U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

Ensuring the Quality of Training Providers under the 

Workforce Investment Act (WIA) 

Prepared by: 

Lauren Eyster, Doctoral Student 

Public Policy and Administration 

George Washington University 

Washington, DC 

This paper was funded, either wholly or in part, with Federal funds from the U.S. Department of 

Labor, Employment and Training Administration (ETA) under Contract Number 

DOLJ061A20380. The contents of this publication do not necessarily reflect the views or 

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

organizations imply endorsement of same by the U.S. Government. This paper was one of 

seven prepared under the 2009 ETA Research Papers Program, which competitively awarded 

doctoral and post-doctoral students funding to conduct original research and prepare scholarly 

papers on topics relating to the workforce investment system. 
 

Acknowledgments

Thank you to the U.S. Department of Labor, Employment and Training Administration (ETA) 

for its support for this research through the 2009 Research Paper Program offered to doctoral 

students. The grant allowed for data collection and early analysis that will be the focus of this 

dissertation research. Lance Grubb of HeiTech Services, Inc. provided helpful guidance 

throughout the grant program. The insightful comments from staff in ETA’s Office of Policy 

Development and Research, Kenneth R. Troske and Christopher Jepsen at the University of 

Kentucky, and Doug Wissoker at the Urban Institute helped to guide and improve this research. 

The assistance of Jonathan Pollak at Johns Hopkins University, who shared his work on the 

Workforce Investment Act Standardized Record Data, and all of the state Workforce Investment 

Act representatives, who took the time to complete the survey, were invaluable. 
     

                     

       

       

   

           

     

           

   

                 

     

   

               

           

               

             

Table of Contents

Introduction 1
 

A Conceptual Framework for Ensuring the Quality of Training Providers for Adults and
 

Dislocated Workers under WIA 3
 

The Need for Training 3
 

Market­Based Policy Intervention 3
 

State Restrictions on Training Provider Markets 5
 

Training Provider Quality 8
 

Moderating Factors in Training Provider Quality 9
 

Policy Outcomes 10
 

Current Research on ITAs and ETP Policies and Their Effectiveness 11
 

Data and Research Methodology 13
 

Data 13
 

Research Methodology 14
 

Limitations 17
 

Findings and Conclusions 19
 

Descriptive Results of the State ETP Policy Survey 19
 

Multivariate Analysis of State ETP Policies 25
 

Policy Implications 31
 

References 33
 

Appendix A – State Eligible Training Provider Survey 36
 

Appendix B – Tables on Multivariate Results 38
 
 

Introduction

The Workforce Investment Act of 1998 (WIA) authorized the use of a voucher-like 

mechanism, called Individual Training Accounts (ITAs), by which the WIA participants can 

purchase training services. The motivation behind ITAs is to promote maximum customer 

choice and encourage a market-driven strategy for the workforce investment system. To ensure 

that customers have a selection of high-quality training providers whose training will lead to 

employment, states are required to provide ITA participants with a list of state-approved training 

providers. However, states have a great deal of flexibility in the criteria they use to certify 

training providers, how they construct and distribute their lists, and if they devolve the process to 

local workforce investment areas. 

WIA created a framework for states to customize their eligible training provider (ETP) 

systems from which ITA participants can select the provider that most meets their workforce 

goals. The legislation requires initial and subsequent eligibility procedures for training providers 

to be established but allows states to design their own application and certification process, allow 

for local flexibility in the eligibility process, and seek a waiver to extend eligibility periods. 

Standard performance data on the aggregate employment and wage outcomes of training 

programs must be collected and shared by states but states can set the performance levels and 

allow local workforce investment boards (WIBs) to increase those levels. WIA also requires that 

states publish a statewide ETP list but states can present the information to meet their needs such 

as including providers from other states or providers that are not approved on their lists. Thus, 

while WIA provides a framework for the ETP system, states have a great deal of flexibility to 

develop their own ETP policies, ostensibly to help ensure that their state’s ITA participants 

receive high-quality training services that will lead to gainful employment. 

Little is known about how states have designed their ETP policies and if these policies 

increase the quality of approved training providers as measured by the employment and training 

outcomes of ITA participants. To better understand states' ETP policies and their possible 

effects, this study attempts to answer the following questions: 

 What ETP policies have states implemented and how do policies vary across states? 

 How much do the various state ETP policies have an effect on ITA participants’ 

employment and training outcomes? 

1 
 

It is expected that while states set ETP policies that encourage the inclusion of high-quality 

training providers (training that leads to employment), ETP eligibility criteria that are too strict 

or onerous may deter some providers from applying which decrease the number of quality 

providers. In addition, the more colleges – both two- and four-year postsecondary institutions – 

included on the ETP lists may also encourage ITA participants to select a higher quality training 

program that leads to gainful employment. Finally, the states that have more flexibility through 

having a waiver and by giving local workforce investment areas some control to develop their 

own ETP policies and practices may lead ITA participants to select higher quality training 

programs because the eligibility process can be customized to state and local needs. 

To answer these questions and test these hypotheses, a survey entitled, the State Eligible 

Training Provider Survey, of the 50 states (and the District of Columbia) was fielded to inquire 

about their ETP policies over the past five program years. The short survey, which was 

administered January-May 2010 and completed by 24 states, asked overall ETP policies (e.g., 

use of waiver, local flexibility), criteria used for provider eligibility, percentage of colleges on 

the state ETP list, and state experiences administering ETP policies. The state survey data are 

then linked to Workforce Investment Act Standardized Record Data (WIASRD) files for the four 

most recent program years available (2004-2007), and provide data on ITA participant 

characteristics, program participation, pre-program employment experiences, and post-program 

employment and training outcomes for the analysis. Both descriptive and multivariate regression 

analyses are used to understand how state ETP policies affect individual-level employment and 

training outcomes. 

This paper first offers a conceptual framework for how states under WIA can ensure the 

quality of training providers through their ETP policies in order to improve outcomes for adults 

and dislocated workers. A summary of research on ITAs and ETP policies is then presented. 

Next, a description of the primary and secondary data and the methodology used to analyze the 

effects of state ETP policies on individual employment and training outcomes is provided. The 

final section summarizes the results of these analyses and offers several policy implications from 

the research. 

2 
3 

A Conceptual Framework for Ensuring the Quality of Training

Providers for Adults and Dislocated Workers under WIA

A conceptual framework is needed to provide a theoretical understanding of how state 

ETP policies ensure training provider quality in order to improve individual employment and 

training outcomes under WIA. The framework, depicted in Figure 1, focuses on WIA adult and 

dislocated worker participants.1

 The following section provides the rationale for this framework 

and how it offers a basis for the analytical task presented later in this paper. 

The Need for Training

At the starting point of the conceptual framework, it is important to ask why adults and 

dislocated workers need publicly-provided training. As shown in the first column of the 

conceptual framework, there are two reasons for entering into WIA training. First, dislocated 

workers may need retraining because they have skills that are considered obsolete and cannot 

qualify for new employment. For example, an assembly person who lost his or her job at a 

manufacturer may need a skills upgrade or training in a new occupation to find a job. Second, 

adults who have insufficient skills or education may also need training in order to find a “good” 

job, meaning employment that provides a self-sufficient wage and opportunities for promotion. 

These could be individuals who have only worked in low-paying jobs that require few 

occupational skills to perform and provide no opportunities for career growth. Under WIA, 

adults and dislocated workers who cannot find employment because of a lack of necessary skills 

are assessed at a local One-Stop Career Center and may be considered eligible for training. 

Market‐Based Policy Intervention

The second column of the conceptual framework provides an understanding of how the 

need for training is addressed by government using a market-based policy intervention – the 

ITA. ITAs operate similarly to a voucher, defined by Judith Resnik as “a subsidy that grants 

limited purchasing power to an individual to choose among a restricted set of goods and 

services.” WIA participants use ITAs to select and purchase training services from the market of 

eligible training providers. While the subsidy transaction remains between the entity issuing the 

ITA (e.g., a One-Stop Career Center or workforce investment board) and the training provider, 

the power lies with the participant choose which training provider will best meet his or her

 

1 Although with a waiver local areas can provide training to older and out-of-school youth through ITAs, the analysis 

focused on ITAs for adults and dislocated workers.
Figure 1. Conceptual Framework for Ensuring the Quality of Training Providers for Adults and Disloca

under WIA 

Need for Training Obsolete skills due to changes in workforce demands for industry (dislocated workers) Insufficient skills or education needed to obtain a “good” job (adults) 

Market-based 

Policy Intervention 

Selection of Policy Tool: Use of a voucher-like mechanism called an Individual Training Account (ITA) under the Workforce Investment Act of 1998 Policy Goals: Empowerment of individuals through choice, a strong role for the private sector, & state and local flexibility 

State Restrictions on Training Provider Market 

State Policies to Ensure Provider Quality: Initial & subsequent eligibility determinations, provider performance reporting, design & publication of ETP list, use of waivers, & local flexibility in eligibility process 

Training Provider Quality 

Moderating Factors 

State ITA Design: Cap on ITA amount and limit on ITA training duration State Experiences and Challenges with the Provider Market: Provider market that meets demand, quality of information, customer use of list, and process burden ITA Participant Characteristics: Demographics, program participation, WIA services, & pre-WIA wages State Economic Factors: Unemployment rate 

4 
           

individual needs and preferences. However, this choice is limited to the providers that have been 

approved by the state and are on the ETP list. 

Vouchers are often used as a tool for government to provide goods and services and can 

be politically popular (Steuerle and Twombly 2002). Policymakers recognize that vouchers can 

create competition among businesses vying for vouchers from which produce goods and services 

more efficiently, meaning that supply will meet demand at fair market pricing (Steuerle and 

Twombly 2002). Policymakers may also view them as a way to reduce the need for government 

resources to administer the voucher and, thus, reduce government size and spending. Voucher 

recipients may favor having the choice among goods and services rather than having a very 

limited or lack of choice through direct government provision, grants, and contracts (Salamon 

2002). In addition, business may prefer vouchers because they open the market to all providers 

of a good or service and expand opportunities for the private sector to tap into government 

spending. This popular appeal of vouchers has led to many Federally-funded social programs to 

use a voucher mechanism to deliver services, including food stamps, child care, and housing, 

among others. 

The goals of WIA and the use of vouchers as a policy tool, as shown in the conceptual 

framework, embody many of the principles in presented by Osborne and Gaebler (1992) in their 

seminal book, Reinventing Government. As provided in the final rules of WIA (U.S. Department 

of Labor 2000), the key principles include empowering individuals through choice, a strong role 

for the private sector, and state and local flexibility, which directly align with Osborne and 

Gaebler framework. The use of training vouchers – because they are a market-driven, permit 

customer choice and are administered at a local level – will help to meet the goals of WIA. 

State Restrictions on Training Provider Markets

In the conceptual framework, the next step is to implement the market-based policy 

intervention but with restrictions on the policy tool selected, a voucher. These restrictions are to 

ensure that the government is receiving a certain standard of quality in the goods and services it 

procures—in this case, training. Vouchers as policy tools also use the power of the market to 

ensure that there is an adequate supply of quality goods and services to meet customer demand. 

However, the market of providers of ITA-funded training may not perform as intended and a 

market failure may occur (Barnow 2000; Ellis 2001; Hipp and Warner 2008). In their study of 

German and U.S. training vouchers, Hipp and Warner observe that market failures can occur on 

5 
both the supply and demand sides of vouchers. On the supply side, it can be difficult for 

government to manage the quality of the providers and the viability of the market. For example, 

in training vouchers, developing performance standards for providers may be necessary to ensure 

the quality of the training as measured by employment and earnings outcomes of participants. 

Solutions to supply side market failures can also include oversight and regulation of providers, 

both in their selection and performance. Market formation issues because of a lack of scale in 

the demand for training services can be addressed by making non-voucher services available to 

customers, such as those in rural areas. 

Market failures on the demand side can also be corrected by government action. Voucher 

users can have preferences that do not align with the goals of the program and government must 

restrict the choices they can make, according to Hipp and Warner. This solution is evident in 

programs such as food stamps where the U.S. Department of Agriculture restricts the types of 

food participants can buy because of nutritional concerns. In training programs, voucher use 

may be restricted to training that is most likely to lead to employment. Restrictions on the 

vouchers may lead to their underuse, which may hinder the formation of markets. Not enough 

demand by consumers for training, especially in early implementation stages, will stunt the 

development of a viable market. Governments sponsoring training vouchers have to carefully 

structure voucher choice to encourage use by consumers and incentivize provision by private 

firms. Poor or uninformed choices can occur when the consumer holding the voucher does not 

have enough information to make good choices for themselves. In the case of training vouchers, 

a consumer may not be able to make a reasonable judgment as to whether their interests and 

abilities will lead to successful completion of training and placement in a job. Thus, a system 

that provides the data needed to make an informed decision and guidance from an intermediary, 

such as the ETP list under WIA, can help to overcome a lack of adequate information. 

While ITAs are designed to allow for maximum choice in training options, the risk of 

ITA participants making poor choices that did not lead to positive outcomes, such finding 

employment and earning a living wage, is evident. WIA specifies that certain restrictions 

imposed at the state or local level should be used to vet providers for their quality, determined by 

the training providers’ past performance, and their ability to provide training in high-demand, 

high-wage occupations. States and local WIBs must make information on training providers and 

6 
their programs available to WIA participants and are encouraged in the WIA regulations to 

provide assessment and counseling to ITA participants so they can make an informed choice. 

