Analysis of the Adult Activities Allotment Formula under the Workforce Innovation and Opportunity Act

June 11, 2024 R48100

Analysis of the Adult Activities Allotment
June 11, 2024
Formula under the Workforce Innovation and
Benjamin Collins
Opportunity Act
Analyst in Labor Policy

The Workforce Innovation and Opportunity Act of 2014 (WIOA; P.L. 113-128) is the primary
federal workforce development statute. Congress has considered bills in the 117th and 118th
For a copy of the full report,
Congresses that would reauthorize WIOA.
please call 7-5700 or visit
www.crs.gov.
Title I of WIOA authorizes three formula grant programs that provide funding to state workforce
systems. These grant programs include the Adult Activities program, which supports career services and training for
jobseekers. The formula that allots Adult Activities funds is complex and its nuances may not be well understood. In support
of WIOA reauthorization, this report presents a series of analyses with the goal of providing policymakers with a
comprehensive understanding of how the Adult Activities formula is designed and how it allocates funds.
Under current law, the Adult Activities formula initially allots funding to states based on their relative shares of three formula
factors. One of the formula factors considers population within specified income parameters and the remaining two consider
concentrations of unemployment. More specifically, the three formula factors are the following:
• Disadvantaged Adults (DA factor). This factor generally considers individuals with income under the
poverty line or below other specified levels.
• Unemployment in Areas of Substantial Unemployment (ASU factor). This factor considers unemployed
individuals in areas of substantial unemployment (ASUs) with unemployment rates of at least 6.5%.
• Excess Unemployment (EU factor). This factor considers unemployed individuals in excess of 4.5% of the
civilian labor force, either statewide or within ASUs.
The three formula factors established in WIOA are specialized indicators that are not otherwise published by federal
statistical agencies. They must be developed specifically for their application in the formula. After making initial allotments
based on the three factors, the formula then applies several adjustment provisions that are common to formula grant
programs. These adjustment provisions establish minimum grant levels and limit grant fluctuations from year to year.
During the WIOA reauthorization process, numerous questions have arisen related to the design of the Adult Activities
formula and how it allocates funds. These include questions about how the specialized formula factors are derived and how
the existing factors compare to more traditional indicators. Other questions have arisen about alignment between the formula
factors and the populations the program serves and the sources of year-to-year volatility in states’ formula funding.
This report presents a combination of qualitative and quantitative analyses that are designed to address common inquiries and
clarify how the formula is designed and implemented as well as how various elements of the formula affect states’ Adult
Activities funding. The report’s analysis and findings are divided into five parts:
• Review of formula implementation. This section uses a combination of sources to review how the formula
factors are developed in compliance with statutory definitions. In each case, developing the factors requires
using data that are not regularly published. The DA factor is partially based on a regional income indicator
that has not been fully updated since the 1980s. In describing the construction of the ASU factor, this
section illustrates that in the approximately 80% of cases since program year 2015 in which states were
responsible for defining their own areas of substantial unemployment, establishing these definitions was
largely a strategic exercise to maximize the state’s formula factor.
• Comparison of the formula factors and the Adult Activities population. The Adult Activities program is a
universal access program (no eligibility requirements) that provides priority to low-income and other high-
need workers. This analysis compares the populations that are reflected in the formula factors to the
program populations. It finds that the DA factor has some alignment with the priority population but that
the other two factors may not be aligned with the more general population that is eligible for services under
the program or the more specific populations that the program prioritizes. Because two of the three factors
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Analysis of Adult Activities Allotment Formula under WIOA

are not clearly aligned with the service population, the benefits of using complex statute-specific factors are
unclear from a targeting perspective.
• Analysis of the formula factors’ relationship with traditional metrics. This analysis compares the formula
factors to more traditional indicators that are more widely used and may be more widely understood. It
compares the DA factor to a traditional persons in poverty indicator and finds that, compared to their shares
of persons in poverty, states in the West and Northeast regions tend to have higher relative shares of the
DA factor while states in the Midwest and South tend to have lower shares of the DA factor. The analysis
compares states’ relative shares of the ASU and EU factors to states’ relative shares of total unemployment
that are more widely reported. It finds that states with higher unemployment rates typically have higher
shares of the ASU and EU factors than their respective shares of traditional unemployment. It also finds a
close relationship between the ASU and EU factors and that, in many cases, the EU factor largely mirrors
the ASU factor. Correspondingly, many of the distributional considerations related to the ASU factor also
apply to the EU factor.
• Analysis of year-to-year changes in factor data. This analysis calculates year-to-year changes in individual
states’ relative shares of the formula factors and compares these changes to states’ changes in their shares
of traditional labor market indicators like unemployment and civilian labor force. This analysis finds that
the states’ relative shares of the ASU and EU factors are much more likely to have large year-to-year
changes than their relative shares of traditional labor market indicators. The more limited dynamism of the
traditional labor market indicators suggests that the volatility of the ASU and EU factors are specific to the
design of those factors and not inherent to labor market indicators in general.
• Analysis of adjustment provisions. This section tabulates the instances of the application of adjustment
provisions and calculates the provisions’ effect by comparing states’ shares of funding to their shares of the
formula factors. A central finding is that while the adjustment provisions (particularly the hold harmless)
limit some year-to-year fluctuation in grant levels, they also lead to grants that are, in some cases,
disconnected from states’ most recent relative shares of the formula factors and instead more closely
indicate states’ shares of (volatile) formula factors from a prior year. For example, the analysis finds that
the hold harmless can significantly increase funding for certain states, sometimes for several years in a row.
The report concludes with policy options to potentially revise the formula during a reauthorization of WIOA. Critiques of the
current formula factors focus on a lack of transparency, year-to-year volatility, and misalignment with the program and
priority populations. New factors could be based on factors that are some combination of well understood, less volatile, and
potentially better aligned with the populations the Adult Activities program serves. Adjusting the weighting of formula
factors could also emphasize or de-emphasize the role of certain metrics. Formula changes can impact the distribution of
funds, and the final section of the report also discusses legislative options to moderate the immediate effects of a transition to
a new formula if such a transition was pursued.
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Introduction
The Workforce Innovation and Opportunity Act of 2014 (WIOA; P.L. 113-128) is the primary
federal workforce development statute.1 Title I of WIOA authorizes three formula grant programs
that support state workforce systems.2 State workforce agencies use these funds to meet local
workforce needs, including providing career services and training to individual workers.
WIOA authorized appropriations for FY2015 through FY2020. Since FY2021, the systems and
programs authorized by WIOA have continued to be funded through the annual appropriations
process. Congress has engaged in ongoing efforts to reauthorize WIOA, including a House-
passed reauthorization bills in each of the 117th and 118th Congresses, but no consensus has
emerged.3
Title I of WIOA authorizes three formula grants— Adult Activities, Dislocated Worker Activities,
and Youth Activities—that target specific populations. The Adult Activities grant is the most
flexible of the three formula grants. States can use these funds to support any worker age 18 or
over, though certain statutory provisions give priority for more intensive services to certain low-
income individuals and other disadvantaged subpopulations.
The formula that allots Adult Activities funds is complex and its nuances may not be well
understood. During the WIOA reauthorization process, numerous questions have arisen related to
the design of the Adult Activities formula and how it allocates funds. These include questions
about how the specialized formula factors are derived and how the existing factors compare to
more traditional indicators. Other questions have arisen about alignment between the formula
factors and the populations the program serves and the sources of year-to-year volatility in states’
formula funding.
In support of WIOA reauthorization, this report uses a combination of qualitative and quantitative
analyses that are designed to address common inquiries and provide policymakers with a
comprehensive understanding of how the Adult Activities formula is designed, how it is
implemented, and how it allocates funds to state workforce agencies.4
The report begins with background on the WIOA statute to provide context, situate the analysis
within the federal workforce strategy, and provide a brief legislative history.5 It then provides a
brief description of the formula to establish terminology and key principles.
The bulk of the report presents a series of qualitative and quantitative analyses with the goal of
illuminating each step of the formula, from the initial development of the formula factors to the
final application of the adjustment provisions. The analysis is divided into five sections. Each
section has somewhat discrete methodologies and findings. The introduction to each of the five
sections summarizes the major findings associated with the accompanying analysis.

1 For more information on WIOA, see CRS Report R44252, The Workforce Innovation and Opportunity Act and the
One-Stop Delivery System.
2 WIOA defines state to include the 50 states, the District of Columbia, and Puerto Rico; see WIOA §3(56). Discussion
of states in this report will typically include these 52 jurisdictions.
3 For information on the House-passed bill from the 117th Congress, see CRS Report R47099, Workforce Innovation
and Opportunity Act of 2022 (H.R. 7309). For information on the House bill in the 118th Congress, see CRS Report
R47905, A Stronger Workforce for America Act (H.R. 6655): In Brief.
4 This report focuses on the WIOA formula as it applies to the allotments to states. Similar factors and principles are
used for the substate allocation of WIOA funds. See WIOA Section 133 for information on substate allocations, which
are generally not discussed in this report.
5 A more detailed legislative history is provided in Appendix A.
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The report concludes with a discussion of potential options for revising the formula. Options to
revise the formula factors include adopting factors that are more transparent or more closely
aligned with the program population. Each of these factor strategies options has advantages and
potential drawbacks. Some potential factor changes may have a large distributional effect. The
final section of the report discusses policy options to limit the immediate effects of any factor
changes on states’ grants.
Context and Background
WIOA authorizes grant programs and state systems that coordinate federal funding streams to
meet local labor market needs. Title I of WIOA authorizes formula grants to states that support
the operation of state workforce systems. These funds are allotted to states based on their relative
shares of specified formula factors. States can use the funds to operate a system of One-Stop
Career Centers that provide career services and training. States and One-Stop Career Centers
coordinate these activities with other federal funding.6
Title I of WIOA authorizes three formula grant programs that support workforce development
interventions for specified populations. Youth Activities supports training and other workforce
preparation services for in-school and out-of-school youth who are low-income or meet other
criteria. Dislocated Worker Activities supports reemployment efforts for workers who have been
terminated or laid off and meet other criteria. Adult Activities funding (the focus of this report)
has the most flexibility in the populations it serves. It can be used to provide career services to
any person age 18 or over, though it prioritizes low-income workers and other disadvantaged
populations. To receive training under the Adult Activities program, a participant must
demonstrate a need for training to obtain or retain employment that leads to self-sufficiency.7
WIOA formula funds are appropriated on a fiscal year basis but support activities that operate on
a program year (PY) basis. The program year runs nine months behind the fiscal year (e.g.,
PY2020 ran from July 1, 2020, through June 30, 2021). Funds appropriated in a given fiscal year
support activities in the corresponding program year (e.g., funding from FY2021, beginning
October 1, 2020, supports the operation of programs in PY2021, beginning July 1, 2021).8 Post-
appropriation activities (such as formula allotments) are typically reported on a PY basis.9 Most
of this report will discuss the allocation of funds associated with program years.
Table 1 presents annual funding for WIOA Title I formula grants since the law’s enactment. In
PY2023, funding for Adult Activities formula grants to states was about $881 million, accounting
for roughly 30% of the formula grant funding in Title I of WIOA. The funding level for formula
grants in Table 1 is slightly lower than the total appropriation due to set-asides and reservations,
including dedicated funding for outlying areas.10

6 See WIOA §121(b) for required and optional partner programs.
7 WIOA §134(c)(3).
8 In general, a portion of the annual appropriation for WIOA programs becomes available late in the budget year (i.e.,
July 1) and remains available through the succeeding fiscal year, a period of availability known as forward funding.
The remainder of the annual appropriation for WIOA programs becomes available starting one fiscal year after the
budget year and remains available through the end of that fiscal year, a period of availability known as advance
appropriations.
9 See, for example, DOL Training and Employment Guidance Letter, 19-20, which provides details on WIOA
allotments for PY2021, at https://www.dol.gov/agencies/eta/advisories/training-and-employment-guidance-letter-no-
19-20.
10 In the Adult Activities program, WIOA Section 132(b)(1)(A) specifies that the Secretary of Labor shall reserve no
more than 0.25% of funding for grants to the outlying areas. Outlying areas are defined in WIOA Section 3(45).
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Table 1. Formula Grant Funding Allotted to States Under Title I of WIOA: PY2015-
PY2023
(dollars in millions)
Program
PY2015 PY2016 PY2017 PY2018 PY2019 PY2020 PY2021 PY2022 PY2023
Total Formula Grants
2,600
2,685
2,685
2,771
2,770
2,801
2,820
2,850
2,900
Adult Activities
773
811
811
843
843
852
859
865
881
Dislocated
1,013
1,018
1,018
1,041
1,039
1,052
1,059
1,072
1,092
Worker Activities
Youth Activities
815
856
856
887
888
897
903
913
927
Source: CRS analysis of DOL budget justifications and implementation documents, including state-level
allocations posted at https://www.dol.gov/agencies/eta/budget/formula/state.
Notes: Funding levels in the table are the total allotted via formula to the 52 jurisdictions defined as states in the
statute. Amounts do not include the Dislocated Worker National Reserve or other pre-allotment reservations.
Details may not sum to total due to rounding.
Brief Legislative History
WIOA was enacted in 2014 and took effect beginning in PY2015. The structure and funding
formula for the Adult Activities program in WIOA generally follow the formulas for similar
programs in the two major predecessor statutes: the Workforce Investment Act of 1998 (WIA;
P.L. 105-220) and the Job Training Partnership Act of 1982 (JTPA; P.L. 97-300). Both WIA and
JTPA authorized a formula grant to states that had three factors and similar calculation processes
to those used in the current WIOA Adult Activities formula.
A more detailed legislative history and descriptions of formulas in each of JTPA, WIA, and
WIOA are in Appendix A.
Brief Overview of the Adult Activities Funding
Formula
Funding for the Adult Activities program is allotted to states via formula. The formula allots
funds based on states’ relative shares of three equally weighted formula factors and then applies
several adjustment provisions.11 States may use the funds to support career services and training
for program participants. The Adult Activities program is a universal access program (i.e., it has
no eligibility requirements), but it has several mechanisms to target services to workers who are
low-income and those with barriers to employment.

11 WIOA §134(b)(1)(B).
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Concept of Relative Share
Relative share refers to a state’s portion of a numerical amount relative to the total amount. For example, if the
total population of disadvantaged adults nationally is 10 mil ion and a single state has 1 mil ion such adults, the
state’s relative share is 10%.
In the case of a grant allotted on the basis of relative share, the state’s relative share of a factor is applied to the
total funds for grants. To continue the example above, if total funding for a grant were $20 mil ion and allotted
whol y on the basis of each state’s relative share of disadvantaged adults, the state with a 10% relative share of
economically disadvantaged adults would be eligible for a formula grant of $2 mil ion (10% of $20 mil ion).
Formula Factors and Calculation of Initial Grants
Initial grant calculations are made on the basis of each state’s relative share of three factors. One-
third of the funding for grants is allotted based on each state’s relative share of each factor (see
the text box for more detail on the concept of relative share). The factors are the following:
• Disadvantaged adults (DA factor). This factor considers the number of adults
aged 22 to 72 “who received an income or [are] a member of a family that
received a total family income” that does not exceed the higher of the poverty
line or other specified thresholds.12
• Unemployment in Areas of Substantial Unemployment (ASU factor). This factor
considers the number of unemployed individuals in areas of the state that (1) are
“of sufficient size and scope to sustain a program of workforce investment
activities” and (2) have an unemployment rate of at least 6.5%. In cases where a
state’s overall unemployment rate is at least 6.5%, the state is considered a
whole-state ASU and all unemployed persons in the state are counted in the
calculation of the ASU factor. Note that this report will use the term ASU when
referring to a geographic area of substantial unemployment and the term ASU
factor when referring to the count of unemployed individuals within an ASU that
is considered by the formula.
• Excess unemployment (EU factor). This factor considers the higher of (1)
unemployed individuals in excess of 4.5% of the civilian labor force in the state
or (2) unemployed individuals in excess of 4.5% of the civilian labor force in the
ASUs in the state. For a state that qualifies as a whole-state ASU, these two
numbers will be identical.
Notably, all of the factors are composite indicators that are developed solely for the purpose of
application in the WIOA formulas. The factors cannot be easily constructed or replicated using
regularly published data.

12 Statute establishes an alternative threshold of “70 percent of the lower living standard income level”; see WIOA
§131(b)(1)(v). This metric is discussed in more detail in the “Disadvantaged Adults Factor” section of this report.
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Relationship Between the Adult Activities Formula and Youth Activities Formula
Youth Activities is another formula grant program authorized by Title I of WIOA. The formula for the Youth
Activities program shares two factors with the Adult Activities program: the ASU factor and the EU factor. The
third factor in the Youth Activities formula (disadvantaged youth) is similar to the DA factor in the Adult Activities
formula but considers a younger age group. The adjustment provisions in the Youth Activities formula are similar
(but not identical) to the adjustment provisions in the Adult Activities formula.
This report does not explicitly focus on the Youth Activities formula, but due to the similarities between the two
formulas, much of the analysis of the Adult Activities formula is applicable to the Youth Activities formula.
Adjustment Provisions
WIOA has several provisions that establish minimums for small states and limit year-to-year
fluctuations in individual states’ Adult Activities grants. These adjustment provisions are applied
after the calculation of each state’s initial grant based on the state’s relative share of the three
formula factors.
• Small state minimum: Each state’s funding must equal at least 0.25% of the
total funds for grants. If a state’s initial grant is less than 0.25% of the total
funding for grants, the state’s grant is increased to 0.25% of the total amount and
other states’ grants are ratably reduced.13
• Hold harmless: Each state’s relative share of funding may be no less than 90%
of its relative share of the funding from the prior year. If a state’s initial grant is a
relative share of total funding that is less than 90% of its relative share from the
prior year, the state’s relative share is increased to 90% of its relative share from
the prior year and other states’ grants are ratably reduced.
• Maximum grant/stop gain: No state may receive a relative share of funding that
is more than 130% of its relative share of funding from the prior year. If a state’s
initial grant is more than 130% of its relative share from the prior year, the state’s
relative share is reduced to 130% of its relative share from the prior year and
other states’ grants are ratably increased.
The hold harmless and maximum grant provisions are based on relative share, not actual grant
levels. This means that if total funding for grants increases from one year to the next (a common
scenario), the hold harmless dollar amount is more than 90% of the prior year’s grant dollar
amount and the maximum grant is more than 130% of the prior year’s grant dollar amount.14

13 A ratable reduction consists of reducing the initial grant of each state not affected by the adjustment provisions by an
equal percentage to increase the grant level of other states to comply with limiting provisions. For example, in PY2023
states that were not subject to any limiting provision had a final relative share of funding that was 6.3% less than their
relative share of the formula factors.
14 For example, between PY2019 and PY2020, the total funding for grants increased about 1.30% from $841,378,282
to $852,337,815. In PY2019, Florida’s relative share of funding was 5.51% of the total funding for state grants. In
PY2020, Florida qualified for a hold harmless grant, meaning that its relative share of total funding for grants was
about 4.96% of the total funding for state grants (90% of about 5.51%). If funding had been consistent, Florida’s grant
would have been 10% less than the prior year. However, due to the overall increase in funding, Florida’s PY2020 grant
of $42,259,570 (about 4.96% of $852,337,815) was about 8.83% less than its PY2019 grant of $46,351,320 (about
5.51% of $841,378,282). See DOL published grant levels at
https://www.dol.gov/sites/dolgov/files/ETA/budget/pdfs/20adu%24.pdf.
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Figure 1. Overview of the Adult Activities Formula

Source: CRS analysis of WIOA Section 132(b)
Overview of Analysis, Recurring Themes, and
Issues for Consideration
The following sections discuss the results of an in-depth analysis of the Adult Activities formula
and issues for consideration as Congress pursues reauthorization of WIOA. The combination of
analyses are designed to provide policymakers with a comprehensive understanding of how the
Adult Activities formula is designed, how it is implemented, and how it allocates funds.
The Adult Activities formula factors are unique to WIOA. A recurring theme in the analysis is
how the statute-specific formula factors compare to more traditional labor market indicators. An
implicit question underlying these comparisons concerns what advantages or disadvantages the
complex and somewhat opaque indicators established in WIOA offer over more common and
perhaps more widely understood indicators.
The report includes both qualitative and quantitative analysis. The qualitative analysis is divided
into two parts. The first part reviews the implementation of the formula. It focuses on the
complex procedures necessary to develop formula factors that are in compliance with the
statutory requirements, including some practices that may not be widely understood. The second
part compares the populations that are reflected by the formula factors with the populations that
the Adult Activities program serves and prioritizes.
The quantitative analysis is divided into three related parts. The first part analyzes the formula
factors relative to more traditional labor market indicators. It aims to identify the characteristics
of states that benefit more and less from the complex statutory factors in the Adult Activities
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formula compared to more traditional indicators. The second part calculates states’ year-to-year
changes in their relative shares of the formula factors. The third part analyzes the role of the
adjustment provisions (e.g., hold harmless) in determining final grant levels and measures the
effects that these provisions have on final grant levels.
Traditional Labor Market Indicators
A recurring theme of this analysis is comparing the more specialized metrics that are used to allocate Adult
Activities funds with more traditional labor market indicators. In most cases, traditional labor market indicators refer
to total unemployment (i.e., a standard estimate of unemployed persons) and civilian labor force (i.e., the sum of
employed and unemployed persons). These indicators serve as a ready point of comparison for at least two
reasons.
•
They are common indicators. Total unemployment and civilian labor force are widely understood and reported
on a regular basis for a range of geographies.
•
They are used as formula factors in other WIOA core programs.15 The WIOA Dislocated Worker Program and
the Employment Service (ES) state grants both use states’ relative shares of total unemployment.16 The ES
program also uses a civilian labor force factor to allocate funds.
This report wil use the terms unemployment, total unemployment, and regular unemployment interchangeably to
refer to standard unemployment counts regularly published by the Bureau of Labor Statistics.
Formula Factor Design and Implementation:
Compliance with Statutory Provisions Requires a
Complex Process
This section describes the statutory requirements of each of the three formula factors and how
these requirements are implemented. As noted previously, the Adult Activities formula factors are
not regularly published metrics but rather are developed exclusively for use in the formula. One
of the three factors (the DA factor) is a population-based factor. Two of the factors (the ASU
factor and EU factor) are based on specialized measures of unemployment.
This section synthesizes published and unpublished sources to offer a thorough overview of how
the three formula factors are developed. Developing these factors in accordance with their
statutory specifications requires federal agencies to engage in a series of calculations, sometimes
using nontraditional metrics or delegating certain calculations to state agencies. The complexity
of these metrics and their associated development means the formula is less transparent than a
formula that uses commonly understood and widely available metrics.
Disadvantaged Adults Factor
The DA factor uses published data on persons in poverty in a specified age range as a baseline,
but then introduces complexity primarily by adding additional income thresholds that may
increase the factor population.

