Contract No.: K-4279-3-00-80-30

National Job Corps Study: 

Job Corps Applicants’ 

Programmatic Experiences

Final Report

September 14, 2000

Terry Johnson 

Mark Gritz

Mary Kay Dugan

Submitted to: Submitted by:

U.S. Department of Labor Battelle Memorial Institute

Employment and Training Administration P.O. Box 5395

Office of Policy and Research 4500 Sand Point Way NE 

Room N-5637 Suite 101

200 Constitution Ave., NW Seattle, WA 98105-0395

Washington, DC 20210

Project Officer: In conjunction with:

Daniel Ryan Mathematica Policy Research, Inc.

(Prime Contractor)

Project Director: P.O. Box 2393

Princeton, NJ 08543-2393

John Burghardt

Decision Information Resources, Inc.

Principal Investigators: (Subcontractor)

610 Gray St., Suite 200

Terry Johnson Houston, TX 77002

Charles Metcalf
This report has been produced under Contract Number K-4279-3-00-80-30 with the U.S. Department of 

Labor, Employment and Training Administration. The contents of the report do not necessarily reflect the 

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

organizations imply endorsement of these by the U.S. Government. Battelle Memorial Institute assumes full 

responsibility for the completeness and accuracy of the information presented in this report.
i

Table of Contents

Page

ACKNOWLEDGEMENTS ........................................................................................................................iii

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

OUTREACH AND ADMISSIONS TYPOLOGIES.....................................................................................ES-2

CENTER TYPOLOGIES.................................................................................................................ES-2

IMPLICATIONS ..........................................................................................................................ES-3

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

A. OVERVIEW OF JOB CORPS........................................................................................................1-4

1. OUTREACH AND ADMISSIONS.............................................................................................1-5

2. CENTER OPERATIONS ......................................................................................................1-8

3. PLACEMENT ................................................................................................................. 1-10

B. OBJECTIVES........................................................................................................................ 1-10

1. HYPOTHESES CONCERNING OA CHARACTERISTICS.............................................................. 1-11

2. HYPOTHESES CONCERNING CENTER CHARACTERISTICS....................................................... 1-12

C. DATA SOURCES FOR ANALYSIS............................................................................................... 1-14

D. OVERVIEW OF THE REPORT ................................................................................................... 1-15

II. OVERALL RESULTS ................................................................................................................2-1

A. INTRODUCTION.....................................................................................................................2-1

B. REPRESENTATIVE CASES.........................................................................................................2-1

C. RESULTS..............................................................................................................................2-5

1. 16-17 YEAR-OLD APPLICANTS..........................................................................................2-7

2. 18-19 YEAR-OLD APPLICANTS..........................................................................................2-8

3. 20-24 YEAR-OLD APPLICANTS..........................................................................................2-9

D. SUMMARY.......................................................................................................................... 2-10

III. RESULTS FOR OUTREACH AND ADMISSIONS COUNSELOR TYPOLOGIES.........................3-1

A. INTRODUCTION.....................................................................................................................3-1

B. OA COUNSELOR TYPOLOGIES..................................................................................................3-1

C. RESULTS..............................................................................................................................3-5

1. 16-17 YEAR-OLD APPLICANTS..........................................................................................3-5

2. 18-19 YEAR-OLD APPLICANTS..........................................................................................3-6

3. 20-24 YEAR-OLD APPLICANTS..........................................................................................3-7

D. SUMMARY............................................................................................................................3-8

IV. RESULTS FOR JOB CORPS CENTER TYPOLOGIES ...............................................................4-1

A. INTRODUCTION.....................................................................................................................4-1

B. JOB CORPS CENTER TYPOLOGIES.............................................................................................4-1

C. RESULTS..............................................................................................................................4-6

1. 16-17 YEAR-OLD APPLICANTS..........................................................................................4-6

2. 18-19 YEAR-OLD APPLICANTS..........................................................................................4-7

3. 20-24 YEAR-OLD APPLICANTS..........................................................................................4-8

D. SUMMARY............................................................................................................................4-9

V. SUMMARY AND CONCLUSIONS.............................................................................................5-1

A. INTRODUCTION.....................................................................................................................5-1

B. OUTREACH AND ADMISSIONS COUNSELOR TYPOLOGIES...............................................................5-2

C. CENTER TYPOLOGIES.............................................................................................................5-3

D. CAVEATS AND NEXT STEPS ....................................................................................................5-5
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APPENDIX A METHODOLOGY......................................................................................................... A-1

APPENDIX B RESULTS FOR VOCATIONAL CHOICES OF JOB CORPS STUDENTS .................................... B-1

APPENDIX C RESULTS FOR APPLICANT TYPOLOGIES ........................................................................ C-1

APPENDIX D ESTIMATING IMPACTS FOR DIFFERENT JOB CORPS PROGRAM EXPERIENCES..................... D-1

LIST OF EXHIBITS

Page

2-1 Summary of Representative Case for Applicant Characteristics, by Age Group ...............................2-3

2-2 Summary of Representative Case for Outreach and Admissions Counselor Characteristics and 

Practices.................................................................................................................................2-4

2-3 Summary of Representative Case for Center Characteristics.........................................................2-6

3-1 Summary of Outreach and Admissions Typologies ......................................................................3-3

4-1 Summary of Job Corps Center Typologies..................................................................................4-2

C-1 Summary of Applicant Typologies: 16-17 Year-Olds................................................................... C-9

C-2 Summary of Applicant Typologies: 18-19 Year-Olds..................................................................C-10

C-3 Summary of Applicant Typologies: 20-24 Year-Olds..................................................................C-11

C-4 Summary of Applicant Geographic Proximity Typologies ...........................................................C-13

D-1 Comparison of Estimated Impacts on Average Earnings per Week in 10th Quarter Following 

Random Assignment for Job Corps Participants....................................................................... D-11

LIST OF TABLES

Page

2-1 Predicted Probability of Arrival, GED Attainment, Vocational Completion, and Paid Days 

on Center: Overall, by Sex, Residential Status, and by Age Group................................................ 2-11

3-1 Predicted Probability of Arrival, GED Attainment, Vocational Completion, and Paid Days 

on Center: Outreach and Screening Typologies by Age Group.......................................................3-9

4-1 Predicted Probability of Arrival, GED Attainment, Vocational Completion, and Paid Days on 

Center: Center Typologies by Age Group..................................................................................... 4-12

B-1 Predicted Probability of Vocational Trade Choices by Applicant Typologies: 16-17 Year 

Olds B-5

B-2 Predicted Probability of Vocational Trade Choices by Applicant Typologies: 18-19 Year 

Olds B-6

B-3 Predicted Probability of Vocational Trade Choices by Applicant Typologies: 20-24 Year 

Olds B-7

B-4 Predicted Probability of Vocational Trade Choices by Center Typologies: 16-17 Year Olds...............B-8

B-5 Predicted Probability of Vocational Trade Choices by Center Typologies: 18-19 Year Olds...............B-9

B-6 Predicted Probability of Vocational Trade Choices by Center Typologies: 20-24 Year Olds.............B-10

C-1 Predicted Probability of Arrival and Paid Days on Center: Role of Having a Child by Age 

Group...................................................................................................................................C-19

C-2 Predicted Probability of Arrival, GED Attainment, and Paid Days on Center: Applicant 

Typologies by Age Group .......................................................................................................C-20

C-3 Predicted Probability of Arrival and Paid Days on Center: Role of Geographic Proximity by 

Age Group ............................................................................................................................C-21
iii

ACKNOWLEDGEMENTS

Many individuals from numerous organizations made valuable contributions to this report that 

describes Job Corps applicants’ programmatic experiences.

The authors are especially indebted to the multitude of Job Corps staff who provided key data 

on the practices of outreach and admissions counselors and center characteristics that were used to 

construct the different typologies examined in this report. In particular, we would like to thank the 

nearly 500 OA counselors that completed a detailed telephone survey on the practices they follow in 

recruiting and screening students for the program and the 110 center directors nationwide and their staff

who contributed to completing a comprehensive mail survey of center characteristics. We are also 

grateful for the assistance of the Job Corps Data Center for providing student program records used in 

the analysis. 

The authors also wish to acknowledge the DOL and current and former National Office of Job 

staff who reviewed preliminary results from this analysis and helped shape the focus and format of this 

report. We appreciate the support and guidance of Dan Ryan of the Office of Policy and Research, as 

well as his careful review of an earlier draft of the report. We would also like to particularly thank three 

former National Office staff, Mary Silva, Pete Rell, and Jenny Gallo, for their time in meeting with the 

research team on several occasions to review preliminary findings and for their insights on the types of 

analyses that would be of most policy interest to the program.

Within the research team, we are particularly grateful to John Burghardt, Peter Schochet and 

Steve Glazerman for their thorough and thoughtful reviews of preliminary versions of this report. Their 

comments were extremely valuable in creating a revised document that we hope is of interest to Job 

Corps policy makers. We would also like to thank Fred Dong for expert data processing and analysis 

assistance. Finally, we would like to thank Kate Lynch for her excellent help in producing this report.
ES-1

EXECUTIVE SUMMARY

Job Corps plays a central role in federal efforts to provide employment assistance to disadvantaged 

youths ages 16 to 24. The program’s goal is to help these individuals become “more responsible, 

employable, and productive citizens” by providing them with comprehensive services that include basic 

education, vocational skills training, counseling, and residential support. Each year, Job Corps serves 

more than 60,000 new enrollees at a cost of more than $1 billion. The National Job Corps Study is 

expected to provide Congress and program managers with the information they need to assess how well 

Job Corps is attaining its goal.

This report is one of a series presenting findings from the study. It builds directly on the results of 

the process analysis study that described the basic Job Corps program model and identified variations in 

the way the basic model is implemented. It also supplements the descriptive findings of student 

programmatic experiences presented in the 30-month impact report. Specifically, in this report, we 

examine the outreach, admissions (OA) and center characteristics and practices that appear to promote 

positive programmatic experiences for Job Corps applicants, and for the students that enroll in the 

program. The programmatic experiences of interest are measured using agency records and include the 

likelihood of enrollment in Job Corps and, conditional on enrollment, whether the student attains a GED, 

completes vocational training and program length of stay. A secondary purpose of the report is to 

develop preliminary statistical models of various aspects of program participation that could be used in 

subsequent analyses of the effects of programmatic experiences on students’ post-program labor 

market outcomes. 

The results summarized below are based entirely on data for youth who were randomly selected 

for the program group and who were eligible to enroll in Job Corps. Because all of the comparisons are 

among program group members only, it is important to emphasize that the findings below do not 

represent program impacts, but merely reflect the differences in programmatic experiences of youth who

were exposed to various OA or center practices. In addition, it is important for the reader to keep in 

mind that the results do not represent causal relationships. That is, the analysis addresses the effects of 

particular OA or center practices for contractors or centers that chose to adopt specific practices. 

However, the results cannot be used to address how a specific practice would work in other OA 

agencies or centers that chose to adopt other practices. Nevertheless, the analysis provides important 

suggestive findings about the success of particular program practices if they were to be adopted on a 

broader scale. 

In the report, we illustrate the results of the statistical models of programmatic experiences for 

different typologies of OA agencies and centers. For example, to illustrate the results for OA agency 

characteristics and practices, we calculate the predicted probability of arrival and the probabilities of 

other programmatic experiences conditional on arrival, for a representative OA agency (evaluated at the 

characteristics of the representative center and applicant) and compare them with the predicted 

probabilities derived from other typologies of OA characteristics. The representative cases are not 
ES-2

based on the mean or median characteristics or practices and, as such, do not have a strictly statistical 

interpretation. Rather, the representative case reflects a set of specific characteristics and practices that 

when taken together yield a predicted value of the likelihood of the programmatic experience that is 

approximately equal to the actual value observed in the data. The cases considered are broadly 

representative of the OA agencies and centers that operate the Job Corps program. Because 

programmatic experiences and impacts appear to differ by age, we present results separately for three 

applicant age groups: 16-17, 18-19, and 20-24.

OUTREACH AND ADMISSIONS TYPOLOGIES 

Outreach and admissions services are provided by three main types of contractors: (1) State 

Employment Security (ES) Agencies and other state and local agencies; (2) private organizations 

affiliated with a Job Corps center; and (3) private organizations not affiliated with a center. For the 

most part, we find that the type of OA contractor does not significantly affect the programmatic 

experiences of the students recruited, although students recruited by private organizations affiliated with 

Job Corps centers appear to be slightly more likely to enroll in the program than students recruited by 

other agencies. Other important findings include:

• The outreach and screening practices of OA counselors play a very significant role in 

an applicant’s programmatic experiences. For all age groups, OA counselors who use 

aggressive screening practices achieve higher arrival rates among their eligible applicants. For 

the two youngest groups, counselors who follow passive outreach practices and effectively put 

the burden on the applicant to follow-through and enroll in Job Corps recruited students who 

were likely to remain in the program longer.

• OA counselor knowledge and experience level play a key role in an applicant’s

programmatic experiences. Uniformly across all age groups, applicants served by well-informed 

and experienced OA counselors are much more likely to have better programmatic experiences 

than applicants served by less experienced and less knowledgeable counselors. 

CENTER TYPOLOGIES

The process analysis study identified several variations in the ways in which centers implement the 

Job Corps program model that could be related to applicants’ programmatic experiences. We find that 

whether the center has an OA contract and the extent to which staff match students in race/gender 

composition do not seem to be related to student programmatic experiences. In addition, we thought 

that since Job Corps is a performance-driven system that we might find that students who attend higher 

ranking centers (using OMS performance ranking as the measure) would have more positive program 

experiences. We did not find this be the case. Other results presented in Chapter IV confirm the 

general nature of the findings obtained from the process analysis, including:

• Center operator type, size and location play an important role in students’ 

programmatic experiences. For all age groups, students that attend small CCC centers 
ES-3

(that are primarily located in rural areas) are more likely to complete their vocational trade and 

more likely to stay on center for longer periods of time compared to students that attend other 

types of centers. This is consistent with the process analysis findings indicating that CCC 

centers are more focused on vocational training than other centers.

• The particular vocational areas a center offers and the range of vocations offered do 

not affect a student’s programmatic experiences. For all age groups, there are very small 

or no differences in predicted programmatic experiences depending on the comprehensiveness 

of the range of vocations offered by a center. However, it is important to note that this result 

refers strictly to the set of trades that were offered at the time of the study, and does not 

necessarily hold for new vocational training program offerings.

• Students that attend centers with strong vocational and academic programs have 

much more positive programmatic experiences than students that attend centers with 

relatively weak programs. In the process report, we found many differences across centers 

in the strength of their vocational programs, from the amount of OEP provided to students, to 

policies for assigning students to trades and switching among trades, as well as in the extent to 

which centers supplement the basic academic curriculum with other programs. For all age 

groups we consistently find that applicants to centers with strong vocational and academic 

programs have more positive programmatic experiences, and especially GED attainment and 

vocational completion than students served by other centers. 

• Limited residential living facilities are associated with shorter lengths of stay, but 

exceptionally good facilities do not appear to promote longer lengths of stay. In the 

process report, we found that the quality of center residential facilities and recreation 

opportunities vary considerably. Here we find that limited residential facilities are associated 

with low student retention, but that extensive facilities do not seem to promote retention.

IMPLICATIONS

These results have potentially important implications for the Job Corps program. They indicate the 

importance of ensuring that up-to-date materials/information about centers and videos depicting life on 

center are available and used by OA counselors. They also emphasize the importance of providing training 

to OA counselors concerning screening and outreach strategies, as well as developing recruitment/retention 

strategies to reduce the high turnover of OA counselors.

The results concerning center practices emphasize the importance of strong vocational and academic 

programs, as well as the potential problems associated with limited residential living facilities.
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I. INTRODUCTION

Job Corps plays a central role in federal efforts to provide employment assistance to disadvantaged 

youths ages 16 to 24. The program’s goal is to help these individuals become “more responsible, 

employable, and productive citizens” by providing them with comprehensive services that include basic 

education, vocational skills training, counseling, and residential support. Each year, Job Corps serves 

more than 60,000 new enrollees at a cost of more than $1 billion.

The National Job Corps Study, funded by the U.S. Department of Labor (DOL), is expected to 

provide Congress and program managers with the information they need to assess how well Job Corps 

is attaining its goal.1 The central feature of the study was the random assignment of all youths found 

eligible for Job Corps to either a program group or a control group. Program group members were 

permitted to enroll in Job Corps, and control group members were not (although they could enroll in 

other training or education programs). The research sample for the study consists of approximately 

9,400 program group members and 6,000 control group members randomly selected from among 

nearly 81,000 eligible applicants nationwide. Sample intake occurred between November 1994 and 

February 1996.

This report presents findings on the outreach, admissions and center practices that appear to 

promote positive programmatic experiences for Job Corps applicants, and for the students that enroll in 

the program. We also present information on the applicant characteristics that are associated with 

better programmatic experiences. A secondary purpose of the report is to develop preliminary 

 

 

1

The study is being conducted by Mathematica Policy Research, Inc. (MPR) and its subcontractors, Battelle Memorial 

Institute and Decision Information Resources, Inc.
1-2

statistical models of program participation that can be used in subsequent analyses of the effects of 

programmatic experiences on students’ post-program labor market outcomes. 

This report builds on the analysis and findings presented in two earlier reports. First, it builds on 

the findings of the process study, described in Johnson et. al. (1999) National Job Corps Study: Report 

on the Process Analysis. That report described the Job Corps program model and how it was 

implemented at the time of the study. The process study concluded that Job Corps uses a well-defined 

program model, which is offered at all centers, with students generally receiving the same basic program 

services at each center. At the same time, however, the process analysis also identified important 

variations in program elements that could affect applicant programmatic experiences. In this report, we 

provide insights on the program features that appear to promote positive programmatic experiences.

Second, this report builds on the analysis and findings contained in the main report on short-term 

impacts (Schochet, Burghardt and Glazerman, 2000), National Job Corps Study: The Short-Term 

Impacts of Job Corps on Participants’ Employment and Related Outcomes. That report included 

descriptive information on the education and training experiences of the program group and of the 

control group based on self-reported information. It also provided detailed information on the impacts 

of Job Corps on exposure to education and training, as well as impacts on post-program labor market 

and other outcomes based on differences between the program group and the control group. In this 

report, we provide information on programmatic experiences based on official agency records.

This report examines the programmatic experiences of program group applicants and attempts to 

identify OA and center practices that promote positive experiences for youth who apply and enroll in 

Job Corps. For example, we compare the experiences of representative youth in the program group 

who were recruited and screened by an OA contractor that followed a certain set of practices with the 
1-3

experiences of otherwise similar youth in the program group who were recruited and screened by an 

OA contractor that followed different practices. We also examine the programmatic experiences of 

various program group youth exposed to other OA practices, as well as to different center 

characteristics and practices. Because programmatic experiences and impacts appear to differ by age, 

we present results separately for three applicant age groups: 16-17, 18-19, and 20-24.

In interpreting the results presented in this report, as well as in understanding how these results fit in 

with earlier reports, it is important to keep in mind the nature of the comparisons being made and the 

limitations of the analysis. First, the results presented in this report reflect comparisons of programmatic 

experiences among program group members only. As such, comparative statements indicating that 

certain types of applicants or students have more positive programmatic experiences than a different 

group has nothing to say about the relative impacts of the program for those groups, as such conclusions 

would require information about control group experiences, which are not included in this report. 

Second, because the results concerning OA and center practices rely on statistical models, it is difficult 

to conclude that these findings represent causal relationships. That is, the analysis addresses the effects 

of particular OA or center practices for contractors or centers that chose to adopt the specific practice. 

 However, the results cannot be used to address how that practice would work in other OA agencies or 

centers that chose to adopt other practices. Nevertheless, the analysis provides important suggestive 

findings about the success of particular program practices if they were to be adopted on a broader 

scale. 

In the remainder of this chapter, we provide additional background information concerning the Job 

Corps program, focusing on the key roles that outreach and admissions agencies and centers play. We 

then discuss the goals of this analysis and provide additional details concerning the specific research 
1-4

questions that we address in this report and the data sources that underlie the analysis. The chapter 

concludes with a description of the organization of the report.

A. OVERVIEW OF JOB CORPS

The Job Corps program, established by the Economic Opportunity Act of 1964, at the time of the 

study operates under provisions of the Job Training Partnership Act (JTPA) of 1982.2 The operational 

structure of Job Corps is complex, with multiple levels of administrative accountability, several distinct 

program components, and numerous contractors and subcontractors. DOL administers Job Corps 

through a national office and nine regional offices. The national office establishes policy and 

requirements, develops curricula, and oversees major program initiatives. The regional offices procure 

and administer contracts and perform oversight activities, such as reviews of center performance.

Through its regional offices, DOL uses a competitive bidding process to contract for operations of 

the three main program elements: recruiting and screening of new students, center operations, and 

placement of students into jobs and other educational opportunities after they leave the program. At the 

time of the study, 80 centers were operated under such contracts. In addition, the U.S. Departments of 

Agriculture and of the Interior operated 30 centers, called Civilian Conservation Centers (CCCs), under 

interagency agreements with DOL.3 Next, we briefly outline the roles of the three main program 

elements.

 

 2 As of July 2000, Job Corps operates under provisions of the Workforce Investment Act of 1998.

 

3

 Currently, 91 contract centers and 28 CCCs provide Job Corps training.
1-5

1. Outreach and Admissions 

Recruitment and screening for Job Corps are conducted by outreach and admissions (OA) 

agencies, which include private nonprofit firms, private for-profit firms, state employment agencies, and 

Job Corps centers. These agencies provide information to the public through outreach activities (for 

example, by placing advertisements and making presentations at schools), screen youth to ensure that 

they meet the eligibility criteria, assign eligible youth to centers (when the regional office delegates this 

function), and arrange for their transportation to centers.

