Analysis of the WIA 

Allotment Formula for 

Distributing Funds to the 

States 

Final Report 

Authors: 

Louis Jacobson 

Carrie Markovitz 

Amy Shimshak 

November 2002 

Prepared for:

US Department of Labor/ 

Employment and 

Training Administration 

Office of Policy and Research

Prepared by: 

WESTAT

Rockville, Maryland 
Analysis of the WIA 

Allotment Formula for 

Distributing Funds to the 

States 

Authors: 

Louis Jacobson* 

Carrie Markovitz 

Amy Shimshak 

November 2002 

*Dr. Louis Jacobson served as the project director for this 

task. He was responsible for development and execution of 

the analyses, and supervised the work by other team 

members. Carrie Markovitz and Amy Shimshak assisted Dr. 

Jacobson by assembling the data required for this work and 

assisting in writing the final report. Ellen Tenenbaum is the 

manager of the Rapid Response contract and was responsible 

for submitting progress reports and ensuring ETA that the 

work met its schedule and budget targets. Greg Knorr, a 

former employee of ETA who is expert on the allocation 

formulas, served as advisor and reviewer throughout the 

project, and Sherryl Bailey from ETA supplied the basic 

formulas for calculating the allotments to states as well as 

other necessary information. 

This project has been funded, either wholly or in part, with Federal funds from the Department of Labor, 

Employment and Training Administration. The contents of this publication do not necessarily reflect the 

views or policies of the Department of Labor, nor does mention of trade names, commercial products, or 

organizations imply endorsement of same by the U.S. Government.
iii 

TABLE OF CONTENTS 

Chapter Page

 EXECUTIVE SUMMARY ............................................................................. vii 

1 INTRODUCTION ........................................................................................... 1 

1.1 Background......................................................................................... 2 

1.2 Issues To Be Addressed...................................................................... 4 

1.3 Methodology....................................................................................... 4 

2 WIA ALLOTMENT FORMULAS ................................................................. 7 

2.1 JTPA Title II-A and Title II-B Allotment Formulas........................... 7 

2.2 How the JTPA Title II-A Allotment Was Made for Program 

Year 1997............................................................................................ 9 

3 EFFECT OF EACH FACTOR ON THE JTPA TITLE II-A ALLOTMENT 

 IN PY93 ........................................................................................................... 16 

3.1 Redistributive Effects of the Minimum Funding Provisions .............. 19 

3.2 The Relationship Between Eligibility and Title II-A Allotment 

Shares.................................................................................................. 22 

3.2.1 The Relationship Between Eligibility and State Shares 

in PY93 ............................................................................... 23 

3.2.2 The Relationship Between Eligibility and State Shares 

in PY97 ............................................................................... 26 

3.3 Changes in Each State’s Share Between PY93 and PY97.................. 29 

3.4 Changes in State Allotments for Non-Dislocated Adult Programs 

Over Time........................................................................................... 33 

3.5 Effects of WIA Minimum Funding Provisions................................... 36 

3.6 Summary and Conclusions ................................................................. 45 

4 ANALYSIS OF JTPA AND WIA DISLOCATED WORKER 

 ALLOTMENTS............................................................................................... 47 

4.1 Effect of Each Factor on the Dislocated Worker Allotments in PY93, 

PY97, and PY01 ................................................................................. 47 

4.2 Effect on the Dislocated Worker Allotments in PY93, PY97, and 

PY01 of Giving 100 Percent Weights to Each Factor ........................ 53 

4.3 Change in State Dislocated Worker Allotments PY93-PY01 and 

PY93-PY97......................................................................................... 54 

4.4 The Relationship Between Eligibility and State Dislocated Worker 

Allotments........................................................................................... 57 
iv 

TABLE OF CONTENTS (continued) 

Chapter Page

4.4.1 The Relationship Between the Distribution of Dislocated 

Workers and State Shares in PY93 and PY99..................... 58 

4.4.2 Ability of Each Factor To Match Dislocation Shares ......... 63 

4.4.3 Share Changes by State between PY93 and PY99.............. 63 

4.4.4 Share Changes by State Between PY97 and PY99 ............. 66 

4.5 Summary and Conclusions ................................................................. 68 

5 RECOMMENDATIONS................................................................................. 70 

List of Tables 

Table

1 Unemployment rates for the United States by year for 1985-2000 ................. 6 

2 FY1997 JTPA Title II-A allocation ................................................................. 10 

3-93 Distribution of total Title II-A allotment to the states and distribution of 

 each state’s allotment by factor for PY93 ordered by share to states .............. 17 

4-93 Table 4-93: Distribution of total title II-A allotment to the states and 

 distribution of each allotment by factor for PY93 ordered by hold harmless/ 

 minimum contribution ..................................................................................... 21 

5 PY93 allotment per disadvantaged adult ......................................................... 22 

6-93 Actual per-person Title II-A allotment versus use of New Census poverty 

 figures for PY93............................................................................................... 24 

7 Redistribution of Title II-A funds based on alternative formulas in PY93...... 25 

8 Redistribution of Title II-A funds based on alternative formulas in PY97...... 26 

6-97 Actual per-person Title II-A allotment versus use of New Census poverty 

 figures for PY97............................................................................................... 27 

9 Changes between PY93 and PY97 in allotments, poverty, and funds 

 per disadvantaged adult ................................................................................... 28 

10 Percentage change in Title 11-A shares PY93 – PY97 based on actual 

 formula and annual poverty ............................................................................. 30 
v 

TABLE OF CONTENTS (continued) 

List of Tables (continued) 

Table Page

11 Change in ASU unemployed, excess unemployed, and adults in poverty 

 between PY93 and PY97 ................................................................................. 32 

12 Changes in State Adult Program Shares PY93-PY01 and PY93-PY97 .......... 34 

13 WIA disadvantaged youth activities allocation for PY01................................ 37 

14 State youth program allotments under WIA and JTPA formulas PY01 .......... 43 

15-93 Distribution of Title III Allotment to the States and Distribution of Each 

 State’s Allotment by Factor for PY93, Ordered by State Shares..................... 48 

16-93 PY93 ratio of excess unemployment shares to total shares ............................. 49 

15-97 Distribution of Title III allotment to the states and distribution of each 

 state’s allotment by factor for PY97, ordered by state shares.......................... 51 

15-01 Distribution of Title III allotment to the states and distribution of each 

 state’s allotment by factor for PY01, ordered by state shares.......................... 52 

16-01 PY01 ratio of excess unemployment shares to total shares ............................. 53 

17 Shifts in state allotments based on giving a 100 percent weight to each factor 53 

18 Changes in Dislocated Worker State Shares PY93-PY01 and PY93-PY97 .... 55 

19 Comparison of shifts in state allotments PY93-PY01 for dislocated 

 worker and adult programs .............................................................................. 56 

20 State Allotments per Dislocated Worker in PY93 and PY99 .......................... 58 

21-93 Allotment per dislocated worker, differences in share of dislocated workers 

 versus share of allotment by state for PY93 .................................................... 59 

21-99 Allotment per Dislocated Worker, Differences in Share of Dislocated 

 Workers versus Share of Allotment by State for PY99 ................................... 62 

22 Reallocations needed to equalize dollars per dislocated worker if each 

 factor was used alone....................................................................................... 63 

23-93 Change in dislocated worker and allotment shares PY93-PY99 ..................... 65 
vi 

TABLE OF CONTENTS (continued) 

List of Tables (continued) 

Table Page

24 Effect on PY93-PY99 share changes of removing California, New York, 

 Michigan, Massachusetts, and Ohio ................................................................ 66 

23-97 Change in dislocated worker and allotment shares PY97-PY99 ..................... 67 

25 Quartile change in allocations and dislocations PY93-PY99 and PY97-PY99 

 when states are ordered by allocation change.................................................. 68 
vii 

EXECUTIVE SUMMARY 

This study examines the way federal funds are distributed to the states by formula-driven 

block grants. Much of this analysis examines the Job Training Partnership Act (JTPA) formulas for 

funding programs targeted on economically disadvantaged adults and dislocated workers. We also 

examine the formulas used to fund Workforce Investment Act (WIA) programs, which were almost 

identical to the JTPA formulas. The adult formula gives equal weight to three factors: decennial poverty 

and two measures of unemployment. However, minimum and hold-harmless provisions also affect the 

distribution. The dislocated worker formula gives equal weight to three unemployment measures, has no 

hold-harmless or minimum provisions, but reserves 20 percent of funds to handle unforeseen 

contingencies. 

This study describes: 

 How the actual year-to-year allotments changed because of changes in the 

unemployment factors used to make the adult and dislocated worker calculations; 

 How much difference the hold-harmless provisions made to the adult allotments; 

 How closely the disadvantaged adult allotments matched the distribution of program 

eligibles as measured by new Census poverty statistics; and 

 How closely the dislocated worker allotments matched the distribution of program 

eligibles based on estimates we produced. 

Our analysis of JTPA and WIA funding formulas determined that the large reductions in 

unemployment rates that occurred during the 1990s caused large shifts in allotments. However, the shifts 

were far larger for dislocated worker than for disadvantaged adult programs. This was because the 

dislocated worker excess unemployment factor was calculated on a statewide basis, while the adult 

formula was calculated for Areas of Sustained Unemployment (ASUs). Over the 1990’s, unemployment 

in more and more states fell below the 4.5 percent excess threshold. In contrast, most states had had some 

areas that met the 6.5 percent ASU threshold, and therefore also met the 4.5 percent excess 

unemployment threshold. 

We also found the hold-harmless provisions, which only existed for adult programs, had the 

desired effect of preventing large, sudden, reductions in funding. As a result, the effect of a sharp drop in 

unemployment took several years to be fully felt. In addition, the share-floor of 0.25 percent for adult 
viii 

programs substantially boosted the amount of funds going to a number of states. While the gains to those 

states were in the neighborhood of 30 percent, the cost to the remaining states was tiny, only about 0.5 

percent. 

We also found that state shares of economically disadvantaged adults changed slowly as 

economic conditions improved. In contrast, state shares of dislocated workers showed substantial shifts 

that were often negatively correlated with shifts in unemployment. However, compared to disadvantaged 

adults, most dislocated workers left dislocated status relatively quickly. 

With respect to overall equity we found that the unemployment measures in the dislocated 

worker allotment formula did an especially poor job of distributing funds in proportion to the number of 

dislocated workers. The problem was exacerbated because the sharp unemployment decline led the excess 

unemployment factor to distribute funds to only a few states with high unemployment, but relatively few 

dislocated workers. 

Equally important, the use of unemployment measures did not distribute adult funds in 

proportion to the number of adults in poverty. In fact, and levels of unemployment were not highly 

correlated with the initial levels of poverty. A secondary problem was that changes in unemployment 

were much greater than changes in poverty, even if usually in the same direction. 

We drew the following recommendations for improving the allocation formulas from our 

analysis. 

 A simple way to equitably allocate adult program funds would be to use new Census 

Bureau poverty measures as the sole factor in the formula. Even though these 

estimates lag by 3 years, the changes in poverty are so small that this would make 

little difference. 

 It would make sense to double the amount of worker dislocation funds that are setaside for unforeseen contigencies from 20 to 40 percent. Because the distribution of 

worker dislocations are large and difficult to predict. 

 It also would make sense to drop the excess unemployment factor from the dislocated 

worker formula since it is an especially poor indicator of the given state’s share of 

dislocated workers. 
1 

1. INTRODUCTION 

This paper describes Westat’s analysis of the Workforce Investment Act (WIA) allotment 

formulas. This work is designed to help the Employment and Training Administration (ETA) respond to a 

congressional requirements for a study about the effectiveness of those formulas. 

Starting during the fall of 2000, we assembled and analyzed much of the data required to 

fully understand: 

 The way the formulas work. 

 The strengths and weaknesses of the formulas. 

 Options for improving the formulas. 

More specifically, this report thoroughly describes: 

 How the highly complex hold-harmless and minimum funding provisions affect the 

distribution of adult program funds. 

 How well the unemployment factors used in the formulas distribute funds in 

proportion to each state’s share of adults in poverty and share of dislocated workers. 

 How changes in economic conditions affect the distribution of funds and the 

distribution of key target groups. 

However, this work suffers from several limitations. First, we studied only the way Federal 

funds are allotted to the states. A separate group is studying substate allotments. Second, we lack the data 

needed to examine: 

 How well the youth program formulas distribute funds in proportion to the program’s 

target group. 

 How severe declines in economic conditions in the early 1990s affected the allotments 

and size of the target populations. 

 How broadening WIA eligibility to all adults, not just economically disadvantaged 

adults, affected who received WIA aid and how much WIA funds different groups 

received. 

