i

A Compilation of Selected Papers from 

The Employment and Training Administration’s 

2003 Biennial National Research Conference 

Edited by 

Joshua Riley 

Aquila Branch 

Stephen Wandner 

Wayne Gordon 

United States Department of Labor 

Employment and Training Administration 

Office of Policy Development, Evaluation, and Research 
ii

PREFACE 

The United States Department of Labor’s Employment and Training Administration held 

its second Biennial National Research Conference in June of 2003. Workforce 

development researchers and practitioners were drawn to this exciting event by the 

promise of learning about innovative research findings and engaging in active discussions 

with colleagues. The two-day event proved to be a rewarding experience through which 

the greater dialogue on important workforce investment issues was broadened and 

advanced. 

The Employment and Training Administration has undertaken the task of publishing 

selected works from the conference to offer even greater exposure to these issues. This 

book will provide conference participants with an opportunity to review some of the 

presented literature and reflect on the proceedings, while giving those workforce 

investment specialists who were unable to attend a glimpse at what was offered. 

From a large pool of submitted papers, we used a competitive process to choose those 

works that would best reflect the activities of the conference while shedding light on 

current policy debates and furthering our understanding of important ETA programs. It 

should be noted that some of the papers presented at the conference have been or will be 

published as separate ETA occasional papers as well. 

In addition to several papers, we have included Assistant Secretary Emily Stover 

DeRocco’s keynote speech from the event, an agenda that contains a list of all papers that 

were presented, and the Federal Register notice, which provides general information 

about the conference and its main objectives. 

We would like to acknowledge Crystal Woodard, Peggie Edwards-Jeffries, Monica 

Thomas, Mia Bruce, and Carlos Pinto. Without the hard work and dedication of these 

people, the research conference and this publication would not have been possible. 

We hope that you enjoy this publication and look forward to seeing you at the next 

Biennial National Research Conference. 

Maria K. Flynn 

Acting Administrator 

Office of Policy Development, Evaluation, or Research 

Employment and Training Administration 

U.S. Department of Labor 
iii

TABLE OF CONTENTS 

Keynote Speech Delivered by Assistant Secretary Emily 

Stover DeRocco.......................................................................................1 

 

“The Relationship Between the Workforce Investment Act 

and the Charitable Choice Initiative,” by Bender, April.........................................6 

“A Frontline Decision Support System for Georgia Career Centers” 

by Randall Eberts and Christopher O’Leary.....................................................80 

“A New WPRS Profiling Model for Michigan” by Randall Eberts 

and Christopher 

O’Leary.............................................................................................130 

“Former TANF Recipients Monetary Eligibility for 

Unemployment Insurance Benefits: An Empirical Study” 

by Douglas Sanford, et al.........................................................................185 

 “Employment and Training Services Provided by Faith-Based 

Organizations: An Exploratory Study and Issues for Consideration 

in the use of Public Funds” by Fredrica Kramer, et al.........................................208 

“Reconceptualizing Education as an Engine of Economic 

Development: A Case Study of the Central Educational Center,” 

by MacAllum, Keith and Amy Bell Johnson...................................................244 

“Low Income and Welfare Client Priorities: Patterns of Earnings 

and Welfare Receipt for Workforce Investment Act Participants” 

by Peter Mueser and David Stevens............................................................282 

“The Participation of Older Workers in Employment and Training 

Administration Programs” by Jonathan Simonetta............................................296 

“The Role of One-Stop Career Centers in Serving Welfare Recipients” 

by Demetra Smith Nightingale, et al............................................................330 

Federal Register Notice...........................................................................419 

Conference Agenda................................................................................423
1

KEYNOTE SPEECH: 

DEVLIERED BY ASSISTANT SECRETARY EMILY STOVER DEROCCO 

Good afternoon, and welcome to the ETA Biennial National Research Conference. Am I 

glad to see you! My staff has prepared an exciting agenda. I hope you leave here with a 

lot of new ideas and strategies that you can utilize. 

I have always had a great appreciation for research that is done well. Throughout my 

career, I’ve taken great pride in the work I do. I have tried to set a course, and lead 

people in the direction I think they, or the organization they work for, should go. I have 

tried to be decisive, and I know that making the right decision depends on having the 

right information. As Assistant Secretary for Employment and Training, it is my job to 

make sure our nation’s workforce investment system is as effective and efficient as it can 

possibly be. 

I must be able to decide how we are going to spend the $12 billion Congress invests in us 

each year. In order to do that, I need access to the information that tells me: 

• What has worked in the past and why? 

• Has the same thing worked everywhere? 

• How has the program varied between urban and rural settings? 

• What caused similar programs/initiatives to fail? 

• What changes would make the program more effective? 

• What are the results from the program that I can point to? 

The list of questions I need answered is almost endless. I know because every time I go 

before a Committee of Congress, I get many of them. Unfortunately, the pool of research 

I can draw on to defend the decisions I have to make every day is somewhat limited. 
2

Congress asks extremely tough questions about the resources they commit to our agency, 

and you know what? They should. 

We have a limited number of resources and the competition for them is very tight. In 

effect, we are competing for appropriations against education and health programs. 

Members of the Appropriations committee must decide whether the money they invest in 

workforce programs will gain more benefit than the money they spend on education, or in 

fighting disease. 

The only way I can ever know what is effective and efficient and defend it to the 

Congress, the media or the general public, is with good research. 

Your work is critical for me and the Congress who funds these programs in order to: 

• Assess program effectiveness 

• Identify process improvements 

• Reform and improve the way we do things. 

That is why I am calling on you today to join me in thinking about the future direction for 

the Employment & Training Administration’s research strategy. 

Before I go any further, let me just acknowledge the fact that as professional researchers I 

know you are professionals and will do a tremendous job of conducting the research we 

request. However, in the past, I don’t believe ETA has been as specific as we needed to 

be in articulating the types of research we need or want from the research community. 

So, today I ask you to set aside any preconceived notions you may have about the types 

of research ETA has traditionally asked for. This is a new beginning. We commit you to 

be as specific with you as possible. We will also attempt to clearly define our 

expectations going into projects to make sure you know what we expect in the form of a 

final product. 
3

Because this is a new beginning, I think everything should be on the table. We need to 

look at new ideas. We need to question accepted practices and we need to probe areas 

that have not been examined in order to find the most effective and efficient ways of 

doing business. 

I am also committed to ensure that ETA’s program officials are engaged in our agency’s 

research process, both in terms of identifying areas to research as well as working to 

ensure research findings are built into program operations and future policy development. 

I am pleased that so many of our top Administrators and managers are serving as panel 

chairs during this Conference. I think that is representative of the type of connections 

between research and program operations that we are trying to build within our agency. 

I realize we don’t have much time this morning, but let me just pose some questions to 

you that will hopefully get your minds revving to help kick off this conference. I have 

identified five key areas that I would like all of you to think about: 

1) Partnerships with Education: 

• What are the appropriate roles of public K-12 and higher education in an 

integrated Workforce Investment System? 

• How can the Department of Labor build better connections with such 

program as Adult Education and Vocational Education? 

2) Services to Out-of-School Youth: 

• What are the most effective strategies for serving this population? 

• How do we ensure that school dropouts, court-involved youth and 

other at-risk youth have access to further education as well as gainful 

employment? 

3) Lessons from Welfare Reform: 

• What has our experience with welfare reform taught us about helping 

low-income individuals succeed in today’s economy? 
4

• What are the most effective retention and advancement strategies? 

4) Reassessing New Deal Programs: 

• How can the Unemployment Insurance Program effectively adapt to an 

evolving U.S. economy? Areas of exploration include program 

administration, coverage, eligibility, benefit adequacy, benefit duration, 

recipiency, benefit financing, economic stabilization, special populations and 

changing work patterns. 

• Is UI the right economic safety net? 

5) Measuring Program Success: 

• How do we know if our programs are truly successful in achieving their 

goals? 

• What information do we need to accurately tell our story to our investors and 

our customers? 

These are just some of the questions we are faced with on a regular basis as we work to 

develop a budget, make policy decisions, and manage our programs. I’m here to tell you 

that we need you and the studies you develop so that we can have data to lean on when 

these tough decisions need to be made. 

Right now, we are working with Congress to reauthorize the Workforce Investment Act. 

This effort is made stronger by the lessons learned the hard way in the field and through 

research and evaluation. 

Most of you probably know, the provisions of the WIA direct the Secretary of Labor to 

prepare a five year research plan for research, pilot and demonstration initiatives every 

two years. This plan reflects a strategic vision for stakeholders in employment and 

training research, a review of recent efforts, an identification of areas where future 
5

research may be needed, and a review of possible research methodologies. ETA 

published the first plan in June 2001 and is nearing publication of the second issue of the 

plan. A plenary session is planned for tomorrow morning on the recently completed five 

year plan. 

It is in this environment of change in the macro-economy which has taken place in the 

areas of technological transformation, increasing globalization and changing 

demographics over the recent years, and the resulting effects of rising workforce 

insecurity, wherein the Employment and Training Administration decided to continue the 

research conference series. 

Hopefully, you will find this conference both informative and engaging, whether you are 

a researcher, a state liaison, a federal employee, a student or a contractor. The goal of 

this conference is to provide all participants a forum for the presentation and discussion 

of the studies commissioned by ETA; to provide an opportunity for presenting external 

research studies that are relevant to the Employment and Training Administration; and to 

serve as a springboard for developing partnerships between ETA and outside 

organizations that conduct research of interest to ETA. 

We intend to publish selected papers presented at the conference that broadly represent 

this years themes as an Occasional Paper, the hard part will be deciding which ones to 

select. 

I again welcome you to the Biennial National Research Conference. 

Since there are a few minutes before the breakout sessions begin, I would be happy to 

answer any questions you may have. 
6

The Relationship Between the Workforce 

Investment Act and the 

Charitable Choice Initiative 

April M. Bender, Ed.D. 

Partnerships for Quality 

Sponsored by the Charitable Choice Center of the State University of New York, 

University Center for Academic and Workforce Development. 

© 2003 State University of New York, University Center for Academic and Workforce 

Development 

This document is protected under copyright law and may not be reproduced in any way 

without explicit, written permission. Violation of copyright will be vigorously pursued 

via all possible legal means. 

This document can be secured through the Charitable Choice Center (315.265.409)
7

Section I 

Introduction 

Background 

 On January 29, 2001, in an effort to expand opportunities for faith-based and other 

community organizations and to strengthen their capacity to better meet the social needs in 

America’s communities, President Bush issued Executive Order 13198 to establish the White 

House Office for Faith-Based and Community Initiatives, along with five cabinet centers created 

within the Departments of: (a) Education, (b) Health and Human Services (USDHHS), (c) 

Housing and Urban Development (USDHUD), (d) Justice, and (e) Labor (USDOL). Since the 

inception of the five cabinet centers, two other centers have been developed, in the Agency for 

International Development and the Department of Agriculture. 

 The purpose of each Center for Faith-Based and Community Initiatives (CFBCI) is to 

coordinate their department’s efforts to eliminate regulatory, contracting, and other 

programmatic obstacles in an effort to facilitate the participation of faith-based organizations 

(FBOs) and other community-based organizations (CBOs) in providing social services. 

According to a review of the literature by the U.S. General Accounting Office (USGAO), the 

term FBOs is commonly defined as religious organizations or religiously affiliated not-for-profit 

entities that could be classified into two major categories for purposes of providing social 

services. Those categories are: sectarian or pervasively religious organizations such as 

churches, temples, synagogues, mosques, and congregations; and non-sectarian or separate, 

secular organizations created by a religious organization to provide social services, such as 

Jewish Family Services, Catholic Charities USA, Lutheran Social Services, and the Salvation 

Army (USGAO, 2002b). 

 According to the Executive Order, each of the centers will: (a) conduct a departmentwide audit to identify all existing barriers to the participation of FBOs/CBOs as they relate to the 

delivery of social services with respect to rules, orders, procurement, internal policies and 

practices, and outreach activities; (b) coordinate a comprehensive effort to include FBOs/CBOs 

in programs and initiatives to the greatest extent possible and eliminate regulatory, contracting, 

and other programmatic obstacles so they can fully participate in the provision of social services; 

(d) develop innovative pilot and demonstration programs in Federal, state, and local initiatives 

which would include FBOs/CBOs; (e) ensure information is disseminated more effectively to 

FBOs/CBOs through communication and technical assistance; (f) conduct a comprehensive 

review of policies and practices affecting existing funding streams governed by charitable choice 

legislation (to be completed by the Centers for the USDHHS and USDOL); (g) create a 

hospitable environment for groups which have not traditionally collaborated with government; 

(h) implement special programs designed to showcase and pioneer innovative efforts; and (i) 

submit a report within 180 days, and annually thereafter, that will include identification and 

analysis of the barriers preventing full participation of FBOs/CBOs in the delivery of social 

services, strategies to eliminate them, and identification of technical assistance and other 

information available for the purpose of preparing grants, cooperative agreements, contracts, and 

procurement and performance indicators (Executive Order 13199, 3 C.F.R.). 
8

 Shortly after the Executive Order, on February 28, 2001, President Bush addressed 

Congress and shared his budget and vision for the coming year. Within the President’s address, 

A Blueprint for New Beginnings, he called upon Americans to champion compassionate 

conservatism. 

Compassionate conservatism means providing vigorous and thorough support for 

those in need, while preserving the dignity of the individual and fostering 

personal responsibility. It means that caring must be accompanied by more than 

education and assistance. It must come with encouragement, and an expectation 

of success. It means that every compassionate effort must extend beyond the 

temporary amelioration of want toward independence and personal authority ... 

With this budget, the President commits our Nation to mobilizing the armies of 

compassion - charities and churches, communities and corporations, ministers and 

mentors – to transform lives. These groups are proving that real change comes 

from the bottom up, not the top down. Moreover, these faith-based and 

community organizations will be permitted to compete for Federal funds as long 

as secular alternatives are also available. Faith-based organizations can maintain 

their religious characteristics, but the Federal Government cannot fund inherently 

religious activities” (White House, 2002a). 

 The Blueprint for New Beginnings called for the following: (a) creating a Compassion 

Capital Fund (CCF) to invest in charitable best practices; (b) allowing community groups, 

churches, and charities to conduct after-school programs; (c) making Federal funds available on 

a competitive basis for faith-based pre-release programs at Federal facilities; (d) allowing 

FBOs/CBOs to focus on improving the prospects of low-income children of prisoners to apply 

for grants; (e) ensuring that faith-based and other non-medical drug treatment programs have 

equal access to increased drug treatment funding; (f) establishing second chance homes for 

unwed teenage mothers; (g) promoting responsible fatherhood; (h) increasing the adoption tax 

credit to $7,500 and making it permanent; (i) expanding efforts to help low-income families pay 

rent and avoid homelessness; (j) expanding charitable choice to all applicable Federal laws that 

authorize the government to use non-governmental entities to provide services to beneficiaries of 

Federal dollars; and (k) encouraging the establishment of state offices of faith-based action 

(White House, 2002a).1

 

 

 The first report required by the Executive Order, Unlevel Playing Field: Barriers to 

Participation by Faith-Based and Community Organizations in Federal Social Service 

Programs, was released on August 16, 2002. According to the report, the President’s Charitable 

Choice Initiative (CCI) helps to: (a) clarify and codify the right of faith-based groups to 

participate by addressing the misperceptions and doubts about whether religious groups may 

deliver Federally-funded social services; (b) replace government suspicion of religious providers 

with a welcoming environment by giving a green light to expanded collaborations with 

government and making such partnerships plausible and possible; (c) ratify and give a legal 

foundation to current flexible practice by clarifying that the Constitution does not require 100% 

secularism, but neutrality and equal opportunity instead; (d) overcome anti-faith barriers in 

Federal programs by overturning restrictions on participation and activities not required by the 

 

1

 See Table 7 for details on the CCF. 
9

Constitution; (e) enrich the mix of service providers in many states; (f) enable formerly excluded 

groups to offer their effective services by freeing local officials to create new collaborations that 

involve faith-based charities previously wary of partnering with government; (g) better fulfill the 

service mission of current religiously affiliated providers by permitting established groups to get 

rid of the excessive government-imposed limits that have wrongly hobbled services and kept 

them from better integrating a moral dimension into their programs; and (h) build on successful 

principles in other areas of Federal funding which are based on principles of accountability, 

performance, pluralism, and religious liberty (White House, 2002b). The President’s Executive 

Order and subsequent related activities clarified and expanded the potential of the charitable 

choice provision of the Personal Responsibility and Work Opportunity Reconciliation Act of 

1996 (PRWORA) and became known as the aforementioned CCI. 

 As early as 1996, Section 104 of the PRWORA addressed the services provided by 

charitable, religious, or private organizations including state programs funded under Part A of 

Title IV of the Social Security Act and any other program established or modified under Title I 

or II of the Act. Section 104, known as the charitable choice provision of the PRWORA, allows 

states to contract with religious organizations, or to allow them to accept certificates, vouchers, 

or other forms of disbursement “ ... on the same basis as any other nongovernmental provider 

without impairing the religious character of such organizations, and without diminishing the 

religious freedom of beneficiaries of assistance funded under such programs” (Personal 

Responsibility and Work Opportunity Reconciliation Act, 1996). Programs must be 

implemented consistent with the Establishment Clause of the United States Constitution: 

“Congress shall make no law respecting an establishment of religion, or prohibiting the free 

exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people 

peaceably to assemble, and to petition the Government for a redress of grievances” (First 

Amendment: Establishment Clause of the United States Constitution). Organizations cannot be 

discriminated against on the basis of their religious character. 

The Act makes the following provisions: (a) religious organizations will retain their 

independence from Federal, state, and local governments, including such organizations’ control 

over the definition, development, practice, and expression of its religious belief, and will not 

require a religious organization to alter its form of internal governance or remove religious art, 

icons, scripture, or other symbols; (b) if an individual has an objection to the religious character 

of the organization or institution, the state must provide an alternative provider that is accessible 

to the individual and of equal or greater value; (c) a religious organization’s exemption under 

702 of the Civil Rights Act of 1964 (42 U.S.C.2000e-1a) regarding employment practices will 

not be affected by its participation in, or receipt of, funds from programs authorized by this 

section; (d) religious organizations cannot discriminate against an individual in regard to 

providing assistance on the basis of religion, religious beliefs, or refusal to actively participate in 

a religious practice; (e) religious organizations are subject to the same regulations as other 

contractors and must use generally accepted auditing principles for the use of funds under these 

programs (if they maintain separate accounts, only the accounts with Federal funds will be 

subject to audit); (f) any party which seeks to enforce its rights under this section may assert a 

civil action for injunctive relief exclusively in an appropriate State Court against a FBO, which 

allegedly commits such violation; and (g) no funds provided directly to institutions or 

organizations to provide services and administer programs will be expended for sectarian 
10

worship, instruction, or proselytizations (Personal Responsibility and Work Opportunity 

Reconciliation Act, 1996, Section 104).2

While these provisions apply to the passage of Welfare-to-Work in 1997, Community 

Services Block Grant in 1998, and Substance Abuse and Mental Health Services Act Block 

Grant and Drug Treatment Funds in 2000, the funding stream most immediately influenced by 

the CCI appears to be Temporary Assistance for Needy Families (TANF) under the provision of 

the PRWORA and administered by the USDHHS. While some Federal, state, and local 

government agencies had long-standing relationships with FBOs/CBOs prior to the passage of 

the PRWORA, the language of Section 104 and the President’s focus on, and support of, the 

initiative served to help expand some of the existing relationships and forge new ones. Faithand community-based organizations themselves have an established commitment to, and history 

of, serving those in need with and without government funding. How do these provisions impact 

other pieces of legislation such as the Workforce Investment Act (WIA) of 1998? 

The WIA is an effort by Congress to consolidate, coordinate, and improve employment, 

training, literacy, and vocational rehabilitation programs in the United States “in an effort to 

provide universal access to services to the satisfaction of job seekers and businesses” 

(Workforce Investment Act of 1998). “The overriding principles behind the legislation were to 

create a locally driven, State-coordinated System that: (a) improves individual choices; (b) 

reflects local conditions; (c) results in increased employment, retention and earnings of 

participants; and (d) results in less welfare dependency and a higher quality workforce” 

(USDOL, 2002, June 14). The System, as depicted in Figure 1, is part of the community and it 

partners with many local agencies and organizations. By partnering with others, it is possible to 

provide more comprehensive services and to be more effective in achieving goals by leveraging 

the collective resources of the community. 

The Act, implemented July 1, 2000, required local implementation by the Chief Elected 

Official of an area that appoints a local Workforce Investment Board (WIB) comprised primarily 

of local employers and mandated partners. The local WIB is responsible for achieving the 

performance standards through the One-Stop Career System (See Table 1). The state WIB, 

appointed by the state’s governor, coordinates activities of local WIBs. This System, according 

to the Act, will eliminate fragmentation among training, education, and employment programs. 

Partners enter into Memorandums of Understanding (MOUs) with WIBs in order to participate 

in the One-Stop Career System. These agencies and others wishing to provide services within 

the System must meet specific criteria and become approved as eligible providers. Those 

involved in the One-Stop Career System agree to be part of a performance-driven System that is 

accountable to job seekers and business customers, the state and local WIBs, and the USDOL, 

the Federal agency responsible for administering the WIA (See Table 2). 

Collaboration implies a willingness on the part of organizations to change the way 

services are delivered by: Jointly developing and agreeing to a set of common goals and 

directions, sharing responsibility for obtaining those goals, and working together to 

achieve those goals, using the expertise of each collaborator (Bruner, 1991, p. 6). 

 

2

 The Act refers to an individual receiving services as one who receives, applies for, or requests to apply for 

 assistance. 
11

The WIA is an attempt to change the way services are delivered on a local level by developing a 

set of common goals and creating partnerships with a variety of organizations in an effort to 

obtain these goals. 

Services take place through a One-Stop Career Center and satellites that provide 

integrated services through a seamless One-Stop Career System. According to the Federal OneStop Career Center System Request for Proposal (RFP), the One-Stop Career Center is the 

organizing vehicle for transforming the current fragmented array of employment and training 

programs into a coordinated information and service delivery system for individuals seeking 

first, new, or better jobs and for businesses seeking to build a world class workforce. The focus 

of such integration includes a system customized to the particular needs of the local labor market 

and connected to state and national systems (USDOL, Employment and Training Administration, 

1996). 

. . . this system is characterized by its emphasis on serving its customers. It 

should meet the needs of all customers by providing a common core of 

information and services, which are standard and universal at any access point ... 

The system should be easy to locate and use, be information-rich, and offer 

customers choice in where and how to get services. Finally, this system must be 

focused on constant improvement by gauging customer satisfaction with services 

and using the information to improve the system ... This system should be 

flexible, comprised of entities that are learning organizations with staff capable of 

leading and evolving. This flexible system is also high-tech where technology is 

used to give and expand high quality services to customers in a variety of manners 

and media (USDOL, Employment and Training Administration, 1996, p. 1). 

“A One-Stop Career Center is a place where local, State and Federal employment, 

education and training programs are brought together as a single network of public and private 

resources” (USDOL, 2002). This relationship is depicted in Figure 2. There are three levels of 

service provided through the One-Stop Career System: (a) core, (b) intensive, and (c) training. 

Elements of the services are included in Table 3. 

 In an effort to build upon the local partnerships and capacity created through the WIA, on 

April 17, 2002, the USDOL issued a Training and Employment Guidance Letter (No. 17-01) 

requesting: “...that states take actions to broaden the number of grassroots community-based 

organizations, including faith-based organizations, which partner with local WIBs and One-Stop 

Career Centers.” On July 1, 2002, the USDOL became the first Federal agency to award grants 

targeted specifically toward states and intermediary organizations. As a result, $17.5 million was 

awarded to 12 states and 29 organizations in an effort to link faith-based and grassroots 

community organizations to the One-Stop Career System. 

 

While it is too early to study the results of these initiatives by the USDOL, it is possible 

to identify the characteristics of relationships that exist between state and local WIBs with 

FBOs/CBOs. Research on the role of government funding of faith-based initiatives has been 

more extensive than specific research on the use of Federal funding under the PRWORA. Even 

less research has been conducted on the relationship between other pieces of legislation and 
12

faith- and community-based initiatives, such as the WIA. This is so primarily because 

researchers have focused their efforts on studying the pieces of legislation directly impacted by 

the provision. A very limited number of studies have included organizations funded through the 

WIA and in most cases only because they administer the Welfare-to-Work grant program (WtW) 

authorized through the Balanced Budget Act of 1997 as part of the TANF Block Grant, 

sometimes administered through the WIB. The WtW funding is administered by the USDOL, 

the same Federal agency that administers the WIA. 

The spirit of the CCI would suggest the provisions of Section 104 be applied to other 

Federal funding sources. The USGAO found by the end of the year 2001 there was no “... 

national picture of the extent to which States have responded to charitable choice provisions” 

with respect to the funding provided through TANF and substance abuse treatment and 

prevention programs under amendments to the Public Health Services Act in 2000 (U.S. GAO, 

2002, September). 

After an exhaustive review of the literature, it is evident the role of the WIA and the CCI 

has not been studied on a national level. Two studies are relevant, one by the California State 

Employment Development Department that has yet to be published, and the second by the Urban 

Institute. The California study evaluates the California Community and Faith-Based Initiative, 

partially funded through the WIA: The findings have yet to be released (Campbell, 2003). The 

Urban Institute, under contract with the USDOL, conducted a study of five communities for the 

purpose of providing a basic understanding of the extent to which FBOs are providing 

employment-related services. The authors state the findings are exploratory, but they aid in 

providing insight into the possible scale of activity by FBOs in the One-Stop Career System. 

The focus of this study included three inquiries: (a) how much Federal employment and training 

funding is going to FBOs, (b) what type of employment-related services are provided by FBOs, 

and (c) how many employment-related services are provided by FBOs and to whom. 

The value added to this study, in addition to interviewing staff of the WIBs, is the 

interviews conducted with congregations to determine the level of employment-related services 

they provide. While the study is limited by its focus on a small population, it does serve to 

provide information on the following: (a) the number of contracts with FBOs as a percentage of 

the WIB’s budget, (b) the scope of funding provided to FBOs (c) the source of funding, (d) 

services provided by FBOs, and (e) the types of organizations. The findings indicate: (a) there 

are great variations between the levels of relationships between various WIBs and FBOs with 

respect to the amount of money contracted to the FBOs by the WIB; (b) approximately half of 

the congregations interviewed did not provide employment services, while approximately onethird provided informal or episodic services; (c) the nature of the facility may influence the type 

and level of services provided; (d) the nature of services consisted of a comprehensive mix of 

employment, education, training, and support services for the majority of FBOs; (e) 

approximately half the programs offered by the FBOs received public funding for employment 

related services, but less than half received Federal funding; and (f) the majority of Federal 

funding came from the USDHUD with other funding from the USDOL and the USHHS 

(Kramer, 2002, pp. 10, 21). The authors conclude there are three additional questions to be 

posed: (a) what is the level of interest of faith-based organizations in expanding their services or 

receiving public funding under public rules; (b) what is the capacity of congregations or other 
13

faith-based community organizations to expand their services; and (c) what types of services are 

faith-based organizations best suited to deliver and how does the effectiveness of current FBO 

services and service models compare to current Federally funded programs providing such 

services (Kramer, 2002)? 

In addition to this study by the Urban Institute, other researchers such as Amy Sherman 

with the Hudson Institute, April Bender under contract with the State University of New York, 

and John Bartkowski and Helen Regis who have worked with various organizations, have 

authored several works focusing on faith and community collaborations within the context of 

TANF and to a lesser extent the WtW Block Grant Program (See the bibliography). Amy 

Sherman has completed what may be the most comprehensive research on the relationship to 

date between TANF and the CCI with respect to FBOs (See Sherman, 2002 and Sherman, 

1998b). Her study of the WIBs has been primarily limited to the WtW funding they administer, 

and not to the WIA. 

In addition, various religious organizations and agencies such as the American Muslim 

Council and Interfaith Funders have conducted surveys of their constituents. The American 

Muslim Council focused on the perceptions of FBOs with respect to the role they should play in 

government. Seventy-five percent of the respondents were in favor, in principle, of the initiative 

to allow FBOs of all religions to compete to provide social services using public funds and 83 

percent would favor using public funds to support the social service work of their organization 

provided the government agreed to not direct, advise, or restrict the character and mission of 

Muslim organizations (American Muslim Council, 2001, p. 4). 

A survey conducted by Chaves, The National Congregations of Churches, found 23 

percent of the key informants from congregations in the study were aware of the charitable 

choice provision in the legislation. In addition, 15 percent of congregations were so opposed to 

receiving public funds that they have policies forbidding working with the government in this 

capacity. The study suggests as many as 36 percent of the congregations are potentially willing 

to apply for government money to support human service programs (Chaves, 1999, pp. 6-7, 14). 

Interfaith Funders conducted a study of organizations involved in faith-based community 

organizing (FBCO) in 2001. There are 133 local organizations and they include 4,000 member 

institutions, of which 87 percent are religious congregations and 13 percent are composed of 

unions, public schools, and other CBOs (Interfaith Funders, 2001, p. 2). The report addresses 

three types of collaborations beyond the local FBCO: network, cross-network, and local area 

collaborations. Approximately 50 percent of their respondents reported engaging in economic 

and social service projects such as: (a) housing initiatives, (b) worker training, (c) worker 

cooperatives, (d) job cooperatives, (e) credit unions, (f) micro loans, (g) gang prevention, (h) 

homework centers, (i) welfare-to-work transition services, (j) immigrant naturalization 

assistance, (k) land trust funds, and others (Interfaith Funders, 2001, p. 19). The perspective of 

FBOs regarding the relationships their organizations should and/or can have with the government 

is, according to Smith and Sosin (2001), based on a relationship of faith to organizational culture 

and is complex. Coffin states that while conservatives are promoting and liberals opposing the 

CCI, liberal religious congregations are more likely to be interested in receiving public funds to 

provide faith-based services to the poor; however, race appears to be more significant than 
14

theological orientation: “Larger African-American congregations are the most likely to act on 

their interest and actually develop new partnerships” (Coffin, 1999, p. 20). 

There is a variety of non-empirical information from various WIBs and provider agencies 

identifying the value of faith and community organizations in their proposals for funding, 

published reports and documents and other work.3 As an example, the State of Colorado, 

Department of Labor and Employment, has published their findings on promising practices from 

Texas, which has a long history of relationships between TANF and FBOs, California, New 

Jersey, and Colorado, although there are no criteria utilized to define what constitutes a 

promising practice (Policy Studies, Inc., 2002). Sherman worked with a task force in Florida to 

offer recommendations on strategies for building collaborations between Florida’s faith 

communities and One-Stop Career Centers. In her report to the task force, she states: “In 

addition to resources, Florida’s faith community can help the State’s System meet the challenges 

of recruitment and retention” (Sherman, 2002, p.12). In addition, she states many faith-based 

nonprofits are providing job training, mentoring, literacy, drug rehabilitation, and transportation 

programs to the poor. Her report does not indicate if these services are currently provided 

through the One-Stop Career System and if these organizations are eligible providers of services. 

This review of the literature does not expand upon the research conducted on the 

relationship between TANF and the CCI. It may be impossible to draw a correlation between the 

research conducted on this relationship and the one between the CCI and the WIA for several 

reasons. First, agencies working with TANF have a longer history of working with the 

charitable choice provision, while the WIA, a separate piece of legislation apart from the 

PRWORA, was not implemented until 2000, and the USDOL did not formally request states to 

broaden the number of grassroots community-based partnerships with the WIBs until 2002. 

While the intent of President’s Bush’s Executive Order 13198 appears to have implications for 

all Federal agencies and funding, Federal agencies have formally applied the charitable choice 

provision to their agencies in different ways, some earlier than others. Some Federal agencies 

have been working with FBOs/CBOs effectively prior to and since the passage of the PRWORA. 

Sixty percent of the FBOs surveyed by the USGAO in 2001 reported contracting with the 

government before the passage of the charitable choice legislation (U.S. GAO, 2001, p. 13). 

Research conducted on the established relationships between agencies administering TANF and 

FBOs/CBOs may not adequately reflect the same characteristics of relationships just beginning 

or in their infancy with other Federal legislation. For example, research from the Hudson 

Institute and the USGAO indicates the number of faith-based providers of social services 

utilizing TANF funding has grown: Without additional data, it is unknown if this increase is 

mirrored with respect to WIA funding (See Sherman, 2002, and USGAO, 2002). 

Second, TANF and the WIA are two separate pieces of legislation, each with its own 

purpose, despite the fact they share many of the same goals for the population being served by 

TANF. Populations served by TANF and the WIA differ, as do some of the services offered, 

eligibility requirements, and perceptions regarding the purpose of the funding. In many states 

and counties, recipients of TANF are served by the WIB, but WIBs also serve a variety of other 

job seeker and employer customers. It is unknown whether these differences may make some 

FBOs/CBOs more or less likely to partner with government agencies administering TANF versus 

 

3

 See Van Stine, 2001, and Falgout, 2003 
15

the WIA. Thus, the existing relationships, issues, strategies, and outcomes may be very different 

between TANF and WIA agencies partnering with FBOs/CBOs. 

The California State Employment Development Department is in the process of 

evaluating their Community and Faith-Based Initiative, and they are identifying the services 

provided; however, their report is not ready for dissemination. Sherman’s work identifies 40 

distinct services of FBOs funded through TANF and WtW (Sherman, 2002a). Bender’s study, 

while focusing on the relationship between TANF and the CCI, analyzed the comprehensive 

services provided by FBOs/CBOs and the variety of funding streams utilized to achieve the 

outcomes required by the state and local agencies administering TANF programs. In her study, 

state level respondents identified 30 different services provided through 11 different funding 

streams including TANF and the Maintenance of Effort required: These funds also included 

money from the WIA. Local level respondents identified 40 different services funded through 

14 different funding streams (Bender, 2003). There is also the need to analyze the difference 

between services funded through the legislation versus those offered without the assistance of 

government funding. Given the three categories of service for WIA job seekers, FBOs/CBOs 

may be able to provide additional support services that cannot be funded by the WIA. It would 

be helpful to know the difference between services offered by FBOs/CBOs through government 

contracts, specifically those funded through the WIA, versus the ones offered through other 

funding sources.4

Third, while there appears to be a lack of awareness by FBOs/CBOs of the potential to 

access federal funding, there also appears to be more of an awareness regarding services and 

funding traditionally linked with welfare reform and the CCI, as opposed to workforce 

development and the services provided through the WIBs. While research has documented the 

social services provided by FBOs/CBOs, little has been done to document the services provided 

by organizations typically associated with preparing someone for a job, helping them retain the 

job, building a career pathway, and providing services directly to employers. This awareness 

may have influenced the number and type of relationships established with TANF versus WIA 

administered agencies. For example, research indicates many FBOs offer services on an 

informal basis, never reaching the definition of what could be considered a relationship and/or 

something identified through an MOU and/or contract (Colorado Partnerships, 2002). It may be 

impossible or inappropriate to draw conclusions from the prevalence of FBOs/CBOs that provide 

welfare services and their level of funding to those providing workforce services through MOUs 

and/or contracts from agencies administering the WIA. 

In their survey, the USGAO found FBOs’ lack awareness of funding opportunities, have 

limited administrative and financial capacity, lack experience with government contracting, and 

hold beliefs about the separation of church and State, and that these issues constrain the ability of 

small FBOs to contract with the government (U.S. GAO, 2001). These findings are consistent 

with research conducted by the State University of New York, University Center for Academic 

and Workforce Development, of state and local TANF agencies. Approximately 71 percent of 

those involved in the study stated lack of awareness was the greatest barrier to the establishment 

 

4

 Maintenance of Effort is the Federally mandated level of spending that states are required to continue to provide in 

order to qualify in return for the receipt of TANF. 
16

of relationships between FBOs/CBOs and the government with respect to TANF (Bender, 2002b, 

p. 42). 

Fourth, the oversight of TANF on a local level rests ultimately within the hands of 

government. The WIBs, although appointed through the county elected official, are primarily 

composed of employers, and they have the oversight role on a local and state level. It is 

unknown whether the level of authority resting with local employers may influence the 

prevalence and/or level of relationships government agencies have with FBOs/CBOs. 

 Fifth, the level of funding through TANF and the WIA available for, and contracted to, 

FBOs/CBO initiatives may differ, and the extent to which funding either predicts or influences 

the relationship is unknown. There may be more or less money available from one Federal 

funding stream to collaborate with other agencies. Other partnerships may not require more than 

sharing the same mission, population, and services, each of which is different for TANF and 

WIA funded agencies. It may not be accurate to suggest the research on the funding provided to 

FBOs/CBOs under PRWORA can shed light on the funding provided to the same organizations 

under the WIA. 

A survey conducted by the USGAO found contracts with faith-based organizations 

accounted for 8 percent of the one billion dollars in Federal and state TANF funds spent by state 

governments on contracts with non-governmental entities in 2001, with contracting occurring at 

the state level in 24 states, at the local level in 5 states, and at both levels in 20 other states and 

the District of Columbia (USGAO, 2002a, p. 8). Sherman’s study adds that ten additional states 

are developing government-faith collaborations (Sherman, 2002, p. 5). For example, Coffin 

believes devolution has been a catalyst for the CCI, possibly irrespective of whether initiatives 

are funded by TANF or the WIA (Coffin, 1999). 

There is increasingly less Federal money and more emphasis on local flexibility, control 

and responsibility. Bender, in her study of the implementation of TANF and the WIA, found 

many One-Stop Career Systems did not have the funding to provide services to existing 

customers, let alone trying to expand to different populations and providers: “There isn’t enough 

money in the system to serve the people we’ve got” (Bender, 2001, p. 184). This frustration 

would be echoed by informants and survey respondents in this study. Trying to spread funding 

too thinly across providers could threaten the capacity of providers and the System. It appears 

these factors may influence a more visible and/or renewed role for FBOs/CBOs across funding 

streams. 

Finally, researchers have not fully identified and described the criteria necessary for a 

relationship between FBOs/CBOs with government to be considered successful, and whether 

these criteria differ from relationships with other organizations. Without these criteria, it is 

difficult to do more than study the characteristics of the relationship. The Colorado study 

identifies strategies that can be used to develop successful partnerships, but they do not identify 

the criteria necessary to describe the elements of a successful partnership. The work by the 

California Employment Development Department may provide additional information necessary 

to identify such criteria. 
17

It may be necessary to create some of the same baseline data collected on the relationship 

between the CCI and TANF for the relationship between the CCI and the WIA in order to 

understand if the research specific to TANF can provide insight into the relationship between the 

WIA and the CCI. By studying TANF and the WIA funded agencies in isolation, it is difficult to 

conclude what role FBOs/CBOs have in developing and leveraging resources in their community 

to better serve their community. A more comprehensive environmental scan of the entire 

community across Federal funding streams may be necessary in order to fully understand the 

benefit of relationships between FBOs/CBOs and the government. The results from this study 

may help to create some of the baseline data needed to understand how the government is 

working with FBOs/CBOs across the nation. 

Methodology 

 This national study identifies some of the characteristics of the relationships that exist 

between state and local WIBs with FBOs/CBOs. Each state and local WIB was surveyed in the 

winter of 2002 and extending into 2003, with interviews and a focus group taking place in the 

spring of that year (n=643). The following characteristics of these relationships are analyzed in 

this study: (a) prevalence of relationships between WIBs and FBO/CBOs, (b) types of 

FBOs/CBOs working with local WIBs, (c) types of services provided by FBOs/CBOs, (d) 

funding provided to FBOs/CBOs through the WIBs, (e) prevalence of Memorandums of 

Understanding with FBOs/CBOs, (f) how WIBs determine the services provided by FBO/CBOs, 

(g) prevalence of FBO/CBOs’ co-location at One-Stop Career Centers and satellites, (h) 

effectiveness of FBOs/CBOs at meeting the WIA performance standards, and (i) the value WIBs 

place on working with faith- and community-based initiatives.5

Both quantitative and qualitative methods were utilized. The primary source of data 

collection was a survey sent to each state and local WIB. The survey questions consisted of: (a) 

open-ended questions requiring identification and explanation, (b) yes/no response, and (c) use 

of a ten point Likert scale. Respondents were asked to attach copies of their MOUs and any 

other information helping to explain the relationship existing between their One-Stop Career 

System and FBOs/CBOs. The survey was e-mailed and faxed, and a hard copy was mailed to 

each state and local WIB. Each state and local WIB was contacted by telephone a minimum of 

once to ensure they received a copy of the survey, and to request they complete and return the 

survey. Approximately five percent of respondents answered the survey over the telephone. 

In addition to the survey, structured interviews were conducted with approximately 10 

percent of respondents to help clarify existing information or secure missing information from 

their surveys. A focus group was facilitated to validate information from local WIB directors in 

an effort to clarify and validate information from the survey. Primary sources such as MOUs, 

procedures, press releases, and other documents shared by respondents were analyzed. An openended question on the survey and during the interviews provided the respondents and informants 

with the opportunity to provide other relevant information. 

 

5 This report focuses on data collected from the respondents that have relationships with FBOs/CBOs. Data 

 collected from the respondents that do not work with FBOs/CBOs will be evaluated in the future. 
18

Where relevant, data was analyzed with respect to the following five categories of focus: 

(a) responses from the total population of State and local WIBs, (b) state level WIB responses, 

(c) local WIB responses, (d) local WIB responses from metropolitan counties, and (e) local WIB 

responses from non-metropolitan counties. The total population consists of each state and local 

WIB including the 50 states, the District of Columbia, Guam, Puerto Rico, Marshall Islands, 

North Marianna, and America Samoa (n=643).6 There was a 37 percent response rate (n=238) 

(See Table 4). Responses were received from 92 percent of the states with representation from 

each part of the nation (n=46/50) (See Figure 3). The response rate for state level WIBs was 40 

percent (n=20/50). Approximately 50 percent of the state WIBs that received funding from the 

USDOL in an effort to link faith-based and grassroots community organizations to the One-Stop 

Career System responded to the survey (n=6/12) (See Table 5). The response rate for locals 

WIBs was 37 percent (n=218/593). The response rate for local WIBs from metropolitan counties 

was 38 percent (n=153/400). The response rate for local WIBs from non-metropolitan counties 

was 34 percent (n=65/193). 

The following percentages represent the composition of the total population that 

responded: approximately 8 percent were from state WIBs (n=20); 92 percent were from local 

WIBs (n=218); approximately 64 percent of the local WIBs were from metropolitan counties 

(n=153); and approximately 27 percent of the local WIBs were from non-metropolitan counties 

(n=65).7

 Approximately 9 percent of the state level WIB responses were the only responses from 

the state (n=4). Approximately 57 percent of the states that responded had responses only from 

local WIBs. Approximately 35 percent of the states that responded had responses from both 

state and local WIBs (n=16). The breakdown of the number of responses by state and 

metropolitan and non-metropolitan counties can be found in Table 5.8

There are at least three limitations to this study. First, data collected from the survey took 

longer than expected, leaving limited time for interviews: Survey respondents were contacted a 

minimum of four times in an effort to secure a response (n=643). In an effort to shorten the 

survey instrument and decrease the response time to something appealing to respondents, some 

of the questions that would have yielded further clarity were omitted. For example, respondents 

were asked whether they had relationships with FBOs/CBOs, and to name the organizations for 

which they have relationships. The type and level of relationship, beyond the existence of a 

MOU or contract and type of services provided by each FBO/CBO, was not requested. 

Second, only 53 percent of the respondents rated the effectiveness of FBOs/CBOs at 

meeting the WIA performance standards (n=85). Some of the reasons for not answering this 

question included: (a) unwillingness to rate or discomfort with rating these organizations; (b) 

some organizations are not required to meet standards as they are reimbursed for allowable costs; 

 

6

 Delaware, South Dakota, West Virginia, Wyoming, Guam, Puerto Rico, Marshall Islands, North Marianna, and 

 America Samoa did not respond to the survey. 

7

 There were three anonymous surveys that were not included in the analysis of the study given there was no way to 

 contact the respondents to clarify their response. 

8 The 1999 standards from the U.S. Office of Management and Budget were used to define Metropolitan counties: 

 one city with 50,000 or more inhabitants, or a Census Bureau-defined urbanized area (of at least 50,000 

 inhabitants and a total metropolitan population of at least 100,000 or 75,000 in New England) (U.S. Census, 

 2003). 
19

(c) there was no way to analyze outcomes achieved by FBOs/CBOs separate from all providers 

of services; and (d) they had not worked with them long or closely enough to rate their 

effectiveness. These reasons are consistent with findings by the USGAO in their study related to 

TANF in 2001 (USGAO, 2002b, pp. 22-23). 

Third, respondents were asked to list the FBOs/CBOs they work with, irrespective of 

whether they were a national organization or local initiative and faith or community-based 

organization. As Coffin describes in his research, “Vendors may voluntarily choose to report 

their religious or charitable choice status” therefore, not even the survey respondent may know 

the status of the organizations they are working with (1999, p. 10). While it was possible to 

identify national organizations, it was not always possible to identify the difference between 

FBOs and CBOs, those operating under a separate 501(c) (3) from their religious affiliation, and 

those with sectarian and/or non-sectarian purposes. 

In order to reduce the limitations of this study, the survey would have been much longer 

and more demanding of respondents. The initial draft consisted of several charts and matrices to 

be completed in addition to open-ended questions. The draft would have required respondents 

an average of 30 to 45 minutes to complete. The instrument used was estimated to require 15 to 

20 minutes to complete and return contingent upon the extent of the relationship. Given the 

purpose of the study was to provide a national perspective that would help provide policy makers 

with the first national blush of this information and to help frame future studies, the density of 

the data collected was intentionally less than it could have been, in an effort to take as broad a 

snapshot as possible and in a reasonable amount of time. As it was, the human resources 

dedicated to achieving a 37 percent response rate was immense and intense. 

The results of this study can serve to illuminate the path other researchers will take in an 

effort to fully study the relationship between the WIA and the CCI on a national level. 

Information gleaned from respondents and informants should help the USDOL and elected 

representatives develop a context for future research. 
20

Section II 

Overview of Findings and Implications 

Introduction 

 

The relationship between the WIA and the CCI has not been identified and explored on a 

national level. There appear to be at least five separate perspectives from which to study this 

relationship: (a) WIBs, (b) FBOs, (c) CBOs, (d) government and (e) customers. As a subset of 

these groups, researchers could study the perspective of organizations that receive money from 

the WIB, those providing services without reimbursement and the WIBs providing money to 

these organizations, as well as those that do not provide money, but benefit from informal 

services provided by FBOs/CBOs. In addition, researchers can study these perspectives in 

isolation or in tandem with one another. For example, the Urban Institute’s study conducted 

interviews with the local WIBs in addition to those FBOs/CBOs they contracted with and local 

FBOs that did not have contracts with the WIB (Kramer, 2002). These relationships may also be 

studied from a local, state, and/or national perspective. 

This study focuses on the perspective of state and local WIBs in an effort to identify how 

the relationship between them and FBOs/CBO help achieve the performance standards of the 

WIA. The relationship between the WIA and the CCI, for the purpose of this study, is defined in 

the broadest sense and within the spirit of the charitable choice provision and Executive Order 

13198 that would expand the provisions of Section 104 of the PRWORA to other Federal laws 

and funding. The relationships being studied may have no informal or formal connection to the 

funding released through the CCF nor formal CCIs of Federal and/or state and local governments 

(See Table 6). For the purpose of this study, a relationship between the WIA and the CCI is 

defined as any informal or formal arrangement between the WIB and FBOs and/or CBOs for the 

purpose of providing services to job seekers and/or employers in the community in an effort to 

meet the performance standards of the Act. The relationships described by respondents and 

informants did not always include formal MOUs or contracts. 

The following characteristics of these relationships are analyzed in this study: (a) 

prevalence of relationships between WIBs and FBO/CBOs, (b) types of FBOs/CBOs working 

with local WIBs, (c) types of services provided by FBOs/CBOs, (d) funding provided to 

FBOs/CBOs through the WIBs, (e) prevalence of Memorandums of Understanding with 

FBOs/CBOs, (f) how WIBs determine the services provided by FBO/CBOs, (g) prevalence of 

FBO/CBOs’ co-location at One-Stop Career Centers and satellites, (h) effectiveness of 

FBOs/CBOs at meeting the WIA performance standards, and (i) the value WIBs place on 

working with faith- and community-based initiatives. 

Prevalence of Relationships Between Workforce Investment Boards and Faith- and 

Community-Based Organizations 

The WIA requires the One-Stop Career System to create a variety of relationships with 

other agencies in an effort to provide a comprehensive, integrated, universal System that will 

meet the needs of job seeker and employer customers. The Act requires specific partners to be 

part of the WIB. In addition to required partners, the Act identifies additional partners. Table 7 
21

includes a list of required and additional partners. According to Section 117 of the WIA, the 

local WIB composition must include representatives of CBOs, including organizations 

representing individuals with disabilities and veterans, for a local area in which these 

organizations are present (Workforce Investment Act of 1998). There is nothing in the Act 

specifying other types of CBOs that may be included or the role of FBOs. Interviews with 

informants revealed WIBs have been contracting with CBOs to provide services primarily to 

youth since the implementation of the Act. Some of the WIBs retaining staff from the Job 

Training Partnership Act, the Act replaced by the WIA, stated they have had contractual 

relationships with CBOs for several years prior to the implementation of the WIA. The full 

extent to which WIBs work with CBOs, and to a greater extent FBOs, is unknown, especially 

within the context of the CCI. This study attempts to identify and describe some of these 

relationships. 

State and local WIBs were asked whether they work with FBOs/CBOs. There was a 100 

percent response rate to this question. Of the total population surveyed, 67 percent stated they 

work with FBOs/CBOs (n=159). Of the state WIBs that responded, 80 percent stated they work 

with FBOs/CBOs (n=16). Of the local WIBs that responded, 66 percent stated they work with 

FBOs/CBOs (n=143). Of the local WIBs that responded from metropolitan counties, 71 percent 

stated they work with FBOs/CBOs (n=109). Of the local WIBs that responded from nonmetropolitan counties, 52 percent stated they work with FBOs/CBOs (n=34). 

Less than two percent of the respondents that work with FBOs/CBOs stated they had 

negative feelings and/or experiences with, or reservations about, working with these 

organizations. The majority of these responses fell into one of two categories. One category of 

response included comments from respondents who believe the services of the One-Stop Career 

System should be left to them, the experts, and not FBOs/CBOs. Another category of response 

included comments from respondents that have found these organizations do not have the 

capacity to receive Federal funding and implement programs: “[name of organization] has 

proven more challenging due to the need of the community-based organizations to develop 

increased capacity to implement programs and coordinated funding.” Another respondents adds: 

... However, some that have not had much experience with contract services or 

dealing with government agencies have great difficulties with data entry, 

reporting, financial management, and contract administration in general. By 

contrast, more experienced CBOs generally do very well even administratively. 

“Don’t use the F word. We can work with CBOs, but we are scared to death of working 

with faith-based organizations.” When this informant was asked why she felt this way she 

explained: 

We are afraid that they are going to take our money. Are they going to take our 

money? We don’t know how to work with churches. It is the whole church and 

state thing, trying to keep them separate. What if something goes wrong and there 

is a conflict? The WIB gets the black eye. The church goes on with their 

mission. We struggle to regain our position in the community. We can’t afford 

that. 
22

These feelings were not prevalent among respondents; however, those that expressed 

these sentiments were worried about the perceived increasing role of FBOs in their local OneStop Career System. 

 “Money seems to be the driving issue for WIA – everyone thinks that WIA can 

fund their initiative. Because of the war efforts and the state of the economy, 

faith/community based organizations are experiencing a decrease in donations and 

fierce competition for government and private funds. With our federal mandated 

WIA funds, it is difficult to fund any of the agencies, but we are trying to work 

with any agency that is willing to partner with us in our One-Stops.” 

These respondents do raise an issue inherent in working with the One-Stop Career 

System. Most of the partners are responsible for achieving the performance standards; it appears 

very few are reimbursed for costs incurred without some performance measure tied to their 

funding. If the partner does not achieve their performance standards their contract could be 

terminated or they may not receive another contract. The WIB must then rely on other partners 

to achieve additional numbers or risk not meeting their performance standards. Underperforming WIBs may receive less funding, secure another operator for the One-Stop Career 

Center, change the staff of the WIB, or in some states be placed under corrective action if they 

do not achieve their performance standards. It appears the majority of respondents have positive 

relationships with a variety of FBOs/CBOs. 

Types of Faith- and Community-Based Organizations Working With Local Workforce 

Investment Boards 

 

 In order to provide all of the services required of the WIA and to meet the diverse needs 

of customers within the One-Stop Career System, the WIB must access and integrate a variety of 

services across the community. This study attempts to identify which FBOs/CBOs WIBs are 

working with. Respondents were asked to provide the names of the FBOs/CBOs they work with 

in their local One-Stop Career System. The organizations range from those which are nationally 

affiliated to one-of-a-kind organizations found only in their local communities. Of the 

respondents that work with FBOs/CBOs, 65 percent responded to this question (n=104). 

Seventy-three percent of the local WIBs responded (n=104). Seventy-five percent of the 

metropolitan counties responded (n=82) and 65 percent of the non-metropolitan counties 

responded (n=22). The state WIB data was not included in the analysis given the question was 

targeted to local WIBs. 

 

The nationally affiliated organizations with the most frequency of response, in 

descending order are: (a) Catholic Charities (n=22); (b) Community Action (n=16); and (c) 

Goodwill (n=15.) The local WIBs were approximately three times as likely to work with 

organizations with a local basis of operation, as they were organizations with a national 

association. The question did not require respondents to identify the organizations by 

FBOs/CBOs, nor into those providing sectarian, non-sectarian or both types of activities. It 

appears an increasing number of faith-based organizations are establishing separate entities 

under the 501(c) (3) status. Unless identified by their name, it would be difficult to associate 
23

their religious affiliation, if any, with their organization. Therefore, it is possible that 

community-based organizations could have a religious nature and/or affiliation unknown to the 

WIB. It may not be enough to distinguish between faith and community-based organizations by 

their name alone: Other criteria might need to be considered. 

Sixty-seven percent of the respondents have relationships with 1022 FBOs/CBOs in their 

One-Stop Career System. A complete list of all organizations can be found in Table 8. The 

Table is organized to demonstrate the variety and number of organizations working with each 

local WIB by metropolitan and non-metropolitan areas. Workforce Investment Boards in 

metropolitan areas work with an average of approximately eleven FBOs/CBOs whereas those in 

non-metropolitan areas work with an average of approximately five of these organizations. The 

average number of relationships is only one variable. The types of services provided by 

FBOs/CBOs, the scope of those services, and the value they bring to the One-Stop Career 

System are other important elements. 

Types of Services Provided by Faith- and Community-Based Organizations 

 Local WIBs have flexibility in determining the service constellation they will fund for 

each customer through the WIA. Given WIA funding is not intended to cover the entire cost of 

the System, it becomes necessary to rely on partners and other community-based organizations to 

provide the additional services necessary for the customer to be successful and for the WIB to 

meet their performance standards. It appears important for the WIB to find ways to strengthen 

their capacity through existing community resources according to respondents: “They fill the 

‘gap’ in underserved needs that One-Stops cannot so that individuals can get to work more 

quickly, i.e., transportation, child care, clothing.” 

State and local WIBs were asked to identify the types of services provided by 

FBOs/CBOs in conjunction with their One-Stop Career System. The survey included an openended question for respondents to list the services provided by CBOs/FBOs they work with. 

Respondents referenced services provided to a wide range of population groups as identified in 

Table 9. Of the number of respondents that stated they work with FBOs/CBOs, there was a 92 

percent response rate to this question (n=147) with 88 percent of the state WIBs responding 

(n=14). Of the 93 percent of the local WIBs that responded (n=133), 95 of the metropolitan 

counties responded (n=104) and 85 percent of from non-metropolitan counties responded (n=29). 

Youth appear to be the largest group for which services are provided by FBOs/CBOs. Of the 

respondents that work with FBOs/CBOs, 43 percent of the respondents from metropolitan 

counties (n=45), and 52 percent from non-metropolitan counties (n=15), state these organizations 

provide youth services to their customers. 

 The services most frequently identified by respondents as being provided by FBOs/CBOs 

involve job training and placement. Fifty-three percent of the local WIBs that work with 

FBOs/CBOs receive job-training services from these organizations (n=71) and 40 percent 

receive job placement and employment services (n=53). The frequency of job training and job 

placement and employment services in metro- and non-metropolitan counties is similar. The 

complete list of services provided by FBOs/CBOs and their frequency is included in Table 10. 

Given many of the services reported by respondents may be arranged through the MOU or RFP 
24

process, it is possible FBOs/CBOs provide other services free of charge and/or outside the 

relationship with the WIB and therefore are not accounted for in this study. This listing should 

not be considered a comprehensive list of services provided by FBOs/CBOs, but a list of services 

provided through and/or in tandem with the One-Stop Career System to help the System achieve 

its goals. 

In keeping with the intent of the Act, the majority of services provided by FBOs/CBOs 

fall within the purview of workforce services, whether: (a) job training, (b) readiness/lifestyle 

skills, including assessment, literacy and subsequent educational services, (c) placement; or (d) 

hiring. In addition, the majority of the FBOs/CBOs cited by the WIBs also provide social and 

human services. The types of these services vary between WIBs, and may include offerings as 

diverse as: (a) emergency housing, (b) refugee resettlement, and (c) immigrant acclimation. 

Some respondents and informants alluded to an additional dimension of the services 

provided by FBOs/CBOs, but only one respondent identified it as being a “spiritual” component 

of what they bring to the relationship “ ... because of the multiple services and the community 

support systems they provide, worker training, mentoring, personal growth, spiritual growth and 

in most cases, these organizations lead by example.” Respondents provided more insight into 

this and other dimensions of services provided by these organizations when they described the 

funding provided to them and the value these organizations bring to the One-Stop Career 

System. 

Funding Provided to Faith- and Community-Based Organizations Through the 

Workforce Investment Board 

The local WIBs rely on more than just funding from the WIA to sustain their System and 

capacity. The System relies heavily on the resources of local partners. Since the WIA provides 

for a comprehensive, integrated service delivery System that is responsible at the local level for 

meeting the needs of job seeker and business customers, it also requires a variety of local 

agencies to contribute their federal, state, and local dollars to the System. This survey explored 

the prevalence of funding for FBOs/CBOs from two Federal funding streams, the WIA and 

TANF. Under Federal legislation, TANF administrative agencies are not required to be 

members of the local WIB however, given the WIA requires a focus on those job seeker 

customers most in need, many recipients receive services funded by TANF and may be eligible 

for the same or additional services provided by local WIBs: “It provides agencies with the 

opportunity to identify and reach out to those ‘most in need’.” Some of the services provided 

through the WIA and TANF have different outcomes. There are differences between eligibility 

requirements, services that can be provided, and sometimes the duration of services. 

There are two primary reasons for focusing on these funding streams. First, local WIBs 

report working with FBOs/CBOs, but it was unknown how many of them provide funding to 

these organizations. Many of the relationships described by respondents and informants are 

informal and do not consist of the exchange of funds for services. Some relationships consist of 

including FBOs/CBOs in their network and/or making referrals to the services they provide, but 

the outcomes they achieve through these informal relationships cannot always be included as 

part of the local WIB’s performance standards. 
25

In order to credit the outcomes of the job seeker to the System, he or she must be 

registered in the System. A portion of those registered are required to achieve the performance 

standards. This is a two-edged sword as one informant explained: 

If you put them in the denominator [referring to the job seeker] you want to be 

able to put them in the numerator. You are taking a risk that they will indeed be 

successful and become part of the numerator. If they don’t, they work against you 

in meeting your performance standards. 

Core services, or self-help services, are offered to job seekers prior to registering them in 

the System. It appears the additional services provided by FBOs/CBOs prior to registration may 

be effective in helping individuals and their families achieve their personal, educational, and 

employment related goals: “The faith- and community-based organizations are often able to 

work on barriers which the WIA funds are not.” They may not be reimbursed by the WIB for 

these services. Since these job seekers are not registered in the System, it is very difficult for the 

System to track their progress. To what extent do WIBs fund these and other services provided 

by FBOs/CBOs? 

This survey became a vehicle by which to identify the number of WIBs that have formal 

relationships with FBOs/CBOs based on MOUs specifying their contribution to the performance 

standards required by the Act, and to a lesser extent the informal roles FBOs/CBOs have in 

providing services and achieving outcomes that may not be captured by the One-Stop Career 

System. If the local WIB provides funding to FBOs and/or CBOs, they would be in a position to 

identify, describe, and verify the more formal elements of the relationships. 

Second, if the local WIBs utilize TANF funding as part of the network of resources that 

sustains their One-Stop Career System, it would be beneficial to recognize the connection 

between the WIA and TANF with respect to funding FBOs/CBOs. Given the majority of 

research conducted on the CCI has focused on TANF, it may be possible, now, with additional 

research, to draw correlations between the role of TANF and the CCI with the WIA and CCI in 

the future. It does appear some WIBs utilize TANF funding to meet the needs of those eligible: 

“Without their services [referring to FBOs/CBOs], we could not reach out to the homeless 

community, assist all of the low and moderate low income persons seeking employment, and 

provide welfare assistance to TANF participants.” There does appear to be a connection 

between funding provided by TANF and the WIA with respect to the role of FBOs/CBOs in the 

System. Another respondent adds: “Those CBOs are instrumental in the recruitment, outreach, 

marketing, and delivery of WIA/TANF services to inner city youth and adults. They play a major 

role in our Workforce Development System.” 

Respondents were asked to identify whether they fund FBOs/CBOs through the WIA, 

TANF, or both sources of funding. Ninety-two percent of those working with FBOs/CBOs 

answered this question (n=147). Eighty-one percent of the state WIBs responded (n=13) and 94 

percent of the local WIBs responded to this question (n=134). Of the local WIBs that responded, 

95 percent of the metropolitan counties responded (n=103) and 91 percent of the nonmetropolitan counties responded (n=31). Of the respondents that work with FBOs/CBOs, 64 
26

percent fund them with the WIA (n=94), 48 percent with TANF (n=71), 43 percent with both the 

WIA and TANF (n=63), and 48 percent using other funding sources (n=71). Other funding 

sources included: (a) Welfare-to-Work, (b) volunteers, (c) Community Services Block Grant, (d) 

USDOL faith based grant, (e) USDOL Employment and Training Administration, (f) adult 

education funding from state education agencies, (g) Refugee Employment Training Program, 

(h) private donations, (i) governor’s general fund, (j) USDOL, (k) National Education Grant, (l) 

Child Care and Development Fund (CCDF), (m) state outreach grant, (n) Wagner-Peyser, (o) 

governor’s general fund, (p) Consolidated Act of 1988, (q) foundations, (r) Displaced 

Homemaker grant.9

 Less than one percent of the respondents stated they were obligated to fund 

FBOs/CBOs: “We statutorily [have] to spend certain levels of funds with faith- and communitybased organizations that provide after school activities. Otherwise, we would probably not 

contract with some of the agencies that we do business with.” 

Fifty-four percent of the state WIBs use WIA funding to fund FBOs/CBOs (n=7); 54 

percent use TANF (n=7); 46 percent use both WIA and TANF (n=6); and 61 percent use other 

sources of funding (n=8). Sixty-five percent of the local WIBs use WIA funding to fund 

FBOs/CBOs (n=87); 48 percent use TANF (n=64); 43 percent use both WIA and TANF (n=57); 

and 47 percent use other sources of funding (n=63). Sixty-one percent of the local WIBs located 

in metropolitan counties use WIA funding to fund FBOs/CBOs (n=63); 49 percent us TANF 

funding (n=50); 44 percent use both (n=45); and 52 percent use other funding (n=54). Seventyseven percent of the local WIBs located in non-metropolitan counties use WIA funding to fund 

FBOs/CBOs (n=24); 45 percent use TANF (n=14); 39 percent use both (n=12); and 29 percent 

use other funding (n=9). If WIBs provide funding to other organizations, it appears there must 

be a MOU and/or contract with the organization that details the specifics of the relationship. 

Prevalence of Memorandums of Understanding with Faith- and Community-Based 

Organizations 

 The WIA requires the WIB, with the agreement of the Chief Elected Official, to develop 

a MOU between the local WIB and the One-Stop Career Center partners regarding the operation 

of the One-Stop Career System. The MOU must contain the following: (a) the services to be 

provided through the System; (b) how the costs of the services and the operating costs of the 

System will be funded; (c) methods for referral of individuals between the One-Stop Career 

Center operator and the partners for appropriate services and activities; and (d) the duration of 

the memorandum and the procedures for amending the memorandum during the term of the 

memorandum (Workforce Investment Act of 1998). Memorandums of Understanding allow the 

One-Stop Career System to ensure that necessary operating costs and services will be provided 

by the partners. The partners in turn know what they can expect to receive. Relationships based 

in part or in their entirety on MOUs are more formal than those that may merely include a verbal 

agreement to make referrals or exchange information, for example. The existence of a MOU 

does not imply that a relationship is more positive or beneficial than informal relationships or 

those negotiated through a contract. 

 

9 None of the respondents indicated they use the MOE from the TANF to fund services provided by FBOs and 

 CBOs, however, the category of TANF could be perceived by respondents to include MOE funds.
27

 The formality of the relationships defined by a MOU may restrict and/or threaten 

relationships established prior to the implementation of the WIA. Bender’s study of the 

implementation of the WIA and TANF in rural areas found several counties in the study resisted 

the MOU process: 

It appears the Workforce Investment Act would make this system less fluid and 

formal by requiring provider agencies to stipulate the elements of their 

relationship in a time-limited Memorandum of Understanding: County A was in 

the process of doing this, whereas County B was avidly preserving their existing 

relationships and success despite the implementation of the one size fits all 

Workforce Investment Act approach (Bender, 2001). 

Some partners who had a history of positive relationships were now required to 

renegotiate those relationships with new criteria, often, according to informants of Bender’s 

study, favoring the WIB with too much reliance on funding from partners. In the process of 

renegotiations sometimes old wounds surfaced and embers rekindled. Informants in the focus 

group conducted to provide clarity on this, among other issues, shared the MOU process is 

primarily utilized to ensure operating costs and procedures are clarified with the partners. 

Funding for the exchange of services is done primarily through the RFP process and the 

contracts that result. 

According to informants, contracts are just as formal and binding as MOUs. The MOU 

is, however, the mechanism by which partners enter into a relationship with the WIBs. For this 

reason, it is beneficial to know how many WIBs have MOUs with FBOs/CBOs. For example, 

informants explained they could have both MOUs and contracts with partners and throughout the 

course of the fiscal year the MOU can be amended and new contracts awarded, changing the 

composition of the relationship. It may be necessary to study these issues from the perspective 

of FBOs/CBOs to determine the likelihood of them being in a position to enter into a MOU and 

the value they place on relationships based on a MOU, contract, or no formal mechanism for the 

provision of services. Data from this study did not reveal whether WIBs or FBOs/CBOs initiated 

the relationship. 

 Respondents were asked whether or not they had MOUs with FBOs/CBOs. Ninety-two 

percent of the respondents working with FBOs/CBOs answered this question (n=147). Eight-one 

percent of the state WIBs responded (n=13) and 94 percent of the local WIBs responded 

(n=134). Ninety-five percent of the metropolitan counties responded (n=103) and 91 percent of 

the non-metropolitan counties responded (n=31). Of the number of WIBs working with 

FBOs/CBOs, 37 percent of the WIBs have MOUs with these organizations (n=55). Of the 

number of WIBs working with FBOs/CBOs, 64 percent use funding from the WIA to fund 

services provided by these organizations (n=94). At least 33 percent of the WIBs that have 

relationships with FBOs/CBOs have some type of contractual agreement with them based on a 

competitive RFP process (n=49). In addition, at least 41 percent of the WIBs having MOUs with 

FBOs/CBOs also have engaged in the RFP process with these organizations (n=61). 

Approximately 52 percent of the WIBs providing WIA funding to FBOs/CBOs do not have 

MOUs with these agencies (n=48). Approximately 9 percent of the WIBs utilizing WIA funding 
28

do not have MOUs or contracts and have not used the RFP process with these organizations 

(n=9). Twenty-one percent of the respondents have MOUs with FBOs/CBOs but do not provide 

WIA funding to them (n=18). 

Twenty-three percent of the state level WIBs have MOUs with FBOs/CBOs 

(n=3) and 54 percent of them utilize funding from the WIA to fund services provided by these 

organizations (n=7). Thirty-nine percent of the local WIBs have MOUs with FBOs/CBOs 

(n=52), while 65 percent of them use funding from the WIA to fund services provided by these 

organizations (n=87). In metropolitan counties, 41 percent of the local WIBs have MOUs with 

FBOs/CBOs (n=42) while 61 percent use funding from the WIA to fund services provided by 

these organizations (n=63). In non-metropolitan counties, 32 percent of the local WIBs have 

MOUs with FBOs/CBOs (n=10) while 77 percent use funding from the WIA to fund services 

provided by these organizations (n=24). It appears more WIBs in non-metropolitan counties use 

funding from the WIA to fund services provided by FBOs and CBO than metropolitan counties. 

The process of negotiating a MOU and/or contract is not the only mechanism by which WIBs 

determine the services provided by FBOs/CBOs. 

How Workforce Investment Boards Determine the Services Provided by Faith- and 

Community-Based Organizations 

State and local WIBs have the flexibility to identify the services needed on a local level 

in order to achieve the performance standards required (See Table 1). In addition to the services 

funded through the Act, additional services customers need in order to be successful in achieving 

all of their personal, educational, and employment related goals are leveraged by partners and 

provider organizations in the community: “Because we have many of these CBOs providing 

services through our One-Stops, we are able to leverage their resources and provide enhanced 

services we would not be able to afford through WIA funding alone.” As demonstrated, some of 

these services are provided at no cost to the System while others are arranged through formal 

MOUs and contracts. 

Respondents were asked how they determine what services will be provided by 

FBOs/CBOs. There was a 94 percent response rate from respondents that work with 

FBOs/CBOs (n=150). Approximately 81 percent of the state WIBs responded (n=13) and 95 

percent of the local WIBs responded (n=137). Of the local WIBs that responded, 95 percent of 

the metropolitan counties responded (n=104) and 97 percent of the non-metropolitan counties 

responded (n=33). The most frequently cited means of determining services on a local level was 

through the RFP process. Of the number of WIBs working with FBOs/CBOs, 39 percent use the 

RFP process to determine services (n=59). It appears that 31 percent of state level WIBs 

determine services primarily based on RFPs (n=4). They also stated sometimes the FBOs/CBOs 

determine the services that are necessary and the needs of the customers and the One-Stop 

Career System as a whole are considered. State WIBs did not use contracts with any 

FBOs/CBOs. On a local level, 40 percent of the WIBs use the RFP process to determine 

services (n=55), 10 percent utilize contracts (n=13). Forty-three percent of the respondents from 

metropolitan counties stated they use the RFP process (n=45) and 8 percent use contracts (n=8). 

Approximately 30 percent of the respondents from non-metropolitan counties determine services 

through the RFP process (n=10) whereas 15 percent determine services through contractual 
29

arrangements (n=5). It would appear for every RFP, some type of contract or formal 

arrangement would be made between the WIB and the organization, although respondents did 

not provide evidence to suggest this. In addition, some respondents stated organizations 

determine the service they provide. It is unknown whether the organizations identify these 

services on their own or from a menu of services the WIBs provided in a RFP. One plausible 

answer would be that services are identified on the RFP for all providers and each provider 

identifies the service they want to provide through the RFP process with the contract specifying 

those services. 

 While the response rate to this question was high, 94 percent, the frequency of responses 

ranged over 47 categories: It is impossible to account for the variety of ways services are 

determined. It is apparent WIBs do not always use a MOU or RFP/contractual process to 

determine the services that will be provided by FBOs/CBOs. This information does help to 

explain information shared by informants regarding the function of MOUs. It is not necessary 

for FBOs/CBOs to have a MOU in order to provide services or to receive funding from the WIB: 

The RFP process is another means by which funds from the WIA can flow to FBOs/CBOs. At 

least 9 percent of the respondents that stated they have MOUs with FBOs/CBOs also stated that 

they use the RFP process to identify services. In some areas, WIBs do not make distinctions 

between FBO/CBOs and other agencies: “We do not distinguish between CBOs, FBOs, etc. in 

terms of who we seek to deliver services. We do our best to set quality thresholds and have 

groups meet those standards.” These services may be provided at One Stop Career Centers, 

satellites, at the organization’s location, and/or through other co-location arrangements. 

Prevalence of Faith- and Community-Based Organizations’ Co-Location at One-Stop 

Career Centers and Satellites 

 The WIA requires services be co-located at a centralized physical space called a OneStop Career Center. In order to ensure customers have access to services and a variety of points 

at which at enter the System, satellite sites are created throughout the community as 

demonstrated in Figure 2. Local WIBs have the flexibility to determine where services will be 

located throughout the community and whether they will be co-located with other services. 

Respondents were asked to identify whether FBOs/CBOs were co-located with One-Stop Career 

Centers, satellites, or in both locations. Eight-five percent of the respondents working with 

FBOs/CBOs answered this question (n=136). Eighty-one percent of the state WIBs responded 

(n=13) and 86 percent of the local WIBs responded (n=123). Of the local WIBs that responded 

86 percent of the metropolitan counties responded (n=94) and 85 percent of the non-metropolitan 

counties responded (n=29). 

Of the WIBs working with FBOs/CBOs, 63 percent have these organizations co-located 

at the One-Stop Career Center (n=86), 52 percent are co-located at satellites (n=71), 51 percent 

are co-located at both the One-Stop Career Center and satellites (n=69) and 55 percent have 

other co-location arrangements (n=75). Forty-six percent of the state WIBs stated FBOs/CBOs 

are co-located at the One-Stop Career Center (n=6), 46 percent stated they are co-located at 

satellites (n=6), 46 percent stated they are co-located at both the One-Stop Career Centers and 

satellites (n=6), and 69 percent stated there are other co-location arrangements (n=9). 
30

Sixty-six percent of the local respondents that work with FBOs/CBOs stated these 

organizations are co-located at their One-Stop Career Center (n=80), 53 percent are co-located at 

satellites (n=65), 51 percent are co-located at both One-Stop Career Centers and satellites 

(n=63), and fifty-four percent of the local WIBs stated that they have other co-location 

arrangements (n=66). Of the local WIBs located in metropolitan counties, 65 percent have 

FBOs/CBOs located at One-Stop Career Centers (n=61), 54 percent at satellites (n=51), 53 

percent co-located at both One-Stop Career Centers and satellites (n=50), and 52 percent state 

there are other co-location arrangements (n=49). Of the local WIBs located in non-metropolitan 

counties, 66 percent are co-located at the One-Stop Career Center (n=19), 48 percent at satellites 

(n=14), 45 percent are co-located at both One-Stop Career Centers and satellites (n=13), and 59 

percent state there are other co-location arrangements (n=17). One hundred percent of the 

respondents that have FBOs/CBOs co-located at the One-Stop Career Centers also have them colocated at satellites. Faith- and community-based organizations may serve as satellites in some 

communities and partners and/or providers may be co-located with these organizations. 

 Some respondents and informants explained that many of the FBOs/CBOs, while not colocated at a the One-Stop Career Center or satellite, do provide services on site: 

They are not co-located as agencies at our One-Stops; however, some of their 

services are provided at the One-Stop as part of their contract, e.g., English as a 

Second Language classes offered at the One-Stop are all provided by agencies 

listed above [referencing the FBOs/CBOs they work with]. 

Bender, in her research on the implementation of the WIA in rural areas, found many 

rural areas did not have the resources required to be financially contributing partners of the OneStop Career Center and maintain their existing location(s) in the community: “Despite the 

apparent benefits of co-location, there may be other more effective ways of delivering services in 

rural areas and still preserving the tenets of the System ... Due to low population density there 

may only be few people who need a service. If they are scattered across the county, it may only 

be possible to co-locate five or six of them in a central location” (Bender, 2001, p. 441). The 

central location for these individuals may or may not be the One-Stop Career Center. 

Co-location implies a permanent presence in a physical location at least for the duration 

of the MOU; however, the limits of physical space and funding to develop the capacity necessary 

to house everyone the WIB may want and/or need to have co-located may not always be 

possible. Some organizations have established networks within their community and serve 

specific populations and therefore may have no desire or need to extend beyond the boundaries 

of their community on a permanent basis. The value of co-location of FBO/CBO with One-Stop 

Career Centers and satellites is unknown from the perspective of the WIB and these 

organizations. One of the greatest values respondents place on working with FBOs/CBOs is 

their grassroots presence. To remove them from their local environments may be to the 

detriment of the One-Stop Career System. It may be more effective for these organizations to 

become a part of the System through the satellites existing throughout the community or remain 

in their location and become networked with the One-Stop Career System. 
31

Effectiveness of Faith- and Community-Based Organizations at Meeting the Workforce 

Investment Act Performance Standards 

The WIA requires each WIB to be accountable for meeting performance standards. It is 

unlikely a WIB would choose to work or continue to work with an organization that was 

ineffective in helping them achieve these performance standards, however, little has been done to 

document the effectiveness of FBOs/CBOs within the context of the WIA. Fifty-seven percent 

of the respondents working with FBOs/CBOs did not answer this question. While many did not 

provide reasons for omitting their response, some shared they would prefer not to rate these 

organizations: 

Although I would rather not rate the effectiveness on a numeric scale, I will say 

that when the Workforce Centers and the faith- and community-based 

organizations truly support and rely on one another through a referral process and 

information sharing, targeted clients have more chance of success - this makes the 

performance measures more easily attained in the client’s case. We have begun 

stressing the importance and built-in benefit of collaborating with the 

organizations as a means to support their efforts and provide the most benefit to 

our clients who may have needs outside the range of services that the Workforce 

Centers offer. 

Others stated FBOs/CBOs were not required to meet the performance standards either 

because they are working with them informally and no WIA funding is provided to them, or 

because they are reimbursed for expenditures, not performance, therefore it would not be 

possible to rate their effectiveness. Some respondents stated they did not have a way to analyze 

the performance standards of individual organizations. Some stated they had not worked with 

them long enough or closely enough to rate their effectiveness: “One organization has just 

recently become a provider of WIA services, so we have no performance information to date.” 

 Some respondents and informants stated the performance measures of the Act are not the 

only measure of success. One respondent comments: 

Some are highly effective, particularly working with youth, while others do not 

contribute positively to the areas of performance. This does not mean they are not 

good organizations, they simply have values and goals at times that differ from 

the grant. 

Respondents were asked on a scale of one to ten, with one being low and ten being high, 

how effective FBOs/CBOs are at meeting the performance standards of the WIA. The ten point 

Likert scale used was patterned after the scale and the calculation for the American Customer 

Satisfaction Index used by the USDOL to gauge customer satisfaction from customers of the 

One-Stop Career System. The survey did not require respondents to submit performance 

outcomes and compare these outcomes to those from other organizations nor to those achieved 

collectively by the WIB. 
32

There was a 53 percent response rate from local WIBs (n=85). Nineteen percent of the 

state WIBs responded (n=3) and 57 percent of the local WIBs responded (n-82). Fifty-eight 

percent of the metropolitan counties responded (n=63) and 56 percent of the non-metropolitan 

counties responded (n=19). State WIBs rated the effectiveness of FBOs/CBOs as 8.67 (n=3). 

Local WIBs rated the effectiveness of FBOs/CBOs as a 6.9 (n=82). Respondents from 

metropolitan counties rated their effectives as 6.63 (n=63) whereas respondents from nonmetropolitan counties rated their effectiveness as 7.16 (n=19). The weighted average was 6.68. 

It appears the respondents that did share their perceptions find FBOs/CBOs to be more effective 

than not and a contributing factor to their success. The comments provided by respondents on 

the value they place on FBOs/CBOs helps qualify the effectiveness of these organizations. One 

state WIB respondent shared: “[name of state] met and exceeded all performance measures in 

Program Year 2001. This would not have been possible if these service providers were not 

effective at what they do in the Workforce Development System.” 

The Value Workforce Investment Boards Place on Working With Faith- and CommunityBased Initiatives 

One-Stop Career Systems are driven by performance standards. To that end, they are 

primarily interested in brokering services that are in the best interest of their customers. These 

services must be of the level and quality necessary for WIBs to achieve their performance 

standards. It is apparent respondents and informants place value on the relationships they have 

with FBOs/CBOs. Ninety-five percent of the respondents that work with FBOs/CBOs answered 

this question (n=151). One hundred percent of the state WIBs responded (n=16) and 94 percent 

of the local WIBs responded (n=135). Ninety-five percent of the local WIBs from metropolitan 

counties responded (n= 104) and 91 percent of the local WIBs from non-metropolitan areas 

responded (n=31). 

It is not surprising the contribution most valued by respondents is the expanded services 

provided by FBOs/CBOs, however, it appears it is their modus operandi that makes these 

services of value to the WIBs. The value described by respondents and informants focuses less 

on the specific types of service provided and more on who these organizations are as people and 

their mission and commitment that provides the context in which they deliver services. A 

respondent explains the various dimensions of the value FBOs/CBOs bring to the WIB: 

These organizations often bring resources (both financial and human) to the 

workforce system that would otherwise not be available to individuals. The 

individual involvement with volunteers associated with faith or community based 

organizations as mentors, tutors, etc., is a significant benefit. While it is more of a 

quality measure than a quantity or performance measure, it is often reflected in 

retention and follow-up. These are areas where faith and community based 

organizations often excel because of their commitment to maintain a long-term 

relationship with individuals receiving their services. 

Their modus operandi includes: (a) who they are, their beliefs, mission, commitment and overall 

culture of their organizations; (b) location in which the organization exists and provides services; 

(c) the type and quality of services provided; (d) the delivery strategies used to provide services; 
33

and (e) the population they serve. The majority of respondents have found these to be positive 

attributes FBOs/CBOs contribute to the relationship. 

These organizations operate from their mission to be of service to others, and to their 

community: “[They have] Both an excellent connection to targeted groups and a sincere desire 

to help serve the community.” Respondents’ descriptions of staff from FBOs/CBOs as being 

compassionate, committed, and sincere describe some of the less tangible characteristics that 

appear to make these organizations effective. As respondents describe: “. . . .Staff are often 

passionate about the organization’s goals” and “The faith- and community-based organizations 

are of great value especially in the TANF program because they have developed a reputation of 

doing whatever needs to be done to make the customer successful.” These characteristics may 

contribute to customers having a more positive perception of staff of FBOs/CBOs than of 

government employees: “We believe that faith-based and community-based organizations are 

good resource to access. They do not represent government and sometimes that allows 

individuals to feel less threatened ... Neither type of organization is ‘caught up’ in a bureaucracy 

and can be more compassionate toward people being served.” 

 Services provided by FBOs/CBOs are primarily delivered where they exist, in their local 

communities: “They have a strong connection with the neighborhoods they serve.” Many 

respondents referred to this as a “grassroots” or “community” approach. This connection to the 

community appears to place staff and services directly with the people who most need them. 

Physical location, combined with caring, committed, and sincere staff make FBOs/CBOs 

accessible, knowledgeable of the needs of the community, and a safe place for customers: “They 

have a closer connection with the community, and residents see the church as a ‘safe’ haven.” It 

appears they have demonstrated the ability to serve many of the customers of the WIA that live 

in the same community: “Faith-based and community-based organizations often have strong ties 

to their neighborhoods and employers in the neighborhoods. In some cases they focus on a 

particular population and have ‘expertise’ with [the] population.” Location and the relationships 

established within the community also make outreach and recruitment for services easier. 

Faith-based and CBOs provide services of value to the WIB and also serve as a conduit 

or referrals. These organizations recruit individuals from their communities to access services of 

the One-Stop Career Center and the Center makes referrals to FBOs/CBOs for services they do 

not provide or for services contracted to these organizations. In addition, at least 63 percent of 

the respondents working with FBOs/CBOs state these organizations provide services on-site at 

the One-Stop Career Centers and/or satellites (n=86). While respondents listed all of the services 

provided by these organizations (See Table 10), the ones they reference specifically as bringing 

value to the WIB include: (a) after school programs; (b) career advancement; (c) career 

planning; (d) child care; (e) clothing; (f) counseling; (g) drug and alcohol services; (h) education; 

(i) follow-up; (j) housing; (k) job placement; (l) job search; (m) literacy instruction; (n) 

mentoring; and (o) services provided to families. 

There are several other elements regarding the quality services provided by FBOs/CBOs 

that are important to their relationship with the WIBs. First, they have an established history and 

experience working with at least a significant portion of the same population: “The advantage of 

working with established faith and community based agencies is their history of quality service.” 
34

It appears respondents find value in their ability to work with customers: “... Their experience 

in human investment is unmatched ... ” They have the operational framework in place to 

respond immediately to the WIBs’ needs: “In many cases these organizations already have 

mechanisms in place that could allow you to expand services, lower costs, and/or allow both 

groups to leverage resources available within a community.” Second, they are cost effective in 

some counties: “Quite often [FBOs/CBOs] can provide quality services cheaper than 

government operated programs, however it is largely because they pay very low wages in 

comparison.” Third, by working with these organizations, the entire resources of the community 

can be leveraged on behalf of the customer: “In addition, these community-based organizations, 

many times, tend to bring match (both in-kind and cash) to the table as well as other wrap-around 

services that can be incorporated into their program.” Fourth, by leveraging resources, 

comprehensive and holistic services can be provided to customers and their families: 

The value of connecting with FB [FBOs] entities is in the flexibility of the 

organization to serve numerous customers with workforce development needs, as 

well as the needs of the entire family. This holistic approach to service is a strong 

asset for faith-based activities and our One-Stop Operators. 

 

Fifth, they can assist WIBs with meeting their performance standards: “They are very familiar 

with resources and the community and they provide excellent case management. Usually meet 

or exceed performance standards.” Finally, some of these organizations may do more than help 

WIBs achieve their performance standards by providing additional services and continuing 

relationships with job seekers after services funded from the WIA have ended: 

Many times, the CBOs and FBOs bring a more compassionate (as opposed to 

governmental) flavor to the mix. Although all of our contracts with community 

and faith-based organizations have performance outcomes as part of the 

contractual agreement, these smaller organizations tend to focus more on 

performance as it relates to the success of the participant as opposed to simply 

meeting state or federally mandated performance targets. 

Another respondent adds: “They will allow services to be coordinated and delivered locally. 

Non-profits are usually more flexible than state agencies. They also bring unique, value-added 

resources that complement service delivery.” These elements are summarized by another 

respondent: “ ... These organizations have the ability to offer a broad range of services to a very 

diverse population at a high quality and reasonable cost.” The other benefits they bring to the 

WIB are the strategies they utilize to deliver services. 

 The delivery strategies utilized by FBOs/CBOs are partly inherent upon their location. 

One of the largest advantages of being a FBO/CBO is that they are located where services need 

to be delivered. Services are often available outside of the schedule of the One-Stop Career 

Centers and satellites with staff available during the evenings and weekends and beyond the time 

period funded through the WIA or other funding sources: “ ... FBOs/CBOs can provide 

services/assistance outside of the Mon-Fri, 8-5 window, which is often when people need them 

the most.” This flexibility appears to make them particularly effective in responding to crises: 

“The faith-based community's greatest asset is that it is very adept at providing needed crisis 
35

services. They have the ability to provide customized, flexible, immediate service, something no 

government agency is able to do.” Relationships with FBOs/CBOs appear to aid the community. 

