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S O CIA L P O L ICY R ESEA R C H

A S S O CIATE S

Prepared for: 

U.S. Dept. of Labor/ETA 

200 Constitution Ave., N.W. 

Room N-5637 

Washington, D.C. 20210 

Contract No. G-7713-9-00-87-30 

Project No. 1375

Analysis of the WIA Substate 

Allocation Formulas 

Final Report 

October 10, 2003 

Prepared by: 

Social Policy Research Associates 
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DISCLAIMER 

This report was prepared for the U.S. Department of Labor, Employment and Training 

Administration, Office of Policy Development and Research, by Social Policy Research 

Associates. Since contractors conducting research and evaluation projects under government 

sponsorship are encouraged to express their own judgment freely, this report does not necessarily 

represent the official opinion or policy of the U.S. Department of Labor. 
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Acknowledgements and Attributions 

This report was conducted by Social Policy Research Associates. The project was directed by 

Andrew Wiegand of SPR, who also wrote this Final Report. 

We wish to acknowledge the contributions and support of DOL staff, especially our project 

officers, Alberta Baker and Dan Ryan. Their advice and steadfast support are much appreciated. 

Thanks, too, to the invaluable contributions of the State workforce staff, whose responses to the 

evaluation survey are a critical component of this report. 
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CONTENTS

EXECUTIVE SUMMARY..........................................................................................ES-1

I. INTRODUCTION ......................................................................................................I-1

CURRENT ALLOCATION FORMULAS .........................................................................I-3

Comparison with Allocation Formulas under JTPA.....................................I-7

ISSUES IN THE USE OF THE ALLOCATION FORMULAS...............................................I-8

Financial and Organizational Stability ..........................................................I-8

Inequities Inherent in Formula Design ........................................................I-11

Data Quality and Access..............................................................................I-16

Alternatives to the Standard Formulas........................................................I-17

Questions Concerning Substate Dislocated Worker 

Allocations...................................................................................................I-19

Defining Need..............................................................................................I-21

PROPOSED NEW FORMULAS ...................................................................................I-24

Adult and Dislocated Worker Formulas......................................................I-24

Youth Formula .............................................................................................I-27

CONCLUSION ..........................................................................................................I-29

II. STATES’ CURRENT ALLOCATION FORMULAS ..............................................II-1

ADULT AND YOUTH ALLOCATIONS .........................................................................II-2

Discretionary Formulas................................................................................II-2

Statewide Activities......................................................................................II-8

Perceptions of Allocation Formulas’ Suitability..........................................II-8

Fluctuations in Funding Under Current Formulas......................................II-13

DISLOCATED WORKERS.........................................................................................II-17

Allocation Formulas ...................................................................................II-19

Funding Stability and Recapture of Funds .................................................II-22

SINGLE WORKFORCE AREA STATES......................................................................II-23

SUMMARY.............................................................................................................II-24

III. COMPARISONS OF CURRENT ALLOCATIONS WITH 

ALTERNATIVE MODELS ................................................................................... III-1

REPLICATING CURRENT ALLOCATION FORMULAS ................................................ III-2

Excess Unemployed.................................................................................... III-4

Areas of Substantial Unemployment .......................................................... III-4

Disadvantaged Adults/Youth...................................................................... III-6
ii

Factors Used in Current Discretionary Formulas....................................... III-7

COMPARISONS OF ALTERNATIVES TO CURRENT ADULT AND YOUTH 

FORMULAS ............................................................................................................ III-8

Applying Discretionary Formulas to All States.......................................... III-9

ADDITIONAL ALTERNATIVE FORMULAS.............................................................. III-25

Adult Formulas......................................................................................... III-25

Youth Formulas ........................................................................................ III-27

DISLOCATED WORKER FORMULA........................................................................ III-28

IMPLEMENTING PROPOSED FORMULAS................................................................ III-32

Adult Allocations...................................................................................... III-32

Youth Allocations..................................................................................... III-42

MECHANISMS TO PROMOTE ORGANIZATIONAL AND FINANCIAL 

STABILITY ........................................................................................................... III-46

Hold Harmless .......................................................................................... III-51

The Impact of Replacing Factors Causing Instability .............................. III-56

Incorporating Carry-In Funds to Subsequent Allocations ........................ III-60

CONCLUSION ....................................................................................................... III-65

IV. SUMMARY ...........................................................................................................IV-1

BACKGROUND .......................................................................................................IV-1

STATES’ CURRENT ALLOCATIONS.........................................................................IV-3

COMPARISONS WITH CURRENT ALLOCATIONS ......................................................IV-4

Applying Discretionary Formulas ..............................................................IV-4

Dislocated Workers....................................................................................IV-6

Applying Proposed Formulas.....................................................................IV-8

Mechanisms to Promote Organizational and Financial 

Stability.....................................................................................................IV-10

CONCLUSION AND RECOMMENDATIONS..............................................................IV-12

APPENDIX A: FACTORS INCLUDED IN SIMULATED FORMULAS .................................. A-1
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EXECUTIVE SUMMARY

In this report, we examine the formulas that states use to allocate funds to local 

workforce investment areas under the Workforce Investment Act (WIA) Title I adult, 

dislocated worker, and youth programs. 

In Chapter I we identify several key issues of concern in using the formulas. These 

issues, which represent a summation of several earlier research efforts as well as concerns 

expressed by states and local areas as part of this study, include concerns about the 

organizational and financial stability of local areas under WIA, inequities in targeting 

funds to meet the goals of the legislation, data quality and access, and the use of 

discretionary formulas. Many of these issues arose under JTPA, and remain concerns 

under WIA, because the formulas are actually quite similar. 

STATES’ CURRENT ALLOCATIONS

In Chapter II, we describe the results from a survey of all states concerning their 

current allocation formulas under WIA for the adult, youth, and dislocated worker 

programs. We received completed surveys from 44 states, plus Washington, D.C and 

Puerto Rico. Results showed that very few states actually chose to use a discretionary 

formula for their adult and youth programs, even though WIA allows states to do so for 

up to 30% of their allocated funds. Those four states that did adopt discretionary 

formulas did so for both their adult and youth programs, and typically used indices of 

excess unemployment, or unemployment above the state average, as their discretionary 

measures. 

Most states were primarily concerned that the formula used to allocate funds under 

WIA target those funds to areas with the greatest need. Though the survey results would 

suggest that states’ concern for targeting need is paramount, the fact that only four states 

opted to adopt a discretionary formula in allocating funds for the adult and youth program 

suggests that perhaps stability in funding among local areas may instead be the critical 

factor in this allocation. In an effort to ensure this stability, the vast majority of states 

invoked their authority to implement a hold harmless provision in substate funding, even 

though WIA did not mandate one until two years after WIA implementation.

Substate funding for the dislocated worker program relied primarily on several 

factors, including most prominently unemployment, unemployment concentrations, and 
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long-term unemployment, which are similar to the factors used in inter-state allocations. 

Several other factors often were used in substate allocations, though typically these other 

factors received less weight in the allocation formula. 

Finally, eight of the states in our sample were single workforce area states. Of 

these, three reported allocating funds to substate areas, often using the standard WIA 

adult and youth formulas, or slight variants of them, as the mechanism by which to make 

these allocations. The remaining five states did not allocate funds to substate areas and, 

instead, directed funds as needed from the state level.

COMPARISONS WITH CURRENT ALLOCATIONS

In Chapter 3, we describe the results of several simulations conducted to examine 

both how allocations are currently made and how the amounts allocated to local areas 

would change under several alternative formulas. First, we describe the process by which 

we simulated current allocations, obtaining data from the 2000 Census and several years 

of data from the Local Area Unemployment Statistics (LAUS) and ES_202 programs. 

Using these data, we were able to mimic current allocations by either applying the 

standard formula, for those states who were utilizing only this formula for adult and 

youth funding, or a combination of the standard and discretionary formulas, for those 

states that had adopted a discretionary formula, as well as the dislocated worker formula 

identified by states in their survey.

Once we simulated the current allocations, we then simulated several alternative 

allocation formulas to examine how the allocations made as a result of these formulas 

differ from the allocations made under the current formulas. Among the alternatives, first 

we simulated the four discretionary formulas adopted by states. Next, we simulate two 

alternative formulas for the dislocated worker program, including adopting the standard 

adult formula and adopting the inter-state dislocated worker formula to make substate 

allocations. Following this, we examine several alternative using formulas that have been 

proposed as part of WIA reauthorization.

Although none of the formulas examined as part of this report can be said to be the 

ideal one, because there are many competing interests and concerns involved in the 

allocation of funds under WIA, we can identify those formulas that would be best suited 

to achieve specific goals. For example, although any change to the allocation formulas is 

bound to create significant shifts in funding at least in the first year, these shifts will 

benefit certain local areas more than others. To the extent that those which benefit have 
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more need for WIA funds, as a result of their greater demand, greater numbers of 

unemployed or disadvantaged individuals, or greater capacity to serve this demand, then 

these shifts could be considered a temporary but necessary challenge to overcome in the 

effort to target greater proportions of funds to those areas better suited to receive them. 

Thus, changes, even dramatic ones, may not be a poor result given that the current 

allocations do not target the eligible population perfectly.

Among the alternatives considered in this report, we have seen that several of the 

discretionary formulas currently in use by four states would have the impact, if applied 

across all states, of targeting greater proportions of funds to those local areas with greater 

populations and LWIAs that are primarily in cities, while areas with smaller populations, 

and those in rural or suburban areas, would experience losses. The formulas also tended 

to shift funds away from areas of low poverty toward areas with moderate, but not very 

high, poverty levels. Thus, these formulas may be well suited to targeting funds toward 

areas that have relatively greater numbers of “universal” customers, because the funds are 

targeted toward areas with greater populations. Further, these formulas tend to target 

funds to areas that have, in PY 2001, served greater numbers of customers per WIA 

dollar. Additional funds targeted toward these areas thus would be one way of directing 

resources to areas in which there is a clear demand.

In contrast, an alternative formula developed by one state for their youth program, 

is especially well suited to targeting areas with high proportions of youth poverty, 

because it utilizes poverty above the state average as the discretionary factor, and 

therefore allocates 30% of the state’s available funds based on this factor. Given that the 

vast majority of youth must be disadvantaged in order to be eligible for WIA, this is an 

especially appealing result. Florida’s formula, which relies on unemployment in excess 

of 1.25% also has the effect of targeting greater proportions of funds toward those local 

areas with the highest rates of poverty. 

As part of this study, we examined the impact of implementing formulas that have 

been proposed as part of WIA reauthorization. These formulas would significantly 

overhaul the current allocation formulas by merging the adult and dislocated worker 

funding streams together with Wagner-Peyser funds, and by removing two of the three 

factors currently in place for allocating youth funds.

The general result of implementing the proposed formula for adults, at least at the 

substate level, would be that a sizable percentage of local areas would experience 
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significant shifts in allocations, and areas with larger populations would tend to benefit, 

while areas with smaller populations would experience losses. Further, LWIAs that are 

primarily in cities would experience increases, while those in suburbs and rural areas 

would experience losses. Finally, the proposed formula would have the somewhat 

surprising effect of targeting more funds to areas with lower poverty rates, and directing a 

lower proportion of funds to those areas with higher rates of poverty.

An alternative to this proposed formula is to use this formula to allocate 85% of 

adult funds, and use a discretionary factor to allocate the remaining 15%. This is 

consistent with the proposed legislation, and makes somewhat of a difference in the 

actual allocations obtained. For example, when using unemployment in ASUs as a 

discretionary factor, relatively fewer areas experience dramatic changes in their 

allocations. Further, LWIAs with different population sizes are somewhat less

differentially impacted and, although those in cities do tend to benefit under this formula, 

those in rural and suburban areas do not experience dramatic reductions. Further, this 

formula targets areas in the middle two quartiles in terms of their poverty rates, while 

directing funds away from areas that have extremely high or low poverty rates. A second 

alternative, using unemployment above the state average, creates somewhat greater 

fluctuation from the current allocations, and directs funds toward areas of high population

and those with high rates of poverty, but away from areas that have served higher 

numbers of customers per dollar.

Because two of the three current factors are replaced, the proposed youth formula 

creates substantial shifts in allocations, with nearly one-third of all LWIAs experiencing 

shifts of more than 50%. Areas with greater populations, and those in cities and suburbs, 

tend to benefit the most, while smaller areas and rural areas experience substantial 

reductions in their allocations. Interestingly, the formula would tend to shift funds away 

from areas that have high poverty rates. This does not mean it shifts funds away from 

areas with the greatest numbers of youth in poverty, but that areas that have high rates of 

poverty do not fare well under this formula. This result is reversed when poverty above 

the state average is used as the discretionary factor on which 20% of the allocation is 

based. Doing so targets more funds to areas with the highest poverty rates. 

We also examined several ways in which the formulas could be used to reduce 

fluctuation in local areas’ allocations, and the results suggest that there are several 

plausible ways to do so. Hold-harmless provisions are quite effective in limiting the 

losses areas will experience from year-to-year, but they have the potential downside of 
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artificially reducing some areas’ allocations simply because others’ allocations are lower 

than their previous levels.

A second means by which one could reduce fluctuation is to eliminate the threshold 

measures currently in place, and replace them with the conceptually similar measure of 

the absolute number of unemployed individuals. Because the threshold causes instability 

due to areas just meeting or just falling below the threshold level, simply replacing these 

measures with an absolute measure significantly reduces the fluctuation in yearly 

allocations experienced by local areas. Similarly, by extending the reference period (i.e., 

taking the average of two or more years of the total number of unemployed, rather than a 

single year measure), one can also substantially reduce fluctuation.

Additionally, we examined the extent to which incorporating carry-in funds to a 

subsequent year’s allocation pool would help to minimize fluctuations. Although this did 

serve to minimize the theoretical “gains” experienced by areas that had carry-in funds, 

and redistribute those funds to areas that did not have trouble spending their funds, there 

was substantial concern about this, because areas often have obligated these funds even 

though they have not been spent by the end of the program year. We therefore 

recommend using great caution, and providing substantial notice of any intent to use 

carry-in funds in this way.

Finally, we offer several specific recommendations for DOL’s consideration as 

they contemplate modifying the formulas used to allocate funds to substate areas under 

WIA. Among these recommendations are to:

• Include factors that measure raw counts of individuals. 

• Include measures that target the eligible population, specifically: 

− For the adult program, a measure of the number of individuals in 

the labor force, and measures of underemployment. 

− For the youth program, a measure of the number of youth with 

any of the barriers that are required for eligibility for WIA 

services. 

• Include measures of growth industries or in-demand jobs as an allocation 

factor. 

• Eliminate the use of threshold measures in the substate allocation 

formulas. 

• To promote organizational and administrative stability, continue use of 

hold harmless provisions.
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• Exercise great caution if including carry-in funds as a factor in 

subsequent allocations. 

• If the adult and dislocated worker streams of funding are to be merged, 

enable states to retain a higher proportion of funds to be distributed based 

on dislocations as they occur. 

• Support efforts to develop new data sources that would provide data to be 

used for substate allocations more quickly. 
I-1 Social Policy Research Associates

I. INTRODUCTION

This is the final report of a two-year study of the formulas that states use to allocate 

funds to local workforce investment areas under the Workforce Investment Act (WIA) 

Title I adult, dislocated worker, and youth programs. The allocation formulas used under 

WIA, and, before it, under the Job Training Partnership Act (JTPA), have been the 

subject of much discussion and scrutiny over the last several decades, largely because 

small alterations in the formula can produce relatively large shifts in the allocation of 

funds, and because the amount of funds at stake is very large. For example, in PY 2002 

alone, nearly $3.3 billion was given to states to operate these three programs under WIA, 

with the largest allocation (more than $1.2 billion) made to the dislocated worker 

program, an additional $1.1 billion allocated for WIA youth programs, and more than 

$900 million allocated for WIA adult services. 

A further reason for the heavy interest in the allocation of these funds is that 

changes in the economic conditions of local areas can produce shifts in the level of need 

for funds in particular areas. Ideally, the allocation formulas would be able to capture 

these shifts and thus target funds to the areas in greatest need. But over the course of 

several decades, questions have been raised about the adequacy of the formulas to reach 

this goal, and this study has examined the degree to which the current allocation 

formulas, and several potential variants of them, are able to create relative stability in 

local area funding (thereby enabling areas to offer consistent levels of services from yearto-year) while also shifting funds (based on economic characteristics) to better target 

changes in the need for these funds. 

Attempting to achieve these somewhat competing objectives can be a difficult 

balancing act. On the one hand, DOL, in its role of providing overall policy guidance 

and direction to the workforce development system, has a clear interest in ensuring that 

funds are allocated across states and local areas in a way that ensures that proportionally 

more funds are made available to areas with demonstrably greater needs. Despite this 

interest, however, it is not always clear what constitutes a need for areas who receive the 

funding. For example, it is plausible to identify an area’s level of need by identifying the 

number of individuals within that area who are unemployed (at a given time, or over a 

given period of time, such as a calendar year). In such cases, it would seem quite logical 

to allocate greater proportions of the available funds to those designees with higher 

numbers of unemployed individuals. 
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On the other hand, changes in the number of unemployed individuals from time to 

time, or from period to period, could cause significant fluctuations in the specific amount 

of funds allocated to each local area from one program year to the next. Clearly, any 

substantial deviations in funding levels from one year to the next will be highly 

contentious, as, if some areas stand to benefit by receiving greater levels of funding, 

others will lose funding commensurately and can thereby be expected to argue 

vociferously against any change. Further, areas would argue so vociferously because 

some level of consistency is essential to be able to provide a specific level of services 

across program years. Without such stability in funding, local areas could not be certain 

whether they will have the staff necessary, or other resources available to offer to 

customers and, thus, there would be significant hesitance to offer a full array of services 

within any given program year. Indeed, these competing dynamics were one important 

reason why the reform of the workforce development system brought about by WIA had 

been delayed for so long, as the tension between attempting to target funds to areas with 

greater needs, determining how one could best measure these “needs,” and ensuring 

enough consistency in funding levels from one year to the next to enable areas to provide 

a stable set of services to their customers could best be resolved. Examining the impact 

of the allocation formulas that emerged as a result of these deliberations, therefore, 

represents an important undertaking that should provide critical additional information on 

how states are actually allocating their funds, the degree to which the formulas target 

levels of need and create stability among funding levels, and whether other potential 

formulas might better meet one or both of these demands. 

This report is divided into four chapters, each of which addresses discrete tasks 

conducted as part of this evaluation. In the remainder of this chapter, we describe the 

current allocation formulas under WIA and discuss the key issues involved in each of 

these formulas. Throughout the discussion of key issues, we synthesize the results of 

previous efforts that have been made to study the allocation formulas, first under JTPA 

and then, for inter-state allocations only, under WIA. Also, we discuss some of the 

differences between inter-state formulas used to allocate WIA funds, and substate 

allocation formulas, and provide an overview of some of the critical issues in allocating 

funds within states. Finally, we provide an overview of alternative formulas that have 

been proposed in pending legislation. These formulas would significantly alter both 

inter- and substate funding under WIA.
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Chapter II presents an overview of the findings from a state-level survey conducted 

as part of this project. This survey was mailed to all 50 states, plus Puerto Rico and 

Washington, D.C., and focused on how states are currently allocating their WIA funds, 

whether they feel the formulas accurately target the levels of need within their local 

areas, and the extent to which they considered alternative formulas to better meet their

desire to target funds based on specific indicators of need for WIA allocation. 

In Chapter III we present the results from a wide variety of simulations of different 

funding allocation strategies. We begin this chapter by simulating the current funding

mechanisms, using information gleaned from the state survey. Subsequent to this, we 

present a series of alternative formula allocations and examine how funding levels would 

change as a result of implementing these alternatives. For each alternative, we consider 

the extent to which it would more accurately target the levels of need (defined in various 

ways to capture a range of possible “needs”), the degree to which certain types of local 

areas receive greater or lesser shares of funding, and whether the alternative has a 

significant impact upon the year-to-year stability of local areas by dramatically altering 

their funding levels. 

Finally, in Chapter IV, we provide a summary of the earlier chapters, and discuss 

how proposed changes to the formulas would impact funding levels among local areas. 

Additionally, we offer several suggestions concerning how formulas could be established 

to ensure greater targeting of needs within local areas, as well as greater stability of 

funding across program years.

CURRENT ALLOCATION FORMULAS 

WIA specifies the formulas to be used in allocating funds across states for each of 

the Title I adult, dislocated worker, and youth programs. In each program, three separate 

factors are taken into consideration when allocating funds to the states, although the 

specific three factors vary somewhat by program. For adults and youth, two-thirds of the 

allocation formulas are identical and are based upon unemployment factors using two 

separate threshold levels, while the remaining third is based on the relative number of 

disadvantaged adults, or youth, respectively. Funding to states for the dislocated worker 

program is currently allocated based equally upon three factors, including the relative 

number of unemployed individuals, the number of excess unemployed individuals, and 

the number of long-term unemployed individuals within each state. A separate study was 

funded by DOL to examine the state-level allocation formulas, and thus this level of 

allocation is not part of the current study. The results from this separate study, as well as 
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a more detailed description of the inter-state allocation formulas, are described where 

relevant throughout chapter. In the remainder of this section, we discuss only the 

substate formulas used to allocate adult, youth, and dislocated funds to local workforce 

areas, which is the central focus of this study. 

These within-state allocations, like the inter-state allocations, typically are made 

according to a formula set out explicitly in WIA. With respect to the allocation of adult 

funds, WIA stipulates that the Governor of a state may reserve an amount of not more 

than 15% of the state’s allocation for statewide workforce investment activities. The 

remainder of the adult funds is to be allocated within each state according to the 

following provisions:

• Use of the Standard Formula. WIA requires that the Governor use the 

following formula for allocating funds across local areas:

− One-third of the funds are to be allocated on the basis of the 

relative number of unemployed individuals in areas of substantial 

unemployment, compared to the total number of unemployed 

individuals in areas of substantial unemployment (ASU).1

− One-third of the funds are to be allocated on the basis of the 

relative excess number of unemployed individuals, compared to 

the total excess number of unemployed individuals.2

− One-third of the funds are to be allocated on the basis of the 

relative number of disadvantaged adults, compared to the total 

number of disadvantaged adults in the state.

3

• Use of Discretionary Allocation. In lieu of making the allocation using 

the standard formula, the state may distribute:

 

1 An area of substantial unemployment (ASU) refers to 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% for the most recent 12 months. Although entire local workforce areas, or even states, can be an 

ASU, ASUs also can be smaller than LWIAs and, thus, are not restricted exclusively to the LWIA 

boundaries. 

2 The “excess number” represents the higher of the number of unemployed individuals in excess of 

4.5%, or the number in excess of 4.5% in areas of substantial unemployment.

3 For states with at least one local area that is designated as a rural concentrated employment 

program grant recipient, the allotment is based on the higher of the number of adults in families with an 

income below the low-income level in such areas, or the number of disadvantaged adults. Disadvantaged 

adults represent those 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 the poverty line or 70% of the lower 

living standard income level. To the extent practical, college students and members of the Armed Forces 

are to be excluded from the determination of the number of disadvantaged adults.
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− At least 70% of the funds using the standard formula.

− Up to 30% of the funds using an alternative formula that relates to 

the excess poverty or excess unemployment (above the state 

average) in urban, rural, and suburban local areas, so long as the 

formula was developed by the state Board and approved by DOL 

as part of the state plan.

• Hold-harmless Provisions. No local area shall receive an allocation 

percentage for a fiscal year that is less than 90% of the average allocation 

it received for the two preceding fiscal years. Although this provision 

takes effect only at the end of the second full fiscal year after the date the 

local area was designated under WIA, DOL issued a policy ruling that 

gave the Governors authority to use a hold-harmless during the first two 

years of WIA operations.4

• Transfer Provisions. Local areas may transfer up to 20% of their funds 

between the adult and dislocated worker programs, with the Governor’s 

approval.

Although there are no transfer provisions in the youth program, its allocation 

methodology is otherwise quite similar. For this program, Governors again may reserve 

an amount of the state’s total youth allocation of not more than 15% for statewide 

workforce investment activities. The remainder of the youth funds is to be allocated 

within each state according to the following provisions:

• Use of the Standard Formula. With the exception of the third subpart, the 

standard allocation formula is the same as that used for allocating adult 

funds. Thus, under the standard youth allocation formula:

− One-third of the funds are to be allocated on the basis of the 

relative number of unemployed individuals in areas of substantial 

unemployment, compared to the total number of unemployed 

individuals in areas of substantial unemployment. 

− One-third of the funds are to be allocated on the basis of the 

relative excess number of unemployed individuals, compared to 

the total excess number of unemployed individuals.

− One-third of the funds are to be allocated on the basis of the 

relative number of disadvantaged youth, compared to the total 

number of disadvantaged youth in the state.

 

4 Fully 33 of the states invoked the hold-harmless provision in their first year of WIA 

implementation, according to our state survey. 
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• Use of Discretionary Allocation. The discretionary allocation formula is 

also quite similar. According to WIA, in lieu of making the allocation 

using the standard formula, the state may distribute:

− At least 70% of the youth funds using the standard formula.

− Up to 30% of the youth funds using an alternative formula that 

relates to the excess youth poverty or excess unemployment 

(above the state average) in urban, rural, and suburban local areas, 

so long as the formula was developed by the state Board and 

approved by DOL as part of the state plan.

• Hold-harmless Provisions. As with the adult program, no local area shall 

receive an allocation percentage for a fiscal year that is less than 90% of 

the average allocation it received for the two preceding fiscal years. 

Again, while this provision takes effect only at the end of the second full 

fiscal year after the date the local area was designated under WIA, 

Governors have authority to use a hold-harmless during the first two 

years as well.5

Finally, within-state allocation in the dislocated worker program allows for far 

greater flexibility than that allowed under the adult or youth programs, largely because 

dislocations are less predictable in incidence than is the number of unemployed or 

disadvantaged adults and youth within a given state or local area. Although Governors 

may only hold up to 25% of the funds to be used for statewide rapid response activities, 

and an additional 15% for other statewide activities, there are no required formulas 

specified for governors to allocate the remaining funds under the dislocated worker 

program. Rather, WIA specifies that within-state allocations shall be made “based on the 

allocation formula prescribed by the Governor of the state.” This formula cannot be 

modified more than once in any given program year, and is to be based on “the most 

appropriate information available to the Governor...to address the state’s worker 

readjustment needs.”6 Included among the information to be used are:

• Unemployment data,

• Unemployment concentrations,

• Plant closing and mass layoff data,

• Declining industries data,

 

5 31 of the states invoked the hold-harmless provision for their youth allocations in the first year of 

WIA implementation, according to the state survey conducted as part of this study.

6 WIA Section 133b(2)B(i).
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• Farmer-rancher economic hardship data, and

• Long-term unemployment data.

• Other factors which may be added by the state.

Thus, the within-state formulas used under the dislocated worker program can vary 

substantially more than those used to allocate adult or youth funds, because states are 

given nearly entire discretion in their allocation decisions.

Comparison with Allocation Formulas under JTPA

The stipulations regarding the allocation of adult and youth funds are quite similar 

to those used under JTPA, with several noteworthy exceptions. First, JTPA did not allow 

a discretionary formula to be used for allocating any part of adult or youth funds across 

service delivery areas within the state. Thus, the standard allocation formula was used in 

all cases. Second, state set-asides were somewhat greater under JTPA than WIA. Third, 

through appropriation act riders and waivers in the last years of the program, JTPA 

allowed locals to transfer certain funds among all three of the adult, dislocated worker, 

and youth programs, while fund transfers into and out of the youth program are not 

allowed under WIA. Fourth, WIA, like JTPA, incorporates a hold-harmless provision to 

minimize year-to-year fluctuations in funding levels, but, according to the terms of the 

WIA legislation, this provision takes effect only after two full program years, although 

under a Governor’s authority it can be invoked earlier.7 The delay in the hold-harmless 

provision was intended to facilitate the transition from smaller SDAs to larger LWIAs by 

removing the potential effects of funding legacies from decisions on how local areas 

should be formed under WIA. However, the elimination of the hold-harmless provision 

after so many years of application may have a similar effect to a dam bursting, in that 

wild swings could be evident in funding to local areas within those states who do not opt 

to implement the provision early. However, even states that did not substantially change 

their substate structure could face potentially large changes in their local areas’ 

allocations unless the Governor invokes hold-harmless authority at the outset. As noted 

above, approximately 70% of all states reported that they invoked the hold-harmless 

authority even in the first year of WIA implementation, presumably as a means to limit 

the considerable funding fluctuation that would have occurred in its absence. 

 

7 For JTPA, the hold-harmless applied to 90% of the local area’s allocation in the previous fiscal 

year; under WIA, it is 90% of the local area’s average allocation in the previous two fiscal years. Thus, the 

hold-harmless provision under WIA is more conservative and, thereby, ensures greater funding stability 

than the provision under JTPA.
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Because of the similarity of the stipulations regarding the allocation of funds to 

local areas under both JTPA and WIA, many of the important and contentious issues that 

emerged under JTPA remain important under WIA. Consistent with the findings from an 

assessment of the funding allocation under JTPA,8 these issues and concerns can be 

generally classified into three broad categories: (1) financial and organizational stability, 

(2) inequities in targeting funds to meet the goals of the legislation that result from the 

formula specification and design, and (3) data quality and access. Additionally, new 

issues have arisen as a result of the differences in formulas specification between WIA 

and JTPA, including the use of discretionary formulas, questions about allocations for the 

dislocated worker program, and whether there is a uniform definition that one can use to 

identify areas of greater need or demand. Each of these categories, and the various 

questions they raise, are discussed below.

ISSUES IN THE USE OF THE ALLOCATION FORMULAS

Several key issues of concern have been raised, first under JTPA and subsequently 

under WIA, concerning the use of these specific formulas to allocate funds, both from the 

federal government to the states and, in turn, from the states to their local areas. 

Financial and Organizational Stability

Among the most prominent issues raised by the allocation formulas is their ability 

to hold relatively stable the allocation amounts from one program year to the next. 9 This 

is of clear concern to local areas because shifts in funding levels may well impact their 

ability to provide a uniform and consistent level of services. Such instability in the 

number, types, or consistency of services that is based upon data that are at least a year 

old may well inhibit local areas’ ability to meet the current needs of their customers, 

especially as these needs, and the number of customers seeking services, can vary 

substantially from one year to the next. Finally, reductions in funding between program 

years force local areas to make decisions about whether they will reduce staff, 

 

8 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986), Abt 

Associates, Cambridge, MA.

9 Although exactly half the states initially reported that they expected the allocation formulas to 

create significant changes to local funding (“Workforce System Information and Evaluation, Volume VI,” 

Social Policy Research Associates, Nov. 2000. This report was produced as part of a national Evaluation 

of the Implementation of the Workforce Investment Act, begun in 1999), results from our survey indicate 

that closer to 40% actually observed such changes under WIA. Many of those who did not see significant 

changes, however, report that this was due to their early invocation of hold-harmless provisions. These 

data will be discussed in greater detail in Chapter II.
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I-9 Social Policy Research Associates

infrastructure investments, or services to their customers. Each of these choices, of 

course, has consequences for how well the local area will be able to continue its 

operations and effectively serve a broad range of customers.

The results from earlier investigations of the allocation formulas suggest that these 

concerns are quite valid. For example, a study of the JTPA allocation formulas found 

that during the initial two program years of this Act, over 40% of the local areas (then 

called Service Delivery Areas, or SDAs) experienced at least a ten percent change in 

funding, and more than one in ten experienced changes in excess of 25%.10 Those who 

experienced the most substantial changes were those whose initial allocations were 

among the smallest, thereby heightening the effect since they have fewer options for 

accommodating such changes. A more recent study of inter-state allocations 

demonstrated that over a five-year period (between PY 1993 and PY 1997), several states 

received dramatically different allocations, with one in ten states receiving at least 30% 

greater allocations in PY 97 than in PY 93, while a similar one in ten experienced 

allocation reductions of at least 30%.11

One mechanism for ensuring greater funding stability between program years has 

been the invocation of the hold-harmless provision. This provision ensures that local 

areas receive no less than 90% of some previous allocation (the average of their previous 

two years’ allocation, under WIA, or their previous year’s allocation, under JTPA), 

thereby injecting some lower limit on the amount a local area can expect to receive for an 

upcoming program year.12 As noted above, however, the hold-harmless provision was 

only required after two years of WIA implementation. Thus, in the initial two years, 

local areas were not guaranteed at least the 90% threshold, unless the state specifically 

chose to invoke their hold-harmless authority during those years. Although 

approximately 70% of all states did so, a significant number of states did not, thereby 

potentially throwing their local areas into periods of uncertainty concerning their funding 

stability. Further, even among the 70% of states who invoked the hold-harmless 

 

10 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986).

11 “Analysis of the WIA Allotment Formula for Distributing Funds to the States,” (2002), Westat, 

Rockville, MD.

12 According to a recent study of inter-state allocations of WIA funds, the hold-harmless provisions 

for adult program achieved the goal of preventing dramatic and rapid reductions in funding to states. 

