a)(1) of P.L. 89-10.
47 National Institute of Education, Department of Health, Education and Welfare, Title I Funds Allocation: The Current
Form ula, 1977, pp. 57-58 (hereinafter referred to as “ NIE, Title I Funds Allocation”); and U.S. Office of Education,
Department of Health, Education and Welfare, Title I ESEA: Selecting Target Areas (hereinafter, “ USOE, Title I ESEA:
Selecting Target Areas”).
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 “other” data sources.48
Whatever measure was chosen, the same measure had to be applied to al public schools in the
LEA, although LEAs could choose to focus Title I-A services on only one or more grade levels
(e.g., only elementary schools). In addition, schools could be selected for participation on the
basis of either the percentage or number of students from low -income families either residing in
school attendance areas or actual y enrolled in schools. There was also a limited option to serve
schools with higher incidences of educationally deprived students (a term not specifical y
defined) over schools with higher concentrations of students from low-income families. The
provisions for al ocation of Title I-A funds among eligible schools were also broad—funds were
to be al ocated in proportion to the number of students to be served and their educational needs,
as determined by the LEA. A 1977 report by the National Institute of Education found that "the
number of free lunch recipients is the most readily available source of poverty data for most
school districts," and that 66% of a sample of LEAs used these data for school selection and
al ocations, while 51% of LEAs used AFDC data, and some LEAs were found to use a
combination of free lunch, AFDC, and other authorized data sources.49
This pattern of broad authority for LEAs to select measures of low family income for school
selection and funding al ocation under Title I-A continued until the ESEA was reauthorized by the
IASA in 1994. The IASA contained the first explicit statutory specification of the data that may
be used for selection of schools to participate in Title I-A and al ocation of funds among them.50 It
provided that schools are to be selected on the basis of their percentage (not number) of children
from low-income families, while funds are to be al ocated among eligible schools on the basis of
their number children from low-income families.51 With updates of programmatic references, the
IASA provisions remain in effect today, and are described below.
Current Provisions for Selection of Participating Schools and
Allocation of Funds Among Them
The general policy for Title I-A school selection and al ocation established under the IASA
continues under the ESEA as most recently reauthorized by the ESSA and related policy
guidance.52 These provisions are described in greater detail below.
While there are several rules related to selection of schools to participate in Title I-A, LEAs must
general y rank their public schools by their percentage of students from low-income families, and

48 NIE, Title I Funds Allocation, pp. 57-58; and USOE, Title I ESEA: Selecting Target Areas.
49 It was also reported that 67% of the LEAs in the study sample used 1970 Census data as a partial source of
information for school selection under T itle I-A (virtually no LEAs could have used this Census data as a sole source of
such information). NIE, Title I Funds Allocation, p. 60.
50 ESEA Section 1113(a)(5), as amended by P.L. 103-382.
51 T his includes a provision, which is still applicable, for a minimum T itle I-A grant amount per child from a low-
income family to be allocated to participating schools. T his minimum amount was to be at least 125% of the amount
received by the LEA per child from a low-income family. However, this rule applies only to LEAs serving any schools
with fewer than 35% of their students from low-income families.
52 T he statutory provisions may be found in Section 1113 of the ESEA, as amended by P.L. 114-95. Detailed policy
guidance regarding the selection of schools to receive T itle I-A grants and the allocation of funds among them may be
found in the ED policy guidance document , U.S. Department of Education, Local Educational Agency Identification
and Selection of School Attendance Areas and Schools and Allocation of Title I Funds to Those Areas and Schools,
2003, http://www2.ed.gov/programs/titleiparta/legislation.html#waiver (hereinafter referred t o as “ ED, LEA
Identification and Selection of School Attendance Areas”).
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serve them in rank order. 53 LEAs may choose to consider only schools serving selected grade
levels (e.g., only elementary schools) in determining eligibility for grants, so long as al public
schools with 75% or more of students from low-income families and, at the LEA’s discretion,
high schools with 50% or more students from low-income families, receive grants first, to the
extent that funds are available.54
Al participating schools must general y have a percentage of children from low -income families
that is equal to or above the LEA’s average, or 35%, whichever is lower.55 The percentage of
students from low-income families for each public school is usually measured directly, although
LEAs may choose to measure this percentage indirectly for middle or high schools, based on the
measured percentages for the elementary or middle schools that students attended previously
(feeder schools). LEAs have the option of setting school eligibility thresholds higher than the
minimum in order to concentrate available funds on a smal er number of schools, and this is the
practice especial y in many large, urban LEAs.
The current Title I-A statute al ows LEAs to use the following low-income measures for school
selection and al ocations:
 eligibility for FRPL under the federal child nutrition programs (the Richard B.
Russel National School Lunch Act),
 eligibility for TANF,
 eligibility for Medicaid,
 Census poverty estimates (in the very rare instances where such estimates are
available for individual schools or school attendance areas), or
 a composite of two or more of these measures.
While FRPL data are not used by ED to determine grants to LEAs, in school year 1997-98,
approximately 90% of LEAs receiving Title I-A funds were using this data—sometimes alone,
sometimes in combination with other authorized criteria—to select Title I-A schools and al ocate
funds among them.56 According to a more recent ED report, “school districts subal ocate most of
their Title I funds to eligible schools based on each school’s number of low-income children,
typical y using data from the free or reduced-price lunch program.”57
Individual children become eligible for FRPL in one of two ways: (1) submission of application
forms indicating that household income is below specified thresholds for free or reduced-price
lunch eligibility; or (2) Direct Certification, through which eligibility for FRPL is established
through household participation in one of a number of public benefit programs (see below). In

53 T here is an exemption from all of the T itle I-A school selection requirements for small LEAs—defined in this case as
those with enrollments of 1,000 or fewer students. Such small LEAs do not have to meet any of the school ranking
requirements discussed here.
54 Some LEAs run out of T itle I-A funds before serving all schools where the percentage of students from low-income
families is 75% or above. T his usually occurs in LEAs with exceptionally high percentages of students from low-income
families in a high proportion of their schools. T hese LEAs are to serve schools in rank order, based on their percentages
of students from low-income families, until they run out of funds.
55 T his minimum percentage is reduced from 35% to 25% for schools participating in certain desegregation plans.
56 U.S. Department of Education, Study of Education Resources and Federal Funding: Final Report, 2000, p. 33,
http://eric.ed.gov/?id=ED445178.
57 U.S. Department of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies
Service, Study of Title I Schoolwide and Targeted Assistance Program s: Final Report, 2018, p. 11,
https://www2.ed.gov/rschstat/eval/title-i/schoolwide-program/report.pdf.
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addition, children may obtain access to free school meals through school or LEA participation in
CEP (discussed later in this report). Overal , the national average percentage of public school
students who are eligible to receive free or reduced price lunches was 52.3% for the 2016-2017
school year.58
The income eligibility thresholds for households submitting applications specifical y for free and
reduced-price lunches are higher than the poverty levels used in the al ocation formulas to states
and LEAs: 130% of the poverty income threshold for free lunches, and 185% for reduced-price
lunches.59 From the perspective of consistency, it would be preferable to use the same measure of
poverty in determining children to be counted in the al ocation of Title I-A funds both to LEAs,
and to individual schools within LEAs. However, that has thus far proven to be a practical
impossibility. SAIPE and other Census data that provide estimates of school-age children in poor
families are not designed to provide reliable estimates at levels of geography smal er than LEAs.
Thus, it is currently possible only rare instances to obtain Census poverty estimates for individual
school attendance areas. Until recently (as is discussed further below), data on the number of
students eligible for or receiving free and/or reduced-price lunches has been the only indicator of
student low-income status for individual schools, and those data are only available at the income
thresholds of 130% and 185% of poverty, not 100%.
Data on students whose family income is at 100% of the poverty level could be collected for
individual schools through state or local surveys of student family income, but such surveys are
currently conducted by only a limited number of states and LEAs. Final y, one alternative to free
and/or reduced-price lunch data currently under consideration in some LEAs is counts of children
directly certified for free lunches through family participation in programs such as SNAP, among
others. Eligibility for SNAP and other direct certification programs is determined by low family
income, with somewhat varying income thresholds, plus a variety of other factors.
Focusing on Title I-A grants to LEAs, and to schools within LEAs, for the 2020-2021 school year,
the grants to LEAs are based on SAIPE estimates published in December 2019, based on income
in calendar year 2018. For that year, the weighted average poverty threshold (100%) for a four-
person family was $25,701.60 For that same year, in LEAs that use FRPL data to determine
school-level al ocations, grants to schools general y would be based on FRPL participation in the
preceding school year, 2019-2020.61 For 2019-2020, the income eligibility thresholds for four-
person families were $33,475 for free lunches, and $47,638 for reduced-price lunches.62
As noted above, students may also qualify for free school meals, through direct certification, if
their household participates in one of more of the following federal benefit programs: SNAP,
TANF, Food Distribution Program on Indian Reservations, or Medicaid in areas approved for the
U.S. Department of Agriculture’s (USDA’s) Medicaid Direct Certification Demonstration

58 U.S. Department of Education, National Center for Education Statistics, Digest of Education Statistics, 2018, T able
204.10, https://nces.ed.gov/programs/digest/d18/tables/dt18_204.10.asp.
59 For general information on the school lunch program, see CRS Report R46234, School Meals and Other Child
Nutrition Program s: Background and Funding.
60 See U.S. Bureau of the Census, Income and Poverty in the United States: 2018, https://www.census.gov/library/
publications/2019/demo/p60-266.html.
61 Personal communication between CRS and ED regarding the data used by LEAs to determine T itle I -A school-level
allocations. October 28, 2020.
62 U.S. Department of Agriculture, Food and Nutrition Service, "Child Nutrition Programs: Income Eligibility
Guidelines," 84 Federal Register 10295-10298, March 20, 2019. These income thresholds are somewhat higher for
Alaska and Hawaii.
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Projects.63 Students may be approved as wel without an application if they are in foster care or
are migrant, homeless, runaways, or attending a Head Start program. Direct certification takes
place through matching of school enrollment data with participation data for the indicated benefit
programs. Income and status eligibility standards for these programs vary across different
programs, and in some cases across different states.64
While FRPL data have long been the primary basis for al ocating Title I-A funds among
schools—and for a variety of other purposes related to the identification of students from low -
income families and of schools with large proportions of such students65—this data source has
always had a number of imperfections. For example, a 2015 USDA study, based on data from the
2012-2013 school year, concluded that an estimated 20.2% of FRPL applicants were placed in the
wrong category (among the three categories of free, reduced-price, or denied). The authors of the
USDA study further found that 60% of these errors were due to incorrect reporting of income or
other information by applicant families, 30% were due to administrative errors, and 10% were
due to a combination of these or miscel aneous causes. Administrative errors in the certification
process were found to be much higher when participation was based on family paper applications
submitted specifical y for the school nutrition programs (an error rate of 14% of al applicants)
than when participation was based on direct certification (through family participation in TANF
or SNAP, for example), or under CEP (error rates of 4% and 2%, respectively).66 Other studies
have found that substantial proportions of students eligible to participate in FRPL do not do so,
and/or their families do not apply for the program, and that the non-participation rates vary
among regions of the nation, urban-suburban-rural locales, immigration/English Learner status,
and age/grade levels of students.67