The WIA legislation provides an overall structure to the ITA system that limits 

customers’ choices to training providers that have been certified through a state-approved 

process and ensures that WIA customers have access to information about the performance of 

participants who had been served by these providers. The Governor of each state must establish 

minimum eligibility criteria and procedures for use by local WIBs to screen training providers to 

be eligible to serve ITA participants. The purpose of the eligibility process is not only to ensure 

that ITA participants have adequate information from which to choose training services but also 

to create a market-driven system of training providers that must compete against each other for 

ITA participants. There are two eligibility processes for training providers seeking state 

approval to serve ITA participants: 1) initial eligibility, which can be waived for colleges and 

registered apprenticeship programs; and 2) subsequent eligibility, which all providers must 

undergo after the initial period ends. 

The eligibility process can be administered at the state or local level but the states have 

final approval of training providers for the ETP list. To be placed on the ETP list, training 

providers must submit performance data on the training programs they want to be approved for 

use by ITA participants and the training program must be for an occupation that is considered 

“high demand and high wage” for the local labor market area. These program performance 

measures include (U.S. Department of Labor 2000): 

 Completion rates for WIA and all training participants 

 Employment (unsubsidized) rates for WIA and all training participants 

 Average wages at the time of placement into employment of all participants 

 Employment retention rates for WIA participants completing training 

 Average earnings at six months for WIA participants completing training 

 Credential attainment (if applicable) for WIA participants completing training 

If permitted by the state, local WIBs can apply more stringent criteria to evaluate the provider 

performance for the eligibility process. States can also seek a Federal waiver to extend the initial 

eligibility period for the training provider approval. 

If the providers are approved, they are put on the ETP list that is linked to a Consumer 

Reporting System (CRS), which provides the performance data on approved training programs to 

7 
     

 

 

ITA participants. ETP lists have to be available through One-Stop Career Centers and are 

typically available on the Internet (U.S Department of Labor 2000). States can also choose to 

place training providers on the list that are not approved for ITAs. In addition, a state can 

develop reciprocal agreements with other states to place their approved training providers on its 

own list. These policies can expand the training provider market from which ITA participants 

can choose a training program. 

Thus, the conceptual framework shows the policies that states enact to implement these 

restrictions and quality controls on the training provider market. These include: making initial 

eligibility determinations for ETPs through an application process; receiving a Federal waiver to 

extend the initial eligibility period; permitting local flexibility in the eligibility process setting 

provider performance levels for subsequent eligibility determinations; and designing and 

publishing a state ETP list may includes providers from border states or providers that are not 

approved for ITA use. The results from the survey, provided in a later section, describe how 

states have designed these policies to implement their ETP systems. 

Training Provider Quality

Up to this point, the quality of training providers has been discussed somewhat 

ambiguously. However, as shown in the conceptual framework, the notion of “quality” is key to 

meeting the intended outcomes of WIA, which are to help participants complete training, earn a 

credential, and improve their employment and earnings. WIA provides some signals for what its 

creators saw as the key elements of training provider quality. First, WIA allows states to make 

post-secondary educational institutions (eligible under the Higher Education Act2

) and registered 

apprenticeship programs automatically eligible, at least initially, as approved training providers. 

These providers offer participants widely recognized credentials upon graduation – a certificate 

or college degree – or lead to licensure in a particular occupation such as a journeyman’s license 

or a certified nursing assistant license. This indicates that post-secondary educational institutions 

and registered apprenticeship programs are considered higher quality than other providers, who 

need additional screening by states or local WIBs. Second, the training offered by providers 

must be in high-demand, high-wage occupations to be eligible. The rationale is that training in 

2

 Title IV of the Higher Education Act provides federal financial aid funds including Pell grants to post-secondary 

educational institutions. Institutions that receive these funds are initially eligible to be approved training providers 

under WIA. 

8 
           

these occupations are more likely to lead to gainful employment by WIA training participants. 

Finally, WIA stipulates that training providers during the eligibility process must demonstrate 

that the training program(s) it offers is likely to lead to completion of or graduation from the 

program, credential attainment, employment, and wages. This requirement shows that the 

outcomes of participants are a crucial indicator of the quality of training providers. 

There are other potential indicators of training provider quality that WIA does not 

directly address. For example, training providers that use evidence-based learning models may 

be considered higher quality as they use instructional methods or program features that have 

shown some success in achieving positive participant outcomes. Thus, the use of evidence-based 

programs can be considered an indicator of high-quality training. In addition, hiring and 

retaining qualified instructors that are trained in the latest industry advances or are highly 

experienced in the classroom may also be a key indicator of training provider quality. Finding 

qualified instructors for training in a particular industry can be challenging because they may be 

able to earn a higher wage working in the industry, such as a registered nurse, rather than as an 

instructor at a post-secondary institution (Nightingale et al. 2008). Thus, some training providers 

may not have enough qualified instructors for their training program due to competition for their 

skills in the industry. Another indicator of training quality may be providers that offer services 

to participants that help them stay in and complete a training program (e.g., child care, 

counseling or peer support, monetary incentives) and find employment after program 

completion. While many of the providers approved for ITA training may have any of attributes 

mentioned, WIA does not address them as possible measures of training provider quality. This is 

indicated by the focus of the WIA regulations on the use of only training outcomes to measure 

quality, rather than inputs. 

Moderating Factors in Training Provider Quality

As described in the conceptual framework, the state ETP policies are designed to ensure 

the quality of training providers under WIA but moderating factors can influence the 

effectiveness of the training providers. First, some states put a cap on the amount of the ITA 

available to recipients and limit the duration of training received (Barnow and King 2005). In 

addition, some states allow local WIBs to develop their own ITA policies to limit ITA use (GAO 

2005). These limits to ITAs may, in turn, limit the training options that ITA participants can 

pursue, especially longer-term or higher cost training programs. 

9 
   

Another factor that may moderate the quality of training provider available to ITA 

participants is the potential market failures that may occur in the implementation of ETP 

policies. Limits on provider eligibility and the burden of the eligibility process may keep 

providers from applying (D’Amico and Salzman 2004). There may also be a lack of qualified 

providers in the workforce area such as those that have programs in high-wage, high-demand 

occupations. In addition, there may be a lack of quality data on the eligible training providers in 

order for ITA participants to make informed training decisions or the ITA participants use other 

means to select training providers other than the performance data provided by the ETP list (U.S. 

Department of Labor WIA Readiness Review 2002). 

The characteristics of ITA participants may also moderate the effect of the quality of 

training providers on participants’ outcomes. Studies of the effectiveness of training programs 

take into account the individual demographics of participants, other government programs they 

participate in, the training program’s services they use, and their preprogram employment and 

wages. In particular, participant characteristics are important in determining who may see more 

success in their employment and training outcomes compared to others. State economic factors 

may also moderate the effectiveness of training provider quality. It is important to consider how 

the state economy, such as unemployment rates may influence the employment outcomes of 

individuals participating in a training program. 

Policy Outcomes

There are three potential policy outcomes to the training offered by eligible providers, as 

shown in the conceptual framework. ITA participants may be better prepared for the workforce 

(and may have a credential in an occupation) and see improvements in their employment and 

earnings. These are the major outcomes that are tracked and monitored through the performance 

reporting by states under WIA. Specifically, states must meet performance goals set by the U.S. 

Department of Labor, which are entered employment, employment retention, and average postprogram earnings (U.S. Department of Labor 2006). The Department also tracks credential 

attainment for WIA participants. Thus, the second policy outcome shown in the conceptual 

framework is the state’s ability to meet these performance goals, which having a pool of highquality training providers should improve. Finally, while a less directly measurable outcome, 

industry in a state may be strengthened through the efforts to train a better workforce. 

10 
                 

 

 

Current Research on ITAs and ETP Policies and Their Effectiveness

The conceptual framework offers an analytical guide from which to understand how state 

ETP policies affect ITA participants’ employment and training outcomes by ensuring the quality 

of training providers. While little is known about the ETP policies and ensuring provider 

quality, some studies provide offer some insight into the effects of early training voucher 

experiments and of the more recent ITAs. More recent studies also offer some understanding of 

how states and local areas have implemented their ETP policies. 

In the 1970s, an experimental evaluation of a subsidy for vocational education for lowincome individuals was conducted as a part of the negative income tax experiments. Researchers 

found that individuals who received a 100-percent subsidy for training received one additional 

year of training compared to those who did not receive the subsidy (Dickenson and West 1983). 

However, there was no statistically significant difference in their post-program earnings. Thus, 

these subsidies increased participation in job training but did not result in higher earnings. A 

nonexperimental evaluation of the Career Management Account (CMA) demonstration, a 

precursor to ITAs, showed mixed results; there were small positive impacts on earnings for those 

who received CMAs compared to those who did not (Public Policy Associates 1998). However, 

selection bias was a possible issue that could not be overcome in the nonexperimental design so 

the results cannot be solely attributed to the CMA.3 

Three, more recent ETA-sponsored evaluations have studied the implementation, and 

sometimes the outcomes, of ITAs and ETPs in numerous states and local areas. They are: 

 National Evaluation of the Implementation of the Workforce Investment Act. An 

implementation study of WIA in the five years since its passage, with 21 states and 40 

local workforce investment areas participating in the study (Social Policy Research 

Associates 2004). 

 An Evaluation of the Individual Training Account/Eligible Training Provider 

Demonstration. The implementation component of an experimental evaluation to 

understand the implementation of three approaches to ITAs in 13 states and local areas 

(D’Amico and Salzman 2004). 

3

 Selection bias can occur when the participation in programs is not determined randomly and that those who enter 

the program may be systematically different in immeasurable ways (e.g., ability, motivation) from those who did 

not. 

11 
 The Workforce Investment Act in Eight States. An evaluation of eight states with indepth case studies of the state and local administration of WIA (Barnow and King 2005). 

In all three evaluations, more direct evidence of the effects of ITAs and the implementation of 

the ETP policies and processes in states and local areas is provided. Researchers found that 

many of the local staff were using a “guided” or “informed” choice model in which they worked 

with the ITA customers to make training selections but that the choices were ultimately being 

made by the customers after receiving information on their options. Both evaluations concluded 

that some of these states and training providers indicated that the application process to become 

an eligible training provider was burdensome and expensive. D’Amico and Salzman (2004) also 

noted that most of the lists were made up of community colleges and proprietary schools but that 

many community colleges “balked” at the eligibility requirements and considered it “not worth 

the trouble.” These evaluations also highlighted the strong role of the local WIBs and One-Stop 

Career Center staff in guiding ITA participants the selection of an eligible training provider and 

the development of ITA policies such as caps on amount and duration. States allowed for local 

discretion in developing the ETP processes but still created a structure in which the local entities 

operate. 

The U.S. Government Accountability Office (GAO) (2005) surveyed a sample of local 

WIBs on training under WIA. The survey asked respondents to provide information on how 

local boards spend training dollars and manage the use of ITAs, and what outcome data on 

training are available. The study identified some of the challenges of local boards that imply 

some level of market failure in local workforce systems. The boards were challenged by a lack 

of good performance data on training providers (and, for rural areas, a lack of training providers), 

which did not permit them to evaluate the effectiveness of these providers. However, these 

findings described local-level systems and did not indicate the state-level structures that may 

mitigate the challenges local boards faced. 

While the current literature offers insight of how ETP policies are implemented, no 

research to date has been conducted to describe state-level ETP policies, and how state-level 

decisions on ETP policy can affect the outcomes of ITA participants. The research presented in 

this paper contributes to the current literature on ITAs and training vouchers by examining the 

various state ETP policies and how they vary. These include criteria for provider eligibility, 

processing of applications, the composition of the ETP list, and local flexibility to institute ETP 

12 
     

policy – and how different combinations of these elements may affect ITA participants’ 

employment and training outcomes. 

Data and Research Methodology

The following section provides a description of the data and analytical methods used to 

answer the research questions on state ETP lists. A discussion of the study’s limitations is also 

included. 

Data

To answer the questions outlined, a research design involving the use of primary and 

secondary data and multivariate analytic methods was necessary. Primary data collection 

entailed the fielding of a survey to capture state-level ETP policy data, the key independent 

variables of interest to understand how these factors influence state performance and participant 

training outcomes. See Appendix A for a copy of the State Eligible Training Provider Survey. 

Secondary data drawn from WIASRD provided individual-level WIA records of ITA 

participants (adults and dislocated workers). State-level economic data were accessed through 

the Bureau of Labor Statistics. All data used were for the program years 2004-2007. 

State-Level Data. State documents such as policy manuals, WIA state plans, and ETP 

lists were reviewed for ETP data but the state information sought was inconsistent in format and 

availability. Thus, to obtain standardized state-level ETP policy data, a web-based survey of 

state and the District of Columbia WIA administrators was conducted and fielded from JanuaryMay 2010 and completed by 24 states. The following questions were asked of state respondents 

to ascertain basic information on their ETP policies and experiences over the past five program 

years: 

 Overall state policies such as receipt of a Federal waiver for the initial eligibility 

period and local flexibility to design their own ETP eligibility process and set higher 

performance levels for eligibility. 

 Minimum performance standards for ETP for completion rates (for WIA and all 

participants), employment (unsubsidized) rates (WIA and all participants); average 

wages at the time of placement into employment of all participants; employment 

retention rates for WIA completers; average earnings at six months for WIA 

completers; and credential attainment (if applicable) for WIA completers; 

13 
 Composition of the ETP list such as the percentage of colleges on the state ETP list 

and inclusion of providers that are not approved and from other states on the list; and 

 Perceived difficulty of the eligibility process and challenges with the provider market. 