 Faith-based and community-based organizations provide great value in the 

delivery of services in our community. In many instances, faith- and communitybased organizations are the most appropriate agency to provide services to end 

users, those most in need. Generally, [faith- and community-based organizations] 

are located in our communities, are accessible, are familiar to the populous and 

can easily connect to individuals in need of services. The success of FBOs/CBOs 

in delivering services is directly tied to the economic and community 

development in our region. 

Being in close proximity to customers allows these organizations to have a better understanding 

of the needs of the customers and the environments in which they live and work. One 

respondent states their location provides “Natural access to clients, expertise in service provision, 

and service to niche groups.” Some respondents believe they have “ ... extreme hands-on 

experiences with the targeted customers” that make them successful. This experience also 

allows them to customize services for each customer: “A lot - many agencies are maxed out and 

they provide much needed one-on-one customer services.” 

 Partnering with these organizations also allows the WIB to expand its capacity and to 

enrich the comprehensiveness of services offered to customers. 

[Name of agency] partners with organizations and agencies, including those that 

are faith-based and community-based, to collaborate to create a seamless system 

of service delivery that will enhance access to programs and services and improve 

the long-term employment outcomes for individuals. 

The value, then, of the services provided by FBOs/CBOs includes not only the provision 

of services, but access to the population they serve: “We believe that working with such 

organizations has great value because of their community contacts and their commitment to 

working within the community and with the citizens who are ultimately the recipients of many of 

our services.” They serve a population that may not access the One-Stop Career Center or 

respond to recruitment efforts from those they do not trust: “They often have the trust of the 

customers who may not have had good experiences in public or private for-profit school.” 

Respondents identified the variety of populations served by FBOs/CBOs. When 

describing the value FBOs/CBOs bring to the WIB, they referenced the following populations: 

(a) at-risk youth; (b) hard to reach; (c) hard to serve; (d) homeless; (e) individuals from different 

ethnic backgrounds; and (f) recipients of TANF. These populations appear to have one 

characteristic in common: “CBOs and FBOs often have technical expertise/experience working 

with disadvantaged populations.” 

While these organizations provide access to some of the same populations served by the 

WIA, they may make connections with individuals that would not access the One-Stop Career 

Center and they can leverage the expertise necessary to assist them: “ ... they usually have 
36

particular segments of the community that they focus on serving and through [the] consortium 

they are sharing information so they can direct customers to organizations with experience in 

addressing the particular need of the customer.” 

 

The elements of their modus operandi contributes to the value the WIBs place on 

relationships with FBO/CBOs. These relationships result in diverse and additional services 

being offered to a population sometimes unserved and/or unreachable by the WIB. In some 

cases, WIBs benefit from leveraging their combined resources. 

Working with faith-based and community-based organizations enables the Local 

Workforce Investment Area to leverage resources available through non-profits. 

They also have access to residents in the community. Establishing strong 

partnerships enables both systems to more effectively serve the needs of our 

community. 

The benefits of the relationship are evidenced in the performance standards achieved by 

the WIBs and in the lives of the customers receiving services, sometimes receiving 

additional services beyond the scope of the WIA. 

There is value in working with faith- and community-based initiatives. Primarily, 

the value lies in maximizing the effectiveness of resources employed to meet the 

needs of those on the local service area, minimizing duplication of efforts, 

providing a quicker response mechanism for sharing information and resources, 

and providing services to eligible individuals that otherwise might not be reached. 

Implications 

 The WIBs currently working with FBOs/CBOs place value on the type and quality of 

services they provide, the delivery strategies they employ, the population they have access to, 

and the overall relationships they have with them. These relationships allow them to collectively 

marshal and combine resources to create the type of comprehensive, integrated, and accountable 

System identified through the WIA and the community envisioned through the CCI. It appears 

many WIBs are applying the principles of the CCI to the WIA and that customers and local 

communities benefit from their efforts. 

This research provides a national perspective from approximately 37 percent of all state 

and local WIBs (n=238). Approximately 67 percent of the respondents are working with 

FBO/CBOs (n=159). While much has been learned from their response, there is much more that 

can be gleaned from this research. First, additional analysis will provide a deeper understanding 

of the relationships that exist between WIBs having MOUs with FBO/CBOs. Second, some of 

the limitations of the study will be addressed. More time will be leveraged in an effort to glean 

responses from states that did not participate and from states whose responses were limited to 

either the state WIB or local WIB. Interviews will be conducted with respondents in an effort to 

better understand the effectiveness of the FBOs/CBOs in meeting performance standards. 

Respondents will be provided with a matrix of the FBOs/CBOs they work with and asked to 

identify additional elements of the relationship they have with them. Third, responses from the 
37

33 percent of the respondents that do not work with FBOs/CBOs will be analyzed. Fourth, 

additional information will be secured from states that received funding from the USDOL in an 

effort to link FBOs/CBOs to the One-Stop Career System. Fifth, local state profiles could be 

developed for those states for which there was a high response rate. Finally, a paper will be 

issued focusing on the relationship between FBOs/CBOs and the WIB in non-metropolitan 

counties. 

Researchers will be able to use the information from this study to frame other national 

and local efforts. First, researchers will want to study the growing number of WIBs that 

establish relationships with FBOs/CBOs and the characteristics of those relationships. Second, it 

may be beneficial to understand why some WIBs do not have relationships with FBOs/CBOs: 

Why are some afraid of the “f” word and what have WIBs done to overcome this fear? Third, 

documentation on the outcomes achieved by FBOs/CBOs is needed in order to more fully 

understand their role. Fourth, the outcomes achieved by these organizations with respect to the 

WIA performance standards need to be correlated with the amount of funding provided by the 

WIBs and other funders in order to understand their effectiveness and efficiency. Fifth, the 

informal role of the FBOs/CBOs needs to be explored. It appears many of these organizations 

are providing services without MOUs or contracts. Does this imply they are not being 

reimbursed for these services and if so, what is the monetary value of these services to the 

System and what is their source of funding? Sixth, is there value in FBO/CBOs, given their 

grassroots approach, being co-located with others in the One-Stop Career Center and/or satellites 

and what are the characteristics of the other types of co-location arrangements identified by 

respondents? Finally, researchers may want to gain a more comprehensive understanding of 

these relationships through the perceptions of FBOs/CBOs and job seeker and employer 

customers. 

 Faith and CBOs have a long-standing tradition of providing many of the services 

authorized through the WIA and needed by customers of the System. They provide these 

services to a population unserved or underserved by the current System and in ways sometimes 

outside the service delivery structure and funding provided through the Act. Many have 

partnered with their WIBs, contributing their experiences and resources to the System. They can 

be “powerful allies” in the WIBs’ efforts to meet performance standards and the needs of job 

seeker and employer customers. 

Most of the Workforce Investment Boards that are on the cutting edge realize that 

strategically we have to redefine our role ... from providers of programs for 

certain groups of people to builders of systems that keep industries competitive 

and our customers jobs that pay a family-sustaining wage. To do that, we need to 

network with every resource in the community that will support that mission. 

CBOs and FBOs are powerful allies that do things that our public system can't ... 

that's why our system embraces them. 
38

Section III 

Tables 

Table 1 

Workforce Investment Act Performance Measures

Indicator Population 

Adult Dislocated Youth 

Ages 

19-22 

Youth 

Ages 

14-18 

Entry into unsubsidized employment X X X 

Retention in unsubsidized employment after 

entry into employment 

X X X 

Earnings received in unsubsidized 

employment six months after entry into 

employment 

X X X 

Attainment of educational credential, 

occupational skills credential for adults 

entering employment after training 

X X 

Attainment of educational credential, 

occupation skills credential for youth ages 19 

to 22 entering post-secondary education, 

advanced training, or employment after 

training 

 X 

Attainment of basic skills and, as appropriate, 

work readiness or occupational skills 

 X 

Attainment of secondary school diplomas 

and their recognized equivalents 

 X 

Placement and retention in post-secondary 

education or advanced training, or placement 

and retention in military service, 

employment, or qualified apprenticeships 

 X 

Participant satisfaction X X X X 

Employer satisfaction All Employees 

(Workforce Investment Act of 1998, and Bender, 2002a)
39

Table 2 

The Underlying Tenets of the Workforce Investment Act 

Tenets 

Network of services 

Integration of services and governance 

Performance-driven 

Customer choice 

Customer satisfaction 

Universal access to core services 

Increased accountability 

Strong roles for business 

Improve youth programs 

Local Workforce Investment Boards led by local businesses; 

Flexibility on state and local levels; 

Common goals, objectives, and outcomes; definitions; intake and assessment; referral 

procedures; and accountability; 

Coordinated case management 

Continuous improvement 

Full utilization of technology 

(Workforce Investment Act of 1998)
40

Table 3 

Three Categories of Services Authorized Under the Workforce Investment Act 

Services* 

Core Intensive Training

Determination of the types of 

assistance for which a person 

qualifies 

An initial assessment of a 

person’s needs 

Assistance in job search 

Career counseling 

Information about the current 

labor market 

Information on training and 

other providers 

Information on activities at 

the One-Stop Career Center 

Information on filing 

Unemployment Insurance 

claims 

Assistance establishing 

eligibility for Welfare-toWork and financial assistance 

Follow-up services 

Assessment of skill levels 

Development of an individual 

employment plan 

Group counseling 

Individual counseling and 

career planning 

Case management 

Short-term pre-vocational 

services 

Evaluation to determine if 

additional assistance is 

needed and level of skills 

and qualifications necessary 

to benefit from training 

Training services must be 

linked directly to 

occupations that are in 

demand in local areas or 

relocation areas 

Welfare recipients and 

other low-income 

individuals may receive 

priority in training funds are 

limited 

Information about training 

providers and their 

performance and graduate 

placement is provided 

*Eligible job seekers receive the level of services necessary to secure employment. 

(USDOL, 2002, June 14). 
41

Table 4 

All Respondents, Listed in Alphabetical Order 

Agency City State 

Adams County One-Stop Career Center Commerce City CO 

Alachua/Bradford Jobs & Ecucation Partnership, BCN 

 Associates, Inc. 

Gainesville FL 

Anaheim Workforce Investment Board Anaheim CA 

Anchorage Metanuska Susitna Borough Local Workforce 

 Investment Board 

Anchorage AK 

Arizona Department of Commerce Phoenix AZ 

Arizona Department of Commerce, Workforce Development Phoenix AZ 

Arkansas Workforce Investment Board Little Rock AR 

Atlantic and Cape May Counties Workforce Investment Board Atlantic NJ 

Bennington County Workforce Investment Board North Bennington VT 

Bergen County Workforce Investment Board Paramus NJ 

Berkshire County Regional Employment Board, Inc. Pittsfield MA 

Berrien-Cass-Van Buren Office of Michigan Works Benton Harbor MI 

Broward Workforce Development Board, Workforce One Lauderhill FL 

Bureau of Targeted Services, Office for Workforce 

 Development 

Columbus OH 

Burlington County Workforce Investment Board Mt. Holly NJ 

Calhoun Workforce Development Board Marshall MI 

Camden County Workforce Investment Board Cherry Hill NJ 

Cameron Works - Cameron County Workforce Development 

 Board 

Brownsville TX 

Capital Area Michigan Works! Lansing MI 

Capital Region Workforce Development Board Hartford CT 

Career Development Office Wichita KS 

Carons/Lomita/Torrance Workforce Investment, WiN 

 Worksource Center 

Carson CA 

CDO Workforce - Chenango/Delaware/Otsego Counties 

 Workforce Investment Board 

Norwich NY 

CDO Workforce - Chenango/Delaware/Otsego Counties 

 Workforce Investment Board 

Norwich NY 

Center of Workforce Innovations Valparaiso IN 

Central Area Michigan Works! Greenville MI 

Central Career Center, Fulton County Workforce Preparation & 

 Employment System 

Atlanta GA 

Central Florida Jobs & Education Partnership Winter Park FL 

Central Iowa Employment and Training Center Des Moines IA 

Central Oklahoma Workforce Investment Board Oklahoma City OK 

Central Texas Workforce Belton TX 

Central Western Maine Workforce Investment Board Lewiston ME 
42

Agency City State 

Centralina Workforce Development Board Charlotte NC 

Chautauqua Works Jamestown NY 

Chemung/Schuyler/Steuben Workforce New York Corning NY 

Chipola Regional Workforce Board Chipley FL 

Circle Seven Workforce Investment Board Greenfield IN 

City of Los Angeles Community Development Department, 

Planning/Contracts Unit 

Los Angeles CA 

CobbWorks Workforce Development System Marietta GA 

Cook County President's Office of Employment and Training, 

Local Workforce Investment Area #7 

Chicago IL 

County of Essex Department of Economic Development East Orange NJ 

Crater Region Workforce Investment Board (15) Petersburg VA 

Cumberland County Service Delivery Area Fayetteville NC 

DC Workforce Investment Council Washington DC 

Deep East Texas Local Workforce Investment Board, Inc. Lufkin TX 

Department of Community Development and Housing Glendale CA 

Detroit Workforce Development Board Detroit MI 

Douglas Workforce Investment Board Region 6 Roseburg OR 

Dutchess County Workforce Investment Board Poughkeepsie NY 

Eastern Area Workforce Development Board, Eastern Plains 

 Council of Governments 

Clovis NM 

Eastern Maine Development Corporation, Tri-County 

 Workforce Investment Board 

Bangor ME 

Eastern Upper Peninsula Michigan Works! Sault Ste. Marie MI 

Eastern Washington Partnership Workforce Development 

 Council 

Colville WA 

First Planning District Consortium Chalmette LA 

Frederick County Job Training Agency, Frederick County's 

 Workforce Development Resource 

Frederick MD 

Fresno County Workforce Investment Board Fresno CA 

Gaston County Workforce Investment Act Gastonia NC 

Georgia Mountains Workforce Investment Area 2 Region 2 Gainesville GA 

Gloucester County Workforce Investment Board Woodbury NJ 

Golden Crescent Workforce Development Board Victoria TX 

Greater Nebraska Workforce Investment Board Lincoln NE 

Greater New Bedford Workforce Investment Board New Bedford MA 

Greater Peninsula Workforce Development Consortium Hampton VA 

Greenlee Workforce Investment Board - Greenlee Career 

 Center 

Clifton AZ 

Grundy, Livingston, Kankakee Workforce Investment Board Kankakee IL 

Gulf Coast Business Services Corp. Gulfport MS 

Gulf Coast Workforce Development Board Panama City FL 

Heartland Workforce Investment Board Avon Park FL 

Henry County Department of Job and Family Services Napoleon OH 

Howard County Employment & Training Columbia MD 
43

Agency City State 

Idaho Governor's Workforce Development Council Boise ID 

Illinois Valley Community College Oglesby IL 

Iowa Western Workforce Development Region 13 Council Bluffs IA 

Iowa Workforce Development Des Moines IA 

Iowa Workforce Development (Region 10) Cedar Rapids IA 

Iowa Workforce Development Region 14 Creston IA 

Jefferson Parish Workforce Investment Board Jefferson LA 

Jefferson-Lewis Workforce Investment Board Watertown NY 

Job Training Center, Inc. Fort Pierce FL 

Kalamazoo-St. Joseph Michigan Works! Kalamazoo MI 

Kings County Job Training Office Hanford CA 

Lake County Department of Job & Family Services Painesville OH 

Lake County Workforce Development Board Waukegan IL 

Lancaster County Workforce Investment Board Lancaster PA 

Land of Lincoln Workforce Invesment Board Springfield IL 

Lowcountry Workforce Investment Area Yemassee SC 

Lower Savannah Council Of Governments Aiken SC 

Lumber River Job Training Consortium Lumberton NC 

LWIA-1, Alliance for Business and Training Elizabethon TN 

Macomb/St Clair Workforce Development Board, Inc. Clinton Township MI 

Madison County Employment and Training Department Edwardsville IL 

Maine Department of Labor Augusta ME 

Man-Tra-Con Corporation Marion IL 

Maricopa County Human Services Department Phoenix AZ 

Maryland Governor's Workforce Investment Board Baltimore MD 

Maryland Institute for Employment and Training Professionals Columbia MD 

Massachusetts State Workforce Investment Board, MassJobs 

 Council 

Boston MA 

Mayor's Office of Employment Development Baltimore MD 

Mendocino County Workforce Investment Board Ukiah CA 

Merced County Department of Workforce Investment Merced CA 

Merimack Valley Workforce Investment Board Lawrence MA 

Michigan Works! Association Lansing MI 

Michigan Works! Region 7B Consortium Harrison MI 

Mid-Carolina Council of Governments Fayetteville NC 

Middle Georgia Consortium, Inc. Warner Robins GA 

Missouri Career Center Paris MO 

Missouri Department of Economic Development Jefferson City MO 

Monmouth County Workforce Investment Board Red Bank NJ 

Monroe County Workforce Investment Board Rochester NY 

Montana Job Training Partnership Helena MT 

Morris/Sussex/Warren Workforce Investment Board Morristown NJ 

Mountain Area Job Training Services Asheville NC 

 
44

Agency City State 

Navajo Department of Workforce Development, The Navajo 

 Nation 

Window Rock AZ 

Nebraska Workforce Development Lincoln NE 

New York State Workforce Investment Board Albany NY 

New York State Workforce Investment Board, Department of 

 Labor 

Canadaigua NY 

Niagara County Employment & Training Niagara Falls NY 

North Central Massachusetts Workforce Investment Board Leominster MA 

North Central Pennsylvania Regional Planning and 

 Development Commission 

Ridgeway PA 

North Central Workforce Development Council (fka Pentad) Wenatchee WA 

North Dakota Workforce Development Division Bismarck ND 

North Texas Workforce Development Board, Inc. Wichita Falls TX 

Northeast Workforce Investment Board Claremore OK 

Northern Area Local Workfroce Development Board Santa Fe NM 

Northern Indiana Workforce Investment Board South Bend IN 

Northest Indiana Workforce Investment Board Fort Wayne IN 

Northwest Georgia Workforce Investment Area Region Rome GA 

Northwest Piedmont Workforce Development Board Winston-Salem NC 

NW Iowa Planning (Regions 3&4) Spencer IA 

Ocean City Workforce Investment Board Toms River NJ 

Office of Housing and Community Development Hilo HI 

Ohio Option Area 7/27, Darke County Department of Job & 

 Family Services 

Greenville OH 

Oklahoma Employment Security Commission, Employment & 

 Training Division 

Oklahoma City OK 

Orange County Employment & Training Administration Goshen NY 

Oswego County Employment and Training Mexico NY 

Ouachita Parish Workforce Investment Board Monroe LA 

Pasco Hernando Jobs and Education Partnership Regional 

 Board, Inc. 

Brooksville FL 

Pee Dee Region Workforce Area Asheboro NC 

Pee Dee Workforce Investment Board Florence SC 

Pennsylvania Partners, Pennsylvania's Workforce Development 

 Association 

Camp Hill PA 

Permian Basin Workforce Development Board Midland TX 

Pike's Peak Workforce Center Colorado Springs CO 

Pima County Workforce Investment Board Tucson AZ 

Polk County Workforce Development Board Barstow FL 

PolkWorks Barstow FL 

Region 9 Workforce Investment Board, Columbia Gorge 

 Community College 

The Dalles OR 

Regional Employment Board of Hampden County, Inc. Springfield MA 

Regional Partnership Local Area Asheboro NC 
45

Agency City State 

Rensselaer County Department of Employment & Training Troy NY 

Rhode Island Tri County Consortium RI 

Richmond/Burke Job Training Authority, WIA/Career 

 Workforce Community Link 

Augusta GA 

RochesterWorks! Rochester Resource Alliance, Inc. Rochester NY 

Rockland County Workforce Investment Board Spring Valley NY 

Sacramento Works, Inc. Sacramento CA 

San Benito County Community Service & Workforce 

 Development 

Hollister CA 

San Joaquin County WorkNet Stockton CA 

San Luis Obispo County Workforce Investment Board San Luis Obispo CA 

San Mateo County Workforce Investment Board Belmont CA 

Saratoga County Employment & Training Ballston Spa NY 

SE/CT Workforce Investment Board Norwich CT 

Sonoma County Workforce Investment Board Santa Rosa CA 

South Bay Workforce Investment Board Hawthorne CA 

South Carolina Workforce Development Board Columbia SC 

South Central Idaho Works! Area 4 Twin Falls ID 

South Central Indiana Workforce Investment Board Vincennes IN 

South Central Michigan Works Hillsdale MI 

South Central Oklahoma Workforce Investment Board Duncan OK 

South Central Workforce Council Mankato MN 

South Florida Workforce Board - Region 23, Miami-

 Dade/Monroe Counties 

Miami FL 

South Texas Workforce Development Board Laredo TX 

Southeast Georgia Workforce Investment Board Waycross GA 

Southeast Michigan Community Alliance, SEMCA Michigan 

 Works! 

Taylor MI 

Southeast Texas Workforce Development Board Nederland TX 

Southern Allegheny Planning & Development Commission Altoona PA 

Southern Essex Workforce Investment Board Salem MA 

Southern Maryland Workforce Investment Board Waldorf MD 

Southern Nevada Workforce Investment Board Las Vegas NV 

Southern Seven Workforce Investment Board, Inc. New Albany IN 

Southwest Florida Workforce Development Board, Inc. - 

 Region 24 

Bonita Springs FL 

Southwest Georgia Workforce Investment Board Camilla GA 

Southwest Human Resource Agency, LWIA #11 Henderson TN 

Southwestern NC Service Delivery Area, SW Planning 

 Commission 

Bryson City NC 

St. Lawrence County Office of Economic Development Canton NY 

Stanislaus County Department of Employment and Training Modesto CA 

State of Vermont Vocational Rehabilitation Springfield VT 

Suffolk County Department of Labor, Suffolk County One-Stop Hauppauge NY 
46

Agency City State 

Suncoast Workforce Board, Inc. Sarasota FL 

Tarrant County Work Advantage Fort Worth TX 

Team Pennsylvania Workforce Investment Board Harrisburg PA 

Tecumseh Area Partnership Lafayette IN 

Tennessee Workforce Investment, Tennessee Department of 

 Labor and Workforce Development 

Nashville TN 

The Center for Capacity Development, A Project of the 

 WorkPlace, Inc. 

Bridgeport CT 

The Coordinating & Development Corporation Shreveport LA 

The Work Connection Klamath Falls OR 

Three Rivers Workforce Investment Board Pittsburgh PA 

Tompkins County Workforce Investment Board Ithaca NY 

Town of Hempstead Department of Occupational Resources Hempstead NY 

Tribal Workforce Investment Board Phoenix AZ 

Tri-County Workforce Center Golden CO 

Tulare County Workforce Investment Boards, Inc. Visalia CA 

Ulster County Workforce Investment Board Kingston NY 

Union/Wallowa/Baker County Workforce Investment Board LaGrande OR 

Utah Department of Workforce Services Salt Lake City UT 

Ventura County Workforce Investment Board Ventura CA 

Vermilion County Workforce Investment Board Danville IL 

Vermont Human Resources Investment Council Montpelier VT 

Virginia Workforce Council Richmond VA 

West Central Arkansas Planning & Development District Hot Springs AR 

West Central Michigan Works! MI 

West Central Workforce Development Board Abilene TX 

West Kentucky Workforce Investment Board Hopkinsville KY 

Western Arkansas Economic Development Area Van Buren AR 

Western Maryland Consortium Hagertown MD 

Western Upper Peninsula Michigan Works! MI 

Western Upper Peninsula Workforce Investment Board Ironwood MI 

Westmoreland/Fayette Workforce Investment Board Youngwood PA 

White River Planning and Development Batesville AR 

Wood County Employment Resource Center Bowling Green OH 

Workforce Board of South Central Wisconsin Madison WI 

Workforce Board of the Treasure Coast Port St Lucie FL 

Workforce Boulder County Boulder CO 

Workforce Connection of Central New Mexico Albuquerque NM 

Workforce Development Board of Okaloosa and Walton 

 Counties 

Shalimar FL 

Workforce Development Board of St. Louis County St Louis MO 

Workforce Development Board of the Treasure Coast Port St Lucie FL 

Workforce Development Division, Alabama Department of 

 Economic & Community Affairs 

Montgomery AL 
47

Agency City State 

Workforce Development One-Stop Corning NY 

Workforce Florida, Inc. Tallahassee FL 

Workforce Investment Board #61, Rapides Parish Office of 

 Economic & Workforce Development 

Alexandria LA 

Workforce Investment Board of Herkimer, Madison, Oneida 

 Counties, Inc. 

Utica NY 

Workforce Investment Board of Southeast Missouri Cape Girardeau MO 

Workforce Investment Board of the Southwest Region Joplin MO 

Workforce Tulsa Tulsa OK 

Worknet Pinellas Clearwater FL 

WorkSOURCE Boise ID 

Worksource Greater Austin Area Workforce Board Austin TX 

WorkSource of the South Plains Lubbock TX 

Yonkers Employment Center Yonkers NY 
48

Table 5 

Responses by State 

State State Response Local Response 

 Metro NonMetro Number of Local 

Workforce 

Investment Boards 

Percent Response form 

Local Workforce 

Investment Boards 

Alabama 1 0 0 2 0 

Alaska 0 0 1 2 50 

American Samoa 0 0 0 0 0 

Arizona* 0 5 2 19 37 

Arkansas 1 1 2 10 30 

California 0 13 5 52 35 

Colorado* 0 4 0 10 40 

Connecticut* 0 3 0 8 38 

Delaware 0 0 0 0 0 

District of 

Columbia* 

0 1 0 1 100 

Florida* 1 11 6 25 68 

Georgia 0 4 4 20 40 

Guam 0 0 0 0 0 

Hawaii 0 0 1 4 25 

Idaho 1 1 1 6 33 

Illinois 0 6 2 27 30 

Indiana* 0 6 1 15 47 

Iowa 1 3 2 15 33 

Kansas 0 1 0 7 14 

Kentucky 0 1 0 10 10 

Louisiana 0 5 0 17 29 

Maine 1 2 0 4 50 

Maryland 1 6 0 15 40 

Massachusetts* 1 6 0 18 33 

Michigan 1 7 7 26 54 

Minnesota 0 0 1 17 6 

Mississippi 0 1 0 6 16 

Missouri 0 2 3 15 33 

Montana 1 0 0 1 0 

Nebraska 1 1 0 3 33 

Nevada 0 1 0 2 50 

New Hampshire 0 0 0 0 0 

New Jersey 0 9 0 19 47 

New Mexico 0 2 1 4 75 

New York 1 14 9 33 70 

North Carolina 0 6 4 24 42 

North Dakota* 1 0 0 0 0 

Ohio* 1 2 2 23 17 

Oklahoma 1 3 1 13 31 

Oregon 0 1 3 15 27 
49

State State Response Local Response 

 Metro NonMetro Number of Local 

Workforce 

Investment Boards 

Percent Response form 

Local Workforce 

Investment Boards 

Pennsylvania* 1 6 0 23 26 

Puerto Rico 0 0 0 4 0 

Rhode Island 0 0 1 1 100 

South Carolina 1 2 1 12 25 

South Dakota 0 0 0 0 0 

Tennessee 0 3 0 8 38 

Texas 0 11 1 28 43 

Utah 1 0 0 5 0 

Vermont 1 0 2 2 100 

Virginia* 1 2 0 11 18 

Washington 0 0 2 2 100 

West Virginia 0 0 0 5 0 

Wisconsin* 0 1 0 4 25 

Wyoming 0 0 0 0 0 

Total 20 153 65 593 

Totals 

Total State Response 20 

Total Local Response 218 

Total Local Metro Response 153 

Total Local Non- Metro Response 65 

Total Responses 238 

*State WIBs funded through the United States Department of Labor to work with grassroots and 

community-based organizations, including faith-based organizations. 
50

Table 6 

Elements of the Compassion Capital Fund 

 Elements Description 

Funding 

 

Funding is authorized by section 1110 of the Social Security Act 

governing Social Services Research and Demonstration activities and: 

the Departments of Labor, Department of Health and Human Services, 

Department of Education, and Related Agencies Appropriations Act, 

2003, Public Law 108-7. 

Purpose The purpose of the CCF is to: help build capacity and knowledge among 

faith- and community-based organizations; increase efficiency and 

effectiveness of these organizations by expanding and diversifying their 

funding; assist in creating collaborations that act to serve those most in 

need; and encourage the replication of effective approaches and 

programs. 

Eligibility Intermediary organizations with demonstrated expertise in working with 

and providing technical assistance to faith- and community-based 

organizations in a variety of areas. These organizations will serve as a 

bridge between the Federal government and small faith- and communitybased organizations.

Funded Activities Activities include: conducting a needs assessment; strategic planning and 

project development; legal assistance; development and implementation 

of internal operating controls and procedures; grant writing and business 

plans; information and referrals; access to funding sources; training and 

information on applicable Federal and other funding requirements; 

financial management and accounting; development and use of outcome 

measurements and methods of evaluation; and linking to and networking 

with other organizations. 

(U.S. Health and Human Services, 2003) 
51

Table 7 

Mandated and Additional Partners of the Workforce Investment Board 

Mandated Partners Additional Partners 

Programs authorized under the Act 

Programs authorized under Wagner-Peyser 

Act 

Adult education and literacy activities 

authorized under Title II 

Programs authorized under Title I of the 

Rehabilitation Act of 1973 

Programs authorized under section 403(a) (5) 

of the Social Security Act 

Activities authorized under Title V of the 

Older Americans Act of 1965 

Postsecondary vocational education activities 

authorized under the Carl D Perkins 

Vocational and Applied Technology 

Education Act 

Activities authorized under chapter 41 of Title 

38 United States Code 

Employment and training activities carried out 

under the Community Services Block Grant 

Act 

Employment and training activities carried out 

by the Department of Housing and Urban 

Development 

Programs authorized under State 

unemployment compensation laws 

Programs authorized under part A of Title IV 

of the Social Security Act 

Programs authorized under section 6(d) (4) of 

the Food Stamp Act of 1977 

Work programs authorized under section 6(0) 

of the Food Stamp Act of 1977 

Programs authorized under the National and 

Community Service Act of 1990 

Other appropriate Federal, state, or local 

programs, including programs in the private 

sector. 

(Workforce Investment Act of 1998)
52

Table 8 

Faith- and Community-Based Organizations That Work With the Workforce Investment Boards 

by Metropolitan and Non-Metropolitan Counties 

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

1 Cumberland Community Action Program. 

1 Catholic Charities Services of Lake County 

28 Isaiah:58, St Vincent DePaul, Salvation Army, Christian Home, Santa Rosa 

Food Bank, Lutheran Food Bank, West Texas Food Bank, Catholic Charities, El 

Buen Vecino, Pecos County Community Action, First Presbyterian Church, 

Rose of Sharon Baptist Church, Golf Course Church of Christ, Casa de Amigos, 

Christian Women's Job Core, Kelview Baptist Church, Crestview Baptist 

Church, First Baptist Church, Church of Jesus Christ of Latter Day Saints, Our 

Lady of Guadalupe, Holy Trinity Episcopal Church, Bellview Baptist Church, 

Church of Christ Main Street, First United Methodist, Christian Church of 

Midland, West Texas Opportunities, Westside Lions Club, Fort Stockton 

Ministerial Alliance 

7 Kankakee County Community Services, Inc., Salvation Army, Catholic 

Charities, Gateway Coalition, Futures Unlimited, Options Center for 

Independent Living, Kankakee County Youth Intervention 

4 Faithworks, Empower Lewiston, Samali Community, varied Church mentoring 

program 

4 Atlantic City Rescue Mission, Atlantic City Covenant House, Vision 200, Inc., 

Catholic Charities 

1 Coordinated Youth Services 

2 St. Francis Community Center, Temple Community Development Corp 

4 In July 02 CT/DOL received a Grant to pilot a one-year FB/CB initiative. See 

attached "Overview." The State agencies linked to this project are: CT DSS, 

Connecticut Judicial Branch, HUD, and the State Department of Education. 

4 Center for Child and Family Services (CCFS), Regional Job Support Network 

(RJSN), Alternatives, Inc. (AI), St. Paul's Episcopal Church (SPEC) 

3 Goodwill Industries of Southern New Jersey, Occupational Training Center, 

Burlington County Community Action Program 

12 Operation Bootstrap, North Shore Community Action Program, Action Inc., 

Salem Family Investment Center, Salem Harbor CDC, MassJob Training, Girls 

Inc., Wellspring House, Independent Living Center, Catholic Charities, Jewish 

Family Services, Essex County Community Organization. 

5 Catholic Family Center, Urban League of Rochester, Action for a Better 

Community, Ibero-American Action League, Center for Youth Services. 

7 Friendship of Women, Women Together, TDHS, Tropical Texas, CDCD, 

Harlingen Housing, Cameron County Housing Authority 

 

 

 
53

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

22 Jericho Road Ministries, Habitat for Humanity of Hernando County, Holy 

Trinity Lutheran Church, Salvare Inc. d/b/a Dawn Center, St. Vincent DePaul St. 

Theresa Conference, St. Joan of Arc Catholic Church, Catholic Charities 

Diocese of St. Petersburg, First Church of God, Christian Life Assembly of God, 

Sunrise of Pasco County, Christian Social Services, Lighthouse Pentecostal 

Church International Inc., St. Stephen's Episcopal Church, Resource Center for 

Women, Gulf Coast Community Care, Stepping Stones to Independence/Our 

Lady Queen of Peace Catholic Church, Some of My Best Friends, Deaf Service 

Bureau of West Central Florida, The Salvation Army Domestic Violence 

Program of West Pasco County, New Beginnings Miracle and Deliverance 

Center, Pasco Family Protection Team/Healthy Families. 

3 Community Action Program of Western Indiana, Wabash Center Inc., Abilities 

Services Inc. 

1 Chaplains at Work 

4 RECAP, NCAC, Occupations Inc., Best Resources 

4 CareerWorks, SER Metro Detroit, Detroit AAA, Wings of Faith 

1 St. Marks 

2 Greater Deliverance Church, Friends of the Franklin County Public Library 

2 Lee Economic Development Partnership, Harnett Prod. Enterprises 

5 People Acting in Community Endeavors, Coastline Elderly Services, MY TURN 

Inc., Educational Opportunity Center, Lifestream, Inc. 

4 Goodwill Industries, Tableland Services, Family Services of Blair County, 

Bedford-Fulton Human Services 

8 Merced County Community Action Agency, WIC, CHERISH Senior Nutrition 

Program, Seniors Brown Bag and Surplus Food Programs, Community Service 

Centers, CAP Weatherization Program, CAP Housing and Shelter Program, 

CAP Workforce Development Department 

5 I Care, Inc., Salisbury- County Service Council, Inc., Union County Community 

Action, Inc., Greater St. Matthews Lighthouse Gospel Word Ministries, 

Richmond County Support Center 

3 Goodwill Industries of San Joaquin County, California Human Development 

Corporation, Council for Spanish-Speaking (Concilio) 

6 Urban League, Literacy Volunteers of America, Norwescap, Morris County 

College Women's Center, Employment Horizons, United Way 

9 Family Partnership, Office of Children and Families, Bishop Claggett Center, 4-

H, YMCA, Stay Station, Frederick Works Project, Community Action, City 

Housing. 

1 Economic Security Corporation 

100 The Suffolk WIB through its partnership system has worked with an excess of 

over 100 local faith and community based agencies. There are far too many to 

provide an itemized listing of the agency names. 

 
54

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

15 Trinity Church, Lutheran Services, Jewish Community Services, Catholic 

Charities, Center for Independent Living, Spinal Cord Living Assistance 

Development Inc., Florida Institute for Workforce Innovation, James E. Scott 

Community Association, Jobs for Miami, SER Jobs for Progress, SABER Inc., 

Youth Co-op, Inc., Adult Mankind Organization, Miami Beach Development, 

ASPIRA 

3 Adelente! Youth Center, Methwen Arlington Neighborhood, Hope Street Youth 

Center 

2 Catholic Charities, Oswego Career Opportunities. 

4 Methodist Church of Valparaiso, Catholic Charities, Starke County Ministerial, 

Faith Works of Jasper and Newton County 

6 Catholic Social Services, Laredo Food Bank, Holding Institution, Casa De 

Misericordia, Buckner Children and Family Services, Centro Aztlan 

2 Mill Street Loft, Youth Resource Development Corporation 

1 St. Marks 

3 Catholic Charities, Genesis Group, Goodwill Industries of the Chesapeake 

1 Emerald Coast Promise 

35 Urban League of Broward County, Catholic Charities, Goodwill Industries of 

Broward County, Hispanic Unity of Florida, SER Jobs for Progress Inc., Family 

Central, First Call for Help of Broward County, Liberia Economic & Social 

Development Corp., OIC of Broward County, Victory Living Programs Inc. 

Additionally, we have a Faith-Based Organization/Community Based 

Organization (FBO/CBO) Grant from the USDOL to work with 25 FBO/CBO in 

the area. 

4 Catholic Charities, Interfaith Hospitality, United Way, Dress for Success 

4 Education and Assistance Corporation, Economic Opportunity Commission of 

Nassau County (EOC), Goodwill Industries of Greater New York and Northern 

New Jersey, Circulo de la Hispanidad. 

4 Ministerial Alliances, Rogers County Drug Abuse, Grand Lake Mental Health, 

Community Action 

1 Gulf Coast Community Action Agency 

6 Catholic Charities, Goodwill Industries, United Methodist Urban Ministries, 

Wichita Children's Home, Job Readiness Training, Kansel 

12 Asian Resources, Crossroads Diversified Services, Greater Sacrament Urban 

League, LaFamilia Counseling Center, Mutual Assistance Network of Del Paso 

Heights, Northern California INALLIANCE, PRIDE Industries, Sacramento 

Chinese Community Service Center, Sacramento Lao Family Community, Inc., 

Sacramento Occupational Advancement Resources, Inc., Turning Point 

Community Programs, Visions Unlimited, Inc. 

3 Easter Seals, Goodwill, El-Ada Community Action Agency 

5 Goodwill Industries, Catholic Charities, Latin American Association for 

Development, Community Outreach Program for the Deaf, Independent Living 

Resource Center 

2 Arizona Call-A-Teen, St. Joseph the Worker 
55

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

5 Center for Family Services, Abilities Center for Southern New Jersey, St. 

Matthews Baptist Church, Second Baptist Church, Youth Advocacy Program 

17 Lifespan, Native American Cultural Center, Action for a Better Community, 

American Red Cross, Baden Street Settlement, Center for Youth Services, 

Community Place of Greater Rochester, Ibero American Action League, 

PRISM, Rochester Landscape Technician Program, Urban League, YWCA, 

Threshold Center for Youth, Outreach Temple, Boys and Girls Club, Catholic 

Youth Organization, Puerto Rican Development 

11 Friends Outside, Central Valley Opportunity Center, Center for Human Services, 

Center for Senior Employment, Excell Center, YMCA, Westside Community 

Center, United Way, NorCal Center on Deafness, The Great Valley Center, 

Youth for Christ 

10 Jeffco Action Center, STRIDE, Seniors' Resource Center, Family Tree, Lutheran 

Refugee Services, Colorado Homeless Families, Interfaith Task Force, Loaves & 

Fishes, Cerebral Palsy of Colorado, Job Corps 

9 Community Action Program, Handicrafters, Spanish-American Civic 

Association, Urban League of Lancaster County, Literacy Council of Lancaster 

County, Salem United Methodist Church, Neighborhood Services, Lancaster 

Council of Churches, BASE. 

4 Catholic Charities, Shiloh Baptist Center, OIC, Centro de Communidad. 

25 Baptist Temple Church, Christ of Vicar Lutheran Church, Church of Christ, 

County Extension Office, Cuero Christian Academy, Cuero ISD, First Baptist 

Christian Day Care, First Baptist Church, First Presbyterian Church, First United 

Methodist Church, Housing Authority, Jerusalem Baptist Church, Lord’s Little 

Angels, Mid Coast Family Services, Minnehulla Baptist Church, Nazareth 

Academy, Our Lady of the Gulf, Salvation Army, Solid Rock Christian Learning 

Center, St. James Catholic Church, STAR Family Service, Trinity Episcopal, 

YMCA, Shiloh Baptist Church 

21 A-Prep Center, United Community Centers, Heavenly Gospel Church 

Transformation Center, Tarrant Area Community of Churches/Family 

Pathfinders, The Women’s Center of Tarrant County, MHMR of Tarrant 

County, East Fort Worth Montessori School, Tarrant Council on Alcoholism and 

Drug Abuse, The Institute for Responsible Fatherhood and Family 

Revitalization, Tarrant County ACCESS, Cassata Learning Center, 

TCU/CCWW, Job Bank, Emergency Assistance of Tarrant County, Faith-InAction Committee (United Way), Boys & Girls Clubs of Greater Fort Worth, 

Tarrant County College, Near Northside Partners Council, Community Learning 

Center, CCPP Program, STYEP Program 

1 Advent House Ministries 

5 Crowley's Ridhe Development Council, City Youth Ministries, Consolidated 

Youth Services, Boys/Girls Club, Parks & Recreation 

5 Experience Works, Rural Opportunities Inc., WSOS Community Action 

Commission, YW Child Care Connections, Behavioral Connections of Wood 

County 
56

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

34 Catholic Charities of the Diocese of Beaumont Inc., Beaumont Housing Authority, 

Beaumont ISD Adult Basic Education Program, City of Orange Housing 

Authority, Orange County Housing Authority, Greater Orange Area Literacy 

Service, Lamar University - Orange, Lamar University - Port Arthur, Port Arthur 

ISD Adult Basic Education, Port Arthur Housing Authority, Port Arthur Literacy 

Support, Hardin County Indigent Health Care, Some Other Place, The United 

Board of Missions, US Army Recruiting Company Beaumont, STERPC National 

and Community Services Act Programs, Advocacy Incorporated, UBI-Caritas 

Project Welcome, Texas Rehabilitation Commission, Programs for Human 

Services - Senior Aide Program Title V, Texas Commission for the Blind, South 

East Texas Management Network, Texas Workforce Commission, South East 

Texas Regional Planning Commission - Transportation Planning Commission, The 

Texas Educational Foundation/Job Corps, US Probation Department of Beaumont, 

Samaritan Counseling Center of Southeast Texas, Port Arthur ISD Memorial High 

School, Beaumont ISD Ozen High School, BISD Central High School, West 

Orange-Cove Consolidated ISD West Orange Stark High School, West Hardin 

County Consolidated ISD, Richard Milburn Academy, Lamar Institute of 

Technology, Lamar State College - Orange, LamarUniversity 

3 Experience Works, Ya-Ka-Ama, West County Community Services 

6 Connellsville Ministerium (sp), United Way of Westmoreland County, New 

Kensington YMCA, Communities in Schools, Goodwill of Fayette County, Adam 

Memorial Library 

2 30901 Development Corporation, Beulah Grove Community Resource Center 

4 Chautauqua Opportunities, The Resource Center, Trinity Church, Lutheran Social 

Services 

6 Catholic Charities, Camden County Office on Economic Opportunity, The Work Group, 

Hispanic Family Center, Respond Inc., PRUP 

21 The Training Institute - America, Chicana Action Service Center, Build 

Rehabilitation, Carson Lomita Torrance WIB, MCS Rehabilitation, Career Planning 

Center, Goodwill Industries, ACS, Los Angeles Mission College, Community 

Centers Inc., United Auto Workers LETC, Watts Labor Community Action 

Committee, El Proyecto del Barrio, Los Angeles Urban League, Community Career 

Development, Pacific Asian Consortium for Employment, Chinatown Service 

Center, Los Angeles Community College District, Housing Authority of Los 

Angeles, Career Encores, South Bay WIB, Advanced Computing Institute 

2 First United Methodist Church, St. Paul Methodist Church 

20 Atlanta Enterprise Center Inc., Communities in Schools of Atlanta, Community 

Concerns Inc., Covenant House Georgia, Families First Inc., Genesis Prevention 

Coalition Inc., Literacy Action Inc., Literacy Volunteers of America - 

Metropolitan Atlanta, Nonprofits for Nonprofits Inc., Project Connect of Jewish 

Family & Career Services, Project Open Hand/Atlanta, Quality Living Services 

 

 
57

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

 Inc., Samaritan house of Atlanta Inc., SCLS W.O.M.E.N. Inc./Women's 

Organization Move for Equality Now), SERO-NSSFNS, South Fulton Community 

Coalition Inc., Spectrum Technical Institute Inc., The Sullivan Center Inc., 

Turning Point Enterprises Inc., Viewpoint of Metropolitan Atlanta Inc. 

2 Mt. Zion Human Services, Catholic Charities 

2 Bethel Community Facility, Visions of Restoration Inc. 

10 Crosspoint, The Redemption Center, Prairie Center, Consumer Credit Counseling 

Services, Community Action Agency, Boys & Girls Club, YMCA/YWCA, Dave 

Coleman Ph.D., Salvation Army, various social services 

3 CHR Inc., GNJ Family Life Center, Victory Neighborhood Services Center 

221 Community Action Council of Central WI, Community Action Council of South Central 

WI, CAP Services, Dodge County Multi-Cultural Council - Dodge English 

Language Earner, Employment and Training Assoc Inc., Forward Service 

Corporation, Madison Literacy Council, Marquette County Literacy Council, 

Operation Fresh Starter, Opportunities Inc., Salvation Army. We also work with 

about 200 different community based and faith based organizations for the 

purpose of identifying support services to meet our customers barriers. There are also 10 

community based organizations who are individual training account 

vendors. 

2 Springfield Urban League, Springfield Community Federation 

6 First Presbyterian Church, Housing Resources, Deacon's Conference, Ministry with 

Community, Hope Network, New Genesis, Inc. 

2 MERS Goodwill, Urban League 

73 Fresno Interdenominational Refugee Ministries, One By One Leadership, 

American Indian Center Of Central CA, Big Brothers/Big Sisters, Ca State 

University Foundation, Care Line, Central Valley Children’s Services Network, 

Central Valley Crisis Pregnancy Center, Chicano Youth Center, Community Link, 

Inc, Comprehensive Youth Services, Inc. Exceptional Parents Unlimited, Family 

First Health Care (Planned Parenthood), Family P.A.C.T., Fresno Barrios Unidos, 

Fresno Career Development Institute, Inc, Fresno Institute For Urban Leadership, 

Fresno Metro Ministry, Genesis, Inc, Girl Scouts, Golden Valley Council, 

 Glorybound Ministries, HIV/Aids Program, Hope Now For Youth, Inc, House Of 

Hope For Youth, Inc, Juvenile Justice Ministries (Youth For Christ, League Of 

Hispanic Women, Local Conservation Corp, Marjaree Mason Center, New Life 

For Girls, Parenthood Mar Monte, Phone-A-Friend, Planned Parenthood, Rescue 

The Children, Safe Place Mentor Program, Sanctuary Safe Place Youth Shelter, 

SPCA Education Department, State Center Consortium (STC), Summitt 

Adventure, Teen Connection (YMCA), Teen Pregnancy Resource Center, Teen 

Smart Outreach Program, Tobacco Program, Turn On To Teens, Upward Bound 

(FCC), Youth For Christ, Boys & Girls Club Of Fresno County, Fresno Indian 

Education, Project Access, School Age Child Enrichment, Stone Soup 

 

 
58

Number Faith- and Community-Based Organizations, in Metropolitan Counties 

 Fresno, 4-H Development Programs, Parlier Youth Center, Bear Roots, Kings 

River Corps, Partnership For Better Living, Police Explorer Youth Group, West 

Fresno Pregnancy Prevention Program, Pastoral Counseling For Youth, San 

Joaquin Youth Center, Westside Youth Center, Latino Issues Forum, Proteus, Inc, 

Ser Jobs For Progress, Inc, I-5 Social Services Corporation, Fresno County 

Economic Opportunities Commission, I-5 Business Development Corridor, 

Fresno Regional Foundation, Encourage Tomorrow, Catholic Charities, Central 

California Consortium, FCC, YMCA/YWCA, Goodwill 

 

Number Faith- and Community-Based Organizations, in Non-Metropolitan Counties 

3 Community Action Council, SkillSource, Youth Dynamics 

2 Umpqua Community Action Network, Umpqua Training & Employment 

8 Lowcountry Community Action Agency, Beaufort-Jasper Economic Opportunity 

Corporation (CSBG Block Grants), Colleton County Literacy Association, 

Beaufort County Literacy Association, Hampton County Literacy Council (WIA 

Title II funds). These agencies provide staff and/or information at each One-Stop. 

Each agency has provided written information on the services they offer. Each of 

these agencies has entered into a MOU. Trinity Ministries, Greater Community 

Foundation, New Life Center. These agencies provide staff and/or information at 

each One-Stop. Each agency has provided written information on the services they 

offer. Each of the agencies has entered MOU. These agencies do not receive 

TANF or WIA funding. 

5 Gateway Community Industries, St. Cabrini Home, YMCA, SCORE, Family of 

Woodstock 

21 Hepburn Library of Lisbon, Canton Free Library, Norwood Library, Potsdam 

Library, Massena Public Library, St. Mary's Church and School, Trinity Catholic 

School, First United Methodist Church, St Vincent DePaul, Sacred Heart 

Church/Calvary Cemetery, Historical Society of St Lawrence County, NYS ARC, 

North Country Freedom Homes, SLC Community Development Program, Head 

Start, American Red Cross, Can AM Youth Service, Rose Hill, Massena 

Neighborhood Center, Salvation Army, Massena Independent Living Center 

3 Lake Wales Care Center, Help of Fort Meade, Luster-All Pastoral Care 

5 ZOE Christian Center Duncan, ZOE Christian Center Lawton, WIB Board 

members, WIA youth contacts, Chamber of Commerce 

1 Goodwill Industries 

3 United Christian Ministries, United Methodist Churches, Catholic Churches 

3 Catholic Charities of the Southern Tier, Pro Action of Steuben & Yates, Economic 

Opportunity Program 

1 Saint Joseph's Mercy Care Services, Inc., d/b/a Mercu Senior Care, Inc. 

2 Robeson County Church and Community Center, Center for Strategic Action 

3 Hawaii Island Catholic Social Ministries., Hawaii County Economic Opportunity 

Council, Goodwill Industries 

 

 
59

Number Faith- and Community-Based Organizations, in Non-Metropolitan Counties 

5 Quality Life Centers of Southwest Florida, Goodwill Industries, Workforce 

Council of Southwest Florida, Southwest Florida Employee Assistance Program, 

Planned Parenthood of Collier County 

4 Catholic Charities, Washington County EOC/Employment & Training Center, 

Cornell Cooperative Extension, CWI, many others 

3 The Jobs Partnership of Florida, Inc., The Holden Heights Front Porch 

Revitalization, The Goldsboro Front Porch Council 

6 Gateway Community Industries, St. Cabrini Home, YMCA, WDB Representation, 

SCORE, Family of Woodstock 

16 Ionia County Literacy Council, RAVE Domestic Violence Program, EightCAP, 

Ionic Economic Alliance, Montcalm Alliance, Montcalm Adult Reading Council, 

Community Closet, Business and Professional Women, Salvation Army, 

Community Ministerial Services, Experience Works, Greater Gratiot Economic 

Development, Middle Michigan development Corp., America's Promise, Goodwill 

Industries, and local services clubs. 

1 Joint Orange Chatham Community Action Agency 

1 Lutheran Social Services 

1 Concerted Services Inc. 

1 Rural Challenge Initiative or RCI 

16 List is too long to fax - we have 16 providers that are owned by faith-based 

organizations 
60

Table 9 

Populations Served by Faith- and Community-Based Organizations, Listed in Alphabetical 

Order

Populations Identified by Groups and by Specific Needs 

Adults Illiterate 

Alcohol and substance abuse issues Immigrants 

Child Abuse (victims of) Incarcerated juveniles

Disabled Judicial system (involved with) 

Dislocated workers Mental health issues (adversely affected) 

Displaced homemakers One-stop operator staff 

Educationally disadvantaged individuals Parents 

Ex-offenders Pregnant and parenting youth 

Emergency food and shelter Refugees 

Ethnic backgrounds (differences) Seniors 

Hard to reach Social service recipients 

Hard to serve Victims of domestic violence 

Health Issues Youth 

Homeless 
61

Table 10 

Services Provided by Faith- and Community Based Organizations in Conjunction With the 

Workforce Investment Board 

Types of Services Frequency in Metropolitan 

Counties 

Frequency in NonMetropolitan Counties 

Alcohol and substance abuse 5.7% 3.4% 

Advocacy 1.9% 3.4% 

Assessment 5.7% 3.4% 

Case management 8.6% 10.3% 

Child abuse prevention <1% 0% 

Child care/after school care 6.7% 6.8% 

Clothing 9.6% 0% 

Counseling 18.3% 13.7% 

Education (GED, post-high, etc) 17.3% 6.8% 

English as a second language 3.8% 3.4% 

Financial/fiscal 7.6% 0% 

Food 16.3% 6.8% 

Fuel assistance 7.6% 0% 

Health/medical 10.6% 3.4% 

Housing 23.1% 10.3% 

Job placement 21.1% 20.7% 

Job training 53.8% 52% 

Life skills/soft skills 21.1% 13.8% 

Literacy 4.8% 6.8% 

Mental health 9.6% 3.4% 

Mentoring 12.5% 6.8% 

Parenting 3.8% 3.4% 

Pregnancy/prevention 1.9% 3.4% 

Referrals 13.5% 6.8% 

Refugee resettlement <1% 0% 

Technical assistance 3.8% 10.3% 

Transportation 9.6% 6.8% 

Tutoring 1.9% 3.4% 
62

Section IV 

Figures 

Figure 1. The One-Stop Career Center System provides a common system across agencies, 

organizations, and programs within the community for job seekers and employers (Adapted from 

Bender, 2001).

One 

Stop 

and 

WIA

Education and 

Training 

Agencies 

Other 

Service 

Agencies 

Economic 

Development
63

Figure 2. The One-Stop Career System links the One-Stop Career Center with satellites and 

provider agencies. The One-Stop is where agencies and services are co-located. Satellites may 

not have all of the services within the physical confines of their agency, but they have the 

capacity to make appropriate referrals and engage customers in the System. A solid line 

indicates formal linkages between agencies signified by a Memorandum of Understanding. A 

dotted line signifies less formal relationships between the One-Stop and partners, satellites, 

and/or providers. Together, they provide a network of services for job seekers and employer 

customers (Adapted from Bender, 2001). 

One-Stop

Provider/ 

Partner 

Partne

r 

Provider 

Agency

Provider

Agency

Provider

Agency

Partne

r 

Satellite 

Satellite

Partner/ Satellite

 Workforce 

 Development System 
64

Figure 3: Responses were received from state and local Workforce Investment Boards from 82 

percent of the nation’s states, exclusive of the District of Columbia. 

Striped areas: State Workforce Investment Board response only 

Dotted areas: State and local Workforce Investment Board response 

Dark gray areas: Local Workforce Investment Board response only 
65

Section V 

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 Ammerman, N. T. (2001). Doing good in American communities: Congregations and 

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 Ammerman, N. T. (1997). Congregation & community. New Brunswick, NJ: Rutgers 

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 Anderson, S. et al. (2000). Can we make welfare reform work? The California religious 

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A Frontline Decision Support System 

for Georgia Career Centers

Randall W. Eberts and Christopher J. O’Leary 

Upjohn Institute 

JEL Classification Codes: J68, J65, J64, H49 

This paper is based on work done for the Georgia Department of Labor by the W.E. Upjohn 

Institute for Employment Research under a grant from the U.S. Department of Labor. We thank 

Helen Parker of the Georgia Department of Labor, Stephen Wandner of the U.S. Department of 

Labor, and their staffs for technical guidance and constructive comments. Outstanding research 

assistance was provided by Ken Kline. Kim Kornokovich and Terry Durden of the Georgia 

Department of Labor coordinated implementation efforts. System programming was expertly 

done by Angela Simpson of the Georgia Department of Labor. Kelly DeRango, Wei-Jang 

Huang, Kristine Heffel, and Kris Kracker made substantive contributions. Clerical assistance 

was provided by Claire Black and Phyllis Molhoek. Opinions expressed are our own, and do not 

necessarily represent those of the W.E. Upjohn Institute for Employment Research. We accept 

responsibility for any errors.
81

BACKGROUND

The Workforce Investment Act (WIA) of 1998 emphasizes the integration and 

coordination of services to promote employment. This objective is fostered by the federal 

requirement that local areas receiving WIA funding must establish one-stop centers where 

providers of various employment services are assembled in one location. 

WIA also broadens access to employment services by reducing eligibility requirements. 

As a consequence, a significant increase in customer volume is expected. Coupled with limited 

program resources, the challenges now facing the public employment system are to coordinate 

programs and streamline service delivery. 

Meeting these challenges is hindered by the fact that prior experience of frontline staff is 

often specific to a single program, while customers of the new one-stop system will arrive with a 

broad variety of needs. An additional complication is the WIA emphasis on accountability. 

WIA requires that program success be measured by employment, earnings, job retention, and 

knowledge or skill attainment. 

The Frontline Decision Support System (FDSS) is a set of administrative tools being 

developed to help frontline staff in one-stop centers to quickly identify customer needs and 

choose appropriate services. FDSS includes new tools to promote effective job search and 

identify employment services most likely to be effective. 

The U.S. Department of Labor commissioned the W.E. Upjohn Institute for Employment 

Research to design, develop, test, and implement FDSS in the state of Georgia. FDSS is being 

structured in a way that should permit other states to easily integrate the decision tools into their 

specific computer operating systems. After testing FDSS in Georgia, USDOL intends to offer 

the tools to other interested states. 

The W.E. Upjohn Institute is in a unique position to undertake this project since the 

Institute both conducts employment-related research and administers state and federal 

employment programs for the local Workforce Investment Board. The Institute has been the 

administrator of state and federal employment-related programs for the Kalamazoo, Michigan 

area continuously since the early 1970s. During that period, the Institute has operated programs 

under the Comprehensive Employment and Training Act (CETA), the Job Training Partnership 

Act (JTPA), and currently, the Workforce Investment Act (WIA). 

Over the past twenty years the Institute has also worked closely with employment 

security agencies in several states and countries to conduct applied employment policy research. 

This work has included a number of random trial field experiment evaluations of employment

program innovations. Conducting employment research and operations within the same 

organization provides the Institute with valuable experience coordinating the type of analytical 

and administrative tasks required to develop and test FDSS within one-stop centers. 
82

This paper provides an overview of FDSS and explains the analysis underlying the 

decision algorithms that form the backbone of FDSS tools. In the next section, we summarize 

the overall concept of FDSS and indicate where elements of FDSS could fit into the typical client 

flow through one-stop centers. Section 3 provides technical details of the statistical models 

behind the decision support tools in FDSS. Section 4 provides an example of a typical FDSS 

decision support session using prototype screens from the internet-based Georgia Workforce 

System. The final section of our paper provides a summary of FDSS and describes current plans 

for field testing and implementation in Georgia. 

Pilot testing of FDSS in Georgia began in July of 2002. The examples provided in this 

paper are drawn from the prototype system pilot-tested in the Athens and Cobb-Cherokee 

Georgia Career Centers. 

FRONT LINE DECISION SUPPORT WITHIN ONE-STOP CENTERS

To clarify the role of FDSS, we begin with a brief overview of one-stop centers, the 

services they provide, and the way in which staff members interact with customers. Since onestop centers vary across states, we can provide only a stylized description. However, this 

summary will suffice for our purpose of describing how FDSS can be integrated into one-stop 

centers. 

As mandated by WIA, one-stop centers are a central physical location for the provision of 

services by the following federal and state programs: Unemployment Insurance, Employment 

Service, Dislocated Worker and Youth Training, Welfare-to-Work, Veterans Employment and 

Training Programs, Adult Education, Post-secondary Vocational Education, Vocational 

Rehabilitation, Title V of the Older Americans Act, and Trade Adjustment Assistance. Other 

programs may also be included under a one-stop center’s umbrella of services. One-stop centers 

are designed to serve customers within local Workforce Investment Areas, which usually 

encompass the population of one or more counties within a state. Workforce Investment Areas 

with large populations or those which span a large geographical area may choose to establish 

several one-stop centers. WIA required that each state develop a system of one-stop centers that 

would be fully operational by July 2000, and most states met that target date. 

Services provided by the one-stop centers are divided into three levels: core, intensive, 

and training. Services within each level are characterized by the amount of staff involvement 

and the extent to which customers can access the service independently. Core services typically 

have the broadest access and the least staff involvement of the three categories. Many core 

services are accessible on a self-serve basis. All adults and dislocated workers can access core 

services, which include assessment interviews, resume workshops, labor market information, and 

interviews for referral to other services. 

Intensive services require a greater level of staff involvement and, consequently, access is 

more limited than for core services. Services within the intensive category include individual 

and group counseling, case management, aptitude and skill proficiency testing, job finding 

clubs, creation of a job search plan, and career planning. Training services, the third and highest 
83

level of service intensity, are open to customers only through referrals. Typically, a list of 

approved organizations is set outside of one-stop centers to provide these services. Training 

services typically include adult basic skills education, on-the-job-training (OJT), work 

experience, and occupational skills training. 

The first challenge for one-stop center operators is the expected large volume of 

customers. Nationally, nearly 50 million people are expected to use one-stop centers each year. 

The move toward integrating services raises another challenge: staff will be asked to serve 

clients who may have unfamiliar backgrounds and needs. For instance, a staff person who 

worked extensively with dislocated workers under JTPA may now be asked to work with welfare 

recipients as well. WIA does not provide additional resources for staffing or cross-training. 

Another challenge for operators of one-stop centers is to refer customers to services in the 

most effective matter. The efficiency and effectiveness of a center’s operations are driven by the 

difference in cost of providing the three levels of services. As shown in Figure 1, the cost of 

services increases dramatically and the anticipated number of participants falls as one moves 

from core to intensive to training services. Therefore, the ability to identify the needs of 

individuals and to refer them to the appropriate service as early as possible in the process will 

determine the cost effectiveness of the one-stop centers. 

To address the challenges of effectively operating one-stop centers, FDSS has two basic 

sets of tools or modules. Figure 2 shows how the two modules fit into the operation of the onestop center. The first is the systematic job search module (SJSM). The SJSM is a set of tools to 

provide customized information about several aspects of the job search process. Initial job 

search activities are concentrated in the core services, and consequently this is where the 

systematic search module will be incorporated. The second module of FDSS is the service 

referral algorithm (SRM). The SRM is based on information about the characteristics of recent 

participants in services offered by one-stop centers. Statistical models of participant labor 

market success provide the basis for referral algorithms in the SRM, which will be available to 

support staff recommendations. 

THE ANALYTIC FOUNDATION OF FDSS TOOLS

In this section we explain the analytic foundation for each of the tools in FDSS using 

examples drawn from the Atlanta region of the Georgia FDSS project. To review the tools, we 

sequentially consider the components of the SJSM and SRM. 

Systematic Job Search Module

The SJSM contains tools which can be used to inform the customer about the: 1) 

probability of return to work in the prior industry, 2) expected job growth in the prior occupation, 

3) likely reemployment earnings, 4) available suitable job vacancy listings, and 5) related 

occupations. 

Probability of Return to Work in the Prior Industry
84

Most customers who use one-stop centers will not return to their prior employer, but 

instead will gain reemployment with a different employer. In our sample of Georgia UI clients, 

at most 19.1 percent returned to work with their prior employer.10 Furthermore, the great 

majority of new jobs are in a different industry. A change in the industry of employment often 

means a loss in the value of industry specific skills, with an associated negative impact on 

reemployment earnings.11 The quickest way to return to the prior lifetime earnings path is to 

resume employment and begin building firm-specific human capital in a new job. To help 

clients more realistically assess job prospects and therefore return to work more quickly, FDSS 

provides an estimate of the probability of returning to employment in the prior industry. 

Reliable data are available from UI wage records in Georgia to identify the industry in 

which the person was employed before and after displacement. Table 1 shows an industry 

transition matrix for UI clients in Metropolitan Atlanta. Industries are separated into nine 

categories with the prior industry category in the left column and the reemployment industry 

listed along the top row. In each row the largest element is on the diagonal of the matrix, 

indicating that the largest share of industry UI recipients return to work in the same industry. 

However, only for two industry groups is the aggregate average probability of returning to work 

in the same industry greater than 50 percent: mining-construction and services. For all other 

industry groups there is a better than even chance of changing the industry of employment. 

Table 2 summarizes the gross average percentage change in quarterly earnings associated 

with the industry employment changes in the Atlanta metropolitan area. The diagonal of Table 2 

is positive for all industries except public administration, indicating that those who manage to be 

reemployed in their prior industry have earnings gains associated with changing jobs. The vast 

majority of off-diagonal elements in Table 2 are negative. The greatest earnings losses are 

experienced by those who switch industries and move into either agriculture, retail trade, 

services, or public administration. 

To provide individual estimates of the probability of getting reemployed in the prior 

industry, we estimated logit models for each industry transition. The logit model relates whether 

or not an individual stays in the same industry to a set of explanatory variables including prior 

earnings, age, educational attainment, the quarter of the year in which UI was applied for, and 

indicator variables for prior occupation.12 The logit model also includes variables to indicate 

 

10For UI clients in Georgia, return to the prior employer is judged using wage records for the five quarters 

immediately following the quarter of initial claim. Three definitions of prior employer were applied. All three 

definitions considered the employer paying the greatest share of quarterly earnings. The three definitions (and rates 

of return) were: the preceding quarter (19.1%), during the UI base period quarter with highest earnings (16.0%), the 

UI base period quarter with the highest earnings matched the employer of the quarter with the highest earnings in the 

five quarters immediately following the claim (11.5%). 

11As suggested by Becker’s (1964) theory of human capital. 

12Age, gender, and race are prohibited variables in Worker Profiling and Reemployment Services (WPRS) models 

(Eberts and O’Leary 1996). However, unlike WPRS the FDSS system does not set criteria for program eligibility. 
85

whether an individual was a member of the following population groups: youth, veterans, 

currently employed, receiving public welfare assistance, and dislocated workers.13 Because of 

eligibility conditions, UI beneficiaries include very few people currently enrolled in school, so 

that category was not included in the return to prior industry model. 

Table 3 reports parameter estimates of the return to prior industry logit models computed 

on a combined sample of UI recipients and ES registered customers in the Atlanta region whose 

prior job was in the manufacturing industry. The model includes an indicator variable for UI 

recipients. To illustrate model sensitivity it is evaluated for three examples. Example 1 is a 

person aged 35, with a high school education, who earned $30,000 per year in a sales or related 

occupation and became eligible for UI in the second calendar quarter.14 The probability of return 

to the same industry was estimated to be 0.317 in the Atlanta region. Doubling prior earnings 

from $30,000 to $60,000 raised the chance of returning to manufacturing to 0.340 in the Atlanta 

area. The third example illustrates the effect of having a lower prior annual earnings of $10,000; 

the direct correlation results in the probability of return to the prior industry falling to 0.205. 

Expected Job Growth in the Prior Occupation

Data were available on the industry of both the previous and the new employer, making 

estimation of the probability of return to prior industry possible. However, no similar data are 

available by occupation. To provide some information on the chance of return to prior 

occupation, we simply present the estimated annual employment growth rate in the prior 

occupation based on the ten-year forecast produced using the U.S. Department of Labor 

methodology by the Workforce Information and Analysis Division of the Georgia Department of 

Labor. 

This type of labor market information (LMI) is occasionally presented to customers to 

help them understand the market context of their job search. However, the data usually 

presented are aggregated over the labor market. By providing information specific to a 

customer’s prior occupation and local labor market, the information is both customized and 

relevant to decisions during the job search process. The estimated employment growth rates 

may be positive, negative, or zero. Since the change may be small, the Georgia Workforce 

Information and Analysis Division reports growth with statistical significance to the onehundredth of a percentage point. FDSS presents occupational employment growth estimates at 

the same level of precision. 

 The FDSS computer screens display age, gender, and race as customer background characteristics. However, 

among these only age is used in FDSS statistical models. Age is used to identify youth. 

13These categories are defined by Employment Service (ES) practice. The dislocated worker definition is consistent 

with that in the Economic Dislocation and Worker Adjustment Assistance Act (EDWAA) of 1988. The EDWAA 

definition includes those with significant prior job attachment who have lost their job and have little prospect of 

returning to it or to another job in a similar occupation and industry. 

14Note that the earnings variables in the models are quarterly figures, not annual figures. 
86

Analysis of 786 occupations measured by the Georgia Department of Labor’s Workforce 

Information and Analysis Division reveals that the median projected annual job growth rate is 

1.62 percent over the next five years. This means half of the occupations will grow faster and 

half will either grow more slowly or decline. One-quarter of occupations are predicted to have 

growth rates above 2.78 percent and one quarter are predicted to grow less than 0.54 percent. 

Only computer scientists are forecast to have double-digit job growth. Employment will be 

steady or declining for about 20 percent, or approximately 157 occupations. The prototype 

FDSS informs a system user about the estimated growth in jobs by occupation for the local 

Workforce Investment Area. 

Likely Reemployment Earnings

The WIA legislation permits intensive services to include “evaluation to identify 

employment barriers and appropriate employment goals,” and also “the development of an 

individual employment plan, to identify appropriate employment goals, appropriate achievement, 

and appropriate combinations of services for the participant to achieve their employment 

goals.”15 An underlying principle of WIA is that the best training is a job. Moderating wage 

objectives in order to win a new job may be the quickest way to return to the prior earnings path. 

This establishes a need for a system like FDSS and requires that outcomes be judged relative to 

individual targets. FDSS provides an algorithm to estimate the expected reemployment earnings 

for each customer. By providing the customer with a realistic assessment of earnings prospects, 

he or she can conduct a more informed job search that can hasten the employment process. 

Displaced workers and those who have had little attachment in the workplace, such as 

welfare recipients, may have little understanding of the earnings level that they might expect to 

find in the local labor market given their skills and opportunities. Displaced workers, for 

example, may expect to receive wages in their new jobs comparable to those in the job held prior 

to displacement. However, research suggests that displaced workers can expect a significant 

drop in earnings (Ashenfelter 1978). Most of the loss in earnings is due to a loss in the value of 

firm-specific skills (Jacobson, LaLonde, and Sullivan 1993). 

It is important to point out that the FDSS earnings assessment is only suggestive. 

Customers who find the recommended target to be out of line with their expectations may 

discuss their differences with a staff person in the one-stop center. The staff person may use 

several means in addition to FDSS to establish a realistic earnings target, including recent wage 

surveys and current labor market conditions. 

A median regression model was used to estimate earnings. The model relates quarterly 

earnings to personal characteristics and labor market conditions. Many of these factors may be 

similar to those used by employment counselors to match customers to openings. The model 

assesses those factors in a systematic and consistent way, so that customers with similar needs 

 

15Section 133(d)(3)(i) and (ii), Workforce Investment Act (WIA), Public Law 105-220BAugust 7, 1998. 
87

and characteristics are treated similarly. We used a median regression model since FDSS will 

present a range of reemployment earnings estimates by giving quartiles of the reemployment 

earnings distribution. The median is the second quartile. 

The earnings models were developed using quarterly earnings data from UI wage 

records. However, workers do not usually measure their compensation in terms of quarterly 

earnings. Rather, earnings are typically expressed as hourly, weekly, monthly, and yearly rates 

of compensation. Converting the quarterly earnings to any of these other units is problematic, 

since wage records do not indicate the number of hours worked or even the number of weeks 

worked during a quarter. By using the maximum earnings in the year before and the year after 

receiving reemployment services, we anticipate that quarterly earnings will reflect full-time 

hours. Conversion from quarterly earnings to hourly earnings can then be achieved by applying 

the usual hours of work observed in each occupation and industry group using national survey 

data.16

We report the results from the median regression models for the manufacturing sector in 

metropolitan Atlanta, which is the same region and industry used in the Areturn-to-priorindustry@ models discussed above. As shown in Table 5, the model includes variables typically 

used in earnings models, such as educational attainment, prior job tenure, occupation, and 

industry. Of course, the industry of reemployment is known only after a person finds a job. 

Since it is an endogenous variable, it would be appropriate to find an instrument for this variable, 

such as the industry transition regression described in the previous section. However, since our 

primary purpose is to construct a relatively simple model that offers the best prediction of future 

wages, we have not instrumented the variable in the estimation process. Instead, when 

estimating the earnings for individuals, we use whether or not they actually returned to the same 

industry as data. When FDSS is used to predict a customer’s earnings, however, we substitute 

the prediction of the probability the person will find a job in the same industry as the value for 

this variable in the earnings equation. Earnings models for Georgia also include age and agesquared terms to capture the earnings cycles over one’s working life. 

Georgia data permit the inclusion of additional explanatory variables measuring tenure on 

the previous job, possession of a driver’s license, availability for rotating shifts, employer 

attachment, and current school enrollment status. The model also includes indicator variables for 

population groups that are typically identified with the various programs offered by one-stop 

centers. These groups include youth, veterans, currently employed, receiving public welfare 

assistance, dislocated workers, and economically disadvantaged workers. 

Results of the median regressions on the Atlanta data, as shown in Table 5, are broadly 

consistent with previous earnings research. Prior earnings, education, and age are positively 

correlated with future earnings. The variables indicating prior occupation are significant 

 

16Using data from the Current Population Survey for a comparable time period we computed a (8Η10) industryoccupation matrix of average hours worked using one digit industry and occupation groups. The matrix appears as 

Table 4 in this paper. 
88

predictors of future earnings. In addition, returning to the industry of prior employment raises 

earnings by 15.7 percentage points and the coefficient estimate is highly statistically significant. 

Indicators for the various population groups are not statistically significant, except for veterans 

and the economically disadvantaged. 

 

Coefficient estimates related to other special variables add further insight into the 

determinants of a worker’s compensation. Possession of a driver’s license increases 

reemployment earnings, and longer tenure on the previous job reduces reemployment earnings. 

This latter result is consistent with WPRS models that find increased prior job tenure associated 

with an increased chance of UI benefit exhaustion. 

To compute median estimated earnings for a one-stop customer, the regression 

coefficients are multiplied by the individual’s characteristics. Consider again the same three 

examples used above for evaluating the probability of returning to work in the manufacturing 

industry. Person 1 is 35 years old, has a high school education, earns $30,000 per year (or 

$7,500 per quarter) in a clerical/sales occupation, and applied for UI in the second calendar 

quarter. Median reemployment earnings for this individual in metropolitan Atlanta are predicted 

to be $6,661 per quarter. Consider person 2, who is identical to person 1, except that her prior 

earnings are doubled. This change has the effect of raising predicted median reemployment 

quarterly earnings in metropolitan Atlanta to $11,705. Person 3 has characteristics similar to the 

first two, except that prior annual earnings are $10,000. For this example, predicted median 

reemployment quarterly earnings fall to $3,070. 

We attempted to estimate quartile earnings models (i.e., separate models for the 25th, 50th, 

and 75th percentiles of the earnings distribution). However, small sample sizes for some 

industries in some regions resulted in distributions of the prediction sampling errors of the 

quartile models, which greatly overlapped. This sometimes caused predicted reemployment 

earnings quartiles for an individual to appear to be out of order. Consequently, we adopted an 

alternative strategy for estimating the first and third quartiles. 

Following the same sample structure as that used for earnings model estimation, we 

considered maximum quarterly reemployment earnings for each customer in the combined UI 

and ES sample by region of Georgia, occupation (10 SOC groups), and industry (8 groups), or 

youth or economically disadvantaged status. Within each cell we identified the first, second 

(median), and third quartiles. We then computed ratios of the quartiles. The ratio of the first 

quartile to the second yields a number between zero and one, and this ratio serves as the 25th

percentile multiplier. The ratio of the third quartile to the second yields a number greater than 

one, and this serves as the 75th percentile multiplier. Table 6 lists the ratios for manufacturing in 

the Atlanta region. The earnings example in Table 5 for manufacturing in the Atlanta region 

assumes an occupation in the sales and related group. The ratios applied to this example are 

approximately 0.73 and 1.32. 

Available Suitable Job Vacancy Listings
89

The heart of the SJSM is examination of job vacancy listings—called job orders by onestop center staff—to identify the best available prospects for reemployment. The SJSM 

customizes this process by first reviewing the probability of returning to the prior industry, 

expected local job growth in the prior occupation, the quartile distribution of likely 

reemployment earnings, and the customer’s reservation wage. The reservation wage is labeled 

as the “minimum salary desired.” It is set by the customer when registering for services in 

response to the question: “What is your desired hourly wage at reemployment?” 

With frontline staff assistance, customers may then view selected job orders available in 

the system screened by occupation, local area, and wage requirements. If no suitable openings 

are available, frontline staff may turn to the SRM to identify other core or intensive services 

which may be useful, or they may broaden the scan of job orders by considering listings for 

related occupation. The algorithm for identifying related occupations is the last part of the 

SJSM, and it is explained in the next sub-section. 

Related Occupations

The FDSS algorithm for identifying related occupations provides frontline staff with a list 

of occupations that are related to the occupation that a customer most recently held. The purpose 

of the algorithm is to provide a customer who does not immediately find a suitable job match 

within existing job orders with a list of occupations that require similar skills and aptitudes, so 

that other relevant listed job orders may be considered. Displaced workers are paid less upon reemployment than those who change occupations voluntarily, in part because of the poor match 

between their current occupational skills and their new job skill requirements. Providing 

customers with reliable information on alternatives to their previous occupation may improve 

their re-employment earnings and reduce the amount of time spent unemployed. 

A study by Markey and Parks (1989, p. 3) found that “more than half of the workers in 

the United States who changed occupations did so because of better pay, working conditions, or 

advancement opportunities; however about 1 in 8 workers changed occupations because they lost 

their previous jobs.” Fallick (1993) found evidence that displaced workers increase the intensity 

of their job search in other industries when the employment growth rate in their previous 

industry is low. Shaw (1987) estimates that a 25 percent increase in the transferability of 

occupational skills leads to an 11 to 23 percent increase in the rate of occupational change, 

depending on the age of the worker. Taken together, these results suggest that workers 

concentrate their search efforts in industries and occupations similar to their own. Successful job 

search could be promoted by identifying related occupations and providing clients with timely 

information on the prospects for work in those areas. 

The related occupations algorithm is based on the O*Net system. It identifies 

occupations that are closely related to the previously held occupation with respect to a person’s 

qualifications, interests, work values, and previous work activities. O*Net, developed by the 

U.S. Department of Labor, incorporates the expert opinions of human resource professionals and 

analysts about the characteristics of more than 1,000 occupations, and then relates the various 

occupations by prioritizing the importance of these attributes for each occupation. This 
90

methodology addresses the decision to change occupations by asking the question: “What 

occupations are most related to my previous occupation with respect to my qualifications, 

interests, and aspirations?” This approach assumes that the person was qualified for the job that 

he or she previously held. O*Net matches the characteristics of the previous job with the 

characteristics of other related occupations. However, these transfers are hypothetical and are 

not based on actual occupational transfers. It does not take into account the actual demand for a 

worker’s skills. 

The O*Net related occupations methodology is based on extensive information about the 

characteristics required by an occupation. Furthermore, because of its comprehensive 

assessment of skill requirements for specific occupations, this methodology allows one to link 

this information to possible course offerings at local training and educational institutions in order 

to fill specific skill gaps. One of the major drawbacks of this methodology is that it does not 

consider the actual labor market demand by employers for those skills embodied in the 

occupation. We investigated two alternatives to the O*Net approach which embodied elements 

of labor demand as well as skill relations. One approach used Current Population Survey (CPS) 

data and the other used Georgia ES placement data. Because of required conversions across 

alternative occupational coding systems, neither approach yielded a sufficiently rich menu of 

related occupations in terms of the Standard Occupation Code (SOC), however, which is the 

standard for the Georgia Workforce System. 

To illustrate the O*Net approach which is used in the Georgia FDSS, we found 

occupations related to the occupation of cashier (O*Net Occupation Code 41-2011.00).17 As 

shown in Table 7, O*Net identified occupations that appear to be closely related in terms of the 

type of tasks required and the level of autonomy in executing the task—elements which O*Net 

focuses on in categorizing occupations. Since it is based on standard occupation codes (SOC), 

the FDSS for the Georgia Workforce System will provide related occupations for 674 SOC 

categories. Mapping all O*Net occupations into SOC yields 824 SOC groups, but for 150 of 

these groups O*Net does not identify a related occupation. 

Service Referral Module

The SRM provides the frontline staff with two tools: 1) a ranking of the core and 

intensive services estimated to be most effective for clients with similar characteristics, and 2) a 

ranking of the effectiveness of job training types for clients with similar characteristics. To 

summarize client characteristics, we estimate employability models and group customers with 

similar scores. We first discuss employability estimates, and then turn to service referral and 

training effectiveness statistics. 

Employability Estimates

 

17In making the comparisons, considerable effort was required in converting the occupation codes from O*NET to 

the occupation codes used in the CPS and by the Georgia Employment Service. Complete matching was not 

possible, but we came as close as possible (DeRango, et al. 2000). 
91

The employability algorithm estimates the relationship between recent stable 

employment, personal characteristics, and local office indicators as a measure of local labor 

market conditions. The aim is to produce an “Employability Index” which summarizes 

characteristics influencing the likelihood of being employed. The model uses data on the 

experience of customers who have recently enrolled with the employment service or with other 

programs provided through one-stop centers. Since we are attempting to identify employability 

before receiving services, the dependent variable and all exogenous variables in the model are 

based on values before job search registration. The data come from the same administrative 

records that are used to estimate the components of the systematic job search module described 

in the previous section of this paper. The index will be used to create groups of customers 

having similar employability characteristics so as to examine the effectiveness of employment 

services for these different groups. 

Since it is based on prior values of exogenous variables, the employability index can be 

viewed as a summary of client characteristics. Interacting the employability index with service 

indicators is a type of sub-group analysis (Heckman, Smith and Clements 1997). The planned 

approach is analogous to that used by Eberts (2002) for assigning welfare-to-work clients to 

alternative bundles of reemployment services. This method is also similar to the procedure 

applied by O’Leary, Decker, and Wandner (2002), who essentially interacted an unemployment 

insurance benefit exhaustion probability index with reemployment bonus intervention indicators 

to identify the best exhaustion probability group for targeting a bonus. 

The employability model is similar to the earnings algorithm, except that a binary 

employment indicator is used as the dependent variable instead of earnings, and the model is 

estimated by logit. The sample includes both customers who have had steady work just prior to 

enrolling in one-stop programs, and those without recent steady work. Our model parameterizes 

the effects of measurable attributes on the likelihood of having or not having recent steady 

employment. The expectation is that those with recent work experience are more employable, 

even before they receive services.18 The model is estimated using either UI or ES administrative 

data for each of four separate regions of Georgia (metropolitan Atlanta, Northern, Coastal, and 

Balance of the state) on a selected program population. 

As an example, an employability model for UI recipients in metropolitan Atlanta is 

presented in Table 8. The explanatory variables in the model include the number of prior 

employment services used, age, age squared, educational attainment, whether the most recent 

prior UI claim exhausted benefits, months of tenure on prior job, tenure squared, number of prior 

employers in a recent prior quarter, prior industry, prior occupation, and the Georgia field service 

office where UI benefits were claimed. Most estimated coefficients in the model are statistically 

 

18In algebraic notation the model can be written as: e = a + B=X + u, where e is an indicator variable having a 

value of one if the customer had significant steady employment before registering for job search and zero otherwise, 

X is a matrix of personal and labor market explanatory variables, and B is a conformable vector of regression 

parameters. The error term, u, is assumed to have the logistic distribution and the model is estimated by the logit 

regression routine. 
92

significant. Our measure of employability tends to be positively correlated with age, high school 

education, use of prior intensive services, the number of employers in a quarter before 

registration, and tenure on the prior job (positive but diminishing). Employability is negatively 

related to other than high school education, and not having a driver’s license. Using an 

employability model of the type summarized in Table 8, the employability score for each 

customer using the FDSS in a Georgia Career Center is computed. 

Ordering employability scores from low to high, we divide the distribution of predicted 

employability by quintiles and present information about the effectiveness of alternative services 

for each of the five employability quintile groups. Table 9 shows the quintile employability 

scores. Each quintile group contains 20 percent of all observations. For UI clients in the Atlanta 

region, the quintiles are at employability scores of approximately 0.717, 0.846, 0.922, and 0.969. 

We decided to break the employability distribution into five groups for the purpose of examining 

patterns of service effectiveness, since that number clearly delineated the variation in service 

effectiveness across employability classes. There was more variation than represented by three 

classes, and variation diminished across neighboring classes when ten were used. Furthermore, 

several infrequently used services could not be meaningfully examined across more than five 

groups because of small sample size. 

An indication of the power of the employability score to distinguish differences in 

customer characteristics is given in Table 10, which shows the mean values of descriptive 

characteristics for Atlanta UI claimants referred to the reemployment unit (REU). The low 

employability quintiles had lower values for prior earnings, educational attainment, age, and 

tenure on the prior job. The low quintiles also had higher values for number of prior employers 

in a recent quarter, the likelihood of a prior UI claim, the likelihood that a prior UI claim was 

exhausted, likelihood of being dislocated, and for those who are economically disadvantaged. 

Service Referral 

The service referral module algorithm identifies the set of activities that most often lead 

to successful employment for a customer in a particular employability quintile, in a particular UI 

or ES service subgroup, and a particular region of the state. Information about the characteristics 

and outcomes of individuals who have recently participated in services is used to estimate the 

relative impact of alternative services. It should be emphasized that this algorithm does not 

replace the staff’s referral decisions. Rather, it provides additional information to better inform 

the decision. 

To rank service effectiveness for customers grouped by employability score, impact 

estimates of alternative services were computed while correcting for selection bias. This was 

done using the least squares methodology with observable control variables. These estimates 

were validated using a propensity score matching approach, which accounts for all possible nonlinear influences of observable factors on selection for program participation (Rosenbaum and 

Rubin 1983, Heckman, Ichimura, and Todd 1997, Heckman, LaLonde, and Smith 1999, and 

Smith 2000). 
93

Least squares estimates of relative service impacts were computed using data on only 

those who received services. Unfortunately, because of small sample sizes for some services, the 

resulting estimates of the relative effects of services were useful for reliably ranking only a few 

of the more than 20 available core and intensive services in Georgia Career Centers (Eberts, 

O’Leary, and DeRango 2002).19 Fortunately, rankings based on these parametric estimates were 

nearly identical to rankings based on the simple gross outcome of interestBreemployment as 

measured by the proportion of customers with earnings of at least $2,500 in each of two 

consecutive quarters in the four calendar quarters immediately following registration for job 

search. Consequently, FDSS relies on a non-parametric approach and simply ranks service 

effectiveness by the proportion of an employability group achieving the reemployment criterion. 

Along with these rankings, information is provided on the number of customers in this 

employability quintile and region who used the service in a recent period. 

Tables 11a to 11e separately provide full information for each of the five quintiles 

respectively on the gross effectiveness of alternative core and intensive services for UI clients 

sent to the reemployment unit in Atlanta region Career Centers. Rows in each table are sorted 

from most effective to least effective service as measured by the gross outcome “percentage of 

service users getting steady work.” The display in these tables has the same layout as the service 

referral section of FDSS in the Georgia Workforce System. To put the gross outcome measure 

in context, the first column of numbers reports the total number of clients with similar 

employability characteristics and similar program orientation in the same geographic area of 

Georgia using the service recently. The second column of numbers shows the percentage of 

clients in that region/program group/quintile who used the service. The third column is the 

outcome measure of reemployment success. The far right column in each of these tables reports 

the relative effectiveness index. 