63 For information on the Medicaid Direct Certification Demonstration Projects, see Lara Hulsey, Andrew Gothro, and
Joshua Leftin et al., Evaluation of the Direct Certification with Medicaid for Free and Reduced -Price Meals
Dem onstration (DCM-F/RP), Year 1, Mathematica Policy Research, August 2019, https://www.fns.usda.gov/cn/
evaluation-direct-certification-medicaid-free-and-reduced-price-meals (hereinafter referred to as “ Hulsey et al.,
Evaluation of the Direct Certification with Medicaid for Free and Reduced -Price Meals Dem onstration”).
64 See, for example, Erica Greenberg, New Measures of Student Poverty, Replacing Free and Reduced-Price Lunch
Status Based on Household Forms with Direct Certification, Urban Institute, November 2018, https://www.urban.org/
research/publication/new-measures-student -poverty.
65 Examples include T itle I-A school performance reporting and accountability provisions discussed elsewhere in this
report, and several state school finance program allocation formulas; see CRS Report R45827, State and Local
Financing of Public Schools.
66 U.S. Department of Agriculture, Measuring and Reducing Errors in the School Meals Programs: The APEC II Study
and FNS Actions, 2015, pp. 2-6, https://www.mathematica.org/our-publications-and-findings/publications/measuring-
and-reducing-errors-in-the-school-meal-programs-summary.
67 See, for example, Quin Moore, Lara Hulsey, and Michael Ponza, Factors Associated with School Meal Participation
and the Relationship Between Different Participation Measures, Mathematica Policy Research, May 27, 2009,
https://www.mathematica.org/our-publications-and-findings/publications/factors-associated-with-school-meal-
participation-and-the-relationship-between-different-participation-measures. Also see Frederic B. Glantz, Regina Berg,
and Diane Porcari et al., School Lunch Eligible Non-Participants, U.S. Department of Agriculture, Office of Analysis
and Evaluation, Food and Nut rition Service, 1994, https://www.fns.usda.gov/sites/default/files/EligNonPart -Pt1.pdf;;
Peter W. Cookson, Measuring Student Socioeconomic Status: Toward a Com prehensive A pproach, Learning Policy
Institute, 2020, https://learningpolicyinstitute.org/product/measuring-student-socioeconomic-status-report; U.S.
Department of Education, National Center for Education Statistics, Forum Guide to Alternative Measures of
Socioeconom ic Status in Education Data System s, June 2015, p. 40, https://nces.ed.gov/pubs2015/2015158.pdf;
Michael Harwell and Brandon LeBeau, "Student Eligibility for a Free Lunch as an SES Measure in Education
Research," Educational Researcher, vol. 39, no. 2 (March 2010), pp. 120 -131; T hurston Domina, Nikolas Pharris-
Ciurej, and Andrew M. Penner et al., "Domina, T hurston, et al., Is Free and Reduced-Price Lunch a Valid Measure of
Educational Disadvantage?," Educational Researcher, vol. 47, no. 9 (December 2018), p. 539=555; and Jessica A.
Carson, "Carson, Jessica A., Many Eligible Children Don't Participate in the School Nutrition Programs, University of
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Title I-A funds are al ocated among participating schools in proportion to their number of
students from low-income families, although grants to eligible schools per pupil from a low -
income family need not be equal for al schools. LEAs may choose to provide higher grants per
child from a low-income family to schools with higher percentages of such students. For
example, an LEA could choose to provide higher grants per child to a school where 75% of
students are from low-income families than to a school where 45% of students are from such
families.
Determination of the Share of Title I-A Grants to Be Used to Serve
Eligible Students Attending Private Schools
The share of funds to be used by each recipient LEA to serve educational y disadvantaged
students attending private schools is determined based on the number of private school students
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 al ocate funds among public schools (i.e., usual y FRPL data) or one of a specified
range of alternatives.68 In cases where a state or LEA deems itself to be unable to provide Title I-
A services to eligible private school students, or where the U.S. Secretary of Education
determines that such services have been inadequate, the Secretary arranges for services to be
provided via a bypass arrangement, under which the services are provided by a third-party entity.
Suballocation of Title I-A LEA Grants to Charter Schools and Other
"Special LEAs"
Under current law, the al ocation calculations by ED do not take into account charter schools that
are treated under state law as separate LEAs, nor do they take into account LEAs that provide
specialized services (such as vocational-technical education) to multiple traditional LEAs as no
SAIPE data are available to calculate grants to these entities. Thus, per regulations, the grants as
calculated by ED must be adjusted to provide funds to eligible LEAs in these categories, al of
which are referred to in ED policy guidance as “special LEAs.”69 With respect to charter schools,
these adjustments apply only to charter schools that are treated under state law as separate LEAs;
charter schools that are not treated as separate LEAs under state law receive Title I-A grants in the
same manner as other public schools within a traditional LEA.

New Hampshire, Casey School of Public Policy," Casey Research, University of New Ham pshire, Casey School of
Public Policy, vol. 85 (Summer 2015), pp. 1-4, https://scholars.unh.edu/cgi/viewcontent.cgi?article=1245&context=
carsey.
68 According to ED, LEA Identification and Selection of School Attendance Areas, p. 16, “T o obtain a count of private
school children, an LEA may use: (1) T he same poverty data it uses to count public school children. (2) Comparable
poverty data from a survey of families of private school students that, to the extent possible, protects the families’ identity.
T he LEA may extrapolate data from the survey based on a representative sample if complete actual data are not available.
(3) Comparable data from a different source, such as scholarship applications, so long as the income level for both sources
is generally the same. (4) Proportional data based on the poverty percentage of each public school attendance area applied
to the total number of private school children who reside in t hat area. (5) An equated measure of low-income correlated
with a measure of low-income used to count public school children.”
69 34 C.F.R. 200.72. More detailed information about the process by which SEAs make these adjustments is included in
U.S. Department of Education, State Educational Agency Procedures for Adjusting Basic, Concentration, Targeted,
and Education Finance Incentive Grant Allocations Determ ined by the U.S. Departm ent of Education , May 23, 2003,
https://www2.ed.gov/programs/titleiparta/seaguidanceforadjustingallocations.doc.
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ED’s policy guidance70 describes two different methods for determining Title I-A grants to charter
school and other special LEAs, one for states that are able to determine the sending LEAs in
which charter school and other special LEA students reside, and one to be used by states that do
not have this information. Under both of these methods, SEAs must estimate the number of
Census poverty children who enroll in a charter school LEA or other special LEA.71 As with the
process of subal ocating grants to schools within traditional LEAs, this is most often done with
FRPL counts or other measures of low-income authorized for school-level al ocations (discussed
above).
Under the first method, each charter school or other special LEA reports to the SEA its total
enrollment as wel as its enrollment of students from low-income families, and identifies the
traditional (geographical y based) LEA in which each of these students resides. SEAs then use the
ratio of FRPL (or other authorized measures of low-income) students to Census poverty children
in the specific LEA in which each charter school student from a low-income family resides to
estimate the number of Census poverty children (as counted when determining the federal
al ocation to the state) for each charter school or other special LEA. SEAs add to this Census
poverty estimate for the charter school LEA the number of other formula children included in the
Title I-A formulas72 to derive a total formula child count for each charter school LEA. For each
such formula child, the charter school LEA receives an amount equal to the Title I-A grant per
formula child associated with the sending LEA in which the child’s family resides. At the same
time, an equivalent amount is deducted from the grant for the sending LEA.
Under the second method, the enrollment data reported by charter schools and other special LEAs
again are used to estimate the number of formula children for each special LEA, but in this case
using the statewide average ratio of Census poverty and other formula children to FRPL (or other
low-income measure) students. These formula child counts for each special LEA73 are summed to
determine the share of al formula children in the state who attend charter school and other special
LEAs. In this case, the grants to al traditional LEAs in the state, not just the specific LEAs in
which charter school students reside, are reduced by this percentage, and each special LEA
receives a grant based on the statewide average Title I-A grant per formula child.
Recent Developments Regarding Data on Students
Eligible for Free and Reduced-Price Lunches: The
Community Eligibility Provision (CEP)74
The Healthy, Hunger-Free Kids Act of 2010 (P.L. 111-296) created CEP as a new option for how
schools can operate the National School Lunch and School Breakfast Programs authorized under

70 U.S. Department of Education, State Educational Agency Procedures For Adjusting Basic, Concentration, Targeted,
And Education Finance Incentive Grant Allocations Determ ined by the U.S. Department of Education , May 23, 2003,
pp. 3-24, http://www2.ed.gov/programs/titleiparta/seaguidanceforadjustingallocations.doc.
71 Regulations require SEAs to adjust ED calculated T itle I-A LEA grant amounts to provide grants under each of the
four formulas for LEAs that are not included in the SAIPE data (34 C.F.R. 200.72). T he aforementioned ED guidance
provides information on how these adjustments should be made.
72 Neglected, delinquent, and foster children, plus children in families receiving T ANF payments in excess of the poverty
income threshold for a family of four.
73 Including neglected, delinquent, and foster children, plus children in families receiving T ANF payments in excess of
the poverty income threshold for a family of four.
74 See also CRS Report R44568, Overview of ESEA Title I-A and the School Meals’ Community Eligibility Provision;
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the Richard B. Russel National School Lunch Act. Under CEP, free meals are provided to al
students at participating schools.75 CEP is designed to ensure access to school meals by students
from low-income families, especial y in high-poverty schools, and simplify administration of the
school meal programs by eliminating the use of applications to collect family income information
and the need to track children by eligibility category in the lunchroom.
Community eligibility was initial y phased in in a few states at a time and became available in al
states beginning with the 2014-2015 school year, when 14,214 schools serving more than 6.6
mil ion children participated in the program. Since then, participation in CEP has increased
steadily, rising to 28,614 schools, serving more than 13.6 mil ion students in 4,698 LEAs in the
2018-2019 school year.76 In that year, participating schools constituted an estimated 64.6% of al
schools eligible to participate in CEP,77 or 28.7% of al public schools.
Implementation of CEP by LEAs and schools has important implications for Title I-A. As
discussed above, for the vast majority of public schools eligibility to receive FRPL has been the
sole, or at least the primary, indicator of low family income under Title I-A. This data source is no
longer available for 28.7% of al public schools as of the 2018-2019 school year, and that
percentage is likely to continue to rise in the future. As is discussed below, alternatives to FRPL
are available as indicators of the percentage of students from low -income families in CEP
schools. However, on average these sources tend to include a more limited segment of the low -
income population than FRPL, due to differences in income thresholds and other eligibility
criteria, as wel as differing program administrative procedures. Therefore, these alternative
sources of data on students from low-income families in individual schools are not directly
comparable to the FRPL counts used in the past for CEP schools, or used currently by most non-
CEP schools. Such changes in the sources of data on students from low -income families, and use
of different sources for different schools in the same LEA, can result in substantial shifts in the
patterns of Title I-A eligibility and al ocation levels among schools, and potential y inconsistent
treatment of schools within the same LEA. Further, as is discussed later in this report, changes in
the sources of data on students from low-income families affect policies requiring schools to be
accountable for Title I-A grants by reporting achievement levels specifical y for students from
low-income families and taking appropriate actions when those achievement levels are
inadequate
School meals data never have any effect on state total grants. In the great majority of cases, there
is also no consideration of school meals data in the calculation of grants to LEAs. This section