Ranges rather than point estimates were used to capture the data because of the temporal factor 

of looking at several years of data. It was assumed that state ITA and ETP policies stabilized by 

2004 and those changes would occur only around the margins so that a range estimate would be 

acceptable for this analysis. Using this temporal consideration for the state-level variables also 

allows for analysis to be continued once WIASRD files are released for program years 2008 and 

2009. A set of questions also asked states about their ITA policies including ceiling on ITA 

amount, training duration limit, and degree of guidance provided to ITA participants at local 

One-Stop Career Centers. The data derived from these questions controlled for other state ITA 

policies such as ceilings on training duration and costs to isolate the effects of state ETP policies 

on state performance levels and individual training outcomes. 

Individual-Level Data. The largest data source for this research was the WIASRD file 

for the four most recent program years available – 2004-2007. This source provided 

administrative data at the individual WIA participant level from which ITA participants from the 

Adult and Dislocated Worker programs were extracted. Demographic (e.g., race, gender, age, 

education, preprogram earnings) and program participation variables (e.g., unemployment 

insurance, Temporary Assistance for Needy families) were provided for these individuals and are 

used as controls for the multivariate analysis. Other variables of interest were WIA program 

experiences such as receipt of a Pell grant, needs related payments, and support services. Data 

on ITA participant outcomes of entered employment, employment retention, and average wages, 

the key common measures of performance for WIA, as well as credential attainment (not a 

general equivalency degree), were used for the analysis (U.S. Department of Labor 2006). 

Annual state unemployment rates (U.S. Department of Labor, Bureau of Labor Statistics 2010) 

from 2004-2007 were also used. 

Research Methodology

Descriptive and multivariate regression analyses were used to answer the research 

questions posed: 1) what ETP policies have states implemented and how do policies vary across 

states?; and 2) how much do the various state ETP policies have an effect on ITA participants’ 

employment and training outcomes? The main independent variables of interest were the set of 

14 
 

state ETP policies collected from the survey to describe state ETP policies and to better 

understand how each of the state policies influenced ITA participant outcomes. Descriptive 

analysis of the state ETP policies and experiences – eligibility criteria and processes, 

composition of the list, and local flexibility – was used to answer the first research question. 

Fixed and random effects models provided answers to the second question of interest. 

The multivariate models were used to better understand how state-level variation in ETP 

list policies – after controlling for experiences implementing ETP policies, ITA policies, ITA 

participant characteristics, program participation, and state economic conditions – may influence 

or be associated with changes in individual-level training outcomes of ITA participants. Fixed 

effects models, which do not identify the variance in the model at the state level, were estimated 

first. Using ordinary least squares (OLS) regression, the continuous dependent variables of 

wages during the first quarter after exit and average wages in the second and third quarter after 

exit were regressed on the independent variables to estimate the predicted change in wages due 

to state ETP policies. A logistic regression estimation for the fixed effects model provided the 

probability of employment conditional on the state ETP policies and other independent variables. 

The fixed effects models are specified as follows:4 

	 OLS Model: 

Y = β0 + β1Pi + β2Mi + β3Di + β4Ei 

	 Logistic Model: 

log[π(x)/1-π(x)] = β0 + β1Pi + β2Mi + β3Di + β4Ei
 

Where Y = outcome variable of interest (wages); 

π(x) = the probability of the outcome variable of interest (employment and credential 

attainment); 

Pi = state ETP policies 

Mi = key moderating variables for state ETP experiences and ITA policy 

Di = variables for ITA participant characteristics, program participation, and WIA 

services received; 

Ei = state economic conditions 

4

 For multivariate models, it is possible to cluster the standard errors by a higher-level variable, which provides more 

robust standard errors. In this analysis, the fixed effects models were estimated but the results are not reported in 

detail because there were too few states to permit to calculate a model test (F or chi-squared test). In general, fewer 

estimates of the key ETP policy variables and moderating factors were statistically significant when clustering the 

standard errors by state. However, the random effects models were able to achieve more robust standard errors and 

provided a goodness-of-fit test so those estimates are reported in detail. 

15 
       

                                   

     

   

                           

   

                           

           

                               

                     

   

   

       

                   

               

                   

             

                 

                     

                   

Table 1 describes the variables included in each of these components. 

Table 1. Description of Variables in Model Components 

Model Component Variables Included

Earnings Outcomes (Y) Wages in 1st quarter after exit, average wages at 2nd and 3rd quarters after exit

Employment and Credential

Outcomes (π(x))

Employed in 1st quarter after exit, employed in 2nd and 3rd quarters after exit,

attained credential

State ETP Policies (Pi

) ETP waiver, local ETP flexibility, no minimum criteria, reciprocal agreement, non‐

approved providers on the ETP list

Key Moderating Variables (Mi

) Less than 50 percent colleges on ETP list, provider difficulties, performance data

difficulties, state ITA cap on training amount or on training duration

ITA participant characteristics,

program participation, and

WIA services received (Di

)

Gender, ethnicity, race, older worker, disabled, limited English speaker, single

parent, education level, veteran, displaced homemaker, public assistance

recipient (i.e., Temporary Assistance for Needy Families and state general

assistance), unemployment insurance claimant or exhaustee, Rapid Response,

Trade Adjustment Assistance, received supportive services or needs related

payments, Pell grant recipient, employed in quarter prior to program entry

State Economic Conditions (Ei) State unemployment rate by program year

Each model was run separately for adults and dislocated workers. This was because the 

two groups are inherently different (e.g., all dislocated workers have recent work experience) and 

the models for each group require the use of different control variables. 

The random effects models, which identify state-level variance, were estimated next. 

The overall models were the same but estimates of the within state and between state variance 

were provided. While the parameter estimates did not change much (or at all, in some cases) 

between the fixed and random effect models, the random effects models provide more robust 

standard errors and a measure of within-state variance and between-state variance. These models 

were specified as follows: 

	 Random Effects Generalized Least Squares Model: 

Y = μ + αi +β0 + β1Pi + β2Mi + β3Di + β4Ei + εij
 

	 Random Effects Logistic Model: 

log[π(x)/1-π(x)] = μ + αi +β0 + β1Pi + β2Mi + β3Di + β4Ei + εij
 

Where Y = outcome variable of interest (wages); 

π(x) = the probability of the outcome variable of interest (employment and credential 

attainment); 

μ = overall mean of the outcome variable, which is an unknown constant 

αi = a random effect due to the ith state 

Pi = state ETP policies 

Mi = key moderating variables for state ETP experiences and ITA policy 

Di = variables for ITA participant characteristics, program participation, and WIA 

services received; 

16 
= 

 

Ei state economic conditions 

εij = the within state variance among ITA participants 

Because the random effect models identifies the state-level variance, estimates for these 

models are reported in the findings section below and the full results are provides in Tables B.1-

B.5 in the Appendix. To compare the model estimates, the tables with the fixed effects models 

are provided in Tables B.6-B10 in the Appendix. 

Limitations

There are several limitations of this study. First and foremost, the effectiveness of WIA 

training against no training or another type of training is not being tested or measured in this 

research. This study only examines how different state ETP policies affect employment and 

training outcomes, not whether using ITAs works better than contracting (as used under the Job 

Training Partnership Act, the precursor to WIA) nor whether the ETP framework ensures the 

quality of training providers for ITA participants. This analysis can only provide some 

understanding of which state ETP policies, which are intended to ensure the quality of training 

providers, may show better employment and training outcomes within the adult and dislocated 

worker programs. It is hoped, though, that this study can serve as a starting point for 

understanding ETP policies as a means to ensure training provider quality under a voucher 

system. It would be ideal if the research could build upon by using experimental or 

nonexperimental analyses that involve a comparison group for measuring the impact of such 

policies. 

The State Eligible Training Provider Survey has two specific challenges. First, the 

survey analysis is limited to 24 states instead of all states. A longer survey period was used to 

increase the response rate but it is suspected that survey “fatigue” exists among WIA 

administrators.5

 The 24 states that responded show a range in characteristics such as size and 

DOL region but caution is used in generalizing the results to all states. A second concern is the 

measures of ETP and ITA policies collected in the survey. Precision of the estimates is 

comprised by asking state WIA administrators for ranges rather than point estimates over 

5

 Requests for state WIA administrators to participate in surveys and other research activities have increased greatly 

over the past year with the implementation of the WIA and other workforce provisions in the American Recovery 

and Reinvestment Act of 2009 (ARRA). Thus, it is expected that some states have had difficulty meeting research 

requests because of their intensive efforts to respond to the ARRA and recession and have experienced what 

researchers call “survey fatigue.” 

17 
multiple years but the design of the data collection was necessary for ease of answering the 

survey and using state ETP and ITA policy variables across program years. 

Another challenge that exists is the bias inherent in the WIASRD dataset. WIA 

performance standards encourage local staff to keep participants enrolled in WIA until they find 

employment and only terminate them once they do so. Thus, as WIASRD population is 

comprised of only WIA exiters, the findings from this analysis are likely to be biased toward 

positive participant outcomes, especially immediately after exiting the program. Missing data 

can also be a problem with WIASRD, with some local workforce investment areas (LWIAs) 

systematically not reporting some information. Another challenge in using the WIASRD is that 

it only provides three quarters of post-program earnings and employment. Human capital theory 

shows that gains to education and training should increase over time but it is not possible to test 

the hypothesis of whether ETP policies have any influence on long-term employment and 

earnings outcomes with the available data. 

A final limitation of the study is omitted variable bias. First, as is typical of analysis that 

study human capital improvements, having measures of participants’ ability and motivation are 

critical because they contribute to an individual’s selection into a particular field of training and 

their ability to successfully complete the training and find employment. There are no variables 

that capture these data for ITA participants and could bias the results. In addition, local-level 

ETP policies and administration clearly have the potential to influence the experiences of ITA 

participants (Barnow and King 2005; D’Amico and Salzman 2004; SPR 2004). This analysis 

can only capture whether states allow for local flexibility in ETP policies but not the precise 

nature of those policies. 

With all of the limitations of the study, the multivariate models of state ETP policies 

cannot be interpreted as causal models, where it can be said that one ETP policy caused an 

increase or decrease in earnings or employment, for example. The models can only indicate how 

the state ETP models may be associated with or have an influence on the employment and 

training outcomes of interest. 

18 
     

               

 

Findings and Conclusions

This final section provides a summary of the results from the descriptive and multivariate 

analyses and their implications for policy, especially in light of possible WIA reauthorization. 

Full analytic results are provided in Tables B.1-B.11 in the Appendix. 

Descriptive Results of the State ETP Policy Survey

As mentioned, 24 states, which range in characteristics such as size and geographic 

region, completed the state ETP policy survey. These states catalogued their state ETP policies 

and experiences implementing these policies as presented here.6 

First, states were asked about policy options they have exercised in designing their ETP 

system over the past five program years. These options include having a Federal waiver 

extending the time limit on the initial eligibility period for training providers and allowing local 

WIBs to increase minimum performance criteria set by the state. Half of the state respondents 

(12) said that they had a Federal waiver for the initial eligibility period. This is consistent with 

the number of all states (25) that currently have this Federal waiver (U.S. Department of Labor 

2010). Local flexibility to set higher minimum performance levels than the state is also a policy 

option but only 4 of the respondents said that they had opted to permit such flexibility. 

The survey then asked state respondents about the minimum performance levels they 

require for providers to maintain eligibility over the past five program years. It should be 

mentioned that there is a correspondence, although not perfect, between states that report having 

waivers and not using the performance criteria for subsequent eligibility described in the WIA 

legislation. This is to be expected as since their initial eligibility period for providers has been 

extended and performance criteria for subsequent eligibility are not needed. 

The survey asks about the subsequent eligibility criteria used by state and the levels they 

have set to maintain eligibility, as described in the WIA regulations. Table 1 summarizes these 

results. The first eligibility criterion is training program completion for all participants and WIA 

participants. While most state respondents (5) that use this criterion for all participants tend 

toward the middle option of 50-75 percent completion, more state respondents (4) indicate that 

they set a higher minimum completion rate for WIA participants (75 percent or more). 

6

 To encourage participation in the survey, the states were assured that their individual answers would be kept 

confidential. Thus, the results do not identify specific state answers and are provided in the aggregate. 

19 
           

       

                           

                         

                           

                         

         

     

                   

         

   

                   

                       

                                 

   

       

                                   

 

 

The employment rate is also a key criterion used for maintaining provider eligibility. 

While over half the states that responded did not use this performance criterion, of those that did, 

more states (7) report the their minimum performance level for post-program employment is in 

the middle range (50-75 percent) for all participants and evenly distributed between the mid- and 

high-range (4 states each) for WIA participants. As observed in the completion rate criterion, the 

these states more often use employment rate minimums that are set higher for WIA participants 

than for all participants that attended an eligible training provider. 

Two other possible performance criteria for training providers – employment in a related 

occupation and employment at six months – are also identified in the WIA regulations. More 

states than with other employment criteria indicate that they do no use employment in a related 

occupation for all participants as a criterion for eligibility. Of the 6 states that use this criterion, 

half (3) report setting the minimum rate of 50-75 percent. Ten states report using employment 

rate at six months for WIA participants that completed training as a criterion. Of these, 6 states 

use a minimum level within 50-75 percent, 3 states set a higher minimum at over 75 percent, and 

1 state has a minimum level of less than 50 percent. 