As can be seen in Tables 11a to 11e, there is a bundle of five services which is most 

commonly received by UI claimants in the Atlanta profiling/REU/CAP group. These services 

include: service needs evaluation, orientation, eligibility review program (ERP), customer 

service plan, and counseling. For the first quintile group, Table 11a shows a common 

reemployment rate of 37.6 percent among customers receiving these services; however, the 

present summary provides service effectiveness information singly rather than in bundles. It is 

likely that patterns of service receipt under WIA will be different than that observed in these 

tables which are based on pre-WIA data. 

There is not a common pattern of service effectiveness across quintiles. This can be seen 

in Table 12 which presents services ranked by effectiveness for quintile 1 and simply lists the 

rank of services for each of the other quintiles. Each quintile group has a different ranking of 

services, and for any particular service the ranking differs across quintile groups. For the UI 

profiling/REU/CAP clients in the Atlanta area, the bundle of five most common services tend to 

 

19Of the 21 relative service impacts listed in their tables, the 6 is the most estimated with precision among any of the 

5 UI quintiles in Atlanta. Three of the five quintiles had only 4 out of 21 relative service impacts estimated with 

statistical significance (Eberts, O’Leary, and DeRango 2002, Table 8). 
94

be most effective for the quintile five group, moderately ranked for the quintile one group, and 

ranked lower in effectiveness for the middle three groups. Job Referrals and call-ins (for job 

referral) are ranked as highly effective for the quintile five group who appear to be most job 

ready, but are very low on the list for quintile one. Service coordination is high on the list of 

effectiveness for quintile one, but ranked very low for all other quintiles. 

Training Statistics

WIA organizes reemployment services into three classes: core, intensive, and training. 

To complement ranking of core and intensive services, FDSS provides similar information on 

four broad categories of training types which receive funding from the federal government. The 

four types of training are: on-the-job (OJT), occupational skills, comprehensive assessment, and 

adult education-basic skills-literacy. Small numbers of participants in these services mean that 

finer distinctions in service types are not possible. The bulk of training in Georgia is funded by 

the state lottery through Hope grants and Hope scholarships. Counts of these participants are not 

included in the FDSS tabulated statistics. The data in the pilot version of FDSS are from the 

federally funded job training program which preceded WIA—the Job Training Partnership Act 

(JTPA) program. 

In FDSS, information on training effectiveness is presented as “training statistics,” rather 

than suggesting a true ranking since only two training types had appreciable levels of activity: 

occupational skills training and comprehensive assessment. The other two types received little 

federal funding, and consequently had few participants counted in the JTPA data. Nonetheless, 

Table 13 shows differences in the ordering of occupational skills training and comprehensive 

assessment across the five employability quintile groups for UI in the Atlanta region. The lesserused training types also appear to be more effective than the popular services for some quintiles. 

There are separate quintile rankings for UI and ES, and for each of the four Georgia regions. 

A PROTOTYPE SYSTEM FOR GEORGIA

Appendix A to this paper presents prototype screens that have been integrated into the 

Georgia Workforce System (GWS) for pilot testing of FDSS in Athens and Cobb-Cherokee 

Career Centers. The GWS is the internet based combined intake and service referral record 

system for Georgia Career Centers. There are five screens in the prototype FDSS, which can be 

scrolled through once the FDSS internet web page is loaded for a particular client. 

A frontline staff person conducting the FDSS session can quickly jump among the five 

screens without reloading the page by simply clicking on any of the titles which appear across 

the top of each screen. Each of the five screens lists the titles of the other four screens. The five 

screens are: 

Customer Background Information

Reemployment Probability and Estimated Earnings

Related Occupations

Service Referral
95

Training Statistics

The customer background information screen is the starting point for an FDSS session. 

This page lists critical information needed to evaluate FDSS algorithms. The frontline staff 

person enters a customer’s client ID number and then hits carriage return. This causes the entire 

FDSS web page to update and report information based on data about the client existing in the 

system. Much of this information is assembled from the most recent combined intake (UI/ES) 

registration, which may happen earlier on the same day of the first FDSS session. 

One background variable merits special description. Special arrangements were made for 

the coding of prior occupation, since data from several different occupation coding systems are 

being used in FDSS. When the FDSS web page loads, the system identifies the prior occupation 

using the DOT (Dictionary of Occupational Titles) occupation code in the work history file. 

Since the related occupations algorithm is based on the O*Net occupation coding system, known 

as SOC (Standard Occupation Code), a translation is required. Rules for the translation are 

presented in Table 14. 

Occasionally, information in the GWS for a particular client may be incorrect or missing. 

The customer background information screen permits a frontline staff person to temporarily 

change some fields to values that the client claims to be appropriate for the current FDSS 

session. Temporary changes to these fields will not be recorded by the system; the values are 

only used in the currently active FDSS session. Values of variables which cannot be changed are 

listed above a line of demarcation, while changeable fields are below that line. Changeable 

fields include: education level, school enrollment status, employment status, geographic region 

of Georgia, and recent quarterly earnings. For each changeable field, a drop down menu is 

provided. 

UI claimants find that it is often necessary to have missing wages added to their existing 

records to establish a claim. This procedure requires reliable documentation as evidence of the 

prior earnings. Unlike the UI eligibility process, the FDSS session requires no documentation to 

temporarily change values in these special fields. However, FDSS is not a means to correct 

erroneous wage records. Such information is provided only to produce recommendations from 

FDSS, and that advice is contingent on the accuracy of the data provided. Any values entered in 

these fields are not permanently recorded when the session is over. 

After seeing FDSS results in other screens, a frontline staff person or client may wish to 

return to the customer background information screen later in an FDSS session in order to 

change values, and run “what if” scenarios. For example, what if the client enrolls in school?Yor 

takes a part-time job?Yor gets a driver’s licence?Yor locates in another region of Georgia? If the 

prior occupation is changed to a different one of ten SOC occupation groups, the SOC group is 

mapped into a DOT code based on the map presented as Table 15. 

Clicking on the reemployment and earnings estimates title at the bottom of an FDSS 

screen jumps the view to that screen, evaluated at the most recent values given in the customer 

background information. Listed on this page are results of algorithms discussed in previous 
96

sections of this paper. The frontline staff person will see estimates for the client of the 

probability of returning to the prior industry, expected employment growth in the prior 

occupation, and a distribution of expected reemployment earnings. Also appearing on this 

screen is the customer’s self-reported “minimum salary desired.” Taken together, this 

information should help the frontline staff and customer identify reasonable reemployment goals, 

and then conduct a systematic search of vacancy listings (job orders). 

If the search of job orders fails to turn up any good job prospects, it may be useful to 

identify job openings in occupations related to that of the prior job. Frontline staff may identify 

related occupations by clicking on that title at the top of the screen. The related occupations

screen lists up to five occupations identified by the O*Net system as related to the prior 

occupation currently displayed on the customer background information screen. For each of 

the five occupations listed, the approximate starting hourly wage and the average annual job 

growth rate are provided for the local workforce area together with the O*Net occupation code. 

Using the occupation codes, the frontline staff person may then identify appropriate job openings 

for the customer to consider. 

If systematic job search yields no immediate candidates for job interviews, clicking on 

service referral at the top of any screen will jump the view to that screen. The result is an 

ordered list of core and intensive reemployment services ranked by effectiveness for clients with 

employability characteristics similar to those in the customer background information screen. 

For each service, the service referral screen displays information on the number of clients using 

the service, the percentage of clients using the service, the percentage of service users getting 

steady work, and the relative effectiveness index. The services are listed in order of the 

percentage of service users getting steady work, which is defined as the percentage having two 

consecutive quarters with earnings in each quarter exceeding $2,500 in the four quarters after 

seeking services at a Georgia Career Center. 

SUMMARY

The Workforce Investment Act of 1998 required creation of a national network of onestop centers where intake and referral of customers to various programs are done in a 

coordinated fashion. Resource constraints dictate that each Workforce Development Area can 

serve only a fraction of the population that might benefit. Funding levels from state and federal 

sources affect how many workers can be served. Choosing which individuals are served depends 

on decision rules applied by frontline staff in one-stop centers. Statistical tools can help make 

these decisions more cost effective for society by targeting services to customers who will 

benefit the most, thereby maximizing the net social benefit of program expenditures. 

The Frontline Decision Support System (FDSS) offers a set of tools that can help inform 

frontline staff and customers in their job search efforts and in their selection of reemployment 

services. The tools are based on statistical techniques that use administrative data to estimate the 

chance of returning to work in the prior industry, reemployment earnings prospects, related 

occupations, and the likely outcomes of alternative reemployment services. The concept of 

FDSS is an extension of the Worker Profiling and Reemployment Services (WPRS) system, 
97

which all states have operated since 1994. The W.E. Upjohn Institute for Employment Research 

is working closely with the U.S. Department of Labor and the Georgia Department of Labor to 

design, pilot test, and implement FDSS in selected Georgia Career Centers. 

This paper documents the analytic foundation of each of the tools in FDSS using 

examples drawn from the Atlanta region of the Georgia FDSS that is currently being pilot tested 

in the Cobb-Cherokee Career Center. Pilot testing is also underway in the Athens Career Center 

based on algorithms for the Northern Georgia geographic region. To review the tools, we 

sequentially consider the elements of the systematic job search module (SJSM) and the service 

referral module (SRM). 

The SJSM contains tools which can be used to inform the customer about the: 1) 

probability of returning to the prior industry, 2) likely employment growth in the prior 

occupation, 3) likely reemployment earnings, 4) available suitable job vacancy listings, and 5) 

occupations related to the prior one. The SRM provides the frontline staff with two tools: 1) a 

ranking of the core and intensive services estimated to be most effective for clients with similar 

characteristics, and 2) information about the effectiveness of alternative types of job training for 

clients with similar employability characteristics. To summarize client characteristics, we 

estimate employability models and group customers with similar scores. 

Field testing of FDSS in the two Georgia pilot sites commenced in July, 2002. Based on 

the experience of field testing and using updated administrative data, the Georgia FDSS will be 

refined during the second half of 2002 with statewide implementation expected in early 2003. 

An evaluation of FDSS is planned after the system is fully operational. The internetbased Georgia Workforce System will record frontline staff use of FDSS as a service in client 

records. This will provide a basis for future objective evaluations of FDSS effectiveness using 

administrative records. 
98

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100

Table 1 Industry of Employment Transition Matrix; Percent of Unemployment 

Insurance Clients, Metropolitan Atlanta, Georgia

Reemployment Industry 

Prior Industry 

Agriculture 

Forestry 

Fishery 

Mining 

Constr. 

Manufacturing

Transportation, 

Communication,

Utilities 

Wholesale 

Trade 

Retail 

Trade 

Finance 

Ins, RE Services Public 

Admin 

Ag., For., Fish 26.3 10.1 10.9 4.9 10.5 11.7 3.2 20.6 1.6

Mine, Construct 0.5 60.1 5.8 3.9 5.3 5.1 2.5 15.0 1.6

Manufacturing 0.3 3.8 40.1 5.7 11.7 8.9 3.0 24.8 1.6

Trans,Comm,Util. 0.4 2.9 6.4 41.8 8.0 7.2 4.7 26.6 2.0

WholesaleTrade 0.4 4.5 14.2 7.4 28.6 11.7 3.9 27.8 1.5

RetailTrade 0.3 2.4 6.2 5.5 7.3 45.5 4.7 26.6 1.5

Finance, Ins, RE 0.3 2.5 4.2 4.7 5.1 6.8 38.3 35.7 2.4

Services 0.3 2.6 6.2 6.2 6.2 8.4 5.9 61.6 25.3

Public Admin. 0.5 3.6 5.4 7.9 4.0 7.8 6.1 39.4 25.3

Table 2 Mean Percentage Change in Earnings for the Industry of Employment 

Transition Matrix, Metropolitan Atlanta, Georgia

Reemployment Industry 

Prior Industry 

Agriculture 

Forestry 

Fishery 

Mining 

Const. 

Manufacturing 

Transportation, 

Communication, 

Utilities 

Wholesale 

Trade 

Retail 

Trade 

Finance 

Ins, RE Services Public 

Admin 

Ag., For., Fish 1.6 1.6 !3.0 !0.9 32.4 !12.1 12.8 !3.5 !16.6

Mine, Construct !30.6 6.4 !7.8 !0.9 !2.1 !25.4 3.3 !9.9 !25.5

Manufacturing !34.3 !14.3 6.6 !0.5 !2.1 !29.4 !9.0 !15.7 !21.4

Trans,Comm,Util !25.8 0.1 !2.1 6.2 !4.3 !25.2 !9.3 !15.8 !19.0

WholesaleTrade !28.3 !2.0 !2.0 1.3 7.1 !21.4 !0.7 !7.4 !26.8

RetailTrade !12.1 0.8 9.0 6.0 10.1 1.9 10.2 !3.1 !9.7

Finance, Ins, RE !28.3 !9.9 !6.6 !10.1 1.4 !26.4 8.6 !11.2 !23.4

Services !20.3 6.3 8.7 9.3 14.4 !20.0 6.7 3.9 !8.4

Public Admin. !22.7 !7.7 1.7 2.2 12.2 !21.5 !8.6 !2.4 !4.2
101

Table 3 Logistic Model for the Probability of Returning to the Same Industry 

(UI and ES Clients in Atlanta whose Prior Industry was Manufacturing)

Hypothetical Workers Variable Description Parameter

Estimate 

Standard 

Error 

Marginal

Effect 1 2 3 

 Log of Maximum Prior Earnings 0.723** 0.061 0.180 8.923 9.616 7.824 

 UI Client !0.663** 0.058 !0.157 1.000 1.000 1.000

 Age as of Reference Date 0.042** 0.017 0.011 35.000 35.000 35.000

 Age Squared !0.000* 0.000 !0.000 1225.000 1225.000 1225.000

 Education, Less than High School 0.208* 0.086 0.052 0.000 0.000 1.000

 Education, GED !0.006 0.119 !0.001 0.000 0.000 0.000

 Education, Some College !0.244** 0.060 !0.060 0.000 0.000 0.000

 Education, Bachelor Degree !0.399** 0.085 !0.097 0.000 1.000 0.000

 Education, Advanced !0.527** 0.177 !0.127 0.000 0.000 0.000

 Veteran !0.129** 0.065 !0.032 0.000 0.000 0.000

 Dislocated Worker !0.205** 0.057 !0.051 0.000 0.000 0.000

 Employed 0.386** 0.077 0.096 0.000 0.000 0.000

 Reference Date in 2nd Quarter !0.071 0.059 !0.018 1.000 1.000 1.000

 Reference Date in 3rd Quarter !0.153** 0.065 !0.038 0.000 0.000 0.000

 Reference Date in 4th Quarter !0.218** 0.070 !0.054 0.000 0.000 0.000

 Prior Occ: Mgmt, Business, Finance !0.822** 0.101 !0.191 0.000 0.000 0.000

 Prior Occ: Professional and Related !0.822** 0.088 !0.191 0.000 0.000 0.000

 Prior Occ: Services !0.662** 0.132 !0.157 0.000 0.000 0.000

 Prior Occ: Sales and Related !1.097** 0.118 !0.243 1.000 1.000 1.000

 Prior Occ: Office and Admin Support !0.934** 0.078 !0.213 0.000 0.000 0.000

 Prior Occ: Farming, Fishing, Forestry 0.158 0.550 0.039 0.000 0.000 0.000

 Prior Occ: Construction, Extraction !0.565** 0.169 !0.136 0.000 0.000 0.000

 Prior Occ: Install, Maintenance, Repair !0.409** 0.118 !0.100 0.000 0.000 0.000

 Prior Occ: Transp, Material Moving !0.414** 0.066 !0.100 0.000 0.000 0.000

 Intercept !6.461** 0.564 !0.475 1.000 1.000 1.000 

Return to Same Industry Probability: 0.317 0.340 0.205

Example 1: Age: 35, Educ: HS grad, Annual income: $30,000, Occupation: sales, Months tenure: 24. 

Example 2: Age: 35, Educ: post-HS (Bachelors), Annual income: $60,000, Occupation: sales, Months tenure: 48. 

Example 3: Age: 35, Educ: less than HS, Annual income: $10,000, Occupation: sales, Months tenure: 8. 

* Parameter significant at the 90 percent confidence level in a two-tailed test. 

** Parameter significant at the 95 percent confidence level in a two-tailed test. 
Table 4 Industry-Occupation Matrix of Usual Quarterly Hours Worked based on 1996 to 199 CPS Mar

Standard Occupation Code (SOC) Occupation Group 

11

B13 15

B29 31

B39 41 43 45 47 49 

Industry Group  Ag, forest, fish 521.5 515.9 444.4 559.9 346.8 459.9 416.7 629.4  Mining, Constr 564.1 539.0 405.0 601.9 411.1 518.6 403.9 322.7  Manufacturing 578.3 537.8 456.8 560.9 492.0 483.3 492.8 509.8  Trans, Comm, Util 570.8 530.7 423.5 550.0 483.0 500.9 478.8 520.0  Wholesale Trade 545.5 518.6 425.4 587.2 513.3 429.6 417.7 650.0  Retail Trade 566.6 507.5 323.9 559.2 334.5 417.7 482.2 514.3  Finance, Ins, RE 533.0 519.1 416.0 572.2 481.2 475.8 337.9 440.0  Services 508.3 480.3 392.1 462.4 382.4 413.1 323.0 333.0  Public Admin 517.7 517.4 541.6 545.3 472.0 467.4 383.9 520.0 
103

Table 5 Median Regression Coefficient Estimate and Examples of Predicted Earnings 

for Recent Manufacturing Employees Among UI Recipients in Metropolitan 

Atlanta, Georgia

Median Hypothetical Workers 

Variable Description Parameter 

Estimate 

Standard 

Error 1 2 3 

Log of Maximum Prior Earnings 0.656** 0.011 8.923 9.616 7.824 

UI Client !0.058** 0.011 1 1 1 

Age as of Reference Date !0.001 0.003 35 35 35 

Age Squared !0.000 0.000 1225 1225 1225 

Education, Less than High School !0.055** 0.015 0 0 1 

Education, GED !0.048** 0.020 0 0 0 

Education, Some College 0.041** 0.011 0 0 0 

Education, Bachelors Degree 0.131** 0.015 0 1 0 

Education, Advanced 0.174** 0.031 0 0 0 

Veteran 0.027** 0.012 0 0 0 

Dislocated Worker !0.002 0.010 0 0 0 

Education Status !0.020 0.027 0 0 0 

Employed 0.035** 0.014 0 0 0 

Exhausted Prior UI Claim !0.082** 0.042 0 0 0 

Weeks of UI Collected Prior Claim 0.005** 0.002 0 0 0 

Does Not Have Driver’s License !0.068** 0.019 0 0 0 

Available for Rotating Shifts 0.023** 0.011 0 0 0 

Months of Tenure, Most Recent Job !0.001** 0.000 24 48 8 

Months of Tenure Squared 0.000** 0.000 576 2304 64 

Reference Date in 2nd Qtr 0.002 0.010 1 1 1 

Reference Date in 3rd Qtr !0.006 0.012 0 0 0 

Reference Date in 4th Qtr !0.007 0.013 0 0 0 

Ref Date 3 Qtrs After Max Wage !0.003 0.010 1 1 1 

Ref Date 4 Qtrs After Max Wage 0.002 0.012 0 0 0 

Ref Date 5 Qtrs After Max Wage !0.004 0.011 0 0 0 

Days Left in Current Quarter 0.000 0.000 54 54 54 

Unemployment Rate, t!3 0.189 0.526 0.040 0.040 0.040 

Employ Yr-Over-Yr Pct. Chg., t!3 0.070 0.253 0.060 0.060 0.060 

Post Industry Same as Prior Industry 0.156** 0.009 0.292 0.325 0.171 

Occup: Mgmt, Business, Finance 0.045** 0.018 0 0 0 

Occup: Professional and Related 0.074** 0.016 0 0 0 

Occup: Services !0.012 0.024 0 0 0 

Occup: Sales and Related 0.042** 0.020 1 1 1 

Occup: Office and Admin Support !0.005 0.014 0 0 0 

Occup: Farming, Fishing, Forestry !0.158 0.097 0 0 0 

Occup: Construction, Extraction !0.017 0.031 0 0 0 

Occup: Installation, Maintenance 0.101** 0.021 0 0 0 

Occup: Transportation, Material Move !0.023** 0.012 0 0 0 

Intercept 3.029** 0.114 1 1 1 

Predicted 25th 4871 8559 2245 

Predicted 50th 6661 11705 3070 

Predicted 75th 8799 15462 4055 

Example 1: Age: 35, Educ: HS grad, Annual income: $30,000, Occupation: sales, Months tenure: 24. 

Example 2: Age: 35, Educ: Bachelors degree, Annual income: $60,000, Occupation: sales, Months tenure: 48. 

Example 3: Age: 35, Educ: less than HS, Annual income: $10,000, Occupation: sales, Months tenure: 8. 

* (**) Parameter significant at the 90 (95) percent confidence level in a two-tailed test. 
104

Table 6 Atlanta Metro Area - Manufacturing Industry Ratios to Calculate 25th and 75th 

Earnings Percentiles

Occupation Code Description 

Ratio for 

25th Percentile 

Ratio for 

75th Percentile 

soc1113 Management, Business, Financial 0.716465824 1.334754150 

soc1529 Professional and Related 0.750614349 1.364359733 

soc3139 Services 0.796787908 1.413971832 

soc41 Sales and Related Occupations 0.731179242 1.320901332 

soc43 Office and Administrative Support 0.772121670 1.310724935 

soc45 Farming, Fishing, Forestry 0.702977445 1.317053626 

soc47 Construction and Extraction 0.757809558 1.327728492 

soc49 Installation, Maintenance and Repair 0.736310733 1.370156660 

soc51 Production 0.728655695 1.409237099 

soc53 Transportation and Material Moving 0.729236288 1.344032112 

SOC: Standard Occupation Code. 
105

Table 7 Occupations Related to Cashier (O*Net SOC 41-2011)

O*Net SOC Title O*Net SOC 

Food preparation and serving 35-3021 

Counter and retail clerks 41-2021 

Parts sales persons 41-2022 

Insurance sales agents 41-3021 

Receptionists 43-4171 

SOC: Standard Occupation Code. 
106

Table 8 Employability Model: Atlanta Metro, UI Sample

Hypothetical Workers Variable Parameter 

Estimate 

Standard

Error 

Marginal 

Effect 1 2 3 

Intercept !0.092 0.126 !0.016 1 1 1 

Months Tenure on Prior Job 0.012** 0.001 0.002 24 48 8 

Months Tenure Squared !0.000** 0.000 !0.000 576 2,304 64 

Number of Employers, Qtr T!5 !0.032** 0.015 !0.005 1 1 1 

Prior Wages, 5 Qtrs Before Ref Date 0.000** 0.000 0.000 7,500 15,000 2,500 

Age as of Reference Date 0.059** 0.006 0.010 35 35 35 

Age Squared !0.000** 0.000 !0.000 1,225 1,225 1,225 

Education, Less than High School !0.225** 0.034 !0.040 0 0 1 

Education, GED !0.093* 0.049 !0.016 0 0 0 

Education, Some College 0.069** 0.025 0.011 0 0 0 

Education, Bachelor Degree 0.226** 0.038 0.035 0 1 0 

Education, Advanced 0.233** 0.076 0.036 0 0 0 

Youth, Ages 14 through 21 !0.665** 0.051 !0.131 0 0 0 

Veteran !0.021 0.033 !0.004 0 0 0 

Dislocated Worker 0.138** 0.024 0.022 0 0 0 

Welfare Recipient !0.632** 0.052 !0.123 0 0 0 

Economically Disadvantaged !0.656** 0.022 !0.129 0 0 0 

Exhausted Prior UI Claim !1.053** 0.045 !0.222 0 0 0 

Has No Drivers’ License !0.356** 0.029 !0.065 0 0 0 

Available for Rotating Shifts !0.045 0.031 !0.008 0 0 0 

Reference Date in 2nd Quarter 0.147** 0.025 0.023 1 1 1 

Reference Date in 3rd Quarter 0.411** 0.029 0.060 0 0 0 

Reference Date in 4th Quarter 0.285** 0.030 0.043 0 0 0 

Prior Industry: Ag, Forestry, Fish !0.277* 0.157 !0.050 0 0 0 

Prior Industry: Mining and Construction !0.252** 0.061 !0.045 0 0 0 

Prior Industry: Trans, Comm, Utilities 0.043 0.059 0.007 0 0 0 

Prior Industry: Wholesale Trade 0.052 0.056 0.008 0 0 0 

Prior Industry: Retail Trade !0.401** 0.034 !0.074 0 0 0 

Prior Industry: FIRE 0.213** 0.064 0.033 0 0 0 

Prior Industry: Services !0.159** 0.032 !0.028 0 0 0 

Prior Industry: Public Admin !0.156** 0.073 !0.027 0 0 0 

Prior Occupation: Management, Business, Financial 0.072 0.057 0.012 0 0 0 

Prior Occupation: Professional and Related !0.068 0.051 !0.011 0 0 0 

Prior Occupation: Services !0.655** 0.046 !0.128 0 0 0 

Prior Occupation: Sales and Related Occupations !0.303** 0.053 !0.055 1 1 1 

Prior Occupation: Office and Administrative Support !0.301** 0.044 !0.054 0 0 0 

Prior Occupation: Farming, Fishing and Forestry !0.135 0.125 !0.023 0 0 0 

Prior Occupation: Construction and Extraction !0.046 0.063 !0.008 0 0 0 

Prior Occupation: Install, Maintenance and Repair 0.037 0.074 !0.006 0 0 0 

Prior Occupation: Transport and Material Moving !0.189** 0.047 !0.033 0 0 0 

Field Service Office: DeKalb !0.034 0.034 !0.006 1 1 1 

Field Service Office: Gwinnett 0.184** 0.043 0.029 0 0 0 

Field Service Office: North Metro 0.007 0.038 0.001 0 0 0 

Field Service Office: South Metro !0.156** 0.033 !0.027 0 0 0 

Field Service Office: Cobb/Cherokee !0.006 0.038 !0.001 0 0 0 

Employability Score: 0.953 0.996 0.786 

Example 1: Age: 35, Educ: HS grad, Annual income: $30,000, Occupation: sales, Months tenure: 24. 

Example 2: Age: 35, Educ: Bachelors degree, Annual income: $60,000, Occupation: sales, Months tenure: 48. 

Example 3: Age: 35, Educ: less than HS, Annual income: $10,000, Occupation: sales, Months tenure: 8. 

* (**) Parameter significant at the 90 (95) percent confidence level in a two-tailed test. 
107

Table 9 Employability Score Quintiles

Region Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Georgia - UI Profiling/REU/CAP Clients 

 

Atlanta 0.717 0.846 0.922 0.969 1.000

Northern 0.649 0.809 0.899 0.958 1.000

Coastal 0.470 0.664 0.829 0.939 1.000

Balance 0.467 0.654 0.809 0.920 1.000

 

Georgia - UI NON-Profiling/REU/CAP Clients 

 

Atlanta 0.610 0.761 0.860 0.940 1.000

Northern 0.510 0.680 0.818 0.917 1.000

Coastal 0.356 0.540 0.719 0.883 1.000

Balance 0.367 0.542 0.701 0.856 1.000

 

Georgia Training Referrals 

Based on UI Employability Score Model 

 

Atlanta 0.756 0.878 0.935 0.970 1.000

Northern 0.685 0.841 0.911 0.960 1.000

Coastal 0.500 0.694 0.825 0.928 1.000

Balance 0.480 0.700 0.833 0.923 1.000
108

Table 10 Characteristics of Employability Quintile Groups Atlanta UI 

Profiling/REU/CAP Clients

Description Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 

Prior Wages, 5 Qtrs Before Ref Date 1,969 3,074 4,491 6,279 11,268 

Number of Employers, Qtr T-5 1.39 1.33 1.26 1.18 1.12

Had Prior UI Claim 0.167 0.086 0.072 0.052 0.037

Exhausted Prior UI Claim 0.086 0.011 0.010 0.004 0.003

Age as of Reference Date 37 38 39 40 42 

Months Tenure on Prior Job 19 24 35 49 70 

Educ, LT High School 0.174 0.082 0.069 0.049 0.017

Educ, GED 0.051 0.038 0.032 0.023 0.014

Educ, HS Grad 0.495 0.430 0.409 0.360 0.225

Educ, Some College 0.229 0.294 0.297 0.301 0.255

Educ, Bachelor 0.042 0.122 0.163 0.224 0.379

Educ, Advanced 0.008 0.033 0.030 0.042 0.109

Education Status, 1=In School 0.017 0.018 0.016 0.013 0.011

Youth, Ages 14 through 21 0.089 0.010 0.002 0.000 0.000

Veteran 0.084 0.119 0.114 0.100 0.122

Dislocated Worker 0.623 0.608 0.598 0.608 0.576

Welfare Recipient 0.081 0.005 0.003 0.001 0.000

Economically Disadvantaged 0.740 0.331 0.209 0.091 0.031

Employment Status, 1=Employed 0.039 0.028 0.025 0.019 0.011

Has No Drivers License 0.247 0.090 0.052 0.023 0.007

Available for Rotating Shifts 0.113 0.113 0.106 0.106 0.103
109

Table 11a Service Referral Rankings for Quintile 1; Atlanta UI Profiling/REU/CAP Clients

Rank Description 

Number of 

Clients Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

1 Job Finding Club 1 0.0 100.0 2.66

2 Testing 1 0.0 100.0 2.66

3 Service Coordination 10 0.2 60.0 1.59

4 Job Referrals 1,356 28.0 42.1 1.12

5 Resume Preparation 157 3.2 40.8 1.08

6 Order Search 1,279 26.4 40.0 1.06

7 Specific LMI 640 13.2 39.7 1.05

8 Service Needs Evaluation 4,698 97.1 37.6 1.00

9 Orientation 4,696 97.0 37.6 1.00

10 ERP 4,726 97.6 37.6 1.00

11 Job Search Assistance 542 11.2 37.6 1.00

12 Customer Service Plan 4,705 97.2 37.6 1.00

13 Counseling 4,709 97.3 37.6 1.00

14 Workshops 3,139 64.9 36.6 0.97

15 Job Search Planning 837 17.3 34.1 0.91

16 Referred to Support Services 44 0.9 34.1 0.91

17 Job Development 115 2.4 33.9 0.90

18 Call-In 495 10.2 33.5 0.89

19 Referred to Training 138 2.9 31.2 0.83

20 Expanded Workshop 8 0.2 25.0 0.66

21 Bonding Assistance 3 0.1 0.0 0.00
110

Table 11b Service Referral Rankings for Quintile 2 Atlanta UI Profiling/REU/CAP Clients

Rank Description 

Number of 

Clients Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

1 Testing 1 0.0 100.0 2.02

2 Job Referrals 1,212 25.0 55.6 1.12

3 Job Search Assistance 513 10.6 51.9 1.05

4 Referred to Support Services 45 0.9 51.1 1.03

5 Specific LMI 699 14.4 50.9 1.03

6 Order Search 1,629 33.7 50.8 1.02

7 Call-In 413 8.5 50.1 1.01

8 Expanded Workshop 2 0.0 50.0 1.01

9 Orientation 4,744 98.0 49.4 1.00

10 ERP 4,763 98.4 49.4 1.00

11 Service Needs Evaluation 4,742 98.0 49.3 0.99

12 Customer Service Plan 4,754 98.2 49.3 0.99

13 Counseling 4,754 98.2 49.3 0.99

14 Workshops 3,287 67.9 48.8 0.98

15 Job Development 115 2.4 47.8 0.96

16 Resume Preparation 166 3.4 47.0 0.95

17 Service Coordination 15 0.3 46.7 0.94

18 Job Search Planning 457 9.4 46.0 0.93

19 Referred to Training 81 1.7 43.2 0.87

20 Bonding Assistance 3 0.1 33.3 0.67

21 Job Finding Club 1 0.0 0.0 0.00
111

Table 11c Service Referral Rankings for Quintile 3 Atlanta UI Profiling/REU/CAP Clients

Rank Description 

Number of 

Clients Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

1 Job Finding Club 2 0.0 100.0 1.85

2 Testing 1 0.0 100.0 1.85

3 Expanded Workshop 4 0.1 75.0 1.39

4 Job Development 125 2.6 63.2 1.17

5 Job Referrals 1,045 21.6 61.8 1.15

6 Resume Preparation 152 3.1 61.8 1.15

7 Referred to Training 72 1.5 61.1 1.13

8 Referred to Support Services 46 1.0 58.7 1.09

9 Call-In 365 7.5 55.6 1.03

10 Job Search Assistance 479 9.9 55.5 1.03

11 Order Search 1,698 35.1 54.2 1.00

12 Service Needs Evaluation 4,760 98.3 53.6 0.99

13 Orientation 4,762 98.4 53.6 0.99

14 ERP 4,776 98.7 53.6 0.99

15 Customer Service Plan 4,768 98.5 53.6 0.99

16 Counseling 4,774 98.6 53.6 0.99

17 Specific LMI 634 13.1 53.0 0.98

18 Workshops 3,421 70.7 52.9 0.98

19 Job Search Planning 283 5.8 51.2 0.95

20 Service Coordination 7 0.1 42.9 0.80

21 Bonding Assistance 5 0.1 40.0 0.74
112

Table 11d Service Referral Rankings for Quintile 4 Atlanta UI Profiling/REU/CAP Clients

Rank Description 

Number of 

Clients Using

Service 

Percentage 

of Clients 

Using Service

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

1 Testing 1 0.0 100.0 1.71

2 Bonding Assistance 1 0.0 100.0 1.71

3 Job Referrals 954 19.7 67.8 1.16

4 Resume Preparation 162 3.3 63.0 1.08

5 Job Development 124 2.6 61.3 1.05

6 Order Search 1,838 38.0 60.8 1.04

7 Job Search Assistance 440 9.1 58.9 1.01

8 Specific LMI 671 13.9 58.7 1.00

9 Service Needs Evaluation 4,785 98.9 58.3 1.00

10 Orientation 4,784 98.8 58.3 1.00

11 ERP 4,788 98.9 58.3 1.00

12 Customer Service Plan 4,787 98.9 58.3 1.00

13 Counseling 4,793 99.0 58.3 1.00

14 Workshops 3,434 71.0 57.3 0.98

15 Call-In 298 6.2 54.4 0.93

16 Referred to Support Services 43 0.9 53.5 0.91

17 Job Search Planning 239 4.9 52.7 0.90

18 Referred to Training 60 1.2 51.7 0.88

19 Service Coordination 8 0.2 50.0 0.85

20 Job Finding Club 0 0.0 na na

21 Expanded Workshop 0 0.0 na na
113

Table 11e Service Referral Rankings for Quintile 5 Atlanta UI Profiling/REU/CAP Clients

Rank Description 

Number of 

Clients Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

 

1 Job Referrals 635 13.1 64.9 1.15

2 Job Development 90 1.9 58.9 1.04

3 Call-In 247 5.1 58.7 1.04

4 Service Needs Evaluation 4,792 99.0 56.6 1.00

5 Orientation 4,793 99.0 56.6 1.00

6 ERP 4,797 99.1 56.6 1.00

7 Customer Service Plan 4,795 99.1 56.6 1.00

8 Counseling 4,797 99.1 56.6 1.00

9 Order Search 1,897 39.2 56.2 1.00

10 Job Search Assistance 362 7.5 55.5 0.98

11 Specific LMI 515 10.6 55.1 0.98

12 Workshops 3,372 69.7 54.5 0.97

13 Job Search Planning 107 2.2 53.3 0.94

14 Resume Preparation 106 2.2 51.9 0.92

15 Referred to Support Services 29 0.6 51.7 0.92

16 Service Coordination 8 0.2 50.0 0.89

17 Referred to Training 30 0.6 40.0 0.71

18 Bonding Assistance 3 0.1 33.3 0.59

19 Job Finding Club 1 0.0 0.0 0.00

20 Testing 2 0.0 0.0 0.00

21 Expanded Workshop 0 0.0 na na
114

Table 12 A Ranking of Service Effectiveness by Quintile Group Atlanta UI 

Profiling/REU/CAP Clients

Quintile Description 1 2 3 4 5 

 

Job Finding Club 1 21 1 na 19 

Testing 1 1 1 1 20 

Service Coordination 3 17 20 19 16 

Job Referrals 4 2 5 3 1 

Resume Preparation 5 16 6 4 14 

Order Search 6 6 11 6 9 

Specific LMI 7 5 17 8 11 

Service Needs Evaluation 8 11 12 9 4 

Orientation 8 9 12 9 4 

ERP 8 9 12 9 4 

Workshops 8 14 18 14 12 

Job Search Assistance 8 3 10 7 10 

Customer Service Plan 8 12 12 12 4 

Counseling 8 12 12 13 4 

Referred to Support Services 15 4 8 16 15 

Job Search Planning 16 18 19 17 13 

Job Development 17 15 4 5 2 

Call-In 18 7 9 15 3 

Referred to Training 19 19 7 18 17 

Expanded Workshop 20 8 3 na na 

Bonding Assistance 21 20 21 1 18 
115

Table 13 Atlanta Training Referral Based on UI Employability Model

Rank Service 

Variable Description 

Number of 

Clients Using 

Service 

Percentage of 

Clients Using 

Service 

Percentage 

of Service 

Users 

Steadily 

Working 

Relative 

Effectiveness 

Index 

Quintile 1 

1 jtpa40 On-the-Job Training 29 2.0 48.3 1.36

2 jtpa41 Occupational Skills Training 532 37.3 37.8 1.06

3 jtpa5 Comprehensive Assessment 837 58.7 33.9 0.95

4 jtpa39 Adult Ed, Basic Skills, Literacy 38 2.7 28.9 0.81

 

Quintile 2 

1 jtpa5 Comprehensive Assessment 312 55.1 53.2 1.03

2 jtpa39 Adult Ed, Basic Skills, Literacy 2 0.4 50.0 0.97

3 jtpa41 Occupational Skills Training 248 43.8 49.2 0.96

4 jtpa40 On-the-Job Training 5 0.9 40.0 0.78

 

Quintile 3 

1 jtpa39 Adult Ed, Basic Skills, Literacy 9 1.7 66.7 1.21

2 jtpa5 Comprehensive Assessment 257 47.3 56.0 1.02

3 jtpa41 Occupational Skills Training 283 52.1 54.4 0.99

4 jtpa40 On-the-Job Training 2 0.4 50.0 0.91

 

Quintile 4 

1 jtpa39 Adult Ed, Basic Skills, Literacy 3 0.5 66.7 1.12

2 jtpa41 Occupational Skills Training 301 53.7 60.8 1.02

3 jtpa5 Comprehensive Assessment 259 46.2 57.9 0.98

4 jtpa40 On-the-Job Training 0 0.0 na na

 

Quintile 5 

1 jtpa40 On-the-Job Training 1 0.1 100.0 1.59

2 jtpa39 Adult Ed, Basic Skills, Literacy 4 0.6 75.0 1.19

3 jtpa5 Comprehensive Assessment 255 38.1 66.3 1.06

4 jtpa41 Occupational Skills Training 412 61.6 60.4 0.96
116

Table 14 Mapping from DOT to SOC Occupation Codes

SOC name SOC number DOT range of codes 

Management, business and financial 11-0000 to 13-0000 161-168 

Professional and related occupations 15-0000 to 29-0000 00-05, 07, 09-16, 19, 96-97 but 

excluding 161-168 

Services including military 31-0000 to 39-0000 30-38 

Sales and related occupations 41-0000 25-27, 29 

Office and administrative support 43-0000 20-24 

Farming, fishing and forestry 45-0000 40-46 

Construction and extraction 47-0000 85, 86, 89, 93 

Installation, maintenance and repair 49-0000 62, 63, 82 

Production 51-0000 50-61, 64-81, 84, 95 

Transportation and material moving 53-0000 90-92 

SOC: Standard Occupation Code. 

DOT: Dictionary of Occupational Titles. 

Table 15 Mapping from an SOC Group to a DOT Code

SOC name SOC number DOT number DOT name 

Management, business and financial 11-0000 to 13-0000 162157018 Buyer 

Professional and related occupations 15-0000 to 29-0000 091227010 Teacher, secondary school 

Services including military 31-0000 to 39-0000 352367010 Airline Flight Attendant 

Sales and related occupations 41-0000 003151010 Sales Engineer, electrical 

products 

Office and administrative support 43-0000 201362030 Secretary 

Farming, fishing and forestry 45-0000 401137010 Area Supervisor 

Construction and extraction 47-0000 824137010 Chief Electrician 

Installation, maintenance and repair 49-0000 184167050 Maintenance Supervisor 

Production 51-0000 641562010 Corrugator Operator 

Transportation and material moving 53-0000 168167082 Transportation Inspector 

SOC: Standard Occupation Code 

DOT: Dictionary of Occupational Titles. 
117
118
119

Appendix A 

The Frontline Decision Support System: 

Prototype Screens for the Georgia Workforce System
120

FRONTLINE DECISION SUPPORT SYSTEM

Customer Background Information

Wednesday May 08, 2002 at 09:13:07 ET. 

Reemployment and Earning Estimates | Related Occupations | Service Referral | Training Statistics

SSN: 123456789 

Name - JOHN SMITH 

 

Current Age: 35 Resides in: COBB COUNTY 

Gender/Race: WHITE Claimant: YES 

Hispanic Origin: No Last Chk: 05/05/02 

Veteran Status: No Wks paid: 2 

 Recently Separated: BYE: 04/01/03 

 Disability: POTENTIAL Dislocated Worker: No 

Citizenship: Yes TANF: No 

Economically disadvantaged: No Employment Status: Not Employed 

Disability: No Currently in school: No 

Exhausted previous UI claim: No Prior Industry: MANUFACTURING 

_________________________ 

County of Employment: COBB 

Education Level: HS GRADUATE 

High School Graduate: YES Prior Occupation: SALES 

GED: NO Months Experience in Prior Occupation: 24 

Driver's license: YES Prior Hourly Wage Rate: $15.00 

Available for all shifts: NO Minimum Salary Desired: $10.00 Per: HOUR 

 

Wage Information: 

Qtr Year Wages 

4 2001 $ 6,000 

3 2001 $ 7,000 

2 2001 $ 8,000 

1 2001 $ 9,000 

RECALCULATE VALUES RESET ORIGINAL VALUES

_______________________
121

Reemployment Probability and Estimated Earnings

Customer Background Information | Related Occupations | Service Referral | Training Statistics

SSN: 123456789 Name: JOHN SMITH 

Probability of Return to Work in Your Prior Industry: 

 The chance of returning to the MANUFACTURING industry in COBB county is 61%. 

Expected Job Growth in Prior Occupation: 

 Over the next 5 years, employment in the SALES occupation is expected to grow by + 

2.25% per year in COBB county. 

Likely Reemployment Earnings: 

Individuals with a similar background had the following estimated reemployment earnings: 

25% had earnings less than $8.10 per hour 

50% had earnings less than $9.05 per hour 

75% had earnings less than $10.20 per hour 

Minimum Salary desired $10.00 per hour 
122

Related Occupations

Customer Background Information | Reemployment Probability and Estimated Earnings | Service Referral | Training Statistics

The following occupations are related to Cashiers . For each related occupation listed, the 

approximate starting hourly wage and the average annual job growth rate in the Cobb County 

Workforce area are given. 

SSN: 123456789 Name: JOHN SMITH 

Related 

Occupations

Approximate 

Starting 

Hourly Wage

Average 

Annual Job 

Growth Rate O*Net Code

Food preparation and serving $5.93 1.02% 35-3021

Counter and retail clerks $6.29 4.29% 41-2021

Parts sales persons $6.29 2.29% 41-2022

Insurance sales agents $8.69 3.52% 41-3021

Receptionists $7.20 5.67% 43-4171
123

Service Referral

Customer Background Information | Reemployment Probability and Estimated Earnings | Related Occupations | Training Statistics

The following is a list of services ranked in order of effectiveness for recent clients in the 

ATLANTA METRO region with characteristics similar to those in the Customer Background 

Information screen. 

SSN: 123456789 Name: JOHN SMITH 

Service 

Number of 

Clients Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

Job Finding Club 2 0.0 100.0 1.85 

Testing 1 0.0 100.0 1.85 

Expanded Workshop 4 0.1 75.0 1.39 

Job Development 125 2.6 63.2 1.17 

Job Referrals 1045 21.6 61.8 1.15 

Resume Preparation 152 3.1 61.8 1.15 

Referred to Training 72 1.5 61.1 1.13 

Referred to Support Services 46 1.0 58.7 1.09 

Call-In 365 7.5 55.6 1.03 

Job Search Assistance 479 9.9 55.5 1.03 

Order Search 1698 35.1 54.2 1.00 

Service Needs Evaluation 4760 98.3 53.6 0.99 

Orientation 4762 98.4 53.6 0.99 

ERP 4776 98.7 53.6 0.99 

Customer Service Plan 4768 98.5 53.6 0.99 

Counseling 4774 98.6 53.6 0.99 

Specific LMI 634 13.1 53.0 0.98 

Workshops 3421 70.7 52.9 0.98 

Job Search Planning 283 5.8 51.2 0.95 

Service Coordination 7 0.1 42.9 0.80 

Bonding Assistance 5 0.1 40.0 0.74 
124

Training Statistics

Customer Background Information | Reemployment Probability and Estimated Earnings | Related Occupations | Service Referral

The following is information about the recent use of the four general types of adult training by 

clients in the ATLANTA METRO region with characteristics similar to those in the Customer 

Background Information screen. 

SSN: 123456789 Name: JOHN SMITH 

Service 

Number of Clients 

Using 

Service 

Percentage 

of Clients 

Using Service 

Percentage of 

Service Users 

Getting Steady 

Work 

Relative 

Effectiveness 

Index (*1) 

Adult Ed, Basic Skills, Literacy 9 1.7 66.7 1.21 

Comprehensive Assessment 257 47.3 56.0 1.02 

Occupational Skills Training 283 52.1 54.4 0.99 

On-the-Job Training 2 0.4 50.0 0.91 

^ Back to Top 
125

Appendix B 

An Accounting of Samples and Models Underlying the 

FDSS for the Georgia Workforce System
126

Appendix B 

An Accounting of Samples and Models Underlying the 

FDSS for the Georgia Workforce System

This paper presents examples of algorithms for the FDSS prototype being used in the 

internet-based Georgia Workforce System (GWS) at two pilot one-stops in Georgia: Athens 

Career Center and Cobb-Cherokee Career Center. All the examples provided in this paper are 

for the Atlanta metropolitan area of Georgia which includes Cobb-Cherokee. This appendix 

provides a quick accounting of the samples used and the models estimated for the full set of 88 

models, plus 60 service referral summaries and 40 training type rankings which form the basis 

for decision support algorithms in FDSS statewide. 

Systematic Job Search Module (SJSM)

Models predicting return to prior industry and likely reemployment earnings in the SJSM 

are estimated on data combined from both the unemployment insurance (UI) and employment 

service (ES) programs with an indicator variable for UI included in each model. While UI 

beneficiaries who are not job-attached (awaiting employer recall or union hiring hall members) 

are required to register for job search with the ES, only one observation for each client identity 

number is retained in the pooled data. In the combined data sample, three separate sub-samples 

are used for estimation: 1) youth (clients aged 14 to 21 who are not welfare recipients or 

economically disadvantaged), 2) economically disadvantaged and welfare recipients, and 3) 

others. Models for the first two sub-groups—youth and economically disadvantaged and welfare 

recipients—are estimated on data pooled across all prior industries. Models for the third 

subgroup—other—are estimated separately for each of eight industry groups (agriculture, 

mining, and construction were combined because of small sample sizes in some regions, and an 

indicator variable was included in those equations for agriculture). 

Since the earnings models are intended to predict full time earnings, for the eight industry 

specific models a sample inclusion restriction was imposed that quarterly earnings must equal or 

exceed $2,500 in at least one of the four quarters between two and five quarters before 

registration. However, no such prior earnings restriction was imposed on the youth and welfare 

recipients or economically disadvantaged samples. Models are estimated for four separate 

regions of Georgia: Atlanta metropolitan, northern, coastal, and balance of the state. 

Considering the return to prior industry plus the median earnings models to be a group, then 10 

groups of models are estimated for each of four regions in Georgia for a total of 80 models—40 

return to prior industry models and 40 earnings models. 
127

Service Referral Module (SRM)

Service referral rankings are compiled for groups formed using an employability score. 

The employability score summarizes characteristics related to prior employment stability. For 

the full FDSS system, employability models are estimated for two program data samples, UI and 

ES, on data for each of the four geographic regions of Georgia. These eight employability 

models are used to set up the quintile groups for the service referral algorithm. That is, based on 

each model, the ordered distribution of employability scores is divided into five equal parts. 

Within the UI sample, rankings of service effectiveness are prepared for two 

programmatically distinct subgroups. The first group is those who are sent directly to the 

reemployment unit (REU) for a special work search orientation workshop and a scheduled series 

of eligibility review interviews and workshops. The REU handles clients who are either profiled 

and referred by the state worker profiling and reemployment services (WPRS) system, or 

referred by the Georgia claimant assistance project (CAP). CAP refers to the REU all UI 

beneficiaries who qualify based on earnings only in the state of Georgia and are entitled to at 

least 14 weeks of benefits. For FDSS service referral, the non-REU UI beneficiaries are 

collected into a second UI group. ES clients who are not UI eligible form the third group for 

service referral ranking. 

For each of the three program groups, service referral rankings were prepared for distinct 

quintile groups within each of the four geographic regions of Georgia. That is, service referral 

quintiles (5), for two UI groups and one ES group (3), in four regions (4), for a total of 60 service 

rankings (5 Η 3 Η 4). Also, training statistics are summarized separately for UI and ES in four 

regions with five quintile groups each for a total of 40 training type rankings. 

A one page summary of this information which identifies the sample sizes used for all 

computations of FDSS models and service referral rankings for the prototype system is provided 

as appendix Table B.1. The following notes apply to the summary given in Table B.1. 

1. In the service referral section, for the REU/Profiling/CAP rows, the total for 

REU/Profiling/CAP is the same as the total for those REU/Profiling/CAP people who used 

services since all persons in that sample received some services. 

2. The sum of persons used for service referral is slightly less than the total sample for the 

employability score models. For example, for UI clients across all four regions, 204,771 persons 

were used in the UI employability score regression. The sum of the UI categories is 202,346 

(52,112 REU/Profiling/CAP + 150,234 Other UI). The shortfall is due to the identification of 

the 15-day period in which the use of services was totaled. The end of that 15-day period had to 

have occurred during or before the fourth quarter of 1998 (98:4). This constraint was applied so 

that for all persons we had at least 4 quarters of wages to observe successful outcomes. 

3. Across all four regions, there were 150,234 persons who were in the Other UI category (nonREU/Profiling/CAP). Of those, 107,178 used services. The drop off comes from two sources: 

A) Persons who did not receive any services, and B) Persons who received some services but 
128

their 15-day period of most services received was more than one year after the reference date. 

For example, for the UI people, if the service activity used to identify the 15-day period occurred 

more than one year after their benefit year begin date, the flurry of activity does not apply to that 

benefit year. The same one year constraint was also applied to those in the ES sample. 
Table B.1 Sample Sizes for Estimating FDSS Algorithms

Sample Size Summary for Return to Prior Industry and Earnings Median Models 

Group Atlanta Metro Area Northern Georgia Coastal Georgia Balance of State Total Sample Size 

Youth (UI and ES samples combined) 7875 9826 4623 8010 30334 

Economically Disadvantaged (UI and ES) 38154 26378 16780 45983 127295 

Other (UI and ES combined):  Agriculture, Mining, Construction  Manufacturing  Transportation, Communication, Utilities  Wholesale Trade  Retail Trade  Finance, Insurance, Real Estate  Services  Public Administration  Total Other 

3317 8256 4795 5642 8585 4087 

18460 

1764 

54906 

3733 

19859 

2019 3217 5536 1362 8370 1499 

45595 

2066 5320 1241 1138 3440 

557 

4724 

862 

19348 

4636 

13907 

2047 2611 5939 1450 8971 2711 

42272 

13752 47342 10102 12608 23500 

7456 

40525 

6836 

162121 

Sample Size Summary for Employability Score Models 

 UI 75055 45513 27514 56689 204771 

 ES 63584 54046 34969 68554 221153 

Sample Size Summary for Service Referral Summaries 

 UI REU (Profiling/CAP)  Used Services  Other UI (Non-Profiling/CAP)  Used Services 

24200 24200 50105 30763 

10180 10180 34772 26897 

6721 6721 

20543 15314 

11011 11011 44814 34204 

52112 52112 

150234 107178 

 ES Total  Used Services 

62779 49795 

53495 49526 

34607 31970 

67918 61890 

218799 193181 

 JTPA Total  Used Services 

9563 3781 

7800 2791 

4104 1194 

9199 3959 

30666 11725 
130

A New WPRS Profiling Model for Michigan 

 Randall W. Eberts and Christopher J. O’Leary 

 Upjohn Institute 

This document prepared at the Upjohn Institute and was prepared for the Michigan 

Bureau of Workers and Compensation. Prepared for presentation at the Employment and 

Training Administration National Research Conference, June 4-5, 2003, Holiday Inn 

Capitol at Smithsonian, Washington, DC. It is based on a report prepared for the 

Michigan Bureau of Workers' and Unemployment Compensation. Eberts is Executive 

Director and O'Leary is Senior Economist at the W.E. Upjohn Institute for Employment 

Research. We thank Kay Knight and Dennis Hunt for useful suggestions. Ken Kline 

provided excellent research assistance. Clerical assistance was provided by Claire Black 

and Phyllis Molhoek. Opinions expressed are our own, and do not necessarily represent 

those of the W.E. Upjohn Institute for Employment Research. We accept responsibility 

for any errors.
131

I. Background

The Worker Profiling and Reemployment Services (WPRS) system was 

established nationwide following the 1993 enactment of Public Law 103-152. The law 

requires state employment security agencies to establish and utilize a system of profiling 

all new claimants for regular unemployment insurance (UI) benefits. Profiling is 

designed to identify UI claimants who are most likely to exhaust their regular benefits, so 

they may be provided reemployment services to help them make a faster transition to new 

employment. 

In November 1994, the Michigan Employment Security Commission (MESC) 

began profiling new UI claimants to identify those at risk of long-term unemployment. 

To do this, MESC adopted a statistical methodology that ranks dislocated workers 

according to their likelihood of exhausting UI benefits. MESC developed the 

methodology with technical assistance from the W. E. Upjohn Institute for Employment 

Research (Eberts and O’Leary 1996). In January 1995, the first cohort of profiled 

unemployment insurance recipients were referred to reemployment services. 

The same profiling model implemented in Michigan eight years ago is still being 

used to refer UI claimants to WPRS services. However, nearly all other aspects of UI in 

Michigan have changed in the intervening years. The MESC has been abolished. It was 

replaced by the Michigan Unemployment Agency, and now UI is administered by the 

Michigan Bureau of Workers’ and Unemployment Compensation (MBWUC). Within 

the next few months, the process of taking UI claims will shift from in-person interviews 

at local offices around the state to telephone claims taken by staff at three call centers to 

be located in Detroit, Grand Rapids, and Saginaw. Furthermore, UI has become a 

partner in new one-stop centers for employment services established in each workforce 

development area in the state as required by the Workforce Investment Act (WIA) of 

1998. 

When the Michigan WPRS was first implemented in 1994, linkages between UI 

and the employment service and Job Training Partnership Act (JTPA) agencies were 

either established or strengthened in each local labor market (Eberts and O’Leary 1997). 

Those relationships which have flowered in the WIA one-stop centers are crucial for 

maintaining active reemployment efforts for those at greatest risk of long-term UI benefit 

receipt. Currently, UI claimants who are neither job attached nor union hiring hall 

members are required to register for job search with Michigan Works to establish benefit 

eligibility. With UI call centers, internet, employer-filed claims, and mail claims 

available in the near future, personal interaction with claimants will be greatly reduced. 

Under this new system, a WPRS referral to orientation may be the most active 

reemployment assistance that many UI claimants will experience during a new spell of 

joblessness. 
132

Also since 1994 Michigan has changed from a wage-request state for UI eligibility 

determination to a wage-reporting state. This means that each claimant’s full benefit year UI 

entitlement is now known at the time that eligibility is established, a fact that will permit new 

approaches to WPRS modeling. When call centers are implemented, the MBWUC will also 

switch to using the new Standard Occupation Code (SOC) and North American Industrial 

Classification System (NAICS). 

To develop a new Michigan WPRS profiling model which is in harmony with the new 

institutional realities, the MBWUC has once again chosen to partner with the W.E. Upjohn 

Institute for Employment Research. This brief paper offers a new WPRS model for Michigan 

which improves on the original model by applying lessons learned nationwide in the years since 

WPRS models were first implemented. A variety of alternative specifications were considered, 

the best of these is proposed as the new Michigan WPRS model. 

The purpose of this paper is to provide the staff of the Michigan Bureau of Workers’ and 

Unemployment Compensation with a detailed description of the new profiling model that we 

recommend the state adopt. In the next section we briefly review the existing Michigan WPRS 

profiling model and the describe the profiling and referral process as it existed when WPRS was 

originally implemented. Section III summarizes the findings from two evaluations of WPRS, 

which help in understanding the expected effects of the program. Section IV delineates the 

recommendations from a study sponsored by the U.S. Department of Labor data to identify the 

best ways to simplify and improve the statistical profiling models. This section is followed by a 

description of the data used to estimate the new profiling model. Section VI presents the 

specification of the new model and its variations. Section VII contrasts the two top variations of 

the new model using several criteria, which shows why we recommend one model over the 

others. The final section offers a brief summary. 

II. The Original Michigan WPRS System

Unemployed workers who are issued a first payment within five weeks of filing a claim 

are eligible for profiling in Michigan. As in all states, profiling in Michigan entails a two-stage 

process (this section is drawn from Eberts and O’Leary 1996). First, UI recipients who are 

expecting to be recalled to their previous job or who are members of a union hall are dropped 

from the pool of workers to be profiled. These two groups are excluded because they are not 

expected to undertake an active independent job search. Second, among the remaining UI 

recipients, some are identified as the best candidates for early reemployment services. Michigan, 

like most states, performs the second sorting using a statistical model that ranks claimants by 

their likelihood of exhausting regular UI benefits.20 Beneficiaries are then referred to orientation 

and reemployment services in order of their ranking until the capacity of local agencies to serve 

them is depleted. 

 20Kelso (1998) and Dickinson et al. (1999, 2002) report that only a few states use non-statistical 

characteristics screens to refer UI claimants to WPRS services. 
133

The profiling model is run at the state level and profiling scores are generated for each 

eligible worker statewide. To implement profiling, each local office draws from the statewide 

ranking of profiled UI claimants who live in their jurisdiction. Each office arrays the selected 

individuals from highest to lowest predicted probability of exhausting UI benefits. Service 

providers (or coordinating organization) determine the maximum number of claimants who can 

be served in a given period, based on the funds that office receives for the WPRS program. 

Profiled UI claimants are referred to service providers based on their probability of benefit 

exhaustion and the referral agreement.21 After assessing the referred claimant’s needs, the 

service provider offers a set of reemployment services best suited to the individual claimant. 

The original Michigan WPRS statistical model includes a UI claimant’s personal 

characteristics: educational attainment, industry and occupation of last job held, and tenure on 

their last job. Industry and occupation codes are also included to reflect differences in demand 

for labor across these sectors and occupations as well as differences in worker qualifications, 

particularly across occupations. If the plastics industry, for example, is experiencing a downturn 

in the state, then workers who have been employed in that sector may have more difficulty 

finding reemployment than those in a sector experiencing growth. The occupational indicators 

followed the codes in the Dictionary of Occupational Titles (DOT). These codes, which provide 

indicators of the people and things complexity of occupations, were also included in the 

statistical model to provide additional detail on the requirements of the job held by the UI 

beneficiaries. Service Delivery Areas (SDAs), defined for administering Job Training 

Partnership Act (JTPA) programs, were included in the statistical model to identify local labor 

markets, with the understanding that local economic conditions, and other local circumstances, 

may differ across these regions of the state. 

Based on this model, the probability assigned to each eligible UI recipient is a weighted 

average of the effects of each of these characteristics on the likelihood an individual exhausts UI 

benefits. The weights reflect the relationship between these variables and the likelihood of 

exhaustion at the time the model is estimated. Since these relationships may change over time, it 

is necessary to reestimate the model periodically. 

 

 For purposes of the WPRS in Michigan, all individuals who receive first payments 

within the same week are considered as one group. UI recipients within this group are ranked 

according to their predicted probability of exhausting. Those estimated to be most likely to 

exhaust are placed at the head of the queue for reemployment services. 

Once a week, each local MESC office receives a list of profiled and ranked eligible UI 

recipients who are beneficiaries through that office. The list includes the name, social security 

number, and estimated probability of exhausting UI benefits for each profiled beneficiary. The 

 21Berger, Black, Smith and Noel (2001) devised a rationing rule to accommodate local WPRS capacity 

which provides for an ideal impact evaluation. 
134

ranking of eligible UI recipients on the list is derived from the statewide estimation of the 

probability of exhausting UI benefits. The local beneficiary with the highest state ranking is 

placed first on the list followed by the beneficiary with the next highest state ranking and so 

forth. 

 

The number of UI recipients actually referred to reemployment services at any specific 

local office depends upon the amount of resources received by that office to provide WPRS 

services. Since funding to local offices is largely based on labor market conditions, one would 

expect that those local offices with the greatest need should be able to serve a larger proportion 

of their UI claimants. UI recipients from local offices with tight labor markets or with industries 

experiencing few layoffs will have state-wide rankings much lower than those from local offices 

with high unemployment rates, and they will serve a smaller proportion of beneficiaries through 

the WPRS. 

III. Evaluation of the Effectiveness of WPRS

The purpose of WPRS is to identify UI beneficiaries who are most likely to exhaust their 

regular UI benefits and to direct them to reemployment services as quickly as possible so that 

they can actively pursue reemployment. Two evaluations have been conducted to determine the 

success of this program. A national evaluation of WPRS, sponsored by the U.S. Department of 

Labor, was based on claimant-level data from a sample of states (Dickinson et al. 1999, 2002). 

In each of the study states (Connecticut, Illinois, Kentucky, Maine, New Jersey, and South 

Carolina), labor market outcome data were compiled from administrative records on all new 

initial UI claimants between July 1995 and December 1996 who were eligible for referral to 

mandatory WPRS job search assistance (JSA). The combined samples included 92,401 profiled 

and referred claimants, and 295,920 claimants who were profiled but not referred to WPRS JSA. 

The impact estimates were statistically significant in all states except South Carolina. For those 

five states with statistically significant results, the largest impact was !0.98 weeks in Maine with 

the other impacts ranging from !0.21 to !0.41 weeks of UI benefits. 

The State of Kentucky also sponsored an assessment of their WPRS system. A feature of 

the Kentucky evaluation that sets it apart from the national evaluation was that the evaluation 

design was incorporated into the profiling modeling and implementation process. This allowed 

for the randomized assignment of claimants to treatment and control groupsBan improvement 

over the design of the national evaluation. A team of economists at the Center for Business and 

Economic Research at the University of Kentucky developed the profiling model and conducted 

the evaluation (Berger, et al. 1997, 2001). 

 

To accommodate the random assignment of claimants, the Kentucky approach to 

profiling divides the predicted UI exhaustion distribution into 20 groups spanning 5 percentile 

points each. Each week the local WPRS capacity is met within one of the 20 groups. For 

example, for a particular week, sufficient capacity was available to accommodate claimants from 

the top three percentile groups, but there was not enough capacity to extend the referrals into the 
135

fourth percentile group. Thus, claimants were randomly selected from the percentile group 

which was third from the top until the capacity was exhausted. The authors referred to this group 

as the profiling tie group (PTG). Justification for this approach is based on the fact that the 

precision of the profiling model is such that it is not possible to distinguish statistically at any 

reasonable confidence level between individuals in that group. Therefore, randomization is 

appropriate for assigning claimants to JSA. 

From among these profiling tie groups (PTGs), experimental treatment and control 

groups were formed to conduct an evaluation of the WPRS in Kentucky. Data were collected 

starting from the very beginning of WPRS implementation in Kentucky, October, 1994 through 

June, 1996. The PTGs yielded a total sample of 1,981 with 1,236 of these assigned to mandatory 

WPRS JSA. 

The impact estimates for WPRS in Kentucky were more dramatic. With regard to the 

three outcomes of interest, the estimated impacts were a reduction of 2.2 weeks of UI, a 

reduction of $143 in UI benefits per beneficiary, and an increase of $1,054 per beneficiary in 

earnings during the UI benefit year. The differences in these estimates from those of the national 

WPRS evaluation are most likely due to the fact that Black et al. (2001) essentially confined 

their comparisons within PTGs, thereby achieving a closer counterfactual. Dickinson et al. 

(1999) compared those assigned to WPRS, who had the highest probability of benefit 

exhaustion, with all those profiled but not referred, including many with very low exhaustion 

probabilities. This meant that the comparison group in the national evaluation was likely to have 

a shorter mean benefit duration than program participants, even in the absence of WPRS 

services. The ideal approach is to use beneficiaries from the same percentile group to make the 

comparison between the outcomes of those who were referred to orientation with those who were 

not. 

The two studies suggest that WPRS has been successful in meeting its original purpose. 

Findings from these evaluations are important not only for providing a better understanding of 

the overall effect of the program, but also for helping states improve the precision of their 

profiling models and the effectiveness of their service delivery systems. In a separate evaluation 

of the U.S. Department of Labor’s Significant Improvement Demonstration Grants (SIGs), which 

were awarded to 11 states, it was recommended that states continue to find ways to improve their 

models (Needels, 2002). In addition to updating and revising the model more often, they also 

recommended that states improve their models through assessing the performance of their own 

WPRS system. The Kentucky approach offers an excellent framework in which to integrate an 

evaluation design into the profiling process. The approach is efficient, inexpensive, and 

incorporates a random assignment technique, which is regarded as the most reliable method of 

evaluation. We recommend that such an approach be incorporated into the implementation of 

the new profiling model. 
136

IV. Lessons Learned from WPRS Modeling

A. Recommendations from a Study Sponsored by the USDOL 

In addition to sponsoring an evaluation of the WPRS, the U.S. Department of Labor 

commissioned a study to identify the best ways to simplify and improve statistical WPRS models 

(Black, Smith, Plesca, and Plourde 2002). Our proposed model takes into consideration the 

lessons learned from this study. 

The study identified five areas in which the model can be simplified without reducing 

predictive performance: 1) use ordinary least squares (OLS) instead of logit, probit or tobit 

(quantal choice models), 2) define the dependent variable as the proportion of entitlement used, 

3) drop the local labor market values of the unemployment rate and industry employment, 4) add 

covariates that contribute to the predictive power of the model, and 5) there is no need to have 

separate models for separate regions of the state-use dummies. The study also recommended 

that using UI administrative records which are maintained at a high standard would improve the 

precision of the model. 

We briefly summarize the reasons that the authors of the study gave for each of their five 

recommendations and then indicate whether or not we have incorporated these features into the 

new model that we propose. First, the study concluded that the functional form for the model 

should be linear. The authors found no evidence that the more involved statistical techniques, 

such as tobit, logit, or probit, outperformed the simple linear probability model (applying 

ordinary least squares (OLS) to estimate a dichotomous dependent variable). Therefore, they 

recommended the use of OLS for both dichotomous and continuous dependent variables. We 

will adopt this recommendation for the new model. 

Second, the study suggested that the dependent variable should be a continuous variable 

that measures the fraction of weeks of entitled benefits that the claimant has drawn. This 

measure is calculated as the actual benefits drawn divided by the total amount of benefits the 

claimant is entitled to in his/her current benefit year. Unlike the dichotomous variable that 

indicates whether or not a beneficiary has drawn his/her total entitlement, the fraction of benefits 

drawn differentiates among those who have not yet exhausted. The authors contend that this 

additional information can improve the predictive power of the model. Their results, however, 

show little difference in predictive power between the two models. Furthermore, they report 

discrepancies in the construction of the continuous variable across the three states for which they 

analyzed data. Therefore, while we offer a model that uses the continuous variable for 

comparison purposes, we recommend adoption of the model that uses a dichotomous variable 

indicating whether or not the individual has exhausted benefits. 

Third, the study recommended dropping the local labor market values of the 

unemployment rate and industry employment. The reason behind this suggestion is that virtually 

all claimants applying for UI in a given WIA area at a given time face the same unemployment 

rate. Consequently, the regional variation in unemployment rates will not help distinguish among 
137

workers applying in the same WIA area at the same time. We recognized this problem when 

developing the original model and left it out of the specification, and we will do so again in the 

new model. We retain the occupation and industry variables, however, to reflect structural 

differences in labor demand and supply in the various occupations and sectors. 

Fourth, the study found that a few additional variables improved the predictive power of 

the model. In addition to the variables that we included in the original Michigan model, their 

study suggests considering a few others: 1) UI benefits exhausted in the most recent prior UI 

spell, 2) an indicator for previous UI claims, 3) welfare dependency, 4) food stamps recipiency, 

public transportation available for getting to work, 5) JTPA (or WIA) eligibility, 5) quarterly 

wages within the last year, and 6) enrolled in school or employed at time of claim. Some 

variables from this list were not available from the Michigan’s administrative records to include 

in the model. Others were tried but were not statistically significant and did not add to the 

predictive power of the model. We included variables 1 and 5 (in the form of base wages) from 

the list above. In addition, we included reasons for job separation and length of UI entitlement. 

Fifth, the study found that estimating separate models for different regions of the state did 

not improve the predictive power of the model. We had come to a similar conclusion when 

experimenting with different specifications, and thus will estimate a single model for the state of 

Michigan. We do include regional indicators, associated with each WIA area, which account for 

Ashifts@ in the probability of exhaustion across regions but which do not incorporate possible 

differences in the coefficients of the variables across regions. 

B. Model Specification Changes Related to Intervening Events 

 

The occurrence of three events since the original model was developed prompts the need 

for additional changes to the specification. First, the occupation variables in the original model 

were based on the Dictionary of Occupational Titles (DOT). These codes included detailed 

descriptions of the degree of complexity encompassed by the various occupations in relating to 

people and in manipulating things. DOT codes are being replaced with standard occupation 

codes based on the O*Net occupational classification system. Consequently, the detailed 

descriptions of occupations with respect to people and things are no longer available and must be 

deleted from the model. 

The second event is the simple fact that WPRS has been in operation since 1994 and has 

shown in the evaluations it has had a significant impact on exhaustion rates, at least for the states 

studies. Thus, the model must now include an indicator for those beneficiaries who have been 

profiled and referred to orientation. The original model was estimated on data that recorded the 

experience of beneficiaries before WPRS was implemented. But any reestimation of the model 

since then must take into account the effect of the program on the behavior of the claimants. As 

described in the previous section, WPRS has been shown to reduce the duration of UI benefits by 

as much as 2.2 weeks. Therefore, those claimants who are profiled and referred to orientation 

will on average have a different duration than those who were not referred to orientation. If there 
138

were no way to distinguish between the two groups, the model would be misspecified, thus 

reducing its predictive power.22 

The third event is the initiation of extended benefits during the period in which the new 

model was estimated. The recent economic downturn and the increased difficulty experienced 

by displaced workers in finding jobs prompted Congress and the President to establish the 

Temporary Extended Unemployment Compensation (TEUC), which provides claimants who 

have exhausted regular state benefits up to 13 weeks of additional benefits. Under federal law, 

unemployed workers may qualify for benefits if they 1) are not currently working full-time, 2) 

have exhausted all rights to regular state UI benefits, 3) have no entitlement to other UI benefits, 

and 4) have a new or additional claim for state UI benefits and a benefit year ending after March 

10, 2001. An additional period of TEUC, called TEUC-X , is payable if the state’s insured 

unemployment rate (IUR) reaches 4.0 percent or higher at the time a jobless worker exhausts 

his/her original TEUC benefits. These benefits are the same length and amount as offered under 

the TEUC. Jobless workers will generally receive the same weekly benefit amount in TEUC as 

they did in their most recent regular state UI claim and be eligible for half the number of weeks 

to which they were entitled in their most recent benefit year. The first week for which TEUC 

was payable was the week ending March 16, 2002. TEUC was still in effect at the time this 

paper was written. Figure 1 shows the jump in the percentage of claimants establishing TEUC 

entitlements soon after the program was implemented. The percentage climbed steadily until 50 

percent of the beneficiaries established entitlements. The same pattern occurred for TEUC-X but 

with small percentages. Therefore, it is important to account for this program in the new model. 

By offering claimants an additional 13 weeks of benefits beyond the regular entitlement, 

those who are eligible to establish this extended entitlement may be more likely to exhaust their 

regular benefits than those who are not eligible. Studies have shown that extended benefits tend 

to increase the rate of UI benefit exhaustion (Woodbury, 1997, p. 245; Jurajda and Tannery, 

2003). Therefore, estimating the model on data that includes a period in which the TEUC is in 

effect requires the ability to sort out the effect of extended benefits on the likelihood of 

exhausting regular benefits. The difficulty in doing so is the inability to distinguish between 

those who during their benefit year recognized that benefits could be extended beyond the 

regular period and those who did not have this option. It is compounded by the inability to 

separate out the effects of economic downturns (demand conditions) on reemployment from the 

effects of extended benefits (supply response). However, since we are not concerned about 

estimating the separate effect of extended benefits on exhaustion (that is, to separate its effect 

from the other variables in the model), we need only to enter separate categorical variables for 

 22However, empirical studies have found that the degree of misspecification from using such data is usually 

minor and does not significantly affect the parameter estimates (Olsen, Kelso, Decker and Klepinger 2002). This 

finding may appear to run counter to the evaluation results, which found a significant effect of WPRS on exhaustion 

rates. The difference in results can be explained by the fact that entering the referral indicator in the model is not a 

valid method of testing for the impact of the program, due to selection bias and other factors. The orientation 

variable is not used to calculate the profiling score, since it of course is not observed at the time profiling takes 

place. 
139

each week. In this way, we have taken into account the effect of any event unique to that week 

on the probability of exhaustion. These events, of course, include among other things, the 

claimant’s eligibility for extended benefits. Therefore, the recommended model includes 

categorical weekly variables.23 

V. Data for Estimating a New Michigan WPRS Model

The MBWUC provided data on UI claimants who filed on or after October 1, 2000. The 

reason for this starting date is that this is the time that the MBWUC started using quarterly wage 

record information to determine UI eligibility. Prior to that time, Michigan used a wage request 

system, which relied on contacting employees for weekly wages and separation information 

whenever a former employee filed a claim for jobless benefits. For consistency of data, it is 

necessary to start the estimation when the wage record system was initiated, after October 2000. 

Wage record data are also the only source of information on the full benefit year of UI 

entitlement, which other studies (as well as our estimation) have shown to be an important 

variable. Under the old Michigan wage request system, the full potential benefit year 

compensation might never be known for a claimant drawing less than 26 weeks of benefits. The 

data extract provides information on valid claims that ended after September 30, 2001, which 

means that the claims started on or after October 1, 2000. A claim is valid if the claimant met all 

monetary (sufficient earnings and hours) and non-monetary (not fired, quit, etc.) conditions for 

establishing a valid UI claim. 

Table 1 summarizes the number of profiled claimants in our sample by the week ending 

date of their first payment. Table 2 shows the frequencies of benefit durations in our sample. 

More than 50 percent of claimants in the sample exhausted their initial entitlement. Table 3 

shows the number of profiled workers in each of the 24 workforce development areas (WDA) of 

the State of Michigan. WDA 19 includes the City of Detroit. 

VI. Variables included in the New Michigan WPRS Model

Specification of the new proposed Michigan WPRS profiling model is shown in Table 4. 

The table contrasts the variables used in the original model with those used in the new model. 

The major difference is the addition of five variables that were not available at the time the 

previous model was developed. These variables include: base wages, entitlement length, 

exhausted benefits in prior UI spell, reasons for separation, and referred to orientation, as well as 

the weekly categorical variables described above. Adding these variables is justified as a way to 

better model the behavior of claimants with respect to exhausting benefits. Base wages are 

added in order to offer additional information about the individuals prior job, since the level of 

compensation reflects a person’s qualifications relative to other individuals in the same sector 

and occupation. It also indicates the likelihood that an individual is able to find a job with 

 23 The categorical variables are used in the estimation to avoid omitted variable bias in the other 

coefficients. The categorical variables, however, are not used to estimate the profiling scores when the model is 

implemented. 
140

similar attributes. The entitlement length suggests the claimant’s prior attachment to work. The 

reason for including the referral to orientation has been discussed in the previous section. The 

variable indicating whether the claimant exhausted benefits in a prior UI spell is added to the 

model to reflect behavioral tendencies of the claimant. The reasons-for-separation variables are 

included to distinguish among those claimants who were laid off from those who quit or were 

fired, since there appears to be a difference in the likelihood of exhausting benefits depending 

upon the reason for separation. The weekly categorical variables are included to account for the 

idiosyncratic effects of weekly events on the probability of exhausting benefits, particularly the 

availability of extended benefits. The means for the estimation sample are shown in Table 5. 

We also experimented with various combinations of the new variables. We found, 

however, that the full model outperformed the models that included only subgroups of the 

variables listed above. In particular the weekly categorical variables, which were included to 

account for TEUC, helped to improve the model. Table 6 shows the correlation between the 

three most promising models, with and without the weekly variables. We see that the logit and 

the OLS estimation of the specification with UI exhaustion as the dependent variable yield 

virtually identical rankings, which is the reason we recommend the simpler estimation technique 

of OLS. Also, although the addition of the weekly variables increases the fit of the models, it 

does not change the ranking significantly, as indicated by the higher correlations between those 

two variations of the model. 

VII. Choosing the Appropriate Dependent Variable 

Accepting the full set of variables as the preferred specification, the remaining issue with 

regard to specification is the choice of the dependent variable. The original model, along with 

most state profiling models, used a dichotomous dependent variable indicating whether or not a 

beneficiary exhausted his/her benefits. Black et. al. (2002) experimented with the fraction of 

benefits drawn in a benefit year and recommended it as an alternative measure. We also will 

experiment with the two forms of the dependent variables and show that according to several 

criteria the alternative measure (fraction of benefits drawn) is not superior and in most cases 

slightly inferior to the model with exhaustion as the dependent variable. 

 

Estimates of the two models are shown in Tables 7 and 8.24 We find that the coefficients 

are similar across the two specifications, particularly for the claimant’s personal characteristics 

such as tenure and education. Estimates suggest that claimants with more tenure (up until about 

26 years as a result of the negative sign on the tenure squared term) and education are less likely 

to exhaust benefits. Those referred to orientation are more likely to exhaust benefits. This result 

seems counterintuitive to what we learned from the evaluations. However, the positive sign may 

 24The dichotomous variables, such as those used for education, separation, occupations, industries and WIA 

areas, are normalized against the mean instead of against an omitted variable from each of the groups of variables. 

Therefore, all categories are included in the tables, as opposed to the customary omission of one variable from each 

group. 
141

reflect the fact that those claimants who were referred to orientation were most likely to exhaust 

benefits during their previous claim (according to the statistical profiling model) and thus may 

have the same tendency in this benefit period.25 It is interesting that the coefficients for the first 

two reasons for separationBlack of work and quit/firedBdiffer between the two models. The 

exhaustion model (Model A) suggests that those who quit or are fired are more likely to exhaust 

benefits, while the fraction-of-benefits model (Model B) suggests the opposite. The signs are 

reversed for the lack-of-work variable. The coefficients on both variables are statistically 

significant in each model. 

The relationship between the predicted values and key variables can be illustrated by 

graphing these relationships by constructed percentiles. We choose three variablesBtenure at the 

last employer, college graduation, and exhaustion of prior UI spellBand construct 20 percentile 

groups in order to record the percentage of college graduates and the prior exhaustion rates 

across the distribution. For illustrative purposes, we use only the predicted values from Model 

A, recognizing that the same relationships hold for Model B. As shown in Figure 2, prior 

exhaustion is positively related to the profiling score, with most of the variation affecting the 

upper end of the profiling score distribution. Figure 3 shows that college graduation and the 

profiling score are negatively correlated, with the percentage gradually falling throughout the 

distribution while the profiling score increases. As shown in Figure 4, tenure exhibits a 

quadratic relationship with the profiling score, which is the reason for entering that variable as a 

quadratic. In the graph, we see that tenure increases throughout the distribution until it begins to 

decrease after reaching the 16th percentile group (the top 25 percent of the distribution). 

In order to judge the predictive power of the various models, it is appropriate to base 

these comparisons on out-of-sample predictions generated by each model. Out-of-sample 

validation involves excluding a random sample from the data used for model estimation, and 

then using that sample to check the forecasting accuracy of the model. Following Black et al. 

(2002), the validation sample is constructed by randomly selecting claimants who filed claims in 

four different weeksBone week from each of four quarters of data. This process generated a 

sample of 15,074, which is 7 percent of the estimation sample. The means of the explanatory 

variables for the validation sample are displayed in Table 9. 

A. Selection Criteria of Minimizing False Positives26

A statistical profiling model ranks individual claimants according to their estimated 

probability of exhausting benefits (or the fraction of benefits drawn, as is the case with Model 

B). Therefore, referrals to orientation are drawn first from the top of the distribution of predicted 

values, working down through the distribution until the capacity of the system to serve 

individuals has been met. Therefore, an optimal profiling model is one in which the model 

 25The positive sign on the orientation coefficient may reflect selection bias, which underscores the need for 

random assignment when evaluating the WPRS program. 

26 The authors wish to thank Tim Bartik for suggesting the framework for this criterion. 
142

precisely selects for referral all individuals who would, if not referred, exhaust their benefits. 

Models that generate a greater number of false positives (that is, those who were identified by 

the model as exhausting but did not) yield less efficient profiling procedures. 

Two costs result from imprecise estimates, as shown in Table 10. The first cost is from 

false negatives. These are individuals who were not referred to orientation because their 

profiling score was below the cutoff point, but should have been. By exhausting their benefits, 

they draw more UI benefits than they would if referred to orientation, thus costing the UI system 

additional dollars and reducing the prospect of returning to work. According to the Kentucky 

evaluation results, individuals are likely to stay on UI 2.2 weeks longer, collect $143 more in UI 

benefits and forgo $1,054 in earnings during the UI benefit year than if they would have been 

referred. 

The second cost relates to false positives. These are individuals who were identified as 

having a high probability of exhausting benefits and referred to services but would have likely 

found a job without assistance before exhausting benefits. The cost associated with this group is 

the opportunity cost of occupying a position in the orientation session (and subsequent services) 

that could have been used by someone who would have actually exhausted benefits without this 

assistance. There is also the cost of delaying an individual’s job search activities and asking an 

individual to participate in a program that he or she may not have wanted to attend.27 

Therefore, it is obvious from Table 10 that the goal of an optimal profiling model is to 

minimize the number of claimants who are false positives in the upper range of the profiling 

distribution from which people are drawn to attend orientation. The converse of this goal is to 

maximize the number of true positives, that is, those who are identified as exhausting benefits 

who actually do exhaust. 

As a way of using this criterion to compare the two models, first suppose that capacity 

exists to serve 3,000 people per week out of 20,000 people profiled. Following the procedure 

used by Kentucky, these 20,000 are divided into 20 groups of 1,000 each, that is, into 20 groups 

each with an interval of five percentile points. Selection for referral to orientation starts with the 

top percentile group of 1,000 and then works down the distribution until all the slots are filled. 

Table 11 displays the cumulative number of claimants who are profiled as exhausting benefits 

and who actually exhausted, for each of the two models. In order to refer 3,000 people to 

orientation, all 1,000 people from each of the first three groups are selected. If Model A were 

used to identify who among the claimants is likely to exhaust benefits, 2,074 people (or 69.1 

percent) would have actually exhausted (true positives). If Model B were used to profile the 

 27Under the proposal Personal Reemployment Accounts, these costs become even more significant to both 

the individual claimant and the system. According to the PRA, a claimant is entitled to up to $3,000 if they are 

eligible. One criteria of eligibility is to have a high probability of exhausting benefits. If a false positive occurs for 

someone with a high probability of exhausting benefits, then that individual becomes entitled to the $3,000 account, 

which since there are limited funds, would prevent someone who was actually more likely to exhaust benefits from 

receiving the funds. 
143

claimants, 2,064 (68.8 percent) would be identified correctly. The difference is 10 people who 

are accurately identified as exhausting. For this part of the distribution, the models are 

comparable in meeting the goal of referring to orientation as many people as possible who would 

actually exhaust benefits. 

It should be noted that statistical profiling does much better than randomly selecting 

claimants from the entire pool of 20,000. Under random selection, the probability of referring 

someone to orientation who would have actually exhausted benefits is 52 percent (the mean 

percentage of exhaustees in the sample).28 The two models exceed this rate by at least 16.8 

percentage points. For the 3,000 assigned, this means that an additional 504 people have been 

accurately identified as exhausting, thus significantly reducing the cost of misclassification. For 

example, wrongly classifying this group of 3,000 people would cost the system $72,072 per 

benefit year in additional UI payments (504x$143), according to the Kentucky evaluation, since 

the false positives are taking up space in the programs that could have been used by those who 

actually exhausted. 

Suppose that capacity is increased to 3,500. To add 500 more claimants, profiled 

workers would be drawn from the next lowest percentile groupBthe 17th. However, only half of 

the 1,000 people included in this group can be accommodated. One solution would be to 

randomly draw 500 people from the group. This approach is similar to the one suggested by 

Black et. al (2001) and used by Kentucky. Following Black’s terminology, the 17th percentile 

group is referred to as the profiling tie group, since not everyone from this percentile group is 

referred to orientation due to limited capacity. Under Model A, 65.7 percent of the 500 people 

drawn from the 17th percentile would actually exhaust benefits, whereas under Model B, 65.1 

percent would exhaust. Of the 500 people drawn, the difference between the two models in the 

number of people drawn who actually would exhaust benefits is very small, only 3 people. 

An alternative approach is to draw the 500 claimants sequentially from highest to lowest 

profiling score from within the 17th percentile group until the 500 referrals are reached. A 

convenient way to contrast the two approaches is to divide those claimants in the profiling tie 

group (17th percentile group in the case of the previous example) into decile groups (10 groups of 

equal number of claimants). We consider only the distribution generated from Model A in order 

to illustrate the differences between the two sampling techniques. Table 12 displays the number 

of actual exhaustees for each decile group within the 17th percentile group. Drawing from the 

top half of the distribution to obtain 500 additional referrals results in 66.8 percent of those 

drawn actually exhausting benefits. This proportion is slightly more than the 65.7 percent of 

actual exhaustees that was obtained by randomly selecting from the entire 1,000 claimants within 

 28 Random selection may not be the decision rule used instead of profiling. Traditionally, referral decisions 

are based on the judgment of front-line staff. It is interesting to note, however, that Gueron and Pauly (1991) cite 

two studies that show little correlation between the job-readiness ratings by frontline staff and participants’ 

performance in the program. 