The primary purpose of the outreach and admissions component of Job Corps is to ensure a steady 

flow of young people who are eligible for and can benefit from the program. Operating the program at near 

capacity is important for taking full advantage of the resources devoted to the program. Short stays in the 

program are viewed as wasteful because the participants derive little benefit if they leave home to attend but 

then do not complete the program. In this context, the twin challenges for the outreach and admissions 

system are (1) ensuring that qualified candidates are ready to enroll when slots become available, and (2) 

ensuring that these candidates fully understand what is expected of them and what life on center will be like, 

so as to avoid disappointment and early program termination. Sometimes tension exists between these two 

objectives.

Outreach and admissions involves several related activities. Outreach makes young people aware of 

the opportunities Job Corps offers and gives potentially interested persons the information on which to base 

a decision to enroll. Screening for eligibility entails determining whether the numerous eligibility criteria are 

met. The eligibility criteria include readily verifiable factors (such as age, income, and citizenship), factors 

requiring the exercise of judgement (need for removal from the home, and capacity to benefit from the 

program), and factors that may require review by professionally qualified third parties (evidence of serious 
1-6

health or behavioral problems). After they are determined eligible, students are assigned to enroll at a 

specific center on a particular date. In this final phase of OA counseling, the outreach and admissions 

system prepares candidates for enrollment, arranges transportation, and sees the student off.

Several types of organizations share responsibility for outreach and admissions, including Job Corps 

regional offices, outreach and admissions agencies, and Job Corps centers. Regional offices are responsible 

for contracting with agencies to perform outreach and admissions work. They are also responsible for 

determining eligibility and assigning students to centers, although most regional offices delegate some or all of 

these decisions to OA agencies and centers. Outreach and Admissions agencies play a pivotal role in Job 

Corps, because they generally are the first contact with the program for youths and their families. As part of 

their outreach activities, OA agencies communicate to potentially eligible youths, as well as to other 

individuals and organizations who can identify and refer potentially eligible youths, about the availability of 

the Job Corps and its opportunities. OA agencies must conduct outreach by using activities that are 

appropriate for their needs (for example, flyers, billboards, center tours, off-center presentations, and 

broadcast outlets), and by developing liaisons and working relationships with relevant community

organizations (for example, schools, court officers, employment services, and welfare agencies). 
1-7

OA contractors are responsible for determining whether a youth who has expressed interest in and 

applied to Job Corps is eligible for the program. The information used to determine eligibility is collected 

directly from the applicant (over the telephone or face-to-face) by an OA counselor, using an agencydeveloped pre-application form or the Job Corps ETA-652 application form.4

 The screener must also 

obtain any additional information from appropriate authorities needed to determine an applicant’s eligibility. 

If a candidate has a serious health problem or evidence of a behavioral problem, the regional office provides 

for a review of the case to ensure that eligibility rules are applied fairly and consistently, and that persons 

who could benefit are not denied admission.

The OA contractor is also responsible for notifying eligible applicants of their assignment to a center, 

and for arranging for their departure. Often, an OA counselor will contact a youth periodically, between the 

time eligibility is determined and a center assignment is made, to determine whether he or she continues to 

be interested in Job Corps. After a youth has been assigned to a center, the OA counselor contacts the 

applicant to determine whether he or she will accept the assignment, and to arrange travel and a departure 

date. At the beginning of this assignment interview, the OA counselor also checks for changes in the youth’s 

health and emotional or behavioral condition, including recent arrests. The OA counselor then briefs the 

youth about the center, describes the programs’ requirements and expectations, and makes a final check on 

eligibility.

The OA counselor will brief youths who decide to participate on travel arrangements; advise about 

baggage, clothing, and documentation; and warn about the consequences of bringing drugs, alcohol, or 

weapons to the center. The OA counselor will also inform youths that a physical examination, which will 

 

4Currently, application processing is automated; information is entered to a computer and transferred to SPAMIS if 

the student enrolls. At the time of the study a paper-based system was used.
1-8

include an HIV test, will be required after arrival on-center. If a youth is on probation or parole, the OA 

counselor must notify the appropriate probation or parole officer. Finally, the OA counselor makes all 

departure arrangements, including escorting or providing an escort for youths to the initial transportation site 

and providing all travel and meal tickets required for the trip to the center. In addition, some OA counselors 

maintain contact with youths after they have arrived on-center and throughout their Job Corps experiences. 

Center staff are also often involved in the center assignment decision, and staff at most centers make prearrival calls to students assigned to the centers.

2. Center Operations

Centers are the cornerstone of Job Corps as they provide a comprehensive and intensive set of 

program services. The major services provided by centers include basic education, vocational training, 

residential living (including training in social skills), health care and education, counseling, and job 

placement assistance. Services in each of these components are tailored to meet the needs of each 

student. 

Education. The goal of the education component is to enable students to learn as fast as their 

individual abilities permit. Education programs in Job Corps are individualized and self-paced and 

operate on an open-entry and open-exit basis. The programs include remedial education (emphasizing 

reading and mathematics), world of work (including consumer education), driver education, home and 

family living, health education, classes designed for those whose primary language is not English, and a 

General Educational Development (GED) program of high school equivalency for students who are 

academically qualified. In addition, about one-fourth of the centers can grant state-recognized high 

school diplomas.
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Vocational Training. As with the education component, the vocational training programs 

individualized, self-paced, and operate on an open-entry and open-exit basis. Each Job Corps center 

offers training in several vocations, typically including business and clerical occupations, health 

occupations, construction, culinary arts, and building and apartment maintenance. Instruction is 

provided by staff with occupational experience that are hired by the center, as well as by national labor 

and business organizations under national training contracts at many centers.

Residential Living. Residential living is the component that distinguishes Job Corps from most 

other publicly funded employment and training programs. From its inception in 1964, residential living 

has been considered a key element of the program because most students come from disadvantaged 

environments and it is believed they require new and more supportive surroundings to derive the 

maximum benefits from education and vocational training. A key part of residential living consists of 

formal social skills training that all students must participate in, including nonresidential students. The 

residential living component also includes meals, dormitory life, entertainment, sports and recreation, 

center government, and other related activities. Historically, regulations had limited the number of 

nonresidential students to 10 percent, but JTPA amendments raised that limit to 20 percent in July 

1993.

Health Care and Education. Job Corps centers also provide comprehensive health services to 

both residential and nonresidential students. Services include medical examinations and treatment; 

biochemical tests for drug use, sexually transmitted diseases, and pregnancy; immunizations; dental 

examinations and treatment; counseling for emotional and other mental health problems; and instruction 

in basic hygiene, preventive medicine, and self-care. 
1-10

Counseling and Other Ancillary Services. Job Corps centers offer students a range of other 

supportive services including providing counselors and residential advisers. These staff help students 

plan their educational and vocational curricula, offer motivation, and create a supportive environment. 

Support services are also provided during recruitment, placement, and the transition to regular life and 

jobs.

3. Placement

The final step in the Job Corps program is placement. This component of the program helps 

students find jobs in training-related occupations with prospects for long-term employment and 

advancement. Placement contractors may be state employment offices or private contractors, and 

sometimes the centers themselves perform placement activities. Placement agencies help students find 

jobs by providing assistance with interviewing and resume writing and services for job development and 

referral. They are also responsible for distributing the readjustment allowance -- a stipend students 

receive after leaving Job Corps.

B. OBJECTIVES 

This report presents the results of statistical models designed to improve our understanding of the 

programmatic factors that affect the experiences of youths recruited for Job Corps. The underlying 

objectives of the report incorporate both practical and methodological considerations. First, from a 

practical/policy perspective, it is important to better understand the outreach, admissions and center 

characteristics and practices that appear to promote positive programmatic experiences for Job Corps 

applicants and for the students who enroll in the program. To meet this objective, the statistical models 

we estimate are used to provide insights regarding hypotheses developed from the process analysis 
1-11

study concerning the relationship between programmatic experiences and OA and center characteristics 

and practices. Below we provide additional information concerning the different hypotheses that we 

explore in the analysis.

The statistical models estimated here also serve a second, methodological objective. Specifically, 

the Department of Labor and the Office of Job Corps are interested in understanding the impacts of 

specific programmatic experiences on post-program labor market outcomes. Developing reliable 

estimates of such impacts, however, presents a formidable challenge because it requires an extensive 

understanding of the processes determining the program experiences and accomplishments of 

individuals, as well as the processes that determine these individuals’ labor market and other related 

outcomes. Specifically, to answer this question, analysts must separate out the effects of the factors that 

determine which individuals have a particular set of programmatic experiences (e.g., vocational training 

program, vocational completion status, GED attainment status, length of stay) from the effects of these 

experiences on outcomes. For this report, the statistical models used to meet the first objective 

described above also serve as the preliminary models of program participation that are used in 

subsequent analyses of the impacts of programmatic experiences on students’ post-program labor 

market outcomes. In Appendix D, we report selected results from a preliminary exploratory analyses of 

whether the impacts of Job Corps on post-program weekly earnings differ for various program-defined 

subgroups using interview data for the first 30 months after application.

1. Hypotheses Concerning OA Characteristics

OA Counselor Outreach and Screening Practices. In the process report we found that 

although OA counselors on average devote considerable time to outreach activities, some counselors 

follow a more passive approach to recruiting and, for the most part, essentially require interested youth 
1-12

to contact them. Although such an approach may produce fewer applicants and make it more difficult 

for counselors to meet their arrival goals, such a process also results in a more committed pool of youth 

that make it to the application stage. Based on these results, we hypothesize that OA counselors that 

follow a passive approach to outreach will recruit youth who are more committed to Job Corps and as 

such, more likely to enroll and have better programmatic experiences, than applicants recruited by OA 

counselors that follow an aggressive outreach strategy. 

The process analysis also documented that most OA counselors primarily screen into the program 

youth that meet the “traditional,” easily documentable eligibility criteria related to age, U.S. citizenship, 

income and so forth; relatively few youth that are otherwise eligible are found ineligible by OA 

counselors for other criteria related to capability and aspirations to participate. Thus, we hypothesize 

that OA counselors that follow aggressive screening practices (e.g., counselor has 30-day stay goal, 

assess attitudes and goals, conducts home visits, strongly encourages enrollment) are more likely to 

identify youth that can benefit from the program and achieve more positive programmatic experiences.

OA Counselor Experience and Knowledge. In the process report we found that OA counselors 

had limited job tenure and limited firsthand knowledge of the centers for which they recruit. Moreover, 

center staff reported that this high turnover and lack of knowledge led to many students arriving on center 

without a good idea of what to expect and what the specific center offers. Thus, we hypothesize that 

applicants recruited by OA counselors that have less experience and less knowledge about the program are 

likely to stay shorter periods in Job Corps and have less positive programmatic experiences than applicants 

served by OA counselors with more experience and program knowledge. 
1-13

2. Hypotheses Concerning Center Characteristics

Center Operator Type, Size and Location. In the process analysis we found that CCC centers 

are more focused on vocational training than other centers. Thus, we hypothesize that students who 

attend small CCC centers will have higher vocational completion rates than the representative center.

Range of Vocational Offerings. In the process report we found that the range of vocational 

offerings, and the extent to which the training programs offered are “in demand,” varies considerably 

across centers. We hypothesize that centers that offer a wider range of vocational programs, as well as 

trades that are more in demand, are more likely to produce positive programmatic experiences for 

students.

Strength of Vocational and Academic Program. In the process report, we found many 

differences across centers in the strength of their vocational programs, in terms of the amount of 

Occupational Education Program (OEP) provided, and policies for assigning students to trades and 

switching among trades. Also, although all centers offer the standard Job Corps academic program, 

some centers supplement the basic curriculum with other academic programs that provide additional 

services to youth. Thus, we hypothesize that centers with stronger vocational and academic programs 

(e.g., more time spent by student in OEP to decide which trades to select, moving students quickly into 

their vocational trades, pre-college classes available, and academic instructor on Progress Performance 

Evaluation Panel) are more likely to produce positive programmatic experiences for students.

Quality of Residential Living Facilities. In the process report, we found that the quality of 

center facilities varies considerably. This was particularly true for residential living facilities, which is 

likely to significantly affect program length of stay. Thus, we hypothesize that students who attend 

centers with facilities that provide greater student privacy will stay longer and have better program 
1-14

experiences than students who attend centers with less privacy. In addition, we found that recreation is 

an important part of residential living and hypothesize that students who attend centers that provide 

more recreational opportunities will experience longer lengths of stay and better program experiences 

than students who enroll in centers with limited recreational opportunities.

Degree of Student/Staff “Match”. Students are more likely to find positive role models and 

mentors in centers where the backgrounds of the students match the backgrounds of the staff. In the 

process report we found that centers differ appreciably in the extent to which the characteristics of their 

staff (e.g., race/ethnicity, gender) match the characteristics of their students. We hypothesize that 

students who attend centers in which the racial and gender backgrounds of the staff match the 

backgrounds of the students will have more positive experiences than students who attend centers that 

are not as well matched.

OMS Center Performance Rankings. Job Corps is a performance-driven system, and includes 

a number of program outcomes in its Outcome Measurement System. We hypothesize that students 

who attend higher-ranked centers are more likely to obtain positive programmatic experiences than 

students who attend lower-ranked centers.

C. DATA SOURCES FOR ANALYSIS

Our analysis of the factors that affect programmatic experiences draws on multiple data sources. 

Student demographic (and other) characteristics at application and programmatic experiences were 

obtained from the Job Corps Student Pay and Allotment Management Information System (SPAMIS). 

 Additional applicant personal background characteristics were obtained from the baseline survey. We 

also used Bureau of Labor Statistics data to construct county-level measures of the unemployment rate 
1-15

and average annual earnings that were linked to the applicant’s address at the time of application to Job 

Corps.

Two surveys conducted as part of the process analysis also contributed important information to 

our analysis. This includes a telephone survey of a large sample of OA counselors in all OA agency 

offices nationwide that were operating at the time of sample intake for the study. This survey obtained 

information on OA counselor characteristics and OA practices that were then linked to specific sample 

members based on the OA office that processed his or her Job Corps application. We also conducted 

a mail survey of all Job Corps centers that were in operation at the time of sample intake. This survey 

obtained detailed information on center characteristics and center operational practices that could affect 

students’ programmatic experiences, which were linked to the center the student attended. Additional 

information on these two surveys can be found in Johnson et al. (1999).

D. OVERVIEW OF THE REPORT

The remainder of this report is organized as follows. In Chapter II, we overview the approach 

used to examine factors that are related to applicants’ programmatic experiences. We also present 

overall findings for the major programmatic experiences of interest, including whether applicants arrive 

on center and, conditional on arrival, whether students complete their vocational training, attain a GED, 

and length of stay in Job Corps. To focus attention on the policy variables that are of most interest, the 

results in the next two chapters are organized not by programmatic experiences but by the different 

factors that shape those experiences. Specifically, in Chapter III, we present the results of how certain 

characteristics of outreach and admissions counselors and their practices are related to Job Corps 

programmatic experiences. Chapter IV provides information on the role of center characteristics and 
1-16

practices in programmatic experiences. Finally, Chapter V summarizes our findings and discusses 

implications for program operations. 

The report also includes four appendices. Appendix A provides additional details concerning 

the statistical models underlying the results presented in Chapters III and IV. Appendix B summarizes 

the factors that are related to the choices Job Corps applicants make regarding the vocational area 

where they receive the most training. Appendix C presents findings illustrating the effects of applicant 

personal characteristics on selected programmatic experiences. Appendix D presents some exploratory 

results from statistical models designed to estimate the extent to which program impacts differ for 

applicants with different programmatic experiences.
2-1

II. OVERALL RESULTS

A. INTRODUCTION

In this chapter we describe the general approach we use to examine the OA and center 

characteristics and practices that are associated with positive programmatic experiences and present our 

overall results. We estimate models in four general areas of programmatic experiences: (1) enrollment 

(arrival on center); (2) GED attainment; (3) vocational training; and (4) length of stay. We develop 

statistical models that focus on these programmatic experiences because of their policy interest and 

because they represent the broad categories of experiences for which the subsequent component impact 

analysis will attempt to estimate program impacts. The details of the statistical approach are provided in 

Appendix A and B.

We begin by describing the applicant characteristics, OA counselor practices and characteristics 

and center characteristics used to illustrate the results. Then, for each of the programmatic experiences 

of interest, we present the results of the statistical models by age, gender and by the student’s likely 

residential status for these typical or representative characteristics. The chapter concludes with a 

summary of the main findings that broadly apply across all age groups. 

B. REPRESENTATIVE CASES

The statistical models used to characterize the programmatic experiences of Job Corps applicants 

were estimated separately for three applicant age groups: 16-17 year olds, 18-19 year olds, and 20-24 
2-2

year olds.5 To evaluate the implications of these statistical models, we calculate the predicted 

probabilities of arrival on center for a number of representative types of applicants, OA agencies and 

Job Corps centers. Among those who enroll in Job Corps, we calculate the conditional predicted 

probabilities of GED attainment, vocational completion, and length of stay for the representative Job 

Corps student. The cases considered here are broadly representative of the youth that apply to Job 

Corps and they typify the OA agencies and centers that operate the Job Corps program. 

In this section, we define the representative cases for applicants, OA agencies and Job Corps 

centers that we use to illustrate the results of the statistical models. Exhibit 2-1 summarizes the set of 

assumptions that define the representative student characteristics for each of the three age groups. As 

shown, the representative characteristics are selected to reflect the primary differences in education and 

employment backgrounds among applicants of different ages. For example, the representative youth 

aged 16-17 at application is assumed to have been in school for a significant part of the prior year, have 

only completed 9th grade, and to have relatively little work experience (i.e., worked 2 months last year 

with total earnings of at least $500). In contrast, the representative youth for the two older applicant 

groups is assumed to have more work experience but less recent participation in education programs. 

More specifically, we assume that applicants aged 18-19 and aged 20-24 have completed 11th grade 

and have worked 6 months in the last year, with earnings of at least $3,000. The one difference in the 

representative case between the two older age groups is that youth aged 20-24 are assumed to not 

have been enrolled in school in the last year, whereas the representative applicant aged 18-19 is 

assumed to have been in school for 3 months during the last year.

 

5

The models are estimated separately by age because results from Schochet, Burghardt and Glazerman 

(December 1999) indicate that the impacts of Job Corps on participants’ post-program employment and earnings 
2-3

Exhibit 2-1

Summary of Representative Case for Applicant Characteristics, by Age Group

Characteristics 16-17 Year Olds 18-19 Year Olds 20-24 Year Olds

Personal 

background

Age 16

Black

Native English speaker

In good health

Age 18

Black

Native English speaker

In good health

Age 21

Black

Native English speaker

In good health

Background 

experiences

Family receiving welfare

No criminal/drug history

Not working at application

Worked 2 months in last year

Earnings of $500 last year

Completed 9th Grade

Enrolled in school for 9 

months last year

Does not live with a spouse 

No children

Family receiving welfare

No criminal/drug history

Not working at application

Worked 6 months in last year 

Earnings of $3,000 last year

Completed 11th Grade

Enrolled in school for 3 months 

last year

Does not live with a spouse 

No children

Family receiving welfare

No criminal/drug history

Not working at application

Worked 6 months in last year 

Earnings of $3,000 last year

Completed 11th Grade

Not enrolled in school in last 

year

Does not live with a spouse 

No children

Knowledge and 

expectations of 

program

Knows someone who 

attends/attended Job 

Corps

Believes Job Corps will help 

with social skills, selfcontrol, math and reading 

skills

Knows someone who 

attends/attended Job Corps

Believes Job Corps will help 

with social skills, selfcontrol, math and reading 

skills

Knows someone who 

attends/attended Job Corps

Believes Job Corps will help 

with social skills, selfcontrol, math and reading 

skills

Geographic Region 4

Lives in a Metropolitan 

Statistical Area

Lives 25 miles from likely 

center

Region 4

Lives in a Metropolitan 

Statistical Area

Lives 25 miles from likely center

Region 4

Lives in a Metropolitan 

Statistical Area

Lives 25 miles from likely center

As indicated in Exhibit 2-1, the representative case for other characteristics that do not typically 

vary significantly by age are defined similarly for all age groups. For example, we 

assume the representative case for each of the age groups does not have any criminal or drug history, is 

 

outcomes differ by age.
2-4

in good health, and has similar knowledge and expectations of the program. 

Exhibit 2-2

Summary of Representative Case for Outreach and Admissions Counselor Characteristics 

and Practices

Characteristics Description

OA contractor type Works for an OA contractor affiliated with a Job Corps 

Center

OA screening practices Counselor encouraged applicant to go to Job Corps

Counselor believed applicant was very likely to enroll

Applicant told to expect to wait between 4 to 8 weeks before 

he/she could enroll at a center

Applicant told to expect to stay in Job Corps for 12 months

Applicant told he/she can leave Job Corps center after 60 days 

to visit home 

Attempts to contact no-shows

Does not have a goal for number of students who stay at least 

30 days

Has a video for 25-50% of the centers he/she recruits students 

for

Does not make center assignments

Does not assess students in terms of experience or 

attitudes/goals

Does not make visits to applicant’s home

OA outreach practices Has a target goal for number of applicants that enroll

Applicant first saw counselor in Job Corps office

Applicant spent 1-2 hours with counselor

OA counselor experience CCC/Contractor type matched likely center 

Has worked as an OA counselor for 2 years

In Exhibit 2-2, we describe the representative case for OA counselor characteristics and practices. 

As shown, we organize these factors into four categories: OA contractor type, OA screening practices, 

OA outreach practices, and OA counselor experience. The representative case reflects the relatively 

short time (1-2 hours) that OA counselors typically spend with prospective students, as well as the 

relatively high job turnover of OA counselors (only 2 years work experience), and the limited amount of 

information available to the counselor about specific centers (e.g., has video for less than one-half of the 
2-5

centers for which he/she recruits students). The representative case also reflects relatively limited OA 

outreach efforts (e.g., applicant first saw counselor in OA office), and the limited active applicant 

screening that is performed (e.g., counselor does not conduct experience or attitude/goal assessments of 

students). Other specific assumptions about OA counselor characteristics and practices that define the 

representative case are described in Exhibit 2-2.