Table 2. State’s Use of Minimum Performance Levels to Maintain Eligibility (N=24) 

Minimum Performance Criteria < 50% 50‐75% >75%

Do Not

Use

Do Not

Know

Completion Rate (all individuals) 2 states

(8%)

5 states

(21%)

2 states

(8%)

14 states

(58%)

1 state

(4%)

Completion Rate (WIA

participants)

1 state

(4%)

3 states

(13%)

4 states

(17%)

15 states

(63%)

1 state

(4%)

Employment Rate (all individuals) 0 states

(0%)

7 states

(29%)

2 states

(8%)

14 states

(58%)

1 state

(4%)

Employment Rate (WIA

participants)

1 state

(4%)

4 states

(17%)

4 states

(17%)

13 states

(54%)

2 states

(8%)

Rate of Employment in Related

Occupation (all individuals)

1 state

(4%)

3 states

(13%)

2 states

(8%)

16 states

(67%)

2 states

(8%)

Employment Rate at 6 Months

(WIA completers)

1 state

(4%)

6 states

(25%)

3 states

(13%)

13 states

(54%)

1 state

(4%)

<$7 per

hour

$7‐10 per

hour

$11‐14 per

hour

$15 or

more

Do Not

Use

Do Not

Know

Hourly Wage at Placement (all

individuals)

0 states

(0%)

7 states

(29%)

0 states

(0%)

0 states

(0%)

13 states

(54%)

4 states

(17%)

<$5,000

$5,000‐

$9,999

$10,000‐

$14,999

$15,000

or more

Do Not

Use

Do Not

Know

Average Wages at 6 Months (WIA

completers)

1 state

(4%)

3 states

(13%)

2 states

(8%)

1 state

(4%)

14 states

(58%)

3 states

(13%)

Source: 2010 Survey of State Eligible Training Provider Policies 

Note: Percentages across cell rows may not total 100 percent due to rounding.
 

20 
Minimum wage levels are also used as eligibility criteria. Of the 7 states that report using 

hourly wage at placement into employment for all participants as a criterion, all of these states 

indicate that they set their minimum hourly wage at $7-10. While this may be a realistic goal for 

training providers to meet for wages in entry-level jobs, an hourly wage of $10 and working 

fulltime would barely put a single-headed household of three above 100 percent of the national 

poverty level (U.S. Department of Health and Human Services 2010). Thus, this may not be a 

difficult performance level to meet. The minimum performance level for average wages at six 

months for WIA participants who complete the training is not set as low. Of the states that use a 

longer-term wage minimum criterion, three states set the average six-month wages at $10,000 

and over and 4 states set it at below $10,000. 

As allowed by WIA, several states set additional criteria for maintaining provider 

eligibility. Six states use rates of licensure, certification, or attainment of academic degrees of 

WIA participants and 1 state uses the criterion for all participants to determine subsequent 

eligibility. In addition, 4 states use the rate of attainment of industry-recognized occupational 

skills as an eligibility criterion. Two states report using retention rates in employment at six 

months for all participants and two states use average wages at six months for all individuals. 

Fourteen states report using no additional criteria for setting minimum performance levels for 

maintaining provider eligibility. 

These minimum performance levels for provider eligibility can be a contentious issue for 

some states. A workgroup of state representatives hosted by the U.S. Department of Labor met 

in 2001 to discuss and develop recommendations for the implementation of subsequent eligibility 

policies under WIA (U.S. Department of Labor WIA Readiness Review 2002). The states 

indicated that there is confusion in using the seven criteria used to determine subsequent 

eligibility as states can weight each criterion differently. In addition, the workgroup suggested 

that an adequate system is not in place to capture performance data in “a uniform, meaningful, 

and cost-effective manner across all programs on the ETP list.” The workgroup’s main concern 

was that implementing these eligibility criteria would reduce the number of providers on the ETP 

list and restrict the choice of training for ITA participants. 

Three questions were asked about how states have designed their ETP lists – reciprocal 

agreements with other states, non-approved providers on the ETP list, and the proportion of postsecondary institutions on the ETP list. These options are intended to help to increase the size of 

21 
               

     

                       

   

       

           

         

   

           

               

     

   

           

                   

                       

                         

         

                     

         

         

           

 

 

 

the provider market for ITA participants. Nearly half of the respondents (11) also said that they 

had entered into a reciprocal agreement with another state that permits eligible training providers 

from one state to accept ITAs provided by another state. Over a third of state respondents (9) 

report allowing training providers to be placed on their state ETP list that have not been 

approved for ITA use. States were also asked about the approximate proportion of two- and 

four-year colleges and universities on the ETP list. Nearly half of the respondents (11) indicate 

that at least half of the providers on the list are two- and four-year institutions, which could lead 

to more ITA participants selecting training programs that would lead to a college degree. 

States were asked their perceptions of their experiences implementing ETP policies over 

the past five years. On a four-point scale, the state respondents indicated how successfully they 

believed they addressed issues around ETP policies.7 Table 2 provides a summary of these 

results. 

Table 3. State Respondents’ Experiences Address ETP Issues (N=24) 

Issues

With Great or Some

Success

With Great or Some

Difficulty Does Not Apply

Getting new providers onto the ETP

list

21 states

(88%)

1 state

(4%)

2 states

(8%)

Getting degree‐bearing institutions

onto the ETP list

19 states

(79%)

3 states

(13%)

2 states

(8%)

Getting unqualified providers off the

ETP list

12 states

(50%)

5 states

(21%)

7 states

(29%)

Lack of providers on the ETP list that

offer training in high‐demand, high‐

wage occupations

11 states

(46%)

6 states

(25%)

7 states

(29%)

Lack of qualified providers 11 states

(46%)

3 states

(13%)

10 states

(42%)

Lack of quality data on provider

performance

5 states

(21%)

16 states

(67%)

3 states

(13%)

Lack of customer use of ETP list 15 states

(63%)

1 state

(4%)

8 states

(33%)

Negative feedback from providers on

the burden of the application

process

15 states

(63%)

4 states

(17%)

5 states

(21%)

Negative feedback from providers on

the burden of maintaining eligibility

9 states

(38%)

8 states

(33%)

7 states

(29%)

Source: 2010 Survey of State Eligible Training Provider Policies 

Note: Percentages across cell rows may not total 100 percent due to rounding.
 

7

 GAO (2005) developed a survey of local workforce investment areas that included questions on their experiences 

implementing ITA and ETP policies. Several of the survey questions for this study were adapted from the GAO 

survey. 

22 
The first five issues on which states provided their perceptions of the experiences dealt 

with various aspects of the viability of the provider market for their state over the past five 

program years. Of the 24 state respondents, most either had success in addressing provider 

market issues or the issues did not apply to their state. These responses may indicate that the 

ETP policies and systems have been in place long enough to have achieved a steady operating 

state and that most of the issues were addressed earlier in WIA implementation. However, 

several states, especially in getting unqualified providers off the list (5 states) and getting 

providers that offer training in high-demand, high-wage occupations (6 states), have experienced 

some difficulty in addressing provider market issues. 

As mentioned, an issue that was at the forefront of Federal-State discussions on ETP 

policy implementation in 2001—a lack of quality data on provider performance—remained an 

issue through the past five program years. Two-thirds of the state respondents (16) indicate that 

they have had some or great difficulty addressing the issue of quality data on provider 

performance over the past five program years. The current requirements under WIA for 

performance data on training providers (and their programs) are still a considerable challenge for 

the states that responded to the survey. However, customer use of the list has not been a 

perceived challenge for state respondents. Only 1 state reports that it has been a problem over 

the past five program years. 

The final two issues address the experience of states with negative feedback on the 

burden of the application and subsequent eligibility processes. As mentioned, early studies on 

ETP policies indicated that training providers were concerned about the burden of becoming and 

remaining an eligible training provider for ITA participants. Some state respondents report that 

they have experienced difficulties addressing this issue over the past five program years, 

particularly for the burden of the subsequent eligibility process. A third of state respondents (8) 

indicates that they have had some or great difficulty addressing the negative feedback from 

providers on the subsequent eligibility process. Only 17 percent of respondents (4) report having 

difficulty addressing negative provider feedback on the application process. 

The survey also asks states about its ITA policies on capping the ITA amount and 

limiting the duration of training at the state level. Half of the state respondents report capping 

the amount of ITAs at the state level. Of those states that cap the amount, 5 states set the cap at 

less than $3,000, 4 states between $3,000-$4,999, and 3 states between $5,000-$7,499. Of the 7 

23 
states that limit the duration of ITA training, 5 states limit the duration to less than 12 months 

and 2 states limit the duration to 12-23 months. While not the focus of this study, these 

responses are used to understand one of the potential moderating factors that are associated with 

ITA participant outcomes and are included in the multivariate analysis. 

Survey respondents were offered the opportunity to further explain their state’s ETP 

policies and to comment on what they would change about the ETP framework under WIA. 

Several of the comments addressed the issue of performance data required by providers. Some 

states have centralized and automated the ETP list, which has streamlined the data collection 

process for eligibility and reporting purposes. Other state respondents indicate that the challenge 

still exists to collecting performance data consistently, especially data on all individuals in a 

training program. One state respondent reported that two large universities have pulled out of 

the ETP list because the requirement that they track the performance of all students was too 

onerous. Another respondent recommended that the requirement that performance data on all 

individuals be eliminated and only data on WIA participants be captured. 

Other comments addressed issues regarding the types or training providers and their 

programs. One respondent noted that not all post-secondary institutions automatically placed on 

the ETP list offer training in high-demand, high-wage occupations and would want to have 

greater discretion to remove those programs that do not offer such training. Another respondent 

thought that the emphasis for WIA-funded training should be on developing work-based learning 

models such as on-the-job training, apprenticeship, and customized training rather than only 

traditional classroom training. A third respondent said that determining the quality of training 

providers is becoming more difficult as for-profit providers aggressively pursue enrollment but 

may not deliver a job after training. 

Finally, several state respondents indicate that they would recommend eliminating the 

ETP component of WIA altogether. One respondent thought that the ETP has not achieved its 

intended purpose. Another respondent indicated that it is the counselors in One-Stop Career 

Centers that do the work to ensure the quality of the training provided, not the ETP processes 

that have been established. 

Overall, these comments highlight some of the issues that some states have experienced 

in implementing ETP provisions and some point out several challenges in determining the 

quality of a provider in order to allow ITA participants to make informed training choices. 

24 
           

 

Multivariate Analysis of State ETP Policies

The analysis presented in this section builds on the descriptive data on state ETP policies 

and experiences discussed above. The conceptual framework provides an understanding of how 

various state ETP policies influence the quality of training providers used by ITA recipients in 

order to improve their employment and training outcomes. The models discussed in the 

methodology section run each outcome variable of interest against the key variables of interest 

and control variables separately for adults and dislocated workers. 

One challenge to the analysis that bears addressing upfront is the problem of 

multicollinearity, which occurs when variables are highly correlated with each other, among the 

key ETP policy variables. This caused large standard errors, which lead to imprecision in the 

model estimates (i.e., wide confidence intervals). To address the issue, some survey questions 

were collapsed into dichotomous variables to capture overall state ETP policy in a particular 

topic area. For example, one variable created captures whether states used minimum 

performance criteria for eligibility determinations. In addition, a single variable was created to 

capture whether the state experienced any difficulty addressing provider market issues. In 

addition, the information provided variable on the burden of training providers in the ETP 

application and subsequent eligibility process was captured in the variable on the difficulty of 

obtaining quality performance data so the burden variable was omitted. A simple correlation 

table and principal component analysis of the state ETP policy and moderating variables were 

used to test for multicollinearity. All models were then tested using variance inflation factors to 

understand where continued issues may be present.8

 The results of the analysis show that while 

multicollinearity exists between the key variables of interest, particularly between ETP waiver 

and having no minimum criteria, between ETP waiver and state ITA policy, and between having 

no minimum criteria and state ITA policy.9

 With the existence of multicollinearity in the model 

and other limitations, it is again important to note that interpretations are limited to associations 

rather than causal inferences. 

8

 See Berenson, M.L., D.M. Levine, and M. Goldstein (1983), Intermediate Methods and Applications: A Computer 

Package Approach, and Neter, J., M.H. Kutner, and C.J. Nachtsheim (2003), Applied Linear Regression Models (4th
 

Edition) for more information on multicollinearity tests. 

9

 The results of the correlation table are provided in Tables B.11 in the Appendix. 

25 
Addressing the issue of multicollinearity, yet adhering to the conceptual framework 

designed for this study, has focused the analysis on five key predictor variables of interest. They 

are: 

1.	 Use of a Federal ETP waiver; 

2.	 Local ETP flexibility 

3.	 Use of any minimum performance criteria for subsequent eligibility determinations; 

4.	 Use of a reciprocal agreement with another state to allow use of ITAs with the other 

state’s eligible training providers; and 

5. Inclusion of training providers on the ETP list that have not been approved. 

The random effects models were run separately for adults and dislocated workers. Included in 

the models are three moderating variables that capture the state ETP experiences: less than 50 

percent two- and four-year colleges and universities on the ETP list; some or a great degree of 

difficulty experienced with the provider market; and some or a great degree of difficulty 

experienced in obtaining quality data on provider performance. A moderating variable that 

captures state ITA policy was also included in the model. Demographics, program participation, 

WIA services, preprogram wages, and state economic conditions were used as control variables. 

Table B.1 in the Appendix presents the results of the first set of results for understanding 

the effect of ETP state policies on the wages of adults and dislocated workers in the quarter after 

exit. For dislocated workers, being in a state with a Federal ETP waiver raises the earnings in 

the first quarter after exit by $297. However, being in a state with no minimum criteria for 

subsequent eligibility decreases these first quarter earnings for dislocated workers by $506. For 

these two policies, the findings are similar for adult workers’ first quarter earnings but are of a 

greater magnitude. These findings indicate that having a Federal waiver is associated with 

higher first-quarter earnings but having no standards by which to measure the quality of training 

providers could be detrimental to earnings in the first quarter. It also appears that adults may see 

negative earnings from two state ETP policies—local ETP flexibility and allowing non-approved 

providers on the ETP list—which do not influence the post-program earnings of dislocated 

workers. Adults see a $623 decrease in first quarter earnings in states that permitted local ETP 

flexibility and see a $93 decrease in states which allowed non-approved providers on the ETP 

list. These findings contradict of the hypothesis that local flexibility in using higher minimum 

performance levels improves ITA participant outcomes. In addition, the inclusion of non26 
approved providers on the list was hypothesized to strengthen the provider market but it is 

possible that lower-quality providers are being selected by ITA participants from the ETP list. 