and CRS Report R46371, Serving Free School Meals through the Com m unity Eligibility Provision (CEP): Background
and Participation.
75 In addition to CEP, there are two other provisions under which school meals may be served to all students in a school
at no cost to the students or their families. T hese special assistance alternatives are referred to as Provision 2 and
Provision 3 of the National School Lunch Act, Section 11(a)(1). Un der Provision 2, schools make student eligibility
determinations in a base year, then make no new eligibility determinations for the next three years. Reimbursement
during years two to four is based on applying the percentages of free, reduced-price, and paid meals in the base year to
the number of meals served in years two to four. Under Provision 3, schools may receive for a four -year period the
same level of federal cash and commodity assistance as in a base year (preceding the four -year period), adjusted to
reflect changes in enrollment and inflation. Under both provisions, schools must pay the difference between the federal
school meals reimbursement and the cost of providing free meals to all students from non -federal sources. See
https://www.fns.usda.gov/school-meals/provisions-1-2-and-3.
76 Food Research and Action Center (FRAC), Community Eligibility: The Key to Hunger-Free Schools School Year
2018-19, May 2019, https://frac.org/wp-content/uploads/community-eligibility-key-to-hunger-free-schools-sy-2018-
2019.pdf.
77 Ibid., p. 3.
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provides an overview of CEP, followed by discussion of the implications of this option for Title I-
A.
Overview of CEP
A school, group of schools, or an entire LEA may operate under CEP if the LEA chooses to do so
and if at least 40% of the total enrollment is approved for free school meals without an
application. Students enrolled without an application are referred to as Identified Students
because they have been identified by another program as low-income or especial y vulnerable.
The share of enrolled students who are Identified Students is referred to as the Identified Student
Percentage (ISP). These counts of Identified Students are the alternative to FRPL counts (referred
to above) that are now available for CEP schools, and potential y available for non-CEP schools
as wel .
Identified Students include those whose families receive SNAP benefits, TANF cash assistance,
Food Distribution Program on Indian Reservations benefits, or Medicaid in areas approved for the
USDA’s Medicaid Direct Certification Demonstration Projects.78 Such students are deemed to be
directly certified for free lunches through family participation in the specified programs. Students
may also be approved without an application if they are in foster care or are migrants, homeless,
runaways, or attending a Head Start program. For purposes of CEP, ISPs need to be updated once
every four years, although LEAs are encouraged to update these data more often.79
It should be noted that SAIPE estimates of the number of school-age children in poor families,
which are used to al ocate Title I-A grants to states and LEAs, are more comprehensive than
counts of Identified Students in schools under the CEP program. SAIPE estimates include all
children ages 5-17 in families with income below the standard federal poverty level, regardless of
whether they attend public or private schools, or any school at al ; the immigration or citizenship
status of the children or their parents; whether their parents have applied for assistance under any
governmental aid program; the employment or educational status of their parents; and whether
their parents may have been convicted of violating any laws. In contrast, parents must meet a
number of eligibility criteria related to the above factors in order for the parents and children to
receive assistance under SNAP or other programs under which students may be directly certified
and thereby included in their schools’ ISP. (For a discussion of eligibility criteria for SNAP, see
CRS Report R42505, Supplemental Nutrition Assistance Program (SNAP): A Primer on
Eligibility and Benefits, and CRS Report R46371, Serving Free School Meals through the
Community Eligibility Provision (CEP): Background and Participation.) Similarly, data on
students receiving free or reduced-price lunches are limited by either the requirement for parents
to complete applications or to meet the eligibility standards for SNAP or other programs that are
the basis for direct certification of eligibility for free lunches.
CEP schools serve meals free to al students and are reimbursed by USDA at the free meal rate
based on the ISP multiplied by 1.6, which is intended to reflect the average ratio of the number of
students receiving free or reduced-price meals to the number of Identified Students.80 As a result,

78 For information on the Medicaid Direct Certification Demonstration Projects, see Hulsey et al., Evaluation of the
Direct Certification with Medicaid for Free and Reduced -Price Meals Dem onstration.
79 If LEAs update data on Identified Students more often than required (every four years), and the updated ISP is higher
than previously, schools and LEAs may use the increased percentage as the basis for reimbursement by USDA. If it is
lower, schools and LEAs can continue to use their original percentage for the full four -year period.
80 According to the USDA, "An analysis conducted around the time that the HHFKA [Healthy, Hunger -Free Kids Act
of 2010] was being drafted showed that, for every 10 children directly certified, up to 6 additional children relied on the
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schools participating in CEP are fully reimbursed at the free meal rate for providing free school
meals to al students if their ISP is 62.5% or above; if the ISP is between 40% and 62.5%, they
must provide at least some revenue for school meals from other sources.81
CEP and Title I-A Implications
The implementation of CEP has implications with respect to the determination of Title I-A grants
to the school level and accountability measures. First, CEP schools need to identify an alternative
data source (other than FRPL eligibility) to determine school-level Title I-A grants. The data
source selected, however, must al ow for comparability between CEP and non-CEP schools to
ensure fairness. Second, these data choices affect al ocation of Title I-A funds among schools, and
Title I-A policies requiring schools to be accountable for these funds by reporting achievement
levels specifical y for students from low-income families and taking appropriate actions when
those achievement levels are inadequate, as discussed below.
U.S. Department of Education Policy Guidance on CEP
ED published detailed policy guidance on the administration of Title I-A in LEAs with one or
more schools participating in CEP in March 2015.82 For schools participating in CEP, the counts
of students approved to receive free and reduced-price meals discussed above are no longer
available, though such schools do have counts of Identified Students.83 ED’s March 2015 policy
guidance lists several alternative sources of data that states and LEAs may use for selection of
Title I-A schools and al ocating funds among them. These alternative data sources are listed
below.
 Identified Student Percentage multiplied by 1.6. The 1.6 multiplier is an
estimate of the ratio of the total number of students approved for FRPL to the
number of students approved for free meals without an application (through
direct certification). For schools that participate in CEP individual y, this
percentage is identical to the percentage of meals for which they can claim
reimbursement at the free rate from USDA. A school that participates in CEP as

application process to access free or reduced price meal benefits. An evaluation of CEP in pilot States also showed that
the 1.6 multiplier appears to be an accurate reflection of the relationship between the free and reduced-price student
percentage and the ISP in a typical participating LEA." (U.S. Department of Education, Food and Nutrition Service,
"National School Lunch Program and School Breakfast Program: Eliminating Applications T hrough Community
Eligibility as Required by the Healthy, Hunger-Free Kids Act of 2010," 81 Federal Register 50210, July 29, 2016).
Major reasons why the average number of students participating in FRPL would be approximately 1.6 times the
average number of Identified Students include the relatively high income eligibility standard, particularly for reduced-
price meals under FRPL (185% of the standard federal poverty measure) , and differing citizenship eligibility standards
for FRPL versus SNAP and other programs that are the basis for counts of Identified Students. T he 1.6 multiplier was
originally applicable through at least June 30, 2015 , and was to remain in place for any four-year CEP cycle begun by
that date. For cycles beginning after that date, USDA could have changed the multiplier within the range of 1.3 -1.6,
although as of the cover date of this report no such change has yet taken place, and the multiplier remains at 1.6.
81 T he 1.6 multiplier for reimbursement multiplied by 62.5% equals 100% of students. If a school or LEA ISP is 62.5%
or higher, the reimbursement from USDA is at the free meal rate for all meals served. If the ISP is between 40% and
62.5%, then the percentage of meals served in excess of the ISP multiplied by 1.6 are reimbursed by USDA at the much
lower paid meal rate.
82 U.S. Department of Education, T he Community Eligibility Provision and Selected Requirements Under T itle I, Part
A of the Elementary and Secondary Education Act of 1965, March 2015, https://www2.ed.gov/programs/titleiparta/
legislation.html.
83 For the first year in which a school operates under CEP, FRPL data from the prior school year remain available and
may be used to ease the transition to CEP for participating schools and their LEAs.
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part of a group must calculate this percentage for the individual school and uses
this individual percentage in the context of Title I-A, while using its group
percentage in the context of operating the school meal programs. Where the ISP
multiplied by 1.6 is used for CEP schools, non-CEP schools in the LEA may use
any other authorized data source, such as children approved to receive FRPL.
Alternatively, LEAs may use the number of Identified Students multiplied by 1.6
as the share of the school’s enrol ment that are from low-income families for al
schools, whether or not they participate in CEP, providing consistent treatment of
al schools.

This approach al ows LEAs to continue using FRPL or other data they have used
in the past for non-CEP schools, while using a measure for CEP schools that is,
on average, comparable. In addition, LEAs could use the same measure—ISP
multiplied by 1.6—for both CEP and non-CEP schools, ensuring comparability
between them. One potential difficulty is that the estimated ratio of students
approved for free meals without an application to FRPL students for the nation as
a whole may not apply to individual schools or LEAs. Further, if LEAs use the
ISP multiplied by 1.6 for al of their public schools, such use of a new data
source may result in changes in the identification of Title I-A schools and
al ocation of funds among them.84
 Identified Student Percentage without use of the 1.6 multiplier (if used
consistently for both CEP and non-CEP schools). Because students approved
for free meals without an application are, on average, a subset of students who
would qualify for free or reduced-price school meals if their families completed
an application, this approach to identifying low-income students wil general y
lower the percentage of students considered low income at al schools. Therefore,
LEAs that adopt this approach may wish to adjust by funding schools whose
shares of low-income students are lowered as a result. Under the approach, the
same data are used for CEP and non-CEP schools, ensuring comparable
treatment. However, this would general y require the use of new data sources for
al schools, possibly resulting in shifts in the identification of Title I-A schools
and al ocation of funds among them.
 Shares of students from low-income families as determined by state or local
income surveys. While ED’s policy guidance discourages their use due to
concerns about administrative burdens,85 states and LEAs could design and