In Exhibit 2-3, we define the center characteristics included in the representative case. These center 

characteristics are grouped into several categories: broad center characteristics, pre-arrival contact and 

intake frequency, center facilities, OEP and vocational assignment practices, academics and class 

schedules, PPEP panels, student body and staff characteristics and vocational program area offerings. 

As this exhibit indicates, the representative center case assumes a medium-sized center (226-495 

student slots) of average measured performance that is operated by a private contractor and receives 

new student input on a weekly basis. The representative center case is assumed to offer the most 

common Job Corps vocational training programs (with student preferences guiding the trade selection 

and with no restrictions on changing trades) and the standard Job Corps academic program is assumed, 

with no pre-college classes offered. Other specifics concerning the representative case for Job Corps 

centers are detailed in Exhibit 2-3.

C. RESULTS

In this section we present the overall findings for the major programmatic experiences for the three 

age groups described above. For each of the age groups, we present overall results for 
2-6

Exhibit 2-3

Summary of Representative Case for Center Characteristics

Characteristics Description

Broad center characteristics Operated by a private contractor

Medium sized center with 226-495 students

Located in a city with a population of 50,000

Average measured performance

Center holds a contract for placement services

Does not hold an Outreach and Admissions contract

Pre-arrival contact and intake 

frequency

Students are warned about possible vocational waiting list

New student intake on weekly basis

Center facilities New arrivals are placed in dorm rooms based on space availability only and not in 

rooms designated for new arrivals

Dorm rooms have average number of beds per-room

Center has athletic field on grounds

OEP and vocational assignment New students spend 5 to 11 hours in OEP

Student preference guides trade selection

Students placed in second choice trade if first choice is full

Vocational trade classes begin 2 - 4 weeks after arrival

10 - 20 % of available trades have waiting list for enrollment

No restrictions on changing trades

Academics and class schedules No pre-college classes available 

SST classes held during evening hours

PPEP panels Counselor chairs panel

Student’s vocational instructor on panel

Student body and staff 

characteristics

Average student and staff characteristics

Vocational program area 

offerings

Building and apartment maintenance

Business/clerical occupations

Carpentry

Food service occupations

Health occupations

Masonry

Other construction occupations

arrival, GED attainment, vocational completion, and length of stay experiences for the representative 

case and then examine the extent to which these results differ for males, females 
2-7

and for applicants who were assessed by OA counselors to be likely residential or likely non-residential 

students.

1. 16-17 Year-Old Applicants

The results for 16-17 year-old applicants are summarized in Table 2-1. Table 2-1 shows the 

predicted probability of arrival and, among students who enroll in Job Corps, the conditional GED 

attainment and vocational completion rates overall and by gender. In addition, Table 2-1 shows the 

predicted probabilities of staying at least 30 paid days, at least 90 paid days and at least 270 paid days 

(conditional upon arrival) overall and by gender. 

Overall, these figures indicate that the representative 16-17 year-old applicant is very likely to enroll 

in Job Corps, but conditional on enrollment, is not likely to stay very long. As a result, the 

programmatic accomplishments of representative 16-17 year-old students are modest. For the most 

part, there are only minor differences in the programmatic experiences of 16-17 year-olds by gender. 

Some of the most notable findings include: 

• Approximately 80 percent of representative 16-17 year-old applicants are predicted to arrive 

on center. 

• Conditional upon arrival, about one-quarter (25.9 percent) of all 16-17 year old students are 

predicted to attain a GED and about 40 percent are predicted to complete their vocations.

• About 80 percent of eligible 16-17 year-old students who arrive on center are likely to stay at 

least 30 paid days on center. About one-half of students (50.1 percent) are likely to stay 90 

paid days on center and relatively few students (16 percent) are likely to stay 270 paid days or 

more on center. 

• The likelihood of each of these programmatic experiences for 16-17 year-old applicants is very 

similar for males and females.

• There are generally very small differences in the predicted probabilities of arrival and, 

conditional on arrival, of GED attainment and vocational completion among 16-17 year-old 

applicants by the OA counselors’ assessment of likely residential status, especially for males. 
2-8

For females, likely residential applicants are slightly more likely than non-residential applicants to 

arrive on center, stay at least 30 days, complete a GED and complete vocational training. 

2. 18-19 Year-Old Applicants

Table 2-1 provides a similar set of predicted probabilities for 18-19 year-old applicants. Overall, 

the representative 18-19 year-old applicant is less likely to enroll in Job Corps than younger applicants, 

but nevertheless has slightly better programmatic experiences. This is because, conditional on 

enrollment, 18-19 year-old applicants are much more likely than their younger counterparts to have 

positive programmatic experiences. In contrast to 16-17 year-old applicants, there are large differences 

in programmatic experiences by gender for this age group, with male applicants more likely to arrive on 

center and complete their vocations as compared to female applicants. There are also large differences 

between likely residential and likely non-residential applicants for this age group that are consistent for 

both males and females, with likely residential applicants more likely to have better programmatic 

experiences than likely non-residential applicants. Notable findings from our analysis include:

• About 71 percent of representative applicants’ aged 18-19 are predicted to arrive on center.

• Conditional on arrival, about one-third (32.4 percent) of students are expected to obtain their 

GED, and less than one-half (47.6 percent) are expected to complete their vocational training 

program.

• Male applicants aged 18-19 are significantly more likely to arrive on center (74.9 percent vs. 

64.5 percent) and complete their vocations (51.5 percent vs. 42.4 percent), as compared to 

female applicants. After arriving on center, male students are also particularly more likely to 

stay on center at least 270 paid days (33.9 percent vs. 25.9 percent for females). 

• There are large differences in the programmatic experiences by likely residential status for both 

males and females. 18-19 year-old applicants who are likely to be assigned to non-residential 

status are significantly less likely to arrive on center, to remain on center for certain periods, to 

attain a GED and much less likely to complete a vocational training program, than otherwise 

similar applicants who are likely to be assigned to residential status. 

3. 20-24 Year-Old Applicants
2-9

The same set of results for applicants’ aged 20-24 are summarized in the lower portion of Table 2-

1. For the most part, these older out-of-school applicants seem to have very similar programmatic 

experiences to their (slightly younger) 18-19 year-old counterparts. 

Although their enrollment rates are slightly lower, the predicted probabilities that 20-24 year-old 

applicants have positive programmatic experiences are similar to or higher than the predicted 

probabilities for 18-19 year-old applicants. This is due to older applicants being more likely than 18-19 

year-old applicants to have better programmatic experiences conditional on having enrolled in Job 

Corps. We also observe the same consistent pattern of better programmatic experiences by likely 

residential status where residential applicants have better programmatic experiences than likely nonresidential applicants. Highlights of our findings include:

• Overall, 67 percent of representative 20-24 year-old applicants are predicted to arrive on 

center.

• Conditional on arrival, about one-third (33.2 percent) of these students are expected to 

complete a GED, and over one-half (54.2 percent) are expected to complete their vocational 

trades. 

• Male applicants aged 20-24 are more likely than female applicants to arrive on center (70.6 

percent vs. 62.5 percent) and, once on center, to complete their vocational trades (56.5 percent 

vs. 51.2 percent). 

• Likely residential applicants aged 20-24 are estimated to be more likely to enroll in Job Corps; 

and, after arriving on center to obtain a GED, and complete their vocational training program 

compared to likely non-residential applicants. The pattern and size of the differences by 

residential status is similar for both males and females. 

D. SUMMARY

Looking across the results for these three age groups, several common patterns emerge. The most 

notable of these patterns are: 
2-10

• The applicant enrollment rate declines with age, from 79.8 percent for 16-17 year-old 

applicants, to 70.5 percent for 18-19 year-old applicants and to 67.0 percent for 20-24 yearold applicants. 

• Despite the higher enrollment rate of 16-17 year-old applicants, the predicted probabilities of 

attaining a GED while in Job Corps and of completing a Job Corps vocational training program 

are lower for 16-17 year-olds than they are for representative older students. This is 

particularly true for vocational training completion, where 54.2 percent of representative 20-24 

year-old students are predicted to complete their vocational training, as compared to 47.6 

percent for 18-19 year-old students and 40.4 percent for 16-17 year-olds. This is because the 

likelihood of completing a vocational training program conditional on enrollment is much larger 

for older applicants than for younger applicants.

• Conditional on arrival, older students are more likely to stay on center than comparable 16-17 

year-old students. The increased likelihood of older students to stay longer periods on center is 

in part responsible for their better programmatic accomplishments. 

• With the exception of 16-17 year-olds, there are consistent differences in programmatic 

experiences by gender. Specifically, male applicants are more likely to enroll and, conditional 

on arrival, to complete their vocational training than female applicants. However, female 

students are more likely to complete their GED – particularly among the 20-24 year old age 

group. 

• With the exception of 16-17 year-olds, there are strong and consistent differences in 

programmatic experiences by likely residential and non-residential status for both males and 

females. Regardless of gender, likely non-residential applicants are less likely to have favorable 

programmatic experiences (i.e., less likely to enroll, less likely to attain their GED, less likely to 

complete their vocation, less likely to stay on center for any length of time) than otherwise 

similar likely residential applicants. 

• The problems inherent in serving 16-17 year-old applicants identified in the process study are 

widespread and do not appear to be dependent on gender or residential status. However, there 

are very few applicants of this age that are assessed to need non-residential services, especially 

males.
Table 2-1. Predicted Probability of Arrival, GED Attainment, Vocational Completion, 

and Paid Days on Center: Overall, by Sex, Residential Status, and by Age Group

Conditional Upon Arrival

Arrival GED 

Attainment

Vocational

Completion

30 Paid 

Days

90 Paid 

Days

270 Paid 

Days 

Age 16-17

Overall 79.8 25.9 40.4 80.3 50.1 16.0

Males 80.3 25.4 39.8 79.5 45.4 15.4

Females 79.0 26.8 41.4 81.5 58.3 16.8

Male residential 80.3 25.5 39.9 79.4 46.0 15.2

Male non-residential 81.2 23.7 37.9 82.6 34.4 18.1

Female residential 79.7 27.1 41.6 82.6 59.6 15.7

Female non-residential 74.9 25.1 39.7 75.1 50.3 22.7

Age 18-19

Overall 70.5 32.4 47.6 86.6 68.2 30.3

Males 74.9 31.5 51.5 86.9 70.1 33.9

Females 64.5 33.6 42.4 86.1 65.6 25.9

Male residential 76.3 32.2 52.8 86.7 70.3 35.0

Male non-residential 58.6 23.1 35.9 88.2 67.0 22.1

Female residential 68.3 36.0 46.2 86.7 70.2 30.7

Female non-residential 51.9 26.0 30.0 84.3 50.9 13.6

Age 20-24

Overall 67.0 33.2 54.2 81.4 61.2 28.6

Males 70.6 29.7 56.5 81.3 59.0 25.4

Females 62.5 37.5 51.2 81.5 64.0 32.9

Male residential 72.2 30.4 57.2 81.5 59.4 25.3

Male non-residential 55.1 22.9 50.0 79.1 55.6 26.4

Female residential 69.7 41.1 53.9 82.0 62.9 31.3

Female non-residential 50.5 31.6 46.7 80.7 65.7 35.7

2-11
3-1

III. RESULTS FOR OUTREACH AND ADMISSIONS 

COUNSELOR TYPOLOGIES 

A. INTRODUCTION

In this chapter we present our findings concerning how various outreach and admissions counselor 

practices and characteristics are related to programmatic experiences. We first describe the specific 

OA counselor typologies developed to illustrate the estimated empirical relationships between OA 

counselor practices and characteristics and applicants’ programmatic experiences. We then present the 

results for the key programmatic experiences for these OA typologies separately for the three applicant 

age groups. The chapter concludes with a summary of the main findings that broadly apply across all 

three age groups. 

B. OA COUNSELOR TYPOLOGIES

In Chapter II, we described the set of OA practices and characteristics that were included in the 

representative case. As described in that chapter, the representative case reflects an OA contractor 

that is affiliated with a Job Corps center. It also reflects the relatively high job turnover of OA 

counselors (only 1-2 years of work experience) and the limited amount of time they typically spend with 

prospective applicants. It also reflects the limited amount of information available to the counselor about 

specific centers (e.g., has a video for 25-50 percent of the centers for which they recruit). The values 

selected for the representative case also reflect the relatively limited OA outreach efforts (e.g., applicant 

first saw counselor in OA office), and the limited active applicant screening that is performed (e.g., 

counselor does not conduct experience or attitude/goal assessments of applicants, counselor does not 

make home visits). The representative case also assumes that the counselor has a target goal for the 
3-2

number of new youth recruited that enter Job Corps, but the goal is not dependent on whether the 

recruited youth stay a certain minimum length of time (e.g., 30 days).

In Exhibit 3-1, we describe several different OA typologies that are based on differences in key 

OA counselor characteristics and practices in four broad categories: (1) OA contractor type; (2) OA 

screening practices; (3) OA outreach practices; and (4) OA counselor experience level and program 

knowledge. In the first column, we summarize the representative OA case that was described in 

Chapter II. In subsequent columns we highlight the OA characteristics that change from the 

representative case displayed in column (1); blank cells indicate that the values of the OA characteristics 

are the same as the values for the representative case. Below we provide additional details concerning 

the specific comparisons that can be made using these typologies.

The first category of OA characteristics and practices that differ across the typologies concerns 

contractor type. In Columns (2) and (3) we modify only the type of OA contractor that applies to the 

representative case. Column (2) presents the typology for private OA contractors that are not affiliated 

with a Job Corps center and Column (3) presents the typology for ES agencies. Because the only 

characteristic that changes from the representative case is the type of OA organization, the results 

presented below for these typologies can be interpreted as the simple difference in applicants’ 

programmatic experiences associated with being recruited by the three categories of OA contractor 

types.

The second area examined in these typologies concerns the OA counselor’s level of aggressiveness 

in conducting outreach and screening. As described in Columns (4) through (7) of Exhibit 3-1, we 

created four typologies that represent various combinations of specific OA practices that can be 
3-3

summarized into four categories: (1) aggressive outreach and aggressive screening; (2) aggressive 

outreach and passive screening; (3) passive outreach and aggressive 
3-4

Exhibit 3-1

Summary of Outreach and Admissions Typologies

OA Typologies

Characteristics

(1)

Job Corps Center

(2)

Private NonCenters

(3)

Employment 

Service

(4)

Aggressive 

Outreach, 

Aggressive 

Screening

(5)

Aggressive 

Outreach, Passive 

Screening

(6)

Passive Outreach, 

Aggressive 

Screening

(7)

Passive Outreach, 

Passive Screening

(8)

Very 

Experienced, 

Well- Informed

(9)

Less Experienced, 

Less Informed

OA contractor 

type

Job Corps Center Private NonCenter

Employment 

Service

Private NonCenterEmployment 

Service

Attempted to contact 

no-shows

No contact 

with no-shows

No contact 

with no-shows

No 30-day stay goal Had 30-day 

stay goal

Had 30-day 

stay goal

Video for 25-50% of 

the centers 50%+ <25% 50%+ <25% 50%+ <25%

No assessment of 

experience or 

attitudes/goals

Assessed 

attitudes/ 

goals

Assessed 

attitudes/ 

goals

No home visits Conducted 

home visits

Conducted 

home visits

Encouraged applicant 

to enroll in Job Corps

Strongly 

encouraged 

enrollment

Strongly 

encouraged 

enrollment

OA Screening 

Practices

Discussed wait time 

and length of stay 

expectations with 

applicants

Wait times 

and length of 

stay not 

discussed

Wait times 

and length of 

stay not 

discussed

Target goal for 

number enrolled No goals No goals

OA Outreach 

Practices Applicant came to 

counselor’s office

Counselor 

called 

applicant first

Counselor 

called 

applicant first

CCC/Contractor type 

matched likely center

Contractor 

type did not 

match center OA counselor 

experience

2 years 3 years Less than 1 

year

Note: Blank cells indicate that the characteristic is the same as the representative case in Column (1).
3-5

screening; and (4) passive outreach and passive screening. There are a number of different OA 

counselor practices that are used to define the degree of aggressiveness in outreach and 

 screening. The screening practices include whether the counselor typically contacts applicants 

who do not show up for their scheduled enrollment at a center (no-shows), whether the OA counselor 

has a target goal for recruits that stay at least 30 days on center, the percentage of centers that the 

counselor has a video for, the types of assessments the counselor usually conducts with applicants, 

whether the counselor conducts home visits, whether the counselor encourages an applicant to enroll in 

Job Corps, and the specific topics that are generally discussed with applicants. The outreach practices 

used in the typologies include whether the counselor has a target goal for the number of applicants who 

enroll and where applicants first come into contact with the counselor. As described in Exhibit 3-1, we 

create these four typologies using different assumptions for these factors.

The final OA typologies developed address the extent to which counselors’ experience and 

knowledge are related to the programmatic experiences of the applicants they recruit. In particular, 

Column (8) shows the typology for very experienced and well-informed OA counselors and Column (9) 

defines the typology for less-experienced and less-informed OA counselors. The typology in Column 

(8) differs from the representative case only in that the counselor has more work experience (3 years or 

more of experience as an OA counselor) and also has access to videos for the majority of the centers 

for which he or she recruits. Less-experienced and less-informed OA counselors have less than a year 

of work experience and have access to videos for less than one-quarter of the centers for which they 

recruit.

The final element of the less experience/knowledge typology presented in Column (9) concerns the 

counselor’s knowledge of the type of contractor that operates the center an applicant is likely to attend. 
3-6

 Specifically, as part of the data collection for the National Job Corps Study, OA counselors filled out a 

supplemental form that provided information about the Job Corps center each applicant was most likely 

to be assigned to, as well as whether the applicant would likely attend a center that was operated by a 

CCC or by a private contractor. In some cases, they often didn’t know whether a center was a CCC 

or contract center. We consider such inconsistent information as an indicator of less OA counselor 

knowledge/experience. 

C. RESULTS

In this section, we present results illustrating the extent to which applicants’ major programmatic 

experiences differ across the key OA counselor typologies.6Specifically, we compare applicants’ 

programmatic experiences for three different categories of typologies: (1) contractor type; (2) outreach 

and screening practices; and (3) OA counselor experience level and program knowledge. The results 

are discussed separately by age group.

1. 16-17 Year-Olds 

The results for 16-17 year olds are summarized in the top of Table 3-1. Table 3-1 shows the 

predicted probabilities of arrival; and, among applicants who enroll in Job Corps, the predicted

probabilities of GED attainment, vocational completion, and length of stay (paid days) on center for all 

of the OA contractor typologies. Notable findings from our analysis include: 

• Although arrival rates are lower for applicants recruited by OA counselors employed by State 

Employment Service (ES) agencies and private contractors not affiliated with a Job Corps 

center, the differences are not very large. Specifically, 80 percent of applicants recruited by the 

 

6

It should be noted that the results presented in this chapter for GED attainment and for vocational completion 

typically do not vary across the OA typologies. This is because many of the OA characteristics were omitted from 

these models that were estimated only among students who arrived on center because they were not found to be 

statistically significant. 
3-7

representative OA counselor employed by a contractor affiliated with a center enroll in the 

program as compared to about 75-77 percent of applicants recruited by other contractors. The 

likelihood of staying in the program at least 30, 90, and 270 paid days is lower for students 

recruited by a counselor employed by the ES as compared to counselors employed by private 

non-centers and by Job Corps centers. 

• There are significant differences in applicants’ programmatic experiences that are related to OA 

outreach and screening practices. Specifically, 16-17 year-old applicants served by OA 

counselors that follow passive outreach practices (i.e., placing the burden on the applicant to 

initiate contact with the program) and aggressive screening practices (i.e., conducting 

assessments of applicants and having a goal for enrollees who stay 30 days) are more likely than 

other applicants to have positive programmatic experiences. Applicants recruited by OA 

counselors who follow aggressive outreach and passive screening practices have the lowest 

applicant arrival rates. Over 80 percent of the applicants recruited by counselors following 

passive outreach and aggressive screening practices arrive on center. This compares to 50-55 

percent of the applicants served by counselors that follow passive screening strategies. 

Conditional on arrival, students served by counselors that follow this latter approach are also 

much less likely to stay on center after arrival than students recruited by counselors that follow 

aggressive screening practices.

• Applicants recruited by more experienced and well informed OA counselors are much more 

likely to arrive on center compared to applicants recruited by less experienced and informed 

counselors. For example, 83 percent of applicants recruited by more experienced and informed 

counselors enroll in Job Corps, as compared to only 73 percent for those served by less 

experienced and less-informed counselors and to 80 percent for the representative case. 

However, among those who arrive on center, students recruited by OA counselors with 

different experience levels have similar lengths of stay.

2. 18-19 Year-Old Applicants

Table 3-1 provides a similar set of predicted probabilities for 18-19 year old applicants. Notable 

findings from our analysis include: 

• The probability of arrival on center and length of stay on center for 18-19 year old applicants 

recruited by counselors working for different OA contractors are very similar. For example, 

whereas 71 percent of applicants aged 18-19 recruited by OA contractors affiliated with a 

center or the ES enroll in Job Corps, 68 percent of those recruited by private contractors not 

affiliated with a center enroll. Unlike the younger applicants, conditional on arrival, 18-19 year 

old students recruited by counselors working for a Job Corps center are slightly less likely to 

stay 30, 90 and 270 paid days on center compared to students recruited by counselors working 

for other OA contractors.
3-8

• There are substantial differences in 18-19 year-old applicants’ programmatic experiences 

across the various categories of OA counselor outreach and screening practices. Specifically, 

18-19 year-old applicants served by OA counselors that follow passive outreach practices and 

aggressive screening practices are more likely than other applicants to arrive on center. For 

example, about 75 percent of the applicants recruited by such counselors arrive on center as 

compared to between 28-53 percent of the applicants recruited by counselors that follow 

different strategies. In addition, conditional upon arrival, students recruited by counselors that 

follow aggressive screening practices (and either passive or aggressive outreach practices) are 

more likely to remain on center than students recruited by counselors that follow other practices.