 How changes in the number of program eligibles translate into changes in 

participation and the need for services with different costs. 
2 

Because of these data limitations, we did not fully analyze youth allotments or the effects of 

economic downturns. We also assumed that most adult funds still are targeted on economically 

disadvantaged individuals. However, we are planning to assemble data that should help us determine how 

changes in the number of dislocated workers affect program participation and take-up rates for different 

types of services. Despite these limitations, the report contains a lot of useful information and provides an 

excellent foundation for pursuing additional work. 

1.1 Background 

The Workforce Investment Act (WIA) of 1998 established one of several major federal 

programs that distribute funds to the states through formula-driven block grants. In Fiscal Year 2000 

(October 1999 through September 2000) the U.S. Department of Labor (DOL) distributed more than 

$5.37 billion to run state-level WIA programs during program year 2000 (July 1, 2000 through June 

30, 2001). WIA replaced the Job Training Partnership Act (JTPA), but used almost identical formulas for 

distributing funds to the states to serve the same target populations.1

Because WIA is so new, most of our examination focuses on the actual allotments made 

under JTPA. The majority of JTPA funds supported employment and training programs for economically 

disadvantaged adults and youth under JTPA Title II-A and II-C respectively. Additional amounts were 

made available under Title II-B for the operation of summer youth programs and under Title III for the 

provision of training and reemployment services to displaced workers. 

The allotment formulas for all three JTPA Title II programs are highly similar both in how 

they distribute funds to the states and how the governors are required to pass the majority of each state’s 

funds to local service delivery areas (SDAs), which under WIA are now called local workforce 

investment areas. JTPA Title III uses a different allotment formula and, unlike Title II programs, gives the 

governors complete flexibility in determining if and how Title III funds will be distributed at the substate 

levels. 

 

1

 Adult WIA programs are no longer restricted to economically disadvantaged adults. Indeed, states are obligated to establish One-Stops that 

provide core services to all-comers. However, it is our understanding that most of the funds needed to provide the more intensive and more 

costly services still primarily go to economically disadvantaged adults, as WIA requires that preference be given to that group. Thus, in this 

paper we assume that the intent of Congress in continuing use of the JTPA disadvantaged adult formula was to continue providing funds in 

proportion to the number of economically disadvantaged adults. 
3 

Under JTPA and WIA the allotment formula for economically disadvantaged adults divides 

the total funds earmarked for the states into three equal parcels and then allots each parcel using states’ 

shares of the nationwide number of, respectively: (1) economically disadvantaged (ED) adults, (2) 

unemployed individuals in areas of substantial unemployment (ASUs)—areas with unemployment rates 

at least 6.5 percent, and (3) excess unemployed individuals—number of unemployed above 4.5 percent 

unemployment rate (the higher of statewide or ASUs). 

However, in JTPA every state must receive at least 0.25 percent of the total allotment. Thus, 

usually the six or seven states with the smallest populations receive the floor share. In addition, no state 

can receive a share that is less than 90 percent of its share in the preceding year. Thus, usually 10 or more 

states have their shares determined by these hold-harmless provisions.

2

The formula for distributing WIA youth and adult program funds is identical to the JTPA 

disadvantaged youth and adult formula described above, with several important exceptions. When total 

nationwide funding for the states exceeds a specified amount ($960 million for adults and $1 billion for 

youth), a three-part set of somewhat more complex minimum funding provisions are applied (which are 

described in detail in Section 2.0). In addition, WIA, unlike JTPA, limits the percentage gain in states’ 

share from the preceding year. 

Dislocated worker funds under JTPA Title III and WIA funds earmarked for the states also 

are divided into three equal parts with each part allocated based on states’ shares of, respectively: (1) 

regular unemployment (2) excess unemployment (state unemployment over 4.5 percent, not 

unemployment in ASUs, as is the case for adults and youth funds), and (3) long-term unemployment 

(individuals unemployed for 15 weeks or more). 

No minimum funding provisions are required for the dislocated worker allotments in either 

JTPA or WIA. However, the unpredictable nature of worker dislocation motivated Congress to establish a 

substantial Federal set-aside amounting to 20 percent of the total dislocated worker appropriation mainly 

to deal with emergency layoffs. As a result only 80 percent of the total funds appropriated for dislocated 

workers are distributed to the states using the above formula. 

 

2

 Under JTPA the states then used essentially the same formulas to allocate funds to sub-state areas, although governors could set aside up to a 

fixed percentage for statewide programs. This is basically true under WIA, however, governors can use an alternative formula to distribute 30 

percent of the substate funds. 
4 

1.2 Issues To Be Addressed 

The main focus of this study is to describe: 

 How the actual year-to-year allotments changed because of changes in the 

unemployment factors used to make the Title II-A and Title III calculations; 

 How much difference the hold-harmless provisions made to the Title II-A allotments; 

 How closely the actual Title II-A allotment for disadvantaged adults matched the 

distribution of program eligibles as measured by new Census poverty statistics; and 

 How closely the actual Title III allotment for dislocated workers matched the 

distribution of program eligibles based on our own estimates of the distribution of 

dislocated workers by state. 

We then use this information to reach conclusions about how well the current formula meets 

the goals of: 

 Distributing funds in proportion to the number of eligibles; 

 Ensuring funding levels are relatively stable and predictable; and 

 Providing sufficient funds for small states to effectively run their programs. 

We then discuss what changes in the formulas and the data used to produce the calculations 

might lead to better meeting the above goals. 

1.3 Methodology 

 Title II-A Funds for Disadvantaged Adults 

It seems clear that the prime intent of the funding formulas was to distribute funds 

proportionally to eligibility, which for Title II-A meant proportionally to the adult poverty level. Thus, a 

crucially important element of our work is comparing the actual allotment to adult poverty for the same 

period. However, an adequate measure of annual poverty has only recently been formulated by the Small 
5 

Area Income and Poverty Estimates (SAIPE) Project of the Census Bureau, which began to develop 

county level poverty statistics for use in computing federal block-grants in the mid-1990s.3

In the absence of better data, the existing formula used the two unemployment measures in 

the hope that they would adequately reflect changes in poverty. The new annual Census poverty data, 

therefore, provide the first opportunity to assess how successfully the two unemployment measures are as 

proxies for poverty levels and also determine how the new measures might be used to produce formulas 

that better match funding to eligibility. 

To reach the key goals of this study we primarily focus on: (1) how the actual Title II-A 

allotment of funds differed across the states from PY93 through PY01; (2) how the allotments would be 

modified if we: (a) eliminated the hold-harmless and minimum provisions, (b) gave more weight to 

current poverty measures; and ultimately, (3) how well various formulas distribute funds proportionally to 

eligibility. 

We primarily focus on changes between PY93 and PY97 because: (a) the required allotment 

and new Census poverty data are available for both these periods, (b) the periods are sufficiently far apart 

to examine whether poverty levels substantially diverged from 1990 levels measured with decennial 

census data alone, and (c) there were substantial changes in the distribution of the two unemployment 

factors over this period. 

Importantly, we examined the working of the formulas for each program year between PY93 

and PY99, as well as for PY01. We concluded that effects on the allotments of sharp drops in 

unemployment that occurred in most areas during the period are very well described by the PY93-PY97 

comparisons (see Table 1), and little would be gained either by including data between those two program 

years or extending the analysis beyond PY97. Thus, we do not regard our lack of the required new Census 

poverty data beyond 1998 to be a serious problem. As noted in the introduction, our inability to observe 

the effect of the recessions in the early 1990’s is a much more serious drawback to the study. 

 

3

 In this paper we assume that a “good” formula is one that distributes funds in proportion to eligibility. However, we hope to examine alternative 

criteria in future work, as ETA requested that we assess whether the formulas distribute funds in proportion to “need.” Need could be a superior 

criterion because eligibility does not take into account variations in the severity of the problems faced by individual participants nor does it 

consider variations in the number of eligibles willing to participate. Thus, program operators in two locations with the same number of eligibles 

(and same funding) may face large differences in the funds needed to adequately serve each person wanting aid. 
6 

Table 1. Unemployment rates for the United States by year for 1985-2000 

Year 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Unemployment Rate 7.2 7.0 6.2 5.5 6.1 5.6 6.9 7.5 6.9 6.1 5.6 5.1 4.9 4.5 4.2 4.0 

 Title III Funds for Dislocated Workers: 

In terms of the Title III allotment formula, the intent of the formula was to distribute funds 

based on the proportion of dislocated workers in each state. Unfortunately, no published data report the 

number of dislocated workers by state and year. However, we will use the Displaced Worker Surveys 

(DWSs), biennial supplements to the Current Population Survey, to develop estimates of the number of 

dislocated workers by state for each calendar year 1993-1999. These estimates can then be used to 

analyze the extent to which the JTPA Title III unemployment-based allotment formula distributed funds 

in proportion to the number of dislocated workers in each state. 

For the dislocated worker analysis, we primarily will focus on: (1) how the actual allotment 

of funds differed across the states, (2) how the allotments would be modified if we gave more weight to 

one or more of the unemployment measures, and (3) how well various formulas distribute funds 

proportionally to eligibility, based on our estimates of dislocated workers. We will confine most of our 

analysis to the PY93 and PY97 allotments for the same reasons stated in the Title II methodology section. 
7 

2. WIA ALLOTMENT FORMULAS 

Before examining the attributes of the current WIA allotment formula, it is important to 

understand how the factors that make up the formula are calculated and how the formula is used to 

distribute funds to states. This section fully describes the JTPA Title II formulas and then demonstrates 

precisely how the formulas distributed funds for Program Year 1997 (PY97). The JTPA Title III 

allotment formula, which is much less complex due to the absence of minimum funding provisions, is 

then described. 

2.1 JTPA Title II-A and Title II-B Allotment Formulas 

As noted in Section 1.1 the WIA allocation formula for adults and the JTPA formula for 

economically disadvantaged adults are almost identical. They use the following three factors: 

1. Each state’s share of 1990 Poverty for adults age 21 to 72 as derived from the 1990 

decennial census. 

2. Each state’s share of ASU unemployment (ASU is an area of substantial 

unemployment as defined by the states to include a contiguous area with average 

unemployment rates at least 6.5 percent and a population of at least 10,000). 

3. Each state’s share of excess unemployment where excess unemployment is the higher 

of the excess number of state total unemployed over 4.5 percent or excess number of 

state total ASU unemployment over 4.5 percent in the state. 

The JTPA formulas also use the following two minimum funding conditions: 

1. The floor requirement that no state’s share can be less than 0.25 percent of the total 

allotment; and 

2. The hold-harmless provision that no state’s share can be less than 90 percent of the 

previous year’s share. 

Factor 1 is easy to apply once decennial census data become available, and does not change 

until the next decennial data is available. If state unemployment is at least 6.5 percent, then factor 2 is 

easily calculated. If not, factor 2 is based on a highly complex aggregation of unemployment across areas 

within each state to create ASUs (areas of substantial unemployment). This process is described below. 
8 

Factor 3 is always easily calculated because it is derived from factor 2. Both minimum conditions are also 

easily computed from the allotment in a given year and the preceding year. 

Under both JTPA and WIA the process of defining areas of substantial unemployment 

(ASUs) for a given program year begins by subdividing the prior year’s statewide number of unemployed 

individuals and number of workers by whatever geographic sub-unit the state decides to use. In this 

illustration we will assume the sub-unit of choice is the Census tract. Statewide unemployment is derived 

from standard, but highly complex, Bureau of Labor Statistics (BLS) computations. Statewide 

employment is derived from a simpler and more accurate BLS computation. These numbers are 

apportioned across Census tracts by assuming that the distribution of statewide unemployment and 

employment across the Census tracts observed in 1990 Census data remain constant over time. More 

specifically, if there were 50,000 unemployed workers in state-A in 1990, and tract-A contained 500 of 

those individuals, then the number unemployed in tract-A in year-t would be assumed equal to 1/100th 

(500/50,000) of state-A’s unemployment in year-t. 

The second step is to aggregate unemployment and employment statistics across contiguous 

census tracts to maximize the number of unemployed included in those areas. To do this, states identify 

all tracts with unemployment at least 6.5 percent and then add contiguous tracts with lower 

unemployment starting with those with the highest unemployment until total unemployment averages at 

least 6.5 percent in each ASU. The last step is to make sure that every area of substantial unemployment 

meets the requirement of having a population of at least 10,000. To meet this requirement it sometimes is 

necessary to combine small ASUs by building bridges of contiguous Census tracts with relatively low 

unemployment and dropping other Census tracts with slightly higher unemployment. 