84 Under this option, when comparing t he ISP multiplied by 1.6 to total enrollment in order to determine a school’s
low-income student percentage, this percentage is capped at 100% if it would otherwise exceed that amount. For
example, if a school has 400 students and 300 are Identified Students, the school’s low-income student percentage for
the purpose of selecting schools to participate in T itle I-A would be 100%, not 120% ((300 /400)* 1.6 = 1.2). However,
in the allocation of T itle I-A funds among participating schools, ED’s policy guidance allows LEAs to vary the T itle I-
A grant per child from a low-income family among CEP schools where the ISP multiplied by 1.6 is capped at 100%,
based on variations in the share of the schools’ total enrollment that consists of Identif ied Students. At the same time,
all CEP schools capped at 100% must receive a per-child grant that is at least as high as that for any non -CEP school at
or below 100%.
85 T his option differs from previous policies in that it explicitly allows states and LEAs to use their own family income
surveys for purposes of school ranking and allocations under T itle I -A. If state or local income surveys are used, they
must be accurate and have an income threshold that is consistent with the Census poverty definition or the threshold
used for free or reduced-price lunches, T ANF assistance, or Medicaid. States or LEAs conducting the surveys must not
in any way indicate that the surveys are required by either ED or USDA, school nutritio n funds may not be used for the
surveys, and they must clearly indicate that receipt of free school meals is not tied to them. T itle I-A funds may be used
to conduct an income survey, but only under specific circumstances (e.g., the survey cannot be needed to meet any state
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administer income surveys to meet their needs, not only for Title I-A, but
potential y for other programs (such as state school finance formulas) as wel .
Aside from administrative burdens, there may be issues with the reliability and
accuracy of state or LEA income surveys.
 The Medicaid, TANF, Census (where available), or composite data
authorized under the ESEA statute. These data sources are already explicitly
authorized under the Title I-A statute—and have been for many years, though
few LEAs have chosen to rely on them. Census data, in particular, are rarely
available for individual schools. Note that data on the number of children whose
families participate in SNAP are not included here, presumably because that
program is not among those specified in the statutory text of the ESEA.
With some of the options al owed under ED’s policy guidance for Title I-A school selection and
al ocations in LEAs with CEP and non-CEP schools, there is potential concern about a lack of
comparability in the low-income student data used for CEP and non-CEP schools. LEAs
following ED’s CEP policy guidance wil in some cases be using different data sources on low -
income students for CEP and non-CEP schools (e.g., Identified Students * 1.6 for CEP schools
and students approved for FRPL for non-CEP schools). Also, while the data for non-CEP schools
are updated annual y, the data for CEP schools might be updated only once every four years
(although the ED guidance encourages more frequent updates).
In addition to public schools, private schools may be eligible to participate in CEP if their ISPs
are 40% or more. Thus, in determining the share of their Title I-A grant that must be used to serve
private school students, LEAs may have to consider a variety of scenarios in which public and
private schools may or may not be CEP schools. Options for counting students in low -income
families for CEP private schools include those described above for CEP public schools.
Nevertheless, the basic principles regarding equitable determination of low-income student
counts for participating public and private schools remain unchanged, and ED’s March 2015
policy guidance offers several il ustrative examples.
State Adjustments of LEA Grants as Calculated by ED
As noted previously, school meals data never have any effect on state total grants. In the great
majority of cases, there is also no consideration of school meals data in the calculation of grants
to LEAs. However, states may, with ED approval, real ocate grants among their smal LEAs—
defined as those serving areas with a total population of fewer than 20,000 persons. In states
exercising this option, total funds calculated by ED for smal LEAs are aggregated and then
real ocated based on the statutory formulas, but using alternatives (approved by ED) to the
statutory population factor. Currently, nine states (Alaska, Iowa, Kansas, Maine, Montana,
Nebraska, New Hampshire, North Dakota, and Oklahoma)86 exercise this option to use alternative
population data for real ocation of funds among their smal LEAs, and many of these states use
counts of children eligible for FRPL as at least a partial population factor.
For states that use alternatives to Census poverty estimates to al ocate Title I-A funds among their
smal LEAs, ED’s March 2015 policy guidance provides two options in cases where those states
use school meals data in their formulas. First, states may use the number of Identified Students as
their measure of low family income for al schools in compiling data for affected LEAs. Second,
states may use the number of Identified Students multiplied by 1.6 for CEP schools, and use

or local requirements, it must be necessary to properly operate the T itle I-A program in the school district, and costs
must be reasonable).
86 Personal communication between CRS and ED, March 6, 2020.
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either the number of Identified Students multiplied by 1.6 or an unduplicated combination of
Identified Students plus FRPL students for non-CEP schools.
With respect to SEAs’ possible use of FRPL data to adjust ED al ocations for some of their LEAs
due to recent boundary changes or creation of new LEAs, or for charter schools treated as
separate LEAs under state law, ED’s policy guidance would al ow use of any of the alternatives
to FRPL data for CEP schools discussed above.
Possible Impact on CEP of Recent Changes in the Supplemental
Nutrition Assistance Program (SNAP)
SNAP, formerly known as the Food Stamp program, assists low-income households obtain a
nutritional y adequate diet. As is discussed below, counts of children ages 5-17 in households
participating in the SNAP program, and changes in those counts over time, affect the al ocation of
Title I-A funds to states and LEAs, as wel as to individual schools.
SNAP policies assume that households devote 30% of their monthly cash income to the purchase
of food.87 If, given the household’s income level, this contribution is insufficient to meet the full
cost of a diet set at the level of USDA’s Thrifty Food Plan, SNAP benefits are available to
eligible households to help make up the difference.88 SNAP benefits are provided via Electronic
Benefit Transfer (EBT) cards, which operate similar to debit cards when used to purchase eligible
foods at participating food retailers.
In general, to be eligible for SNAP benefits applicants must have gross income below 130% of
the poverty level and/or net income (after taking into account a number of al owable deductions)
below 100% of the poverty level, plus have limited financial resources (such as cash or money in
a bank account).89 Households may also be categorically eligible for SNAP if they receive
benefits under SSI, TANF, or state-run General Assistance (GA) programs.90
There are a number of additional eligibility requirements for SNAP beneficiaries. For example,
only U.S. citizens and certain legal y present noncitizens may receive benefits. In addition, a
number of work/training requirements apply to al SNAP beneficiaries aged 16-59, and a special,
additional set of work/training requirements apply specifical y to Able Bodied Adults Without
Dependents (ABAWD) aged 18-49. In particular, ABAWD wil general y need to meet specific
work or training requirements in order to receive SNAP benefits for more than three months in
any three-year period. Persons may be excused from either the general requirements or the
specific ABAWD requirements under specified conditions.91

87 For background information on SNAP, see CRS Report R42505, Supplemental Nutrition Assistance Program
(SNAP): A Prim er on Eligibility and Benefits. Also see https://www.fns.usda.gov/snap/supplemental-nutrition-
assistance-program.
88 In general, the household's net monthly income is multiplied by 0.3, then this amount is subtracted from the
maximum monthly allotment (based on the cost of the USDA's T hrifty Food Plan) for a household of the relevant size
to determine the household's monthly SNAP allotment.
89 Exceptions or adjustments to these requirements may apply to persons who are elderly or disabled, or who live in
Alaska or Hawaii.
90 See CRS Report R42054, The Supplemental Nutrition Assistance Program (SNAP): Categorical Eligibility.
91 See https://www.fns.usda.gov/snap/work-requirements. Also see CRS Report R42505, Supplemental Nutrition
Assistance Program (SNAP): A Prim er on Eligibility and Benefits.
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SNAP programs are administered by state SNAP agencies,92 and policies regarding some aspects
of eligibility and benefit amounts may vary somewhat among the states. One example is state
implementation of categorical eligibility of households for SNAP if they already participate in
certain other programs, including SSI, state-run GA programs, or TANF. With respect to TANF,
states have been al owed, if they so choose, to confer SNAP eligibility on households based not
only on cash assistance but also on receipt of a wider range of often low-cost TANF benefits or
services, such as provision of a brochure or pamphlet, or referral to a telephone hotline for
additional information.93 These categorical y eligible households are able to bypass the usual
SNAP asset limits, although their net income stil needs to be below the standard threshold for
SNAP eligibility.94
A number of changes to SNAP eligibility provisions have been proposed or adopted in 2019 or
2020. (A comprehensive or detailed description of changes, whether proposed or enacted, is
beyond the scope of this report. They are discussed in CRS Report R42054, The Supplemental
Nutrition Assistance Program (SNAP): Categorical Eligibility, and CRS Insight IN11250, USDA
Domestic Food Assistance Programs’ Response to COVID-19: P.L. 116-127, P.L. 116-136, and
Related Efforts) Data on SNAP beneficiaries are incorporated into SAIPE estimates of children
ages 5-17 in poor families for states, counties, and LEAs. In addition, counts of Identified
Students via household participation in SNAP are a key element in determining school eligibility
for the CEP child nutrition program. Even in schools that do not participate in CEP, students may
become eligible for free school lunches through household participation in SNAP (direct
certification). Thus, any change in the number of households with school-age children in the
SNAP program could affect the al ocation of Title I-A funds to states and LEAs, as wel as to
individual schools.
Title I-A Accountability, Reporting Requirements, and Data on
Low-Income Students
To receive Title I-A funds, states, LEAs, and schools must comply with a number of educational
accountability requirements relating to standards and assessments. Under the ESEA, states
participating in Title I-A are required to develop and adopt standards and assessments in
mathematics, reading, and science.95 States are also required to establish long-term and interim
goals, including goals related to performance on assessments. States must annual y measure the
performance of al students and each subgroup of students in schools relative to these goals using
a set of state-developed indicators. Subgroups for accountability and reporting purposes include
economical y disadvantaged students, students from major racial and ethnic groups, students with
disabilities, and students with limited English proficiency.96 Thus, for outcome accountability and
reporting purposes, schools, LEAs, and states must be able to identify economical y
disadvantaged students.

92 Depending on the state, these may be welfare, social services, family services, or other state government agencies.
93 See CRS Report R42054, The Supplemental Nutrition Assistance Program (SNAP): Categorical Eligibility.
94 See CRS Report R42054, The Supplemental Nutrition Assistance Program (SNAP): Categorical Eligibility.
95 For detailed information about T itle I-A accountability and reporting requirements, see CRS Report R46245, ESEA:
Title I-A Standards, Assessm ents, Accountability, Report Cards, and Frequently Asked Questions.
96 For reporting purposes, data must be additionally disaggregated by gender and migrant status. Also, data for specific
indicators must be disaggregated by homeless status, status as a child in foster care, and status as a student with a
parent who is a member of the Armed Forces.
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A state must use a set of indicators that are based, in part, on the long-term goals it established to
measure annual y the performance of al students and each subgroup of students to evaluate
public schools.97 These indicators must include the following:
 Student Proficiency on Reading/Language Arts (RLA) and Mathematics
Assessments. For al public schools, student performance on the RLA and
mathematics assessments as measured by student proficiency, and for high
schools, this may also include a measure of student growth on such
assessments98;
 Measures of Student Growth or Another Indicator of School Performance.
For public elementary and secondary schools that are not high schools, a measure
of student growth or another indicator that al ows for meaningful differentiation
in school performance99;
 Graduation Rates. For public high schools only100;
 English Language Proficiency. For al public schools, English Learners’ (ELs’)
progress in achieving English language proficiency101; and
 School Quality or Student Success. For al public schools, at least one indicator
of school quality or student success (e.g., a measure of student engagement,
postsecondary readiness, school climate) that al ows for meaningful
differentiation in school performance.102
Based on the performance of al students and each subgroup of students, states must meaningfully
differentiate school performance using al of the required indicators. States are required to
identify (1) at least the lowest-performing 5% of al schools receiving Title I-A funds, (2) al
public high schools failing to graduate one-third or more of their students, (3) schools required to
implement additional targeted support (see below) that have not improved in a state-determined
number of years, and (4) additional statewide categories of schools, at the state’s discretion, for
comprehensive support and improvement (CSI). States also are required to identify for targeted
support and improvement (TSI) any school in which a subgroup of students is consistently
underperforming. Schools in which one or more subgroups were performing at the same level as
schools identified for CSI must be identified for additional targeted support and improvement
(ATSI) activities. Thus, being able to disaggregate students by subgroup, including the
economical y disadvantaged student subgroup, is required for the identification of schools for
improvement.
States and LEAs are required to prepare and disseminate annual report cards that include a range
of information. LEAs are also required to prepare and disseminate report cards for each of their
public schools. These report cards must include data disaggregated for economical y
disadvantaged students on student achievement on the mathematics, RLA, and science
assessments required under Title I-A at each level of achievement103; student performance on the
other academic indicator included in the state’s accountability system for elementary schools and