• Applicants in the 18-19 year old age group recruited by more experienced and informed 

counselors are more likely to arrive on center. For example, 72 percent of applicants recruited 

by more experienced and well-informed OA counselors enroll in Job Corps, as compared to 

only 63 percent of applicants recruited by less experienced and less-informed counselors. 

Conditional on arrival, however, there are no differences in length of stay for these students. 

3. 20-24 Year-Old Applicants

Results for applicants’ aged 20-24 are summarized in the third panel of Table 3-1. Notable findings 

of our analysis are summarized below: 

• Overall, 67 percent of 20-24 year-old applicants recruited by counselors working for centers 

holding OA contracts are predicted to arrive on center, compared to about 58 percent of 

applicants recruited by other OA contractors. Conditional on arrival, students recruited by 

counselors working for centers or ES agencies are more likely to remain on center compared to 

students recruited by OA contractors affiliated with private (non-centers) agencies. 

• About 73 percent of 20-24 year-old applicants recruited by counselors that follow aggressive 

screening practices enroll in Job Corps, as compared to 20-35 percent of applicants recruited 

by OA counselors that follow passive screening practices. Among those recruited by OA 

counselors that follow passive screening, students recruited by counselors that follow passive 

outreach are more likely to stay on center as compared to students recruited by counselors that 

follow more aggressive outreach practices.

• The experience and knowledge levels of OA counselors are significantly related to the 

programmatic experiences of 20-24 year-old applicants. For example, applicants recruited by 

more experienced and well-informed counselors are about 18 percentage points more likely to 

enroll in Job Corps than otherwise similar applicants recruited by less experienced and lessinformed counselors. However, among the oldest age groups that arrive on center, students 

recruited by experienced and informed counselors are somewhat less likely to remain on center.
3-9

D. SUMMARY

Several common patterns emerge from examining the results for the various OA typologies across 

these three age groups. Specifically, there are a number of relationships between applicants’ 

programmatic experiences and counselor characteristics and OA practices that consistently appear in 

the results described above. The most notable of these patterns are:

• Applicants recruited by Employment Service agencies or private contractors not affiliated with Job 

Corps centers tend to be slightly less likely to enroll as compared to applicants recruited by 

counselors employed by an OA contractor affiliated with a Job Corps center. We find that the 

patterns regarding length of stay by OA agency type vary across age groups. As such, there 

appears to be no single OA agency type that leads to longer stays on center for all age groups of 

students.

• The outreach and screening practices of OA counselors play a very significant role in applicants’ 

programmatic experiences. For all age groups, there are large differences in the likelihood of arrival 

on center associated with aggressive screening practices of OA counselors. Moreover, for the two 

younger age groups, there are substantial differences in the length of stay of applicants recruited by 

counselors that follow aggressive screening as opposed to passive screening practices. For 

example, conditional on arrival, 16 - 19 year old students recruited by a counselor following 

aggressive screening practices are likely to stay on center longer than those recruited by counselors 

that follow passive screening practices. The differences by outreach practices are not as apparent 

for 20-24 year-old applicants.

• The experience level and knowledge of the OA counselor is also significantly related to applicant 

experiences. In particular, applicants recruited by more knowledgeable and experienced OA 

counselors are consistently much more likely to arrive on center than applicants recruited by less 

experienced and knowledgeable counselors, although length of stay is similar for students recruited 

by both groups of OA counselors. 
Table 3-1. Predicted Probability of Arrival, GED Attainment, Vocational Completion, 

and Paid Days on Center: Outreach and Screening Typologies by Age Group

Conditional Upon Arrival

Arrival GED 

Attainment

Vocational

Completion

30 Paid 

Days

90 Paid 

Days

270 Paid 

Days 

Age 16-17

Job Corps Center 79.8 25.9 40.4 80.3 50.1 16.0

Private non-centers 76.8 25.9 40.4 80.6 47.5 15.2

Employment service 74.8 25.9 40.4 76.4 43.0 13.8

Aggressive outreach/aggressive screening 67.5 24.4 32.6 82.7 42.9 13.0

Aggressive outreach/passive screening 50.2 24.4 32.6 65.6 38.6 12.6

Passive outreach/aggressive screening 80.5 25.9 40.4 82.9 53.4 17.1

Passive outreach/passive screening 55.6 25.9 40.4 61.2 37.7 12.9

Less experienced and less informed 73.3 25.9 40.4 81.1 55.9 17.9

Very experienced and well informed 83.0 25.9 40.4 80.3 57.5 18.4

Age 18-19

Job Corps Center 70.5 32.4 47.6 86.6 68.2 30.3

Private non-centers 67.6 32.4 47.6 89.0 73.1 32.5

Employment service 70.5 32.4 47.6 89.2 71.7 31.9

Aggressive outreach/aggressive screening 52.9 30.2 50.9 85.7 66.9 29.5

Aggressive outreach/passive screening 28.0 30.2 50.9 65.0 47.9 19.9

Passive outreach/aggressive screening 74.6 32.4 47.6 85.0 67.3 29.9

Passive outreach/passive screening 47.2 32.4 47.6 73.9 59.4 25.7

Less experienced and less informed 63.1 32.4 47.6 87.2 63.8 28.4

Very experienced and well informed 71.8 32.4 47.6 83.5 65.4 29.1

Age 20-24

Job Corps Center 67.0 33.2 54.2 81.4 61.2 28.6

Private non-centers 57.7 33.2 54.2 68.0 50.5 23.6

Employment service 57.6 33.2 54.2 76.5 62.6 29.3

Aggressive outreach/aggressive screening 72.5 32.1 55.6 62.3 37.7 15.8

Aggressive outreach/passive screening 35.0 32.1 55.6 63.8 51.5 23.9

Passive outreach/aggressive screening 73.4 33.2 54.2 84.4 62.8 29.4

Passive outreach/passive screening 20.0 33.2 54.2 67.6 62.2 31.9

Less experienced and less informed 52.4 33.2 54.2 85.6 65.5 30.6

Very experienced and well informed 70.6 33.2 54.2 77.1 59.0 27.6

3-9
4-1

IV. RESULTS FOR JOB CORPS CENTER TYPOLOGIES

A. INTRODUCTION

In this chapter we summarize the implications of our empirical model relating Job Corps center 

characteristics to the key programmatic experiences discussed in previous chapters. We first describe 

the specific center typologies that we developed to illustrate the estimated empirical relationships 

between center characteristics and student experiences. We then present results of the various 

programmatic experiences for key center typologies by student age group. The chapter concludes with 

a summary of the main findings that broadly apply across all age groups. 

B. JOB CORPS CENTER TYPOLOGIES

Chapter II discussed the characteristics used to describe the representative center. Specifically, the 

representative center is a medium-sized center (226-495 student slots), located in a city with a 

population of at least 50,000, operated by a private contractor that also has a contract to provide 

placement services to former students. This representative center offers the most common Job Corps 

vocational training programs (with student preferences guiding the trade selection and with no 

restrictions on changing trades), a standard Job Corps academic program, and does not offer precollege classes. Exhibit 4-1 summarizes these key characteristics of the representative center and 

explains the differences in these characteristics that define the specific center typologies created to 

illustrate the association between various center factors and students’ programmatic experiences.
4-2

Exhibit 4-1 Summary of Job Corps Center Typologies

Center Typologies

Characteristics (1)

Representative 

Center

(2)

Center 

with 

OA&P

(3)

Large 

Contract 

Center

(4)

Small CCC 

Center

(5)

Strong 

Vocational 

and 

Academics

(6)

Weak 

Vocational 

and 

Academics

(7)

Only High 

Demand 

Vocations

(8)

Center with 

All 

Vocations

(9)

Extensive 

Facilities

(10)

Limited 

Facilities

226-495 slots >735 

slots

<226 

slots

City of 50,000 Rural Broad Center 

Characteristics

Placement contract only Both 

OA&P

Both 

OA&P

No OA 

or P

5 - 11 hours in OEP

12+ hours 

in OEP

Less than 

5 hours in 

OEP

Vocational trade begins 2-4 weeks 

after arrival

Less than 

2 weeks

More 

than 4 

weeks

Student selects own trade No

OEP and 

Vocational 

Assignment

Placed in second choice trade if 

first choice full

Can wait 

for first 

choice

No 

waiting 

lists

No pre-college classes Yes

Academics/PPEP Student’s counselor and vocational 

instructor on PPEP

Add 

academic 

instructor 

on PPEP

PPEP 

counselor 

only

Vocational 

Program Offerings

Building and apartment 

maintenance

Business/ Clerical

Carpentry

Food Service

Health

Masonry

Other construction

Add 

Welding,

Mechanic 

and

Other 

Service

Drop 

Health 

Add

Welding

Drop

Carpentry 

and

Masonry;

Add 

Mechanic

All 

vocation

s 

available

No special arrangements for new 

students

Separate 

rooms 

for new 

students

Athletic field No

Center Facilities

Average number of beds per room

2-4 beds 

per room

9 or more 

beds per 

room

Note: Blank cells indicate that the characteristic is the same as for the representative center.
4-3

The typologies described in Exhibit 4-1 change center characteristics in five broad categories: (1) 

center involvement in outreach, admissions and placement (OA&P) (column 2); (2) center operator, 

size and location (columns 3 and 4); (3) quality of vocational and academic programs (columns 5 and 

6); (4) the specific vocational training programs available at the center (columns 7 and 8); and (5) the 

range of residential living facilities (columns 9 and10). In the remainder of this section, we provide 

additional details regarding the specific changes in center characteristics that define the typologies 

described in Exhibit 4-1.

The first typology varies the center’s involvement in OA & P. As described above, our 

representative center also holds a contract for placement services, but does not have an outreach and 

admissions contract. The typology described in Column (2) changes this characteristic such that the 

center has both a contract for outreach and admissions, as well as placement services. Thus, a 

comparison of results for centers with the characteristics presented in the first and second columns of 

Exhibit 4-1 illustrates the differences in students’ programmatic experiences that are associated with 

expanding center involvement to include all major components of the Job Corps program.

The second typology illustrates the combined effects of center operator, size and location. 

Specifically, Column (3) depicts a large contract center (735 or more slots) that also holds contracts for 

both OA and placement services and Column (4) describes a small center (less than 225 slots), 

operated by a CCC agency (located in a rural area) that is not involved in any OA or placement 

contracts. Results for the two typologies described in Columns (2) and (3) illustrate how students’ 

programmatic experiences are related to increasing center size from medium to large and expanding 

vocational offerings accordingly. Similarly, comparing results for our representative center (Column 

(1)) and the small CCC center depicted in Column (4) illustrates how students’ programmatic 
4-4

experiences differ between the representative privately operated center and a representative center 

operated by a CCC. In interpreting the results presented below showing the predicted programmatic 

experiences for students who are likely to attend the representative center as compared to small CCC 

centers and large contract centers, it is important to keep in mind how all of these different 

characteristics are defined under these typologies.

The next two columns of Exhibit 4-1 describe center typologies that vary features of the vocational 

and academic programs to illustrate the differences in applicant programmatic experiences associated 

with what we term “strong” and “weak” vocational and academic programs. The distinction between 

strong and weak programs involves a number of characteristics that change from the representative case 

to capture some of the different features of vocational and academic programs. For example, Column 

(5) depicts a center with strong vocational and academic programs. This center is characterized by 

having students spend more time in the Occupational Exploration Program (OEP) to decide which 

trades to select, moving students quickly into their vocational programs, allowing students to wait for an 

opening in their desired vocation if it was initially full, offering pre-college classes and having one of the 

student’s academic instructors included in his/her P/PEP. In contrast, centers with weak vocational and 

academic programs are defined in Column (6). These centers are characterized by students spending 

less than 5 hours in OEP, taking over 4 weeks to assign students to a vocational program, no “waiting 

lists” for vocational trade choices, and not having any of a student’s vocational or academic instructors 

involved in his/her P/PEP. As our description of these center typologies indicates, any differences in 

students’ programmatic experiences across the different center typologies are a result of numerous 

characteristics changing and cannot be attributed to any single center characteristic.

The next two columns alter the vocational offering available at the representative center. As 
4-5

indicated earlier, the representative center is defined as offering the most common Job Corps vocational 

training programs. The typology defined in Column (7) modifies this characteristic by changing the 

group of trades offered from the most common vocational programs to offering only the vocational 

programs that were identified as having the largest number of average annual job openings among the 

training areas currently available in Job Corps, which we refer to as “high-demand” vocational 

programs. This involves expanding the trades offered on center to include mechanics occupations 

training, as well as eliminating offerings in carpentry and masonry. Column (8) depicts a center that 

offers programs in all of the 13 broad occupational areas currently available in Job Corps.7These 

typologies illustrate the extent to which students’ programmatic experiences differ by vocational 

program offerings in terms of the most common vocations, high-demand occupations only, and all 

vocations.

Finally, the last two columns describe centers with different levels of residential living facilities. 

Specifically, the typology in Column (9) depicts a center with extensive residential living facilities. 

Consistent with the representative center, this typology assumes that the center has an athletic field. In 

addition, under this typology, the center provides new students with separate rooms, and all students are 

housed in rooms with no more than three other students. In contrast, Column (10) defines a center 

typology with limited residential living facilities as this center not only lacks an athletic field, but all 

students live in rooms with eight or more other students. Thus, comparisons in students’ experiences 

across these typologies involve differences

 

7

The 13 occupational areas currently available at Job Corps include: Masonry, Food Service, Carpentry, Building 

Maintenance NTC, Building Maintenance Non-NTC, Health Occupations Business, Mechanic, Service, Construction 

NTC, Construction Non-NTC, Welding, and Other.
4-6

in the availability of athletic fields, as well as differences in student privacy in the dormitories during their 

Job Corps stay.

C. RESULTS

To illustrate the extent to which programmatic experiences are related to these various center 

typologies, this section presents the predicted probabilities conditional on arrival for the following 

programmatic experiences: GED attainment, vocational completion, and length of stay (30, 90 and 270 

paid days).8 Specifically, we present results comparing students’ programmatic experiences across the 

ten different center typologies described above. The results are presented separately for students in the 

three age categories.

1. 16-17 Year-Old Students

The results for 16-17 year old students are summarized in Table 4-1. Table 4-1 presents the 

predicted probabilities of GED attainment, vocational completion, and length of stay (paid days) on 

center for all of the center typologies. Notable findings from our analysis include: 

• There are no substantial differences in students’ programmatic experiences that are related to 

center involvement in an OA contract over and above the center holding a placement contract. 

• There are more substantial differences in students’ programmatic experiences across the 

typologies that vary center operator, size and location. Specifically, 16-17 year-old students 

who are likely to enroll at small CCC centers are less likely to complete their GED and more 

likely to complete their vocational training and stay on center longer than similar students to a 

representative center. Further students who are likely to enroll at larger centers operated by 

private contractors are less likely to complete their GED and more likely to complete their 

 

8

In this chapter, we do not present results for the likelihood of arrival on center. This is because the only center 

characteristic that significantly affected the predicted probability of arrival was center size, with higher predicted 

probability of arrival for students likely to be assigned to large contract centers and small CCCs. As a result, the 

predicted arrival rates for applicants do not vary by center characteristics and the results in this chapter focus on the 

relationship between center characteristics and in-program experiences for students who arrive on center.
4-7

vocational training despite shorter stays on center as compared to similar students at the 

representative center. 

• Although vocational completion rates at large contract centers are lower than at CCCs, they are 

substantially higher than at the representative center despite serving students that stay for 

significantly shorter periods. These large centers appear to produce a relatively high volume of 

vocational completers within a relatively short average duration on center. This higher rate of 

vocational completion also does not appear to come at the cost of any apparent reduction in 

GED attainment as the slight drop in GED attainment that is shown in Table 4-1 for large 

contract centers was already apparent for centers that also perform OA activities.

• 16-17 year-old students who are likely to attend a center with strong vocational and academic 

programs are much more likely to have positive programmatic experiences. For example, about 

59 percent of students served by centers with strong academic and vocational programs are 

predicted to attain a GED, compared to 26 percent of students served by the representative 

center, and about 13 percent of students served by centers with weaker programs. In addition, 

students served by centers with stronger programs are also much more likely to complete their 

vocational trade programs and to stay at a center for longer periods as compared to students 

served by the typical center or by centers with weaker programs. 

• Young students at centers with all vocations available have more positive experiences as 

compared to students at either the representative center or at a center with only high demand 

vocations available. Specifically, these students are more likely to obtain a GED, complete 

vocational training and stay on center for longer periods. 

• Students aged 16-17 who are likely to attend centers with more extensive residential living 

facilities have more positive programmatic experiences in terms of GED completion and length 

of stay on center than those likely to attend centers with limited residential living facilities or a 

representative center. However, unexpectedly, students in centers with more limited residential 

facilities are more likely to complete their vocational training compared to students in centers 

with more extensive facilities. 

2. 18-19 Year-Old Students

Table 4-1 provides the same set of predicted probabilities for 18-19 year-old students. Notable 

findings from our analysis include: 

• The role of center involvement in outreach and admissions contracts along with placement 

contracts is quite minimal with respect to GED attainment and vocational completion for 

students 18-19 years old. However, students in this age group who are likely to attend centers 

that hold both outreach and admissions, and placement contracts are slightly more likely to stay 

on center than students who attend the representative center, which only has a placement 
4-8

services contract.

• Center size and center operator are significantly related to students’ programmatic experiences. 

 The results show that 18-19 year-old students who are likely to attend small CCC centers are 

consistently more likely to attain a GED and complete vocational training relative to students 

likely to attend a representative center. They also achieve these programmatic experiences 

through relatively longer durations of stay on center. In contrast, students in this age group who 

are likely to attend large contract centers also tend to complete their GED at higher levels than 

their counterparts who are likely to attend a representative center, but these better experiences 

are also associated with shorter program stays. 

• Students in this age group who are likely to attend centers that have strong vocational and academic 

programs are much more likely to have positive programmatic experiences compared to students 

who are likely to attend a representative center, and significantly more likely to have more positive 

experiences compared to students who are likely to attend centers with weak vocational and 

academic programs. 

• Similar to younger students, students aged 18-19 years who attend centers with all vocations 

available fare significantly better in terms of vocational completion compared to students at either the 

representative center or a center with only high demand vocations available. However, these 

students do not appear to stay as long on center and have slightly lower GED attainment rates than 

students that attend the representative center or a center with only high demand vocations.

• Students in this age group who are likely to attend centers with more limited residential living 

facilities have much less positive programmatic experiences as compared to 18-19 year old 

students at a representative center. However, students at centers with more extensive 

residential living facilities generally experience only slightly better accomplishments with respect 

to GED attainment, vocational completion, and length of stay as compared to students at the 

representative center.

3. 20-24 Year-Old Students

The empirical results for 20-24 year-old students are summarized in Table 4-1. Notable findings 

from the empirical models for this age group include: 

• Students aged 20-24 who are likely to attend a center that holds both an outreach and 

admissions contract and a placement contract experience slightly less favorable programmatic 

accomplishments compared to students at a representative center with responsibility only for 

placement activities. Specifically, these students are much less likely to attain their GED and 

less likely to complete their vocational trade.

• Center size and operator type is significantly related to programmatic experiences for students 
4-9

aged 20-24. For example, students who are likely to attend small CCC centers are much more 

likely to complete their vocational training and to stay on center longer relative to students at a 

representative center, and to those at large contract centers. However, students at a 

representative center are more likely to achieve a GED than students at large contract centers or 

small CCCs.

• 20-24 year old students who are likely to attend centers that have strong vocational and 

academic programs are much more likely to have positive programmatic experiences compared 

to students at a representative center and centers with weaker programs. For example, about 

66 percent of students at centers with strong vocational and academic programs are expected 

to attain a GED, compared to 33 percent of students at a typical center and compared to less 

than 1 percent of students at centers with weak academic and vocational programs. This same 

pattern holds for vocational completion. In addition, students at centers with strong vocational 

and academic programs are more likely to stay on center for longer periods of time relative to 

students at all other centers. 

• In contrast to the younger age groups, older students at centers with all vocations available have 

poorer programmatic experiences than similar students in centers that offer only high demand 

vocations.

• Students likely to attend centers with limited residential living facilities are less likely to have 

positive programmatic experiences. However, similar to the younger age groups, there does not 

appear to be a strong relationship between programmatic experiences and more extensive 

residential facilities for 20-24 year old students. For example, these students have very similar 

experiences with respect to GED attainment and vocational completion to students at a 

representative center. Thus, it appears that although residential living facilities do matter, once 

you meet a certain minimum level, more extensive facilities do not seem to be related to better 

programmatic experiences.

D. SUMMARY

Looking across the results for these three age groups, several common patterns emerge concerning 

the effects of various center characteristics and practices. The most 

notable of these patterns is:

• There are no consistent differences in students’ programmatic experiences that are related to 

center involvement in an OA contract over and above the center holding a placement contract. 
4-10

• Center operator type, size and location play an important role in students’ programmatic 

experiences. For all age groups, students who are likely to attend small CCC centers are more 

likely to complete their vocational trade training and more likely to stay on center for longer 

periods of time compared to students at other types of centers. Further, students in the 

youngest and oldest age groups who are likely to attend small CCC centers are also more likely 

to complete a GED.

• Across all age groups we consistently find that students who are likely to attend centers with 

strong vocational (as evidenced by strong vocational exploration, student choice of occupation, 

start of vocation training soon after arrival) and academic programs (as evidenced by precollege classes and participation of the academic counselor in PPEPS) have the most positive 

programmatic experiences. As expected, this is particularly true with respect to GED 

attainment and vocational completion. Moreover, the findings also suggest that older students 

tend to leave centers with weaker programs more quickly, and with less positive program 

experiences, compared to younger students facing similar circumstances. 

• A center’s particular mix of vocational offerings appears to play a role in an applicant’s 

programmatic experiences although the impact is very different by age group. Among 16-17 

year olds, students that attend centers with all vocations available are likely to have more 

positive programmatic experiences. However, among the older age groups, students that attend 

centers with all vocations available are likely to have less positive programmatic experiences 

than students who attend the representative center.