According to this study, sharp drops in funding were fully felt only after three to four years, due to the 

hold-harmless provisions. Ibid, pg. 45.
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Social Policy Research Associates I-10

provision, it is far from clear that 90% is the precise threshold that enables local areas to 

continue their operations smoothly from one year to the next. Finally, it is conceivable 

that the admirable quality of hold-harmless to limit the funding losses of any particular 

local area may also have the negative effect of limiting the amount of funds that can be 

targeted to areas that have greater need for the funds.13

In addition to concerns over dramatic reductions in funding from year-to-year, local 

areas might also struggle were they to receive a substantial influx of additional money in 

a given year. Although measures to limit increases, or so-called “stop-gain” provisions, 

could be implemented, they are not currently included in the substate funding formulas 

and, thus, there is no limit on the potential increase a given local area can receive from 

one year to the next. In such cases, they would be forced to significantly increase their 

staff, service capacity, and perhaps even their infrastructure in order both to expend their 

resources and to meet the need that was implied from the increased funding level. Given 

that such increases would need to occur quickly, in response to a suddenly larger budget, 

rather than as a result of a sustained effort to gradually increase staff and capacity, there 

would likely be complications and difficulties that arise from this situation. These funds 

can, according to the legislation, be spent over a two-year period, which could serve to 

lessen the impact of any such rapid increases in funding.14 Assuming they were to 

receive a similarly increased amount of funds in the subsequent year, though, the need to 

ramp up their operations and services may well be similar despite this provision. Thus, 

although it seems fairly clear that local areas would be less distraught about dramatic 

increases in their yearly funding levels than about decreases, the potential impact on their 

overall financial and organizational stability may well be similar. As a result, 

considerations surrounding the organizational and financial stability of local areas must 

factor in the potential instability of increases, as well as of reductions, in year-to-year 

funding.

In addition to relatively exogenous constraints on allocations, such as holdharmless or stop gain provisions, the results from earlier studies suggest that one could 

 

13 In contrast to the adult and youth formulas, however, there is no hold-harmless provision for the 

dislocated worker allocations, either at the inter- or substate levels, which likely contributes to the increased 

volatility in funding under this program, as noted below.

14 This provision does require that the area obligate at least 80% of its funds in the initial program 

year.
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I-11 Social Policy Research Associates

introduce greater consistency in funding across program years by simply extending the 

period for which data used in the formulas are calculated. For example, in a study of 

formula allocation under JTPA, allocations made to local areas when using two-year 

averages of unemployment reduced the amount of year-to-year fluctuation considerably 

over single-year measures of unemployment.15 The theory underlying such allocations is 

that although single-year indicators of need, such as unemployment, can fluctuate wildly 

based on the specifics of local area economies, longer-term averages better reflect general 

trends within the areas and are thus more indicative of broader needs. Further, allocating 

funds based on a single year’s worth of data can be subject to substantial increases for a 

given local area being allocated for a future program year based on a single anomalous 

past year, while two-year or longer averages would lessen the likelihood of such 

anomalies dramatically impacting future funding.

One final means of understanding the organizational stability of local areas, within 

the context of this study, is by recognizing that some local areas easily expend all their 

funds in a given program year, while others under expend. As new program year funds 

are introduced, it is plausible to suggest that those areas with unexpended funds from the 

prior year should not receive their full new allocation, as it would thereby leave them 

with greater overall funds than their relative share and, thus, may introduce the problems 

with sudden increases described above. Perhaps, then, local areas with unexpended funds 

should have their new allocations reduced such that they are not asked to expend even 

more resources in the new program year when they could not expend their lower amount 

of funds in the prior year. In the interests of ensuring or increasing organizational and 

financial stability, then, one way to do so may be to factor in all “carry-in” funds to each 

local area’s new allocation and ensure that they are not allocated even greater amounts in 

a new year as a result of being unable to expend funds from the prior year.

Inequities Inherent in Formula Design

In addition to concerns about funding stability, the formulas also raise important 

questions about whether the factors used in allocating funds, as well as the weight each 

factor is given, adequately reflect the primary goals of WIA. For example, does the 

heavy reliance on unemployment factors cause allocations to be overweighted toward 

areas with particular characteristics, at the expense of similarly needy areas whose 

customers have different characteristics? By allocating two-thirds of the standard 

 

15 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986).
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Social Policy Research Associates I-12

formula using unemployment factors, the allocation is thus heavily weighted toward 

cyclical factors caused by short-term business downturns, perhaps at the expense of areas 

with higher concentrations of economically disadvantaged individuals who may have 

longer term needs for workforce development services. Although the adult program 

under WIA has removed the explicit eligibility requirement that the individual be 

economically disadvantaged, this requirement still exists for the youth program.16 Thus, 

for the youth program an allocation formula using a two-thirds weight for unemployment 

factors may be inappropriately targeting areas with high concentrations of people who are 

not even eligible for the program or its funds.

In addition, both unemployment factors used in the adult and youth formulas are 

threshold factors, meaning that a local area must exceed the threshold to receive any 

allocation under that factor. For example, a local area whose unemployment rate is no 

more than 4.5% cannot receive any funding under the excess unemployed allocation 

factor. While this may make sense because the funds are targeted to areas with higher 

concentrations of unemployment, the potential for relatively perverse actual allocations 

can be demonstrated using an admittedly extreme example. Imagine a state that has two 

local areas, each of which has exactly 1,000 people. In one of these local areas, 45 

people are unemployed, making the area’s unemployment rate exactly 4.5%. In the 

second local area, 46 people are unemployed, making the unemployment rate greater than 

4.5%. Because of the cut-off value used in the formula, the second local area would 

receive the entire one-third of the funds to be allocated for this factor, while the first 

receives nothing. Thus, a difference of a single unemployed individual in otherwise 

equal populations can sway the entire one-third of the allocation, though it would be 

extremely difficult to argue that the second local area has a need for the entire amount 

while the first local area has no need at all.

A similar argument could be made for the Areas of Substantial Unemployment 

(ASU) unemployment factor. In this case, the discrepancy can be even greater. 

Although an entire local area, or even state, can qualify as an ASU and, thus all 

unemployed persons above an average of 6.5% can count toward this factor, ASUs also 

can be defined by boundaries smaller than those of local workforce areas. Because an 

 

16 Technically, the requirement is that youth be low-income, rather than economically 

disadvantaged. Although we will dis cuss this difference in some detail below, for the purposes of this 

section we treat these as interchangeable.
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I-13 Social Policy Research Associates

entire local area need not qualify as an ASU to receive some portion of the funds 

distributed under this factor, it is conceivable that the entire allocation made under this 

factor could be made based upon a small subsection of a single local area that can be 

drawn to show an unemployment rate of at least 6.5% (assuming no other are in the state 

had such an area).

In each of these cases, then, a critical concern arises because of the all-or-nothing 

aspect of the threshold, which could cause areas that are actually quite similar in their 

unemployment rates to receive dramatically different allocations depending on whether 

they fell above or below the threshold. In addition, the actual values used for these 

thresholds were initially established in the early 1980s, when unemployment hovered 

near double digits. Although it has increased in recent years, such that it now hovers near 

6% overall, for many years in the late 1990s the rate was closer to 4.5%. This may imply 

that those areas exceeding the threshold values are far needier than those areas that do 

not, given the lower overall unemployment rates, but it also may suggest that many areas 

who have relatively high unemployment rates currently will receive very little funding 

because their rates are relatively low when compared to the standards of twenty years 

prior. 

An additional concern about using a threshold for these factors is that local areas’ 

funding levels could vary dramatically from year-to-year based on very slight changes in 

their unemployment rates, should the rates change from just below to just above the 

threshold, or vice versa. Thus, in addition to overweighting those areas who just meet the 

threshold at the expense of those who do not, a single local area could experience a 

dramatic influx or outflow from one year to the next based on similar criteria. This could 

have significant impacts on the local areas’ stability and consistency in service provision. 

More broadly, the fact that the standard formula for adult allocations utilizes the 

same factors that were employed under JTPA raises some concern, given that the eligible 

population has changed dramatically. Under JTPA, an individual had to be 

disadvantaged to be eligible to receive services. WIA, however, places a strong emphasis 

on serving the universal customer. Thus, the formula’s heavy reliance on unemployment 

and disadvantaged populations may well place too much emphasis on areas that are 

experiencing short-term business downturns or areas of high poverty rather than focusing 

funds on areas in which there are somewhat different needs for workforce development 

services. Given the broad definition of serving the universal customer, perhaps, then, 

adult funds should be targeted explicitly based on the number of individuals in the 
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Social Policy Research Associates I-14

civilian labor force, since any of these individuals would by definition be eligible for 

program services. Although it is true that many who are employed may have little 

current need for workforce services, administrative data from the most recent program 

year suggest that, among adult registrants under WIA, 20% were employed at the time of 

registration. Thus, targeting funds based heavily on unemployment would discount the 

employed population, a sizable number of whom may well be interested in receiving 

workforce services.

One of the critical complaints about the formulas under JTPA was that they had the 

effect of directing funds away from central cities despite the fact that these areas tend to 

house the largest concentrations of economically disadvantaged individuals. This 

complaint was due to the heavy reliance on unemployment data, which is twice as 

heavily weighted as data identifying disadvantaged individuals. Were the two factors 

highly correlated, the argument would have little merit. But the results of prior studies of 

the allocation formulas, first under JTPA and, more recently, under WIA, revealed that 

the two factors are not highly correlated.17 Indeed, relying on Census data from 1980, 

one study concluded that the two populations (the unemployed and the disadvantaged) 

had very different geographical distributions. Specifically, while more than one-third of 

those who were economically disadvantaged lived in urban areas, only slightly more than 

one-fourth of the unemployed lived in such areas. Further, only about one in sixteen of 

those who were economically disadvantaged were unemployed; the vast majority was 

considered out of the labor force entirely. Similarly, fewer than one in five unemployed 

persons were also categorized as economically disadvantaged. Indeed, nearly threefourths of the unemployed population had family incomes (used to determine economic 

disadvantage) in excess of 125% of the poverty level. Citing these statistics, the study 

concluded that “the weight attached to the unemployment-based factors results 

in...resources being ‘pulled’ away from areas with the highest concentrations of 

economically disadvantaged residents...,” and recommended that there be a decreased 

emphasis in the formulas on the unemployed population.18 A more recent analysis of 

inter-state allocations similarly concluded that unemployment and poverty measures were 

poorly correlated, suggesting that although changes in the measures were generally in the 

 

17 “An Assessment of Funding Allocation Under the Job Training Partnership Act,” (1986); 

“Analysis of the WIA Allotment Formula for Distributing Funds to the States,” (2002).

18 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986), pg. 5.
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I-15 Social Policy Research Associates

same direction from year to year, changes in unemployment typically were much greater 

than changes in poverty.19 Given that the highest concentrations of economically 

disadvantanged residents are in central cities, relying so heavily on unemployment factors 

thereby redirects funds away from central cities and toward areas with higher 

unemployment. Given that the formulas for adult and youth programs are very similar 

under WIA, similar concerns likely exist about the ability of the allocation strategies to 

target appropriate levels of funds to inner cities.

Finally, significant concern has been raised about the two-tiered distribution 

process, in which funds are first allocated to the states, then re-pooled and allocated to 

local areas. The reason for this concern is that the weight of each factor that was 

primarily responsible for the overall state allocation is not identical to the weight used in 

the substate allocation. In other words, though a state may have received the vast 

majority of their funds due to a high number of disadvantaged individuals within the 

state, once they receive these funds they must allocate them to local areas using the 

standard formula and, thus, would only allocate one-third of their available funds using 

this factor. Indeed, earlier studies have shown that nearly every state was forced into 

some degree of redistribution while allocating funds to their local areas.20 The funding 

pool based on the economically disadvantaged factor was the most adversely impacted by 

this two-tiered system. Specifically, in 60% of the states, funds that were initially 

allocated to states based on their relative share of economically disadvantaged individuals 

were ultimately redirected in making substate allocations based on the two 

unemployment factors. Because this is a two-step process, rather than a direct allocation 

to local areas, the money that actually is allocated to local areas is thus not in direct 

proportion to these local areas’ share of the national pool of funds, despite the fact the 

formulas are the same for inter- and substate allocations. This creates some concern 

because, even if the allocation factors were perfectly suited to target the level of need in 

local areas across the nation, some inequity would result because of the two-tiered 

process mandating that factors are weighted equally at each step. Indeed, an earlier study 

of the JTPA allocation formulas recommended eliminating the two-tiered system in favor 

of a single allocation from the federal government to local areas, arguing that this would 

 

19 “Analysis of the WIA Allotment Formula for Distributing Funds to the States,” (2002).

20 Ibid.
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Social Policy Research Associates I-16

promote “a more equitable distribution...[and] would reduce the complexity and enhance 

the intuitive appeal” of the formulas.21

Data Quality and Access

In order to allocate funds using the factors in the standard formulas, or alternative 

factors, one must have readily available data that can be used to generate each local 

area’s share of the overall total. Thus, allocating funds to states requires data on each 

state’s overall number of unemployed individuals as well as their total labor force, the 

number of disadvantaged adults and youth within each state, and the number of long-term 

unemployed individuals in each state. While these data, or highly accurate estimates of 

them, are readily available at the state level using the Current Population Survey, which 

is conducted yearly, such data cannot be used to draw reliable estimates for substate areas 

such as local workforce areas or ASUs. Thus, the data necessary for substate allocations 

are not as widely available and, in many cases, rely upon state-specific data collection. 

Not all states collect such data and, thus, must rely on whatever is available to them from 

external sources. As such, it is problematic when factors are used for which there are no 

available data at the substate level.

One solution to this dilemma is to utilize Census data, which can easily be drawn 

for even very small areas, such as the level of Census tracts. These data are very 

complete, and include measures of poverty and unemployment, but there is significant 

concern about the within-decade shift away from the currency of the data. In other 

words, while the Census data are extremely reliable and current for the year in which they 

were collected, over the ten-year period between such data collection efforts, significant 

shifts occur in the population and economic characteristics that these data measure, and 

no such shifts can be captured until the next Census.

Although the Census data may not be ideal, given the substantial time lag that 

occurs between collections, they are one of the few sources of data on economic 

disadvantage and, thus, traditionally have been used for this factor in the allocation 

formulas. Thus, data on disadvantaged individuals to be used in the substate allocation 

formulas for program year 2002 still relied on Census data from 1990.22 The actual 

 

21 Ibid, pg. 6.

22 Although data from the 2000 Census were used to make allocations to states for PY 2003, it is 

unclear that these data were used for substate allocations for this program year. Regardless, as currently 
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I-17 Social Policy Research Associates

numbers of disadvantaged individuals within a given local area is likely to be 

dramatically different from the number tabulated in the 1990 Census, but reliable and 

more up-to-date numbers simply are unavailable at the substate level necessary for LWIA 

allocations. One possible solution to this problem is to utilize new Census Bureau 

poverty measures that are calculated yearly,23 though there is still an approximately threeyear time lag before the data are accessible. These measures are available at least at the 

county level, such that they could largely approximate local area boundaries. Indeed, a 

recent study of the inter-state allocations under WIA recommended replacing the entire 

adult and youth formulas with this poverty measure, because it: reduces the need for 

calculating complex ASU measures; is more closely related to the targeted population, 

especially for youth; and largely eliminates the effect of hold-harmless provisions.24 

While this suggestion may more accurately target those in poverty, given that under WIA 

services for adults are not exclusive to such individuals, this potential solution may have 

adverse effects on services to universal customers.

A further concern that emerges under WIA is that, because WIA allows states to

target up to 30% of their allocation based on other indicators they believe are important, 

states may have great interest in utilizing additional factors in their allocation decisions, 

but the data necessary to make such decisions may be unavailable at the substate level. 

Thus, it is theoretically possible that if the data were available, states could precisely 

target their funds based on the exact relative need within each local area. The data 

necessary to do so, of course, may not exist, thus forcing states to rely on what data they 

do have available. And given that different states may well have different types of data 

available for substate allocation, some states would be less able to target funds based on 

important indicators of need because they simply do not have the necessary data to do so.

Alternatives to the Standard Formulas

As discussed above, WIA enables states to base up to 30% of their adult and youth 

WIA allocation based on an alternative formula. Although nearly twenty percent of all 

 

arranged, these data from 2000 will remain as the operative measure of the number of individuals living in 

poverty until data from the 2010 Census become available.

23 The Small Area Income and Poverty Estimates (SAIPE) Project, which began developing countylevel poverty statistics in the mid-1990s.

24 “Analysis of the WIA Allotment Formula for Distributing Funds to the States,” (2002). It should 

be noted that this elimination of the effect of the hold-harmless provision was shown only at the state level 

and not at the substate level.
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Social Policy Research Associates I-18

states initially reported that they were considering adopting a discretionary formula,25

according to the results from our survey only four actually did so. Each of these four 

states utilized the discretionary formula for fully 30% of their funding allocations. This 

raises several questions about the extent to which these states were better able to target 

their funds to meet their local areas’ levels of need. Further, these states were forced to 

obtain the data necessary for measuring the factors included in their discretionary 

formulas. Such data may not be available to all states and, if so, they could not be used in 

allocation formulas regardless of whether these other states agree that they more 

accurately target funds. Related to this, some states may well have considered adopting a 

discretionary formula but decided against it simply because they lacked access to the data 

necessary to implement it.

Clearly, allocation decisions in those states that have adopted a discretionary 

formula will differ from those states that use only the standard formula. Further, their 

allocation will differ from what it would have been had these states themselves used only 

the standard formula. Thus, a critical question is just how the allocations differ in states 

that have adopted discretionary formulas from the levels that would have occurred using 

the standard formula. If these discretionary formulas help states to target their local 

areas’ levels of need, then questions can be raised as to whether all states should adopt 

such formulas, or whether states should be allowed to allocate more than 30% of their 

funds using the discretionary formulas. In so doing, perhaps, states would thus be better 

able to target the needs of their workforce.

An additional issue is the experience of single workforce-area states. These states 

need not allocate funds to local areas and, thus, can distribute funds with far more 

flexibility than can states with multiple workforce areas. Such flexibility may well help 

them target the needs of their workforce extremely well, enabling them to rapidly 

distribute money to the subareas shown to be most needy. Understanding how these 

states distribute funds, and perhaps learning from their experiences in deciding how to do 

so, could be extremely useful for thinking broadly about how all states might better be 

able to target funds to their local areas. 

Finally, although hold-harmless provisions were included in WIA, as they were in 

JTPA, some key changes were made to these provisions. These changes, discussed 

 

25 “Workforce System Information and Evaluation, Volume VI,” Social Policy Research Associates, 

Nov. 2000.
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I-19 Social Policy Research Associates

above, were intended in part to enable states to restructure their local areas and target 

funds to them without restrictions due to prior funding levels. Learning about the 

experiences of states in invoking the hold-harmless provision or not will enable DOL to 

better understand how such lapses might be beneficial or harmful to local areas in both 

targeting the needs of customers and ensuring the financial stability of local boards.

Questions Concerning Substate Dislocated Worker Allocations

The flexibility inherent in the substate dislocated worker allocation formula raises 

even further questions, as there is no standard formula at all for this funding stream. As 

such, states have substantial freedom to select from a wide variety of possible factors on 

which to base their funding decisions. This means that there may well be little 

consistency across states in the way in which they allocate funds for dislocated workers. 

This, of course, is largely the point of introducing such flexibility, but it decreases the 

federal government’s ability to target funds in any logical way toward local areas. This 

dilemma is exacerbated due to the two-tiered system of allocating funds, because the two 

tiers allocate funds using very different criteria. For example, the federal government 

allocates funds to states using the three factors identified above, each of which employs 

some measure of the unemployed population. Thus, the total amount of states’ funding 

for the dislocated worker program is determined simply by their relative share on these 

three unemployment factors. However, when states make their substate allocation 

decisions, they are encouraged to do so using a much broader set of factors, including 

plant closing data and mass layoff statistics, declining industries data, and farmer-rancher 

economic hardship data, as well as other factors the state believes to be useful in targeting 

funds. While these factors may well be highly related to the need for dislocated worker 

funds, they are not incorporated in any way into the states’ overall funding received from 

the federal government and, thus, there is opportunity for funds to be redistributed away 

from local areas that were primarily responsible for the state receiving the funds in the 

first place. This may be of less concern for the dislocated worker program than it is for 

the adult and youth programs because by their very nature dislocations are difficult to 

predict. Thus, although the original allocation to the state was based on that state’s share 

of each of the formula factors, due to the unpredictability of dislocations, this allocation 

may bear very little resemblance to the actual need for dislocated worker funds within 

any given local area.

An additional concern surrounding the dislocated worker allocation formula is that 

the data to be used in making allocations may be unavailable or difficult to obtain. 
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Social Policy Research Associates I-20

Unlike much of the data used in the adult and youth allocation standard formulas, which 

can be obtained through the Census or the Local Area Unemployment Statistics program, 

data such as mass layoff statistics must be compiled by the state, or obtained from less 

readily available sources. As such, the manner in which these data are collected varies 

from state to state and, thus, even if two states were using the same factors for allocating 

their dislocated worker funds, they would likely be allocating funds in quite different 

ways because of these differences. 

Finally, recent studies of the inter-state allocation formulas observed that shifts in 

funding between states were particularly large for the dislocated worker funding stream, 

as compared to shifts that occurred in the adult and youth funding streams.26 According 

to these studies, this was in part due to the fact that measures of excess unemployment 

relied on statewide indices of excess unemployment, rather than smaller concentrations of 

such employment (such as might be used for ASUs), or relative numbers of unemployed 

individuals within a state. In other words, because the period included in the study was 

one of sharply declining unemployment rates, substantial numbers of states received no 

funding under this factor because they had unemployment rates lower than the 4.5% 

required for excess unemployment. Such dramatic shifts for dislocated workers could 

create serious concerns about the ability of particular states whose unemployment rate as 

a whole is less than 4.5% to meet the needs of their dislocated workers. Given that the 

formula for substate allocation is discretionary, and thus need not rely on the specific 

measure of excess unemployed employed in inter-state allocations, the volatility in 

substate allocations is less clear. In Chapter III, we examine this volatility compared to 

the fluctuation observed in funding for the adult and youth programs, and offer 

suggestions for the reasons behind the fluctuations that do exist.

One further concern about this volatility and dependence on unemployment figures 

is that a recent General Accounting Office (GAO) report suggested that changes in the 

numbers of workers affected by mass layoffs from year to year was negatively related to 

the actual state-level dislocated worker allocations for those years.27 This is significant 

because it implies that the factors used to allocate dislocated worker funds to states may 

 

26 “Analysis of the WIA Allotment Formula for Distributing Funds to the States,” (2002). “Issues 

Related to Allocation Formulas for Youth, Adults, and Dislocated Workers,” (2003), General Accounting 

Office Report # GAO-03-636.

27 “Issues Related to Allocation Formulas for Youth, Adults, and Dislocated Workers,” (2003).
I. Introduction

I-21 Social Policy Research Associates

be inversely related to the mechanisms by which states then allocate funds to local areas, 

as mass layoff data are not used in inter-state allocations, but often are a key component 

for substate allocations. As such, there are significant concerns about how equitable are 

the local area allocations for dislocated worker funds.

Defining Need

Throughout the preceding sections, we have referred to states’ ability to target the 

level of need within each local area. Yet, developing a single standard definition of the 

need within a given local area seems difficult at the least, and perhaps impossible given 

the variety of objectives or goals of WIA. One critical task of this project was to identify 

a range of potential definitions of need, and model how various allocation formulas fare 

in targeting funds to meet these needs. In this section, we identify several of the various 

definitions one might adopt for level of need, and discuss the strengths or weaknesses in 

adopting each definition. In Chapter III of this report, we implement some of these 

definitions of need, and compare how alternative formulas used for allocation were able 

to target funds to meet each definition. 

Perhaps the most obvious definition of need is the number of individuals in an area 

who fit the eligibility criteria for the programs. However, unlike under JTPA, the criteria 

established for eligibility are not easily modeled quantitatively for each category of 

funds. For example, although only disadvantaged adults were eligible under JTPA, there 

are no similar low-income criteria for adults under WIA. Rather, WIA places an 

emphasis on serving the universal customer. Thus, it is unclear how one would define 

the eligible adult population in need in any way that would be stricter than simply 

defining the number of adults in a local area. Another alternative that perhaps more 

closely targets the potential demand for workforce services would be to utilize each local 

area’s relative share of the adult labor force within a given state. This would ensure that 

allocations are based not simply on the adult population in an area, but the portion of that 

population who are actively in the labor force and, thus, who may wish to receive 

workforce services. A third alternative, which reflects the fact that far more unemployed 

customers seek services than those who are employed, would be to weigh the number of 

unemployed individuals within an area more heavily, while allowing some portion of the 

funding to be based on the overall size of the labor force. This would have the effect of 

giving greater weight in allocation decisions to those customers who are more likely to 

utilize workforce and training services, while still recognizing that some employed 

individuals do seek out such services. We examine the use of each of these potential 
I. Introduction

Social Policy Research Associates I-22

indicators of need—the size of the overall population within local areas, as well as the 

size of the adult labor force, and a weighted balance between the size of the unemployed 

population and the size of the labor force—in alternative specifications for WIA substate 

allocation in Chapter III, and detail how their use would alter current funding levels.

Allocations for the youth program under WIA, in contrast, can be targeted 

somewhat more specifically to the eligible population than they can under the adult 

program. This is because the low-income eligibility criteria for youth participation in 

WIA remains largely intact; thus, one could simply calculate the number of youth who fit 

this definition within each local area and assign appropriate weights to each area based on 

their proportion of the overall state total, which we do in Chapter III.28 Even for the 

youth program, in which one can more clearly model the number of youth in an area who 

would be eligible for the program, it is not certain that this accurately defines the level of 

need for a particular area. For example, the number of youth who actually access or wish 

to access services in a given local area may be only weakly correlated with the size of the 

eligible population. Thus, areas in which there is a relatively small number of eligible 

youth may, in fact, have much greater demand for services than do areas that have greater 

numbers of eligible youth. But if we were to establish “true” levels of need simply based 

upon the number of youth eligible for services, we would instead conclude that the larger 

area has greater need. Thus, even in cases in which the simple number of eligible 

individuals can be calculated, this may not correspond well to alternative definitions of 

need.

Related to this, it is conceivable that in some local areas, more money may be 

available than is required to serve those who wish to receive services. In such 

circumstances, much of the money may go unspent in some local areas, while others may 

use all their funds and still have individuals waiting for services. Although there are 

specific regulations surrounding recapture, it is unlikely that any such redistribution 

would occur quickly enough in any particular year to impact those waiting for services. 

Should certain areas consistently evidence their ability to expend their funds by providing 

services, while others do not, it could be argued that those areas utilizing their full 

 

28 This calculation would still not perfectly fit the eligible population, as youth must both be lowincome and have one of several potential barriers to employment, including being: a school dropout; 

deficient in basic literacy skills; homeless, runaway, or in foster care; pregnant or a parent; an offender; or 

requiring help completing an educational program or securing and holding a job. Nevertheless, youth who 

live in poverty could be deemed a reasonable approximation of the eligible youth population.
I. Introduction

I-23 Social Policy Research Associates

allocation have greater needs than those with resources remaining. And this fact could be 

entirely independent of the number of eligible individuals across the local area, generally. 

Such a definition would suggest that the areas that have the greatest needs are those 

which consistently expend their funds, as compared to those who routinely have funds 

remaining at the end of a program year. If this were to be the case, then subsequent 

allocation decisions may well factor in an area’s unexpended funds into the overall total 

to be allocated, or simply reduce a given local area’s recommended allocation amount by 

the amount left unexpended at the conclusion of the prior program year. We explore such 

options in Chapter III, examining how these decisions would impact funding compared to 

current allocation formula results. 

It should be noted that the current evaluation takes no a priori stance on which of 

these definitions of need, among other possible definitions, is the correct one to adopt. 

Indeed, the purpose of this report is to present DOL with potential alternative formulas 

and to define how such alternatives would alter the overall distribution of funds. Thus, it 

should be clear that we do not presume that excess funds for a given local area are a sign 

that there is less need within that area. Nor do we presume the opposite: that shortage of 

funds in a particular area indicates greater need. Rather, we propose these as potential 

alternatives to the current formulas, which govern at least 70% of all within-state 

allocations, and, in Chapter III, we will explore how utilizing such approaches would 

alter the distribution allocations to local areas. 

An important consideration above is that WIA requires only that 70% of withinstate allocations be made according to the funding formulas. This provides states with 

some amount of flexibility in their allocation strategies, which as noted above represents 

a new development under WIA. Although a very few states (4) indicated they had 

actually adopted an alternative formula to distribute funds, according to the results of our 

survey, the alternative factors used by these states suggest further plausible options for 

defining need. These additional factors, such as unemployment above the state average, 

or excess poverty, which have been incorporated into the funding formulas of these four 

states, are intended to more accurately target funds to those local areas most in need. In 

Chapter III, we explore how these additional factors have altered the funding levels 

within these states (i.e., how the funding differs from what it would have been under the 

standard formulas only) and also examine how funding would be altered if all states 

adopted these potential alternatives for their own use in allocating funds to their local 

areas. 
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Social Policy Research Associates I-24

PROPOSED NEW FORMULAS

Partially in response to the issues cited above, new legislation has been introduced 

in Congress that, in addition to reauthorizing WIA, overhauls the allocation formulas and 

streamlines two of the three funding streams discussed in this report. Known as the 

Workforce Reinvestment and Adult Education Act of 2003,29 this bill would integrate 

funds under the adult and dislocated worker funding stream, as well as funds directed to 

states through the Wagner-Peyser Act, and significantly alter the factors used in 

allocating funds both to and within states. Because this proposal and the formulas laid 

out in it represent in part a response to the concerns identified above, and because this bill 

represents a very possible and comprehensive modification to formula allocation under 

WIA, we describe below the newly proposed formulas.

Adult and Dislocated Worker Formulas

Perhaps the most significant change to the formula allocation in this bill is that the 

adult and dislocated worker funding streams, as well as Wagner-Peyser funds, would be 

consolidated into a single funding source. Given this consolidation, there no longer 

would be separate allocation formulas for adults and dislocated workers. Rather, what 

had been three pots of money would now be merged into a single stream and funding to 

states and to local areas would thus be based on this single stream of funds.

In addition to the consolidation of these three programs, there are significant 

changes to the allocation process, as well. First, the Secretary of Labor would be able to 

reserve 10% of the overall funds available, mostly to be used for national emergency 

grants (this figure used to be 20% of the overall dislocated worker funds). Once this 

amount has been set aside, the remainder is to be allocated to states using the standard 

formula described below, which differs markedly from the formulas used currently. 

Once allocations for the adult (and dislocated worker and Wagner-Peyser) program have 

been made to states using the standard formula (shown below), substate allocations 

would then be made. First, of the total amount allocated to each state, the Governor can 

reserve up to 50% of this amount for Rapid Response and other statewide activities, at 

least fifty percent of which must be used to support the provision of core services. The 

remainder of the funds is to be allocated in the following manner: 

• Use of the Standard Formula. 85% of the amount to be allocated (the 

state total minus the 50% reserved for Rapid Response and other 

 

29 H.R. 1261
I. Introduction

I-25 Social Policy Research Associates

statewide activities) is to be allocated using this standard formula. The 

factors on which this formula is based are:

− 60% based on the relative number of unemployed individuals 

within each local area, compared to the total number of 

unemployed individuals in the state.30

− 25% based on the relative number of excess unemployed 

individuals within each local area, compared to the total number 

of excess unemployed individuals in the state.31

− 15% based on the relative number of disadvantaged adults within 

each local area, compared to the total number of disadvantaged 

adults in the state.32

• Use of Discretionary Allocation. 15% of substate allocations would be 

made using a discretionary formula of the state’s own choosing.33

• Hold-harmless/Stop-gain Provisions. No local area shall receive an 

allocation percentage for a fiscal year that is less than 90% or greater than 

130% of its allocation in the prior fiscal year. 

If enacted, this formula would make several significant changes to the allocation 

mechanisms used by states. First, the overall total to be distributed to local areas would 

be reduced by as much as 50%, due to the allowance of state set-asides for rapid response 

and other statewide activities. This is not unlike set-asides under the current dislocated 

worker program, but under the proposed formula it would apply both to adult and 

dislocated worker funds, as well as Wagner-Peyser funds, since they would be merged 

into a single stream. The effect of this is somewhat unclear, in part because currently 

Wagner-Peyser does not have similar restrictions on the amount of funds states can set 

aside, given that it is a statewide program and, thus, need not allocate funds to substate 

areas. 

Second, the formula eliminates the use of ASUs which, as noted above, have long 

been a source of confusion and concern. Eliminating this factor removes one of the two 

 

30 This factor does not rely on a threshold and, thus, is calculated simply by taking the overall 

number of unemployed individuals within a local area.

31 Calculated similar to the current factor using excess unemployment.

32 Disadvantaged adults would be calculated in the same manner as they are in the current allocation 

formula.

33 This formula is to be developed in consultation with the State and local boards, and is to be 

objective and geographically equitable and include appropriate demographic and economic factors.
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Social Policy Research Associates I-26

“threshold” factors, thereby making it less likely that there will be dramatic swings in 

funding from year to year. Additionally, this formula adds an entirely new factor, the 

relative number of unemployed individuals, which is not coincidentally one of the two 

factors used to allocate funds to states under the Wagner-Peyser Act. In addition to being 

a primary basis for current Wagner-Peyser allocations, this factor was described above as 

a possible mechanism by which WIA funds could be more accurately targeted toward 

areas with the highest need. By focusing on the raw number of unemployed individuals 

(rather than exclusively through the use of certain thresholds) those areas with greater 

numbers of unemployed persons would receive a higher proportion of the available 

funds. Further, this factor is to be given sixty percent of the weight of the formula which 

makes clear that this formula still places a premium on those who are actively looking for 

work. 

The remaining two proposed factors are holdovers from the current WIA adult 

formula, though both receive less weight in this formula than currently. The continued 

inclusion of the excess unemployed factor maintains at least one threshold factor within 

the formula, though its 25% weight means that the influence of such thresholds would be 

slightly reduced (from their current one-third weight) and, perhaps, so too will be the 

substantial swings in funding from year to year. Similarly, the use of the economic 

disadvantage factor is a hold over from the current formula, but the weight assigned to 

this factor would be reduced by more than one half from one-third to only fifteen percent.