 
144

the 17th percentile. However, whether one approach is superior to another for any portion along 

the distribution of profiling scores depends upon the idiosyncrasies of those claimants.29 

B. Steepness of the Distribution 

The criterion of maximizing the number of referrals who actually would exhaust benefits is 

comprised of two parts. The first is the steepness of the distribution, that is the ability to 

distinguish among claimants according to their likelihood of exhausting benefits. The second is 

the accuracy of that prediction, as measured by the percentage of individuals along each segment 

of the distribution that actually exhausts benefits. First consider the steepness of the distribution 

for the two profiling models. Steepness is one of the primary criteria used by Black et. al (2002) 

to select profiling models.30 A profiling model with a steeper distribution is able to distinguish 

among the UI claimants more precisely. Figures 5 and 6 display the predicted probabilities 

derived from Model A and Model B, respectively, estimated on the validation sample. Note that 

both curves follow a logistic function. The distribution generated by Model A ranges from 0.17 

to 0.92, while the distribution generated by Model B spans a shorter interval from 0.51 to 0.99. 

Figure 7 compares the steepness of the two distributions by dividing the distributions into 20 

percentile groups and indexing the lowest value (upper cutoff value for the lowest percentile 

group) of each distribution to 1. The points plotted in Figure 3 are the upper percentile values 

for each of the 20 groups. It is apparent from this graph that predicting exhaustion events 

(Model A) generates a distribution that is considerably steeper than predicting the fraction of 

benefits (Model B). The ending value for Model A is 77 percent greater than the beginning 

value, whereas the ending value for Model B is only 28 percent greater than its beginning value. 

Based on this measure, the slope of Model A’s distribution is 2.7 times steeper than that of 

Model B. 

Since most claimants who are referred to orientation are drawn from the top 25 percent of 

the distribution, it is also instructive to take a closer look at this portion of the curve. Once again 

using upper percentile values for each of the 20 groups, it is evident that, for the upper 25 percent 

of the distribution, the distribution of the predicted values of Model A is steeper than that of 

Model B. The difference between the cutoff values for the 20th percentile group and the 15th is 

 29As will be shown later in the paper, the actual exhaustion rates do not perfectly track the predicted 

probability of exhausting benefits. As shown in table 12, the actual exhaustion rate is higher in the 17th percentile 

than in neighboring percentiles, whereas it should decline continuously from top to bottom of the top of the 

distribution. One possible reason for the non-monotonic nature of the actual exhaustion rate for small segments of 

the distribution is the relatively small sample size for each percentileB750. The model was estimated on a sample of 

more than 200,000 claimants. In reality, however, WIA areas will be drawing relatively small samples each week 

and should expect some anomalies as shown here. It should also be noted that the actual exhaustion rate when 

Model B is used to delineate the 17th percentile is even less monotonic when compared to the exhaustion rate of the 

neighboring percentiles. 

30Steepness of the distribution is one of the criteria that we used to select the original profiling model for 

Michigan. 
145

0.119 for Model A versus 0.077 for Model B. Thus the spread of the distribution for Model A is 

55 percent greater than that of Model B for this upper quarter of the distribution. 

C. Accuracy of the Model 

To measure the accuracy of each model, we follow an approach referred to as the running 

sum of proportion of ones (exhausting benefits equals one), or CUSUM.31 Ideally for our 

purposes, the profiling score should perfectly distinguish between those who exhaust and those 

who do not exhaust. If this were true, the relationship between the profiling score and the event 

of exhausting benefits would be such that all those who exhaust would be in the top portion of 

the distribution of predicted exhaustion probabilities and all those who do not exhaust would be 

in the lower portion of the distribution. Thus, there would be no interspersing of those who 

exhausted with those who did not exhaust. In this case, plotting the running sums of ones against 

the continuous profiling score would yield a pyramid-shaped graph with its peak at the sample 

proportion of those who exhausted. Figures 8 and 9 show the graphs of the running sum of ones 

for each model. The graphs show a pronounced inverted U-shaped plot for each model, 

indicating a strong positive monotonic relationship. The trend for each model is confirmed by a 

highly statistically significant linear cusum statistic (7.72 for Model A and 7.27 for Model B). 

Examining the upper 25 percent of the distribution, as shown in Figures 10 and 11, shows 

a less pronounced inverted U-shaped relationship, but the statistic shows a highly statistically 

significant linear relationship, with Model B exhibiting a slightly higher statistic than Model A 

(3.41 for Model A and 4.45 for Model B). Therefore, according to this measure of fit, the two 

models are comparable in their relationship between the exhaustion event and the profiling score. 

D. Comparing How Each Model Ranks Claimants 

 

While the two models are comparable with respect to fitting the data and satisfying the 

criteria of maximizing the number of referrals who would actually exhaust benefits, their ranking 

of specific individuals according to their profiling scores differs. Thus, some individuals 

referred to orientation by one model may not be referred to orientation by the other model. The 

rank correlation of the profiling scores generated from the two models is 0.885. A score of 1.00 

indicates that each model ranked individuals identically. 

To see the effect of the different rankings on referrals to orientation, we return to the 

previous example of selecting 3,000 claimants for referrals. As shown in Table 11, selecting 

claimants from the 18th, 19th, and 20th percentile groups would meet this capacity. Table 13 

shows the overlap between the two models in selecting claimants as well as the outliers. Cross 

tabulations were derived for each of the 20 percentile groups, but we show only the 13th

percentile group and higher, since this is the region of the distribution that is affected by the 

selection of referrals. We find an overlap of 2,423 (or 80.8 percent of) individuals who were in 

the 18th through 20th percentile groups for each model. If referrals are based on Model A, then 

 31See the description of CUSUM in the STATA Reference Manual, Release 6, Volume One, pp. 285-288. 
146

576 individuals would have been included in the top 3 percentile groups who would not have 

been included if Model B were used. Conversely, Model A does not include 570 people in the 

top 3 percentile groups that Model B would have included. Since the outliers under Model B 

vis-a-vis Model A extend farther down in the distribution than the outliers under Model A, the 

exhaustion rate of the Model B outliers is slightly lower than that of the outliers under Model A 

(60.8% versus 61.7%). 

E. Contrasting the Preferred New Model with the Original Model 

The original model and the new model assign different profiling scores to the same 

people, thus yielding significantly different rankings. Using the same out-of-sample group of 

claimants, we find that the rank-order correlation coefficient is 0.33, which is considerably lower 

than the ideal value of 1.00, which indicates all individuals are ranked the same by each model. 

The value of 0.33 is also much lower than the rank correlation coefficient between the two 

versions of the new model. 

We also find that the distribution of profiled scores between the new and original models 

differs. As shown in Figure 12, the new model is considerably steeper than the original model 

and tends to increase more monotonically than the original model. The new model is about 40 

percent steeper than the original model for the entire distribution and 15 percent steeper for the 

top 25 percent of the distribution. Therefore, adding the variables included in the new model 

improves the performance of the model based on this simple criteria of model performance. The 

performance of the model is also improved by updating the estimates of the coefficients. 

VIII. Summary

The Michigan Bureau of Workers’ and Unemployment Compensation has asked the 

Upjohn Institute to revise and update the statistical profiling model that it uses to identify UI 

claimants who are most likely to exhaust their regular benefits. The Institute developed the 

original model, which Michigan has used since 1995. Several studies sponsored by the U.S. 

Department of Labor underscore the need to reestimate profiling models periodically and to 

update them if new variables are made available. The new model that we propose includes new 

variables that are now available since Michigan became a wage-record state. In addition, the 

new model is estimated using the most recent data available. The proposed model predicts the 

probability that a UI beneficiary exhausts his or her regular benefits. An alternative specification 

was explored that predicts the fraction of benefits drawn during the benefit year. Both models 

incorporate most of the suggestions outlined in the report by Black et al. (2002) sponsored by the 

U.S. Department of Labor. While the two models are fairly comparable according to several 

criteria, we recommend adopting the model based that predicts the exhaustion of benefits (Model 

A). This model performed slightly better, and it is easier to interpret. 

We also recommend that the profiling model be implemented following the method 

recommended by Black et. al. (2002) and used by Kentucky. This method divides the 

distribution of profiling scores into 20 percentile groups and refers claimants to orientation 
147

starting with the group with the highest profiling scores and working down the distribution. 

When the capacity of the service providers is met within a specific percentile group, claimants 

are randomly drawn from that group, referred to as the profiling tie group, until capacity is met. 

We showed that this approach yields results that are similar to that obtained from using a 

sequential selection approach. This approach is justified because the models are not sufficiently 

precise to distinguish among claimants within a given percentile group with an acceptable 

statistical significance. It also provides MBWUC with a valuable evaluation tool that can be 

used to periodically revise the profiling model and to improve the effectiveness of the WPRS 

system. 
148

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150

Table 1 

Number of Profiled Workers, Exhaustion Rate and Fraction of Entitlement Used by Date of First UI Payment 

Estimation Sample Validation Sample 

Week Ending 

Date of First 

Payment 

Sample Size Exhaustion 

Rate 

Fraction of 

Entitlement 

Used 

Sample Size Exhaustion 

Rate 

Fraction of 

Entitlement 

Used 

7-Oct-00 1645 0.5605 0.7583 . . . 

14-Oct-00 1285 0.5440 0.7493 . . . 

21-Oct-00 1381 0.5583 0.7601 . . . 

28-Oct-00 1363 0.5657 0.7676 . . . 

4-Nov-00 1686 0.5623 0.7817 . . . 

11-Nov-00 72 0.6250 0.8075 1665 0.5351 0.7721

18-Nov-00 1847 0.5322 0.7778 63 0.4762 0.7185

25-Nov-00 1179 0.5191 0.7656 27 0.5185 0.6937

2-Dec-00 1483 0.5192 0.7698 2 0.5000 0.5962

9-Dec-00 1726 0.5110 0.7600 . . . 

16-Dec-00 1154 0.4905 0.7541 . . . 

23-Dec-00 1117 0.5031 0.7519 . . . 

30-Dec-00 1197 0.5313 0.7484 . . . 

6-Jan-01 2220 0.5279 0.7556 . . . 

13-Jan-01 2751 0.5049 0.7538 . . . 

20-Jan-01 2331 0.4848 0.7343 . . . 

27-Jan-01 2550 0.4624 0.7101 . . . 

3-Feb-01 2329 0.4693 0.7127 . . . 

10-Feb-01 3136 0.4790 0.7213 . . . 

17-Feb-01 2242 0.4777 0.7189 1 0.0000 0.5385

24-Feb-01 2306 0.4679 0.7178 . . . 

3-Mar-01 2324 0.4819 0.7158 . . . 

10-Mar-01 166 0.4518 0.6920 2399 0.5227 0.7444

17-Mar-01 2374 0.5430 0.7664 87 0.4713 0.7252

24-Mar-01 2459 0.5336 0.7538 44 0.4773 0.7355

31-Mar-01 2059 0.5294 0.7518 6 0.8333 0.8590

7-Apr-01 3637 0.5428 0.7571 . . . 

14-Apr-01 142 0.4648 0.6890 1988 0.5302 0.7517

21-Apr-01 2114 0.5553 0.7681 59 0.4407 0.6724

28-Apr-01 2334 0.5338 0.7572 38 0.5789 0.7642

5-May-01 2380 0.5458 0.7649 9 0.5556 0.6410

12-May-01 2054 0.5755 0.7751 . . . 

19-May-01 1938 0.5681 0.7740 . . . 
151

Table 1 

Number of Profiled Workers, Exhaustion Rate and Fraction of Entitlement Used by Date of First UI Payment 

Estimation Sample Validation Sample 

Week Ending 

Date of First 

Payment 

Sample Size Exhaustion 

Rate 

Fraction of 

Entitlement 

Used 

Sample Size Exhaustion 

Rate 

Fraction of 

Entitlement 

Used 

26-May-01 2014 0.5645 0.7665 . . . 

2-Jun-01 1475 0.5715 0.7679 . . . 

9-Jun-01 2297 0.5485 0.7477 . . . 

16-Jun-01 1956 0.5557 0.7559 . . . 

23-Jun-01 2041 0.5654 0.7606 . . . 

30-Jun-01 1695 0.5971 0.7850 . . . 

7-Jul-01 3186 0.6058 0.7829 . . . 

14-Jul-01 2242 0.6258 0.7891 . . . 

21-Jul-01 2065 0.5835 0.7619 . . . 

28-Jul-01 1822 0.6153 0.7815 . . . 

4-Aug-01 2022 0.6078 0.7816 . . . 

11-Aug-01 2059 0.5872 0.7622 . . . 

18-Aug-01 101 0.6733 0.8297 1754 0.6009 0.7702

25-Aug-01 1709 0.6220 0.7796 70 0.5714 0.7987

1-Sep-01 1794 0.6472 0.8062 28 0.6786 0.8559

8-Sep-01 2069 0.6191 0.7877 5 0.6000 0.6923

15-Sep-01 1883 0.6325 0.8001 . . . 

22-Sep-01 2159 0.6373 0.8021 . . . 

29-Sep-01 3136 0.5858 0.7679 . . . 

6-Oct-01 3593 0.5967 0.7802 . . . 

13-Oct-01 145 0.6207 0.8047 . . . 

20-Oct-01 61 0.6393 0.8174 . . . 

27-Oct-01 10 0.7000 0.8615 . . . 

3-Nov-01 1 0.0000 0.1923 . . . 

17-Nov-01 3 0.0000 0.1031 . . . 

1-Dec-01 1 1.0000 1.0000 . . . 

102520 0.5517 0.7607 8245 0.5434 0.7569
152

Table 2 

Number of Profiled Workers and Proportion of Entitlement Used by Weeks of Benefits Drawn 

Estimation Sample Validation Sample 

Weeks of Benefits 

Drawn Sample Size Proportion of 

Entitlement Used Sample Size Proportion of 

Entitlement Used 

1 1003 0.0416 75 0.0401

2 3611 0.0850 296 0.0862

3 1823 0.1242 165 0.1195

4 3390 0.1654 297 0.1666

5 1681 0.2026 145 0.1990

6 3066 0.2457 240 0.2479

7 1629 0.2863 122 0.2828

8 2607 0.3310 227 0.3317

9 1427 0.3663 122 0.3577

10 2453 0.4150 182 0.4132

11 1277 0.4468 94 0.4590

12 2432 0.5001 194 0.4923

13 1278 0.5498 102 0.5559

14 2681 0.6397 227 0.6374

15 2033 0.7793 153 0.7745

16 3197 0.7702 258 0.7795

17 2357 0.8377 173 0.8523

18 3479 0.8383 234 0.8268

19 2426 0.8907 165 0.8890

20 3279 0.8820 266 0.8697

21 2140 0.9204 188 0.9192

22 3039 0.9151 250 0.9164

23 2002 0.9489 163 0.9386

24 3058 0.9548 251 0.9551

25 2176 0.9834 195 0.9803

26 42991 1.0000 3462 1.0000

Total 102535 0.7607 8246 0.7569
Table 3 

Number of Profiled Workers, Exhaustion Rate and Fraction of Entitlement Used by WIA Area 

Estimation Sample Validatio

Sample Size Exhaustion Rate

Fraction of 

Entitlement 

Used 

Sample Size Exhaustio

1 WIA Area, Western UP 864 0.5671 0.8055 70 2 WIA Area, Central UP 981 0.5178 0.7615 73 3 WIA Area, Eastern UP 352 0.5199 0.7590 33 4 WIA Area, North West 3765 0.4874 0.7372 316 5 WIA Area, North East 2101 0.5621 0.7988 181 6 WIA Area, West Central 1755 0.5197 0.7562 147 7 WIA Area, Region 7B 1185 0.5932 0.8010 77 8 WIA Area, Muskegon-Oceana 3122 0.5208 0.7454 196 9 WIA Area, Ottawa County 2273 0.4809 0.7142 148 

10 WIA Area, ACSET 6505 0.5191 0.7352 598 11 WIA Area, Central 2003 0.5142 0.7463 176 12 WIA Area, Saginaw-Midland-Bay 5087 0.5546 0.7697 384 13 WIA Area, Thumb 2393 0.5687 0.7781 153 14 WIA Area, Capital 1508 0.4973 0.7305 116 15 WIA Area, Genesee-Shiawassee 3840 0.5646 0.7702 310 16 WIA Area, Livingston County 913 0.4907 0.7201 69 17 WIA Area, Oakland County 12869 0.5225 0.7378 1066 18 WIA Area, Macomb-St. Clair 11709 0.5415 0.7501 998 19 WIA Area, Wayne-Monroe 26469 0.6060 0.7883 2002 20 WIA Area, Washtenaw County 2283 0.4823 0.7161 170 21 WIA Area, Calhoun ISO 1924 0.5405 0.7632 183 
Table 3 

Number of Profiled Workers, Exhaustion Rate and Fraction of Entitlement Used by WIA Area 

Estimation Sample Validatio

Sample Size Exhaustion Rate

Fraction of 

Entitlement 

Used 

Sample Size Exhaustio

22 WIA Area, South Central 2848 0.5488 0.7523 221 

23 WIA Area, Kalamazoo-St. Joseph 3229 0.5636 0.7635 359 24 WIA Area, Berrien-Cass-Van Buren 2386 0.5746 0.7783 188 

999 Out-of-State Resident 171 0.4971 0.7091 12 

Total Overall 102535 0.5517 0.7607 8246 
155

Table 4: Variables in the Original and New Michigan WPRS Profiling Models 

Original Model New Model Comments 

y = UI exhaustion (1, 0) y = UI exhaustion (1, 0) Use OLS instead of logit 

Education - 5 Education - 5 LTHS, HS, SC, ColGrad, Adv 

Tenure - 2 Tenure - 2 tenure, tenure squared 

Occupation - 9 DOT Occupation - 10 SOC coding system changed 

Industry - 11 SIC Industry - 20 NAICS coding system changed 

SDA - 25 areas WIA - 24 areas + out of state claim coding system changed 

Complexity - 6 no longer available 

 

 Variables added 

 

 Base_wages Earnings in UI base period 

 Entitle_length Maximum UI weeks available 

 Exhaust_prior Exhausted previous UI spell 

 Orient_ref* Proxy for referred to WPRS 

 Weekly categorical variable Controls for weekly events such as 

TEUC 

 sep_reason Reasons for job separation 

 byb___* Weekly time indicator

_________________________________________________________________________________________ 

Note: Variables marked with an asterisk (*) are included in the regression model but are not used to calculate the 

profiling score for each individual. 
156

Table 5 

Means of the Estimation Sample 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Means Standard 

Deviation 

tenure Tenure at Last Employer (Years) 3.540 5.760 

tenure2 Tenure Squared 45.840 154.720 

educ1 Education, less than high school 0.135 0.340 

educ2 Education, High School Graduate 0.529 0.499 

educ3 Education, Some College 0.216 0.411 

educ4 Education, College Graduate 0.084 0.277 

educ5 Education, Advanced 0.035 0.184 

exhaust_prior Exhausted Recent Prior Unemployment Claim 0.168 0.374 

base_wages Base Period Wages ($1000) 28.440 21.340 

entitle Entitlement Length (Weeks) 24.670 2.910 

orient_ref Referred to Orientation 0.054 0.226 

sep_reason1 Separation Reason, lack of work 0.793 0.405 

sep_reason2 Separation Reason, Quit/Fired 0.194 0.395 

sep_reason3 Separation Reason, Still Employed 0.002 0.042 

sep_reason4 Separation Reason, Other 0.011 0.106 

soc1113 Occup (SOC), Management, Business, Financial 0.068 0.251 

soc1529 Occup (SOC), Professional and Related Occ 0.105 0.307 

soc3139 Occup (SOC), Services 0.039 0.193 

soc41 Occup (SOC), Sales and Related Occ 0.034 0.180 

soc43 Occup (SOC), Office, Administrative Support 0.120 0.325 

soc45 Occup (SOC), Farming, Fishing and Forestry 0.029 0.170 

soc47 Occup (SOC), Construction and Extraction 0.070 0.255 

soc49 Occup (SOC), Installation, Maintenance, Repair 0.019 0.138 

soc51 Occup (SOC), Production 0.390 0.488 

soc53 Occup (SOC), Trans and Material Moving 0.126 0.331 

indnaics1 Ind (NAICS): Agric,, Forestry, Fishing 0.006 0.077 

indnaics2 Ind (NAICS): Mining 0.005 0.073 

indnaics3 Ind (NAICS): Utilities 0.001 0.028 

indnaics4 Ind (NAICS): Construction 0.098 0.298 

indnaics5 Ind(NAICS)L Production 0.339 0.473 

indnaics6 Ind (NAICS): Wholesale Trade 0.049 0.216 

indnaics7 Ind (NAICS): Retail Trade 0.083 0.276 

indnaics8 Ind (NAICS): Transportation, Warehousing 0.039 0.193 

indnaics9 Ind (NAICS): Information 0.018 0.133 
157

Table 5 

Means of the Estimation Sample 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Means Standard 

Deviation 

indnaics10 Ind (NAICS): Finance and Insurance 0.024 0.154 

indnaics11 Ind (NAICS): Real Estate, Rental, Leasing 0.013 0.112 

indnaics12 Ind (NAICS): Prof, Scientific, Technical 0.074 0.262 

indnaics13 Ind (NAICS): Company/Enterprise Management 0.003 0.055 

indnaics14 Ind (NAICS): Admin, Support and Waste Mgmt 0.113 0.316 

indnaics15 Ind (NAICS): Educational Services 0.012 0.108 

indnaics16 Ind (NAICS): Health Care/Social Assistance 0.04 0.20 

indnaics17 Ind (NAICS): Art, Entertainment, Recreation 0.014 0.117 

indnaics18 Ind (NAICS): Accommodation and Food Services 0.367 0.188 

indnaics19 Ind (NAICS): Other Services (Except Pub Admin) 0.023 0.148 

indnaics20 Ind (NAICS): Public Administration 0.010 0.097 
158

Table 6: Estimation Sample Correlation of Rankings by Model Specification 

Client Inflow: October 1, 2000 through September 30, 2001 

Logit, 

Exhaust, 

New 

Logit, 

Exhaust, 

New Plus 

Dummies

OLS, 

Exhaust, 

New 

OLS, 

Exhaust, 

New Plus 

Dummies 

OLS, 

Fraction, 

New 

OLS, 

Fraction, 

New Plus 

Dummies 

Logit, Exhaust, New 1.0000 0.8735 0.9999 0.8714 0.8907 0.7941

Logit, Exhaust, New Plus Dummies 0.8735 1.0000 0.8735 0.9999 0.7811 0.8884

OLS, Exhaust, New 0.9999 0.8735 1.0000 0.8715 0.8903 0.7936

OLS, Exhaust, New Plus Dummies 0.8714 0.9999 0.8715 1.0000 0.7785 0.8878

OLS, Fraction, New 0.8907 0.7811 0.8903 0.7785 1.0000 0.8853

OLS, Fraction, New Plus Dummies 0.7941 0.8884 0.7936 0.8878 0.8853 1.0000

Validation Sample Correlation of Rankings by Model Specification 

Logit, 

Exhaust, 

New 

Logit, 

Exhaust, 

New Plus 

Dummies

OLS, 

Exhaust, 

New 

OLS, 

Exhaust, 

New Plus 

Dummies 

OLS, 

Fraction, 

New 

OLS, 

Fraction, 

New Plus 

Dummies

Logit, Exhaust, New 1.0000 0.9941 0.9999 0.9942 0.8884 0.8671

Logit, Exhaust, New Plus Dummies 0.9941 1.0000 0.9939 0.9999 0.9128 0.9000

OLS, Exhaust, New 0.9999 0.9939 1.0000 0.9941 0.8879 0.8665

OLS, Exhaust, New Plus Dummies 0.9942 0.9999 0.9941 1.0000 0.9125 0.8997

OLS, Fraction, New 0.8884 0.9128 0.8879 0.9125 1.0000 0.9955

OLS, Fraction, New Plus Dummies 0.8671 0.9000 0.8665 0.8997 0.9955 1.0000
159

Table 7: Model A 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on 0/1 Exhaustion Dummy as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

Intercept Intercept 0.82600 0.00974 84.79

tenure Tenure at Last Employer (Years) 0.01009 0.00051 19.62

tenure2 Tenure Squared -0.00019 0.00002 10.33

educ1 Education, Less Than High School 0.02896 0.00279 10.38

educ2 Education, High School Graduate 0.00271 0.00104 2.61

educ3 Education, Some College -0.00823 0.00205 4.01

educ4 Education, College Graduate -0.03030 0.00374 8.10

educ5 Education, Advanced -0.02904 0.00588 4.94

exhaust_prior Exhausted Recent Prior Unemployment Claim 0.14826 0.00292 50.70

base_wages Base Period Wages ($1000) -0.00134 0.00006 21.21

entitle Entitlement Length (Weeks) -0.01269 0.00041 30.84

orient_ref Referred to Orientation 0.03894 0.00498 7.82

sep_reason1 Separation Reason, Lack of Work -0.00251 0.00058 4.37

sep_reason2 Separation Reason, Quit/Fired 0.01023 0.00230 4.45

sep_reason3 Separation Reason, Still Employed -0.03041 0.02528 1.20

sep_reason4 Separation Reason, Other 0.00613 0.00999 0.61

soc1113 Occup (SOC), Management, Business, Financial 0.00223 0.00416 0.54

soc1529 Occup (SOC), Professional and Related Occ -0.00076 0.00334 0.23

soc3139 Occup (SOC), Services 0.00667 0.00572 1.16

soc41 Occup (SOC), Sales and Related Occ -0.00019 0.00591 0.03

soc43 Occup (SOC), Office, Administrative Support 0.00706 0.00303 2.33

soc45 Occup (SOC), Farming, Fishing and Forestry -0.05131 0.00811 6.33

soc47 Occup (SOC), Construction and Extraction -0.01574 0.00425 3.70

soc49 Occup (SOC), Installation, Maintenance, Repair -0.00594 0.00765 0.78

soc51 Occup (SOC), Production 0.00658 0.00151 4.36

soc53 Occup (SOC), Trans and Material Moving -0.00787 0.00314 2.51

indnaics1 Ind (NAICS): Agric,, Forestry, Fishing 0.02393 0.01411 1.70

indnaics2 Ind (NAICS): Mining -0.17222 0.01632 10.55

indnaics3 Ind (NAICS): Utilities 0.03425 0.03789 0.90

indnaics4 Ind (NAICS): Construction -0.02751 0.00351 7.84

indnaics5 Ind (NAICS): Manufacturing -0.00291 0.00165 1.76

indnaics6 Ind (NAICS): Wholesale Trade 0.01592 0.00475 3.35

indnaics7 Ind (NAICS): Retail Trade 0.00405 0.00368 1.10

indnaics8 Ind (NAICS): Transportation, Warehousing -0.02450 0.00542 4.52
160

Table 7: Model A 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on 0/1 Exhaustion Dummy as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

indnaics9 Ind (NAICS): Information 0.03622 0.00796 4.55

indnaics10 Ind (NAICS): Finance and Insurance 0.03555 0.00689 5.16

indnaics11 Ind (NAICS): Real Estate, Rental, Leasing 0.01186 0.00947 1.25

indnaics12 Ind (NAICS): Prof, Scientific, Technical 0.02813 0.00393 7.15

indnaics13 Ind (NAICS): Company/Enterprise Management -0.01638 0.01952 0.84

indnaics14 Ind (NAICS): Admin, Support and Waste Mgmt 0.00996 0.00306 3.26

indnaics15 Ind (NAICS): Educational Services -0.01726 0.00992 1.74

indnaics16 Ind (NAICS): Health Care/Social Assistance -0.00562 0.00539 1.04

indnaics17 Ind (NAICS): Art, Entertainment, Recreation -0.02645 0.00913 2.90

indnaics18 Ind (NAICS): Accommodation and Food Services -0.01217 0.00585 2.08

indnaics19 Ind (NAICS): Other Services (Except Pub Admin) 0.03583 0.00706 5.08

indnaics20 Ind (NAICS): Public Administration -0.01467 0.01094 1.34

wia1 WIA Area, Western UP -0.00086 0.01245 0.07

wia2 WIA Area, Central UP -0.04219 0.00880 4.79

wia3 WIA Area, Eastern UP -0.04293 0.01265 3.39

wia4 WIA Area, North West -0.06385 0.00591 10.80

wia5 WIA Area, North East 0.00752 0.00811 0.93

wia6 WIA Area, West Central -0.04117 0.00837 4.92

wia7 WIA Area, Region 7B 0.00185 0.00835 0.22

wia8 WIA Area, Muskegon-Oceana -0.04281 0.00667 6.42

wia9 WIA Area, Ottawa County -0.04900 0.00719 6.82

wia10 WIA Area, ACSET -0.01395 0.00378 3.68

wia11 WIA Area, Central -0.07847 0.00748 10.48

wia12 WIA Area, Saginaw-Midland-Bay 0.00007 0.00546 0.01

wia13 WIA Area, Thumb 0.00683 0.00628 1.09

wia14 WIA Area, Capital -0.05616 0.00603 9.31

wia15 WIA Area, Genesee-Shiawassee 0.01489 0.00459 3.24

wia16 WIA Area, Livingston County -0.02457 0.01046 2.35

wia17 WIA Area, Oakland County -0.00180 0.00326 0.55

wia18 WIA Area, Macomb-St. Clair 0.01158 0.00307 3.77

wia19 WIA Area, Wayne-Monroe 0.04232 0.00207 20.42

wia20 WIA Area, Washtenaw County -0.04111 0.00823 4.99

wia21 WIA Area, Calhoun ISO -0.03035 0.00696 4.36

wia22 WIA Area, South Central -0.00806 0.00594 1.36
161

Table 7: Model A 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on 0/1 Exhaustion Dummy as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

wia23 WIA Area, Kalamazoo-St. Joseph 0.00141 0.00648 0.22

wia24 WIA Area, Berrien-Cass-Van Buren 0.01692 0.00700 2.42

wia999 Out-of-State Resident 0.01574 0.00989 1.59

byb100100 BYB = 10-01-2000 0.02337 0.00919 2.54

byb100800 BYB = 10-08-2000 0.02384 0.01010 2.36

byb101500 BYB = 10-15-2000 0.02087 0.00996 2.09

byb102200 BYB = 10-22-2000 0.02525 0.00960 2.63

byb102900 BYB = 10-29-2000 -0.00871 0.00826 1.06

byb111200 BYB = 11-12-2000 0.00001 0.00797 0.00

byb111900 BYB = 11-19-2000 -0.02013 0.00937 2.15

byb112600 BYB = 11-26-2000 -0.02347 0.00828 2.83

byb120300 BYB = 12-03-2000 -0.02577 0.00757 3.40

byb121000 BYB = 12-10-2000 -0.04553 0.00845 5.39

byb121700 BYB = 12-17-2000 -0.09099 0.00762 11.94

byb122400 BYB = 12-24-2000 -0.06613 0.00615 10.76

byb123100 BYB = 12-31-2000 -0.02240 0.00702 3.19

byb010701 BYB = 01-07-2001 -0.03657 0.00552 6.62

byb011401 BYB = 01-14-2001 -0.05105 0.00645 7.92

byb012101 BYB = 01-21-2001 -0.06646 0.00611 10.88

byb012801 BYB = 01-28-2001 -0.05193 0.00656 7.92

byb020401 BYB = 02-04-2001 -0.04442 0.00628 7.08

byb021101 BYB = 02-11-2001 -0.05281 0.00712 7.42

byb021801 BYB = 02-18-2001 -0.05283 0.00719 7.35

byb022501 BYB = 02-25-2001 -0.04744 0.00709 6.69

byb031101 BYB = 03-11-2001 -0.00908 0.00728 1.25

byb031801 BYB = 03-18-2001 -0.01433 0.00728 1.97

byb032501 BYB = 03-25-2001 -0.01809 0.00806 2.25

byb040101 BYB = 04-01-2001 0.00744 0.00628 1.19

byb041501 BYB = 04-15-2001 -0.00763 0.00793 0.96

byb042201 BYB = 04-22-2001 -0.00287 0.00772 0.37

byb042901 BYB = 04-29-2001 0.02296 0.00741 3.10

byb050601 BYB = 05-06-2001 0.02464 0.00791 3.11

byb051301 BYB = 05-13-2001 0.03056 0.00793 3.85

byb052001 BYB = 05-20-2001 0.01089 0.00808 1.35
162

Table 7: Model A 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on 0/1 Exhaustion Dummy as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

byb052701 BYB = 05-27-2001 0.03973 0.00909 4.37

byb060301 BYB = 06-03-2001 0.01838 0.00733 2.51

byb061001 BYB = 06-10-2001 0.01854 0.00824 2.25

byb061701 BYB = 06-17-2001 0.03279 0.00775 4.23

byb062401 BYB = 06-24-2001 0.04036 0.00829 4.87

byb070101 BYB = 07-01-2001 -0.04744 0.00457 10.39

byb070801 BYB = 07-08-2001 0.00531 0.00650 0.82

byb071501 BYB = 07-15-2001 0.04503 0.00774 5.82

byb072201 BYB = 07-22-2001 0.06967 0.00848 8.21

byb072901 BYB = 07-29-2001 0.06820 0.00819 8.32

byb080501 BYB = 08-05-2001 0.06451 0.00802 8.04

byb081901 BYB = 08-19-2001 0.06990 0.00872 8.02

byb082601 BYB = 08-26-2001 0.09578 0.00851 11.25

byb090201 BYB = 09-02-2001 0.08128 0.00820 9.91

byb090901 BYB = 09-09-2001 0.10588 0.00831 12.74

byb091601 BYB = 09-16-2001 0.07383 0.00773 9.55

byb092301 BYB = 09-23-2001 0.06076 0.00671 9.06

byb093001 BYB = 09-30-2001 0.07111 0.00627 11.34 

Education Restriction -5.313e-11

Separation Reason Restriction 1.835e-10

Occupation Restriction -5.592e-11

Industry Restriction -1.248e-10

WIA Area Restriction -1.899e-10

BYB Restriction -1.901e-10

Adjusted R-Square: 0.0455 
163

Table 8: Model B 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on Fraction of Benefits Used/Exhausted as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

Intercept Intercept 0.93900 0.00633 148.27

tenure Tenure at Last Employer (Years) 0.00578 0.00033 17.29

tenure2 Tenure Squared -0.00010 0.00001 8.60

educ1 Education, Less Than High School 0.01784 0.00181 9.84

educ2 Education, High School Graduate 0.00161 0.00068 2.37

educ3 Education, Some College -0.00541 0.00133 4.05

educ4 Education, College Graduate -0.01859 0.00243 7.64

educ5 Education, Advanced -0.01501 0.00382 3.93

exhaust_prior Exhausted Recent Prior Unemployment Claim 0.09147 0.00190 48.12

base_wages Base Period Wages ($1000) -0.00086 0.00004 20.95

entitle Entitlement Length (Weeks) -0.00766 0.00027 28.63

orient_ref Referred to Orientation 0.03055 0.00324 9.44

sep_reason1 Separation Reason, Lack of Work 0.00239 0.00037 6.38

sep_reason2 Separation Reason, Quit/Fired -0.00875 0.00149 5.86

sep_reason3 Separation Reason, Still Employed -0.01123 0.01643 0.68

sep_reason4 Separation Reason, Other -0.01576 0.00649 2.43

soc1113 Occup (SOC), Management, Business, Financial -0.00488 0.00270 1.81

soc1529 Occup (SOC), Professional and Related Occ -0.00703 0.00217 3.24

soc3139 Occup (SOC), Services -0.00212 0.00372 0.57

soc41 Occup (SOC), Sales and Related Occ -0.01144 0.00384 2.98

soc43 Occup (SOC), Office, Administrative Support 0.00031 0.00197 0.16

soc45 Occup (SOC), Farming, Fishing and Forestry -0.01434 0.00527 2.72

soc47 Occup (SOC), Construction and Extraction 0.00682 0.00276 2.47

soc49 Occup (SOC), Installation, Maintenance, Repair -0.00979 0.00497 1.97

soc51 Occup (SOC), Production 0.00456 0.00098 4.64

soc53 Occup (SOC), Trans and Material Moving -0.00111 0.00204 0.54

indnaics1 Ind (NAICS): Agric,, Forestry, Fishing 0.04480 0.00917 4.88

indnaics2 Ind (NAICS): Mining -0.00418 0.01061 0.39

indnaics3 Ind (NAICS): Utilities 0.02314 0.02464 0.94

indnaics4 Ind (NAICS): Construction 0.02149 0.00228 9.42

indnaics5 Ind (NAICS): Manufacturing -0.00707 0.00107 6.58

indnaics6 Ind (NAICS): Wholesale Trade 0.00129 0.00309 0.42

indnaics7 Ind (NAICS): Retail Trade -0.00606 0.00239 2.53

indnaics8 Ind (NAICS): Transportation, Warehousing -0.02254 0.00352 6.40
164

Table 8: Model B 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on Fraction of Benefits Used/Exhausted as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

indnaics9 Ind (NAICS): Information 0.01689 0.00518 3.26

indnaics10 Ind (NAICS): Finance and Insurance 0.01644 0.00448 3.67

indnaics11 Ind (NAICS): Real Estate, Rental, Leasing 0.00180 0.00616 0.29

indnaics12 Ind (NAICS): Prof, Scientific, Technical 0.01348 0.00256 5.27

indnaics13 Ind (NAICS): Company/Enterprise Management -0.04273 0.01269 3.37

indnaics14 Ind (NAICS): Admin, Support and Waste Mgmt 0.00486 0.00199 2.45

indnaics15 Ind (NAICS): Educational Services -0.01949 0.00645 3.02

indnaics16 Ind (NAICS): Health Care/Social Assistance -0.01835 0.00350 5.24

indnaics17 Ind (NAICS): Art, Entertainment, Recreation 0.01641 0.00593 2.77

indnaics18 Ind (NAICS): Accommodation and Food Services -0.01335 0.00380 3.51

indnaics19 Ind (NAICS): Other Services (Except Pub Admin) 0.01297 0.00459 2.83

indnaics20 Ind (NAICS): Public Administration 0.01243 0.00711 1.75

wia1 WIA Area, Western UP 0.02025 0.00809 2.50

wia2 WIA Area, Central UP 0.00134 0.00572 0.23

wia3 WIA Area, Eastern UP -0.00230 0.00822 0.28

wia4 WIA Area, North West -0.02592 0.00384 6.74

wia5 WIA Area, North East 0.02396 0.00527 4.54

wia6 WIA Area, West Central -0.01641 0.00544 3.02

wia7 WIA Area, Region 7B 0.01995 0.00543 3.67

wia8 WIA Area, Muskegon-Oceana -0.02824 0.00433 6.52

wia9 WIA Area, Ottawa County -0.03093 0.00467 6.62

wia10 WIA Area, ACSET -0.01137 0.00246 4.62

wia11 WIA Area, Central -0.04513 0.00487 9.27

wia12 WIA Area, Saginaw-Midland-Bay 0.00628 0.00355 1.77

wia13 WIA Area, Thumb 0.01302 0.00408 3.19

wia14 WIA Area, Capital -0.03610 0.00392 9.20

wia15 WIA Area, Genesee-Shiawassee 0.00987 0.00298 3.31

wia16 WIA Area, Livingston County -0.01600 0.00680 2.35

wia17 WIA Area, Oakland County -0.00241 0.00212 1.14

wia18 WIA Area, Macomb-St. Clair 0.00629 0.00199 3.15

wia19 WIA Area, Wayne-Monroe 0.01948 0.00135 14.46

wia20 WIA Area, Washtenaw County -0.02819 0.00535 5.27

wia21 WIA Area, Calhoun ISO -0.01611 0.00453 3.56

wia22 WIA Area, South Central -0.00719 0.00386 1.86
165

Table 8: Model B 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on Fraction of Benefits Used/Exhausted as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

wia23 WIA Area, Kalamazoo-St. Joseph -0.00588 0.00421 1.40

wia24 WIA Area, Berrien-Cass-Van Buren 0.01084 0.00455 2.38

wia999 Out-of-State Resident -0.01047 0.00643 1.63

byb100100 BYB = 10-01-2000 -0.00276 0.00597 0.46

byb100800 BYB = 10-08-2000 0.00380 0.00656 0.58

byb101500 BYB = 10-15-2000 0.00405 0.00648 0.63

byb102200 BYB = 10-22-2000 0.00658 0.00624 1.05

byb102900 BYB = 10-29-2000 0.00034 0.00537 0.06

byb111200 BYB = 11-12-2000 0.01466 0.00518 2.83

byb111900 BYB = 11-19-2000 0.00884 0.00609 1.45

byb112600 BYB = 11-26-2000 0.00535 0.00538 0.99

byb120300 BYB = 12-03-2000 0.01266 0.00492 2.57

byb121000 BYB = 12-10-2000 0.00211 0.00550 0.38

byb121700 BYB = 12-17-2000 -0.04993 0.00496 10.08

byb122400 BYB = 12-24-2000 -0.06329 0.00400 15.83

byb123100 BYB = 12-31-2000 -0.00651 0.00456 1.43

byb010701 BYB = 01-07-2001 0.00758 0.00359 2.11

byb011401 BYB = 01-14-2001 -0.00899 0.00419 2.14

byb012101 BYB = 01-21-2001 -0.03183 0.00397 8.02

byb012801 BYB = 01-28-2001 -0.02486 0.00427 5.83

byb020401 BYB = 02-04-2001 -0.02027 0.00408 4.97

byb021101 BYB = 02-11-2001 -0.02762 0.00463 5.97

byb021801 BYB = 02-18-2001 -0.02905 0.00468 6.21

byb022501 BYB = 02-25-2001 -0.02969 0.00461 6.44

byb031101 BYB = 03-11-2001 0.00490 0.00473 1.04

byb031801 BYB = 03-18-2001 0.00256 0.00474 0.54

byb032501 BYB = 03-25-2001 -0.00771 0.00524 1.47

byb040101 BYB = 04-01-2001 0.00815 0.00408 2.00

byb041501 BYB = 04-15-2001 0.00345 0.00516 0.67

byb042201 BYB = 04-22-2001 0.00390 0.00502 0.78

byb042901 BYB = 04-29-2001 0.01930 0.00482 4.01

byb050601 BYB = 05-06-2001 0.01925 0.00514 3.74

byb051301 BYB = 05-13-2001 0.02951 0.00516 5.72

byb052001 BYB = 05-20-2001 0.00191 0.00525 0.36
166

Table 8: Model B 

New Michigan Profiling Model Specification Adding Dummies and Restrictions 

OLS Regression on Fraction of Benefits Used/Exhausted as Dependent Variable 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Parameter 

Estimate 

Standard 

Error T-Statistic

byb052701 BYB = 05-27-2001 0.02749 0.00591 4.65

byb060301 BYB = 06-03-2001 0.00765 0.00476 1.61

byb061001 BYB = 06-10-2001 0.00763 0.00536 1.42

byb061701 BYB = 06-17-2001 0.01715 0.00504 3.40

byb062401 BYB = 06-24-2001 0.02013 0.00539 3.74

byb070101 BYB = 07-01-2001 -0.04975 0.00297 16.76

byb070801 BYB = 07-08-2001 -0.01992 0.00423 4.71

byb071501 BYB = 07-15-2001 0.00822 0.00503 1.63

byb072201 BYB = 07-22-2001 0.02763 0.00551 5.01

byb072901 BYB = 07-29-2001 0.02699 0.00533 5.07

byb080501 BYB = 08-05-2001 0.02072 0.00522 3.97

byb081901 BYB = 08-19-2001 0.02729 0.00567 4.81

byb082601 BYB = 08-26-2001 0.04851 0.00553 8.77

byb090201 BYB = 09-02-2001 0.03652 0.00533 6.85

byb090901 BYB = 09-09-2001 0.05575 0.00540 10.32

byb091601 BYB = 09-16-2001 0.03247 0.00503 6.46

byb092301 BYB = 09-23-2001 0.02178 0.00436 5.00

byb093001 BYB = 09-30-2001 0.03719 0.00408 9.12 

Education Restriction 1.994e-09

Separation Reason Restriction 2.631e-09

Occupation Restriction 9.731e-10

Industry Restriction 1.316e-09

WIA Area Restriction 1.411e-09

BYB Restriction 1.666e-09 

Adjusted R-Square: 0.0394 
167

Table 9 

Means of the Validation Sample 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Means Standard 

Deviation 

tenure Tenure at Last Employer (Years) 3.584 5.866 

tenure2 Tenure Squared 47.257 161.710 

educ1 Education, less than high school 0.135 0.342 

educ2 Education, High School Graduate 0.529 0.499 

educ3 Education, Some College 0.217 0.412 

educ4 Education, College Graduate 0.090 0.286 

educ5 Education, Advanced 0.037 0.189 

exhaust_prior Exhausted Recent Prior Unemployment Claim 0.174 0.379 

base_wages Base Period Wages ($1000) 29.415 22.620 

entitle Entitlement Length (Weeks) 24.689 2.902 

orient_ref Referred to Orientation 0.064 0.245 

sep_reason1 Separation Reason, lack of work 0.765 0.424 

sep_reason2 Separation Reason, Quit/Fired 0.220 0.414 

sep_reason3 Separation Reason, Still Employed 0.003 0.055 

sep_reason4 Separation Reason, Other 0.011 0.106 

soc1113 Occup (SOC), Management, Business, Financial 0.085 0.279 

soc1529 Occup (SOC), Professional and Related Occ 0.112 0.316 

soc3139 Occup (SOC), Services 0.042 0.200 

soc41 Occup (SOC), Sales and Related Occ 0.037 0.189 

soc43 Occup (SOC), Office, Administrative Support 0.125 0.331 

soc45 Occup (SOC), Farming, Fishing and Forestry 0.025 0.159 

soc47 Occup (SOC), Construction and Extraction 0.066 0.248 

soc49 Occup (SOC), Installation, Maintenance, Repair 0.021 0.142 

soc51 Occup (SOC), Production 0.356 0.479 

soc53 Occup (SOC), Trans and Material Moving 0.130 0.336 

indnaics1 Ind (NAICS): Agric,, Forestry, Fishing 0.008 0.090 

indnaics2 Ind (NAICS): Mining 0.001 0.033 

indnaics3 Ind (NAICS): Utilities 0.001 0.028 

indnaics4 Ind (NAICS): Construction 0.098 0.297 

indnaics5 Ind(NAICS)L Production 0.320 0.466 

indnaics6 Ind (NAICS): Wholesale Trade 0.052 0.222 

indnaics7 Ind (NAICS): Retail Trade 0.097 0.295 

indnaics8 Ind (NAICS): Transportation, Warehousing 0.033 0.179 

indnaics9 Ind (NAICS): Information 0.020 0.141 
168

Table 9 

Means of the Validation Sample 

Client Inflow: October 1, 2000 through September 30, 2001 

Variable Description Means Standard 

Deviation 

indnaics10 Ind (NAICS): Finance and Insurance 0.030 0.170 

indnaics11 Ind (NAICS): Real Estate, Rental, Leasing 0.014 0.117 

indnaics12 Ind (NAICS): Prof, Scientific, Technical 0.067 0.250 

indnaics13 Ind (NAICS): Company/Enterprise Management 0.003 0.053 

indnaics14 Ind (NAICS): Admin, Support and Waste Mgmt 0.113 0.316 

indnaics15 Ind (NAICS): Educational Services 0.010 0.100 

indnaics16 Ind (NAICS): Health Care/Social Assistance 0.05 0.21 

indnaics17 Ind (NAICS): Art, Entertainment, Recreation 0.016 0.125 

indnaics18 Ind (NAICS): Accommodation and Food Services 0.038 0.191 

indnaics19 Ind (NAICS): Other Services (Except Pub Admin) 0.025 0.156 

indnaics20 Ind (NAICS): Public Administration 0.010 0.099 
169

 Table 10: Costs of Misclassification of Claimants 

Actual Event (=1) 

0 1 

0 

True Negative False Negative 

Cost/benefit year: 

2.2 more weeks of UI 

$143 more in benefits 

$1054 lost in earnings 

Predicted 

Event 

(=1) 

1 

False Positive 

Cost/benefit year: 

Use services that could 

have been used by those 

who need it 

Forego job search 

True Positive 
170

Table 11: Distribution of Profiling Scores and Actual Exhaustion for Models A and B 

Percentile Group Model A 

(Exhaust) 

Model B 

(Fraction of Benefits) 

Difference in 

Number 

Exhausting 

(Model AModel B) 

Number 

in Group 

Cumulative 

Number 

Actually 

Exhausting 

% 

Exhausting 

Cumulative 

Number 

Actually 

Exhausting 

% Exhausting 

20 1000 759 75.9 761 76.1 -2 

19 1000 1427 66.8 1437 67.6 -10 

18 1000 2074 64.7 2064 62.7 10 

17 1000 2731 65.7 2715 65.1 16 

16 1000 3356 62.5 3318 60.3 38 
171

Table 12: Deciles of the Profiling Score within the 17th Percentile for Model A 

Profiling Score 

Decile Lower Cutoff Values Upper Cutoff Values Proportion 

Exhausting 

Number Referred 

10 .659 .662 0.680 100 

9 .656 .659 0.624 100 

8 .652 .656 0.662 100 

7 .649 .652 0.747 100 

6 .646 .649 0.626 100 

5 .643 .646 0.592 100 

4 .640 .643 0.653 100 

3 .637 .640 0.640 100 

2 .635 .637 0.586 100 

1 .632 .635 0.671 100 
Table 13: Comparison of Rankings from Model A and Model B 

Percentiles of Profiling Scores from Model B 

Percentiles 13 14 15 16 17 18 

13 226 

22.55 

64 

6.37 

46 

4.64 

38 

3.81 

35 

3.45 

17 

1.72 

14 242 

24.17 

227 

22.71 

90 

9.03 

36 

3.59 

29 

2.92 

29 

2.92 

15 112 

11.16 

130 

13 

261 

26.1 

100 

9.96 

43 

4.25 

51 

5.05 

16 49 

4.91 

33 

3.32 

277 

27.72 

284 

28.38 

92 

9.15 

66 

6.63 

17 0 

0 

0 0 

118 

11.82 

293 

29.28 

332 

33.2 

102 

10.23 

1

18 0 

0 

0 0 

25 

2.52 

127 

12.73 

316 

31.56 

349 

34.88 

1

19 0 

0 

0 0 

0 0 

7 

0.66 

102 

10.21 

338 

33.82 

4

Percentiles of Profiling Scores From Model A 

20 0 

0 

0 0 

0 0 

0 0 

0 0 

27 

2.65 

2
Figure 1 

TEUC and TEUC-X Entitlement

0

0.10.20.30.40.50.6

10/1/2000

11/1/2000

12/1/2000

1/1/2001

2/1/2001

3/1/2001

4/1/2001

5/1/2001

6/1/2001

7/1/2001

8/1/2001

9/1/2001

BYB Date

Proportion Establishing Entitlement TE

TE
Figure 2 

Prior Exhaustion vs. Predicted Exhaustion

0

0.10.20.30.40.50.60.70.80.9

1

1 2 3 4 5 6 7 8 9 1011121314151617181920

Percentiles

00.10.20.30.40.50.60.70.8

Prior ExhaustionPredicted Exhaustion
Figure 3: College Graduate Vs. Predicted Exhaustion 

College Graduate vs. Predicted Exhaustion

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

00.10.20.30.40.50.60.70.8

Copre
Figure 4: Distribution of Predicted Exhaustion Using Model A 

Tenure vs. Predicted Exhaustion

01234567

1 2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20

00.10.20.30.40.50.60.70.8
177

Figure 5: Distribution of Predicted Exhaustion Using Model A 
178

Figure 6: Distribution of Predicted Fraction of Benefits 
Figure 7: Distribution of Exhaustion Rate vs. Fraction of Benefits 

Distribution of Exhaustion Rate vs. Fraction of Benefits

0

0.20.40.60.8

1

1.21.41.61.8

2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Percentiles

Index (1st pct=1) Exh

Fra
180

Figure 8: Fitness Test for Model Accuracy, Model A 
181

Figure 9: Fitness Test for Model Accuracy, Model B 
182

Figure 10: Fitness Test for Model Accuracy, Model A Upper 25% of Distribution 
183

Figure 11: Fitness Test for Model Accuracy, Model B Upper 25% of Distribution 
Figure 12: Exhaustion Rates using Percentiles Derived rom Predicted Values of the Old and New Models 

Exhaustion Rate by Percentiles

0.0000.1000.2000.3000.4000.5000.6000.7000.800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Percentile

RateOld New
185

Former TANF Recipients’ Monetary Eligibility 

for Unemployment Insurance Benefits: 

An Empirical Study

Douglas M. Sanford, Jr. 

Michael M. H. Ye 

Lester Coffey 

William F. Sullivan, Jr. 

Coffey Communications, LLP 
186

Monetary eligibility is a primary requirement for individuals to receive 

Unemployment Insurance (UI) benefits. The achievement of monetary eligibility is the 

UI system’s means of measuring a substantial attachment to the labor force. 

Understanding what factors may have impacts on an individual’s monetary eligibility 

enables policymakers to identify individuals, such as TANF leavers, most likely to 

succeed in the workforce and therefore benefit from UI during downturns in the 

economy, particularly while in transition to permanent attachment to the workforce. In 

addition, identification of factors that negatively associate with monetary eligibility will 

enable policymakers to develop programs that can meet the needs of these vulnerable 

individuals. 

With the recent reduction in TANF caseloads, the policy emphasis on helping 

individuals join the workforce, and the reduction in government budgets, there is 

considerable interest in targeting an array of public support programs to individuals who 

are most likely to benefit from them. In addition, early intervention can prevent 

individuals from needing more costly support in subsequent time periods. This paper is 

an investigation of the individual characteristics and exogenous conditions that help 

identify TANF leavers who are most and least likely to become attached to the labor 

force. In addition, public support programs are analyzed for their role in contributing to 

employment. 

We investigate a cohort of TANF leavers and their probability of becoming 

monetarily eligible for UI. Our research focuses on three related questions. 

• What individual characteristics associate with monetary eligibility for UI? 

• What support programs associate with individuals’ becoming eligible for UI? 
187

• What exogenous conditions affect individuals’ becoming eligible for UI benefit? 

For individual characteristics, we investigate possible impacts of age, race, 

education and number of children. For support programs, we investigate the possible 

impacts of food stamps, medical care coverage, child care, and employment programs. 

For exogenous conditions, we investigate possible impacts of county economic growth 

and county wage rate, and industry in which former TANF recipients are employed. 

We find that individuals with children less than 6 years of age are less likely to be 

monetarily eligible for UI benefits. Education, medical care coverage and county of 

residence wage rate have a positive association with eligibility. Additionally, industry of 

employment has a strong association with monetary eligibility. 

The following sections include a review of the background of welfare and 

unemployment programs, a description of the Wisconsin case from which individuals’ 

monetary eligibility are estimated, explanations of the model, estimation results, and a 

concluding section that discusses implications for policymakers and legislators. 

 Background 

 The Social Security Act of 1935, in part, sought to enable states to better provide for 

the general welfare of dependent and crippled children and maternal and child welfare, as 

well as workers unemployed through no fault of their own. The Act created two 

programs that helped frame the socio-economic relationship between the federal and state 

governments, employers, and workers. They are Aid to Dependent Children (ADC) that 

was later termed Aid to Families with Dependent Children (AFDC) and the federal-state 

system of Unemployment Insurance (UI). The two programs provide income support to 

two different groups of unemployed individuals. 
188

Aid to Families with Dependent Children gave federal support to the state system 

of pensions for widows with children. As a grant program, it enabled states to provide 

cash welfare payments for needy children who had been deprived of parental support or 

care because their father or mother is generally absent from the home, incapacitated, 

deceased, or unemployed. States defined “need,” set their benefit levels, established 

income and resource limits, and administered the program. 

The “Personal Responsibility and Work Opportunity Reconciliation Act” 

(PRWORA) was signed into law by President Clinton in August 1996 and replaced the 

AFDC programs. TANF was a major section of the Act. It eliminated federal funding for 

three programs: AFDC, Job Opportunities and Basic Skills Training (JOBS), and 

Emergency Assistance (EA) and replaced them with a cash welfare block grant. All 

states were required to implement TANF by July 1, 1997. 

Unlike AFDC benefits, TANF benefits are tied to an individual’s economic 

behavior, because the program requires that most adult recipients work after two months 

of aid (Savner, Strawn & Greenberg 2002). TANF broadly defines work as labor force 

participation, such as working for the community or state in order to receive benefits 

(workfare), looking for employment or being employed. Work may also include workrelated activities, such as schooling, vocational training, general skill development, or 

other types of training. TANF has a lifetime limit of five years (60 months) for the 

amount of time a family with an adult may receive assistance with federal funds. States 

have broad flexibility on their program design but must meet increasing work 

participation rate requirements (Schumacher & Rakpraja, 2002). 

 Unemployment Insurance (UI) provides workers with partial replacement of wages 
189

lost during periods of temporary unemployment due to no fault of their own. The 

program has two purposes: economic stabilization and wage replacement (Advisory 

Council on Unemployment Compensation, 1996: 27). First, UI provides countercyclical 

macroeconomic stability. Second, UI provides income support and assistance for 

individuals to enable them to remain attached to the labor force. UI is administered as a 

federal-state partnership. The program is financed by payroll taxes levied on employers 

that are collected by the states. States vary considerably as to their administration of UI 

programs (U.S. Department of Labor, 2002). For example; benefit levels and employer 

contribution rates vary widely from state to state as do both monetary and non-monetary 

eligibility requirements for workers to receive benefits. 

Because of its role providing assistance to the involuntarily unemployed, the UI 

program has a role assisting former welfare recipients to transition to full employment. 

UI is a social insurance program and provides cash support and reemployment services to 

workers who have lost their jobs through no fault of their own. These unemployed 

workers are attached to the workforce in covered employment, and TANF leavers may 

not have demonstrated a sufficient enough attachment to the labor force to qualify 

monetarily for UI benefits. 

 In order to qualify for UI benefits, unemployed workers must first be monetarily 

eligible. States have varying UI monetary eligibility requirements. In 2002, required 

earnings for the base year, usually the first four of most recent five completed quarters, 

ranged from $130 (Hawaii) to $3586.26 (North Carolina) with most states falling in the 

range of $1,000 to $3,000 (United States Department of Labor, 2002). To make $2000, a 

worker earning a rate of $6.00 per hour with a 30-hour work week, needs to work 11 
190

weeks to be eligible for UI in a state requiring that level to meet monetary eligibility 

requirements. 

 State UI programs are designed to support workers who have been unemployed from 

covered employment. The ability of UI to support individuals in transition to the labor 

force, such as TANF leavers, has not been extensively studied. In addition, other 

programs may be developed to support individuals least likely to become attached to the 

labor force. This paper is an effort to facilitate such targeting of TANF leavers. 

Methodology

We analyzed data on 3085 individuals, or nearly all of the 3,097 in Wisconsin who left 

TANF in the second quarter of 1998. Limitations on availability of county data account 

for the loss of 12 individuals. For each individual, data obtained from the TANF 

program include: individual characteristics, county of residence, and public assistance 

usage. In addition, data on earnings and employer characteristics were matched and 

provided by Wisconsin’s Department of Workforce Development. Data on program 

usage include monthly access to food stamps, medical assistance, childcare, and 

Wisconsin’s W-2 (“Wisconsin Work” or return to work) program. Characteristics of 

employers for each TANF leaver include earnings per employer per quarter, industry 

code for the employer, and whether the employer provides health insurance. Finally, we 

collected county-level data that reflect local economic conditions throughout Wisconsin 

and matched these variables with each individual’s county of residence. 

 There are three criteria to access UI benefits set by Wisconsin’s law. The first is the 

total earnings in the four quarters of the base period (first four of the most recently 

completed five). For the second quarter of 1999, Wisconsin required that earnings had to 

be $1320 during the base period, or the four quarters of 1998. The second criterion is 
191

high quarter earnings, which refers to the minimum level of earnings for the quarter 

during the base period with most earnings. Wisconsin required high quarter earnings of 

at least $1100 in one quarter of 1998 to qualify for UI benefit. The third criterion is the 

distribution of earnings requirement, which refers to the minimum total amount earned in 

3 quarters of the base period outside of the high quarter. Wisconsin required distribution 

of earnings of at least $308 during 1998 in order to qualify for UI benefits in the second 

quarter of 1999. 

Variables 

 To explain the difference between eligible and non-eligible individuals, we used a 

binary logistic regression model with the dependent variable taking a value of one when 

an individual is monetarily eligible for UI and zero when an individual is not. This 

variable measures an individual’s attachment to the labor force. 

 The independent variables include monthly program usage, individual and exogenous 

variables. For the base period, the number of months using food stamps, child care, 

medical care, and W-2 support from July to December of 1998 indicate the effect of these 

programs on employment. The programs may have either a complementary effect with 

employment, in which they positively associate with monetary eligibility, or a 

substitution effect with employment, in which they would negatively associate with 

monetary eligibility. These variables are integer values ranging from 0 to 6 for 1998 due 

to data limitations. 

Individual variables include the following. 

• Age in years and a binary indicator for age greater than 41 to indicate the various 

effects of age on earnings. 
192

• Categorical indicators for Black and Hispanic ethnicity to measure the possible 

effect of ethnicity on labor market opportunities 

• High school graduation status to measure the effect of education on earnings 

• Number of children less than six years old and number of children six and older 

measure the impact of child care responsibilities, including those for prekindergarten children, on employment 

Exogenous variables include the following: 

• Ratios of earnings in industrial groups to total earnings in the base year measure 

industry effects. These industries are health, manufacturing, temporary services, 

food and accommodations, retail, and finance/insurance/real estate/information, 

which are the predominant employers of former TANF recipients. 

• Ratio of earnings in employment covered by health insurance to total earnings in 

the base year measures the effect of health insurance-covered employment on 

eligibility. 

• County economic growth between 1999 and 2000 measures local economic 

conditions 

• County wages-per-job in 2000 measure local economic conditions. 

Descriptive statistics for the variables are shown in Table 1. Tables 2 contains 

correlations between the dependent and independent variables included in the model. 

Table 3 contains correlations between the independent variables. The first row of Table 1 

shows that 59% of the individuals in our cohort become monetary eligible for UI within a 

year of leaving TANF. This percentage is generally consistent with findings by 

Rangarajan et al (2001), who analyzed potential UI eligibility for a sample of New Jersey 

TANF leavers (438 individuals) who left welfare for work between July 1997 and June 
193

1998 and found 75 percent were monetarily eligible. We would expect Rangarijan et al’s 

percentage to be higher because she selected individuals who left TANF for work, while 

we study all TANF leavers. However, Kaye’s (1997) study found that only 35 percent of 

former AFDC recipients would be monetarily eligible for UI benefits. According to 

administrative data reported by the Department of Labor (1999), approximately 89 

percent of all workers applying for UI benefits had sufficient wages to qualify in 1998. 

Table 1 Descriptive Statistics 

Mean Std. 

Deviation

N

Monetary eligibility 2nd Quarter of 1999 .59 .49 3085

Months on W2 in 1998 .83 1.65 3085

Months receiving medical assistance in 1998 5.88 2.18 3085

Months receiving child care support in 1998 1.24 2.23 3085

Months receiving food stamps in 1998 4.18 2.78 3085

Proportion of earnings in the health care industry in 1998* .19 .32 3085

Proportion of earnings in manufacturing sector in 1998* .08 .22 3085

Proportion of earnings in the temporary services industry in 1998* .20 .31 3085

Proportion of earnings in the food and accommodations industry in 1998* .12 .26 3085

Proportion of earnings in the retail trade industry in 1998* .11 .25 3085

Proportion of earnings in the finance, insurance, real estate, and information 

industries in 1998* 

.05 .17 3085

Proportion of earnings covered by health insurance in 1998* .37 .38 3085

Age 41 or older .09 .28 3085

Number of children aged 6 or older 1.30 1.44 3085

Age 28.76 7.93 3085

Number of children aged under 6 1.22 1.01 3085

Growth in county output in 1998 3.08 .91 3085

Wage per job in the county in 1998 30294 2775 3085

High school graduate .45 .50 3085

Black .55 .50 3085

Hispanic .07 .26 3085

* includes only individuals who have greater than 0 earnings during 1998. 
194

Table 2 Correlation between Monetary Eligibility and Other Variables

Months on W2 in 1998 -.21 

Months medical assistance in 1998 .19 

Months child care support in 1998 .31 

Months food stamps in 1998 .06 

Proportion of earnings health care industry .13 

Proportion of earnings manufacturing sector .07 

Proportion of earnings temporary services industry -.17 

Proportion of earnings food and accommodations industry -.06 

Proportion of earnings retail trade industry -.04 

Proportion of earnings fire industries .06 

Proportion of earnings covered by health insurance -.02 

Age 41 or older -.09 

Children 6 or older -.03 

Age -.09 

Children under 6 .04 

Growth in county output -.03 

Wage per job in the county .05 

High school graduate .13 

Black .03 

Hispanic -.01 
195

Table 3 Correlation among Independent Variables32

 A B C D E F G H I J K L M N O P Q R S 

A Months on W2 in 

1998 

 

B Months medical 

assistance 

.18 

C Months child 

care support 

-.01 .22 

D Months food 

stamps 

.25 .52 .17 

E % of earnings in 

health care 

industry 

-.06 .04 .07 .04 

F % of earnings in 

manufacturing 

sector 

-.04 .00 -.01 -.07 -.16 

G % of earnings in 

temporary 

services industry 

.12 -.04 -.07 .00 -.25 -.12 

H % of earnings in 

food and 

accommodations 

industry 

.03 -.01 -.03 .04 -.19 -.12 -.18 

I % of earnings in 

retail trade 

industry 

-.02 -.01 -.03 .00 -.18 -.11 -.18 -.11 

J % of earnings in 

fire industries 

-.02 -.02 .06 -.05 -.11 -.07 -.10 -.09 -.07 

K % of earnings 

with health 

insurance 

-.01 -.01 .02 -.01 -.04 .10 .13 -.09 .00 -.03 

L Age 41 or older -.04 -.05 -.14 .02 .02 .02 -.01 -.02 -.03 -.01 -.03 

M Children 6 or 

older 

.03 .02 -.15 .16 .02 .05 .05 -.05 -.10 -.07 -.01 .15 

N Age -.03 -.07 -.21 .07 .03 .05 .00 -.08 -.08 -.03 -.01 .65 .48 

O Children under 6 -.01 .07 .26 .09 .02 .01 .00 .03 .00 -.01 .02-.22 -.25 -.40 

P Growth in county 

output 

-.13 -.05 .06 -.10 -.01 .04 -.10 .07 .04 -.02 -.04 .05 -.07 .02 .03 

Q Wage per job in 

the county 

.13 .02 -.03 .13 .04 -.09 .12 -.08 -.03 .05 -.10-.06 .09 -.01 .00 -.54 

R High school 

graduate 

-.12 -.01 .14 -.08 .05 -.01 -.06 -.09 .01 .08 .03-.01 -.10 .04 -.07 .06 -.06

S Black .15 .01 .01 .11 .08 -.10 .04 -.03 -.06 .01 -.06-.08 .12 -.01 -.02 -.29 .42 .03

T Hispanic -.04 .01 -.03 -.01 .01 .00 .09 -.04 -.04 -.00 -.01 .00 .02 .00 .01 -.06 .10 -.07 -.31

 

32 Boldfaced numbers indicate significant at 95% or higher level. 
196

Analysis and Results 

 We used logistic regression analysis, which is appropriate in models with 

dichotomous dependent variables (Greene, 1997; Hosmer & Lemeshaw, 1989; Maddala, 

1983). One modification to the model was made necessary by the data. For 546 of the 

3085 individuals there were no earnings during 1998. For these individuals, the ratio of 

earnings in any one industry and the ratio of earnings covered by health care are 0/0, or 

undefined. Our solution to this problem was to treat these undefined ratios as ‘missing 

cases’ in the data and to estimate the undefined ratios for the 546 individuals. We 

calculated the estimates as suggested by Griliches (1986) using linear regression. For the 

estimation regression model, we included all the other independent variables and 3 others 

(the number of months on TANF of the last 60 prior to exit, the number of months 

continuously on TANF prior to exit, and a dichotomous variable for residence in 

Milwaukee) to estimate the industry ratios for the 2539 individuals for which all variables 

are available. The parameter estimates for the coefficients are then used for the 546 

individuals to generate estimates for the missing variables. This approach uses 

information about intercorrelations between independent variables and information from 

the 3 new variables to generate estimated values. 

 The estimated variables are valid in the model provided that a) the parameters for the 

model with 2539 individuals apply to the 546 individuals, and b) the disturbance for the 

model that generates the new estimates is independent of the disturbance for the overall 

model (Griliches, 1986). For a), we assume that individuals, even those who have very 

small earnings, are from the same population as individuals with zero earnings. Many 

individuals with no earnings in 1998 do have earnings in subsequent and previous years, 
197

so there is no systematic factor that prevents them from participating in the labor force 

permanently. For b), we assume that the common elements of the process that explains 

proportion of earnings in an industry and the process that explains monetary eligibility 

are accounted for by the included variables. 

 Our reported results incorporate these industry and health care variables estimates for 

the 546 individuals with no reported earnings. We also ran the model with average 

values for the undefined variables, and we ran the model for the 2539 individuals with no 

undefined observations. All three models give very similar results33. We report the 

model with estimates because it reduces selection bias relative to the model with 2539 

individuals and incorporates more information than the average values model. 

 Table 4 shows the overall model’s performance. The chi-squared, likelihood, and 

Hosmer and Lemeshow chi square show that the model explains a significant proportion 

of the variation in monetary eligibility. The Cox & Snell R Square and the Nagelkerke R 

square show that the independent variables explain 20% to 30% of the variance in 

monetary eligibility. The classification table shows that the model will predict monetary 

eligibility about 71.2% of the time. 

 

33 See authors for these alternative analyses. 
198

Table 4 Overall Model Performance 

Omnibus Tests of Model Coefficients 

Chi-square Degrees of freedom Significance 

784 20 0 

 

Model Summary 

-2 Log likelihood Cox & Snell R Square Nagelkerke R Square 

3389.72 0.225 0.303 

 

Hosmer and Lemeshow Test 

Chi-square Degrees of freedom Significance 

14.10 8 0.079 

 

Classification Table 

Observed Predicted 

 Monetary eligibility Percentage Correct 

 0 1 

Monetary eligibility 0 751 511 59.5

 1 376 1447 79.4

Overall Percentage 71.2

 We performed other tests for multicollinearity and non-spherical disturbances. Table 

5 shows the tolerance and variance inflation factor (VIF) for each independent variable. 

The VIF values are all less than 4, the level at which multicollinearity conventionally 

represents a destabilizing effect. Table 6 and Figure 1 show our analysis of the 

standardized residuals. The tests of normality show conformity with econometric 

assumptions and that the model is not overly impacted by outlying observations. We also 

tested for heteroskedasticity using the artificial regression methodology developed by 

Davidson and Mackinnon (1993). This test showed no evidence of heteroskedasticity 

(F=0.025, p=1.000). 
199

Table 5 Collinearity Statistics 

 Tolerance VIF 

 

Months on W2 in 1998 0.87 1.15

Months receiving medical assistance in 1998 0.68 1.46

Months receiving child care support in 1998 0.83 1.21

Months receiving food stamps in 1998 0.63 1.59

Proportion of earnings in the health care industry in 1998 0.67 1.49

Proportion of earnings in manufacturing sector in 1998 0.79 1.27

Proportion of earnings in the temporary services industry in 1998 0.65 1.54

Proportion of earnings in the food and accommodations industry in 1998 0.73 1.37

Proportion of earnings in the retail trade industry in 1998 0.76 1.32

Proportion of earnings in the finance, insurance, real estate, and 

information industries in 1998 0.86 1.16

Proportion of earnings covered by health insurance in 1998 0.94 1.06

Age 41 or older 0.53 1.87

Number of children aged 6 or older 0.66 1.51

Age 0.38 2.66

Number of children aged under 6 0.77 1.29

Growth in county output in 1998 0.69 1.45

Wage per job in the county in 1998 0.58 1.72

High school graduate 0.91 1.10

Black 0.66 1.53

Hispanic 0.82 1.23

Table 6 Residual analysis: Tests of Normality 

Kolmogorov-Smirnov Shapiro-Wilk 

Statistic 

Degrees of 

freedom Significance Statistic 

Degrees of 

freedom Significance 

0.10 3085 0.00 0.96 3085 0.00 
200

Figure 1 Histogram of Residuals 

 The results in Table 7 show parameter estimates for the coefficients and their 

level of significance. The parameter estimates show the significance of the 

relationship between each independent variable and the probability that an individual 

will achieve monetary eligibility. The column labeled ‘Sig.’ shows the probability 

that the distribution of the coefficient estimate includes 0, so values less than .05 meet 

the conventional 5% level of significance criterion. 

Difference between observed and predicted probabilities

.7.88

5

.3.50.63

8

.25

0.0.13

0

--.13

.25

-1.00-.88-.75-.63-.50-.38

300

200

100

0

Std. Dev = .43 

Mean = 0.00

N = 3085.00
201

Table 7 Variable Coefficients 

 B S.E. Wald Sig. Exp(B)

Months on W2 in 1998 -0.345 0.028 146.875 0.000 0.708

Months receiving medical assistance in 1998 0.176 0.022 61.877 0.000 1.192

Months receiving child care support in 1998 0.392 0.029 179.474 0.000 1.479

Months receiving food stamps in 1998 -0.017 0.019 0.843 0.358 0.983

Proportion of earnings in the health care industry in 

1998 0.594 0.168 12.435 0.000 1.810

Proportion of earnings in manufacturing sector in 

1998 0.651 0.219 8.821 0.003 1.917

Proportion of earnings in the temporary services 

industry in 1998 -1.001 0.163 37.493 0.000 0.368

Proportion of earnings in the food and 

accommodations industry in 1998 -0.284 0.182 2.429 0.119 0.753

Proportion of earnings in the retail trade industry in 

1998 -0.418 0.189 4.879 0.027 0.659

Proportion of earnings in the finance, insurance, real 

estate, and information industries in 1998 0.623 0.301 4.265 0.039 1.864

Proportion of earnings covered by health insurance in 

1998 -0.025 0.117 0.045 0.833 0.976

Age 41 or older -0.180 0.196 0.843 0.359 0.836

Number of children aged 6 or older 0.054 0.035 2.405 0.121 1.056

Age -0.021 0.008 5.960 0.015 0.980

Number of children aged under 6 -0.158 0.047 11.397 0.001 0.854

Growth in county output in 1998 -0.075 0.056 1.786 0.181 0.928

Wage per job in the county in 1998 0.0000

757

0.0000

199 14.506 0.000 1.000

High school graduate 0.299 0.088 11.606 0.001 1.348

Black 0.093 0.103 0.810 0.368 1.097

Hispanic -0.034 0.175 0.038 0.846 0.967

Constant -2.034 0.741 7.532 0.006 0.131

 The parameter estimates do not show a linear relationship between the independent 

and dependent variables. Rather, the column labeled ‘Exp(B)’ gives the change in the 

odds of an individual being monetarily eligible for UI with an increase of 1 in the 

variable of interest (Hosmer & Lemeshow, 1989). For example, and increase of one 

month on W2 in 1998 will associate with a reduction in the odds of an individual being 

monetarily eligible for UI by about 31%. The odds would be multiplied by .708. 

 Estimates for variable coefficients in Table 7 showed the following results. The notes 

in parentheses indicate consistency with analysis of the full-data subsample for 1998, 

1999, and 2000. The note “robust” indicates similar result for all models. 
202

• access to the W2 program was negatively associated with monetary eligibility 

(robust) 

• access to medical care was both strongly positively associated with monetary 

eligibility (insignificant in 2000) 

• access to medical care and child care were both strongly positively associated 

with monetary eligibility (robust) 

• access to food stamps was not significantly associated with monetary eligibility 

(negative and significant for 1999 and 2000 models) 

• age was negatively associated with monetary eligibility, meaning that older 

individuals were less likely to be monetarily eligible for UI than younger ones 

(positive and significant in 1999 model, insignificant in other models) 

• age greater than 41 was not significant (negative and significant in 1999 model, 

insignificant in other models) 

• the number of children less than 6 years old had a significant negative association 

with monetary eligibility (robust) 

• the number of children over 6 had an insignificant effect (robust) 

• education, as measured by high school graduation, was strongly and positively 

associated with monetary eligibility (robust) 

• ethnicity was not a significant impact in the model (robust) 

• the proportion of earnings in the health industry was were positively associated 

with monetary eligibility (insignificant in 1999) 

• the proportion of earnings in the manufacturing sector was positively associated 

with monetary eligibility (robust) 
203

• the proportion of earnings in the finance/insurance/real estate/information 

industries was positively associated with monetary eligibility (insignificant in 

1999 and 2000) 

• the proportion of earnings in the temporary services and retail industries were 

negatively associated with monetary eligibility (robust). 

• the proportion of earnings in the food industry was insignificantly associated with 

monetary eligibility (negative and significant in 1999 and 2000) 

• employers’ provision of health care was not a significant factor in determining 

monetary eligibility for UI (robust) 

• average wages per job in the county had a strong positive association with 

monetary eligibility (robust, but in 2000 the p value was .12) 

• economic growth in the county did not represent a significant influence (robust). 

Policy Implications 

 This study on UI eligibility is conducted using the actual data containing a complete 

cohort of TANF leavers rather than a set of survey data. Therefore, we have potentially 

reduced bias and found a more accurate relationship between monetary eligibility for UI 

benefits and factors associated with former TANF recipients. 

The results derived in this paper are intended to provide policymakers, legislators 

and other interested individuals with useful information for deliberations on possible 

program options to assist this vulnerable population. They include the possible excess 

burden that children less than six (6) represent to those entering or re-entering the 

workforce; the need for schooling and educational attainment of those entering or reentering the workforce; and the need to tailor programs in an age-appropriate manner. 
204

Programs that support former TANF leavers’ efforts to enter the workforce may 

include access to either employer-provided and/or subsidized medical care coverage and 

child care support. 

There is also a need to tailor workforce development programs and supportive 

services to the local economy. Special attention may be needed for counties where wages 

per job are low. Policy makers may also want to pay special attention to industry effects. 

Special care should be given to the temporary services industry, the industry that employs 

the largest number of TANF leavers. This industry provides a great potential means for 

introducing individuals to employment, but should be treated as a stepping stone, rather 

than a permanent job, to other more stable jobs in higher-paying industries such as 

manufacturing and health services. 

 
205

References 

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Roles in Unemployment Insurance, January 1996. 

Davidson, Russell and James G. Mackinnon, Estimation and Inference in 

Econometrics, New York: Oxford University Press, 1993. 

Emsellem, Maurice, et al., Failing the Unemployed - A State by State 

Eexamination of Unemployment Insurance Systems, Washington, DC, Economic Policy 

Institute, Mar., 2002. 

Greene, William H. Econometric Analysis, 3rd Edition, Upper Saddle River, NJ: 

Prentice-Hall, Inc., 1997. 

Griliches, Zvi, “Economic Data Issues,” In Z. Griliches and M. Intriligator, eds. 

Handbook of Econometrics, volume 3, Amsterdam: North Holland, 1986. 

Gustafson, Cynthia, and Phillip Levine, “Less-Skilled Workers, Welfare Reform, 

and the Unemployment Insurance System,” Working Paper, University of California at 

Berkeley, Dept. of Economics, 1997. 

Holzer, Harry, Unemployment Insurance and Welfare Recipients: What Happens 

When the Recession Comes? Washington, DC, The Urban Institute, 2002. 

Hosmer, David W. and Stanley Lemeshow, Applied Logistic Regression, New 

York: John Wiley & Sons, 1989. 

Kaye, Kelleen, “Re-examining the Potential Role of Unemployment Insurance as 

a Safety Net for Workers at Risk of Public Assistance Receipt (Unemployment Insurance 

as a Potential Safety Net for Former Welfare Recipients),” DOL Workforce Security 

Conference of U.S. Department of Health and Human Services, Sept., 2002. 
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Maddala, G. S. Limited Dependent and Qualitative Variables in Econometrics, 

Cambridge, UK: Cambridge University Press, 1983. 

O’Leary, Christopher J. and Stephen A. Wandner, editors, Unemployment 

Insurance in the United States, W.E. Upjohn Institute for Employment Research, 

Kalamazoo, 1997. 

Rangarajan, Anu, Walter Corson, and Robert Wood, Is the Unemployment 

Insurance System a Safety Net For Welfare Recipients Who Exit Welfare for Work?

Mathematica Policy Research, Princeton, 2001. 

Savner, Steve Julie Strawn, and Mark Greenberg, TANF Reauthorization: 

Opportunities to Reduce Poverty by Improving Employment Outcomes, Center for Law 

and Social Policy, Jan. 2002. http://www.clasp.org. 

Schumacher, Rachel, and Tanya Rakpraja, States Have Slowed Their Use Of 

TANF funds and Child Care, Center for Law and Social Policy, Sept. 2002. 

http://www.clasp.org . 

Strewn, Julie, Greenberg, Mark; Savner, Steve (February 2001): Improving 

Employment Outcomes Under TANF. Vroman, Wayne, Effects of Welfare Reform on 

Unemployment Insurance, The Urban Institute, Washington, DC, May 1998. 

United States Census Bureau, Work and Work-Related Activities of Mothers 

Receiving Temporary Assistance to Needy Families: 1996, 1998 and 2000, Washington, 

DC, May, 2002. http://www.sipp.census.gov/sipp/p70s/p70-85.pdf 

United States Department of Health and Human Services, 2002 Indicators of 

Welfare Dependence Appendix A: Program Data: AFDC and TANF, Washington, DC, 

2002. Available Online at http://aspe.hhs.gov/hsp/indicators02/ch1.htm. 
207

United States Department of Health and Human Services, Administration for 

Children and Families, Change in TANF Caseload Since Enactment of New Welfare Law, 

Washington, DC, June 2002. Available online at 

http://www.acf.dhhs.gov/news/stats/aug-dec.htm.

U. S. Department of Labor, Table 1-1: Unemployment Insurance Eligibility, by 

Type of Determination and Outcome, United States, fiscal year 1998, 1999, Available 

online at http://ows.doleta.gov/unemploy/pdf/table1.pdf. 

U. S. Department of Labor, Comparison of State Unemployment Insurance Laws 

2002, Employment and Training Administration Office of Workforce Security, 2002 

United States General Accounting Office, Unemployment Insurance, Role as 

Safety Net for Low-Wage Workers is Limited, Report GAO-01-181, Washington, DC, 

2000. 

The Urban Institute, Issue Brief on Unemployment Insurance, Washington, DC, 

Jan. 2002.

The Urban Institute, Low Benefit Recipiency in State Unemployment Insurance 

Programs, Washington, DC, Oct. 2001. 

The Urban Institute, Unemployment Insurance Primer: Understanding what’s at 

Stack as Congress Reopens Stimulus Package Debate, Washington, DC, Jan. 2002. 

Vroman, Wayne, Effects of Welfare Reform on Unemployment Insurance, 

The Urban Institute, Washington, DC, May 1998
208

Employment and Training Services Provided by 

Faith-Based Organizations: An Exploratory 

Study and Issues for Consideration in the Use of 

Public Funds 

Fredrica D. Kramer 

Demetra Smith Nightingale 

John Trutko 

Shayne Spaulding 

Burt S. Barnow* 

Urban Institute 

* Johns Hopkins University 

This report was prepared at the Urban Institute based in part on research funded by the U.S. 

Department of Labor, Employment and Training Administration contract number 1-6830-8-00-

80-30, Task Order #6. Opinions expressed are those of the authors, and do not represent official 

positions of the Department of Labor, the Urban Institute, its Trustees or its sponsors 
209

1. INTRODUCTION 

Employment and training services in the United States have historically been 

delivered through a decentralized network of local public, private, and nonprofit agencies 

and programs. Among the many service providers, some faith-based entities, particularly 

nonprofit service organizations, have provided a variety of employment-related services, 

for example, to help people prepare for work, improve basic skills, find jobs or new jobs, 

or arrange for social or emergency services that might be needed. Some of these 

organizations receive some public funds and deliver publicly-funded services in a secular, 

not a religious context. Others use only private resources (e.g., from philanthropic 

foundations, charitable donations, and religious groups), and may have religious features 

or activities as part of the services they provide. 

 The Personal Responsibility and Work Opportunity Reconciliation Act 

(PRWORA) of 1996 included a “charitable choice” provision, which allows faith-based 

organizations to compete for state and federal welfare funds on the same basis as other 

organizations and maintain their religious character. Legislation governing other federal 

programs, including substance abuse education, prevention and treatment, and 

community services block grant services, subsequently also included a charitable choice 

provision. The Bush Administration has placed new emphasis on expanding the role of 

community-based and faith-based organizations in the provision of services to meet the 

social and emotional needs of families and individuals. 
210

 While faith-based organizations are an important partner in the workforce 

development area, there is little systematic information about the extent of their 

involvement in the formal public employment and training system, the amount of federal 

funds they receive, particularly through the Workforce Investment Act (WIA) or the 

nature of work-related services they provide. 

 The purpose of this study, therefore, is to provide a basic understanding of the 

extent to which faith-based organizations are providing employment-related services, 

drawing upon exploratory information compiled for five communities: Baltimore, 

Maryland; Fort Worth, Texas; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; and San 

Diego, California. The communities were purposively (not randomly) selected to provide 

geographic diversity to address three general questions: 

• How much federal employment and training funding is going to faith-based 

organizations? 

• What sorts of employment-related services do faith-based organizations 

provide? 

• How much employment-related services do religious congregations provide 

and to whom? 

The examination and the findings are exploratory, but nonetheless offer insight into the 

possible scale of activity by faith-based organizations in the workforce development 

system, and suggest the feasibility of conducting more empirical and comprehensive 

research to examine this issue more fully. 

Definitions 

There is no single generally-accepted definition of what a faith-based organization 

(FBO) is. For the purposes of this study, a faith-based organization is an organization 

that holds religious or worship services, or is affiliated with a religious denomination or 
211

house of worship.34 This includes churches and other congregations or houses of 

worship; nonprofit organizations affiliated with churches, congregations or religions; and 

local nonprofit organizations affiliated with an umbrella or national faith-based network. 

Faith-based nonprofit organizations generally maintain a faith-based mission but the 

services they deliver may or may not have a faith-based content and they do not 

necessarily restrict participants to those who adhere to that faith.35

 For simplicity, the discussion in this report refers to two types of FBOs: (1) local 

congregations of churches, synagogues, mosques, and other houses of worship; and (2) 

nonprofit organizations with some religious or faith-based association. Admittedly, the 

second category encompasses a wide range of organizations, each worthy of separate 

examination but which are considered together in this exploratory review: local affiliates 

of national networks such as Catholic Charities, Jewish Family Services, Lutheran Social 

Services, the Salvation Army, as well as local independent organizations or ministry 

groups operating in a single community. 

 Some large national organizations originally founded with a religious affiliation no 

longer consider themselves faith-based, even though some local affiliates may so define 

themselves. Goodwill Industries and the YMCA/YWCA are perhaps the largest of the secular 

nonprofit service organization networks with a faith-based origin. A few local Goodwill affiliates 

consider themselves to be FBOs, although the vast majority of Goodwill affiliates and local 

YMCA/YWCAs, like their parent organizations, consider themselves secular. In this study, 

 

34 A broader conception of faith-based organizations could include secular entities that are not religiously 

affiliated or religiously-based but have specific missions to promote certain values, beliefs, character, or 

moral behavior, or that are affiliated with groups with particular philosophic objectives. This study, 

however, focuses only on those associated with or founded according to a religion or religious faith. 

35 This study does not address whether the services provided by the organizations include any 

religious content or whether individuals do or are asked to adhere to (be receptive to) that content. 