• In general, we find that limited facilities (i.e., no athletic fields, 9 or more students per dorm 

room) are associated with relatively poorer applicant programmatic experiences, but that 

extensive facilities provide little if any extra value over typical facilities. The relationships are the 

strongest with length of stay on a center. Across all age groups, more limited residential facilities 

are generally associated with a lower probability of staying on center, particularly for longer 

lengths of stay.

• In the process report we found that centers differ appreciably in the extent to which the 

characteristics of their staff (e.g., race/ethnicity, gender) match the characteristics of their 

students. We reported that students are more likely to find positive role models and mentors if 

the backgrounds of the students match the backgrounds of the staff. Thus, we hypothesized 

that centers in which the racial and gender backgrounds of the staff match the backgrounds of 

the students would result in more positive experiences. However, we found no consistently 

significant relationship when we examined the effects of the racial/gender composition of both 

students and staff on applicant programmatic experiences. 

• Finally, we find that the OMS center performance rankings are not related to the students’ 

measured programmatic experiences. Since Job Corps is a performance-driven system, we 

hypothesized that higher-ranking centers are more likely to obtain positive programmatic 

experiences. However, in analyses not shown in this report, we found no relationship between 
4-11

OMS rankings and applicant arrival, length of stay, GED attainment or vocational completion. 

There are at least two possible interpretations of this lack of a relationship between OMS 

rankings and programmatic experiences. First, it may indicate that the OMS system does not 

adequately adjust for differences in student characteristics, since after controlling for 

background characteristics, programmatic achievements seem to be unrelated to OMS ranking. 

 Second, the lack of a relationship could be due to the fact that the OMS system at the time of 

the study included many other measures in addition to those examined here. 
4-12

Table 4-1. Predicted Probability of GED Attainment, Vocational Completion, 

and Paid Days on Center: Center Typologies by Age Group

Conditional Upon Arrival

GED 

Attainment

Vocational

Completion

30 Paid 

Days

90 Paid 

Days

270 Paid 

Days

Age 16-17

Representative center 25.9 40.4 80.3 50.1 16.0

Center with outreach admissions and placement contract 21.5 43.0 79.0 46.6 16.0

Large contract center 20.6 52.9 70.7 43.2 14.5

Small CCC center 23.4 67.8 87.2 64.0 28.4

Strong vocational and academics 59.1 55.5 80.1 61.6 18.0

Weak vocational and academics 12.8 41.9 77.4 53.5 18.8

Only high demand vocations 25.8 41.4 80.3 51.9 15.6

Center with all vocations 31.5 51.5 86.9 70.1 33.9

Extensive facilities 26.1 35.0 81.9 54.8 20.3

Limited facilities 12.8 45.7 78.8 36.5 8.0

Age 18-19

Representative center 32.4 47.6 86.6 68.2 30.3

Center with outreach admissions and placement contract 35.5 47.6 82.6 64.4 27.0

Large contract center 45.0 47.6 77.2 61.2 22.3

Small CCC center 42.0 57.2 92.8 74.8 31.5

Strong vocational and academics 44.2 57.3 92.4 68.8 38.9

Weak vocational and academics 9.9 37.7 80.4 62.0 12.8

Only high demand vocations 33.1 46.2 86.6 67.9 27.2

Center with all vocations 29.7 56.5 81.3 59.0 25.4

Extensive facilities 35.0 47.9 90.6 66.9 29.9

Limited facilities 20.1 28.8 82.5 53.6 13.2

Age 20-24

Representative center 33.2 54.2 81.4 61.2 28.6

Center with outreach admissions and placement contract 26.4 44.2 73.8 63.7 28.3

Large contract center 18.7 44.1 78.9 69.4 25.6

Small CCC center 18.7 65.6 88.3 73.6 34.4

Strong vocational and academics 66.2 66.9 81.7 69.4 46.1

Weak vocational and academics 0.4 18.9 86.0 10.7 0.5

Only high demand vocations 34.5 54.0 81.4 61.1 29.0

Center with all vocations 25.1 41.3 79.7 47.5 15.2

Extensive facilities 33.8 52.6 87.3 57.3 30.5

Limited facilities 16.4 36.9 90.5 51.2 6.3

4-12
5-1

V. SUMMARY AND CONCLUSIONS

A. INTRODUCTION

In this report, we presented the results of statistical models designed to improve our understanding 

of the factors that are related to Job Corps applicants’ programmatic experiences. In particular, we 

examined the role that outreach and admissions counselor characteristics and practices, and center 

characteristics and practices play in key applicant programmatic experiences and accomplishments. 

The Job Corps programmatic experiences and accomplishments we examined include the likelihood of 

arrival, GED attainment, vocational completion and length of stay. The results presented in this report 

form the basis for subsequent analysis of the impacts of programmatic experiences on applicants’ labor 

market outcomes.

It is important to note that the findings from the statistical models presented in this report have their 

roots in the process analysis study (Johnson et. al., February 1999). More specifically, the process analysis 

identified the basic Job Corps program model, as well as numerous variations in the way the model is 

implemented across OA counselors and centers. The process analysis report hypothesized that these 

variations in practices could lead to variations in students’ programmatic experiences and, in turn, in postprogram outcomes. This report represents the results of analyses designed to test key hypotheses about the 

relationships between program characteristics or practices and students’ program experiences. However, 

whether these different experiences affect applicants’ post-program labor market outcomes will be 

addressed in subsequent analyses. 

In this concluding chapter, we summarize the main findings from this report and link them to the 

findings from the process analysis. This chapter is organized by the two types of typologies described in 
5-2

previous chapters: (1) OA contractor/counselor typologies; and (2) center typologies. The chapter 

concludes with some caveats and a discussion of next steps.

B. OUTREACH AND ADMISSIONS COUNSELOR TYPOLOGIES

Three main types of contractors are provided by outreach and admissions services: (1) ES and 

other state and local agencies; (2) private organizations affiliated with a Job Corps center; and (3) 

private organizations not affiliated with a center. The process analysis identified several key findings and 

potential hypotheses concerning OA contractor/counselor characteristics and practices and the potential 

impacts on applicant programmatic experiences. The results reported in Chapter III generally support 

the hypotheses from the process analysis. Specifically, we find:

Applicants served by ES agencies or private contractors not affiliated with Job Corps centers 

tend to be less likely to enroll in Job Corps compared to applicants served by OA contractors 

affiliated with a Job Corps center. Although in this report we found that these differences are 

relatively small, they are important in light of possible efforts to increase the role that centers have in OA 

activities. The patterns regarding OA contractor type and length of stay on center vary by age group. 

As such, there does not appear to be a single agency type that is associated with longer stays across all 

age groups. 

The outreach and screening practices of OA counselors also play a very significant role in an

applicant’s programmatic experiences. In the process report we found that although OA 

counselors on average devote considerable time to outreach activities, some counselors follow a more 

passive approach to recruiting and, for the most part, essentially require interested youth to contact 

them. Although such an approach may produce fewer applicants and make it more difficult for 

counselors to meet their arrival goals, such a process also results in a more committed pool of youth that 

make it to the application stage. Based on these results, we hypothesized that OA counselors that 

follow a passive approach to outreach will recruit youth who are more committed to Job Corps and as 

such, more likely to enroll and have better programmatic experiences, than applicants recruited by OA 

counselors that follow an aggressive outreach strategy. 

The process analysis also documented that most OA counselors primarily screen into the program youth 

that meet the “traditional,” easily documentable eligibility criteria related to age, U.S. citizenship, income 

and so forth; relatively few youth that are otherwise eligible are found ineligible by OA counselors for 

other criteria related to capability and aspirations to participate. Thus, we hypothesized that OA 

counselors that follow aggressive screening practices are more likely to identify youth that can benefit 
5-3

from the program and achieve more positive programmatic experiences.

In this report we found that the outreach and screening practices of OA counselors play a very 

significant role in an applicant’s programmatic experiences. For all age groups, there are large 

differences in the likelihood of arriving on center due to the aggressive screening practices of OA 

counselors. For the two younger groups, we also find that there are large increases in the likelihood of 

having more positive programmatic experiences for applicants served by counselors that follow passive 

outreach practices. The differences by outreach practices are smaller for 20-24 year-old applicants. 

These results suggest that it is important to provide training so that OA contractors and counselors 

understand the effects that different outreach and screening practices have on youths’ programmatic 

experiences so that they can adjust their strategies to maximize program experiences for applicants.

The experience level and knowledge of the OA counselor that serves an applicant plays a key role 

in an applicant’s experiences. In the process report we found that OA counselors had limited job tenure 

and limited firsthand knowledge of the centers for which they recruit. Moreover, center staff reported that 

this high turnover and lack of knowledge led to many students arriving on center without a good idea of 

what to expect and what the specific center offers. Thus, we hypothesized that applicants recruited by OA 

counselors that have less experience and less knowledge about the program are likely to stay shorter 

periods in Job Corps and have poorer programmatic experiences than applicants served by OA counselors 

with more experience and program knowledge. 

In this report we found that the experience level and knowledge of the OA counselor that recruits an 

applicant plays a key role in an applicant’s experiences. Uniformly across all age groups, applicants 

served by experienced and well-informed OA counselors are much more likely to arrive at Job Corps 

than applicants served by less experienced and less knowledgeable counselors. This emphasizes the 

importance of ensuring that up-to-date materials/information about centers and videos depicting life on 

center are available and used by OA counselors. It also emphasizes the importance of developing 

recruitment/retention strategies to reduce the high turnover of OA counselors.

C. CENTER TYPOLOGIES

The process analysis study identified several variations in the ways in which centers implement the 

Job Corps program model that could be related to applicants’ programmatic experiences. The results 

presented in Chapter IV confirm the general nature of the findings obtained from the process analysis. 

These findings include:

For the most part, adding center involvement in OA activities (in addition to placement 

activities) is minimally related to an applicant’s programmatic achievements. In Chapter IV, 

we reported that only for older out-of-school youth does center involvement in outreach and admissions 
5-4

matter, and in this case it is associated with a slightly lower likelihood of positive programmatic 

experiences as compared to the representative case.

Center operator type, size and location play an important role in students’ programmatic 

experiences. We found that for all age groups, students at small CCC centers are more likely to 

complete their vocational trade and more likely to stay on center for longer periods of time compared to 

students at other types of centers. This is consistent with the process analysis findings indicating that 

CCC centers are more focused on vocational training than other centers. In addition, we find that 16-17 

year-old students who attend large contract centers have higher vocational completion rates than the 

representative center, but with much shorter lengths of stay. This relationship was not anticipated in the 

process study.

A center’s particular vocational offering plays a role in an applicant’s programmatic 

experiences. In the process report we found that the range of vocational offerings, and the extent to 

which the training programs offered are “in demand,” varies considerably across centers. We 

hypothesized that centers that offer a wider range of vocational programs, as well as trades that are 

more in demand, are more likely to produce positive programmatic experiences for students.

A center’s particular mix of vocational offerings appears to play a role in a student’s programmatic 

experiences although the impact is very different by age group. Among 16-17 year old students, 

students that attend centers with all vocations available are likely to have more positive programmatic 

experiences. However, among the two older age groups, students that attend centers with all vocations 

available generally have poorer programmatic experiences. The more positive experiences for young 

students could reflect the value of having a wider choice of vocations available for students who are less

sure of what vocational training they are interested in. It is also important to note, however, that this 

result refers strictly to substitutions of existing trades for others, or by offering trades that are already 

available at other centers, and do not necessarily hold if Job Corps were to introduce entirely new 

vocational training programs.

Applicants to centers with strong vocational and academic programs have the most positive 

programmatic experiences. In the process report, we found many differences across centers in the 

strength of their vocational programs, in terms of the amount of OEP provided, and policies for 

assigning students to trades and switching among trades. Also, although all centers offer the standard 

Job Corps academic program, some centers supplement the basic curriculum with other academic 

programs that provide additional services to youth. Thus, we hypothesized that students who attend 

centers with stronger vocational and academic programs will be more likely to have positive 

programmatic experiences.

The results presented in this report support this hypothesis. Specifically, across all age groups, we 

consistently find that applicants to centers with strong vocational and academic programs have the most 

positive programmatic experiences. As expected, this is particularly true with respect to GED 

attainment and vocational completion. 
5-5

Limited residential living facilities are associated with relatively poor applicant 

programmatic experiences, but extensive facilities do not provide “extra value.” In the process 

report, we found that the quality of center facilities varies considerably. This was particularly true for 

residential living facilities, which is likely to significantly affect program length of stay. Thus, we 

hypothesized that centers with facilities that provide greater student privacy will result in longer student 

lengths of stay and better program experiences. In addition, we found that recreation is an important 

part of residential living and hypothesized that centers that provide more recreational opportunities for 

students will result in longer student lengths of stay and better program experiences.

In this report, we found that limited residential facilities are associated with relatively poor applicant 

programmatic experiences, but that extensive facilities do not provide extra value over the typical 

facilities provided by our representative center. As suspected, the differences associated with 

residential living facilities are largest for applicants’ length of stay on a center. Across all age groups, 

more limited residential facilities are associated with a lower probability of staying on center --

particularly for longer lengths of stay. 

The “match” between the racial/gender composition of students and staff is not related to 

student programmatic experiences. In the process report we found that centers differ appreciably in 

the extent to which the characteristics of their staff (e.g., race/ethnicity, gender) match the characteristics 

of their students. We reported that students are more likely to find positive role models and mentors if 

the backgrounds of the students match the backgrounds of the staff. Thus, we hypothesized that centers 

in which the racial and gender backgrounds of the staff match the backgrounds of the students would 

result in more positive experiences. However, in preliminary analysis not reported here, we found no 

relationship between the racial/gender composition of students and staff and applicant programmatic 

experiences.

The OMS center performance rankings are not related to the students’ measured 

programmatic experiences. Job Corps is a performance-driven system. Thus, we hypothesized that 

higher-ranking centers are more likely to obtain positive programmatic experiences. However, in 

analyses not shown in this report, we found no relationship between OMS rankings and applicant 

programmatic experiences related to arrival, length of stay, GED attainment or vocational completion. 

There are at least two possible interpretations of this lack of a relationship between OMS rankings and 

programmatic experiences. First, it may indicate that the OMS system does not adequately adjust for 

differences in student characteristics, since after controlling for background characteristics, 

programmatic achievements seem to be unrelated to OMS ranking. Second, the lack of a relationship 

could be due to the fact that the OMS system at the time of the study included many other measures not 

examined here. 

D. CAVEATS AND NEXT STEPS

In this report, we presented the results of the first step of a two-step process to understand how 
5-6

different Job Corps programmatic experiences impact applicants’ post-program labor market 

outcomes. Specifically, we summarized our findings of the statistical relationship between various 

programmatic experiences and OA and center typologies. We also linked the findings to the results of 

the process analysis study. 

In reviewing these results, it is important for the reader to keep in mind that they do not represent 

causal relationships. That is, the analysis addresses the effects of particular OA or center approaches 

for contractors or centers that chose to adopt the approach. However, the results cannot be used to 

address how the approach would work in other OA agencies or centers that chose to adopt other 

approaches. Nevertheless, the analysis provides important suggestive findings about the success of 

particular program approaches if they were to be adopted on a broader scale. 

The second step of this analysis is to examine how programmatic experiences impact applicant 

labor market outcomes. In Appendix D, we provide selected exploratory results of the effects of 

programmatic experiences on weekly earnings measures constructed from the 30-month post-program 

survey. For many reasons detailed in Appendix D, the results of this exploratory analysis were not very 

satisfactory. The next step involves conducting similar analyses with post-program data from the 48-

month survey to determine whether more consistent patterns of results emerge.
A-1

APPENDIX A

METHODOLOGY

In the first subsection of this appendix, we present a description of the statistical models used to 

examine youth’s programmatic experiences. The second subsection describes the empirical 

specifications used to estimate the universal multinomial logistic models for the eight probabilities Pr(e | 

X, O, A), Pr(v | X, C, e = 1), Pr(g | X, C, e = 1), Pr(c | X, C, e = 1, v), Pr(d > 30 | X, O, A, C, e = 

1), Pr(d > 90 | X, O, A, C, e = 1, v, d > 30), Pr(d > 180 | X, O, A, C, e = 1, v, d > 90), and Pr(d > 

270 | X, O, A, C, e = 1, v, d > 180). 

A.1 Description of Statistical Models

To examine the sequencing of youths’ programmatic experiences from application through 

termination from the program, we utilize multinomial logistic models. The formulation of the sequential 

probability models used here characterizes eight key aspects of applicants’ programmatic experiences:

1. The probability that an applicant arrives at a Job Corps center and enrolls in the 

program;

2. The likelihood of selecting one of 14 vocational training areas offered by Job Corps, 

including the possibility of not participating in any vocational training program, 

conditional on enrollment;9

3. The likelihood of receiving a GED while enrolled in Job Corps, conditional on 

enrollment;

 

9Although a substantial percentage of Job Corps students participate in more than one vocational training 

program, the specific vocational training area modeled here is based on the area in which the student completed the 

highest level of training. If the same level was attained in more than one vocational area, the vocation in which the 

student participated for the longest time is considered as the selected vocational training area.
A-2

4. The likelihood of completing vocational training, conditional on enrollment and the 

student’s chosen vocational training program;

5. The likelihood of remaining in Job Corps for more than 30 paid days of enrollment, 

conditional on enrollment;

6. The likelihood of remaining in Job Corps for more than 90 paid days, conditional on 

enrollment, the student’s chosen vocational training program, and remaining in Job 

Corps for at least 30 paid days;

7. The likelihood of remaining in Job Corps for more than 180 paid days, conditional on 

enrollment, the student’s chosen vocational training program, and remaining in Job 

Corps for at least 90 paid days; and

8. The likelihood of remaining in Job Corps for more than 270 paid days, conditional on 

enrollment, the student’s chosen vocational training program, and remaining in Job 

Corps for at least 180 paid days.

Together, these eight probabilities fully characterize the programmatic experiences of Job Corps 

applicants of interest to the current study. They also represent all of the programmatic experiences that 

will be needed in subsequent analyses of the impact of the program for different vocational training 

areas, as well as the impacts for applicants who achieve a GED, complete a vocation or remain in the 

program for a certain length of time.

To formalize this empirical specification, we define each of these probabilities using the following 

notation:

Pr(e | X, O, A) = Probability enroll in Job Corps, given individual 

characteristics X, OA practices O, and OA counselor 

characteristics A.

Pr(v | X, C, e = 1) = Probability participate in vocational training area v, given 

individual characteristics, center characteristics C, and 

enrollment.

Pr(g | X, C, e = 1) = Probability receive a GED while enrolled in Job Corps, 

given individual characteristics, center characteristics and 
A-3

enrollment.

Pr(c | X, C, e = 1, v) = Probability complete vocational training at a completer or 

advanced completer level while enrolled in Job Corps, 

given individual characteristics, center characteristics, 

enrollment, and participation in vocational training area v.

Pr(d > 30 | X, O, A, C, e=1) = Probability enrolled in Job Corps for more than 30 paid 

days given individual characteristics, OA practices, OA 

counselor characteristics, center characteristics, and

enrollment.

Pr(d > 90 | X, O, A,C, e=1, v, d>30) = Probability enrolled in Job Corps for more than 90 paid 

days given individual characteristics, OA practices, OA 

counselor characteristics, center characteristics, enrollment, 

participation in vocational training area, and enrollment for 

more than 30 paid days.

Pr(d >180| X,O,A,C,e = 1,v,d >90) = Probability enrolled in Job Corps for more than 180 paid 

days given individual characteristics, OA practices, OA 

counselor characteristics, center characteristics, enrollment, 

participation in vocational training area, and enrollment for 

more than 90 paid days.

Pr(d>270|X,O,A,C,e=1,v,d>180)= Probability enrolled in Job Corps for more than 270 paid days 

given individual characteristics, OA practices, OA counselor 

characteristics, center characteristics, enrollment,

participation in vocational training area, and enrollment for 

more than 180 paid days.

The choice of these specific eight programmatic experiences was made to address the goal of 

developing estimates of the impacts of key program components on post-program employment and 

earnings. The first step in this process requires an understanding of an applicant’s decision to enroll in 

Job Corps and the factors that affect that decision. Conditional on enrollment, the goal of the 

component impact analysis is to estimate the post-program employment and earnings impacts for 

students who attain a GED, participate in specific vocational training programs, complete their vocation, 
A-4

and for those who remain in the program for various periods of time. An analysis of the factors that 

affect the likelihood of each of these programmatic experiences is a necessary first step in the process of 

determining the impacts of these experiences on post-program outcomes. 

Consistent with the empirical framework, we estimated the statistical models for the different 

programmatic experiences as a series of conditional probabilities. It is important to note, however, that 

the results of these models are used to calculate probabilities that are more meaningful to program 

managers. Specifically, we use the results from the various conditional models estimated above to 

calculate the following:

• The likelihood that an applicant arrives on center; and 

• Conditional on arrival, the likelihood that a student:

- Obtains a GED

- Participates in a specific vocational training program area

- Completes a vocational training program

- Stays at least 30 paid days

- Stays at least 90 paid days

- Stays at least 180 paid days

- Stays at least 270 paid days

In the main body of the report, we present the results for enrollment into Job Corps, and conditional on 

enrollment, we present the results for GED attainment, vocational completion, and whether the student 

stays at least 30, 90, and 270 paid days. The vocational choice results are presented in Appendix B. 

Although we calculated four different length of stay measures, we chose to present the results for 30, 90 

and 270 days based on discussions with Job Corps National Office staff to focus on early dropouts, 

students who stay long enough to begin to receive substantial services, and long-term stayers. 
A-5

A.2 Specification of Estimating Equations

Universal multinomial logit specifications are used to estimate all of the probabilities characterizing the 

relationships between program experiences and applicant characteristics, OA practices, OA counselor 

characteristics and center characteristics. Simple binary logit specifications are used to estimate the 

likelihood of enrolling in Job Corps, the attainment of a GED, completion of a vocational program, and the 

four duration of stay measures. More complex multinomial logit specifications are implemented to estimate 

the parameters of the probabilities that determine the likelihood a student enrolls in a specific vocational 

training area given the vocations available at the Job Corps center where he or she enrolled. Below we 

provide additional details of the models estimated for each of the different program experiences.