In contrast to the similarities between this formula and the current WIA adult 

formula, there are fewer similarities with the current WIA dislocated worker formula. As 

noted above, the current dislocated worker formula allows for great state flexibility in 

allocating funds to local areas, and includes unemployment and unemployment 

concentration data, which are included in this proposed formula. But in addition to these 

two factors, several others can currently be included, such as plant closing and mass 

layoff data, declining industries data, farmer-rancher economic hardship data, and longterm unemployment data; none of these is incorporated into the proposed formula. Thus, 

allocations made under the current WIA dislocated worker formulas (as defined by the 

states) are quite likely to vary substantially from any allocations that might be made 

under this proposed formula.

Yet another difference between the current and the proposed formula is that, while 

up to 30% of WIA adult funds currently are allowed to be distributed based on a 

discretionary formula, any such formula was not mandatory (states could, and most did, 
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I-27 Social Policy Research Associates

use only the standard formula). Further, the current discretionary formula is limited to 

including only factors that relate to excess poverty or excess unemployment. The 

proposed formula requires that 15% (compared to 30% currently) of the overall amount 

be allocated using a discretionary formula developed by the state, and no specific limits 

are placed on the factors to be used in this formula. Thus, depending on what factors 

states adopt under the proposed discretionary formula, dramatic shifts in allocations 

between the current and the proposed formulas may well occur.

Even assuming states simply adopt the standard formula, or something close to it, 

as their discretionary formula, there still are significant differences between the proposed 

and the current formula. As a result of the changes in the factors used, as well as the 

significant changes in the weights assigned to the various factors, adoption of the 

proposed formula for adult, dislocated worker, and Wagner-Peyser funds would likely 

lead to substantial shifts in allocations from what local areas currently receive. As part of 

Chapter III, we will examine this possibility quantitatively by modeling the effects of the 

proposed formula and comparing it to current allocation results.

Youth Formula

The proposed formula to allocate youth funds to local areas is also somewhat

different than the current youth formula, and also differs in one specific factor from the 

formula for adults, as well as in the weights that are assigned to each of the respective 

factors. For the youth program, first, funds are allocated to states using the standard 

formula identified below. Once these funds have been distributed to states, Governors can 

set aside up to 10% of this total for statewide activities (down from 15% currently). The 

remainder is to be allocated to local areas using the following approach:

• Use of the Standard Formula. 80% of the amount to be allocated is to be 

allocated using this standard formula. The factors on which this formula 

is based are:

− One-third based on the relative number of youth (ages 16-19) in 

the civilian labor force within each local area, compared to the 

total number of youth (ages 16-19) in the civilian labor force in 

the state.34

 

34 This measure does not rely on a threshold but is, instead, a count of the number of individuals 

within the civilian (i.e., non-military) labor force.
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Social Policy Research Associates I-28

− One-third based on the relative number of unemployed individuals

within each local area, compared to the total number of 

unemployed individuals in the state.35

− One-third based on the relative number disadvantaged youth (ages 

16 to 21) within each local area, compared to the total number of 

disadvantaged youth (ages 16 to 21) in the state.36

• Use of Discretionary Allocation. 20% of substate allocations would be 

made using a discretionary formula of the state’s own choosing.37

• Hold-harmless/Stop-gain Provisions. As with the adult allocation, no 

local area shall receive an allocation percentage for a fiscal year that is 

less than 90% or greater than 130% of its allocation in the prior fiscal 

year. 

This proposed formula differs markedly from the formula currently being used to 

allocate youth funds under WIA. Although one of the factors—the number of 

disadvantaged youth—remains the same, including its weight in the formula, the other two 

factors are entirely new and would replace the current unemployment threshold variables 

described at length above. Indeed, no longer would unemployment thresholds play any 

role in the allocation of youth funds. 

Instead, the two new factors seem to address some of the limitations of the current 

formula, which have been described earlier in this chapter. Specifically, the factor using the 

number of unemployed individuals was described above as a possible mechanism by which 

WIA funds could be more accurately targeted toward areas with the highest need. By 

focusing on the raw number of unemployed individuals (rather than exclusively through the 

use of certain thresholds) those areas with greater numbers of unemployed persons would 

receive a higher proportion of the available funds. The proposed formula does not limit this 

factor only to the number of unemployed youth, however, meaning that allocations would 

still be made based on a factor that was at least in part unrelated to youth eligibility for WIA 

services. 

 

35 Note that this factor, like the unemployment factor in the current youth formula is not based on 

the number of unemployed youth but, rather, the numbert of unemployed individuals.

36 Disadvantaged youth would be calculated in the same manner as they are in the current allocation 

formula.

37 This formula, similar to the proposed discretionary formula for adults, is to be developed using 

appropriate demographic and economic factors.
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I-29 Social Policy Research Associates

This is mitigated somewhat by the inclusion of the second new factor, the relative 

number of youth (ages 16 to 19) in the civilian labor force. By limiting this factor to youth, it 

should focus funds more specifically to arewas with higher numbers of youth in need of, or 

potentially desirous of receiving services. Although the number in the civilian labor force 

may help to measure some form of “need” within a local area, it, along with factor 

incorporating the number of unemployed individuals, does little to target funds based on the 

eligibility of youth for WIA services. Whether this formula is better suited to targeting the 

eligible youth population than is the current formula remains to be seen. Without question, 

however, we should expect to see substantial variation between the allotments made under 

the current formula and those made under the proposed formula. We will examine this 

variation in Chapter III.

Unlike the adult formula (in which 15% of the allocation must be made using a 

state-defined discretionary formula), under the proposed youth formula 20% of the 

substate allocation would be made using a discretionary formula. Unlike the current 

option for using a discretionary formula, however, this alternative formula would be 

mandatory and states are not limited in the range of factors they could include in such a 

formula.

CONCLUSION

In this chapter we have provided an overview of the standard allocation formulas 

currently used for substate allocations in the adult and youth programs under WIA, as 

well as the general guidelines for allocating funds for the dislocated worker program. In 

so doing, we described the differences between the inter- and substate allocation 

formulas, as well as how the formulas differ from those used under JTPA. Further, we 

identified several key issues of concern in using these formulas, which represent a 

summation of several earlier research efforts as well as concerns expressed by states and 

local areas. These key issues form the context for the remaining chapters of this report. 

In the next chapter, we describe the results from our state survey, including a description 

of the number of states who have adopted discretionary formulas for the adult and youth 

programs, and what these formulas include as factors in allocating funds. The results 

presented in Chapter II provide a national snapshot of the mechanisms by which states 

are currently allocating funds to their local areas for their adult, youth, and dislocated 

worker programs. 
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II. STATES’ CURRENT ALLOCATION FORMULAS

In the previous chapter, we described the general formulas that states are to use for 

allocating funds under the WIA adult, youth, and dislocated worker programs. In 

addition, we described several of the key issues that have arisen from the use of these 

formulas, including concerns about whether these formulas accurately target funds to 

local areas most in need of them and whether they can adequately ensure that there is 

sufficient stability in funding from one year to the next. Although these concerns have 

been voiced well before the passage of WIA, additional concerns have emerged as a 

result of changes made under WIA. Specifically, the substate discretionary formulas that 

are allowed under WIA are of particular interest as such flexibility has never been a part 

of workforce funding mechanisms. To this point, however, many of the issues and 

concerns raised from these formulas have been largely anecdotal. There has not been a 

formal and uniform effort to gauge states’ perceptions of the formula and its success in 

targeting funds to areas most in need of them. Nor has there been an effort to assess 

whether states are actually exercising their flexibility by utilizing discretionary formulas 

in allocating funds under the adult and youth programs. 

In an effort to obtain these state perceptions and to identify exactly how states are 

allocating funds under WIA, as part of this project we conducted a mail survey of states. 

We sent this survey in the fall of 2002 to each state, as well as to the District of Colombia 

and Puerto Rico. In this chapter, we describe the results from this survey effort, and 

provide an overview of the current mechanisms by which states are allocating funds to 

their local areas for the adult, youth, and dislocated worker programs. 

We received completed responses from 46 of the 52 possible respondents, for a 

response rate of 88.5%.1 Of these respondents, eight (or 17% of the total) were single 

workforce area states while the remaining 38 were multiple workforce area states. The 

funding experiences of these single workforce area states is quite different in that they are 

not bound by the same allocation formulas because they do not have local workforce 

areas to which they must allocate their funds. As a result, we separate these eight states, 

and will describe their experiences and views of the WIA allocation process later in this 

 

1 Despite repeated mailings, reminders, and phone calls, we were unable to obtain completed 

surveys from Alaska, Idaho, Indiana, Kentucky, Missouri, and Mississippi.
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Social Policy Research Associates II-2

chapter. In the next section, though, we describe the results from the remaining 38 states 

that do allocate their WIA adult, youth, and dislocated worker funds to local areas using 

the standard and, in some cases, discretionary formulas. Because many of the questions 

in the survey concerning adult and youth funding were identical, and many of the 

responses were highly similar, we describe the results from these two separate streams of 

funding in a single section. Following this, we describe the results from questions 

concerning dislocated worker funding allocations.

ADULT AND YOUTH ALLOCATIONS2

The survey contained multiple questions concerning adult and youth allocations, 

including questions about the use or consideration of alternatives to the standard 

formulas, as well as respondents’ views concerning whether the standard formula 

accurately targets funds based on local areas’ needs or whether it ensures stability in 

funding from year to year and, if not, why it does not do so. We also asked states to 

describe the percentage of each stream of funds they held back for statewide activities, 

and for what these statewide funds were then used. Additionally, we asked states about 

the extent to which states implemented hold-harmless provisions, and whether they 

believed such provision had their desired effect. Further, we asked about whether local 

areas had any unexpended, or “carry-in” funds, and, if so, to what states attributed this 

fact. Each of these topic areas will be discussed in turn below.

Discretionary Formulas

A primary concern with adult and youth funding under WIA is whether states have 

adopted the use of discretionary formulas to supplement the required standard formulas in 

an effort to more accurately target funds to their local areas. Interestingly, despite the 

flexibility granted under WIA, very few states have opted to implement discretionary 

formulas. Indeed, only four states, or 10.5% of the multiple area states, reported using a 

discretionary formula for their adult and youth substate allocations.3 All four states 

implementing such discretionary formulas did so for both their adult and youth funds, and 

used this formula to allocate the maximum 30% of their funds. Thus, those states who 

 

2 Because single workforce area states need not allocate funds to designated LWIAs, we did not ask 

questions about the allocation processes of these states. Thus, the numbers reported throughout this section 

are valid only for the 38 multiple workforce area states from whom we received completed surveys, and 

percentages are thus based on this total of 38.

3 These four states, each of which used a discretionary formula for both their adult and youth 

programs, were AL, AZ, FL, and IA.
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adopted discretionary formulas did so for the maximum percentage allowable under 

WIA, suggesting that these states firmly believed that their discretionary formula would 

help them to target funds to their local areas.

In each case, the allocation process is conceptually very similar to a process of 

using only the standard formula. In other words, prior to making the allocations, the total 

pool of funds to be allocated is split into separate pots, based on the percentage assigned 

to each factor. Because only 70% of the funds are allocated using the standard formula, 

each of these three factors receives 23.3% of the overall pool of funds (calculated by 

multiplying the weight given to each factor under the standard formula, .333, by the 

share of funds to be allocated using this formula, or .7). The three factors in the standard 

formula therefore comprise 70% of the overall total to be allocated. The remaining 30% 

of the funds are allocated on the basis of the discretionary factors, described in detail 

below. In each case, however, a pre-assigned percentage of the funds is to be allocated 

on the basis of a given factor, and each local area receives their relative share for that 

factor. Each of the individual shares is then summed to provide an overall total allocation 

for each local area. Table II-1 shows the weights assigned to each factor, using the 

standard formula and using the various discretionary formulas.

Table II-1

Actual Weights for each Factor Using Standard and Discretionary Formulas

Standard 

Formula

Arizona 

Formula

Alabama 

Formula

Florida 

Formula

Iowa 

Formula

ASU Unemployment .333 .233 .233 .233 .233

Excess Unemployment .333 .233 .233 .233 .233

Disadvantaged .333 .233 .233 .233 .233

Unemployment Above 

State Avg.

0.0 .15 .225 0.0 .3*

Unemployment Above 

1.25%

0.0 0.0 0.0 .3

Excess Poverty 0.0 .15 .075 0.0 .3*

Total 1.0 1.0 1.0 1.0 1.0

* All 30% of this state’s discretionary funds are allocated based on a single factor, which varies 

depending on the stream of funding. For adult program funds, the 30% discretionary weight is 

assigned to the unemployment factor. For youth funds, the 30% is assigned to the excess youth 

poverty factor.
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As can be seen in Table II-1, the factors the four states used in their discretionary 

formulas were quite similar, in keeping with WIA’s restriction that any discretionary 

formula be based on excess unemployment or poverty. For example, two states 

(Alabama and Arizona) divide the weight of their discretionary formulas between excess 

unemployment and excess poverty (using the relative number of youth and adults living 

in poverty, respectively, for the two streams of funds). In Arizona, the split between 

these two factors is even, such that 15% of the overall allocation is based on excess 

unemployment, and 15% is based on excess poverty. In Alabama, excess poverty 

receives three-fourths of the discretionary formula weight (or 22.5% of the overall 

allocation weight) while excess unemployment receives one-fourth (7.5%). 

In both of these states, the term “excess” means the local area’s share that is above 

the state average, and each is a threshold measure that local areas either reach or do not. 

Thus, to calculate “excess” unemployment for this discretionary formula, one first must 

calculate the state’s, and then each local area’s, overall unemployment rate. If the local 

area’s unemployment rate is above the state’s rate, then the number of unemployed 

individuals within the area that are in excess of the number necessary to meet the state’s 

unemployment rate is summed. This represents that local area’s share of the state’s total 

number of excess unemployed, and this weighted share is then used in determining the 

amount they receive from the discretionary factor.

Similarly, the excess poverty factor is calculated by determining each local area’s 

poverty rate – the number of individuals (adults or youth, depending on the funding stream) 

living in poverty divided by the population. If a given local area’s rate exceeds that of the 

state, then the number living in poverty above the state average is summed, and this sum 

represents that local area’s relative share for the excess poverty factor. Both of these 

factors, then, are threshold measures, although their thresholds are not established, such as 

the 6.5% for ASUs or the 4.5% excess unemployment figure fixed under WIA. Rather, the 

threshold value is dependent on the overall state average. Thus, local areas that receive 

shares under these two discretionary factors can be said to have unemployment or poverty 

rates that exceed the state average. The result of including such factors in the discretionary 

formula is to more precisely direct funds toward those local areas whose unemployment, or 

poverty, is greater than the average of the state overall. Although this is conceptually 

similar to the standard formulas’ use of thresholds for ASUs and excess unemployment, the 

critical difference is that for these discretionary factors, local areas must exceed the actual 
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state average, rather than some pre-established baseline, which may be remarkably high or 

low, depending on the economic conditions within a given state. 

A third state, Florida, utilized a measure conceptually similar to the standard excess 

unemployment factor, but set the threshold level at 1.25% unemployment rate (compared 

to the 4.5% rate used in the standard formula). In so doing, this state was clearly trying 

to balance the targeting of funds to ensure that even local areas with lower levels of 

unemployment received some share of the funds. 

Using this factor allows some local areas with relatively low unemployment rates 

to still receive a share of the funds allocated based on unemployment figures and, in 

effect, acts somewhat like using the simple number of unemployed individuals because it 

is highly unlikely that any area would have a lower rate of unemployment than this. 

Should areas with unemployment rates lower than the 4.5% threshold for excess 

unemployment but above the 1.25% used as a discretionary factor be large in size, the 

impact on overall allocation within the state could be quite dramatic. For example, 

imagine that a single local area in a given state has half of the overall number of 

individuals in that state, but a relatively low unemployment rate, while remaining areas 

are generally much smaller but with higher unemployment rates. Using the standard 

formula, the larger area is unlikely to receive much in funds based on unemployment 

(though they may receive some share based on the number of disadvantaged individuals 

in their area), because its unemployment exceeds neither 4.5% nor 6.5%, yet their actual 

number of unemployed individuals may be greater than the rest of the areas combined. 

By incorporating a factor in the discretionary formula that allocates funds using a fairly 

low unemployment rate as the cutoff, this large local area would likely receive a 

substantial portion of funds, because their absolute number of unemployed individuals is 

quite high, compared to the smaller areas elsewhere in the state. As a result, this factor is 

conceptually closer to using the number of unemployed individuals in that it ensures that 

larger areas with lower relative unemployment rates still receive some share of funding. 

Of course, there remains a threshold, though it is doubtful that any areas would observe 

an unemployment rate below 1.25%.

The fourth state, Iowa, uses two separate factors in its discretionary formulas, but one factor 

is given zero weight in the adult formula, while the other is given zero weight in the youth 

formula. Specifically, for the adult formula, all of the discretionary 30% is allocated based upon 

excess unemployment, or the number of unemployed individuals above the state average. This is 

calculated similar to the other states, described above. In contrast, for youth allocation, this state 
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Social Policy Research Associates II-6

allocates the entire 30% of its discretionary funds using excess youth poverty. By using a different 

factor for adult funds as compared to youth funds, this state is targeting funds differently for the 

two streams. For adults, they are placing greater emphasis on the unemployed population than 

would have been the case under the standard formula, because they are adding an additional 

unemployment factor on which 30% of the funds are based (and, in so doing, they reduce the 

percentage of funds being allocated on non-unemployment factors, by 10%, since only the number 

of disadvantaged adults is a non-unemployment factor in their formula). Similarly, for youth, this 

state is placing greater weight on youth who are disadvantaged, by placing a 30% weight on youth 

poverty, in addition to the 23.3% weight assigned as a result of the standard formula. Thus, more 

than half of all youth funds are targeted based explicitly on the number of low-income youth or 

those living in poverty.

Despite these four states implementing discretionary formulas for the adult and youth 

programs, it is perhaps surprising that so few states took advantage of the flexibility offered 

to them under WIA.4 As noted in the previous chapter, however, despite the increased 

flexibility and the possibility for more accurately targeting funds to local areas, states may 

well have significant concerns about implementing such formulas, including concerns about 

data availability and quality, as well as disagreement over which factors should be utilized in 

these alternative formulas. Many more states, in fact, reported that they considered adopting 

alternatives to the standard formula, but ultimately did not do so. In fact, more than 40% 

(N=16) of all multiple workforce area states reported that they considered adopting an 

alternative formula to allocate adult funds, yet only one-fourth of those contemplating such a 

formula actually implemented one, as noted above. For youth allocation, approximately onethird of all multiple area states considered adopting an alternative formula, but here, too, only 

four states ultimately did so. It would seem, then, that many more states gave some thought 

to adopting an alternative, but most chose to rely on the standard formula to allocate funds.

The reasons cited for not adopting alternative formulas despite considering them 

focused heavily on three primary concerns, which are shown in Figure II-1. First, and 

foremost, states reported that the alternative formulas did not make a significant enough 

impact in the allotments assigned to local areas, either because the factors chosen were not 

 

4 None of the six states for whom we have no data reported that they were considering adopting 

discretionary formulas during their initial start-up under WIA (“Workforce System Information and 

Evaluation, Volume VI,” Social Policy Research Associates, Nov. 2000). Thus, we are confident that, even 

though we do not have updated information from these states, only four states have adopted discretionary 

formulas for the adult and youth programs under WIA.
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different enough from those included in the standard formula, or because the desire to 

implement hold-harmless provisions (discussed below) prevented substantial change from 

occurring even if it would have as a result of the discretionary formula. Thus, for many states, 

implementing a discretionary formula did not have the desired effect of targeting funds 

towards areas in greater need, though often this was because the importance of doing so was 

apparently outweighed by the necessity of utilizing hold-harmless provisions.

0

1

2

3

4

5

6

7

Number of 

Respondents

Did not change

allocation significantly

Could not agree on

factors to use

Problems with data

access and quality

Reason

Figure II-1: Reasons for Not Adopting Alternative Formula

A second, and likely related, reason why states considered but did not adopt 

alternatives to the standard allocation formulas was that those involved in the consideration 

could not agree on what factors should be used in the discretionary formula. In general, this 

inability to agree on the factors to be used reflected a concern that the factors discussed did 

not substantially change the allocation. Thus, similar to the states that chose not to adopt a 

discretionary formula because it did not produce meaningful alterations in allocation results, 

several other states simply could not agree on which factors to use, in part because various 

factors considered led to little noticeable change in results.

The third reason cited for considering, but not adopting, an alternative formula 

was that some states did not have access to the necessary data, or they had specific 

concerns about the reliability or quality of the data they could obtain. This was noted in 

the previous chapter as a significant concern, and at least three states mentioned this 

difficulty as a primary reason why they did not adopt an alternative formula to allocate 

funds for the adult and youth programs under WIA.
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Statewide Activities

Nearly all multiple workforce area states reported setting aside the maximum of 

15% of total adult and youth funds for statewide activities. Indeed, fully 92% (N=35) of 

the states reported they set aside the full 15% of adult and youth funds,5 while the 

remaining three states each set aside approximately 10% for statewide activities. This is 

important for the substate allocations because this percentage of funds is removed from 

the overall state allocation before it is distributed to substate areas.

For the adult funding stream, these funds were used for a relatively wide variety of 

purposes, including for: general administration, incentive funds, technical assistance, 

capacity building, incumbent worker programs, maintaining the eligible training provider 

(ETP) list, and maintaining/developing MIS. Thus, the statewide activities are used for 

many different purposes. In general, however, states set aside the maximum allowable 

under WIA, keeping 15% of their overall state total for both the adult and youth 

programs, and allocating the remaining 85% using the standard and, in a few cases, 

discretionary formulas.

Perceptions of Allocation Formulas’ Suitability

Respondents’ views on the suitability of the standard allocation formulas were quite 

mixed. Although half of the respondents reported that the formulas either very accurately or 

at least somewhat accurately target funds according to need, by definition this means that 

half the respondents did not feel the formulas were very accurate in targeting funds. Indeed, 

sixteen respondents, or 42% of the multiple workforce area states, reported that the standard 

adult allocation formula is somewhat inaccurate in targeting funds to local areas based on 

their need for them. Fully 37% of respondents reported similarly for the youth allocation 

formula. An additional state or two reported that the formulas were very inaccurate in 

meeting this objective. Thus, a substantial percentage of states believe the allocation formula 

is not particularly well-suited to targeting funds under the adult and youth programs to their 

local areas that are most in need.

 

5 These numbers were identical for both the adult and youth funding streams, so they are reported as 

one in this section.
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0%

20%

40%

60%

80%

100%

% of Respondents

Areas of

Substantial

Unemployment

(ASU)

Excess

Unemployment

DisadvantagedOther

Reasons

Factors

Figure II-2: Which Factors Contribute to 

Standard Adult Formula's Inaccuracy?

Figure II-2 displays, from among those who believed the formula was at least 

somewhat inaccurate in targeting funds, the percentage of respondents who believed any of 

the three formula factors contributes to the inaccuracy. As can be seen in this figure, in 

general, respondents believed that the two unemployment factors are the primary reasons 

for this poor ability to target funds more accurately. Specifically, fully three-fourths of 

those respondents who thought the formulas were not particularly accurate believed that the 

formula’s reliance on ASUs was a contributing factor to this inaccuracy. Similarly, 65% of 

respondents believed the reliance on excess unemployment led to inabilities to accurately 

target funds to areas most in need of them. To a somewhat lesser degree, 41% of 

respondents believed the reliance on disadvantaged adults leads to the formula being 

inaccurate in targeting funds based on local area needs. Interestingly, nearly fifty percent 

of all respondents noted that “other” reasons caused the adult formula’s allocation to 

inaccurately target funds to local areas. The vast majority of these responses were due to 

the reliance on Census data for data on disadvantaged adults. Many complained that data 

that are as much as 12 years old and hence are virtually useless in targeting funds, and 

several voiced hope that new methods of computing such figures could be established. In 

addition, two respondents noted that reliance on the three factors does not help in targeting 

WIA’s “universal customer,” and expressed the hope that a shift could be made toward 

targeting the adult population, which is a better reflection of WIA’s target population. 

Nearly identical proportions of respondents expressed concerns about these factors for the 

youth allocation, as well.
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Given that we asked respondents to assess how well the allocation formulas target local 

areas’ needs, we also asked them to describe what they meant by the term need. Although this 

question was an open-ended one, asking respondents to identify their definition of local area 

need, we coded responses into several categories and present these in Table II-2. As can be 

seen in this table, very few respondents asserted that the factors used in the current formula 

were the best definition of need, though approximately half the respondents reported that the 

number of disadvantaged adults was one indicator of need. Interestingly, just as substantial a 

percentage of respondents reported that the number of recipients eligible to receive WIA 

services (29%) or the number of individuals in the labor force (17%) is a key indicator of need 

within the adult program. Of course, a major factor in the proposed formula is the number of 

unemployed adults, and this was mentioned by approximately 20% of respondents as a key 

indicator of local areas’ need. Other potential indicators of need, raised by one or two 

respondents each, were the number of WIA registrants in a given local area, the number of 

adults overall, the average income level, the number of long-term unemployed, the number of 

employers in an area, and the number of adults with basic skills levels at less than 9th grade.

Table II-2

Factors that Define Local Areas’ “Need”

Adult 

Formula

Youth 

Formula

# of Disadvantaged 45% 55%

# of Eligible Recipients 29% 29%

# in Labor Force 17%

# of Unemployed 21% 8%

# of Adults/Youth 8% 8%

# of WIA Registrants 8% 3%

# of Employers 8%

Long-Term Unemployed 5% 3%

Average Income Level 5%

Basic Skills Deficient 3% 5%

Dropouts 12%

Pregnant/Parenting Youth 3%

Youth in Criminal/Juvenile Justice System 5%

Concerning youth program funds, an overwhelming number of respondents 

reported that the number of disadvantaged youth, or eligible recipients, was a key 

indicator of local areas’ need for funds. Additionally, 12% of respondents reported that 
II. States’ Current Allocation Formulas

II-11 Social Policy Research Associates

the number of youth in an area who were high school dropouts is a key indicator of need. 

Eight percent of respondents identified the number of unemployed older youth as a 

critical indicator of need. This is somewhat similar to one of the new factors in the 

proposed formula, which focuses on the number of unemployed individuals in an area, 

though this is not specific to unemployed youth. Additionally, one or two respondents 

each identified the number of WIA youth registrants in an area, the total youth 

population, and the number of: pregnant or parenting youth; long-term unemployed 

youth; youth in the criminal/juvenile justice system; and youth who are basic skills 

deficient.

In general, then, though there was not broad support for the current allocation 

formula factors as critical indicators of local areas’ needs, especially the ASU measure, 

there was some support for several of the factors that have been proposed in the pending 

WIA reauthorization legislation, suggesting perhaps that these proposed formulas might 

better target funds to local areas more accurately based on the areas’ needs. In Chapter 

III, we will examine how the proposed formulas would distribute funds differently than 

the current formulas, but here it is important to note that, at the least, the factors included 

in the proposed formulas are seen by at least some states as more accurately targeting 

local areas’ needs.

Finally, we asked states to assess how important several principles are in guiding 

substate allocation formulas. For both the adult and the youth program funds, we asked 

states to rate, on a scale from one to five, with one representing “least important” and 5 

representing “most important,” their assessments of the importance of accuracy, equity, 

and consistency in the funding formulas. Results for this question are displayed in Figure 

II-3. As can be seen in this figure, among these, accuracy was cited as the most 

important principle. Fully 86% of all respondents asserted that accuracy in targeting 

adult program funds to areas with the greatest need was very important (defined as “4” or 

“5” out of 5). All but one of the remaining states reported this was at least somewhat 

important (defined as a “3” out of 5). Under the youth program, the comparable figures 

were 92% asserting this was very important, and again all but one of the remainder 

agreeing it was at least somewhat important. Thus, states foremost consideration in 

assessing a formula to allocate funds under WIA is that it be accurate in targeting areas 

with greater levels of need for services.
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0

5

10

15

20

25

Number of States

Accuracy Equity Consistency

Ratings of the Importance of Principles

Figure II-3: Most Important Principles in 

Guiding Adult Formula Allocation

Less Important

Most Important

Less Important

Most Important

Less Important

Most Important

Although not nearly as uniformly, states also are concerned that the allocation formulas 

promote consistency in funding levels for local areas from year to year. For example, 

76% of states reported that such consistency was very important as a guiding principle 

for the formulas used to allocate funds to local areas for adult program services, and 

83% of states agreed this was very important for youth program allocations. Thus, 

consistency is seen as a key component that should guide substate allocation formulas, 

though this component is somewhat less important that ensuring that funds are targeted 

to where they are most needed. This is interesting, however, in light of the fact that so 

few states opted to adopt an alternative formula to allocate up to 30% of their funds, as 

WIA allows. If states truly believed that “need” was the most important principle in 

allocating funds, one would assume that greater numbers of states would have adopted 

such a formula, especially given the relatively tepid support given to the factors used in 

the standard formula. Perhaps, then, states identify need as critical in allocating funds, 

but ultimately believe that such need is approximated well enough by the standard 

formula in light of the potential instability caused by introducing new factors.

Still fewer states are heavily concerned that the allocation formulas promote equity 

across LWIAs to ensure that they can offer consistent levels of services. Specifically, 63% 

of states identified this as very important in adult program allocations (and 64% for youth 

program allocations). The remaining states were relatively evenly split, viewing this factor 

as either somewhat or not very important in guiding substate allocations. 
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Fluctuations in Funding Under Current Formulas

One of the primary concerns under the current formulas is that there is a high likelihood 

that local areas’ funding allotments will fluctuate substantially from one year to the next. 

Responses to questions asked about funding fluctuation are displayed in Table II-3. In terms 

of the adult formula allocation, in only one case did our respondents report that there was no 

fluctuation from year to year, and an additional 47% indicated that any such fluctuation had 

been within ten percent of the prior year’s funding. But half of all respondents reported that, 

had a hold-harmless provision not been enacted, their local areas’ funding would have 

fluctuated between ten and twenty percent (36% of respondents) or even more than twenty 

percent from one year to the next (14% of respondents). Thus, at least half the states in our 

sample reported local areas would have had greater than 10% changes from one year to the 

next in the adult program allotments they received. Very similar figures were reported for the 

youth program formula allocation (i.e., 46% of all respondents reported there would have 

been changes of greater than 10%). Substantial fluctuations thus would be quite common in 

both the youth and the adult program allotments in the absence of hold-harmless provisions, 

and local areas would be forced to adapt to these changes. Even with hold-harmless in place 

in many states, 41% of all respondents noted that local areas in their state had experienced 

problems in providing adult services (and 35% in providing youth services) because of 

funding instability from year to year.

Table II-3

Fluctuation in Allocations and its Impact on Servi ces

Adult Youth

No Fluctuation 3% 3%

Less than 10% 47% 51%

Between 10 and 20% 36% 37%

Greater than 20% 14% 9%

Experienced Problem Providing Services Because of Fluctuation 41% 35%

_________

Note: Columns in rows 1 through 4 sum to 100%. Row 5 is a separate question asked of all respondents in 

a yes-no format. The percentage shown reflects the percentage answering affirmatively.

Fluctuations in yearly allotments are clearly a concern, given their impact on 

services for both the adult and youth programs. Several respondents noted that 

individuals already receiving services within a given program year expect to continue to 

receive those services until they complete them, even if that is in a subsequent program 
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year. Fluctuations in funding create concerns about what services can be paid for, 

leaving significant concerns about being able to serve customers effectively. In addition 

to causing problems for local areas in planning and knowing what services they will be 

able to offer, respondents also noted that fluctuations prevent local areas from being able 

to enter long-term contracts with providers, because they do not know whether they will 

have the funds to honor such contracts in the future. This creates greater possibility for 

turnover among providers; thereby reducing the experience and understanding of WIA 

among those providers who will be in place in future years. 

0%

20%

40%

60%

80%

100%

% of Respondents

ASU Excess

Unemployment

Diadvantaged Lapse in Hold

Harmless

Factors Causing Fluctuation

Figure II-4: Causes of Funding Fluctuation

ADULT

YOUTH

As a means of understanding the roots of these fluctuations, respondents reported 

what about the formulas caused these shifts. Results for this question are shown in 

Figure II-4. As with the results concerning the inaccuracies of the formula, most 

respondents pointed to the two unemployment factors as the primary cause of year-toyear fluctuation. For example, 89% of respondents reported that the adult formula’s 

reliance on ASUs helped to create the fluctuations, while all respondents (100%) cited the 

reliance on excess unemployment as helping to create funding instability. Similar figures 

for the youth formula were 88% citing ASUs and 76% citing excess unemployment. In 

contrast, only 17% of respondents cited the use of the economically disadvantaged factor 

in creating instability for adult funding (and 12% for youth), likely because the factor 

relies on Census data that have not changed for local area allocation since the 1990 

Census data first became available. Additionally, only 6% of respondents blamed the 

lapse in hold-harmless for funding instability in either the youth or adult programs, 
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II-15 Social Policy Research Associates

though many others may have if their state had not invoked this provision through WIA 

implementation.

Use of Hold-harmless. One mechanism by which states can limit the fluctuations 

in allotments to local areas is the hold-harmless provision. This provision, described in 

some detail in Chapter I, enables states to limit any potential funding decreases to local 

areas to 90% of their average allocation over the previous two years. Although this 

provision was not mandated in the first two years under WIA, it was required as of the 

third program year. Further, DOL allowed states to invoke this provision in the first two 

years if they chose to do so. Two-thirds of state respondents reported their local areas 

encouraged them to invoke the hold-harmless provisions for adult allocation prior to it 

becoming mandatory under WIA, and 80% of states reported their local areas encouraged 

them to do so for the youth program. This makes sense, given that local areas must be 

concerned with stability in their funding, and the hold-harmless provision is one key way 

of accomplishing this goal.