The moderating variables of interest also show statistically significant results for first 

quarter earnings. For both adults and dislocated workers, being in a state in which less than half 

of its ETPs are two- or four-year colleges or universities is associated with a $526 decrease in 

first quarter earnings for dislocated workers and a $294 decrease for adult workers. This finding 

is expected as having fewer colleges available to ITA participants may lead to the selection of 

lower-quality providers. Being in a state that has a cap on its ITA amount or limited ITA 

duration decreases the first quarter earnings of dislocated workers by $812 and by $324 for 

adults. As hypothesized, caps on ITAs and limits to training duration may deter ITA recipients 

from completing their training program because of the cost or length of the program. Some 

differences in the effects of the moderating variables for adults and dislocated workers exist. 

Being in a state with performance data difficulties shows a $156 decrease in first quarter 

earnings for adults. Challenges with data quality may lead to a lack of information for ITA 

participants on the providers they are selecting. The analysis also indicates that being in a state 

with provider difficulties is associated with a $251 increase in first quarter earnings for 

dislocated workers. This result is surprising as it is an indicator of challenges to the provider 

market. 

The effects of state ETP policies on average post-program wages are presented for the 

second and third quarters after exit for adults and dislocated workers. As shown in Table B.2 in 

the Appendix, the positive association with being in a state with a waiver for adults ($2,472) and 

dislocated workers ($834) and the negative association with being in a state with no minimum 

eligibility criteria for adults ($895) and dislocated workers ($635) are consistent with the 

findings for first quarter earnings. Adults in states with local ETP flexibility see a $1,357 

decrease in earnings and being in a state with non-approved ETP see a $694 decrease in average 

quarterly post-program earnings. 

The moderating variables show a somewhat similar pattern of the effects on second and 

third quarter earnings to the effects on first quarter earnings. Being in a state with less than half 

of its ETPs as two- and four-year colleges and universities indicates a decrease in second and 

third quarter earnings for both adults ($186) and dislocated workers ($706) but the magnitude of 

the association for adults is not as strong as it is for adult first quarter earnings. Dislocated 

27 
worker ITA participants in states that experience provider difficulties see a $423 increase in 

second and third quarter earnings. The estimates for performance data difficulties show some 

instability across earnings time periods as the second and third quarter earnings estimate 

becomes positive and significant for dislocated workers and nonsignificant for adults, compared 

to the results for first quarter earnings. This may indicate that this variable is a poor influential 

factor in the conceptual model or that the limitations of the analysis may be causing instability in 

the estimates. The estimate of second and third quarter earnings for adult exiters who are in a 

state with a cap or limited duration on ITAs also changes to nonsignificant from the first quarter 

earnings estimates. 

The third outcome variable of interest is entered employment (or employed in the first 

quarter after exiting WIA) and the results of the analysis are provided in Table B.3 in the 

Appendix. For adults and dislocated workers, there is no statistically significant change in 

employment associated with being in a state with a waiver, local ETP flexibility, no minimum 

eligibility criteria, and reciprocal agreement. However, both adults and dislocated workers see 

an increase in the odds of being employment (41 percent and 32 percent, respectively) if they are 

in a state where non-approved providers are allowed on the ETP list. 

The estimates of the moderating variables on employment in the first quarter after exit are 

different for adults and dislocated workers. As with wages in the first quarter after exit, there is a 

negative association for dislocated workers (28 percent) with being in a state with less than 50 

percent of its ETPs as colleges on employment in the first quarter after exit. Dislocated workers 

also see the odds of employment increase by 39 percent when the state where they received WIA 

services has experienced any difficulties with its provider market. This is consistent with the 

findings for wage at placement but is contradictory to the original hypothesis. Being in a state 

with performance data difficulties decreases the odds of employment for adults by 29 percent, an 

expected and consistent finding. There were no statistically significant changes in employment 

in the first quarter after exit for adults and dislocated workers associated with the state having an 

ITA cap on duration limit. 

The magnitude of the association with state ETP policies on employment becomes 

slightly stronger for employment in the second and third quarters after exit for both adults and 

dislocated workers. As shown in Table B.4 in the Appendix, being in a state with non-approved 

training providers on the ETP list increases the odds of retaining employment by 83 percent for 

28 
dislocated workers and 22 percent for adults. A state with local ETP flexibility also increases the 

odds of retaining employment for dislocated workers by 84 percent. Finally, the odds of being 

employed in these two quarters increases for adults by 30 percent when being served in a state 

with an ETP waiver. Neither adults nor dislocated workers see any change in the odds of 

employment retention when in a state with a reciprocal agreement or with no minimum criteria 

for subsequent eligibility. 

The estimates of the moderating variables change for the second and third quarter of 

employment from the first quarter. The only moderating factor showing some association with 

increased employment retention is having an ITA cap or duration limit. The odds of retaining 

employment into the second and third quarters increase by 49 percent for dislocated workers and 

by 31 percent for adults. 

Credential attainment is the final outcome of interest for this analysis and the results are 

presented in Table B.5 in the Appendix. Consistent with the positive findings for earnings, the 

analysis shows that being in a state with a waiver increases the odds of earning a credential by 87 

percent for adults but shows no change in odds for dislocated workers. Adults in a state with a 

reciprocal agreement see their odds of attaining a credential decrease by 30 percent. No state 

ETP policies show any change in the likelihood of credential attainment for dislocated workers. 

As is consistent with the direction of the findings for other earnings variables, being in a 

state that has less than half of its ETPs as colleges decreases the odds of credential attainment for 

adults (62 percent) and dislocated workers (41 percent). In addition, having a state ITA cap or 

duration limitation increases the odds of credential attainment increases the odds for adults by 54 

percent. As discussed, this may be because training participants may choose shorter-term training 

because resources and time for ITA-funded training may not be sufficient for longer-term 

training such as a community college or apprenticeship program. 

Overall, the findings from the multivariate analyses provide mixed and limited evidence 

of how state ETP policies may be associated with ITA participant outcomes as explained in the 

conceptual framework. The analysis shows a positive association of being in a state with a 

Federal waiver with the earnings and credential variables; having this waiver may allow states to 

have more providers on the ETP list and do so in a way that they use they own screening criteria 

to ensure the quality of training providers for ITA participants. The influence of no minimum 

criteria on employment and training outcomes is inconsistent but is negative, as hypothesized, 

29 
for earnings. The two variables identifying state efforts to expand the provider market – the 

reciprocal agreements and policy to allow non-approved providers on the state ETP list – also 

show mixed findings for employment and training outcomes. This could be because it is not 

possible to account for the quality and use of providers across state borders and non-approved 

providers. 

The moderating factors of interests also had mixed results. For the most part, being in a 

state that has less than half of its ETPs as two- and four-year colleges and universities indicates 

negative employment and training outcomes of ITA participants. In addition, having a cap on 

the ITA amount or a limit on the duration of training has inconsistent findings – negative for 

earnings outcomes and positive for employment and credential outcomes. However, the 

evidence was more mixed or nonsignificant on the difficulties states experienced in dealing with 

issues of providers. It could be that states, while they experienced difficulty in these areas, were 

able to overcome these challenges successfully and thus did not affect ITA participants’ 

outcomes. 

It is possible that the inconsistencies in the findings are due to the limitations presented 

earlier. The state ETP policy variables may too diffuse in their construction (because of 

temporal issues and collapsing of similar variables) to capture the effects of the policies on ITA 

participant outcomes. The multicollinearity and omitted variable bias are major challenges to the 

analysis. Finally, the attempt to capture some of the state-level variance in the models may not 

have been sufficient. 

While this first attempt at analyzing the association of state ETP policies with ITA 

participant outcomes is limited, it provides a basis for future research to examine what state 

policies and factors may affect the quality of eligible training providers and their relevancy to 

ITA participants’ outcomes. A more qualitative examination of how states design and 

implement their ETP systems under WIA and how training providers and ITA participants 

respond to these systems would help refine the conceptual framework and provide a better 

understanding of state ETP policies and promising practices. Case studies of the different 

approaches states use to implement their ETP systems may shed light on what standards states 

are using to determine the quality of training providers for their ETP lists. This research could 

then inform the development of better, possibly annually collected, measures of the state ETP 

policies (and potentially all state WIA policies) to conduct more rigorous analyses that provides 

30 
stronger evidence of the effects of these policies on ITA participants’ outcomes. A database of 

state Temporary Assistance for Needy Families policies, called the Welfare Rules Database, has 

been operated since 1996 and a similar database could be constructed for WIA. 

The importance of the local WIBs and One-Stop Career Centers should also be examined 

in more depth. While several studies have studied how states and local WIBs have implemented 

ITAs and the ETP system that supports customer choice, it is unclear the relationship between 

the state and local WIBs in designing this system and how much local flexibility is permitted. In 

addition better understanding of how ITA participants are using the ETP list and performance 

data may help states and local WIBs improve how One-Stop staff work with ITA participants to 

use the ETP list and performance data, and potentially offer supplemental information on 

training programs such as reviews by WIA completers and informational interviews with 

graduates who are working in the field to help them make training decisions. 

Policy Implications

The research on state ETP policies conducted for this study contributes new information 

for policymakers to better understand the role of training provider quality in publicly-funded job 

training efforts. The conceptual framework shows how states’ efforts to design a training system 

that incorporates customer choice but must ensure that participants find and keep employment 

have to consider the quality of the providers in the system. Under WIA, the quality of the 

training providers is measured by aggregate participant outcomes for employment, credential 

attainment, and earnings. The conceptual framework shows that the inputs of training such as 

evidence-based instructional models, highly qualified instructors, and supportive services can 

also matter in looking at the quality of training providers. While the notion of evaluating 

program inputs is counter to the results-oriented management movement in government, overall 

program performance should consider evaluating both inputs and outcomes to support the 

management of successful program. Having this level and type of performance data would help 

the Department of Labor identify and provide promising practices for states to test and replicate. 

Greater knowledge of the states’ ETP policies has also been gained through this research. 

Of the 24 states that responded to the survey, many do not use the minimum criteria for 

subsequent eligibility as provided in WIA. Many states also have pursued a Federal waiver to 

extend the initial eligibility period of ETPs. In addition, states have shared anecdotes of the 

challenges for providers and states to collect performance data needed to maintain eligibility on 

31 
the list. As this research shows that the collection and use of ETP performance criteria and data 

may not be fully implemented as intended in the WIA legislation, future research could establish 

how states have designed their ETP systems and interacted with local WIBs in establishing substate ETP systems to approved and maintain providers on the ETP list. They may be able to 

offer lessons for WIA reauthorization in how to amend the current regulations to better develop 

less burdensome state ETP systems but maintain a focus on ensuring the quality of the training 

providers. 

This research also sought to provide insights into which ETP policies states have 

developed may have an effect on ITA participants’ outcomes than states that do not. Because of 

the limitations of the study, answering this question was challenging. However, there are some 

indications of promising state ETP policies and efforts that should be explored. The importance 

of having higher education represented on the ETP lists is supported by the analyses conducted. 

The findings show that states that have a significant presence of two- and four-year colleges and 

universities on their ETP lists is often associated with improvements the employment and 

training outcomes of their ITA participants. With the current policy discussion about the use of 

community colleges as a major training provider, it is important to consider how higher 

education can connect to the workforce investment system and respond to its participants’ needs. 

These needs may include student retention incentives and supportive services such as child care 

and transportation to assist ITA participants succeed in their pursuit of better skills and 

education. The promising findings from MDRC’s Opening Doors study, which tested retention 

incentives and services (Scrivener and Weiss 2009), and the ongoing evaluation of the 

Community-Based Job Training Grants, a Department of Labor demonstration program, could 

provide some ideas to incorporate into future community college-workforce investment system 

initiatives. 

Any efforts to improve the states’ ETP systems and test new approaches would take 

considerable federal government resources to implement. It would also be challenging for 

overburdened state and local staff, especially as they respond to the economic crisis, to focus on 

such activities. However, with more WIA participants being placed into training more than ever 

and the potential reauthorization of WIA in the near future, the time may be ripe to examine 

states’ ETP systems and how states are ensuring the quality of the training providers within these 

systems. 

32 
 

References

Barnow, Burt S. 2000. “Vouchers for Federal Targeted Training Programs.” Edited by C. 

Eugene Steuerle, Van D. Ooms, and George Peterson in Vouchers and the Provision of 

Public Services. Washington, DC: Brookings Institution Press, 224-250. 

Barnow, Burt S., and Christopher T. King. 2005. The Workforce Investment Act in Eight States. 

Prepared for the U.S. Department of Labor, Employment and Training Administration. 

Albany, NY: The Rockefeller Institute of Government, February. 

D’Amico, Ron, and Jeffrey Salzman. 2004. An Evaluation of the Individual Training 

Account/Eligible Training Provider Demonstration. Prepared for the U.S. Department of 

Labor, Employment and Training Administration. Oakland, CA: Social Policy Research 

Associates, December. 

Decker, Paul, and Irma Perez-Johnson. 2004. “ITAs, ETP Lists, and Consumer Report 

Systems.” Edited by Christopher O’Leary, Robert A. Straits, and Stephen A. Wandner in 

Job Training Policy in the United States. Kalamazoo, MI: W.E. Upjohn Institute of 

Employment Research. 