We parameterize the enrollment probabilities using a binary logit specification with a general form given 

by

 ,

1+exp

exp

Pr ( e | X,O, A )= e e e

e e e

X +O + A 

X + O + A 

a b g

a b g

where X represents characteristics of the Job Corps applicant, O represents measures of the practices 

used by the OA agency that recruited the applicant, A denotes characteristics of the OA counselor who 

recruited the applicant, and áe , âe , and ãe are suitably dimensioned parameter vectors. To estimate this 

specification, we apply a conventional maximum likelihood procedure using a sample consisting of all 

program group members who completed a baseline interview.

The statistical model introduced to parameterize the vocational choice probabilities is a modified version 

of the universal multinomial logit class. A modification to the standard multinomial logit model is required to 

account for the fact that not all vocational training areas are available at all Job Corps centers, thereby 

limiting the choices of students at such centers. Specifically, the modification needs to accommodate the 
A-6

fact that some vocational choice probabilities are constrained to zero because vocational programs in that 

area are not offered at the student’s center. We incorporate this modification via the specification:

 , k =1, , 14 ,

 exp

exp Pr ( v | X,C,e = 1 )=

X +C + d 

14

j = 1

X +C + d 

k

vj vj k

vk vk k

K

a q y

a q y

∑

where vk represents participation in vocational training area k, X again represents characteristics of the 

Job Corps applicant, C denotes characteristics of the Job Corps center, dk is an indicator variable 

defined below, and ávk, èvk and ø are suitably dimensioned parameter vectors. The only difference 

between this specification and a more standard multinomial logistic model is the presence of the quantity 

ø dk. The indicator variable dk takes into account the availability of 

vocational training programs at specific centers such that dk = 1 if vocational training area k is not 

available at the student’s Job Corps center; otherwise, we set dk = 0. Further, we set the value of ø to 

minus infinity, which implies that Pr(vk | X, C, e = 1) = 0 if vocational training area k is not available at 

the student’s center. Finally, as a normalization, we constrain the coefficients of the no vocational 

training area option (k = 14) to zero (i.e., áv14 = 0 and èv14 = 0).10 We apply standard maximum 

likelihood estimation methods to this specification using a sample of all program group members who 

completed a baseline interview and enrolled in Job Corps within the first 30 months after random 

assignment.

The specification of Pr(g | X, C, e = 1) used in the estimation takes the form of a simple binary 

logit model given by

 

10 It is crucial to recognize that d14 = 0 for all centers because students can always choose to 

never participate in a vocational training program.
A-7

 ,

1+exp

exp

Pr ( g | X,C, e = 1)= X + C 

X + C 

g g

g g

a q

a q

where X represents applicant characteristics, C denotes center characteristics and ág and èg are suitably 

dimensioned parameter vectors. Estimates of ág and èg are obtained by applying standard maximum 

likelihood methods using a sample of all program group members who completed a baseline interview, 

enrolled in Job Corps within the first 30 months after random assignment and who did not possess a 

high school diploma or GED at the time of application.

The statistical model used to estimate the likelihood of completing a vocational program also uses a 

simple binary logit specification given by

 ,

1+exp

exp

Pr ( c | X,C,e = 1, v )= c c c

c c c

X +C + v 

X + C +v 

a q p

a q p

where the variables X,C, and v are the same as above and ác, èc and ðc are suitably dimensioned 

parameter vectors. Maximum likelihood methods are used to estimate the parameters of this 

specification using the sample of program group members who completed a baseline interview, enrolled

in Job Corps within the first 30 months after random assignment and who participated in one of the 13 

specific vocational training areas, which effectively excludes students who did not participate in any 

vocational training program.

A simple binary logistic model is also used for the sequence of conditional probability models that 

characterize the likelihood applicants stay in Job Corps for a specified number of days. To present the 

specification of this sequence of probabilities in as concise a way as possible, let d0 represent zero paid 

days of enrollment (i.e., did not enroll in Job Corps), d1 denote 30 paid days in Job Corps, d2 represent 

90 paid days in Job Corps, d3 denote 180 paid days in Job Corps, and d4 represent 270 paid days in 
A-8

Job Corps. Then we can express the probability that an applicant stays in Job Corps for a specified 

number of days as:

 ,

1+exp

exp

Pr ( d > d | X,O, A,C,v d > d )= di di di di di

di di di di di

X +O + A +C v

X + O + A +C v

i i-1 a b g q p

a b g q p

+

+

,

where X, O, A, and C are defined as above, ádi , âdi , ãdi , èdi, and πdi are suitably dimensioned 

parameter vectors, and d > 0 signifies enrollment in Job Corps. In the estimation we impose the 

constraint that πd1 = 0 because students do not begin participating in their selected vocations during the 

first few weeks of enrollment. These four logit models are also estimated using maximum likelihood 

methods that are sequentially applied to smaller samples of treatment group members who completed a 

baseline interview and had lengths of stay in Job Corps greater than 0, 30, 90 and 180 days, 

respectively.

As indicated above, the conditional probabilities of vocational choice, GED attainment, vocational 

completion and duration of stay are all estimated conditional on enrollment in Job Corps. As such, the 

center characteristics that are included in these conditional models are the characteristics of the actual 

center that the student attended. 
B-1

APPENDIX B

RESULTS FOR VOCATIONAL CHOICES OF JOB CORPS STUDENTS

This appendix presents the implications of the models estimated to characterize the vocational 

choices of Job Corps applicants. More specifically, we present the predicted probabilities of vocational 

trade choices for the different student typologies described in Appendix C and for the different center 

typologies described in Chapter IV. Tables B-1 through B-3 show the predicted probabilities that 

representative students with different background characteristics will select among the vocations 

available to them for 16-17 year olds, 18-19 year olds, and 20-24 year olds, respectively. Tables B-4 

through B-6 present the predicted probabilities that representative 16-17, 18-19, and 20-24 year old 

students, respectively, will select each of the available vocations for centers with different 

characteristics.11 These percentages average over male and female students with the specified 

characteristics.

Before discussing the results, it is important to briefly describe how we characterize the vocational 

trade choice set available to Job Corps students. Job Corps offers vocational education in 

approximately 75 trades nationwide, with approximately 75 percent of the slots in eight broad 

occupational areas: clerical, health, carpentry, masonry, building and apartment maintenance, food 

service, mechanic and welding. We group the remaining slots into three general occupational areas: 

construction, service, and other. 

The other aspect of vocational choice that we incorporate in the model concerns whether the 

 

11 The percentages in the rows of these tables add to 100%, as they represent all of the 

vocational options available to students.
B-2

training is provided by the center or by a National Training Contractor (NTC). Although some training 

slots are provided by NTCs in each of these 11 occupational areas, because of sample size limitations 

we were only able to distinguish NTC from center-provided training in two occupational areas: 

construction, and building and apartment maintenance. As a result of these decisions, the results 

presented below assume that a student’s vocational choice set is comprised of 13 possible vocational 

training programs: clerical, health, carpentry, masonry, NTC-provided building and apartment 

maintenance, non-NTC provided building and apartment maintenance, food service, mechanic, welding, 

NTC-provided construction, non-NTC provided construction, service, and other. Taking into account 

the possibility that a student will not participate in any vocational training program, the overall vocational 

choice set includes 14 options.

The predicted probabilities that a representative student aged 16-17 will select each of the 14 

possible vocational training options available to them for the different student typologies are presented in 

Table B-1. As indicated in the first row, health occupations (15.7 percent) and business occupations 

(14.9 percent) are the most prevalent vocations among 16-17 year-old students; carpentry (7.3 

percent) and building and apartment maintenance (7.1 percent) are among the least chosen vocations 

when they are available. The last column of the first row shows that about 12 percent of representative 

16-17 year old students do not enroll in any vocations. We also find that 16-17 year-old Job Corps 

students who have a criminal and drug use history are much less likely to participate in business 

occupations and service occupations programs and more likely to participate in one of the constructionrelated or food service trades. Aside from the large differences due to criminal and drug use histories, 

we find few differences in these predicted probabilities across these typologies, implying that vocational 

choices are unrelated to other characteristics of the youngest students. 
B-3

The vocational choices of 18-19 year old Job Corps students presented in Table B-2 follow a 

pattern similar to their younger counterparts. Again, health (16.1 percent) and business occupations 

(17.8 percent) are the most prevalent, with carpentry (4.9 percent), building and apartment maintenance 

provided by NTCs (7.9 percent) and other NTC provided construction-related trades (9.8 percent) 

among the least chosen vocations. Moreover, 18-19 year old students with criminal and drug use 

histories (and who are early dropouts with no work experience) are much less likely to select business 

or other service occupations; they are more likely to either not participate in a vocation or to select one 

of the construction-related trades. Finally, the results in the fifth and six rows show that students who 

graduated from high school before enrolling in Job Corps are much more likely to select business (21.4 

percent) or health occupations (19.8 percent) over the other available vocational programs.

The results in Table B-3 suggest that the vocational choice patterns of 20-24 year-old Job 

Corps students are generally similar to that of their younger counterparts. However, there is a 

noticeable increase in the percentage of these older students who participate in the business and 

health occupations. In addition, older students are much less likely than 16-17 year-old students to 

select food services (6.6 percent). However, like their younger counterparts, 20-24 year old 

applicants with criminal and drug histories (with limited work experience and early high school drop 

outs) are less likely to select business and other service occupations and more likely to participate 

in construction-related trades. Finally, similar to 18-19 year old high school graduates, older high 

school graduates are more likely to select health occupations.

The results presented in Tables B-4 through B-6 focus on predicted probabilities of vocational 

trade choices by center typologies. Because these results exhibit very similar patterns across the three 
B-4

age groups, we discuss the implications of changing center characteristics without distinguishing by 

student age. The major implications of the results in these tables are:

• The likelihood that a student will participate in construction-related trades (masonry, carpentry, 

welding, BAM, and other construction) is much higher at small CCC centers than at larger centers 

operated by private contractors. The vocational choices of students at small CCC centers reflect the 

greater relative availability of these trades at such centers.

• With the exception of older students (aged 20-24), expanding the number of available vocational 

choices at a typical center increases the percentage of students who never participate in any 

vocational program.

• Students likely to attend centers with strong vocational and academic programs are much less likely 

to not participate in any vocational program while in Job Corps than students likely to attend centers 

that offer somewhat weaker vocational and academic program.

Taken together, these results imply that centers offering a wide range of strong vocational and academic 

programs are more likely to have students that participate in a vocational training program, regardless of 

the specific programs available.
Table B-1

Predicted Probability of Vocational Trade Choices by Student Typologies: 16-17 Year Olds

Student Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative student 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

Early high school dropout 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

Early high school dropout, no work 

experience with criminal/drug history 11.1% 15.1% 7.4% 8.7% 0.0% 13.8% 6.8% 0.0% 7.9% 14.8% 0.0% 0.0% 0.0%

14.3%

Early high school dropout with more 

work experience 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0%

12.0%

Criminal/drug history 9.1% 15.0% 8.1% 8.6% 0.0% 16.0% 6.8% 0.0% 7.2% 16.2% 0.0% 0.0% 0.0% 12.9%

Late high school dropout with no 

work experience 13.0% 12.3% 6.6% 7.1% 0.0% 13.4% 14.7% 0.0% 11.6% 8.2% 0.0% 0.0% 0.0%

13.1%

Late high school dropout with work 

experience 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0%

12.0%

Applicant has a child 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

1

 These vocations were not considered to be available in this typology.

B-5
Table B-2

Predicted Probability of Vocational Trade Choices by Student Typologies: 18-19 Year Olds

Student Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative student 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

Early high school dropout, no work 

experience 12.7% 10.3% 4.4% 7.9% 0.0% 13.8% 17.6% 0.0% 13.2% 8.8% 0.0% 0.0% 0.0%

11.3%

Early high school dropout, no work 

experience with criminal/drug history 11.0% 13.1% 5.0% 10.0% 0.0% 14.7% 8.4% 0.0% 9.2% 16.1% 0.0% 0.0% 0.0%

12.5%

Early high school dropout with 

limited work experience 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0%

10.3%

High school graduate 8.7% 8.2% 4.1% 6.1% 0.0% 19.8% 21.4% 0.0% 14.9% 9.6% 0.0% 0.0% 0.0% 7.1%

High school graduate with no work 

experience 10.6% 8.2% 3.7% 6.2% 0.0% 17.1% 21.3% 0.0% 16.3% 8.7% 0.0% 0.0% 0.0%

7.9%

Extensive work experience 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

Applicant has a child 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

1

 These vocations were not considered to be available in this typology.

B-6
Table B-3

Predicted Probability of Vocational Trade Choices by Student Typologies: 20-24 Year Olds

Student Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative student 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

Early high school dropout, limited 

work experience with criminal/drug 

history

6.4% 8.4% 5.8% 9.8% 0.0% 21.6% 10.2% 0.0% 7.3% 17.6% 0.0% 0.0% 0.0% 13.0%

Extensive work experience 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

High school graduate 5.5% 6.7% 4.9% 7.7% 0.0% 26.9% 12.4% 0.0% 9.0% 17.7% 0.0% 0.0% 0.0% 9.2%

High school graduate with extensive 

work experience 6.1% 5.1% 4.2% 5.9% 0.0% 24.1% 24.8% 0.0% 12.5% 9.3% 0.0% 0.0% 0.0% 8.0%

Recent educational experience 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

Applicant has a child 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

1

 These vocations were not considered to be available in this typology.

B-7
Table B-4

Predicted Probability of Vocational Trade Choices by Center Typologies: 16-17 Year Olds

Center Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative center 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

Center with outreach admissions and 

placement contract 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

Large contract center 7.5% 9.4% 5.1% 5.0% 0.0% 13.1% 12.1% 9.7% 8.3% 6.4% 0.0% 10.1% 4.5% 8.8%

Small CCC center 16.1% 10.2% 12.5% 5.5% 0.0% 0.0% 14.5% 0.0% 0.0% 10.2% 0.0% 18.6% 0.0% 12.4%

Strong vocational and academics 11.1% 14.2% 5.9% 4.5% 0.0% 14.8% 17.5% 0.0% 7.0% 9.3% 0.0% 0.0% 0.0% 15.6%

Weak vocational and academics 6.6% 14.0% 5.9% 3.5% 0.0% 10.7% 15.1% 0.0% 11.8% 8.5% 0.0% 0.0% 0.0% 24.0%

Only high demand vocations 0.0% 12.9% 0.0% 7.4% 0.0% 16.1% 15.3% 14.8% 11.2% 9.6% 0.0% 0.0% 0.0% 12.6%

All vocations available 15.9% 10.5% 7.7% 11.1% 0.0% 7.1% 10.0% 0.0% 11.4% 14.1% 0.0% 0.0% 0.0% 12.4%

Extensive facilities 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

Limited facilities 10.8% 12.4% 7.3% 7.1% 0.0% 15.7% 14.9% 0.0% 10.8% 9.1% 0.0% 0.0% 0.0% 12.0%

1

 These vocations were not considered to be available in this typology.

B-8
Table B-5

Predicted Probability of Vocational Trade Choices by Center Typologies: 18-19 Year Olds

Center Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative center 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

Center with outreach admissions and 

placement contract 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

Large contract center 7.7% 8.2% 3.5% 5.9% 0.0% 13.9% 15.0% 8.0% 9.8% 7.2% 0.0% 8.0% 4.7% 7.9%

Small CCC center 17.1% 9.4% 9.1% 6.7% 0.0% 0.0% 18.7% 0.0% 0.0% 12.0% 0.0% 15.5% 0.0% 11.6%

Strong vocational and academics 10.9% 12.1% 4.0% 5.1% 0.0% 15.3% 21.1% 0.0% 8.0% 10.0% 0.0% 0.0% 0.0% 13.5%

Weak vocational and academics 6.6% 12.0% 4.0% 4.0% 0.0% 11.1% 18.4% 0.0% 13.6% 9.3% 0.0% 0.0% 0.0% 21.0%

Only high demand vocations 0.0% 10.9% 0.0% 8.4% 0.0% 16.6% 18.4% 11.7% 12.8% 10.4% 0.0% 0.0% 0.0% 10.9%

All vocations available 11.7% 7.0% 8.4% 11.4% 0.0% 9.4% 12.6% 0.0% 10.2% 14.5% 0.0% 0.0% 0.0% 14.7%

Extensive facilities 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

Limited facilities 10.5% 10.4% 4.9% 7.9% 0.0% 16.1% 17.8% 0.0% 12.2% 9.8% 0.0% 0.0% 0.0% 10.3%

1

 These vocations were not considered to be available in this typology.

B-9
Table B-6

Predicted Probability of Vocational Trade Choices by Center Typologies: 20-24 Year Olds

Center Typology

Masonry

Food Service

Carpentry

Building 

Maintenance NTC

Building 

Maintenance NonNTC

1

Health Occupations

Business

Mechanic

1

Service

Construction NTCConstruction NonNTC

1

Welding

Other

1

No Vocation

Representative center 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

Center with outreach admissions and 

placement contract 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

Large contract center 5.3% 5.1% 3.6% 5.6% 0.0% 17.1% 17.6% 9.6% 8.2% 6.9% 0.0% 8.3% 3.8% 8.8%

Small CCC center 12.3% 6.1% 9.7% 6.7% 0.0% 0.0% 23.0% 0.0% 0.0% 12.0% 0.0% 16.8% 0.0% 13.5%

Strong vocational and academics 7.7% 7.6% 4.1% 5.0% 0.0% 18.9% 24.8% 0.0% 6.8% 9.8% 0.0% 0.0% 0.0% 15.2%

Weak vocational and academics 4.6% 7.6% 4.1% 3.8% 0.0% 13.9% 21.8% 0.0% 11.6% 9.0% 0.0% 0.0% 0.0% 23.6%

Only high demand vocations 0.0% 6.6% 0.0% 7.7% 0.0% 20.0% 20.9% 13.5% 10.3% 9.5% 0.0% 0.0% 0.0% 11.6%

All vocations available 0.0% 13.1% 0.0% 9.9% 0.0% 7.8% 9.3% 21.4% 10.6% 13.1% 0.0% 0.0% 0.0% 14.8%

Extensive facilities 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

Limited facilities 7.5% 6.6% 5.1% 7.8% 0.0% 20.1% 21.1% 0.0% 10.4% 9.7% 0.0% 0.0% 0.0% 11.7%

1

 These vocations were not considered to be available in this typology.

B-10
C-1

Appendix C

RESULTS FOR APPLICANT TYPOLOGIES

A. INTRODUCTION

This appendix summarizes the empirical results relating various applicant and student personal 

characteristics to key programmatic experiences: arrival at a center, GED attainment, vocational 

program completion, and enrollment in Job Corps for at least 30, 90 and 270 paid days. We first

describe the specific applicant typologies that we developed to illustrate the estimated empirical 

relationships between applicant characteristics and programmatic experiences. We then present results 

for the different programmatic experiences for several applicant typologies for the three age groups of 

interest (16-17, 18-19 and 20-24). The appendix concludes with a summary of the main findings that 

broadly apply across all age groups. 

B. APPLICANT TYPOLOGIES

In Chapter II, we described the typical applicant characteristics included in the representative cases 

for each of three age groups. The representative characteristics were selected to reflect the primary 

differences in education and employment backgrounds among applicants of different ages. For

example, the representative case for younger applicants has fewer years of schooling and less work 

experience than the representative case for older applicants. Other characteristics that do not typically 

vary significantly by age were defined similarly in the representative cases for all age groups. For 

example, we assume the representative case for each of the age groups does not have any criminal or 

drug history, is in good health, and has similar knowledge and expectations of the program. 
C-2

Below, we briefly describe the applicant characteristics of the representative case and the different 

typologies used to illustrate the role of various personal background characteristics for each age group. 

In addition to various personal background characteristics, we also consider the proximity of applicants 

to their likely Job Corps center. Geographic proximity is taking on a higher level of importance under 

the Workforce Investment Act of 1998 as this legislation emphasizes that applicants should, all else 

being equal, attend a center near their homes. To illustrate the potential relationship between applicants 

distance to their likely Job Corps center, we developed two geographic proximity typologies that are 

the same for all three age groups. These two typologies are described at the end of this section.

1. 16-17 year-old Typologies

Exhibit C-1 depicts the characteristics of the representative 16-17 year-old applicant and 

summarizes the key differences in the characteristics that define the applicant typologies we created to 

illustrate the relationships between personal characteristics and applicants’ programmatic experiences. 

As shown in Column (1), the representative 16-17 year-old applicant was in school for 9 months during 

the prior year, completed 9th grade, and had relatively little work experience (i.e., worked only 2 

months in the prior year with total earnings of $500). In addition, our representative 16-17 year-old 

applicant also is assumed to not have a child and to not have a history of drug use or criminal activities. 

Other features of the representative applicant in this age group were presented in Chapter II.

The other typologies described in Exhibit C-1 change one or more personal characteristics of 

applicants in four broad categories: (1) the presence of a child; (2) educational experiences; (3) work 

experience and earnings; and, (4) criminal/drug history. Cells in these categories in subsequent columns 

that are blank indicate that the same value of the characteristic is used as in the representative case. The 

first applicant typology changes the presence of a child. Specifically, Column (2) defines a typology that 
C-3

is identical to the representative case except that the applicant has a child. As such, a comparison of the 

programmatic experiences of applicants across these two typologies enables us to illustrate the 

relationship between having a child and Job Corps programmatic experiences. 