Unless state unemployment is at least 6.5 percent, making these calculations is so difficult 

that states purchase (or devise) specialized computer software to make these calculations. Also, because 

of the extreme complexity of computing these two factors, it is virtually impossible to reproduce 

historical data at the national level should the data become lost and analyze the source of changes in the 

allotment.4

 

4

 The spreadsheets used in determining the allotments prior to PY93 are no longer available in DOL. If these data were available, it would be 

highly worthwhile to examine how the allotments changed during the recessions that occurred in the early 1990’s. It also would be useful to 

determine how different the results would be if a far simpler calculation was used based on use of statewide unemployment and unemployment 

in large metropolitan areas. 
9 

WIA slightly modified the allotment of JTPA Title II funds in that the minimum funding 

provisions change when the total nationwide funding for the states reaches a maximum threshold of $960 

million for adults and $1 billion for youth and, for the first time, instituted a maximum of 130 percent of 

the prior year’s share. However, WIA did not modify the allotment formula for title III (dislocated 

worker) programs. 

When the thresholds are reached the WIA minimum funding conditions for non-dislocated 

adults and youth are as follows: 

 The hold-harmless provision that no state’s share can be less than 90 percent of the 

share in the preceding year (same as JTPA); 

 The floor requirement that no state’s share can be less than 0.30 percent of $960 

million for adults and $1 billion for youth, plus 0.40 percent of any excess over the 

threshold; and 

 The additional floor requirement that no state can receive an amount less than the 

dollar amount received by the state in the 1998 allotment. 

Due to the limited time that WIA has been in place, it is not clear what effect these new 

provisions have had on the distribution of WIA funds. Section 3.5 discusses the analysis of the effects of 

the WIA minimum funding provisions for the first two years WIA has been in place. 

2.2 How the JTPA Title II-A Allotment Was Made for Program Year 1997 

Table 2 presents a spreadsheet that describes precisely how ETA determined the allotment of 

Title II-A funds to the states in PY97. This table was supplied by Sherryl Bailey of ETA and modified by 

Carrie Markovitz to address key questions about how the five allotment factors affect the distribution of 

the funds to the states. The allotment process involves five steps: 

1. Determine the minimum funding grant based on the hold-harmless and 

minimum floor share provisions. This is done by calculating the hold harmless 

floor, which is 90 percent of the prior year’s share times the total amount of funds to 

be distributed this period. The state minimum floor is calculated by multiplying .0025 

times the total amount of funds to be distributed. The allotment going to each state 

must be at least equal to the larger of these two amounts. (In our subsequent 

discussion we refer to this one amount as the state minimum.) 
10 

Table 2. FY1997 JTPA Title II-A allocation 

Column A Column B Column C Column D Column E 
11 

Table 2. FY1997 JTPA Title II-A allocation (continued) 

 Column 

F 

Column 

G 

Column 

H 

Column 

I 

Column 

J 

Column 

K 

Column 

L 

Column 

M 
12 

Table 2. FY1997 JTPA Title II-A allocation (continued) 

 Column 

N 

Col. 

O 

Col. 

P 

Col. 

Q 

Col. 

R 

Col. 

S 

Col. 

T 

Col. 

U 
13 

Table 2. FY1997 JTPA Title II-A allocation (continued) 

 Column 

W 

Col. 

X 

Col. 

Y 

Col. 

Z 

Col. 

AA 

Col. 

AB 

Col. 

AC 

Col. 

AD 
14 

2. Make an initial allotment by calculating the proportion of funds allocated to each 

state for each of the three major factors (one-third: unemployment in areas of 

substantial unemployment, one-third: excess unemployment, and one-third: 

economically disadvantaged adults). The amount of funds to be received by each state 

is calculated by multiplying each state’s share for each factor by one-third of the total 

amount to be distributed and then totaling the three amounts. 

3. Apply the hold-harmless and state minimum floor allotment provisions. This is 

done by substituting the minimum funding allotment (the higher of the 0.25 percent 

floor and hold-harmless amounts) in cases where the initial allotment is smaller than 

the state’s minimum funding allotment (as determined in step 1). 

4. Reduce the allotment going to states that are above the minimum funding 

allotment (so the total across states equals the total allotment). This is done by 

subtracting the amount of funds going to states where the minimum holds from the 

total allotment and repeating step 2 for the states above the minimum. 

5. Repeat steps 3 and 4 as many times as needed until no state is below its minimum 

funding and the total equals the funds allocated. These steps are required because the 

reductions needed to pay the states’ minimums occasionally cause additional states to 

fall below their minimums. In most years, two iterations beyond the initial allotment 

are needed to ensure that the minimum funding provisions are met. 

To demonstrate step 1, Column B of Table 2 shows that Alabama’s PY96 allotment was 

$13,665,742 out of a total allotment of $847,746,734. Column D shows that 90 percent of the PY96 

allotment equals $803,364,700. Column C shows Alabama’s PY96 share was .01612. Thus, based on the 

hold-harmless provisions, Alabama must receive at least $12,950,300 (01612 times the 90% figure). 

Column E shows the higher of the hold-harmless amount or .0025 times the PY97 allotment. 

To demonstrate step 2, Column G of Table 2 shows that the state of Alabama should receive 

$5,201,456 based on ASU unemployment. This amount was calculated by dividing the number of ASU 

unemployed in Alabama (on line 12 of Column F) by the total number of ASU unemployed in the entire 

country (on line 10 of Column F) to get a proportion of .0175. Then this percentage is multiplied by the 

amount of Federal funding available based on ASU unemployment (one-third of the total funding 

available) shown on line 10 of Column G. The amount of money allocated to Alabama based on the other 

two factors is determined in the same way, and then the three totals are added together to produce the total 

allotment for each state (Column L). 

To demonstrate step 3, Column M of Table 2 shows the 17 states where the total initial 

allotment shown in Column L is less than the higher of the hold-harmless and floor provisions shown in 
15 

Column E. For these states the amount in Column M is substituted for the initial allotment in Column L. 

The allotment for these states is now complete, and they are removed from further calculations. 

However, the allotment to the remaining states now needs to be reduced to cover the added 

funds going to the states where funding is determined by the hold-harmless or 0.25 percent floor 

provisions. To make this calculation, the total amount going to the 17 states covered by hold-harmless or 

floor provisions, $101,063,552 on line 10 of Column M, is subtracted from the total allotment. Thus, the 

remaining $791,563,892 is allocated among the remaining 35 states in precisely the same fashion as was 

the initial allotment. These calculations are shown in Columns N through T of Table 2. For example, 

Alabama’s allotment falls from $15,680,080 in Column L to $15,421,777 in Column T. 

The end result is that two additional states now fall below the hold-harmless threshold, thus 

their total allotment is replaced by the threshold (Column U). Then the money is redistributed again based 

on the total amount left ($770,148,555). This process is continued for a second iteration at which point 

the hold harmless and floor provisions are satisfied (Column BB for all states). Generally, two iterations 

are necessary to satisfy the floor or threshold requirement. 
16 

3. EFFECT OF EACH FACTOR ON THE JTPA TITLE II-A ALLOTMENT IN PY93 

In this section we examine how the allotment of funds differed across the states in PY93. To 

do this, we first constructed the equivalent of Table 2 for PY93, and then extracted key pieces of 

information from that table to create Table 3-93. The actual allotment of PY93 Title II-A funds and the 

share of funds are displayed in the first two columns of Table 3-93. We order the states by the size of their 

PY93 Title II-A allotment and shade the figures for states whose allotments were determined by the 

minimum funding provisions—the combination of hold-harmless and the share floor of 0.25 percent 

provisions. In all, 24 states have their allotments determined by the minimum funding provisions, leaving 

28 determined by the three factors plus reductions needed to boost the shares of the hold-harmless/floor 

states. 

The minimum funding provisions affected many states in PY93 because: (a) the switch from 

1980 to 1990 poverty data was first made for this program year, and (b) several areas hard-hit by the 

recessions in 1990-92 experienced substantial reductions in the two unemployment factors between 1992 

and 1993.5

 

The seven states at the bottom of Table 3-93 are all affected by the share floor of 0.25 percent. Seventeen 

states are affected by the hold-harmless provisions. The 17 hold-harmless states are widely distributed by 

size of their shares. Five Southern states with substantial shares—Louisiana, Georgia, Alabama, 

Mississippi, and Arkansas—gained between 32.8 and 9.1 percent of their allotment due to these 

provisions. In contrast, three Midwestern states with relatively large shares—Michigan, Missouri, and 

Indiana—received increases of less than 2.5 percent due to hold-harmless provisions. 

At the bottom of the table we show how the share of the allotment is divided by quartile. The 

13 states in the first quartile received approximately two-thirds of the entire allotment. Indeed, the three 

states with the greatest funding, California, New York, and Texas, accounted for 27.4 percent of the entire 

allotment. At the same time, the entire fourth quartile received a mere 3.7 percent of the total allotment. 

 

5

 We cannot precisely estimate these effects because the allotment data prior to PY93 have not been preserved. 
17 

Table 3-93. Distribution of total Title II-A allotment to the states and distribution of each state’s 

allotment by factor for PY93 ordered by share to states 
18 

Columns C, D, and E of Table 3-93 show the percentage of each state’s allotment that was 

derived from the two unemployment factors and the poverty factor. Column F shows the effect of the 

minimum funding provisions. What is striking about this breakdown is that the minimum funding 

provisions increase the funds going to the states with the smallest shares by a whopping 21.5 percent, 

while the average minimum funding provision funds going to the states in the other quartiles range from 

3.3 percent to -0.7 percent. 

A similar pattern is observed in every year. Thus, a key conclusion from our analysis is that 

the minimum funding provisions have very large effects on the distribution of funds going to many states, 

even though they do not redistribute a large proportion of total funds. Although not shown in Table 3-93, 

the net gain due to the minimum funding provisions equals only 3.0 percent of the total allotment.6

The summary statistics on the bottom of Table 3-93 show distinct differences in the per-state 

effect of the poverty factor relative to the unemployment factors. The poverty factor accounts for a little 

less than one-third of the funds going to the 13 states receiving the largest shares, but well over one-third 

of the funds going to states in the remaining quartiles. 

However, by far the biggest difference is in the bottom quartile, where the poverty factor 

accounts for almost as much funding going to the 13 states with the smallest shares as the two 

unemployment factors combined, even though together, the two unemployment factors carry twice the 

weight as the poverty factor. 

As with the effect of the minimum funding provisions, similar patterns occur every year. 

Thus, a second key observation is that states that have a large share of the allotment tend to have higher 

levels of unemployment and lower levels of poverty than states that have a small share of the allotment. 

This suggests that analysis using the new annual poverty numbers will provide a very important test of 

whether too much weight is given to the unemployment factors, if the goal is to distribute funds 

proportionally to the number of program eligibles.7

 

6

 The net gain due to the minimum provisions was calculated by dividing the total amount of funds received by the minimum states due to 

minimum provisions by the total allotment for all of the states. 

7

 The summary statistics at the bottom of Table 3-93 show that the poverty factor distributed slightly more than one-third of all funds over all of 

the states, even though the effect of the minimum provisions washed out to zero across all of the states. It took considerable effort to figure out 

why this was the case. The explanation stems from the way funds are allocated after the minimum provisions are invoked. Essentially, the funds 

left over after the minimum funds are set aside are distributed equally across the three main factors, but the distribution of those factors across 

the remaining states is not identical to the initial distribution across all states. Because states affected by minimum provisions have substantially 

higher than average poverty shares the remaining states experience a bigger boost to their poverty shares than their unemployment shares. As a 

result, more than one-third of the funds are distributed based on poverty. 
19 

Finally, the summary statistics show that the overall effect of each of the two unemployment 

factors are similar, but not identical. The excess unemployment factor is slightly more influential than the 

ASU factor in the states in the top quartile. The reverse is the case for all other groups. The difference in 

favor of ASU unemployment is especially large in the second quartile. 

However, unlike the first two patterns we discussed that were constant over time, the 

difference between the two unemployment measures declined over time. The average difference fell from 

about 30 percent in PY93 to only about 10 percent by PY97. The main reason for this change is that 

unemployment in ASUs fell substantially in a way that narrowed the differences in the proportion of 

unemployed in areas with unemployment rates of at least 6.5 percent. 

Overall, we conclude that substantially different distributions would have occurred if the 

weighting of the two unemployment factors was different in PY93, but the difference would greatly 

diminish as overall unemployment rates fell. 

3.1 Redistributive Effects of the Minimum Funding Provisions 

Table 3-93 shows that the minimum funding provisions increase funds going to many states 

with small shares by large percentages. However, because many states with large shares also benefit from 

the minimum funding provisions it is difficult to see precisely how those provisions redistribute funds. 

We, therefore, make it easier to see the redistributive effects by ordering the data in Table 3-93 by the size 

of the effect of the minimum funding provisions. 

The reordered data displayed in Table 4-93 shows that 13 states lost over 4 percent of their 

allotment in order to boost funds going to states positively affected by the minimum funding provisions. 