Perhaps in response to this encouragement, 76% of all state respondents reported 

implementing hold-harmless provisions prior to being required to do so for the adult 

allocation, and 74% for the youth allocation. Thus, states, like their local areas, were 

quite concerned about ensuring stability in funding to the localities that determine service 

provision under WIA.

Implementing the hold-harmless provisions was seen as critical in preventing 

dramatic changes in local area allotments. Results for these questions are shown in 

Figure II-5. As can be seen in this figure, 65% of all respondents noted that local areas 

would not have been able to serve about the same number of adult customers from year to 

year had the hold-harmless provision not been enacted. And more than half of all 

respondents reported that adult allocations would have been very different for all (9%) or 

some (48%) of their local areas, while an additional 40% reported these allocations would 

have been at least somewhat different. 
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50%

60%

70%

% of Respondents

Could Not Serve Similar Number of Customers

Figure II-5: What Would Have Been the Impact of Not Implementing Hold 

Harmless?

ADULT

YOUTH

Similarly, 60% of respondents noted their local areas would not have been able to serve 

similar numbers of youth customers from one year to the next. And similar numbers also 

reported that youth allocations would have been very different for all (10%) or some 

(47%) of their local areas had hold-harmless provisions not been invoked, while an 

additional 40% reported that these allocations would have been at least somewhat 

different for local areas. Clearly, then, the hold-harmless provisions have achieved the 

desired effect of reducing fluctuation in local area allocations and, according to these 

results, in ensuring that local areas can serve similar numbers of adult and youth 

customers from year to year.

Unexpended Funds. One obvious potential indicator of funds being allocated in 

disproportion to the level of need within a local area is the extent to which some areas 

had unexpended funds for a given program year at the conclusion of that year. If areas 

were unable to expend their allocated funds, while others expended their entire allotment 

prior to the end of the program year, this would indicate, perhaps, that funds were 

mismatched based on local areas’ needs, or at least their ability to use the available funds. 

Further, because such “carry-in” funds are not incorporated into subsequent years’ 

formula allocations, those local areas that carry funds from one year to the next simply 

add these funds to their subsequent allocation. This situation could be seen as doubly 

troubling in that it is the areas who had trouble spending their full allotment in the 

previous year who now are asked to spend not only a full year’s allotment, but also to 

spend the remainder from the previous year. Thus, unexpended funds create multiple 

layers of potential inaccuracy in targeting levels of need within states.
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Interestingly, nearly all states (92%) reported that some of their local areas had 

unexpended adult program funds at the conclusion of a given program year, while a 

lower but still large percentage of states reported that local areas had unexpended youth 

program funds (81%). Such sizable percentages suggest that, indeed, the annual 

allotments made to local areas do not closely mirror the amount they are able, or need to 

spend in that year.

Most states did not, however, believe that these unexpended funds were the result 

of inaccuracies in the funding formulas’ ability to target funds to local areas in need. 

Indeed, only 6% of states reported that unexpended adult program funds (and 4% of 

youth program funds) were the result of the allocation formula being inadequate to target 

funds to local areas based on their levels of need. Instead, most states (79% for adult 

funds and 67% for youth funds) believed that the primary reason for the leftover funds 

was that some local areas were slower to start up under WIA, thereby leaving them with 

less time to spend their allotments. This would suggest that the formulas were not to 

blame for the unexpended funds because the problem was not one of demand within a 

local area but, rather, the local areas’ ability to supply the services to meet demand. 

Similarly, one-third of all states noted that a key reason for unexpended funds was that 

local areas found it difficult to recruit or enroll individuals into WIA. Although this 

could suggest limited demand, states noted instead that this difficulty was a pervasive one 

that affected all or most of their local areas. This suggests that, rather than identifying 

areas of low demand specifically, the unexpended funds were allocated during a 

transition from JTPA to WIA, and this transition created temporary gaps between the 

services offered and the customers who may have wanted access to them. In general, 

then, states were quick to note that, although many of their local areas had experienced 

unexpended funds, this was less the result of inaccuracies or inefficiencies in the 

formulas used to allocate funds than it was the result of difficulties in ramping up or 

making the transition from JTPA to WIA. 

DISLOCATED WORKERS

Given that the allocation formula for the dislocated worker program is substantially 

different from the adult and youth formulas, and because the amount that the state can set 

aside differs as well, we asked questions of states separately for this funding stream. In 

general, it may be expected that formulas to allocate funds under the dislocated worker 

program will less accurately target the need in a local area, because almost by definition 

those who are eligible to receive funds under this stream will be unknown until layoffs 
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actually occur. Unlike under JTPA, in which long-term unemployed individuals were 

generally eligible to receive such services, under WIA these individuals are not 

automatically eligible. As a result, no data are available that directly measure the eligible 

population and, thus, the allocations can be expected to be less accurate in targeting need 

than formulas for the adult and youth programs. 

All states, even those who are single workforce area states, were asked to complete 

the section on dislocated workers; as a result, the universe for this section is the 46 states 

from whom we received completed surveys. A majority of states set aside the maximum 

25% for rapid response activities, as shown in Figure II-6. Twenty-seven of the forty-six 

states (59%) reported setting aside this maximum amount, with the remainder setting 

aside somewhat less. An additional 17% of states set aside between fifteen and twenty 

percent, while 13% of states set aside ten percent and the remaining 11% set aside less 

than ten percent. Additionally, all but two states reported setting aside their maximum 

15% of dislocated worker funds for statewide activities. The remaining two states 

reported setting aside 10% for these activities.

0%

20%

40%

60%

80%

100%

% of States Setting Aside 

the Amount

Less Than 10% 10% 15% to 20% 25%

% Set Aside for Rapid Response

Figure II-6: Percentage of Dislocated Worker 

Funds Set Aside for Rapid Response

States also received substantial additional amounts through the National 

Emergency funds, which were set aside at the federal level to award based on needs that 

arise during a given program year, such as large plant closings or mass layoffs. Results 

for this question are shown in Table II-4. As can be seen in this table, 17% of states 

(N=8) reported having received no money from this fund, and an additional three states 
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II-19 Social Policy Research Associates

reported receiving only an average of a few thousand dollars per program year. The 

remainder of the states received substantially more than this over the previous three 

program years. For example, nine percent of the states received an average of between 

$100,000 and $750,000 over those three program years. An additional 39% of states 

reported receiving between $750,000 and $5 million per year over that time. Fifteen 

percent of states received between $5 and $10 million, while the remaining thirteen 

percent of states reported receiving $10 million or more on average from the National 

Emergency fund over the previous three program years. Despite these amounts, 

however, each state reported that the National Emergency fund has no impact on their 

substate dislocated worker allocation strategy, largely because such funds cannot be 

anticipated, as they are awarded based on unexpected mass layoffs that occur within a 

given year. Thus, states make their allocations based on current trends they can identify 

in advance of a given program year, and reserve specific percentages for rapid response 

activities. Further, local areas often receive National Emergency funds based on 

unexpected events that occur during the years, though the state has no control over such 

funds and, as such, cannot utilize them in any allocation strategy.

Table II-4

Amounts States Receive in National Emergency Funds

Average Amount Received in Previous Three Years Percentage of States

Zero 17%

Less Than $10,000 7%

Between $100,000 and $750,000 9%

Between $750,000 and $5 million 39%

Between $5 million and $10 million 15%

More than $10 million 13%

Allocation Formulas

Five states have adopted the standard adult allocation formula for dispensing funds 

to local areas for the dislocated worker program. Thus, these five states make allocations 

for dislocated workers based on the identical factors used in dispensing adult program 

funds. In effect, then, these states are allocating both the adult program and the 

dislocated worker program funds using a single formula. As noted in the previous 

chapter, proposed changes to the allocation formulas would achieve this same effect in 
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that the adult and dislocated worker program funds would be merged into a single 

funding stream (along with funds from Wagner-Peyser). 

The remaining states (89%), however, have not adopted the standard adult formula 

to dispense dislocated worker funds. Rather, these states rely on several of the factors 

identified in WIA (and presented in Chapter I) to allocate dislocated worker funds to their 

local areas. Indeed, most of these states use many or all of the recommended factors 

under WIA, including data on unemployment, unemployment concentrations, plant 

closing and mass layoffs, declining industries, farmer-rancher economic hardship, and 

long-term unemployment.

The percentage of states that reported using each of the factors, as well as the average 

weight assigned to each factor, is displayed in Table II-5. The most frequent factor used in 

allocation formulas for dislocated worker funds is unemployment. Slightly more than threefourths of all states report using this factor as a key determinant in allocating dislocated 

worker funds. This is perhaps not surprising, since unemployment does represent two-thirds 

of the factors that constitute the inter-state allotment formula for dislocated workers. In this 

sense, states’ substate formulas are thus incorporating one of the primary factors that is 

responsible for their state-level allotment. 

Table II-5

Factors Used in Dislocated Worker Allocation, and Their Average Weights

Percentage of States 

Using Factor Average Weight

Unemployment 77% 28%

Mass Layoff 72% 10%

Declining Industries 72% 5%

Long-Term Unemployment 69% 24%

Unemployment 

Concentrations

65% 25%

Plant Closing 52% 3%

Farmer-Rancher Hardship 46% 4%

That unemployment is heavily utilized in the current dislocated worker allocation, 

as it is in the current adult allocation formula, is important, because unemployment will 

be the dominant factor in both adult and dislocated worker allocations should the 

proposed changes to the allocation formulas be enacted. Given the fact that substate 
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allocations already are often based partly on unemployment, changes in allotments to 

local areas as a result of the proposed changes to the allocation formulas may not be as 

severe as they would were unemployment not currently being used. Even though, on 

average, states reported that unemployment received slightly more than a 50% weight in 

the allocation, proposed changes to the formulas would significantly increase this 

percentage. Thus, changes may well still be observed, as we explore in Chapter III.

A similarly high percentage of states reported using mass layoff data and declining 

industries data in their allocation formulas for the dislocated worker funding stream. 

Fully 72% of states used these factors in their allocations, though typically the weight 

they were given was relatively small. For example, most states applied a weight of ten 

percent or less to these factors, and the average weight applied to these factors was 

between five and ten percent, respectively. Thus, while they were included in the vast 

majority of states’ allocation formulas, their influence remained somewhat small due to 

the small weight given them in the formula.

The number of individuals who are long-term unemployed also was utilized 

widely. Seven in ten states reported using these data in making their allocation, and often 

they carried a weight of 25% in the formula, though, on average, this factor was weighted 

slightly less than 25%. Not unlike the unemployment data described above, this is 

perhaps not surprising as it represents one-third of the weight used in the inter-state 

allocation formula. Thus, states utilizing these data can be seen as simply replicating in 

their substate allocation the mechanism by which they were allotted funds for dislocated 

workers in the first place. Further, unemployment concentrations (generally in the form 

of areas of substantial unemployment) were used by 65% of states. That these factors 

and the unemployed factor described above are both widely used, and given heavy 

weight, in substate formulas means that these formulas relatively closely mimic the interstate formula which relies on them exclusively.

Somewhat fewer states also incorporated plant closing data and farmer-rancher 

economic hardship data. Slightly more than half of all states reported using plant closing 

data, while 46% of states use hardship data. Where these two factors were used, typically 

they were given relatively little weight. In fact, often these factors were used for 5% or 

less of the overall dislocated worker allocation and, on average, they were weighted as 

only three to four percent of the formula.
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In general, then, most states used a wide variety of factors in their dislocated 

worker allocation formula, but the bulk of the weight was given to the three factors that 

are used in inter-state allocations for this program, namely unemployment, 

unemployment concentrations, and long-term unemployment. Typically, at least 60%, 

and often 75% or more, of each state’s allocation was made on the basis of these three 

factors. 

Typically, these formulas were developed by a combination of state workforce 

department officials and representatives from the state WIB. In approximately one-third 

of the states, a workgroup was composed of state and local officials in the effort to 

develop a substate allocation formula. In fewer cases, the Governor or representatives 

from the state legislature were involved in the decision-making. In each of these cases, 

only 15 to 20% of states reported such involvement

Funding Stability and Recapture of Funds

Slightly more than three-fourths of all states reported that their local areas had 

already or were planning to invoke their transfer authority to shift funds from the 

dislocated worker program to the adult program, presumably because the dislocated 

worker funds were not going to be spent by the conclusion of a given program year. 

Although this could suggest that the formula used to allocate funds for the dislocated 

worker program was inaccurate, such inaccuracies are likely more inevitable in this 

program as compared to the adult or youth programs, because there is no way to predict 

where layoffs will occur prior to a given program year and, thus, the need for dislocated 

worker funds can vary dramatically from year to year. 

Despite the difficulties in estimating the level of need in a given local area, most 

states do report that each local area should receive a baseline amount of dislocated 

worker funds, rather than setting all funds aside for the state to allocate as the need arises. 

This baseline amount would be used, according to states, to assure that each local area is 

capable of operating a dislocated worker program and, thus, is equipped to respond when 

layoffs in their area do occur. Fully sixty percent of states reported that baseline funding 

for local areas was important. Given that this is the manner in which the program 

currently operates, perhaps it is unsurprising that the majority of states believe this is the 

most effective means of operating the program. Interestingly, though, one in four states 

do not believe such a baseline is necessary (the remainder reported they were unsure 

whether baseline funds were necessary), suggesting perhaps that some states believe the 

program might be run more effectively by reserving all funds at the state level until such 
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time as dislocations occur, at which point funds could be directed toward the area 

experiencing these layoffs. Despite this, however, most states did believe that allocations 

should be made to local areas, even when these allocations cannot be targeted to the 

eligible population due to problems with data availability and variability in the 

occurrence of dislocations.

When local areas are unable to expend their dislocated worker funds, they typically 

face recapture of the funds by the state. Nearly eighty percent of states require their local 

areas to meet an 80% obligation or expenditure rate by a specific date within the program 

year or they face recapture of their funds to be reallocated to those areas in need of them. 

This is largely driven by the fact that states themselves must obligate at least 80% of their 

funds or face national recapture. As a result, states pass this requirement on to their local 

areas. After recapturing unexpended funds from local areas, the states’ reallocation 

process often mirrors the original allocation, as nearly sixty percent of states use the 

original allocation formula in reallocating recaptured funds. The remaining states 

reallocate based on local areas’ needs, which are by then known since layoffs have 

occurred, and on local areas’ expenditure rates. Those local areas who have expended 

their allotments, and can still demonstrate a need for funds are thus given preference in 

reallocation. In this way, then, recapture and reallocation can be seen as mechanisms by 

which a state can supplement any shortcomings in the original formula, or more 

accurately target needs that arise during a given year.

SINGLE WORKFORCE AREA STATES

Eight states6 are single workforce area states, meaning that they need not dispense 

funds to local areas using any allocation formula prescribed by WIA. All eight of these 

states returned completed surveys, in which we asked a set of questions about whether 

and how they dispense funds to sub areas within their state.

All states reported setting aside money, both under the youth and the adult 

programs, for statewide activities. One state set aside 10% of both adult and youth funds, 

while the remaining states set aside the maximum 15% for statewide activities for both 

the youth and adult programs. Similarly, one state set aside 10% of dislocated worker 

funds and the remaining states set aside 15% of dislocated worker funds for state 

 

6 Seven of these are states, and the remainder is the District of Colombia. For ease of exposition, 

we simply refer to all as states.
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activities, and all states set aside the maximum of 25% of dislocated worker funds for 

rapid response activities.

Despite these general set asides, only a few states reported actually allocating 

money to substate areas. Specifically, three of the eight states allocate funds to substate 

areas, each by using a formula developed by their state. Indeed, each of these three 

reported that the ability to develop a discretionary formula, and thus target funds to the 

substate areas most in need, had an impact on the state’s decision to become a single-area 

state. 

One of these states used exactly the same three factors as the standard formula to 

allocate adult and youth program funds to their substate areas; in this case, although the 

same factors are used as in the standard formula, the weights are slightly different (with 

the number of disadvantaged adults/youth receiving more than one-third of the weight). 

A second state allocates both adult and youth funds based on several factors, including 

the three used in the standard formula, as well as long-term unemployment data and the 

number of individuals living in poverty. This state allocates funds to its three counties 

using these formulas. Finally, the third state that allocates funds to substate areas does 

so, for adults, by focusing solely on unemployment concentrations (i.e., ASUs). For 

youth, this state incorporates two factors into its allocation formula: the number of 

disadvantaged youth and the number of individuals living in poverty. In each case, the 

state allocates money to substate areas based on the location of the state’s One-Stop 

centers.

SUMMARY

In this chapter, we have described the results from a survey of all states concerning 

their current allocation formulas under WIA for the adult, youth, and dislocated worker 

programs. We received completed surveys from 46 of the 52 states to which we sent the 

survey.7 As noted, very few states actually adopted the use of a discretionary formula for 

their adult and youth programs, even though WIA allows states to do so for up to 30% of 

their allocated funds. Those four states who did adopt discretionary formulas typically 

used indices of excess unemployment, or unemployment above the state average, as their 

discretionary measures. 

 

7 In addition to the 50 states, we also mailed surveys to Washington, D.C., and Puerto Rico.
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In general, states allocated money under the adult and youth programs very 

similarly. Each of the four states using discretionary formulas did so for both programs. 

All remaining states used the standard formula to allocate their entire amount of youth 

and adult program funds.

Most states were primarily concerned that the formula used to allocate funds under 

WIA target those funds to areas with the greatest need. Additionally, states believed that 

consistent funding levels were important so that local areas could rely on sufficient funds 

to provide a stable set of services from one year to the next. Though the survey results 

would suggest that states’ concern for targeting need is paramount, the fact that only four 

states opted to adopt a discretionary formula in allocating funds for the adult and youth 

program suggests that perhaps stability in funding among local areas may instead be the 

critical factor in this allocation. In an effort to ensure this stability, the vast majority of 

states invoked their authority to implement a hold-harmless provision in substate funding, 

even though WIA did not mandate one until two years after WIA implementation.

Substate funding for the dislocated worker program relied primarily on several factors, 

including most prominently unemployment, unemployment concentrations, and long-term 

unemployment, which are similar to the factors used in inter-state allocations. Several other 

factors often were used in substate allocations, though typically these other factors received 

less weight in the allocation formula. A few states used the standard WIA adult formula to 

allocate funds under the dislocated worker program, thereby foreshadowing to some degree 

proposed changes to the allocation formulas under WIA that would integrate adult and 

dislocated worker funds (as well as those under Wagner-Peyser).

Finally, eight of the states in our sample were single workforce area states. Of 

these, three reported allocating funds to substate areas, often using the standard WIA 

adult and youth formulas, or slight variants of them, as the mechanism by which to make 

these allocations. The remaining five states did not allocate funds to substate areas and, 

instead, directed funds as needed from the state level.

In the subsequent chapter, we utilize the information gleaned from our state survey to 

simulate current allocation strategies. In addition, we again draw on results from the survey to 

model alternative funding strategies and compare how these alternatives create different 

allotments than the current formulas. In each case, we refer back to the results gathered 

through the survey to describe the results of the simulated allocation strategies.
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III. COMPARISONS OF CURRENT ALLOCATIONS WITH 

ALTERNATIVE MODELS

In the previous chapters, we have provided an introduction to the formula 

allocation process used to allocate WIA funds to substate areas, and described the results 

from a survey of states conducted as part of this evaluation. In so doing, we have 

identified several of the key concerns that have been expressed about the allocation 

formulas, and discussed why these concerns have arisen. Additionally, in presenting the 

results from our state survey, we have detailed how states currently are allocating their 

WIA funds, and the problems they have experienced in doing so. In this chapter, we use 

the knowledge gleaned from the survey to reproduce the actual allocations made to local 

areas by states by mimicking the allocation formulas they are using. Next, we develop 

alternative formulas, utilizing the discretionary formulas used by states implementing 

such formulas, as well as other plausible alternatives, in an effort to examine how the 

actual allocations made under such alternatives would differ from the current allocation. 

Such comparisons enable us to examine whether one or more of these alternatives is 

better able to target the level of need existing within local areas by focusing more funds 

to those areas with higher levels of need.

This chapter is divided into several key parts. First, we describe how we replicated 

states’ allocation formulas for adults and youth, and discuss our approach to reproducing 

actual allocations, including the development of Areas of Substantial Unemployment 

(ASUs) and the collection of all the data required to reproduce these allocations. In the 

next section, we make comparisons between the current allocations for these two 

programs and the results that would be obtained by using one of several alternative 

formulas both without and then with specific exogenous constraints such as a hold 

harmless provision. Additionally, we detail how the various alternatives presented in the 

chapter impact local areas with differing levels of need, such as those with high rates of 

poverty or high numbers of unemployed individuals. In this section, we describe how 

local areas of different types are impacted by each of the alternatives presented in this 

chapter, by detailing, for example, how cities are impacted by each alternative as 

compared to suburbs and rural areas. Next, we present a similar, though somewhat 

shorter, discussion of the dislocated worker allocation formulas and alternatives to them. 

Finally, we discuss several ways in which the formulas could be used to ensure 

organizational and financial stability for local boards. In this section, we discuss the 
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utility of various exogenous factors, such as stop-gain and hold-harmless provisions, as 

well as factoring in carry-in funds into local areas’ subsequent year allocations, in 

promoting greater stability in local area funding levels from one year to the next.

REPLICATING CURRENT ALLOCATION FORMULAS

Our first step in examining the impact of alternative formulas was to replicate the 

existing formula, using the information gathered from the state survey. Because single 

area states do not allocate funds directly to local areas whose boundaries could be 

replicated through Census or other data, we exclude these eight states from our efforts to 

replicate allocations. Thus, the discussion that follows holds for all 44 multiple-area 

states. For each of the three funding streams (adult, youth, and dislocated worker), we 

asked states to identify the specific factors they use in the allocation formula, and the 

weights they assign to each of these factors.1 Using these data, then, we could construct a 

funding formula for each state that replicates their actual funding formulas simply by 

utilizing the specific factors and weights identified in their survey. Thus, if a state 

reported using solely the standard formula for adult allocation, we constructed a formula 

for that state relying on the three standard factors and applying to each of them a weight 

of one-third. In other words, local areas in states using the standard formula would be 

assigned an allocation formula as follows:

Allocation Share = .333*(ASU share) + .333*(Excess Unemployed Share) + 

.333*(Disadvantaged Share)2

 For states that relied on discretionary formulas in addition to the standard formula, 

we assigned a weight of .233 to each of the standard factors (i.e., one-third times .7, 

which represents the 70% weight the standard formula must receive even when using a 

discretionary formula), and the appropriate weights assigned to the discretionary factors 

(i.e., if a state used two factors in the discretionary formula and weighted them equally, 

each received a weight of .15 in the formula. This is derived by dividing the 30% weight 

 

1 Although we did not receive responses from six of the states, each of these states reported as part 

of a separate study that they planned on using the standard formula for adult and youth allocation. Thus, 

for adult and youth allocations replicated in this chapter, we simply used the standard formula for these 

states.

2 In this equation, the disadvantaged share is either for youth or adults, depending on the formula 

being examined. The calculation of each LWIA’s share, shown in parentheses, is described in detail below.
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given to a discretionary formula by the two factors weighted equally). This is denoted 

below:

Allocation Share = .233*(ASU share) + .233*(Excess Unemployed Share) + 

.233*(Disadvantaged Share) + .x*(Discretionary Factor 1 Share) + 

.y*(Discretionary Factor 2 Share), 

where .x equals the weight assigned to the first discretionary factor, and .y equals 

the weight assigned to the second discretionary factor, and so on. 

Combining the various formulas across states, we can thus replicate the allocations 

made to each local area under these funding streams. 

Results obtained from these formulas do not exactly mirror the actual allocations 

made in a given program year, because they do not account for exogenous constraints 

such as hold-harmless provisions which, as noted in the previous chapter, most states 

implemented even before being required to under WIA. But we were interested in 

producing the results that would be obtained in the absence of such external constraints 

so that we could more properly examine how allocations would differ under alternative

formulas. After we discuss the impact of alternative formulas in comparison to the 

current formula being used by each state in the absence of such constraints, we then 

describe these impacts when coupled with a hold-harmless provision. In this way, we can 

better explicate how simply changing the formula would have an impact on allocations, 

as well as how this impact may be mitigated through the use of hold-harmless provisions.

Thus, our initial goal was to develop a formula for each state that mimics their 

current formula, and to produce allocations to local areas using this formula. Having 

already accomplished the construction of the requisite formulas, we next needed to 

acquire the data upon which the formulas are based. In general, as noted in the previous 

chapter, the adult and youth formulas were based largely upon the standard formulas, as 

well as a few highly similar factors. Thus, we needed to compile unemployment figures 

and tallies of the number of disadvantaged adults or youth within local areas. For these, 

we relied upon two primary data sources, the 2000 Census and the yearly Local Area 

Unemployment Statistics (LAUS) series. These are the same data sources states rely on 

for their own allocations and, thus, we are able to mimic their allocations with our own.

For each of these data sources, we had to develop configuration files so that we 

could aggregate the data grouped by Census tract, for the Census, or by county, city, or 
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other geographic region, in the LAUS, into the LWIA boundaries necessary for allocating 

funds under WIA. This required sorting through each LWIA’s boundaries to determine 

which counties, cities, or other regions were included in the LWIA. We then used this 

information to group data from the two data sources into the respective LWIA’s, thereby 

providing us with aggregate counts for each LWIA of the total number of people, the 

number of unemployed individuals, the number of disadvantaged individuals, and several 

other critical measures on which our analyses are based.

Excess Unemployed

For each factor to be used in our calculations, we computed each local area’s share 

by comparing its relative amount on that factor to the overall state total. Thus, for 

example, to compute a local area’s share of the excess unemployed population, we first 

had to determine the number of unemployed individuals necessary for the local area to 

have an unemployment rate of 4.5% (by simply multiplying the total number of 

individuals within the labor force by .045). We then subtracted this number from the 

total number of unemployed individuals within the local area. This difference 

represented the absolute number of excess unemployed individuals within the local area.3 

Summing across all local areas within a state, we then computed the total number of 

excess unemployed within the state. Any given local area’s share for this factor, then, 

was simply their own number of excess unemployed divided by the state total of excess 

unemployed.4 This share could then be attached to its appropriate formula weight (i.e., 

.333 for states using the standard formula; .233 for states using a discretionary formula) 

to determine each local area’s allocation based upon this factor.

Areas of Substantial Unemployment

Although the description of calculating each local area’s share of the excess 

unemployed is relatively straightforward, the remaining calculations are not as simple. 

For example, the ASU calculation is quite tricky, in that entire local areas need not meet 

the definition of an ASU in order for them to receive a share of the funds allocated based 

on this factor. As noted in Chapter 1, all that is necessary for an area to receive some 

share on this factor is for some part of the area (required only to be large enough to carry 

 

3 If this difference was negative, meaning the local area’s unemployment rate was less than 4.5%, 

we simply set the number of excess unemployed within this local area equal to zero.

4 From this calculation, it should be clear that summing all the shares across local areas will total 

one, representing one hundred percent of the total number of excess unemployed for the state.
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out workforce activities) to have an unemployment rate of at least 6.5%. Thus, 

computing the unemployment rate only for the local area as a whole may well miss some 

portions of the area that qualify as an ASU. Further complicating matters, local areas 

have a vested interest in having as much of their unemployed population as possible 

within ASUs, so that if the entire LWIA has an unemployment rate of at least 6.5%, all 

unemployed individuals within that area are counted in tallying the local area’s share on 

the ASU factor. And, one step further removed, if an entire state has an unemployment 

rate above 6.5%, it is considered an ASU and, thus, all local areas in the state also receive 

this designation. 

Thus, to calculate this factor appropriately for each local area, one must first 

examine the state unemployment rate to determine if the state qualifies as an ASU.5 If so, 

all LWIAs also qualify and, thus, each LWIA’s share for the ASU factor is simply their 

total number of unemployed individuals. If the state itself does not qualify as an ASU, 

the second step is to examine whether an entire LWIA qualifies as an ASU. In such 

cases, all unemployed individuals within the LWIAs that qualify as ASUs are counted 

toward their share on this factor. Finally, if neither the state nor the LWIA is an ASU, 

one must examine the individual sub-areas that combine to form an LWIA. If any of 

these sub-areas has an unemployment rate of at least 6.5%, it counts as an ASU, and the 

total number of unemployed individuals within that sub-area counts toward that LWIA’s 

share on the ASU factor. But, because local areas have a vested interest in maximizing 

the number of unemployed individuals within ASUs (because they will receive a higher 

share of the funding allocated based on this factor), it is critical that the total ASU 

population for a local area be calculated by finding the largest contiguous population that 

qualifies as an ASU.6 We accomplished this by first identifying ASUs among the various 

counties, cities, or parts of cities that comprise each LWIA, and then expanding outward 

to areas contiguous to this in order to maximize the unemployed population within the 

sub-area that still maintains an unemployment rate of at least 6.5%. 

 

5 States reported in their survey responses that they have adopted the DOL definition of an ASU as a 

contiguous area with a population of 10,000 or more. Additionally, they noted that they follow the DOL 

procedure for identifying ASUs, as detailed in this section.

6 Of course, it is possible for more than one ASU to be defined within a single local area, because 

two non-contiguous areas within the LWIA could each have an unemployment rate of at least 6.5%. In 

such cases, the number of unemployed individuals within all the ASUs within the local area is summed to 

provide the total number of unemployed individuals within ASUs for that local area.
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Once all the ASUs have been identified, by designating the state as an ASU, entire 

LWIAs as ASUs, or sub-areas within the LWIAs as ASUs, each local area’s share for this 

factor is calculated as described for the excess unemployed variable above, by first 

summing the total number of unemployed individuals in ASUs across LWIAs, and then 

dividing each LWIA’s total by the state total. This relative share can then be multiplied 

by the weight assigned to the share to produce each LWIA’s share of the funds to be 

allocated under the ASU factor.

Disadvantaged Adults/Youth

Calculating the number of disadvantaged adults or youth also is less 

straightforward than calculating the number of excess unemployed. Because an 

individual is classified as disadvantaged if they have a household income that is less than 

the higher of the poverty level or 70% of the lower level standard of living (LLSIL), one 

must know both the household income of all individuals, as well as their family size, 

because these two levels vary depending on one’s family size. Although the Census, 

from which the disadvantaged factor traditionally has been obtained, does provide in its 

summary files an indicator of poverty status for all individuals, to calculate an 

individual’s LLSIL status one needs access to individual-level data. Unfortunately, such 

data were unavailable to us for this study, because the individual-level data had not yet 

been released for the 2000 Census. As a result, our disadvantaged measure used 

throughout this report relies solely on the poverty status of individuals, as recorded by the 

Census. As such, it may reflect an undercount of the total number of disadvantaged 

individuals within any given local area, because some individuals who are not defined as 

living in poverty may well be defined as having a household income less than 70% of the 

LLSIL. These individuals would not be counted in the analyses in this report. Although 

the implications of this omission are not certain, we can speculate as to the likely effect it 

will have on our results. LLSIL values are typically calculated at the regional level (or, 

in some cases, at the metropolitan area level), while poverty levels often are calculated 

for smaller geographic regions. This means that LLSIL values are often applied 

uniformly across a state, while poverty levels can vary significantly within that state. As 

a result, not including LLSIL in our measure of disadvantaged individuals may 

systematically benefit local areas whose poverty level is high, as compared to those 

whose levels are low. This is because areas with low poverty levels would have more 

room between their poverty level and the 70% cutoff for LLSIL than areas with high 

poverty levels (because they would be compared to the same LLSIL in both cases), 

thereby yielding a greater possibility of having more of the area’s residents fall under the 
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LLSIL cutoff. Thus, not being able to include LLSIL as a factor in our measure of 

disadvantaged individuals may systematically favor areas with high poverty levels. 

In addition, the fact that we had to rely upon aggregate-level data from the 

Summary Files of the Census also meant that we were constrained by the breakdowns 

included in these files. Thus, although the number of disadvantaged youth is intended to 

be calculated for ages 16 to 21, we were able to extract only the number for ages 16 to 

20. Those ages 21 were grouped with a much larger set of ages in the Summary Files 

and, as such, we are unable to include them in this calculation. Given these caveats, 

however, the calculation of each LWIA’s share of disadvantaged individuals proceeds 

exactly as described for the two factors above.

Although the calculation of the three factors described above would enable us to 

reproduce adult and youth allocations for the vast majority of states, it would not enable 

us to reproduce allocations for the four states who adopted a discretionary formula to 

allocate 30% of their funds. For these states, we need to calculate each LWIA’s share of 

the three factors described above, but also must calculate shares for the additional factors 

the state includes in their discretionary formula. In the subsequent section, then, we 

describe these factors and their calculation.

Factors Used in Current Discretionary Formulas

There are several additional factors that are used currently by states that have 

adopted a discretionary formula. Although they may help to create markedly different 

allocations as compared to using the standard formula alone, calculating each local area’s 

share for a particular factor is conceptually similar to those described above. The four 

factors to be described in this section are unemployment above 1.25%, unemployment 

above the state average, and excess poverty.

Calculating the factor measuring unemployment above 1.25%, which is used by the 

state of Florida and is assigned the full 30% weight allowable for a discretionary formula, 

is very similar to calculating the number of excess unemployed, described above, except 

that instead of the threshold being 4.5%, the lower value of 1.25% is used instead. 