The next set of typologies varies educational attainment, as well as participation in educational 

activities and the amount of work experience in the prior year.12 Specifically, Column (3) depicts a 16-

17 year-old applicant who has dropped out of school after only completing the 7th grade and, as such, 

was not enrolled in school in the prior year. The typology in Column (4) further modifies the 

representative case to change work experiences in the prior year (worked 6 months with earnings of 

$3,000), as well as the changes in educational experiences introduced in Column (3). A comparison of 

the predicted probabilities across these typologies illustrates the extent to which these characteristics are 

associated with different programmatic experiences for 16-17 year-old applicants.

The next two columns in Exhibit C-1 characterize applicants who drop out of high school later than 

our representative 16-17 year-old applicant and who have different work experiences in the prior year. 

 Specifically, the fourth typology, shown in Column (5), corresponds to a 16-17 year-old applicant who 

dropped out of high school after completing 11th grade with no prior work experience. Column (6) 

defines a fifth typology in which applicants are again assumed to complete the 11th grade before 

dropping out of school but they are also assumed to have a more extensive amount of work experience 

in the prior year (worked 6 months with earnings of $3,000). Hence, a comparison of programmatic 

experiences for the representative case in Column (1), which is assumed to have less education and 

limited previous work experience, with the experiences for applicants with additional years of high 

 

12 In creating the employment and school participation measures used in the empirical analyses, we defined the 

“prior year” as the 12-month period before random assignment. However, as random assignment typically occurred 
C-4

school and work experiences illustrates the relationships between experiences and these personal 

characteristics of 16-17 year-old applicants. It should also be noted that a comparison of the 

programmatic experiences among the applicant typologies defined in Columns (5) and (6) isolates the 

relationships between programmatic experiences and work experience among late high school dropouts.

The final two columns in Exhibit C-1 create typologies for applicants that come from what can be 

considered relatively more disadvantaged backgrounds. Specifically, Column (7) depicts an applicant 

typology that is identical to the representative case except that the applicant has a criminal and drug 

history. The typology shown in Column (8) describes applicants with other characteristics that can be 

considered to be associated with a more disadvantaged background. This last typology changes the 

characteristics of the typology in Column (7) such that it characterizes 16-17 year-old applicants who 

dropped out of school after completing only the 7th grade and with no prior work experience. A 

comparison of applicants’ programmatic experiences across these typologies with the experiences of 

our representative 16-17 year-old applicant in Column (1) illustrates the extent to which applicants with 

progressively more disadvantaged backgrounds have different programmatic experiences.

2. 18-19 year-old Typologies

Exhibit C-2 summarizes the combination of various characteristics used to define our representative 

18-19 year-old applicant as well as the specific typologies created to illustrate the relationships between 

these personal characteristics and the programmatic experiences of different types of 18-19 year-old 

applicants. Column (1) summarizes our representative 18-19 year-old applicants who are assumed to 

have more work experience but less recent participation in education programs than our representative 

 

within a few days of when the youth completed his or her Job Corps application, we will refer to the prior year as the 

12-month period before application.
C-5

16-17 year-old applicants described above. More specifically, we assume that the representative 

applicant aged 18-19 has completed the 11th grade, worked 6 months in the prior year with earnings of 

$3,000, does not have a child and has no criminal/drug history.

The first applicant typology shown in Column (2) alters the assumption regarding the presence of an 

applicant’s child. Specifically, a comparison of the programmatic experiences for our representative 

applicants and this applicant typology, which are shown in the first and second columns of Exhibit C-2, 

illustrates the relationship between having a child for 18-19 year-old Job Corps applicants and their Job 

Corps programmatic experiences.

The next typology shown in Column (3) alters the amount of work experience in the prior year. 

Specifically, this typology depicts applicants who are similar to our representative case except that 

instead of working 6 months in the prior year with earnings of $3,000 we increase their pre-application 

work experience to 12 months of work in the prior year with $12,000 in earnings. Comparing the 

programmatic experiences of applicants in this typology with the experiences of our representative case 

shown in Column (1) illustrates the extent to which additional years of employment are associated with 

programmatic experiences for this age group.

The next two typologies shown in Exhibit 3-2 depict applicants with fewer years of formal education 

and different amounts of work experience in the prior year. Specifically, the typology presented in 

Column (4) depicts 18-19 year-old applicants who dropped out of high school after completing the 9th

grade and with no prior work experience. The typology described in Column (5) represents applicants 

who also dropped out of school after completing the 9th grade but with a limited amount of work 

experience (worked 2 months in prior year with earnings of $500). Results for these two typologies 

illustrate the extent to which programmatic experiences differ for 18-19 year-old applicants who drop 
C-6

out of school earlier and have less work experience compared to our representative applicants in this 

age range.

Columns (6) and (7) in Exhibit C-2 represent applicants with more formal education and different 

levels of work experience. The typology shown in Column (6) represents applicants who completed 

high school (completed the 12th grade) but otherwise have the same characteristics as our representative 

18-19 year-old applicants. Column (7) depicts applicants who completed high school but with no prior 

work experience. Comparing results across these typologies will illustrate the relationships between 

high school graduation and different levels of work experience and programmatic experiences.

Finally, the last column in Exhibit C-2 corresponds to a typology for applicants in this age group 

who come from more disadvantaged backgrounds. Specifically, Column (8) depicts applicants who not 

only dropped out of school early (completed only the 9th grade) and with no prior work experience, but 

who have a history of criminal activities and drug use. This typology enables us to assess any 

differences in programmatic experiences that are related to educational attainment, work experiences 

and a criminal/drug history. Together with the results for the typologies in Columns (1) and (4), this 

typology illustrates the extent to which programmatic experiences are related to these personal 

characteristics.

3. 20-24 year-old Applicants

Exhibit C-3 summarizes the eight typologies developed for 20-24 year-old applicants and describes 

the key differences in these characteristics that define the specific typology. The first column restates the 

key characteristics that describe our representative case for youth aged 20-24, which consists of the 

same characteristics as that for 18-19 year-olds except for their recent participation in educational 

programs. Specifically, the one difference in the representative case between the two older age groups 
C-7

is that youth aged 20-24 are assumed to not have been enrolled in school in the last year, whereas the 

representative applicant aged 18-19 is assumed to have been in school for 3 months during the prior 

year.

As before, the applicant typology for this age group shown in Column (2) changes only a single 

characteristic, namely whether the applicant has a child. Comparing results for this typology with the 

results for our representative 20-24 year-old applicant illustrates the relationships between having a 

child and programmatic experiences.

The typology shown in Column (3) alters the recent educational experiences of this oldest group of 

Job Corps applicants and assumes that applicants of this type were enrolled in school for 9 months in 

the year prior to applying to Job Corps. We can compare the programmatic experiences of this 

typology with the experiences of the representative case in Column (1) that was not enrolled in school at 

all in the prior year to illustrate the relationship between recent educational attainment and programmatic 

experiences.

The next two columns of Exhibit C-3 present applicant typologies that vary both the educational 

attainment and work experience of 20-24 year-old applicants. The typology shown in Column (4) 

depicts applicants who graduated from high school and had work experiences in the prior year that are 

identical to the representative case. Column (5) defines a typology that modifies the representative case 

by assuming applicants of this type are high school graduates with extensive work experience (worked 

12 months in the prior year and earned $12,000). Comparing the programmatic experiences 

experienced by these applicant typologies with the experiences of the representative case in Column (1) 

illustrates the extent to which programmatic experiences are associated with high school graduation and 

different levels of work experience among 20-24 year-old applicants.
C-8

The typology shown in Column (6) alters the amount of work experience in the prior year. 

Specifically, the characteristics of applicants under this typology are the same as our representative case 

except these applicants are assumed to have more extensive work experience (worked 12 months in 

prior year with $12,000 in earnings). A comparison of our representative applicants’ programmatic 

experiences with the experiences of applicants of this type isolates the relationship between these 

programmatic experiences and prior work experience for this oldest group of applicants.

Finally, the last two columns in Exhibit C-3 depict 20-24 year-old applicants who come from more 

disadvantaged backgrounds. Specifically, Column (7) defines a typology for applicants who dropped 

out of high school early (completed only the 9th grade education) and have only a limited amount of 

work experience (worked 2 months in prior year and earned $500). A more disadvantaged group of 

20-24 year-old applicants is described in Column (8). This typology is identical to the applicants 

depicted in Column (7) except that the applicant typology in Column (8) also possesses a criminal/drug 

history. A comparison of results for the last two typologies illustrates the extent to which differences in 

programmatic experiences are related to educational attainment, differences in work experience and any 

criminal/drug history.
C-9

Exhibit C-1

Summary of Applicant Typologies: 16-17 year-olds

Applicant Typology

(1)

Representative Applicant

(2)

Applicant has a 

child

(3)

Early High School 

Dropout

(4)

Early High School 

Dropout with more 

Work Experience

(5)

Late High School 

Dropout with No 

Work Experience

(6)

Late High School 

Dropout with Work 

Experience

(7)

Criminal/ Drug 

History

(8)

Early High School

Dropout, No Work 

Experience, with 

Criminal/

Drug History

Applicant has a 

child

No Yes

Education 9

th grade 7th grade 7th grade 11th grade 11th grade 7th grade

Enrollment in 

Educational 

Program in Past 

Year

Enrolled 9 months 

in past year

None None None

Work 

Experience

Not working at 

application, 

Worked 2 months 

in past year

Working at 

application, 

Worked 6 

months in last 

year

No prior work 

experience

Working at 

application, 

Worked 6 

months in last 

year

No prior work 

experience

Earnings $500 $3,000 $0 $3,000 $0

Criminal/ Drug 

History

No Yes
C-10

Exhibit C-2

Summary of Applicant Typologies: 18-19 year-olds

Applicant Typology

(1)

Representative 

Applicant

(2)

Applicant has a 

child

(3)

Extensive Work 

Experience

(4)

Early High School 

Dropout, No Work 

Experience

(5)

Early High School 

Dropout with 

Limited Work 

Experience

(6)

High School 

Graduate

(7)

High School 

Graduate with no 

Work Experience

(8)

Early High School 

Dropout, No Work 

Experience, with 

Criminal/

Drug History

Applicant has a 

child

No Yes

Education 11th grade 9

th grade 9th grade 12th grade 12th grade 9th grade

Enrollment in 

Educational 

Programs

Enrolled 3 months 

in past year

None None Enrolled 9 

months in past 

year

Enrolled 9 

months in past 

year

None

Work 

Experience

Not working at 

application, 

Worked 6 months 

in past year

Working at 

application, 

Worked 12 

months in last 

year

No prior work 

experience

Not working at 

application, 

Worked 2 

months in past 

year

No prior work 

experience

No prior work 

experience

Earnings $3,000 $12,000 $0 $500 $0 $0

Criminal/Drug 

History

No Yes
C-11

Exhibit C-3

Summary of Applicant Typologies: 20-24 year-olds

Applicant Typology

(1)

Representative Applicant

(2)

Applicant has a child

(3)

Recent Educational 

Experience

(4)

High School Graduate

(5)

High School Graduate 

with Extensive Work 

Experience

(6)

Extensive Work 

Experience

(7)

Early High School Dropout,

Poor Work Experience with 

Criminal/

Drug History

Applicant has a 

child

No Yes

Education 11th grade 12th grade 12th grade 9

th grade

Enrollment in 

Educational 

Program in Past 

Year

N

on

e

Enrolled 9 months 

in past year

Work 

Experience

Not working at 

application, Worked 

6 months in past 

year 

Working at 

application, 

Worked 12 

months in last 

year

Working at 

application, 

Worked 12 

months in last 

year

Not working at 

application, Worked 

2 months in past 

year

Earnings in 

Past Year

$3,000 $12,000 $12,000 $500

Criminal/

Drug History

No Yes
C-12

4. Geographic Proximity Typologies

A potentially important aspect of an applicant’s background concerns the geographic proximity of 

the applicant to the likely center. As such, in this appendix , we not only illustrate the relationships 

between personal background characteristics and programmatic experiences, we also specify alternative 

typologies to examine the association between applicants’ proximity to their likely center and 

programmatic experiences. However, unlike the other background characteristics that differed by age, 

we apply the same geographic proximity typologies to each applicant age group.

The geographic typologies are developed only for applicants who are likely to be residential 

students and are based on distance to the likely center as well as the type of city in which they live at the 

time of application. As indicated in Exhibit C-4, the first typology assumes that our representative 

applicant lives 25 miles from the likely center and resides within a Standard Metropolitan Area (SMA). 

 The second typology extends the distance to the likely center to 200 miles and assumes that applicants 

live in a rural area. The third typology also assumes that applicants live 200 miles from the likely center 

but that they reside within an SMA. Thus, by comparing programmatic experiences for the first and 

third typologies we can assess the extent to which additional distance from the likely center is related to 

the programmatic experiences of likely residential students. Moreover, by comparing the second and 

third typologies we can assess the strength of any association between programmatic experiences and 

residing in large metropolitan areas versus coming from rural areas.
C-13

Exhibit C-4

Summary of Applicant Geographic Proximity Typologies

Applicant Proximity Typology

(1)

25 miles from center

(2)

200 miles from center in 

rural area

(3)

200 miles from center not in 

SMA

Miles from Center 25 miles from 

center

200 miles from 

center

200 miles from center

Area In Standard 

Metropolitan 

Area

Rural Not in Standard 

Metropolitan Area

C. RESULTS

In this section, we present results describing how the major programmatic experiences differ across 

these key applicant characteristics. Specifically, we compare applicants’ arrival experiences and 

students’programmatic experiences across the various personal background and geographic typologies 

described above.

The results for all age groups are summarized in Tables C-1 through C-3. Table C-1 summarizes 

the role of having a child by gender and likely residential status. Table C-2 summarizes all of the 

applicant typologies by age group. Finally, Table C-3 summarizes the geographic typologies by gender 

and age. The results described below are organized by applicant age group.

1. 16-17 Year-Old Applicants

Overall, the three tables for 16-17 year-old applicants indicate that personal characteristics are 

significantly related to the likelihood of having specific programmatic experiences. Notable findings of 

interest include:

• Job Corps students are much less likely to stay in the program if they have a child. The 

reduction in the likelihood of staying in the program is significant for both male and female 
C-14

students with a child, but is particularly acute for residential females. For example, 15 percent 

of residential female students without a child are expected to stay 270 days, as compared to 9 

percent of residential female students with a child. 

• The programmatic experiences of applicants who dropped out of school early (after completing 

only the 7th grade) are substantially different from the experiences of our representative 16-17

year-old applicants. Approximately 80 percent of representative applicants arrive on center, 

compared to about 71 percent of applicants who dropped out of school early. Further, Job 

Corps students who are early high school dropouts are less likely to attain a GED (26 percent 

compared to 16 percent) and less likely to stay on center. However, early dropouts are equally 

likely as the representative student to complete their vocational training programs. 

• More extensive work experiences play a role in offsetting some of the reductions in 

programmatic accomplishments for early high school dropouts. Specifically, early high school 

dropouts with 6 months of work experience and $3,000 in earnings in the prior year are 

expected to have programmatic experiences that are generally similar to the experiences of the 

representative student. The only exception is GED attainment, which is not surprising, since the 

representative student has completed more formal education and is closer to obtaining the GED 

than a youth that dropped out early.

• Applicants aged 16-17 with additional years of education beyond 9th grade do not have 

significantly better programmatic experiences than representative 16-17 year-old students. 

More specifically, late high school dropouts (11th grade education) have very similar 

experiences compared to the representative student with a 9th grade education. Further, late 

high school dropouts with some work experience have very similar programmatic experiences 

except for GED attainment. Students with work experience and an 11th grade education are 

more likely to attain a GED compared to representative applicants and to students who are late 

high school dropouts but with no work experience. 

• Students with prior involvement in the juvenile justice system and with a history of drug use have 

significantly poorer programmatic experiences compared to other similar 16-17 year-old 

applicants. That is, among these youngest students, those with criminal and drug histories are 

much less likely to have positive programmatic experiences compared to the representative 

student. Moreover, very disadvantaged students – early high school dropouts, with no work 

experience, and with criminal/drug histories – can expect the least positive programmatic 

experiences. For example, the most disadvantaged applicants have predicted arrival rates of 53 

percent, compared to 80 percent for the representative applicant and 70 percent for the 

representative applicant with only a criminal/drug history. The lower arrival rates of the very 

disadvantaged group translate into consistently lower lengths of stay and lower student 

programmatic experiences. 

• Finally, our results indicate that applicants’ geographic proximity to the likely center is related to 

their likelihood of arrival on center. Specifically, applicants coming from greater distances are 
C-15

slightly more likely to arrive on center than applicants residing within 25 miles from the likely 

center. However, the length of stay on center appears to be unrelated to applicants’ geographic 

proximity.

2. 18-19 year-old Applicants

Tables C-1 through C-3 provide a similar set of predicted probabilities for 18-19 year-old 

applicants. Notable findings from our analysis include: 

• Similar to the younger age groups, both male and female 18-19 year-old students with a child 

appear to experience significantly shorter stays on center than the representative student.

• Students in this age group who drop out of high school earlier than others with similar 

characteristics are much less likely to attain a GED in Job Corps. Moreover, although not 

nearly as substantial, these early dropouts also experience lower vocational completion rates 

and somewhat shorter lengths of stay. Specifically, 32 percent of representative 18-19 year-old 

students attain a GED compared to 11 percent of similar students who are early high school 

dropouts without any work experience, and 18 percent who are early high school dropouts with 

only limited work experience. The addition of limited work experience among students aged 

18-19 who dropped out early does little to increase positive programmatic experiences.

• As shown in Table C-2, 18-19 year-old applicants with a high school credential at application 

have quite different programmatic experiences compared to similar applicants who dropped out 

after completing the 11th grade. For example, about 85 percent of applicants with a high school 

diploma (regardless of work experience) are expected to arrive on center, compared to 71 

percent of representative applicants. In addition, students with high school diplomas are much 

more likely to complete vocational training. It is important to note that even high school 

graduates who lack any work experience also have these higher levels of programmatic 

experiences. Specifically, students who are high school graduates have very similar 

programmatic experiences regardless of work experience level. In fact, high school graduates 

with no work experience are more likely to stay for longer periods than other groups. 

• Among 18-19 year-old students, those with extensive work experience achieve slightly better 

GED and vocational accomplishments while at the same time staying for shorter periods in the 

program.

• The most disadvantaged applicants in this age group – those early dropouts with no work 

experience and with prior criminal and drug histories – are less likely to have positive 

programmatic experiences compared to all other types of applicants. Specifically, these 

severely disadvantaged applicants have lower predicted arrival rates, once on center these 

students experience much lower rates of GED attainment and vocational completion, and 

significantly lower lengths of stay. 
C-16

• For both male and female applicants in this age group, those living further from their likely center 

are more likely to arrive. In contrast to younger applicants, 18-19 year-old applicants living 

closer to the likely center are more likely to stay longer periods at the center compared to 

applicants living greater distances from the center.

2. 20-24 Year-Old Applicants

The same set of results for applicants’ aged 20-24 is summarized in Tables C-1 through C-3. 

Notable findings include:

• Female students in this age group (regardless of residential status) with a child are less likely to 

have positive programmatic experiences compared to their counterparts who do not have a 

child. Specifically, those with a child are somewhat less likely to arrive on center, attain a GED, 

and complete their vocation. Moreover, in general, they are also less likely to stay on center –

particularly longer periods of time. 

• Among male students, those who are likely to be in a residential status with a child have more 

positive programmatic experiences compared to their counterparts without a child. In contrast, 

males who are likely to be non-residential students with a child are less likely to arrive and to 

stay on center compared to male non-residential students without a child. These differences in 

programmatic experiences by gender and likely residential status may reflect differences in child 

care arrangements, as female students are more likely than males to have the child on center.

• The results presented here show a significant relationship between recent participation in 

education and/or schooling programs and the programmatic experiences of 20-24 year-old 

applicants. For example, applicants with recent educational experiences are somewhat more 

likely to arrive on center and once on center, significantly more likely to stay longer, achieve 

their GED, and complete their vocation compared to our representative applicants/students in 

this age range who were not enrolled in school in the prior year.

• Among this older age group, applicants with a high school diploma at entry are less likely to 

arrive on center but equally likely to attain a GED, complete their vocational training, and stay 

on center.

• The relationship between more extensive work experience and programmatic experiences 

appears to be weaker for 20-24 year-old applicants than for 18-19 year-old applicants. 

Specifically, our representative 20-24 year-old applicants and their similar counterparts with 

more extensive work experience in the prior year have very similar programmatic experiences. 

This pattern holds for arrival rates, GED attainment, vocational completion and length of stay.
C-17

• Similar to the other age groups, 20-24 year-old applicants with more disadvantaged 

backgrounds are much less likely to have positive programmatic experiences. For example, 

while about 67 percent of our representative applicants in this age group arrive on center, only 

57 percent of applicants who are early high school dropouts with limited work experience and a 

criminal/drug history arrive on center. The differences in vocational completion and length of 

stay are even larger among the subset of applicants that arrive on center. One exception to this 

is in the probability of GED attainment where students from the most disadvantaged 

backgrounds have a slightly higher probability of GED attainment relative to representative 

applicants.

• Residential applicants (20-24) who live 200 miles from their likely center are much more likely 

than residential applicants residing within 25 miles from their likely center to arrive and stay in 

Job Corps longer. This is true for both male and female applicants.

D. SUMMARY

Looking across the results for these three age groups, several common patterns emerge concerning 

the relationships between various personal characteristics and programmatic experiences. The most 

notable of these patterns are:

• Programmatic experiences differ more across female students with and without a child 

compared to male applicants with similar characteristics. Specifically, programmatic 

experiences are substantially lower for female applicants with a child relative to those without a 

child, whereas the negative differences for males are less pronounced. However, even for 

females, the differences in GED attainment and vocational completion for those with a child 

appear to be relatively small.

• Prior education plays a key role in the programmatic experiences of all student groups. 