Dickenson, Katherine, and Richard W. West. 1983. “Impacts of Counseling and Education 

Subsidy Programs.” Final Report of the Seattle-Denver Income Maintenance Experiment, 

Vol. 1. Menlo Park, CA: SRI International. 

Gore, Al. 1995. Common Sense Government. Works Better and Costs Less. Third Report of the 

National Performance Review. Washington, DC: U.S. Government Printing Office, 

September 7. 

Hipp, Lena, and Mildred E. Warner. 2008. “Market Forces for the Unemployed? Training 

Vouchers in Germany and the USA.” Social Policy & Administration 42:1, 77-101. 

King, Christopher T. 1999. “Federalism and Workforce Policy Reform.” Publius 29:2, 53-71. 

Nightingale, Demetra S., Lauren Eyster, John Trutko, Carolyn O’Brien, and Kate Chambers. 

2008. Implementation Analysis of the High Growth Job Training Initiative Programs. 

Washington, DC: The Urban Institute, June. 

Osborne, David, and Ted Gaebler. 1992. Reinventing Government: How the Entrepreneurial 

Spirit Is Transforming the Public Sector. Reading, MA: Addison-Wesley. 

Public Policy Associates. 1998. Career Management Account Project Evaluation: Final 

Report. Lansing, MI, December. 

Resnik, Judith. 1982. “Managerial Judging.” Harvard Law Review 96, 374. 

33 
Salamon, Lester. 2002. Tools for Government Action. New York: Oxford University Press. 

Scrivener, Susan, and Michael J. Weiss, with Jedediah J. Teres. 2009. More Guidance, Better 

Results? Three-Year Effects of an Enhanced Student Services Program at Two Community 

Colleges. New York: MDRC, August. 

http://www.mdrc.org/publications/524/overview.html. 

Social Policy Research Associates. 2004. The Workforce Investment Act after Five Years: 

Results from the National Evaluation of the Implementation of WIA. Prepared for the U.S. 

Department of Labor Employment and Training Administration. Oakland, CA, June. 

_____. 2006. “Workforce Investment Act Standardized Records Database, Program Year 

2005.” Prepared for the U.S. Department of Labor, Employment and Training 

Administration. Oakland, CA. 

_____. 2007. “Workforce Investment Act Standardized Records Database, Program Year 

2006.” Prepared for the U.S. Department of Labor, Employment and Training 

Administration. Oakland, CA. 

_____. 2008. “Workforce Investment Act Standardized Records Database, Program Year 

2007.” Prepared for the U.S. Department of Labor, Employment and Training 

Administration. Oakland, CA. 

Steuerle, C. Eugene, and Eric C. Twombly. 2002. “Vouchers.” Edited by Lester Salamon in 

Tools for Government Action. New York: Oxford University Press, 445-465. 

U.S. Department of the Commerce, U.S. Census Bureau. 2010. Current Population Survey, 

Primary Families by Poverty Status, by State, 2005-2008. Washington, DC. 

http://www.census.gov/hhes/www/cpstc/cps_table_creator.html. 

U.S. Department of Commerce, Bureau of Economic Analysis. 2009. News Release: GDP by 

State, Table 1, Real GDP by State, 2004-2008. Washington, DC, June 2. 

U.S. Department of Health and Human Services, Assistant Secretary of Planning and Evaluation. 

2010. “The 2009 HHS Poverty Guidelines.” Washington, DC. 

http://aspe.hhs.gov/poverty/09poverty.shtml. 

U.S. Department of Labor, Bureau of Labor Statistics. 2010. Local Area Unemployment 

Statistics, Annual Unemployment Rates, by State. Washington, DC. http://bls.gov/lau/. 

U.S. Department of Labor, Employment and Training Administration. 1998. Workforce 

Investment Act of 1998. Washington, DC, September. 

_____. “Workforce Investment Act: Final Rule,” Federal Register 65:156 (11 August 2000). 

34 
_____. 2006. “Common Measures Policy for the Employment and Training Administration’s 

(ETA) Performance Accountability System and Related Performance Issues.” Training and 

Employment Guidance Letter 17-05. Washington, DC, February 17. 

http://wdr.doleta.gov/directives/corr_doc.cfm?DOCN=2195. 

_____. 2010. Workforce Investment Act (WIA) Waiver Summary Report Program Year 2009 

(July 1, 2009-June 30, 2010). Washington, DC, June 3. 

http://www.doleta.gov/waivers/pdf/WIA-summary-waiver-report-6-01-10.pdf. 

U.S. Department of Labor WIA Readiness Review. 2002. Draft Technical Assistance Guide: 

Addressing Subsequent Eligibility Implementation Issues. Prepared by the Eligible Training 

Provider List Work Group. Washington, DC, March 25. 

U.S. Government Accountability Office. 2005. Workforce Investment Act: Substantial Funds 

Are Used for Training, but Little Is Known Nationally about Training Outcomes. Report to 

Congressional Requesters. Washington, DC, June. 

Workforce Investment Act of 1998. Public Law 105-220, U.S. Statutes at Large 112 (1998): 

936-1247. 

35 
Appendix                A – State Eligible Training Provider Survey
 

36 
37 
38 
39 
40 
41 
42 
43 
44 
45 
46 
             

   

                       

     

   

     

     

   

         

       

       

   

     

           

                 

   

       

     

   

       

   

       

     

   

     

   

Appendix B – Tables on Multivariate Results

Table B.1. Dependent Variable Is Wage at Placement (Random Effects) 

Wages in 1st Quarter after Exit

Dislocated Workers Adults

Coefficient (Std. Er.) Coefficient (Std. Er.)

State ETP Policies

ETP Waiver

296.5184***

(93.2657)

961.8756***

(69.9830)

Local ETP Flexibility

15.32332

(77.7260)

‐623.3708***

(62.5893)

No Minimum Criteria

‐506.2482***

(83.3760)

‐629.9391***

(67.0033)

Reciprocal Agreement

‐37.5340

(61.2808)

70.92884

(44.25134)

Non‐Approved Providers on ETP List

91.06931

(62.5639)

‐93.48729**

(45.5373)

Moderating Factors

Less than 50% Colleges

‐525.8137***

(58.9208)

‐294.1053***

(44.7627)

Provider Difficulties

250.7538***

(74.4034)

71.08679

(55.3828)

Performance Data Difficulties

39.3094

(67.0033)

‐156.3243***

(48.2135)

State ITA Cap or Duration Limit

‐812.4335***

(96.2491)

‐323.9131***

(74.3258)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1227.886***

(35.01888)

419.1516***

(32.2434)

Hispanic

‐325.968***

(57.8690)

‐285.3029***

(45.3916)

Black

‐727.6263***

(42.0410)

‐965.6244***

(32.2692)

Age 45 and Older

‐460.4658***

(35.1199)

‐454.2002***

(40.0297)

Disabled

‐575.6758***

(96.0549)

‐814.2557***

(69.5075)

Limited English Speaker

‐607.8044***

(78.3176)

‐272.8541***

(78.4771)

Single Parent

‐60.3922

(46.3104)

‐118.377***

(32.6958)

Less than High School

‐2765.303 ***

(72.8470)

‐2590.482***

(67.6988)

High School/GED

‐2280.776***

(55.0550)

‐2067.17***

(56.6032)

Some College/No Degree

‐1460.937***

(60.1817)

‐855.0988***

(60.7986)

Veteran

10.2492

(60.9838)

175.6869***

(60.5430)

Displaced Homemaker

‐525.6269***

(126.8887)

n.a.

Public Assistance Recipient

‐510.3727**

(207.2375)

‐676.4217***

(36.8733)

UI Claimant

387.4343***

(42.2135)

252.3147***

(40.2462)

47 
       

   

     

   

     

   

           

   

               

               

                   

             

               

             

             

             

               

UI Exhaustee

199.9681***

(71.2133)

261.5851***

(69.9007)

Rapid Response

224.2406***

(76.0052)

n.a.

Trade Adjustment Assistance

‐626.8225***

(48.2242)

n.a.

Supportive Services

‐157.247***

(38.1581)

69.5116**

(69.9007)

Needs Related Payments

‐145.2554

(114.7422)

110.4907

(80.0742)

Pell Grant

102.7376*

(58.4758)

416.32***

(38.9580)

Employed in Quarter Prior to Entry

854.4655***

(38.30149)

1074.606***

(28.1801)

State Unemployment Rate

‐220.2359***

(26.6321)

‐139.845***

(17.2806)

Constant

8402.926***

(229.0521)

6822.97***

(175.2454)

N = 76769; groups=20 N = 87038; groups=21

Wald chi2 = 6538.29 Wald Chi2 = 7458.10

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

R‐squared = 0.0785 R‐squared = 0.0789

*** statistically significant at the .01 level

Within state variance =

0.0678

Within state variance =

0.0718

** statistically significant at the .05 level

* statistically significant at the .10 level

Between state variance =

0.7198

Between state variance =

0.5001

48 
   

                             

     

   

     

     

   

         

   

       

   

     

           

               

       

     

   

       

   

     

     

   

   

   

Table B.2. Dependent Variable Is Average Wages (Random Effects) 

Average Wages at 2nd and 3rd Quarters after Exit

Dislocated Workers Adults

Coefficient (Std. Er.) Coefficient (Std. Er.)

State ETP Policies

ETP Waiver

834.1603***

(195.1461)

2472.078***

(165.2124)

Local ETP Flexibility

32.83617

(163.7634)

‐1356.786***

(147.0612)

No Minimum Criteria

‐634.9872***

(174.4487)

‐895.4289***

(157.5376)

Reciprocal Agreement

11.9598

(129.3904)

124.2513

(103.5055)

Non‐Approved Providers on ETP List

‐66.8821

(134.2795)

‐694.0967***

(109.8091)

Moderating Factors

Less than 50% Colleges

‐705.6964***

(126.7546)

‐186.1961*

(107.3687)

Provider Difficulties

423.4538***

(157.8244)

‐3.8601

(132.6738)

Performance Data Difficulties

318.5417**

(141.3519)

‐155.1761

(113.9033)

State ITA Cap or Duration Limit

‐988.3405***

(203.6050)

63.40015

(176.3354)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

2595.662***

(74.0409)

832.0147***

(75.7095)

Hispanic

‐607.3506***

(123.5732)

‐681.7535***

(106.6948)

Black

‐1445.316***

(88.9030)

‐1921.733***

(75.6244)

Age 45 and Older

‐1144.567***

(74.0903)

‐1072.619***

(94.1694)

Disabled

‐1251.139***

(201.9849)

‐1610.379***

(162.7435)

Limited English Speaker

‐1366.966***

(166.9149)

‐597.2806***

(181.7183)

Single Parent

‐35.00702

(98.1519)

‐228.0564***

(76.5432)

Less than High School

‐5894.185***

(155.1219)

‐5520.787***

(160.0845)

High School/GED

‐4656.926***

(116.6158)

‐4364.031***

(133.9699)

Some College/No Degree

‐3013.139***

(127.6603)

‐1842.07***

(144.1763)

Veteran

68.3890

(128.0103)

503.5552***

(141.2892)

Displaced Homemaker

‐1093.566***

(269.8455)

n.a.

Public Assistance Recipient

‐1039.214**

(451.5161)

‐1455.665***

(86.0368)

UI Claimant

715.8291***

(89.9724)

628.7289***

(93.218)

UI Exhaustee

232.8817

(151.7431)

682.6655***

(164.8945)

Rapid Response

634.7572***

(161.2314)

n.a.

49 
     

   

     

   

           

   

               

              

                   

             

               

             

             

             

               

Trade Adjustment Assistance

‐966.6388***

(101.0130)

n.a.

Supportive Services

‐340.5629***

(80.6350)

156.5555**

(71.4604)

Needs Related Payments

‐402.6591*

(239.5110)

352.5914*

(183.3252)

Pell Grant

99.7124

(124.8827)

1017.728***

(91.5865)

Employed in Quarter Prior to Entry

1666.538***

(80.9645)

2125.872***

(65.9615)

State Unemployment Rate

‐363.5331***

(57.7559)

‐117.9064***

(41.0121)

Constant

15786.83***

(489.6641)

11880.90***

(411.299)

N = 65763; groups=20 N = 72767; groups=21

Wald chi2 = 6037.62 Wald chi2 = 5799.46

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

R‐squared = 0.0841 R‐squared = 0.0738

*** statistically significant at the .01 level

Within state variance =

0.0748

Within state variance =

0.0674

** statistically significant at the .05 level

* statistically significant at the .10 level

Between state variance =

0.6941

Between state variance =

0.5041

50 
   

                           

     

   

     

     

   

         

   

       

   

     

           

                 

       

     

   

       

   

     

     

   

   

Table B.3. Dependent Variable Is Entered Employment (Random Effects) 

Employed in 1st Quarter after Exit

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

1.1127

(0.2028)

1.2711

(0.1896)

Local ETP Flexibility

1.0178

(0.0198)

0.6887

(0.1896)

No Minimum Criteria

1.1422

(0.1833)

1.3317

(0.3005)

Reciprocal Agreement

0.8832

(0.1134)

0.7644

(0.1331)

Non‐Approved Providers on ETP List

1.3245**

(0.1830)

1.4072*

(0.2643)

Moderating Factors

Less than 50% Colleges

0.7200**

(0.1020)

1.1011

(0.2188)

Provider Difficulties

1.3873**

(0.2166)

1.3260

(0.2914)

Performance Data Difficulties

0.8426

(0.1117)

0.7134*

(0.1330)

State ITA Cap and/or Duration Limit

1.1909

(0.2075)

1.3302

(0.3243)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1.0668***

(0.0248)

0.9116***

(0.0209)

Hispanic

1.1895***

(0.0469)

1.0348

(0.0339)

Black

0.9520*

(0.0259)

0.7975***

(0.0183)

Age 45 and Older

0.7037***

(0.0159)

0.7627***

(0.0208)

Disabled

0.7039***

(0.0454)

0.6250***

(0.0272)

Limited English Speaker

0.9309

(0.0463)

0.9891

(0.0540)

Single Parent

1.0540*

(0.0324)

1.0709***

(0.0255)

Less than High School

0.7580***

(0.0361)

0.6261***

(0.0311)

High School/GED

0.8993***

(0.0341)

0.7437***

(0.0325)

Some College/No Degree

0.9600

(0.0394)

0.9243*

(0.0433)

Veteran

0.9295*

(0.0363)

1.005

(0.0423)

Displaced Homemaker

0.7796***

(0.0587)

n.a.