These are important issues, but beyond the scope of this report. 
212

organizations such as Goodwill and YMCA/YWCA which are primarily secular, are excluded 

from the broader faith-based organization category. Local affiliates of other large national 

organizations, such as Jewish Family Services and Lutheran Social Services, which have 

carefully distinguished their secular services and activities, may still provide some separate 

services that are more religious in nature; these types of organizations are included in the study.

Approach and Limitations 

This is an exploratory study, examining FBO provision of employment-related 

services in five communities. Within each site, information was collected along three 

tracks: 

1. Extent of WIA contracting with FBOs, based on telephone 

discussions with the local WIA administrator or designated staff person 

who was asked whether they contract with FBOs and the dollar amount of 

the contracts; and discussions with the 9 largest WIA contractors, who 

were also asked whether they subcontract to FBOs. 

2. Nature of employment-related services provided by 

congregations, based on telephone discussions with the nine largest 

congregations and houses of worship and nine smaller ones (100-700 

members) selected at random from a list obtained from the American 

Church Lists. 

3. Nature of employment-related services by faith-based 

nonprofit organizations, based on telephone discussions with nine 

organizations selected at random from organizations presumed to be 

religiously affiliated, identified from an on-line telephone directory search 

(switchboard.com) under the categories “social services/welfare” or 

“employment agencies and opportunities.” 
213

The information compiled from these informal telephone discussions in the 

Spring of 2001 is summarized in the following sections. 

There are obvious limitations. First, the study is exploratory—while the 

congregations and nonprofit organizations were randomly chosen, the selection process 

did not involve statistical random sampling,36 and only a small number of congregations, 

nonprofit organizations, and workforce investment agencies37 were contacted. 

 Second, because we drew our samples from incomplete listings and contacted a 

limited number, the estimates presented in this report may understate the activity by faithbased organizations. It is likely that there are more faith-based organizations than we 

were able to identify. The extent of the underestimate, however, is unknown, and a 

precise estimation would require further research with larger samples. 

Thus, the samples of congregations and nonprofit organizations are not 

statistically representative, and the findings cannot provide a valid generalization of the 

sites in the study or of any other sites. The patterns of findings in this exploratory study, 

however, provide a useful starting point and offer a rough estimate, or approximation, of 

the extent of FBO involvement in employment-related services. 

 

36 Statistical random sampling requires a complete enumeration of the entire population, but we used 

listings that were likely to incomplete. For example, we sampled only churches for which we had a known 

congregation size in order to contact both large and small institutions. However, in identifying faith-based 

nonprofits, we attempted to avoid introduction of systematic bias by identifying every nth entry with 

presumed religious affiliation to produce a total of nine from the list, and substituting the entry immediately 

next if the organization was either not faith-based or did not offer employment-related services. 

37 For consistency throughout the text, the term workforce investment agency is used to characterize the 

local organization that administers federal workforce investment funds under the Workforce Investment 

Act. The appointed board as well as the administering agency are sometimes both referred to generically as 

the Workforce Investment Board (WIB). 
214

2. WORKFORCE INVESTMENT AGENCIES’ CONTRACTING WITH FAITHBASED ORGANIZATIONS 

Five workforce investment agencies38 (WIAs) (in Baltimore, Ft. Worth, 

Milwaukee, Pittsburgh, and San Diego) were contacted to determine the extent to which 

they contracted with FBOs in the last full 12-month period for which data were available 

(usually this was program year 2000). Table 1 summarizes the information by workforce 

investment agency in terms of: the number of FBOs under contract, the number of FBO 

contracts, the total value of the FBO contracts, the program funding sources (such as 

WIA/JTPA or Welfare-to-Work grants) for FBO contracts, and the percent of the 

agency’s total budget that goes to FBOs. Two of the five cities, Milwaukee and San 

Diego, have programs operated under national competitive Welfare-to-Work (WtW) 

grants. Appendix A provides additional information on FBO participation in the WtW 

program. 

 The extent of FBO contracting by workforce investment agencies in these sites 

varied, ranging from less than 1 percent (in three cities) to as much as 10 percent (in 

Pittsburgh). A total of 19 FBOs received contracts from these five boards in the most 

recent program year. Several of these FBOs had multiple workforce board contracts from 

different funding sources—and so, the total number of contracts let to FBOs by the 

boards was 26. Across the five boards the median value of an FBO contract was about 

$98,000, and FBO contracting represented about 4 percent of the boards’ total budget. 

Appendix B summarizes the FBO contracting reported by each of the five agencies. 

 

38 As noted in Chapter 1, the terms workforce investment agency and workforce investment board are often 

used interchangeably. Local agency names may vary depending on historical precedent, but each serves as 

the official body responsible for administering federal funds under the Workforce Investment Act. 
215

There was, however, much variation across the boards, as indicated in Table 1. With the 

exception of Three Rivers Workforce Investment Board in Pittsburgh (which contracted 

with 11 FBOs), the boards included in our sample contracted with between one (two of 

the boards) and three FBOs. The total contract value of awards to FBOs ranged from 

under $150,000 (at two of the boards) to slightly over $3.5 million (at the San Diego 

Workforce Partnership Board). Sources of funding were principally WIA/JTPA and 

Welfare-to-Work, though some FBOs received contracts from the local board under the 

Youth Opportunities program and the Temporary Assistance for Needy Families, or 

TANF, employment program. The total amounts contracted by local boards in 2000 to 

individual FBOs in the five cities ranged from $26,145 to nearly $3 million (for Metro 

United Methodist Urban Ministries in San Diego). However, half of the FBOs received 

contracts of less than $100,000. 

Table 1: Selected WIA Agencies' Contracting with FBOs (most recent year)

Workforce Investment 

Area 

# of FBOs as 

contractors 

# of FBO 

contracts 

Total value 

of FBO 

Contracts 

FBO 

contracts as 

a % of 

Agency’s 

budget 

San Diego Workforce 

Partnership Board, San 

Diego, CA 

3 5 $3.6 million 6% 

Mayor’s Office of 

Employment 

Development, Baltimore, 

MD 

1 1 $300,000 1% 

Three Rivers Workforce 

Investment Board, 

Pittsburgh and Allegheny 

County, PA 

11 15 $1.8 million 10% 

Tarrant County Work 

Advantage Board, Ft. 

Worth,Tarrant County, TX 

3 3 $144,000 1% 

Private Industry Council of 

Milwaukee County, 

Milwaukee and Milwaukee 

County, WI 

1 2 $36,000 Less than 1% 
216

After reviewing program and fiscal data obtained from the five workforce 

investment agencies, brief telephone discussions were also held with administrators in the 

largest non-FBO contractors for each of the local boards to determine if these non-FBOs 

were subcontracting a portion of their funding to FBOs. Of the 26 non-FBOs with 

contracts from the WIA agency, just one had in turn sub-contracted with an FBO. This 

particular contractor had subcontracted about 15 percent of its workforce funds to an 

FBO to conduct intake, client assessment, and case management. However, a number of 

other non-FBO contractors to the WIA agency indicated that while they did not have a 

contractual or financial relationship with FBOs, they were well connected with the faithbased community in their localities. Some of these contractors noted that FBOs can be 

very helpful in recruiting individuals from targeted neighborhoods and providing in-kind 

support services to which program participants can be referred (e.g., emergency food, 

clothing, housing, and counseling services). 

Future Funding Efforts 

Some local board administrators and non-FBO service contractors were likely to 

expand their contracting with FBOs in the near future. Several board administrators 

observed that they either were in negotiations with additional FBOs for employment and 

training services for the upcoming program year or that they expected an expansion in the 

number of FBOs they contracted with in the future. For example, one board 

administrator noted that while the Request for Proposals (RFP) process has always been 

open to FBOs on the same competitive basis as for other non-faith-based organizations 

and the board has funded some FBOs in the past, awareness on the part of FBOs and 
217

desire to compete for contracts/grants had increased significantly in the faith-based 

community over the past year. 

 However, as would be true for many small organizations, especially those new to 

contracting within the workforce development system, workforce development board 

representatives and FBO administrators were concerned about the ability of FBOs to 

meet the stringent audits imposed under programs such as WIA. As one administrator of 

a non-faith-based community-based organization with a long track record of contracting 

under JTPA/WIA noted, FBOs—especially churches—are “particularly viable service 

providers at the street level,” but often lack the infrastructure and expertise needed to 

administer contracts/grants under the exacting requirements imposed under DOL. He 

was in active discussions with several local FBOs (including a large church), and his 

organization was offering to act as a fiscal agent for local FBOs to manage funds and the 

documentation required under the contract/grant process. 
218

3. WORK-RELATED SERVICES PROVIDED BY RELIGIOUS 

CONGREGATIONS 

 In each of the five communities, informal telephone discussions were also held 

with representatives of a small number of religious congregations. The congregations 

were selected non-scientifically from lists obtained from the American Church Lists but 

represent a diverse mix of sizes and denominations. Telephone calls were made to 

selected congregations and the representative was asked whether they provide any 

employment-related services, casting the net widely to include, for example "...anything 

from job readiness or job retention services, training or education explicitly related to 

employment, or support services such as transportation, tools or clothing to enable 

individuals to work" but excluding services that were not explicitly employment-related. 

If employment-related services were provided, they were asked to describe them and 

estimate approximately how many people received the services in a given year, and how 

much resources were dedicated to providing those services. About half the congregations 

contacted provided no employment services, about one-third provided informal or 

episodic services, several collaborated with other congregations or organizations to refer 

people to services, and a few directly provide more substantial or formal services. 

Informal or episodic assistance 

Congregations and church groups commonly provide humanitarian help to 

individuals in need; if members of their congregation or the community ask for or need 

help, that help will be provided, usually on an informal basis as needed. This help may 

at times be employment-related, such as providing bus tokens to get to work. While 

about half the congregations contacted indicated that they provided no employment-
219

related assistance, over a third explained that they did provide some employment 

assistance, but that it tended to be primarily informal or episodic, involving very little 

expenditure of money, and serving very few people in the course of a year. 

For example, some congregations report providing one-time or time-limited 

transportation (for a few trips or a week) to a job or job interview. For example, one 

large Ft. Worth church39 provided bus tokens, paid for by the City, for one-time 

transportation to a confirmed job or job interview, and arranged access to a "career 

clothing closet" to persons referred by a caseworker from a service agency, such as a 

local women's shelter. In the past year, “hundreds” of bus tokens were distributed. 

Volunteers from that church also helped another local church tutor and assist students 

with schoolwork. 

Informal help might also include general job search assistance, such as posting 

job opportunities on a bulletin board, and referring individuals who say they are looking 

for a job or training to agencies that do provide skills training or education, depending on 

the individual’s needs. In addition, churches may house child care facilities, which may 

enable a parent to work, but such facilities are not considered employment-related for this 

study unless the facility was established for that purpose. None that we identified were 

established for that purpose. 

Referral services and other cross-institutional relationships 

In some cases, churches have established relationships with other institutions that 

offer employment services, although these relationships have varying degrees of 

formality and may or may not involve explicit expenditures by the church. For example, 

one Baltimore church was a pick-up location for a "Bridges to Work" program that 

 

39 Some representatives contacted asked that published information not specifically identify them. 

Therefore, in general, congregations are not identified by name in the report. 
220

provided transportation to work. In Ft. Worth, one church referred, and contributed a 

small amount of funds, to a larger church that operated a formal program for employment 

assistance. That program is profiled below. 

Some churches support activities as part of a consortium of churches or through 

an umbrella institution. One Ft. Worth church contributed about $5,000 a year to an 

inter-church social service agency that provides a variety of employment-related services. 

In Milwaukee a formal consortium of churches made referrals to a faith-based nonprofit 

organization that worked with 29 Lutheran churches and a larger network of multi-faith 

congregations and some employers to provide life skills, career development, counseling, 

and placement services. One church in San Diego made routine contributions and ran 

yearly fundraisers to support an organization within the United Church of Christ that 

provided services. In Pittsburgh, volunteers from an inter-faith reemployment 

organization provided speakers to help in reemployment issues, such as resume 

preparation. 

 These types of collaborations were identified in the course of conversations with 

representatives of the congregations contacted, but it was beyond the scope of the study 

to pursue further information specifically from or about the networks. That would be a 

useful activity in future research to gain a fuller understanding of the role of 

congregations. 

Formal Congregation Services and Programs 

In each of the five cities, typically one or two of the large congregations contacted 

provided what could be characterized as substantial services explicitly related to 

employment. As defined here, substantial employment-related services include routine 

programmatic services or a formal organizational component, such as a social service 
221

ministry dedicated to employment-related and perhaps other support services. Small 

congregations seem less likely to provide such services and provide services on a much 

smaller scale. 

Twelve congregations were identified that provided some type of service related 

to employment that appeared to be fairly substantial and somewhat formal. This 

represented around 10 to 15 percent of the congregations contacted. While this is not a 

statistically valid estimate of the percent of congregations that provided services, it does 

offer a rough idea of the extent of congregation involvement with employment services. 

Of those churches that described more formal programs or services to help individuals 

with employment needs, the levels of service and the numbers served were extremely 

wide ranging, as summarized in Table 2. 

Some of these churches had a social welfare ministry through which they offer 

counseling, job search assistance, clothing, or tutoring, but in general the services 

provided were modest or episodic. For example, a church in Baltimore (Case 1) used its 

social welfare ministry to offer counseling in employment and education, provided by 

retired educators or other individuals in the church as needed. The church also offered 

tutoring to high school drop-outs and adult education students through a local community 

college. But the level of expenditures ($500 in 2000), number of volunteers, and 

numbers of participants (25 to 50) suggest the services were quite modest. 

Similarly, a church in Pittsburgh (Case 1) received an endowment from a private 

individual to provide tuition scholarships to needy students for college or trade school. 

Another Pittsburgh church (Case 2) was the site for an English as a Second Language 

(ESL) program and a site for the Pittsburgh Lutheran Center for the Blind. The ESL 

program, first subsidized by the church and now funded with a small grant from a church 
222

in another state, offered two semesters of two classes per week and a language lab for 

adults who were principally spouses of foreign graduate students (most of whom 

eventually return to their home country). The Center for the Blind provides computer 

classes for a fee to a small number of blind individuals. A church in Baltimore (Case 2) 

provided a comprehensive program for refugees where employment assistance was a 

major component. The employment assistance consisted of job search assistance, job 

counseling, and any supportive services necessary over a three-month period to help the 

refugees find and maintain employment. The program served four individuals in 2000. 

At a somewhat higher level of service, a church in Pittsburgh (Case 1) offered 

computer training courses throughout the year and courses in how to build computers 

(mostly to young men). This program served approximately 80-100 individuals in 2000. 

A church in Ft. Worth (Case 2) had developed a system within the church for matching 

individuals needing work with older parishioners needing homecare or household 

assistance. While this program developed informally, the social service director reported 

receiving two to three calls per week from parishioners needing assistance, although the 

actual extent of job creation or the duration of employment was unclear. The church also 

provides approximately 400 gas vouchers per year for individuals who need assistance in 

getting to work. 

Some churches provide even more comprehensive services, but the number of 

participants and the level or intensity of service may still vary widely. For example, a church in 

San Diego (Case 1) contributed $12,000 of congregation funds (they are currently exploring 

foundation support) to provide mentoring, using teams of volunteers and a small commitment of 

paid staff, to a small number (18 in 2000) of single mothers and their families, all of whom had 

been on welfare. The mentoring wais done by phone or in person, and continued for as long as 

needed, sometimes six months to a year. Initially the church received referrals from the state 
223

welfare program (CalWorks), but now received referrals mainly from another service 

organization. Comprehensive services were also provided by a church in Baltimore (Case 3), 

which relied heavily on volunteers and served 1,200 individuals in 2000. Services included 

workshops on resume preparation, job search assistance, GED classes, life skills training, and 

vocational training. They also offered a range of supportive services including transportation to 

work, work clothing, and help with work expenses. 

Table 2 

Summary of Employment-Related Services Provided by Selected Congregations40

 

40 Total expenditures, funding sources, and participants served are for 2000. Education services include 

literacy training, English as a Second Language (ESL), adult basic education, GED/ high school diploma 

preparation, and related services. Job or training services include job search assistance, job or career 
224

Education Job/Trng. 

Services 

Support

Services

Total Annual 

Expenditures 

Number 

Served41

Public 

Funding 

Baltimore 

 Case 1 X X X $500 25-50 None 

 Case 2 X Minimal 

(refugee supp. 

up to 3 mos.) 

4 None 

 Case 3 

 

X X X $0 (in-kind, 

volunteers) 

90-480 

(1200) 

None 

Ft. Worth 

 Case 1 X X $0 

(volunteers) 

(100s)42 None 

 Case 2 X X $2,500 2-3 job 

referrals/

wk 

None 

 Case 3 X $140,000 30-300 

(530) 

None 

Milwaukee 

 Case 1 X X X n.a.43 10-200 

(620) 

None 

Pittsburgh 

 Case 1 X $8000 (tuition 

scholarships) 

50-100 

(100) 

None 

 Case 2 X X ESL: $2500 4-17 (21) None 

 Case 3 X n.a. 80-100 

(100) 

n.a. 

San Diego 

 Case 1 X $12,000 18 None 

 Case 2 X $1.1M 50-500 

(800) 

None 

Although expenditure and participation data were not consistently available, three 

congregation programs identified appeared to be providing extensive services (brief 

profiles of each are presented on the following page). Richland Hills Church of Christ 

 counseling, job mentoring or job coaching, life skills training, work experience, and specific trade or 

customized job training. Support services include transportation, tools or clothing provided specifically to 

enable work, help with work expenses, legal assistance related to employment, and any other support 

offered specifically to help individuals get or keep jobs. 

41 Indicates range of individuals served in each of several program components. Numbers in parentheses 

suggest maximum total if no individuals participated in more than one component. 

42 Although 100s of individuals may receive services, these are limited and episodic access to bus tokens or 

clothing assistance. 

43 Expenditure data not available, but church staff estimated that about two staff were directly engaged in 

providing employment- related services. 
225

(identified on Table 2 above as Case 3 in Ft. Worth) provided weekly evening job fairs 

led by a deacon as a ministry within the church. Among the ministry activities, they also 

offered a 12-week course four times a year in financial planning and budgeting. About 

300 persons participated in that course in 2000. In addition, they had a social worker on 

site under the supervision of a church elder to provide case management to about 200 

persons a year and were arranging short-term (ranging from 10 hours to two weeks) work 

experience opportunities for a small number of persons (30 in 2000) coming out of the 

criminal justice system or otherwise unemployed. 

The Holy Redeemer Institutional Church of God in Christ (Case 1 in Milwaukee) 

operated several institutions within the church, including a social service agency that 

offered a range of employment services: 12-week on-the-job training (about 20 

individuals received services in 2000) and vocational rehabilitation (20-30 in 2000); an 8-

week life skills training course twice a year (about 30 received services in 2000); job 

mentoring/coaching services biweekly (about 200 individuals received services in 2000); 

GED/HS preparation and computer technology in partnership and paid for by a local 

junior college; and literacy and ESL (about 100 received services). It also provided bus 

tickets (to about 25 per year) and referred a small number of participants (10-20) to 

outside agencies for legal assistance. 

Impact Urban America (Case 2 in San Diego) operated a $1.1million program in 

2000 providing services ranging from job readiness to job placement. The program was 

in its second year of implementation, with funding coming from community members 

and from employers with whom it made temporary, transitional, or permanent 

placements. The program used a model aimed at developing job and life accountability 

skills, and had partnered with the San Diego Housing Authority to apply the model. 
226

Profiles of Selected Congregation-based Employment Programs

Impact Urban America, a self-described "faith-based social entrepreneurial organization," is a 

project of the Maranatha Chapel, a Christian non-denominational church in San Diego, 

California. It has developed a model that combines leadership training with job and life 

accountability skills and customer service, using a seminar format and follow-up derived from a 

well-known executive leadership training model, to help residents of low income urban 

communities prepare for and find jobs. It has partnered with the San Diego Housing Authority to 

apply the model within the context of the HUD-funded Self-Sufficiency program. In addition, the 

program develops temporary, transitional, and permanent placements with local employers. 

Employers pay salaries for temporary or transitional workers through the program, thereby 

limiting their own costs for a direct hire. Now in its second year of operation, the program had a 

budget of $1.1 million in 2000, $100,000 of which came through community member 

ib i d $1 illi id f b l Th j i i i f h

The Richland Hills Church of Christ in North Richland Hills, Texas, in the Fort Worth area, operates 

several ministries that offer a range of humanitarian assistance, some of which is explicitly employmentrelated. Their "Christian Career Network" is a deacon-led series of once-per-week evening workshops 

offering job search assistance. Job counseling is now offered as part of once-per-week job fairs. A social 

worker on site, under the direction of church elders, provides ongoing case management and served about 

200 individuals in 2000. They also offer work experience, ranging from 10 hours to two weeks, to 

individuals coming out of the criminal justice system or otherwise unemployed. About 30 individuals 

received those services last year, including some who were referred from other churches in the area. Other 

services that may indirectly help with employment include their Crown Ministry, a course offered four times 

a year that teaches financial planning and other life skills in the context of biblical teachings. About 300 

individuals took that course in 2000. They also operate clothing resale shops whose profits go to homes for 

battered women, and they offer gas vouchers to help individuals get to work. 

About $140,000 was spent on employment and training-related services last year. All funds are raised by 

the congregation of about 2800 families (5000 individuals). 

Holy Redeemer Institutional Church of God in Christ in Milwaukee operates several educational and 

social service programs sponsored by the church. Among the services they provide are job search assistance 

(weekly), job counseling, job mentoring and job coaching, an eight-week course twice a year in life skills 

training (about 30 received services last year), case management (about 50 received services in 2000), onthe-job training and vocational rehabilitation in 12-week segments (20-30 received services in 2000), and 

career advancement assistance, GED preparation and computer technology through workshops in 

partnership with a local junior college. Also, about 100 individuals received literacy training, a similar 

number received assistance in English as a Second Language (ESL) training, and GED/High School 

preparation assistance. In addition the program provides bus tickets (about 25 a year), referrals for legal 

assistance (about 20 a year) and, less frequently, tools or clothing for work (about10 per year). The church 

has been providing employment-related services for about 10 years, with funds raised entirely by the 

congregation. 
227

4. WORK-RELATED SERVICES PROVIDED BY FAITH-BASED 

NON-PROFIT ORGANIZATIONS 

Telephone calls were also made to a select number of nonprofit organizations in 

each of the five cities. The organizations were identified through a search of an on-line 

telephone directory. Those listings in the categories “social services/welfare” and 

“employment and opportunities” that were presumed to be religiously affiliated (based on 

the name of the organization only) were identified. A non-scientific but fairly random 

group of nonprofit organizations in each of the five cities were called to ask (1) their 

religious affiliation (to verify whether or not they indeed were FBOs), and (2) whether 

they provide employment related services. 

 The results of these calls are discussed in this section, and Table 3 summarizes the 

types of organizations contacted, their sources of funds, and the nature of employmentrelated services they offered. 

Types of Organizations 

The entities contacted include some that were independent and some that were 

part of a larger social service or other organization. Similarly, in some of the 

organizations employment-related activities were part of a larger social service or faith 

mission, while in others, employment assistance was the principal purpose of the 

organization. 

For example, some programs were part of larger multi-purpose social service 

institutions, such as Catholic Charities, St. Vincent de Paul, Lutheran Social Services, 

Jewish Family Services (and related organizations), or the Salvation Army. Others, while 

still multi-purpose, had single discrete facilities, perhaps supported by a church or a 

consortium of churches and therefore quite modest in size. Even programs that are part 

of larger social service organizations take very different forms in different locations. 
228

Some are totally independent of the larger institution except for their receipt of funds and 

other resources, and others are programmatically quite connected. Since we were more 

concerned in this exercise to explore the nature of services being delivered, we cannot 

generalize about the magnitude of services or interconnections between the facility we 

contacted and the umbrella organization. 

As noted in Table 3, many of the faith-based programs contacted were in shelters 

or transitional housing facilities, in which employment assistance might be one 

component of a broader humanitarian mission to assist homeless individuals or families 

become stabilized, or part of a considerably narrower mission to offer short-term 

emergency assistance. A program might house individuals for six months to a year, 

provide them with a range of social services, offer them in-house work experience, or full 

employment services to transition into independence. Or a program might be an 

emergency night shelter dealing with individuals with severe substance abuse or other 

challenges and less receptive to intervention, but the facility might still offer them small 

jobs within the shelter. 

A much smaller number of faith-based programs contacted serve special 

populations such as refugees. A still smaller number were those whose sole mission was 

employment and training. 
229

Table 3 

Program Types, Funding Sources and Services Provided by Faith-Based Organizations 

BALTIMORE 

(n=8) 

FT. WORTH 

(n=9) 

MILWAUKEE

(n=944)

PITTSBURGH 

(n=8)

SAN DIEGO 

(n=9) 

 

Nature of Facility45 

 

Shelter, Transitional, 

Supportive Housing 

3 3 1 3 4 

Social Service 

Agency 

2 646 4 4 4 

Program for Special 

Population 

3 2 1 

Employment/Training 

Program 

 1 1 

Public Funding47

 

HUD 2 2 1 148 2 

DOL 1 1 2 1

FEMA 2 1 

HHS 1 

State of Local 3 1 2 1 

Other Federal 1 1 249 1 250

Any Public Funding 4 4 4 5 5 

Nature of Services 

 

Comprehensive Mix 

of employment, 

educ/training, & 

support services 

5 3 5 5 6 

Basic Employment/ 

job search assistance 

2 3 4 1 3 

Education only 1 

Support only 3 2 

 

 

44 Facility type uncertain in one case. 

45 Reflects the nature of the program at the site of interview. 

46 One program may also operate a shelter. 47 Programs may receive funding from more than one source and therefore be counted in more than one 

cell. Five cases did not report on funding sources. 

48 HUD emergency shelter funds, through city. 

49 Uncertain federal funding source through state and city. 

50 One program receives Community Development Block Grant funds, source of federal funds for other 

program not known. 
230

Funding Sources 

About half of the programs reported receiving some public funding for 

employment-related services, but less than half received federal funding. Of the 43 

programs contacted, 22 received some public funding, 19 of which receive federal 

funding. Funding typically came from either the church or churches with which the 

program was affiliated or from foundations or private donations. The majority of federal 

funding reported (in seven of the programs) was from HUD, principally funds to support 

housing for the homeless. Five of the programs received funds from DOL, three from 

FEMA, two from HHS and six cited other federal sources, four of which were uncertain 

of specific sources (summarized in Table 3). Other public funding sources mentioned 

included state and local monies and could represent pass-through of federal funds about 

which the interviewee was unaware. 

The HUD funding was principally from the McKinney Act and reflected the 

predominance of shelter and supportive housing in the sample. At least one program 

cited funding from HOPE VI, a program to revitalize the most distressed public housing 

and one which typically engages community-based organizations. The DOL funds were 

either not specifically identified other than as ETA grants, or were identified as "Welfareto-Work" funds, although there was some uncertainty about whether these funds were 

from the DOL Welfare-to-Work Grant program or from other efforts related to TANF 

and welfare reform. One program was the conduit for the refugee resettlement program 

funded by the HHS Office of Refugee Resettlement and under which they and several 

other social service agencies delivered services in the city. 
231

Nature of Services 

 The nonprofit FBOs contacted represent a variety of types of employment-related 

programs. As summarized at the bottom of Table 3, based on our very brief and informal 

conversations, it seems that some of the programs provided just one type of service—

such as remedial education, or basic job search assistance or short-term job preparation, 

or support services such as transportation assistance. Most contacted, though, provided 

what appeared to be a more comprehensive mix of services designed to improve 

employability and which included not only job search assistance and job placement, but 

also training or education as well as supportive services. The comprehensive programs, 

for example, might include a range of employment preparation services, such as life skills 

or job readiness training, accompanied by remedial education and/or support services 

such as transportation or clothing. 

 The general categorization of types of services in Table 3, does not reflect a sense 

of the magnitude of the programs, however. Although we asked for estimates of the 

number of individuals receiving services last year, that information was not consistently 

available for this study. 

5. CONCLUSIONS 

 Faith-based organizations, both religious congregations and nonprofit service 

providers, have been delivering a variety of social services, sometimes including 

employment-related services, for many years. Some of these organizations are attached 

to congregations, some are community-based organizations, and some are affiliated with 

national social service organizations. One question on the minds of many policymakers 

concerns understanding better the potential for expanding the role of faith-based 
232

organizations in providing social services, and, for the purposes of this report, 

specifically in providing employment-related services. This study explores the nature of 

current participation of congregations and faith-based organizations in the provision of 

employment-related services in order to contribute to the policy discussions. 

General Findings 

Congregations. Although many congregations provide limited humanitarian 

assistance that may help in employment—such as job postings, clothing, or occasional 

transportation—most that we contacted did not provide programmatic or extensive 

services for employment-related needs of church or community members. Except in two 

cities (Ft. Worth and San Diego), the majority did not provide even limited employment 

assistance. Of the one to three, usually large, congregations in each of the cities that did 

provide substantial programmatic employment-related services, none operated these 

services with public funds. Rather, most used funds raised directly by the congregation, 

or in some cases from private charitable foundations. Whatever the funding source, the 

numbers served in these programs was also generally quite limited, ranging from under 

10 to a little over a thousand in the largest program in the most recent year. 

Nonprofit Faith-based Organizations. Many faith-based social services 

organizations, whether independent or local affiliates of national networks, did provide 

employment-related assistance. Characteristically the assistance in the programs studied 

was one component of a broader array of services; few programs were devoted solely to 

employment and training services. The numbers served in these programs were also 

often limited, although about one-quarter of the programs contacted served over 500 in 

the year 2000. 
233

Workforce Investment Act and Other Public Funds. About half of the programs 

that we contacted received public funding. Of those that received federal funding, that 

funding was more likely to come from HUD to support services for the homeless than 

from other sources, including DOL through Workforce Investment Act or Welfare-toWork grant programs. Consistent with those findings, WIA funding for faith-based 

organizations in the areas studied represented only a small portion of the local workforce 

investment agency's total contracting activity (less than 10 percent of total funding, and 

often for relatively small contracts to a small number of organizations). Though public 

funds and especially WIA funds were not typically supporting employment-related 

services provided by FBOs, local workforce investment agencies in the areas studied 

frequently work collaboratively with the faith-based community in recruitment and 

referral of individuals and in providing supportive services to complement workforce 

development services. There was some indication that local workforce investment 

agencies might expand the participation of faith-based organizations in the near future. 

Magnitude of FBO Employment Service Activity. Because this is an exploratory 

study, involving only brief discussions with staff who were available at the time, and 

because the sample of FBOs is not scientifically representative, we cannot provide 

precise estimates of the magnitude of services or service levels provided by FBOs in the 

cities studied. But to provide some sense of the possible magnitude of FBO activity, we 

have attempted to develop a range of estimates, as presented in Table 4, based on the 

information compiled and using different explicit assumptions. 

To estimate the spending of all congregations on employment services in a city, 

we applied the percentage that provided employment services to the total number of 

congregations on the American Church Lists. For a conservative estimate ("Low 

Estimate" in Table 4), we assumed that congregations would spend on average $1,000 per 
234

year51 and therefore combined the spending identified in the congregations contacted 

with $1,000 for each of the remaining congregations to arrive at an estimate of total 

spending among all congregations. To produce a high estimate, we used the same 

extrapolation but included one congregation at a $50,000 annual expenditure. 

To estimate spending by nonprofit FBOs, we applied the percentage that provided 

employment services of all those contacted in the city to the total number of nonprofits 

presumed to be faith-based in that city. We then assumed that half of those 

organizations spend $10,000, 42 percent spend $100,000, and the remaining 8 percent 

spend $1 million a year on employment-related services. 

To produce the estimates of total spending on employment and training from local 

workforce investment agencies (WIAs) and WtW grantees that appear in Table 4, we 

combined the estimates of congregation and FBO spending with our estimates of 

allocations to FBOs made from WIA and WtW sources. 

The detailed estimates for each of these calculations appear in Table 4. A very 

rough sense of the magnitude of FBO provision of employment-related services emerges. 

It suggests that local spending on employment services by congregations and nonprofit 

FBOs might range from perhaps $2.4 million a year (low estimate for Baltimore) to as 

much as $6.9 million a year (high estimate for San Diego). To put this in perspective, the 

level of spending by congregations and FBOs, largely not supported by public funds, was 

an amount equivalent to under 10 percent (in Baltimore) to a little over 20 percent (in 

Pittsburgh) of the public funds spent in these cities on WIA and WtW activities.52

 

51 Congregations providing substantial services, noted in Chapter 3, often spent minimal or no direct funds. 52 This percentage does not capture funds spent on employment and training-related services by FBOs that 

derive from all public and non-public sources, but is simply meant to provide some perspective on the 

magnitude of spending by congregations and FBOs for employment-related services in comparison with 

total WIA and WtW spending. 
235

Table 4: Rough Estimates of Magnitude of Spending for Employment-Related Services by 

Congregations and FBOs in Five Cities (Year 2000) 

Estimates and assumptions Baltimore Ft. Worth Milwaukee Pittsburgh San Diego 

CONGREGATIONS 

 Approx. # 

Approx. % with substantial53

employment-related services 

Spending On Empl. 

Services Low Estimate54 

Spending On Empl. 

Services High Estimate 55

120056

15% (180) 

$177,500 

$226,500 

900 

15% (135) 

$274,500 

$323,500 

700 

5% (35) 

$84,00057

$133,000 

700 

15% (105) 

$112,500 

$161,500 

600 

10% (60) 

$1,170,000 

$1,229,000 

NONPROFIT FBOS (nonWIA)58 

Possible # with empl. 

services 

Spending On Empl. 

Services (Non-WIA) 

(assumes 50% spend 

$10,000; 8% spend $1 

million; 42% spend 

$100,000) 

16 

$1,962,000 

15 

$1,905,000 

17 

$2,169,000 

15 

$1,905,000 

15 

$1,905,000 

APPROX. WIA $s TO 

FBOS 

% of all WIA $s 

$300,000 

1% 

$144,000 

1% 

$36,000 

1% 

$1,800,000 

10% 

$3,600,000 

6% 

APPROX. WtW $s TO 

FBOS (non-WIA) 

0 0 $147,577 0 $147,577 

TOTAL SPENDING BY 

CONGREGATIONS 

AND FBOS 

 Low Estimate 

 High Estimate 

$2,439,500 

$2,488,500 

$2,323,787 

$2,372,787 

$2,436,577 

$2,485,577 

$3,817,500 

$3,866,500 

$6,822,577 

$6,881,577 

TOTAL WIA, WtW, and 

other misc. E/T funds 

$33,419,931 $26,495,077 $26,420,441 $17,981,382 $56,899,822 

 

53 As defined in Chapter 3. 

54 Dollars identified + $1,000/per congregation times remaining number of congregations assumed to 

provide employment services.

55 Dollars identified + 1 more congregation at $50,000 + $1,000/per congregation times remaining number 

of congregations assumed to provide employment services. 

56 Possible inclusion of surrounding county may be responsible for larger number relative to other cities 

studied. 

57 Imputes $50,000 for church program for which no expenditure estimates were available, using estimates 

of number of staff providing services. 

58 In order to estimate the number of FBOs in each city that provided employment-related services, the 

percentage who provided employment services of all those contacted was applied to the total presumed to 

be faith-based. 
236

Outstanding Issues 

There is heightened interest on the part of the Bush Administration, Congress, and some 

local workforce development boards to engage faith-based organizations more fully in 

providing publicly-funded services. There is also interest on the part of some faith-based 

organizations to participate more fully in assisting individuals in employment-related and 

other human services and to use public funding to help support their efforts. The 

discussion is drawing sufficient attention that the governing bodies of several national 

religious organizations are reexamining the tenets and conditions under which they would 

receive or continue to receive public funds. 

While this study is exploratory, the findings suggest a number of questions that 

need to be addressed in order to understand appropriate directions for expanded support 

of faith-based efforts in employment and training, and are likely to apply more generally 

to public funding for the provision of a range of human services. Questions might begin 

with the following: 

• What is the level of interest of faith-based organizations in expanding their 

services or receiving public funding under public rules? 

• What is the capacity of congregations or other faith-based community 

organizations to expand their services? 

• What sorts of services are faith-based organizations best suited to deliver: how 

does the effectiveness of current FBO services and service models compare to 

current federally-funded programs providing such services? 

It is difficult to interpret from our research how much interest there is, particularly 

on the part of small congregations, in gaining public funding. Both the interest and the 

capacity to participate in publicly-funded programs will differ depending on the nature 

and size of the organization. In addition, some analysts have suggested that FBOs may 

fear compromising their “prophetic voice”—both their faith content and their 

independence from any public policy agenda—by taking public funds. Our conversations 

with congregations and faith-based nonprofits suggested that some organizations who are 
237

faith-based but whose social services have no religious content may still resist oversight 

around content that federal participation would entail. The level of interest and the 

ability of these organizations to balance their faith missions with responsibilities for 

fulfilling public purposes are empirical questions that require further research. 

Our conversations with local administrators and faith-based organizations 

delivering employment-related services also suggested that there is likely widespread 

need for technical assistance in conjunction with expanded federal funding for FBOs. As 

with many first-time federal grantees, adjustments may need to be made by many FBOs 

in order to fulfill federal reporting requirements. Federal and state governments may 

need to look for ways to help interested organizations build the necessary internal 

organizational capacity, both from the standpoint of building effective service delivery 

systems and of safeguarding expenditures of public funds. 

The capacity of faith-based organizations to expand their services also may differ 

depending on their size and nature. Questions of capacity are especially relevant to small 

organizations whether they are religiously based or not. The mission of many local 

organizations, including FBOs and especially local congregations, is to provide highly 

flexible, often low cost, responses to local needs. Many such organizations have 

relatively little experience in the formal contracting requirements, financial tracking, and 

caseload tracking, required to meet audit requirements of federal, state, and local 

programs. Some may not want to invest the time and managerial attention required to 

operate more complex programs under more complex reporting requirements. Some may 

be loathe to operate in the outcomes-driven environment of public programs. Many are 

particularly concerned with remaining highly accessible to populations in need and have 

little incentive to ask the kinds of questions that might better track clients but put that 

accessibility at risk. Some could easily make the adjustment to meet the requirements of 

public funding; others might find it difficult to focus on tracking and outcomes and 

maintain the character of their mission. 

Larger organizations, both faith-based and otherwise, often have greater 

experience managing and monitoring programs that deliver a variety of social services. 

Indeed many large faith-based organizations, some part of national affiliates, have long 

histories of using public funds to help deliver services; some could apply such experience 

to new or expanded use of public funds. In addition, small congregations might 
238

collaborate with larger ones, or with faith-based or other non-profit organizations that are 

not-faith-based, to provide services. Small, community-based institutions may, for 

example, be uniquely capable of doing outreach and recruitment while larger 

organizations may have greater competence in delivering more extensive services and in 

monitoring and reporting in order to meet public funding requirements. Collaborations 

may be one way to involve FBOs in meeting the objectives of federal programs and to 

capitalize on the different expertise of each type of organization. Although identifying 

such collaborations was beyond the scope of this study, we found a few examples in the 

cities studied. Future research might explore more precisely how prevalent such 

relationships are and how they function to deliver desired services, in order to assess the 

potential for expansion or replication. 

Expanding the involvement of FBOs in the delivery of federally-funded programs 

requires an understanding of the sorts of services they are best suited to deliver. Many 

community-based organizations, including FBOs are in better touch with disenfranchised 

populations, have widely perceived legitimacy, and are therefore relatively advantaged in 

recruitment of individuals otherwise less likely to come forward for services. Spiritual 

and faith content may themselves be powerful motivators, particularly in the context of 

services aimed at personal transformation. This again is an area with little empirical 

evidence to guide public decisionmaking. Apart from legal issues, which are not the 

focus here, there has been little systematic study of the program content of faith-based 

organizations as they compare to other organizations delivering social services, or the 

nature of faith content in different program models, and little guidance from program 

evaluations on the importance of such content in program effectiveness. As the recent 

“charitable choice” provisions in some federal legislation explicitly recognize, it may be 

important when serving disadvantaged populations to ensure that alternative services be 

made available for individuals who do or do not wish to participate in certain programs 

with faith content. There is little empirical research on how well vulnerable populations 

can exercise this option, for example, when program services provide vital income 

support and are tied to mandatory participation in a service component, or perhaps are a 

court-ordered drug rehabilitation as an alternative to imprisonment. Whatever the faith 

content, there are no formal evaluations of the performance of faith-based organizations 
239

delivering social services in order to compare their effectiveness to other organizations 

delivering similar services. 

In short, if the public interest in faith-based organizations is rooted in a desire to 

expand the pool of community-based organizations to deliver publicly-funded services, 

many of the same concerns that accompany involvement of small organizations would 

attend the involvement of FBOs. If FBOs with little prior experience in administering 

publicly-funded programs jump in aggressively, they might be easily overwhelmed by the 

administrative responsibilities attached to operating under government requirements, 

while rapid service expansion might also challenge their ability to maintain quality 

programs. If the public interest in FBO involvement in the delivery of publicly-funded 

programs is to tap a special expertise rooted in spiritual or faith mission, either in the 

special capabilities of staff or the content of services, considerably more research is 

needed to understand the content of those services and their effectiveness in achieving 

program objectives. 
240

APPENDIX A: FAITH-BASED ORGANIZATIONS RECEIVING 

WELFARE-TO-WORK GRANTS 

In addition to federal funds through WIA (and previously, JTPA), the Welfare to Work 

(WtW) grants represent a fairly large source of funding for communities and workforce boards. 

Grantees received WtW funds between 1999 and 2000, and were allowed three years in which to 

spend those funds (later extended by Congress to five years). Seventy-five percent of the $3 

billion Congress authorized for WtW grants was distributed by formula to states, which in turn 

must distribute 85 percent to local workforce boards or, in a few instances, to other sub-state 

organizations. Local boards operate programs with the grant funds or, more typically, contract 

with service providers to operate separate programs or deliver particular services. This means 

local boards may contract with FBOs for WtW services, just as they do for WIA services. 

The other 25 percent of WtW funds has been distributed by DOL competitively, based on 

applications submitted by nonprofit organizations, public agencies, workforce boards, and 

consortia of various entities. About $40.3 million in WtW competitive funds nationwide were 

distributed directly to faith-based organizations, of the total $383 million total competitive grants 

allocated, representing about 11 percent of the total competitive grant amount. The twelve faithbased WtW grantees, most of which are nonprofit organizations affiliated with a house of 

worship, are:

• DePaul University, Chicago ($5,000,000) (Jesuit) 

• Benedict College, Columbia, South Carolina ($4,771,156) (Baptist affiliated, 

Historically Black College) 

• Vorhees College, Denmark, South Carolina ($1,990,859) (Episcopal affiliated, 

Historically Black College) 

• Bethel New Life, Chicago ($2,739,506) 

• Catholic Social Services of Albuquerque, Inc. ($1,351,541) 

• Catholic Charities of Los Angeles ($3,037423) 

• International Association of Jewish Vocational Services ($4,204,777) (multi-site 

grantee) 

• Catholic Community Services of Southern Arizona ($1,978,125) 

• So Others Might Eat, Washington, DC ($963,865) 

• St. John the Baptist Roman Catholic Church/North Brooklyn Coalition for Welfare 

Reform and Economic Development, New York City ($2,926,751) 

• Catholic Social Services, Charlotte North Carolina ($1,086,006) 
APPENDIX B: WORKFORCE DEVELOPMENT BOARDS’ CONTRACTING WITH FAITH-BASED O

Note: Data are for period 7/1/2000-6/30/2001, except for Tarrant County (9/1/1999-8/30/2000) and PIC of Milw(7/1/1999-6/30/2000). 

Board Service Area # of FBOs # of FBO Total Value Program Sources B

Contracted Contracts of FBO Contracted to R

Contracts FBOs 

San Diego Workforce San Diego Co. 3 5 $3,568,880 WtW, 

 Partnership Board (incl. San Diego) Youth Opportunities Mayor's Office of Employ- Baltimore City 1 1 $300,000 WtW  Ment Development Three Rivers Workforce Allegheny Co., 11 15 $1,768,706 WIA (Adult, Youth, DW)  Investment Board (incl. Pittsburgh) WtW Tarrant County Work Tarrant County 3 3 $144,287 TANF (CHOICES)  Advantage Board (incl. Ft. Worth) Private Industry Council Milwaukee Co. 1 2 $35,855 JTPA-Adult, Older Worker of Milwaukee County (incl. Milwaukee) Totals 19 26 $5,817,728 
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APPENDIX C: NUMBER OF CONGREGATIONS IDENTIFIED THAT PROVIDE 

EMPLOYMENT- RELATED SERVICES, BY CITY 

Large Congregations Small Congregations 

Baltimore 2 (n=10) 1 (n=9) 

Ft. Worth 3 (n=1159) 0 (n=10) 

Milwaukee 1 (n=10) 0 (n=9) 

Pittsburgh 1 (n=10) 2 (n=11) 

San Diego 2 (n=10) 0 (n=10) 

Informal discussions indicated that the following congregations provide employment-related 

services: 

Baltimore: Concord Baptist Church 

 Bethel A.M.E. 

 St. Stephen United 

Ft. Worth: First United Methodist Church 

 Richland Hills Church of Christ 

St. John the Apostle 

Milwaukee: Holy Redeemer Institutional 

Pittsburgh: First Trinity Evangelical Lutheran 

Monumental Baptist 

St. Thomas More Church 

San Diego: First United Methodist Church 

Maranatha Chapel 

 

59 Includes one church identified in the sample of faith-based affiliated organizations. 
243

APPENDIX D: NONPROFIT FAITH-BASED ORGANIZATIONS 

Informal discussions were held with the following nonprofit FBOs, which indicated that they 

provide employment-related services. 

Baltimore: Spanish Apostlate; Young Christian Workers; Salvation Army; Christopher Place; 

St. Ambrose Family Outreach; Payne Memorial Outreach; Brown’s Memorial Church Shelter 

Ft. Worth: Eastside Ministries; Catholic Charities; Bread Basket Ministries; Northside InterChurch Agency; Presbyterian Night Shelter; Cornerstone New Life Center; Restoring Hope 

Center; South Central Alliance of Churches; Salvation Army 

Milwaukee: House of Peace; Holy Family House; Catholic Charities Archdioces; Islamic 

Family and Social Services; Service Empowerment and Transformation (SET) Ministry; 

Milwaukee Christian Center; Salvation Army; Jewish Family Services; St. Vincent de Paul 

Pittsburgh: Holy Family Institute; Jubilee Kitchen; Northside Common Ministries; St. Vincent 

de Paul; St. Joseph's House of Hospitality; Salvation Army; Light of Life Ministries 

San Diego: First Step Ministries; Catholic Charities; Mid City Christian Service Agency; Center 

for Urban Ministry; Uptown Interfaith Services; Lutheran Social Services; San Diego Rescue 

Mission; St. Vincent de Paul; United Jewish Federation 
244

Reconceptualizing Education as an 

Engine of Economic Development: 

A Case Study of the Central Educational Center 

Keith MacAllum, Ph.D. 

Amy Bell Johnson 

Academy for Educational Development 

National Institute for Work and Learning 
245

Educational change starts with imagining new solutions to old problems.

Perhaps you’re the superintendent of schools or the president of a technical college, a teacher or 

guidance counselor. Maybe you’re the mayor or the governor. You might be a parent or the 

owner of a small business. How do you want education to be structured in your community? 

What is education supposed to accomplish? Can education support the development of our 

young people and our communities at the same time? What needs do you have that education, if 

it was organized just a little differently, could meet more effectively? 

These questions led the citizens of Coweta County, Georgia to reconceptualize high school 

education—a reconceptualization that ultimately led to the creation of the Central Educational 

Center (CEC), a unique partnership based in Newnan among business and industry, the Coweta 

County Schools, and West Central Technical College. Through partnership, CEC addresses the 

mutual concerns of educators, parents, business owners and managers, college administrators, 

and the students themselves. 

A few years ago, Coweta County was like many communities in the United States. Most 

educators and community members thought college was the natural next step after high school. 

Therefore, high school ought to prepare young people for college. And it had a school system 

that did a pretty good job preparing the top students for just that. But like most communities, 

close to half of Coweta’s students don’t go to college. And about half of those who enroll, never 

complete their degree. Thus we end up with a K-12 educational system that is designed to 

effectively serve about one-quarter of the student population. What about the rest? 

At the same time, employers in Coweta and elsewhere began to notice two trends. First, the 

demands of the workplace were increasing as a result of technological innovation and 

globalization. Employees need to have more advanced skills to do the same type of work and to 

do it more efficiently. Second, employers were having a harder time finding qualified 

employees, ones with fundamental skills and an ability to learn. The problem was especially 

acute among non-college degreed applicants. Though the employers may not have recognized it 
246

at the time, their needs were changing—but the traditional goals and processes of education were 

not. More and more jobs in Coweta County are requiring special skills or technical training. 

Inside the schools, teachers struggled with two interrelated problems—a declining level of 

motivation among students coupled with unacceptable dropout rates. Parents were likewise 

discouraged because they knew their children “didn’t really like school and were not being 

successful in school.” Still, while many parents were not satisfied with the status quo, they were 

not necessarily motivated or seemed unsure of how to promote change. 

Meanwhile, far beyond the school grounds, economic developers and county planners wrestled 

with the challenges of attracting new businesses to the area while helping to grow the businesses 

already there. It was becoming increasing clear that the vitality of the community and 

opportunities for economic development were inextricably linked to the quality of education and 

training. It has become a truism that the “currency of the new economy is knowledge and 

information.” 

Along those lines but at the state level, a similar round of deliberations were taking place, 

exploring the strategic options to grow the state’s economy. What policies and practices could 

the state employ to attract business and industry to Georgia? The QuickStart program, which 

provides customized training services to relocating businesses, had already demonstrated its 

value. In this model, 34 technical colleges throughout the state provide the platform for 

education and training services to new and expanding businesses, on everything from new 

technology to retention of employees. “QuickStart is high profile,” explained Dr. Ken Breeden, 

Commissioner of Georgia’s Department of Technical and Adult Education. “We’re a real part of 

the economic development team.” According to Breeden, the Governor has regularly stated that 

“QuickStart is the economic development strategy for the state.” 

But more needed to be done to develop the local workforce. “The market for skilled and trained 

workers is growing so fast,” Breeden observed. “We’re interested in competing for highskill/high-wage jobs. The number one thing we need for economic development is an available 

and well-trained workforce. Education is everything.” 
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In Newnan and Coweta County, the employer community, West Central Technical College, and 

the school district each had distinct problems they were trying to solve. The employer 

community was having a hard time finding qualified workers. West Central Technical College 

(WCTC), a leader in workforce development serving four counties in the region, had been 

grappling with how to strengthen programs and reach more individuals in Coweta County. As a 

start, WCTC leaders wanted to centralize course offerings and activities in Newnan under one 

roof. At the same time, the superintendent of schools and school board were struggling to find a 

way to improve programs for non-college bound students. 

Personal conversations among individuals evolved into extended deliberations among the 

organizations. Vague concerns evolved into strategic goals. Others in the community joined in 

the deliberations. The deliberations evolved into a series of meetings. “Once this community’s 

leaders were mobilized, we really started to see movement,” observed one employer. In 1997, 

the loosely affiliated group decided to formalize a steering committee comprised of 20 influential 

movers and shakers representing a broad cross-section of stakeholder groups. Said one member 

of the steering committee, “Within a relatively short period of time, everyone was at the table.” 

Interest and excitement surrounded what appeared to be a unique opportunity to forge new 

alliances among high schools, the technical college, and the private sector. All the necessary 

ingredients were on the table. All that was needed was a process to help the group work together 

in a winning combination. 

Dr. Joe Harless, a nationally respected consultant who for 30 years helped business, industry, and the 

military improve the performance of their employees, just happened to live in town. In 1998, Harless 

published “The Eden Conspiracy,” which explored how education could be reformed around 

accomplishment-based curriculum, an approach that was uniquely suited to addressing the multitude of 

concerns the various stakeholders brought to the table. “I invited myself to get involved,” confided 

Harless. Given his experience and interest, Harless was designated as the chair of the steering committee. 
248

In retrospect, when asked how CEC came into being, many of those involved nonchalantly 

replied, “The stars just aligned.” And with respect to gathering the input and commitment of key 

organizations, this may be true. But the ease of forming initial alliances belies a tremendous 

planning effort. The committee reviewed the literature on successful school-business 

partnerships, explored promising and effective pedagogical strategies, visited exemplary 

programs, was steeped in accomplishment-based curriculum development, and most importantly, 

developed a deep and authentic understanding of the unique needs of each stakeholder group. 

From this understanding, a common vision was formed. 

CONCEPTUAL VISION

CEC would be a place where the highest levels of learning for all could be achieved. Drawing on the 

best research and practice available, the Steering Committee identified the basic building blocks on which 

CEC would be built. Expectations for student performance would be high, given the expanding demands 

of the new economy. Experiential learning through applied and hands-on projects would be a common 

part of the daily classroom. Young people would be given new flexibility to “design” a program of study 

that prepared them for multiple pathways beyond high school—pathways dictated by changes in the 

economy. These would combine advanced technical training with a higher level of academic instruction 

than traditionally seen in connection with vocational education. Seemingly separated levels of education, 

secondary and technical college, would be vertically integrated into a seamless mix through instructor 

collaborations and dual-enrollment opportunities. This core instructional package would be topped with 

heavy doses of work-based learning—real opportunities to practice classroom learning in the local 

economy. Along the way, local business would provide advice, counsel, direction, and expertise in the 

classroom. The effort at the secondary level linked to a technical college would provide both competitive 

pressure and an opportunity to attract adults back into the classroom for retraining. 

After 18 months of intensive work, the committee produced an action plan. It would take approximately 

another year before CEC opened its doors to students in August 2000. 

WHO ARE WE AND WHY ARE WE INTERESTED IN CEC? 

Founded in 1961, the Academy for Educational Development (AED) is an independent, 

nonprofit organization committed to solving critical social problems in the United States and 
249

throughout the world. Major areas of focus include health, education, youth and leadership 

development, and the environment. 

The authors of this paper are affiliated with the National Institute for Work and Learning (NIWL), which 

is housed within the U.S. Education and Workforce Development Group of AED. We focus our attention 

on the intersection of education and employment. Our projects regularly cross the traditional boundaries 

that separate schools, workplaces, and communities. Indeed, we seek to blur those boundaries by 

reforming education and improving the interrelationships between education, work, and the surrounding 

community—goals we believe are shared by CEC. 

Our mission is to help local, state, and national agencies transform the educational enterprise by exploring 

new models of instructional practice, building organizational capacity, and providing professional 

development opportunities together with the tools to assess and improve effectiveness. 

As part of our ongoing work, we at NIWL believe it is imperative that educational research focus more 

directly on documenting and quantifying the economic benefits of educational innovations such as CEC 

to the county and region in which they are situated. In doing so, we chose to concentrate our attention 

more on the external relationships that helped establish and sustain CEC rather than its internal 

components and practices. Therefore, readers will not find many details in this paper on instructional 

practice or student progress. 60 Our goal was to learn as much as possible about the promise and potential 

of the CEC partnership model so that we can share developmental design features, promising practices, 

and lessons learned with others. 

Which brings us back to how education was reconceptualized in Coweta County. 

CEC As School 

“It’s a totally different atmosphere. Kids want to be there, teachers want to be there, 

and it shows.” Ellis Cadenhead, Assistant General Manager, Newnan Utilities 

 

60 For a thorough evaluation of these issues, readers are referred to a parallel study undertaken by Florida State 

University (FSU). Focusing on the internal structure of CEC using surveys of faculty, staff, parents, and alumni, 

FSU will report on the operation of the CEC instructional system and measure its performance. We believe the 

inquiry undertaken by AED complements the programmatic focus of the FSU evaluation by examining how CEC, as 

a model of educational improvement, relates to the wider community. Ours is a perspective that examines CEC’s 

relationships with other institutions and processes, namely workforce development and economic development. 

Contact Anthony Chow, Department of Instructional Systems, FSU, for additional information. 
250

The planning and development of CEC began years before the first class enrolled. Coming to 

appreciate the related concerns of education, workforce development, and economic 

development took time in and of itself. From there, the relationships between the public and 

private sectors, and among their respective organizations, took shape. The story of how CEC 

was formed, and to a large degree how it operates, is intimately wound up in relationships among 

local community partners. Later in this report we focus on the nature and value of partnership. 

In this section, we provide a general overview of how CEC looks today, as it evolved from 

original concept to reality. Details on instructional practice are addressed in a parallel study by 

Florida State University. Here we report on the following major dimensions of CEC's 

organization: charter school status, leadership, course offerings, student enrollment, and faculty. 

ORGANIZATION AND STRUCTURE OF CEC 

In order to understand how CEC was implemented and the reconceptualized structure of 

education shared by community stakeholders, it is essential to recognize that CEC was 

established as a charter school.61 Charter school status affords schools a remarkable degree of 

freedom with respect to organizational structure, management, and instructional practice. 

Viewed as a vital strategy to address local educational needs, charter schools have grown 

exponentially in the U.S. from 430 only six years ago to over 2, 800 today. 

Charter school status gives school planners and administrators considerable, but not unlimited, 

flexibility. The charter must adhere to certain state and school district parameters and the school 

is obligated to report to the superintendent of schools. Yet, CEC is deliberately positioned to be 

directly accountable to business and parents. In the case of CEC, flexibility is most visibly 

manifested in its mandate to respond to business and community needs. CEC’s charter makes it 

possible for partners to create and maintain a school culture and climate distinct from that of 

traditional high schools. 

 

61 More specifically, CEC is technically considered a start-up charter, created de novo, as opposed to a conversion 

charter that converts an existing school to charter status. 
251

In the words of Dr. Peggy Connell, Superintendent of Coweta County Schools, “CEC is not 

limited by the rules and regulations that have become ingrained in the more traditional education 

system. Its flexibility allows for actions that couldn’t be taken and decisions that couldn’t be 

made when dealing with regular school. For example, in a regular school environment, adjusting 

the number of hours of seat time a student needs while offering work-based learning and off-site 

experiences would have been an arduous task. It’s doable in the CEC environment under the 

CEC governance structure.” 

The following organizational chart delineates the lines of reporting and governance structure of 

CEC. 

CENTRAL EDUCATIONAL CENTER ORGANIZATIONAL CHART 

Coweta 

County 

School Board

CEC Board of 

Directors 

Technical 

College Board

CEO 

Director of 

High 

School

Programs 

Director of 

Technical & 

Career 

Education

Director of 

College 

Operations 

Technical 

College 

Directors 

CEC Directors 

Technical 

College 

Operations

High 

School 

Operations 

CEC 

Administrative 

Staff 
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LEADERSHIP

“Leadership transforms vision into reality.” Warren Bennis, Ph.D., Professor of 

Business, University of Southern California 

In our studies of best practices and effective programs, we have consistently found a correlation 

between success and strong leadership. Leadership can come in at least two major forms. In the 

first case, it references vision and the determination and ability to move that vision forward. 

Such leadership often extends beyond the school and includes chief school and college 

administrators, civic leaders, and other stakeholder groups, like parent and business 

organizations. Clearly, there was evidence of this dimension of leadership in Coweta County. 

The second type is organizational leadership. By organizational leadership we are referring to 

building-level administration. In the case of CEC, this includes the CEO and the Directors of 

High School Programs, Technical and Career Education, and College Operations. The 

organizational chart presents the reporting relationships of these positions to their respective 

Boards. It’s instructional to note the deliberate use of nontraditional titles for these positions. 

For example, in a traditional high school, the Director of High School Programs would be called 

the Principal. The terminology of “CEO” and “Director” is more consistent with the businesslike culture that pervades CEC. 

At CEC, the CEO holds responsibility for oversight and integration of high school, technical and 

career education and college operations. Applying a matrix management model, he acts as a 

facilitator, building and strengthening connections among business partners, the school district, 

WCTC, parents, state and local political officials, and the community at-large. The CEO is the 

public “face” of CEC. 

 

“Charter school flexibility accommodates the idea of a CEO,” said a staff member. “It makes it 

possible to bring in a businessman with real world experience to run the school.” Indeed, Mark 

Whitlock, the CEO, comes to CEC with a strong background in banking and financial services. 

However, Whitlock is no stranger to the field of education. His undergraduate degree is in social 
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science education and he continues to be well read in the field of education. In addition, he 

played a leadership role in the establishment of a Montessori school serving as a founding 

member and chair of its board. But the comment we heard most in interviews is that he “clearly 

brings a business perspective through his training and practice.” 

The fact that the CEO comes with a business background is viewed as extremely beneficial. “It didn’t 

concern us that the CEO was from business and not education,” said one board member. “He brings a 

healthy new perspective.” Perhaps more importantly, the CEO’s business pedigree helps bridge the 

chasm between public education and the private sector. He’s fluent in both educational and business 

parlance and being from “their” environment, he’s trusted by business partners. 

Outsider and Insider 

When asked to describe the characteristics of good leadership, the individuals we interviewed tended to 

cite strong communication skills, a clear commitment to quality education, the ability to convey that 

vision to others while delegating and empowering staff, sincerity and integrity, and the ability to manage 

multifaceted projects. Though often difficult to embody as effectively as the current CEO has, these are 

fairly standard descriptors of good leadership. Two other characteristics were identified as being 

especially important. First, the fact that “Mark is from Newnan but has been outside of Newnan.” This 

was described as a potent combination since it provides the important objectivity of an outsider with the 

credibility of an insider. 

The second was a conscious commitment to extend a high level of autonomy and respect to the 

administrative directors, with an emphasis on professionalism, that they in turn extend to the 

instructional staff. “You’d be amazed at how far this goes to help compensate for the low pay 

educators have,” confided one staff member. It’s worth noting that the terminology of 

“directors” is applied to the classroom instructor, thereby elevating the professional status 

typically afforded “teachers.” (For the purposes of this paper, we employ the term instructor to 

reference the directors of program instruction, to avoid potential confusion.)

The CEO reports to a board of directors as dictated in the school’s charter. The board meets every other 

month to conduct strategic planning and reflect on progress. The board considers and advises on issues 
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such as student attendance, busing schedules, tracking outcomes, resource acquisition and distribution, 

communications, and marketing. CEC recognizes the need to generate parent support and include 

parental input when setting policy. A majority (9) of the seats on the 17-member board are held by 

parents, with four held by business representatives and four by educators, features mandated by the 

charter. In addition, the school board reviews the CEC curriculum at regular intervals. With the charter 

ultimately awarded by the State, the CEO encounters three layers of audit via the CEC board, the Coweta 

County School Board, and the Department of Education for the State of Georgia. Additional layers of 

audit—through the West Central Technical College and its governing Board as representatives of the 

State’s Department of Technical and Adult Education—stem from the design of CEC to seamlessly 

integrate secondary and postsecondary education.

SEAMLESSNESS BETWEEN HIGH SCHOOL AND TECHNICAL COLLEGE

“During my junior year of high school, I took advantage of a Health Occupations 

program offered at Central Educational Center or CEC as we call it. Believe it or not, I 

graduated with a technical college certificate before I even graduated high school! Most 

importantly (to me and to the economy of my community) I am able to work as a 

Certified Nursing Assistant in a nursing home or hospital.” Jamie Rodriguez, Class of 

2002

CEC itself physically houses a high school and technical college under its roof. As a founding 

partner, West Central Technical College sought a physical presence in Newnan to serve its 

historically older population of adults wanting to enhance their employability skills and gain 

industry-recognized certification. The co-location of WCTC on a high school campus provided 

the opportunity to deliver an extended array of dual-enrollment options to a younger population. 

As a result, 52 percent of students enrolled in West Central at CEC are under the age of 25. Due 

to the influx of CEC graduates at other WCTC campuses, the average age of technical college 

students is declining overall. 

Dual enrollment provides an excellent mechanism to create smooth, “seamless” transitions between high 

school and higher education. This is a key feature of CEC and one highly touted at the state level by the 

governor and others. On a practical level, dual enrollment offers high school students the opportunity to 

obtain their diploma and a certificate of credit simultaneously. As a result, students find themselves better 
255

positioned to participate in the labor market immediately after graduation and/or make thoughtful 

decisions with respect to postsecondary education. 

The powerful social and psychological effects of dual enrollment should not be ignored. Instructional 

staff reported that having high school-age and adult-age college students in the same class has a positive 

effect on the younger students’ maturity. Graduates of CEC commented on the value of having 

opportunities to interact with adult students while still in high school, claiming an increased comfort level 

in the adult workplace and an increased sense of self-confidence that is not generally derived from peer 

interaction. 

COURSES, CERTIFICATE PROGRAMS, AND CURRICULUM

“CEC doesn’t treat its students as just kids. They are team members and are treated as 

such. The learning environment at CEC emphasizes character, work ethic, and how to 

make it in the business world today. By offering hands-on educational experiences, CEC 

is meeting those standards while paving the way for the future of education.” 

CEC Graduate 

Through an initial needs analysis, local business and industry helped identify the major areas of 

concentration. Programs of study are organized under four broad career paths: Business and Computer 

Information Systems; Health and Medical; Technology and Engineering; and Services. 

According to the Director of High School Programs, “Every course in the CEC curriculum responds to 

needs in the local labor market.” Students can choose from programs that range from high tech (e.g., 

computer repair, computer networking, and CAD) to construction and production (e.g., certified 

manufacturing specialist, machine tool technology, and metal joining) to health care (e.g., dental 

assisting, patient care assisting), travel and tourism, and broadcasting. 

With respect to curriculum development, teachers and central office curriculum developers sit 

down with representatives from business who serve as subject matter experts (SMEs) to identify 

skills, knowledge, attitudes, and behaviors needed in the workplace. The curriculum is built 

around those parameters. An important feature of this reconceptualized education is the ability 

for business representatives to inform, design, and help deliver the curriculum. To ensure that 
256

classrooms are adequately equipped, discussions with employer partners include identification 

and acquisition of state-of-the art equipment and technology. 

Through open lines of communication with the community and through the more formalized role of the 

SMEs, business and industry have the opportunity to guide and influence curriculum development. 

Subsequently, if a program no longer aligns with labor market demands and does not have minimal 

placement, retention, or graduation rates, that program can be eliminated.

In addition to academic grades, students receive a “work ethic grade” comprised of scores along ten 

factors or themes. These themes rotate on a weekly basis. Productivity was the theme during the week of 

our visit and a large banner with the word “PRODUCTIVITY” in big bold letters hung in the main 

corridor as a prominent reminder. All instructors are expected to work the themes into their curriculum 

and lesson plans. The work ethic grade does not currently appear on students’ high school transcripts. In 

the future, the district would like to see all high school classes give both academic and work ethic grades, 

a strategy we would endorse. 

Even administrative concerns like attendance are used to convey an understanding of workplace 

expectations. For example, absenteeism and tardiness are not considered behavioral problems, 

but performance related. At CEC, the thinking is that if you’re not there, you can’t learn. This 

again mirrors the business model—if you are not at work, you can’t do your job. A point reward 

and deduction system linked to students’ class participation grade is used to drive the concept 

home. 

Much of the curricula is self-directed and self-paced, a feature that reinforces the need for selfdiscipline. “I had access to a teacher when I needed it, but actually I finished ahead of schedule. 

That allowed me to do extra stuff,” explained one student proudly. 

Research on work-based learning, offered at CEC through internships, simulations in labs, or 

paid work experiences, has shown that it helps students acquire general workplace competencies; 

explore and plan careers; and acquire knowledge and skills in particular industries. But it also 

creates another level of learning for the student, one that engages them in the learning process. 

Students report that, “The whole school has a different atmosphere. You’re not a student at your 
257

desk all day.” As expected, the work-based learning and project-based learning approaches have 

made a strong impression on the students. “Hands-on is just great,” one alumnus beamed. She 

highlighted the emphasis on developing products rather than testing while another commented on 

her performance-based assessment in front of a professional RN. Both underscore the fact that 

performance is what gets measured in “the real world.” 

STUDENT ENROLLMENT AND DEMOGRAPHICS

“Most high school students just want to get out. The students at CEC are more 

motivated. They’re working for a job or preparation for college. They act more grown 

up.” CEC Graduate

One student described CEC as “a full-service educational hub.” Indeed, CEC offers a rich array 

of educational services to a diverse student population. CEC serves students in both high school 

and technical college. Some of the high school students are dual-enrolled in technical college 

certification programs. In addition, adult students prepare for their GED in evening courses and 

high school students in need of remediation and course make-up attend evening high school. 

Lastly, the well-resourced school offers local employers the opportunity for off-site training. 

The FSU study referenced earlier focuses on the dual-enrolled population. Our interests are 

broader, but still generally limited to the high school and technical college programs as the 

demographics presented below attest. 

CEC Enrollment Count August 2002 

High Schools AM PM Totals % of Total Distance from CEC

Newnan 186 146 332 39% 2 miles 

East Coweta 226 139 365 43% 10 miles 

Northgate 102 57 159 18% 10+ miles 

Totals 514 342 856 100% 
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CEC draws students from across the three high schools in Coweta County and the school’s 

enrollment mirrors that of the county. About 28 percent of the students identify themselves as a 

racial minority. As a charter school, CEC is not allowed to establish admissions requirements, 

however, individual programs can require specific criteria for enrollment. Transportation to and 

from CEC is not an issue. About 85 percent of the students drive to CEC. The remaining 15 

percent take school buses from their base high schools. 

Since opening their doors in the fall of 2000, CEC has witnessed substantial growth as depicted 

in the chart below. 

Last year (2001-2002) about 10 percent of the student body attended CEC for the full day, but 

that proportion increased to about 13 percent this year. In 2001, CEC graduated 96 high school 

students with technical college certificates of credit (TCCs) from WCTC (58 percent female, 29 

percent minority). This past spring (2002) CEC graduated 128 students with at least one TCC 

from WCTC. That number, too, is expected to increase. In the first semester this year, 63 

students are dual enrolled already. 

In addition to the 63 dual-enrolled high school students, WCTC enrolls 312 adults. Over half of 

these students are well under the average age of typical technical college students. About 24 of 

these young adult students attend classes with the dual-enrolled high school students while the 

remaining 288 attend night courses through WCTC. 

CEC Program Growth 

 Total Students Unique Individuals 

Program Year School Year per Semester Served per Year

1st year 2000-2001 400 650 

2nd year 2001-2002 800 1246 

3rd year 2002-2003 856 TBD 
259

Approximately 190 adults attend evening classes generally focused on GED test preparation, 

while 72 high school students attend evening high school, with 80 percent taking classes in order 

to stay on track for graduation with their class. 

FACULTY 

According to the students, the instructors at CEC are among the best they ever had. Some 

qualities mentioned include: knowledge of the subject matter, personally involved with students, 

with a knack for making learning fun. The students felt they were treated more maturely by their 

teachers, “like we’re adults,” said one female graduate. Not surprisingly, students tend to react 

positively to such treatment. In turn, the students tend to have more respect for their teachers, a 

benefit not lost on the teachers themselves. 

There appears to be growing consensus that the ideal teacher has some real-world industry experience. 

Indeed, applied experience can be a real asset in the classroom. It allows instructors to link academic 

concepts to occupational applications. In addition, students seem to respond more favorably to those who 

have been in the real world. “I have a lot more credibility with the students,” said a former plant 

manager. 

It’s safe to say that the staff come from diverse backgrounds. Charter school status affords CEC the 

luxury of recruiting staff who might not hold conventional teaching certification. While some staff have a 

combination of academic and applied experience, others come directly from the world of business or the 

military. Still, the majority of instructors appear to have conventional teacher training and traditional 

teaching backgrounds. What makes instructional practice at CEC unique is that staff have the freedom to 

approach education in a non-traditional way. The climate of the school, the direction from leadership, 

and the school’s charter status encourage this flexibility. “The rule here is to be different,” said last year’s 

teacher of the year. 

Given CEC’s career development focus, career guidance and career development are integrated 

into the culture of CEC. That is to say, guidance is not simply the counselor’s role, but rather a 

responsibility shared by all. “Every Director is a counselor,” says the CEO. In turn, the 
260

counselor’s role is evolving to a managerial position focusing on course selection and credit 

attainment. 

It was suggested, half-jokingly, that the staff was “hand picked.” In truth, many of the teachers 

who joined the CEC staff during the first year volunteered for the position. However, now, as 

CEC adds new staff, administrators are seeking out people who are creative—who are still 

motivated and energized by the job. 

Likewise, efforts were made to identify appropriate college staff that would thrive in the 

CEC environment. To support the recruitment process, administrators brought teachers from 

other West Central campuses to tour CEC facilities. 

THE STUDENT EXPERIENCE

“What really stood out for me? I thought it was going to be easier. I didn’t think it was 

going to be as serious with respect to job building and career building. I just thought I’d 

have something to put on my resume. I have so much more now.” CEC Alumnus 

As mentioned earlier, the FSU evaluation is concentrating on instructional practice and student-level 

outcomes. While it would be premature to present detailed statistics at this point in time, several early 

indicators point toward success. 

For example, since CEC began operations, the annualized drop-out rate, grades 9 to 12 in 

Coweta County Schools, has dropped from around 7 percent to around 3 percent. 

According to graduates, “Students [at CEC] are more mature, more disciplined. And if they’re not, they 

will be when they graduate.” A good mechanism CEC uses to promote this trait is the emphasis placed 

on performance-based assessment. For example, students in the Health Occupations program 

demonstrate the skills they have learned under the critical eye of a registered nurse. As one student 

acknowledged, “I was nervous, yeah, but afterward, I felt more pride than if it had simply been a test.” 
261

WCTC administrators suggested that students experience a boost to self-esteem when 

they realize that they can perform at the college level. Eighty percent of students in the Class of 

2001 graduating with a technical certificate indicated a likelihood that they would pursue 

postsecondary education and anecdotal evidence bears this out. The local average in Coweta 

County is about 40 percent. What accounts for the large difference? Like the WCTC 

administrators, FSU researchers attribute the high proportion to the familiarity CEC students 

gain with college expectations and environment and a newfound confidence that they can 

perform at the college level. Other contributing factors likely include the benefits of smaller 

learning communities and a more positive outlook toward education. At the same time, it is 

possible that a more motivated student is attracted to the CEC program.62

Regardless, CEC is having an impact. One student described her experience as follows: “Thanks 

to CEC and its business partners, I have a great start on my future. They have opened the door 

for me to launch my career as a physical therapist. I am one of many set apart from the average 

student, and I have gained a greater sense of professionalism, self-esteem, and purpose.”63 

CEC As Partnership 

In Coweta County, CEC is more than a school—it’s a partnership. The relationships that key stakeholder 

groups in Coweta County formed to develop the CEC concept, turn it into reality, and, now, provide 

ongoing support to operations, strategic planning, and growth are a vital part of CEC. 

Over the past decade, NIWL has examined dozens of partnerships between schools and 

businesses, primarily through intensive case studies. From coast to coast and from the most rural 

areas to the most urban, it has been our experience that strong and meaningful collaboration and 

partnerships among a wide range of groups in a community are necessary to create meaningful 

change in educational practices and strategies. Formalized partnerships provide stakeholders 

with clear goals; a concrete management and governance structure; clearly delineated roles and 

responsibilities; networks to facilitate communications; and mechanisms to broker connections 

 

62 See also FSU Study. Report 1, July 2002. Additional reports forthcoming. 

63 Excerpts from an essay by Jamie Rodriguez, CEC Class of 2002. Excerpted with permission of the author. 
262

and ensure fairly precise coordination of activities. Through partnerships, stakeholders function 

as one unified team in accomplishing common goals. 

With business involvement contributing an estimated $2.4 billion and 109 million volunteer 

hours to schools each year, these partnerships have an impact on the lives of an estimated 35 

million students.64 We have observed growing evidence that today’s relationships between 

schools and business are notably different from those of the past which resulted in one-way 

philanthropic efforts from businesses to schools, or commercial relationships in which schools 

provided a marketplace for the sale of products and services. Today’s thoughtful educators and 

business leaders seek true partnerships that build on a shared understanding of values and culture 

that support mutual needs. 

A Meeting of the Minds 

 

We truly believe that in order to effect change, schools need support and therefore must rely on 

resources—human, financial, and material—from outside the school walls. But establishing the necessary 

relationships and trust to build truly effective partnerships is much easier said than done. Who were the 

key stakeholder groups that came together to form the CEC partnership and how did the partnership come 

about? 

The School District. While the community in general was satisfied with the quality and rigor of 

the college preparatory programs offered at all three of the county’s high schools, only a fraction 

of the county’s high school graduates were going on to postsecondary education, and even fewer 

were finishing with degrees. With 65 percent of the jobs in the county requiring special skills or 

technical training, district administrators knew they needed to improve career and technical 

education programs so that students not going on to postsecondary education would have viable 

and meaningful alternatives. In order to offer the kind of high tech career and technical 

education programs that would improve postsecondary outcomes for “middle of the road” 

students, the district would need to make major investments in equipment and facilities at the 

 

64 Lamar Alexander and Richard W. Riley, “A Compass in the Storm.” Education Week, October 9, 2002.
263

new high school and revamp programs at the two existing high schools. Administrators began 

exploring possible strategies for consolidating the career and technical education programs 

offered across the county under one roof, with the goals of strengthening programs and 

eliminating the duplication of costs and efforts. 

Postsecondary Involvement. West Central Technical College had been trying to establish a 

presence in Coweta County for years. It had been “borrowing” space at local high schools in 

order to offer night classes but wanted to provide more flexibility in scheduling and maybe reach 

a younger population of students. It, too, was looking for a base for activities in Newnan. But 

college administrators wanted more than just a site for classes, they wanted to develop a business 

and industry partnership to inform course development and ensure that programs were truly 

responsive to local labor market needs. 

Community Leaders. At the same time, local real estate agents were having a hard time selling 

commercial real estate in Coweta County and were just beginning to realize that a shortage of 

skilled workers and limited training opportunities might be to blame. Faced with the possible 

loss of one of the community’s largest employers, business leaders, educators, and 

representatives from local government formed 21st Century Coweta, an economic development 

initiative. Led by Scott Frederick, 21st Century Coweta provided a framework to discuss what 

might be done to encourage existing businesses to stay and others to relocate and invest in the 

county. They began developing Vision 2020, a plan for supporting “smart growth.” 

Employers. Don Moore, the plant manager at Bon-L, a major manufacturer in Newnan, noticed 

that many of his employees—even the ones that had graduated from high school—had limited 

academic proficiency. While he was confident that his company had the subject matter expertise 

to successfully train people in the technical skills needed to work in the plant, he was hesitant to 

get into the business of providing whole-scale remediation in the areas of reading and writing. 

He felt that the overall lack of basic skills evidenced by his workforce was a symptom of a much 

larger systemic problem that would be more appropriately addressed by the local school system. 

At Chamber of Commerce meetings, other employers were voicing similar concerns. In 

addition, some small business owners were complaining that their employees would often miss 
264

work in order to meet with their children’s teachers to discuss problems at school and that this 

was having a big impact on operations and productivity. Business leaders knew that in order to 

solve their problems something would have to be done to change the way education was 

delivered in Newnan, and they wanted a seat at the table. 

On behalf of the employer community and in the spirit of enlightened self-interest, the plant 

manager at Bon-L approached the superintendent of schools with his concerns and a partnership 

was formed. A twenty-person committee with representatives of all of the key stakeholder 

groups—the school district, WCTC, local government, business, and industry—met to identify a 

common solution to their unique but related problems. Because the superintendent had also been 

grappling with the need for change, he was very receptive to the idea. 

 

ACTIVE EMPLOYER INVOLVEMENT

“The commitment of the employer community in Newnan extends beyond buying 

 donuts and t-shirts.” School District Administrator

The business community is often credited with being the catalyst for the partnership, with 

residents pointing to the plant manager at Bon-L as the initial driver of change. When the 

steering committee needed to build support for CEC at the state level and generate the additional 

funds that would be required to renovate and expand their proposed site, the business 

community, through the Chamber of Commerce, was instrumental in lobbying efforts. Chamber 

members successfully promoted CEC to the governor and general assembly, requesting support 

and calling in favors. 

Since CEC’s inception, the Chamber of Commerce has been a vehicle for sharing information 

about CEC with its members, generating interest, and coordinating employer involvement. In 

addition, a number of local employers sit on CEC’s board of directors and advisory bodies, while 

others donate equipment, lend subject matter expertise to the curriculum development process, 

and provide young people with access to work-based learning opportunities. To date, over 150 

local employers have provided CEC students with work-based learning opportunities. 
265

There is a growing awareness among business leaders that their active involvement in 

educational reform is essential for their own survival and growth. Indeed, the number of schoolbusiness partnerships is on the rise. Ed Rust, Jr., Chairman and CEO of State Farm, has stated 

that “business leaders who are not actively involved in education are short sighted.” Under the 

No Child Left Behind legislation, the role of business is probably more crucial than ever before. 

However, it has been our experience that it takes the combined efforts of a diverse cross-section 

of individuals and groups, including educators, community-based organizations, parents, and 

students, to improve education and employment outcomes for young people. Each of these 

groups contributes a distinct set of talents and resources to partnership activities. 

SEAMLESSNESS REQUIRES INTERINSTITUTIONAL RELATIONSHIPS

 

We were frequently told that CEC would not exist without the support of the business 

community. While this is true, it was our observation that CEC’s existence is actually the result 

of commitment from the highest levels of leadership on many fronts. It is clear that in order to 

implement an initiative like CEC, the business community needs to be on board to drive change, 

and its ongoing support is vital to sustain and develop further efforts. Still, we feel it is 

important to note that each of the major stakeholder groups was equally important in the 

development of CEC, and their continued involvement will be essential for future success. 

For instance, what would have happened if the superintendent and school board hadn’t been 

willing to work with business and industry on addressing their concerns? Business leaders 

would have had to find alternative solutions, which may have included relocation. Across the 

country, businesses and schools are constantly talking about the need to work together. In 

Coweta County, the superintendent was not only willing to hear the concerns of the business 

community, he really listened. Then he took action so that the outcome was more than just an 

amicable but meaningless dialogue. The school district’s involvement made it possible for West 

Central Technical College to connect with a much younger population of students. Further, with 

the school district’s endorsement, parents were confident that CEC programs would be 
266

academically rigorous and that their children would not be left behind if they were to enroll in 

CEC. If the current superintendent didn’t understand the links between education and economic 

development and firmly support CEC’s efforts, the partnership’s activities would have been 

derailed, if not stopped entirely. 

Further, it would have been impossible to change classroom practice if teachers hadn’t been 

willing to broaden their views on the purpose of education and how it should be delivered. 

According to one teacher, “If we can employ and keep graduates on the job, they stay and move 

up with a company. That’s good for everyone.” Another remarked, “It’s key to have people 

from business who are very visible and very involved. It helps kids build a network.” It’s this 

perspective, and a willingness on the part of educators to “step out of their comfort zones,” that 

makes education at CEC different and effective partnership feasible. 

From our earlier studies of partnership efforts between businesses and schools, we found that the 

best programs provide connections to post-high school education and training options. These 

arrangements at once greatly expand the training immediately available to high school students 

and offer them a ladder of opportunity toward progressively more advanced training and 

advantageous employment after high school. In Coweta, if the business community and school 

district had decided to move forward without the assistance of WCTC, they might have been able 

to strengthen vocational programs across the county, but they wouldn’t have been able to provide 

students with a seamless transition from secondary to postsecondary education. 

Because WCTC and CEC’s secondary vocational programs are housed within the same facility, 

students can earn technical certificates while still in high school and have an opportunity to get a 

head start on the next phase of life regardless of whether that next phase involves a four-year 

college or university, further technical training, community college, or the workplace. Upwards 

of 80 percent of high school students who are also dual enrolled in WCTC through CEC pursue 

additional postsecondary education and training—twice the local average.65 Administrators, 

faculty, and staff suspect that dual-enrolled students experience a “boost in self esteem” when 

 

65 FSU Study. Report 1, July 2002. 
267

they realize that they can perform at the college level and the prospect of college seems less 

intimidating. The partnership with the technical college makes it possible to attract students who 

might otherwise become disengaged from school and either drop out or squander their senior 

year.66 

Nobody’s Feathers Have Gotten Ruffled 

When asked why CEC partners had been able to build such a strong partnership, one observer 

noted, “The funny thing is, there were two things that didn’t happen. First, there were no turf 

battles. Second, partners didn’t mind spending the time to ‘do it right.’” The CEO of CEC 

suggested, “We don’t have all of the answers, but we have the desire to figure it out. We run into 

barriers, sure, but we figure out how to overcome them...” 

One of the most unique aspects of the CEC partnership is the way that partners recognize the value of 

collaboration and work together without feeling the need to establish “territory,” take credit when things 

go as planned, or point fingers when they don’t—characteristics that are almost considered endemic to 

partnerships in general. As a result, everyone was eager to contribute, but no one felt the need to control 

partnership activities. Each partner had an area of expertise and took on corresponding roles and 

responsibilities. At the same time, partners felt empowered to define strategies and policies that they felt 

would better serve students and translate them into action. Partners were accountable to one another, and 

with egos in check, seemed ready, willing, and able to jump in, but also to cede control when necessary. 

A second aspect of CEC that distinguishes it from other partnerships we have studied is that partners 

seem to understand that they are making a long-term investment and that change will take time. They 

made sure not to rush the development phase. In fact, the original committee spent approximately three 

years developing the concept, identifying and generating resources to support it, and designing the model. 

Because stakeholders were empowered to be active partners in supporting systemic change from the very 

start, rather than passive contributors, all of the partners have made considerable investments in CEC, and 

all are strongly committed to seeing it succeed. We were told that, “Nobody’s feathers have gotten 

ruffled. That’s because people’s expectations have been met. They’re being met because all of the key 

players are at the table. They’re actively involved.” 

 

66 For more on the importance of restructuring the senior year see, Raising Our Sights: No High School Senior Left 

Behind. The National Commission on the High School Senior Year, October 2001. 
268

We feel that one of the partnership’s most outstanding accomplishments was its ability to merge the silos 

that traditionally separate K-12 and postsecondary technical college systems. Across the country, 

separate accountability systems established at the federal and state levels have created unintentional 

barriers to joint efforts. It has been extremely hard for people responsible for overseeing money for 

education and training to merge funds into a common pot. Key players in the CEC partnership were able 

to stay focused on the “big picture.” In order for this to be possible, partners needed to really trust one 

another, and a lot of energy was devoted to building the necessary trust ahead of time. 

THE POWER OF WORKING TOGETHER

“CEC demonstrates what can happen if we all work together.” Greg Wright, 

President, Newnan-Coweta Chamber of Commerce 

The president of the Coweta County Chamber of Commerce observed, “If we can do this as a community, 

there’s nothing we can’t do. Now that we see the success of this effort, it will lead to future 

collaborations.” Interestingly, the success of the CEC partnership is already having carryover effects in 

the community. For instance, employers have started supporting the local Boy’s and Girl’s Club and 

some have become involved in adult literacy programs. Bon-L recently gave dictionaries to all third 

grade students in the county in the hopes of enhancing the academic rigor of programs at the elementary 

school level. Finally, and perhaps most significantly, the school district and business community are 

jointly developing a large plot of land in the county, soon to be the home of the school district’s new 

amphitheater and the county’s new convention center. 

During one conversation, the CEC model was described as “a grassroots movement,” a fairly 

astute observation. In the beginning, everyone came to the table with a slightly different agenda. 