97 ESEA, §1111(c)(4)(B).
98 ESEA, §1111(c)(4)(B)(i).
99 ESEA, §1111(c)(4)(B)(ii).
100 ESEA, §1111(c)(4)(B) (iii).
101 ESEA, §1111(c)(4)(B)(iv).
102 ESEA, §1111(c)(4)(B)(v).
103 States are required to administer science assessments to students in specified grade levels. T he results of these
assessments are not included in the state’s accountability system but are reported on the report cards.
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secondary schools that are not high schools; high school graduation rates; student performance on
the indicator(s) of school quality or student success used in the state’s accountability system, as
wel as their progress toward meeting the state’s long-term accountability system goals, including
interim progress; and the percentage of economical y disadvantaged students who were assessed
and not assessed.
Thus, for outcome accountability and reporting purposes, schools need to be able to identify
students in the economical y disadvantaged subgroup. LEAs and schools have general y used
FRPL data to comply with accountability and reporting requirements relating to subgroup
performance for low-income students. As these data are no longer available in schools
participating in CEP, ED has published guidance providing schools with a series of options for
identifying students from low-income families (see below).
Data Options for LEAs Not Participating in CEP
The ESEA does not specify how to determine which students should be included in the
economical y disadvantaged subgroup. In practice, this subgroup has historical y been based on
FRPL data. For schools participating in the NSLP but not CEP, these data are stil available.
Data Options for LEAs Participating in CEP
As FRPL data are no longer available for schools participating in CEP, ED has provided policy
guidance that gives states and LEAs three options for determining low -income status for
accountability and reporting purposes: (1) to consider al students in CEP schools to be from low-
income families, (2) to consider only Identified Students to be from low-income families, and (3)
to use income surveys to identify students from low-income families.104 Each of these options,
and its alignment with ESEA accountability and reporting requirements, is discussed in more
detail below.
Assume All Students Are from Low-Income Families
LEAs may consider al students in CEP schools to be from low -income families. In this situation,
the al students group is the same as the economical y disadvantaged subgroup. ED has pointed
out that the rate of students from low-income families is relatively high in CEP schools; thus, it is
not unreasonable to consider al of the students in these schools as being from low -income
families.105 However, the rate of Identified Students in a CEP school can general y be as low as
40%.106 Thus, it can be argued that although a school has a relatively high rate of Identified
Students, the rate is not high enough to assume that 100% of students are from low -income
families. Additional y, this approach obscures any achievement gaps for students from low -
income families as it does not al ow schools and LEAs to differentiate between al students and
those from low-income families.

104 U.S. Department of Education, T he Community Eligibility Provision and Selected Requirements Under T itle I, Part
A of the Elementary and Secondary Education Act of 1965, as Amended, 15 -0011, March 2015, http://www2.ed.gov/
programs/titleiparta/15-0011.doc (hereinafter referred to as “ ED, T he CEP and Selected Requirements Under T itle I -
A”).
105 ED, T he CEP and Selected Requirements Under T itle I-A.
106 Schools participating in CEP because they are in an LEA or group of schools eligible for CEP may have an
Identified Student rate below 40%. Additionally, schools not in the first year of their four -year CEP cycle may have an
Identified Student rate of below 40%.
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Consider Only Identified Students to Be from Low-Income Families
LEAs may consider Identified Students in CEP schools to be from low-income families. Unlike
the previous option, this would most likely underrepresent the size of the economical y
disadvantaged subgroup, as Identified Students do not include al of the students who would be
included in FRPL student counts.107
One argument in favor of this approach is that, unlike the previous option, it al ows schools and
LEAs to differentiate between al students and those from low-income families. However, this
approach may exclude students who previously would have been considered to be from low-
income families because they would be eligible for FRPL based on a school lunch application.
Identify Students Based on Surveys
LEAs can use a household income survey to identify students from low-income families. As
previously discussed, the data from a survey could be used for Title I-A purposes and for other
programs. As with the previous option, income surveys al ow schools and LEAs to differentiate
between students from low-income families and the general student body. At the same time,
income surveys essential y reintroduce some of the paperwork that CEP is intended to eliminate.
Alternative Ways to Measure School-Level Poverty
or Related Indicators
Throughout the history of the Title I-A program, its focus has remained on providing funds to
areas with concentrations of poverty. Thus, Congress has needed to identify which children
should be considered as living in poor or low-income families. This has made it necessary to
define poverty, identify a data source for measuring poverty, and decide which other categories of
children, if any, should be included in the determination of Title I-A grants. These choices al
have implications for state, LEA, and school grant amounts.
As discussed previously, data for the number of children in each school living in families in
poverty are not readily available, so the number and percentage of children eligible to receive
FRPL is often used as a proxy measure. According to the National Center for Education Statistics
(NCES) at ED, “Because the free/reduced price lunch eligibility is derived from the federal
poverty level, and therefore highly related to it, the free/reduced price lunch percentage is useful
to researchers from an analytic perspective.”108 However, utility of the FRPL measure has
changed substantial y with the introduction of CEP, complicating its use as a measure to
determine school-level grant amounts, meet Title I-A accountability and reporting requirements,
and meet the needs of other programs or research that relies on FRPL data as a measure of
school-level poverty.
ED summarizes issues related to the use of existing poverty measures and their limitations as
such:

107 According to USDA, "An analysis conducted around the time that the HHFKA [Healthy, Hunger-Free Kids Act of
2010] was being drafted showed that, for every 10 children directly certified, up to 6 additional children relied on the
application process to access free or reduced price meal benefits"; Federal Register, July 29, 2016, p. 50201.
108 U.S. Department of Education, National Center for Education Statistics, Free or reduced price lunch: A proxy for
poverty?, NCES Blog, April 16, 2015, https://nces.ed.gov/blogs/nces/post/free-or-reduced-price-lunch-a-proxy-for-
poverty.
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Although federal, state, and local education programs focus billions of dollars each year to
improve educational opportunities for children in low-income schools and neighborhoods,
the information available to identify and target high -need areas is limited. Most
compensatory education programs use some type of poverty indicator to determine
program eligibility and/or funding levels, but the development and accessibility of poverty
data has not kept pace with the needs of these programs. The existing poverty thresholds
do not fully reflect nonfood expenses needed to maintain household well-being, and they
do not fully account for noncash in-kind benefits provided to individuals and families
participating in federal need-based initiatives like the Supplemental Nutritional Assistance
Program (SNAP) or the Women, Infants, and Children program (Citro and Michael 1995).
Nor do they reflect important interactions between poverty status and other attributes like
occupational prestige and educational attainment. More importantly, the structure and
accessibility of poverty data is too limited. Carefully constructed measures of
socioeconomic status (SES) provide little benefit if the resulting data are not available at
the necessary geographic scale or for required analytic areas.109
In response, ED has been studying possible alternative measures of school-level poverty as wel
as measures of SES that include components that address school-level poverty. These efforts are
discussed below.
Alternative Measures of School-Level Poverty
ED currently is working on developing a new school-level poverty measure. In the request for
applications for the Statewide Longitudinal Data Systems (SLDS) program released by the
Institute of Education Sciences (IES) at ED in June 2019,110 ED indicated that applicants awarded
grants under any of the three SLDS grant priorities (infrastructure, education choice, and equity)
also were eligible to receive $250,000 to assist ED in testing a proposed school-level poverty
measure. The new measure would be based on student addresses rather than FRPL eligibility.
Participating states111 would be required to create geocoded student address directories and to link
these data with other geographic information provided by NCES. States would then use these data
to produce summaries of the existing poverty measures based on FRPL and the proposed poverty
measure to share with ED. Neither the geocoded student address directory created by a state nor
individual student information would be shared outside the state. States would be required to
participate in up to six webinars each grant year to discuss existing and proposed poverty

109 U.S. Department of Education, National Center for Education Statistics, Education Demographics and Geographic
Estimates (EDGE) Program, Sidestepping the Box: Designing a Supplem ental Poverty Indicator for School
Neighborhoods, November 2018, p. 1, https://www.google.com/url?client=internal-element -cse&cx=
011774183035190766908:dac6vpluw5k&q=https://nces.ed.gov/programs/edge/docs/2017039.pdf&sa=U&ved=
2ahUKEwjnoPPz_7vrAhVQknIEHdsVAh4QFjAAegQIAhAB& usg=AOvVaw3MZKwNlLuT T 2Ovr7ART t -_
(hereinafter referred to as “ED, Sidestepping the Box”).
110 U.S. Department of Education, Institute of Education Sciences, Grants for Statewide, Longitudinal Data Systems:
Request for Applications, June 19, 2019, https://ies.ed.gov/funding/pdf/2020_84372.pdf.
111 As the project will involve data available from the American Community Survey (ACS), only the 50 states, the
District of Columbia, and Puerto Rico are eligible to participate. T he ACS is not conducted in American Samoa, the
Commonwealth of the Northern Mariana Islands, Guam, or the U.S. Virgin Islands. For more information, see U.S.
Department of Education, Institute of Education Sciences, Statewide Longitudinal System s Data Grant Program :
Frequently Asked Questions Regarding the FY19 RFA, 2019, Item 27, https://nces.ed.gov/programs/slds/
faq_rfa19.asp#27. In addition, 28 states were awarded grants in the FY2019 grant competition. Publicly available
abstracts summarizing each state’s proposal were not required to indicate whether the state expressed interest in the
school-level poverty measure project. For more information, see U.S. Department of Education, Institute of Education
Sciences, Statewide Longitudinal Data System s Grant Program : Inform ation Relation to FY19 Grants,
https://nces.ed.gov/programs/slds/grant_information.asp.
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measures. While a specific timeline for these activities was not specified, the SLDS grant awards
are for 48 months, so it is possible that work on the development of a new school-level poverty
measure wil occur over this period of time as wel .
NCES previously examined the feasibility of creating a flexible neighborhood poverty indicator
that could be used to identify schools in low-income neighborhoods based on data from the
ACS112 and estimation techniques developed for spatial statistics.113 To determine what was
involved with actual y producing the proposed indicator, researchers developed neighborhood
poverty estimates for almost 1,800 Ohio elementary schools.114 Based on the results of the study,
the researchers determined the proposed indicator “may provide a useful supplement to existing
school-level poverty indicators.”115 They note there are many benefits to this approach, including
the ability to create estimates for al schools, the use of a wel -known and widely used poverty
standard, and the use of data that “originate from a reliable, authoritative source that uses a
consistent method of measuring income and poverty across the country.”116 In addition, the data
could be updated annual y, would be “relatively cost-effective to produce,” and would not suffer
from disclosure limitations that often restrict the release of poverty estimates for smal areas. The
researchers also note the possible utility of using the estimates to determine Title I-A grants to the
school level.
The proposed measures also have downsides. For example, the data only provide estimates for
school neighborhoods, not for counties, cities, or congressional districts. They also do not provide
the percentage of children in poverty actual y enrolled in a school. In addition, the proposed
indicator works less wel for schools of choice that may draw students from many neighborhoods
or for non-public schools that also enroll students from many areas. The researchers also note
concerns about the sources of income included in the Census data used for the analysis and
relatively large standard errors in the estimates for the new indicator. Despite these limitations,
the researchers recommended the development of a set of indicators for al public schools for
further use and study.
ED also has examined the utility of creating school-level poverty estimates using data from the
ACS, which are used to develop the SAIPE data employed in determining Title I-A LEA grant
amounts. As part of the NCES School Attendance Boundary Survey (SABS),117 NCES examined
whether it was possible to “integrate school attendance boundaries with data from the ACS to
develop demographic estimates for individual school areas.”118 While NCES determined that
estimates could be created, the “average quality for these smal geographic areas was too
unreliable for NCES to create and release as a regular public data product.” NCES also identified