Public Assistance Recipient

0.8961

(0.1132)

0.7855***

(0.0199)

UI Claimant

1.2644***

(0.0347)

1.1620***

(0.0356)

UI Exhaustee

1.2127***

(0.0535)

1.3140***

(0.0660)

51 
   

     

   

     

   

           

   

               

               

                   

               

             

             

             

               

Rapid Response

1.0853

(0.0571)

n.a.

Trade Adjustment Assistance

0.6997***

(0.0211)

n.a.

Supportive Services

1.1410***

(0.0302)

1.0912***

(0.0248)

Needs Related Payments

0.8404**

(0.0618)

1.015

(0.0589)

Pell Grant

0.9681

(0.0361)

1.0602**

(0.0309)

Employed in Quarter Prior to Entry

1.8245***

(0.0430)

2.4205***

(0.0478)

State Unemployment Rate

1.0759***

(0.0280)

1.4180***

(0.0242)

N = 76769; groups=20 N = 87038; groups=21

Wald chi2 = 1408.83 Wald Chi2 = 3268.66

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

*** statistically significant at the .01 level

Within state variance =

0.0621

Within state variance =

0.0913

** statistically significant at the .05 level

* statistically significant at the .10 level

Between state variance =

0.9379

Between state variance =

0.9087

52 
                   

               

     

   

     

     

   

         

   

       

   

     

           

                 

       

     

   

       

   

     

     

   

   

   

Table B.4. Dependent Variable Is Employment Retention (Random Effects) 

Employed in Both 2nd and 3rd Quarters after

Exit

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

1.1466

(0.2836)

1.3023*

(0.2089)

Local ETP Flexibility

1.8426**

(0.4907)

0.1.1619

(0.1953)

No Minimum Criteria

0.9423

(0.2048)

1.0383

(0.1473)

Reciprocal Agreement

0.9021

(0.1585)

1.0767

(0.1142)

Non‐Approved Providers on ETP List

1.8307***

(0.3522)

1.2212*

(0.1430)

Moderating Factors

Less than 50% Colleges

0.8243

(0.1965)

1.1130

(0.1352)

Provider Difficulties

0.9190

(0.2186)

0.9996

(0.1352)

Performance Data Difficulties

1.2163

(0.2186)

1.0231

(0.1159)

State ITA Cap and/or Duration Limit

1.4938*

(0.3543)

1.3094*

(0.2073)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

0.8508***

(0.0246)

0.7138***

(0.0187)

Hispanic

1.0390

(0.0471)

1.0134

(0.0381)

Black

0.8483***

(0.0291)

0.7937***

(0.0212)

Age 45 and Older

0.8484***

(0.0245)

0.8842***

(0.0286)

Disabled

0.8172***

(0.0594)

0.8089***

(0.0449)

Limited English Speaker

0.8654**

(0.0504)

0.9393

(0.0554)

Single Parent

1.0068

(0.0389)

1.0029

(0.0279)

Less than High School

0.7561***

(0.0426)

0.5651***

(0.0322)

High School/GED

1.0281

(0.0457)

0.7361***

(0.0368)

Some College/No Degree

1.0967*

(0.0538)

0.9466

(0.0514)

Veteran

0.9132*

(0.0440)

0.9468

(0.0448)

Displaced Homemaker

0.8501

(0.0847)

n.a.

Public Assistance Recipient

0.8093

(0.1334)

0.7987***

(0.0245)

UI Claimant

1.1624**

(0.04005)

1.2196***

(0.0414)

UI Exhaustee

1.0866

(0.0596)

1.2052***

(0.0692)

Rapid Response

1.3200***

(0.0915)

n.a.

53 
     

   

     

   

           

   

               

               

                   

               

             

             

             

               

Trade Adjustment Assistance

1.3065***

(0.0603)

n.a.

Supportive Services

0.9802

(0.0303)

1.0569**

(0.0270)

Needs Related Payments

1.0046

(0.0984)

1.1404*

(0.0474466)

Pell Grant

1.1470***

(0.0596)

1.2456***

(0.0449)

Employed in Quarter Prior to Entry

1.4809***

(0.0441)

1.5885***

(0.0365)

State Unemployment Rate

1.1385***

(0.0378)

1.1135***

(0.0307)

N = 56914; groups=20 N = 61897; groups=21

Wald chi2 = 535.56 Wald chi2 = 1236.99

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

*** statistically significant at the .01 level

Within state variance =

0.0825

Within state variance =

0.0556

** statistically significant at the .05 level

* statistically significant at the .10 level

Between state variance =

0.9175

Between state variance =

0.9444

54 
   

                   

     

   

     

     

   

         

   

       

   

     

           

               

       

     

   

       

   

     

     

   

   

   

Table B.5. Dependent Variable Is Credential Attainment (Random Effects) 

Credential Attainment

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

1.5771

(0.5044)

1.8669***

(0.4079)

Local ETP Flexibility

1.3703

(0.4959)

0.8007

(0.1916)

No Minimum Criteria

0.6957

(0.1826)

0.8532

(0.1513)

Reciprocal Agreement

1.0771

(0.2432)

0.6974**

(0.0997)

Non‐Approved Providers on ETP List

0.7572

(0.1832)

1.2297

(0.1917)

Moderating Factors

Less than 50% Colleges

0.3767***

(0.0905)

0.5876***

(0.0912)

Provider Difficulties

0.9250

(0.2394)

1.2009

(0.2059)

Performance Data Difficulties

1.3918

(0.3327)

0.9814

(0.1548)

State ITA Cap and/or Duration Limit

1.1347

(0.3232)

1.5386**

(0.2963)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1.0096

(0.0214)

1.0724***

(0.0245)

Hispanic

1.1215**

(0.0538)

0.8646***

(0.0363)

Black

0.7938***

(0.0189)

0.7355***

(0.0160)

Age 45 and Older

1.0025

(0.0215)

1.0831***

(0.0315)

Disabled

0.7867***

(0.0434)

0.8046***

(0.0358)

Limited English Speaker

0.8565***

(0.0470)

1.1738***

(0.0730)

Single Parent

0.8952***

(0.0248)

0.9181***

(0.0209)

Less than High School

0.6632***

(0.0294)

0.5247***

(0.0270)

High School/GED

1.0163

(0.0361)

0.7351***

(0.0334)

Some College/No Degree

0.9864

(0.0378)

0.9242

(0.0447)

Veteran

1.1443***

(0.0424)

1.0136

(0.0427)

Displaced Homemaker

1.1178

(0.0865)

n.a.

Public Assistance Recipient

1.342

(0.3049)

0.8195***

(0.0229)

UI Claimant

0.9476*

(0.0265)

0.9348**

(0.0281)

UI Exhaustee

0.9812

(0.0403)

0.9140**

(0.0414)

Rapid Response

1.0775

(0.0601)

n.a.

55 
     

   

     

   

           

   

              

               

                   

               

             

             

             

               

Trade Adjustment Assistance

0.6848***

(0.0200)

n.a.

Supportive Services

1.4032***

(0.0371)

1.1595***

(0.0272)

Needs Related Payments

0.9902

(0.0713)

1.1373*

(0.0750)

Pell Grant

0.7705***

(0.0247)

0.8519***

(0.0219)

Employed in Quarter Prior to Entry

1.1579***

(0.0257)

1.0604***

(0.0209)

State Unemployment Rate

0.8397***

(0.0222)

1.4148***

(0.0250)

N = 61849; groups=19 N = 66887; groups=20

Wald chi2 = 864.64 Wald chi2 = 1233.48

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

*** statistically significant at the .01 level

Within state variance =

0.1023

Within state variance =

0.0731

** statistically significant at the .05 level

* statistically significant at the .10 level

Between state variance =

0.8977

Between state variance =

0.9269

56 
   

                       

     

   

     

     

   

         

       

       

   

     

           

                 

   

   

       

     

   

       

   

       

     

   

     

       

       

   

Table B.6. Dependent Variable Is Wage at Placement (OLS) 

Wages in 1st Quarter after Exit

Dislocated Workers Adults

Coefficient (Std. Er.) Coefficient (Std. Er.)

State ETP Policies

ETP Waiver

296.5184***

(93.2657)

961.8756***

(69.9830)

Local ETP Flexibility

15.32332

(77.7260)

‐623.3708***

(62.5893)

No Minimum Criteria

‐506.2482***

(83.3760)

‐629.9391***

(67.0033)

Reciprocal Agreement

‐37.53401

(61.2808)

70.9288

(44.2513)

Non‐Approved Providers on ETP List

91.06931

(62.5639)

‐93.4873**

(45.5373)

Moderating Factors

Less than 50% Colleges

‐525.8137***

(58.9208)

‐294.1053***

(44.7627)

Provider Difficulties

250.7538***

(74.4034)

71.0868

(55.38275)

Performance Data Difficulties

39.30938

(67.0033)

‐156.3243***

(48.2135)

State ITA Cap or Duration Limit

‐812.4335***

(96.2491)

‐323.9131***

(74.3258)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1227.886***

(35.0189)

419.1516***

(32.2434)

Hispanic

‐325.968 ***

(57.8690)

‐285.3029***

(45.3916)

Black

‐727.6263***

(42.0410)

‐965.6244***

(32.2692)

Age 45 and Older

‐460.4658***

(35.1199)

‐454.2002***

(40.0297)

Disabled

‐575.6758***

(96.0549)

‐814.2557***

(69.5075)

Limited English Speaker

‐607.8044***

(78.3176)

‐272.8541***

(78.4771)

Single Parent

‐60.3922

(46.3104)

‐118.377***

(32.6958)

Less than High School

‐2765.303 ***

(72.8467)

‐2590.482***

(67.6988)

High School/GED

‐2280.776***

(55.0550)

‐2067.17***

(56.6032)

Some College/No Degree

‐1460.937***

(60.1817)

‐855.0988***

(60.7986)

Veteran

10.2492

(60.9838)

175.6869***

(60.5430)

Displaced Homemaker

‐525.6269***

(126.8887)

n.a.

Public Assistance Recipient

‐510.3727***

(207.2375)

‐676.4217***

(36.8733)

UI Claimant

387.4343***

(42.2135)

252.3147***

(40.2462)

UI Exhaustee

199.9681***

(71.2133)

261.5851***

(69.9007)

Rapid Response

224.2406***

(76.00512)

n.a.

57 
     

   

     

   

           

   

           

                   

                   

            

             

             

                             

Trade Adjustment Assistance

‐626.8225***

(48.2242)

n.a.

Supportive Services

‐157.247***

(38.1581)

69.5116**

(30.5937)

Needs Related Payments

‐145.2554

(114.7422)

110.4907

(80.0742)

Pell Grant

102.7376*

(58.4758)

416.32***

(38.9580)

Employed in Quarter Prior to Entry

854.4655***

(38.3015)

1074.606***

(28.1801)

State Unemployment Rate

‐220.2359***

(26.6321)

‐139.845***

(17.2806)

Constant

8402.926***

(229.0521)

6822.97***

(175.2454)

N = 76769 N = 87038

F( 31, 76737) = 210.91 F( 28, 87009) = 266.36

*** statistically significant at the .01 level

** statistically significant at the .05 level

* statistically significant at the .10 level

Prob > F = 0.0000 Prob > F = 0.0000

R‐squared = 0.0785 R‐squared = 0.0789

Adj R‐squared = 0.0781 Adj R‐squared = 0.0787

58 
   

                             

     

   

     

     

   

         

   

       

   

     

           

               

       

     

   

       

   

     

     

   

   

   

Table B.7. Dependent Variable Is Average Wages (OLS) 

Average Wages at 2nd and 3rd Quarters after Exit

Dislocated Workers Adults

Coefficient (Std. Er.) Coefficient (Std. Er.)

State ETP Policies

ETP Waiver

834.1603***

(195.1461)

2472.078***

(165.2124)

Local ETP Flexibility

32.8362

(163.7634)

‐1356.786***

(147.0612)

No Minimum Criteria

‐634.9872***

(174.4487)

‐895.4289***

(157.5376)

Reciprocal Agreement

11.9598

(129.3904)

124.2513

(103.5055)

Non‐Approved Providers on ETP List

‐66.8821

(134.2795)

‐694.0967***

(109.8091)

Moderating Factors

Less than 50% Colleges

‐705.6964***

(126.7546)

‐186.1961*

(107.3687)

Provider Difficulties

423.4538***

(157.8244)

‐3.8601

(132.6738)

Performance Data Difficulties

318.5417**

(141.3519)

‐155.1761

(113.9033)

State ITA Cap or Duration Limit

‐988.3405***

(203.6050)

63.4002

(176.3354)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

2595.662***

(74.0409)

832.0147***

(75.7095)

Hispanic

‐607.3506***

(123.5732)

‐681.7535***

(106.6948)

Black

‐1445.316***

(88.9030)

‐1921.733***

(75.6244)

Age 45 and Older

‐1144.567***

(74.0903)

‐1072.619***

(94.1694)

Disabled

‐1251.139***

(201.9849)

‐1610.379***

(162.7435)

Limited English Speaker

‐1366.966***

(166.9149)

‐597.2806***

(181.7183)

Single Parent

‐35.00702

(98.1519)

‐228.0564***

(76.5432)

Less than High School

‐5894.185***

(155.1219)

‐5520.787***

(160.0845)

High School/GED

‐4656.926***

(116.6158)

‐4364.031***

(133.9699)

Some College/No Degree

‐3013.139***

(127.6603)

‐1842.07***

(144.1763)

Veteran

68.38895

(128.0103)

503.5552***

(141.2892)

Displaced Homemaker

‐1093.566***

(269.8455)

n.a.