People shared their unique problems but solutions were developed in the sense of moving the 

group’s overall agenda forward. Over time, people formed a shared vision and began working 

toward the big picture. Partners were not just involved in the creation, they continue to be 

involved in ongoing operations. Further, partnership makes sense—when communities lose 

businesses, they lose their economic base, then there’s no money to support schools, no money 

for taxes, and no money for goods and services. The school district had a vested interest in 

finding a way to make people and businesses productive. At the same time, employers realized 
269

that not only do they depend on schools to provide them with a trained and well-prepared future 

workforce, good schools mean that employees aren’t worried about their kids’ education and can 

focus on doing their jobs. 

Finally, from a teacher’s perspective, the partnership makes it possible to tap into other resources 

in the community to strengthen classroom content and delivery. One teacher observed, “As an 

educator, you need to work closely with business and industry. Education moves so slowly that 

without business input in the form of equipment, facilities, and expertise, you can’t provide a 

good education.” Another echoed this sentiment, saying, “I don't have to learn it all or to deliver 

it all. I can't keep up on all the technology, the subject matter, all by myself. I need to 

collaborate with business partners and students, and the local community, parents, all these nontraditional avenues. You can't just do that anywhere. But I can do that here.” 

CEC As Engine of Economic Development 

“While CEC is bringing many desired results to the local school system—almost singlehandedly lowering the county’s dropout rate from 7 percent to 3 percent in its first full 

year of operation—CEC is having an even more dramatic effect on the interaction 

between the school system and the county’s economic development efforts.” Russ Moore, 

Business Owner, Consultant, Chamber of Commerce Member 

The success of our schools in graduating successful students is directly related to the success of our 

communities. The more economically prosperous our communities, the stronger the tax base backing our 

school systems, which in turn offers more resources to provide a quality education. It seems obvious. But 

the relationship is a dynamic one as well. Quality education produces successful graduates with talent 

and skills that local employers can leverage to improve productivity and support economic expansion. 

Yet, for much of the last century, schools operated as independent institutions. Schools receive state 

funding and increasingly must administer standardized statewide tests, but for the most part control rests 

in the hands of local school boards. Businesses have increasingly sought to lend assistance, but that 

assistance, coming in the form of subsidized computer labs and in-kind contributions, tended not to affect 

the instructional status quo. Civic leaders, too, remained at arm’s length. For example, John DeStefano, 
270

Jr., Mayor of New Haven, Connecticut confided, “I’ve been in politics a long time, and before I was 

mayor, I worked for the mayor. And in all my experience, I’d have to say that the view from city hall was 

that education was something that took place over there. It wasn’t really our job, it was the job of 

educators.”67 

RECONNECTING SCHOOLS TO THE REAL WORLD

For their part, educators have done a fairly good job of keeping “outside influences” at arm’s length from 

the schools. Consider the language of our students when they refer to the “real 

world,” the world that exists beyond the four walls of the school. Students clearly recognize a tangible 

distinction between the world of school and the world that surrounds it. The phrase itself makes clear 

which one they perceive as having more salience and relevance in their lives. 

Our educational system, structurally and culturally, has been insulated from external forces of “the real 

world” that threaten to corrupt the academic integrity of the educational process. In the “ivory tower,” the 

entire process of teaching and learning is detached and above the crude and mundane forces of the 

everyday world. Consider the negative connotations that have been associated with vocational education, 

or career education, or more recently, the school-to-work legislation. The pervasiveness of viewing K-12 

education as stepping stones to higher education at a four-year college rather than as preparation for life 

benefits the few at the peril of the many. It is a simple fact that the vast majority of Americans do not 

hold a bachelor’s degree. And the jobs with the highest demand at present require some postsecondary 

training but not a bachelor’s degree. 

We do not mean to imply that the resistance to social and market forces is entirely bad. We suspect that 

the commercialization of the classroom could lead to more negative effects than positive ones, and to 

maintain the mission of public education, quality must be made available to all and not sold to the highest 

bidder. However, protecting the sanctity of the classroom at all costs is not without its own dangers. A 

position that isolates schools from their surrounding communities prevents many positive changes from 

taking place. We don’t advocate tearing down the ivory tower, simply lowering a few of the drawbridges. 

Public schools are not just for the students. They belong to the community. As Joe Harless, chair of the 

CEC Steering Committee, might argue, it seems self-evident that everyone should benefit from them. We 

 

67 These remarks were shared following the October 7, 2002 American Youth Policy Forum session on the role of 

municipal government in promoting educational improvement. Under the leadership of Mayor John DeStefano, 

New Haven, Connecticut has become a model city demonstrating the potential municipal leaders can play in 

supporting schools for the benefit of students and, in turn, local economic development. 
271

believe that schools have an institutional role to play in addition to their role of developing the academic 

growth of their individual students. That role has a strong economic component in the form of workforce 

development, supporting local business and industry, attracting new business, and contributing to a 

general improvement in the quality of life within the community. 

APPRECIATING THE RELATIONSHIP BETWEEN EDUCATIONAL DEVELOPMENT AND ECONOMIC 

DEVELOPMENT

“Strong local economies support better education systems; good education systems, in 

turn, strengthen local economies. School systems that can consistently prepare students 

for today’s—and tomorrow’s—fast-paced, fast-changing workplace help communities 

develop good businesses with good jobs. Those jobs lead to greater prosperity for the 

whole community, which, in turn, leads to better schools. But how does a community get 

this cycle started?” Conference Program on Education and the Economy68

We believe that Coweta County has much to teach us in answer to that question. 

As with all municipal leaders, Mayor DeStefano of New Haven recognized that economic development 

was definitely a part of his job. Somehow, he and his colleagues just never made the connection between 

education and economic development. Newnan Mayor Keith Brady helps make the link. “The goal of 

education is ultimately individualized for each student. But more generally, it boils down to producing 

citizens who can effectively engage themselves in our society. It offers a grounding that allows them to 

be productive.” Mayor Brady emphasizes the importance of embedding teaching strategies that develop 

work ethic, pre-employment skills, and job readiness in the school curriculum. But he closes by saying, 

“we need strategies that produce not just job readiness, but society readiness.” 

In this manner, schools begin to depart from their singular focus on academic achievement and move in 

the direction of Joe Harless’s “accomplished citizen”—graduates who are prepared for the world of work, 

prepared to contribute to their community, and capable of developing a healthy and safe family and 

making informed decisions for themselves. While focused on applied outcomes, much of this 

preparation requires a strong academic grounding. 

 

68 Appalachian Regional Commission Conference Program, October 29-30, 2002, Maryville, TN 
272

To clarify the point, Dr. Harless drew a Venn diagram with two circles, one representing education, the 

other economic development. They overlap to a considerable degree, but not completely, indicating they 

are not completely mutual—and that education serves many ends in addition to economic ones. He points 

to the significant overlap between the circles. “Here, education is key and paramount to economic 

development, for attracting and keeping business. This is manifested through a capable workforce that 

possesses relevant skills, knowledge, and information.” 

WORKFORCE DEVELOPMENT

“Workforce development is education.” Parent of CEC student 

The most obvious and direct relationship between education and economic development is through 

workforce development. While workforce development is an important and explicit role of the technical 

college system, the relationship is less well-established and more controversial at the secondary school 

level. 

On the one hand, educators have long recognized their role with respect to workforce development, 

however, this role was delimited to a narrow band of students typically categorized as part of a separate 

vocational education system. Responsibilities associated with workforce development goals tended to be 

compartmentalized and discrete from the core mission of the school (i.e., college preparation) and those 

students identified as non-college bound typically channeled toward a vocational track. The recent 

School-to-Work Opportunities Act sought to challenge this bifurcation. 

In contrast, a more inclusive approach toward workforce development assumes that it is the school’s 

obligation to help empower all of its students to effectively participate in a global 

economy as world-class workers and citizens.69 Workforce development at CEC falls into two 

broad categories. The first is generalized preparation for the world of work. All students are better 

served by gaining transferable employability skills. The second is specialized training for specific 

careers. Through specific skill development, certification, and credentializing, graduates are better 

positioned to launch fulfilling and lucrative careers. 

 

69 One state that has made considerable strides in this direction is North Carolina. “Workforce Development 

Education,” begins with exploratory courses in grade 6 and leads to specialized classroom instruction in grades 11 

and 12. Programs in Workforce Development are not compartmentalized but rather integrated into the broader 

curriculum. Recent statistics indicate that nearly 70 percent of all students enrolled in grades 9-12 took at least one 

workforce development education course. These courses are designed to contribute to the broad educational 

achievement of students, including basic skills such as reading, writing, and mathematics as well as enhance their 

ability to work independently and as part of a team, think creatively, solve problems, and utilize technology.
273

By extending the goals of workforce development down into the secondary grades, especially in 

collaboration with community and technical colleges, some significant benefits to both students and 

community stakeholders are gained. Through career guidance, career preparation, college guidance, and 

more efficient alignment of personal skills and organizational needs, students gain a better appreciation 

for the relationship between education, training, and employment. In short, it helps to tighten the linkages 

between school, college, and the workplace. 

A review of the many statements expressed during our interviews in Newnan reflects a similar 

orientation. It is noteworthy that this orientation is shared by both those within and outside the school 

system. A curriculum developer told us that “CEC is a dynamic place that is responsive to its customers. 

This includes the students but extends to the private sector as well. CEC can be envisioned as a 

mechanism for workforce development, anticipating and addressing the workforce needs of local business 

and industry.” 

A technical college instructor put it this way, “Education at CEC prepares students with the skills 

to go right into work. Businesses have a larger pool of potential employees to draw from. Kids 

who aren’t ready to go to college can test a career path out.” 

Effective workforce development requires education to be responsive to local labor market 

conditions. Community and technical colleges have more experience in this regard than do high 

schools. The partnership with postsecondary education and local business, therefore, is critical. 

A high school instructor stated that “CEC programs quickly match up employer needs with the 

education and training of the labor supply. As a result, it reduces the training time employers 

need to provide, thereby increasing efficiency.” 

Perhaps the Commissioner of Adult and Technical Education summed it up best. “Our state is 

interested in competing for high-skill/high-wage jobs. In that competition, education is 

everything.” 
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SUPPORTING LOCAL INDUSTRY

“If we can employ and keep graduates on the job, they stay with company, they move up 

within the company. That’s good for everyone.” CEC High School Director 

Developing the basic and applied skills of young people equips them for productive careers and selfsufficiency. However, the advantages of workforce development at the high school- and technical 

college-level translates into some very powerful benefits for local industry as well. It is worth noting that 

today’s business leaders have a deeper appreciation for the value of an educated and well-prepared 

workforce. The early experience of CEC helps bear this out. Russ Moore, a local businessman and 

Chamber member, explains this story best. 

“The traditional approach for cities and counties to recruit and retain large manufacturers 

is for them to cooperate with states and offer significant incentive packages, often 

including breaks on taxes and free land. Recently, Coweta County was faced with the 

reality that its largest manufacturer was considering relocating and was actively being 

courted by other Georgia counties and several states. The situation was not helped by a 

formal policy against offering incentive packages to industries. 

“The public-private entity [21st Century Coweta County] that was working most closely 

with the manufacturer [Yamaha] to keep it in Coweta had its hands tied until CEC stepped 

up to the plate with an offer to establish a lab that would train students using the 

manufacturer’s actual equipment and products. This lab would guarantee the company an 

affordable way to recruit and train its own workforce locally. 

“CEC’s offer was enough to keep the manufacturer in Coweta. In fact, the company 

decided to build a $40 million expansion and create an additional 300 jobs. The fact that 

the economic impact of Yamaha’s decision to stay and expand is many times greater than 

Coweta’s investment in CEC makes the educational center something of a catalyst.” 

CEC was the deciding factor to keep Yamaha and encourage them to grow. Education and 

training in the service of workforce development was seen as more valuable than other incentives 
275

like deferred taxes, lower property taxes, even free land. Strong educational systems can be a 

tremendous asset, since today’s workers have to be skilled to a greater and greater degree. And 

if training is done prior to employment, the employer can experience a tremendous savings in 

time and money. 

Other communities have discovered the power of thoughtful school-business partnerships in 

retaining major employers. Louisville, Kentucky almost lost UPS before developing Metro 

College, an outgrowth of their school-to-work initiative. Understanding that employee 

recruitment and retention had been an obstacle for UPS, city and state officials along with local 

education administrators pooled their resources to begin an education program that would 

produce the workforce needed to operate its new mega-hub and keep UPS in Louisville. 

“Through CEC, we have a real articulation between private sector needs and educational 

delivery,” stated an educator. “There’s a real connection between the two.” A business manager 

offers the following perspective. “From an economic development point of view, CEC moves us 

to the next level of flexibility. We can customize and create curriculum on an as-needed basis. 

We can be targeted. You can’t turn the whole school system around overnight; it’s like a big 

ship. CEC affords a measure of maneuverability.” 

ATTRACTING NEW INDUSTRY

Coweta County possesses a number of cultural and geographic advantages that help attract 

business. It is easily accessible via the interstate highway system, it is within an hour’s drive to a 

major airport, and it boasts a population of hard-working, conscientious people that are 

incredibly invested in giving back to the community. Yet a lot of communities within a fiftymile radius of Atlanta tend to promote the same things. One of the more exciting dimensions of 

a reconceptualized educational system is the role it can play in helping to attract new industry. 

Initiatives like CEC make Coweta County unique. 

It is now common for communities to tout their educational resources when attracting new residents and 

businesses. The following example from Virginia is a case in point: “The Hampton Roads region 
276

contains a greater concentration of learning institutions than any other area of Virginia. Whether you're 

looking for a prestigious graduate program, training for employees, or a good school for your thirdgrader, Hampton Roads provides a long list of options.” 

What is less common, but becoming recognized as a viable economic development tool, is the 

active partnership between educators, the private sector, and regional economic development 

entities to strategically position and reconfigure education. Much in the same way communities 

develop their physical and technological infrastructure, Coweta has decided to develop its 

educational infrastructure to offer potential new businesses and industries a more attractive 

environment—in this case, an environment characterized by a reconceptualized approach to 

education, training, and workforce development. 

Smart Growth 

Coweta County has given more thought to what type of industry it wants to attract and in turn seeks to 

create the characteristics those industries are going to look for. As a small business owner stated, “People 

are trying to bring a higher tech focus and clean industries into the community. Education can supply 

employees to work in these jobs.” Proactive approaches to economic growth such as this stand in 

contrast to traditional ones of identifying what features a community has, advertising those, and accepting 

whatever businesses that attracts. 

Local leaders recognized that the existing labor market in the county would not support highwage, high-tech employers. This placed Coweta at a disadvantage with respect to its economic 

development goals. By providing a platform of operations for West Central Technical College 

and linking its certificate programs to high school education, Newnan, and Coweta County more 

generally, address this concern. As a manager remarked, “CEC makes us appear more 

sophisticated. More importantly, CEC is a practical solution to this problem.” 

DIRECT AND INDIRECT BENEFITS 

In order to fully appreciate the impact education can have as an engine of economic 

development, it is important to recognize that the community gains through both direct and 
277

indirect benefits. The most obvious benefits are the direct ones—especially the ones that accrue 

to the students and the graduates. As we saw above, CEC students value the innovative 

educational experience, the demonstrable gains in skill, and the employer-recognized credentials 

they earn. Indeed, the student effects of educational reforms that embrace the principles of 

school-to-career are becoming increasingly well documented.70 

In our own work that tracks the educational and career trajectories of high school students, we 

have found that participants in career development programs, in contrast to their nonparticipating peers, tend to pursue postsecondary education at higher rates; maintain good 

grades; report having been better prepared for the transition from high school to college and 

employment; take more tangible steps toward achieving their career goals; and report earning 

higher wages.71

Of course, companies that hire the graduates of these programs experience direct benefits as well 

in the form of improved productivity, reduced turnover, and savings with respect to training 

costs. Our work has uncovered additional benefits for firms that actively partner with schools 

and colleges. For example, partnering firms reported higher levels of morale among employees, 

increased levels of communication between management and labor, reduced recruitment costs, 

and improved corporate image within the community.72

However, our visit to CEC and Coweta County drew our attention to an entirely new range of 

benefits that often go undocumented in typical studies. Individuals we spoke with, from 

education, business, municipal government, and economic development alike, referenced what 

can only be termed indirect benefits. These include the production of capable and involved 

 

70 See, for example, K.L. Hughes, et al. (2001) School-to-Work: Making a Difference in Education. New York, NY: 

Institute on Education and the Economy, Teachers College, Columbia University. 

71 See MacAllum, K., et al. (2002) Moving Forward: College and Career Transitions of LAMP Graduates. 

Washington, DC: Academy for Educational Development, and Bozick, R. and MacAllum, K. (2002) Does 

Participation in School-to-Career Limit Students’ Educational and Career Options? Journal of Career and 

Technical Education. Vol. 18, No. 2. 

72 See MacAllum, K., and Charner, I. (2000) Beyond the Success of Students. Washington, DC: Academy for 

Educational Development. 
278

citizens, the secondary effects of industry staying and new businesses arriving, increased 

property values, reduced brain drain, enhanced quality of life, and a genuine sense of civic pride.

The importance of these indirect benefits came through clearly during a conversation with the 

managers at Newnan Utilities, a strong supporter of CEC, when they stated that “we’ve hired 

only one or two CEC graduates. But CEC is nevertheless critical to us, because we need 

companies like Bon-L to be happy, and to stay, since they’re big customers of ours. For our 

organization to thrive, we need a vital economic environment.” 

In a conversation with a hospital administrator, we learned that the major workforce development issue 

facing the healthcare industry is a severe nursing shortage. While CEC offers a Patient Care Assisting 

certificate program, the school is not producing registered nurses which would directly address this crisis. 

“Some of those graduates might decide to continue on for their RN and that would be great,” said Steve 

Anderson of Emory Peachtree Hospital. But the real benefits that Mr. Anderson focused on were more 

indirect in nature. 

“As a hospital, we’re a catalyst for economic development and a recipient of it,” he explained. “As 

people move in and new company’s open up, those people will need healthcare, and we will have more 

folks to serve. Economic development is necessary for a robust healthcare system, otherwise we can’t 

afford to serve our population. And as we grow, we create more jobs, including those considered high 

tech and high skill.” 

The Newnan-Coweta Chamber of Commerce, instrumental in gaining political and financial 

support at the state level for CEC, clearly recognizes the connection between education and 

economic development. The president of the Chamber, Greg Wright noted that initiatives like 

CEC “make a contribution to the entire community, a contribution to the economic health of the 

entire area.” 

An Education Hub To Better the Economy 

It is important to recognize how educational institutions like CEC fit in to what has been termed the 

knowledge supply chain. By producing graduates with marketable skills, schools add value to their 

communities that go far beyond the obvious. A focus on direct and immediate results often obscures the 
279

value of indirect benefits.73 Worse, an emphasis on immediate results can lead to a reduction in 

commitment and support since we rarely do an adequate job of documenting outcomes that are a step or 

two removed from high school graduation, college enrollment, or that first job placement. CEC staff and 

partners embrace a philosophy that encompasses the bigger picture—one that is notable for its forward 

vision. 

Coming to consensus on this philosophy took lots of intentional work and communication. A school 

administrator stated that “we have educated our business community on what it takes to support lasting 

change.” As if to prove her point, a plant manager put it this way: “We have to take the perspective that 

with CEC, we are seeding the field. We’re not looking for major outcomes just yet. Overtime, as fewer 

kids drop out of our schools, more kids will graduate and hopefully stay in Newnan. It takes time to seed 

change.” 

The instructional staff at CEC are not only comfortable with their reconceptualized mission, they 

embrace it. The following quote is fairly typical of responses we received. “Our number one 

goal is to provide a supply of workers for the economy—people who can be productive at their 

jobs. We aim to employ and keep these graduates on the job, staying with the company and 

moving up within the company.” 

One of the most intriguing findings from our interviews in Coweta and elsewhere is how 

positively students react to a reconceptualized educational process that intentionally places 

education in the center of an economic development strategy. When asked how CEC differs 

from her base school, one student’s reply reflects the value from her perspective for tightening 

the linkage between school and work. “Teachers and staff don’t let you slack off. They push 

you. They show you all of your options, you know, in school and in the work world. It’s an 

 

73 Several other indirect benefits were referenced during our interviews. These include: Duplication reduction: 

Because students can earn both secondary and postsecondary credits at the same time, tax payers win since the 

schools are not duplicating services. Strengthened linkages: The new superintendent cited as one of the benefits of 

the partnership the fact that she immediately had a network with postsecondary institutions, and links to financial 

resources, human resources, and the business community. Enhanced quality of life: Employers noted that as 

employees skills go up, they can pay them more. As wages go up and those dollars circulate throughout the 

community, given the multiplier effect, the general quality of life goes up. Carry over to other grade levels: For 

example, an outgrowth of the deliberations surrounding CEC led to smaller classes at the elementary level and lower 

teacher/student ratios. 
280

education hub to better the economy. The kids are graduating with technical credentials and are 

getting jobs. It creates so many opportunities.” 

WIN, WIN, WIN

The advantages gained by graduates of reconceptualized educational initiatives that offer career 

building skill development and seamless transitions to higher education and employment are 

significant. Indeed, the life-long effects on graduates and their families can be profound. 

However, it is important to recognize that initiatives like CEC benefit more than the individual 

students who attend them. 

Such approaches offer employers a means for recruitment and training of new employees, a 

stronger pool of job candidates, upgraded skills for existing employees, and improved retention. 

Economic development agencies and government officials gain as well since the model helps 

ensure the vitality and growth of the local and regional economy by helping employers and 

supporting key economic sectors. The general quality of life within the community is enhanced 

as employment and individual wages go up, smart growth takes place, the tax base increases, and 

economic vitality is robust. To put it simply, “It’s a win-win-win.” 
281

References 

Alexander, Lamar and Riley, Richard W. (2002). A Compass in the Storm. Education 

Week. Vol. 22, No. 6, p. 48. October 9, 2002. 

Bozick, Robert and MacAllum, Keith. (2002). Does Participation in School-to-Career 

Limit Students’ Educational and Career Options? Journal of Career and Technical 

Education. Vol. 18., No. 2. 

Chow, Anthony. (2002). The Central Educational Center: Research Report 1. 

Tallahassee, Florida: Florida State University. 

Harless, Joe. (1998). The Eden Conspiracy: Educating for Accomplished Citizenship. 

Wheaton, IL: Guild V Publications. 

Hughes, Katherine L., Bailey, Thomas.R., and Mechur, M.J. (2001). School-to-Work: Making a 

Difference in Education. New York, NY: Institute on Education and the Economy, 

Teachers College, Columbia University. 

MacAllum, Keith and Charner, Ivan. (2000). Beyond the Success of the Students. 

Washington, DC: Academy for Educational Development. 

MacAllum, Keith, Yoder, Karla, Kim, Scott, and Bozick, Robert. (2002). Moving 

Forward: College and Career Transitions of LAMP Graduates. Washington, DC: 

Academy for Educational Development. 

Moore, Russ. (2002). Georgia’s Central Educational Center: Catalyst for Progress in 

Education and Economic Development. Newnan, Georgia: Author. 

National Commission on the High School Senior Year. (2001). Raising Our Sights: No 

High School Senior Left Behind. Washington, DC: National Commission on the 

High School Senior Year. 

Rodiguez, Jamie. (2002). Student essay submitted to Newsweek magazine. Newnan, 

Georgia: Author. 
282

Low-Income and Welfare Client Priorities: 

Patterns of Earnings and Welfare Receipt for 

Workforce Investment Act Participants 

Peter R. Mueser and David W. Stevens 

This paper was prepared under the Administrative Data Research and Evaluation 

(ADARE) project with support from the Employment and Training Administration, U.S. 

Department of Labor (Agreement K-6558-8-00-80-60, Evaluation Project No. 2). 

ADARE project partners John Baj, Julie Hotchkiss, Christopher King, and Phillip 

Rokicki provided WIA, wage record, and TANF recipient data for their ADARE project 

states. The authors accept full responsibility for the tabulations and text as they appear 

here. No attribution of agreement with this content should be made to any other person 

or organization.
283

1. INTRODUCTION 

The Workforce Investment Act of 1998 (WIA) represented a major overhaul of 

job training services in the U.S, replacing the Job Training Partnership Act (JTPA), 

which had been in effect since the 1980s. It altered administrative structures at both the 

state and local level, mandated changes in the way services were provided to individuals, 

and, perhaps most significantly, required that programs meet newly-designed 

performance standards. All states were required to implement WIA by July 2000, 

although a small number implemented the act as early as July 1999. 

A primary goal of the program is to improve employment outcomes for 

disadvantaged individuals, moving individuals and their families toward self-sufficiency 

and reducing reliance on government subsidized programs. A large number of studies 

have attempted to examine the impact of such training programs, and there is an 

important experimental study that examines JTPA.74 Although performance measures 

required under WIA are available, given its recent implementation, analyses examining 

outcomes from WIA participants are much more limited. One experimental study has 

been designed to compare alternative counseling approaches under WIA, although it is 

still in progress.75 

We are not aware of any random assignment study, even at the design stage, that 

will allow a comprehensive evaluation of the impact of WIA on participant employment 

outcomes. One may question the extent to which nonexperimental designs are valid in 

providing meaningful evaluations of program outcomes. There is an active literature that 

attempts to develop and test alternatives to experimental design.76 Even if such concerns 

could be addressed, there are other difficulties in estimating WIA program effects. Under 

WIA, states and local areas have much latitude in the design of their job service and 

training programs, so effects are very likely to differ across areas. 

 

74 Daniel Friedlander, David H. Greenberg and Philip K. Robins, “Evaluating 

Government Training Programs for the Economically Dsadvantaged.” American 

Economic Review 35(4, December 1997):1809-1890; and Larry L. Orr et al., Does 

Training for the Disadvantaged Work? Evidence from the National JTPA Study. 

Washington, DC: Urban Institute Press, 1996. 

75 Paul Decker and Irma Perez-Johnson, “What Can We Expect Under Personal 

Reemployment Training Accounts? A Discussion Based on Findings from the Individual 

Training Account Experiment, Reemployment Bonus Demonstration and Other 

Evaluations.” Paper presented at the USDOL 2003 National Research Conference. 

76 Rajeev H. Dehejia and Sadek Wahba, “Propensity Score-Matching Methods for 

Nonexperimental Causal Studies.” The Review of Economics and Statistics 84 (February 

2002): 151-161; Kenneth Troske, Peter R. Mueser and Alexey Gorislavsky, “Using State 

Administrative Data to Measure Program Performance.” Draft, 2003; and Jeffrey Smith 

and Petra Todd, “Does Matching Overcome LaLonde’s Critique of Nonexperimental 

Estimators.” Journal of Econometrics, forthcoming. 
284

This paper identifies observed patterns of earnings and welfare receipt for WIA 

exiters in six states by type of service received. Our approach examines quarterly 

earnings over a four-year period, including two years prior to and one year following the 

exit year. Since all measures are calculated on the same sample of participants, changes 

in earnings or welfare receipt represent actual experiences of these individuals. On the 

other hand, it is important to recognize that observed changes are not necessarily due to 

program impact. It has long been recognized that the decision to participate in a program 

selects individuals at particular points in their employment history. Decisions to exit 

WIA may also be selective. Without a comparison group with which to compare these 

patterns, we cannot separate out actual program effects from variation that would have 

occurred in the absence of the program.77

The analysis here focuses on Program Year 2000 (July 2000-June 2001) WIA 

Title I-B Adult and Dislocated Worker exit flows. Although recruiting channels differ 

across states and local areas, those classified as Adults are disproportionately low-skilled 

individuals, often unemployed or with unstable jobs and low pay. In contrast, Dislocated 

Workers are often recruited by WIA staff when plants close and they generally have 

much higher skill levels and greater prior earnings. 

We divided individuals between those who are coded as receiving Training 

services and those who do not. While there is some latitude in how such services are 

defined, generally any classroom or on-the-job training activity that takes more than a 

few days must be classified as Training. Although training programs often last several 

months, few extend for as much as a year. In contrast to Training, “Core” and 

“Intensive” services include most job search and job readiness programs, various kinds of 

employment counseling, in addition to a host of programs of very short duration. We 

chose not to distinguish Core and Intensive services because states often differ in how 

these services are defined. 

The six states from which we have data are Florida, Georgia, Illinois, Maryland, 

Missouri and Texas. Although the current analyses combine results in all states, we also 

make some reference to the separate state estimates where these are of interest. Status 

descriptors are drawn from the Workforce Investment Act Standardized Record 

(WIASRD) data file provided by each of the ADARE project states. Section 2 describes 

the data that were assembled to prepare the tabulations. Section 3 presents and interprets 

the results. Section 4 provides a brief conclusion. 

 

77 Kevin Hollenbeck, Christopher King, and Daniel Schroeder (“WIA Intensive and 

Training Service Net Impact Estimates,” paper presented at the USDOL 2003 National 

Research Conference) provide preliminary analyses that attempt to identify the impacts 

of WIA in our states using a matching methodology to construct a comparison group 

from those receiving less intensive services. 
285

2. DATA SOURCES AND PROCESSING 

Data Sources 

The base data for WIA client information derive from the Workforce Investment Act 

Standardized Record (WIASRD) file, listing WIA exiters in PY2000 (July 2000-June 

2001), provided to each of the ADARE project partners by the WIA administrative 

entity in the state. Among the ADARE project states, Florida and Texas were 

voluntary early implementers of the Workforce Investment Act of 1998. This means 

that the 2000 WIA Program Year covered in this report was the second year of WIA 

reporting for Florida and Texas but only the first year of such reporting for Georgia, 

Illinois, Maryland, and Missouri. 

In order to examine the labor market experience of WIA clients, the WIASRD data 

were merged by Social Security number to Unemployment Insurance wage record 

data maintained by the state. The wage record data provide total earnings in each 

quarter for each employee in the state who is in a job overseen by the state’s 

Unemployment Insurance system. These records include the overwhelming majority 

of earnings received by residents in the state, although it does not include earnings 

from self-employment, informal or illegal employment, or employment in a small 

number of exempt jobs. It also omits any earnings obtained by residents working 

outside the state. A partial exception is that, in the case of Missouri, wage record data 

for both Missouri and Kansas are included in the analysis. Among the states in the 

study, Missouri contains the only major city, Kansas City, for which a substantial 

portion of residents are employed outside the state, and these individuals are 

employed in Kansas. Although St. Louis is also on a state border, very few of its 

residents work in Illinois. 

In each state, the WIASRD data were also merged with state administrative records 

identifying receipt of Temporary Assistance for Needy Families (TANF) cash 

payments. We have not identified as recipients those individuals who received only 

in-kind benefits, such as Medicaid, childcare support, or related benefits. In each 

case, except that of Illinois, all individuals in households receiving such cash 

payments were classified as “recipients.” This means that a small number of 

individuals identified as household members who were not themselves eligible for 

TANF payments are included. Excluding such individuals would not have influenced 

our results. Our analysis also includes individuals who were dependents in families 

receiving TANF, although again the number of such individuals is very small and has 

no material impact on our results. In the case of Illinois, our analysis is limited to 

female payees receiving TANF cash payments in the single-parent program, so all 

males as well as dependents are omitted. In a separate analysis, we examined the 

impact of this selection for Missouri and found that such selection did not alter results 

in any important way. 

All the TANF information is coded by quarter, so an individual is identified as a 

recipient in any quarter in which TANF payments were received. All these TANF 
286

indicators derive from data sources used to administer the TANF program in the state, 

not from the variables on the WIASRD database. In addition to greater accuracy, the 

measure we use has the advantage that it can be traced for an extended period—in the 

case here, for a full four years spanning the WIA participation year. 

Data Processing 

WIASRD data element 303 Date of WIA Exit was used to ensure that only exit dates 

between July 1, 2000 and June 30, 2001 were included for the Adult and Dislocated 

Worker populations. This includes ‘hard’ exits, where the relevant agency explicitly 

chose to code an individual as completing or leaving the program, and ‘soft’ exits, 

where the participant is define as exiting because 90 days elapsed since the last 

recorded service. 

WIASRD data elements 304 Adult (Local) and 305 Dislocated Worker (Local) were 

used to select the two sub-populations of interest.78 WIASRD data element 333 Date 

of First Training Service was used to assign an individual to the Training subpopulation. Any Adult or Dislocated Worker with a valid Date of First Training 

Service was assigned to the Training Services sub-population. The remaining clients 

were assigned to the Core or Intensive Services only sub-population. These steps 

resulted in the assignment of each person to one of the two mutually exclusive 

categories of WIA services—Core or Intensive, and Training. 

3. WIA CLIENT OUTCOMES 

In the analysis here, we have combined data from Florida, Georgia, Illinois, 

Maryland, Missouri, and Texas. The total population consists of 31,282 Adult and 

23,516 Dislocated Worker WIA clients, which is the total universe of participants 

who exited during Program Year 2000. For all quarters in the period beginning two 

years prior to the program year through the year following the program year (1998:3 

through 2002:2), we plot employment, earnings and TANF receipt for the full set of 

individuals in the specified category. If no wage record data matches for an 

individual in a given quarter, that individual is coded as not employed. 

Patterns of Employment and Earnings for Adult Participants 

Figure 1, which graphs employment of Adults participating in Core and Intensive 

services, and those participating in Training, provides a useful window into the 

employment experiences of WIA clients. The white bar shows that in the two years 

prior to the program year, levels of employment are between 53 and 56 percent. In 

the first quarter of the program year, employment increases above prior levels, 

 

78 Duplication is permitted and does occur, but the number of duplicated cases is very 

small. Youth, statewide activities supported by the 15 percent provision in the federal 

legislation, including Displaced Homemakers, Rapid Response, and National Emergency 

Grant funded services to clients, are not included. 
287

increasing each quarter, approaching 74 percent in the last quarter of the program 

year. In the subsequent year, employment declines somewhat but is still over 62 

percent in the last quarter. It is clear that participation in the program is associated 

with substantial gains in employment, but the employment increment partly dissipates 

following the program year. The pattern for Adults receiving Training services 

(striped bar) shows a very similar pattern. 

The trend for earnings for those employed is indicated in Figure 2.79 Focusing first 

on those receiving Core and Intensive services (square mark), we see that during the 

program year, earnings do not increase much initially, but do increase modestly 

toward the end of the program year. Notably, these gains continue into the following 

year. Average quarterly earnings in the two prior years are $3,123, and they increase 

by 22 percent to $3,814 in the year following participation. Not only are clients 

getting jobs following their WIA participation, but they are getting better jobs.80

The growth in earnings for those who participate in training (triangle mark) is much 

greater. If we examine the prior earnings for these individuals, they are somewhat 

lower than those who receive only Core or Intensive services, but they increase much 

more in proportional terms. Quarterly earnings in the two years prior to the program 

year average only $2,791, which increases a remarkable 69 percent in the year after 

participation. 

One obvious question is whether there are important differences across states. Figure 

3 presents quarterly earnings for employed Adult clients receiving Training services 

in our six states. We see there are differences in the levels of earnings in each of the 

states, and that growth in earnings differs somewhat by state. On the other hand, the 

basic pattern is remarkably similar across states: There is little growth in earnings 

prior to program participation and substantial net growth beginning in the 

participation year. Although we do not present disaggregated statistics by state in 

what follows, in each case we have tabulated results separately, and the basic patterns 

are similar across states. 

Patterns of Employment and Earnings for Dislocated Workers 

Figure 4 shows employment and earnings for those classified as Dislocated Workers. 

For those who participated only in Core or Intensive services, as might be anticipated, 

 

79 Earnings are calculated each quarter for the set of individuals employed in that quarter. 

In order to obtain unconditional mean earnings, the reported earnings can be multiplied 

by employment rates in Figure 1. Since employment, and earnings for those employed 

tend to follow similar patterns, this product measure shows the same pattern, somewhat 

accentuated, as do Figures 1 and 2. 

80 All earnings are reported in current dollars, and therefore do not take account of 

inflation. Basic patterns would not be altered substantially by price adjustments, given 

modest levels of inflation over this period. 
288

there is a decline in earnings in the year prior to the program year, reflecting the loss 

of jobs. Those receiving Training Services differ in important ways, as their 

employment declines by much more than those receiving Core or Intensive Services, 

but then their employment returns to similar levels. 

Differences between Core and Intensive service participants and those in Training are 

highlighted by patterns of earnings for those who are employed (Figure 5). Training 

participants have slightly lower earnings initially, and experience substantially larger 

declines prior to the program year. Apparently, the loss of high paying jobs is 

particularly critical for those who move into Training. Earnings for both groups, but 

especially those in Training, increase by the end of the program year. Between the 

first quarter of our series and the first quarter of PY2000, their earnings drop by 24%, 

as compared to the 15% drop experienced by those not receiving Training services. 

However, interestingly, in the post-participation period, their earnings experience an 

almost complete (99.8%) recovery as compared to the initial year of our series. This 

improves on the 92.6% recovery experienced by those receiving only Core or 

Intensive Services. 

TANF Use for Adults and Dislocated Workers 

Figure 6 shows that TANF participation is near 10 percent at the beginning of our 

time period for Adults in both service categories. This rate begins to rise in the year 

prior to WIA participation and continues a steady if modest increase through the first 

quarter of PY2000. However, beginning in the second quarter of the program year, 

this rate declines sharply and continues to do so throughout the survey period, ending 

at below 7% for those receiving Core or Intensive services and dropping to a 

remarkable 4.3% for those in the Training cohort. 

Figure 7 shows that Dislocated Workers are very unlikely to participate in TANF, as 

participation rates are under 2 percent in all cases. We observe a downward trend in 

these rates, with participation less than 1 percent at the end of our period. 

4. CONCLUSION 

We find that adults have large employment gains associated with participation in 

WIA, whether they receive core and intensive, or training services. Training 

recipients have particularly large earnings gains. Following losses, employment and 

earnings of dislocated workers largely recover, especially for those in training. Adult 

TANF receipt declines, especially for those in training activities, dislocated workers 

are very unlikely to receive TANF. Despite some differences in the detailed patterns, 

similarities between states in basic patterns are striking. 
FIGURE 1

Employment for WIA Exiters July 2000 - June 2001

Adult Clients Receiving Core & Intensive, or Training Services

0%

10%20%30%40%50%60%70%80%

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 

Year-Quarter

% Employed

Core & Intensive Training

PY 2000
FIGURE 2

Quarterly Earnings for Employed WIA Exiters July 2000 - June 2001

Adult Clients Receiving Core & Intensive, or Training Services

0

1,0002,0003,0004,0005,0006,000

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 2

Year-Quarter

Quarterly Earnings

Core & Intensives Training

PY 2000
FIGURE 3

Quarterly Earnings for Employed WIA Exiters July 2000 - June 2001

Adult Clients Receiving Training Services by State

0

1,0002,0003,0004,0005,0006,000

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 

Year-Quarter

Quarterly Earnings

State 1 State 2 State 3 State 4 State 5 

PY 2000
FIGURE 4

Employment for WIA Exiters July 2000 - June 2001

Dislocated Worker Clients Receiving Core & Intensive, or Training S

0%

10%20%30%40%50%60%70%80%90%

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 2

Year-Quarter

% Employed

Core Intensive Training

PY 2000
FIGURE 5

Quarterly Earnings for Employed WIA Exiters July 2000 - June 2001

Dislocated Worker Clients Receiving Core & Intensive, or Training Service

0

10002000300040005000600070008000

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 2

Year-Quarter

Quarterly Earnings

Dislocated Workers--Core & Intensive Dislocated Workers-T

PY 2000
FIGURE 6

TANF Receipt for WIA Exiters July 2000 - June 2001

Adult Clients Receiving Core & Intensive, or Training Services

0%2%4%6%8%

10%12%14%

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 

Year-Quarter

% TANF

Core & Intensive Training

PY 2000
FIGURE 7

TANF Receipt for WIA Exiters July 2000 - June 2001

Dislocated Worker Clients Receiving Core & Intensive, or Training Servic

0%2%4%6%8%

10%

1998-3 1998-4 1999-1 1999-2 1999-3 1999-4 2000-1 2000-2 2000-3 2000-4 2001-1 2001-2 2001-3 

Year-Quarter

% TANF

Core & Intensive Training

PY 2000
296

The Participation of Older Workers in Employment 

and Training Administration Programs: An Overview 

Jonathan Simonetta 

DOL/ETA 

The author wishes to thank the Employment and Training Administration’s Office of 

Workforce Security for their help in securing the necessary Unemployment Insurance and 

Employment Service data for this exercise. The views expressed in this paper represent 

those of the author and not necessarily those of the Department of Labor. 
297

The following document is a series of tables and analysis concerning the status of 

older workers in ETA programs. Older workers, for this purpose, are defined as active 

labor market participants at or above the age of 55. This document has been prepared for 

a Special Populations panel for the Employment and Training Administration’s 2003 

National Research Conference. A collateral benefit of this document is also the 

showcasing of ETA’s current administrative data capabilities. The data for all but one of 

the tables are taken from internally available ETA data repositories. 

Data tables using JTPA/WIA participant files come from the Six State (Texas, 

Florida, Missouri, Maryland, Illinois, Georgia) Administrative Data Research and 

Evaluation (ADARE) alliance Grant K-6558-8-00-80-60. Data were taken from PY 

1995-PY2000. The data tables that highlight older workers participation in the 

employment service come from the U.S. Department of Labor (USDOL) – Employment 

Service Programs ETA 9002 Quarterly reports. Data were taken from PY1992-PY 2000. 

For the employment service program data, we are limited to state aggregated data only. 

The tables dealing with unemployment insurance recipients come from the unpublished 

benefit accuracy measurement (BAM) data repository housed in the Office of Workforce 

Security (OWS) of DOL’s Employment and Training Administration. The BAM data 

cover calendar years 1991-2001. Data for the final table are taken from the searchable 

data archive at the Bureau of Labor Statistics website (www.bls.gov). Data used to adjust 

average weekly wages and weekly UI benefit amounts for inflation were taken from this 

site as well. 

This paper will be broken up into six sections. The first section discusses the data 

sources used for the graphical analysis. The second section presents a review of the 
298

recent literature concerning older workers. The third section presents and analyzes the 

graphs produced from the ADARE data. The fourth section presents and discusses the 

graphs produced from the ETA 9002 data and the Bureau of Labor Statistics. The fifth 

section exhibits and comments on the graphs produced using the unpublished BAM data. 

The paper then concludes with recommendations from the author concerning future 

research about older workers and their role in the 21st century labor market. A technical 

appendix will appear at the end of the document. 

Section #1: Data Sources

ADARE – Administrative Data Research and Evaluation 

ETA has been funding administrative data research on all workers in seven states: 

Maryland, Missouri, Texas, Florida, Georgia, Illinois, and Washington. These detailed 

longitudinal data on individuals are maintained confidentially by remaining in the hands 

of state officials at state universities. These longitudinal data sets include UI wage 

records, JTPA/WIA participant files, TANF records, and Perkins/Voc Ed data. 

Currently, they are adding UI claims data and employment service records to the data 

sets. All data sets cover a minimum of six years with some states having access to data 

up to eleven years. 

The ADARE data set has an advantage in that it can be sorted by age, sex, 

industry, state, and a host of other factors. Currently, the ADARE data set is being 

modeled to answer critical questions for which the Department needs answers. The seven 

ADARE state partners are executing analytical research papers to address questions 

concerning the current WIA reauthorization effort, unemployment insurance, low-income 

TANF recipients, and special populations including older workers and the disabled. 
299

Employment Service ETA 9002 Data 

The ETA 9002 system houses all the data for Employment Service (ES) programs 

from PY1991-PY2000. The data can be sorted by quarter and by state. The fourth 

quarter national program year summary compiles ES data for the whole program year. 

The data can only be obtained through authorized access via the ETA intranet. The unit 

of measure for the ETA 9002 data is the ES applicant. Applicants are broken down and 

categorized by over 50 data elements. Data element categories include: 

1. Race 

2. Sex 

3. Age 

4. Employment status 

5. UI eligibility 

6. Education level 

7. Disabled status 

8. Dislocated worker status 

9. Veterans status 

An annual report of ETA 9002 data exists as well. The purpose of the Annual Report is 

to share data from the quarterly ETA 9002 reports and other sources for the period of July 

1999 through June 2000 (PY 1999). This report includes state-by-state labor exchange 

activities and outcomes, as well as a national summary. The report includes data 

describing services and outcomes of states public labor exchange programs, displays 

items focusing on the emerging electronic labor exchange, and includes a discussion of 

reemployment services. The data are based on quarterly ETA 9002 reports submitted by 

states to the Employment and Training Administration.

The Benefit Accuracy Measurement (BAM) Data System 

The BAM data system is the mechanism for intensively reviewing UI payments to 

determine if they were made to eligible claimants and, if so, whether payments were 

made in the proper amounts. Each case selected for BAM is an original payment for a 
300

specific week of unemployment referred to as the “key week.” Each key week is 

investigated to verify that all information pertaining to eligibility and payments is treated 

in conformity with State written law and policy. The data obtained for the investigations 

can and are used to draw inferences about the claimant population as a whole. The BAM 

investigation involves gathering data longitudinally linked by key identifier about the 

claimants and claims sampled for entry into the automated BAM database. The BAM 

database holds records back to 1988 and contains 83 data elements per individual record. 

The BAM database can only be accessed by request from OWS personnel in the Division 

of Performance Management. 

Section #2 – Literature Review

 This review discusses the relevant literature concerning the current labor market 

situation of older workers. It begins with a basic discussion of older worker labor force 

demographics and the impending retirement of the baby-boomers. Next, given these 

demographics, some general and specific labor force implications are introduced. The 

review then details both the private sector and retirement system response to the 

demographic situation of older workers. The review ends with a treatment of policy 

implications concerning older workers and the unemployment insurance program. 

Demographics 

 The United States General Accounting Office (GAO) details the effects of the 

impending retirement of the unusually large “baby boom” generation, comprised of those 

individuals born between 1946 and 1964. According to the GAO, the number of older 

workers will grow substantially over the next two decades, and they will become an 

increasingly significant proportion of all workers. The GAO study cites the Current 
301

Population Survey (CPS) in saying that in the year 2000 18.4 million workers were in the 

labor force, a number that the Bureau of Labor Statistics (BLS) projects to swell to over 

31 million workers by 2015. This expected increase is a consequence both of the aging 

of the baby boom generation and a general trend in greater labor force participation 

among older persons. Thirty percent of all persons over age 55 participated in the labor 

force in the year 2000, and according to BLS projections, this percentage is expected to 

rise to 37% by 2015. Also, GAO states that older workers are employed in a diverse 

group of occupations but are more likely than younger workers to be white-collar 

managers or professionals. GAO projections suggest that older workers will become an 

increasing proportion of some occupations. For example, from 2000 to 2008 the percent 

of teachers older than age 55 will increase from 13% to 19% and the percent of nurses 

and related occupations older than age 55 will increase from 12% to 18%. Due to an 

increase in full-time employment and a change in the composition of the older workforce 

toward white-collar jobs, older workers have experienced substantial real earnings 

increases from 1989 to 1999 compared with younger workers. Over this period, earnings 

increased by an aggregate 11% for workers age 55 to 74 compared with a 2% gain for 

workers 40 to 54. Finally, older workers are less likely than younger workers to lose a 

job; however, when they do they are less likely than younger workers to find 

employment. Fifty-seven percent of older workers who lose their jobs retire, partially or 

fully, following a job loss. The GAO asserts that once older workers fully retire, most do 

not re-enter the labor force even for part-time work.1

General Labor Force Implications

 

1

 The United States General Accounting Office (GAO). Older Workers: Demographic Trends Pose 

Challenges for Employers and Workers. November 2001. GAO-02-085. 
302

Quinn (1998) points out that labor force participation rates among older 

Americans are no longer declining. Many more older men and women are working today 

than pre-1985 trends would have predicted. Quinn analyzes retirement patterns in the 

1990’s and finds that many Americans withdraw from the labor market gradually, often 

utilizing bridge jobs between full-time career employment and complete labor force 

withdrawal. Bridge employment, according to Quinn, is work between the career job and 

complete retirement. Quinn estimates that a minimum of 49% of women and 34% of 

men engage in bridge employment and that the great majority of bridge employment 

involves fewer hours per week and less compensation per hour than the career job.2

Quinn, Burkhauser, and Myers (1990) assert that while an abrupt and complete 

transition from full-time work is still the most common avenue to retirement, a variety of 

other paths are taken. A crucial concept in their research is that of the career job. The 

career job is the one in which a worker spends the bulk of their working life, usually 

working full-time. If transition from the career job to retirement is not immediate, it may 

involve a reduction in hours to part-time work on the career job. Alternatively, there may 

be an exit from the career job to full or part-time work on another job, which most often 

is not in the same industry and occupation.3

Specific Labor Force Implications

Arlene Dohm (2000) details the effects of the retirement of the baby-boomers on 

particular occupations and industries. Dohm uses Current Population Survey (CPS) data 

 

2

 Quinn, Joseph. 1999. “New Paths to Retirement” In Forecasting Retirement Needs and Retirement 

Wealth, Brett Hammond, Olivia Mitchell and Anna Rappaport, ed. Philadelphia, PA:University of 

Pennsylvania Press. 

3

 Quinn, Joseph F., Richard V. Burkhauser, and Daniel A. Myers. 1990. Passing the Torch: The Influence 

of Economic Incentives on Work and Retirement. Kalamazoo, MI: W.E. Upjohn Institute for Employment 

Research. 
303

on occupation and age from 1998 to 1999 to base her analysis. According to Dohm, the 

industry that will be most affected by baby-boomer retirements is educational services. 

Occupations such as special education teachers, teacher’s aides, educational 

administrators, elementary school teachers, and secondary school teachers will be the 

most affected. Forty-seven to sixty-six percent of workers aged 45 and over will be 

leaving these occupations by 2008. People in this industry generally retire because of 

pensions that often provide full coverage for qualified employees after 30 years of 

service. Also, a slowdown in hiring in the 1980s and early 1990s rose the average age of 

teaching professions and left fewer workers to move into vacant slots. Transportation 

industries such as railroads, bus service, urban transit, air transportation, trucking, and 

water transportation will be hard hit as well. The reasons for this include the relatively 

high age requirements for operating vehicles and other modes of transportation, high 

wages and low turnover rates in the highly unionized railroad and airline industries, and a 

large number of part-time workers, especially in bus and taxicab services, occupation 

often preferred by older workers. 

Another industry that will be strongly affected according to Dohm will be the 

health services industry. In particular, registered nurses (62.6%) and licensed practical 

nurses (59.1%) are expected to leave the hospital industry in the largest numbers by 2008. 

Other healthcare occupations such as physicians (37.2%), dentists (40.3%), and 

psychologists (36.9%) will be affected as well. For a number of occupations, however, 

the attrition generated by the upcoming retirements may be the preferred mechanism to 

reduce the size of the occupation in the face of declining demand. Farmers, bookkeepers, 
304

sewing machine operators, and other occupations face declining prospects due to 

productivity improvements or declining U.S. production.4

 

Private Sector Response

The Committee for Economic Development (1999) (CED) asserts that society’s 

goal should be increased work opportunities and incentives for older Americans that meet 

the needs of these workers and their employers. According to CED, older Americans 

who want to work face numerous obstacles: pension plans that strongly discourage them 

from working, workplace attitudes and practices that hinder their employment, federal 

regulations that inhibit flexible work arrangements, and sometimes unrealistic 

expectations on the part of workers themselves. CED also maintains that employer 

attitudes and policies must change if older workers are to remain in the workforce longer. 

Businesses have demonstrated a preference for early retirement to make room for 

younger workers. This preference, though, is a relic from a bygone era of labor 

surpluses; it will not be sustainable when labor becomes scarce. A stagnant labor supply 

will force many employers to rethink their attitudes toward older workers and reverse 

policies that inhibit their employment. 

Some of the major findings of the CED study include: 

1. Americans are retiring earlier and living longer, healthier lives. The retirement span 

has increased by 5 years for male workers and four years for female workers since 

1965. 

2. The workforce is aging. In 1950, there were seven working-age persons for every 

person age 65 and older in the United States; by 2030 there will be fewer than three. 

3. These two trends will cause tighter labor markets and lower national growth due in 

particular to the exploding costs of our old-age entitlement programs. 

4. Older Americans face multiple barriers to employment including financial 

disincentives to work, workplace discrimination, and inadequate training. 

5. Disincentives to employers hiring older workers include that fact that older workers 

often fail to maintain and upgrade their skills and older workers can cost more than 

younger workers due to practices related to earnings, health insurance, and pensions. 

 

4

 Dohm, Arlene. 2000. Gauging the labor force effects of retiring baby-boomers. Monthly Labor Review. 

July 2000. U.S. Department of Labor: Bureau of Labor Statistics. 
305

6. Some older workers face discrimination in the work place and job market. Older 

workers who believe they are likely to face discrimination are less inclined to remain 

in the workforce. 5

 

Retirement System Response

Purcell (2000) discusses possible changes to both the public and private 

retirement systems due to the retirement of the “baby boom” generation. Purcell pays 

particular attention to rising plan eligibility ages and introducing “phased retirement” 

plans. In terms of rising plan eligibility ages, Purcell concentrates on the Social Security 

system. 

The rules that govern eligibility for Social Security benefits can have a substantial 

influence on workers’ decisions about when to retire. Empirical evidence, according to 

Purcell, indicates that more retirements occur at age 62, the earliest age at which reduced 

retired worker benefits are available and at age 65, the earliest age at which full-retired 

worker benefits are available, than at other ages. The Social Security Amendments of 

1983 mandated a gradual increase in the age at which individuals are eligible for full 

retirement benefits from its current level of 65 years to 67 years in 2022. As a result, the 

actuarial reduction in Social Security benefits for those who retire at age 62 will increase 

from 20% to 30%, creating a financial disincentive to delay receipt of Social Security and 

continue working. Another important development, according to Purcell, was the recent 

amendment to the Social Security Act to repeal the earnings test for beneficiaries who are 

65 or older. In 2000, the earnings test was eliminated for people at full retirement age. 

Unfortunately, for Social Security Recipients who are under the age of 65, their benefits 

will be reduced by $1 for each $2 of earnings in excess of $10,080. 

 

5

 Committee for Economic Development. 1999. New Opportunities for Older Workers. Washington, D.C. : 

Committee for Economic Development. 
306

Purcell argues that as members of the baby-boom generation begin retiring, 

millions of skilled and experienced workers will exit the labor force. As this occurs, 

employers may find it necessary to alter their employment practices and pension plans to 

induce some of those who would ordinarily retire completely to remain on the job, 

perhaps on a part-time or part-year schedule. Purcell refers to this process as phased 

retirement. Advocates of phased retirement contend that more pension-eligible 

individuals would choose to continue working if employers could offer them the 

opportunity to continue working if employers could offer them the opportunity to collect 

pension benefits while remaining on the employer’s payroll. Some employers are calling 

on Congress to amend the tax code and the Employee Retirement Income Security Act 

(ERISA) to allow employers greater flexibility in designing phased retirement programs 

for their employees. One proposed amendment would permit pension in-service 

distributions to employees who have not reached the pension plan’s normal retirement 

age. This, employers say, would allow them to offer older employees the chance to cut 

back their work schedules to part-time, while supplementing their reduced salaries with 

pension income. Another proposed amendment would allow in-service pension 

distributions to begin when a participant has reached the earliest of the plan’s normal 

retirement age or the completion of 30 years of service. This amendment, according to 

Purcell, would promote continued employment among older workers who would 

otherwise have chosen to retire (if given the choice between full-time employment and 

taking early retirement).6

 

 

6

 Purcell, Patrick J., 2000.Older workers: employment and retirement trends. Monthly Labor Review. July 

2000. U.S. Department of Labor: Bureau of Labor Statistics. 
307

Policy Implications: Unemployment Insurance 

 O’Leary and Wandner (2000) stress that in taking account of economic security 

for older workers, considerable attention should be given to unemployment insurance 

(UI) as a source of income security and as a potential influence on work incentives. 

Demographic patterns in U.S. labor markets at the end of the twentieth century suggest 

that it would be wise to investigate and develop policies to encourage the continued labor 

market participation of older workers. The authors cite a recent survey sponsored by the 

American Association of Retired Persons (AARP) that found that four-fifths of all 

workers born between 1946 and 1964 intend to continue working after retirement. Also, 

employer groups are increasingly concerned about maintaining labor market participation 

of older workers, given the small labor force cohorts that will follow. 

 The authors believe that changes in UI rules concerning initial eligibility, 

continuing eligibility, wage replacement, and partial benefits should all be examined to 

evaluate effects on the likely employment patterns of older workers. UI features most 

relevant to older workers are concentrated in the areas of self-employment, part-time 

worker, seasonal work, and agricultural work. In short, the authors believe that as society 

tries to retain older workers in the labor force, the current and potential role of UI as both 

a built-in stabilizer of aggregate expenditure and as an influence on work incentives for 

older workers needs to be examined.7

 

 

7

 O’Leary, Christopher J., and Stephen A. Wandner. 2000. Unemployment Compensation and Older 

Workers. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. 
308

Section #3 – ADARE Graphs

Graph 1 

JTPA Title III and WIA Dislocated Worker Program 

Participation Age Groups 55-59, 60-64, and 65+ for 

Program Years 1995-2000

0

500

1000

1500

2000

2500

3000

3500

4000

PY1995 PY1996 PY1997 PY1998 PY1999 PY2000

Program Year 

Number of Participants

55-59 60-64 65 and up

Graph 1 Summary 

 Graph 1 describes JTPA Title III and WIA Dislocated worker program 

participation for the age groups 55-59, 60-64, and 65+. The data were taken from four of 

the six states in the ADARE alliance. These states include Florida, Texas, Maryland, and 

Missouri. The dislocated worker category was used because, from JTPA to WIA, the 

definition of a dislocated worker stayed the same. The most interesting characteristic of 
309

this graph is the three-year spike in program participation in the 55-59 cohort over 

program years 1996-1998. Age group 60-64 also exhibits a similar increase over these 

same years but not to the magnitude of the 55-59 cohort. 

Graph 2 

JTPA Title III and WIA Dislocated Worker Program 

Participation by Age Group from Program Years 1995-

2000 

8.60%

10.68%

9.81% 10.42% 10.62%

11.80%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

PY1995 PY1996 PY1997 PY1998 PY1999 PY2000

Program Year

Percentage of Total Participants Age 55 and Over Linear Trend Line

 

Graph 2 Summary 

 This graph again shows JTPA Title III and WIA Dislocated Worker program 

participation but from a different perspective. Here, older workers are aggregated into a 

55+ category and compared as a percentage to all other participants in the dislocated 
310

worker program. In viewing the linear trend line, it is clear that the number of older 

workers, as a percentage of all participants in the dislocated worker program, is 

increasing from PY 1995-PY 2000. It can be inferred from viewing graph 2 in the 

context of graph 1, that from the program years 1996-1998 a general increase in 

participation in the dislocated worker program occurred. This is because given the spike 

in older workers participation during these years, one did not see a concomitant rise in 

their overall program participation relevant to other age categories. 

Section #4 – Employment Service and Bureau of Labor Statistics Graphs

Graph 3 

Percentage of Total Applicants to Employment Service 

Programs that are Age 55 and Over, United States Totals, 

PY1992-PY2000

7.15% 6.86% 6.48% 6.42% 6.55% 6.70%

7.26%

8.40%

7.71%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

PY1992 PY1993 PY1994 PY1995 PY1996 PY1997 PY1998 PY1999 PY2000

Program Year

Percentage

Age 55 and Over Linear Trend Line
311

Graph 3 Summary 

 Graph 3 shows the percentage of total applicants to the Employment Service that 

were age 55 and over for the program years 1992-2000. Relative to the other age groups, 

older worker participation in the employment service has generally increased. A 

noteworthy characteristic of graph 3 is the trough in participation from PY 1993-PY 

1997. A possible explanation of this trough could be that given the economic prosperity 

of the middle 1990’s, older workers had less of a need for the labor exchange relative to 

other age cohorts. 
312

Graph 4 

Unemployment Rate for the Civilian Labor Force 55 years and 

over for Program Years 1991-2001

3.93%

4.80%

4.33%4.09%

3.69%3.43%3.27%

2.73%2.76%2.63%

3.03%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

PY1991PY1992PY1993PY1994PY1995PY1996PY1997PY1998PY1999PY2000PY2001

Program Year

Unemployment Rate 55 years and Older

Linear (Unemployment Rate 55 years and Older)

Graph 4 Summary 

Graph #4 is the basic unemployment rate for the civilian labor force 55 years and 

older for program years 1991-2001. This graph clearly shows that older workers in the 

labor force enjoyed continuing job security throughout the 1990’s. During program years 

1998-2000, unemployment rates dipped below 3% for older workers. These 

unemployment rates for older workers were the lowest in the recorded history of 

unemployment figures for this cohort. 
313

Section 5: BAM Data Graphs

Graph 5 

Breakdown of UI Recipients by Gender Ages 55-64

61.00% 60.30% 58.90% 58.40% 56.90% 56.70%

59.30%

54.80%

58.90%

56.10%

61.40%

39.00% 39.70% 41.00% 41.60% 43.10% 43.30%

40.70%

45.20%

41.10%

43.90%

38.60%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Percentage of UI Recipients

Male Female

 

Graph 5 Summary 

 Graph 5 shows the gender breakdown of unemployment insurance (UI) recipients 

for the age group 55-64. It is clear that more male older workers are receiving UI relative 

to female workers. A possible explanation for this difference could be that, traditionally, 

males have held the household’s full-time job. Because these males hold the primary job 

for the household, they would be more readily eligible to receive UI benefits. Another 
314

interesting characteristic of this graph is that the largest male/female difference is seen in 

years of relative economic downturn (1991 & 2001). 

Graph 6 

Gender Breakdown of UI Recipients Age 65+

51.50%

56.70%

51.00%

53.90% 53.70%

48.30%

52.50%

49.20%

54.90%

52.40%

59.80%

48.50%

43.30%

49.00%

46.10% 46.30%

51.70%

47.50%

50.80%

45.10%

47.60%

40.20%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Percentage of UI Recipients

Male Female

 

Graph 6 Summary 

 Graph 6 shows the gender breakdown for UI recipients for the age group 65 years 

and older. In this graph, a much more even distribution exists between males and 

females. A possible explanation of this could be that the primary male wage earners of 

the household have mostly retired by this age. Another explanation could be that women 

generally live longer than men so there exists more of them to collect UI at this age level. 
315

Graph 7 

Unemployment Insurance Benefit Duration 

Age Group 55-64

17.7535

17.5864

17.0037

17.15

17

17.887

17.44

16.5739

16.93

17.12

16.16

15

15.5

16

16.5

17

17.5

18

18.5

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Benefit Duration in Weeks

Graph 7 Summary 

Graph 7 shows the duration of UI benefits in weeks for the age group 55-64. The trend 

line shows a general downward movement mirrored by the success of the economy 

during the 1990’s. 
316

Graph 8 

Unemployment Insurance Benefit Duration for 

Age Group 65+

17.8944

18.3702

18.6856

17.77 17.59

18.87

18.57

16.71

18.53

17.888

15.76

14

14.5

15

15.5

16

16.5

17

17.5

18

18.5

19

19.5

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Benefit Duration in Weeks

Graph 8 Summary 

Graph 8 shows unemployment insurance benefit duration for the age group 65 and 

over. Other than for calendar years 1998 and 2001, UI benefit duration for the 65+ age 

group has been consistently high. A possible explanation for this could be age 

discrimination against the oldest of the older workers in job search. Another explanation 

could be a functional disconnect in current “high-tech” job search techniques for older 

workers. 
317

Graph 9 

Percentage Distribution of UI Recipients by Education Level 

Age Group 55-64

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Population Percentage

None HS Grad Assoc BA/BS Graduate

Graph 9 Summary 

Graph 9 shows the percentage distribution of UI recipients by education level for 

the age group 55-64. From this graph we can reasonably infer general population 

education levels for this age group. An important characteristics of this graph is that UI 

recipients 55-64 with less than a high school education has become virtually nil by 

CY2001. This could mean that either 55-64 year olds without a HS education are just not 

applying for UI or that 55-64 year old UI recipients without a HS education have become 
318

virtually nonexistent. Another important characteristic of this graph is that the 

percentage of UI recipients in the 55-64 age group in the BA/BS, associates, and graduate 

degree categories at least doubled from CY 1991-CY 2001. 

Graph 10 

Percentage Distribution of UI Recipients by Education Level for Age Group 65+

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Population Percentage

None HS Grad Assoc BA/BS Graduate

 

Graph 10 Summary 

Graph 10 shows the percentage distribution of UI recipients by Education level for the 

age group 65 and over. From this graph we can reasonably infer general population 

education levels for this age group. An important characteristics of this graph is that UI 

recipients 65 and over with less than a high school education has become virtually nil by 

CY2001. Another important characteristic of this graph is that the percentage of UI 

recipients in the 65+ age group in the BA/BS, associates, and graduate degree categories 
319

at least doubled from CY 1991-CY 2001. The HS grad category, although showing a 

spike in 1995, remained fairly steady state in the upper 30% range. 

Graph 11 

Percentage Distribution of UI Recipients

 By Race Age Group 55-64

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Population Percentage

White 55-64 Black 55-64 Hispanic 55-64

Graph 11 Summary 

Graph 11 shows the percentage distribution of UI recipients by race for the age 

group 55-64 from CY 1991-2001. From this graph, we can reasonably infer general 

racial distribution for the age group 55-64 for this time period. The Black and Hispanic 

categories remained fairly stable, slightly increasing during this period while the white 

category slightly decreased during the same period. 
320

Graph12

Percentage Distribution of UI Recipients 

by Race Age Group 65+

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Population Percentage

White 65+ Black 65+ Hispanic 65+

Graph 12 Summary 

Graph 12 shows the percentage distribution of UI recipients by race for the age 

group 65 and over from CY 1991- CY 2001. From this graph, we can reasonably infer a 

general racial distribution for the age group 65 and over for this time period. The Black 

and Hispanic categories remained fairly stable slightly increasing from CY 1991-CY 

2001. The white category showed a significant decrease during the same period. 
321

Graph 13 

Replacement Ratio Age Group 55-64 Compared to All Other UI 

Beneficiaries

0.424 0.424 0.437 0.433 0.441 0.428 0.437 0.434 0.432 0.4310.412

0.371

0.307

0.504

0.375 0.376 0.383 0.378 0.383

0.339

0.3810.374

0

0.1

0.2

0.3

0.4

0.5

0.6

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Replacement Ratio

All Others Age Group 55-64

Graph 13 Summary 

 Graph 13 shows the replacement ratio for the age group 55-64 compared to all 

other UI beneficiaries for CY 1991- CY 2001. The replacement ratio is defined as the 

ratio of average weekly wages to weekly unemployment insurance benefits. A ratio of 

.30 would mean that the UI recipients benefit replaces 30% of what his/her previous 

average weekly wage was. This graph clearly shows that older workers in the 55-64 age 
322

range have a lower replacement rate than other UI recipients. A possible explanation for 

this could be that older workers in this age range are at the peak of their earning curves 

hence UI does not “replace” a large percentage of their incomes. 

Graph 14 

Replacement Ratio Age Group 65+ Compared to All Other UI Beneficiaries

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Replacement Ratio

65+ All Others

 

Graph 14 Summary 

 Graph 14 shows the replacement ratio for the age group 65 and over compared to 

all other UI beneficiaries for CY 1991-CY 2001. Replacement ratios of the age group 65 

and over generally lagged behind other UI beneficiaries until CY 1996 when the ratios 

tended to converge. 
323

Graph 15 

Average Weekly UI Benefit Amount Age Groups 55-64 and 65+ 

Adjusted for Inflation

$165.37

$134.66

$170.33 $171.06 $171.33 $170.45 $170.10 $172.86 $175.99 $176.32

$190.000

$138.67 $140.48 $137.00

$149.52

$138.53 $134.81

$128.14

$139.42 $141.50 $142.03 $146.21

$0.00

$20.00

$40.00

$60.00

$80.00

$100.00

$120.00

$140.00

$160.00

$180.00

$200.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Benefit Level

Age Group 55-64 Age Group 65+

Graph 15 Summary 

 Graph 15 shows the average weekly UI benefit amount for UI recipients in the 55-

64 and 65 and over age group for calendar years 1991-2001. These figures are adjusted 

for inflation using 1991 as the base year. Benefit levels in real dollars generally remained 

the same for the 65 and over category. Benefit levels went up almost 20% for the 55-64 

age category during the same period. At least for the 55-64 age group, increased benefit 

levels mirror the labor market success of this age group during the 1990’s. 
324

Graph 16 

Average Weekly Wage for 

Age Groups 55-64 and 65+ Inflation Adjusted

$0.00

$100.00

$200.00

$300.00

$400.00

$500.00

$600.00

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Weekly Wage

Age Group 55-64 Age Group 65+

 

Graph 16 Summary 

Graph 16 details the average weekly wages, inflation adjusted, for the age groups 

55-64 and 65 and over for the calendar years 1991-2001. These figures are adjusted for 

inflation using 1991 as the base year. Average weekly wages for the 55-64 age group 

have increased over 10 percent during this time period after inflation adjustment. The 

same measure for the age group 65 and over shows a slight decrease during this period. 
325

A possible explanation for the drop in average weekly wages for the oldest of the older 

workers could be higher rates of final retirement or the pickup of post retirement bridge 

employment at a lower wage level. 

Conclusions:

An important dilemma facing America’s workforce as we proceed into the 21st

century will be the loss of human capital associated with the retirement of the baby-boom 

generation. That is to say that, as baby boomers retire from the labor force, they will take 

their lifetimes of accumulated skills, knowledge and expertise with them out of the labor 

force. This problem is especially exacerbated by the fact that most of these older workers 

may take part-time low skill jobs after initial retirement that “bridge” the gap between 

initial and final full retirement from the labor force. I argue that this dilemma is a 

dysfunction of the labor market. Certain labor market externalities such as outdated 

pension rules, employer retirement systems, social security rules, unemployment 

insurance, and length of the workweek may create an “exit effect” that drives older 

workers from the labor market. The implications of this question for labor market 

research are many. Some of these research questions may include: 

1. Given the current retirement landscape, are older workers exiting the labor force at a 

higher rate than in previous years? 

2. Is there a relationship between pension rules, social security rules, unemployment 

insurance rules, length of the workweek, and older worker’s decision to exit primary 

employment? 

3. If older workers retirement decisions are shaped by incentives created by the retirement 

landscape what can be done to identify and ameliorate this problem? 

One of the ways in which we can examine this issue is to examine ways in which barriers 

in the retirement landscape can be alleviated or removed. A research demonstration that 

tests multiple incentive systems that create incentives for older workers to stay in the 

labor market should merit some attention. 
326

For example, one of the primary reasons older workers leave the workforce for 

retirement is the length of the workweek. Many older workers take part-time “bridge 

jobs” after retirement from their primary employment. These older workers do not wish 

to continue the full-time schedule but still wish to continue working in the labor market. 

Many older workers, if given the opportunity, would scale down their labor force 

attachment to part-time status while retaining their current job with their career employer. 

A research demonstration could be constructed that tests this assumption in the 

marketplace. Under such a demonstration, employers would be offered a subsidy to 

construct job-sharing contracts for two older workers seeking retirement. Each set of two 

older workers would “share” one 40 hour per week job. Participating employers would 

be obligated to continue the job-sharing agreement for one year and could continue the 

arrangement after the demonstration is over. This demonstration would be run as a 

classical experiment with a treatment and control group. The treatment group would be 

the “job sharing” older workers while the control group would be a similar set of retiring 

older workers not offered the subsidy. The potential benefits of such a demonstration 

would be enormous. These might include increased tax revenue, decreased receipt of 

Social Security, increased productivity for the employer, and increased labor force 

attachment of older workers in America’s workforce. 

In short, we need to begin researching now on how to keep older workers in 

productive areas of the labor market where their wealth of knowledge, experience, and 

skills are not underutilized. The potential benefits to society could be enormous. 

Likewise, the potential costs of inaction could be equally as enormous. 
327

References:

Committee for Economic Development. 1999. New Opportunities for Older Workers. Washington, D.C.: 

Committee for Economic Development. 

Dohm, Arlene. 2000. Gauging the labor force effects of retiring baby-boomers. Monthly Labor Review. 

July 2000. U.S. Department of Labor: Bureau of Labor Statistics. 

O’Leary, Christopher J., and Stephen A. Wandner. 2000. Unemployment Compensation and Older 

Workers. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.

Purcell, Patrick J., 2000.Older workers: employment and retirement trends. Monthly Labor Review. July 

2000. U.S. Department of Labor: Bureau of Labor Statistics. 

Quinn, Joseph. 1999. “New Paths to Retirement” In Forecasting Retirement Needs and Retirement Wealth, 

Brett Hammond, Olivia Mitchell and Anna Rappaport, ed. Philadelphia, PA: University of Pennsylvania 

Press. 

Quinn, Joseph F., Richard V. Burkhauser, and Daniel A. Myers. 1990. Passing the Torch: The Influence of 

Economic Incentives on Work and Retirement. Kalamazoo, MI: W.E. Upjohn Institute for Employment 

Research. 

The United States General Accounting Office (GAO). Older Workers: Demographic Trends Pose 

Challenges for Employers and Workers. November 2001. GAO-02-085. 
328

Technical Appendix 

Technical Notes pertaining to JTPA Title III/WIA Dislocated Worker Graphs

I. The data from these graphs were taken from the Six-State (Texas, Florida, 

Missouri, Maryland, Illinois, Georgia) Administrative Data Research Alliance 

Grant K-6558-8-00-80-60. 

II. The states providing data for this exercise were Texas, Maryland, Florida, and 

Missouri 

III. Data were based on longitudinal data from these states for Dislocated workers 

only because the definition from JTPA to WIA remained the same during the 

program years considered. 

a. The definition of a dislocated worker remained the same from JTPA to 

WIA 

b. Changes in definitions between JTPA and WIA can create problems for 

longitudinal data analysis though because under JTPA an adult was 

defined as being both 21 years and older and economically disadvantaged. 

Under the WIA, an adult is defined as being over 18 years of age. 

IV. This analysis can be extended to adults and youth, however, because the six 

state research alliance site partners actually house the data, they have the 

ability to sort JTPA records under the WIA definition and make estimates so 

that, with data uniformity, valid conclusions could be made concerning Adults 

and Youth. 

V. Given the various JTPA/WIA data elements, a wide variety of analyses could 

be executed including, for example, participation in JTPA/WIA of welfare 

recipients. 

VI. The linear trend line present in each of the JTPA/WIA graphs is a linear 

regression line calculated using the least-squares method. 

Technical Notes pertaining to the Employment Service Program and Bureau of 

Labor Statistics Graphs

I. The data from these graphs were taken from the US Department of LaborEmployment Service Programs ETA 9002 Quarterly Reports PY1992-

PY2000. 

II. The data in the ETA 9002 quarterly reports are based on aggregate data taken 

from each of the states. 

III. For each age category the age of the applicant is the age at which the 

application to the Employment Service was received. 

IV. It is understood that the Employment Service is currently precipitating 

changes to some of these definitions and discontinuing others. However, the 

definitions for all data presented in these graphs fall under ET Handbook NO. 

406. 
329

V. The linear trend line present in each of the ES graphs is a linear regression 

line calculated using the least-squares method. 

Technical Notes pertaining to the Benefit Accuracy Measurement (BAM) Graphs

I. The data from these graphs were taken from the US Department of LaborOffice of Workforce Security Benefit Accuracy Measurement database 

CY1991-CY2000. 

II. The inflation adjustment done in graphs 15 & 16 use 1991 as a base year. 

III. The linear trend line present in graphs 7 & 8 is a linear regression line 

calculated using the least-squares method. 
330

The Role of One-Stop Career Centers 

 in Serving Welfare Recipients in 2002 

Demetra Smith Nightingale 

Fredrica D. Kramer 

John Trutko 

Michael Egner 

Burt S. Barnow* 

Urban Institute 

 

*Johns Hopkins University Institute for Policy Studies 

This paper was prepared by the Urban Institute for the U.S. Department of Labor, Employment and 

Training Annual Research Conference, Washington, DC June 4-5, 2003. It is based on a report prepared 

for the U.S. Department of Labor, Employment and Training Administration, Under Contract No. K-6830-

8-00-80-30, Task Order #7. Opinions expressed are those of the authors and do not represent official 

positions of the Department of Labor, the Urban Institute, its Trustees or its sponsors.
331

I. INTRODUCTION 

 Public assistance recipients have historically been an important target group for 

employment and training programs, but the types of services and programs available 

have varied depending on federal and state legislation, policies and funding. With the 

passage in 1996 of federal welfare reform legislation in the Personal Responsibility 

and Work Opportunity Reconciliation Act (PRWORA), and the passage of the 

Workforce Investment Act (WIA) in 1998, the role of workforce development 

programs in serving welfare recipients—and other individuals—has changed 

considerably. 

 The Temporary Assistance for Needy Families (TANF) authorized by PRWORA 

dramatically changed the nation’s welfare system, particularly by emphasizing work 

and employment activities. Welfare benefit time limits were also instituted under 

TANF, adding to the urgency some clients and case managers feel about helping 

individuals move quickly into employment. The increased work focus suggests that 

more welfare recipients might seek services through the workforce development 

system. 

 A major component of the revamped workforce investment system under WIA 

has been the development of a nationwide network of local One-Stop Career Centers, 

through which workers and employers can access all work-related services and 

information. Many different federal funding sources are available to provide 

employment-related services. Some funding is restricted by eligibility criteria that 

determine who can be served (e.g., intensive training funded under WIA, workrelated services funded by Welfare-to-Work grants and TANF block grant funds). 
332

Other funding is available to all individuals currently working as well as those 

seeking work (e.g., WIA core services and Wagner-Peyser funded employment 

services). At the local level, all of these funding streams—and others—may be used 

to fund One-Stop Center operations. 

 This paper examines how welfare recipients are being served through the 

workforce development system, particularly the One-Stop Centers. The important 

changes that have occurred in both the workforce development system and the 

welfare system make this exploration especially timely. 

WIA and One-Stop Centers: An Overview 

WIA is the primary federal legislation governing employment and training-related 

programs. The broad intent of WIA, which replaced the Job Training Partnership Act

(JTPA) of 1982, is to improve overall national economic productivity and 

competitiveness by simultaneously investing in developing and upgrading the skills of 

the nation’s workforce and responding appropriately to the changing labor needs of 

employers. How these two objectives—developing worker skills and serving employer 

needs—are balanced defines the types of workforce investment systems that are now 

operating around the nation. 

The WIA legislation includes several provisions that together are changing the 

nation’s employment and training system. WIA calls for streamlined services through 

comprehensive One-Stop Career Centers; enhanced customer choice in selecting training 

programs; greater universal access to services; increased emphasis on work, rather than 

training, for those who already have employment skills; enhanced local flexibility; 

increased private-sector involvement through new local boards; maximum use of 
333

information technology; and refocused youth programs that emphasize long-term and 

year-round youth development. Most of these features already existed in many localities 

before 1998, but WIA now requires them nationwide. 

 Defining Workforce Development and Workforce Investment. The term “workforce 

development” is fairly recent, used since the mid-1990s to convey an expanding mission 

of federal employment and training policy that aims both to provide employment services 

or skills training to workers seeking services and to address the broader objective of 

improving the overall functioning of the workforce itself. Thus, WIA is more than just 

the latest employment and training legislation; it is the legislative mechanism for 

operationalizing this broader concept of workforce development. As such, WIA reflects 

the assumption that in order to improve the overall quality of the workforce, the nation’s 

employment and training investments should go beyond job training, that all resources 

and programs related to skills or employment should be coordinated, and that the 

modernized workforce investment system should be more responsive to the needs of the 

private business sector as well as make available a full range of labor market services to 

all workers who seek them (i.e., universal services). 

 Therefore, in addition to repealing and replacing JTPA, WIA also amended 

legislation related to the Employment Service (ES) and had provisions regarding 

coordination of other work-related programs, including vocational rehabilitation and 

adult education. As noted below, the role of the Wagner-Peyser Act’s Employment 

Service (ES) in the evolving workforce development system is as central as the adult, 

youth, and dislocated worker programs authorized by WIA (and previously by JTPA). 
334

One-Stop Centers’ Role in the Workforce Investment System. To enhance these 

various objectives—universal services to workers and businesses, a better trained 

workforce, and improved coordination among programs—WIA also mandated a system 

of One-Stop Centers nationwide. The One-Stop Career Center system is a main 

component of the employment and training service delivery; and welfare recipients, like 

other workers and potential workers, can access various services through the local 

Centers. Each local workforce investment system is to have networks of partnering 

agencies and programs and streamline the way customers (i.e., workers, potential 

workers, and employers) access services. At least one physical One-Stop Center must 

operate in each local workforce investment area designated by the Governor, and that 

Center must ensure that all available services from partnering programs are accessible to 

customers—either on site at the Center or through other institutional arrangements. Other 

arrangements could, for example, include “virtual” information about programs and 

services using computer-assisted technology, or setting up appointments with 

representatives of other agencies who have regularly scheduled hours at the One-Stop. 

 Thus, the network of physical One-Stop Centers is the most concrete component of 

the workforce investment system. Other aspects of the system include computer-based 

technology to access program information and services (“virtual” One-Stops to access 

labor market, career, and job information), individual training accounts for self-directed 

access to skills training, and coordination with partnering programs and agencies located 

elsewhere. 

 There is no single One-Stop service delivery model. Local workforce boards and 

elected officials have wide latitude in designing their local delivery system, including 
335

deciding on the types of physical One-Stop Centers they operate and whether there are 

other service delivery efforts as well. Nor do all physical One-Stop Centers necessarily 

offer the same mix or range of services or have the same partners on-site. Some OneStop Centers have been developed around the Wagner-Peyser ES program, which has had 

a universal labor-exchange service mission since 1933; others have evolved mainly from 

the adult training and employment services that had been central under JTPA; and some 

Centers are a full merger of both the Wagner-Peyser and adult WIA programs. To some 

extent the variation across One-Stops in how they developed may influence the amount 

of emphasis or priority placed on services to employers and businesses, economic 

development, and job skills development and training. 

 One-Stop Partners. Local workforce investment boards (WIBs) have wide 

latitude in deciding what programs and services are offered at that Center, and the WIBs 

along with local partnering agencies decide how much of the services through various 

programs are offered at the One-Stop Center versus at other locations. The mix of OneStop partners, the extent to which partners provide a full range of services at the OneStop, and the degree to which partnering programs are fully integrated or function 

independently vary across Centers. 

WIA specified certain mandatory One-Stop Partners, and suggested a number of 

additional optional partners, as noted in Table I.1. In addition, states can add other 

mandated partners. According to DOL information, about half the states have designated 

TANF programs as mandatory partners. If an agency or organization is a mandatory 

One-Stop partner, it means that at a minimum—just as with other mandatory One-Stop 
336

partners—individuals should be able to access complete information at the One Stop 

Center about its programs and services and how to access those services. 

TABLE I.1 

Required and Optional Partners in One-Stop Centers 

Required Partners

• Adult, Dislocated Worker, and Youth Activities under WIA 

• Employment Service under Wagner-Peyser Act 

• Adult Education 

• Postsecondary Vocational Education under Perkins Act 

• Vocational Rehabilitation 

• Welfare-to-Work Grant-funded Programs 

• Title V of the Older Americans Act 

• Trade Adjustment Assistance 

• NAFTA Transitional Adjustment Assistance 

• Veterans Employment and Training Programs 

• Community Services Block Grant 

• U.S. Department of Housing and Urban Development (HUD) 

Employment and Training Programs 

• Unemployment Insurance 

Optional Partners Specifically Noted in the Legislation 

 

• Temporary Assistance for Needy Families 

• Food Stamps Employment and Training Program 

• National and Community Service Act programs (e.g., Americorps) 

• Any other appropriate federal, State, or local programs (e.g., 

transportation, child care, community colleges, and economic 

development) 

The specific details of the TANF mandated role differ across states. For example, 

in some states, the TANF-work program is a mandatory One-Stop partner, while in other 

states the entire TANF program (i.e., cash assistance as well as the work program) is the 

mandated partner. On the other end of the continuum, TANF intake and eligibility 

determination as well as the TANF work program might physically occur at the One-Stop 
337

Center. As discussed in sections below, in most localities the linkages fall somewhere in 

between these extremes. 

 One-Stop Operators. Each local area (i.e., the workforce investment area, as 

designated by the Governor) must have at least one physical “full service” One-Stop 

Center, and a One-Stop “operator” is designated by the local Workforce Investment 

Board (WIB) to oversee day-to-day functioning of the Center. The legislation specifies 

that organizations eligible to be One-Stop operators include: 

• Postsecondary educational institutions 

• Local Employment Service offices 

• Community-based organizations 

• For-profit organizations 

• Government agencies 

• Others 

 Neither the WIB nor its administrative entity can directly provide services or 

operate a One-Stop Center; instead One-Stop Center Operators must be selected 

competitively by the WIB. There are a few exceptions. For example, if there are no 

viable competitive alternatives, the WIB could select a particular operator. The move 

toward more privatization of public services is an important change in job training. 

Although most private industry councils (PICs) which oversaw JTPA also contracted out 

most, if not all service delivery, they had the discretion to decide how to deliver services, 

including whether to operate any programs or deliver services directly. 

 One-Stop Funding. Appropriations for adult, youth, and dislocated worker 

programs are authorized under WIA, and the level of appropriated funds are similar to 

what had previously been appropriated under JTPA for similar purposes. However, 

Congress did not appropriate any additional funds for the One-Stop system or for training 

or employment services. The expectation is that resources from the adult, youth and 
338

dislocated programs as well as from pre-existing programs, such as the Wagner-Peyser 

Act’s Employment Service and other partnering programs and agencies, would be used to 

operate a coordinated system of employment-related services offered through the OneStop Centers. 

 Individual Training Accounts. Congress specified that to the maximum extent 

possible, WIA-funded job training should be provided only to those who lack adequate 

skills to obtain employment, and that training must be provided through a voucher-type 

mechanism called Individual Training Accounts (ITAs). Under JTPA, much of the job 

training was contracted to training providers or programs. If funds are limited, the 

legislation specified that priority should be given to recipients of public assistance and 

other low-income individuals for intensive services and training services. 

One-Stop Centers Visited as Part of this Study 

As part of this study, the extent and nature of service to welfare recipients through 

seven One-Stop Centers are addressed: Asheville, North Carolina; Burlington, 

Vermont; Hayward, California; Kansas City, Missouri; Nashville, Tennessee; Oakland, 

California; and Philadelphia, Pennsylvania. The objective of the exploratory visits was to 

understand how welfare recipients are served though these One-Stops (the name of each 

Center and the local TANF and WIA agencies for each Center are listed in Table I.2). 

The seven sites were purposively selected mainly to obtain geographic diversity, and no 

attempt was made to include sites that served high proportions of welfare recipients. 

While this is not a random sample of sites, these seven localities may be typical in many 

ways of other One-Stops. However, since the sites were not selected to be representative 

of all One-Stop Centers, care should be taken in generalizing results from this small 
339

sample of sites, especially given the extensive flexibility states and localities have in 

structuring local arrangements.1

The next section provides a summary of the interaction of welfare agencies and 

workforce development programs based on existing literature, discussions with several 

representatives of key national organizations, and review of national workforce 

development program data. The operations and delivery of services to welfare recipients 

in the selected local One-Stop Centers are described in Section III. 