In contrast, three Western states—North Dakota, Wyoming, and South Dakota—gained over 50 percent 

of their allotments from the minimum funding provisions. 

 

 A second by-product of this computational method is that the states with shares not determined by the minimum provisions do not contribute an 

equal percentage of their allotment to increase the funds going to the states that are affected by the minimum provisions. The contributions are 

inversely proportional to their share of poverty. As a result states that are not covered by the minimum provisions can, nevertheless, increase the 

amount of funds they receive as a result of invoking the minimum provisions, For example, Utah received a small boost to its funds of about 

$5,000 in PY97, while California’s funds were reduced by over $4 million. 

 This somewhat odd result would not occur if instead of recalculating the shares based on each factor after states receiving minimum amounts 

are removed from the estimates, the initial shares were kept constant and an equal percentage reduction applied to each state’s total. 
20 

Overall the minimum funding provisions shifted about $30 million out of roughly a $1 

billion total allotment. This amounts to only 3 percent of the entire allotment. At the same time, the 

bottom of column B shows that the states whose share was determined by minimum funding provisions 

(shaded states) received 26.2 percent of the entire allotment. 

Column E of Table 4-93 shows the share of the total allotment determined by the poverty 

factor. Without exception, for the 28 states whose budgets were primarily determined by the 

unemployment and poverty factors, the poverty share monotonically increases as the reduction in share 

due to the minimum funding provisions decrease. 

As discussed in footnote 7, this pattern stems from the fact that the states whose entire 

allotment was determined by the minimum funding provisions had considerably higher poverty levels 

than the remaining states and the way funds are reallocated after the minimum funding provisions are 

invoked. 

Thus, an important observation is that the minimum funding provisions redistribute funds 

from states with relatively low poverty to states with relatively high poverty. This is consistent with the 

goal of distributing funds in proportion to eligibility, but it is largely coincidental. It just happens that 

states with relatively high poverty are small-share states that experienced reductions in poverty between 

1980 and 1990 or states that experienced declines in unemployment. States with these characteristics tend 

to be positively affected by the minimum funding provisions. 

However, there could be a small systematic component because there is no upper cap on the gains in total 

shares to states whose share of unemployment increases. Thus, to some extent, states with large gains in 

funding will have decreases in shares derived from poverty. However, there is no necessary connection 

between having a large share of unemployment in one year, and having a large increase in the 

unemployment share the following year. 
21 

Table 4-93. Table 4-93: Distribution of total title II-A allotment to the states and distribution of each 

allotment by factor for PY93 ordered by hold harmless/minimum contribution 
22 

3.2 The Relationship Between Eligibility and Title II-A Allotment Shares 

We now examine the extent to which the allotment formula distributed funds in proportion 

to the numbers of eligible individuals. The only legislative requirement for entering JTPA programs for 

economically disadvantaged adults was that family income must be near or below poverty levels (or the 

person was receiving food stamps, which amounts to about the same thing). Thus, measures of the 

number of adults in poverty should be highly correlated with the number of eligibles for Title II-A 

programs.8

To start our assessment of the relationship between state allotment shares and economically 

disadvantaged adult shares we compute the nationwide dollars per disadvantaged adult using the new 

Census data covering 1993.9

 As shown in Table 5, the PY93 allotment provided $40.75 for each adult in 

poverty. 

Table 5. PY93 allotment per disadvantaged adult 

1. PY93 JTPA Title II-A allotment $1,012,330,280 

2. Census estimate of adults in poverty in 1993 24,844,937 

3. Funds available in PY93 per adult in poverty $40.75 

4. Funds available in PY93 per adult in poverty in 

states that do not gain from the minimum funding 

provisions 

$38.11 

 

8

 In practice, ETA has established additional criteria related to giving preference to the hard-to-serve among those eligible. Hard-to-serve is 

defined by having multiple impediments to gaining permanent employment. Those impediments include physical handicaps, low literacy, lack 

of knowledge of English, and especially poor prospects because of low skills. It may be possible to use Census Bureau data to develop 

additional factors that measure cross-state variation in the number of individuals being given preference. However, producing such measures 

was well beyond the scope of the work reported here. For the purposes of this study we, therefore, assume that the legislative intent of the 

allotment formula would be adequately served if funds were distributed roughly in proportion to poverty levels. In addition, the WIA legislation 

eliminated poverty as an eligibility requirement. Under WIA, any individual can receive a set of core services. However, persons who fit the 

original disadvantaged criteria are given preference for more intensive services. So we still assume that the bulk of funds is still being dedicated 

to disadvantaged participants. 

9

 New Census estimates are based on data from the March Current Population Survey (CPS) Supplement which are used in modeling the 

relationship between poverty and summary data from Federal income tax returns, data about participation in Food Stamp programs, economic 

data from the Bureau of Economic Analysis, and data from the most recent decennial census. It takes three years to produce estimates for one 

year because the estimates are based partly on CPS data from two years after the year of estimation. Thus, March 1998 CPS data were used to 

produce the 1996 poverty estimates, so, consequently, the new Census poverty figures for 1996 were not released until 1999. 
23 

However, rather than compare the actual state allotment to the national average of funds 

available per adult in poverty, we compare the actual state allotment to the allotment based on funds per 

adult in poverty after the minimum funding provisions are applied. In other words, we first compute the 

actual allotment using the existing formula with its minimum funding provisions and then recompute the 

allotment substituting the new Census poverty figures for 1993 for the two unemployment and 1990 

poverty factors. This makes sense because the actual allotment was strongly affected by the minimum 

funding provisions, and it is likely that the minimum funding provisions would be maintained even if 

other aspects of the formula were modified. 

3.2.1 The Relationship Between Eligibility and State Shares in PY93 

Column A of Table 6-93 shows the actual per-person allotment based on the existing 

formula. Column B shows the per-person allotment that would be available in each state if the three 

factors were replaced by the new Census data, but the existing minimum funding provisions were 

retained. The figures are derived by dividing each state’s allotment by the Census Bureau’s estimate of 

the number of adults in poverty in 1993. Column C shows the percentage difference between these two 

figures. 

The states in Table 6-93 are ordered by the percentage change in their share that would occur 

if the present formula was switched to one using the Census poverty figures and the minimum funding 

provisions were maintained. Kansas and Nebraska top the list with gains of 81.7 percent and 65.9 percent, 

respectively. Five other states have gains of 22.4 percent or more, and five states have gains between 16 

and 20 percent. 

Connecticut is at the bottom of the list, losing 27.6 percent. Nine other states have loses 

ranging from 15.6 percent to 10.1 percent. In general, the increases are larger than the decreases because 

hold-harmless provisions limit the size of the declines. Overall, 16 states would be affected by minimum 

funding provisions using either formula and therefore show no difference. Another 13 states show gains 

or losses of less than 10 percent. 
24 

Table 6-93. Actual per-person Title II-A allotment versus use of New Census poverty figures for PY93 
25 

Column E of Table 6-93 shows the precise effect of the minimum funding provisions applied 

to the formula using poverty as the sole factor. States that do not benefit from the minimum funding 

provisions uniformly experience reductions of 6.9 percent, which reduces the per person allotment in 

those states from $40.75 to $38.11. In contrast, when the existing formula is used, the reductions varied 

from state-to-state based on its share of poverty. Seven states gain 25 percent or more from the minimum 

funding provisions. Those states include four New England states—Massachusetts, New Hampshire, 

Rhode Island, and Vermont—plus Michigan, Wyoming, and Alaska. 

Finally, column G of Table 6-93 shows what would happen if the new poverty shares were 

substituted for the 1990 poverty shares in the existing formula. It turns out that the switch would make a 

large difference only in a few states. By far the largest effect is in Montana, which would lose 16.0 

percent. Wisconsin and Minnesota would lose about 4 percent each, while Florida would gain about 4 

percent. Most of the remaining states would experience, little, if any, change. 

The small effect of switching to the new poverty figures is primarily due to the poverty 

shares changing very little between 1990 and 1993. Thus, the primary reason that the formula fails to 

distribute funds in proportion to eligibility in the states not gaining from the minimum funding provisions 

is that the two unemployment factors are not especially highly correlated with the poverty share. 

However, it also is important to recognize that although there is substantial cross-state 

variation in the funds available per adult in poverty, the total dollar difference between a perfect 

proportionality and the actual allotment is modest. As shown in Table 7, the shift in funds based on 

substituting the 1993 adult poverty shares for the existing three factors would redistribute an amount 

equal to only 3 percent of the allotment. Even if funds were distributed solely based on each state’s share 

of adults in poverty, the reallotment would equal only 8 percent of the full allotment. 

Table 7. Redistribution of Title II-A funds based on alternative formulas in PY93 

1. Change to formula based solely on poverty 8.0% 

2. Change to formula based on poverty plus minimum funding provisions 3.0% 

3. Change to formula based on the existing formula with Census poverty 

figures for 1993 substituted for 1990 decennial census figures 0.2% 
26 

3.2.2 The Relationship Between Eligibility and State Shares in PY97 

Table 6-97 replicates Table 6-93 for the PY97 Title II-A allotments. Importantly, this table shows that the 

differences between using the new annual Census poverty measures versus the actual formula are 

substantially larger in PY97 than in PY93. For example, in PY93 the average gain was 29.8 percent for 

the 13 states receiving the largest percentage gains, and the average loss was 12.7 percent for the 13 states 

with the largest losses. In contrast, comparable figures for PY97 show the gains were 52.3 percent, and 

the losses 21 percent—a significant increase for both. 

As shown in Table 8, an amount equal to 9 percent of the total allotment would be shifted if 

the formula was switched in PY97 from two-thirds unemployment and one-third 1990 poverty to 100 

percent current poverty, and the minimum funding provisions remained unchanged. If the hold-harmless 

and 0.25 floor share provisions were dropped, the shift would only increase to 9.5 percent. In contrast, the 

shift in PY93 would be only 3 percent if the minimum funding provisions were continued, and the 

increase would be 5 percentage points greater if those provisions were eliminated. 

Table 8. Redistribution of Title II-A funds based on alternative formulas in PY97 

1. Change to formula based solely on poverty 9.5% 

2. Change to formula based on poverty plus minimum funding 

provisions 

9.0% 

3. Change to formula based on the existing formula with Census 

poverty figures for 1993 substituted for 1990 decennial census 

figures 0.1% 

The redistribution of funds with the minimum funding provisions in place in PY97 is much 

larger than in PY93. This is because our PY97 estimates are based on switching to the new poverty 

measures in PY93 and leaving them in place through PY97. Thus, the effect of the hold-harmless 

provisions, which were very strong in PY93, greatly diminish over time. As will be discussed in Section 

3.3, the difference also is great because: (a) the distribution of poverty across states does not change very 

much from year-to-year, but (b) the two unemployment factors changed substantially between 1992 and 

1996 in a way that was not highly correlated with changes in poverty. 
27 

Table 6-97. Actual per-person Title II-A allotment versus use of New Census poverty figures for PY97 
28 

As shown in Table 9, the total allotment and the number of adults in poverty fell by 11.8 

percent and 8.2 percent, respectively, between PY93 and PY97. Because the reduction in the allotment 

was only a little greater than the reduction in poverty, per capita funding fell a modest 3.9 percent, from 

$40.75 in PY93 to $39.28 in PY97. 

Table 9. Changes between PY93 and PY97 in allotments, poverty, and funds per disadvantaged adult 

PY93 PY97 

Difference between 

PY93 and PY97 

PY97 JTPA Title II-A allotment $1,012,330,280 $892,627,443 -11.8% 

Census estimate of adults in poverty 24,844,937 22,736,575 -8.2% 

Funds available per adult in poverty $40.75 $39.28 -3.9% 

Funds available per adult in poverty 

in states that do not gain from the 

minimum funding provisions $38.11 $39.07 2.0% 

However, column E of Table 6-97 shows that if the new Census poverty figures were used to 

calculate shares, only 0.6 percent of all funds going to states not benefiting from minimum funding 

provisions needed to be transferred to the 10 states gaining from the minimum funding provisions. In 

sharp contrast, 6.9 percent of total funds going to states not benefiting from minimum funding provisions 

needed to be transferred to the 27 states gaining from the minimum funding provisions in PY93 (Column 

E in Table 6-93). As a result, the per capita allotment in the states not gaining from the minimum funding 

provisions rose 2.0 percent from $38.11 in PY93 to $39.07 in PY97. 