112 T he ACS is an ongoing demographic survey conducted by the Census Bureau that was designed to provide detailed
data on a wide variety of topics for local communities. Unlike the decennial census, the ACS provides data on an
annual basis and includes questions on topics that are not included in the census. ACS survey forms are sent out every
month to a sample of U.S. addresses, for a total of approximately 3.5 million addresses per year. T he ACS collects
information similar to the decennial census long form, which was sent to one-sixth of all U.S. households and was
discontinued after the 2000 Census. By pooling five consecutive y ears (60 consecutive months) of survey responses,
ACS five-year estimates are based on a sample size roughly comparable to the old decennial census long form. For
more information about the ACS, see https://www.census.gov/programs-surveys/acs/data.html.
113 For detailed information about the study, see ED, Sidestepping the Box.
114 T he estimates are referred to as spatially interpolated demographic and economic (SIDE) estimates.
115 ED, Sidestepping the Box, p. 1.
116 ED, Sidestepping the Box, p. 25.
117 For more information about SABS, see https://nces.ed.gov/programs/edge/SABS.
118 ED, Sidestepping the Box p. 3.
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chal enges related to how often school area boundaries were updated and to schools with open-
enrollment policies that may not have neighborhood boundaries.
Alternative Measures of Family Characteristics That May Be
Related to Student Achievement
While FRPL data can serve as an indicator of relative poverty, they are not actual measures of
poverty or changes in poverty rates. In addition, neither FRPL nor poverty measures are measures
of socioeconomic status (SES, which “measures a broader spectrum of family characteristics
[e.g., parental education and occupations] that may be related to student performance”119). This
section of the report discusses ongoing efforts at ED to develop SES measures that would include
components that could be used to measure family income.
In 2012, in response to a request for the National Assessment Governing Board (NAGB), NCES
convened an expert panel to examine ways in which the measurement of SES for purposes of the
National Assessment of Educational Progress (NAEP) could be improved. The panel reached
consensus on the following definition of SES:
SES can be defined broadly as one’s access to financial, social, cultural, and human capital
resources. Traditionally, a student’s SES has included, as components, parental educational
attainment, parental occupational status, and household or family income, with appropriate
adjustment for household or family composition. An expanded SES measure could include
measures of additional household, neighborhood, and school resources.120
NCES also established an Alternative Socioeconomic Status Measures Working Group
(hereinafter, Working Group), which was part of the National Forum on Education Statistics.121 In
response to the potential loss of the NSLP-eligibility indicator, the group was tasked with
“identifying alternative measures of SES that meet the needs of the education community.”122 The
working group was composed of staff from NCES and ED as wel as state and LEA staff.
In 2015, the Working Group published the Forum Guide to Alternative Measures of
Socioeconomic Status in Education Data Systems.123 This document was created to provide
information to the education community as it considers alternatives to FRPL data as a proxy for
student and family SES. The guide highlights three chal enges with continuing to use FRPL data.
First, FRPL data are being used and interpreted in ways that were not intended by the data’s
collection. FRPL is used as a proxy for SES even though it only measures family income and
does not include other relevant measures such as parents’ occupation and education. Second,

119 U.S. Department of Education, National Center for Education Statistics, Free or reduced price lunch: A proxy for
poverty?, NCES Blog, April 16, 2015, https://nces.ed.gov/blogs/nces/post/free-or-reduced-price-lunch-a-proxy-for-
poverty.
120 U.S. Department of Education, National Center for Education Statistics, Improving the Measurement of
Socioeconom ic Status for the National Assessm ent of Educational Progress: A THEORETICAL FOUNDA TION,
November 2012, p. 14, nces.ed.gov/nationsreportcard/pdf/researchcenter/Socioeconomic_Factors.pdf.
121 For more information, see U.S. Department of Education, National Center for Education Statistics, National Forum
on Education Statistics, Alternative Socioeconom ic Status (SES) Measures Working Group , https://nces.ed.gov/forum/
alternative_ses.asp.
122 U.S. Department of Education, National Center for Education Statistics, National Forum on Education Statistics,
Alternative Socioeconom ic Status (SES) Measures Working Group, https://nces.ed.gov/forum/alternative_ses.asp.
123 U.S. Department of Education, National Center for Education Statistics, National Forum on Education Statistics,
Forum Guide to Alternative Measures of Socioeconom ic Status in Education Data , NFES 2015-158, June 2015,
https://nces.ed.gov/pubs2015/2015158.pdf (hereinafter referred to as “ ED, Forum Guide to Alternative Measures of
SES in Education Data”).
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FRPL data at the individual level are available only for purposes of administering the school
meals program; “NSLP prohibits other education staff from using the data to determine the
instructional and non-instructional (service eligibility) needs of individual students.”124 Third,
with the increasing use of CEP, the availability of individual student-level FRPL data is declining
in participating LEAs.
The Working Group focuses on eight alternative SES measures:
1. eligibility for other means-tested programs,
2. household-provided information,
3. student/family categorical status (e.g., homeless, migrant, foster care, runaway),
4. family/household income,
5. highest level of education completed by resident parent/guardian,
6. occupation of resident parent/guardian,
7. neighborhood SES, and
8. school district poverty estimate.
While the Working Group notes that any of these components could be used as a stand-alone
proxy for SES, it does not recommend this approach. Rather, it recommends using the measures
in combination with one another. According to the Working Group, when measures 1-3 are
combined, they replicate past methods of identifying FRPL eligible students and, therefore, may
be consistent with historical values. Measures 4-6 “reflect the three components of
socioeconomic status commonly recognized by the research community.”125 The last two
measures describe community-related aspects of an individual’s SES.
Current and Next Steps on Measures of School-Level Poverty
As discussed previously, Title I-A always has relied on measures of family income to determine
grant amounts. In addition, at the school level Title I-A grants have historical y been made and
continue to be made to schools with relatively high concentrations of students from low -income
families. According to ED:
The impact of poverty on student achievement, educational attainment, and other
educational outcomes has been a concern for educators and federal policymakers since the
passage of the Elementary and Secondary Education Act (ESEA) in 1965. Educational
programs like Title I, Head Start, Promise Neighborhoods, E-Rate, and the National School
Lunch Program (NSLP) target federal resources to help mitigate the effects of poverty on
low-income students, families, and neighborhoods.126
Title I-A is not the only ESEA program that uses measures of poverty to make grants at the state
or LEA level. For example, there are two formula grant programs authorized by the ESEA under
which grants are made to states and subsequently to LEAs in proportion to prior-year Title I-A
grants—Student Support and Academic Enrichment Grants program (Title IV-A) and 21st Century
Community Learning Centers (Title IV-B).127 Other programs also use measures of poverty to
determine grant amounts or eligibility to receive a grant. The Supporting Effective Instruction

124 ED, Forum Guide to Alternative Measures of SES in Education Data , p. 6.
125 ED, Forum Guide to Alternative Measures of SES in Education Data , p. 15.
126 ED, Sidestepping the Box, p. 1.
127 For more information about these and other ESEA programs, see CRS Report R45977, The Elementary and
Secondary Education Act (ESEA), as Am ended by the Every Student Succeeds Act (ESSA): A Prim er.
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program (Title II-A) uses a combination of Census population data for children ages 5-17 and
SAIPE poverty estimates for children ages 5-17 to determine grants to states and subgrants to
LEAs. Similarly, under the Rural and Low-Income Schools (RLIS) program (Title V-B-2), LEAs
are eligible for a grant if, among other eligibility requirements, at least 20% of the children ages
5-17 served by the LEA are from families with income below the poverty line, which can be
determined using SAIPE data.
Title I-A is unique among federal education programs administered by ED with respect to its
statutory requirements related to the distribution of funds to the school level. It also includes, as a
condition of accepting Title I-A funds, numerous accountability and reporting requirements for
states, LEAs, and schools that require the disaggregation of data by low -income student status. As
FRPL data become less useful for meeting these needs and the needs of other programs or
research that may also rely on FRPL data in some capacity (e.g., reduction in school fees for
students who are FRPL eligible, philanthropic organizations that use FRPL data in grant
determinations), this raises the issue of what school-level poverty measure could be used instead
of FRPL data.
While there is continued congressional interest in using poverty measures to determine grant
al ocations under federal education programs, those that are currently being used are imperfect as
measures of poverty and, some may argue, are too limited in their focus. As discussed above, ED
currently is engaged in a study with multiple states to develop a new school-level measure of
poverty. Should this measure come to fruition, it could be used to make Title I-A grants to
schools, for Title I-A accountability purposes, and for research requiring school-level measures of
poverty. In the meantime, ED notes that despite the limitations and data quality issues associated
with the FRPL counts, they “continue to serve as the standard for identifying school-level poverty
for educational programs and surveys because they satisfy core conditions needed to serve as
useful program indicators.”128 Drawing on the work of the researchers Harwel and LeBeau,129
ED lists these conditions and describes how FRPL data meet the criteria of universal
participation, uniform criteria, regular updates, stable infrastructure, flexible application, easy
access, and cost-effective development.130 With respect to the last condition, ED points out that
the development of a new school-level poverty measure that requires the collection of new data
from al schools in the United States would be cost prohibitive and would require new statutory
authority and new funding. ED notes that trying to repurpose existing data would be a more
“convenient and cost-effective solution.”
Brief Considerations for Congress
As previously discussed, LEAs currently lack a consistent measure to identify low -income
children across schools for determining Title I-A school-level al ocations and for academic
accountability purposes under the ESEA. Congress could consider requiring ED to report on its
previous and current efforts to identify a new measure of low -income students that would be both
reliable and consistent across schools. In addition, Congress could consider engaging with ED to
determine whether a better measure could be developed if new statutory authority and new
funding were provided and what specific authority and funding levels would be needed to create a
new measure.