Unemployment above the state average is assigned a 15% weight in Arizona’s 

allocation formulas, a 22.5% weight in Alabama’s formulas, and the full 30% weight in 

Iowa’s adult allocation (though it receives no weight in the youth formula). This is 

calculated similar to the factors described previously, with the exception that the 

threshold used in this calculation is the statewide average of unemployment.
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Finally, excess poverty is assigned a 15% weight in Arizona’s allocation formulas, 

a 7.5% weight in Alabama’s formulas, and the full 30% weight in Iowa’s youth allocation 

(but receives no weight in the adult formula). Excess poverty is defined by the states that 

use it as poverty above the state average; thus its calculation is very similar to that of 

previous measures. In this case, however, the threshold is the statewide average poverty 

level, rather than any form of unemployment. 

Although when comparing alternative formulas, we utilize several additional 

factors in our analyses, we describe each of these factors as we introduce them into our 

simulations. The factors described above are those necessary to replicate current 

allocation mechanisms for the adult and youth formulas and, thus, to produce current 

funding allocations for each LWIA. In the next section, we compare the allocations 

derived from these models to several alternative models, exploring how the alternatives 

impact local areas’ funding, and the extent to which they create significant shifts in 

allocations. Further, we examine how these alternative allocation formulas impact local 

areas of different types, including those that have high versus low rates of poverty, those 

that are located primarily in cities versus suburbs or rural areas, and other factors, and 

discuss what these impacts mean in terms of targeting levels of need within LWIAs.

COMPARISONS OF ALTERNATIVES TO CURRENT ADULT AND YOUTH 

FORMULAS

In this section, we compare a series of alternative funding formulas for the WIA 

adult and youth program to the current formulas, examining their impact on the 

allocations made to local areas both generally, and with a specific focus on how local 

areas of different types are impacted. We begin by using the discretionary models 

developed by several states, and applying these models to all states to gauge how the 

adoption of such formulas would impact allocations across all states. Next, we examine 

how the incorporation of several alternative factors not currently in place impact the 

allocations made to local areas. These additional factors were raised, by either our survey 

respondents or by one of several interested stakeholders consulted as part of this project, 

as intriguing possibilities for producing better allocation results than are currently 

produced. Following this discussion, we then present results for the formulas that have 

been proposed under pending H.R. 1261, described in Chapter I. In this discussion, we 

merge funding allocations from the WIA adult and dislocated worker funds, as well as 

Wagner-Peyser, to mimic the funding strategy that is proposed under this impending 

legislation. 
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 Alternative Models

III-9 Social Policy Research Associates

For each of the alternative formulas presented below, we discuss how its 

implementation would impact the allocations received by local areas by comparing the 

results of the alternative formula to those of the current formulas. In many cases, we 

present the results for adult and youth formulas together, as many of the alternative 

formulas have similar effects on these two funding streams because the formulas are 

quite similar. In those cases in which the two sets of formulas differ substantially, 

however, we present results separately for adult and youth allocations, and describe the 

differential impact the alternative formula has on each funding stream. Unless otherwise 

noted, the comparisons made between alternative and current formula allocations use data 

for PY 2002. Although we replicated the allocations made to local areas for each of PYs 

2000, 2001, and 2002, we primarily use the earlier two years’ data to assist in calculating 

the average allocation level for the two-year period prior to PY 2002, which is used in the 

hold-harmless calculations below. Thus, in the subsequent sections, we compare how 

alternative allocations made using PY 2002 data compare to actual allocations made in 

PY 2002 using the current allocation formulas.

Applying Discretionary Formulas to All States

Formulas Using Unemployment and Poverty Above the State Average. The 

first set of alternative formulas we examined were those that used two factors in addition 

to the three used in the standard formula. In each case, these two factors were 

unemployment and poverty above the state average, but the weights assigned to each was 

different depending on the state adopting the formula. Each will be discussed in turn.

Alabama’s Formula. First, we examine the discretionary formula adopted by 

Alabama. This formula relies on the standard adult and youth formula for the minimum 

70% of its allocation, but incorporates the two additional factors, according to its survey 

responses, to produce a more equitable allocation of funds. For the remaining 30% of its 

allocation. This formula applies a 7.5% weight to the unemployment above the statewide 

average measure and a 22.5% weight to the poverty above the statewide average 

measure. Thus, the formula is calculated as follows:

Allocation = .233*(ASU Share) + .233*(Excess Unemployed Share) + 

.233*(Disadvantaged Share) + .075*(Unemployment Above State 

Average Share) + .225*(Number in Poverty Above State Average Share) 

As can be seen, this formula applies greater weight to those areas with high rates of 

poverty, and substitutes the threshold of the state average of unemployment in place of a 
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Social Policy Research Associates III-10

small percentage of the two standard formula threshold measures (i.e., ASU, Excess 

Unemployed).

Applying this formula to all states, even this relatively straightforward change has 

significant impacts on the allocations to local areas. Specifically, as shown in Figure III-1, 

nearly eleven percent of all local areas observe a change in excess of 50% to their WIA adult 

funding levels when using this formula as compared to the standard formula, and nearly 

twelve percent of local areas observe similar changes in their youth allocation. Keep in mind 

that in these and subsequent comparisons, we are comparing how the allocations would 

change by implementing the alternative formula in the absence of any exogenous constraints, 

such as hold-harmless provisions. Thus, changes of more than 50% are possible because no 

local area is ensured that it will receive at least 90% of its prior allocation. Later in this 

chapter we discuss how the implementation of hold harmless provisions would mitigate the 

changes evident in this section.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

% of Local Areas

Less than 10% 11% to 25% 26% to 50% More than 50%

% of Change in Allocation

Figure III-1: Changes in Allocation Due to 

Alabama's and Arizona's Formulas

Alabama Formula

Arizona Formula

In addition to the fairly sizable percentage of local areas whose allocation would be 

changed dramatically (i.e., by more than 50%), a substantial percentage of local areas 

would observe changes to their adult allocations of somewhat smaller amounts. For 

example, as shown in Figure III-1, the adult allocations of nearly one in six local areas 

would change between 25% and 50%, and an additional 37% of local areas would 

observe fluctuations in their adult allocations of between 10% and 25%. Slightly more 

than one in three local areas would receive an adult allocation within 10% of their current 

allocation, using this alternative formula.
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Similar, although slightly larger, changes would occur in youth allocations. 

Specifically, nearly one in four local areas’ youth allocation would change between 25 

and 50%, while 39% of local areas would see changes of between 10% and 25%. Only 

one in four local areas’ youth allocation would change by less than 10% if this alternative 

formula was adopted across all states.

These changes would impact local areas of different types quite differently. For 

example, local areas with greater population bases would, on average, see an increase in 

their allocations, while local areas with smaller populations would generally lose funds 

under this formula. These figures are nearly identical for the youth and the adult 

allocations. In Figure III-2, we show this effect in adult allocations by breaking local 

areas into one of four quartiles, based on their population size. For this and similar 

calculations below, we divide LWIAs into quartiles based on a given characteristic, in 

this case population size. For each quartile of LWIAs, we compute the average 

percentage change experienced as a result of a particular formula or set of assumptions. 

Thus, LWIAs in, for example, the lowest quartile, each experience a specific percentage 

change in their allocation. These percentage changes are averaged over all the LWIAs in 

the quartile, which produces the average percentage change for the quartile.

As can be seen in this figure, local areas in the highest quartile of population size 

(i.e., the top 25% of local areas in terms of their population size) see an average increase 

of nearly 5% under this alternative formula, while those in the lowest quartile, on 

average, lose nearly 9% of their allocations. Further, local areas in the middle two 

quartiles also lose money, on average, using this alternative formula.7 Thus, the effect of 

this formula, when applied across all states, is to divert money away from areas of 

smaller population and toward areas with larger populations. These trends reflect the 

average allocation across the various LWIA population quartiles; any individual LWIA 

may receive more or less in funding as a result of this formula, no matter its rank in 

population size. But the pattern is clear: on average, larger (in terms of population)

 

7 It is possible for three of the four quartiles to be negatively impacted, with only one gaining in 

allocation, because the highest quartile receives a greater proportion of current funds than do the smaller 

areas. Thus, although the actual dollars gained by the larger quartile must be exactly equal to the number 

of dollars lost by the others, a 5% gain for the larger LWIAs allocation represents a much larger percentage 

loss for the smaller areas, because of the differing dollar amounts in current allocations.
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Figure III-2: Alabama and Arizona's Formulas' Impact on Funds Directed 

to LWIAs Based on Population Size

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

Quartile with

Smallest

Populations

2nd Quartile 3rd Quartile Quartile with

Largest

Populations

Quartiles of Population

Average Change

Alabama Formula

Arizona Formula

LWIAs are the beneficiaries of this alternative formula while smaller ones tend to lose 

funds, both for adult and for youth allocations.

Similarly, cities tend to be the beneficiaries of this alternative formula for adults, 

rather than suburbs or rural areas. This is not surprising given the above findings, since 

cities tend to have higher populations than do these other areas. But the effect of this 

alternative formula is to direct nearly 8% more funds toward LWIAs that have heavy 

concentrations of their population in cities, and to direct less of the funds toward LWIAs 

in suburbs and rural areas. Specifically, LWIAs that are primarily suburban receive 

nearly 4% less in funds, while rural areas receive just over 5% less when applying this 

formula. Although youth allocations show a similar pattern, for these funds, both cities 

(3.9%) and suburban areas (1.8%) receive slightly more, on average, when applying this 

alternative formula, while only rural areas (-6%) lose funds. 

As noted above, one of the primary features of this alternative formula is to weight 

more heavily those areas with higher numbers of people living in poverty. What the 

above results suggest is that poverty concentrations tend to occur more in LWIAs with 

larger populations and in cities than in other areas. This is consistent with prior research 

conducted on formula allocations, which have showed that the large percentage of the 

allocation that is based on unemployment, rather than poverty, tends to direct money 
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 Alternative Models

III-13 Social Policy Research Associates

away from central cities.8 One effect of this alternative formula, then, may well be to 

direct money toward those areas with greater poverty.

We can examine this possibility by looking at the effect this formula has on LWIAs 

with greater rates of poverty. Results of this analysis for adult allocations are shown in 

Figure III-3, which breaks LWIAs down into quartiles based on their rates of poverty. 

Interestingly, this figure suggests that it is not the areas with the highest rates of poverty 

that benefit from this alternative formula. Rather, it is areas that have somewhat higher 

rates of poverty that benefit the most. Specifically, on average, LWIAs in the quartile 

with the highest rate of poverty receive nearly the same amount of adult funds as they do 

under the current formula. However, LWIAs in the next two quartiles receive 

substantially more, on average, using this alternative formula. For example, LWIAs in 

the third quartile (the 50th to the 75th percentile) receives nearly 8% more in adult funds, 

on average, than they do under the current formula. In contrast,

Figure III-3: Alabama and Arizona's Formulas' Impact on 

Funds Directed toward Poverty

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

Quartiles

w/Lowest Poverty

Rates

2nd Quartile 3rd Quartile Quartile w/Highest

Poverty Rates

Quartiles of Poverty

Average Change

Alabama Formula

Arizona Formula

those LWIAs with the lowest rates of poverty would receive, on average, 17% less in 

adult allocations using the alternative formula than they do in the current formula. Thus, 

this formula does target areas with higher poverty, although the areas with the highest 

 

8 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986), Abt 

Associates, Cambridge, MA.
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Social Policy Research Associates III-14

poverty change very little. Coupled with the findings concerning cities and rural areas, 

this suggests that some rural areas with very high rates of poverty receive less under this 

alternative formula, perhaps because the absolute number of individuals within these 

areas pales in comparison to areas with poverty rates that are not as high, but whose 

absolute numbers of individuals in poverty is substantial.

Another consideration in targeting funds to local areas is to do so based on the 

LWIAs’ ability to serve customers. As noted in Chapter I, one might wish to target funds 

to those local areas who serve greater numbers of customers per WIA dollar, on the 

notion that these areas are serving customers somewhat more efficiently than areas 

serving fewer customers per dollar. Such a measure could be used, albeit relatively 

crudely, to represent the demand for services relative to the overall allocation made to the 

local area. This is, of course, not the only way in which one could assess efficiency, and 

it does not assess the quality of WIA services provided. Rather, it is intended as a 

potentially useful way to categorize LWIAs on a dimension that captures demand for 

LWIAs’ services in relation to the amount they are given to provide them. As such, it 

should not be taken as the benchmark of quality but, rather, as one way by which LWIAs 

can be categorized while standardizing for the size of their allocation.

As a means of examining this, we obtained from the WIASRD data files 

information on the number of customers enrolled in WIA for each local area in PY 

2001 (the latest year for which data are available), and divided this number by the total 

allocation the LWIA was allocated in our simulation for PY 2001, to provide a rough 

estimate of the number of individuals served per WIA dollar. In Figure III-4, we 

group LWIAs into four quartiles from lowest to highest, with the higher quartiles 

serving greater numbers of adults per WIA dollar. As can be seen in this figure, this 

alternative formula does a very good job of targeting more adult funds toward those 

LWIAs with higher numbers of customers served per dollar, while also diverting funds 

away from those local areas serving fewer customers per WIA dollar. Indeed, the 

LWIAs in the top quartile would receive, on average, 4% greater adult allocations than 

under the current formula, while LWIAs in the next highest quartile would receive 

nearly 11% more, on average. In contrast, LWIAs below the median in customers 

served per dollar would lose money under the alternative funding, with those in the 

lowest quartile losing, on average, more than 5% of their adult allocation.
III. Comparisons of Current Allocations with

 Alternative Models

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III-4: Alabama's and Arizona's Formulas' Impact on Funds 

Directed toward LWIAs Based on Customers Served per Dollar

-8.0%

-4.0%

0.0%

4.0%

8.0%

12.0%

Quartile Serving

Fewest Youth

Per Dollar

Allocated

2nd Quartile 3rd Quartile Quartile Serving

Most Youth Per

Dollar Allocated

Quartiles of Customers per Dollar

Average Change

Alabama

Arizona

Thus, this alternative formula, which uses the standard formula for 70% of adult 

and youth allocations, and employs two additional factors, including unemployment and 

poverty above the state average, for the remaining 30%, would substantially alter the 

allocations made to LWIAs under both the adult and youth programs. Primarily, it would 

direct money toward areas with greater populations and areas that are or include cities, 

and away from LWIAs with smaller populations and which are primarily rural. Although 

this alternative would direct more funds toward areas that are higher in poverty, it would 

not direct them toward those areas highest in poverty, likely because the areas with 

highest poverty often tend to be rural areas with smaller populations. Thus, their relative 

amount of individuals living in poverty, or who are unemployed, is likely to be small in 

comparison to areas with larger populations, even though their rates of poverty or 

unemployment may be higher. Additionally, this formula would have the effect of 

directing more funds toward areas that serve more individuals per WIA dollar, while 

directing funds away from those areas that serve fewer customers per dollar.

Arizona’s Formula. The next alternative formula we examined was the 

discretionary formula adopted by Arizona. Similar to the formula described above, this 

relies on the standard adult and youth formula for the minimum 70% of its allocation, and 

incorporates unemployment and high poverty (both of which are defined as rates that 

exceed the state average), to make up the remaining 30% of the allocation. In its survey 

responses, Arizona reported that this formula was intended to produce a more equitable 

allocation of funds, as well as produce the least negative impact on its local areas. To 

accomplish this, Arizona gives equal weight to these two additional measures, thereby 
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Social Policy Research Associates III-16

applying a 15% weight to both the unemployment measure and the poverty measure. 

Thus, the formula is calculated as follows:

Allocation = .233*(ASU Share) + .233*(Excess Unemployed Share) + 

.233*(Disadvantaged Share) + .15*(Unemployment Above State Average 

Share) + .15*(Number in Poverty Above State Average Share) 

Due to its similarity, it is not surprising that the impact of this formula is highly 

similar to that described above. Results for this formula are shown alongside the results 

for Alabama’s formula, in the figures above. As can be seen in Figure III-1, similar to 

the formula used by Alabama, just over 10% of local areas would observe a change of at 

least 50% in their adult allocation. Greater percentages, however, would experience 

changes of between 25% and 50% (i.e., 24% of all LWIAs) as well as between 10% and 

25% (i.e., 38%). Slightly more than one-fourth of all LWIAs would experience a change 

of less than 10% in their adult allocation, even in the absence of hold-harmless 

provisions. Very similar percentages occur for the youth formula as well. These results 

are very similar to those shown for Alabama, precisely because the two formulas rely 

upon very similar factors. The sole difference is the amount of weight attached to 

unemployment and poverty above the state average. Arizona’s formula attaches greater 

weight to unemployment, while Alabama’s attaches greater weight to poverty.

Given this disparity, we would expect that the impact of applying Arizona’s 

formula to all states would be that specific types of local areas are affected somewhat 

differentially than by Alabama’s formula. This impact should be most evident when 

examining LWIAs based on their poverty rates, because it is the weight of this factor that 

is primarily responsible for differences in the allocations between the two alternative 

formulas. The results displayed in Figures III-2 and III-3 largely confirm this hypothesis. 

As seen in Figure III-2, the impact on targeting funds based on population of applying 

Arizona’s formula across all states is remarkably similar to the impact of Alabama’s 

formula – areas with the largest populations receive, on average, more funds, while 

smaller areas receive fewer funds. In each case, the direction is identical, and the actual 

percentage is extremely similar as well. Not shown in this figure are results when 

examining the impact of Arizona’s formula on cities, suburbs, and rural areas. These 

results, too, are quite similar to those presented for Alabama’s formula. Cities would 

receive, on average, just over 5% more, while suburbs would experience very small 

reductions, and rural areas would experience losses of about 5%.
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In contrast, though, Figure III-3 shows that the impact of the two formulas differs 

significantly when considering the poverty rates within the LWIAs. Specifically, the 

Arizona formula, which applies a lower weight to the poverty measure, has a lesser effect 

on LWIAs with different poverty rates than does the Alabama formula. For example, the 

Alabama formula impacts LWIAs with low poverty rates very negatively, as the lowest 

quartile receives over 17% less than when using the current formula. In contrast, using 

the Arizona formula, this lowest quartile receives approximately the same amount as they 

do under the current formula. Similarly, the Alabama formula targets approximately the 

same amount of funds toward the highest quartile of LWIAs, while the Arizona formula 

directs nearly 4% less toward LWIAs in this quartile. In general, then, this second 

alternative formula is quite similar to the first, but is less effective at directing funds away 

from areas with low poverty rates, largely because it gives less weight in the formula to 

poverty factors.

Finally, applying Arizona’s formula to all states has a similar effect on our measure 

of WIA efficiency. As can be seen in Figure III-4, generally speaking areas that serve a 

greater number of WIA customers per dollar would experience increases in their 

allocation, while those areas serving fewer customers per dollar would experience 

reductions in their allocations.

Iowa’s Formula. The third discretionary formula that uses these same two factors 

is Iowa’s. In this case, however, only one of the two factors is used, depending on which 

program funds are being allocated. For the adult program, the poverty measure is 

assigned a zero weight so that unemployment above the state average is assigned the 

maximum 30% weight. In contrast, for the youth program, unemployment is assigned a 

zero weight and, thus, poverty is given the maximum 30% weight in the formula. These 

formulas are, then, conceptually similar, and because 70% of the allocation is exactly 

identical, they should be expected to produce results relatively similar to that of the first 

two alternative formulas described above. Yet, because the adult formula assigns no 

additional weight to the poverty measure, one might expect that it would be less suited 

for targeting areas of poverty than the formulas presented above. Indeed, because more 

than three-fourths of the allocation is made entirely based upon unemployment, one 

would expect that this formula would target the unemployed almost exclusively. In 

contrast, for youth allocation, more than 50% of the formula is based on poverty factors 

and, thus, this formula should be best suited for targeting areas of high poverty, at least 

among the alternatives and current formulas discussed thus far.
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Results for these formulas are displayed in Figures III-5 and III-6. The results shown 

in Figure III-5 suggest that applying Iowa’s adult and youth formulas across all states would 

have a substantial impact on the allocations received by local areas, though 

the impact would be greater for youth funds than for adult funds. For example, while only 

8% of LWIAs would experience a change of at least 50% in their adult allocations, nearly 

fourteen percent of LWIAs would experience similar shifts in their youth funding. 

Continuing this trend, while 23% of LWIAS would see a change of between 25% and 50% 

in their adult funds, were Iowa’s adult formula to be applied across all states, slightly less 

than 29% of LWIAs would see similar fluctuation under the youth program. Across the 

board, then, shifts produced by applying Iowa’s formulas to all states are greater for youth 

allocations than for adults. This is readily explainable because the discretionary formula 

used by Iowa for its adult allocation is not dramatically different than the current standard 

formula. Although it reduces the share allocated based on poverty from one-third to .2333, 

and increases the share of unemployment from two-thirds to just over three-fourths, the 

overweighting of unemployment as compared to poverty remains. In contrast, Iowa’s youth 

allocation switches this balance such that poverty is given more weight in the formula than 

the two unemployment factors

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

% of Local Areas

Less than 10% 11% to 25% 26% to 50% More than 50%

% of Change in Allocation

Figure III-5: Changes in Allocation Due to Iowa's Formulas

Iowa Adult

Iowa Youth

combined. As a result, one might expect there to be greater fluctuation among youth 

allocations to LWIAs, and less among adult allocations, just as is shown in Figure III-5.

The impacts of Iowa’s formula on LWIAs of different populations and different 

regions is quite similar to those seen for the prior two formulas. Specifically, more funds 

are targeted toward LWIAs with greater populations while fewer funds are directed 
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III-19 Social Policy Research Associates

toward suburban and rural areas. The impact is particularly pronounced for the youth 

formula, in which cities would expect to receive nearly nine percent more on average, 

while suburbs and rural areas each would lose between four and five percent. 

Given the above discussion, one would certainly expect that Iowa’s youth formula would 

better target areas with high poverty than would its adult formula. Figure III-6 shows that 

this is, indeed, true. Specifically, applying Iowa’s adult formula across all states has little 

impact on LWIAs based on their rates of poverty. What little fluctuation does exist likely 

does so because of the fact that the weight assigned to poverty is reduced from one-third 

to .233 in this formula. In contrast, however, applying the youth formula across all states 

has substantial impact on LWIA’s youth allocation, depending on the poverty rates 

within the LWIAs. For example, the quartile of local areas with the lowest poverty rates 

loses just over 10% of its funds when applying this alternative formula. The next lowest 

quartile similarly loses portions of their funds, though only about 6.5% on average. 

LWIAs in the top two quartiles, on the other hand, gain substantially when applying this 

formula. Indeed, LWIAs in the highest quartile, meaning those with the highest rates of 

poverty, would experience an increase of just over 11% in their youth allocation were 

Iowa’s formula adopted across all states. Thus, this formula does a very good job of 

targeting youth

Figure III-6: Iowa's Formulas' Impact on 

Funds Directed toward Poverty

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

Lowest Poverty

Quartile

2nd Quartile 3rd Quartile Highest Poverty

Quartile

Quartiles of Poverty

Average Change

Iowa Adult

Iowa Youth

 

funds toward those local areas with higher rates of poverty. Figure III-7 shows that this 

formula has somewhat less ability to target funds toward those areas that are serving a 

greater number of customers per dollar. For both adults and youth, this formula directs 
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Social Policy Research Associates III-20

less money to the lowest and highest quartiles in terms of efficiency, while directing 

more money toward the middle two quartiles. 

Figure III-7: Iowa's Formulas' Impact on Funds Directed toward LWIAs 

Based on Customers Served per Dollar

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Quartiles w/Lowest

Customers per Dollar

2nd Quartile 3rd Quartile Quartile w/Highest

Customers per Dollar

Quartiles of Customers per Dollar

Average Change

Adult

Youth

Florida’s Formula. The fourth formula currently being used as a discretionary 

formula allocates funds somewhat differently than those discussed above. Florida uses 

the standard formula for the minimum 70% of its allocation for both youth and adult. 

The remaining 30% is allocated on the basis of the number of unemployed in excess of 

1.25% unemployment within the local area. Thus, for both youth and adult allocations, 

the formula is calculated as follows:

Allocation = .233*(ASU Share) + .233*(Excess Unemployed Share) + 

.233*(Disadvantaged Share) + .3*(Unemployment Above 1.25% Share)

Similar to the formulas described above, implementing this alternative across all 

states would result in substantial changes to both adult and youth allocations. Results for 

these two sets of allocations are shown in Figure III-8. As can be seen in this figure, 

although only a very small percentage of local areas would see either their youth or adult 

allocations change by at least 50% under this formula, nearly half would experience 

changes of between 25% and 50%. An additional eighteen percent would experience 

changes of between 10% and 25%, with the remaining 29% experiencing changes of less 

than 10%, even in the absence of a hold harmless provision. Thus, although fewer 

LWIAs would experience dramatic shifts in funding (of more than 50%), the vast 

majority of local areas would experience shifts of at least 10 percent, with most 

experiencing changes of at least 25% for both their adult and youth allocations.
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0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

% of Local Areas

Less than 10% 11% to 25% 26% to 50% More than 50%

% of Change in Allocation

Figure III-8: Changes in Allocation Due to Florida's Formulas

Adults

Youth

Given these sizable shifts in funding across all LWIAs, it is critical to identify how 

areas of different types would be impacted by implementing this formula. Interestingly, 

relying on the lower threshold of unemployment, as this formula does, has significantly 

different effects than those produced by the alternative formulas described above. Figure 

III-9 displays how implementing Florida’s formula across all states would impact LWIAs 

based on their population size. As can be seen in this figure, unlike the alternative 

formulas described above, which targeted more funds toward areas with larger 

populations, this formula does just the opposite. Indeed, LWIAs in the highest quartile in 

population would lose 4% of their adult allocations, on average, were this formula to be 

implemented. In contrast, LWIAs in all remaining quartiles stand to gain from 

implementation of this formula, with those in the lowest quartile (the smallest LWIAs, 

based on population) experiencing gains in adult allocation of just over 8%. The figures 

are nearly identical for youth allocations as well, which is unsurprising given that the 

formulas used for the two sets of allocations are virtually identical.9

 

9 The sole difference between them is that the disadvantaged factor is calculated for the number of 

disadvantaged adults for adult allocation, and youth for the youth allocations. Because Florida uses a 

discretionary formula for 30% of its allocation, this sole difference receives a weight of only .233, or less 

than one-fourth of the overall total allocation. Further, this difference is already accounted for because we 

are comparing the impacts of the alternative formula, across all states, to the current formula, in which the 

initial difference between the two allocations was the one-third weight assigned to the disadvantaged 

factors. As a result, the “true” difference between the two sets of allocations is actually only ten percent (or 

.333-.233). Given that the number of disadvantaged youth in a given LWIA is highly correlated with the 
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Social Policy Research Associates III-22

Figure III-9: Florida's Formulas' Impact on Funds Directed to LWIAs 

Based on Population Size

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Quartile with

Smallest Populations

2nd Quartile 3rd Quartile Quartile with Largest

Populations

Quartiles of Population

Average Change

Adult

Youth

These findings are echoed by the results shown in Figure III-10, which depict how 

implementation of Florida’s formula would impact LWIAs that are primarily in central 

cities, suburbs, and rural areas. As can be seen in this figure, LWIAs that are primarily 

located in central cities and suburbs either experience very little change as a result of the 

implementation of this formula, or would experience small decreases in their funding. 

LWIAs in rural areas, on the other hand, experience gains of slightly more than 2% as a 

result of this formula. This is due to the fact that there are substantial numbers of such 

LWIAs who do not meet the threshold for either excess unemployment (4.5%) or ASUs 

(6.5%), but do meet the lower threshold used in this formula. Thus, incorporating this 

factor into the allocation formula increases the allocation shares targeted to these areas.

 

number of disadvantaged adults, this difference in 10% of the allocation would be expected to result in an 

even smaller difference in actual allocations. 
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III-23 Social Policy Research Associates

Figure III-10: Florida's Formulas' Impact on Funds Directed to Cities, 

Suburbs, and Rural Areas

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

Cities Suburban Areas Rural Areas

Type of Area

Average Change

Adult

Youth

Interestingly, this alternative formula also targets funds well to areas of high 

poverty. Despite the fact that the formula differs from the standard formula only in that it 

reduces the weight assigned to poverty and increases the weight assigned to 

unemployment, the impact of the formula is to allocate more funds toward areas with the 

highest levels of poverty, and away from areas with low poverty. These results are 

displayed in Figure III-11. As can be seen in this figure, only LWIAs in the highest 

quartile of poverty rates experience an increase in both their adult and youth funds. In 

each case, this average gain is approximately 5.5%. The remaining LWIAs experience 

decreases in their allocations, with LWIAs in the lowest quartile of poverty experiencing 

an average reduction of almost 5% in their adult and youth allocations.
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Social Policy Research Associates III-24

Figure III-11: Florida's Formulas' Impact on 

Funds Directed toward Poverty

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Quartiles w/Lowest

Poverty Rates

2nd Quartile 3rd Quartile Quartile w/Highest

Poverty Rates

Quartiles of Poverty

Average Change

Adult

Youth

To understand why this happens, it is important to note the opposite results took 

place when using the three previous formulas. Each of those targeted more funds toward 

areas with greater populations (and, hence, cities) and toward poverty, but not 

specifically the areas with the absolute highest rates of poverty. When discussing those 

results, we hypothesized that the reason for this was that the formulas were directing 

allocations toward areas with reasonably high rates of poverty, but that had high absolute 

numbers of individuals in poverty, while the areas with the highest rates of poverty were 

likely to have lower numbers of individuals within them because they were rural. The 

results from Florida’s formula provide the remaining evidence for this argument. 

Although Florida’s formula specifically targets unemployment above the threshold value 

of 1.25%, in doing so it has the effect nationwide of allocating greater shares of funds 

toward rural areas. These areas tend to be those with the highest rates of poverty, even if 

they do not have the largest numbers of individuals living within poverty. Thus, 

Florida’s formula happens to greatly benefit areas with very high poverty rates, though it 

actually directs funds away from areas that have the greatest number of people living in 

poverty, as evidenced both by the reduction in weight given in the formula to the number 

of disadvantaged people in an area, as well as by the fact that the majority of areas with 

high numbers of people in poverty, despite not having the highest rates of poverty, 

receive lower allocations using this formula.

Interestingly, Florida’s formula has the effect of targeting money away from areas 

that serve more customers per dollar and toward areas serving fewer customers per dollar. 
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III-25 Social Policy Research Associates

As shown in Figure III-12, both the top two quartiles in efficiency experience losses 

under this formula, while the bottom two quartiles experience gains. Thus, one effect of 

adopting this formula nationwide is that areas that tend to serve fewer customers per WIA 

dollar would receive a greater proportion of the funds than they do currently.

Figure III-12: Florida's Formulas' Impact on Funds Directed toward 

LWIAs Based on Customers Served per Dollar

-12.0%

-8.0%

-4.0%

0.0%

4.0%

8.0%

12.0%

Quartiles w/Lowest

Customers per Dollar

2nd Quartile 3rd Quartile Quartile w/Highest

Customers per Dollar

Quartiles of Customers per Dollar

Average Change

Adult

Youth

ADDITIONAL ALTERNATIVE FORMULAS

Adult Formulas

In addition to the discretionary formulas described above, we also conducted 

numerous other alternative formulas in an effort to gauge how well these formulas could 

target funds toward local areas with particular characteristics. Although we do not 

present a series of graphs to depict the results of these additional simulations, so as not to 

overwhelm the reader with too many figures, in this section we provide a brief summary 

of some of our findings. For a complete list of the alternative factors that we used in 

simulations as part of this project, please see Appendix A.

As noted above, one key alternative to the unemployed population that currently is 

targeted in the adult and youth formulas is the relative size of a local area’s civilian labor 

force. The reasoning behind this is that since WIA aims to serve the universal customer, 

one cannot assume that only unemployed individuals would have a need for workforce 

services. Indeed, nearly 20% of adult WIA registrants in PY 2001 were employed at the 

time of registration. Whether they are looking to upgrade their skills to obtain a 

promotion with their current employer, or whether they would like to look for other 

employment while they are currently employed, often those interested in workforce 
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Social Policy Research Associates III-26

services are not unemployed. As a result, allocating funds on the basis of the 

unemployed alone can perhaps direct funds away from employed individuals that would 

very much like to access workforce services.

In an effort to assess what formulas might best target funds to areas with relatively 

greater numbers of individuals in the civilian labor force, we began by simulating a 

model that used only this factor in allocating adult funds. Although this was quite 

successful in targeting funds to areas with greater numbers in the labor force, for obvious 

reasons, we could then examine how variants of this “pure” formula were able to 

replicate the targeting of funds to this population. Among models that might plausibly be 

implemented, one in which the total number of unemployed individuals, the relative size 

of the civilian labor force, and the number of disadvantaged adults, each weighted 

equally, was able to still target areas with high numbers of unemployed individuals while 

also better targeting areas with large civilian labor force populations. Additionally, 

similar models employing the population of a given LWIA in place of the civilian labor 

force performed nearly as well. Not surprisingly, these formulas tend to benefit areas that 

have larger populations and LWIAs that are in cities, and while rural areas and those with 

small populations experience reductions in their allocations.

In contrast to the notion of the universal customer, we devised a formula to 

simulate the number of adults in poverty who were also unemployed. We created this 

simulation because these individuals represent the combination of the two current types 

of factors employed in the standard adult formula. Although there is no requirement 

under WIA that adult customers be disadvantaged, most states acknowledge giving such 

customers priority when funds are scarce. Thus, disadvantaged individuals can be seen to 

be a target for WIA services. Of course, not all disadvantaged individuals will want to 

use workforce services. Many have become discouraged by their employment prospects 

and simply dropped out of the workforce. Others are caretakers, for children or other 

family members, and these individuals, too, are unlikely to be looking for employment. 

Thus, the combination of being disadvantaged and unemployed would seem a likely 

indicator of both a need for and a desire to receive workforce services. As a result, we 

created several formulas that used the relative number of unemployed and disadvantaged 

individuals as the target population. 