The above analysis produces several important conclusions about the differences between 

switching to a formula that solely uses the new poverty statistics (plus the minimum funding provisions) 

versus using the actual formula that gives two-thirds of its weight to unemployment factors and one-third 

of the weight to an unchanged measure of poverty. We concluded that: 

 By reallocating only about 9 percent of the total allotment, the program dollars per 

eligible person could be equalized across states; 

 The hold-harmless provisions were highly effective in preventing states from being hit 

with exceptionally large reductions in funding; and 

 The effect of the hold-harmless provisions greatly diminished between PY93 and 

PY97. Thus, after about four years, funding per eligible person became much more 

uniform across all states. 
29 

3.3 Changes in Each State’s Share Between PY93 and PY97 

So far we examined the cross-state equity of the present formula based on whether funds 

were distributed proportionally to the number of program eligibles. In this section we focus on several 

related issues: (1) how each state’s funding shares change between PY93 and PY97, (2) why the shares 

changed, and (3) why the changes increased disparities in per-person funding across states. 

Because the one-third poverty weight is based on an unchanging measure—the 1990 

decennial rate—the only way the current formula could reduce disparities in per-person funding is if 

changes in the two unemployment factors were highly correlated with changes in poverty levels. 

(However, even if this was the case it is possible for the minimum funding provisions to reduce the effect 

of the correlation.) 

Column B of Table 10 shows the overall percentage change in the allotment share between 

PY93 and PY97 for each state based on the actual formula. Columns C and D show the change in the 

share distribution of each of the two unemployment factors that largely determined that change. The states 

are ordered from largest increase to greatest reduction in total allotment. 

Hawaii experienced an enormous percentage gain in share of 83.3 percent. This largely was 

because its unemployment factors were exceptionally low in PY93. The District of Columbia also 

experienced a very large increase in share of 45.3 percent. Three Western states—Nevada, California, and 

Washington—experienced large gains of more than 30 percent. Six states, including Texas, experienced 

gains of 15 percent or more, and eight states, including New York, experienced gains of less than 10 

percent. 

In contrast, five states—Iowa, Massachusetts, Michigan, Arkansas, and Colorado—

experienced overall reductions of more than 30 percent. Five states experienced reductions of about 21 

percent or more, and 10 states experienced reductions of 11 percent or more. Seven states experienced 

reductions of less than 10 percent, and six states experienced neither gains nor losses because they were 

covered by the 0.25 percent floors in both periods. 
30 

Table 10. Percentage change in Title 11-A shares PY93 – PY97 based on actual formula and annual 

poverty 
31 

Importantly, 13 of the 19 states with gains based on unemployment also increased their share 

of adults in poverty, but the average increase in the poverty shares was considerably less than the increase 

in the actual share. On average, states with gains experienced an increase of 19.2 percent, but the average 

increase in poverty share was only 5.4 percent. 

Similarly, 17 of the 27 states with losses based on unemployment also decreased their share 

of adults in poverty, but the average decrease in poverty was considerably less than the decrease in the 

actual share. On average states with losses experienced a decrease of 18.0 percent, but the average 

decrease in poverty share was 2.8 percent. 

Despite the overall positive correlation between changes in actual funding (due to changes in 

unemployment) and changes in poverty, there were several notable cases where the positive correlations 

were weak or absent. For example, New Jersey’s actual share increased by 15.4 percent, but its share of 

poverty increased by only 1.6 percent, and New Jersey would have experienced a reduction in share of 

14.5 percent had poverty been used as the only factor (along with the minimum funding provisions) 

beginning in PY93. Similarly, Ohio and Pennsylvania both experienced substantial reductions in poverty 

shares of about 9.5 and 5.7 percent respectively, but showed small gains in actual shares of about 3.7 and 

2.5 percent. 

In contrast, Florida, Utah, and Virginia experienced increases in their poverty shares of 

between 6 and 10 percent, but reductions in share of the actual allotment of between 15 and 26 percent. In 

fact, of the five states with reductions above 30 percent, only Michigan showed a substantial decrease in 

poverty of 12 percent. Two of the states, Colorado and Arkansas, showed small increases in poverty, and 

both Iowa and Arkansas would have shown increases in share had poverty been used as the sole factor 

along with the minimum funding provisions. 

Percentage changes in share can be very large even when the absolute change in funding is 

small, and vice versa. Thus, states receiving small shares with relatively small changes in their shares can 

dominate the statistics, and thereby, give a misleading picture of the overall changes. We, therefore, also 

use the total percentage point change in share to assess the difference between changes in funding and 

changes in unemployment and poverty. 

Percentage point changes show that, overall, states that increased their share between PY93 

and PY97 gained 8.8 percentage points, and by necessity, states losing share showed an identical decline. 
32 

Over this same period, the shift in excess and ASU unemployment was about 13 and 11 percentage 

points, respectively. Together, these changes are roughly two-thirds of the total shift, in keeping with their 

two-thirds weight in the allotment formula.10

States that would have gained total share had poverty been substituted for the two 

unemployment factors and the 1990 poverty factors would have experienced an increase of about 6 

percentage points. The percentage point change in the actual share of these states was 5.4 percentage 

points. However, the actual shift in shares from losers to gainers using the actual formula was about 9 

percentage points, about 45 percent more than the shift based on poverty. 

While there is no necessary connection between overall shifts in poverty or unemployment 

and the way the shifts are distributed across the states, it is noteworthy that, as shown in Table 11, the 

overall decline in the two unemployment factors was very large, but the decline in poverty was modest. 

Table 11. Change in ASU unemployed, excess unemployed, and adults in poverty between PY93 and 

PY97 

 PY93 PY97 Change 

ASU 8,825327 6,372,835 -27.8% 

Excess 3,541,628 2,145,139 -39.4% 

Poverty 24,844,937 22,724,572 -8.5% 

Overall, we conclude that: 

 Changes in the two unemployment factors were positively correlated with changes in 

poverty. However, the correlation was far from perfect. Several states where poverty 

increased, unemployment decreased, and vice versa. 

 Shifts in shares of unemployment tended to be far greater than shifts in shares of 

poverty. As a result, changes in the actual allotment tended to be much greater than 

shifts in poverty. Thus, if the goal of the formula was to keep funding proportional to 

eligibility, shifts in unemployment were given far more weight than would have been 

appropriate. 

 

10 The shift in unemployment shares is not exactly two-thirds of the shift in the total allotment shares because of the effect of the minimum 

provisions and because the Census Bureau made minor revisions in its poverty figures in 1994. As a result there was a small decrease in poverty 

in states where unemployment shares declined, and vice versa. 
33 

 Reductions in unemployment between 1993 and 1997 were large for the nation as a 

whole, but they were not distributed evenly across the states. Unemployment became 

relatively higher in the far West, and lower in the Midwest and Northeast. 

 Minimum funding provisions had a large effect on the shift in allotments. The 0.25 

percent floors held constant the shares of the seven states with the smallest shares. The 

hold-harmless provisions prevented sharp year-to-year declines in several states, and 

instead, led to declines occurring over several years.11

3.4 Changes in State Allotments for Non-Dislocated Adult Programs Over Time 

Even though we lack the data needed to examine state poverty trends in the period 

subsequent to WIA’s inception, we can examine how allotments changed subsequent to PY97. Table 12 

summarizes the way the allotments changed over time. The states in Table 12 are ordered by their change 

in allotment between PY93 and PY01 from the greatest reduction to the greatest increase. The states are 

grouped by their percentage change. Group 1 experienced large reductions in share ranging from 57.0 

percent to 30.2 percent. Group 2 experienced moderate reductions from 27.0 percent to 10.5 percent. 

Group 3 showed little change spanning reductions of 5.2 to increases of 3.8 percent. Group 4 experienced 

moderate gains from 13.2 percent to 15.6 percent. And group 5 had large gains, from 27.2 percent to 

129.8 percent. 

All of the New England states except Vermont, which was protected by the 0.25 percent 

floor, had large reductions. Three of the 10 large reduction states were in the Midwest—Michigan, 

Indiana, and Iowa—and the remaining two are Colorado and Virginia. At the opposite extreme, eight of 

the 11 large increase states are in the West, including all five states touching the Pacific Ocean. The three 

remaining members of group 5 are Puerto Rico, the District of Columbia, and Ohio. Six of the 13 small 

change states had their share determined by the 0.25 percent floor. 

As shown in columns E and F of Table 12, the change in shares was due to the way 

unemployment changed across the states. There were large reductions in unemployment in New England, 

and several Midwestern states and moderate reductions elsewhere in the Midwest, Middle Atlantic, and 

South. There were large increases in unemployment in most of the Mountain and Pacific states, as well as 

in New York and Ohio. Indeed, changes in unemployment had to be the source of the shifts in shares 

because the poverty shares remained constant as they were based on the 1990 decennial rates over the 

entire period. 

 

11 One odd result stemming from the effect of the hold-harmless provisions being especially large in PY93 was that the base for the percentage 

change was artificially high in PY93 in many states. This substantially increases the observed size of the decline between PY93 and PY97. 
34 

Table 12. Changes in State Adult Program Shares PY93-PY01 and PY93-PY97 
35 

The declines in ASU and excess unemployment shares were about equal in the states with large overall 

share reductions, but excess unemployment reductions were considerably larger than ASU unemployment 

reductions in the states with moderate reductions. In contrast, states with large and moderate increases 

gained considerably more from increases in excess unemployment than ASU unemployment. However, 

greater than proportional increases in excess unemployment would be expected because many of the 

states experiencing unemployment increases initially had low unemployment rates and tiny excess 

unemployment levels. 

In terms of the timing of the changes in unemployment, a bit more than half of the change 

for the groups with the largest decreases and largest increases occurred between PY93 and PY97. Threequarters of the change for the groups with moderate decreases or moderate increases occurred between 

PY93 and PY97. That much of the change occurred during the recovery from the relatively mild 

recessions of the early 1990’s makes sense, as these recessions were heavily concentrated in the Midwest 

and Northeast, and the recovery in those sections was relatively swift. In contrast, the relative increases in 

unemployment in the West were less severe, but fairly persistent. They were linked in part to the decline 

in high-tech firms and continued recession in Japan. 

Overall, the results clearly show that there were sharp and persistent shifts in unemployment 

in many states. Because shifts in poverty were far smaller than shifts in unemployment during the 1990’s, 

our results suggest that giving so much weight to unemployment shifts did not do a good job in equalizing 

funding per person in poverty. Moreover, while tighter labor markets modestly reduce poverty, not all 

changes in unemployment are likely to have the same effect on poverty. 

For example, declines in traditional “smokestack” manufacturing industries probably have a 

much stronger affect on local poverty than declines in high tech computer-oriented or aerospace 

industries. Because of the strong regional differences in unemployment and industry mix it is unfortunate 

that we did not have more recent state poverty statistics to more precisely pin-down the unemploymentpoverty relationship. 

It is similarly unfortunate that we have no measure of demand for JTPA/WIA services 

(among individuals who are not dislocated workers). While we are confident that changes in 

unemployment are sharper than changes in poverty, it still could be the case that shifts in unemployment 

trigger sharp shifts in demand for employment and training assistance. 
36 

We consider the lack of good measures of service demand to be a major impediment for 

improving the allotment system. Even if, as is quite likely, new poverty data will make it possible to 

develop a formula that equalizes funding per person in poverty, that step may not provide equal access to 

WIA programs. This is because demand may be more of a function of changes in economic circumstances 

than levels of poverty. Equitably distributing WIA funds is further complicated because adult programs 

are no longer narrowly targeted on economically disadvantaged individuals. 

3.5 Effects of WIA Minimum Funding Provisions 

As explained previously, under WIA, minimum funding provisions change when funding 

available for the states reaches a defined threshold ($960 million for adults and $1 billion for youth). In 

the first year WIA was applicable (PY2000), special minimum funding provisions were not used because 

funding did not reach the designated threshold. However, in PY2001, the youth funding did exceed $1 

billion, and the special hold-harmless provisions affected state allotments. 

In this section, we examine the PY2001 allotment for youth to demonstrate the use of the 

special WIA hold-harmless provisions. Table 13 describes how ETA allocated youth funds for PY2001 

using the new minimum funding provisions. The allotment process is similar to the process described 

previously under JTPA, with a few significant exceptions. The hold-harmless floor continues to be 90 

percent of the prior year’s share (Table 13, Column D); however, the calculation of the state minimum 

floor is no longer 0.25 percent of the total amount of funding. 

At the top of Table 13 in Column B, the calculation of the minimum floor is shown in two 

parts. The first part of the minimum is 0.30 percent of the first maximum set limit (.003 times $1 billion) 

plus .40 percent of any excess over the maximum set limit (.004 times 82.98 million). In PY2001 this 

calculation came to $2.7 million dollars. An additional floor requirement was also added under WIA—

that no state might receive a share that is less than the dollar amount received by their state in the 1998 

allotment. The 1998 allotment for each state is presented in Column B in the lower portion of Table 13. 