128 ED, Sidestepping the Box, pp. 3-4.
129 M. Harwell and B. LeBeau, Student Eligibility for a Free Lunch as an SES Measure in Education Research.
Educational Researcher, 39(2): 120–131, 2010.
130 ED, Sidestepping the Box, p. -4.
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Appendix A. Overview of Title I-A Formula Factors
Table A-1. Overview of ESEA Title I-A Allocation Formula Characteristics

Current Law
Education
Finance
Formula
Concentration
Incentive Grants
Characteristic
Basic Grants
Grants
Targeted Grants
(EFIG)
Formula child count
Children ages 5-17:
Same as Basic
Same as Basic
Same as Basic
(1) in poor families,
Grants
Grants
Grants
(2) in institutions for
neglected or
delinquent children
or in foster homes,
and (3) in families
receiving Temporary
Assistance for
Needy Families
(TANF) payments
above the poverty
income level for a
family of four
Formula child
10 or more formula
More than 6,500
10 or more
Same as Targeted
eligibility threshold
children and a
formula children or
formula children
Grants
for LEAsa
formula child rate of
a formula child rate
and a formula child
more than 2%
of more than 15%
rate of 5% or more
and must meet the
eligibility
requirements for
Basic Grants
Weighting of formula
None
None
At al stages of the
For al ocation of
child count
al ocation process,
funds within states
formula children
only, formula
are assigned
children are
weights on the
assigned weights on
basis of each LEA’s
the basis of each
number of formula
LEA’s number of
children and
formula children
formula child rate,
and formula child
with each LEA’s
rate, with each
grant determined
LEA’s grant
based on the most
determined based
favorable measure
on the most
(child count or
favorable measure
formula child rate)
(child count or
formula child rate)
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Current Law
Education
Finance
Formula
Concentration
Incentive Grants
Characteristic
Basic Grants
Grants
Targeted Grants
(EFIG)
Expenditure factor
State average
Same as Basic
Same as Basic
Same as Basic
expenditures per
Grants
Grants
Grants, except that
pupil for public K-12
the minimum is
education, subject
85% and the
to a minimum of
maximum is 115%
80% and maximum
of the national
of 120% of the
average
national average,
further multiplied by
0.40
Minimum state grantb
Up to 0.25% of total
Same as Basic
Up to 0.35% of
Same as Targeted
state grants, subject
Grants
total state grants,
Grants
to a series of caps
subject to a series
of caps
LEA hold harmless
85%–95% of the
Same as Basic
Same as Basic
Same as Basic
previous-year grant,
Grants except that
Grants
Grants
depending on the
LEAs are eligible
LEA’s formula child
for the hold
rate, applicable only
harmless for up to
to LEAs meeting the
four years after
formula’s eligibility
they no longer
thresholds
meet the eligibility
threshold
Stages in the grant
Grants are
Same as Basic
Same as Basic
Grants are first
calculation process
calculated at the
Grants
Grants
calculated for states
LEA level, subject to
overal , then state
state minimum
total grants are
provisions
al ocated to LEAs in
a separate process
Additional formula
None
None
None
State effortc and
factors
equityd factors are
applied in the
calculation of state
total grants
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Current Law
Education
Finance
Formula
Concentration
Incentive Grants
Characteristic
Basic Grants
Grants
Targeted Grants
(EFIG)
Funding trigger
None
None
Receives a share of
Receives a share of
Title I-A
Title I-A
appropriations that appropriations that
are in excess of the are in excess of the
amount provided
amount provided
for Basic Grants
for Basic Grants
and Concentration
and Concentration
Grants in FY2001;
Grants in FY2001;
for FY2016,
for FY2016,
appropriators
appropriators
determined how to determined how to
divide these funds
divide these funds
between Targeted
between Targeted
Grants and EFIGe
Grants and EFIGe
beginning in
beginning in
FY2017, statutory
FY2017, statutory
provisions require
provisions require
that al funds in
that al funds in
excess of FY2001
excess of FY2001
levels be divided
levels be divided
evenly between
evenly between
Targeted Grants
Targeted Grants
and EFIG
and EFIG
Source: Table prepared by CRS based on an analysis of the ESEA.
a. The formula child rate is the percentage of children ages 5-17 residing in a given LEA who are formula
children. It is calculated by dividing the number of formula children in an LEA by the number of children
ages 5-17 who reside in the LEA.
b. Formula child counts are used to determine the caps on the minimum grants under al four formulas. Under
Basic Grants, Concentration Grants, and Targeted Grants only formula children in LEAs eligible for Title I -A
are included in the determination of the state minimum grant amounts. Under EFIG, al formula children,
regardless of whether or not they reside in an LEA eligible for Title I-A, are included in the determination of
the state minimum grant amounts.
c. The effort factor is calculated based on average per pupil expenditures for public K-12 education compared
to personal income per capita for each state compared to the nation as a whole.
d. The equity factor is determined based on variations in average per pupil expenditures among the LEAs in
each state.
e. Funds provided to Basic Grants and Concentration Grants have fal en below their FY2001 levels, due in part
to across-the board reductions and rescissions. In recent years, appropriators have divided funds not
appropriated for Basic Grants and Concentration Grants evenly between Targeted Grants and EFIG.

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Appendix B. Current Issues Regarding the Standard
Federal Poverty Measure as Applied in the SAIPE
Estimates Used to Calculate Title I-A Grants to LEAs
Since adoption of the Education Amendments of 1974 (P.L. 93-380), ESEA Title I-A grants have
been determined by estimates of the number of related children ages 5-17 in families with income
below the standard federal poverty measure's thresholds. This Appendix discusses the
development and characteristics of this measure of poverty, as wel as major proposals to modify
the measure over recent decades—including a Supplemental Poverty Measure (also published by
the Census Bureau).
Characteristics of the Standard Federal Poverty Measure131
As noted above, the standard federal poverty measure has been the criterion for determining
poverty level family income, as applied in the al ocation of Title I-A grants to states and LEAs,
for statistical purposes. Thus, al of the poverty estimates provided by the SAIPE program, and
used to calculate state and/or LEA grants under Title I-A and other ESEA programs, are based on
this standard for determining whether school-age children are in families that have income that is
below the poverty level. In addition, standards for determining whether students are eligible for
FRPL, or whether their families are eligible for such programs as SNAP, TANF, or Medicaid are
also based on the same standard federal poverty measure. Therefore, this measure is a critical
underlying factor in al aspects of fund al ocation under Title I-A and several other ESEA
programs.
The measure, initial y developed in the early 1960s, and adopted as the standard federal poverty
measure in 1969, is sometimes cal ed the Orshansky measure, after its developer, Mollie
Orshansky, an economist working for the Social Security Administration. The system for setting
the standard federal poverty income thresholds was original y based on survey data indicating
that low-income families spent one-third of their income on food. Thus, poverty income
thresholds were set, in general, at three times the estimated cost of food to meet minimal y
adequate dietary guidelines, based on a survey conducted in 1955.132 Then, as now, the standard is
based solely on gross cash income. Different income thresholds were established based on family
size, number of children in the family, the sex of the head of the household, and farm versus non-
farm residence. Beginning in 1980, the number of different income thresholds was reduced to
consider only two categories—family size and number of children.133 For two-person households
only, the thresholds also vary depending on whether the household includes an adult whose is 65
or older.

131 For additional information on this and related topics, see CRS Report R44780, An Introduction to Poverty
Measurem ent.
132 T hese thresholds were based on the Economy Food Plan published by USDA. For more information, see Gordon M.
Fisher, The Developm ent of the Orshansky Poverty Thresholds and Their Subsequent History as the Official U.S.
Poverty Measure, U.S. Census Bureau, https://www.census.gov/library/working-papers/1997/demo/fisher-02.html.
133 See https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.
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The standard federal poverty income thresholds are updated annual y, based on annual changes in
the Consumer Price Index for Urban Consumers (CPI-U).134 The most recent published poverty
income thresholds, applicable to income for calendar year 2019, are listed in Table B-1, below.
Table B-1. 2019 Poverty Thresholds by Family Size and Number of Related Children
Under 18 Years

Related Children Under 18 Years
Eight
or
Size of Family Unit
None
One
Two
Three
Four
Five
Six
Seven
More
One person (unrelated individual):

Under age 65
$13,300

Aged 65+
$12,261

Two people:

Householder under 65
$17,120
$17,622

Householder 65+
$15,453
$17,555

Families of three or more members:

Three people
$19,998
$20,578
$20,598

Four people
$26,370
$26,801
$25,926
$26,017

Five people
$31,800
$32,263
$31,275
$30,510
$30,044

Six people
$36,576
$36,721
$35,965
$35,239
$34,161
$33,522

Seven people
$42,085
$42,348
$41,442
$40,811
$39,635
$38,262 $36,757

Eight people
$47,069
$47,485
$46,630
$45,881
$44,818
$43,470 $42,066
$41,709

Nine people or more
$56,621
$56,895
$56,139
$55,503
$54,460
$53,025 $51,727
$51,406 $49,426
Source: Table prepared by CRS based on data available from the U.S. Census Bureau at
https://www.census.gov/data/tables/time-series/demo/income-poverty/historica l-poverty-thresholds.ht ml.
Concerns About the Standard Federal Measure of Poverty:
“Measuring Poverty: A New Approach”
A major study of the current federal poverty standard, with recommendations for changes to it,
was published by the National Research Council (NRC) of the National Academy of Sciences in
1995. This report, titled "Measuring Poverty: A New Approach,"135 critiqued the then (and stil )
current standard federal measure of poverty, and recommended a number of changes to it.136 The