15 WIOA has six core programs that are coordinated for state planning purposes and subject to similar performance
accountability metrics. All six programs are formula grants to states. One of the core programs, the Employment
Service state grants, operates within the WIOA system, but has a permanent authorization of appropriations in the
Wagner-Peyser Act and is therefore not technically authorized under WIOA.
16 The WIOA Dislocated Worker formula also uses other BLS-published indicators related to long-term unemployment
and unemployment in excess of 4.5%; see Section 131(b)(2) of WIOA.
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Definition of DA Factor
Section 132 of WIOA defines a disadvantaged adult as:
an adult who received an income, or is a member of a family that received a total family
income, that, in relation to family size, does not exceed the higher of—
(aa) the poverty line; or
(bb) 70 percent of the lower living standard income level.17
Other provisions in Section 132 establish that for the purposes of the factor, an adult is an
individual between the ages of 22 and 72.18 The DA factor definition includes additional
provisions that require, in the construction of the factors, “to the maximum extent practicable,
exclu[sion of] college students and members of the Armed Forces[.]”19
Statute does not establish a specific reference period for the factor, but it does specify that “all
data relating to disadvantaged adults ... shall be based on the most recent satisfactory data from
the Bureau of the Census.”20
Implementation of the DA Factor
The Census Bureau provides DOL with updated estimates of disadvantaged adults every five
years. Data are based on a special tabulation of American Community Survey (ACS) data that
consider a five-year reference period. The data, including some intermediate calculations, are
published on the DOL website.21 DOL publishes a quinquennial Training and Employment
Guidance Letter (TEGL) that focuses on these data.22 It is not clear if the complexity of the
calculations contributes to the infrequency of the updates.
Reference Periods and Timeliness
The reference periods for each set of DA factors that have been used since the effective date of
WIOA are listed in Table 2. The five-year reference period and the application of data for five
years can create a gap between the data that are used for awarding funds and the applicable award
year. For example, DA data from the 2011-2015 period were used to allocate Adult Activities
funds for each of PY2018 through PY2022.
While the DA factors are updated infrequently, the changes in states’ relative shares of this factor
tend to be somewhat minor when the factors are updated (see the “Year-to-Year Variation in the
Disadvantaged Adults Factor is Limited” section).

17 WIOA §132(b)(1)(B)(v)(IV). Separate provisions establish different income thresholds for states with designated
local areas served by rural concentrated employment program grants. See WIOA Section 132(b)(1)(iii), which
establishes the exception, and WIOA Section 132(b)(1)(v)(VII), which defines the separate income level. This policy
effectively applies to three states: Kentucky, Minnesota, and Wisconsin. See also page A-1 of DOL Training and
Employment Guidance Letter 01-23, https://www.dol.gov/agencies/eta/advisories/tegl-01-23.
18 WIOA §132(b)(1)(B)(v)(I).
19 WIOA §132(b)(1)(B)(v)(V).
20 WIOA §182(a).
21 U.S. Department of Labor (DOL), “Data for Persons Defined as Disadvantaged Youth and Adults (2016-2020),
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults.
22 For example, the data used for PY2023 were published in TEGL No. 01-23,
https://www.dol.gov/agencies/eta/advisories/tegl-01-23.
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Table 2. Reference Periods for the Disadvantaged Adult Factor: PY2015-PY2023
Program Year (PY)
PY Dates
Data Reference Years
PY2015
July 1, 2015, to June 30, 2016
PY2016
July 1, 2016, to June 30, 2017
2006-2010
PY2017
July 1, 2017, to June 30, 2018
PY2018
July 1, 2018, to June 30, 2019
PY2019
July 1, 2019, to June 30, 2020
PY2020
July 1, 2020, to June 30, 2021
2011-2015
PY2021
July 1, 2021, to June 30, 2022
PY2022
July 1, 2022, to June 30, 2023
PY2023
July 1, 2023, to June 30, 2024
2016-2020
Source: U.S. Department of Labor, Employment and Training Administration, “Data for Persons Defined as
Disadvantaged Youth and Adults,” https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults.
Calculating Disadvantaged Adults with Incomes Below 70% of the Lower Living
Standard Income Level
A distinct characteristic of the DA factor is the statutory requirement that the factor consider
individuals in poverty as well as persons with incomes below 70% of the Lower Living Standard
Income Level (LLSIL). The latter metric generally allows the DA factor to capture additional
adults with incomes above the poverty line but below the specified LLSIL metrics. The use of the
additional LLSIL income metric is likely the greatest source of complexity in the DA factor.
The LLSIL is a series of income thresholds determined by family size and geographic location.
There are LLSIL metrics for about 35 regions and metropolitan areas.23 The differences in the
metrics across regions and metro areas generally reflect differences in cost of living between the
regions and metro areas.24
DOL last fully updated the LLSIL metrics in 1981, but the department uses the Consumer Price
Index (CPI) to update the numerical metrics each year solely for use in the WIOA formulas.25 As
such, the LLSIL metrics reflect regional differences in cost of living as of 1981, adjusted for
inflation.26 In the most recent publication of the LLSIL metrics that were applied to the

23 Annual updates are published through a combination of Federal Register notices and tables on the DOL website. For
links to both notices and tables, see DOL, “Lower Living Standard Income Level Guidelines,”
https://www.dol.gov/agencies/eta/llsil.
24 See U.S. Department of Labor, “Lower Living Standard Income Level Guidance,”
https://www.dol.gov/agencies/eta/llsil.
25 In the annual Federal Register notices announcing the publication of the latest LLSIL thresholds, DOL states
“Publication of these figures is only for the purpose of meeting the requirements specified by WIOA ... [the Bureau of
Labor Statistics] has not revised the lower living family budget since 1981, and has no plans to do so.... [T]hese figures
should not be used for any statistical purposes, and are valid only for those purposes under WIOA as defined in the
law.” See, for example, Federal Register, April 30, 2020, p. 24036,
https://www.dol.gov/sites/dolgov/files/ETA/llsil/pdfs/2020%20LLSIL%20FRN.pdf. For related information, see
technical information associated with the “Family Budgets” section beginning on page 324 of the Bureau of Labor
Statistics’ Handbook of Labor Statistics from December 1980, archived at
https://fraser.stlouisfed.org/files/docs/publications/bls/bls_2070_1980.pdf.
26 The annual Federal Register notices with updated LLSIL levels note that WIOA defines the LLSIL as “based on the
(continued...)
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calculation of DA factors, many (but not all) geographies had family sizes with LLSIL levels
where 70% of the LLSIL was above the poverty line.27
Census applies the LLSIL thresholds to data from the ACS to calculate the number of persons
from households with incomes below 70% of the applicable LLSIL. These data are then
combined with the poverty data to create a data set of persons with incomes below poverty or
below 70% of the applicable LLSIL.28
Other Adjustments to the DA Factor
Subsequent adjustments are made to exclude college students and active-duty military, per
statute.29 Other adjustments are made for the three states (Kentucky, Minnesota, and Wisconsin)
where separate income metrics apply.30
Analysis of the DA Factor
Because the DA factor is only updated once every five years, each state’s relative share of the
factor is fixed for five years when the factor is updated. For example, in each of PY2018 through
PY2022, Ohio’s relative share of the DA factor was 3.413%.31 This also means that, after the DA
factor is updated, a state can accurately predict the factor that will be used to initially allot one-
third of the Adult Activities funding for the remainder of the five-year period.
The combination of the five-year reference period for calculating the DA data and the five-year
period for using each set of data means that in the final year that a data set is used, grants will be
partially allotted on the basis of data that are more than 10 years old. For example, the most
recent DA data are based on data from the 2016-2020 period. These data were first used in
PY2023 and will continue to be used through PY2027.32
Unemployment in Areas of Substantial Unemployment
The ASU factor is an estimate of unemployed individuals within specified geographies. In cases
where a state’s unemployment rate is at least 6.5%, the state qualifies as a whole-state ASU and
the state’s ASU factor is simply its total unemployment. In cases where a state’s unemployment
rate is less than 6.5%, the factor captures only the number of unemployed workers in substate

most recent lower living family budget issued by the Secretary” and subsequently notes that “the most recent lower
living family budget was issued by the Secretary in fall 1981.” See, for example, Federal Register, April 30, 2020,
page 24036, https://www.dol.gov/sites/dolgov/files/ETA/llsil/pdfs/2020%20LLSIL%20FRN.pdf.
27 See Tables 4 and 5 associated with each year’s LLSIL guidelines at https://www.dol.gov/agencies/eta/llsil.
28 DOL, “Data for Persons Defined as Disadvantaged Youth and Adults (2016-2020),”
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults, Tables 1-5.
29 DOL, “Data for Persons Defined as Disadvantaged Youth and Adults (2016-2020),”
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults, Table 6.
30 The states of Kentucky, Minnesota, and Wisconsin, which have designated local areas served by rural concentrated
employment program grant recipients under WIOA Section 107(c)(1)(C), must use the higher of the number of
disadvantaged adults in such areas or the number of adults aged 22 to 72 in families with an income below the "low-
income level" in such area. As a result of this policy, the final published DA levels in other DOL formulas for
Kentucky, Minnesota, and Wisconsin are typically higher than their metrics in Table 6 on the DA website. See DOL
Training and Employment Guidance Letter 01-23 for more details, at https://www.dol.gov/agencies/eta/advisories/tegl-
01-23.
31 DOL, “Workforce Innovation and Opportunity Act Adult Activities: Data Factors for PY 2019 State Formula
Allotments,” https://www.dol.gov/sites/dolgov/files/ETA/budget/pdfs/19adudat.pdf.
32 See Table 2 for other examples of grant award periods and DA factor reference periods.
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ASUs with an unemployment rate of 6.5% or higher. DOL delegates the responsibility of
identifying ASUs to state agencies. Following DOL guidance and using DOL-provided data, the
state agencies must construct an area or a series of areas that each have an unemployment rate of
at least 6.5%. Trends in ASU data suggest that the states’ construction of their ASU factors is
largely a strategic exercise to maximize the size of the eligible population rather than
identification of traditionally defined regions (e.g., county or city) with high unemployment.
Definition of ASU Factor
The statutory formula allots funds based on the “relative number of unemployed individuals in
areas of substantial unemployment in each state.” WIOA defines an ASU as “any area that is of
sufficient size and scope to sustain a program of workforce investment activities … and that has
an average rate of unemployment of at least 6.5 percent for the most recent 12 months, as
determined by the Secretary.”33 Statute further specifies that “determinations of areas of
substantial unemployment shall be made once each fiscal year.”34
Implementation of the ASU Factor
Unemployment in ASUs is not a metric that is otherwise published by federal statistical agencies
and it must be constructed annually for its application in the WIOA formulas. Unemployment
data used in the ASU calculations are based on the preliminary Local Area Unemployment
Statistics (LAUS) data published by the Bureau of Labor Statistics (BLS). The preliminary data
are revised in subsequent BLS publications, but the initial preliminary data remain as the
foundation for the ASU calculations.35
The ASU factor has been implemented by DOL so that the reference period is the 12 months
ending June 30 of the year prior to the program year. (See Table 3.) For example, funds for
PY2022 (July 1, 2022, through June 30, 2023) were allotted using the ASU metrics for the period
beginning July 1, 2020, and ending June 30, 2021, which corresponds with PY2020.

33 WIOA §132(b)(1)(B)(v)(III)
34 Ibid.
35 This means that the revised unemployment data for a state published on the BLS website will not match the data used
for the WIOA calculations, even for whole-state ASUs.
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Table 3. Reference Period for the ASU and EU Factors, PY2015-PY2023
Program Year
Reference Period
PY2015
PY2013
PY2016
PY2014
PY2017
PY2015
PY2018
PY2016
PY2019
PY2017
PY2020
PY2018
PY2021
PY2019
PY2022
PY2020
PY2023
PY2021
Source: DOL, “State Statutory Formula Funding,” https://www.dol.gov/agencies/eta/budget/formula/state.
Notes: Program years run July 1 to June 30. Reference period is the year providing the underlying data that are
used for the allocation of Adult Activities funding.
Whole State ASUs
As noted previously, in cases where a state’s unemployment rate is equal to or greater than 6.5%,
the whole state is considered an ASU. In these cases, the state’s ASU factor equals the total
regular unemployment in the state. Qualifying as a whole-state ASU is the only way that a state
can have 100% of its unemployment considered in the Adult Activities formula.
Within State ASUs
When a state does not qualify as a whole state ASU, state agencies are responsible for identifying
ASUs within the state. BLS provides data and issues annual guidance to state agencies to aid in
the construction of the ASUs, but the actual construction is done by states. BLS confirms that
state submissions are compliant.
In cases where a state must construct its ASUs, unemployment in portions of the state that do not
qualify as ASUs are excluded from the ASU factor. This is effectively a cliff in which 100% of
unemployment in constructed ASUs is counted in the ASU factor while 0% of the unemployment
in non-ASU areas is counted.
Statute does not constrict ASUs to traditional political boundaries such as cities or counties; it
only specifies that an ASU must be “of sufficient size and scope to sustain a program of
workforce investment activities.”36 DOL guidance has operationalized this provision to require
that ASUs have a population of at least 10,000.37
This combination of parameters means that components of ASUs may be (and typically are) as
granular as census tracts. This can allow states to strategically construct ASUs in a variety of
ways.

36 WIOA §132(b)(1)(A)(v)(III)
37 BLS provides annual technical memoranda to state workforce agencies to facilitate the calculation of ASU data. For
example, states produced ASU data for PY2023 following LAUS Technical Memorandum No. S-22-13. (This
memorandum is not published online but is on file with the author.)
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In cases where a state does not qualify as a whole-state ASU but the statewide unemployment rate
is close to 6.5%, the state can use a subtraction method to create large ASUs or a large single
ASU. Under this method, states remove low-unemployment census tracts from consideration until
the remaining contiguous tracts yield a collective unemployment rate of at least 6.5% and
therefore qualify as an ASU.
Alternately, a state can use an addition method to construct ASUs in the state. This method
consists of identifying all tracts with unemployment above 6.5% and adding adjacent tracts with
lower unemployment rates until the overall unemployment rate for each ASU averages 6.5%.38
States’ strategic construction of ASUs may result in traditionally associated geographies (such as
census tracts within a city) being separated. For example, ASUs may be strategically constructed
so that lower-unemployment parts of a jurisdiction or area are excluded and higher-
unemployment tracts of an adjacent jurisdiction are included, creating a larger area with an
unemployment rate of at least 6.5%.
Analysis of the ASU Factor
A critical consideration related to the ASU factor is whether the state qualifies as a whole-state
ASU or if the state must construct its ASUs. If a state qualifies as a whole-state ASU, it will have
all of its unemployment captured by the ASU factor. If the state’s unemployment rate is less than
6.5%, the state must construct its ASUs and some portion of the state’s unemployment will fall
outside of ASUs and therefore not be captured by the ASU factor.
Since WIOA took effect in PY2015, states have constructed their ASUs in more than 80% of
cases. As presented in Table 4, cases where larger numbers of states qualified a whole-state ASUs
tend to be concentrated in reference periods when overall unemployment rates are high.

38 Both the addition and subtraction methods are described in the annual notice BLS provides to state labor market
information agencies to provide guidance in constructing ASUs.
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Table 4. Count of States That Constructed ASUs or Qualified as Whole-State ASUs:
PY2015-PY2023
Total
Whole State
Constructed
Unemployment
PY
ASU
ASUs
Ratea
Reference Period
2015
28
24
6.8%
July 2013-June 2014
2016
11
41
5.7%
July 2014-June 2015
2017
2
50
5.0%
July 2015-June 2016
2018
3
49b
4.7%
July 2016-June 2017
2019
2
50b
4.2%
July 2017-June 2018
2020
1
51
3.8%
July 2018-June 2019
2021
17
35
5.9%c
July 2019-June 2020
2022
24
28
6.9%
July 2020-June 2021
2023
1
51
4.2%
July 2021-June 2022
Source: CRS analysis of unemployment data and formula factors. See Appendix B for ful sources.
Note: The reference year for each program year is the second preceding program year.
a. Rate reflects aggregate unemployment in all states divided by civilian labor force in all states.
b. In PY2019 and PY2020, Vermont did not qualify as a whole-state ASU (unemployment rates of 2.9% and
2.3%, respectively) and was unable to designate any areas within the state that met the ASU criteria. Thus,
Vermont’s constructed ASU factor was zero.
c. Civilian labor force data for Puerto Rico are not available for PY2021. The sum of state unemployment rates
in PY2021 reflects the sum of unemployment in the 50 states and the District of Columbia divided by the
civilian labor force in the same 51 jurisdictions.
Strategic Construction of ASUs
As noted previously, states that do not qualify as a whole-state ASU must construct ASUs
following the procedures described in the “Within State ASUs” section of this report. Optimized
construction of these areas typically yields ASUs with overall unemployment rates at or slightly
above 6.5%.39 If a state constructed an ASU with an unemployment rate well above 6.5%, it is
likely to be to the advantage of the state to add additional census tracts to the ASU, even if those
tracts had low unemployment rates that would reduce the unemployment rate of the constructed
ASU to closer to 6.5%. As long as the additional tracts contained at least some unemployed
individuals and did not reduce the overall unemployment rate within the ASU below 6.5%, the
additional tracts would increase the total number of unemployed individuals in the ASU and
therefore increase the corresponding ASU formula factor for the state.40

39 DOL does not typically publish data on the labor force in ASUs. CRS obtained the labor force data that were used to
calculate unemployment rates within ASUs directly from DOL. See Appendix B.
40 This also means that if the ASUs within a state have an unemployment rate well above 6.5%, the state may not be
maximizing the share of total unemployment that could be captured by the ASU factor. For example, in PY2023
Georgia had an unemployment rate of 7.2% in its constructed ASUs. Georgia’s overall unemployment rate during the
reference period was 3.2% and about 9% of Georgia’s total unemployment was captured by the ASU factor. Among
nearby states in the same period, Florida had a total unemployment rate of 3.3% but captured 43% of its total
unemployment in its ASU factor, and South Carolina had an overall unemployment rate of 3.5% but captured about
48% of its total unemployment in its ASU factor. Both Florida and South Carolina constructed ASUs with an
unemployment rate of 6.5%.
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Table C-2 presents data on states’ unemployment and ASUs for PY2023. Because only one state
qualified as a whole state ASU, nearly every state had to construct their ASUs. Several trends
emerge from the table:
• Most states were able to construct ASUs with an unemployment rate of almost exactly
6.5%. Of the 51 states that had to construct ASUs, 46 were able to construct ASUs that
had overall unemployment rates that were (or at least rounded to) 6.5%.41
• Many states constructed a single, contiguous ASU. Of the 51 states that had to construct
their ASUs, 27 created a single ASU. This included several large states: of the six states
with the largest civilian labor forces, five of them created a single ASU within the state.42
These large states’ construction of a single contiguous ASU may support the point that
the construction of ASUs is a strategic exercise rather than identification of specific areas
of economic disadvantage within the state.
Excess Unemployment Factor
The EU factor measures unemployment (i.e., unemployed individuals) in excess of 4.5% of the
civilian labor force in either a whole state or within the state’s ASUs. In the approximately 80%
of cases since PY2015 where a state did not qualify as a whole-state ASU, the state’s EU factor
was largely a function of the state’s ASU factor.
Definition of EU Factor
Statute establishes two definitions of excess number of unemployed and specifies that the higher
number applies to the formula allotment. Excess number of unemployed is the higher of
• “the number of unemployed individuals in excess of 4.5 percent of the civilian
labor force in the State”, or
• “the number of unemployed individuals in excess of 4.5 percent of the civilian
labor force in areas of substantial unemployment in such State.”43
In cases where a state qualifies as a whole-state ASU per the ASU definition, the two excess
unemployment metrics will be the same. The construction of the second factor option
(unemployment in excess of 4.5% of the civilian labor force in ASUs) means that every state that
has at least some unemployment in ASUs will have a nonzero EU factor because ASUs, by
definition, have an unemployment rate of at least 6.5%.44
Implementation of the EU Factor
The calculation of each state’s EU factor is relatively straightforward. Each state calculates two
potential factors based on the two criteria described in statute (see above) and the higher one is
used in the allotment formula.