The allotment going to each state must be at least equal to the largest of these three amounts (presented in 

Column H). 
37 

Table 13. WIA disadvantaged youth activities allocation for PY01 

 Column 

A 

Col. 

B 

Col. 

C 

Col. 

D 

Col. 

E 

Col. 

F 

Col. 

G 

Col. 

H 
38 

Table 13. WIA disadvantaged youth activities allocation for PY01 (continued) 

 Column 

A 

Column 

B 

Column 

C 

Column 

D 

Column 

E 

Column 

F 
39 

Table 13. WIA disadvantaged youth activities allocation for PY01 (continued) 

 Column 

A 

Column 

B 

Column 

C 

Column 

D 

Column 

E 
40 

Table 13. WIA disadvantaged youth activities allocation for PY01 (continued) 

 Column 

A 

Column 

B 

Column 

C 

Column 

D 

Column 

E 
41 

Table 13. WIA disadvantaged youth activities allocation for PY01 (continued) 

 Column 

A 

Col. 

B 

Col. 

C 

Col. 

D 

Col. 

E 

Col. 

F 

Col. 

G 

Col. 

H 
42 

Table 14 offers a comparison between the allotments given to states under WIA in 2001 and 

the allotments that would have been distributed to states if the JTPA formula was still in effect in 2001. 

First, it is important to point out that more states were directly affected by the new minimum 

funding provisions. Under WIA, 30 states received minimum funds under the minimum funding 

provisions, while under JTPA only 23 states would have received minimums. The same 23 minimum 

funding states in JTPA received minimum funding in WIA, with 19 of those states receiving more 

funding under WIA, while 4 received the same level. Overall, since more states received their funding 

based on the minimum funding provisions, many states received more funding in 2001 under the WIA 

formula than they would have received under the JTPA formula. Seven states that received more funds 

under WIA minimum funding provisions would not have received minimum funds under JTPA 

provisions. These states are Florida, Indiana, Kentucky, Minnesota, Nevada, South Carolina, and 

Virginia. Of the seven states, all but Florida and Nevada would have received less funding under JTPA. 

The WIA formula caused all of the states affected by the minimum floor of .30 and .40 

percent to receive 23 percent more funding ($624,463 more per state) than they would have received 

under JTPA, where the minimum floor is only 0.25 percent of the total allotment. The number of states 

where the floor was applied remained about the same when using both of the formulas, eight states under 

JTPA and nine states under WIA. Utah was the only state that received funds based on the minimum floor 

under WIA that would not have under JTPA. 

The largest differences were due to the use of the new 1998 minimum funding provision, 

which stipulated that states could not receive less funds than their actual allotment in 1998. This WIA 

provision directly affected the funds going to 17 states, and 15 of these states received more funding than 

they would have using the JTPA formula. The other 2 states (Florida and Nevada) received less funding 

under WIA because in the initial calculation steps, funds were shifted from them to the other minimum 

states. 

In addition, under the WIA formula, the 90 percent threshold directly affected fewer states 

than would have been affected under JTPA (15 under JTPA and four under WIA). However, this is due to 

the extended use of the new 1998 minimum funding provision, which tended to provide a higher dollar 

amount per state than the 90 percent threshold. All four of the states that received their funding based on 

the WIA 90 percent threshold would have received the same amount of funding under JTPA. 
43 

Table 14. State youth program allotments under WIA and JTPA formulas PY01 
44 

Table 14 shows that 24 states received more funding under WIA than they would have using 

the JTPA minimum funding provisions. Twelve of these states received at least 20 percent more. Four 

states remained the same in terms of funding. Although 24 states lost funding in order to boost the funds 

of the 24 hold-harmless states, their percentage losses were small, between 1.9 percent and 4.9 percent. 

The WIA minimum funding provisions shifted $30.4 million dollars out of the 

approximately $1 billion total allotment, which amounts to only 3 percent of the total allotment. In 

addition, the states whose allotments were determined by the minimum funding provisions accounted for 

only 27 percent of the total allotment. This result is consistent with our earlier findings that the states 

which generally receive less funding are more positively affected by the hold-harmless provisions. 

Because the new WIA provisions tended to positively affect the same states as the JTPA 

provisions, we observed a similar redistributive effect in terms of high poverty and the minimum funding 

provisions. In other words, the WIA hold-harmless provisions serve a similar function as the JTPA holdharmless provisions in that the WIA provisions redistribute funds from states with relatively low poverty 

to states with relatively high poverty. In fact, the WIA provisions appear to be distributing even more 

funds to the high poverty states than JTPA would, which would indicate that the WIA provisions are even 

more generous to states that have more eligible individuals. 

Although the WIA formula also includes a provision which limits the total amount of funds 

allowed to a state to 130 percent of the previous year’s share, this new provision had little effect on the 

amount of funds distributed by the formula in PY2000 or PY2001. It appears that this provision would 

rarely come into use due to the high threshold of 130 percent and the redistributive effects of the other 

minimum funding threshold provisions. 

As explained earlier, since WIA funding did not otherwise reach the designated threshold, 

we were only able to examine the use of these special provisions for the PY2001 youth funding. We, 

therefore, have to be cautious about drawing conclusions based on a single year of data, particularly about 

the full impact of the minimum funding provisions and how changes in unemployment may affect use of 

those provisions in the future. The 1998 floor requirement in particular, because it maintains fixed dollar 

levels, could produce unforeseen results when either funding or unemployment levels change 

substantially. 
45 

3.6 Summary and Conclusions 

Our analysis of JTPA and WIA programs for adults reached several conclusions with respect 

to the way the formulas allotted funds over the 1990s: 

1. The primary determinant of changes in allotments was changes in unemployment. In 

other words, large shifts in unemployment created large shifts in allotments. 

2. During the 1990’s there were large reductions in unemployment rates, but changes in 

unemployment rates were not uniform across the United States. In relative terms, 

unemployment fell sharply in the Northeast and Midwest and rose in the West. 

3. The hold-harmless provisions for adult programs had the desired effect of preventing 

large, sudden, reductions in funding. As a result, it took about 3 to 4 years for the 

effect of a sharp drop in unemployment to be fully felt. 

4. The share-floor of 0.25 percent for adult programs substantially boosted the amount of 

funds going to seven states. While the gains to those states were in the neighborhood 

of 30 percent, the cost to the remaining states was tiny, only about 0.5 percent. We 

have no direct evidence that administrative overhead costs drain a much higher 

percentage of these state’s allotment away from the direct provision of services. 

However, this hypothesis seems plausible, and therefore, maintaining the floor seems 

justified. 

5. More states were directly affected by the new WIA minimum funding provisions. For 

this reason, many states received more funding in 2001 under the WIA formula than 

they would have received under the JTPA formula. However, the new WIA provisions 

produced a similar redistributive effect as the JTPA provisions, in that funds from 

states with relatively low poverty were moved to states with relatively high poverty. 

Our analysis also reached conclusion with respect to the way the target population, 

economically disadvantaged adults, shifted over the 1990s. State shares of economically disadvantaged 

adults changed slowly as economic conditions improved. The changes were slow in large part because 

many members of this group either remained poor for long periods or drifted in and out of poverty. 

We also reached several conclusions about overall equity—if we assume that an appropriate 

criterion for the equitable distribution of allotments is that adult program funds are distributed in 

proportion to the number of adults in poverty. 

 Use of unemployment measures did not distribute funds in proportion to the number 

of adults in poverty. A large part of the problem was that the initial levels of poverty 

and levels of unemployment were not highly correlated. A secondary problem was 
46 

that changes in unemployment were much greater than changes in poverty, even if 

usually in the same direction. 

 In PY93 and PY97 redistributions of 8 percent and 9.5 percent of the total allocation, 

respectively, would have equalized dollars available per adult in poverty. However, if 

the hold-harmless and floor provisions were kept in place, switching to a povertybased formula only would have redistributed 3 percent of the total PY93 allocation. 

However, by PY97 the effect of the hold-harmless provisions would have been 

reduced to about zero. 

 Even though relatively small shifts in overall funding could equalize dollars per adult 

in poverty, the changes in many states would be large in percentage terms. This is 

because the 13 states with the largest shares received about two-thirds of the total 

funds, and the 13 states with the smallest shares received less than 3 percent of all 

funds. 
47 

4. ANALYSIS OF JTPA AND WIA DISLOCATED WORKER ALLOTMENTS 

In this section we describe the workings of the dislocated worker allocation formula, which 

is identical under JTPA and WIA. This formula distributes 80 percent of the total Federal dislocated 

worker funds to the states. Twenty percent of the funds are reserved for emergency disbursements to the 

states to deal with layoffs and for other purposes. 

The following three factors are used to establish the state allotments: 

1. Each state’s share of regular unemployment, determined by the total number of 

unemployed in each state. 

2. Each state’s share of excess unemployment, determined by the total number of state 

unemployed less the number equal to 4.5 percent of the state workforce. 

3. Each state’s share of long-term unemployment, determined by the number of 

individuals who have been unemployed 15 weeks or more in each state. 

There are no minimum funding or hold-harmless conditions in the formula. 

Factor 1 is calculated by the Bureau of Labor Statistics (BLS) for the 12 months ending in 

September prior to the start of each program year. So, for instance, the allotment for the 1999 program 

year (PY99), which runs from July 1, 1999, to June 30, 2000, would rely on BLS regular unemployment 

figures for October 1, 1997, to September 30, 1998. Factor 2 uses the same unemployment data as factor 

1, together with BLS’s estimate of each state’s workforce for the same period. Importantly, if the state 

unemployment rate is 4.5 percent or less, the excess unemployment factor is 0. Factor 3 also is calculated 

by BLS, but the base period is the second calendar year prior to the start of each program year. Thus, for 

PY99 the base period for calculating long-term unemployment is calendar year 1997. 

4.1 Effect of Each Factor on the Dislocated Worker Allotments in PY93, PY97, and PY01 

Here we examine how the allotments differed across the states in PY93, PY97, and PY01. 

Column A of Table 15-93 shows the dollar allotment of PY93 Title III funds and column B shows the 
48 

Table 15-93. Distribution of Title III Allotment to the States and Distribution of Each State’s Allotment 

by Factor for PY93, Ordered by State Shares 
49 

share of funds. States are ordered by their PY93 Title III share. Columns C, D, and E show the percentage 

of each state’s allotment that was derived from each unemployment factor. 

At the bottom of the table we show the share of the allotment among states in each quartile. 

As with adult programs, the 13 states in the first quartile received about two-thirds of the entire allotment. 

As expected, the top three states, California, New York, and Texas, accounted for 29.2 percent of the 

entire allotment. At the same time, the entire bottom quartile, consisting mainly of Great Plains and 

Mountain states received only 2.7 percent of the total allotment. 

The bottom of the table also shows that there are notable differences in the effect of each of 

the unemployment factors across the quartiles. The primary source of the difference is that the excess 

unemployment share goes disproportionately to states with the largest shares. Table 16-93 shows that the 

proportion of the excess unemployment share falls from the top quartile to the bottom quartile. 

Table 16-93. PY93 ratio of excess unemployment shares to total shares 

Total share Excess share 

Ratio 

excess/total 

States with 

no excess 

Quartile 1 67.9% 72.8% 107.2% 0 

Quartile 2 19.4% 17.7% 91.5% 0 

Quartile 3 10.0% 7.9% 78.2% 0 

Quartile 4 2.7% 1.6% 60.6% 4 

In contrast, the shares based on regular and long-term unemployment are proportionate to 

the overall share—with one exception—states in the bottom quartile receive a disproportionately high 

fraction of their allocation based on regular unemployment. 

A clear-cut conclusion from the above results is that excess unemployment plays a major 

role in redistributing funds from states with smaller shares to states with larger shares. The distributional 

effects of excess unemployment for dislocated worker programs is much stronger than the effect of excess 

ASU unemployment for adult programs. This is because most states with low overall state unemployment 

have some areas within the state with unemployment above both the 4.5 percent and 6.5 percent 

thresholds. However, by using overall state unemployment to establish the excess unemployment 

measures, the dislocated worker formula makes it far more likely that many states receive little, if any, 

funds based on the excess unemployment shares. 
50 

Tables 15-97 and 15-01 present the same information as Table 15-93 but for program years 

97 and 01. About 5 percent more funds go to states in the top quartile in PY97 compared to PY93, and 

about 14 percent more funds go to states in the bottom quartile. About 15 percent less funds go to states in 

the middle two quartiles. However, there is little difference between the distributions across quartiles in 

program years 97 and 01. 

The distributional effects of excess unemployment grow over time, as overall unemployment 

rates fall. As shown in Table 16-01, over 90 percent of the one-third of funds distributed on the basis of 

excess unemployment go to states in the top quartile. Only about 5 percent of the funds go to states in the 

middle two quartiles, and no funds go to states in the bottom quartile. The reason for this disproportionate 

distribution is that, as overall unemployment declined to record low levels, only a few states had any 

excess unemployment. 