134 T he CPI-U is compiled by the Bureau of Labor Statistics, U.S. Department of Labor. For information on the CPI -U,
see https://www.bls.gov/cpi/home.htm.
135 National Research Council, “Measuring Poverty: A New Approach” (National Academies Press, 1995),
https://doi.org/10.17226/4759.
136 A more limited, but in some respects similar, critique of the standard federal poverty measure was contained in U.S.
Department of Health, Education, and Welfare, The Measure of Poverty, 1976, https://www.census.gov/content/dam/
Census/library/publications/1976/demo/measureofpoverty.pdf. T he preparation of this report was mandated under
Section 821 of the Education Amendments of 1974 ( P.L. 93-380).
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report focused on defining a threshold or budget level below which families would be considered
poor, and the level of income or resources available to families to compare with that threshold.
The report addressed numerous issues and concerns that had arisen regarding the standard federal
poverty measure in the decades following its development. These include the following:
 The standard measure was based on a 1955 survey of family expenditure
patterns. Since that time, family expenditure patterns have changed significantly.
For example, the share of family budgets devoted to food costs has declined,
while the share devoted to certain other basic necessities, such as housing or
medical costs, has increased.
 The standard measure is based on gross cash income, and therefore does not take
into consideration the cost of income, payroll (Social Security), and other taxes
that families must pay from that income. The standard measure also does not
consider the value of tax benefits that families may receive, such as under the
Earned Income Tax Credit (EITC) or the Child Care Tax Credit, especial y in
cases where those tax credits are refundable.137
 Because it is based on gross cash income, the standard measure considers only
benefit programs that provide pre-tax cash income. These include programs such
as Social Security, TANF (previously, AFDC), and Supplemental Security
Income (SSI). Not considered are programs that provide non-cash benefits, such
as SNAP (previously, Food Stamps), Medicaid, or housing subsidies.
 There is no geographic adjustment to the standard thresholds. That is, the same
thresholds are applied to households throughout the nation, regardless of
differences in the cost of living in different areas.
 The standard thresholds are adjusted annual y with respect to a broad measure of
inflation in consumer prices (CPI-U), but this may not accurately reflect changes
in prices for basic necessities for low-income families.
 The standard measure does not consider differences in health status, health
insurance coverage, or out-of-pocket medical costs.
In response, the NRC panel recommended a number of changes to the income thresholds
associated with the standard federal measure of poverty. The most general aspec ts of the proposed
changes regarding poverty income thresholds included the following:
 The thresholds should be based on current estimated needs and consumption
patterns for food, housing (including utilities), and clothing, plus an amount for
miscel aneous expenses (household supplies, personal needs, transportation, etc.).
 The thresholds should be adjusted annual y on the basis of specific measures of
changes in the costs of food, housing, and clothing, not the broad CPI-U
measure, and adjusted over time to consider future changes in patterns of
consumption of these items.
 The thresholds should be adjusted for differences in costs in different areas of the
nation, especial y (but not only) with respect to housing.
With respect to the family income or resource levels that should be compared to the above
thresholds, in order to determine the poverty status of the family or household, the NRC

137 A refundable tax credit is paid to a taxpayer even if its value exceeds the taxpayer's income tax obligations.
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recommended these should be based on money and near-money disposable income. Thus, they
recommended that family income should be reduced with respect to
 income and payroll taxes;
 employment expenses, such as child care where necessary;
 out-of-pocket medical costs, including those for health insurance premiums; and
 child support payments.
The NRC panel recommended that family income levels should be increased with respect to
 a wide variety of in-kind benefits, including those provided under SNAP
(previously, Food Stamps), housing and energy subsidy programs, and child
nutrition programs; and
 tax benefits, particularly the EITC and child care credits.
Alternative Measure of Poverty Published by the Census Bureau:
The Supplemental Poverty Measure138
Since 2011, the Census Bureau has published a series of reports containing data on the number
and characteristics of people in poverty determined by applying a Supplemental Poverty Measure
(SPM).139 The SPM was developed by an Interagency Working Group on Developing a
Supplemental Poverty Measure (ITWG) that included staff from a range of federal agencies. The
SPM incorporates many of the recommendations of the NRC in the 1995 report discussed above.
According to the Census Bureau, the SPM is not intended to be a replacement for the standard
federal poverty measure, or to be implemented in the administration of federal tax or benefit
program policies; it is intended to help il ustrate the effects on the population in poverty of
current federal policies in these areas.
Major differences between the standard federal poverty measure and the SPM are summarized in
Table B-2.
Table B-2. Major Differences Between the Standard Federal Poverty Measure and
the Supplemental Poverty Measure
Poverty Measure
Standard Federal Poverty
Supplemental Poverty Measure
Characteristic
Measure
Measurement unit
Families (persons related by
Resource units residing in the same
marriage, birth, or adoption) or
household, and consisting of either
unrelated individuals
unrelated individuals, or families
plus unrelated resident children,
foster children, and/or unmarried
partners and their relatives

138 For additional information, see CRS Report R45031, The Supplemental Poverty Measure: Its Core Concepts,
Developm ent, and Use.
139 T he latest publication in this series is U.S. Bureau of the Census, The Supplemental Poverty Measure: 2018, P60-
268, October 2019, https://www.census.gov/library/publications/2019/demo/p60-268.html.
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Poverty Measure
Standard Federal Poverty
Supplemental Poverty Measure
Characteristic
Measure
Poverty threshold
The cost of a minimum food diet
Expenditures for food, clothing,
(as determined by the USDA in
shelter, and utilities (FCSU) at the
1963) multiplied by three
33rd percentile of the population

based on the Bureau of Labor
Statistics' (BLS) Consumer
For 2018, the poverty threshold for
Expenditure Survey (CES) over the
a four-person (two adult, two
most recent five-year period, plus
children) family was $25,465
an additional 20% for miscel aneous
expenditures (household supplies,
personal care, and non-work
related transportation)

For 2018, the poverty thresholds
for a four-person (two adult, two
children) family (resource unit)
were $28,342 for homeowners
with mortgages, $24,173 for
homeowners without mortgages,
and $28,166 for renters (see below)
Poverty threshold adjustments
The thresholds are adjusted
The thresholds are adjusted
according to family size;
according to family size,
composition; and in some cases,
composition, housing tenure
whether an adult 65 or older is
(owner with mortgage, owner
included
without mortgage, or renter), and
regional costs of housing
Poverty threshold updates
Thresholds are updated annual y
Thresholds are updated annual y
according to changes in CPI-U
according to a five-year moving
average of consumer expenditures
specifical y for FCSU
Geographic adjustments
None
Poverty income thresholds are
adjusted for regional variations in
the cost of housing
Resource measure
Gross, before-tax income
Cash income plus the value of non-
cash benefits to meet FCSU needs
(e.g., SNAP, WIC, school lunch,
housing subsidies, home energy
assistance) and refundable tax
credits, minus income and Social
Security payrol taxes, work-related
expenses (including child care paid
to another household), and out-of-
pocket medical expenses (including
health insurance premiums)
Source: CRS analysis of the standard federal poverty measure and the SPM.
a. The Supplementary Nutrition Program for Women, Infants, and Children (WIC), administered by USDA,
provides nutritional screening and nutritional assistance to low-income, nutritional y at-risk pregnant
women and their children through age five. For further information, see CRS Report R44115, A Primer on
WIC: The Special Supplemental Nutrition Program for Women, Infants, and Children.
As shown in Table B-2, the SPM applies different income thresholds, somewhat different units of
analysis, and different mixes of resources to the estimation of the population in poverty.
According to the most recent report on the SPM, based on income for calendar year 2018, the
overal population poverty rate was slightly higher using the SPM (12.8%) than the standard
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federal poverty measure (11.8%). However, application of the SPM versus the standard measure
varies with respect to a number of population characteristics, as il ustrated in Table B-3.140
Table B-3. Estimates of the Percentage of Population in Poverty According to the
Standard Federal Poverty Measure Versus the Supplemental Poverty Measure, Based
on Income in 2018
Standard Federal Poverty
Population Characteristic
Measure
Supplemental Poverty Measure
Al population
11.8%
12.8%
Age Groups

0-17
16.2%
13.7%
18-64
10.7%
12.2%
65 and older
9.7%
13.6%
Region

Northeast
10.3%
12.2%
Midwest
10.4%
9.2%
South
13.6%
13.9%
West
11.2%
14.4%
Metropolitan areas

Al population in
Metropolitan Statistical Areas
(MSAs)
11.3%
12.9%
MSA principal cities only
14.6%
16.0%
Non-MSA areas
14.7%
12.2%
Source: U.S. Census Bureau, The Supplemental Poverty Measure: 2018, P60-268, October 2019, Figure 7 and
Appendix Table A-2, https://www.census.gov/library/publications/2019/demo/p60-268.html.
Notes: Al of the differences between the official poverty measure and the supplemental poverty measure listed
in this table are statistical y significant from zero (difference) at the 90% confidence level.
As indicated in Table B-3, while the overal poverty rate for 2018 was 1.0 percentage point
higher under the SPM than under the standard poverty measure, there were substantial variations
among people in different age groups, regions, and metro-/non-metro areas of the nation.141
Among age groups, the SPM rate of 13.7% for children from birth through age 17 was
substantial y lower than the standard measure rate of 16.2%, in part because of a number of
benefit programs focused on families with children (school nutrition programs, WIC, etc.) that are
considered non-cash income when determining if the SPM threshold is met. Regarding the effects
of specific adjustments to income under the SPM on the poverty rates for children (ages birth
through 17), the adjustments that had the greatest effect on reducing poverty rates were those for
refundable tax credits (EITC and the refundable portion of the child care tax credit), SNAP, and
Social Security payments. The adjustments that had the greatest effect of increasing poverty rates

140 U.S. Census Bureau, The Supplemental Poverty Measure: 2018, P60-268, October 2019, https://www.census.gov/
library/publications/2019/demo/p60-268.html.
141 All of the differences between the official poverty measure and the SPM discussed from this point forward are
statistically significant from zero (difference) at the 90% confidence level.
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for 0-17 year olds were out-of-pocket medical expenses, work-related expenses, and Social
Security payroll taxes.142 The SPM rate of 13.6% for adults aged 65 and older was higher than the
standard measure rate of 9.7%, in part because of the higher out-of-pocket costs for medical care
among seniors.
Among regions of the nation, the SPM rate was higher than the standard measure rate for states in
the Northeast (12.2% vs. 10.3%) and West (14.4% vs. 11.2%), in part due to higher costs of
housing in those regions. The SPM poverty rate was lower than the standard measure rate for
states in the Midwest (9.2% vs. 10.4%), and the two poverty rates were similar for states in the
South (SPM 13.9%, standard measure 13.6%).
For people living in metropolitan versus non-metropolitan areas of the nation, the SPM rate was
higher than the standard measure rate for al persons living in Metropolitan Statistical Areas
(MSAs) (12.9% vs. 11.3%) and specifical y those living in principal cities of MSAs (16.0% vs.
14.6%), while the SPM rate was lower for those living outside MSAs (12.2% vs. 14.7%). This is
in part due to the SPM adjustments for differences in housing costs.

Author Information

Rebecca R. Skinner

Specialist in Education Policy

Acknowledgments
Wayne Riddle, former CRS Specialist in Education Policy and current contractor to CRS, co -authored this
report.

Disclaimer
This document was prepared by the Congressional Research Service (CRS). CRS serves as nonpartisan
shared staff to congressional committees and Members of Congress. It operates solely at the behest of and
under the direction of Congress. Information in a CRS Report should n ot be relied upon for purposes other
than public understanding of information that has been provided by CRS to Members of Congress in
connection with CRS’s institutional role. CRS Reports, as a work of the United States Government, are not
subject to copyright protection in the United States. Any CRS Report may be reproduced and distributed in
its entirety without permission from CRS. However, as a CRS Report may include copyrighted images or
material from a third party, you may need to obtain the permission of the copyright holder if you wish to
copy or otherwise use copyrighted material.

142 U.S. Census Bureau, The Supplemental Poverty Measure: 2018, P60-268, October 2019, Appendix T able A-6,
https://www.census.gov/library/publications/2019/demo/p60-268.html.
Congressional Research Service
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