 

1 Appendix A provides detailed information on each of the One-Stop Centers visited. 
340

TABLE I.2 

One-Stop Centers Visited 

Locality One-Stop Center Local WIA Agency Local TANF Agency 

Ashville, NC Mountain Area 

Joblink Career 

Center 

Mountain Area Workforce 

Development Board 

Buncombe County 

Department of Social 

Services 

Burlington, VT Burlington Career 

Resource Center 

Vermont Department of 

Employment and Training 

Vermont Department of 

Prevention, Assistance, 

Transition, and Health 

Access (PATH), 

Burlington District Office 

Nashville, TN Nashville Career 

Advancement 

Center/Davidson 

County Career 

Center 

Middle Tennessee 

Workforce Investment 

Board/Nashville Career 

Advancement Center 

Tennessee Department of 

Human Services, 

Davidson County Office 

Philadelphia, PA Philadelphia 

County Team 

Pennsylvania 

CareerLink 

Philadelphia Workforce 

Development Corporation 

Philadelphia County 

Assistance Office 

Hayward, CA Eden Area OneStop Career Center Alameda County Social 

Services Agency, 

Department of Workforce 

and Human Services, 

Workforce Investment 

Board Division 

Alameda County Social 

Services Agency, 

Department of Workforce 

and Human Services, 

CalWORKS Division 

Oakland, CA Oakland Career 

Center East 

City of Oakland 

Workforce Investment 

Board 

Alameda County Social 

Services Agency, 

Department of Workforce 

and Human Services, 

CalWORKS Division 

Kansas City, 

MO 

Central Kansas 

City One Stop 

Career Center 

Greater Kansas City Full 

Employment Council 

Missouri Department of 

Social Services, Division 

of Family Services, 

Jackson County DFS 

Office 
341

II. THE INTERACTION OF WORKFORCE DEVELOPMENT PROGRAMS 

AND WELFARE RECIPIENTS 

Since the 1960s, federal employment and training programs have had various 

mandates and priorities, although two overall objectives have remained paramount: 

helping workers seeking jobs, and serving employers and businesses in meeting their 

need for labor. Programmatic priorities about targeting services to specific populations, 

such as the economically disadvantaged or the unemployed, versus providing services to 

all job seekers and all employers (i.e., universal services) have periodically shifted. 

Similarly, the priority placed on skills development and the balance between skills 

training and basic labor exchange (e.g., job placement and job matching) has also varied 

over time. 

Until 1998, the major programs were funded under JTPA, and these programs 

along with the ES represented the main general resources for skills training and job 

placement services. In addition, employment and training programs and agencies have 

had formal and informal roles in welfare-specific employment programs. This chapter 

examines trends in services to welfare recipients, the historic role of employment and 

training programs in serving the welfare population, and summarizes key issues related to 

coordination and interaction between the two systems. 

Welfare Recipients in JTPA and the ES 

Welfare recipients’ participation in JTPA and ES in the 1990s provides a general 

sense of the extent to which workforce development programs have served welfare 

recipients. While a new data reporting system is available for WIA, a number of states 

have not yet submitted data files to DOL and some data inconsistencies were identified 
342

for many other states in the first annual files; therefore, those data are not used in this 

report. Presumably, the general pattern of services to welfare recipients has remained 

similar since the inception of WIA.2 

 JTPA. Welfare recipients were served under all three of the major components of 

JTPA: adult programs (Title II-A), dislocated worker programs (Title III), and youth 

programs (Title II-C). Based on data extracted from the JTPA Standardized Program 

Information Report (SPIR) system, the following two charts show the number of JTPA 

terminees3 on welfare during program years (PY) 1993 to 1999, in aggregate numbers 

(Chart II.1) and as a percent of all JTPA terminees (Chart II.2).4 

 In general, the total number of all persons participating in JTPA programs declined by 

about 13 percent between 1993 and 1999. The number of welfare recipients in JTPA, 

though, declined by a much greater number; 55 percent over the same period—from 

100,000 to 46,000. Similarly, welfare recipients gradually represented a smaller share of 

all JTPA participants, although Title II-A programs continued to serve the most TANF 

(or AFDC) recipients. In 1993, about one-fifth of all JTPA participants (i.e., terminees) 

were AFDC recipients—including about one-third of Title II-A adult program 

participants. By 1999, only about 10 percent of all JTPA participants were on TANF (or 

AFDC), including about 20 percent of Title II-A participants. 

 

2

 Appendix B provides more detailed JTPA data on the state level. 

3

 The reporting convention used in JTPA was to report the individuals once they terminate from the system. 

Since all individuals terminate, this is generally considered an accurate estimate of the number of 

participants. 

4

 Data were not available for Program Year 1999 for 6 states that were early implementers of the 

Workforce Investment Act. For those states, we estimated PY 1999 data by assuming that the number of 

terminees changed by the same percentage as for all other states combined. 
343

A main reason for the decline in the number of welfare recipients in JTPA was 

probably the shift in resources from the adult and youth programs to dislocated worker 

programs. As shown in Charts II.1 and II.2, welfare recipients were never a major factor

CHART II.1 

JTPA Terminees who were Receiving Welfare, by Title, 1993-1999

CHART II.2 

Percent of JTPA Terminees who were Receiving Welfare, 

By Title, 1993-1999

0

20,000

40,000

60,000

80,000

100,000

120,000

1993 1994 1995 1996 1997 1998 1999

Program Year

# of JTPA Terminees 

Title IIA Terminees Title IIC Terminees

Title III Terminees Total Terminees

0%

5%

10%

15%

20%

25%

30%

35%

40%

1993 1994 1995 1996 1997 1998 1999

Program Year

% of JTPA Terminees

Title IIA Terminees Title IIC Terminees

Title III Terminees Total Terminees
344

 in the Title III dislocated worker program, and over the 1993-1999 period, the 

proportion of dislocated workers served who were welfare recipients never exceeded 2.2 

percent. While the reduction in number of welfare recipients in JTPA is similar to the 

nation’s welfare caseload decline, one would not necessarily have expected this decline 

in JTPA since the proportion of all welfare recipients historically served by JTPA had 

been so low. That is, despite the welfare caseload declines, there were still many welfare 

recipients who could potentially have been served by JTPA. 

 In fact, the Title II-A program always served significantly more welfare recipients 

than the other two titles, but as can be seen in Chart II.2, welfare recipients’ share of the 

total served in Title II-A first increased but then declined between PY 1993 and PY 1999. 

Their share rose from 32 percent of Title II-A terminees in PY 1993 to 34 percent in PY 

1994; their share then declined in each subsequent year, reaching 21 percent in PY 1999. 

The number of Title II-A welfare terminees declined from about 57,000 in PY 1993 to 

fewer than 31,000 in PY 1999. 

 Chart II.3 illustrates an important point that is central to understanding the interaction 

and relationship between workforce development programs and TANF programs: 

although welfare recipients comprised a fairly significant percentage (20 to 30 percent) of 

all participants in employment and training programs, they still represented only a very 

small portion of the nation’s welfare recipients. That is, between PY 1993 and PY 1999, 

fewer than two percent of adults on welfare were enrolled in JTPA each year. 

Interestingly, the proportion rose slightly between 1993 and 1999, mainly because 
345

although the number of welfare recipients enrolled in JTPA fell over this period, the total 

number of TANF recipients declined by a greater percentage. 

CHART II.3 

Percent of Welfare Parents who were Participating 

in JTPA Programs, 1993-1999 

 The Employment Service. In addition to JTPA, the local ES/Job Service is the 

other major workforce development program through which welfare recipients may 

obtain employment assistance. Chart II.4 thus shows the number of ES registrants 

receiving any type of public assistance over the period 1993-2000.5 Several 

important observations can be made from this chart. 

 First, as in JTPA, only a small proportion of public assistance adults register with 

the ES.6

 While more public assistance recipients were in the ES than in JTPA and 

 

5 Unlike the JTPA reporting system which distinguished welfare recipients by the type of benefit program 

from which they receive cash benefits, the ES reports all public assistance recipients in the aggregate rather 

than by each program (i.e., TANF, food stamps, and state general assistance). Appendix C includes 

national and state ES data from ETA 9002A Quarterly Reports. 

6

 We estimate that about 6-10 of percent public assistance recipients may register with the ES. This is 

based on the following: about 700,000 persons who were registered with the ES in 1993 were reported as 

“public assistance,” compared to about 10-12 million adults on TANF, food stamps, or General Assistance. 

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

1993 1994 1995 1996 1997 1998 1999

Program Year

% of TANF/AFDC Adults
346

while a larger share of all public assistance recipients were registered with ES, in 

2000 they represented only about 5 percent of all ES registrants (a bit higher than in 

1993 when 3 percent of ES registrants were on public assistance). 

CHART II.4 

ES Registrants who were Receiving Welfare 

(GA, FS, or TANF/AFDC), 1993-2000

 In addition, total ES registrants declined between 1993 and 2000 by about 20 

percent—from about 21 million to about 17 million, reflecting federal budget 

reductions. However, the number of public assistance recipients registered with the 

ES actually increased by about 20 percent over the same period (from about 650,000 

in 1993 to about 800,000 in 2000). It is possible that the number registering with the 

ES increased as a result of state welfare reform policies in the late 1990s that 

strengthened work requirements and mandated more active job search activities. 

 In FY2000, about 800,000 ES registrants were “public assistance” out of 8 million on TANF, food stamps 

or General Assistance. 

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1993 1994 1995 1996 1997 1998 1999 2000

Program Year
347

 In summary, welfare recipients have enrolled in both the ES and JTPA programs. 

While there were substantially more public assistance clients in the ES than in JTPA 

throughout the 1990s, they comprised a much larger proportion of all JTPA 

participants--about 20 percent of all JTPA terminees in 1999 and 35 percent of Title 

II-A terminees were TANF recipients. Approximately 5 percent of all ES registrants 

were TANF recipients. In addition, it appears that the number of welfare recipients 

registered with the ES increased by about 20 percent during the 1990s, while the 

number in JTPA declined by about 50 percent.7 The welfare population represents a 

greater share of customers in JTPA than the customers in the ES, but in either case 

those enrolled represent just a small portion of the total adults on welfare. 

 The decline in welfare recipients’ use of JTPA and the rise in the ES probably 

also was affected by the changes in welfare policies and philosophies in most states, 

from emphasis on human capital (i.e., training) to immediate employment (i.e., “work 

first”). It is not clear yet, though, how this will translate into the WIA arena. Some 

welfare recipients may continue to use the ES for basic job services, although 

presumably some will also turn to One-Stops for WIA core services. To the extent 

that all ES/Wagner Peyser services are collocated at the One-Stop and some welfarerelated services are also there, welfare recipients may use ES services at that location. 

If welfare agencies increase their focus on training and upgrading, more TANF 

recipients may seek and qualify for intensive services under WIA than has been true 

in the past few years. The next sections examine more closely the changes in the 

welfare-workforce interaction prior to and under TANF, WIA, and the One-Stop 

System. 

 

7

 It was not possible in this study to determine what services they received or service effectiveness. 
348

Historic Role of Employment Agencies’ Services to Welfare Recipients 

The role that One-Stops play in providing services to welfare recipients reflects 

the changing federal policies regarding both systems, the historic interaction and 

relationship between the workforce development system and the welfare system before 

WIA and PRWORA/TANF legislation was enacted, and the more recent relationships 

around WtW and state welfare reform efforts. It is useful to summarize what is known 

about the relationship and interaction between these two systems generally, and different 

perspectives about that relationship. 

Employment and training agencies and programs have had both direct and 

indirect roles and responsibilities for serving welfare recipients. One of the main 

programs targeted to welfare recipients and delivered by the employment and training 

system was the Work Incentive (WIN) program, created in 1967 to assist recipients of 

Aid to Families with Dependent Children (AFDC) to gain employment and to enforce the 

work requirement introduced at the same time. WIN was jointly administered at the 

federal level by the Department of Labor and the Department of Health and Human 

Services (formerly Health, Education, and Welfare). At the state and local level, the 

Employment Service (ES) and the welfare agency were mandated to operate the WIN 

program together—the ES staff handled employment services, and welfare social workers 

in a special staff unit of the welfare agency handled child care, family counseling, and 

other supportive services. In some places WIN was operated out of local ES offices, in 

others it was operated out of welfare offices, and in a few it was located within the job 

training agency. In all cases, personnel from both the employment agency and the 

welfare agency shared responsibility for delivering WIN services. 
349

Beginning in the 1980s, when states were given the option under demonstration 

authority of operating the WIN program under a single (rather than dual) agency 

structure, many state welfare agencies chose to provide employment services directly. In 

some places the ES continued to provide employment services as in the past (but under 

subcontract to the welfare agency); in others the administrative entity for the Job Training 

Partnership Act (JTPA) had a contract from the welfare agency to provide services; and 

in some places the welfare agency operated their own in-house employment units. 

In 1988, the Job Opportunities and Basic Skills (JOBS) Training program, 

authorized by the Family Support Act, replaced WIN. State welfare agencies were given 

full responsibility for administering the JOBS program. As during the WIN singleagency demonstration period, some welfare agencies operated their own JOBS program, 

some contracted with the ES or with the JTPA Private Industry Council (PIC) or the 

JTPA administrative agency, and a few contracted some JOBS services to other 

providers, such as community colleges or community-based organizations. 8

New Roles and Arrangements under TANF and WIA 

With the enactment of federal welfare reform legislation in 1996, the structure 

and institutional arrangements of welfare-employment programs became even more 

varied. States have wide latitude on how to use the federal TANF block grant, including 

deciding how much of the TANF funds should be devoted to work-related programs. 

Some states (e.g., Wisconsin and Utah) have combined the welfare and workforce 

development functions in one agency at the state level, as part of a broader effort to 

 

8 For a detailed description of the links between the two systems in 1997 see, Demetra Smith Nightingale, 

Russell Jones, Carolyn T. O'Brien and Kathleen Brennan, The Structural Link Between JTPA and State 

Welfare Reform Programs in 1997, The Urban Institute, prepared for the U.S. Department of Labor, 

Employment and Training Administration, 1997. 
350

create a clear work-based transitional public assistance policy, consistent with the intent 

of the federal welfare reform law. It is also now quite common for welfare agencies to 

contract with several employment service providers, including non-profit organizations, 

other public institutions such as community colleges, and for-profit companies, to operate 

the TANF work programs. At the local level, One-Stop Centers and workforce 

investment agencies are often one of several providers of employment services to TANF 

recipients. 

 State-Level Interaction Between Workforce Development and TANF. The ES 

and WIA agencies and programs continue to be important collaborators with welfare 

agencies—either as a result of their being housed in the same department or agency at the 

state level, or as contractors to deliver TANF work services. More specifically, there is 

evidence that more states have integrated TANF and workforce development policies at 

the state level, and more TANF agencies are formally contracting with workforce 

agencies to deliver some TANF-work program services. 

In a study of linkages between JTPA and state welfare programs, conducted in 

1997—one year into the 1996 welfare reform changes and on the eve of the passage of 

WIA—the Urban Institute found substantial variation.9

 Utah and Wisconsin had 

integrated their welfare and workforce departments, and four other states had joint 

administration of the TANF work program and workforce development programs. All 

other states had less formal linkages, but much informal collaboration at the state level, 

varied coordination at the local level, and integration initiated by local administrators. 

 

9

 Demetra Smith Nightingale, Russell Jones, Carolyn T. O'Brien and Kathleen Brennan, The Structural 

Link Between JTPA and State Welfare Reform Programs in 1997, The Urban Institute, prepared for the 

U.S. Department of Labor, Employment and Training Administration, 1997. 
351

Utah and Wisconsin are still the only states that are fully integrated at the state 

level, but a number of other states now also appear more integrated than they had been in 

1997. Table II.1 summarizes the state-level linkages in 2002, based on information 

obtained from DOL federal and regional sources, state information maintained by 

national associations, and a review of state TANF and workforce development program 

Internet sites.10 Most recently, major reorganizations in Ohio and Arizona have more 

closely integrated welfare and workforce programs in the same state department. In five 

other states—Connecticut, Florida, Michigan, New York, and Texas—the TANF-work 

programs and WIA are now located in the same overall department (even though the 

TANF cash program is in a separate department). This means that nine states now have 

substantially integrated TANF work activities and workforce activities at the state level 

(compared to six states with that degree of integration in 1997). 

In addition to these nine fairly integrated states, it also appears that compared to 

1997, more state welfare agencies in 2002 formally contract with the state workforce 

agency (20 states in 2002 compared to 16 states in 1997). In the remaining 22 states 

(down from 29 in 1997), there are no formal contractual or organizational linkages at the 

state level, but local contracting and coordination often exists because local 

administrators in these and other states have the authority to develop interagency 

linkages, even if they do not formally exist at the state level. 

 

10 The table simply describes the structural linkages at the state level and is descriptive only. It is not 

intended to measure the quality or effectiveness of the WIA-TANF relationship. 
352

TABLE II.1 

State-Level Organizational Linkages Between TANF and 

Workforce Development Programs 

Type of Linkage Description States 

Full Integration TANF Cash, TANF Work and WIA are 

all administered by the same department 

and agency 

Utah, Wisconsin 

Departmental 

Integration 

TANF and WIA are administered 

within the same department or 

interagency commision; but TANF 

Cash is administered by one agency or 

division, and TANF Work and WIA are 

administered by a different agency or 

division 

Arizona, Ohio 

Formal Work 

Integration 

TANF Work and WIA are administered 

by different agencies within the same 

department or interagency commission; 

TANF Cash is in a different department 

Connecticut, Florida, Michigan, 

New York, Texas 

Formal 

Interaction 

TANF Work and WIA are administered 

by agencies in different departments 

with formal transfer of TANF funds at 

the state level to operate all or part of 

TANF Work, or state TANF agency 

contracts directly with substate local 

WIB's or One-Stops 

Delaware, New Hampshire, 

Pennsylvania, Missouri, Iowa, 

Nebraska, New Jersey, Oregon, 

Oklahoma, Tennessee, West 

Virginia, Minnesota, Georgia, 

Massachusetts, Hawaii, North 

Dakota, South Dakota, Vermont, 

Montana, Wyoming 

Varied Local 

Interaction 

TANF Work and WIA are administered 

by agencies in different departments at 

the state level, with few structural links 

at the state level aside from 

coordinating committees, MOU's, or 

task forces 

California, Colorado, Idaho, 

Indiana, Maryland, North 

Carolina, Virginia, Washington, 

Alabama, Alaska, Arkansas, 

District of Columbia, Illinois, 

Kansas, Kentucky, Louisiana, 

Maine, Mississippi, Nevada, 

New Mexico, Rhode Island, 

South Carolina 

Local-level Interaction Between Workforce Development and TANF. There is 

limited nationwide data about the extent to which local workforce programs in general 

and One-Stop Centers in particular interact with welfare agencies and programs. 

However, a study by the U.S. General Accounting Office (GAO) concludes that local 

interaction between welfare programs and One-Stop Centers appears to be increasing. 
353

GAO conducted a survey of state WIA officials in late 2001 that revealed some 

degree of welfare-workforce coordination in nearly all states and an increase in 

coordination compared to the year 2000. According to the GAO survey, TANF funds 

supported One-Stops in 36 states, One-Stops had linked at least informally with the 

TANF program in 44 states, and TANF-work programs were colocated at One-Stops in 

39 states.11

In some states and localities, the ES, WIB, or One-Stop Centers are contracted to 

deliver just some aspects of the TANF-work program (e.g., conduct work registration or 

organize job search workshops), while in other places the WIB or ES operates the entire 

TANF-work program. Regardless of whether the WIB or the ES has a formal contractual 

arrangement with the welfare agency, whether the welfare agency refers individuals for 

services, or whether TANF is a One-Stop partner, the universal services mission of the 

One-Stop system means that welfare recipients and former recipients, like other persons 

seeking employment services, can seek services in local ES or One-Stop offices on their 

own. 

Coordination Issues 

Despite the shared interest of the welfare and workforce systems in serving 

welfare recipients, the focus and responsibilities of each system create different 

challenges and different expectations of the other, which have persisted throughout much 

of their histories but may now be changing. How well the systems have dealt with these 

challenges is both a function of their perceptions of each other, and a function of different 

organizational missions and cultures, which vary in different localities. 

 

11 Workforce Investment Act: States and Localities Increasingly Coordinate Services for TANF Clients, but 

Better Information Needed on Effective Approaches, U.S. General Accounting Office, GAO-02-696, July 

2002. 
354

 Different Cultures, Different Expectations, and Growing Interdependence. 

Historically, federal welfare programs have been concerned principally with cash 

support. Although AFDC had some form of work requirement beginning in 1968, until 

the imposition of TANF’s strict work participation requirements, only about one-third of 

adult welfare recipients were required to register for work-related services, and only 

about half of those received any employment services or training through the welfare 

agency’s work program (i.e., WIN and then JOBS). While there are differences across 

localities, the general perception of welfare administrators and officials over the years has 

been that the workforce development system was not particularly concerned with serving 

welfare recipients or that programs were not very responsive to the needs of welfare 

recipients. Administrators and staff on the workforce development side, meanwhile, 

often perceived that welfare agencies and staff did not really emphasize employment and 

were more concerned with welfare procedures and requirements surrounding eligibility 

and cash payment accuracy. These long-standing perceptions reflect different priorities 

and missions and result in different perspectives of staff in each system. 

For example, welfare agency staff may correctly assume that the workforce 

development programs and agencies are the primary source of publicly-funded 

employment services in their community. However, these programs have very limited 

resources. By some estimates, JTPA funding could only allow about 2 to 3 percent of 

eligible economically disadvantaged persons to be served.12 The scarce resources meant 

that many who were eligible could not be served. Since there are no more resources in the 

workforce development system today than there were before WIA, it is fair to assume 

 

12 Pamela J. Loprest and Burt S. Barnow, “Estimating the Universe of Eligibles for Selected ETA 

Programs,” The Urban Institute, Washington DC, 1993. 
355

that no more individuals can receive services under these funding streams than in the 

past. Welfare staff, meanwhile, often note that many individuals they refer to local 

workforce development programs are not selected for programs because they do not meet 

some specified requirement (e.g., reading and math levels), or because they have more 

complex problems than other clients, which make it difficult for them to participate in the 

programs as designed. Workforce agencies lack the resources, and often the experience, 

to address the personal deficiencies or social service needs that many welfare recipients 

have that may hinder their successful participation in programs. In addition, the TANF 

emphasis in the late 1990s on immediate employment—rather than training or education 

first—sometimes made it less desirable to consider other employability development 

activities, that TANF staff and workforce program staff agreed might be helpful for 

achieving long-term goals. 

 New Coordination Challenges and Remaining Concerns. Coordination 

generally refers to situations where two or more organizations work together, through 

formal or informal arrangements, to meet one or more of the following goals: (1) 

improve the effectiveness of programs, (2) reduce program costs, (3) avoid unnecessary 

duplication of services, and/or (4) improve measured performance on outcomes of 

interest to program administrators.13 The simplest form of coordination is the sharing of 

information across programs. Other forms of coordination include joint planning, 

coordinated referrals, and coordinated provision of services. The most complete form of 

coordination is program or service integration, where programs merge their funding and 

 

13This definition was developed by DOL/ETA for use in an earlier study of local coordination of Job 

Training Partnership Act (JTPA) programs with other state and local programs: John Trutko, Lawrence 

Bailis, Burt Barnow, and Stephen French, An Assessment of the JTPA Role in State and Local 

Coordination Activities, prepared by James Bell Associates, Inc., for the U.S. Department of Labor, 

Research and Evaluation Report Series 91-D, 1991. 
356

jointly conduct outreach, assessment, service provision, and job placement. In complete 

service integration, a client may receive a range of services from different programs in a 

“seamless” process, without repeated intake and registration procedures, waiting periods, 

or other administrative barriers.14 

Some emerging literature suggests that interest in effective coordination between 

welfare and workforce systems is increasing. The literature on coordination between 

workforce development agencies and other state and local human service programs 

(including TANF agencies) identifies advantages both for program participants and for 

agencies.15 For participants, coordination can result in better access to a wider range of 

services and a reduction in the barriers to accessing services. In addition, both welfare 

and workforce development agencies may benefit, by enhancing their ability to place 

program participants into jobs, being able to better track services and client outcomes, 

and by creating potential cost savings if duplicative efforts are eliminated. 

There are also potential drawbacks to coordination. Disadvantages to the 

agencies appear to be generally more significant than disadvantages to the clients. The 

most significant disadvantage is generally the amount of time and effort required to plan 

and sustain successful coordination. Staff on both sides feel that meetings and other 

regular interagency communication are the unavoidable cost of coordinating services. 

Additional time spent completing paperwork required by different federal or state 

programs is also sometimes mentioned as a cost. Some disadvantages may be 

 

14N. Pindus, R. Koralek, K. Martinson, and J. Trutko, Coordination and Integration of Welfare and 

Workforce Development Systems, The Urban Institute, prepared for the Office of the Assistant Secretary 

for Planning and Evaluation, Department of Health and Human Services, 1999. 

15 For a full review of the literature on both advantages and disadvantages of coordination, and obstacles 

and mechanisms to promote coordination, see J. Trutko et al., op. cit., 1991, and Karin Martinson, 

"Literature Review on Service Coordination and Integration in the Welfare and Workforce Development 

Systems", in Pindus et. al,. Coordination and Integration of Welfare and Workforce Development Systems, 

op. cit., 2000. 
357

ameliorated as agencies become more accustomed to dealing with one another and as the 

time needed to sustain coordination is reduced. 

The recent GAO study mentioned earlier surveyed all 50 states to determine the 

level of coordination between TANF programs and One-Stop Career Centers. GAO 

reports that states are increasingly interested in coordinating across programs, that 

coordination has in fact increased, but that many of the challenges noted above remain, a 

number of which are specific to the TANF and workforce programs and cannot be 

resolved easily at the local level.16 According to GAO, nearly all states reported some 

coordination at the state or local level, from informal information sharing or periodic 

referrals, to formal memoranda of understanding, shared intake, or integrated case 

management. TANF funds are used to support One-Stops in 36 states, and TANF is one 

of the three largest funding sources for One-Stop Centers in 15 states. 

According to GAO, state decisions about coordination depend on several factors, 

including the expertise residing in different staffs, the geographic areas covered by TANF 

programs and One-Stops, space availability, and the size of the welfare caseload. Some 

TANF agencies, for example, operate in state-owned facilities throughout the state or 

under long-term leases, making collocation with One-Stops not an option. Some state 

officials also believe that welfare clients needed a higher level of supervision and more 

structured assistance than can be provided in One-Stop Centers, even if some TANF staff 

are collocated there. 

GAO also found that in many states, welfare and workforce development 

administrators continued to grapple with issues that are known to make coordination 

 

16 Workforce Investment Act: Coordination between TANF Programs and One-Stop Centers in Increasing, 

but Challenges Remain, U.S. General Accounting Office, GAO-02-500T, March 12, 2002. 
358

difficult, such as different definitions and reporting requirements across programs. 

Information sharing is especially challenging for welfare programs because of 

confidentiality restrictions and the deliberate separation of data systems, although some 

states are attempting to overcome this problem. For instance, Connecticut developed a 

self-sufficiency wage standard that could be applied for reporting entry level wages and 

objectives in both programs. 

 Recent conversations with national interest groups and stakeholders conducted 

for this report reinforce GAO’s conclusions that the interest in coordination is growing, 

but also further highlight the differing views on how much and what sort of coordination 

is occurring.17 Whatever the level of coordination, states and localities are still in the 

early stages of implementing WIA and only beginning to understand the best strategies to 

support the goals of welfare reform in the context of reform of WIA. States are therefore 

particularly advocating that they be permitted continued flexibility in both systems, so 

they can identify strategies that best reflect state and local conditions and that will 

support their programs as they attempt to respond to individual client needs. 

For example, there is general agreement that the two funding streams should 

remain separate, but differing approaches in the states on whether major funding 

decisions for welfare employment programs should reside at the state level or at the local 

level and how responsibility should be distributed between TANF agencies and WIA 

agencies or WIBs. There are also differing views on the extent to which some TANF 

funds should be dedicated to the One-Stop system to ensure services to welfare 

recipients. Many in the WIA community feel that their system continues to be funded at 

 

17 Informal discussions were held with representatives of the National Governors Association, Association 

of Public Human Service Agencies, the National Association of Counties, and the National Association of 

Workforce Development Professionals. 
359

levels far below what would be required to serve all welfare recipients eligible for 

services through One-Stops, and that some TANF funds—or more TANF funds—could 

be used to support employment and training activities for welfare recipients. Many in the 

TANF community feel a large portion of TANF funds are already devoted to work 

activities, and are concerned TANF agencies may have to reduce that funding given 

recent fiscal constraints in many states. 

Concern about the content of services provided to welfare recipients through the 

One-Stop system is also complex, and centers around the WIA restrictions on One-Stops 

providing direct services and the related issue of whether the One-Stop system can 

adequately serve individuals with multiple barriers to employment and complex service 

needs. For example, there is concern in both the WIA and TANF communities that the 

loss of Welfare-to-Work (WtW) grant funds will not be replaced by other funding and the 

WIBs and One-Stop Centers will have very limited resources to develop services and 

programs for harder-to-serve populations. In addition, the WtW grants program, enacted 

by Congress in 1997, by many accounts encouraged workforce agencies to develop new 

strategies to serve the types of individuals who may constitute increasing portions of the 

welfare population. As TANF recipients move into jobs and off the welfare rolls, 

workforce programs will need to be prepared to address the variety of challenges that will 

bring these individuals back to the workforce programs for services, perhaps as laid-off 

or dislocated workers and not as TANF recipients. 

Welfare officials express continuing concern about what they perceive to be a 

very strong business focus of some One-Stops, which they worry may not be sensitive to 

the needs of low income women with a range of personal challenges as well as family 
360

responsibilities. Welfare officials would also like One-Stop services to be more "TANFfriendly," in particular making the sequencing of services sufficiently flexible to provide 

easy access to training opportunities at appropriate times. 

In some states and localities, mechanisms for expanded collaborative 

efforts between TANF and workforce development agencies were jump-started by the 

implementation of the WtW Grants program. DOL was responsible for distributing $3 

billion over two years to grantees operating programs to help the hardest-to-employ 

TANF recipients move into jobs. By law, about three-fourths of the WtW grant funds 

were allocated by formula to states, specifically to a state agency designated by the 

Governor (usually the workforce development or employment security agency). The 

state agency in turn passed 85 percent directly to local WIBs, and retained 15 percent for 

discretionary purposes and programs. One-fourth of the federal funds went to other 

grantees who were selected by DOL through a competitive grant application process. 

Most competitive grantees were either local WIBs or non-profit organizations. All WtW

grantees were given five years to spend their funds, meaning that by 2004 all grants will 

have expired. Although WtW grant-funded programs are now winding down, in some 

localities the grants may leave in place a set of relationships, interagency planning 

groups, or improved mutual understanding about respective resources and how to serve 

individuals with a variety of challenges or programs.18

Conclusion 

Welfare—especially the TANF work program—could be an important partner in 

the emerging One-Stop system, but a number of challenges remain. Some of these 

 

18 Demetra S. Nightingale, Nancy Pindus, and John Trutko. The Implementation of Welfare-to-Work Grant

Programs. Washington, DC: The Urban Institute, 2002. 
361

challenges are common to many federally-funded programs that have different legislative 

roots, different objectives, and different administrative requirements. As GAO points 

out, some challenges (such as fiscal reporting inconsistencies) could be resolved at the 

federal level. However, the role that One-Stop Centers play in serving welfare recipients 

and the role that TANF agencies play in the One-Stop system derive from differences in 

the level of devolution of the two programs within a state and historic relationships 

between the two systems at the state and local level. As the GAO report notes, as states 

and localities have begun to recognize the shared goals of the workforce and welfare 

systems, they are developing new ways to interact, but “...these changes, like all culture 

changes, will take time.” 

To understand the interaction between TANF and One-Stops better, the next 

chapter describes how welfare recipients were being served in a sample of One-Stop 

Centers in 2002. 
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III. SERVICES TO WELFARE RECIPIENTS IN ONE-STOP CENTERS 

The enactment of PRWORA, with its emphasis on time-limited welfare benefits, 

“work first,” and moving large numbers of TANF recipients off the welfare rolls into 

full-time, unsubsidized employment, created interest in expanding the use of the 

workforce development system to assist the welfare population in transitioning to work. 

There are a variety of work-related services currently available through local One-Stop 

Centers relevant to the varied needs of welfare populations. Such services may be 

provided through formal collaborative arrangements between the welfare agency and the 

workforce development agency or One-Stop Center. Alternatively, welfare recipients 

may seek services through the One-Stop Centers in their communities just as others in the 

general public do. 

Visits to seven sites around the country suggest that the extent to which local 

One-Stop Centers serve welfare recipients varies considerably, as do the nature of 

services provided and the interaction between welfare agencies and One-Stops. The 

extent and intensity of collaboration between the two systems in the localities visited falls 

along a continuum. At one end of the continuum are sites with both structural and 

financial ties and formal service delivery components in the One-Stop Centers that are 

dedicated to welfare clients. At the other end of the continuum, there are One-Stop 

Centers with no formal organizational or financial linkage with the welfare agency – 

though even in these sites welfare recipients access services available at the One-Stop. In 

between are sites where there are varying types of operational linkages, often involving 

TANF agency staff outstationed at the One-Stop or frequent referral of welfare recipients 
363

for employment and other services readily available at the One-Stop. Regardless of 

whether there are contracts for delivery of services or more informal arrangements for 

referral and/or collocation of staff, across the seven sites, welfare recipients are receiving 

a variety of services through the One-Stop Centers. 

Despite historic differences in approaches between the two systems and the 

evolving nature of One-Stops themselves, in most sites visited there is a formal 

interaction at the administrative level between the welfare agency or its parent agency 

and the local WIB, and in most sites the One-Stop Center provides one or more formal 

components of the TANF agency’s work program. But the type of structural linkage 

does not necessarily correlate with the degree to which welfare recipients are served at 

the One-Stop, the nature or intensity of the services, or the extent to which welfare 

customers are integrated with other client populations in the service delivery system. 

To understand the nature of services to welfare recipients through these One-Stop 

Centers, the sites are described in the sections that follow across the following 

dimensions:19

• One-Stop administering agency and partners; 

• ongoing management of the One-Stops; 

• characteristics of the area served by the One-Stop and proximity to the 

TANF local office; 

• staffing arrangements; 

• types of agreements between One-Stops and TANF agencies; and 

• the services that welfare recipients receive through One-Stop Centers 

(level and types of service, amount and degree of integration with general 

customer population). 

 

19 Appendix A provides detailed information on each of the One-Stop Centers visited. 
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One-Stop Features and Key Partners 

 The lead agency responsible for establishing each of the One-Stop Centers visited is 

the local workforce investment agency, and that agency selects One-Stop Center 

operators. As discussed in greater detail later in this chapter, TANF agencies have some 

level of involvement in operations in all seven of workforce systems and collocate (or 

outstation) TANF staff at most of the One-Stops. In each of the centers visited, the 

Employment Service (ES) and Vocational Rehabilitation (VR) agencies have a physical 

presence, and, in varying combinations across the seven sites, other local partnering 

agencies also provide some services for customers in the One-Stop Centers, including 

community colleges, local community action agencies, economic development agencies, 

Job Corps, and other public programs and non-profit organizations serving disadvantaged 

or special needs populations (e.g., youth, veterans, older workers, disabled individuals, 

low-income students). In two centers visited (Hayward and Philadelphia), the local 

Chambers of Commerce and the local housing authority or HUD office are also formal 

partners in the One-Stop. In one, the family court system is involved. 

 The Hayward One-Stop Center is unique in that it is operated by an 

administrative division of the Alameda County Social Services Agency (SSA), and the 

Alameda County WIA administrative entity is another separate division within that same 

department. Staff in the WIA division, along with the workforce investment board, 

designed the One-Stop system. The Hayward One-Stop Center operated by another SSA 

division is organizationally separate from the WIA division, although both the One-Stop 

director and the WIA administrator report up the SSA channels to the county SSA 

director. 
365

 The JobLink Career Center in Asheville provides an example of the types of agencies 

that have formally agreed (under a Memorandum of Understanding signed by all 

agencies) to outstation staff and make services available at the One-Stop: the Mountain 

Area Workforce Development Board (the local WIA agency); the North Carolina 

Department of Social Services (the TANF agency); the Employment Security 

Commission of North Carolina (providing ES services and unemployment insurance); the 

Asheville-Buncombe Technical Community College; the North Carolina Vocational 

Rehabilitation agency; the Schenck Job Corps Center, the Opportunity Corporation of 

Buncombe/Madison, and Services for the Blind. In larger sites, the One-Stop may 

partner with a number of providers for similar services. For example, Hayward partners 

include four community colleges or adult learning schools, and Philadelphia has more 

than one partner offering literacy services. 

 Each of the sites visited is a comprehensive career center and several work in tandem 

with satellite or specialized centers. In some sites, other centers handle specialized 

services (Oakland) or target special populations (e.g., mini-centers operated directly at 

public housing projects in Philadelphia). Each comprehensive--that is, full service--

center offers a range of services to the general public (see Appendix A for a cross-site 

comparison of services available through the seven One-Stop Centers). All centers 

visited offer some form of job club and/or computer-based job search service, and all 

have a job information or “resource” room open to the public that includes labor market 

information, job openings lists (computerized job banks and hard copy listings), career 

information, and usually computers for short-term use (e.g., to prepare a resume). For 

example, the Nashville Career Advancement Center offers the following: (1) a full-
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service resource center (which includes Internet access to on-line career services, fax 

machines, telephone, labor market information (LMI)); (2) employment services 

(including local and national job searches, help with resume development, resume 

posting, and help with job search); and (3) assessment and job training services 

(including skills assessment, career workshops, and information and referral to education 

and training providers, and payment for training, generally in the form of Individual 

Training Accounts or ITAs). 

 Each center has some services, partners, or facilities on-site that make it fairly 

unique. In Philadelphia, for example, two employers (ARAMARK and United Parcel 

Service) have permanent on-site recruiting offices, and 25 percent of the visitors to 

the One-Stop come for these services. The Philadelphia One-Stop Center also hosts a 

job fair every third Wednesday, coordinated with its partners. Several One-Stop 

Centers make rooms available for partners and contractors to use for routinely 

scheduled classes and workshops, such as literacy training, education or skills testing, 

resume writing and job search, and GED or skills upgrading. Some One-Stop Centers 

feature “rapid response” services on site to help workers affected by plant closings or 

mass layoffs. For example, the Hayward Center has “therapy” rooms, initially set up 

to allow confidential counseling sessions with rapid-response staff. Also, as a 

member of the EastBay Works consortium (of over 100 organizations and three 

workforce boards), Hayward One-Stop customers can access on-line (or in person) 

any one of the 14 One-Stops in Alameda and Contra Costa counties and obtain 

services that might be unique to another One-Stop or geographic area. The EastBay 
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Works Internet-based card swipe system, “Smartware,” allows the centers to track 

activities usage as well. 

 While each of the centers visited provides a range of comprehensive services, their 

priorities and missions differ, as do their involvement with the welfare system. A good 

example is Nashville. In 1997, the One-Stop Center administrators designed and 

implemented Nashville’s DOL-funded Welfare-to-Work (WtW) grant program. The 

WtW program facilitated a dramatic shift and expansion of services for welfare clients 

within the One-Stop Center by contracting with a number of non-profit agencies to 

implement the Pathways model for case management of hard-to-employ welfare 

recipients (developed by Project Match in Chicago and implemented under their 

guidance). However, a change in administration and reconsideration of the mission of 

the One-Stop has prompted the center to shift again toward serving the needs of 

employers more directly, for which it feels best equipped, and therefore, shift away from 

intensive case management and related services to welfare recipients. As of July 2001, 

the welfare agency no longer contracted with the One-Stop for any discrete or core 

services for its clients, although several other TANF service partners continue to serve 

TANF recipients at the One-Stop. 

Ongoing Management of the One-Stop 

Each One-Stop Center has a director who oversees day-to-day operations of the 

facility. In each center visited, this individual has line authority over personnel on the 

payroll of that center operator (e.g., workers who staff the career resource units), but does 

not have direct line authority over individuals working for other partnering organizations. 

Usually some type of management committee of partners meets periodically to discuss 
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operations, resolve outstanding issues/problems, and develop plans for enhancing OneStop operations. For example, in Asheville, a “management team” consisting of key 

management staff from each of the partnering organizations is responsible for providing 

direction and oversight for JobLink Career Center. This group typically meets quarterly 

to make sure services provided through the One-Stop are meeting the needs of the 

customers, to resolve problems, and to plan for future development of the One-Stop. 

Administrators and staff at the seven centers also report substantial informal 

communication and problem-solving among managers and staff of partnering agencies 

located at the One-Stop Center. 

One-Stop Locations and Proximity to Welfare Offices 

To some extent the location of One-Stop Centers and the level of on-site 

participation by partnering agencies reflects the size of the community and the scale and 

responsibilities of different agencies. In general, the geographic areas covered by OneStop Centers do not necessarily correspond to the areas covered by other partners, 

including the local welfare agency. Each of the seven One-Stop Centers interacts with 

just one local or county welfare agency, although in some cases the welfare agency may 

work with more than one One-Stop Center. Appendix A provides a more detailed crosssite comparison of the location and several other key characteristics of the seven OneStops visited. 

 Geographic Areas and Center Location. Geographic areas served by the 

One-Stop Centers visited range from a single city or portions of a city to multi-county 

areas. Some One-Stop Centers also operate satellite centers to serve individuals in 

outlying areas. All centers in this study are located in fairly populated areas – four of the 
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One-Stops serve areas with population in excess of 300,000 persons (e.g., Kansas City, 

Nashville, Oakland, and Philadelphia) and the other three serve local areas with 

population less than 300,000 persons (Asheville, Burlington, and Hayward). Three of 

the centers (Asheville, Burlington, and Nashville) also serve surrounding rural areas. The 

One-Stop Centers in Asheville and Burlington serve single county areas, while the 

Nashville One-Stop is the full-service center for a larger, four-county area, with 

additional satellite offices. The size of the local population and the TANF population in 

the areas served varies substantially—for example, Buncombe County (the county served 

by the Asheville One-Stop) has a population of about 200,000 residents and an active 

TANF caseload of about 400, while the four-county area served by the Nashville OneStop system covers an area with a population of about 850,000 residents and an active 

TANF caseload of about 8,000. 

All seven One-Stops are located in areas accessible by public transportation. The 

Asheville, Burlington, Hayward, Kansas City, Oakland and Philadelphia career centers 

are all located in central downtown areas. The Nashville One-Stop is located in a 

commercial park that is easily accessible by bus from the downtown area. The Hayward 

Career Center, located in the main county social services building, is one of 14 OneStops in the EastBay Works consortium, serving all of Alameda and Contra Costa 

counties. While service areas for each center in the consortium are discrete, career center 

customers may link electronically to services in any one of the 14 centers. 

The Employment Services (ES) is included in One-Stop Centers in various ways. 

In some places, the only ES services in the localities are provided at One-Stop Centers 

(Burlington, Kansas City, and Nashville). In other places, the local ES office is not 
370

physically located at the One-Stop Center, but is nearby (in Asheville the ES office is 

located about two blocks from One-Stop Center). In several places, there may be a 

separate ES office as well as a ES unit collocated at the One-Stop Center to register 

individuals (with the ES) and provide job search assistance, and to provide information 

about applying for Unemployment Insurance. In the Oakland area, for instance, there are 

several full-service One-Stop Centers, each of which includes substantial ES 

involvement, and the ES also operates an additional office where regular Wagner-Peyser 

services are offered.

 Proximity to Welfare Offices. Proximity to welfare offices varies across the 

seven sites. The space in which the Asheville One-Stop Center is located is leased from 

the TANF agency and the building also serves as the headquarters for the local TANF 

program. All TANF recipients come to this same building to apply for and receive public 

assistance benefits, which facilitates involvement of TANF recipients in services 

available through Asheville’s One-Stop Center. In Hayward, the One-Stop is located in 

the same building as the welfare office, and as described above, is operated by a division 

of the local welfare agency. In Philadelphia, separate One-Stop Centers are assigned to 

work with specific welfare offices. For historic reasons the One-Stop Center visited for 

this study is assigned to work with a welfare office located within about a 10-minute 

walk although there is another TANF office located in the same building as the One-Stop. 

The Burlington and Nashville TANF offices are located several miles from the One-Stop 

facility. In both localities, welfare recipients would need to use public transportation or 

have their own private means of transportation (e.g., a car or ride from a family member 

or friend) to travel between the One-Stop Center and the local TANF office. Burlington, 
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Nashville and Asheville also serve surrounding rural areas – hence, accessibility to the 

One-Stop Center for the welfare population may vary within the wider service area. In 

Kansas City, the welfare office is about 1.5 miles from the One-Stop, which also serves 

surrounding county areas with satellite offices. Finally, the Oakland One-Stop is on a 

main bus route from the nearest county welfare office.

Staffing Arrangements 

In developing its requirements for One-Stop delivery of services under WIA 

legislation, DOL extended considerable flexibility to states and localities in determining 

the design of the local systems (e.g., with respect to location, partners included, client 

flow, and day-to-day operations of One-Stop Centers). The flexibility and discretion 

extended to states and localities is reflected in the substantial variation that we observed 

in the way services are delivered. Staffing arrangements are one expression of that 

variation. 

The number of staff in One-Stop Centers in part depends on the size of the local 

population served but also in part on the nature and mode of service delivery (e.g., 

electronic linkage versus on-site staff-administered services). Thus, the two smallest 

study sites (Asheville and Burlington) have about 20 staff each. The other five One-Stop 

Centers in our sample had between 42 staff (Philadelphia) and 125 staff (Oakland). The 

staff located at the One-Stop Centers includes employees from several different agencies, 

especially those officially employed by the One-Stop Operator (usually with WIA 

funding). 

In six of the seven agencies visited (except Philadelphia), staff from the TANF 

agency are either collocated in a One-Stop Center or outstationed for some regular 
372

portion of their time or to provide episodic services. In Asheville, for example, where 

both the TANF and WIA agencies are headquartered in the building that houses the OneStop Center, staff from the two agencies work in separate areas of the building, but one 

TANF worker is located within the One-Stop Center and delivers specialized case 

management services to TANF recipients as needed. Other One-Stop partners operate 

elsewhere in other community locations in Asheville, though each partner outstations 

small numbers of staff at the One-Stop on a full-time (ES/UI, community college, and 

Job Corp) or part-time (VR, community action corporation, and Services for the Blind) 

basis. 

In Burlington, WIA and ES staff are headquartered in the One-Stop building. The 

TANF agency (located at a facility a few miles from the One-Stop) outstations four 

workers at the One-Stop to provide case management services for the small portion of the 

caseload receiving services routinely at the One-Stop -- principal earners in two-parent 

TANF families and a small number of job-ready TANF recipients who volunteer for 

work or are approaching the time limit. TANF funds also pay for a One-Stop case 

manager to provide job retention services as part of the recently implemented “Job 

Keeper” initiative. Other One-Stop partners—Vocational Rehabilitation, Vermont 

Associates (serving older workers), the Vermont Student Assistance Corporation 

(providing student loans), and the Vermont Center for Independent Living (providing 

services for disabled individuals)—are located elsewhere, but outstation small numbers 

of staff at the One-Stop on either a full- or part-time basis. 

In Nashville, WIA and ES staff are all co-located at One-Stop Center. Though 

TANF workers are located elsewhere in the local welfare office, the TANF agency 
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outstations a TANF case manager at the One-Stop to ensure that One-Stop users are 

aware of and can easily access TANF and other types of assistance for which they are 

eligible. Similarly, in Oakland, the welfare agency outstations staff part-time to provide 

case management, crisis intervention, and special counseling. In Kansas City, the welfare 

agency outstations three “intensive case managers” to address TANF support issues (e.g., 

child care, domestic violence services), and other partners also outstation staff at the OneStop. FEC (the lead agency for the Kansas City One-stop) also outstations its staff at 

TANF offices, making them, in effect, satellite One-Stop Centers for career readiness and 

job matching services. 

In Hayward, where the One-Stop Center is operated by a division of the Social 

Services Agency (SSA), most One-Stop staff are SSA employees, operating job clubs, 

staffing the “resource room,” and coordinating with other One-Stop partners and with the 

SSA staff in the TANF units located in a different part of the building. ES staff is also 

located at the One-Stop, but organizationally report to an ES supervisor, not to the OneStop director. Other One-Stop partners include a number of community-based providers 

for specialized services, such as language assistance, and continued case monitoring, 

most of whom use One-Stop conference rooms on regularly scheduled bases. 

Finally, in Philadelphia, all partners except the welfare office outstation some 

staff at the One-Stop. Welfare recipients may receive other services (such as workshops) 

directly from contractors using career center space. 

Agreements Between One-Stops and TANF Agencies 

The interaction between One-Stop Centers and TANF agencies usually is 

specified in a formal memorandum of understanding (MOU) or an interagency 
374

agreement. If the agreement between the agencies involves transfer of funds to the OneStop (e.g., to rent for space occupied by outstationed staff or for services rendered), then 

a contract is negotiated either in addition to or in place of a MOU. Table III.1 provides 

an overview of collaborative arrangements between the One-Stop centers and TANF 

agencies. 

A state or local memorandum of understanding takes different forms. The MOU 

may be enacted at the state-level between the two relevant agencies (as is the case in 

Kansas City and Asheville). A model MOU may be developed at the state level (between 

the state TANF and workforce development agencies) and then crafted to fit the local 

situation. Alternatively, the agreement may be strictly developed at the local level 

between the One-Stop Center and the local TANF agency (and other partnering 

agencies). 
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TABLE III.1: INTERACTION BETWEEN ONE-STOPS AND TANF 

One-Stop Site Signed 

MOU? 

TANF Staff1 CoLocated/Outstationed Description of Collaborative Arrangement 

Asheville, NC Yes Yes 9 2-year “resource sharing agreement” among partners to serve 

employment and training objectives of each agency (TANF is one 

of these partners). Partners locate staff at One-Stop and provide 

resources to maintain staff (i.e., salary, fringe benefits, supplies, 

furniture, equipment, travel, and training). County & WIA pay for 

rent, utilities, parking maintenance, and computer system/IT. 

Burlington, 

VT 

Yes Yes 9 State TANF agency contracts with state Department of Employment 

and Training for One-Stop TANF services: assessment, case 

management, job search, work activity assignments (e.g., 

Community Work Experience Program, education and training), and 

support services for principal earners in 2-parent families, single 

parents nearing time limit, and Food Stamp applicants and recipients. 

Hayward, CA Yes Yes 9TANF (CalWORKS) funds career development and employment 

counselors in One Stop; and provides $1200/client/quarter (up to 

$87,500). Two-year contracts. 

9Employment Information Services at One-Stop, funded by TANF 

provides recipients nearing time limit One-Stop orientation. 

Kansas City, 

MO 

Yes Yes 9State TANF agency developed a 5-year MOU with The Full 

Employment Council, Inc. (KC WIA Agency) with broad 

coordination guidelines, and annual contracts with financial details. 

9TANF provides funding (~500K) for FEC staff positions to serve 

TANF recipients, including job readiness workshops, job placement 

assistance, and post-employment retention) 

Nashville, TN Yes Yes 9Joint memorandum written by state TANF director and state director 

of employment and training. 

9One-Stop contract to provide case management and employment 

services for TANF recipients ended July 2001. TANF agency 

contracts directly with three intermediary service contractors 

(formerly contracted by the One-Stop) to provide services. These 

and other providers use the One-Stop in serving TANF recipients 

Oakland, CA Yes Yes 9MOU with with county TANF agency is resource sharing agreement, 

with One-Stop billing quarterly for services per TANF participant 

served per quarter. TANF staff outstationed at One-Stop on regular 

basis to provide case management, crisis support, and general 

information as needed 

9New Employment Information Services (See Hayward). 

Philadelphia, 

PA 

Yes2 No 9Local TANF agency is not a paying partner of the 1-stop and does 

not have formal agreement for outstationing of TANF staff at 1-stop 

or referral of TANF recipients to 1-stop. 

9County TANF Director sits on consortium responsible to the WIB 

to review broader business plans, services, approve spending and 

sites, and meets monthly to review operations. 

1 TANF staff physically in the One-Stop, whether permanently co-located outstationed for scheduled times, 

are typically performing work-related functions, but some TANF staff are outstationed to attend to 

eligibility and benefit issues. 

2

 MOU is limited to special initiatives to provider retention services and placement services for job-ready 

TANF recipients – no formal agreement or regular flow of TANF recipients to the One-Stop for services. 
376

Such MOUs will identify specific roles and responsibilities of each OneStop partner, particularly relating to co-location of staff and types of services to be 

provided for the target population (i.e., TANF recipients). For example, the MOU 

implemented in Asheville is for a two-year period (through June 2003), and is a “resource 

sharing agreement” among partners “to co-occupy premises” of the JobLink Career 

Center “to more effectively serve the employment and training objectives of each of the 

agencies.” The partnering agencies agree to co-locate staff at the Asheville JobLink 

Center, with (1) WIA, the TANF agency, the local community college, ES, and Job 

Corps agreeing to locate full-time staff at the center; and (2) Vocational Rehabilitation 

and Services for the Blind agreeing to locate part-time staff at the center. In Asheville, 

each partnering agency provides the resources necessary to maintain staff at the career 

center (including paying for salary, fringe benefits, furniture, supplies, telephone, travel, 

and training). The county government pays for costs related to ongoing operation of the 

One-Stop, including personnel costs of career center staff, rent, utilities, parking 

maintenance, and computer system costs (with Job Corps picking up its proportional 

share of cost for space occupied by the Job Corps recruitment office). Finally, each 

partnering agency in the Asheville site must give 30 days advance notice of plans to 

terminate the MOU. 

The MOU covering One-Stop services to welfare recipients in Kansas City is a 

five-year contractual agreement between the Missouri Department of Social Services and 

the Kansas City Full Employment Council, Inc. (FEC), with yearly contract amounts tied 

to the state budget, which detail levels of funding and services to be provided. Similar to 
377

Asheville, the Kansas City contract covers costs of services provided by One-stops for 

TANF recipients, such as job clubs, job search, work experience, internships and training, 

and related support services. 

In Vermont, the state TANF agency and the state Department of Employment and 

Training have historically worked jointly to develop state policies and programs for the 

employment of welfare recipients. But the state contract between the two agencies uses 

the One-Stop in Burlington to provide assessment, case management, job search 

assistance and assignment to work activities and support services for two groups of 

TANF recipients: principal earners in TANF two-parent recipient and applicant families, 

and single parents reaching their time limits. The employment service needs of other 

TANF recipients are arranged by welfare staff in the local welfare office. 

In Philadelphia, the county TANF director is a member of an interagency 

consortium responsible to the WIB for reviewing One-Stop business plans, approving 

spending and sites and reviewing operations monthly. However, the welfare agency is 

not a paying partner in the One-Stop Center visited and does not outstation staff at the 

One-Stop. Rather, the welfare agency contracts directly with specific service providers 

for a variety of training and work-related services as part of their TANF work 

requirement, and some of those providers use One-Stop services and information 

resources. While few welfare recipients reportedly seek employment and training 

services on their own through the One-Stop, the welfare agency funds the ES, which 

operates at the One-Stop, to provide post-employment retention services and placement 

services for job-ready TANF recipients. 
378

In Hayward, a two-year contract between the local WIB and the Alameda County 

Social Services Agency (SSA) uses TANF dollars to pay for a separate unit of career 

development counselors within the One-Stop and provides an additional $1,200 per client 

per quarter reimbursement (up to $87,500 per year) for employment services provided to 

the TANF population. The Alameda SSA also contributes $130,000 annually to the 

Oakland PIC’s One-Stop Center, and as in Hayward, that One-One Stop is reimbursed 

quarterly for participants served. 

Services Provided for Welfare Recipients at One-Stop Centers 

As suggested by the varied institutional descriptions just presented, the seven 

One-Stop Centers visited provide services to welfare recipients in different ways, ranging 

from basic core and intensive services available to the general public, to specific services 

designated just for TANF recipients. In most One-Stop Centers, staff provides services 

to TANF recipients at various points during their involvement in TANF, as specified by 

the interagency agreement or contract (i.e., during the application process, while 

individuals are receiving benefits, and in some cases, after individuals enter the labor 

market and terminate receipt of TANF benefits). At all the centers visited, no matter how 

minimal the formal arrangement is between the welfare and workforce development 

systems for serving TANF recipients through the One-Stop, administrators from both 

systems reported that many TANF recipients were likely to visit the One-Stop at least at 

some point during their tenure on TANF. 
379

Precise data on numbers of TANF recipients served at the seven One-Stops are 

not generally available,20 although One-Stop and TANF administrators provided rough 

estimates of One-Stop usage by welfare recipients as a percentage of total customers. 

Table III.2 provides an overview of estimates of TANF recipients as a percentage of OneStop users and a description of the typical types of services TANF recipients obtain at 

One-Stop Centers in the seven sites. In the five sites for which estimates were provided, 

One-Stop administrators indicate that TANF recipients account for between 10 percent 

(Asheville) and 25 percent (Burlington, Nashville, and Hayward) of all individuals served 

by the One-Stop Centers. In Asheville, when individuals receiving food stamps and other 

adult welfare services are included with TANF recipients, One-Stop administrators 

estimate that this group accounts for between one-third and 40 percent of all users (and 

represent the largest group of One-stop users). 

Staff in some sites note, however, that some issues may affect patterns of use of 

One-Stop services by TANF recipients. For example, the “work first” orientation of 

TANF and need for TANF recipients to meet work requirements makes labor exchange 

services and labor market information available through One-Stop resource rooms 

especially relevant to TANF recipients. However, the constraints imposed by TANF 

work requirements (i.e., limiting time available to attend education or training) and limits 

on duration of training make it difficult for many TANF recipients to utilize ITAs and 

other WIA training services until they have secured a job. 

 

20 One-Stop center staff indicated that collecting such data is often difficult because of the nature of how 

services are made available to TANF recipients and to the general public. Staff noted that they make a 

concerted effort to make it easy for welfare recipients to use their services and are reluctant to ask them to 

provide any more personal information that is necessary. Staff in at least one site expressed reluctance to 

stigmatize users by identifying their welfare recipiency. Users of resource rooms and other facility 

resources may sign-in at the time they use services, but details about whether an individual is or is not a 

TANF recipient is generally not recorded on such logs. 
380

TABLE III.2: ESTIMATES OF NUMBER OF TANF RECIPIENTS SERVED 

AND TYPES OF SERVICES RECEIVED AT ONE-STOPS 

One-Stop 

Site 

Est. of TANF 

Recipients as % 

of One-Stop 

Users 

Principal Services for TANF Recipients at One-Stop 

Asheville, 

NC 

10%1 9TANF clients required to register with dedicated ES staff at One-Stop; new recipients 

visit One-Stop twice, to learn about resources and use computer job search, during 

mandatory 2-week job readiness workshop at community college; all job-ready TANF 

recipients attend job club along with other One-Stop participants, and use other job 

search services (e.g., resource room, workshops, assistance from job club counselors); 

Job Club counselors track and monitor compliance with TANF work requirements; and 

WIA staff refer to training. TANF worker in Career Center for case management as 

needed. In WtW program TANF worker refers to job club and other One-Stop services. 

Burlington, 

VT 

25% 9Assessment, case management, job search assistance and work assignments for 

principal earners in two-parent families and single parents who volunteer or are 

reaching time limits, by TANF case managers outstationed at One-Stop. New program 

uses TANF funds for case manager to provide job retention services to TANF 

recipients. Other TANF recipients’ employment needs arranged at TANF office. 

Hayward, CA 20-30%2 9Career Development Unit (with TANF dollars) provides case management, triage 

welfare recipients to job clubs at the One Stop, or at CBOs contracted for specialized 

services and continued case monitoring. TANF reimburses $1,200 per TANF client 

receiving employment services (resource room, job search, one-week mandatory job 

club for TANF recipients, counseling, community service assignments, other). 

9 “Employment Information Services” orient TANF recipients nearing time limit about 

One-Stop. ES staff at One-Stop can register TANF recipients and provide job search. 

Kansas City, 

MO 

15% 9DSS contracts for job club, job search, work experience, internships, training, and 

support services; outstations three case managers to address TANF support issues (e.g., 

child care, domestic violence). FEC outstations staff at TANF offices, creating minisatellite offices, for career readiness and job matching services. WtW program at OneStop refers to classes and other services at One-Stop. 

Nashville, TN 25% 9WtW program (using Project Match intensive case management model) is ending, 

though WtW Pathways specialist still provides intensive case management and home 

visits to some TANF recipients. TANF agency ended contractual relationship with 

One-Stop for employment services in July 2001 (except special transportation and 

support services historically provided by workforce development system). TANF 

recipients likely to be brought to One-Stop during TANF orientation, job readiness 

workshops, or for labor market information (LMI). TANF agency outstations case 

manager at One-Stop to ensure users can access services for which they are eligible. 

Oakland, CA N/A 9TANF staff outstationed part-time to provide services to TANF participants. No Job 

Club at One-Stop. New “Employment Information Services” initiative for TANF 

recipients raching time limit. WtW program had major outreach program through OneStop, but most WtW education and training services delivered elsewhere. ES staff at 

One-Stops can register welfare recipients and provide job search assistance. 

9“Employment Information Services” (See Hayward) 

Philadelphia, 

PA 

N/A 9Two initiatives contracted directly to and run by ES at the One-Stop provide: (1) postemployment retention services; and (2) assessment, career development, LMI and 

placement services for job-ready TANF recipients. Other employment services 

provided by contractors through welfare agency; no TANF staff at One-Stop. 1

When Food Stamp and other DSS adult welfare referrals are included with TANF recipients, the estimate 

increases to 33-40 percent of all one-stop users. 

2

Estimates include TANF and General Assistance recipients.
381

 Regular One-Stop Services. Some TANF recipients may, and reportedly often 

do, make use of the regular core and intensive services available at One-Stops just as 

anyone from the general public might. While One-Stops generally maintain counts on 

daily use of resource rooms and other One-stop services, sign-in procedures do not 

typically ask whether customers are TANF recipients – hence, no firm counts on use of 

One-Stop Centers by TANF recipients are available (independent of referrals made 

through TANF and other agencies). 

 Indirect Services at One-Stops by Other TANF Contractors. Welfare recipients 

sometimes receive services at One-Stop Centers indirectly as a result of the participation 

in other programs in their communities. For example, in Nashville, until July 2001, the 

WIA agency (located at One-Stop) in Nashville had been contracted by the TANF agency 

to provide soft skills training, job search assistance, and post-employment counseling for 

TANF recipients. Since then, the TANF agency has contracted with other providers for 

these services. However, although systematic data are not maintained on usage of OneStop services by TANF recipients, TANF and One-Stop administrators indicate that most 

TANF recipients still visit the One-Stop at some point during their time on TANF -- 

either as part of orientation, during job readiness workshops offered by other local 

vendors, or for ongoing use of labor exchange resources. Staff report that TANF 

recipients are particularly likely to also use One-Stop services when they are actively 

engaged in searching for a job. Service providers under contract with the TANF agency 

still bring participants to the One-Stop one or two times per week to learn about and use 

the core One-Stop services, attend workshops, and meet with ES counselors. The OneStop is the also local contractor (for the state) to deliver certain transportation and work-
382

related support services (e.g., dental, optical, other work-related medical services, and 

uniforms) to TANF recipients -- a continuation of an historical relationship with the 

workforce development system. Other partners located at the One-Stop provide services 

for subgroups within the TANF population: (1) VR outstations staff at the One-Stop to 

provide direct client services for TANF recipients; (2) the Job Corps outstations staff at 

the One-Stop to provide information about the Job Corps and facilitate the application 

process; and (3) a WtW Pathways specialist provides intensive case management for 

TANF recipients at the One-Stop, as well as home visits. Finally, the TANF agency still 

outstations a TANF case manager at the One-Stop to help ensure that One-Stop users are 

aware of services available through the welfare agency. 

In Philadelphia, the Career Development Unit located at the local welfare office 

provides employability planning services to welfare recipients and refers individuals to 

various employment programs in the community. Local ES staff is located in the local 

welfare office to help individuals register with the ES. While the TANF agency is not a 

paying partner in the general One-Stop operation, the TANF agency contracts with the 

ES for two other initiatives delivered by ES staff on-site at the One-Stop. One new 

program is the “Returners’ Initiative,” that provides post-employment case management, 

including visits to employers to aid in job retention. The other contract with the ES, the 

“New Directions” initiative, refers job-ready welfare clients for up to 60 days of 

assessment, career development, workshops, referral to GED or other education 

opportunities, LMI, and placement services using the facilities in the One-Stop. Clients 

must call in daily and come in for services at least once a week or be sanctioned. In 
383

addition to these special initiatives, some TANF intake workers may routinely inform 

clients about CareerLink services available. 

Direct Services for TANF Recipients. Where One-Stops have formal 

arrangements with TANF agencies to serve welfare recipients, the activities are generally 

designed to inform participants about the variety of services available through the OneStop system, then to provide employment and training-related services tailored to each 

individual’s specific needs (especially related to finding a job). These formal 

arrangements vary across study sites in intensity of services and breadth of population 

served. 

In Asheville, all TANF recipients are required to register for the ES at the OneStop (with one ES staff person dedicated in the One-Stop Center solely for that purpose) 

and virtually all TANF recipients determined to be job-ready and unable to secure a job 

attend a Job Club at the One-Stop and use other One-Stop services (e.g., the resource 

room and workshops offered by the One-Stop). Asheville’s JobLink Career Center 

provides a good example of the several points at which TANF recipients receive services 

at the One-Stop: 

• All TANF applicants must see an ES worker (outstationed at JobLink) during 

the application process for TANF benefits. This involves a short interview to 

determine if the applicant is job-ready, register the individual with the ES, and 

make certain the individual is aware of the various services and resources 

available through the ES. 

• New TANF adult recipients visit the JobLink as a group twice during a 2-

week mandatory job readiness workshop taught at a nearby community 

college to become familiar with One-Stop resources, ongoing workshops, and 

other partners and services available through the One-Stop, and to use 

JobLink computers to search for potential job openings. 

• TANF recipients who have not secured a job are mandated to check in daily 

in-person (or by telephone if on an interview or other job search activity) with 
384

a Job Club counselor. The Job Club, open to the general public from 8:30-

4:00 weekdays, is operated by case workers outstationed from a local 

community college, so welfare recipients are mixed in with others but account 

for most Job Club participants. Job Club counselors provide mostly 

individual placement assistance; participants use databases, fax machines, 

telephones, and other resources to structure their job search and go out on 

interviews. Job Club counselors track individual progress and monitor 

compliance with TANF work requirements. Some Job Club participants 

attend GED preparation classes at JobLink two mornings per week. 

• TANF recipients may walk in and use JobLink Career Center facilities 

(particularly the Resource Room), as anyone from the general public might. 

TANF recipients may also go to the WIA office at the JobLink to obtain 

information and receive referrals for short-term training (though strict 

enforcement of work requirements make it difficult for most TANF recipients 

to attend long-term training until after they have secured a job). 

 The Hayward and Kansas City One-Stop Centers also provide a range of services 

explicitly for TANF recipients. Both operate a mandatory one-week job readiness 

class or job club for TANF recipients. Staff in Hayward note that the CalWORKS 

(TANF) job club at the One Stop Center is distinct from others at that center and 

tailored to special needs many TANF recipients (e.g., more soft skills training than 

other One-Stop customers). The Hayward One-Stop also administers the mandatory 

unpaid CalWORKS community service assignment for those recipients who do not 

find jobs. In 2002, the Hayward One-Stop became the lead agency for a new 

voluntary Employment Information Services (EIS) program to provide welfare 

recipients who are “timing out” of TANF with detailed information and an orientation 

about services available through the One-Stop Centers, particularly the universal core 

services and intensive training options. The EIS is also offered at the Oakland OneStop Centers visited. Oakland administrators are especially concerned with 

approaching time limits for the many individuals who have not participated in an 

employment program. Because of space constraints, there is no job club for TANF 
385

participants at the Oakland One-Stop, and the primary services specifically for TANF 

recipients are provided through the WtW-grant funded program or through referral to 

programs operated by community-based organizations, with supportive assistance and 

help with resume writing and interviewing skills provided by CalWORKS staff 

outstationed at the One-Stop. 

Role of ES in Providing Services to TANF Clients at One-Stops 

In general, the ES serves TANF recipients in One-Stop Centers by registering them 

for ES services and making computerized job listings (e.g., America’s Job Bank) and 

other LMI available to assist TANF recipients with job search and placement. In 

Burlington, the ES is part of the lead agency (the Vermont Department of 

Employment and Training) operating the One-Stop Center, and hence, locates ES 

workers at the One-Stop and provides the full range of ES services at the center. In 

Nashville, the ES has 12 of its staff located at the One-Stop to provide the full range 

of ES services to TANF recipients and the general public. In Asheville, where the ES 

is headquartered two blocks from the One-Stop Center, the ES outstations one worker 

at the One-Stop Center to determine job readiness of TANF applicants and register 

TANF applicants with the ES as part of their fulfillment of the TANF work 

requirement, and provide general information about the availability of ES services. In 

addition, the One-Stop resource room maintains an electronic link to job listings 

maintained by ES. 

In other sites the ES offers more extensive or specialized services to TANF 

recipients. In Kansas City, for example, the ES is located at the One-Stop and is under 
386

contract with the Department of Family Services to provide a range of services 

(registration, job search, counseling, job club, classroom training and support services) to 

“able-bodied” Food Stamp beneficiaries and some TANF recipients. In Philadelphia, 

TANF recipients register with the ES electronically, but, as described earlier, the welfare 

agency also contracts with the ES to provide retention and placement services at the OneStop for TANF recipients. In addition to applying for unemployment insurance at intake, 

TANF recipients may attend weekly Employment Information Services meetings run by 

the ES (or get one-on-one assistance), as part of the new initiative described above. 

Role of the Welfare-to-Work (WtW) Grants at One-Stops 

The role of WtW in serving TANF recipients through One-Stops is gradually 

winding down (in Asheville, Nashville, Kansas City, and Oakland) or ended (Burlington, 

Hayward, and Philadelphia). In Asheville, a TANF worker at the One-Stop Center 

determines whether TANF recipients referred to a two-week job readiness workshop 

(held at the One-Stop Center) are 70- or 30-percent WtW eligible. This same TANF 

worker also conducts an intake interview with the small numbers of non-custodial parents 

(NCP) referred from the courts and the local child support enforcement agency, 

determines WtW eligibility, refers NCPs to a two-week job readiness workshop or job 

club, provides transportation vouchers, assesses, and (as appropriate) refers NCPs to 

other available support services. In Kansas City, the WtW program operates out of the 

One-Stop with one WtW staff person who assigns those referred from the welfare agency 

to orientation, classes and other services. The Oakland PIC (the operator of the One-
387

Stop) trained workers to do outreach under the PIC’s WtW grants, though the site 

reported that few used the career center’s services because they tended to be in school or 

training through the WtW program. 

The Nashville One-Stop Center has operated a WtW program using the Project 

Match Pathways model. Although the program is scheduled to terminate in 2002 and 

will not be picked up by the TANF agency, the experience offers lessons in collaboration 

that may be particularly applicable to serving welfare or other disadvantaged populations 

who may require a combination of service providers with different expertise. In response 

to very poor referral rates at the start of the grant, the WIB partnered the One-Stop with 

community-based organizations so that staff at the One-Stop Center could provide 

administrative and technical support and funding for paid work experience slots, while 

staff at the CBOs, presumably in closer touch with hard-to-serve individuals, could 

provide case management services and employment preparation. The arrangement 

increased referrals dramatically. 

Conclusions 

The site visits provided an opportunity to begin to explore the similarities and 

differences in the structure of One-Stop Centers and the ways in which WIA, TANF, ES, 

and other agencies collaborate to provide services to TANF recipients making the oftendifficult transition from welfare to full-time, unsubsidized work. The One-Stop system 

represents one of a variety of avenues available at the local level to provide TANF 

recipients with the services they need to make that transition. Welfare recipients may 

(and often do) take advantage of the same services that are generally available to the 

public through the One-Stop system (e.g., LMI available in career resource rooms, 
388

workshops regularly run by the One-Stop to enhance job seeking skills). One-Stops may 

also host activities and services (e.g., job readiness workshops, job clubs) that are 

specially designed to serve TANF applicants or recipients and move them quickly along 

the pathway toward employment. 

Despite the differences we observed in the One-Stop systems in the seven 

localities, each brings together a range of services that are particularly relevant to 

enhancing TANF recipients understanding of the labor market and to facilitating the job 

search and placement process. According to many TANF administrators and staff, 

among the most important potential benefits of partnering with One-Stops are that such 

partnerships can contribute to increased knowledge and use of employment and trainingrelated services by TANF applicants and recipients. One-Stops can make it easier for 

TANF recipients to obtain the expertise, services, and resources available through the 

workforce development system (e.g., knowledge of and linkages to employers, expertise 

in job search methods, and capabilities to assess client employment readiness and needs). 

In addition, the One-Stop environment facilitates awareness of services available through 

other One-Stop partners and can make it easier for TANF recipients to take advantage of 

such services (e.g., VR, links to community colleges, specialized services available 

through CBOs). 

As the preceding site observations attest, the ways in which TANF recipients are 

served through the One-Stops studied are varied. One-Stops are evolving and 

refining their service delivery strategies, and their relationships with TANF agencies 

are evolving, reflecting the flexibility extended by DOL to states and localities in 

designing their local workforce systems and in responding to local needs, and also the 
389

evolving nature of welfare reform. Organizational arrangements that determine the 

way in which TANF recipients are served are themselves varied and, importantly, do 

not predict the intensity of services or density of use by welfare recipients. Thus, the 

TANF director in Philadelphia sits on the consortium that oversees the operation of 

the workforce system, but the welfare agency formally uses the One-Stop only as a 

venue for its contract with the ES for special services for portions of its caseload. 

The TANF agency in Nashville has ended its principal contractual relationship with 

the One-Stop work employment services, but TANF recipients continue to receive 

core services through the One-Stop and continue to comprise a substantial portion of 

all One-Stop users. 

The experiences of the One-Stops visited also suggest lessons for improving 

services for welfare recipients. As evidenced by the often separate programs for welfare 

recipients, such as job clubs, workshops or counseling rooms dedicated to providing 

services for welfare recipients, many welfare recipients may need different or enhanced 

services, such as increased soft skills training. While some One-Stops are sensitive to 

the potential for stigmatization that might arise from identifying welfare receipt in 

application procedures, others have also attempted to make accommodations for 

different needs of welfare recipients. Greater sensitivity on the part of the workforce 

development system to both special needs and avoidance of stigma may help to reduce 

some of the frustration reported in the past by both systems as they have attempted to 

work with each other. In the sites that are co-located with the welfare office, familyfriendly designs including on-site child care could make participation in the One-Stop by 

welfare recipients significantly easier. Work requirements and limits on receipt of 
390

training services, especially long-term training, limit the ability of welfare participants to 

take advantage of training-related services. However, One-Stop Centers may facilitate 

access to such training services, especially once TANF recipients begin working. OneStops may also attend to retention services, including intensive case management, which 

are proving increasingly important to newly-employed welfare recipients. Finally, OneStop partners can learn to capitalize on their special expertise and rely on other partners 

or other providers within the context of the One-Stop for services for which they are less 

equipped. This has proven especially helpful in dealing with disadvantaged populations, 

where, for example, community-based organizations with greater access and perceived 

legitimacy can provide the route in to services, and One-Stop partners can provide the 

technical support or specialized training necessary for individuals to advance. 
391

IV. IMPLICATIONS 

 

 The increasing interaction between welfare and workforce development 

agencies means that there are arrangements in place to expand the services to welfare 

recipients, former recipients, and low-income working parents through the One-Stop 

system. The implementation of WIA on the heels of welfare reform, as well as the 

experience of serving welfare recipients through the WtW grant-funded programs, have 

afforded WIBs and One-Stops the opportunity to gain valuable knowledge in how to 

deliver intensive services to special populations. 

 But there are even fewer resources to serve welfare recipients under WIA than there 

were under JTPA, when only about 2 percent of eligible individuals received services. If 

WIA intensive services are expected to serve an increasing number of welfare recipients, 

and if they are to build on their current base of knowledge and partnerships, several 

strategies ought to be added to the thinking of policymakers and program planners. 

 First, if the workforce investment system is expected to increase their emphasis on 

welfare recipients and also maintain services to other groups, the WIA and One-Stops 

will need more resources than they currently receive. The resource constraints at the 

service delivery level are likely to become more serious in the next two years for two 

reasons. WtW grants, which have been a major source of funding for services to welfare 

recipients in many local areas, are ending. At the same time, state TANF agencies no 

longer have substantial discretionary resources. Caseloads in many states have risen, 

meaning more of the TANF block grant must be used for benefit payments, and some 

state policymakers are facing hard choices about whether they can continue to fund some 

special employment initiatives that had been developed with TANF discretionary funds. 
392

Although differences in philosophies between welfare and workforce systems remain, 

there are clear indications (from the literature and from discussions with representatives 

of various groups) that both welfare and workforce officials are eager to collaborate and 

each could expand services with more funding. Specifically, whatever the differences in 

missions and emphases, welfare agencies will continue to look to the One-Stop Centers, 

at a minimum, to expose welfare recipients to the resources available through the centers, 

including training and education as well as access to labor market information, job search 

resources and other assistance in identifying jobs. 

 Second, policymakers may want to consider a new grants program to expand upon the 

WtW grant program experience, focusing on not only welfare recipients, but former 

recipients and low-wage working parents in general. Through the implementation of 

welfare reform and the WtW Grants program, many WIBs and One-Stop Centers have 

gained new programmatic expertise about addressing the needs of special populations, 

especially the hard-to-employ. The WtW grant initiative played an important role in 

developing new models for the workforce development system to serve individuals with 

multiple barriers to employment, and in expanding the participation of community-based 

organizations and faith-based organizations within the workforce development system at 

the local level. It is important that that expertise not be lost with the expiration of WtW. 

 Third, policymakers and local program planners may want to consider ways to 

encourage ongoing services to former welfare recipients. Staff and administrators in 

both the welfare and workforce development systems are increasingly aware of the need 

to address the challenges of job retention for low-wage workers in general and for those 

with multiple barriers, particularly as the economy has softened from its unprecedented 
393

run in the 1990s. The Employment Information Services initiative in Alameda County is 

an example of how welfare recipients nearing their time limits can receive an intensive 

orientation to the One-Stop system and the range of services available to all workers in 

the community. WIA initiatives and programs for incumbent workers (e.g., skills 

upgrading, workplace-based education or training, refresher training) might similarly 

consider modifications that might be appropriate for former welfare recipients who are 

newly employed. 

 This limited exploratory study suggests several directions for further study. Although 

our review of seven sites provided a glimpse into important variations in program 

models, the review also reinforced our sense that there are differences that require more 

in-depth exploration in a greater variety of settings in order to understand how they affect 

the relationship between welfare and One-Stops and the delivery of services to welfare 

recipients. There may be differences in access to services in rural areas, in areas in which 

welfare policy is less devolved to the local level and therefore planning across systems 

may be somewhat more complicated, and differences in program philosophies that play a 

role in how and what services are delivered through the One-Stop and to what effect. 

The continuing reference in the literature and in current conversations with policymakers 

and stakeholders to the challenges in working across the two systems, paired with our 

observations that the two systems are poised and eager to work together, suggest that we 

need a more precise understanding of how geography and other jurisdictional issues play 

a role, and what service allocation and service strategies have been used in different 

settings to satisfy the objectives of both systems to serve welfare recipients and other 

disadvantaged populations with continuing need for services. 
 394

APPENDIX A: 

CROSS-SITE COMPARISON OF ONE-STOP CAREER CENTER 

FEATURES 

AND SERVICES FOR WELFARE RECIPIENTS 
TABLE A.1: CROSS-SITE COMPARISON OF ONE-STOP 

CENTER FEATURES 

AND SERVICES FOR WELFARE RECIPIENTS 

LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Job Center Name Joblink Career 

Center 

Burlington Career Resource Center 

Eden Area One Stop Career Center 

Central Kansas 

City Missouri 

Career Center 

Nashville Career Advancement Center 

OC

Agency Operating OneStop Center 

Mountain Area Workforce Development Board 

Vermont Department of Employment and Training (DET) 

Department of Workforce Development (part of Alameda Co. Social Services) 

The Full Employment Council, Inc. (FEC) 

Nashville Career Advancement Center (a division of Mayor’s office) 

OII

Area Served by One-Stop 

Buncombe County Chittenden County 

(but serve some residents of nearby Franklin & Grand Isle Counties) 

“South County”: Castro Valley, Hayward, San Leandro and San Lorenzo 

“Urban core” of Kansas City (but serve some residents elsewhere in Jackson County) 

Davidson County (includes Nashville) and 3 surrounding counties (Wilson, Rutherford and Trousdale) 

C

Characteristics of the Service Area 

One mid-sized city (Asheville) and several smaller rural towns 

One mid-sized city (Burlington) and several smaller rural towns 

Urban area adjacent to Silicon Valley 

Urban Major city 

(Nashville), but also rural areas in 3 outlying counties 

U

Description of Physical Location of OneStop 

Downtown Asheville, in same building as TANF agency. WIA, WtW, & some TANF staff in 1-Stop; ES located about 2 blocks from 1-Stop 

Downtown Burlington. WIA &ES co-located at 1-Stop; county’s TANF office located about 5 miles from 1-Stop 

Career Center colocated with county welfare department, sharing employment and training facilities in 1-stop. 

One-Stop located in downtown Kansas City. WtW & ES located at One-Stop; county TANF office located about 1.5 miles from 1-stop. 

Commercial park (accessible by bus from downtown). DHS office located several miles away and not easily accessible to 1-stop. 

ClVRWsO
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Year One-Stop Established 

1998 1999 1999 (co-location 

since July 2001, integrated into DSS July 2002) 

1997 (system emerged during the 1990’s) 

Workforce services at current site for many years (but major renovation 3 years ago) 

Oc1

Est. Service Area 2000 Population 

206,330 146,571 297,662 ~350,000 847,982 3

Est. TANF Caseload 2002 (Excluding Child-only Cases) 

393 ~600 ~2,800 total cases, 

~1,880 total adults (March ’02) 

6,100 8,000 (6,500 have 

active work requirement) 

~~(

Nature of Agreement Between OneStop and Local TANF Agency 

Memorandum of Understanding establishes roles of each partner (TANF is one of these partners); key aspects of MOU: -2-year agreement (7/1/01-6/30/03) -A” resource sharing agreement” among partners to serve employment and training objectives of each agency -MOU specifies that partnering agencies co-locate and maintain staff (i.e., pay for salary, fringe benefits, supplies, furniture, equipment, travel, 

-At the state level, the TANF dept. (Prevention, Assistance, Transition, and Health Access – PATH) has contracted with the state DET for provision of assessment, case management, job search assistance, work activities (e.g., Community Work Experience Program [CWEP], education and training), and support services for principal earners in TANF two-parent families and single parents timing out, and for Food Stamp applicants 

-One-Stop is division of Social Services Agency and Career Center Staff report to SSA. -TANF (CalWORKS) funds career development unit and employment counselors in One Stop; plus provides $1200/client/ quarter (up to $87,500), though actual costs considerably higher and not fully reimbursed. -Contract extension every 2 years 

-The Missouri TANF agency (Department of Social Services – Division of Family Services) developed a 5-year MOU with FEC establishing broad guidelines of coordination, and year-by-year contracts (tied to the state budget) spelling out financial details of the relationship. - TANF provides funding (~500K) for FEC staff positions to serve TANF recipients, including providing job readiness workshops, job 

-Joint memorandum written by state TANF director and state director of employment and training -One-Stop ended its contractual relationship in July 2001 to provide case management and employment services for TANF recipients -The Nashville TANF agency continues to contract directly with three contractors to provide these services -- Catholic Charities, YMCA and The Pencil Foundation. 

-CS($raspq-oOrpms
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

and training) at One-Stop - county and WIA will pay for rent, utilities. 

and recipients -Agreement specifies scope of services, payment 

placement assistance, and post-employment retenti 
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Staffing Arrangement for One-Stop (Including Shared Staff with TANF Program) 

-About15 Career Center workers, plus 5 part-time outstationed workers from other agencies staff the 1-Stop Center -WIA, WtW, & TANF agencies staff are located in the same building that houses OneStop Career Center (though TANF staff not considered part of 1-stop) -Other partners are headquartered in other locations, but outstation staff at One-Stop either full-time (ES/UI, community college, Job Corp) or part-time (VR, Job Corps, CAC, and Services for the Blind) 

-About 16 staff at the Career Resource Center, including 2 WIA case managers; 1 DVOT; 4 customer service representatives; 1 employment career counselor; 1 clerical; 2 employer representatives; 2 supervisors; and One-Stop director -TANF outstations 4 workers at OneStop for case management services for principal earners in 2-parent families and other jobready volunteers -WIA and ES staff located at Career Resource Center - Other One-Stop partners are headquartered in other locations, but outstation staff at One-Stop 

- About 82 total (including 24 parttime partners’ staff) ]-Includes about 35 CalWORKS employment counselors; 4 Career Center managers; and Career Development and Job Development teams - DSS and EDD outstation staff at the one-stop for some time (e.g., 2 days per week) - DSS: Employment Counselors for triage to Job Clubs, principally at One Stop, or to CBO for special service (e.g., language problem), and continued case monitoring, while client receives services in one stop or other provider -All partners outstation some staff at One Stop for varying time commitments 

-About 100 total staff at the OneStop (includes about 70 FEC staff and outstationed staff from other partners) - TANF agency outstations three “intensive case managers” at 1-Stop to deal with support issues (e.g., child care assistance, domestic violence services) -Most partners have a staff member outstationed at the one-stop, across the street, or at the One-Stop on a rotating basis - FEC locates staff at main and local TANF offices (making them satellite one-stop offices) for career readiness and job matching services - The ES office and staff are located at the Onestop 

-About 60 staff work at the OneStop -Substantial number of WIA and ES staff located at the OneStop -One TANF staff case manager is outstationed at One-stop (paid for by TANF) to ensure that Onestop users can avail themselves of income benefits programs for which they are eligible 

-(sccwtcsmesdW so- otsd- wEC
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Overview of Services Available to the General Public Through the One-Stop 

-Main services available include: (1) job placement assistance (local job listings, Internet job listings, ES/UI representative, job seeking skills workshops, youth services); (2) career development services (job club, pre-employment certification, job training/ITAs, OJT); (3) computer resource center (access to computers for job search, career exploration, resume preparation; keyboarding and Microsoft tutorials; library of books and resources on job seeking and retention skills); (4) career planning (individual/group career guidance, career assessment, aptitude testing) 

-Main services available include: (1) job placement assistance (local job listings, Internet job listings, ES/UI representative, job seeking skills workshops, youth services); (2) job training and career development services; (3) labor market information and range of computerized resources (access to computers for job search, career exploration, resume preparation; library of books and resources on job seeking and retention skills); (4) career planning (individual/group career guidance, career assessment, aptitude testing) 

-Universal services plus veterans, older workers, disabled, welfare, youth, Job Corps, dislocated workers -Employer services include rapid response for plant closings/layoffs, and job fairs. -Internet-based card swipe system (Smartware) tracks activities usage and grants access to any center in EastBay Works consortium -Staff are available to assist in job search -Universal Job Club -Resource room/career library, job search assistance; computer lab for job search, resumes, training, phone banks, fax machine (20 general access computers, EDD computers for DMV, UI and employer job search, 8 dedicated to LMI services, others for workshops, training, PowerPoint orientation. 