41 Of these 46 states, 39 had unemployment in their ASUs of at least 6.45% and less than 6.50%, allowing them to
round up to the minimum 6.5%.
42 California, Texas, New York, Pennsylvania, and Illinois created a single ASU. Florida established five ASUs.
43 WIOA §132(b)(1)(B).
44 This notably contrasts with the similarly named excess unemployment factor in the WIOA Dislocated Worker (DW)
formula. In the DW formula, excess unemployment only applies to statewide unemployment in excess of 4.5%. This
means that if a state has an unemployment rate below 4.5%, its excess unemployment for the purposes of the DW
program will be zero.
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The potential excess unemployment factors are calculated on the basis of (1) statewide
unemployment data and (2) data related to the ASU factor. Excess unemployment is calculated by
multiplying the size of the labor force in a state (or ASU) by the difference between the actual
unemployment rate in the state (or ASU) and 4.5%. For example, if a state has a labor force of 2
million and 7.5% unemployment, the excess unemployment is 3.0% (7.5% - 4.5% = 3.0%)
multiplied by 2 million, or 60,000. As noted previously, in whole state ASUs the two EU factors
are the same.
The EU factor is updated annually. Like the ASU factor, it has been implemented to consider the
12 months ending June 30 of the year prior to the program year. For example, PY2022 funds were
allotted using the excess unemployment metrics for the 12-month period ending June 30, 2021.
Excess unemployment data are published with other factors in annual guidance and on the DOL
website. Public-facing data do not include the full breadth of data necessary to recreate the
calculation of the EU factor.45
Analysis of the EU Factor
As described previously, the EU metric is the higher of two calculations. In the approximately
20% of cases since PY2015 where the state qualified as a whole state ASU, the calculations are
identical.
In cases where a state does not qualify as a whole state ASU, the calculations yield differing
numbers and the higher metric applies. Analysis of statewide unemployment data and formula
factor data shows that, in the vast majority of cases, a state’s unemployment in excess of 4.5% of
the labor force within ASUs is higher than its statewide unemployment in excess of 4.5% of the
statewide labor force.46
This trend means that, in practice, the EU factor is typically a subset of the ASU factor. In cases
where a state does not qualify as a whole-state ASU, the state’s construction of its ASUs becomes
particularly relevant because it directly impacts two of the three formula factors that determine a
state’s initial allotment.
The relationship between a state’s relative share of the ASU factor and its relative share of the EU
factor varies by overall labor market conditions and how many states qualify as a whole state
ASU. The relationship between the EU factor and the ASU factor is discussed in more detail later
in this report.
Comparison of Formula Factors and the Adult
Activities Population: Alignment is Limited
A potential benefit of complex formula factors may be more precise targeting of federal
assistance. This benefit assumes that complex factors are closely aligned with target populations
or are otherwise responsive to relevant conditions.
This section compares the populations that are captured by the formula factors with the general
population that is eligible for the Adult Activities program and more specific populations that the

45 Calculating the EU factor requires data on the civilian labor force in the state and in ASUs. The civilian labor force
data limited to ASUs are not published but were provided directly to CRS by BLS (see Appendix B).
46 Since WIOA took effect in PY2015, there have been 468 calculations of EU factors (nine years times 52 states). In
instances in which a state did not qualify as whole-state ASU and therefore had to calculate both EU measures, the
metric of unemployment in excess of 4.5% in ASUs was higher in more than 99% of cases.
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program prioritizes. In the case of the Adult Activities formula, one of the three factors (the DA
factor) demonstrates fairly clear alignment with the program’s priority population. The other two
factors (the ASU factor and the EU factor) do not specifically target the general population the
program can serve or the more specialized population it prioritizes.
Adult Activities: Eligible and Prioritized Populations
The Adult Activities program is somewhat unconventional in that it does not have explicit
eligibility requirements. Instead, it is a universal access program (i.e., anyone age 18 or over can
access services) that directs states to give priority for more intensive services to workers with
certain characteristics (“priority groups”): low-income individuals, recipients of public assistance,
and individuals who are basic skills deficient (including English language learners).47 The design
of the program and subsequent agency guidance have made clear that it should not exclusively
serve workers who are members of priority groups.48
Review of program data shows that in PY2022, about 67% of total Adult Activities program
exiters were members of a priority group and about 60% of total exiters were low-income.49 In
PY2017, about 55% of total exiters were members of a priority group and about 50% of total
exiters were low-income.50
One of the stated purposes of WIOA is to support individuals with barriers to employment.51
WIOA defines this population to include low-income individuals, as well as other populations
that may face challenges in the labor market such as ex-offenders, displaced homemakers, older
individuals, and long-term unemployed individuals. Neither the Adult Activities program nor
other programs authorized under WIOA require states to provide priority of service or any other
dedicated priority to workers with barriers to employment who do not qualify as a member of a
priority group. For example, while long-term unemployed individuals and displaced homemakers
are considered individuals with barriers to employment, they are not considered priority groups
under the Adult Activities program.
Disadvantaged Adults Factor
The DA factor, as constructed, is partially aligned with priority groups in the Adult Activities
population. It aligns with the low-income priority population but does not consider program
participants who are not low-income and does not adjust for the likelihood of labor force
participation.

47 See WIOA §134(c)(3)(E). There is also a more general policy that applies to all DOL programs and requires
qualified veterans to receive priority of services in all DOL programs. This analysis does not consider the veterans’
policy.
48 See DOL, “Effective Implementation of Priority of Service Provisions for Most in Need Individuals in the Workforce
Innovation and Opportunity Act (WIOA) Adult Program,” Training and Employment Guidance Letter No. 07-20,
November 24, 2020, https://www.dol.gov/agencies/eta/advisories/training-and-employment-guidance-letter-no-07-20.
49 Low-income exiters are a subset of exiters in priority groups. In other words, about 90% of exiters from priority
groups were low-income. See Table II-1 of PY2022 Data Book: WIOA and Wagner-Peyser, January 2023,
https://www.dol.gov/sites/dolgov/files/ETA/Performance/pdfs/PY2022/PY%202022%20WIOA%20and%20Wagner-
Peyser%20Data%20Book.pdf.
50 Ibid. PY2017 was used as a comparison because it is the earliest date in the cited databook and there was a clear
upward trend during the years included in the cited databook.
51 WIOA §2.
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Central elements of the definition of a disadvantaged adult (income below poverty or 70% of
LLSIL) align with the low-income criteria for priority groups in the Adult Activities program.52
The DA factor does not specifically target any of the other priority group criteria, though it is
likely that significant portions of individuals who meet the other priority criteria (such as TANF
recipients) are captured by the disadvantaged adults metrics.
The Adult Activities program is designed to support individuals in the labor market, but the DA
factor considers individuals in and out of the labor market. This more expansive measure could be
seen as increasing alignment with the prospective WIOA population in that it might capture
workers who may enter the labor force with the assistance of WIOA services. Alternately, the DA
factor, as defined in current law, could be seen as overly inclusive of groups with limited labor
force participation. For example, the DA factor considers adults up to age 72. In calendar year
2023, the labor force participation rate for workers aged 65 to 69 was about 33%, and for workers
aged 70 to 74 it was less than 20%.53
Another consideration pertaining to the design of the DA factor is that its reliance on older data
affects its timeliness. Its basis in a five-year estimate and then its subsequent use for five years
means that, in some years, the factor considers data that are more than 10 years old. For example,
in the PY2018-PY2022 period data were based on the 2011-2015 reference period. However, the
DA factor is not very dynamic. As such, the effects of the lack of timely data tend to be modest.
For example, in PY2023 the reference period for the DA factor shifted from 2011-2015 to 2016-
2020, but a substantial majority of states had a change of less than 5% in their relative shares of
the factor. (See the “Year-to-Year Variation in the Disadvantaged Adults Factor is Limited”
section.)
Employment in Areas of Substantial Unemployment Factor and Excess
Unemployment Factor
The ASU factor and the EU factor are not clearly aligned with either the more general Adult
Activities population or its more specific priority populations. The statute authorizing the Adult
Activities program does not emphasize or even mention targeting services to workers in ASUs or
workers in areas with otherwise high unemployment rates. The only references in WIOA to the
concepts of ASUs and excess unemployment are in the formula provisions.
The service populations specified by WIOA are either general (i.e., the universal access
component of the program) or targeted on the basis of certain personal characteristics, such as
income level. WIOA’s definition of workers with barriers to employment (a population that
receives emphasis in states’ coordinated planning processes but does not get explicit priority in
the provision of services) also emphasizes personal characteristics such as being an ex-offender
or long-term unemployed.
Conversely, the ASU and EU factors are based on unemployment rates, which reflect the overall
labor markets and may or may not align with the personal characteristics of the WIOA target
populations. For example, an area could have high unemployment rates but relatively few low-
income workers, as defined by WIOA. Conversely, areas with high concentrations of low-income
individuals but low unemployment rates that do not qualify as ASUs would not be reflected by
the ASU factor or EU factor.

52 WIOA §3(36).
53 BLS, “Employment status of civilian noninstitutional population by age, sex, and race,” 2023,
https://www.bls.gov/cps/cpsaat03.htm (accessed March 2024).
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Analysis of the Formula Factors’ Relationship with
Other Metrics: Understanding the Factors in More
Common Terms
This section analyzes the relationship between the Adult Activities formula factors and more
traditional metrics such as poverty and unemployment. The goal of the section is to offer
perspectives on the complex and somewhat opaque formula factors by comparing them to more
widely understood metrics. This analysis can offer insight as to how the Adult Activities formula
factors may favor or disadvantage states with certain characteristics compared to more traditional
metrics. This analysis also provides some perspective on how the formula might allot funds
differently if the formula factors were based on more standard measures.
States’ relative shares of the formula factors generally track with their relative shares of
population. For example, California and Texas have large shares of each factor while Vermont
and Wyoming have small shares. As such, much of this section emphasizes state-level differences
in relative shares between the WIOA factors and traditional metrics (e.g., if a state’s relative share
of the ASU factor is higher or lower than its relative share of total unemployment).
This section provides four analyses:
• Comparison between the DA factor and persons in poverty. The analysis shows
that, relative to a traditional poverty count, states with higher LLSIL thresholds
(an income threshold specific to WIOA) tend to have larger relative shares of the
DA factors. These states tend to be concentrated in the Northeast and West.
• Comparison between the ASU factor and total unemployment. Compared to their
relative shares of total unemployment, states with the highest unemployment
rates capture larger shares of the ASU factor. This trend is most prevalent when
nationwide unemployment is low.
• Comparison between the EU factor and the ASU factor. As noted in the prior
section, the EU factor is typically a subset of the ASU factor. Generally, the EU
factor, like the ASU factor, allocates more funding to states with the highest
unemployment rates than would a total unemployment metric. The specific
relationship between a state’s relative share of the EU factor and the ASU factor
varies by whether or not an individual state qualifies as a whole-state ASU
(unemployment rate above 6.5%) and the overall number of states that qualify as
a whole-state ASU.
• Nationwide comparison between the ASU and EU factors and total
unemployment. When the national unemployment rate is high, the ASU and EU
factors capture a large portion of a higher level of unemployment. When the
national unemployment rate is low, the ASU and EU factors capture a smaller
portion of a lower level of unemployment.
This section does not formally analyze the relationship between the EU factor and total
unemployment at the state level. Some relationships between the EU factor and total
unemployment at the state level can be inferred via their corresponding relationships with the
ASU factor. The relationship between total unemployment and the EU factor is briefly discussed
in the “Summary of Relationships Between States’ Total Unemployment, ASU Factor, and EU
Factor” section of this report.
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Relationship Between the DA Factor and Persons in Poverty
The DA factor considers (1) persons aged 22 to 72 in households with income at or below the
poverty threshold and (2) persons from households with incomes above the poverty threshold but
below the 70% of the regional LLSIL (See the “Disadvantaged Adults Factor” section for full
methodology). This section focuses on the difference between the base measure of persons in
poverty and the final DA factor that is used in allocating Adult Activities funds.54
Poverty thresholds are the same for the contiguous states and the District of Columbia.55
However, the secondary income thresholds used by the DA factor (the LLSIL) vary by
metropolitan area and region, meaning that the LLSIL criteria will capture individuals at higher
income levels in some parts of the country.56 Generally, states with areas subject to higher LLSIL
thresholds will capture more individuals beyond the poverty population than states with areas
with lower LLSIL thresholds.
In the most recent data, the national total of individuals in the DA factor (about 27.2 million) was
approximately 17% higher than the national total of persons in poverty in the applicable age
ranges (about 23.3 million).57 The distribution of adults from families with incomes below the
70% LLSIL thresholds but above the poverty thresholds is not uniform across states. Generally, in
cases where a state’s share of adults from families with incomes below the 70% LLSIL threshold
exceeds its share of adults from families with incomes below the poverty threshold, the state
benefits from the inclusion of the LLSIL measure in the DA factor relative to using only a
traditional poverty metric.
The most direct way to measure which states benefit from the LLSIL component of the factor
compared to a traditional poverty count is to compare each state’s relative share of the DA factor
to its relative share of its poverty population.58 For example, Maryland’s relative share of persons
in poverty in the applicable age range is 1.289% and its relative share of the DA factor is 1.414%.
Maryland’s share of the DA factor is 9.7% greater than its share of persons in poverty, meaning
that Maryland’s share of the one-third of formula funding that is allotted under the DA factor is
9.7% more than what it would be if the DA factor were replaced by a traditional poverty metric.
Each state’s relative share of the DA factor that was used to allocate funds in PY2023 compared
to its relative share of poverty is depicted in Figure 2.
The states with the largest percentage difference between their relative shares of the poverty
count and their DA factor are Hawaii and Alaska, both of which benefit from the DA factor.
Hawaii and Alaska have separate, higher poverty thresholds than the other states, but they also

54 Elements to the DA factor beyond the poverty and LLSIL elements, including the exclusion of college students and
individuals in active military service, are challenging to fully assess. For the most recent allocations, DOL did not
publish data on the number of persons excluded from the DA factor on the basis of college enrollment or active military
service, so no analysis of these data elements is included in this report. See DOL, “Data for Persons Defined as
Disadvantaged Youth and Adults (2016-2020),”
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults.
55 There are separate metrics for Alaska and Hawaii. See the collection of Federal Register notices and accompanying
tables with LLSIL levels at https://www.dol.gov/agencies/eta/llsil.
56 The development and application of the LLSIL metric is discussed in more detail in the “Implementation of the DA
Factor” section. DOL publishes annual tables that compare the 70% LLSIL levels with poverty levels, see Tables 4 and
5 at https://www.dol.gov/agencies/eta/llsil.
57 See Table 2 in DOL, “Data for Persons Defined as Disadvantaged Youth and Adults (2016-2020),”
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults, and formula factor data at
https://www.dol.gov/agencies/eta/budget/formula/state.
58 This approach assumes that states have similar shares of individuals excluded from the DA factor on the basis of
being in the military or college.
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have the highest LLSIL levels.59 These higher LLSIL levels allow higher numbers of persons to
be above the poverty levels but below the 70% LLSIL level, and therefore be captured by the DA
factor. In the most recent data, Hawaii’s relative share of the DA factor was 31% above its
relative share of the poverty count, while Alaska’s relative share of the DA factor was 25% above
its relative share of the poverty count.
Among the contiguous 48 states and the District of Columbia, states in the Northeast and West
tend to have higher relative shares of the DA factor than their relative shares of poverty, while
states in the Midwest and South tend to have lower relative shares of the DA factor compared to
their relative shares of poverty.60 This is because the Northeast and West regions, and metro areas
within those regions, tend to have higher LLSIL thresholds, which means those states capture
more individuals between the standard poverty income and the area-specific LLSIL.61
For example, in the 2016-2020 period that will inform allotments for PY2023 through PY2027,
Arizona (West region) had a relative share of the DA factor (2.493%) that was about 10% higher
than its relative share of the poverty population (2.265%). Conversely, Michigan (Midwest
region) had a relative share of the DA factor (2.924%) that was about 8% lower than its relative
share of the poverty population (3.193%). Comparisons of the DA factor and poverty estimates
for the 2016-2020 period that will inform allocations for PY2023 through PY2027 are in Figure
2.

59 See Tables 4 and 5 in DOL, “Lower Living Standard Income Level Guidelines,”
https://www.dol.gov/agencies/eta/llsil.
60 The trends are not uniform. For example, Maryland (South) has a share of the DA factor that is greater than its share
of persons in poverty. Minnesota (Midwest) and Kentucky (South) also defy their regional trends and have higher
relative shares of the DA factor, potentially due to the state-specific adjustments described in the “Other Adjustments to
the DA Factor” section.
61 For example, in 2020 the poverty line for a family of four in the contiguous 48 states was $26,200. Using LLSIL data
from the same year, in the West metro region 70% of the LLSIL for a family of four was $32,245, and in the South
region 70% of the LLSIL for a family of the same size was $27,234. See DOL, “Lower Living Standard Income Level
Guidelines,” https://www.dol.gov/agencies/eta/llsil.
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Figure 2. Percentage Difference Between States’ Relative Shares of the DA Factor
and Relative Shares of Poverty
PY2023 Allocation Data

Source: See Appendix B for data sources and main text for ful calculation methodology.
Note: Positive percentages generally reflect states that benefit from the DA factor compared to a traditional
poverty count.
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Relationship Between the ASU Factor and Total Unemployment
The ASU factor counts a state’s unemployment in areas with an unemployment rate of at least
6.5% and disregards unemployment in areas with lower unemployment rates. A major issue in
assessing the relationship between a state’s ASU factor and its total unemployment is whether or
not it has a statewide unemployment rate of at least 6.5%. States with unemployment rates of at
least 6.5% qualify as whole-state ASUs and 100% of their total unemployment is reflected in the
ASU factor. Conversely, the ASU factor in states with unemployment rates below 6.5% will
include only a portion (i.e., less than 100%) of the state’s total unemployment.
The definition of the ASU factor may suggest potentially diverse outcomes among states with
similar statewide unemployment rates below 6.5%: a state with moderate unemployment
statewide may qualify for a lower ASU factor than a state with similar unemployment rate
through a combination of low unemployment in some areas and high unemployment in other
areas. In practice, however, states with similar unemployment rates tend to have a relatively
consistent share of their unemployment captured by the ASU factor.62 This largely makes the
ASU a reflection of each state’s relative share of unemployment, with greater weight given to
unemployment at higher rates.
Figure 3 depicts each state’s statewide unemployment rate (horizontal axis) and its share of total
unemployment that was included in the ASU factor (vertical axis). The chart includes more than
450 observations (each state over nine years). In the approximately 20% of cases since PY2015
where a state’s unemployment rate was at least 6.5%, the state’s ASU factor considered 100% of
its total unemployment. This is consistent with statutory policy.
The major insight of Figure 3 is that for the approximately 80% of instances in which a state’s
unemployment rate was below 6.5%, there was a consistent positive correlation between a state’s
unemployment rate and its share of total unemployment that was captured by the ASU factor: as
the state’s unemployment rate increased, the share of its total unemployment that was captured by
the ASU factor increased. For example, when a state’s unemployment rate was about 4.0%, the
share of total unemployment that was captured by the ASU factor centered around 40%. When a
state’s unemployment rate was about 5.5%, the share of total unemployment that was captured by
the ASU factor centered around 90%.