Public Assistance Recipient

‐1039.214**

(451.5161)

‐1455.665***

(86.0368)

UI Claimant

715.8291***

(89.9724)

628.7289***

(93.2180)

UI Exhaustee

232.8817

(151.7431)

682.6655***

(164.8945)

Rapid Response

634.7572***

(161.2314)

n.a.

59 
     

   

     

   

           

   

           

                   

                     

                          

             

                             

Trade Adjustment Assistance

‐966.6388***

(101.0130)

n.a.

Supportive Services

‐340.5629***

(80.6350)

156.5555**

(71.4604)

Needs Related Payments

‐402.6591*

(239.5110)

352.5914*

(183.3252)

Pell Grant

99.71238

(124.8827)

1017.728***

(91.5865)

Employed in Quarter Prior to Entry

1666.538***

(80.9645)

2125.872***

(65.9615)

State Unemployment Rate

‐363.5331***

(57.7559)

‐117.9064***

(41.0121)

Constant

15786.83***

(489.6641)

11880.9***

(411.2990)

N = 65763 N = 72767

F( 31, 65731) = 194.76 F( 28, 72738) = 207.12

*** statistically significant at the .01 level

** statistically significant at the .05 level

* statistically significant at the .10 level

Prob > F = 0.0000 Prob > F = 0.0000

R‐squared = 0.0841 R‐squared = 0.0738

Adj R‐squared = 0.0837 Adj R‐squared = 0.0735

60 
   

                           

     

   

     

     

   

         

   

       

   

     

           

                 

       

     

   

       

   

     

     

   

   

Table B.8. Dependent Variable Is Entered Employment (Logistic) 

Employed in 1st Quarter after Exit

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

1.0659

(0.0681)

1.1598***

(0.0579)

Local ETP Flexibility

1.0775

(0.0567)

0.9027**

(0.0391)

No Minimum Criteria

0.9287

(0.0506)

0.9432

(0.0437)

Reciprocal Agreement

0.8050***

(0.0326)

0.7647***

(0.0231)

Non‐Approved Providers on ETP List

1.1882***

(0.0487)

1.1750***

(0.0366)

Moderating Factors

Less than 50% Colleges

0.6982***

(0.0270)

0.8386***

(0.0260)

Provider Difficulties

1.4354***

(0.0726)

1.3226***

(0.0503)

Performance Data Difficulties

0.9149**

(0.0400)

0.8602***

(0.0286)

State ITA Cap and/or Duration Limit

1.1895***

(0.0774)

1.1632***

(0.0606)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1.0664***

(0.0247)

0.9153***

(0.0208)

Hispanic

1.1856***

(0.0469)

1.0446

(0.0336)

Black

0.9518*

(0.0257)

0.7937***

(0.0180)

Age 45 and Older

0.7037***

(0.0159)

0.7816***

(0.0212)

Disabled

0.7847***

(0.0456)

0.6401***

(0.0273)

Limited English Speaker

0.9438

(0.0464)

1.0474

(0.0566)

Single Parent

1.0497

(0.0321)

1.0545**

(0.0248)

Less than High School

0.7724***

(0.0365)

0.6374***

(0.0315)

High School/GED

0.8890***

(0.0335)

0.7591]***

(0.0330)

Some College/No Degree

0.9565

(0.0391)

0.9541

(0.0445)

Veteran

0.9251**

(0.0361)

1.0147

(0.0425)

Displaced Homemaker

0.7445***

(0.0553)

n.a.

Public Assistance Recipient

0.8498

(0.1070)

0.7374***

(0.0183)

UI Claimant

1.2660***

(0.0343)

1.1763***

(0.0357)

UI Exhaustee

1.2229***

(0.0537)

1.3356***

(0.0669)

61 
   

     

   

     

   

           

   

           

              

                                 

             

                             

Rapid Response

1.1022*

(0.0573)

n.a.

Trade Adjustment Assistance

0.6672***

(0.0195)

n.a.

Supportive Services

1.1389***

(0.0296)

1.0804***

(0.0238)

Needs Related Payments

0.8074**

(0.0590)

0.9771

(0.0564)

Pell Grant

0.9888

(0.0367)

1.0677**

(0.0303)

Employed in Quarter Prior to Entry

1.7985***

(0.0419)

2.3846***

(0.0467)

State Unemployment Rate

1.0938***

(0.0197)

1.2479***

(0.0145)

N = 76769 N = 87038

*** statistically significant at the .01 level

** statistically significant at the .05 level

* statistically significant at the .10 level

LR chi2(31) = 2187.76 LR chi2(28) = 3926.65

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

Pseudo R2 = 0.0354 Pseudo R2 = 0.0524

62 
                   

               

     

   

     

     

   

         

   

       

   

     

           

                 

       

     

   

       

   

     

     

   

   

   

Table B.9. Dependent Variable Is Employment Retention (Logistic) 

Employed in Both 2nd and 3rd Quarters after

Exit

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

2.1579***

(0.0958)

1.3613***

(0.0465)

Local ETP Flexibility

1.4870***

(0.0525)

0.9437*

(0.0287)

No Minimum Criteria

1.5727***

(0.0583)

2.2464***

(0.0753423)

Reciprocal Agreement

0.9893

(0.0269)

1.2106***

(0.0264)

Non‐Approved Providers on ETP List

1.6701***

(0.0473)

1.1483***

(0.0251)

Moderating Factors

Less than 50% Colleges

1.0469*

(0.0270)

1.3723***

(0.0294)

Provider Difficulties

0.7049***

(0.0230)

0.8524***

(0.0229)

Performance Data Difficulties

0.7500***

(0.0220)

0.9469**

(0.0221)

State ITA Cap and/or Duration Limit

3.0973***

(0.1392)

2.4617***

(0.0907)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

0.9572***

(0.0151)

0.8419***

(0.0132)

Hispanic

0.9395**

(0.0241)

0.9523**

(0.0210)

Black

0.9016***

(0.0171)

0.8537***

(0.0134)

Age 45 and Older

0.8104***

(0.0128)

0.8394***

(0.0162)

Disabled

0.9244*

(0.0395)

0.9041***

(0.0311)

Limited English Speaker

0.9819

(0.0341)

1.0912**

(0.0418)

Single Parent

0.9787

(0.0204)

1.0811***

(0.0174)

Less than High School

0.8370**

(0.0271)

0.9011***

(0.0294)

High School/GED

1.0584*

(0.0261)

1.0646***

(0.0289)

Some College/No Degree

1.0381

(0.0280)

1.0850**

(0.0317)

Veteran

0.9600

(0.0263)

1.0352

(0.0307)

Displaced Homemaker

0.9778

(0.0546)

n.a.

Public Assistance Recipient

0.8132**

(0.0713)

0.7949***

(0.0143)

UI Claimant

1.1084***

(0.0207)

1.2103***

(0.0242)

UI Exhaustee

1.0667**

(0.0336)

1.1094***

(0.0379)

Rapid Response

1.1110***

(0.0380)

n.a.

63 
     

   

     

   

           

   

           

               

                   

             

             

                           

Trade Adjustment Assistance

0.9906

(0.0218)

n.a.

Supportive Services

1.0138

(0.0173)

1.1059***

(0.0166)

Needs Related Payments

1.0585

(0.0554)

1.1721***

(0.0474)

Pell Grant

0.9638

(0.0253)

1.1253***

(0.0217)

Employed in Quarter Prior to Entry

1.3922***

(0.0238)

1.4476***

(0.0199)

State Unemployment Rate

1.8533***

(0.0224)

1.7684***

(0.0162)

N = 56914 N = 98947

*** statistically significant at the .01 level

** statistically significant at the .05 level

* statistically significant at the .10 level

LR chi2(31) = 4594.34 LR chi2(28) = 6515.86

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

Pseudo R2 = 0.0410 Pseudo R2 = 0.0478

64 
   

                   

     

   

     

     

   

         

   

       

   

     

           

               

       

     

   

       

   

     

     

   

   

   

Table B.10. Dependent Variable Is Credential Attainment (Logistic) 

Credential Attainment

Dislocated Workers Adults

Odds Ratio (Std. Er.) Odds Ratio (Std. Er.)

State ETP Policies

ETP Waiver

1.362047***

(0.0791268)

1.503802***

(0.0738823)

Local ETP Flexibility

1.192903***

(0.0659165)

1.086719*

(0.0501626)

No Minimum Criteria

0.5889517***

(0.0287845)

0.6303801***

(0.0259187)

Reciprocal Agreement

0.8038859***

(0.0308537)

0.7688012***

(0.0231051)

Non‐Approved Providers on ETP List

0.821864***

(0.0305776)

0.9212954***

(0.0285404)

Moderating Factors

Less than 50% Colleges

0.4160928***

(0.0140744)

0.4844944***

(0.0134903)

Provider Difficulties

1.299758***

(0.0581351)

1.232921***

(0.0414332)

Performance Data Difficulties

1.266055***

(0.0551611)

1.041325

(0.0344193)

State ITA Cap and/or Duration Limit

0.9075297***

(0.0493752)

1.084563*

(0.0488886)

Participant Demographics, Program Participation, WIA Services, and State Economic Conditions

Male

1.005608

(0.0212931)

1.085944***

(0.0246675)

Hispanic

1.093782*

(0.0513149)

0.9649167

(0.0388968)

Black

0.7993544***

(0.018882)

0.7013264***

(0.0149626)

Age 45 and Older

0.9955974

(0.0212589)

1.100156***

(0.0318527)

Disabled

0.7362801***

(0.0401095)

0.8082321***

(0.0354811)

Limited English Speaker

0.8514039***

(0.0464474)

1.18101***

(0.072862)

Single Parent

0.8863159***

(0.0245004)

0.912994***

(0.0203986)

Less than High School

0.6698799***

(0.029513)

0.5059487***

(0.0258618)

High School/GED

1.019497***

(0.0357656)

0.7162731***

(0.0323194)

Some College/No Degree

0.9854497

(0.037551)

0.9135837*

(0.043865)

Veteran

1.138998***

(0.042114)

1.013603

(0.0424255)

Displaced Homemaker

1.05

(0.0805919)

n.a.

Public Assistance Recipient

1.383428

(0.3138145)

0.8666843***

(0.0236469)

UI Claimant

0.9576332

(0.0261938)

0.9458527*

(0.0279275)

UI Exhaustee

0.9994911

(0.04076)

0.9224048*

(0.0416234)

Rapid Response

1.056595

(0.0585217)

n.a.

65 
     

   

     

   

           

   

        

                         

                                 

             

             

                                         

Trade Adjustment Assistance

0.6781054***

(0.0195372)

n.a.

Supportive Services

1.372909***

(0.0356113)

1.217608***

(0.0276749)

Needs Related Payments

0.995449

(0.071319)

1.137914**

(0.0746525)

Pell Grant

0.7821315***

(0.0249322)

0.8035012***

(0.0200784)

Employed in Quarter Prior to Entry

1.166207***

(0.0257534)

1.060353***

(0.0207686)

State Unemployment Rate

0.9488461***

(0.0152725)

1.245677***

(0.0135518)

N = 61849 N = 66887

*** statistically significant at the .01 level

** statistically significant at the .05 level

* statistically significant at the .10 level

LR chi2(31) = 5267.81 LR chi2(28) = 4768.57

Prob > chi2 = 0.0000 Prob > chi2 = 0.0000

Pseudo R2 = 0.0761 Pseudo R2 = 0.0649

66 
       

   

                 

                 

             

   

         

   

   

     

         

       

     

   

   

Table B.11. Simple Correlation Table of Key ETP Variables 

ETP

Waiver

Local ETP

Flexibility

No

Minimum

Criteria

Reciprocal

Agreement

Non‐

Approved

Providers

on ETP

List

Less

than

50%

Colleges

Provider

Difficulties

Performance

Data

Difficulties

State

ITA Cap

and/or

Duration

Limit

ETP Waiver 1.0000

Local ETP

Flexibility

0.3122 1.0000

No

Minimum

Criteria

0.8616 0.1860 1.0000

Reciprocal

Agreement

‐

0.1913

‐0.0706  ‐0.1565 1.0000

Non‐

Approved

Providers on

ETP List

0.2007  ‐0.1193 0.0604  ‐0.1565 1.0000

Less than

50%

Colleges

‐

0.0162

0.2238  ‐0.1560  ‐0.0828 0.5457 1.0000

Provider

Difficulties

‐

0.3945

0.3500  ‐0.4166 0.0005  ‐0.2594 0.0115 1.0000

Performance

Data

Difficulties

0.1471  ‐0.0257 0.1581  ‐0.6642 0.2673 0.2579 0.1571 1.0000

State ITA

Cap and/or

Duration

Limit

‐

0.8093

‐0.1594  ‐0.7884 0.1216  ‐0.2303  ‐0.0038 0.5835  ‐0.1118 1.0000

67