Main services available including: (1) resource room (with local and Internet job listings, job postings and referrals, local labor market information, computers and fax machine); (2) assessment services; (3) descriptions of and referrals to education and training providers; (4) financial assistance workshops (to inform customers about TANF and other assistance programs; (5) unemployment insurance filing 

Main services available include: (1) full-service resource center (Internet access to online career services, fax machines, telephone, LMI); (2) employment services (local and national job searchers, help with resume development, resume posting, help with job search); and (3) assessment and job training services (skills assessment, career workshops, information and referral services on education and training options 

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LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Overview of Services Available to TANF Recipients at One-Stop 

-TANF recipients may use core and intensive services generally available to the public (e.g., resource room, training services, etc.) -All TANF applicants see Employment Security Commission worker at OneStop for short interview to determine if applicant is jobready, register for ES, and learn about services available through ES and One-Stop Career Center -During ASPIRE (Additional Support for People in Retraining and Employment) 2-week job readiness workshop (attended by new job-ready TANF participants & volunteers), participants visit One-Stop twice for several hours to learn of available services and begin 

-TANF recipients may use core and intensive services generally available to the public (e.g., resource room, training services, etc.) -TANF program contracts with DET (on statewide basis) to assess and case manage all principal earning parents (PEPs) in 2 parent households (about 1/10 of TANF caseload). DET develops family matrix and family development plan to guide services for PEPs. -The PEP in all 2-parent applicants (and some single parents) are required to report to the One-Stop within 2 days of TANF application for up to 4 weeks of job search -If not placed in jobs, One-Stop is responsible for coordinating and developing workplacements 

Above plus (1) employment counseling services, (2) mandatory weeklong 30-hour separate CalWORKS job club (more soft skills training than general clients), plus (3) three-week networking job search assistance, (4) if no job, mandatory unpaid community service assignment, or (5) voluntary unpaid work experience (for those on cash assistance 18-24 months), (6) job development (job club, job fairs, email distribution, catalog of hot jobs), (7) workshops (interviewing, resumes, career counseling) -Voluntary “Employment Information Services” started in August for cases timing out, to reintegrate clients into One Stop universal and 

All of the above plus (1) career and employment planning; (2) a week-long, 40-hour job readiness class (covering interviewing, resumes, attitude); (3) up to fourweek (30-hour/week) job search/job development/job matching services; (4) as needed, three-month paid work experience assignments for skills development; (5) paid internships and on-the-job training with employers, often with a commitment to hire; (6) up to $350 in funds for work-related expenses (e.g., childcare, supplies) as well as vouchers for housing and other needs; (7) retention incentive payments to customers ($300 for 30 days employment; $1800 for 9 months 

-TANF recipients may use core and intensive services generally available to the public (e.g., resource room, training services, etc.) -WIA agency (located at OneStop) was contractor until July 2001 to provide soft skills training, job search assistance, and post employment counseling for TANF recipients. -Since July 2001, other contractors have taken over from WIA providing these services for TANF recipients, but contractors still bring participants to the One-Stop 1-2 times per week to learn about and use the core OneStop services, attend workshops, meet with ES counselors-TANF agency outstations case manager to One-Stop for TANF services.

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LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

job search -If not employed after ASPIRE, most job-ready TANF recipients attend daily Job Club at One-Stop to meet work requirements – they typically receive help with job leads, coaching from counselors on effective job search techniques, and use LMI and other resources generally available at One-Stop -Though constrained by work requirements and limits on training, small numbers of TANF recipients go to WIA office to obtain information about and referrals for short-term training 

for PEPs (including work, education and training, CWEP, and other subsidized work) -When single parent near the end of their time limits, TANF programs refers individuals to 4-week job readiness/job search workshop held at the OneStop; participants see DET case manager at least once a week; workshop participants receive help with job search and are expected to make between 7-10 contacts/week with employers. 

intensive services. Includes Caretaker Training Initiative, administered by Oakland PIC, for unskilled licensed practical nurses to train up to RN. -University-based evaluation in progress (employer, client satisfaction, employment rate) -Two rooms and one of four units dedicated to TANF and GA 

employment); (8) nine months of formal postemployment follow-up and 12 additional months of informal followup, with job search and matching if needed 

-One-stop is local contractor to state to provide special transportation and work-related support services 
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Estimates of Number/Percent of TANF Recipients Receiving Services Through One-Stop 

-All adult TANF recipients at least visit and are registered for ES at One-Stop -Virtually all TANF recipients determined to be job-ready and unable to secure a job attend Job Club at One-Stop and use other OneStop services (e.g., resource room and workshops offered by One-Stop) 

-Staff indicated that almost all TANF recipients will at least visit the One-Stop at some point -An estimated 1⁄4 to 1/3 of TANF recipients are enrolled in activities at the One-Stop (e.g., case management, help with job search, 4-week job readiness workshop or attend Job Keepers) 

-About 170 CalWORKS clients served per quarter -Average caseload of 135 (staff reported) 

- All non-exempt adult TANF applicants recipients must report to the OneStop for job search - Staff estimates that 70% of TANF recipients use 1-stop at some point; -Between 600-700 TANF recipients were served at the One-Stop in 2001, representing about1/10 of the adult TANF cases in the service area 

-Most TANF recipients visit One-Stop (once or twice a week) to look for a job -Not tracking exact numbers of welfare recipients served by One-Stop 

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Estimate of TANF Recipients as a % of All One-Stop Users 

-TANF recipients estimated at 10% of those served by One-Stop -TANF, FS, and other adult services – estimated at 33-40% of one-stop users 

-TANF recipients estimated at 25% of those served by One-Stop 

-20-30% are on CalWORKS or GA (staff estimate; difficult to track because SmartWare is selfdeclaration) 

-TANF recipients estimated at 15% of those served by One-Stop 

-TANF recipients estimated at 25% of those served by One-Stop 

N
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Role of ES at One-Stop 

-ES headquartered 2 blocks from One-Stop, but outstations 1 worker at OneStop to register and determine job readiness of TANF applicants; also provides information about ES services -One-Stop resource room has electronic link to job listings maintained by ES 

-ES is part of DET -- so ES workers co-located at onestop and provide full-range of ES services at onestop 

-As part of intake, clients apply for UI directly with EDD staff - An Employment Service Information Group meets weekly, on a voluntary basis – one-on-one meetings can be arranged individually 

-ES co-located at One-Stop, and hence, the fullrange of ES services are available at 1-Stop -ES has contract with DFS to provide services to TANF recipients; DFS refers “ablebodied” individuals, mostly Food Stamps but including TANF, recipients to ES for services (e.g., registration, job search, counseling, job clubs, & support services such as transportation) 

-ES has staff (12) co-located at OneStop, and hence, the full-range of ES services are available at the One-Stop 

-oOaaoOa
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Role of WtW grants program in One-Stop 

-Outstationed DSS worker at the OneStop certifies whether TANF recipients referred to ASPIRE are WtW eligible – classify individuals as 70 or 30 percent eligible so state can pay for services under WtW program -Outstationed DSS worker at OneStop receives referrals of noncustodial parents from court and child support enforcement; interviews NCPs; determines WtW eligibility; refers individuals to ASPIRE workshop and job club, and provides help with transportation 

-No role – WtW program no longer operational. 

WtW Retention program ended June 2002. Had three contractors (Hayward Adult School, Vallecitos Center for Employment and Training, and Eden I and R; Veterans stress counseling still operating 

-WtW program operates out of the One-Stop -- site serves both competitive and formula individuals -DFS workers refer eligible persons to the WtW worker at the One-Stop, who assigns them to orientation, classes, and other WtW services 

-The Nashville One-Stop ran a WtW program using the Project Match Pathways model, which is ending in 2002 and will not be picked up by the TANF agency -The WtW Pathways specialist provides intensive case management and home visits to TANF recipients -WtW provided opportunity to collaborate with CBOs serving WtW/TANF recipients, increasing referrals to WtW program 

-cfgp- wWip$$r1 - maae- dststhp
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

Management of One-Stop 

-Management Team consisting of key management staff within partnering agencies responsible for providing direction and oversight that One-Stop services are meeting customers needs -Management Team typically meets quarterly to plan and resolve problems/issues that arise; also issues often resolved through informal discussions of center managers/staff 

-TANF involved from beginning in planning of OneStop; planning group of about 20-30 individuals (including representatives of DET and TANF) met about 6 times -Planning group that originally met monthly in getting One-Stop off the ground, now meets about every 2 months to discuss organization, changes, and problems. 

- Four managers at the Career Center - While CalWORKS staff are under SSA and co-located with them, there is still traditional organization separation - CBO contracting is done for the whole county -Eastbay Works has an internet tracking system for 14 Career Centers 

-Weekly meetings of managers of all agencies within the One-Stop -Monthly LINC meetings with citizens, employers, participants, and workers -Weekly meetings between One-Stop staff and DFS onsite staff -Monthly meetings of DFS staff with One-Stop staff in satellite offices 

-Close relationship between TANF and workforce system at state and local levels: TANF agency represented on state and local WIBs during PRWORA and WIA implementation; TANF agency heavily involved in the creation of the local One-Stop; TANF trained all WIA staff in TANF issues 

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Misc. issues re interface between TANF and OneStop 

-TANF and OneStop staff perceive (1) that TANF clients and TANF administrators may be better aware of various services available through One-Stop because located in same building (2) referral of TANF recipients to other services at the One-Stop is easier and 

-DET brings workforce development expertise to serving TANF recipients -- knowledge of employers, job search methods, and ability to assess job readiness -One-Stop makes other useful services readily available for 

-Colocation has been critical factor in both TANF agency focus on One Stop and in stemming what would otherwise be 50-75% attrition rate in referrals (drop off rate to Job Club was huge, now nearly non-existent). Facilitates childcare in the building. 

-Co-location of TANF and OneStop workers leads to daily informal interaction and closer coordination -Co-location saves time for customers, since job preparation and case management services at the same office 

-Early issues, potential policy conflicts worked out at state level. For example, TN, not a "work first" state, allows anyone with less than 9th grade 

education level to engage in training activities, therefore defined core services and intensive services so that TANF 

- tdnsw
LOCATION ASHEVILLE, 

NC 

BURLINGTON, 

VT 

HAYWARD, CA KANSAS CITY, 

MO 

NASHVILLE, 

TN 

recipients are more likely to show up for services because they are in the same office building (3) by being located in the same building as the One-Stop, DSS case managers can informally talk with One-Stop staff and check to see if TANF recipients are showing up for One-Stop services – so partnering facilitates tracking and feedback of information 

TANF recipients – for example, referrals to VR for more in-depth assessment of disabilities are easier because a VR worker is outstationed at the One-Stop 

-Relationship between Employment Counselors (TANF) and One stop generally good but issues still being ironed out. 

assessments would count as WIA assessment to satisfy WIA performance standards -WIA longer-term training was incompatible with TANF needs, so shorter-term and demand occupation training was developed to serve TANF customers. 
407

APPENDIX B: 

WELFARE RECIPIENTS SERVED THROUGH JTPA, BY STATE 

 
408

TABLE B.1 

TOTAL NUMBER OF JTPA TERMINEES (TITLES II-A, II-C, AND III) 

State Project Year Percentage 

 1993 1994 1995 1996 1997 1998 1999 Change 

Alabama 9,763 7,836 6,936 6,848 7,532 7,893 5,250 (46.2%)

Alaska 799 930 1,052 909 1,126 1,383 1,580 97.7%

Arizona 6,459 6,856 6,368 6,499 5,979 6,649 6,287 (2.7%)

Arkansas 7,380 5,480 5,331 5,668 6,163 4,126 4,507 (38.9%)

California 51,575 58,965 62,480 66,437 63,250 61,773 63,559 23.2%

Colorado 7,225 6,742 7,247 5,899 5,124 5,137 4,525 (37.4%)

Connecticut 5,640 4,957 4,273 6,212 6,282 6,814 8,333 47.7%

Delaware 1,720 1,214 1,280 1,175 1,162 1,233 1,029 (40.2%)

District of Columbia 530 1,363 842 1,247 794 839 1,508 184.5%

Florida 22,714 22,987 22,772 21,224 18,336 22,807 22,503 (0.9%)

Georgia 12,151 12,584 18,391 15,610 16,877 12,749 10,271 (15.5%)

Hawaii 1,285 2,226 1,878 1,847 2,661 3,222 3,215 150.2%

Idaho 1,769 1,745 1,697 1,892 2,133 2,107 2,137 20.8%

Illinois 24,825 24,172 27,434 19,619 17,552 16,102 15,895 (36.0%)

Indiana 8,282 7,803 10,147 8,799 9,126 7,485 7,382 (10.9%)

Iowa 4,766 4,168 2,751 3,022 2,797 2,180 2,001 (58.0%)

Kansas 2,794 2,318 3,576 3,407 3,642 2,407 2,255 (19.3%)

Kentucky 9,041 8,085 6,017 5,261 5,381 6,643 6,496 (28.1%)

Louisiana 14,242 15,193 11,662 10,550 11,025 9,675 11,804 (17.1%)

Maine 2,459 2,349 2,348 2,668 2,505 2,522 2,379 (3.3%)

Maryland 12,395 14,734 14,768 10,643 12,157 11,919 10,595 (14.5%)

Massachusetts 19,328 17,013 14,856 12,121 12,454 9,843 7,991 (58.7%)

Michigan 22,253 20,882 14,139 14,351 12,416 13,514 12,495 (43.9%)

Minnesota 8,409 10,434 8,048 5,550 5,737 5,656 6,028 (28.3%)

Mississippi 9,879 9,366 9,011 9,364 9,457 9,821 10,556 6.9%

Missouri 11,390 10,584 9,943 8,664 8,293 8,224 9,690 (14.9%)

Montana 1,927 1,615 1,551 1,244 1,285 773 1,114 (42.2%)

Nebraska 1,858 1,595 1,218 1,012 1,106 1,075 1,187 (36.1%)

Nevada 2,204 2,601 2,535 2,729 3,215 3,231 2,514 14.1%

New Hampshire 2,373 2,666 1,542 1,782 1,378 1,181 1,425 (39.9%)

New Jersey 11,705 15,944 28,118 22,851 18,235 11,896 12,653 8.1%

New Mexico 2,876 2,966 2,541 2,741 3,370 3,671 4,615 60.5%

New York 21,846 27,752 26,441 33,597 33,578 28,984 22,258 1.9%

North Carolina 10,125 9,876 8,453 8,031 7,957 8,020 5,681 (43.9%)

North Dakota 1,014 1,200 1,192 1,050 1,599 858 955 (5.8%)

Ohio 19,637 17,677 19,842 15,121 14,607 14,782 14,767 (24.8%)

Oklahoma 5,016 4,569 4,806 6,201 3,573 1,968 2,503 (50.1%)

Oregon 6,693 6,995 7,850 6,554 5,969 5,954 4,946 (26.1%)

Pennsylvania 24,825 26,261 26,808 25,292 22,769 20,634 20,161 (18.8%)

Puerto Rico 5,751 4,528 14,303 13,605 13,401 15,470 11,171 94.2%

Rhode Island 2,466 3,219 2,421 2,992 2,229 1,794 1,307 (47.0%)

South Carolina 8,328 9,036 10,897 12,153 9,375 8,090 10,192 22.4%

South Dakota 2,073 2,559 2,714 1,900 2,247 2,178 2,274 9.7%

Tennessee 6,692 7,642 8,193 7,664 7,739 5,523 10,598 58.4%

Texas 39,295 38,527 35,550 30,419 28,941 30,738 30,132 (23.3%)

Utah 2,325 2,127 1,987 1,442 1,289 1,309 1,273 (45.2%)

Vermont 2,112 1,964 1,624 883 853 890 890 (57.9%)

Virginia 10,925 11,026 9,512 9,559 9,559 8,269 7,968 (27.1%)

Washington 8,773 10,583 11,444 10,956 12,399 12,051 11,257 28.3%

West Virginia 6,983 6,052 5,777 4,014 2,945 4,468 5,075 (27.3%)

Wisconsin 10,133 9,299 8,196 6,763 6,503 5,923 7,362 (27.3%)

Wyoming 730 621 719 763 651 538 900 23.3%

U.S. Total 497,758 504,929 521,481 486,804 466,733 442,991 435,449 (12.5%)
409

TABLE B.2 

NUMBER OF JTPA TERMINEES (TITLES II-A, II-C, AND III) 

WHO ARE RECEIVING WELFARE 

State Project Year Percentage 

 1993 1994 1995 1996 1997 1998 1999 Change 

Alabama 1,557 1,109 842 651 446 347 201 (87.1%)

Alaska 189 257 230 222 337 314 274 45.0%

Arizona 1,025 1,227 997 1,093 823 701 569 (44.5%)

Arkansas 1,123 778 466 396 342 270 231 (79.4%)

California 11,517 12,657 12,564 12,113 11,035 9,493 7,981 (30.7%)

Colorado 1,672 1,636 1,414 1,172 916 637 494 (70.5%)

Connecticut 1,178 909 640 567 704 699 615 (47.8%)

Delaware 415 304 262 234 165 127 95 (77.1%)

District of Columbia 54 264 170 215 161 148 220 307.4%

Florida 5,727 5,563 4,614 3,381 2,939 3,394 2,651 (53.7%)

Georgia 2,038 2,125 2,122 1,303 1,239 1,498 1,295 (36.5%)

Hawaii 182 212 248 265 474 554 499 174.2%

Idaho 288 286 246 214 210 103 52 (81.9%)

Illinois 4,759 5,074 3,944 3,012 3,100 2,342 1,539 (67.7%)

Indiana 1,390 1,249 1,041 760 701 622 488 (64.9%)

Iowa 1,014 927 597 481 388 344 307 (69.7%)

Kansas 611 485 595 544 424 214 135 (77.9%)

Kentucky 1,840 1,594 1,226 838 852 956 741 (59.7%)

Louisiana 3,066 3,298 2,044 1,703 1,663 1,124 1,200 (60.9%)

Maine 479 450 391 378 279 238 241 (49.7%)

Maryland 3,125 3,099 2,419 1,090 1,675 1,649 1,437 (54.0%)

Massachusetts 3,140 3,351 2,599 1,579 1,717 1,570 882 (71.9%)

Michigan 5,162 5,251 2,586 2,143 1,796 1,566 855 (83.4%)

Minnesota 2,034 2,378 1,795 1,200 1,250 964 1,057 (48.0%)

Mississippi 1,650 1,280 993 691 651 402 266 (83.9%)

Missouri 1,789 1,791 1,515 1,487 1,373 1,228 904 (49.5%)

Montana 384 329 297 195 225 131 208 (45.8%)

Nebraska 474 444 248 225 211 173 203 (57.2%)

Nevada 299 357 550 479 476 314 119 (60.2%)

New Hampshire 507 619 357 212 189 94 113 (77.7%)

New Jersey 2,708 2,982 2,495 1,914 1,837 1,639 1,197 (55.8%)

New Mexico 445 485 404 515 514 527 603 35.5%

New York 4,636 5,793 4,821 5,427 4,604 3,681 2,353 (49.2%)

North Carolina 2,199 2,296 1,832 1,614 1,507 1,197 690 (68.6%)

North Dakota 152 147 156 113 96 68 50 (67.1%)

Ohio 5,653 4,938 4,355 3,185 3,192 2,771 2,107 (62.7%)

Oklahoma 942 707 763 502 409 245 181 (80.8%)

Oregon 1,068 1,075 926 606 452 362 313 (70.7%)

Pennsylvania 8,046 8,547 8,718 7,359 5,717 5,711 4,421 (45.0%)

Puerto Rico 41 111 378 326 811 847 556 1256.1%

Rhode Island 480 639 387 404 421 378 152 (68.3%)

South Carolina 1,378 1,213 1,165 977 782 498 383 (72.2%)

South Dakota 286 326 304 258 231 149 103 (64.0%)

Tennessee 2,107 2,920 2,057 1,774 1,060 618 1,079 (48.8%)

Texas 6,331 5,714 4,743 3,847 3,179 2,757 2,166 (65.8%)

Utah 473 358 307 211 191 227 176 (62.8%)

Vermont 263 305 269 153 121 150 119 (54.9%)

Virginia 1,796 1,657 1,404 1,145 1,037 754 496 (72.4%)

Washington 1,909 2,055 1,893 1,727 2,012 1,577 1,141 (40.2%)

West Virginia 1,575 1,527 1,157 721 607 748 898 (43.0%)

Wisconsin 2,292 2,202 1,941 1,315 1,009 855 645 (71.9%)

Wyoming 61 0 146 165 73 33 0 (100.0%)

U.S. Total 103,529 104,391 88,633 73,101 66,623 58,008 45,700 (55.9%)
410

TABLE B.3 

PERCENT OF JTPA TERMINEES (TITLES II-A, II-C, AND III) 

WHO ARE RECEIVING WELFARE 

State Project Year Change in Percentage 

 1993 1994 1995 1996 1997 1998 1999 Percent Change 

Alabama 15.9% 14.2% 12.1% 9.5% 5.9% 4.4% 3.8% -12.1% (76.0%)

Alaska 23.7% 27.6% 21.9% 24.4% 29.9% 22.7% 17.3% -6.3% (26.7%)

Arizona 15.9% 17.9% 15.7% 16.8% 13.8% 10.5% 9.1% -6.8% (43.0%)

Arkansas 15.2% 14.2% 8.7% 7.0% 5.5% 6.5% 5.1% -10.1% (66.3%)

California 22.3% 21.5% 20.1% 18.2% 17.4% 15.4% 12.6% -9.8% (43.8%)

Colorado 23.1% 24.3% 19.5% 19.9% 17.9% 12.4% 10.9% -12.2% (52.8%)

Connecticut 20.9% 18.3% 15.0% 9.1% 11.2% 10.3% 7.4% -13.5% (64.7%)

Delaware 24.1% 25.0% 20.5% 19.9% 14.2% 10.3% 9.2% -14.9% (61.7%)

District of Columbia 10.2% 19.4% 20.2% 17.2% 20.3% 17.6% 14.6% +4.4% 43.2%

Florida 25.2% 24.2% 20.3% 15.9% 16.0% 14.9% 11.8% -13.4% (53.3%)

Georgia 16.8% 16.9% 11.5% 8.3% 7.3% 11.7% 12.6% -4.2% (24.8%)

Hawaii 14.2% 9.5% 13.2% 14.3% 17.8% 17.2% 15.5% +1.4% 9.6%

Idaho 16.3% 16.4% 14.5% 11.3% 9.8% 4.9% 2.4% -13.8% (85.1%)

Illinois 19.2% 21.0% 14.4% 15.4% 17.7% 14.5% 9.7% -9.5% (49.5%)

Indiana 16.8% 16.0% 10.3% 8.6% 7.7% 8.3% 6.6% -10.2% (60.6%)

Iowa 21.3% 22.2% 21.7% 15.9% 13.9% 15.8% 15.3% -5.9% (27.9%)

Kansas 21.9% 20.9% 16.6% 16.0% 11.6% 8.9% 6.0% -15.9% (72.6%)

Kentucky 20.4% 19.7% 20.4% 15.9% 15.8% 14.4% 11.4% -8.9% (44.0%)

Louisiana 21.5% 21.7% 17.5% 16.1% 15.1% 11.6% 10.2% -11.4% (52.8%)

Maine 19.5% 19.2% 16.7% 14.2% 11.1% 9.4% 10.1% -9.3% (48.0%)

Maryland 25.2% 21.0% 16.4% 10.2% 13.8% 13.8% 13.6% -11.6% (46.2%)

Massachusetts 16.2% 19.7% 17.5% 13.0% 13.8% 16.0% 11.0% -5.2% (32.1%)

Michigan 23.2% 25.1% 18.3% 14.9% 14.5% 11.6% 6.8% -16.4% (70.5%)

Minnesota 24.2% 22.8% 22.3% 21.6% 21.8% 17.0% 17.5% -6.7% (27.5%)

Mississippi 16.7% 13.7% 11.0% 7.4% 6.9% 4.1% 2.5% -14.2% (84.9%)

Missouri 15.7% 16.9% 15.2% 17.2% 16.6% 14.9% 9.3% -6.4% (40.6%)

Montana 19.9% 20.4% 19.1% 15.7% 17.5% 16.9% 18.7% -1.3% (6.3%)

Nebraska 25.5% 27.8% 20.4% 22.2% 19.1% 16.1% 17.1% -8.4% (33.0%)

Nevada 13.6% 13.7% 21.7% 17.6% 14.8% 9.7% 4.7% -8.8% (65.1%)

New Hampshire 21.4% 23.2% 23.2% 11.9% 13.7% 8.0% 7.9% -13.4% (62.9%)

New Jersey 23.1% 18.7% 8.9% 8.4% 10.1% 13.8% 9.5% -13.7% (59.1%)

New Mexico 15.5% 16.4% 15.9% 18.8% 15.3% 14.4% 13.1% -2.4% (15.6%)

New York 21.2% 20.9% 18.2% 16.2% 13.7% 12.7% 10.6% -10.6% (50.2%)

North Carolina 21.7% 23.2% 21.7% 20.1% 18.9% 14.9% 12.1% -9.6% (44.1%)

North Dakota 15.0% 12.3% 13.1% 10.8% 6.0% 7.9% 5.2% -9.8% (65.1%)

Ohio 28.8% 27.9% 21.9% 21.1% 21.9% 18.7% 14.3% -14.5% (50.4%)

Oklahoma 18.8% 15.5% 15.9% 8.1% 11.4% 12.4% 7.2% -11.5% (61.5%)

Oregon 16.0% 15.4% 11.8% 9.2% 7.6% 6.1% 6.3% -9.6% (60.3%)

Pennsylvania 32.4% 32.5% 32.5% 29.1% 25.1% 27.7% 21.9% -10.5% (32.3%)

Puerto Rico 0.7% 2.5% 2.6% 2.4% 6.1% 5.5% 5.0% +4.3% 598.1%

Rhode Island 19.5% 19.9% 16.0% 13.5% 18.9% 21.1% 11.6% -7.8% (40.3%)

South Carolina 16.5% 13.4% 10.7% 8.0% 8.3% 6.2% 3.8% -12.8% (77.3%)

South Dakota 13.8% 12.7% 11.2% 13.6% 10.3% 6.8% 4.5% -9.3% (67.2%)

Tennessee 31.5% 38.2% 25.1% 23.1% 13.7% 11.2% 10.2% -21.3% (67.7%)

Texas 16.1% 14.8% 13.3% 12.6% 11.0% 9.0% 7.2% -8.9% (55.4%)

Utah 20.3% 16.8% 15.5% 14.6% 14.8% 17.3% 13.8% -6.5% (32.2%)

Vermont 12.5% 15.5% 16.6% 17.3% 14.2% 16.9% 13.3% +0.9% 7.1%

Virginia 16.4% 15.0% 14.8% 12.0% 10.8% 9.1% 6.2% -10.2% (62.1%)

Washington 21.8% 19.4% 16.5% 15.8% 16.2% 13.1% 10.1% -11.6% (53.4%)

West Virginia 22.6% 25.2% 20.0% 18.0% 20.6% 16.7% 17.7% -4.9% (21.5%)

Wisconsin 22.6% 23.7% 23.7% 19.4% 15.5% 14.4% 8.8% -13.9% (61.3%)

Wyoming 8.4% 0.0% 20.3% 21.6% 11.2% 6.1% 0.0% -8.4% (100.0%)

U.S. Total 20.8% 20.7% 17.0% 15.0% 14.3% 13.1% 10.5% -10.3% (49.5%)
411

TABLE B.4 

TOTAL NUMBER OF TANF/AFDC ADULTS 

State Fiscal Year1 Percentage 

 1994 1995 1996 1997 1998 1999 2000 Change 

Alabama 46,786 39,709 33,453 28,046 16,753 12,848 12,007 (74.3%)

Alaska 17,978 17,190 16,737 16,382 14,026 11,175 11,273 (37.3%)

Arizona 83,685 77,877 68,843 59,057 37,854 29,709 26,924 (67.8%)

Arkansas 25,608 22,974 20,842 19,105 11,140 8,480 9,381 (63.4%)

California 1,077,327 1,091,920 1,059,093 967,933 789,221 652,685 393,069 (63.5%)

Colorado 49,913 44,862 39,801 31,143 20,917 13,345 9,095 (81.8%)

Connecticut 70,389 72,900 69,128 66,425 53,708 33,243 25,136 (64.3%)

Delaware 11,532 10,347 9,806 9,408 6,890 5,277 5,738 (50.2%)

District of Columbia 29,850 28,731 28,325 25,668 24,421 19,533 16,284 (45.4%)

Florida 266,843 244,935 213,835 170,455 91,462 60,916 41,492 (84.5%)

Georgia 153,515 147,075 131,579 102,724 63,325 46,996 41,305 (73.1%)

Hawaii 26,984 28,650 28,793 27,763 20,009 18,871 17,323 (35.8%)

Idaho 9,787 9,859 9,051 6,215 1,487 791 547 (94.4%)

Illinois 292,120 281,858 257,749 222,464 197,784 129,495 85,324 (70.8%)

Indiana 91,513 76,821 56,765 44,127 40,871 43,536 35,318 (61.4%)

Iowa 49,967 44,535 38,719 34,320 29,398 24,442 22,121 (55.7%)

Kansas 36,054 32,128 26,407 20,505 13,518 11,877 11,367 (68.5%)

Kentucky 92,032 79,733 70,471 63,788 49,964 36,403 31,537 (65.7%)

Louisiana 88,321 100,394 95,186 81,969 47,018 35,395 22,259 (74.8%)

Maine 31,258 28,823 26,567 23,758 17,843 15,062 11,667 (62.7%)

Maryland 91,802 91,843 82,180 65,540 47,126 36,769 24,716 (73.1%)

Massachusetts 141,602 125,377 108,387 96,616 68,779 50,934 37,442 (73.6%)

Michigan 292,847 258,144 223,462 188,786 142,126 88,626 70,536 (75.9%)

Minnesota 81,217 76,601 70,999 64,217 59,869 46,609 44,733 (44.9%)

Mississippi 55,504 49,424 42,647 34,576 18,130 11,166 8,960 (83.9%)

Missouri 112,314 101,503 89,683 74,647 60,482 46,839 45,727 (59.3%)

Montana 15,650 15,073 13,766 12,112 9,732 6,103 7,157 (54.3%)

Nebraska 18,460 16,705 15,390 14,491 14,359 12,656 8,473 (54.1%)

Nevada 14,806 15,571 13,392 9,316 10,056 6,729 5,008 (66.2%)

New Hampshire 14,172 12,768 10,794 9,007 6,346 6,560 5,673 (60.0%)

New Jersey 139,162 132,687 120,084 104,829 79,156 57,978 42,645 (69.4%)

New Mexico 46,376 47,209 46,144 36,842 27,254 33,746 28,815 (37.9%)

New York 569,851 572,880 533,053 470,270 415,330 348,435 299,977 (47.4%)

North Carolina 141,869 131,775 111,889 100,712 66,176 41,942 29,942 (78.9%)

North Dakota 7,265 6,187 5,608 4,556 3,415 3,069 3,729 (48.7%)

Ohio 296,241 253,558 211,702 200,107 145,191 104,742 84,016 (71.6%)

Oklahoma 52,944 49,150 39,632 30,959 22,375 17,531 10,165 (80.8%)

Oregon 49,152 43,176 34,866 25,814 21,303 18,122 15,306 (68.9%)

Pennsylvania 261,611 249,617 226,564 185,454 155,904 112,531 82,404 (68.5%)

Puerto Rico 75,355 69,671 63,943 58,908 3,640 50,638 36,148 (52.0%)

Rhode Island 27,673 26,631 24,995 23,005 23,337 20,632 18,158 (34.4%)

South Carolina 48,392 42,868 38,850 31,379 22,179 13,154 10,635 (78.0%)

South Dakota 7,063 6,088 5,667 4,399 3,130 2,183 1,645 (76.7%)

Tennessee 124,447 111,423 102,442 72,156 51,279 53,651 56,764 (54.4%)

Texas 307,071 284,952 258,177 234,221 150,289 108,470 116,455 (62.1%)

Utah 21,598 19,454 17,105 14,394 14,627 9,674 8,082 (62.6%)

Vermont 13,705 13,406 12,401 11,284 9,863 8,483 7,478 (45.4%)

Virginia 78,096 72,377 61,788 48,770 49,838 30,996 25,045 (67.9%)

Washington 135,205 131,908 125,046 116,577 95,463 77,102 62,316 (53.9%)

West Virginia 54,800 48,838 43,208 37,308 27,954 15,414 13,103 (76.1%)

Wisconsin 114,005 81,169 61,094 44,479 17,677 10,978 7,366 (93.5%)

Wyoming 6,805 5,844 4,807 3,109 953 525 294 (95.7%)

U.S. Total 5,968,525 5,645,196 5,120,917 4,450,098 3,390,946 2,663,066 2,048,079 (65.7%)

 

1 A project year begins on July 1st of the fiscal year. Therefore, the fiscal year most closely corresponding to Project Year 

1993 is Fiscal Year 1994, Fiscal Year 1995 for Project Year 1994, and so on. 
412

TABLE B.5 

NUMBER OF WELFARE PARENTS WHO ARE PARTICIPATING 

IN JTPA PROGRAMS (TITLES II-A, II-C, AND III) 

State Project Year Percentage 

 1993 1994 1995 1996 1997 1998 1999 Change 

Alabama 1,340 1,031 783 625 436 335 193 (85.6%)

Alaska 162 230 213 207 326 300 258 59.3%

Arizona 835 963 874 973 713 604 515 (38.3%)

Arkansas 677 568 367 348 316 228 202 (70.2%)

California 7,171 8,137 9,241 9,533 8,812 7,536 6,192 (13.7%)

Colorado 1,496 1,468 1,294 1,115 870 604 460 (69.3%)

Connecticut 776 655 533 499 643 644 567 (26.9%)

Delaware 272 279 250 217 141 93 84 (69.1%)

District of Columbia 50 238 168 197 143 145 207 314.0%

Florida 4,125 4,546 3,711 3,062 2,684 3,163 2,453 (40.5%)

Georgia 1,760 1,858 1,908 1,182 1,119 1,283 1,149 (34.7%)

Hawaii 167 179 221 248 422 498 465 178.4%

Idaho 252 245 219 199 198 99 49 (80.6%)

Illinois 1,746 3,355 2,980 2,495 2,767 2,128 1,378 (21.1%)

Indiana 1,080 1,007 858 683 623 562 436 (59.6%)

Iowa 234 567 477 442 373 335 296 26.5%

Kansas 495 434 549 512 398 206 125 (74.7%)

Kentucky 1,059 1,151 983 749 768 912 703 (33.7%)

Louisiana 1,739 2,028 1,344 1,338 1,363 939 1,009 (42.0%)

Maine 415 382 327 321 242 208 204 (50.8%)

Maryland 2,517 2,564 2,041 924 1,526 1,548 1,341 (46.7%)

Massachusetts 2,353 2,549 2,236 1,401 1,580 1,506 824 (65.0%)

Michigan 3,589 4,003 2,208 1,876 1,626 1,386 742 (79.3%)

Minnesota 1,729 1,978 1,623 1,125 1,158 907 991 (42.7%)

Mississippi 1,178 845 686 550 517 306 201 (82.9%)

Missouri 1,459 1,528 1,401 1,397 1,315 1,181 834 (42.8%)

Montana 205 235 249 172 195 126 192 (6.3%)

Nebraska 372 345 228 213 190 158 185 (50.3%)

Nevada 244 322 524 463 457 300 113 (53.7%)

New Hampshire 412 548 297 172 154 53 70 (83.0%)

New Jersey 1,258 2,257 2,201 1,756 1,667 1,543 1,124 (10.7%)

New Mexico 153 253 300 459 454 444 532 247.7%

New York 2,776 4,285 3,802 4,699 3,971 3,089 1,979 (28.7%)

North Carolina 745 1,376 1,369 1,334 1,308 1,057 605 (18.8%)

North Dakota 137 137 150 107 93 63 43 (68.6%)

Ohio 4,332 3,722 3,448 2,822 2,864 2,494 1,846 (57.4%)

Oklahoma 596 527 603 457 382 239 176 (70.5%)

Oregon 411 797 738 539 395 317 284 (30.9%)

Pennsylvania 5,995 7,449 7,998 6,977 5,437 5,487 4,230 (29.4%)

Puerto Rico 16 72 201 247 536 633 412 2475.0%

Rhode Island 310 468 333 359 352 304 128 (58.7%)

South Carolina 1,006 912 976 870 716 459 347 (65.5%)

South Dakota 266 251 266 241 209 134 92 (65.4%)

Tennessee 1,469 2,436 1,851 1,635 956 530 933 (36.5%)

Texas 4,569 4,590 4,014 3,398 2,838 2,471 1,920 (58.0%)

Utah 431 326 290 193 181 222 172 (60.2%)

Vermont 197 213 207 123 91 119 92 (53.3%)

Virginia 1,474 1,375 1,265 1,055 967 700 447 (69.7%)

Washington 653 1,399 1,514 1,446 1,658 1,295 886 35.7%

West Virginia 1,207 1,265 899 675 582 724 882 (26.9%)

Wisconsin 874 1,651 1,546 1,100 866 740 465 (46.8%)

Wyoming 61 0 134 158 67 30 0 (100.0%)

U.S. Total 68,845 79,344 72,898 63,888 58,665 51,387 40,032 (41.9%)
413

TABLE B.6 

PERCENT OF WELFARE PARENTS WHO ARE PARTICIPATING 

IN JTPA PROGRAMS (TITLES II-A, II-C, AND III) 

State Project Year Change in Percentage 

 1993 1994 1995 1996 1997 1998 1999 Percent Change 

Alabama 2.9% 2.6% 2.3% 2.2% 2.6% 2.6% 1.6% -1.3% (43.9%)

Alaska 0.9% 1.3% 1.3% 1.3% 2.3% 2.7% 2.3% +1.4% 154.0%

Arizona 1.0% 1.2% 1.3% 1.6% 1.9% 2.0% 1.9% +0.9% 91.7%

Arkansas 2.6% 2.5% 1.8% 1.8% 2.8% 2.7% 2.2% -0.5% (18.6%)

California 0.7% 0.7% 0.9% 1.0% 1.1% 1.2% 1.6% +0.9% 136.7%

Colorado 3.0% 3.3% 3.3% 3.6% 4.2% 4.5% 5.1% +2.1% 68.8%

Connecticut 1.1% 0.9% 0.8% 0.8% 1.2% 1.9% 2.3% +1.2% 104.6%

Delaware 2.4% 2.7% 2.5% 2.3% 2.0% 1.8% 1.5% -0.9% (37.9%)

District of Columbia 0.2% 0.8% 0.6% 0.8% 0.6% 0.7% 1.3% +1.1% 658.9%

Florida 1.5% 1.9% 1.7% 1.8% 2.9% 5.2% 5.9% +4.4% 282.4%

Georgia 1.1% 1.3% 1.5% 1.2% 1.8% 2.7% 2.8% +1.6% 142.6%

Hawaii 0.6% 0.6% 0.8% 0.9% 2.1% 2.6% 2.7% +2.1% 333.7%

Idaho 2.6% 2.5% 2.4% 3.2% 13.3% 12.5% 9.0% +6.4% 247.9%

Illinois 0.6% 1.2% 1.2% 1.1% 1.4% 1.6% 1.6% +1.0% 170.2%

Indiana 1.2% 1.3% 1.5% 1.5% 1.5% 1.3% 1.2% +0.1% 4.6%

Iowa 0.5% 1.3% 1.2% 1.3% 1.3% 1.4% 1.3% +0.9% 185.7%

Kansas 1.4% 1.4% 2.1% 2.5% 2.9% 1.7% 1.1% -0.3% (19.9%)

Kentucky 1.2% 1.4% 1.4% 1.2% 1.5% 2.5% 2.2% +1.1% 93.6%

Louisiana 2.0% 2.0% 1.4% 1.6% 2.9% 2.7% 4.5% +2.6% 130.2%

Maine 1.3% 1.3% 1.2% 1.4% 1.4% 1.4% 1.7% +0.4% 31.7%

Maryland 2.7% 2.8% 2.5% 1.4% 3.2% 4.2% 5.4% +2.7% 97.9%

Massachusetts 1.7% 2.0% 2.1% 1.5% 2.3% 3.0% 2.2% +0.5% 32.4%

Michigan 1.2% 1.6% 1.0% 1.0% 1.1% 1.6% 1.1% -0.2% (14.2%)

Minnesota 2.1% 2.6% 2.3% 1.8% 1.9% 1.9% 2.2% +0.1% 4.1%

Mississippi 2.1% 1.7% 1.6% 1.6% 2.9% 2.7% 2.2% +0.1% 5.7%

Missouri 1.3% 1.5% 1.6% 1.9% 2.2% 2.5% 1.8% +0.5% 40.4%

Montana 1.3% 1.6% 1.8% 1.4% 2.0% 2.1% 2.7% +1.4% 104.8%

Nebraska 2.0% 2.1% 1.5% 1.5% 1.3% 1.2% 2.2% +0.2% 8.4%

Nevada 1.6% 2.1% 3.9% 5.0% 4.5% 4.5% 2.3% +0.6% 36.9%

New Hampshire 2.9% 4.3% 2.8% 1.9% 2.4% 0.8% 1.2% -1.7% (57.6%)

New Jersey 0.9% 1.7% 1.8% 1.7% 2.1% 2.7% 2.6% +1.7% 191.6%

New Mexico 0.3% 0.5% 0.7% 1.2% 1.7% 1.3% 1.8% +1.5% 459.6%

New York 0.5% 0.7% 0.7% 1.0% 1.0% 0.9% 0.7% +0.2% 35.4%

North Carolina 0.5% 1.0% 1.2% 1.3% 2.0% 2.5% 2.0% +1.5% 284.8%

North Dakota 1.9% 2.2% 2.7% 2.3% 2.7% 2.1% 1.2% -0.7% (38.9%)

Ohio 1.5% 1.5% 1.6% 1.4% 2.0% 2.4% 2.2% +0.7% 50.3%

Oklahoma 1.1% 1.1% 1.5% 1.5% 1.7% 1.4% 1.7% +0.6% 53.8%

Oregon 0.8% 1.8% 2.1% 2.1% 1.9% 1.7% 1.9% +1.0% 121.9%

Pennsylvania 2.3% 3.0% 3.5% 3.8% 3.5% 4.9% 5.1% +2.8% 124.0%

Puerto Rico 0.0% 0.1% 0.3% 0.4% 14.7% 1.3% 1.1% +1.1% .

Rhode Island 1.1% 1.8% 1.3% 1.6% 1.5% 1.5% 0.7% -0.4% (37.1%)

South Carolina 2.1% 2.1% 2.5% 2.8% 3.2% 3.5% 3.3% +1.2% 57.0%

South Dakota 3.8% 4.1% 4.7% 5.5% 6.7% 6.1% 5.6% +1.8% 48.5%

Tennessee 1.2% 2.2% 1.8% 2.3% 1.9% 1.0% 1.6% +0.5% 39.2%

Texas 1.5% 1.6% 1.6% 1.5% 1.9% 2.3% 1.6% +0.2% 10.8%

Utah 2.0% 1.7% 1.7% 1.3% 1.2% 2.3% 2.1% +0.1% 6.4%

Vermont 1.4% 1.6% 1.7% 1.1% 0.9% 1.4% 1.2% -0.2% (14.5%)

Virginia 1.9% 1.9% 2.0% 2.2% 1.9% 2.3% 1.8% -0.1% (5.4%)

Washington 0.5% 1.1% 1.2% 1.2% 1.7% 1.7% 1.4% +0.9% 194.4%

West Virginia 2.2% 2.6% 2.1% 1.8% 2.1% 4.7% 6.7% +4.5% 205.6%

Wisconsin 0.8% 2.0% 2.5% 2.5% 4.9% 6.7% 6.3% +5.5% 723.5%

Wyoming 0.9% 0.0% 2.8% 5.1% 7.0% 5.7% 0.0% -0.9% (100.0%)

U.S. Total 1.2% 1.4% 1.4% 1.4% 1.7% 1.9% 2.0% +0.8% 69.5%
414

TABLE B.7 

PERCENT OF JTPA TERMINEES (TITLES II-A, II-C, AND III) ON WELFARE 

WHO ARE ALSO PARTICIPATING IN JOBS 

State Project Year Change in Percentage 

 1993 1994 1995 1996 1997 1998 1999 Percent Change 

Alabama 40.7% 33.7% 33.3% 31.8% 32.7% 33.4% 41.3% +0.6% 1.4%

Alaska 32.8% 30.0% 34.3% 32.0% 30.3% 23.9% 33.2% +0.4% 1.2%

Arizona 18.3% 13.6% 20.3% 31.4% 34.1% 34.7% 37.3% +18.9% 103.1%

Arkansas 12.6% 15.3% 7.7% 9.8% 7.0% 10.0% 16.0% +3.5% 27.6%

California 15.2% 14.5% 19.4% 24.0% 19.3% 21.3% 28.6% +13.3% 87.5%

Colorado 32.4% 36.5% 39.7% 42.0% 44.1% 27.0% 18.8% -13.5% (41.8%)

Connecticut 25.0% 0.0% 22.7% 14.6% 28.6% 33.8% 24.9% -0.2% (0.7%)

Delaware 48.9% 47.7% 41.2% 37.6% 23.0% 9.4% 1.1% -47.9% (97.8%)

District of Columbia 20.4% 42.0% 31.2% 9.3% 7.5% 6.1% 17.3% -3.1% (15.2%)

Florida 18.3% 21.8% 19.9% 24.1% 26.8% 29.3% 29.1% +10.8% 58.9%

Georgia 16.4% 12.1% 18.3% 21.4% 16.3% 0.3% 4.6% -11.8% (72.2%)

Hawaii 13.7% 14.6% 19.0% 17.0% 13.1% 11.6% 9.2% -4.5% (32.9%)

Idaho 28.5% 26.6% 24.0% 29.9% 26.7% 19.4% 17.3% -11.2% (39.2%)

Illinois 58.7% 57.4% 52.7% 47.4% 54.8% 36.5% 22.6% -36.1% (61.5%)

Indiana 33.5% 34.3% 22.0% 17.0% 15.0% 17.7% 17.7% -15.9% (47.3%)

Iowa 28.2% 31.5% 40.2% 53.0% 53.9% 63.1% 25.4% -2.8% (9.9%)

Kansas 48.6% 56.9% 60.0% 41.5% 44.1% 46.3% 35.6% -13.1% (26.9%)

Kentucky 22.8% 31.2% 38.3% 48.1% 48.0% 40.2% 39.8% +17.0% 74.6%

Louisiana 22.1% 27.6% 27.0% 33.1% 30.1% 25.5% 19.3% -2.9% (12.9%)

Maine 60.5% 33.8% 34.0% 31.2% 28.7% 24.4% 23.2% -37.3% (61.6%)

Maryland 78.0% 75.4% 74.6% 63.7% 56.1% 38.0% 33.2% -44.8% (57.4%)

Massachusetts 68.4% 71.0% 81.7% 88.2% 90.7% 94.1% 93.0% +24.6% 35.9%

Michigan 34.1% 41.1% 36.0% 26.3% 27.4% 20.7% 24.6% -9.5% (28.0%)

Minnesota 41.8% 41.0% 42.0% 44.4% 49.7% 59.1% 79.5% +37.7% 90.2%

Mississippi 21.3% 21.1% 17.4% 17.4% 27.6% 25.9% 10.5% -10.8% (50.7%)

Missouri 25.9% 23.4% 31.2% 36.2% 34.9% 35.7% 28.8% +2.9% 11.1%

Montana 43.5% 48.0% 47.1% 45.1% 44.4% 38.9% 41.8% -1.7% (3.8%)

Nebraska 39.9% 35.4% 32.3% 29.8% 31.8% 22.5% 24.1% -15.7% (39.5%)

Nevada 15.7% 23.0% 37.5% 39.2% 50.2% 54.8% 52.9% +37.2% 236.8%

New Hampshire 66.9% 72.4% 66.1% 67.0% 69.8% 39.4% 46.0% -20.8% (31.2%)

New Jersey 65.4% 71.4% 79.1% 79.0% 84.3% 92.0% 93.4% +28.0% 42.9%

New Mexico 9.2% 18.1% 31.7% 46.4% 40.7% 32.1% 27.7% +18.5% 200.6%

New York 44.8% 42.0% 41.0% 25.2% 32.3% 26.0% 27.4% -17.4% (38.9%)

North Carolina 24.0% 30.3% 33.8% 32.9% 30.3% 31.7% 27.0% +3.0% 12.5%

North Dakota 57.2% 45.6% 48.7% 65.5% 52.1% 51.5% 54.0% -3.2% (5.7%)

Ohio 32.4% 35.3% 38.1% 45.1% 45.7% 48.2% 40.8% +8.4% 25.9%

Oklahoma 34.6% 34.4% 27.4% 21.3% 21.3% 18.4% 25.4% -9.2% (26.6%)

Oregon 49.7% 48.0% 47.6% 55.1% 50.9% 44.5% 56.9% +7.1% 14.4%

Pennsylvania 25.4% 23.5% 22.4% 27.1% 22.0% 15.5% 15.5% -9.9% (39.0%)

Puerto Rico 0.0% 2.7% 2.9% 3.7% 6.5% 7.3% 2.9% +2.9% .

Rhode Island 17.3% 16.9% 16.3% 11.6% 2.9% 2.1% 0.0% -17.3% (100.0%)

South Carolina 20.5% 18.0% 18.2% 10.8% 19.1% 22.7% 15.7% -4.8% (23.4%)

South Dakota 72.0% 57.7% 64.8% 68.2% 39.0% 0.0% 0.0% -72.0% (100.0%)

Tennessee 0.0% 39.7% 79.6% 83.9% 69.3% 58.1% 56.2% +56.1% .

Texas 17.9% 25.4% 30.8% 37.0% 42.7% 36.2% 35.8% +17.9% 100.3%

Utah 30.7% 27.4% 37.5% 41.2% 53.4% 42.7% 42.6% +11.9% 38.8%

Vermont 41.1% 23.0% 32.3% 32.0% 33.1% 27.3% 26.4% -14.6% (35.6%)

Virginia 31.5% 28.7% 35.2% 34.5% 34.1% 43.8% 47.6% +16.1% 51.0%

Washington 35.2% 41.8% 50.0% 56.3% 47.5% 43.7% 40.5% +5.3% 15.0%

West Virginia 24.9% 23.9% 18.6% 28.0% 25.7% 30.5% 47.8% +22.9% 91.9%

Wisconsin 43.4% 49.7% 50.9% 61.2% 65.9% 30.1% 0.0% -43.4% (100.0%)

Wyoming 36.1% . 42.5% 52.1% 69.9% 69.7% . . .

U.S. Total 29.4% 32.4% 34.6% 35.2% 35.2% 31.2% 31.1% +1.7% 5.9%
415

APPENDIX C: 

WELFARE RECIPIENTS SERVED THROUGH EMPLOYMENT SERVICE / JOB

SERVICE, BY STATE 
416

TABLE C.1 

TOTAL NUMBER OF ES REGISTRANTS 

State Project Year Percentage 

 1993 1994 1995 1996 1997 1998 1999 2000 Change 

Alabama 466,337 459,497 453,380 451,836 467,932 464,260 452,443 450,824 (3.3%)

Alaska 108,060 111,316 102,013 97,809 88,324 81,875 70,997 62,459 (42.2%)

Arizona 356,027 330,494 293,244 286,149 260,425 248,849 232,286 219,839 (38.3%)

Arkansas 312,159 298,212 275,574 275,977 260,876 245,580 229,965 226,371 (27.5%)

California 1,505,052 1,186,067 1,041,640 1,038,948 1,122,583 978,463 918,731 1,046,562 (30.5%)

Colorado 303,767 287,409 267,167 256,880 251,833 227,948 209,274 194,905 (35.8%)

Connecticut 255,634 239,093 206,388 197,747 180,542 152,439 148,720 139,112 (45.6%)

Delaware 41,110 42,183 40,885 40,921 42,351 41,641 35,602 32,989 (19.8%)

District of Columbia 94,175 90,271 80,733 70,321 67,853 60,662 49,835 29,894 (68.3%)

Florida 1,329,191 1,274,398 1,247,309 1,114,157 1,210,218 1,297,725 1,304,286 975,074 (26.6%)

Georgia 639,888 624,799 601,469 591,775 605,697 598,869 576,660 554,056 (13.4%)

Hawaii 95,375 64,070 69,443 78,830 83,344 80,099 67,483 66,719 (30.0%)

Idaho 155,411 145,534 142,133 145,126 147,976 173,037 158,809 160,474 3.3%

Illinois 961,114 855,333 710,077 683,315 678,700 627,320 581,290 560,850 (41.6%)

Indiana 401,404 370,291 340,750 316,436 298,609 273,912 285,734 324,742 (19.1%)

Iowa 292,581 288,118 279,200 263,621 257,373 241,400 213,929 215,792 (26.2%)

Kansas 201,091 198,133 194,499 179,252 152,526 143,325 135,121 127,346 (36.7%)

Kentucky 473,846 433,532 428,314 397,830 388,081 368,188 343,445 305,761 (35.5%)

Louisiana 356,957 355,985 343,255 319,394 313,113 298,570 296,569 305,732 (14.4%)

Maine 144,344 132,626 128,894 121,748 118,226 64,373 65,608 63,576 (56.0%)

Maryland 287,542 267,428 264,452 271,961 272,499 251,700 208,755 171,866 (40.2%)

Massachusetts 298,710 237,707 207,729 178,912 135,481 130,909 119,177 103,025 (65.5%)

Michigan1 598,396 556,888 529,582 517,113 525,200 512,003 493,040 572,305 (4.4%)

Minnesota 315,698 295,995 265,448 238,286 225,720 234,094 197,771 150,520 (52.3%)

Mississippi 341,041 326,573 310,318 319,138 319,954 340,764 291,551 290,875 (14.7%)

Missouri 602,517 606,986 654,206 641,969 609,955 605,870 582,310 563,633 (6.5%)

Montana 112,510 105,171 105,862 101,069 93,860 84,357 76,711 77,706 (30.9%)

Nebraska 116,701 118,078 114,423 109,254 110,810 105,096 99,868 94,895 (18.7%)

Nevada 106,034 108,748 93,126 89,420 89,099 84,017 77,445 68,892 (35.0%)

New Hampshire 68,404 60,972 52,205 46,680 41,656 37,898 36,022 32,932 (51.9%)

New Jersey 456,867 276,956 279,084 307,892 329,972 351,065 357,644 355,270 (22.2%)

New Mexico 150,973 156,516 150,002 144,539 152,621 156,917 157,185 146,820 (2.8%)

New York 1,223,987 1,015,516 874,234 1,017,324 1,022,597 927,936 798,006 753,511 (38.4%)

North Carolina 767,913 778,752 780,905 783,835 786,482 780,048 761,612 760,148 (1.0%)

North Dakota 96,440 94,238 86,870 88,305 88,527 89,894 91,601 91,464 (5.2%)

Ohio 606,082 536,775 570,429 687,569 702,360 712,934 757,688 774,218 27.7%

Oklahoma 294,031 295,808 278,031 270,982 259,788 243,099 248,920 235,845 (19.8%)

Oregon 374,096 370,467 361,467 392,907 410,270 483,607 426,778 429,786 14.9%

Pennsylvania1 676,276 629,366 598,506 584,415 593,554 578,639 557,208 465,629 (31.1%)

Puerto Rico 214,291 221,619 232,687 227,193 269,904 236,222 265,726 204,972 (4.3%)

Rhode Island 71,480 68,889 60,965 53,486 55,413 65,266 72,541 72,361 1.2%

South Carolina 412,824 432,647 427,779 423,289 406,031 393,855 352,085 345,450 (16.3%)

South Dakota 96,792 97,809 98,990 99,222 98,488 95,814 91,069 87,989 (9.1%)

Tennessee 456,416 440,057 394,593 393,809 429,327 327,698 394,878 392,246 (14.1%)

Texas 1,804,990 1,731,158 1,643,411 1,561,313 1,507,593 1,428,553 1,393,007 1,446,371 (19.9%)

Utah 225,007 232,100 245,635 256,253 244,407 246,556 265,304 291,649 29.6%

Vermont 76,680 73,496 70,291 68,561 70,046 67,131 60,762 45,881 (40.2%)

Virginia 530,858 505,494 466,123 231,553 438,544 433,672 394,284 374,410 (29.5%)

Washington 415,037 426,843 421,397 420,366 444,520 593,970 650,209 643,931 55.2%

West Virginia 209,957 198,442 198,840 201,058 195,071 210,450 195,890 196,826 (6.3%)

Wisconsin 373,241 367,899 384,337 378,845 391,176 380,115 347,788 339,184 (9.1%)

Wyoming 79,405 78,428 76,688 73,756 74,019 70,704 65,718 57,167 (28.0%)

U.S. Total 20,954,719 19,501,182 18,544,982 18,108,346 18,391,526 17,929,388 17,265,340 16,700,854 (20.3%)

 

1

 Due to data irregularities, ES data for Michigan and Pennsylvania prior to 1999 were extrapolated 

backwards using 1999 data and the year-to-year percentage change from 1999 totals for the remaining 48 

states. 
417

TABLE C.2 

NUMBER OF ES REGISTRANTS WHO ARE RECEIVING WELFARE (TANF/AFDC, 

FS, or GA) 

State Project Year Percentage 

 1993 1994 1995 1996 1997 1998 1999 2000 Change 

Alabama 1,790 1,224 957 618 871 7,501 13,166 11,982 569.4%

Alaska 371 598 1,003 1,727 1,928 1,483 1,281 1,287 246.9%

Arizona 11,824 8,794 5,935 4,649 3,573 2,401 1,606 1,190 (89.9%)

Arkansas 739 420 300 260 323 711 849 837 13.3%

California 32,898 29,425 51,900 63,675 92,559 86,072 49,759 58,596 78.1%

Colorado 2,116 1,839 1,287 1,002 1,139 3,709 4,490 5,438 157.0%

Connecticut 38,681 38,169 32,899 21,947 13,356 2,395 15,674 12,385 (68.0%)

Delaware 2 0 0 0 0 770 217 89 4350.0%

District of Columbia 2,275 2,468 2,175 1,900 4,758 8,621 5,185 1,328 (41.6%)

Florida 3,385 1,465 315 125 252 65 1 0 (100.0%)

Georgia 23,498 27,420 28,050 13,266 17,649 66,317 52,136 55,029 134.2%

Hawaii 4,749 2,426 3,196 4,524 5,722 5,241 4,406 4,844 2.0%

Idaho 997 826 727 714 561 1,552 2,021 2,341 134.8%

Illinois 74,810 56,241 46,767 19,102 18,211 12,606 23,486 16,378 (78.1%)

Indiana 1,302 1,210 1,174 831 461 1,465 45,574 70,059 5280.9%

Iowa 12,571 18,255 17,128 11,764 11,157 27,207 24,229 22,130 76.0%

Kansas 3,324 3,255 3,152 3,080 1,316 1,389 757 560 (83.2%)

Kentucky 6,242 8,303 4,107 3,517 3,613 31,133 24,050 3,687 (40.9%)

Louisiana 1,345 1,434 1,422 1,125 1,259 1,677 2,364 1,196 (11.1%)

Maine 505 529 428 381 285 499 540 922 82.6%

Maryland 2,483 1,973 1,472 1,783 1,021 4,937 4,499 2,881 16.0%

Massachusetts 1,086 526 359 174 1,258 2,490 6,612 3,469 219.4%

Michigan1 4,606 4,547 4,653 4,259 4,412 5,996 5,863 6,698 45.4%

Minnesota 8,724 7,590 7,180 4,669 4,443 3,889 2,617 128 (98.5%)

Mississippi 28,993 28,034 27,688 27,829 26,514 77,083 36,498 32,051 10.5%

Missouri 196,051 214,004 219,440 219,678 216,304 209,348 202,597 193,413 (1.3%)

Montana 386 311 201 132 113 111 23 8 (97.9%)

Nebraska 2,753 2,406 1,835 1,685 1,887 1,701 1,489 1,428 (48.1%)

Nevada 93 149 173 162 674 3,512 4,361 4,374 4603.2%

New Hampshire 1,906 1,940 492 73 220 2,349 2,234 1,729 (9.3%)

New Jersey 21,799 18,798 22,393 22,638 19,528 67,469 96,105 83,087 281.2%

New Mexico 1,021 1,538 1,415 1,460 3,021 5,556 5,272 3,136 207.1%

New York 28,105 33,944 22,082 23,453 23,734 27,924 29,326 32,379 15.2%

North Carolina 3,681 4,805 2,035 824 753 1,544 3,642 3,889 5.7%

North Dakota 7,205 7,382 6,115 5,456 4,536 4,344 3,230 4,518 (37.3%)

Ohio 20,232 20,518 40,963 39,937 39,628 39,969 36,996 31,271 54.6%

Oklahoma 7,840 8,821 7,949 7,918 7,426 8,667 10,405 8,574 9.4%

Oregon 3,599 7,976 8,851 11,206 8,511 6,388 6,786 5,558 54.4%

Pennsylvania1 14,052 13,874 14,197 12,993 13,461 18,293 17,888 12,301 (12.5%)

Puerto Rico 5,258 6,900 12,517 12,996 11,544 3,563 15,753 20,205 284.3%

Rhode Island 875 740 357 166 288 582 901 1,729 97.6%

South Carolina 1,187 497 216 108 14 1,146 560 239 (79.9%)

South Dakota 4,485 4,557 4,483 4,341 4,054 4,114 4,105 3,465 (22.7%)

Tennessee 508 458 299 191 128 3,894 9,544 8,036 1481.9%

Texas 10,555 363 312 106 42 33,305 29,779 27,649 162.0%

Utah 1,119 1,006 894 1,035 1,353 3,964 4,838 6,443 475.8%

Vermont 50 32 35 26 17 18 12 10 (80.0%)

Virginia 8,898 9,724 14,468 7,509 14,295 14,128 10,365 8,018 (9.9%)

Washington 7,981 8,589 10,379 19,933 22,448 18,422 3,687 4,804 (39.8%)

West Virginia 21,668 18,692 17,039 15,552 14,299 16,835 9,740 7,671 (64.6%)

Wisconsin 18,450 15,502 11,958 6,322 2,445 1,228 649 444 (97.6%)

Wyoming 626 826 1,098 1,139 4,576 3,194 1,601 1,121 79.1%

U.S. Total 659,699 651,323 666,470 609,959 631,940 858,777 839,768 791,004 19.9%
418

TABLE C.3 

PERCENT OF ES REGISTRANTS WHO ARE RECEIVING WELFARE (TANF/AFDC, 

FS, or GA) 

State Project Year Change in Percentage

 1993 1994 1995 1996 1997 1998 1999 2000 Percent Change 

Alabama 0.4% 0.3% 0.2% 0.1% 0.2% 1.6% 2.9% 2.7% +2.3% 592.4%

Alaska 0.3% 0.5% 1.0% 1.8% 2.2% 1.8% 1.8% 2.1% +1.7% 500.2%

Arizona 3.3% 2.7% 2.0% 1.6% 1.4% 1.0% 0.7% 0.5% -2.8% (83.7%)

Arkansas 0.2% 0.1% 0.1% 0.1% 0.1% 0.3% 0.4% 0.4% +0.1% 56.2%

California 2.2% 2.5% 5.0% 6.1% 8.2% 8.8% 5.4% 5.6% +3.4% 156.1%

Colorado 0.7% 0.6% 0.5% 0.4% 0.5% 1.6% 2.1% 2.8% +2.1% 300.5%

Connecticut 15.1% 16.0% 15.9% 11.1% 7.4% 1.6% 10.5% 8.9% -6.2% (41.2%)

Delaware 0.0% 0.0% 0.0% 0.0% 0.0% 1.8% 0.6% 0.3% +0.3% 5445.5%

District of Columbia 2.4% 2.7% 2.7% 2.7% 7.0% 14.2% 10.4% 4.4% +2.0% 83.9%

Florida 0.3% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% -0.3% (100.0%)

Georgia 3.7% 4.4% 4.7% 2.2% 2.9% 11.1% 9.0% 9.9% +6.3% 170.5%

Hawaii 5.0% 3.8% 4.6% 5.7% 6.9% 6.5% 6.5% 7.3% +2.3% 45.8%

Idaho 0.6% 0.6% 0.5% 0.5% 0.4% 0.9% 1.3% 1.5% +0.8% 127.4%

Illinois 7.8% 6.6% 6.6% 2.8% 2.7% 2.0% 4.0% 2.9% -4.9% (62.5%)

Indiana 0.3% 0.3% 0.3% 0.3% 0.2% 0.5% 15.9% 21.6% +21.2% 6551.1%

Iowa 4.3% 6.3% 6.1% 4.5% 4.3% 11.3% 11.3% 10.3% +6.0% 138.7%

Kansas 1.7% 1.6% 1.6% 1.7% 0.9% 1.0% 0.6% 0.4% -1.2% (73.4%)

Kentucky 1.3% 1.9% 1.0% 0.9% 0.9% 8.5% 7.0% 1.2% -0.1% (8.5%)

Louisiana 0.4% 0.4% 0.4% 0.4% 0.4% 0.6% 0.8% 0.4% +0.0% 3.8%

Maine 0.3% 0.4% 0.3% 0.3% 0.2% 0.8% 0.8% 1.5% +1.1% 314.5%

Maryland 0.9% 0.7% 0.6% 0.7% 0.4% 2.0% 2.2% 1.7% +0.8% 94.1%

Massachusetts 0.4% 0.2% 0.2% 0.1% 0.9% 1.9% 5.5% 3.4% +3.0% 826.2%

Michigan1 0.8% 0.8% 0.9% 0.8% 0.8% 1.2% 1.2% 1.2% +0.4% 52.1%

Minnesota 2.8% 2.6% 2.7% 2.0% 2.0% 1.7% 1.3% 0.1% -2.7% (96.9%)

Mississippi 8.5% 8.6% 8.9% 8.7% 8.3% 22.6% 12.5% 11.0% +2.5% 29.6%

Missouri 32.5% 35.3% 33.5% 34.2% 35.5% 34.6% 34.8% 34.3% +1.8% 5.5%

Montana 0.3% 0.3% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% -0.3% (97.0%)

Nebraska 2.4% 2.0% 1.6% 1.5% 1.7% 1.6% 1.5% 1.5% -0.9% (36.2%)

Nevada 0.1% 0.1% 0.2% 0.2% 0.8% 4.2% 5.6% 6.3% +6.3% 7138.9%

New Hampshire 2.8% 3.2% 0.9% 0.2% 0.5% 6.2% 6.2% 5.3% +2.5% 88.4%

New Jersey 4.8% 6.8% 8.0% 7.4% 5.9% 19.2% 26.9% 23.4% +18.6% 390.1%

New Mexico 0.7% 1.0% 0.9% 1.0% 2.0% 3.5% 3.4% 2.1% +1.5% 215.8%

New York 2.3% 3.3% 2.5% 2.3% 2.3% 3.0% 3.7% 4.3% +2.0% 87.1%

North Carolina 0.5% 0.6% 0.3% 0.1% 0.1% 0.2% 0.5% 0.5% +0.0% 6.7%

North Dakota 7.5% 7.8% 7.0% 6.2% 5.1% 4.8% 3.5% 4.9% -2.5% (33.9%)

Ohio 3.3% 3.8% 7.2% 5.8% 5.6% 5.6% 4.9% 4.0% +0.7% 21.0%

Oklahoma 2.7% 3.0% 2.9% 2.9% 2.9% 3.6% 4.2% 3.6% +1.0% 36.3%

Oregon 1.0% 2.2% 2.4% 2.9% 2.1% 1.3% 1.6% 1.3% +0.3% 34.4%

Pennsylvania1 2.1% 2.2% 2.4% 2.2% 2.3% 3.2% 3.2% 2.6% +0.6% 27.1%

Puerto Rico 2.5% 3.1% 5.4% 5.7% 4.3% 1.5% 5.9% 9.9% +7.4% 301.7%

Rhode Island 1.2% 1.1% 0.6% 0.3% 0.5% 0.9% 1.2% 2.4% +1.2% 95.2%

South Carolina 0.3% 0.1% 0.1% 0.0% 0.0% 0.3% 0.2% 0.1% -0.2% (75.9%)

South Dakota 4.6% 4.7% 4.5% 4.4% 4.1% 4.3% 4.5% 3.9% -0.7% (15.0%)

Tennessee 0.1% 0.1% 0.1% 0.0% 0.0% 1.2% 2.4% 2.0% +1.9% 1740.7%

Texas 0.6% 0.0% 0.0% 0.0% 0.0% 2.3% 2.1% 1.9% +1.3% 226.9%

Utah 0.5% 0.4% 0.4% 0.4% 0.6% 1.6% 1.8% 2.2% +1.7% 344.2%

Vermont 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% -0.0% (66.6%)

Virginia 1.7% 1.9% 3.1% 3.2% 3.3% 3.3% 2.6% 2.1% +0.5% 27.8%

Washington 1.9% 2.0% 2.5% 4.7% 5.0% 3.1% 0.6% 0.7% -1.2% (61.2%)

West Virginia 10.3% 9.4% 8.6% 7.7% 7.3% 8.0% 5.0% 3.9% -6.4% (62.2%)

Wisconsin 4.9% 4.2% 3.1% 1.7% 0.6% 0.3% 0.2% 0.1% -4.8% (97.4%)

Wyoming 0.8% 1.1% 1.4% 1.5% 6.2% 4.5% 2.4% 2.0% +1.2% 148.7%

U.S. Total 3.1% 3.3% 3.6% 3.4% 3.4% 4.8% 4.9% 4.7% +1.6% 50.4%
419

FEDERAL REGISTER NOTICE 

VOL. 68, NO.30 

THURSDAY, FEBRUARY 13, 2003 

Department of Labor 

Employment & Training Administration 

Office of Policy Development 

Evaluation and Research; 

Call for Papers 

Biennial National Research 

Conference on ‘‘Workforce Investment Issues in the United States’’ 

Washington, DC June 4–5, 2003 

Summary: The Employment and Training Administration (ETA) of the U.S. Department 

of Labor (DOL) is the federal government agency responsible for implementing a 

national workforce investment and security system that enables workers and employers to 

compete and succeed in an ever changing economy. This task is accomplished through 

the provision of world class labor market information, labor exchange services, job 

search assistance, training, and unemployment insurance benefits. These programs assist 

in the management of the risks associated with unemployment, declining income and 

skill shortages. They help workers in their search for work and their efforts to upgrade 

their human capital. They help employers find new employees who meet their skill 

requirements and upgrade the skills of their incumbent workers. In 1998, the Workforce 

Investment Act, the nation’s most comprehensive effort at streamlining and transforming 

public employment, job training and education programs and agencies into a system that 

meets the skill needs of today’s economy, was passed. The implementation of the 
420

Workforce Investment Act (WIA) of 1998 combines a variety of workforce development 

programs and initiatives under one umbrella—the One-Stop System—to 

effectively serve the needs of jobseekers, workers and employers in the changing 

workforce development environment at the onset of the 21st century economy. 

Under the provisions of the WIA, the Secretary of Labor is required every two 

years to prepare a five-year research plan for research, pilot and demonstration initiatives. 

This research plan reflects a strategic vision for research efforts based upon input from 

stakeholders in employment and training research, a review of recent efforts, an 

identification of areas where future research may be needed, and a review of possible 

research methodologies. 

In light of major changes in the macro-economy that have taken place in the areas 

of technological transformation, increasing globalization and changing demographics 

over the recent years, and the resulting effects of rising workforce insecurity, the 

Employment and Training Administration will hold its second national research 

conference since the enactment of WIA. The conference will focus on issues related to 

trends, challenges and impacts of the macro-economy to workforce investment, 

significance of recent changes in workforce investment, workforce competitiveness in the 

global economy, changing business requirements, workforce security in our new 

economy, and major policy options to increase productivity by meeting the skill needs of 

business and promoting economic opportunities for the United States workforce. A 

plenary session is planned to discuss the soon-to-be published 2002–2007 plan and lay 

the groundwork for the 2004–2009 research plan. This is a call for empirical/ nonempirical papers related to workforce investment issues. ETA is seeking recently 

completed papers and papers that will be completed prior to the conference. We 

encourage contributions by researchers from academia, state or local agencies, business 

organizations, labor associations, research consulting firms and other relevant 

organizations. Possible topics may include, but are not limited to: 

• Workforce Investment Act (program implementation and administration of adult, 

dislocated worker and youth programs; issues relating to WIA reauthorization). 
421

• Skills Requirements of Employers (skills needed by employers in particular sectors and 

industries, the effect of new technologies, including the internet and e-commerce, on skill 

needs of employers). 

• Changes in the Structure and Organization of Work (changes in tenure and the rise of 

contingent work arrangements). 

• Effect of Contingent Work on Hiring Practices (the effect of hiring practices of 

employers and the job search behavior of individuals). 

• Role of Intermediaries in the Labor Market (whether intermediaries offer new 

approaches and techniques that can be adapted by the public-sector employment and 

training community). 

• Adaptability of the Unemployment Insurance Program to an evolving U.S. economy. 

(program administration, coverage, eligibility, benefit adequacy, benefit duration, 

recipiency, benefit financing, economic stabilization, special populations and changing 

work patterns). 

• Changes in Wages and Compensation (effect of education on 

workers’ earnings). 

• Wages and Compensation Trends (recent trends in the receipt of benefits, including 

health insurance and retirement benefits) Interventions (employment and training 

intervention responses to wage and compensation trends). 

• Impact of technology, Internet and Labor Market Information on labor exchange 

processes. 

• Education—Workforce Training Continuum (appropriate roles of public K–12 and 

higher education integrated with Workforce Investment System). 

Time and Place: The conference will be held from 1 p.m. to 5 p.m. on June 4, 2003 and 

from 9 a.m. to 4:30 p. m. on June 5, 2003 at the Holiday Inn, Capital Hill, 550 C Street, 

SW.,Washington, DC 20024. 

Submission of Papers: All papers submitted will be reviewed by a panel of DOL experts 

in the workforce development arena and presenters will be notified if their papers are 

selected. Papers reporting on research and development, evaluation studies, pilot efforts, 
422

or applied practices are encouraged. Selected papers selected for the conference will be 

published as part of the ETA Occasional Paper Series. Travel and accommodation for 

invited presenters will be paid by the Employment and Training Administration. If 

interested, please submit your paper and abstract if possible in hard copy and diskette/CD 

(Word) by March 1, 2003. Papers should be doubled-spaced and single sided. You will 

be notified by April 4, 2003, if your paper is selected; you will have to confirm your 

attendance by April 15, 2001. Please send your papers and abstract to the logistical 

contractor for this contract, HMA Associates, Inc., 1680 Wisconsin Avenue, NW., 2nd 

Floor, Washington, DC, 20007, Attn: Peggie Edwards-Jefferies. She may be reached at 

202–342–8258. We also encourage submitting abstracts for papers that have not yet been 

completed, but will be completed before the deadline for submission of papers. 

Public Participation: This Conference is open to the public; there is no registration fee. 

For registration information, please send name, address, e-mail address, affiliation, and 

telephone number to H.M.A Associates, Inc., 1680 Wisconsin Avenue, NW., 2nd Floor, 

Washington, DC, 20007, Attn: Peggie Edwards-Jefferies or email them to 

hmaassociates.com. 

Signed at Washington, DC, this 5th day of February. 

Gerard F. Fiala, 

Administrator, Office of Policy Development, 

Evaluation and Research. 

[FR Doc. 03–3559 Filed 2–12–03; 8:45 am] 
423

2003 BIENNIAL NATIONAL RESEARCH CONFERENCE 

AGENDA 

Wednesday, June 4th

12:30-1:15 Registration in Columbia Foyer

1:15-1:45 Opening Session in Columbia Ballroom

• Welcome/Greetings - Wayne Gordon 

Lead Manpower Analyst, Division of Research and Demonstration 

• Introduction of Keynote Speaker - Maria K. Flynn 

Acting Administrator, Office of Policy Development Evaluation 

and Research 

Keynote Speaker - Emily Stover DeRocco 

Assistant Secretary, Employment and Training Administration 

• Overview of Agenda – Wayne Gordon 

1:45-2:00 PM Break in Discovery II

2:00-3:45 Concurrent Session I (Three Panels) 

Panel 1 Education-Workforce Training Continuum

 Room: Saturn/Venus 

 Panel Chair - Bill Sanders 

Building a Career Pathways System: Promising Practices of Community CollegeCentered Workforce Development 

Julian L. Alssid (Workforce Strategy Center)

Connecting Business and the Wyoming Community College System: A Study of 

Employment Outcomes of 2001 Graduates of Wyoming Community Colleges

Tom Gallagher, Sara Saulcy (Wyoming Department of Employment) 

Reconceptualizing Education as an Engine of Economic Development: A Case Study of 

the Central Educational Center

Keith MacAllum, Amy Bell Johnson (Academy for Educational Development) 

Panel 2 Skills Requirements of Employers

Room: Discovery I 

 Panel Chair – Dana Daugherty 

Exemplary Practices in High Skill USDOL H-1B Training Programs

Burt S. Barnow (Johns Hopkins University), Joyce Kaiser (TTW Incorporated), John 

Trutko (Capitol Research) 
424

The Role of Apprenticeship in the Development of the Information Technology 

Workforce: An Economic Perspective

John M. Aaron, Neill J. Hopkins (Workforce Development for the Computing 

Technology Industry Association) 

Creating Effective Workforce Development Networks: Key Findings from an

Evaluation of USDOL’s Skills Shortages Demonstration Programs

Nancy Hewat (Public Policy Associate Inc.) 

Panel 3 One Stop Services: Current and Future 

Room: Columbia Ballroom 

 Panel Chair - Wayne Gordon 

What Can We Expect Under Personal Reemployment Training Accounts? A 

Discussion Based on Findings from the Individual Training Account Experiment,

Reemployment Bonus Demonstrations and other Evaluations

Paul Decker, Irma Perez-Johnson (Mathematica Policy Research, Inc.) 

Considerations for States Planning Personal Reemployment Accounts 

Christopher O’Leary, Randall W. Eberts (W.E. Upjohn Institute for Employment 

Research) 

Project GATE (Growing America Through Entrepreneurship Demonstration Project)

Jacob Benus (IMPAQ International, LLC), Sheena McConnell (Mathematica Policy 

Research Inc. 

An Evaluation of Public Labor Exchange Services in a One-Stop Environment

Lou Jacobson (Westat) 

4:00-5:15 Concurrent Session II (Three Panels) 

Panel 4 Targeted Populations 

Room: Columbia Ballroom 

 Panel Chair - John Beverly 

Medicaid and Employment of People with Disabilities: Findings from the National 

Survey of State Systems and Employment for People with Disabilities

Jennifer Sullivan, Susan Foley, Dana Scott Gilmore (Institute for Community 

Inclusion) 

 

Older Workers Participation in Employment and Training Administration Programs

Jonathan Simonetta (U.S. Department of Labor) 

Panel 5 Performance Measures and Portable Credentials 

Room: Saturn/Venus 

Panel Chair - Eric Johnson 
425

Performance Management of US Training Programs 

Burt S.Barnow, (Johns Hopkins University), Jeffrey A. Smith (University of Maryland) 

Validated Portable Credentials: Why and How-Lessons from the Career 

Management Account Pilot Program 

David J. Pass (Independant) 

Panel 6 Low Wage Workers 

Room: Discovery I 

 Panel Chair – Marilyn Shea 

Interactions of Workers and Firms in the Low Wage Labor Market

Fredrik Andersson (U.S. Census Bureau), Harry J. Holzer, Julia I. Lane (The 

Urban Institute) 

 

Employment Benefits and Work Supports for Disadvantaged Workers

Diane Baillargeon (SEEDCO) 

5:30-7:00 Ice Breaker/Networking Session in Discovery II

Thursday, June 5th

8:30-10:15 Plenary Session-5 Year Research Plan in Columbia Ballroom

 Burt S. Barnow (Johns Hopkins University) 

 Stephen Bell (The Urban Institute) 

 Jonathan Simonetta (U.S. Department of Labor) 

 Carl Van Horn (The Heldrich Center, Rutgers University) 

 Denise Pearson-Balik ((The Heldrich Center, Rutgers University) 

10:15-10:30 AM Break in Discovery II

10:30-12:15 Concurrent Session I (Three Panels) 

Panel 7 Unemployment Insurance (UI) 

Room: Discovery I 

 Panel Chair - Cheryl Atkinson 

Combining New Sources of Information on Pension Benefits with UI Wage Records: The 

5500 File Project at the Census

Anja Decressin, Julia Lane, Kristin McCue, Martha Stinson (The Urban Institute) 

UI Non-Mon Policies and Practices: How Do They Affect Program Participation? 

Mike Fishman , Karen Gardiner, Mary Farrell (The Lewin Group), Burt Barnow 

(Johns Hopkins University), John Trutko (Capital Research Corporation) 
426

 How are States Using their $8 Billion Reed Act Funds?

 Rich Hobbie (NASWA) 

Panel 8 Youth Services 

Room: Saturn/Venus 

 Panel Chair - Lorenzo Harrison 

The Quantum Opportunity Program Demonstration: Implementation and Short Term 

Impacts

Myles Maxfield, Allen Schirm, Nuria Rodriguez-Planas (Mathematica Policy 

Research, Inc.) 

 

Career Academies: Impact of Post Secondary Education and Employment

Jim Kemple, Rob Ivry (MDRC) 

Working with Disadvantaged Youth: Thirty-Month Findings from the Evaluation of the 

Center for Employment Training (CET) Replication Sites 

Cynthia Miller, Kristin E. Porter, Fred C. Doolittle, (MDRC), Johannes M. Bos, Fannie 

M. Tseng, Deana N. Tanguay, Mary P. Vencill (Berkeley Policy Associates) 

Panel 9 Administrative Data Research and Evaluation (ADARE) 

Project Studies 

Room: Columbia Ballroom 

 Panel Chair - Jonathan Simonetta 

Incremental Net Impacts of Participation in WIA Services

 Kevin Hollenbeck (W.E. Upjohn Institute) 

 

Occupations in Demand and Individual Training Accounts

Christopher King (Ray Marshall Center, University of Texas at Austin) 

Analysis of WIA and Low Wage Workers

Peter Mueser (University of Missouri at Columbia) 

WIA Benchmarking Tool

 John Baj (Northern Illinois University), David Stevens (University of Baltimore, 

 Jacob Francis Center) 

12:15-1:30 Lunch in Discovery II

1:30-3:15 Concurrent Session II (Four Panels) 

Panel 10 Workforce Intermediaries and Industry Clusters 

Room: Discovery I 
427

 Panel Chair – Grace Kilbane 

What Do Workforce Intermediaries Do? 

Richard Kazis (Jobs for the Future) 

 Labor-Based Industry Clusters

 David J. Peters (Missouri Economic Research Information Center) 

 

 Financing Workforce Intermediaries-Jobs for the Future

 Jerry Rubin, Marlene B. Seltzer, Jack Mills (Jobs for the Future) 

Panel 11 Workforce Investment Act 

Room: Columbia Ballroom 

 Panel Chair – Delores Beran-Hal 

The Relationship between the WIA and the Charitable Choice Initiative

 April Bender (Partnerships for Quality) 

 A New WPRS Profiling Model for Michigan

 Randall W. Eberts, Christopher O’Leary (W.E. Upjohn Institute for Employment 

 Research) 

Washington WIA Evaluation

Kevin Hollenbeck (W.E. Upjohn Institute for Employment Research) 

Panel 12 Temporary Assistance for Needy Families (TANF) 

Room: Saturn/Venus 

 Panel Chair – Dennis Lieberman 

Impact of Various Criteria on Monetary Eligibility for Unemployment Insurance

Benefits for TANF “Leavers” 

Lester Coffey, Clive Glenn, Douglas M. Sanford Jr., Michael M. H. Ye, 

William F. Sullivan (Coffey Communications, LLC) 

Former TANF Recipients’ Monetary Eligibility for Unemployment Insurance 

Benefits: An Empirical Study 

Lester Coffey, Clive Glenn, Douglas M. Sanford Jr., Michael M. H. Ye, 

William F. Sullivan (Coffey Communications, LLC) 

The Role of One-Stop Career Centers in Serving Welfare Recipients in 2002

(Demetra Nightingale, Fredrica D. Kramer, Michael Enger (The Urban Institute), 

John Trutko (Capital Research Corporation), Burt Barnow (Johns Hopkins 

University) 

Panel 13 Labor Market Information and Low Wage Workers 

Room: Apollo (2nd Floor) 
428

 Panel Chair - Dave Mormon 

Developing New Workforce Information Products: Employee Benefits in Washington 

State

Carolyn Cummins (Washington State Employment Security Department) 

The Hard to Employ, TANF and WIA 

John Heinberg (Policy Research and Evaluation) 

Pathways to Work for Low Income Workers: The Effect of Work in the Temporary Help 

Industry

Julia Lane, Kelly S. Mikelson, Pat Sharkey, Doug Wissoker (The Urban Institute) 

 

3:15-3:30 PM Break in Discovery II

3:30-5:15 Concurrent Session III (Three Panels) 

Panel 14 Making Workforce Investment Programs More Effective 

Room: Columbia Ballroom 

 Panel Chair – Marilyn Shea 

What is the Value Added by Caseworkers?

Jeffrey A. Smith, Michael Lechner (IZA Bonn) 

 

A Frontline Decision Support System for Georgia Career Centers 

Randall W. Eberts, Chris O’Leary (W.E. Upjohn Institute for Employment 

Research) 

Merging Cultural Differences and Professional Identities: Strategies for Maximizing 

Collaborative Efforts during the Implementation of the WIA

Sheila Fesko, Allison Cohen, Jaimie Ciulla Timmons (University of 

Massachusetts Boston) 

Panel 15 Workforce Intermediaries 

Room: Saturn/Venus 

 Panel Chair - Brent Orrell 

 

Faith Based Organizations (FBO) Providing Employment and Training Services 

Fredrica D. Kramer, Demetra Nightengale (The Urban Institute) John Trutko (Capitol 

Research) Burt Barnow (Johns Hopkins University) 

 

A Theoretical Approach to Evaluating the Efficacy of Labor Market Intermediaries

Providing Employment and Training Services And the Implications of Findings 

for Policy

John Harvey, Steve Gaither, Freddie John Martin (DAH Consulting, Inc.) 

 Key Findings on the Role of Intermediaries Under WIA
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 Bronwen Macro, Johanes Bos (Berkeley Policy Associates) 

Panel 16 Employment & Training Research Sponsored by Other Agencies 

Room: Discovery I 

 Panel Chair - Steve Wandner 

Welfare to Work Housing Voucher Experiment: Interim Qualitative Findings

Greg Mills, Michelle Wood, Jennifer Turnham, Alvaro Cortes, Jenny Berrien 

(Abt Associates Inc.) 

Institutional Linkages between the Workforce Investment and Welfare Systems in 

Employment Retention and Advancement Evaluation Sites

Karin Martinson, Barbara Goldman (MDRC) 

Overview of the Market-Response Community College Initiative of the Office of 

Vocational and Adult Education (OVAE) U.S. Department of Education

Lou Jacobson (Westat)
430