62 This consistency may be attributable to the procedures and practices described in the “Implementation of the ASU
Factor” and “Strategic Construction of ASUs” sections.
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Figure 3. State Unemployment Rate and Share of Unemployment Included in the
ASU Factor
PY2015-PY2023

Source: CRS analysis. See Appendix B for data sources and main text for ful calculation methodology.
As state examples, in PY2023 the ASU factor in California (5.2% unemployment rate) captured
about 90% of the state’s total unemployment, while the ASU factor in Wisconsin (3.1%
unemployment rate) captured about 27% of the state’s total unemployment.63
Applied to the formula, the pattern of states with higher unemployment rates capturing a larger
share of their total unemployment in their ASU factor means that states with the highest
unemployment rates in a given year will typically have relative shares of the ASU factor that are
higher than their relative shares of total unemployment and vice versa. To continue the prior
examples from PY2023, California had a 14.5% relative share of total unemployment, while its
relative share of the ASU factor was 18.8%; conversely, Wisconsin had a 1.4% share of total
unemployment but less than a 0.6% share of the ASU factor.
Translated into a ratio, the ratio of California’s relative share of the ASU factor to its relative
share of total unemployment was about 130% in PY2023.64 Conversely, Wisconsin’s relative
share of the ASU factor relative to its share of total unemployment in PY2023 was about 38%.65

63 Underlying data for these calculations are in the PY2023 data for each state presented in Table C-2.
64 Specifically, California’s relative share of the ASU factor (18.797%) divided by its relative share of total
unemployment (14.479%) was 129.818%.
65 Specifically, Wisconsin’s relative share of the ASU factor (0.540%) divided by its relative share of total
unemployment (1.415%) was 38.163%.
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Variations of Distribution by Overall Labor Market Conditions
The distribution of states’ ratios of their relative shares of the ASU factor to their relative shares
of total unemployment varies by the labor market conditions in the reference period. When
national unemployment is high and a large number of states qualify as whole-state ASUs, the
distribution tends to be narrower and states’ relative shares of the ASU factor more closely track
their relative shares of total unemployment. In these environments, each state that qualifies as a
whole-state ASU in a given year has the same ratio of its relative share of the ASU factor to its
relative share of total unemployment (typically just over 100%). Conversely, in cases where
overall unemployment is low, a smaller number of states with higher unemployment rates can
have much higher ratios (over 150% in numerous cases). In other words, relative to a traditional
unemployment metric, the ASU factor allocates funding toward the individual states with the
highest unemployment rates more intensively when overall unemployment is low compared to
when it is high.
Figure 4 illustrates these principles by presenting the ratio of each state’s relative share of the
ASU factor to its relative share of total unemployment in PY2015 and PY2020.66 In PY2015
(depicted in blue below), the national unemployment rate was 6.8%, and 28 states had
unemployment rates of at least 6.5%. In PY2020 (depicted in orange below), the national
unemployment rate was 3.8% and one state had an unemployment rate of at least 6.5%.67
In PY2015, each of the 28 states that qualified as a whole-state ASU had a ratio of about 104%.
In other words, no state’s relative share of the ASU factor was more than 4% higher than its
relative share of total unemployment. For example, Tennessee was one of 28 states with an
unemployment rate of at least 6.5% but its relative share of the ASU factor (2.190%) was only
slightly higher than its relative share of total unemployment (2.107%).
In PY2020, 10 states had a ratio of relative share of the ASU factor that was at least 150% of their
relative share of total unemployment. In these 10 states, the state’s relative share of the ASU
factor was at least 50% higher than its relative share of total unemployment. For example,
Louisiana’s relative share to the ASU factor (2.558%) was 63% higher than its relative share of
total unemployment (1.566%).

66 The outlier unemployment rates in Figure 4 and Figure 6 are in Puerto Rico. In the reference period for PY2015, the
unemployment rate in Puerto Rico was 14.3% (topcoded to 10% in the figures). In the reference period for PY2020, the
unemployment rate in Puerto Rico was 8.4%.
67 This report uses PY2015 and PY2020 as models for high and low unemployment because they both used data from
before the COVID-19 pandemic and the corresponding shocks to the labor market. (As shown in Table 3, the reference
period for PY2020 was July 1, 2018, through June 30, 2019.) Generally, the trends in PY2023 are similar to those in
PY2020, but there were some residual elements from the large fluctuations in unemployment during the pandemic. As
such, PY2020 may reflect a more traditional low-unemployment environment.
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Figure 4. Relationship between Each State’s Relative Share of the ASU Factor and
Relative Share of Total Unemployment, by Unemployment Rate
PY2015 and PY2020

Source: See Appendix B for data sources and main text for ful calculation methodology.
Notes: Ratio on the vertical axis is topcoded at 200%. In actuality, the highest ratio was 333% and the second-
highest was 218%. Unemployment rate on the horizonal axis is topcoded at 10%. In actuality, the highest rate
was 14.3% and the second-highest was 8.9%.
Relationship Between the EU Factor and ASU Factor
As discussed previously, the EU factor is typically a subset of the ASU factor.68 A state’s EU
factor in a given year has distinct relationships with the ASU factor depending on whether or not
the state qualifies as a whole-state ASU. The relationship between the EU factor and ASU factor
is more consistent in the approximately 80% of cases since PY2015 when a state did not qualify
as a whole-state ASU.
Figure 5 presents the share of each state’s EU factor as a percentage of its ASU factor by
unemployment rate since PY2015 (each dot represents one state’s EU factor as a share of its ASU
factor for one year). Figure 5 shows two distinct trends:

68 The relationship between the EU factor and total unemployment is largely a function of the EU factor’s relationship
with the ASU factor. As such, it is more illuminating to discuss the relationship between the EU factor and the ASU
factor. The relationship between the EU factor and total unemployment can be inferred by the EU factor’s relationship
with the ASU factor and the ASU factor’s relationship with total unemployment.
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• In the approximately 80% of cases where a state did not qualify as a whole-state
ASU (i.e., it had an unemployment rate of less than 6.5%), the EU factor was
typically about 31% of the ASU factor. As discussed previously, the
unemployment rate within constructed ASUs is typically close 6.5%. For these
states, the EU factor will be calculated as the 2.0 percentage points of the state
civilian labor force between 4.5% and 6.5% within the ASU. Mathematically, 2
percentage points is approximately 31% of 6.5%, so the EU factor will be about
31% of the ASU factor.69 The close relationship between the ASU factor and EU
factor for states that do not qualify as whole-state ASUs underscores the
significance of a state constructing its ASUs to maximize its ASU factor and
corresponding initial grant.70
• In the approximately 20% of cases where a state qualified as a whole-state ASU,
the EU factor was at least 31% of the ASU and increased as a share of the ASU
as the state unemployment rate increased.71 For example, in a case where a state’s
unemployment rate is 6.6%, the state’s EU factor will be based on the 2.1
percentage points of the state civilian labor force in excess of 4.5%, or about 32%
of its ASU factor. If a state’s unemployment rate is 8.5%, the state’s EU factor
will be based on the 4.0 percentage points of the state civilian labor force in
excess of 4.5%, or approximately 47% of the ASU factor.

69 The exact percentage can vary slightly based on the precise unemployment rate within the constructed ASU. For
example, if the unemployment rate in an ASU is 6.45%, the EU factor will be about 30.2% of the ASU factor. If the
unemployment rate in an ASU is 6.6%, the ASU factor will be about 31.8% of the ASU factor.
70 If a state constructs ASUs with an unemployment rate well above 6.5%, the EU factor may be higher than 31% of the
ASU factor.
71 In these scenarios, the ASU factor will equal total unemployment because the state would qualify as a whole-state
ASU.
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Figure 5. Relationship Between the ASU Factor and EU Factor,
by Unemployment Rate
PY2015-PY2023

Source: See Appendix B for data sources and main text for ful calculation methodology.
Notes: The unemployment rate is topcoded at 10% to simplify presentation. Limited instances in which
unemployment rate data were unavailable or a state had an ASU factor of zero are omitted from the figure.
Relationship between the Relative Share of the EU Factor and the Relative Share
of the ASU Factor
This relationship between an individual state’s relative share of the ASU factor and its relative
share of the EU factor varies by overall labor market conditions. In cases where overall
unemployment is low and few states qualify as whole state ASUs, each state’s relative share of
the EU factor correlates closely with its relative share of the ASU factor. In years where overall
unemployment is high and many states qualify as whole state ASUs, the states with the highest
unemployment rates capture shares of the EU factor that are higher than their relative shares of
the ASU factor while states with lower unemployment rates have shares of the EU factor that are
lower than their relative shares of the ASU factor.
Figure 6 presents each state’s relative share of the EU factor as a percentage of its relative share
of the ASU factor in PY2015 (a high unemployment environment) and PY2020 (a low
unemployment environment). In cases where the percentage is above 100%, the state’s relative
share of the EU factor is greater than its share of the ASU factor. In cases where the percentage is
less than 100%, the state’s relative share of the EU factor is less than its relative share of the ASU
factor. If the EU factor were perfectly correlated with the ASU factor, each state’s relative share
of the EU factor would be 100% of its relative share of the ASU factor.
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The orange dots in Figure 6 depict the relationship between states’ shares of the EU factor and
ASU factor in PY2020 (which had a national unemployment rate of 3.8%). Every state except
one had an unemployment rate below 6.5%. For those 51 states, their relative shares of their EU
factor as a percentage of their relative shares of the ASU factor clustered around 100%. In other
words, for 51 states the relative share of the EU factor was closely correlated to the relative share
of the ASU factor.
The blue dots in Figure 6 show the relationship between the ASU factor and EU factor in
PY2015 (which had a national unemployment rate of 6.8%, and 28 states qualified as whole state
ASUs). In this year, the nine states with the highest unemployment rates had relative shares of the
EU factor that were more than 110% of their relative shares of the ASU factor. The states with
higher unemployment rates captured a larger share of the EU factor, thereby reducing the relative
shares of the EU factor for states with lower unemployment rates. In PY2015, 21 states with
unemployment below 6.5% had relative shares of the EU factor that were between 80% and 83%
of their ASU factor.72
Figure 6. Relationship between Each State’s Relative Share of the EU Factor and
Relative Share of the ASU Factor, by Unemployment Rate
PY2015 and PY2020

Source: See Appendix B for data sources and main text for ful calculation methodology.
Notes: Unemployment rate is topcoded at 10%.

72 Three remaining three states that did not qualify as whole-state ASUs had relative shares of the EU factor that were
between 85% and 90% of their relative shares of the ASU factor: Virginia (85.4%), Nebraska (85.5%), and Wyoming
(89.0%).
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The data in Figure 6 demonstrate two potential roles for the EU factor in the allotment formula.
In years where unemployment is low (like PY2020), each state’s relative share of the EU factor is
largely derivative of its relative share of the ASU factor, essentially making the ASU factor
double weight in allotting funds. In years where unemployment is higher, the EU factor has a
distinct role relative to the ASU factor, allotting more funding to states with the highest
unemployment rates and effectively levying a ratable reduction on the EU factor for states with
unemployment below 6.5%.
Summary of Relationships Between States’ Total Unemployment,
ASU Factor, and EU Factor
Table 5 summarizes the direction and nature of the relationships between total unemployment,
the ASU factor, and the EU factor that were discussed in the prior sections. The relationships vary
based on whether or not a state has an unemployment rate of at least 6.5%.
In two of the four instances, the relationships are fixed: when a state qualifies as a whole-state
ASU, 100% of total unemployment is captured by the ASU factor; and when a state does not
qualify as a whole-state ASU, the EU factor will typically be about 31% of the state’s ASU factor.
In the remaining two instances, the relationship is variable but follows a pattern. When a state
does not qualify as a whole-state ASU, the ASU factor is a fraction of total unemployment, which
typically increases as the state’s unemployment rate approaches 6.5%. When a state qualifies as a
whole-state ASU, the EU factor as a percentage of the ASU factor increases as the state’s
unemployment rate increases.
While the table does not explicitly compare the EU factor to total unemployment, the information
in it can be used to determine these trends. When a state does not qualify as a whole-state ASU,
the EU factor is typically 31% of a share of total unemployment, which generally increases with
the state’s unemployment rate. When a state qualifies as a whole-state ASU, the state’s EU factor
is at least (and usually more than) 31% of the state’s total unemployment and the percentage
increases as the unemployment rate increases.73
Table 5. Summary of the Direction and Nature of the Relationships Between
Total Unemployment, the ASU Factor, and the EU Factor
Relationship Between Total
Unemployment and ASU
Relationship Between ASU
Whole State ASU Status
Factor
Factor and EU Factor
State does not qualify as a whole-
A percentage of total
The EU factor is typically about 31%
state ASU (unemployment rate less
unemployment is captured in the
of the ASU factor.
than 6.5%; approximately 80% of
ASU factor. Typically, higher
cases since PY2015)
percentages are associated with
higher unemployment rates.
State qualifies as a whole-state ASU
100% of total unemployment is
The EU factor typically captures
(unemployment rate of 6.5% or
captured in the ASU factor.
more than 31% of the ASU factor.
higher; approximately 20% of cases
The percentage increases as the
since PY2015)
unemployment rate increases.
Source: CRS analysis.

73 This largely matches the description in the lower right cell of Table 5 because total unemployment equals the ASU
factor when a state’s unemployment rate is at least 6.5%.
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Relationship Between the Aggregate Nationwide ASU and EU
Factors and Total Unemployment
The nationwide relationship between the ASU and EU factors and total unemployment also varies
by labor market conditions. The national trends are a composite of statewide trends: when overall
unemployment is higher, the share of total unemployment that is captured by the ASU and EU
factors increases.
In years with higher overall unemployment rates, a larger number of states qualify as a whole-
state ASU and therefore have 100% of their state’s unemployment considered in the Adult
Activities allotment. Conversely, in years with lower overall unemployment, states have to
construct more restrictive ASUs, which results in larger shares of total unemployment being
excluded from the ASU factor (and therefore the Adult Activities formula) entirely.
Table 6 presents annual data on total unemployment and the share of total unemployment
captured by the ASU and EU factors. In the three years between PY2015 and PY2023 when the
unemployment rate was at least 5.9%, more than 90% of total nationwide unemployment was
considered in states’ ASUs factors. In the reference period for PY2020, when unemployment was
3.8%, less than 50% of total unemployment was considered. The EU factor, which is based on a
smaller portion of unemployment but has a close relationship with the ASU factor, fluctuated in a
similar pattern: higher shares of total unemployment were captured in the EU factor when overall
unemployment rates were higher.
In years with lower unemployment rates, the combination of lower total unemployment and a
smaller portion of total unemployment considered in the ASU factor means that funds are
allocated on the basis of a much smaller population than in higher-unemployment years. For
example, in PY2022 the total ASU population was 10.6 million, nearly four times the 2.8 million
ASU population in PY2020. An even stronger discrepancy exists with the EU factor where the
PY2022 population (about 4.3 million) was nearly five times the PY2020 population (about
873,000). Because a fixed share of funding is allotted on the basis of each formula factor, these
variations mean that the share of funding associated with each individual captured by the factor is
much greater when unemployment is low compared to when it is high.
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Table 6. Total Unemployment, Unemployment in ASUs, and Excess Unemployment:
PY2015-PY2023
A
C
B
D
E
F
G
Total
Excess/
Program
Unemp.
Total
Total ASU
ASU/Total
Total EU
Total
Year
Rate
Unemp.
Factor
Unemp.a
Factor
Unemp. b
2015
6.8%
10,681,651
10,276,322
96.2%
3,841,273
36.0%
2016
5.7%
9,070,150
7,992,257
88.1%
2,558,400
28.2%
2017
5.0%
8,029,604
5,971,262
74.4%
1,874,990
23.4%
2018
4.7%
7,558,904
5,158,345
68.2%
1,621,056
21.4%
2019
4.2%
6,746,542
3,657,551
54.2%
1,140,391
16.9%
2020
3.8%
6,219,523
2,826,339
45.4%
873,426
14.0%
2021
5.9%c
9,736,330
8,798,258
90.4%
2,912,343
29.9%
2022
6.9%
11,191,863
10,583,567
94.6%
4,264,370
38.1%
2023
4.2%
6,915,556
4,804,512
69.5%
1,463,460
21.2%
Source: See Appendix B for data sources and main text for ful calculation methodology.
a. Column E is Column D divided by Column B.
b. Column G is Column F divided by Column B.
c. Civilian labor force data for Puerto Rico are not available for PY2021. For that year, data in the table on
total unemployment and data related to the ASU and EU factors include Puerto Rico. The overall
unemployment rate for PY2021 excludes Puerto Rico and is calculated on the basis of the sum of the civilian
labor force and sum of unemployment for the 50 states and the District of Columbia.
Analysis of Year-to-Year Changes in Factor Data:
Large Fluctuations in the ASU and EU Factors
This section uses formula data between PY2015 (the effective date of WIOA) and PY2023 (the
most recent program year) to analyze the year-to-year changes in formula factors. The analysis
focuses on the ASU and EU factors, which are consistently more dynamic than other labor market
indicators such as unemployment and civilian labor force. The limited dynamism of the DA factor
is discussed in the first section, but it is largely omitted from the subsequent analysis because this
factor sees little year-to-year variation.
A central question underlying this analysis is to what degree volatility in formula factors is
desirable. Some level of volatility is expected, and is desirable inasmuch as it reflects underlying
changes to states’ economic conditions and corresponding need to adapt workforce funding.
However, the presence of adjustment provisions designed to limit annual change (e.g., hold
harmless) suggests that higher levels of volatility are undesirable, even if there are large short-
term changes in states’ relative economic conditions.
This section describes the relative share of formula factors that are applied to states’ initial
allotments, prior to the application of the adjustment provisions. The adjustment provisions
(described in principle previously and in practice subsequently) somewhat temper some of the
large annual swings, though some of the large fluctuations can also have longer-term
implications.
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Measuring Year-to-Year Variation
Because states’ initial grants are determined by their relative share of formula factors, this report measures year-
to-year variation in formula factors as the percentage change in a state’s relative share of a factor. For example, in
PY2021 Ohio’s relative share of the ASU factor was 4.475%, and in PY2022 its relative share declined to 3.253%.
Thus, the state’s year-to-year change in its relative share of the ASU factor was -27.3%. Under this methodology, a
state can have an increase of more than 100% but cannot have a decrease of more than 100% because a state’s
relative share cannot go below zero.
For the primary analysis, CRS categorized each state’s change in its relative share of formula factors by its absolute
value. It considered states with changes of less than 10%, changes between 10% and 30%, changes between 30%
and 50%, and changes in excess of 50%. The 10% and 30% thresholds were chosen to align with the adjustment
provisions in the formula: the hold harmless prevents losses of more than 10% and the maximum grant caps
increases at 30%. The 50% threshold was included due to the large number of instances of variation well in excess
of 30%. The consideration of each state’s absolute value reduces the number of categories and means, for
example, that an increase of 8% and a decrease of 9% are both considered changes of less than 10%.
Underlying data are presented in Table C-4 and summary data are presented in Figure 7. Data on annual
changes in total unemployment and civilian labor force are included for comparison.
This report uses the approach of categorizing changes rather than calculating measures of variation to offer a
simple presentation and to minimize the effect of outlier values. Limited instances where data were not available
or a state’s factor was zero are omitted from the analyses.
Year-to-Year Variation in the Disadvantaged Adults Factor is
Limited
Under current practice, the DA factors are only updated once every five years. As such, in four
out of every five years, states’ relative shares of this factor do not change. Since the effective date
of WIOA, these factors have been updated twice: between PY2017 and PY2018 and between
PY2022 and PY2023.74
Table C-3 presents data on the DA population and relative share for each state and changes when
the reference period shifts. When the DA factors are updated, the changes in relative share tend to
be modest in spite of the time between updates.
• Between PY2017 and PY2018, 49 of the 52 states had a change in relative share
of less than 10%, and within this group 31 states had a change of less than 5%.
• Between PY2022 and PY2023, 45 of the 52 states had a change in relative share
of less than 10%, and within this group 36 states had a change of less than 5%.75

Year-to-Year Variation in ASU Factor, EU Factor, and Traditional
Labor Market Indicators
Figure 7 categorizes states by the percentage change in their relative shares of the ASU factor
and the EU Factor. For comparison, the table also includes changes in relative share in two
traditional labor market indicators (state-level total unemployment and civilian labor force). The
more limited dynamism of the traditional labor market indicators suggests that the volatility of the

74 The changes effective in PY2013 applied to the Adult Activities program under Workforce Investment Act in 2013
and 2014. These same factors were applied under the WIOA Adult Activities formula in 2015 through 2017.
75 Of the seven states with a change of at least 10% in their relative shares of the factor, four were states that qualified
for the minimum grant in both of PY2022 and PY2023 and were therefore unaffected by the change in factor values.
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ASU and EU factors are specific to the design of those factors and not inherent to labor market
indicators in general.
The distribution of changes varied by year. The three program years that included data subsequent
to the beginning of the COVID-19 pandemic (PY2021 through PY2023) generally had more
states with greater year-to-year variations than the prior years, though the volatility of the ASU
factor and EU factor exceeded that of the traditional indicators in all years.76 Table C-4 includes
the underlying data in Figure 7 as well as the average number of states in each category over the
eight-year reference period. The subsequent analysis focuses on these averages.