One significant problem with modeling such formulas is that substate measurement 

of poverty can only be done every so often. Poverty data typically are obtained through 

the Census, which occurs only every ten years. Although there are ways of estimating 
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III-27 Social Policy Research Associates

these data between Census collections, often these data are not available or accessible to 

state to be used for substate allocation. Thus, the factors included in our efforts to target 

the unemployed disadvantaged population all are reliant on Census data.

The simulation model which best targets this population incorporates the total 

number of unemployed individuals, the number of individuals in poverty, and the number 

of individuals in poverty who also are unemployed. The unemployment factor is 

weighted at 50%, while the remaining two factors are each weighted at 25%. A similar 

model using the number of excess unemployed in areas of excess unemployment 

produces very similar results,10 but is likely to be subject to greater fluctuation due to the 

use of the threshold factor. These models have the effect of shifting money toward cities, 

though LWIAs in rural areas do not experience substantial losses. Instead, LWIAs in 

suburbs experience marked declines in their allocations. Areas with larger populations 

stand to benefit under the scenario, while those LWIAs with smaller populations tend to 

experience reductions. 

Youth Formulas

We also simulated several additional youth models, including substituting 

unemployment rates for youth in the standard formula, the number of high school 

dropouts, and the number of jobless and unemployed out-of-school youth. Several of 

these will be described more fully below when we discuss the results from our 

simulations of formulas proposed as part of WIA reauthorization legislation.

Because the specific eligibility requirements for youth include being disadvantaged 

and having one or more of a specified set of barriers, identified in Chapter I, there are no 

data that enable us to accurately target the eligible population at the substate level. We 

can, of course, calculate the relative number of youth in poverty in each local area, but 

there are no individual-level data available that would allow us to determine which of 

these youth have the additional barrier that would make them eligible for WIA services. 

Instead, then, we must simulate models that target these characteristics separately.

The single best formula to target the number of disadvantaged youth is to use a 

single factor in the allocation formula: the number of youth living in poverty in each 

LWIA. This factor is readily available, but because this number is calculated only when 

 

10 In this model, we define areas of excess unemploymentexactly as they are defined in the current 

standard formula model, described above.
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Social Policy Research Associates III-28

each Census is tallied, there would be no variation in allocations for a ten-year period, 

even as the actual numbers of individuals living in poverty changes substantially during 

that period. This simulation produces substantial increases in funds directed toward 

cities, and also toward areas that have larger populations. 

In addition to this model, we also simulated models that examined the inclusion of 

high school dropouts in place of the unemployment in ASUs. This model had the 

interesting impact of directing funds away from areas of poverty, and toward areas with 

lower rates of poverty. It also directed money toward areas with larger populations, 

though the impact on cities was somewhat less than other models examined in this paper.

We also simulated models that incorporated out-of-school youth as a key factor. In 

one model we substituted this factor for unemployment in ASUs, while leaving the 

remaining two factors in the model. The result of this simulation was to direct more 

money to areas with larger populations and to cities, with a lower percentage of resources 

being directed toward rural and suburban areas. Interestingly, this formula also had the 

effect of targeting areas with lower rates of poverty, although the negative impact on 

areas with very high rates of poverty was relatively minimal.

DISLOCATED WORKER FORMULA

We also simulated the current dislocated worker formula for several states.11 These 

simulations were somewhat more complex than those for the adult and youth programs, 

because there are significantly more factors included in many states’ formulas, there is no 

standard formula upon which most states’ models are based, and the weights assigned to 

each factor vary dramatically across states. For example, most states applied a weight of 

25% or more to unemployment factors, but some states applied only a 5% or 10% weight 

to this factor. Similarly, many states utilized farmer-rancher economic hardship data, but 

the definitions of these data varied across states. Further, several states did not include 

any such data as a factor in their formulas. 

 

11 Because WIA allows states to select from a wide variety of factors to include in their dislocated 

worker formulas, some states included to incorporate factors for which only they had access to data. We 

contacted several states in an effort to obtain all relevant data for the dislocated worker formula, but were 

successful in only some cases. As a result, the data presented for dislocated workers is applicable to 23 of 

the 38 multiple-area states from whom we received survey responses (and, thus, could model their 

dislocated worker formulas).
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Nevertheless, we simulated current allocations for the dislocated worker program 

much as we did for the adult and youth programs. Although there was no standard 

formula that could be used to simulate current allocations, we simply included all 

potential factors—including unemployment data, unemployment concentrations, plant 

closing data, mass layoff data, declining industries data, farmer-rancher economic 

hardship data, long-term unemployment data, and the three factors included in the 

standard adult formula—into a single simulation model, and assigned the appropriate 

weights to each factor (assigning a zero weight when a state had opted not to use this 

factor in their formula).

In an effort to simulate alternative models, we did not apply any single state’s 

current formula to all other states because the variation was so great in the specific 

weights and factors used, and the possibilities virtually endless in the specific formula 

that could be applied. Rather, we simulated three primary alternatives. The first of these 

was to simulate funding using the standard formula for adults; because it is 

acknowledged that dislocations are by their nature very hard to predict, we examined 

what the effect would be of not attempting to target funds based on an elaborate formula 

that takes into account seven or eight factors but, rather, to include the relatively 

straightforward formula used to allocate funds to the adult program. The second 

alternative was to apply the inter-state dislocated worker formula to substate areas. This 

formula, which is based equally on three factors, the relative number of unemployed 

individuals within each state, the number of excess unemployed, and the number of longterm unemployed (defined as unemployed for 15 weeks or more), could be used to 

allocate funds to local areas. This formula would thus mitigate the fact that the factors 

producing a state’s dislocated worker allotment are quite different from the factors 

producing each local area’s allocation. Further, in the subsequent section, we simulate 

the effects of three additional models that apply to all adult funds, including current WIA 

adult and dislocated worker funds, as well as Wagner-Peyser funds, and compare the 

allocations made under these alternative formulas to those made by the combined total of 

current allocations for the three formulas. 

Our first alternative to the current formulas used by states was to apply the standard 

adult allocation formula to the dislocated worker program. Doing so should have fairly 

substantial effects on the allocations, though it is unclear that they would be in any way 

less effective in targeting levels of need, because such need is so unpredictable across 

program years. Thus, the first alternative model is calculated as:
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Allocation Share = .333*(ASU share) + .333*(Excess Unemployed Share) + 

.333*(Disadvantaged Share)

Results for this model are displayed in Table III-1. As can be seen in this table, the 

impacts upon the dislocated worker allocation are quite substantial. 

Table III-1

Percent Funding Changes Between Current Dislocated Worker and 

Standard Adult Formula

Percentage 

Absolute Value of Percentage 

Change

0-5% 18.7

5-10% 14.9

10-15% 17.6

15-25% 24.3

More than 25% 24.5

Size of Population

Lowest Quartile -12.7

2

nd Quartile 11.9

3

rd Quartile -3.8

Highest Quartile 7.1

Type of Area

City 3.2

Suburb -5.8

Rural -14.6

Nearly two-thirds of all local areas would experience a shift of more than 10%, 

and more than one-third would lose at least 10%. Nearly one in four areas would 

experience changes of greater than 25%, were no hold-harmless provision enacted. 

Further, these reductions would hit hardest on smaller areas and rural areas, while 

benefiting areas with larger populations and those in cities.

Our second alternative model was to apply the inter-state formula to substate 

allocations. As such, this model is calculated as follows:

Allocation Share = .333*(Unemployed share) + .333*(Excess Unemployed Share) 

+ .333*(Long-Term Unemployed Share)
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III-31 Social Policy Research Associates

The advantage of this formula is that the local area allocation, which ultimately is 

the destination of the majority of funds, would be made in the same fashion as the 

allotments are made to states. Thus, the factors for which states actually receive their 

allotments would be used to allocate funds to their local areas, which implies that the 

funds are being targeted more consistently throughout the process. Of course, as noted in 

Chapter I, the use of a two-tier process introduces some bias because the funds are 

originally allocated to states, and are then re-pooled and allocated to local areas. Despite 

this bias, however, utilizing the inter-state formula for substate allocations would make 

the two distribution processes more similar than is currently the case and, thus, should 

target the resulting allocations more accurately, assuming of course that the inter-state 

formula represents an accurate indicator of WIA’s, or DOL’s, target. Results for this 

model are shown in Table III-2.

Table III-2

Percent Funding Changes Between Current Dislocated Worker and 

Inter-State Formula

Percentage Change

Absolute Change

0-5% 23.3

5-10% 24.7

10-15% 16.5

15-25% 21.9

More than 25% 13.6

Size of Population

Lowest Quartile 6.9

2

nd Quartile -10.2

3

rd Quartile -8.1

Highest Quartile 5.0

Type of Area

City 4.1

Suburb -3.2

Rural -5.6

As can be seen in this table, the impact of this alternative formula is substantially 

less dramatic than applying the standard adult allocation formula, shown above. This is 

likely because the three factors incorporated into this formula are used by many of the 

states in their current substate allocation formula. Thus, slightly more than half of all 
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Social Policy Research Associates III-32

LWIAs would experience shifts in funding of more than 10%, but this means that nearly 

half would experience shifts of less than 10%, even in the absence of hold-harmless 

provisions. Further, the impacts on areas of different types are not as great as under the 

standard adult formula. Both the smallest and the largest group of LWIAs, in terms of 

population size, would stand to gain under this formula, and while cities would stand to 

gain and rural areas to lose under this formula, the size of the shifts is not particularly 

substantial.

IMPLEMENTING PROPOSED FORMULAS

In addition to the discretionary formulas already in use by several states, we also 

sought to examine the potential impact of formulas proposed under recent legislation as a 

modification of the WIA allocation formulas. These formulas, described more fully in 

Chapter I, represent a substantial overhaul of the current allocation mechanisms for 

several reasons. First, rather than allocating funds separately under adult, dislocated 

worker, and youth funding streams, they replace this with only two separate streams of 

funds: youth and adult. The youth allocation remains largely the same in its target, 

though the actual allocation mechanism differs significantly, as described below. 

Adult Allocations

The adult allocation, however, combines the current WIA adult and dislocated 

worker funding streams with that from Wagner-Peyser, and creates a single source of 

funds for these three historically separate programs. Because of this change, in order to 

examine how the allocations would change as a result of the proposed formulas, we first 

needed to combine the three streams of funding and mimic the current funding formula. 

To do so, we had to integrate the total current allocation to local areas for each of the 

three sources of funding. While this is relatively straightforward for the adult and 

dislocated worker allocations, including Wagner-Peyser creates unique difficulties 

because this program currently is a state-run program that has no substate allocation 

formula. Rather, funds are distributed to states, which subsequently distribute funds to 

their employment offices throughout the state in whatever way they deem most 

appropriate. These employment offices need not correspond to current LWIA boundaries 

and, thus, creating allocations for LWIAs using Wagner-Peyser funds must rely on 

reasonable hypotheses rather than actual formulas.

To estimate current Wagner-Peyser allocations, we had to estimate the amount of 

funds that would be withheld for statewide activities, and, for the remainder, we needed 

to develop a formula to allocate funds to local areas. The first of these tasks is made 
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III-33 Social Policy Research Associates

especially difficult in that there is no current estimate for state set-aside funds, as the 

program is a statewide program. We estimated 10% for this percentage, because this is 

the sum allotted to each state that can be reserved for use by the state for specified 

statewide activities, such as performance incentives, services for groups with special 

needs, and extra costs of exemplary models for delivering services.12 Thus, for both the 

current allocation estimate, and the estimate derived from the proposed formulas, we set 

aside 10% of the funds to be held for statewide activities, with the remaining 90% to be 

allocated to local areas. To develop a formula for allocating this remaining 90% of 

Wagner-Peyser funds, we simply relied upon the formula used to allocate funds to the 

states. This formula includes two factors. The first, which is given two-thirds of the 

weight, is the number of individuals within the civilian labor force. The second, given 

the remaining one-third weight, is the number of unemployed individuals within a state. 

Similar to the federal disbursement to states, in order to estimate current Wagner-Peyser 

allocations to LWIAs we assumed states would allocate funds using this same formula. 

Thus, for each local area, we calculated the number of unemployed individuals as well as 

the number in the civilian labor force. The specific formula is thus calculated as:

Allocation = .667*(Civilian Labor Force Share) + .333*(Unemployment Share) 

By summing each local area’s values across LWIAs to receive the state total, and 

then dividing each LWIA’s number by the state total, we are able to develop proxies for 

current Wagner-Peyser allocations to LWIAs. We could then combine these allocations 

with those for dislocated workers and adults to obtain a reasonable estimate of the current 

allocation, against which we can compare the alternative formulas that have been 

proposed under H.R. 1261.

The proposed adult formula represents an amalgamation of the current adult and 

Wagner-Peyser allocations, combining the number of unemployed and the number in the 

civilian labor force from the state-level Wagner-Peyser formula with the excess 

 

12 Given that the Wagner-Peyser program is a statewide program, this ten percent figure cannot be 

taken to mean this is the sole set-aside for states. Indeed, the remaining 90% of funds are to be used by 

states for a broad variety of purposes which include services to job-seekers and employers, but also include 

evaluation, developing linkages with other service agencies, developing and providing labor market 

information, developing management information systems, and administering the work test for the 

unemployment compensation system, many of which could also accurately be described as statewide 

activities. Thus, in addition to setting aside 10% for statewide activities, we also conducted similar 

simulations using a set-aside of 25%, and achieved highly similar results. 
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unemployed and disadvantaged adult factors from the current WIA adult formula. 

Specifically, this formula is calculated as follows:

Allocation = .6*(Number of Unemployed Share) + .25*(Excess Unemployed 

Share) + .15*(Disadvantaged Share)

Though it includes no specific factors currently being used by the dislocated 

worker formula, much of the inter-state and substate allocations for this program depend 

on unemployment factors. Thus, despite the fact that several different funding streams 

with different allocation formulas are merged together, the proposed formula may not 

create dramatic differences in allocations because many of the factors from the current 

formulas are included in the proposed formula, and other factors not included are 

conceptually similar to those factors included in the present formulas.

H.R. 1261 proposes that inter-state allotments be made using this standard formula, 

and that, for substate allocations, 85% of the allocation for adult funds be made using this 

formula, and the remaining 15% be allocated using a formula of the state’s choosing. 

One critical aspect of our simulations is that we only examine the impact of substate 

allocations, rather than incorporating changes in inter-state allotments as well. There are 

two reasons for this. First, our charge was only to examine substate allocations rather 

than inter-state allotments; thus incorporating such changes would have expanded the 

scope of the study beyond what we had been asked to do. Second, and more important, 

however, is that incorporating changes in inter-state allotments would create far more 

substantial changes between current and proposed allocations, which would thus reduce 

our ability to examine the specific impact of the formulas. By this, we mean that by 

holding the state allotments constant, while examining changes produced by the substate 

allocations, the changes due to the differing substate formulas can be isolated. In so 

doing, we gain a better perspective on how the proposed changes impact the substate 

allocations that are the focus of this study. To understand the full impact of 

implementing proposed changes, one must begin with inter-state allotments, and follow 

the allocations through to local areas. 

Although the proposed formula suggests that 15% of the substate allocations would 

be made using a discretionary formula, we have no information on exactly what states 

would choose when implementing this formula. As a result, we examine the effects of 

the formula when calculated in two separate ways. First, we discuss how the allocations 

change when applying this formula for 100% of the allocation, to assess how substantial 
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III-35 Social Policy Research Associates

would be the change were states to adopt the formula wholesale. Following this, we 

discuss how the allocations would change if states used the proposed formula to allocate 

85% of their available funds and, for the remaining 15%, used some discretionary factor, 

as described below. 

Results for the analysis using the proposed formula to allocate 100% of the adult 

funds are shown in Figure III-13. As can be seen in this table, the proposed formula 

actually produces results that are somewhat more similar to the current allocation 

mechanisms than do many of the alternative formulas discussed above. Only 7.1% of 

LWIAs would experience shifts in their funding of more than 50%. And slightly more than 

one in four would experience shifts of between 25% and 50%. Approximately one-third of 

LWIAs would experience a change of between 10 and 25% in their adult allocations, and 

about one in four local areas would experience a change of less than 10% in their adult 

allocation under this proposed formula. This relative stability, at least in comparison to 

some of the alternative formulas discussed above, is due primarily to the fact that the 

Wagner-Peyser funding mechanism remains somewhat intact. Fully 60% of the proposed 

formula uses one of the the two primary factors used in the current Wagner-Peyser formula. 

This overlap creates a consistency within the allocations that limits the change that would be 

experienced if such a formula was adopted. 

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

% of Local Areas

Less than 10% 11% to 25% 26% to 50% More than 50%

% of Change in Allocation

Figure III-13: Changes in Allocation Due to 

Proposed Adult Formulas

Proposed100%

Prop85%, ASU

Prop85%, StateAvg

As noted above, the proposed legislation actually would mandate that only 85% of 

the allocation be made using this formula. The remaining 15% would be allocated using 
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Social Policy Research Associates III-36

a formula of the state’s choosing. Because we do not know exactly what factor(s) states 

would choose to allocate this remaining 15%, in an attempt to simulate the effects of this 

proposed formula, we estimated two different models, each using a different factor for 

the 15% of the allocation that would be left to the state’s choosing. In the first model, we 

use unemployment in ASUs as the discretionary factor. Given that this is the only factor 

from the current adult allocation mechanism not included in the proposed formula, it is 

quite likely that states, in the interest of preserving stability in funding to their local areas, 

would choose to include this factor to allocate their discretionary 15%. For this formula, 

then, the formula is calculated as follows:

Allocation = .51*(Number of Unemployed Share) + .2125*(Excess Unemployed 

Share) + .1275*(Disadvantaged Share) + .15*(ASU share)

Results for this formula are also shown in Figure III-13. As can be seen in this 

figure, this formula also produces relative consistency in LWIA funding, which is 

perhaps not surprising because it includes only factors that currently are a significant 

portion of the formulas. The shifts in funding that do occur are relatively small, in 

comparison to those noted for several of the other alternative formulas identified above; 

less than ten percent of local areas would experience changes in their funding of over 

50%, and an additional ten percent would experience changes of between 25% and 50%. 

But nearly forty percent of LWIAs would experience a shift of between 10% and 25% 

under this alternative of the proposed formula, and slightly more than forty percent of the 

local areas experience less than a 10% shift in funding even with no hold-harmless 

provision in place. 

The second model simulated uses the proposed formula for 85% of the allocation 

and unemployment above the state average as the discretionary 15%. The reasoning 

behind such a model is that three of the four states that currently have adopted a 

discretionary formula used unemployment above the state average as a critical factor in 

their adult formulas. Thus, it is plausible that other states would be interested in adopting 

this factor as a critical feature in their own discretionary allocations. As a result, the 

formula for this estimation is identical to the one just presented, with the sole exception 

that unemployment above the state average is substituted for the ASU factor as the 

discretionary factor with a 15% weight. Results for this formula are shown alongside the 

previous two formulas in Figure III-13. As can be seen, this formula creates much larger 

shifts in funding than either of the previous two formulas. This is perhaps unsurprising, 

given that the discretionary factor chosen is not included in any of the current funding 
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III-37 Social Policy Research Associates

formulas being integrated into the single stream of funds. Further, we have already seen 

how including unemployment above the state average can help to create sizable shifts in 

funding to LWIAs, and this proposed formula adds further evidence to that theme. 

Thus, using either the proposed formula to allocate 100% of the funds, or using it in 

conjunction with unemployment in ASUs would not appear to cause dramatic shifts in 

allocations experienced by the vast majority of LWIAs. Still, however, several local 

areas would experience significant shifts in funds, in the absence of a hold-harmless or 

similar provision. Further, as the model utilizing unemployment above the state average 

shows, some discretionary factors used in concert with the standard proposed formula 

create much more substantial shifts in funding from the current formula. This raises the 

question of how local areas of different types would be affected by these formulas: what 

types of local areas stand to gain from the implementation of these formulas, and which 

stand to lose funds? We present several aspects of this in Figures III-14 through III-16. 

In the first of these figures, we examine how the three variants of the proposed 

formula impact LWIAs of different population sizes. As can be seen in Figure III-14, the 

impact of adopting this proposed formula to allocate 100% of the funds is greatest on 

those LWIAs that have the largest populations. LWIAs in the each population quartile 

stand to gain or lose between 5 and 10 percent relative to their current allocation. For 

example, LWIAs in the highest quartile of population stand to gain slightly less than 7% 

under this proposed alternative formula. LWIAs in the next highest quartile stands to 

gain an almost identical percentage. In contrast, LWIAs in the bottom half in terms of 

population could expect to lose funds under this allocation approach. For example, 

LWIAs in the lowest population quartile would lose 8.1 percent, on average, were this 

formula adopted, while LWIAs in the next lowest quartile could expect to lose 

approximately 7.5% of their funds.
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-38

Figure III-14: Proposed Adult Formulas' Impact on Funds Directed to 

LWIAs Based on Population Size

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

Quartile with

Smallest

Populations

2nd Quartile 3rd Quartile Quartile with

Largest

Populations

Quartiles of Population

Average Change

Proposed100%

Prop85%, ASU

Prop85%, StateAvg

In the second formula simulated, in which the proposed formula accounts for 85% 

of the allocation, and unemployment in ASUs accounts for the remaining 15%, the results 

look fairly similar to those presented above. LWIAs in the highest population quartile 

would stand to gain about three percent, and those in the next highest quartile would gain 

5 percent. In contrast,, LWIAs in the lowest quartiles would lose funds. LWIAs in the 

lowest quartile would lose nearly 9 percent of their funds, while those in the next highest 

quartile would lose a more modest 2 percent. These changes are consistent with what 

was shown above, that the proposed formula with unemployment in ASUs as a 

discretionary factor would cause some shifts in funding among most LWIAs, but that 

these shifts are relatively minimal compared to some of the alternatives described above.

The third formula we estimated shows quite different impacts on local areas. 

Indeed, this formula, which uses unemployment above the state average for the 

discretionary 15% factor, tends to direct greater percentages of funds toward LWIAs in 

the highest population quartile, and directs funds away from those in the lower quartiles. 

And the disparity between the highest and lowest quartiles is relatively great, as LWIAs 

in the highest quartile would experience gains of just more than 6%, while those in the 

lowest quartile would experience losses of nearly 10%. Clearly, then, this third formula 

creates greater disparities between larger and smaller LWIAs, which could be expected 

given the greater instability the formula creates in general.

In Figure III-15, we display the impacts of these three formulas on LWIAs that are 

primarily in cities versus those in suburbs and rural areas. As can be seen in this figure, 
III. Comparisons of Current Allocations with

 Alternative Models

III-39 Social Policy Research Associates

the estimation that uses only the proposed formula tends to direct more funds toward 

cities, while directing significantly fewer funds toward suburbs and rural areas. A 

similar, though somewhat more muted result is observed when using the proposed 

formula and unemployment in ASUs as the discretionary factor given a 15% weight. 

LWIAs in cities stand to gain under this formula, while those in suburbs and rural areas 

would experience small reductions in their allocation. In contrast, however, using 

unemployment above the state average has very different impacts on LWIAs in these 

different categories. LWIAs that are primarily in cities stand to gain nearly 11% under 

such a formula, while those that are primarily suburban would lose 7.3% and rural areas 

would, on average, lose over 13%.

Figure III-15: Proposed Adult Formulas' Impact on Funds Directed to 

Cities, Suburbs, and Rural Areas

-16.0%

-12.0%

-8.0%

-4.0%

0.0%

4.0%

8.0%

12.0%

16.0%

Cities Suburban Areas Rural Areas

Formula Used

Average Change

Proposed100%

Prop85%, ASU

Prop85%, StateAvg

Next, we examine these three alternative formulas in their impact upon LWIAs with high 

versus low poverty. These results are shown in Figure III-16. In this figure, it is clear 

that the three formulas produce substantially different results. For example, under the 

alternative in which the proposed formula is used to allocate 100% of funds, LWIAs in 

the top two quartiles in poverty rates (i.e., have the highest rates of poverty) would 

experience losses in their funds, while LWIAs in the lower two quartiles would 

experience gains. In contrast, using the formula in which 85% of funds are allocated 

using the proposed formula and 15% are allocated using the number of unemployed in 

ASUs, the lowest and highest poverty quartiles of LWIAs experience losses, while 

LWIAs in the middle two poverty quartiles experience gains in their allocations. In each 

case, the gains and losses are relatively small, which is consistent with our general 
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-40

finding that this formula creates relative little shifting in funding among local areas. 

Finally, the alternative in which the proposed formula is used to allocate 85% of funds, 

and unemployment above the state average is used to allocate the remaining 15%, 

produces still different allocations. Under this formula, LWIAs in the lowest two 

quartiles experience reductions in their funding, while those in the highest two quartiles 

experience gains. On average, LWIAs experience larger shifts in funding under this 

formula as compared to the other two alternatives presented in this figure.

Figure III-16: Proposed Adult Formulas' Impact on 

Funds Directed toward Poverty

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Quartiles

w/Lowest

Poverty Rates

2nd Quartile 3rd Quartile Quartile

w/Highest

Poverty Rates

Quartiles of Poverty

Average Change

Proposed100%

Prop85%, ASU

Prop85%, StateAvg

Finally, we examine the effect of adopting these formulas on LWIAs based on their 

relative efficiency in serving WIA customers. These results are shown in Figure III-17. 

As can be seen in this figure, the impact of the three formulas is somewhat different in 

each case. Using the proposed formula to allocate 100% of the funds would direct money 

toward those in the highest two quartiles in terms of customers served per WIA dollar, 

while directing funds away from the lowest two quartiles. Using unemployment in ASUs 

as a discretionary factor would create a quite similar effect, directing money toward the 

highest two quartiles, and away from the lowest two quartiles. Finally, using 

unemployment above the state average has much the opposite effect; in such an 

alternative model, more funds are directed toward those LWIAs that are low in the 

number of customers they serve per WIA dollar.
III. Comparisons of Current Allocations with

 Alternative Models

III-41 Social Policy Research Associates

Figure III-17: Proposed Adult Formulas' Impact on Funds Directed 

toward LWIAs Based on Customers Served per Dollar

-12.0%

-8.0%

-4.0%

0.0%

4.0%

8.0%

Quartiles

w/Lowest

Customers per

Dollar

2nd Quartile 3rd Quartile Quartile

w/Highest

Customers per

Dollar

Quartiles of Customers per Dollar

Average Change

Proposed100%

Prop85%, ASU

Prop85%, StateAvg

In general, then, the proposed formula has several positive features that may well 

enhance its attractiveness to both DOL and LWIAs. When applying the formula 

exclusively, or in combination with unemployment in ASUs, a majority of LWIAs stand 

to benefit in their allocations. This is because the areas that would experience increases 

are, on average, smaller areas with lower initial allocations. As a result, many such areas 

can experience significant changes in their allocations at the expense of a few, relatively 

small reductions for larger areas with much larger initial WIA allocations. Thus, in cases 

in which LWIAs experience losses, these reductions are generally relatively small. In 

contrast, implementing the proposed formula in combination with unemployment above 

the state average, a factor many of the states currently using a discretionary formula have 

chosen to include in their models, would produce much more substantial shifts in funding 

from the current allocation, with the general result that larger LWIAs would experience 

increases in allocation, while smaller and more suburban or rural LWIAs would 

experience losses. Effects are less clear for targeting money toward local areas that serve 

higher numbers of customers per dollar, as the formulas create broadly varying results. 

What is clear from these results, however, is that using unemployment in ASUs as a 

discretionary factor in addition to the proposed formula, or simply using the proposed 

formula to allocate 100% of the funds available, would create relatively similar 

allocations as currently exist. 
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-42

Youth Allocations

In contrast to the proposed adult formula, which streamlines programs and services 

by merging three separate programs into a single one, the proposed youth formula would 

remain targeted toward the same single program it currently is, though the actual formula 

would be altered significantly. For example, neither of the two threshold unemployment 

factors, which constitute two-thirds of the current youth allocation formula, would be 

included under the proposed formula. Instead, these formulas would be replaced by two 

new factors, including the number of unemployed individuals within an area (not limited

to specific ages) and the number of youth (ages 16 to 19) in the civilian labor force. 

Although the number of unemployed individuals involves unemployment, this factor is 

quite different from the previous factors in that it is not based on a threshold measure but, 

rather, a raw count of the number of unemployed individuals. Indeed, the proposed youth 

formula eliminates threshold measures, and instead relies on simple counts of the number 

of individuals falling into any one of the three categories. Both the number of 

unemployed individuals and the number of youth in the civilian labor force would receive 

one-third of the weight under the proposed formula. The final one-third of the allocation 

would be made using the same factor currently in place – the number of disadvantaged 

youth. 

Because the targeted population remains the same, making comparisons between 

the current formula and the proposed formula is relatively straightforward. To calculate 

results for the proposed formula, we used the standard formula, which is identified in 

H.R. 1261. Specifically, this formula is calculated as follows:

Allocation = .333*(Number of Youth in Labor Force Share) + .333*(Number of 

Unemployed Share) + .333*(Number of Disadvantaged Youth Share) 

In a slight difference from the adult formula, H.R. 1261 proposes that 80% of the 

allocation for youth funds be made using this formula (as compared to 85% of adult 

funds), and the remaining 20% be allocated using a formula of the state’s choosing (as 

compared to 15% of the adult funds). Our analysis is parallel to that presented above for 

adults. First, we discuss how the allocations change when applying this formula for 

100% of the allocation, to assess how substantial would be the change were states to 

adopt the formula wholesale. Following this, we discuss how the allocations would 

change if states used the proposed formula to allocate 80% of their available funds and, 

for the remaining 20%, used some discretionary factor, as described below.
III. Comparisons of Current Allocations with

 Alternative Models

III-43 Social Policy Research Associates

Changes produced by the proposed formula are shown in Figure III-18. As can be 

seen in this figure, if states were to adopt the proposed formula to allocate 100% of their 

youth funds to local areas, there would be some significant changes in the allocations 

produced. For example, nearly one-third of all local areas would experience changes of 

greater than 50%. An additional one-third would experience changes of between 25% 

and 50%. Another 19% would experience changes of between 10% and 25%, and only 1 

in 6 LWIAs would experience changes of less than 10 percent. These changes would 

only occur, of course, in the absence of hold-harmless provisions, but it is clear that the 

effect of the proposed youth formula would be to create sizable shifts in allocation to 

LWIAs. 

Also shown in Figure III-18 are results from a second simulation, which uses the 

proposed formula to allocate 80% of the funds under the youth program, and uses youth 

poverty above the state average to allocate the remaining 20%. Because we do not know 

exactly what states would select to include in their formulas for the discretionary 20%, 

we opted to include youth poverty above the state average because three of the four states 

that have currently adopted a discretionary formula have included this measure. Thus, it 

is plausible that other states would be interested in adopting such a measure, especially 

given that the focus of the youth program would remain on serving disadvantaged youth 

and, as a result, including this measure

Figure III-18: Changes in Youth Allocation 

Due to Proposed Formulas

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Less than 10% 11% to 25% 26% to 50% More than 50%

% of Change in Allocation

% of Local Areas

Proposed100%

Prop80%, StateAvg

would further target funds to those local areas with higher rates of youth poverty. The 

specific calculation of this formula is as follows:
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-44

Allocation = .233*(Number of Youth in Labor Force Share) + .233*(Number of 

Unemployed Share) + .233*(Number of Disadvantaged Youth Share) + 

.2*(Number in Poverty Above State Average Share)

The results shown in Figure III-18 are similarly striking in the amount of change 

they would produce in allocations to LWIAs. More than one-third of all LWIAs would 

experience shifts in funding of more than 50%, while an additional one-third would 

experience changes of between 25% and 50%. Far smaller percentages would experience 

less dramatic shifts in their funding.

Figure III-19: Proposed Youth Formulas' Impact on Funds Directed 

toward LWIAs Based on Population Size

-60.0%

-40.0%

-20.0%

0.0%

20.0%

40.0%

Quartile with

Smallest

Populations

2nd Quartile 3rd Quartile Quartile with

Largest

Populations

Quartiles of Population

Average Change

Proposed100%

Prop80%, StateAvg

In Figure III-19 and III-20, we show the impacts of these proposed youth formulas 

on LWIAs with different characteristics. As can be seen in this figure, the impacts of 

these proposed youth formulas on LWIAs are also quite dramatic. For example, both 

formulas direct far greater proportions of funds toward local areas with
III. Comparisons of Current Allocations with

 Alternative Models

III-45 Social Policy Research Associates

Figure III-20: Proposed Youth Formulas' Impact on Funds Directed to 

Cities, Suburbs, and Rural Areas

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

Cities Suburban Areas Rural Areas

Type of Area

Average Change

Proposed100%

Prop80%, StateAvg

the largest populations (as much as 24% more toward areas with the highest population), while 

directing substantially less funds toward areas with smaller populations. Not surprisingly, 

given this finding, each formula also directs significantly more funds to cities and suburban 

areas (between 10% and 16% more in each case), and away from rural areas (which would 

experience reductions of between 30% and 33%).

Further, in Figure III-21, we show the impact of these formulas on areas with different 

poverty rates. As can be seen in this figure, the proposed formula would have the effect of 

directing money away from areas of poverty, with the highest poverty quartile of LWIAs 

experiencing reductions of nearly 11%, while increasing funds in areas that are lower in poverty. 