Finally, within each quartile the shares based on regular and long-term unemployment 

continue to be roughly equal. However, as we might expect, states in the top quartile also have a 

disproportionate share of long-term unemployment, even though the difference is much smaller than the 

difference in excess unemployment. As a result, states in the remaining three quartiles get a somewhat 

larger proportion of their funding from the regular unemployment share than the long-term 

unemployment share. 

Based on the annual patterns for PY93, PY97, and PY01 it is clear that excess 

unemployment is responsible for progressively larger shifts of funds from states with smaller shares to 

states with larger shares. Long-term unemployment causes a shift in the same direction, but much smaller 

in magnitude. 
51 

Table 15-97. Distribution of Title III allotment to the states and distribution of each state’s allotment by 

factor for PY97, ordered by state shares 
52 

Table 15-01. Distribution of Title III allotment to the states and distribution of each state’s allotment by 

factor for PY01, ordered by state shares 
53 

Table 16-01. PY01 ratio of excess unemployment shares to total shares 

Total share Excess share 

Ratio 

excess/total 

States with 

no excess 

Quartile 1 71.2% 90.0% 126.5% 6 

Quartile 2 17.0% 5.5% 32.6% 11 

Quartile 3 8.9% 4.4% 50.0% 9 

Quartile 4 3.0% 0.0% 0.0% 13 

4.2 Effect on the Dislocated Worker Allotments in PY93, PY97, and PY01 of Giving 100 

Percent Weights to Each Factor 

Table 17 shows the shift of funds that would be transferred from states losing funds to states 

gaining funds if, instead of the 1/3 - 1/3 - 1/3 weighting of each factor, each factor, in turn, was the sole 

basis for allocating funds in PY93, PY97, and PY01. The key result is found on line 2, where it is clear 

that, over time, progressively greater percentages of total funds would be shifted if excess unemployment 

was used as the sole factor. 

Table 17. Shifts in state allotments based on giving a 100 percent weight to each factor 

 Shifts as a % of total allotment

 PY93 PY97 PY01 

Formula based solely on: 

1. Regular unemployment 5.1% 11.7% 23.7% 

2. Excess unemployment 7.3% 22.2% 44.7% 

3. Long-term unemployment 6.4% 7.6% 22.2% 

Indeed, in PY93 the excess unemployment shift would be only a bit larger than the shift due 

to use of either regular or long-term unemployment as the sole factor. But the shift tripled from PY93 to 

PY97, and doubled from PY97 to PY01. Also of note, the long-term unemployment shift was relatively 

small in PY93 and PY97, while the regular unemployment shift doubled from one period to the next. 

Overall, Table 17 reinforces the key conclusion that the sharp decline in unemployment 

through the 1990’s caused the excess unemployment factor to distribute one-third of the total allocation to 

a successively smaller number of states. This strongly accentuated the proportion of funds received by 

states with relatively high unemployment. Also, there was a delay in the full effect of the unemployment 
54 

shifts because the base period for the long-term unemployment factor has a greater time lag relative to a 

given program year. 

4.3 Change in State Dislocated Worker Allotments PY93-PY01 and PY93-PY97 

In the preceding subsections we focused on how the allocation shares differed across 

quartiles. In conducting that analysis, states could shift from one quartile to another in different periods. 

In this section we focus on how the share going to each state changed over time. 

Table 18 displays the state shares ordered by the percentage change in their allocation 

between PY93 and PY01 from largest loss to greatest gain. This table contains the same statistics as Table 

12, but for dislocated worker allotments instead of adult program allotments. 

Because both the dislocated and adult allotments are largely based on shifts in 

unemployment, the rank order of states is similar in both tables. All six of the New England states had 

large reductions (reductions from 56.6 percent to 75.5 percent). Four of the 14 states with large reductions 

were abutting states in the Midwest—Michigan, Indiana, Illinois, and Missouri. The remaining four states 

are Virginia, Florida, Oklahoma, and New Jersey. 

The size and extent of the declines was truly remarkable. As shown in Table 19, one-third of 

the total PY93 dislocated worker allotment went to states that subsequently had large declines. By PY01 

the share fell by 17.1 percentage points to roughly half its PY93 value. In contrast, states with large shifts 

in their adult allotments received only 15.1 percent of the PY93 allotments, and their share decline was 

“only” 6.4 percentage points. 

Table 18 shows that states with large increases were predominantly along the Pacific Rim 

and elsewhere in the West, along with Puerto Rico and the District of Columbia. However, among states 

with large gains, the average gain in dislocated worker allocation was more than four times as great as the 

average gain in adult program funds. This was the case whether the gains were measured as a percent of 

the PY93 allotment or as the percentage-point increase in the allotment. 
55 

Table 18. Changes in Dislocated Worker State Shares PY93-PY01 and PY93-PY97 
56 

Table 19. Comparison of shifts in state allotments PY93-PY01 for dislocated worker and adult 

programs 

Share PY93 Share PY01 

Percentage point 

difference 

Dislocated Worker Programs 

Large reductions 33.4% 16.3% -17.1 

Moderate reductions 28.8% 21.2% -7.6 

Small changes 11.9% 11.5% -0.4 

Moderate increases 19.9% 28.8% 8.9 

Large increases 6.1% 22.3% 16.2 

Adult Program 

Large reductions 15.1% 8.7% -6.4 

Moderate reductions 25.4% 19.9% -5.5 

Small changes 16.4% 16.1% -0.4 

Moderate increases 10.1% 11.5% 1.4 

Large increases 32.9% 43.8% 10.9 

As shown in Table 19, states with large increases in dislocated worker allotments received 

6.1 percent of PY93 allotments. But by PY01 those states received 22.3 percent of the total allotment, a 

jump of 16.2 percentage points. In contrast, a much larger percentage of the total PY93 adult allotments 

went to states experiencing large gains. The PY93 share was 32.9 percent for those gainers, and the 

increase was a healthy 10.9 percentage points to 43.8 percent in PY01. The initial share was large because 

the large gainers included California, Texas, and Ohio. However, even though the base was much greater 

for adult programs than dislocated worker programs, the percentage point shift for adult programs was 

much less than the shift for dislocated worker programs. 

As already noted, there could be huge upward or downward shifts in dislocated worker 

allotments because of the way the excess unemployment factor was calculated. Shifts in adult allotments 

were far less because most states had some areas within the state with unemployment meeting the excess 

ASU unemployment criteria. In addition, downward adult program shifts were limited by the minimum 

funding provisions and to a lesser extent by the hold harmless provisions. 

We have already seen that the shifts in adult program allotments were much larger than 

shifts in poverty. Nevertheless, the much larger shifts in dislocated worker allotments than in adult 

program allotments might be justified if there were much larger shifts in the target population of 

dislocated workers. This is the topic of the next several subsections. 
57 

4.4 The Relationship Between Eligibility and State Dislocated Worker Allotments 

In this subsection we examine state dislocated worker shares in PY93, PY97, and PY99 and 

then examine the extent to which the dislocated worker allotments were distributed in proportion to the 

share of eligible individuals. Unfortunately, we have to limit this analysis to the JTPA period because 

1999 was the latest year for which we had a measure of dislocated worker shares. On the other hand, our 

results are more clear cut than for adult programs because WIA did not fundamentally alter the eligibility 

requirement for participation in dislocated worker programs. In contrast, adult program participants 

shifted from disadvantaged adults alone to all adults. 

Under JTPA Title III (and WIA) the vast majority of dislocated workers are individuals who 

lost jobs as a result of permanent closures or substantial layoffs. However, some dislocated workers are 

displaced homemakers and business owners, such as ranchers and farmers, displaced due to changes in 

environmental or other public policies. 

Because there are no published state-level estimates of dislocated workers, we had Professor 

Lori Kletzer (UC-Santa Cruz) develop these estimates for use in this paper. To do this, she used the 1994, 

1996, 1998, and 2000 Displaced Worker Surveys (DWSs), which are supplements to the February 

Current Population Surveys (CPSs). She formed a model based on information contained in the DWSs. 

So, for calendar year 1993 she developed estimates using data from the 1994 survey, for years 1994 and 

1995 she used the 1996 survey, for years 1996 and 1997 we relied on the 1998 survey, and for years 1998 

and 1999 she used the 2000 survey. 

The DWS is asked of regular CPS participants, ages 20 and older. Our estimates are based 

on the following questions: 

 Did you lose a job in the previous three calendar years? 

 Did you not expect recall? 

 Were you not self-employed? 

 Did you lose your job due to: plant closing or relocation, elimination of position or 

shift, or slack work? 

If the answer to all four questions was yes, then the individual were defined as displaced 

worker. 
58 

For the years 1994-99, each observation is weighted by a variable called “wgtdw”, a variable 

added by the Census Bureau as a displaced worker supplement weight.12 These weights were then used to 

estimate the number of dislocated workers in each of the 50 states and the District of Columbia. Weights 

were not available for Puerto Rico, which received 7.32 percent of the PY99 allotment, an increase of 

5.07 percentage points relative to its PY93 share. 

4.4.1 The Relationship Between the Distribution of Dislocated Workers and State Shares in 

PY93 and PY99 

Much as we did for adult programs, we used our estimates of the number of dislocated 

workers to calculate funds available for each potential participant. As shown in Table 20, the state Title 

III allotment per dislocated worker was $102.66 for PY93, and $314.99 for PY99. 

Table 20. State Allotments per Dislocated Worker in PY93 and PY99 

 PY93 PY99 

JTPA Title III state allotment $413,636,802 $1,042,093,538 

Dislocated workers 4,029,029 3,308,292 

Funds per dislocated worker $102.66 $314.99 

4.4.1.1 PY93 Shares 

Column A of Table 21-93 shows that there were large differences in the per-eligible 

allotment across the states. For instance, Hawaii received $18.15 per eligible while Mississippi received 

$219.92 per eligible. 

Column B shows that Hawaii’s allotment would increase 452 percent, if funds were 

redistributed to make each state’s allotment share equal to its share of dislocated workers. Five other 

states would more than double their allotments—Nebraska, North and South Dakota, Colorado, and 

Kansas. 

 

12 For the year 1993, wgtdw is not available. There is an alternative weight, wgtfnl. We used wgtfnl for the years 1994-99 and compared these 

weighted counts to the correct weighted counts using wgtdw. We then developed an adjustment for wgtfnl so that for 1993 it would yield a 

weight similar to wgtdw. 
59 

Table 21-93. Allotment per dislocated worker, differences in share of dislocated workers versus share of 

allotment by state for PY93 
60 

At the same time, Mississippi’s share would decrease by 54 percent. Five other states would 

suffer reductions of 40 percent or more—Arkansas, Alabama, West Virginia, New Hampshire, and Utah. 

The range of gains and losses is exceptionally large because unemployment levels are poor 

indicators of the number of dislocated workers in the Workforce Investment Act (WIA) allotment 

formulas. Looking at the states at the extremes of the distribution, in PY93 Great Plains states tended to 

have low unemployment, but relatively large numbers of dislocated workers, while Southern states along 

the Mississippi River tended to have high levels of unemployment, but relatively small numbers of 

dislocated workers.13

Table 21-93 also compares the share of dislocated workers in each state (in column C) to the 

state’s share of the dislocated worker allotment (in column D). Line 1 shows that Washington State had 

3.24 percent of dislocated workers, but received only 1.63 percent of the dislocated worker allotment. 

Thus, as shown in column E, a 1.61 percentage point increase in Washington’s allotment would be 

needed to make its allotment proportional to its share of dislocated workers. 

At the bottom of Table 21-93 we see that overall a transfer of 14.34 percent of the PY93 

allotment would be needed to bring each state’s allotment into balance with its share of dislocated 

workers. Five states, Washington, California, Texas, Georgia, and California would receive just under 

half of the total redistribution. 

At the opposite end of the redistribution, losses by five states—New York, Michigan, 

Massachusetts, Pennsylvania, and Illinois—would account for a bit more than one-half of the total 

redistribution. Since the “losing” states received 44.37 percent of the total allotment, an overall transfer of 

about one-third of the funds going to those states would be required to equalize funding per dislocated 

worker. 