76 In March 2020, President Trump declared a nationwide emergency for the pandemic under the Robert T. Stafford
Disaster Relief and Emergency Assistance Act (Stafford Act). As noted in Table 3, the ASU and EU data are based on
data from the second preceding program year. Thus, PY2021, which considered data from PY2019 (July 2019 through
June 2020) was the first year in which the reference period included a portion of the presidentially declared nationwide
emergency under the pandemic.
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Analysis of Adult Activities Allotment Formula under WIOA

Figure 7. Distribution of Change of Relative Share from Prior Year of Adult Activities
Factors and Traditional Labor Market Indicators

Source: CRS analysis. See Appendix B for data sources and main text for ful calculation methodology.
How to Read Chart: Vertical bars with more blue (especial y more light blue) indicate more states with small
year-to-year changes. Vertical bars with more turquoise and orange indicate more states with large year-to-year
changes.
Notes: Limited instances where data were not available or a state’s factor was zero are omitted from the
analyses, resulting in fewer than 52 states being included in the analysis for a given year. See Appendix B for
more details.
Compared to Traditional Labor Market Indicators, the ASU and EU Factors
Were Less Likely to Have a Change of Less Than 10%
Over the eight-year period, an average of about 12 states (less than 25%) had a change of less
than 10% in their ASU or EU factor. Conversely, an average of 33 states (more than 60%) had a
change of less than 10% in total unemployment. Annual data are depicted visually in Figure 7
where the light blue section of the bar (indicating changes of less than 10%) is smaller for the
ASU and EU factors than for total unemployment. Civilian labor force was more stable than total
unemployment, with only a single instance of a state reporting a year-to-year change of more than
10% during the reference period.77
Changes of 30% or Higher Were Much More Likely Among the ASU and EU
Factors
Over the course of the reference period, an average of 20 states had a year-to-year change in the
ASU factor and an average of 21 states had a change in the EU factor of at least 30%. Conversely,

77 Between PY2022 and PY2023, the civilian labor force in Puerto Rico increased 11%.
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an average of two states had a comparable change in their shares of total unemployment. In other
words, changes of at least 30% were about 10 times as common in the ASU and EU factors than
in total unemployment.
Prior to the pandemic (up to and including PY2020), the contrast was sharper: an average of 18
states had a change to the ASU factor and an average of 17 states had a change to the EU factor of
at least 30%. In the same time period, an average of less than one state had a change in total
unemployment of at least 30%.
As noted previously, no state’s share of the civilian labor force changed more than 10% from
year-to-year in the reference period.
Analysis of the Adjustment Provisions: Frequently
Applied to Large Effect
As noted previously, the WIOA Adult Activities formula include provisions to establish minimum
grants for small states and other provisions to limit year-to-year fluctuations in states’ grants. See
the “Adult Activities Adjustment Provisions” text box for a brief summary and the “Adjustment
Provisions” section for a more detailed description. The baseline levels for the hold harmless and
maximum grant provisions are established in terms of states’ relative share of funding, not the
grant dollar level.
Since the effective date of WIOA, the adjustment provisions were frequently applied to a large
number of states’ grants. These adjustment provisions are applied after the calculation of each
state’s initial grant based on the state’s relative share of the three formula factors. The most
commonly applied provision was the hold harmless. The minimum grant provision was applied
less frequently, and the maximum grant provision was applied somewhat rarely.
The minimum grant provision applied to a small group of states every year and a rotating group
of states more intermittently. The minimum grant provision could have a large effect on the grant
level for the individual states it applied to but generally reallocated a small amount of overall
funding among the states.
The hold harmless provisions were the most frequently applied adjustment provision. The total
amount of funding that was reallocated to comply with the hold harmless provision typically
exceeded the amount that was reallocated to comply with the minimum grant provisions.
Interpreting the common application and effects of the hold harmless is more complex. The hold
harmless facilitates stability and prevents individual states from experiencing large year- to-year
declines in funding. The provisions also create scenarios in which states’ actual grants are
somewhat decoupled from their shares of the formula factors that are supposed to determine
funding. This decoupling applies to both the states the qualify for the hold harmless and states
with grants that are reduced to accommodate the hold harmless.
In some cases where a state had an uncharacteristically high share of the formula factors in one
year (not unusual in light of the volatility described in the prior section), the hold harmless could
creating a lingering effect. As the state’s share of the factors regressed to more typical levels, the
hold harmless would be applied in subsequent years, resulting in multiple years of funding that
was well above the state’s share of the formula factors.
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Adult Activities Limiting Provisions
Minimum grants equal to 0.25% of total funding are awarded to states with an average relative share of the
formula factors of less than 0.25%. States subject to these provisions have their grant increased to 0.25% of total
funding and other states’ grants are ratably reduced.78
Hold harmless provisions increase the funding for states where the average relative share of the formula factors
is less than 90% of the state’s relative share of funding from the prior year. States subject to these provisions have
their relative share of funding increased to 90% of their relative share from the prior year and other states’ grants
are ratably reduced.
Maximum grant provisions apply when a state’s relative share of the formula factors is more than a 30% increase
from the state’s relative share of grant funding from the prior year. States subject to these provisions have their
relative share reduced to 130% of their relative share from the prior year and other states have their grants
ratably increased.
Adjustment Provisions Are Applied Frequently
Table 7 presents the frequency of the application of the adjustment provisions in the
Adult Activities formula since WIOA took effect in PY2015. The hold harmless is
typically the most commonly applied adjustment provision. The application of the hold
harmless somewhat mitigates the influence of the factor volatility discussed in the prior
section. This report classifies states where no adjustment provisions applied as receiving
calculated grants.79
Table 7. Number of States Subject to Each Adjustment Provision, WIOA Adult
Activities, PY2015-PY2023
Aver

2015
2016
2017
2018
2019
2020
2021
2022
2023
-age
Calculated Grant
37
30
27
25
22
21
21
17
21
24.6
Minimum Grant
8
9
9
9
9
10
5
5
7
7.9
Hold Harmless
7
12
16
18
19
20
17
27
23
17.7
Maximum Grant
0
1
0
0
2
1
9
3
1
1.9
Source: CRS analysis of funding levels and formula factors. See Appendix B for ful sources.
Note: Adjustment provisions applied in PY2015 were relative to allotments under the Workforce Investment
Act of 1998.
In the nine-year reference period, 12 states received the minimum grant at least once. Four states
qualified for the minimum grant every year. Five other states received a minimum grant at least
four times, and three other states received the minimum once or twice. In cases where a state
received intermittent minimum grants, it was typically during a period of low unemployment. For
example, Nebraska received minimum grants for each of PY2015-PY2018, a period during which
its unemployment was between 2.9% and 3.7%.
As shown in Table 7, the hold harmless provisions have regularly applied during the reference
period, with an average of about 18 states qualifying for the hold harmless each year. With the

78 Statute includes additional minimum grant provisions that could take effect if total funding for Adult Activities
grants exceeds $960 million, but these provisions have never been triggered. See WIOA §132(b)(1)(B)(iv)(II). Total
funding for grants in PY2023 was approximately $883 million.
79 Typically, states with calculated grants have relative shares of grant funding that are lower than their relative shares
of the grant factor because the ratable reductions to accommodate the minimum grants or hold harmless exceed any
ratable increases to accommodate the maximum grant.
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exception of PY2015, when only 7 states qualified for the hold harmless, at least 12 states have
qualified for the hold harmless in each year. The hold harmless (which limits decline from the
prior year) is applied more frequently than the maximum grant (which limits increase from the
prior year), likely because the change threshold for triggering the hold harmless (-10%) is less
than the threshold for triggering the maximum grant (+30%).
The frequent application of the hold harmless provisions is likely related to the volatility of some
of the formula factors. For example, the prior discussion noted that it is common for two of the
three factors to vary in excess of 30% from one year to the next. In cases where the relative share
of factors declines, the hold harmless can mute the effect of downward year-to-year changes. In
cases where the relative share of factors increases, the maximum grant can limit changes, though
the threshold to trigger this provision is higher than the threshold to trigger the hold harmless
provision.
The maximum grant provisions were less commonly applied than the hold harmless provisions. In
PY2021 (the first year in which the COVID-19 pandemic labor market was included in the
reference data), nine states were limited by the maximum grant provision. No more than three
states received a maximum grant in any other year and there were three years in which no state
was limited by the maximum grant provisions.
Quantifying the State-Level Effects of the Adjustment Provisions
Compared to what states would have received purely on the basis of their relative share of the
formula factors, the adjustment provisions increase some states’ grants and decrease the grants for
others. Specifically, relative to calculated grants, the adjustment provisions increase grants for
states that qualify for a minimum or hold harmless and reduce grants for states that qualify for a
maximum grant or are not subject to any of the adjustment provisions.80 To quantify the effect of
the limiting factors, CRS compared each state’s share of funding to its average share of the three
formula factors, which would have determined each state’s grant in the absence of the adjustment
provisions. For example, in PY2023, Virginia’s relative share of the formula factors was 1.202%.
However, Virginia’s relative share of funding in the prior year was 1.716%, which meant that the
lowest share of funding it could receive, per the hold harmless provision, was 1.545% of total
funding (90% of 1.716%). The state’s relative share of funding (1.545%) was 28.5% higher than
its relative share of the formula factors (1.202%), so Virginia’s effect from the adjustment
provisions was +28.5%.
Table 8 depicts each state’s final grant relative to its share of the formula factors in PY2023.
Positive effects are generally associated with the minimum grant provisions and hold harmless
provisions. In other words, because of the adjustment provisions, these states’ relative share of
funding is typically higher than their relative share of the three formula factors.81 Negative effects
are associated with the maximum grant provisions and states that were not subject to an
adjustment provision.
All states with calculated grants have the same percentage difference between their grants and
their shares of Adult Activities funding in PY2023 (-6.3%). This uniform decrease reflects the

80 States with calculated grants are affected by the adjustment provisions via the ratable reductions that are necessary
for other states to receive minimum grants or hold harmless amounts as well as ratable increases that accommodate
states with maximum grant levels.
81 In limited instances, a negative effect will be associated with a hold harmless provision. In these cases, the hold
harmless provision stopped a state from being subject to the full ratable reduction that applies to states with calculated
grants. See the “Effect of Hold Harmless Provisions” section for a more detailed description of these cases.
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ratable reduction that is applied to states to accommodate the hold harmless and minimum grant
provisions.82
Table C-5 presents similar data for each year since WIOA took effect in PY2015. In each year,
the ratable reductions to comply with the minimum grant and hold harmless provisions were
greater than the ratable increases to comply with the maximum grant provisions. As such, the
relative shares of funding for states with calculated grants were lower than those states’ relative
shares of the formula factors.
In each year, the share of grant funding for each state with a calculated grant was a uniform
percentage below the state’s relative share of the formula factors. In PY2023, this percentage was
6.3%. The highest uniform reduction since WIOA took effect was in PY2022, when states with
calculated grants had shares of funding that were about 8.4% less than their shares of the formula
factors. The smallest uniform reduction was 1.6% in PY2015.

82 A small ratable increase was applied to each of the calculated states when the funding for one state was reduced to
accommodate the maximum grant provisions.
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Table 8. Effect of the Adjustment Provisions by State, PY2023
Each State’s Final Grant Relative to the State’s Share of the Formula Factors
Calculated
Hold Harmless
Maximum Grant
Minimum Grant
State
Effect
State
Effect
State
Effect
State
Effect
Arkansas
-6.3% Alabama
26.1% Maryland
-9.5%
Idaho
14.3%
California
-6.3% Alaska
42.5%

Montana
53.8%
Connecticut
-6.3% Arizona
38.8%

Nebraska
30.9%
Delaware
-6.3% Colorado
-5.7%

North Dakota
165.7%
Hawaii
-6.3% District of
-0.6%

South Dakota
186.7%
Columbia
Il inois
-6.3% Florida
8.9%

Vermont
267.8%
Iowa
-6.3% Georgia
32.8%

Wyoming
147.8%
Kentucky
-6.3% Indiana
47.2%

Louisiana
-6.3% Kansas
24.8%

Maine
-6.3% Minnesota
59.5%

Massachusetts
-6.3% Mississippi
1.7%

Michigan
-6.3% Nevada
-6.2%

Missouri
-6.3% New Hampshire
134.3%

New Jersey
-6.3% Ohio
-4.8%

New Mexico
-6.3% Oklahoma
29.2%

New York
-6.3% Oregon
-3.2%

North
-6.3% Puerto Rico
12.4%

Carolina
Pennsylvania
-6.3% Rhode Island
7.8%

South Carolina
-6.3% Utah
24.3%

Tennessee
-6.3% Virginia
28.5%

Texas
-6.3% Washington
0.8%

West Virginia
27.1%

Wisconsin
14.9%

Source: CRS analysis of published funding levels and formula factors. See Appendix B for ful sources.
How to Read Table: “Effect” is the percentage difference between the state’s relative share of the formula
factors and its relative share of grant funding in PY2023. For example, Alabama’s relative share of grant funding in
PY2023 needed to be increased 26.1% above the state’s share of the formula factors to comply with the hold
harmless provisions. Montana’s minimum grant (0.25% of total funding) was 53.8% higher than its relative share
of the formula factors (0.1625%). The uniform -6.3% effect among the calculated states reflects the ratable
reduction and smaller ratable increase that was applied to states that were not subject to the adjustment
provisions to accommodate the adjustment provisions.
Effect of Minimum Grant Provisions
The minimum grant provisions typically provide for some of the largest percentage increases
under the adjustment provisions. For example, in PY2023 Vermont’s relative share of the three
factors was 0.07%. The minimum grant provisions increased the state’s share of total funding to
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0.25%, an increase of almost 270%. While states that were subject to the minimum grant tended
to have larger increases relative to their share of the formula factors, states that received the
minimum grant accounted for a relatively small share of total funding. In FY2023, the seven
states that qualified for the minimum share of 0.25% of total funding accounted for 1.75% of total
grants. These states collectively had about 0.9% of the total formula factors, meaning that the
minimum grant provision, on average, almost doubled their grants but also resulted in the
reallotment of less than 1.0% of total funding.
Effect of Hold Harmless Provisions
The effect of the hold harmless on individual states’ grants varies. In PY2023, 23 states were
subject to the hold harmless. Of these, 12 received a relative share of funding that was at least
20% greater than their relative share of the formula factors, and 4 of the 12 received a relative
share of funding that was at least 40% greater.
Five states that were subject to the hold harmless received grants that were less than their relative
share of the formula factors, but the difference was less than the -6.3% for the states that received
a calculated grant. Without the hold harmless provision, these five states would have been
subject to a full ratable reduction that would have pushed their relative share of funding below
90% of their relative share from the prior year. The hold harmless effectively stopped the ratable
reduction in these states and prevented them from having their grant amounts reduced by the
same amount as the reduction for calculated grant states.
Hold Harmless in Consecutive Years
Table 9 divides hold harmless states by the number of consecutive years the state has qualified
for the hold harmless. In many cases, states with the largest effect from the hold harmless are
those that are receiving the hold harmless in consecutive years. For example, in PY2023, of the
12 states where the hold harmless increased their relative shares of funding by at least 20%, 10
were receiving the hold harmless for at least a second consecutive year. Similar trends exist in
prior years. For example, in PY2018 there were nine states where the hold harmless increased
their relative shares of funding by at least 20%, and eight of these states were receiving the hold
harmless for at least the second consecutive year.
Table 9. Application of Hold Harmless (HH) Provisions, PY2015-PY2023