The alternative formula, which uses poverty above the state average to allocate 20% of the funds, 

reverses this effect, such that areas highest in poverty would experience gains of eighteen percent, 

while areas lower in poverty would experience losses of up to fifteen percent.
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-46

Figure III-21: Proposed Youth Formulas' Impact on 

Funds Directed toward Poverty

-20.0%

-10.0%

0.0%

10.0%

20.0%

Quartiles

w/Lowest

Poverty Rates

2nd Quartile 3rd Quartile Quartile

w/Highest

Poverty Rates

Quartiles of Poverty

Average Change

Proposed100%

Prop80%, StateAvg

Finally, although we do not display the results in a figure, both formulas would serve to 

direct somewhat less money toward those local areas that serve higher numbers of youth 

customers per WIA dollar. In both formulas, the top two quartiles on this measure would 

receive slightly less in funds. The highest quartile would experience losses of up to 9%, while 

the lowest quartile would experience gains of 8 to 13%. Thus, the significant shifts caused by 

these proposed youth formulas would tend to favor LWIAs that serve fewer youth customers 

per dollar.

MECHANISMS TO PROMOTE ORGANIZATIONAL AND FINANCIAL 

STABILITY

As has been made clear in the previous sections, many of the alternatives to the current 

formula create substantial shifts in allocations. While these shifts represent the area’s “true” 

allocation, depending on the specific formula being implemented, they do not take into account 

that local areas must have some amount of stability in their year-to-year funding in order to 

provide services to those in need of them. Indeed, many have suggested that changes in excess 

of 10% are significant enough that local areas would be unable to function effectively were they 

to experience funding shifts of more than this amount. These arguments have been at the core of 

the hold harmless provisions that were put into place in the later years of JTPA, and were 

continued (albeit with an allowable two-year hiatus) under WIA.

Before concerning ourselves with such fluctuations, however, it is critical that we examine 

the extent to which they occur with regularity from one year to the next. As we discussed above, 

shifts in excess of 25%, not to mention those in excess of 50%, were quite common in many of 
III. Comparisons of Current Allocations with

 Alternative Models

III-47 Social Policy Research Associates

the alternatives described above, but these were brought about through comparisons of the 

current formula to some alternative that could be adopted, within the same funding year, rather 

than comparisons of the same formula across program years. One would expect that inter-year 

allocations made under the same formula would exhibit somewhat less fluctuation, as the factors 

on which the allocation is based do not vary, and some of the values for those factors (i.e., the 

number of disadvantaged individuals) rarely change as well. 

Our first task in this section, then, will be to examine the extent to which local areas’ 

allocations do fluctuate substantially from year-to-year. In Table III-3, we display the changes in 

allocations between PY 2001 and 2002. We calculated these changes by using the simulation 

procedures described in detail earlier in this chapter for both PY 2001 and 2002 funds, and then 

comparing the two allocations. For PY 2001, we used unemployment data from PY 2000, while 

for PY 2002, we used unemployment data for PY 2001. In both cases, we used counts of the 

number of disadvantaged individuals from the 2000 Census. 

One of the first points to be noted from this table is that we do not show shifts in the 

dislocated worker funds, largely because such funds are not currently subject to hold harmless 

provisions. The reason for this is discussed in detail above; the nature of dislocations and 

layoffs make them highly difficult to predict. As a result, invoking a hold-harmless provision 

would prevent states from directing funds toward those areas that experienced sudden layoffs. 

Of course, by the very nature of the program, allocations for a given year are based on data from 

a prior year, but the flexibility of targeting funds to areas with rapidly changing economic and 

employment climates means that hold-harmless provisions are less useful in the context of the 

dislocated worker program than they may be for adult and youth allocations. 
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-48

Table III-3

Percent Funding Changes under Current Formula, PY2001-2002

Change Between 2001 & 2002

Adult Youth

Absolute Value of Percentage Change

0-5% 26.9 24.7

5-10% 27.2 29.1

10-15% 18.4 16.5

15-25% 10.7 12.1

More than 25% 16.8 17.6

Mean 12.5 12.9

Percentage Change

Less than -10% 27.7 26.1

Between -10% and -.1% 27.8 25.7

Between 0-10% 26.3 28.1

More than 10% 18.2 20.1

Mean -.4 -.2

______

Note: Absolute change reflects the absolute value of the percentage changes experienced by 

LWIAs across program years. Percentage change shows the actual gains or losses experienced 

by these LWIAs. These data were constructed using our simulation model and, thus, the actual 

changes in allocations for LWIAs in these program years may vary slightly.

As can be seen in Table III-3, although the shifts in allocation from year-to-year using 

the current formula are not as dramatic as many we saw when applying alternative formulas, 

there are a sizable number of LWIAs who would experience shifts of more than 10%. And 

approximately one in six LWIAs experience shifts in their adult and youth allocations of 

greater than 25% with no hold harmless provision in place. The lower rows in the table 

display the actual changes experienced by these same LWIAs. In these rows, one can see 

that although just over half the areas receive relatively stable allocations (i.e., no more than 

a 10% gain or loss) from one year to the next, the remaining 45% experience more 

substantial changes. And approximately one in four local areas would experience losses 

from one year to the next of more than ten percent in their adult and/or youth allocations. 

Thus, significant fluctuation is not an isolated occurrence but, rather, a relatively common 

issue that either will upset local areas’ general stability, or must be dealt with in some way 

to ensure that programs and services can continue largely uninterrupted across program 

years. Such instability may cause local areas significant problems because they would be 
III. Comparisons of Current Allocations with

 Alternative Models

III-49 Social Policy Research Associates

unable to offer a stable set of services, or to promise registering customers that the services 

they wish to receive will be available across different program years begin.

Of course, these changes reflect only short-term changes between two program 

years. But it is the year-to-year changes that create the instability that is difficult to 

manage organizationally and financially. As a result, an additional consideration in 

examining these fluctuations is the extent to which LWIAs of a certain type are 

impacted more often than others. If, for example, LWIAs that have smaller 

populations were systematically more likely to be among the areas that experience 

sizable losses, having no mechanism by which to limit these losses would guarantee 

that these local areas would be more subject to programmatic interruptions or 

challenges in meeting the needs of their customers. Any such systematic relationship 

between LWIAs of a given type and funding instability would therefore create a 

situation in which LWIAs were being disproportionately impacted based on 

characteristics that are not under their own control.

Table III-4 displays how various types of LWIAs are impacted by the short-term 

fluctuations presented above. As is apparent in this table, the LWIAs experiencing the 

largest losses due to fluctuation in allocations are those that have smaller populations and 

those with smaller allocations to begin with. Additionally, suburban areas tend to 

experience losses, while urban areas tend to experience greater increases. Further, 

LWIAs with lower rates of poverty also experience losses, on average. Given that the 

poverty measure used in the current formula does not change between the allocations, 

since it is based on Census data, this suggests that the proportion of unemployed 

individuals shifted away from areas of low poverty during these program years, because 

the allocations made based on unemployment shift away from these low poverty areas. 
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-50

Table III-4

Percentage Changes for Different Types of LWIAs

Change Between 2001-2002

Adults Youth

Size of Population

Lowest Quartile -1.9 -2.3

2

nd Quartile -2.0 -1.4

3

rd Quartile 2.4 3.6

Highest Quartile 0.7 0.9

Type of Area

City 5.3 5.4

Suburb -4.7 -4.2

Rural 0.2 -1.6

Poverty Rate

Lowest Quartile -1.4 -2.0

2

nd Quartile -0.6 1.1

3

rd Quartile 1.9 0.5

Highest Quartile 2.3 -0.6

# of Customers Served per Dollar

Lowest Quartile 3.2 1.2

2

nd Quartile -2.4 -2.9

3

rd Quartile 3.1 -1.8

Highest Quartile -1.4 3.1

LWIA Allocation

Lowest Quartile -7.5 -6.3

2

nd Quartile -3.0 -3.7

3

rd Quartile 4.2 5.6

Highest Quartile 2.2 2.4

Thus, not only is there a significant amount of fluctuation in year-to-year 

funding, this fluctuation hits most heavily on those areas that may be least wellequipped to handle it. LWIAs with smaller initial allocations, and those with 

generally smaller populations, tend to be those with fewer staff and services available. 

Experiencing substantial percentage losses from one year to the next may be more 

crippling for these LWIAs than it would be for very large areas, since the larger areas 

have a broader base from which they can help to minimize the impact of funding 

reductions. Further, that an area’s allocation decreases from one year to the next in 

no way implies that the level of need in that area has declined, even if one was to 

accept that the current formulas precisely target need through the factors included in 
III. Comparisons of Current Allocations with

 Alternative Models

III-51 Social Policy Research Associates

the formulas. Rather, it is just as likely that the level of need has remained the same 

in the given local area, or even increased, but that the level of need has increased 

more substantially in other areas. This is especially likely given that smaller areas are 

the ones most adversely impacted by funding fluctuations, because any increases in 

need within these areas would pale in comparison to increases in larger areas. Thus, a 

local area may well be faced with the situation of increasing demand for their services 

while simultaneously receiving significantly less funding to provide these services. 

One key component of the allocation formulas, therefore, may be to ensure that local 

areas have the stability necessary to provide services that remain needed even as the 

areas’ relative share of funding is decreasing.

In the remainder of this section, then, we examine various ways in which the 

formulas used to allocate funds under WIA could be designed to promote organizational 

and financial stability among local areas. Primarily, we do this by exploring the impact 

of implementing the hold harmless provisions. We also, however, examine how 

replacing the factors in the current formula that help to create instability of funding 

impacts the allocations received by LWIAs, and discuss how the alternative formulas 

discussed in this chapter could help to minimize the fluctuations that do occur. In 

addition, we examine the effect of incorporating any carry-in funds into the subsequent 

program year’s allocations. Doing this would ensure that areas that have difficulty 

expending their funds in a single program year would not receive an additional full 

allocation in the following year.

Hold Harmless

One mechanism developed specifically to minimize the fluctuations in allocations 

from year to year is the hold harmless provision. Under JTPA, this provision mandated 

that no local area could receive less than 90% of its prior year’s funding level. This has 

been modified under WIA such that local areas can not receive less than 90% of the 

average of their previous two years’ funding. Thus, the hold-harmless provision under 

WIA is more conservative, and promotes even greater stability because the threshold on 

which their current funds are based is a two-year average rather than a single year. 

Thus, for local areas whose level of need, as measured by the factors included in the 

allocation formula, is falling rapidly, their allocation will fall somewhat less quickly 

because they must receive 90% of their average over the prior two years.

This provision is an external constraint imposed on the allocation formula, 

because it acts largely independent from the formula. First, allocations for each local 
III. Comparisons of Current Allocations with

 Alternative Models

Social Policy Research Associates III-52

area are calculated using the current formulas. At this point, within each state that 

implemented hold-harmless provisions (it became mandatory after the second year of 

WIA implementation), any local area whose allocation is less than the average of the 

prior two years is automatically assigned this average as their allocation for the 

subsequent year. Given that this automatic increase to one or more local areas means 

that less is now available to be allocated to remaining LWIAs, the allocation is 

calculated again, assigning the remaining available funds to those LWIAs not assigned 

the hold-harmless minimum in the first stage. This recursive process is repeated until 

all LWIAs receive at least their minimum and the allocations have been made to all 

LWIAs in the state. This process clearly, then, takes funds from areas that have a 

higher “need,” as identified by the factors included in the formula, and reassigns some 

of those funds to areas with lower levels of “need.” But though it does redirect funds 

away from those areas the formulas have determined have higher levels of need, it 

helps to ensure that LWIAs have some stability in funding so that they can be certain 

that they can offer a relatively stable set of services across program years.

As noted above, certain local areas are much more likely to experience allocations 

that are 10% or more less than they had in the prior program year. Thus, these are the 

LWIAs that stand to benefit from hold-harmless provisions, at the expense of LWIAs that 

did not experience such fluctuations. To examine how the implementation of holdharmless provisions in each state would impact LWIAs of different types, we calculated 

the percentage change in allocations made to different LWIAs as a result of holdharmless. These data are shown in Table III-5. The effects shown in this table are 

largely the opposite of those shown above. This is not surprising because hold harmless 

is designed to limit the losses experienced by certain LWIAs by paring back in the 

allocations to LWIAs not experiencing such losses.
III. Comparisons of Current Allocations with

 Alternative Models

III-53 Social Policy Research Associates

Table III-5

Percent Change as a Result of Hold-Harmless Implementation, 2001-2002

Adult Youth

Size of Population

Lowest Quartile 9.6 11.2

2

nd Quartile 4.1 6.3

3

rd Quartile -0.3 -0.6

Highest Quartile -2.7 -3.0

Type of Area

City -4.6 -5.1

Suburb 6.1 5.0

Rural 0.5 0.3

LWIA Allocation in 2001

Lowest Quartile 13.9 12.7

2

nd Quartile 5.2 6.1

3

rd Quartile -1.3 -1.6

Highest Quartile -1.9 -2.1

What is clear from the table, though, is that the relatively substantial increases 

experienced by smaller LWIAs as a result of hold-harmless provisions are offset by 

relatively small reductions to larger local areas. This is entirely intuitive, in that the 

amount of dollars that need to be transferred to areas with small allocations to ensure they 

receive at least 90% of their prior two years’ funding is relatively small, especially in 

comparison to the amount of funds received by much larger LWIAs. Thus, the holdharmless provision has the average effect of transferring small percentages of larger 

LWIAs’ funds to produce relatively large percentage increases for smaller LWIAs. In 

this way, the hold-harmless provision is very effective in minimizing the percentage of 

negative reductions while creating larger percentage gains for certain local areas.

One problem with looking at such changes, however, is that they are critically 

dependent upon shifting business cycles, such that the areas that stand to benefit from 

hold harmless in the current business cycle are likely to be those that are hurt by it when 

the cycle shifts in the opposite direction. Thus, examining changes over a single year 

period may be incomplete, at best, or misleading at worst because they capture only a
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Social Policy Research Associates III-54

single snapshot of the areas that are hurt or helped by the hold-harmless provision. It 

should be made clear, then, that the effects of the hold-harmless provision noted in this 

section may not hold across different business cycles, and with differing economic 

circumstances. It should be noted, however, that the effects noted here are largely 

consistent with an earlier study examining allocations results, which was completed in a 

much different economic and business context.13

In addition to minimizing the losses that are experienced by some local areas from 

one program year to the next, the hold-harmless provision could also be viewed as a means 

by which allocation reductions could be minimized in shifting from one funding formula to 

another. In an earlier section, we presented a range of potential alternative formulas, and 

described how these formulas would alter the allocations made to local areas from their 

simulated current allocation. Below, in Table III-6, we present how a hold-harmless 

provision would affect implementation of each of the alternative formulas.

Although Table III-6 presents too many figures to discuss each one, we want to draw 

attention to a few key points. First, nearly all the formulas in this table show sizable shifts in 

funds as a result of implementing the hold-harmless provision. This should not be surprising, 

given the significant shifts in funds that occur as a result of implementing these alternative 

formulas. But the size in shifts as a result of hold harmless implementation suggests that 

much of the money to be allocated would be based simply on hold-harmless, rather than on 

targeting any indicator of need. Thus, there is a more stark choice between benefiting some 

smaller LWIAs greatly while taking modest amounts from larger LWIAs, as was the case 

above. As shown in Table III-6, implementing any of these formulas with a hold-harmless 

provision would transfer substantial percentages from certain LWIAs toward other LWIAs, 

thereby lessening the ability to target funds to the levels of need identified within the 

alternative formulas. 

One general exception to this rule, however, is the implementation of the proposed 

formula to allocate 85% of available funds, with unemployment in ASUs used to allocate the 

remaining 15%. With this formula, relatively smaller percentages of funds need to be 

transferred across LWIAs due to hold-harmless provisions and, as a result, more of

 

13 “An Assessment of Funding Allocation under the Job Training Partnership Act,” (1986), Abt 

Associates, Cambridge, MA.
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Table III-6

Impact of Hold-Harmless on Alternative Allocation Formulas

Alabama 

Formula

Arizona 

Formula

Iowa 

Formula

Florida 

Formula

Proposed 

100%*

Proposed 

85%/ASU*

Percent of 

LWIAs 

Receiving 

Increase

34.6 38.9 38.3 37.6 37.2 28.9

Size of 

Population

Lowest Quartile 26.3 25.8 27.2 -0.5 14.1 6.3

2

nd Quartile 12.9 9.7 15.9 -2.1 8.0 4.4

3

rd Quartile -13.6 -10.3 -16.4 -3.6 -10.2 -5.9

Highest Quartile -8.6 -7.4 -8.0 2.7 -8.4 4.3

Type of Area

City -9.5 -9.9 -7.1 -0.3 -8.5 -4.7

Suburb 8.6 4.2 2.7 0.8 7.9 3.2

Rural 17.2 15.4 16.3 1.1 10.4 6.1

LWIA 

Allocation

Lowest Quartile 29.8 26.6 31.2 10.2 18.3 12.1

2

nd Quartile 15.2 16.7 12.9 4.8 15.0 5.8

3

rd Quartile -16.7 -11.9 -14.5 -3.1 -10.2 -7.2

Highest Quartile -8.5 -10.1 -9.9 -1.6 -6.6 -2.3

________

Note: Numbers reflect percent change as a result of implementing hold-harmless 

provision to minimize changes between current formula and identified formula. These 

numbers reflect adult allocations for PY 2002. 

*Proposed formula 100% refers to implementing the formula proposed in H.R. 1261, and 

allocating 100% of available funds using this formula. 

*Proposed Formula 85%/ASU refers to using the above formula to allocate 85% of the 

funds, and unemployment in ASUs to allocate the remaining 15%.

the funds are targeted toward LWIAs identified by the formula as having the highest 

need. Thus, more funds would remain in areas with high numbers of unemployed 

individuals, both generally and in areas of excess unemployment or ASUs, as well areas 

with relatively higher numbers of disadvantaged individuals.
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The Impact of Replacing Factors Causing Instability

Another alternative to reducing the fluctuations that occur across program years is to 

utilize alternative formulas or factors that help to minimize the variation in local areas’ shares. 

As noted above, in the current formula only unemployment in ASUs and excess 

unemployment can contribute to instability in funding, because these are the only two factors 

that fluctuate from year-to-year. The number of disadvantaged individuals remains constant, 

because it is taken from Census data that are updated only once every ten years. Thus, nearly 

all fluctuation between program years that occurs in local area funding is the result of relative 

changes in each local area’s share of unemployed individuals in ASUs or changes in the 

number of excess unemployed in areas of excess unemployment.

As is discussed in great detail above, one of the primary concerns about these two 

factors is that they rely on thresholds. Thus, areas that just meet the threshold are eligible 

to receive some portion of the allocation for that factor, while areas that fall just below 

the threshold receive no share for that factor. In reality, these areas may be relatively 

similar overall, but the all-or-nothing nature of threshold factors treats them radically 

different in terms of the allocation directed toward each. Further, a single area can be 

impacted significantly from one year to the next if they, for example, just meet the 

threshold value in a given year, but fall just below it in a subsequent year. What is an 

absolute change of only a few unemployed individuals can represent an allocation change 

of thousands, or even millions of dollars, because they fluctuate just above or below the 

threshold. This is one reason why many stakeholders have argued that a better, or at the 

very least a more stable factor to be used would be the absolute number of unemployed 

individuals. Although this number can vary substantially across program years, with no 

threshold aspect to the measure local areas would likely not be subject to the dramatic 

swings in funding so typical of these thresholds.

To examine how the incorporation of such a factor into the allocation formulas 

would impact the fluctuation experienced by LWIAs, we simulated separate models that 

replaced each of the ASU and excess unemployment factors with the total unemployment 

factor. To do this, first we simulated the current funding formulas, using the standard 

three factors, to produce allocation results for all LWIAs. Following this, we produced 

separate models in which we replaced the ASU factor with the total unemployment 

factor, and a second model in which we replaced the excess unemployment factor with 

the total unemployment factor. Results for this analysis are shown in Table III-7.
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Table III-7

Impact of Holding Unemployment in ASUs and Excess Unemployment Constant

Adult Youth

Simulated Absolute Percentage Change in LWIA Funding 

(PY2001-2002)

12.5% 12.9%

Unemployment in ASUs

Simulated Change Replacing Unemployment in ASUs with 

Total Unemployment

7.2% 7.3%

Percentage Change from Current to Using Total Unemployment 42.4% 43.4%

Excess Unemployment

Simulated Change Replacing Excess Unemployment with Total 

Unemployment

7.3% 7.8%

Percentage Change from Current to Using Total Unemployment 41.6% 39.5%

As can be seen in this table, the absolute percentage change between PY 2001 and 

2002 experienced by local areas in our standard simulation of the current adult formula 

was 12.5%. For youth, this figure was 12.9%. In simulations in which we replaced the 

value of each local area’s ASU share with their relative share of the total number of 

unemployed individuals, one can see the substantial drop in simulated change, to a value 

of 7.2% for adults, and 7.3% for youth. This represents a decrease of approximately 40% 

in the fluctuation experienced by local areas, simply due to replacing unemployment in 

ASUs with total unemployment across the two program years. The two figures are, 

ostensibly, measuring similar factors – the relative amount of unemployment in a given 

local area – but eliminating the threshold measure creates substantially more stability in 

funding across program years.

Also shown in Table III-7 is a similar result for the excess unemployment variable. 

As with unemployment in ASUs, simply replacing the excess unemployment factor with 

a measure of total unemployment across program years substantially reduces the 

fluctuation experienced by local areas. For adults, replacing excess unemployment with 

total unemployment reduces the fluctuation by just over 40%, while for youth it reduces 

this fluctuation by nearly 40%. Perhaps what is most clear in these models is that both 

unemployment in ASUs and excess unemployment play significant roles in the 

fluctuation experienced by local areas. This raises the question of what the effect of 

removing both threshold measures from the model would be on the fluctuation 

experienced by LWIAs. To answer this question, we simply replaced both the threshold 
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Social Policy Research Associates III-58

factors with a measure of total unemployment. As an initial exercise, we examined the 

extent to which using the number of unemployed individuals to allocate two-thirds of the 

allocation (with the number of disadvantaged adults used to allocate the remaining onethird) helped to lessen the changes experienced by LWIAs across program years. These 

results are shown in Table III-8. 

As can be seen in this table, replacing the two threshold measures with the total 

number of unemployed individuals within a local area as an allocation factor even further 

reduces the fluctuations experienced in allocation across years. Indeed, substituting this 

factor as two-thirds of the weight in the allocation formula (and keeping the number of 

disadvantaged individuals, which does not vary in this simulation, as the remaining 

factor) reduces the fluctuation by nearly three-fourths for both the adult and youth 

formulas. Clearly, then, the threshold aspect of both the ASU and excess unemployed 

factors plays a major role in creating instability in funding across local areas.

We also examined the impact of substituting unemployment measured over an 

extended duration in place of the two threshold measures. For this measure, we 

calculated the average number of unemployed individuals over the prior two years, and 

gave this factor a two-thirds weight in the current formula (with the disadvantaged factor 

receiving the remaining one-third weight). This combination of the number of 

unemployed, averaged over a two-year period, further reduced the fluctuations that 

occurred across program years. In this case, the average fluctuation was reduced by more 

than 80%. Extending the reference period further back, such as to five year, makes a 

modest additional difference, such that five-year averages of the number of unemployed 

individuals reduce the fluctuation in the formulas by just less than 90%.
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Table III-8

Impact of Using Number of Unemployed and Longer-Term Unemployment 

Adult Youth

Simulated Absolute Percentage Change in LWIA Funding 

(PY2001-2002)

12.5% 12.9%

Number of Unemployed Individuals

Simulated Change Using Number of Unemployed 3.2% 3.3%

Percentage Change 74.4% 74.4%

Extended Reference Period for Unemployment

Simulated Change Extending Reference Period for 

Unemployment

2.1% 2.2%

Percentage Change 83.2% 82.9%

This beneficial effect of extending the measurement of factors out over time to 

produce greater stability in funding across program years can also be applied to the two 

threshold measures. For example, were one to calculate the average of an area’s 

unemployment over a five-year period in order to determine its status as an ASU or area 

of excess unemployment, there would similarly be a decrease in the fluctuation 

experienced by local areas. This decrease is shown in Table III-9. As can be seen in this 

table, although the general effect is the same – there is less fluctuation between program 

years in local areas’ funding when calculating ASUs and excess unemployment over a 

five-year period – the magnitude of the effect is much less than that shown for the total 

employment factor above. Calculating both of these factors as five-year averages reduces 

the fluctuation by just over 50%. Thus, there is still an impact of the threshold, as some 

areas will fall just above or just below, even when averaging across five years. Thus, one 

way of minimizing shifts in funds would be to average ASUs and/or excess 

unemployment over a longer period of time, but this would still create greater fluctuation 

than would replacing the threshold measures with an absolute measure, such as total 

unemployment. Further, it may well be impractical to average these threshold measures 

over five years, especially given that ASUs can be calculated at relatively small levels. 

Thus, this alternative seems less fruitful than using a simpler approach such as using total 

unemployment.
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Table III-9

Impact of Using Longer-Term ASUs or Excess Unemployment 

Adult Youth

Simulated Absolute Percentage Change in LWIA Funding 

(PY2001-2002)

12.5% 12.9%

Simulated Change Extending Reference Period for 

Unemployment in ASUs and Excess Unemployment

5.9% 6.0%

Percentage Change 52.8% 53.5%

The factors presented in this section are an alternative to the externally imposed 

constraints brought about by hold-harmless provisions. By using measures similar to the 

current measures, without the threshold aspect, one can reduce fluctuations experienced 

by local areas by substantial amounts. Although some local areas will still experience 

reductions in their allocation of more than 10%, such areas will be a small percentage of 

the overall number of LWIAs. Further, the benefit of adopting such an approach is that it 

ensures that funds are allocated on the basis of one very plausible definition of need – the 

number of unemployed individuals within a given local area – rather than on the basis of 

an external guarantee that no area will receive less than some previous years’ allocation, 

regardless of whether or not there is a need for this amount of funding. Further, even 

greater benefit can be derived by averaging this factor over a period of years, rather than 

allowing it to vary only for a single year. This averaging also would help reduce 

fluctuation while still using the unemployment in ASUs and excess unemployment 

variables, though the impact of this is somewhat less than eliminating threshold measures 

entirely.

Incorporating Carry-In Funds to Subsequent Allocations

Although sudden reductions in funding can clearly create concerns among local 

areas, and difficulties in providing a stable set of services across program years, local 

areas also may struggle because their annual allocation of funds is unspent in a given 

program year, and so the remaining amount is carried in to the new program year. In 

addition to this carry-in amount, however, they also receive a new year’s allocation on 

top of this unspent amount. Thus, local areas are faced with spending not only their latest 

allocation, but also the unspent portion from the prior year. At the same time, other local 

areas that utilized their entire allocation the prior year receive only their subsequent year 
III. Comparisons of Current Allocations with

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III-61 Social Policy Research Associates

funds for the coming year. On the surface, then, it would seem as though areas that are 

able to spend their funds (and perhaps even run out of funds during the program year) 

might well need additional funding in subsequent years because the demand for their 

services is high. In contrast, areas that cannot expend their funds perhaps should receive 

lesser allocations in subsequent years, because the demand for services in these local 

areas may be lower than in other areas. 

Several state representatives and other key stakeholders cautioned against this 

assumption, however. Because local areas have two years to expend their funds, the fact 

an area carries in funds from one program year to the next may not be indicative of an 

inability to spend their funds. Indeed, in most cases, according to state representatives 

and other stakeholders, carry-in funds are already obligated to service providers, and thus 

they make the distinction between unspent and unobligated funds, which are funds that 

are neither spent nor contractually obligated. As noted in Chapter II, the vast majority of 

states have in place provisions that enable them to recapture funds from local areas if 

they are not obligated by some date late in the program year, which enables the state to 

reallocate such funds that likely would not be spent. Thus, in many ways this state-level 

recapture of unobligated funds serves as a basis for addressing fluctuations from year-toyear, because areas that cannot obligate their funds will lose them, to be reallocated 

among the areas that are having no problem obligating and expending funds. 

Unspent funds, on the other hand, currently are not recaptured or reallocated, and 

thus are carried into subsequent program years. Despite the cautions of stakeholders, 

many believe that such funds represent inefficiencies in the allocation of funds, for the 

reasons identified above. Thus, as a final means of examining the promotion of 

organizational and financial stability, we simulated the effects of incorporating carry-in 

funds into the current year’s allocation. 

To do so, we identified all states that had reported in their survey that at least some 

of their local areas had unspent funds at the end of a program year. We then asked 

representatives from each of these states to provide data on the amount of adult, youth, 

and dislocated worker funds each local area carried in to PY 2002, that had been unspent 

for PY 2001. Thirty-four of the thirty-eight multiple-area states in our respondent sample 

were thus asked to provide this information. After repeated solicitations via email and 

telephone, we were able to obtain carry-in information for 20 states. An additional four 

states that reported their areas had no carry-in funds could also be included in our 

analysis, with the carry-in shares being assigned to zero for these states.
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To conduct this simulation, we used a two-stage process. First, we combined all 

carry-in funds (from PY 2001 into PY 2002) into a single pool of funds, which was 

combined with the PY 2002 state total of funds available to be allocated. Thus, if a given 

state had $3 million in carry-in adult funds, and an available pool of adult funds to be 

allocated of $20 million, we treated these as a single pool. Second, we allocated funds 

from this single pool (i.e., the $23 million) in exactly the same way as they are allocated 

under the current formula. What this means, of course, is that local areas who had carryin funds receive some portion of these funds back in their subsequent year’s allocations, 

but they do not receive the entire amount. Indeed, they would receive only their original 

share of the state total, while those local areas that did not have carry-in funds would 

receive greater than their share. Thus, this process increases the percentage of funds 

going to areas that have no carry-in funds, while simultaneously reducing the percentage 

of funds going to those areas with carry-in funds. 

We selected this process, rather than one in which carry-in funds were separated 

from the main pool of funds and allocated only to local areas without carry-in funds, 

because of the possibility of two occurrences. First, it is conceivable that under spending, 

and thus having carry-in funds, is quite common. Indeed, in the states submitting data on 

carry-in funds, more than half of the local areas have such funds. When this is the case, 

taking these funds from local areas that could not spend them, and allocating only to 

those local areas that had spent their entire allocation, would create substantial amounts 

of new resources for a small percentage of local areas. This may, in effect, create the 

reverse problem, providing certain local areas with too much in their allocation. Thus, in 

the attempt to solve the problem of under spending, one might create the contrasting 

problem of needing to overspend. Indeed, in the extreme case in which all LWIAs within 

a state have unspent funds, the state would be faced with the situation of recapturing the 

funds and having no local areas to which they can give them.

To describe the second issue, we will use a hypothetical example of a state with 

two local areas. Should one have no carry-in funds and the other have substantial 

amounts, it makes some sense that the first area receive some portion of the second area’s 

unspent funds. But the idea of directly transferring the entire amount of funds from the 

second area to the first seems extreme, and likely would lead to inequities that are little 

better than the imbalance that spurred the change. 

Thus, we are able to simulate how the allocations would change in the 24 states for 

which we have carry-in data. Results for this simulation are shown in Table III-10. As 
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III-63 Social Policy Research Associates

can be seen in this table, local areas that have carry-in funds do, in fact, receive a lower 

percentage of the overall funds in the subsequent program year. Although these areas 

would have carried-in, on average, between 29% and 41% of their prior year’s funds to 

their subsequent year’s allocation, as a result of this modification these areas do not 

receive nearly this percentage. 

The percentages in Table III-10 reveal that a sizable percentage of local areas have carryin funds. As a result, significant sums of money are available to be recaptured and 

reallocated. Thus, all areas will receive more than they otherwise would have were no 

carry-in funds reallocated. However, as can be seen by comparing the last two rows, 

areas that had carry-in funds experience a much smaller percentage gain as a result than 

they would have had they carried forward their own funds. For example, among those 

local areas with adult carry-in funds, the average percentage of their allocation that was 

to be carried into the subsequent program year is 29%. Under this scenario, this 

percentage can be thought of as the increase they would be expected to see were their 

carry-in funds not recaptured. However, once they are recaptured and reallocated, these 

LWIAs, on average, receive only a 6% increase in their funds. They still experience an 

increase because there is a larger than expected pool of funds available, of which they 

receive their relative share. But the increase is far less than what they otherwise could 

have expected to experience. Further, areas that did not have carry-in funds also 

experience an increase, solely because there are now more funds available, as a result of 

the carry-in funds being redistributed. Each of these areas receives its relative share of 

the additional carry-in funds, thereby providing them with an increase in overall funds. 

Although their overall increase is smaller than the increase experienced by areas with 

carry-over funds, this is entirely due to the fact that the specific areas that had carry-in 

funds are typically allocated a much greater percentage of available funds than are those 

areas that did not have carry-in funds. As a result, the average percentage gain is greater 

for areas with carry-in funds, but the playing field is substantially leveled because of the 

introduction of these funds into the general pool to be allocated.
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Social Policy Research Associates III-64

Table III-10

Changes in Allocation Percentage As a Result of Incorporating Carry-In Funds

Adults Youth Dislocated 

Worker

Percent of Areas with Carry-In Funds 69% 74% 71%

Average Percent of State Total Received by Areas 

with Carry-In Funds

72% 69% 73%

Percent of Total Allocation that Would Have 

Been Carry-In Funds

29% 33% 41%

Actual Percent Increase in Funds from 2001-2002 

Among Areas with Carry-In Funds

6% 5% 6%

Actual Percent Increase in Funds from 2001-2002 

Among Areas with No Carry-In Funds

5% 4% 4%

Thus, including carry-in funds in the overall pool of funds to be allocated would 

serve to even out the “gains” that areas receive from one year to the next. Those areas 

with carry-in funds currently receive all those funds for use in the subsequent year, while 

those without carry-in funds receive nothing. By including carry-in funds in the general 

pool, all local areas experience an increase in what they would have received, and the 

increase experienced by areas with carry-in funds is much smaller. Thus, this procedure 

has the effect of transferring funds that in a particular program year would go to areas 

with unspent funds toward areas that in that same program year that were able to spend 

their funds. In this way, it can be seen as redistributing funds toward areas that have 

more demand, or the capacity to spend all their funds.