 

13 States with equal numbers of dislocated workers, but lower unemployment rates, might need less employment and training funds because 

dislocated workers can more easily find new jobs in tighter labor markets. However, differences in unemployment rates are far from perfect 

indicators of relative labor market tightness, and often skill miss-matches make it difficult for dislocated workers to adjust in states with low 

unemployment rates. Thus, it would be valuable to more thoroughly analyze the factors determining participation rates in different cost 

programs. 
61 

4.4.1.2 PY99 Shares 

Table 21-99 uses the same format as Table 21-93 to display key information about the 

differences between the PY99 allotments and the 1999 share of dislocated workers. At one extreme, New 

Hampshire received only $91.60 per dislocated worker in PY99. Other states that would more than double 

their funding if allotments were proportional to dislocated worker shares are: Utah, Colorado, South 

Dakota, Massachusetts, Alabama, Washington, North Carolina, and Missouri. 

At the other extreme, the District of Columbia received $1,255.65 per dislocated worker. 

Other states whose funding would be cut more than in half are: Hawaii, Louisiana, New York, New 

Mexico, and West Virginia. 

Three states, Colorado, South Dakota, and Washington, were “big gainers” in both PY99 and 

PY93. But Alabama, New Hampshire, Utah, and Massachusetts, which were “big gainers” in PY99, were 

“big losers” in PY93. Louisiana and the District of Columbia were “big losers” in PY93 and PY99. But 

only Hawaii went from a big gainer in PY93 to a big loser in PY99. 

The size of the differences between the dislocated worker and allocation shares in PY99 

reinforces the view that the distribution of unemployment levels poorly matches the distribution of worker 

dislocation. In addition, the major shifts of states from gainers to losers and vice versa between PY93 and 

PY99 suggest that there can be large and rapid shifts in unemployment and worker dislocation. Together, 

both factors suggest that finding an effective formula to match funding to dislocation is a difficult task. 

Turning to differences in dislocated worker and allotment shares, we see at the bottom of 

column E of Table 21-99 that a shift equal to 25.35 percent of total funds would be needed to equalize 

dollars per dislocated workers across the states. Given that the states losing funds started out with 60 

percent of the initial allotment, they would collectively face a reduction of 42.2 percent. 

Two states, New York and California, would contribute 45 percent of the redistribution. 

Four additional states, Louisiana, New Jersey, West Virginia, and New Mexico, would also contribute 

about 45 percent of the redistribution. Seven states would receive just over 50 percent of the gains: Ohio, 

Florida, Massachusetts, North Carolina, Washington, Illinois, and Missouri. 
62 

Table 21-99. Allotment per Dislocated Worker, Differences in Share of Dislocated Workers versus Share 

of Allotment by State for PY99 
63 

4.4.2 Ability of Each Factor To Match Dislocation Shares 

A large redistribution would be needed in PY99 to match dislocation and allotment shares 

mainly because the excess unemployment factor concentrated funds among states with high 

unemployment, but relatively low levels of worker dislocation. Thus, a key step in making the 

distribution of funds more equitable would be to eliminate, or make major modifications to, the excess 

unemployment factor. 

As shown in Table 22, if funds were distributed based solely on excess unemployment a 

reallocation of 60 percent would be needed to equalize dollars per dislocated worker in PY99. In contrast, 

the redistribution would only be about 10.9 percent in PY99 and in PY93, if regular unemployment was 

the only factor. The redistribution would be about 17 percent in both PY93 and PY97, if long-term 

unemployment was the only factor. 

Table 22. Reallocations needed to equalize dollars per dislocated worker if each factor was used alone 

 PY93 PY99 

Regular unemployment 10.8% 10.9% 

Excess unemployment 17.5% 60.2% 

Long-term unemployment 17.2% 16.5% 

Clearly, use of regular unemployment alone would produce a more equitable allocation, and 

little would be gained by using long-term unemployment. As noted several times, excess unemployment 

is an extremely poor indicator of the distribution of worker dislocation. 

4.4.3 Share Changes by State between PY93 and PY99 

In this subsection we switch from examining the difference between allotment and 

dislocated worker shares in individual program years to examining these differences for individual states 

between PY93 and PY99. 
64 

Column C of Table 23-93 shows that North Dakota had the greatest percentage decrease in 

its share of dislocated workers, 57.1 percent. The average decrease, across the 29 states with decreases, 

was 22.5 percent, with a standard deviation of 16.1 percent. Utah had the greatest increase, 487.0 percent. 

The average increase, across the 22 states with increases, was 64.8 percent, with a standard deviation of 

109.8 percent. Thus, there were some very large positive and negative differences in the share of 

dislocated workers across the states and a lot of diversity in the size of those differences. 

Column F of Table 23-93 shows the percentage change in each state’s allotment share 

between PY93 and PY99. There was a similar amount of positive and negative differences and diversity 

in the size of those differences. 

Perhaps the most interesting and important information in Table 23-93 comes from the 

quartile summaries at the bottom of the table. Columns C and F shows that when taken as a whole, the 

changes in the share of dislocated workers in each quartile become progressively more positive (reading 

down the table), but the changes in the share of allotments became progressively more negative. In other 

words, changes in allotments are negatively correlated with changes in dislocated workers, leading to 

precisely the opposite relationship (we assume) the formula was intended to produce. 

Importantly, this relationship is not a statistical artifact due to differences in the PY93 

distribution of the shares of dislocated workers and allocations across the quartiles. In PY93, the 

distribution of dislocated worker and allotment shares was about equal. Also, within each group, the 

shares in the top three quartiles were about equal, but share in the bottom quartile was about half that of 

those in the other quartiles. 

Looking more deeply into the source of the negative correlation, we determined that 

removing five states—California, New York, Michigan, Massachusetts, and Ohio—substantially weakens 

the negative correlation, but does not come close to producing a positive correlation. 
65 

Table 23-93. Change in dislocated worker and allotment shares PY93-PY99 
66 

As shown in Table 24, the change in dislocation shares increases at the extremes, 

accentuating the quartile-by-quartile differences. However, the change in allotment share increased 41 

percentage points in quartile 1, 22 percentage points in quartile 2, and 7 percentages points in quartile 3. 

However, the change in allotment share decreases 16 percentage points in quartile 4. 

Table 24. Effect on PY93-PY99 share changes of removing California, New York, Michigan, 

Massachusetts, and Ohio 

 Change in dislocation share Change in allotment share

 All states 46 states Difference All states 46 states Difference 

Quartile 1 -24.3% -30.9% 6.6 36.1% -5.4% 41.5 

Quartile 2 -11.8% -11.7% -0.1 11.3% -11.2% 22.5 

Quartile 3 7.2% 8.3% 1.1 -21.1% -14.0% 7.1 

Quartile 4 58.9% 79.3% -20.4 -29.8% -13.5% -16.3 

From an equity standpoint it clearly is inappropriate to exclude the effects on five states 

which in PY93 contained 32 percent of all dislocated workers and received 36 percent of the total 

allocation. Indeed, it is hard to see how a “good” formula would lead to a 20 percent increase in the share 

of funds going to those states, which had a 6 percent decline in the share of dislocated workers. 

4.4.4 Share Changes by State Between PY97 and PY99 

Table 23-97 presents the same information as Table 23-93 but for the change in dislocated 

worker and allotment shares between PY97 and PY99. The summary at the bottom of the table shows the 

surprising result that across the quartiles the changes in the dislocation shares were larger between PY97 

and PY99 than between PY93 and PY99. However, this difference is partly explained by the base year 

share of dislocated workers being much smaller, 15 percent versus 29.8 percent, in the PY97-99 

comparison than in the PY93-99 comparison. In general, it is easier to generate large percentage changes 

in share from a small base than a large base. 

Also of considerable importance, we see in the summary section of Table 23-97 that the 

changes in allotment shares show little, if any, correlation with the changes in dislocation shares. While 

the correlation is not nearly as negative as was the case between PY93 and PY99, it still was the case that 
67 

Table 23-97. Change in dislocated worker and allotment shares PY97-PY99 
68 

the dislocation share increased by 70.6 percent in the bottom quartile, but the allocation share decreased

by 4.6 percent. This provides further evidence that even in a short period where overall economic 

conditions were fairly similar, the formula did a poor job of providing funds in proportion to the number 

of targets. 

Finally, it is worth noting that the lack of correlation between changes in dislocation and 

changes in funding was not a result of there being small changes in each state’s allocation. Table 25 

shows that when the states are ordered by the change in allocation, there were large changes in the 

allocations, but small changes in dislocation. Further, the changes in allocations and dislocations 

generally had opposite signs. 

This is further evidence that where there were large changes in allocations, there were small 

changes in dislocations (and vice versa), and those changes were in opposite directions. Both of these 

characteristics are undesirable in an allocation formula. 

Table 25. Quartile change in allocations and dislocations PY93-PY99 and PY97-PY99 when states are 

ordered by allocation change 

 Change PY93-99 in: Change PY97-99 in:

 Allocation Dislocation Allocation Dislocation 

Quartile 1 -38.9 7.0 -45.3 6.9 

Quartile 2 -16.5 -2.9 -25.2 11.6 

Quartile 3 34.0 -6.5 3.9 -4.1 

Quartile 4 59.1 5.2 66.8 -12.1 

4.5 Summary and Conclusions 

Our analysis of JTPA and WIA dislocated workers reached several conclusions with respect 

to the way the formula allotted funds over the 1990s: 

1. The allotment shifts were far larger for dislocated worker programs than for adult 

programs. 

2. Dislocated worker allotment shifts were especially large because its excess 

unemployment factor was calculated on a statewide basis. Over the 1990’s, 

unemployment in more and more states fell below the 4.5 percent excess threshold. In 

contrast, the excess unemployment factor for adult programs was based on 
69 

unemployment in Areas of Substantial Unemployment (ASUs) or the state as a whole. 

Most states had some areas that met the 6.5 percent ASU threshold, and therefore also 

met the 4.5 percent excess unemployment threshold. 

Our analysis also reached conclusion with respect to the way target populations, dislocated 

workers, shifted over the 1990s. State shares of dislocated workers showed substantial shifts that were 

often negatively correlated with shifts in unemployment. However, in contrast to poor adults, most 

dislocated workers relatively quickly left dislocated status. Thus, most dislocated workers in any given 

year recently lost jobs. 

We also reached several conclusions about overall equity—if we assume that an appropriate 

criterion for the equitable distribution of allotments is that the dislocated worker funds are distributed in 

proportion to the number of dislocated workers. 

1. Use of unemployment measures did an especially poor job of distributing funds in 

proportion to the number of dislocated workers. The problem was exacerbated 

because the sharp unemployment decline led the excess unemployment factor to 

distribute funds to only a few states with high unemployment, but relatively few 

dislocated workers. 

2. In PY93 a redistribution of about 15 percent of the total allocation would have 

equalized dollars available per dislocated worker. That figure jumped to over 25 

percent by PY99. However, if regular unemployment were the only factor, the 

redistribution would have only been about 10.8 percent in each period. 
70 

5. RECOMMENDATIONS 

The above results suggest a simple way to allocate adult program funds in proportion to 

adult poverty—use new Census Bureau poverty measures as the sole factor in the formula. Even though 

these estimates lag by 3 years, the changes in poverty are so slow that this would make little difference. 

Use of the new poverty measures is attractive for several additional reasons: 

1. The new measures are available at the county level, so they could be used to allocate 

funds at the substate level. 

2. Use of the new measures would eliminate the need for the states to produce complex 

ASU unemployment estimates. 

3. The new measures have greater statistical integrity than unemployment measures, 

especially at the substate level. This is because the ASU measures are based on the 

untested assumption that within each state the unemployment distribution in 1990 

remains the same across census tracts throughout the rest of the decade. 

4. Use of the new poverty measure would largely eliminate the effect of the holdharmless provisions. After only four years of using the new poverty measures the 

shifts due to hold-harmless provisions fell from about 6 percent of the PY93 allotment 

to almost zero in PY97. 

The central problem with the dislocated worker formula is that the cross-state distribution of 

dislocated workers in any one year is not especially highly correlated with the prior year’s distribution, 

nor with any of the unemployment measures.14 This suggests that it is reasonable to reserve a substantial 

fraction of the total allocation for distribution as events unfold during a given program year. Indeed, 

fluctuations in the distribution of worker dislocation are so large and difficult to predict that it probably 

would be equitable to at least double the set-aside from 20 to 40 percent. The increase would make it 

easier to cover unforeseen dislocations that often occur late in the program year, while ensuring each state 

has sufficient funds on hand to prepare to implement programs as they are needed. 

Whether or not the set-aside is increased, it would be highly desirable to remove the excess 

unemployment measure from the current formula as that measure is an especially poor indicator of the 

given state’s share of dislocated workers. 

 

14 Even if the cross-state distribution in one year was similar to the distribution in the following year, the DWS measures might be unsuitable for 

use in the formula because they are lagged by as much as three years. However, estimates of the dislocated worker distribution with a one-year 

lag can be obtained from the BLS mass-layoff reporting system or other data. Thus, data constraints are not at issue. The problem is difficulty in 

predicting the incidence of dislocation one year in advance.