2015
2016
2017
2018
2019
2020
2021
2022
2023
Total states receiving HH
7
12
16
18
19
20
17
27
23
Not subject to HH prior year
7
9
10
7
8
6
12
13
7
Two consecutive HH
--
3
4
7
2
8
2
11
6
Three or more consecutive HH
--
--
2
4
9
6
3
3
10
Source: CRS analysis of funding levels and formula factors. See Appendix B for ful sources.
Note: Designation of second or third consecutive hold harmless are mutually exclusive. Each state is only
counted once in each of the three categories. Adjustment provisions applied in PY2015 were relative to
allotments under the Workforce Investment Act of 1998.
This phenomenon of the hold harmless being applied in multiple years may be partially related to
the dynamic nature of some of the formula factors. As described in the prior section, it is not
unusual for a factor to change by 50% or more from one year to the next. In some of these cases,
a state receiving a hold harmless in consecutive years can be a residual effect of a single prior
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year in which the state had an unusually high share of the formula factors. For example, in
PY2016 Indiana’s relative share of the formula factors was 1.945% and the state received a
calculated grant. In PY2017, the state’s share of the factors declined to 1.650% (-15.2%), but the
hold harmless limited its change in funding share to -10%, or 1.751% of grant funding. In the
subsequent PY2018, the state’s share of the formula factors declined further to 1.270%, a
decrease of 23% from the prior year, but the hold harmless amount was based on the prior year’s
hold harmless allotment, meaning that the state received 90% of its relative share from the prior
year, or 1.575% of total funding. Between PY2016 and PY2018, Indiana’s share of factors
declined about 35%, but due to the repeated application of the hold harmless provisions, the
state’s share of funding only declined 19%. The controlled decline of the state’s funding in
PY2017 and PY2018 also impacted the state receiving a hold harmless again in PY2019 when its
relative share of formula factors declined further.
Effect of Maximum Grant Provisions
The maximum grant provisions reduce the grants for the applicable states and increase grants for
states with calculated grants. Typically, relatively few states are affected by the maximum grant
provisions. In the nine years since WIOA took effect, there were zero states or one state with a
maximum grant in six years. PY2021 was a major exception, with nine states qualifying for a
maximum grant. PY2021 was the first year in which data from the COVID-19 pandemic were
considered in the allotment formula.
The application of the maximum grant provision does create a ratable increase in funding for
other states, but in each year this increase was more than offset by the ratable reductions to
accommodate the minimum grant and hold harmless provisions.
Calculated Grants
Calculated grants reflect the combination of ratable reductions to accommodate the minimum
grant and hold harmless provisions and ratable increases following the application of the
maximum grant provisions. States with calculated grants consistently have a lower relative share
of grant funding compared to their relative share of the formula factors. In PY2023, the relative
share of grant funding for each state that received a calculated grant was about 6.3% less than its
relative share of the formula factors.
Aggregate Effects of Adjustment Provisions
The aggregate effect of each limiting provision can be estimated by comparing the total share of
the formula factors of a group of states subject to a given provision to the total share of the grant
funding for states in each limiting provision category. These calculations for PY2023 are
presented in Table 10. Generally, the minimum grant provisions and the hold harmless provisions
can be seen as raising the grant for the states benefitting from those provisions at the cost of
reducing grants for other states.
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Table 10. PY2023 Shares of Factors and Funding, by Adjustment Provisions
A
B
C
D
E
F
Relative
Relative
Difference
Number of
Share of
Share of
(Percentage
Difference
Provision
States
Factors
Funding
Point)a
(%)b
Minimum Grant
7
0.9%
1.8%
0.8
89.7%
Hold Harmless
23
27.6%
31.4%
3.8
13.6%
Maximum Grant
1
2.2%
2.0%
-0.2
-9.5%
Calculated Grants
21
69.3%
64.9%
-4.4
-6.3%
Source: CRS analysis of funding levels and formula factors. See Appendix B for ful sources.
Note: Columns C and D are rounded to the nearest one-tenth of one percent. Columns E and F were
calculated on the basis of unrounded data.
a. Column E is Column D minus Column C.
b. Column F is Column E divided by Column C.
The 23 states subject to the hold harmless in PY2023 had 27.6% of the factors but received
31.4% of the funding. This amounts to an average increase of 13.6% for the 23 states relative to
what they would have received in the absence of such a policy. Reallocations due to the hold
harmless were the primary driver of the uniform 6.3% difference between the relative share of
formula factors and the relative share of grant funding for states that were not subject to any of
the adjustment provisions (i.e., states receiving calculated grants).
Reallotments for small state minimums tended to have a larger effect on the states receiving the
minimums but smaller effects on the states losing funds. In PY2023, states subject to the
minimum grant received 1.75% of total funding relative their 0.92% relative share of the factors.
This accounted for large increases for some of the states that received the minimum but
reallocated less than 1.0% of total funds.
Table C-6 presents the relative share of formula factors and grants (similar to Table 10) for each
year since the effective date of WIOA. While magnitudes vary, several trends are present in most
years:
• States subject to the minimum grant provision tend to have the largest percentage
differences between their shares of the factors and their shares of funding.
However, the reallocation of funding to accommodate the minimum grants is
typically 1.0% or less.
• The adjustments for the hold harmless provision are typically the largest
adjustments in terms of the share of formula funding that is reallotted, though the
percentage varied by year. The share of funding that is reallotted under these
provisions is typically related to the number of states that qualify for the hold
harmless. In the nine years since WIOA took effect, the hold harmless
reallocations have accounted for more than 5.0% of formula funding twice and
less than 1.0% twice.
• The adjustments for the maximum grant are typically small and facilitate a small
ratable increase to states not subject to the adjustment provisions. The maximum
grant provisions are applied less frequently than the hold harmless provisions,
due at least partially to the higher change threshold (30% increase to trigger
maximum grant versus a 10% decline to trigger hold harmless).
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• The difference between the relative share of formula factors and grant funds for
states that are not subject to adjustment provisions (i.e., calculated grant states)
tends to be inversely related to the number of states subject to the minimum grant
provision and (especially) the hold harmless. In each year, each state that had a
calculated grant was subject to a uniform reduction. For example, in PY2015
seven states qualified for a hold harmless and states with calculated grants had
relative shares of funding 1.6% below their relative shares of formula factors.
Conversely, in PY2020 20 states qualified for a hold harmless and states with
calculated grants had relative shares of funding that were 6.6% less than their
relative shares of formula factors.
Summary of Key Findings
The analysis in this report has identified a number of issues with the Adult Activities formula that
Congress may choose to consider when reauthorizing WIOA. Many of these issues arise from the
statutorily specified formula factors, and include the following:
• The statutory definitions of the formula factors necessitate complex calculation
procedures. These procedures are not easily understood and the resulting factors
may not be optimal for allocating workforce funding. The DA factor is based on a
series of calculations, including one that utilizes an income indicator that has not
been fully updated since the 1980s. In the 80% of cases since PY2015 where
states constructed their ASUs, both the ASU and EU factors were largely based
on the ability of state agencies to strategically identify contiguous areas within
the state with a collective unemployment rate of at least 6.5%.
• The ASU and EU factors do not clearly align with Adult Activities populations.
Adult Activities is a universal access program that gives priority to low-income
workers and other disadvantaged populations. The ASU and EU factors, which
determine two-thirds of initial state grant allotments, are calculated on the basis
of concentrations of unemployment, which vary from year-to-year and may not
reflect the populations that the WIOA statute emphasizes.
• Relative to more traditional indicators, the formula factors favor states with
certain characteristics. Compared to a traditional poverty indicator or another
indicator that uses a nationwide income threshold, the DA factor uses a regional
metric that favors states with higher costs of living. The ASU and EU factors
capture larger shares of total unemployment in states with higher unemployment
rates and therefore, relative to a traditional unemployment metrics, allocate more
funding to states with higher unemployment rates.
• The ASU and EU factors are typically more volatile than traditional metrics. The
majority of states’ relative shares of the ASU and EU factors frequently vary by
more than 30% from one year to the next. Traditional labor market indicators
such as total unemployment and civilian labor force are typically less volatile.
• The adjustment provisions have significant effects on final grant levels. The
adjustment provisions (particularly the hold harmless) temper some of the factor
volatility but also result in grant levels that are decoupled from states’ shares of
the formula factors. The ratable reductions to accommodate the adjustment
provisions typically lead to grants for some states that are well below their shares
of the formula factors. This is a common issue in formula grant programs where
the desire to have enough stability in year-to-year funding to operate stable
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programs is balanced against allocating funds in a manner that aims to capture
shifts in the formula factors.
Potential Policy Options
Congress may consider modifying the Adult Activities formula as part of WIOA reauthorization.
This section discusses advantages and drawbacks of two formula factor frameworks. It also
discusses policy options to limit large changes in grant amounts that could result from the
implementation of new factors or other changes.
Potential New Factor Strategies and Associated Considerations
Potential new factors would likely fall into two categories: (1) traditional indicators and (2)
targeted indicators. Generally, traditional indicators will be more transparent and less volatile than
the current indicators, while targeted indicators can be more precisely aligned with WIOA priority
populations. In all cases, adjustments to the weight of factors could increase or decrease the
influence of certain metrics.
Traditional Indicators
Traditional indicators would generally include data that are regularly published by major federal
statistical agencies (and are used for comparisons throughout this report). These could include
indicators such as measures of the civilian labor force, total unemployment, and persons in
poverty.
Compared to the current Adult Activities formula factors, the primary advantages of these types
of formula factors are transparency and stability. These indicators are commonly understood and
developed using widely accepted methodologies. As demonstrated in the prior sections of this
report, traditional indicators are also subject to less volatility than the current ASU and EU
factors.
A potential criticism of these indicators is that they are not fully aligned with the WIOA
population. Indicators like civilian labor force and total unemployment offer a sense of the scale
of a potential WIOA population but do not, for example, include individuals who are out of the
labor force and could benefit from WIOA services. Further, similar to the current ASU and EU
factors, most traditional indicators reflect the economic conditions of a place rather than the
personal characteristics that the Adult Activities program targets.
Targeted Indicators
Another option could be to allot Adult Activities funding using factors that are aligned with the
WIOA priority populations. These could include selected existing indicators that could serve as a
proxy for target populations (such as recipients of certain public benefits) or constructed
indicators that try to more comprehensively count members of priority population criteria.
Similar to the existing DA factor, a constructed indicator could be developed to count individuals
that meet a specified set of criteria. For example, existing survey data could be used to develop
estimates of a specified population, such as the WIOA priority populations, perhaps using a
multistage process similar to that used by the DA factor.83

83 See the “Disadvantaged Adults Factor” section for a full description of this process.
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The major advantage of using targeted factors is that they could be more closely aligned with
each state’s relative share of a specific population. Rather than indicating the conditions of a
place, a targeted indicator could focus on the personal characteristics that the Adult Activities
program explicitly targets.
One potential drawback of a new targeted factor is that the construction would be complex and
not transparent. This could effectively be seen as trading one opaque factor for another. If the
factor includes a number of subpopulations, it may not be possible to easily determine what
elements are driving year-to-year changes.
Another potential drawback of a new targeted factor is that the complexity could lead to issues
that are similar to those currently seen in the existing DA factor, including being constructed from
multiple years of data and not being updated each year.
The volatility of a potential new targeted factor could depend on its construction. If it is largely
population-based, it may be relatively stable, similar to the existing DA factor. However, if it
relies on more dynamic elements, such as employment status, it may fluctuate from year-to-year
similar to the existing ASU and EU factors.
Weighting Considerations
Decisions to modify or retain the existing formula factors may be accompanied by considerations
of the appropriate weight for each factor. Currently, the three formula grants in Title I of WIOA
each have three equally weighted formulas.
Weighting could be changed by increasing or decreasing the number of factors but retaining equal
weights for each factor. It could also be possible to design a formula in which some factors have
greater weight than others. For example, a three-factor formula could allocate 50% of funding on
the basis of one factor that legislators wish to emphasize and allocate 25% of funding on the basis
of each of two other factors.
Potential Policy Options to Facilitate Continuity with the Existing
Formula and Allotments
Changing the formula factors would likely be advantageous to certain states and reduce funding
for others. These dynamics could result in changes to the formula being considered controversial
and could require states to make changes to their workforce system operations and strategies.
Should Congress opt to make changes to the formula, it could consider a variety of approaches to
limit the effects of formula factor changes, including the following:
• Construction of adjustment provisions. Hold harmless and maximum grant
provisions can limit the immediate changes that result from formula changes. For
example, the current hold harmless provisions would limit any decline in a state’s
share of funding to 10% from the prior year. A potential related approach may be
to create a separate hold harmless provision based on 100% of a prior year’s
funding level to ensure that no state’s funding would decline in absolute dollar
terms after formula refinements were adopted. This could become a new
foundation that is used every year or it could decline over time.
• Choosing new factors that have some alignment with prior factors. Partial
continuity with the existing formula factors can inform the selection of new
factors. For example, persons in poverty is a key element of the DA factor and if
Congress were looking for a more transparent factor that had significant
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alignment with the existing factor, it might consider replacing the DA factor with
a count of persons in poverty. This approach could be seen as trying to maintain
some of the principles of the current formula while moving to factors that are
more transparent.
• Phasing in a new formula. To limit the immediate effects of a formula change, a
portion of a program’s funds could be allocated using the new formula and the
remainder allocated using the old formula during a phase in period. For example,
it may be possible to structure a change so that in the first year 80% of the
funding would be allotted using the prior formula and 20% allotted using the new
formula. The percentage allotted using the new formula could increase 20% each
year, meaning that by the fifth year, 100% of funds would be allotted using the
new formula.
• Increase total funding for grants. Any of the aforementioned strategies could be
combined with an increase in total funding, if such an increase was deemed
desirable by Congress. Such an approach could increase funding for most or all
states and reduce possible declines in funding among states with shares of the
new factors that are smaller than their shares of the current factors. An increase in
overall funding could mean that even if a state’s relative share of new factors
were less than its relative share of previous factors, the state’s overall funding
may still increase.
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Appendix A. Historical Background and Legislative
History
This section provides a legislative history of federal workforce development statutes, with an
emphasis on placing the current Adult Activities formula in a historical context. The current Adult
Activities formula in WIOA generally follows the approach of a similar program in the Workforce
Investment Act of 1998 (WIA; P.L. 105-220), which largely follows the approach of a similar
program in the Job Training Partnership Act of 1982 (JTPA; P.L. 97-300). CRS’s review of the
legislative history of WIOA, WIA, and JTPA did not reveal a great deal about the congressional
intent behind the development of the formula and subsequent decisions to retain it.
Table A-1 depicts the major formula elements of grants in each of WIOA, WIA, and JTPA. This
review focuses on the formula elements of the relevant programs established in statute and only
notes major changes in other programmatic details. The original JTPA formula grant initially
targeted both disadvantaged adults and disadvantaged youth. It was split into two programs by the
Job Training Reform Amendments of 1992 (P.L. 102-367), which created separate programs for
youth and adults. Only the latter is included in Table A-1.
Table A-1. Major Components of Workforce Formulas
Workforce
Job Training
Workforce
Innovation and
Job Training
Reform
Investment Act of
Opportunity Act
Program
Partnership Act
Amendments of
1998
(2014, P.L. 113-
Element
(1982, P.L. 97-300)
1992 (P.L. 102-367)
(P.L. 105-220)
128)
Program
Training for
Adult Training
Adult Employment
Adult Activities
Disadvantaged Adults
Program
and Training Activities
and Youth
Factor #1
Unemployment in
Same as 1982
Same as 1982
Same as 1982
areas of substantial
provision
provision
provision
unemployment
Factor #2
Excess
Same as 1982
Same as 1982
Same as 1982
Unemployment
provision
provision
provision
Factor #3
Economically
Economically
Same as 1992
Same as 1992
disadvantaged
disadvantaged adults
provisionb
provisionb
individualsa
Minimum
0.25% of total funds
Same as 1982
Same as 1982
Same as 1982
Grant
for grants
provision
provision
provision
Hold
90% of relative share
Same as 1982
Same as 1982
Same as 1982
Harmless
from prior year
provision
provision
provision
Maximum
None
130% of relative share
Same as 1992
Same as 1992
Grant
from prior year
provision
provision
Source: CRS analysis of Section 201 of P.L. 97-300, Section 202 of P.L. 102-367, Section 132 of P.L. 105-220,
and Section 132 of P.L. 113-128.
Notes: Listed formulas focus on allotments to states and do not consider pre-allotment reservations or post-
allotment allocations to substate areas.
a. Factor includes both qualified adults and qualified youth.
b. The factor in WIA and WIOA fol ows the definition of the 1992 provision but uses the term “disadvantaged
adults” rather than “economically disadvantaged adults.”
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Job Training Partnership Act of 1982
JTPA marked a significant change from prior federal workforce legislation for disadvantaged
workers. Prior to JTPA, the primary federal workforce development legislation was the
Comprehensive Employment and Training Act (CETA), which was established in 1973 and
amended several times prior to the enactment of JTPA. The largest component of CETA was
subsidized public service employment, typically facilitated by local “prime sponsors,” which
were usually units of local government. JTPA marked a concerted departure from CETA,
increasing the role of the private sector, emphasizing training over subsidized employment, and
expanding roles for states and governors rather than localities.84
In the 97th Congress, separate bills to replace CETA were reported by the relevant committees in
the House and Senate. Each bill included a formula grant to states to provide job training for
economically disadvantaged individuals:
• H.R. 5320, as reported, had a formula with four equally weighted factors: (1) the
number of unemployed persons, (2) the excess number of unemployed, (3) the
number of unemployed persons living in areas of substantial unemployment, and
(4) the number of adults in low-income families in the jurisdiction. These factors
were similar to the Transitional Public Service Employment program formula in
CETA (one of several formula grants in that law), but the accompanying
committee report did not note the similarity or otherwise establish a rationale for
the factors.85
• S. 2036, as reported, had a formula with two equally weighted factors: (1) the
number of economically disadvantaged persons in the labor force and (2) the
number of long-term unemployed. The bill also established a minimum grant of
0.25% and a hold harmless equal to 90% of the state’s allotment percentage from
the prior year. The accompanying committee report did not explain a rationale for
these factors or adjustment provisions.86
The subsequent bill approved by a conference committee established a formula grant program
under Title II-A of JTPA. The formula generally retained three of the four factors from H.R. 5320
and specified that funds would be allocated on the basis of these three equally weighted factors.
The Senate provisions related to minimum grants and a hold harmless were also retained. The
conference report did not offer a rationale for these decisions.87
The definitions of the unemployment in areas of substantial unemployment factor and excess
unemployment factor in the final JTPA statute largely mirror the current definitions for the ASU
and EU factors in WIOA.88 The disadvantaged individuals factor in JTPA is different from the
current DA factor in that it does not have an age restriction. It is similar to the current DA factor
in that it considers persons with incomes below the poverty line or 70% of the lower living
standard income level.

84 For a more detailed discussion of the evolution and priorities of JTPA, see CRS Report 83-76 EPW, Job Training
Partnership Act: Background and Description, April 19, 1983 (available to congressional clients upon request).
85 See H.Rept. 97-537.
86 See S.Rept. 97-469.
87 See Conference Report 97-889, page 85, which simply describes the House bill, the Senate bill, and the conference
agreement.
88 See Section 4 and Section 201 of JTPA.
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JTPA Amendments of 1992
The JTPA Amendments of 1992 (P.L. 102-367) split the Title II-A program in JTPA into two
separate programs that separately targeted disadvantaged adults and disadvantaged youth. The
two formula programs had two similar factors (ASU and EU) as well as third factors that were
related but distinct (disadvantaged youth and disadvantaged adults). For the adult program, the
law established the current 22-72 age range that is considered by the current DA factor as well as
the policy of excluding college students and members of the Armed Forces.89 The law also
established the current maximum grant of 130% of a state’s relative share from the prior year.
Workforce Investment Act of 1998
The 105th Congress enacted WIA, which authorized appropriations for each of FY1999 through
FY2003. WIA established an Adult Employment and Training Activities formula grant program
for states. Various elements of the program differed from the Disadvantaged Adult program in
JTPA, but the formula had the same factors and adjustment provisions as the Adult Training
Program under the JTPA Amendments of 1992.90
During the development of WIA, bills were reported by the relevant committees in the House and
Senate. Both bills retained the same formula from JTPA for the revised adult program. Both
committee reports noted this similarity.91 The House report noted that a partial rationale for
retaining the JTPA formula was “protecting against major funding shifts among States.”92
Selected Legislative Efforts between WIA and WIOA
The authorization of appropriations under WIA expired after PY2003. Prior to the enactment of
WIOA in 2014, there were several efforts to reauthorize WIA, including bills that passed either
the House or Senate. Some of the bills would have modified the formula for the adult-focused
program. This section discusses some unenacted proposals that would have altered the Adult
Activities formula. It is not a comprehensive review of efforts to reauthorize WIA.
In some cases, proposed formula changes were part of a broader effort to consolidate formula
programs. For example, in the 108th Congress, the House passed a bill that would have combined
the adult program with other programs into a single formula grant. The consolidated program
would have distributed a majority of funding under a new formula that would have allotted 60%
of the funding on the basis of each state’s relative share of total unemployment. Smaller portions
of the funding would have been allocated on the basis of states’ shares of disadvantaged adults,
excess unemployment, and civilian labor force. The bill would have eliminated the ASU factor.
Several mechanisms were in place to limit immediate changes under the new formula.93
Other bills retained a dedicated stream for adult activities, but modified the allocation factors. For
example, in the 109th Congress the Senate passed a WIA reauthorization bill that would have
retained a separate adult-targeted program but would have modified the formula so that 40% of
funds would have been allotted under the ASU factor, 35% would have been allotted on the basis

89 See Section 202(d)(2) of P.L. 102-367.
90 See Section 132(b)(1) of P.L. 105-220.
91 See page 86 of H.Rept. 105-93 related to H.R. 1385 and page 13 of S.Rept. 105-109 related to S. 1186.
92 See page 105 of H.Rept. 105-93.
93 See H.R. 1261 as passed by the House in the 108th Congress.
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of the DA factor, and 25% would have been allotted on the basis of shares of the civilian labor
force (a new factor).94
Workforce Innovation and Opportunity Act of 2014
WIOA was enacted in July 2014 and retained many of the principles and administrative
structures of the predecessor WIA statute. The Adult Activities formula grant in WIOA
had the same formula factors and procedures as the similarly named grant in WIA.
During the development of WIOA, some formula-related changes were considered in the
context of broader consolidation, but they were not enacted. In March 2013, the House
Education and Workforce Committee reported H.R. 803, which would have consolidated
a number of workforce programs (including the Adult Activities program) into a single
formula grant to states. The formula would have allocated funds on the basis of four
equally weighted factors: (1) unemployment in ASUs (existing Adult Activities formula
factor), (2) civilian labor force, (3) long-term unemployment (at least 15 weeks), and (4)
disadvantaged youth. The bill would have established a three-year transition period to
moderate the effects of the new formula.95
In November 2013, the Senate Committee on Health, Education, Labor, and Pensions
reported S. 1356 without a written report. The bill would not have consolidated any of
the major formula grant programs nor did it propose any formula changes to the adult
program.
The final version of WIOA followed the Senate bill and did not consolidate any of the major
formula grant programs or change formulas.96 The adult program was renamed Adult Activities
and retained the formula from WIA.