As noted above, however, there are significant concerns about such an approach. 

Primarily, these concerns center on the fact that unspent funds are not necessarily 

unobligated funds. Thus, recapturing unspent funds from local areas would often, in 

effect, be taking money that these areas had obligated to a service provider or program, 

and redistributing it to other areas. This would make it somewhat difficult to sign longterm contracts with service providers that allow for much of the money in the contract to 

be exchanged in subsequent program years. Rather, local areas would need to focus on 

spending the money they receive in a given year within that year. Thus, adopting such an 
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III-65 Social Policy Research Associates

approach must be done very cautiously, and with sufficient advance warning such that 

local areas could adapt their behavior accordingly.

CONCLUSION

In this chapter we have described the simulation of current formula allocations used 

for the WIA adult, youth, and dislocated worker funding streams. To simulate these 

allocations, we obtained information from our state survey on the specific formulas being 

used by states to allocate their funds under these programs. We then obtained the data 

necessary to simulate the formulas from the Census and other relevant data sources, 

including the LAUS and ES_202 datasets. Using these data, we were able to mimic 

current allocations by either applying the standard formula, for those states who were 

utilizing only this formula for adult and youth allocations, or a combinations of the 

standard and discretionary formulas, for those states that had adopted a discretionary 

formula, as well as the dislocated worker formula they were using, as specified in their 

survey.

After simulating the current allocations, we compared the results of these 

allocations to various alternative models, including the four discretionary formulas 

currently in use, as well as formulas that have been proposed as part of legislation 

reauthorizing WIA. In so doing, we examined how the allocations to local areas would 

change as a result of these alternative formulas. 

Further, we examined how the fluctuation in these formulas could be reduced using 

a variety of approaches, including implementing a hold-harmless provision, substituting 

conceptually similar factors that do not incorporate a threshold, extending the reference 

period during which factors are calculated (i.e., to measure unemployment as an average 

over two or three years, as opposed to a single year), and incorporating carry-in funds to a 

subsequent year’s allocations. Each of these measures was effective in reducing the 

fluctuation experienced by local areas, though they also had drawbacks as well, such as 

reducing allocations to certain types of areas, or taking funds that might already be 

obligated and redistributing them to other areas as part of a reallocation process.

In the subsequent chapter, we provide a summary of the entire report, and offer key 

findings from each of the first three chapters.
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IV-1 Social Policy Research Associates

IV. SUMMARY

In this report, we have examined the formulas that states use to allocate funds to 

local workforce investment areas under the Workforce Investment Act (WIA) Title I 

adult, dislocated worker, and youth programs. We began by describing the current 

allocations formulas under WIA. Although there are standard formulas in use for the 

adult and youth programs, which rely upon unemployment factors and counts of 

disadvantaged individuals within local areas, WIA allows states to employ discretionary 

formulas for up to 30% of their adult and youth allocations. The dislocated worker 

formula, on the other hand, is not specified at the substate level. Rather, states can 

choose from a variety of factors on which to base their substate dislocated worker 

allocations.

BACKGROUND

In Chapter I we described the differences between the inter- and substate allocation 

formulas for adult, youth, and dislocated workers, as well as how the formulas differ 

from those used under JTPA. For example, JTPA did not allow any discretionary 

formulas to be used for allocating funds to local areas. Additionally, states were allowed 

to set-aside a greater portion of their funds for statewide activities under JTPA. Third, 

although JTPA allowed local areas to transfer some funds between the adult, youth, and 

dislocated worker programs, WIA prohibits the transfer of funds into and out of the youth 

program. Fourth, although both JTPA and WIA incorporated hold-harmless provisions to 

help minimize year-to-year fluctuations in funding levels, this provision only took effect 

after two years under WIA (although states were given the option of invoking this 

provision earlier).

In addition, we identified several key issues of concern in using the formulas. 

Many of these issues arose under JTPA, and remain concerns under WIA, because, the 

differences noted above notwithstanding, the formulas are actually quite similar. These 

issues represent a summation of several earlier research efforts as well as concerns 

expressed by states and local areas as part of this study, and include concerns about the 

organizational and financial stability of local areas under WIA, inequities in targeting 

funds to meet the goals of the legislation, data quality and access, and the use of 

discretionary formulas. 
IV. Summary

Social Policy Research Associates IV-2

Because local areas' funding can vary significantly, based on their levels of 

unemployment and other factors that are included in the allocation formula, there is 

legitimate concern that these shifts may create instability in program services or the 

capacity to meet the needs of their customers should they lose substantial funding from 

one year to the next. This concern has typically given rise to the implementation of holdharmless provisions, which limit the amount of loss a local area can experience from one 

year to the next to 90% of either their previous year's allocation (under JTPA) or 90% of 

the average of their two previous years' allocations (WIA). In addition, some have 

suggested that simply extending the reference period (e.g., measuring unemployment 

over more than a single year) used in the allocation formulas over a longer period of time 

would help to minimize funding fluctuation. 

Substantial concern has also been raised about the ability of the funding formulas to 

target funds to those areas in greatest need of them. Because two-thirds of the weight of 

the standard adult and youth formulas is given to unemployment factors, it is conceivable 

the allocation is weighted too greatly toward cyclical factors caused by short-term 

business downturns, rather than toward areas with higher concentrations of economically 

disadvantaged individuals who may have greater long-term needs for workforce services. 

Further, although the WIA adult program has eliminated the requirement (in place under 

JTPA) that adults must be economically disadvantaged to be eligible, this requirement is 

still in place for the youth program. Thus, allocating two-thirds of the youth funds based 

on unemployment, with only one-third allocated based on the number of economically 

disadvantaged youth in an area, may target funds to areas that have relatively few eligible 

youth.

Additionally, because both unemployment factors used in the adult and youth 

formulas are threshold measures (meaning that an area must at least meet a certain level 

of unemployment before receiving any share based on that factor), allocations based upon 

them tend to have dramatic swings from year-to-year, as areas just meet or fall below the 

threshold levels. This has implications for organizational stability, as described above, 

and also raises the question of whether an area that just meets the threshold is necessarily 

far more in need that an area that falls just below the threshold.

A third concern is that, to allocate the funds to local areas, states must have access 

to the necessary data on which the formulas are based. Although the data used in the 

standard adult and youth formulas are readily available, this concern is particularly 
IV. Summary

IV-3 Social Policy Research Associates

relevant should states opt to implement any discretionary formula, because to do so 

requires that they have ready access to these data.

STATES’ CURRENT ALLOCATIONS

In Chapter II, we described the results from a survey of all states concerning their 

current allocation formulas under WIA for the adult, youth, and dislocated worker 

programs. We received completed surveys from 44 states, plus Washington, D.C and 

Puerto Rico. Results showed that very few states actually chose to use a discretionary 

formula for their adult and youth programs, even though WIA allows states to do so for 

up to 30% of their allocated funds. Those four states that did adopt discretionary 

formulas typically used indices of excess unemployment, or unemployment above the 

state average, as their discretionary measures. 

In general, states allocated money under the adult and youth programs very 

similarly. Each of the four states using discretionary formulas did so for both their adult 

and youth programs. All remaining states used the standard formula to allocate their 

entire amount of youth and adult program funds.

Most states were primarily concerned that the formula used to allocate funds under 

WIA target those funds to areas with the greatest need. Additionally, states believed that 

consistent funding levels were important so that local areas could rely on sufficient funds 

to provide a stable set of services from one year to the next. Though the survey results 

would suggest that states’ concern for targeting need is paramount, the fact that only four 

states opted to adopt a discretionary formula in allocating funds for the adult and youth 

program suggests that perhaps stability in funding among local areas may instead be the 

critical factor in this allocation. Indeed, many states reported that they did not believe 

that the standard formulas accurately target levels of need within local areas, yet they still 

opted to use only these formulas for their substate allocation. When asked why they 

would use a formula even though it does not accurately target need, typically, the answer 

was that it was a convenient and well-understood formula that did not create problems 

when implemented. In an effort to ensure this stability, the vast majority of states 

invoked their authority to implement a hold harmless provision in substate funding, even 

though WIA did not mandate one until two years after WIA implementation.

Substate funding for the dislocated worker program relied primarily on several 

factors, including most prominently unemployment, unemployment concentrations, and 

long-term unemployment, which are similar to the factors used in inter-state allocations. 
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Several other factors often were used in substate allocations, though typically these other 

factors received less weight in the allocation formula. A few states used the standard 

WIA adult formula to allocate funds under the dislocated worker program, thereby 

foreshadowing to some degree proposed changes to the allocation formulas under WIA 

that would integrate adult and dislocated worker funds (as well as those under WagnerPeyser).

Finally, eight of the states in our sample were single workforce area states. Of 

these, three reported allocating funds to substate areas, often using the standard WIA 

adult and youth formulas, or slight variants of them, as the mechanism by which to make 

these allocations. The remaining five states did not allocate funds to substate areas and, 

instead, directed funds as needed from the state level.

COMPARISONS WITH CURRENT ALLOCATIONS

In the third chapter, we described the results of several simulations conducted to 

examine both how allocations are currently made and how the amounts allocated to local 

areas would change under several alternative formulas. First, we described the process 

by which we simulated current allocations, obtaining data from the 2000 Census and 

several years of data from the Local Area Unemployment Statistics (LAUS) and ES_202 

programs. Using these data, we were able to mimic current allocations by either applying 

the standard formula, for those states who were utilizing only this formula for adult and 

youth funding, or a combination of the standard and discretionary formulas, for those 

states that had adopted a discretionary formula, as well as the dislocated worker formula 

identified by states in their survey.

Applying Discretionary Formulas

Once we had simulated the current allocations, we then simulated several 

alternative allocation formulas to examine how the allocations made as a result of these 

formulas differ from the allocations made under the current formulas. Among the 

alternatives, first we simulated the four discretionary formulas adopted by states, and 

examined how each impacted the allocations made to local areas across all states. In each 

case, these alternatives used the standard formula to allocate the minimum 70% of their 

funds, while employing a discretionary formula to allocate the remaining 30% of their 

funds. These discretionary formulas typically relied upon unemployment or poverty 

above the state average, although the weights assigned to these two factors differed 

across the states. In contrast, Florida used as its sole discretionary factor unemployment 

above 1.25%, thereby incorporating a threshold factor, albeit one that is likely to be met 
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by virtually every local area. To mimic these formulas, then, we applied the standard 

formula to allocate 70% of funds, and each respective discretionary formula to allocate 

the remaining 30%.

In general, these formulas produced allocations that were markedly different from 

the current allocation formulas. For example, allocations based on these formulas 

typically produced results in which nearly one-third of all local areas experienced 

changes of at least 25% from their current allocations. Often, approximately two-thirds 

of all local areas would experience changes of at least 10% were these alternative 

formulas – using the discretionary formulas already in place – adopted.

The Alabama and Arizona formulas produced quite similar results, which is 

unsurprising given the relative similarity of their discretionary formulas. Specifically, 

both states use indices of unemployment and poverty above the state average in their 

formulas, though Alabama weights the unemployment factor as one-fourth of the 

discretionary formula and poverty as three-fourths, while Arizona weights the two 

equally. Applying these two states' formulas across all states, results showed that LWIAs 

with larger populations, and those in cities, tended to experience increases in their 

funding, while smaller LWIAs and those in rural areas tended to experience losses. In 

addition, these formulas targeted greater amounts of funds toward areas that, in a prior 

program year, had served a higher number of customers per WIA dollar. While this 

measure was not intended to be a measure of WIA effectiveness, formulas that target 

areas that serve higher numbers of customers per dollar could be said to be areas in which 

there is demand for services. The formulas’ impact on poverty was less clear. 

Alabama’s formula shifted substantial amounts of funds away from areas with low 

poverty, but most of the gains went to areas with moderate, rather than very high, levels 

of poverty. Arizona’s formula showed a somewhat similar pattern, although its impacts 

were generally more muted, as the formula did not target funds toward areas of poverty in 

any consistent way. In virtually all cases, the adult and youth formulas produced very 

similar results. 

Iowa’s formula, which uses unemployment above the state average as its sole 

discretionary factor for adults, and poverty above the state average as its sole youth 

discretionary factor, showed similar patterns to Alabama’s and Arizona’s formula results 

in that it directed more funds toward areas of larger population, and toward cities at the 

expense of suburbs and rural areas. But the formula produced quite different allocation 

results based on poverty, especially for the youth formula. These formulas targeted 
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substantially more funds toward areas of high poverty, while shifting funds away from 

areas of low poverty. Indeed, areas in the lowest quartile in terms of poverty rates 

received, on average, 10% less in their youth allocations when using Iowa’s youth 

formula, while areas in the highest quartile received on average slightly more than 10% 

more in their allocations. Clearly, then, using poverty as the discretionary factor has a 

fairly significant impact and causes much greater proportions of funds to be directed 

toward areas of high poverty. Interestingly, Iowa’s formulas shift money away from 

areas that serve both the most and the least number of customers per dollar, while shifting 

money toward those LWIAs in the middle two quartiles on this measure.

The final formula currently in use as a discretionary formula is Florida’s, which 

uses unemployment above 1.25% as its sole factor in both the adult and youth 

discretionary formulas. This formula produces quite different results than those 

described above. For example, the effect of implementing Florida’s formula across all 

states would be to target more funds toward those local areas with smaller populations, at 

the expense of LWIAs with larger populations. LWIAs in the lowest quartile in terms of 

population size would experience gains of approximately 8% for both their adult and 

youth allocations, while LWIAs in the highest quartile would experience losses of about 

4%. Further, rural areas would gain under this formula, while suburban areas would 

stand to lose (cities would remain largely unaffected). This formula also is relatively 

effective at directing funds away from areas with lower poverty and toward areas in the 

highest quartile in terms of poverty rates. Interestingly, this suggests that Florida’s 

formula has the effect of allocating greater proportions of the funds toward rural areas, 

which tend to be highest in terms of their poverty rates, but away from cities, which tend 

to have higher absolute numbers of individuals living in poverty. Finally, Florida’s 

formulas have the effect of shifting funds away from LWIAs that serve the highest 

number of customers per WIA dollar, and toward areas that serve fewer customers per 

dollar.

Dislocated Workers

We also examined how the dislocated worker allocations would shift under 

alternative scenarios. We did not apply any single state’s formula across all states to 

simulate allocations for these funds, because the number of variables and weights 

assigned to them were so disparate no one formula could be selected over any other. 

Rather, we examined allocations under two alternative scenarios, including applying the 

standard adult formula to allocate these funds, and applying the inter-state formula to do 
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so. We were interested in examining the impact of the standard adult formula because a 

few states had chosen to apply this formula for their own allocations due to its relative 

simplicity and the inherent unpredictability of dislocations. States seemed to reason that, 

since one could not accurately predict dislocations in any meaningful way no matter how 

many factors were included in a model, it would be simpler and somewhat more 

equitable to allocate based on a more streamlined model that local areas and states 

already were used to, such as the standard adult formula. Applying this model across all 

states, however, produced sizable changes in allocations. Nearly 50% of all LWIAs 

would experience a change of at least 15%, with one in four experiencing changes of 

more than 25%. Further, more than one-third of all local areas would experience losses 

of more than 10%. Allocations made under this formula would have mixed impacts on 

LWIAs of different sizes. While areas with the smallest populations would experience 

substantial losses (i.e., 12.7%) under this formula, areas with the largest populations 

would experience gains (of just over 7%). Further, cities would stand to benefit at the 

expense of suburbs and rural areas.

The second alternative formula considered for dislocated workers was to apply the 

inter-state formula to these allocations. This formula, which applies equal weight to three 

factors—the number of unemployed individuals, the number of excess unemployed in 

areas of excess unemployment, and the number of long-term unemployed—is actually 

quite similar to what many states are using for their allocations. Although states tend to 

include several other factors in their models, typically these other factors are assigned 

quite small weights, while the three factors used in the inter-state formula are assigned 

the vast majority of the allocation weight. Thus, we expected that this formula would 

produce somewhat similar results to the current allocations made under the dislocated 

worker program. Indeed, results of substate allocations made using the inter-state 

formula were much more similar than those made under the standard adult formula. 

Although one in three LWIAs would experience shifts of more than 15%, nearly half 

would experience shifts of less than 10%. LWIAs in both the lowest and highest 

quartiles in terms of population size would experience gains under this formula, while 

those in the middle quartiles would experience losses. Further, cities would stand to gain 

under this formula, while rural and suburban areas would experience losses. However, 

these gains and losses were typically much smaller than those observed under the 

standard adult formula.
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Applying Proposed Formulas

We also simulated the implementation of formulas that have been proposed under 

H.R. 1261, a bill proposed to reauthorize WIA. These formulas, which would 

substantially alter the funding mechanisms used by WIA adult and dislocated workers, as 

well as Wagner-Peyser, are an attempt to better target the levels of need within local 

areas while streamlining the allocation of several different sources of funds. 

For example, under these proposed formulas, the adult, dislocated worker, and 

Wagner-Peyser programs would be merged into a single funding stream. To mimic this, 

we simulated results for current adult and dislocated worker funds, and combined them 

into a single sum for each local area. To this we added the local area’s current 

hypothesized Wagner-Peyser allocation. Because Wagner-Peyser is currently a state-run 

program, with no such allocations to local areas, we assumed for this exercise that local 

allocations would be made using the formula currently in place under Wagner-Peyser for 

inter-state allotments. This formula places a two-thirds weight on the relative number of 

unemployed individuals and a one-third weight on the relative size of the civilian labor 

force. Using this formula, we were able to simulate the local allocations under WagnerPeyser, which were then added to the adult and dislocated allocations to obtain a current 

total allocation for each local area.

To these sums, we compared the allocations made under the proposed adult 

formula. This formula, which represents somewhat of an amalgamation of the adult, 

dislocated worker, and Wagner-Peyser formulas, includes the number of unemployed 

individuals, the number of excess unemployed individuals, and the number of 

disadvantaged individuals as the factors for allocating funds. According to the 

legislation, this formula is to be used to allocate 85% of the funds, while the remaining 

15% would be allocated using a formula of the state’s choosing. We simulated the 

effects of the proposed formula in three separate ways: first, we allocated 100% of the 

funds using this formula. Next, we allocated 85% of the funds using this formula, and 

the remaining 15% using unemployment in ASU. Finally, we allocated 85% of the funds 

using the proposed formula and 15% using unemployment above the state average, which 

is the factor states that currently have adopted a discretionary formula typically use in 

their formula.

Of these three simulations, clearly the one most similar to current allocations was 

that which allocated 85% of the funds using the proposed formula and 15% using 

unemployment in ASUs. Under this scenario, relatively small percentages of LWIAs 
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experienced shifts of greater than 25%. A sizable number of areas experience shifts of 

between 10 and 25%, but over forty percent of the areas would experience shifts of less 

than 10%, even in the absence of hold-harmless provisions. This is likely due to the fact 

that the formula includes many of the primary factors currently in use by the three 

formulas that would be merged. This formula is largely unrelated to population size, as 

areas with different populations would not experience dramatically different gains or 

losses. Although cities would stand to benefit under this formula, the shift is not large, 

and, similarly, suburban and rural areas, on average, experience only slight reductions in 

their allocations. Further, this formula directs somewhat more funds toward areas that 

are in the middle two quartiles in terms of poverty, while directing funds away from the 

quartiles that are highest and lowest in terms of poverty rates. 

The remaining two simulations produced somewhat greater changes. Allocating 

100% of the funds based on the proposed formula would produce shifts of more than 

25% for more than one in three LWIAs, and only one in four would experience shifts of 

less than 10%. Using unemployment above the state average as a discretionary factor in 

conjunction with the proposed formula, only 21% of LWIAs would experience changes 

of 10% or less, while nearly half would experience changes of greater than 25%. Each of 

the formulas tends to direct more of the funds toward local areas with larger populations, 

and these increases come at the expense of areas with smaller populations. Similarly, 

both formulas also would significantly increase allocations made to cities at the expense 

of suburban areas and, especially for the formula using unemployment above the state 

average as the discretionary factor, rural areas. Finally, using the proposed formula to 

allocate 100% of the funds to local areas has the effect of directing funds toward areas of 

lower poverty, and away from areas of highest poverty. Thus, this formula does a poor 

job of targeting funds to local areas with higher rates of poverty. Using unemployment 

above the state average as the discretionary factor reverses this effect, directing more 

funds toward areas with high poverty and away from areas of low poverty.

We also simulated the effects of adopting the proposed youth formula. This 

formula, which weights three factors—the number of unemployed individuals, the 

number of youth in the civilian labor force, and the number of disadvantaged youth—

equally, would, according to the proposed legislation, be used to allocate 80% of the 

funds, while states could choose a formula to allocate the remaining 20%. To simulate 

the allocations produced by this formula, we conducted two simulations, one in which the 

proposed formula was used to allocate 100% of the funds, and a second in which the 
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proposed formula was used to allocate 80% of the funds, and youth poverty above the 

state average was used to allocate the remaining 20%.

In both cases, very large shifts in allocations were experienced by a majority of 

LWIAs. This is perhaps not surprising, because the proposed formula is quite different 

from the current allocation formula. In both cases, approximately two out of three 

LWIAs would experience shifts of greater than 25%, and approximately one-third would 

experience changes in excess of 50%. In general, far more funds would be directed 

toward areas with the largest populations, at the expense of areas with smaller 

populations. Using the proposed formula to allocate 100% of the funds also tends to 

direct money away from areas of highest poverty, with LWIAs in the highest quartile of 

poverty rates experiencing slightly more than 10% losses in their allocations. Not 

surprisingly, the formula that includes youth poverty above the state average as the 

discretionary factor reverses this effect and directs a greater proportion of the funds 

toward areas of higher poverty.

Mechanisms to Promote Organizational and Financial Stability

 Finally, we examined mechanisms by which the formulas could be used or altered 

to help ensure that local areas receive relatively stable allocations from year-to-year. 

Sizable shifts across years make it far more difficult to offer consistent services and to 

meet the needs of customers. To examine stability in funding across program years, we 

looked at three separate ways in which greater stability could be introduced into the 

formulas, including hold-harmless provisions, extending the reference period for which 

allocation factors are calculated, and including carry-in funds in a subsequent year’s 

allocations.

The need for mechanisms to control fluctuation is readily apparent when examining 

the changes that occur in the absence of them. In the last two program years, only 

slightly more than 50% of all local areas would have experienced shifts of less than 10% 

were no mechanisms in place to limit shifts. Further, approximately one in six local areas 

would have experienced shifts of more than 25%. Thus, the need for mechanisms that 

help to limit these shifts seems clear, as LWIAs cannot be expected to be able to rapidly 

expand or contract, and sign or void contracts with service providers, based on dramatic 

shifts in their funding across years.

The first of the three mechanisms we simulated, implementing a hold-harmless 

provision, was quite effective at minimizing the shifts experienced by local areas. Since 
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it automatically imposes a limit of no more than a 10% loss from one year to the next, 

LWIAs can reasonably predict what their subsequent allocation will be. As a result of 

hold-harmless, then, all local areas receive at least 90% of the average of their two prior 

years’ allocations. In general, this benefits LWIAs with smaller populations, those in 

suburban and rural locations, and those with smaller allocations to begin with. The great 

benefit of hold-harmless applied to current allocation data, however, is that these 

externally-imposed increases come at the expense of larger areas with more WIA 

resources to begin with. As a result, substantial percentage increases directed toward 

smaller areas as a result of hold-harmless provisions require only relatively minimal 

losses on the part of larger areas, and it is these areas that can best afford such small 

losses because of their relatively vast resource base.

A second useful mechanism by which to limit fluctuation in LWIA funding across 

program years is to slightly alter the factors included in the formulas. Rather than 

utilizing threshold measures for unemployment, such as unemployment in ASUs and in 

areas of excess unemployment, one could simply substitute the absolute number of 

unemployed, regardless of whether this number exceeds some externally imposed 

threshold. Doing this reduces the fluctuation in yearly allocations by nearly threefourths, simply because there are not dramatic swings caused by just meeting or falling 

below a given threshold. 

Additionally, if one were to use the average number of unemployed individuals 

over an extended period of time, such as the prior two years, the fluctuation would be 

limited even further still. Using a measure of the average number of unemployed 

individuals over the prior two years, and assigning this factor a weight of two-thirds in 

the allocation formulas (with the number of disadvantaged individuals as the remaining 

one-third) would reduce the fluctuations experienced by LWIAs by more than 80%.

The final mechanism by which we examined stability was by attempting to 

incorporate carry-in funds into subsequent program year allocations. Although this 

mechanism was somewhat effective in limiting the fluctuations local areas experienced, it 

had two serious drawbacks. First, local areas and several interested stakeholders with 

whom we spoke suggested that using carry-in funds as an indicator of an inability to 

expend funds or as a sign of low demand for services was a poor assumption. These 

funds largely had been obligated in the assigned program year, but since LWIAs have 

two years to actually spend them they had not yet been expended. A large percentage of 

these funds, according to local areas, was obligated to service providers under longer-
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term contracts, and limiting subsequent obligations because of apparently available carryin funds would significantly limit these areas’ ability to provide other needed services. 

Secondly, the process by which the carry-in funds would need to be incorporated back 

into subsequent year allocations was trickier than expected, and thus makes the allocation 

process a much less simple one. Given that states and other stakeholders were clearly 

advocating an allocation process that remained relatively simple to understand and 

implement, this must be seen as a significant drawback.

CONCLUSION AND RECOMMENDATIONS

Although none of the formulas examined as part of this report can be said to be the 

ideal one, because there are many competing interests and concerns involved in the 

allocation of funds under WIA, we can identify those formulas that would be best suited 

to achieve specific goals. For example, although any change to the allocation formulas is 

bound to create significant shifts in funding at least in the first year, these shifts will 

benefit certain local areas more than others. To the extent that those that benefit have 

more need for WIA funds, as a result of their greater demand, greater numbers of 

unemployed or disadvantaged individuals, or greater capacity to serve this demand, then 

these shifts could be considered a temporary but necessary challenge to overcome in the 

effort to target greater proportions of funds to those areas better suited to receive them. 

Thus, changes, even dramatic ones, may not be a poor result given that the current 

allocations do not target the eligible population perfectly.

In this report we have presented a variety of alternative formulas and compared 

how these formulas impact the allocations made to local areas. Such comparisons enable 

us to examine how changes that seem slight or subtle may have dramatic effects on the 

allocations made, and that LWIAs with different characteristics may be impacted 

differentially. For example, an alternative formula that changes on an unemployment 

factor may do far more than target more funds toward areas of greater unemployment. 

Because of the way in which the factor is measured within local areas, it may also target 

greater proportions of funds to LWIAs that have higher, or lower, rates of poverty, or 

areas that typically receive larger or smaller shares of WIA funds. These impacts, while 

unintentional, have very real effects on LWIAs, and must be understood and anticipated 

in order to know how any such changes will be received.

Among the alternatives considered in this report, we have seen that several of the 

discretionary formulas currently in use by four states would have the impact, if applied 

across all states, of targeting greater proportions of funds to those local areas with greater 
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populations and LWIAs that are primarily in cities, while areas with smaller populations, 

and those in rural or suburban areas, would experience losses. The formulas also tended 

to shift funds away from areas of low poverty toward areas with moderate, but not very 

high, poverty levels. Thus, if one considers the universal adult customer under WIA to 

be any individual, these formulas may be well suited to targeting funds toward areas that 

have relatively greater numbers of such customers. Further, these formulas tend to target 

funds to areas that have, in PY 2001, served greater numbers of customers per WIA 

dollar. Additional funds targeted toward these areas thus would be one way of directing 

resources to areas in which there is a clear demand.

Other formulas do a better job of targeting funds toward local areas with high rates 

of poverty, largely by including in their allocation formula the number of individuals 

living in poverty as a central component. Other formulas have targeted areas of high 

poverty by using alternative factors, such as unemployment above 1.25%, which targets 

poverty indirectly, but nevertheless has the effect, when combined with the standard 

formula, of directing more funds toward areas with more people living in poverty.

In general, any changes to the allocation formula will tend to produce sizable shifts 

in the actual allocations received by local areas, simply because LWIAs will vary in their 

relative values for the factors in question. Thus, changes proposed to the allocation 

formula as part of WIA reauthorization are likely to produce sizable shifts from current 

allocations, though the analysis presented in this report suggests some ways in which 

those shifts can be minimized. A critical task in determining whether or how to modify 

the existing formulas will thus be to ensure that whatever shifts are produced by the new 

formulas, they have the effect of targeting more funds to areas that have a demonstrated 

need, such as the number of unemployed or disadvantaged individuals, the size of the 

labor force, or other key factors, many of which have been described above. 

Given these concerns, below we offer several recommendations to DOL as they 

consider alternatives to the current formula as specified in the original WIA legislation.

• Include factors that measure raw counts of individuals. Using raw 

counts of individuals measures the actual number of individuals who may 

be in need of services. Relying on such measures will create a more 

uniform and objective formula because the raw counts can be compared 

directly across areas to determine the relative need in each.

• Include measures that target the eligible population. The current 

formulas focus heavily on unemployment factors, as well as measures of 

the disadvantaged population. However, for the adult program, neither 
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being unemployed nor disadvantaged is a requirement to be eligible under 

WIA. Thus, none of the factors used to allocate funds actually targets 

what could be considered the eligible population. Similarly, for the youth 

program, although being disadvantaged generally remains a requirement 

for the youth program, there is no requirement that youth be unemployed. 

Thus, two-thirds of the current formula allocating youth funds does not 

specifically target the eligible population. Thus, we recommend 

specifically that DOL consider including the following measures in the 

allocation formulas:

− For the adult program, a measure of the number of individuals in 

the labor force, as a reasonable indicator of the potential eligible 

population, which includes the “universal customer.”

− For the adult program, consider the use of measures of 

underemployment. 

− For the youth program, a measure of the number of youth with 

any of the barriers that are required for eligibility for WIA 

services. Although several of these barriers would be difficult to 

measure and, thus difficult to implement, we believe it is possible 

to use at least some of the barriers confirming eligibility in the 

allocation barriers. Among these measures could be the number 

of youth who are school dropouts or those who are pregnant or 

parenting.

• Include measures of growth industries or in-demand jobs as an 

allocation factor. Doing so represents one aspect of the “need” within an 

area, in that employers in such industries, or with job openings in these 

areas, have substantial need for a trained workforce.

• Eliminate the use of threshold measures in the substate allocation 

formulas. These measures create significant fluctuation in allocations 

from year to year and can make it more difficult for local boards to 

remain stable. Eliminating them can substantially increase the stability of 

allocations while continuing to allow yearly fluctuation that indicates 

actual need within local areas.

• To promote organizational and administrative stability, continue use 

of hold harmless provisions, at least during the transition period when 

changes, such as those described above, are being implemented. Such 

provisions ensure that local areas can be reasonably sure of the amount 

they will receive from year to year, thereby making their planning efforts 

more feasible and informed.

• Exercise great caution if including carry-in funds as a factor in 

subsequent allocations. Primarily, this is important because often 

unspent funds have already been obligated. Incorporating them into 

subsequent allocations may therefore significantly impair local areas from 

meeting their obligations and providing services to their customers, and 
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may result in local areas being hesitant to enter longer-term contracts with 

their providers. Such contracts often are seen as beneficial, as 

relationships can be developed that help to foster positive outcomes for 

customers.

• If the adult and dislocated worker streams of funding are to be merged,

enable states to retain a higher proportion of funds to be distributed 

based on dislocations as they occur. Because past layoffs are a poor 

predictor of future events, states must remain able to target funds as new 

layoffs do occur. They therefore must retain a significant portion of their 

WIA funds to distribute as these events occur.

• Support efforts to develop new data sources that would provide data 

to be used for substate allocations more quickly. Among these efforts 

could be the use of the SAIPE or the full implementation of the American 

Community Survey. Updating the data on poverty, which currently is 

weighted as one-third of both the adult and youth formulas, more than 

every ten years would better target this need as it shifts within a ten-year 

period.
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APPENDIX A: 

FACTORS INCLUDED IN SIMULATED 

FORMULAS
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APPENDIX A: FACTORS INCLUDED IN SIMULATED 

FORMULAS

The following is a list of the factors used in the various simulations examined as 

part of this study. In addition to the name of the factor, we also include a description of 

the source from which we obtained these data and a description of any key issues that 

should be noted about the factor.

Factor Data Source Key Issues

Disadvantaged Adults Census The number of adults, ages 21 to 

64, living in poverty. 

Excess Unemployment LAUS, Census

Unemployment in Areas of 

Substantial Unemployment

LAUS, Census

Household Income Census Average and Median Income.

Median Earnings Census Household earnings of population 

16 and over.

Number of Individuals with 

Less Than High School 

Education

Census Ages 18 and above.

Population Census

Poverty Above the State 

Average

Census

Total Number of 

Unemployed

LAUS

Total Civilian Labor Force Census, LAUS

Unemployment Rate LAUS

Disadvantaged Youth Census The number of youth, ages 16 to 

20, living in poverty. 

High School Dropouts Census For youth, calculated from ages 16 

to 19. For adults, calculated as 

ages 18 and older.
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Jobless Out-of-School 

Youth (OSY)

Census Combination of unemployed and 

out of labor force, among those 

youth ages 16 to 19 not in school.

Unemployed OSY Census The number of unemployed, ages 

16 to 19, among those not in 

school.

Unemployed Youth Census Number of unemployed youth, 

ages 16 to 21.

Number Employed in 

Declining Industries

ES_202

Mass Layoffs Mass Layoff 

Statistics Series

Farmer-Rancher Economic 

Hardship

Department of 

Agriculture, Statelevel data

Plant Closing Data WARN Notices, 

State-level data