94 See H.R. 27, engrossed amendment Senate, in the 109th Congress.
95 See H.Rept. 113-14 related to H.R. 803.
96 WIOA did eliminate several program authorizations. See “Statement of the Managers to Accompany the Workforce
Innovation and Opportunity Act,” May 21, 2014,
https://edworkforce.house.gov/uploadedfiles/wioa_managers_statement.pdf.
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Appendix B. Data Sources
This report uses a combination of publicly available data and data that were obtained directly
from the Bureau of Labor Statistics (BLS).
DOL publishes state-level formula factor data on its website. Data on disadvantaged adults,
unemployment in areas of substantial unemployment, and excess unemployment were accessed
directly from this site. DOL also publishes details and data on the components of the DA factor.
Specific links are provided in the sources for Table B-1.
Data on state-level civilian labor force and total unemployment that were comparable to the ASU
and EU factor data were obtained directly from BLS. BLS publishes state-level civilian labor
force and unemployment data on a monthly basis. Data are typically published on the third Friday
of the month following the reference month.97 For example, June 2021 data were published on
Friday, July 16, 2021.98 The initial published data are preliminary and are revised for up to five
years. The data on the BLS website reflect the most recent revisions and not the earlier versions
that were used in the creation of the formula factors.
The ASU and EU factor data are calculated on the basis of the state-level unemployment data as
of the preliminary June data in the reference year. Subsequent revisions are not considered in the
ASU and EU calculations. To ensure comparability with the ASU and EU factors, the state-level
civilian labor force and unemployment data as of the preliminary June data for each year are used
in this report. For example, the unemployment data for PY2022 are the data for July 2021 through
June 2022 as of the initial publication of the June 2022 data.
BLS provided CRS with each state’s data for each relevant program year as of the preliminary
June data. It was necessary to obtain this data directly from BLS because, as noted previously,
data on the public BLS website have been revised and would not reflect the data that were used
for the ASU and EU calculations. BLS also provided CRS with unpublished data on the civilian
labor force in each state’s ASUs and the number of ASUs in each state.99
Table B-1 provides sources for the underlying data that were used for the analysis in this report.
Other indicators that were calculated by CRS on the basis of these data are not included in the
table. These other indicators include traditional metrics such as unemployment rate (calculated on
the basis of BLS-provided civilian labor force and unemployment data) and more formula-
specific indicators, such as the percentage of total unemployment considered in the ASU factor or
states’ relative shares of formula factors.

97 Bureau of Labor Statistics, “Local Area Unemployment Statistics: Frequently Asked Questions,”
https://www.bls.gov/lau/laufaq.htm#Q07.
98 See Bureau of Labor Statistics, “State Employment and Unemployment—June 2021,”
https://www.bls.gov/news.release/archives/laus_07162021.htm.
99 State-level unemployment data for Puerto Rico for PY2021 were not available because Puerto Rico could not field its
household survey for March 2020 or April 2020 due to pandemic restrictions. BLS certified Puerto Rico as a whole-
state ASU, as the unemployment rates for the 10 available underlying months ranged from 7.0% to 9.6%. For the
analysis in this report, CRS used the PY2021 ASU factor data and EU factor data for Puerto Rico from the
Employment and Training Administration (ETA) website. Because BLS certified Puerto Rico as a whole-state ASU in
PY2021, CRS assumed that the state’s ASU factor on the ETA website equaled the state’s total unemployment during
the reference period.
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Table B-1. Sources for Data Used in this Report
Geography
Data
Source
State
Civilian labor force
Directly from BLS
State
Unemployment
Directly from BLS
ASU
Unemployment
ETA formula site
ASU
Civilian labor force
Directly from BLS
ASU
Number of ASUs in the state
Directly from BLS
EU
Unemployment
ETA formula site
State
Disadvantaged adults
ETA formula site
State
Components of disadvantaged adults,
ETA disadvantaged adults site
including persons in poverty
State
Grant levels
ETA formula site
Sources:
ETA formula site is https://www.dol.gov/agencies/eta/budget/formula/state, which has data going back to PY2015.
ETA disadvantaged adults site is https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults, and
adjacent pages include data going back to 2006-2010.
Data obtained directly from BLS are based on the preliminary June data for the reference year.
Notes: ETA is the Employment and Training Administration, BLS is the Bureau of Labor Statistics; both agencies
are part of the U.S. Department of Labor.
Zero and Missing Data
For the data used in this report, there are limited instances of zero and missing data.
In the reference periods for PY2019 and PY2020, the state of Vermont had an ASU factor and an
EU factor of zero. This issue led to Vermont being excluded from some analyses in this report.100
For example, because it is not possible to calculate a change from a base period of zero, no
change was calculated for Vermont’s ASU and EU factors for PY2019 to PY2020 or PY2020 to
PY2021. This exclusion is why, for example, the bars for the ASU factor and EU factor in Figure
7 do not include all 52 states for the periods ending in PY2020 and PY2021. The period ending in
PY2019 was classified as a decline of 100%.
In the reference period for PY2021, BLS was unable to estimate the civilian labor force for
Puerto Rico. While this did not affect the formula directly, it did affect some of the traditional
labor market indicators that are used for comparison throughout the report. The lack of civilian
labor force data for PY2021 means it was not possible to calculate the unemployment rate for
Puerto Rico in PY2021, so CRS did not include PY2021 data for Puerto Rico in analyses that
consider unemployment rate. The analyses of changes in civilian labor force excluded Puerto
Rico for the period ending in PY2021 (because reporting a decline of 100% would be misleading)
and for the period ending in PY2022 (because there were no data for the PY2021 base period).
BLS certified Puerto Rico as a whole-state ASU in that reference period. For PY2021, CRS used
the published ASU factor for Puerto Rico for the state’s total unemployment.

100 Because Vermont has qualified for a minimum grant every year since WIOA took effect, these two years of zero
formula factors did not have a direct effect on the state’s allotment.
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Appendix C. Additional Data Tables
Table C-1. Persons in Poverty and Disadvantaged Adults, 2016-2020, Ages 22-72
Disadvantaged Adults Used in PY2023 Adult Activities Program Allotments
A
B
C
D
E
F

Persons in Poverty
Disadvantaged Adults
Difference in
Relative Share
State
Count
Relative Share
Count
Relative Share
(%)a
Alabama
415,955
1.783%
454,600
1.670%
-6.4%
Alaska
41,955
0.180%
61,245
0.225%
25.1%
Arizona
528,200
2.265%
678,605
2.493%
10.1%
Arkansas
256,070
1.098%
284,210
1.044%
-4.9%
California
2,676,250
11.474%
3,461,655
12.717%
10.8%
Colorado
314,960
1.350%
374,475
1.376%
1.9%
Connecticut
196,485
0.842%
239,695
0.881%
4.5%
Delaware
59,040
0.253%
68,010
0.250%
-1.3%
District of Columbia
61,820
0.265%
67,165
0.247%
-6.9%
Florida
1,572,845
6.743%
1,732,870
6.366%
-5.6%
Georgia
786,180
3.371%
870,125
3.197%
-5.2%
Hawaii
74,600
0.320%
114,095
0.419%
31.1%
Idaho
109,030
0.467%
138,705
0.510%
9.0%
Il inois
822,590
3.527%
917,500
3.371%
-4.4%
Indiana
452,510
1.940%
505,640
1.858%
-4.3%
Iowa
182,435
0.782%
194,665
0.715%
-8.6%
Kansas
168,960
0.724%
185,420
0.681%
-6.0%
Kentucky
406,175
1.741%
534,081
1.962%
12.7%
Louisiana
458,045
1.964%
518,180
1.904%
-3.1%
Maine
88,015
0.377%
102,110
0.375%
-0.6%
Maryland
300,720
1.289%
384,845
1.414%
9.7%
Massachusetts
383,665
1.645%
460,480
1.692%
2.8%
Michigan
744,815
3.193%
795,990
2.924%
-8.4%
Minnesota
274,560
1.177%
367,376
1.350%
14.7%
Mississippi
299,410
1.284%
337,485
1.240%
-3.4%
Missouri
428,780
1.838%
472,800
1.737%
-5.5%
Montana
77,065
0.330%
91,540
0.336%
1.8%
Nebraska
103,190
0.442%
112,950
0.415%
-6.2%
Nevada
217,920
0.934%
271,440
0.997%
6.7%
New Hampshire
59,195
0.254%
73,210
0.269%
6.0%
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A
B
C
D
E
F

Persons in Poverty
Disadvantaged Adults
Difference in
Relative Share
State
Count
Relative Share
Count
Relative Share
(%)a
New Jersey
463,170
1.986%
588,650
2.163%
8.9%
New Mexico
210,550
0.903%
257,625
0.946%
4.8%
New York
1,465,500
6.283%
1,788,615
6.571%
4.6%
North Carolina
771,725
3.309%
827,475
3.040%
-8.1%
North Dakota
41,780
0.179%
44,050
0.162%
-9.7%
Ohio
858,095
3.679%
946,130
3.476%
-5.5%
Oklahoma
315,010
1.351%
352,250
1.294%
-4.2%
Oregon
302,735
1.298%
359,110
1.319%
1.6%
Pennsylvania
825,400
3.539%
1,009,730
3.709%
4.8%
Puerto Rico
818,135
3.508%
835,875
3.071%
-12.5%
Rhode Island
67,150
0.288%
78,490
0.288%
0.2%
South Carolina
397,420
1.704%
438,335
1.610%
-5.5%
South Dakota
56,780
0.243%
62,305
0.229%
-6.0%
Tennessee
538,475
2.309%
590,070
2.168%
-6.1%
Texas
2,007,745
8.608%
2,239,865
8.229%
-4.4%
Utah
142,710
0.612%
182,785
0.671%
9.7%
Vermont
38,470
0.165%
45,080
0.166%
0.4%
Virginia
459,160
1.969%
530,370
1.948%
-1.0%
Washington
435,275
1.866%
533,170
1.959%
5.0%
West Virginia
176,475
0.757%
194,740
0.715%
-5.4%
Wisconsin
336,700
1.444%
403,655
1.483%
2.7%
Wyoming
34,395
0.147%
41,080
0.151%
2.3%
Source: Persons in poverty from state data in Table 2 at
https://www.dol.gov/agencies/eta/budget/formula/disadvantagedyouthadults; and disadvantaged adults from
PY2023 data at https://www.dol.gov/agencies/eta/budget/formula/state.
Note: Positive numbers in Column F indicate states with larger relative shares of the DA factor than their
relative shares of persons in poverty. Negative numbers in Column F indicate states with lower relative shares of
the DA factor than their relative shares of persons in poverty.
a. Column F is Column E minus Column C, divided by Column C.

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Table C-2. Unemployment, Statewide and within Areas of Substantial
Unemployment, PY2023
A
B
C
D
E
F
G
H
I

Statewide
Areas of Substantial Unemployment (ASU)
ASU
Factor
# of
as % of
Unemp.
Unemp.
ASUs in
Total
State
CLF
Unemp.
Rate (%)
CLF
Unemp.
Rate (%)
Stateb
Unemp.a
Alabama
2,264,753
66,535
2.9%
388,005
25,265
6.5%
10
38.0%
Alaska
359,604
18,782
5.2%
234,754
15,208
6.5%
2
81.0%
Arizona
3,550,931
131,575
3.7%
1,274,732
82,296
6.5%
1
62.5%
Arkansas
1,342,533
45,265
3.4%
336,132
21,754
6.5%
6
48.1%
California
19,144,470
1,001,322
5.2%
13,999,187
903,089
6.5%
1
90.2%
Colorado
3,201,424
128,686
4.0%
1,015,742
65,775
6.5%
1
51.1%
Connecticut
1,878,087
90,241
4.8%
1,158,688
74,773
6.5%
1
82.9%
Delaware
498,270
23,692
4.8%
293,781
18,972
6.5%
1
80.1%
District of
384,805
22,780
5.9%
347,350
22,661
6.5%
1
99.5%
Columbia
Florida
10,503,088
351,022
3.3%
2,309,280
149,026
6.5%
5
42.5%
Georgia
5,239,767
168,928
3.2%
216,875
15,562
7.2%
2
9.2%
Hawaii
672,988
29,793
4.4%
357,995
23,162
6.5%
5
77.7%
Idaho
933,817
27,283
2.9%
54,321
3,515
6.5%
2
12.9%
Il inois
6,398,010
319,125
5.0%
4,352,609
280,746
6.5%
1
88.0%
Indiana
3,338,079
88,568
2.7%
419,594
27,260
6.5%
11
30.8%
Iowa
1,692,548
56,832
3.4%
279,074
18,251
6.5%
14
32.1%
Kansas
1,500,336
40,233
2.7%
131,435
8,714
6.6%
5
21.7%
Kentucky
2,054,228
86,177
4.2%
984,019
63,479
6.5%
1
73.7%
Louisiana
2,081,201
91,099
4.4%
1,172,476
75,692
6.5%
1
83.1%
Maine
680,035
25,815
3.8%
211,599
13,661
6.5%
1
52.9%
Maryland
3,196,951
153,666
4.8%
1,919,315
123,825
6.5%
1
80.6%
Massachusetts
3,761,395
164,826
4.4%
1,820,016
117,398
6.5%
1
71.2%
Michigan
4,821,125
238,247
4.9%
3,247,974
209,734
6.5%
1
88.0%
Minnesota
3,058,329
77,589
2.5%
140,277
9,159
6.5%
7
11.8%
Mississippi
1,258,480
55,299
4.4%
677,994
43,952
6.5%
8
79.5%
Missouri
3,070,033
105,754
3.4%
758,941
49,065
6.5%
5
46.4%
Montana
558,237
15,669
2.8%
50,709
3,447
6.8%
5
22.0%
Nebraska
1,057,725
22,972
2.2%
58,535
3,794
6.5%
2
16.5%
Nevada
1,505,202
78,228
5.2%
1,057,375
68,285
6.5%
3
87.3%
New
Hampshire
758,986
20,010
2.6%
24,902
1,623
6.5%
2
8.1%
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A
B
C
D
E
F
G
H
I

Statewide
Areas of Substantial Unemployment (ASU)
ASU
Factor
# of
as % of
Unemp.
Unemp.
ASUs in
Total
State
CLF
Unemp.
Rate (%)
CLF
Unemp.
Rate (%)
Stateb
Unemp.a
New Jersey
4,648,753
220,718
4.7%
2,716,823
175,318
6.5%
1
79.4%
New Mexico
946,837
52,281
5.5%
780,700
50,366
6.5%
1
96.3%
New York
9,436,494
487,514
5.2%
6,969,651
449,544
6.5%
1
92.2%
North Carolina
5,040,615
198,959
3.9%
1,985,590
128,108
6.5%
1
64.4%
North Dakota
408,102
11,662
2.9%
43,896
2,865
6.5%
4
24.6%
Ohio
5,763,943
247,393
4.3%
2,961,945
191,354
6.5%
12
77.3%
Oklahoma
1,861,617
55,673
3.0%
157,469
10,179
6.5%
1
18.3%
Oregon
2,177,223
87,529
4.0%
724,633
46,764
6.5%
1
53.4%
Pennsylvania
6,404,328
329,258
5.1%
4,028,177
260,014
6.5%
1
79.0%
Puerto Rico
1,200,386
83,034
6.9%
1,200,386
83,034
6.9%
Whole
100.0%
Rhode Island
571,872
23,348
4.1%
231,450
14,931
6.5%
1
63.9%
South Carolina
2,385,203
83,636
3.5%
621,425
40,091
6.5%
1
47.9%
South Dakota
472,927
12,463
2.6%
11,559
765
6.6%
2
6.1%
Tennessee
3,371,621
119,561
3.5%
1,072,550
69,195
6.5%
1
57.9%
Texas
14,402,536
661,978
4.6%
8,526,168
553,822
6.5%
1
83.7%
Utah
1,706,947
37,763
2.2%
28,208
1,855
6.6%
4
4.9%
Vermont
331,328
8,941
2.7%
14,146
918
6.5%
1
10.3%
Virginia
4,302,102
134,665
3.1%
612,840
39,743
6.5%
15
29.5%
Washington
3,986,932
172,701
4.3%
1,537,283
99,447
6.5%
1
57.6%
West Virginia
793,921
32,093
4.0%
270,873
17,499
6.5%
1
54.5%
Wisconsin
3,138,897
97,854
3.1%
401,229
25,961
6.5%
11
26.5%
Wyoming
290,112
10,519
3.6%
54,916
3,596
6.5%
4
34.2%
Source: See Appendix B for data sources and main text for ful methodology.
Notes: Unemployment in ASUs is the formula factor used in Adult Activities allotments. CLF = civilian labor
force. Unemp. = Unemployed individuals in the state.
a. Column I is Column F divided by Column C. Column I indicates the percentage of the state’s total
unemployment that is captured by the ASU factor.
b. Column H is the number of state-determined ASUs within the state. The instance in which the state
qualified as a whole state ASU and did not have to determine ASUs is labeled “Whole.”

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Table C-3. Disadvantaged Adults Factor: Count and Relative Share
A
B
C
D
E
F
G
H
I

Change in Relative
PY2015-PY2017
PY2018-PY2022
PY2023
Share
Relative
Relative
Relative
PY2017
PY2022
State
Count
Share
Count
Share
Count
Share
to
to
PY2018a PY2023b
Alabama
434,975
1.736%
506,890
1.697%
454,600
1.670%
-2.2%
-1.6%
Alaska
47,265
0.189%
57,090
0.191%
61,245
0.225%
1.3%
17.7%
Arizona
565,775
2.258%
728,505
2.439%
678,605
2.493%
8.0%
2.2%
Arkansas
270,355
1.079%
311,525
1.043%
284,210
1.044%
-3.3%
0.1%
California
3,177,050
12.678% 4,004,545
13.407% 3,461,655
12.717%
5.7%
-5.1%
Colorado
338,910
1.352%
402,520
1.348%
374,475
1.376%
-0.4%
2.1%
Connecticut
210,205
0.839%
250,415
0.838%
239,695
0.881%
-0.1%
5.0%
Delaware
54,865
0.219%
66,540
0.223%
68,010
0.250%
1.7%
12.2%
District of
61,925
0.247%
72,250
0.242%
67,165
0.247%
-2.1%
2.0%
Columbia
Florida
1,462,070
5.835% 1,894,985
6.344% 1,732,870
6.366%
8.7%
0.3%
Georgia
794,240
3.170% 1,002,675
3.357%
870,125
3.197%
5.9%
-4.8%
Hawaii
103,760
0.414%
126,470
0.423%
114,095
0.419%
2.3%
-1.0%
Idaho
120,905
0.482%
146,355
0.490%
138,705
0.510%
1.6%
4.0%
Il inois
891,135
3.556% 1,051,740
3.521%
917,500
3.371%
-1.0%
-4.3%
Indiana
460,470
1.838%
556,305
1.862%
505,640
1.858%
1.4%
-0.3%
Iowa
174,555
0.697%
201,455
0.674%
194,665
0.715%
-3.2%
6.0%
Kansas
179,420
0.716%
204,715
0.685%
185,420
0.681%
-4.3%
-0.6%
Kentucky
521,900
2.083%
570,975
1.912%
534,081
1.962%
-8.2%
2.6%
Louisiana
427,970
1.708%
517,025
1.731%
518,180
1.904%
1.3%
10.0%
Maine
101,645
0.406%
122,935
0.412%
102,110
0.375%
1.5%
-8.9%
Maryland
321,530
1.283%
403,385
1.350%
384,845
1.414%
5.3%
4.7%
Massachusetts
429,270
1.713%
496,210
1.661%
460,480
1.692%
-3.0%
1.8%
Michigan
790,100
3.153%
909,475
3.045%
795,990
2.924%
-3.4%
-4.0%
Minnesota
362,631
1.447%
404,296
1.354%
367,376
1.350%
-6.5%
-0.3%
Mississippi
325,040
1.297%
367,910
1.232%
337,485
1.240%
-5.0%
0.7%
Missouri
443,605
1.770%
524,530
1.756%
472,800
1.737%
-0.8%
-1.1%
Montana
108,316
0.432%
91,340
0.306%
91,540
0.336%
-29.3%
10.0%
Nebraska
106,875
0.426%
122,125
0.409%
112,950
0.415%
-4.1%
1.5%
Nevada
192,925
0.770%
273,310
0.915%
271,440
0.997%
18.8%
9.0%
New Hampshire
66,405
0.265%
76,660
0.257%
73,210
0.269%
-3.2%
4.8%
New Jersey
522,560
2.085%
654,705
2.192%
588,650
2.163%
5.1%
-1.3%
New Mexico
208,350
0.831%
260,015
0.870%
257,625
0.946%
4.7%
8.7%
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A
B
C
D
E
F
G
H
I

Change in Relative
PY2015-PY2017
PY2018-PY2022
PY2023
Share
PY2017
PY2022
State
Count
Relative
Count
Relative
Count
Relative
to
to
Share
Share
Share
PY2018a PY2023b
New York
1,716,375
6.849% 2,019,685
6.762% 1,788,615
6.571%
-1.3%
-2.8%
North Carolina
761,970
3.041%
930,375
3.115%
827,475
3.040%
2.4%
-2.4%
North Dakota
37,870
0.151%
40,670
0.136%
44,050
0.162%
-9.9%
18.9%
Ohio
883,295
3.525% 1,019,380
3.413%
946,130
3.476%
-3.2%
1.8%
Oklahoma
320,780
1.280%
356,120
1.192%
352,250
1.294%
-6.9%
8.5%
Oregon
316,265
1.262%
399,430
1.337%
359,110
1.319%
6.0%
-1.3%
Pennsylvania
922,070
3.680% 1,078,135
3.609% 1,009,730
3.709%
-1.9%
2.8%
Puerto Rico
931,835
3.719%
922,725
3.089%
835,875
3.071%
-16.9%
-0.6%
Rhode Island
73,565
0.294%
88,965
0.298%
78,490
0.288%
1.5%
-3.2%
South Carolina
404,895
1.616%
475,220
1.591%
438,335
1.610%
-1.5%
1.2%
South Dakota
54,180
0.216%
61,355
0.205%
62,305
0.229%
-5.0%
11.4%
Tennessee
560,330
2.236%
642,195
2.150%
590,070
2.168%
-3.9%
0.8%
Texas
2,073,310
8.274% 2,381,090
7.972% 2,239,865
8.229%
-3.7%
3.2%
Utah
159,480
0.636%
203,245
0.680%
182,785
0.671%
6.9%
-1.3%
Vermont
40,140
0.160%
45,410
0.152%
45,080
0.166%
-5.1%
8.9%
Virginia
455,945
1.820%
570,555
1.910%
530,370
1.948%
5.0%
2.0%
Washington
478,825
1.911%
579,345
1.940%
533,170
1.959%
1.5%
1.0%
West Virginia
185,000
0.738%
199,450
0.668%
194,740
0.715%
-9.6%
7.1%
Wisconsin
376,090
1.501%
438,075
1.467%
403,655
1.483%
-2.3%
1.1%
Wyoming
29,550
0.118%
38,680
0.129%
41,080
0.151%
9.8%
16.5%
Source: Compiled from annual factor data at https://www.dol.gov/agencies/eta/budget/formula/state. Listed
years reflect the program year in which the data were used to allot funds. See main text for ful methodology
used to calculate the factors.
a. Column H is Column E minus Column C, divided by Column C. Column H indicates the percentage change
in each state’s relative share of the DA factor when the data were updated between PY2017 and PY2018.
b. Column I is Column G minus Column E, divided by Column E. Column I indicates the percentage change in
each state’s relative share of the DA factor when the data were updated between PY2022 and PY2023.
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Table C-4. Number of States with Changes in Relative Shares of Formula Factors
and Traditional Labor Market Indicators from the Prior Year, by Magnitude of
Change
Magnitude of Change and
PY2016 PY2017 PY2018 PY2019 PY2020 PY2021 PY2022 PY2023
Average
Factor/Indicator
Less than 10% Change

ASU Factor
18
10
18
11
11
9
13
8
12
EU Factor
20
15
19
11
10
6
5
9
12
Total Unemployment
36
30
39
38
41
26
20
31
33
Civilian Labor Force
52
52
52
52
52
51a
51a
51
52
10% to 30% Change

ASU Factor
22
24
20
16
19
11
24
19
19
EU Factor
21
20
18
17
21
13
18
17
18
Total Unemployment
14
22
13
14
10
21
26
19
17
Civilian Labor Force
0
0
0
0
0
0
0
1
0
30%+ Change

ASU Factor
12
18
14
25
21
31
15
25
20
EU Factor
11
17
15
24
20
29
27
26
21
Total Unemployment
2
0
0
0
1
5
6
2
2
Civilian Labor Force
0
0
0
0
0
0
0
0
0
30% to 50% Change

ASU Factor
10
12
13
18
15
9
6
11
12
EU Factor
7
12
14
18
14
8
17
13
13
Total Unemployment
1
0
0
0
1
4
6
1
2
Civilian Labor Force
0
0
0
0
0
0
0
0
0
50%+ Change

ASU Factor
2
6
1
7
6
22
9
14
8
EU Factor
4
5
1
6
6
24
12
13
9
Total Unemployment
1
0
0
0
0
1
0
1
<1
Civilian Labor Force
0
0
0
0
0
0
0
0
0
Source: See Appendix B for data sources. See the “Measuring Year-to-Year Variation” text box for more
details on calculations.
Notes: Categories of change consider each state’s absolute change. In limited cases where relevant state data
were missing or zero and precluded a calculation, observations are excluded from the analysis. The “30% to 50%
Change” and “50%+ Change” categories are subsets of the broader “30%+ Change” category. The method of
categorization would classify a change this precisely at a threshold in the higher percentage category.
a. Civilian Labor Force data are not available for Puerto Rico for PY2021, so it is not possible to calculate
changes for PY2021 and PY2022.

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Analysis of Adult Activities Allotment Formula under WIOA

Table C-5. Adjustment Provisions and Difference Between Share of Grant Funding
and Formula Factors, PY2015-PY2023
State
2015
2016
2017
2018
2019
2020
2021
2022
2023
Alabama
-1.6%
-1.9%
-3.9%
-4.7%
31.2%
12.1%
24.7%
31.2%
26.1%

Calc.
Calc.
Calc.
Calc.
HH
HH
HH
HH
HH
Alaska
22.7%
-1.9%
-3.9%
-4.7%
-19.7%
-6.6%
77.6%
120.4%
42.5%

Min.
Calc.
Calc.
Calc.
Max.
Calc.
HH
HH
HH
Arizona
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-8.9%
39.8%
29.3%
38.8%

Calc.
Calc.
Calc.
Calc.
Calc.
Max.
HH
HH
HH
Arkansas
-1.6%
-1.9%
13.3%
27.4%
-3.4%
-6.6%
-6.3%
-5.5%
-6.3%

Calc.
Calc.
HH
HH
HH
Calc.
Calc.
HH
Calc.
California
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
-6.3%
-8.4%
-6.3%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Colorado
-1.6%
29.2%
70.9%
89.0%
58.9%
-6.6%
-12.0%
-12.6%
-5.7%

Calc.
HH
HH
HH
HH
Calc.
Max.
Max.
HH
Connecticut
-1.6%
-1.9%
-3.9%
1.6%
-5.6%
13.1%
11.2%
-8.4%
-6.3%

Calc.
Calc.
Calc.
HH
Calc.
HH
HH
Calc.
Calc.
Delaware
6.7%
15.6%
15.0%
17.1%
19.0%
74.4%
-6.3%
6.7%
-6.3%

Min.
Min.
Min.
Min.
Min.
Min.
Calc.
HH
Calc.
District of
Columbia
-1.6%
-1.9%
-3.9%
-4.7%
-5.9%
-6.6%
81.4%
51.3%
-0.6%

Calc.
Calc.
Calc.
Calc.
Max.
Calc.
HH
HH
HH
Florida
-1.6%
-1.9%
-3.9%
-4.7%
-0.3%
14.1%
-6.3%
-8.4%
8.9%

Calc.
Calc.
Calc.
Calc.
HH
HH
Calc.
Calc.
HH
Georgia
-1.6%
-1.9%
45.9%
29.9%
105.6%
77.6%
37.1%
9.4%
32.8%

Calc.
Calc.
HH
HH
HH
HH
HH
HH
HH
Hawaii
-1.6%
5.4%
23.7%
34.6%
52.2%
1.6%
-33.8%
-36.6%
-6.3%

Calc.
Min.
Min.
Min.
Min.
Min.
Max.
Max.
Calc.
Idaho
12.0%
28.3%
21.0%
15.2%
26.8%
20.7%
-6.3%
-2.0%
14.3%

HH
HH
HH
HH
Min.
Min.
Calc.
HH
Min.
Il inois
-1.6%
4.5%
-3.9%
-4.7%
-5.6%
-6.6%
10.1%
-8.4%
-6.3%

Calc.
HH
Calc.
Calc.
Calc.
Calc.
HH
Calc.
Calc.
Indiana
2.6%
-1.9%
4.0%
21.7%
16.5%
-6.6%
-6.3%
8.6%
47.2%

HH
Calc.
HH
HH
HH
Calc.
Calc.
HH
HH
Iowa
-1.6%
-1.9%
-3.9%
-2.3%
11.3%
7.7%
-30.3%
-8.4%
-6.3%

Calc.
Calc.
Calc.
HH
HH
HH
Max.
Calc.
Calc.
Kansas
-1.6%
-1.9%
0.5%
-4.7%
13.4%
1.0%
-6.3%
-4.1%
24.8%

Calc.
Calc.
HH
Calc.
HH
HH
Calc.
HH
HH
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Analysis of Adult Activities Allotment Formula under WIOA

State
2015
2016
2017
2018
2019
2020
2021
2022
2023
Kentucky
-1.6%
8.8%
-3.9%
-4.7%
-5.6%
-6.6%
3.1%
24.8%
-6.3%

Calc.
HH
Calc.
Calc.
Calc.
Calc.
HH
HH
Calc.
Louisiana
-1.6%
-18.9%
-3.9%
-4.7%
-5.6%
-6.6%
9.0%
9.9%
-6.3%

Calc.
Max.
Calc.
Calc.
Calc.
Calc.
HH
HH
Calc.
Maine
-1.6%
-1.9%
30.4%
25.2%
53.8%
-4.4%
-6.3%
-8.4%
-6.3%

Calc.
Calc.
HH
HH
HH
Min.
Calc.
Calc.
Calc.
Maryland
-1.6%
-1.9%
-3.9%
-2.7%
-5.6%
-6.5%
3.8%
-8.4%
-9.5%

Calc.
Calc.
Calc.
HH
Calc.
HH
HH
Calc.
Max.
Massachusetts
-1.6%
0.5%
-1.1%
30.7%
-2.9%
-1.0%
-32.5%
-15.4%
-6.3%

Calc.
HH
HH
HH
HH
HH
Max.
Max.
Calc.
Michigan
-1.6%
-2.2%
-1.5%
-4.7%
-5.6%
-6.6%
-6.3%
26.3%
-6.3%

Calc.
HH
HH
Calc.
Calc.
Calc.
Calc.
HH
Calc.
Minnesota
8.9%
14.2%
-3.9%
-4.7%
3.0%
18.8%
-7.7%
-8.4%
59.5%

HH
HH
Calc.
Calc.
HH
HH
Max.
Calc.
HH
Mississippi
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
18.2%
27.3%
1.7%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
HH
HH
Missouri
-1.6%
-1.9%
6.3%
-4.7%
6.0%
5.1%
-6.3%
-4.9%
-6.3%

Calc.
Calc.
HH
Calc.
HH
HH
Calc.
HH
Calc.
Montana
-1.6%
10.3%
1.9%
19.1%
-2.3%
-0.9%
-1.0%
34.5%
53.8%

Calc.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Nebraska
8.1%
23.3%
10.5%
3.4%
-5.6%
-6.6%
-6.3%
22.4%
30.9%

Min.
Min.
Min.
Min.
Calc.
Calc.
Calc.
HH
Min.
Nevada
-1.6%
-1.9%
-3.9%
6.6%
-5.6%
-6.6%
-7.8%
-8.4%
-6.2%

Calc.
Calc.
Calc.
HH
Calc.
Calc.
Max.
Calc.
HH
New Hampshire
15.1%
68.1%
133.7%
151.5%
160.7%
163.0%
-6.6%
78.5%
134.3%

Min.
Min.
Min.
Min.
Min.
Min.
Max.
HH
HH
New Jersey
1.4%
-1.9%
8.4%
3.4%
-5.6%
21.2%
-6.3%
-8.4%
-6.3%

HH
Calc.
HH
HH
Calc.
HH
Calc.
Calc.
Calc.
New Mexico
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
36.2%
0.9%
-6.3%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
HH
Calc.
New York
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-1.1%
-6.3%
-8.4%
-6.3%

Calc.
Calc.
Calc.
Calc.
Calc.
HH
Calc.
Calc.
Calc.
North Carolina
5.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
-6.3%
-8.4%
-6.3%

HH
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
North Dakota
313.2%
307.8%
311.1%
347.8%
326.1%
334.5%
274.0%
107.1%
165.7%

Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
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link to page 58 Analysis of Adult Activities Allotment Formula under WIOA

State
2015
2016
2017
2018
2019
2020
2021
2022
2023
Ohio
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
9.5%
32.7%
-4.8%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
HH
HH
Oklahoma
-1.6%
2.7%
-3.9%
-4.7%
2.1%
52.8%
-6.3%
4.3%
29.2%

Calc.
HH
Calc.
Calc.
HH
HH
Calc.
HH
HH
Oregon
-1.6%
-1.9%
-3.5%
21.6%
1.7%
-6.6%
-6.3%
-8.4%
-3.2%

Calc.
Calc.
HH
HH
HH
Calc.
Calc.
Calc.
HH
Pennsylvania
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
-6.3%
-4.8%
-6.3%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
Calc.
Puerto Rico
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
69.3%
72.9%
12.4%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
HH
HH
Rhode Island
-1.6%
2.3%
-3.9%
0.9%
-5.6%
9.8%
-6.3%
-8.4%
7.8%

Calc.
HH
Calc.
HH
Calc.
HH
Calc.
Calc.
HH
South Carolina
7.1%
-1.9%
-3.9%
23.4%
8.2%
28.2%
2.9%
-7.7%
-6.3%

HH
Calc.
Calc.
HH
HH
HH
HH
HH
Calc.
South Dakota
127.4%
129.4%
172.3%
174.2%
98.7%
133.4%
26.0%
154.4%
186.7%

Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Tennessee
-1.6%
-1.9%
-2.3%
-4.7%
28.4%
-0.5%
-6.3%
-8.4%
-6.3%

Calc.
Calc.
HH
Calc.
HH
HH
Calc.
Calc.
Calc.
Texas
-1.6%
0.6%
-3.9%
-4.7%
-5.0%
0.7%
-6.3%
-8.4%
-6.3%

Calc.
HH
Calc.
Calc.
HH
HH
Calc.
Calc.
Max.
Utah
32.9%
23.4%
1.2%
-4.7%
-5.6%
-2.8%
-13.5%
2.8%
24.3%

HH
HH
HH
Calc.
Calc.
HH
Max.
HH
HH
Vermont
247.9%
241.9%
266.7%
320.4%
393.3%
393.3%
63.6%
243.6%
267.8%

Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Virginia
-1.6%
-1.9%
12.6%
0.7%
5.7%
22.1%
-6.3%
-8.4%
28.5%

Calc.
Calc.
HH
HH
HH
HH
Calc.
Calc.
HH
Washington
-1.6%
-1.9%
-3.9%
-4.7%
-5.6%
-6.6%
0.4%
4.3%
0.8%

Calc.
Calc.
Calc.
Calc.
Calc.
Calc.
HH
HH
HH
West Virginia
-1.6%
-1.9%
-3.9%
7.1%
-5.6%
-6.6%
15.1%
28.2%
27.1%

Calc.
Calc.
Calc.
HH
Calc.
Calc.
HH
HH
HH
Wisconsin
-1.6%
0.9%
-3.0%
22.9%
30.8%
11.9%
-22.4%
-1.7%
14.9%

Calc.
HH
HH
HH
HH
HH
Max.
HH
HH
Wyoming
274.5%
256.4%
74.5%
51.0%
115.3%
103.6%
120.7%
97.8%
147.8%

Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Min.
Source: See Appendix B for data sources and main text for ful methodology.
Min. = Minimum Grant
HH = Hold Harmless
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Analysis of Adult Activities Allotment Formula under WIOA

Max. = Maximum Grant
Calc. = Calculated Grant, not subject to adjustment provisions
Notes: Numbers in each cell reflect the difference between each state’s share of Adult Activities funding and its
share of the formula factors, divided by its share of the formula factors. Positive numbers indicate instances in
which the state’s relative share of grant funding was greater than its relative share of the formula factors.
Negative numbers indicate instances in which the state’s relative share of the formula factors was less than its
relative share of the formula factors. For example, in PY2023 Virginia’s share of Adult Activities funding was
1.545% and its share of the formula factors was 1.202%, and (1.545 – 1.202)/1.202 = the 28.5% in the table.

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Table C-6. Impact of Adjustment Provisions, By Limiting Provision and Program Year
A
B
C
D
E
F
Program
Year/
Relative
Relative
Difference
Adjustment
Number of
Share of
Share of
(Percentage
Difference
Provisions
States
Factors
Funding
Point)a
(%)b
2015

Calculated
37
88.1%
86.7%
-1.4
-1.6%
Hold Harmless
7
10.7%
11.3%
0.6
5.5%
Maximum Grant
–
–
–
–
–
Minimum Grant
8
1.2%
2.0%
0.8
67.3%
2016

Calculated
30
74.8%
73.3%
-1.5
-1.9%
Hold Harmless
12
22.0%
22.9%
0.9
4.1%
Maximum Grant
1
1.8%
1.5%
-0.3
-18.9%
Minimum Grant
9
1.3%
2.3%
0.9
67.3%
2017

Calculated
27
77.2%
74.3%
-3.0
-3.9%
Hold Harmless
16
21.4%
23.5%
2.1
9.8%
Maximum Grant
–
–
–
–
–
Minimum Grant
9
1.4%
2.3%
0.9
65.2%
2018

Calculated
25
80.7%
76.9%
-3.8
-4.7%
Hold Harmless
18
17.9%
20.8%
2.9
16.1%
Maximum Grant
–
–
–
–
–
Minimum Grant
9
1.3%
2.3%
0.9
70.2%
2019

Calculated
22
67.9%
64.1%
-3.8
-5.6%
Hold Harmless
19
29.7%
32.7%
3.0
10.0%
Maximum Grant
2
1.1%
0.9%
-0.1
-13.4%
Minimum Grant
9
1.3%
2.3%
1.0
76.5%
2020

Calculated
21
59.9%
56.0%
-3.9
-6.6%
Hold Harmless
20
34.4%
37.8%
3.4
9.7%
Maximum Grant
1
4.1%
3.8%
-0.4
-8.9%
Minimum Grant
10
1.5%
2.5%
1.0
62.0%
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A
B
C
D
E
F
Program
Year/
Relative
Relative
Difference
Adjustment
Number of
Share of
Share of
(Percentage
Difference
Provisions
States
Factors
Funding
Point)a
(%)b
2021

Calculated
21
62.3%
58.4%
-3.9
-6.3%
Hold Harmless
17
27.3%
32.7%
5.3
19.4%
Maximum Grant
9
9.5%
7.7%
-1.8
-19.3%
Minimum Grant
5
0.8%
1.3%
0.5
59.5%
2022

Calculated
17
62.3%
57.1%
-5.2
-8.4%
Hold Harmless
27
32.6%
37.9%
5.4
16.4%
Maximum Grant
3
4.6%
3.8%
-0.8
-17.5%
Minimum Grant
5
0.6%
1.3%
0.6
106.9%
2023

Calculated
21
69.3%
64.9%
-4.4
-6.3%
Hold Harmless
23
27.6%
31.4%
3.8
13.6%
Maximum Grant
1
2.2%
2.0%
-0.2
-9.5%
Minimum Grant
7
0.9%
1.8%
0.8
89.7%
Source: See Appendix B for data sources and main text for ful methodology.
Notes: Sum of elements may not be 100% due to rounding. Differences were calculated on the basis of
unrounded numbers.
a. Column E is Column D minus Column C. Column E indicates the percentage of total funding from the
program year that was reallocated on the basis of the specified adjustment provision. For example, in
PY2023 3.8% of total grant funding was reallocated to states under the hold harmless provisions, increasing
these states’ share of the funding from 27.6% to 31.4%.
b. Column F is Column E divided by Column C. Column F indicates the percentage change in the group’s
share of funding relative to its initial allotment. For example, in PY2023 the seven states that qualified for
the minimum grant received a total share of funding that was almost 90% greater than their col ective share
of the formula factors.

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66

Analysis of Adult Activities Allotment Formula under WIOA

Author Information

Benjamin Collins

Analyst in Labor Policy

Acknowledgments
John Gorman, CRS Research Assistant, created the graphics in this report. Isobel Sorenson, CRS Research
Assistant, provided assistance in the verification of data in this report. Tom Krolik at the Bureau of Labor
Statistics provided technical assistance related to areas of substantial unemployment and associated formula
factors.

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 not 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
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its entirety without permission from CRS. However, as a CRS Report may include copyrighted images or
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