Specifically, students who drop out of school early experience lower programmatic 

achievements and length of stay compared to their counterparts who completed more years of 

formal education. Not surprisingly, the largest differences in experiences emerge for early 

dropouts in terms of the reduced likelihood of obtaining a GED. In addition, having a high 

school diploma at application is strongly associated with better programmatic experiences for 

18-19 year-old students. However, among 20-24 year-old students who have been away from 

school for a considerable period, having completed high school appears to be only weakly 

related to programmatic experiences. Recent educational experiences are significantly 

associated with improved programmatic experiences in all areas among the older student group.
C-18

• In general, additional work experience prior to entering Job Corps is associated with improved 

programmatic experiences. There is evidence that more extensive work experiences play a 

role in offsetting some of the reductions in programmatic experiences for early school dropouts 

among 16-17 year-old applicants. In addition, older students with extensive work experience 

have similar or better programmatic experiences despite shorter periods on center.

• For all three age groups, students from very disadvantaged backgrounds – defined as early 

dropouts with no work experience and with a criminal/drug history – are the least likely group to 

have positive programmatic experiences. Although this pattern is very strong and consistent 

across all age groups, the youngest Job Corps applicants (16-17 years old) are particularly 

handicapped by the lack of education, no work experience and a criminal and drug history. 

Young applicants with these characteristics are much less likely than the representative case to 

arrive on center (53 percent, as compared to 80 percent) and once on center, only about 6 

percent of these students complete their GED and 14 percent complete vocational training. The 

low measured outcomes are in part due to their very short lengths of stay on center.

• An applicant’s geographic proximity to the likely Job Corps center appears to be related to 

programmatic experiences, especially for the older age groups. For all three age groups, likely 

residential applicants that come from a longer distance (200 miles), are much more likely to 

arrive on center. However, the length of time spent on center after arrival varies by student age. 

Among the youngest age group, there are very small differences in length of stay within gender 

by distance to center. For 18-19 year-old applicants, those living further from the center have 

shorter stays; and, in contrast, among the oldest age group (20-24), applicants living further 

from the center have much longer stays on center compared to applicants located 25 miles from 

the center.
Table C-1. Predicted Probability of Arrival 

and Paid Days on Center: Role of Having a Child by Age Group1

Conditional Upon Arrival

Arrival 30 Paid 

Days

90 Paid 

Days

270 Paid

Days

Age 16-17

Female residential 79.7 82.6 59.6 15.7

Female residential with child 77.9 74.2 48.3 8.8

Female non-residential 74.9 75.1 50.3 22.7

Female non-residential with child 82.8 79.6 50.8 18.0

Male residential 80.3 79.4 46.0 15.2

Male residential with child 83.4 77.7 46.1 11.0

Male non-residential 81.2 82.6 34.4 18.1

Male non-residential with child 81.2 82.6 34.4 14.7

Age 18-19

Female residential 68.3 86.7 70.2 30.7

Female residential with child 64.7 73.7 39.0 13.4

Female non-residential 51.9 84.3 50.9 13.6

Female non-residential with child 57.9 80.4 50.0 9.6

Male residential 76.3 86.7 70.3 35.0

Male residential with child 73.8 73.1 55.0 22.1

Male non-residential 58.6 88.2 67.0 22.1

Male non-residential with child 50.7 85.1 65.4 15.7

Age 20-24

Female residential 69.7 82.0 62.9 31.3

Female residential with child 64.1 86.0 54.3 26.8

Female non-residential 50.5 80.7 65.7 35.7

Female non-residential with child 50.0 70.0 56.5 24.1

Male residential 72.2 81.5 59.4 25.3

Male residential with child 73.8 83.8 63.6 26.9

Male non-residential 55.1 79.1 55.6 26.4

Male non-residential with child 49.1 73.8 51.8 18.6

1GED Attainment and vocational completion are not included because presence of a child is not in the models for these outcomes.

C-19
Table C-2. Predicted Probability of Arrival, GED Attainment, Vocational Completion, 

and Paid Days on Center: Applicant Typologies by Age Group

Conditional Upon Arrival

Arrival GED 

Attainment

Vocational

Completion

30 Paid 

Days

90 Paid 

Days

270 Paid

Days

Age 16-17

Representative applicant 79.8 25.9 40.4 80.3 50.1 16.0

Applicant has a child 81.5 25.9 40.4 76.9 46.7 10.8

Early high school dropout 70.7 15.7 39.0 72.3 43.9 13.6

Early high school dropout, with more work experience 77.9 16.6 40.6 76.8 51.1 22.4

Late high school dropout with no work experience 77.8 29.2 41.2 81.1 56.0 20.5

Late high school dropout with work experience 79.4 33.7 40.4 78.2 53.0 17.1

Criminal/drug history 69.9 24.4 28.7 79.6 38.1 6.6

Early high school dropout, no work experience, with criminal/drug 

history 53.1 12.1 28.1 75.8 38.5 7.2

Age 18-19

Representative applicant 70.5 32.4 47.6 86.6 68.2 30.3

Applicant has a child 68.2 32.4 47.6 74.6 49.7 17.5

Extensive work experience 64.3 36.1 48.1 77.9 56.9 29.1

Early high school dropout, no work experience 70.7 11.4 43.5 80.3 63.3 35.7

Early high school dropout with limited work experience 72.2 18.3 41.4 78.3 56.5 23.8

High school graduate 85.5 N/A 65.4 81.4 64.4 31.3

High school graduate with no work experience 85.4 N/A 67.3 80.3 66.5 43.2

Early high school dropout, no work experience, with criminal/drug 

history 64.4 11.3 33.0 73.0 53.8 21.3

Age 20-24

Representative applicant 67.0 33.2 54.2 81.4 61.2 28.6

Applicant has a child 65.9 33.2 54.2 81.6 59.5 26.1

Recent educational experience 69.4 40.6 66.6 89.5 68.6 47.8

High school graduate 56.0 38.7 53.9 81.7 56.9 32.8

High school graduate with extensive work experience 49.8 30.5 71.2 84.6 68.0 39.7

Extensive work experience 66.3 29.3 59.0 81.5 66.9 29.3

Early high school dropout, no work experience, with criminal/drug 

history 56.8 37.7 40.4 62.2 37.7 9.2

C-20
Table C-3. Predicted Probability of Arrival 

and Paid Days on Center: Role of Geographic Proximity by Age Group1

Conditional Upon Arrival

Arrival 30 Paid 

Days

90 Paid 

Days

270 Paid

Days

Age 16-17

Female residential lives 25 miles from center 79.7 82.6 59.6 15.7

Female residential lives 200 miles from center – rural area 85.4 81.1 59.5 15.1

Female residential lives 200 miles from center –in MSA 82.9 81.1 59.5 15.1

Male residential lives 25 miles from center 80.3 79.4 46.0 15.2

Male residential lives 200 miles from center – rural area 85.8 77.7 46.1 14.9

Male residential lives 200 miles from center – in MSA 83.4 77.7 46.1 14.9

Age 18-19

Female residential lives 25 miles from center 68.3 86.7 70.2 30.7

Female residential lives 200 miles from center – rural area 74.1 78.0 57.9 25.4

Female residential lives 200 miles from center –in MSA 72.2 78.0 57.9 25.4

Male residential lives 25 miles from center 76.3 86.7 70.3 35.0

Male residential lives 200 miles from center – rural area 81.0 78.1 58.0 29.3

Male residential lives 200 miles from center – in MSA 79.5 78.1 58.0 29.3

Age 20-24

Female residential lives 25 miles from center 69.7 82.0 62.9 31.3

Female residential lives 200 miles from center – rural area 75.0 87.8 69.8 42.3

Female residential lives 200 miles from center – in MSA 76.0 87.8 69.8 42.3

Male residential lives 25 miles from center 72.2 81.5 59.4 25.3

Male residential lives 200 miles from center – rural area 77.1 87.4 66.4 35.7

Male residential lives 200 miles from center – in MSA 78.1 87.4 66.4 35.7

1GED Attainment and vocational completion are not included because presence of a child is not in the models for these outcomes.

C-21
D-1

APPENDIX D

ESTIMATING IMPACTS FOR DIFFERENT JOB CORPS PROGRAM EXPERIENCES

One of the primary objectives of the National Job Corps Study is to estimate the impact of Job Corps 

for applicants with different program experiences, including participation in specific vocational training areas, 

completion of academic and vocational programs, and enrollment for specified lengths of time. However, 

unlike the other elements of the impact analysis that can fully exploit the advantages of the random 

assignment of eligible applicants and estimate impacts as the simple difference in outcomes between the 

program and control groups, estimation of the impacts of different programmatic experiences cannot rely 

solely on the random assignment design. This is because the random assignment of Job Corps applicants to 

program and control group status was made at the time of eligibility determination and all subsequent 

programmatic outcomes of program group members, such as enrollment in a particular vocational training 

area and the number of days enrolled at a Job Corps center, partly reflect the specific choices of an 

applicant, as well as program and other factors. As a result, to address this objective of the evaluation we 

must rely on non-experimental methods to estimate the impacts of programmatic experiences on the labor 

market and other related outcomes of Job Corps students.

Estimating program impacts using non-experimental methods is widely recognized as a scientific 

challenge and much debate surrounds whether the statistical models used in these methods yield informative 

impact estimates. Many research studies comparing experimental and non-experimentally derived estimates 

of program impacts for the same program have found that the standard statistical models used to derive 

non-experimental impact estimates have yielded unreliable results. However, several recent studies have 
D-2

yielded more promising results for non-experimental impact estimates under circumstances where the 

statistical models are subject to specification tests.

This appendix presents our preliminary examination of the statistical models we proposed to use to 

estimate the impacts of different program experiences for Job Corps students that are based on the 

empirical results described elsewhere in this report. The first section briefly describes the set of statistical 

models we are examining to derive estimated impacts of different programmatic experiences. The second 

section discusses our approach to examining the different specifications described in the preceding section 

and the assessment of these specifications for estimating program impacts. The next section presents some 

preliminary results from the estimation of the impact of enrollment in Job Corps on employment and earnings 

30 months after application to the program. The appendix concludes by describing the next steps we will 

follow to examine the feasibility of estimating the impacts of the different program experiences using all of the 

data collected up through the interviews conducted 48 months after application.

A. METHODS FOR ESTIMATING IMPACTS FOR PROGRAM EXPERIENCES

It is of great policy interest to estimate the impact of the Job Corps program on the youth who have 

different program experiences and those who achieve specific programmatic outcomes. While impacts may 

vary across many dimensions of program experiences, our primary interest focuses on the estimated impacts 

of five programmatic experiences:

1. Enrollment in Job Corps by eligible applicants;

2. Participation in particular vocational training areas;

3. Completion of a vocational training program;

4. Achievement of a GED among enrollees without a high school credential; and,
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5. Enrollment in the program for a specified length of time, such as 30, 90 or 270 days.

Reliably measuring the impact of Job Corps for eligible applicants with these experiences presents a 

formidable challenge. Moreover, as all subsequent program experiences and achievements are 

dependent upon enrollment in the program, it is critical to obtain reliable estimates of the impacts for 

enrollees in order to estimate impacts for the other four categories of program experiences.

The underlying experimental design of the evaluation provides reliable estimates of the overall impact 

of Job Corps, as well as the impacts for subgroups of eligible applicants that can, for all practical 

purposes, be identified prior to random assignment.13 However, reliable estimation of the impacts of 

Job Corps for subgroups of eligible applicants that can not realistically be identified before 

randomization, such as those defined in terms of their subsequent program experiences, is complicated 

by two factors.

1. The inability to determine the specific control group members who would have program 

experiences that are identical to the experiences of applicants who are offered the 

opportunity to participate in Job Corps.

 

 

13 For example, because the residential and nonresidential components of Job Corps serve applicants with quite 

distinctive characteristics, it was possible to reliably determine the applicants who would attend Job Corps as a 

residential or nonresidential student prior to randomization. This ability permits the estimation of impacts for these 

two program components using comparisons across program and control groups for those most likely to enroll in a 

residential slot and those most likely to enroll in a nonresidential slot.

2. The individuals who choose to have a particular set of program experiences are those 

most likely to benefit from these experiences.

This second complicating factor is often referred to as the selection problem.
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As a consequence of these two complicating factors the estimation of program impacts for the 

subgroups of eligible applicants identified above must rely on one or more assumptions regarding either 

the outcomes for program group members who do not have specific programmatic experiences or the 

behavioral processes that jointly determine program experiences and subsequent outcomes. For 

example, the estimation of impacts for Job Corps enrollees can be derived by assuming the program has 

no impact on the outcomes of eligible applicants who do not enroll in the program or by assuming the 

decision making process for eligible applicants who decide to enroll in the program can be reliably 

summarized by a statistical model that depends on both individual and program characteristics. The 

former assumption is widely used and generally accepted because of the intuitive appeal of the premise 

underlying this assumption.14 Alternatively, the estimation of program impacts for the subset of 

applicants who enroll can also be derived by adopting a specific statistical model summarizing the 

factors that systematically determine which applicants enroll and which applicants do not.

 

 

14 This assumption underlies the estimated impacts per participant of Job Corps over 30 months as reported in 

Schochet, Burghardt, and Glazerman (2000). 

While invoking an assumption about the outcomes for non-enrollees has an intuitive appeal, invoking 

similar assumptions about the outcomes or impacts of the program on other subgroups with specific 

program experiences is much less credible. For example, while it is credible to assume the program has 

no impact on applicants who do not enroll, it is difficult to extend this assumption for applicants who 

enroll but stay in the program for less than 30 days. Because of the lack of credible assumptions, the 

estimation of distinct impacts for the other identified subgroups will rely solely on the specification of a 

statistical model and a set of assumptions that permit the identification of the variation in impacts across 
D-5

subgroups of eligible applicants that are distinguished by participation in specific vocational training 

areas, vocational completion, GED attainment, and different lengths of stay.

There are a wide variety of statistical models and methods available to estimate the impact of 

different program experiences. One of the key distinguishing aspects of these approaches involves 

which of the two complicating factors described above the method attempts to overcome. For 

example, the lack of information regarding which control group members would have a specific program 

experience is analogous to a standard missing data problem and one approach to estimate impacts for 

these subgroups is to use missing data techniques. Alternatively, a large body of research has 

developed to overcome the selection problem by adopting a specific statistical model and invoking the 

assumptions needed to isolate the effects of the characteristics–both measured and unmeasured–of 

individuals who choose to have a particular set of program experiences from the effects of these 

experiences on outcomes. These latter approaches include instrumental variable methods and the 

selection correction models developed by Heckman and Lee.

Although under certain circumstances each of these approaches provides an estimate of the 

program impact for subgroups based on their program experiences, the specific impact being estimated 

differs across these various approaches. The missing data approach, in principal, compares the 

outcomes of applicants with specific program experiences with the subgroup of control group members 

who would have had those same experiences if they had been offered the opportunity to participate in 

the program. Hence, this approach provides an estimate of the average impact of the specific program 

experience for the individuals who, under the current program, choose to have these experiences.15 The 

 

 

15 This approach is analogous to the method used to estimate the impact of the residential and non-residential 

components of the program that are reported in Schochet, Burghardt, and Glazerman (2000).
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instrumental variable approach yields a slightly different impact estimate for program experiences. 

Specifically, this approach provides an estimate of the impact of the program experience for those 

students who came very close to having the experience but did not and, as such, is often referred to as a 

local area treatment effect. For example, an instrumental variable estimate of the impact of achieving a 

GED in Job Corps provides an estimate of the impact for students who were very close to obtaining a 

GED before leaving the program. Finally, the sample selection models provide yet another variant of 

the impact of program experiences. This approach provides an estimate of the impact of a specific 

program experience for an average eligible applicant to Job Corps.

While each of these alternative impact estimates is relevant to the Job Corps program and policy 

makers, efforts here are focused on estimating the impacts of program experiences using the missing data 

approach. This focus has the key advantage of providing impact estimates that are consistent with the 

impacts for the residential and nonresidential components of the program reported in Schochet, Burghardt, 

and Glazerman (2000) and the estimated impacts for center characteristics reported in Burghardt (2000).

To illustrate this approach consider a simple regression model relating an outcome (Y) to a specific 

program experience (E):

Y = a×E×T + b×E

*

×C + c×[1-E]×T + d×[1-E

*

]×C,

where T represents eligible applicants assigned to the program group and C represents the eligible 

applicants assigned to the control group. The impact of program experience E among those applicants 

who would have this experience is given by the quantity (a-b) and the impact of the program for those 

who would not have this experience is given by the quantity (c-d). However, this equation can not be 

estimated because it is infeasible to precisely determine which control group members would have had 
D-7

this program experience (i.e., E

*

 is unobserved), which is analogous to a missing data problem for this 

regression model.

Estimates of the quantities represented by a, b, c, and d can be obtained by replacing the missing 

data by a consistent estimate. In this case, E

*

 is replaced with the estimated probability each control 

group member would have had the specific program experience if they had been offered the opportunity 

to participate in the program. Specifically, the linear regression model can be rewritten as

Y = a×E×T + b×Pr(E

*

)×C + c×[1-E]×T + d×[1-Pr(E

*

)]×C,

where Pr(E

*

) represents a consistent estimate of the probability a control group member would have 

program experience E if offered the opportunity to participate in the program. Generalized variants of 

this simple regression model are used to estimate the impact of different program experiences. In 

addition, a straightforward extension of these regression models incorporates characteristics of 

individuals at the time they applied to Job Corps.

To estimate the impacts of different program experiences, we use the results from the models 

described in Appendix B for Pr(E

*

). Specifically, to estimate the impact for program enrollees we 

replace Pr(E

*

) with the predicted probability derived from the model represented by Pr(e | X, O, A). 

Similarly, when estimating the impacts for other program experiences we replace Pr(E

*

) with an 

appropriate predicted probability.
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B. APPROACH TO ASSESS SPECIFICATION OF STATISTICAL MODELS

The reliability of the estimated impacts for different program experiences using missing data techniques 

hinge critically on the availability of consistent estimates of any missing data elements and the ability of these 

estimates to distinguish between those who would have a program experience and those who would not 

based on observable characteristics. As such, the models used to derive the estimates of the likelihood 

eligible applicants would have different program experiences need to be subject to specification checks. 

This is particularly true for the models distinguishing between eligible applicants who enroll in the program 

and those who do not enroll as all subsequent estimates of the different program experiences depend on this 

model.

The statistical models for enrollment in Job Corps will be subject to two specification checks. As 

noted above, an intuitively appealing estimate of the impact of Job Corps for those who enroll can be 

obtained by assuming the program has no impact on those who choose not to enroll. If correct, this 

assumption provides a benchmark impact estimate that can be compared to the estimated impacts for 

enrollees derived from applying missing data techniques. In addition, building on the foundation of 

selection correction models, if this assumption holds then a selection corrected regression model relating 

outcomes to the characteristics of all control group members and program group members who do not 

enroll in Job Corps should be unrelated to an individual’s program group status.

Ideally, similar assessments of the model specifications summarizing the other program experiences 

would also be examined. However, it is infeasible to develop similar benchmark estimates of impacts 

for these program experiences by imposing credible assumptions regarding the outcomes of program 

group members who do not have a specific program experience. For example, it is impractical to 

assume that the program did not have any impact on the program group members who did not complete 
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a Job Corps vocation. Hence, our specification checks will be limited to the model summarizing 

program enrollment.

C. PRELIMINARY RESULTS

Preliminary estimates of the impact of enrollment in Job Corps on average weekly earnings were 

derived by using the parameter estimates obtained from applying weighted maximum likelihood 

estimation methods to the logistic specification reported in Appendix B in conjunction with the baseline 

characteristics of control group members and the characteristics of their associated OA 

agency/counselor. The combination of the parameter estimates and the characteristics were used to 

construct a value for Pr(e | X, O, A) that was used in the simple linear regression model described 

above where the dependent variable (Y) is the average earnings of individuals in the 10th quarter 

following random assignment as reported in the 30-month interview. In addition to the program group 

indicator variable, control group indicator variable, arrival indicator variable for the program group, and 

the estimated value of Pr(e | X, O, A) for the control group, the estimated version of the linear 

regression included a large number of individual characteristics measured at baseline, including age, sex, 

race, Job Corps region and population density of residence at time of application, educational 

attainment, employment and education, marital status, presence of children, welfare receipt, arrest 

experiences, drug use, and likely residential status.

We estimated two specifications of the linear regression model. The first specification imposed the 

assumption that Job Corps had no impact on the earnings of program group members who did not 

enroll in the program by constraining the parameters c and d to be equal. The second specification did 

not impose this restriction. Table D-1 presents the estimated impacts from these two specifications, 
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along with the estimated impacts for participants reported in Schochet, Burghardt and Glazerman 

(2000) overall for each of the three age groups. Specifically, the second column in the table presents 

the estimated impacts derived by taking the difference between the mean average earnings per week in 

the 10th quarter after random assignment for the program and control group and dividing this by the 

percentage of program group members who enrolled in Job Corps. The third column presents the 

corresponding estimate of this impact from the regression model (i.e., the quantity a - b) replacing E

*

with the predicted Pr(e | X, O, A) for all control group members, imposing the restriction that c - d = 0. 

 The fourth column presents the analogous estimates of the impact of Job Corps on participants without 

this assumption and the fifth column presents the estimated impact of Job Corps for nonenrollees.

The preliminary results presented in Table D-1 provide mixed evidence regarding the reliability of 

our model-based approach to estimating impacts of Job Corps for applicants who have different 

program experiences. The impact estimates derived from our model based approach that imposes the 

same assumption used to derive the estimates in the second column are very close to the non-model 

based estimates overall, as well as for each age subgroup. However, relaxing this assumption yields 

quite different results compared to the approaches that impose this intuitively appealing assumption, 

particularly overall and for the 18-19 year old group. For example, the large increase in the estimated 

impact per participant for the 18-19 year old group and the large negative estimates of the “impact” for 

nonparticipants overall and for the 18-19 year old group raise questions about the reliability of the 

model based approach.

Exhibit D-1

Comparison of Estimated Impacts on Average Earnings per Week in 10th Quarter Following 

Random Assignment for Job Corps Participants
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Applicant Group Estimated Impact

Per Participant 

Reported in 

Schochet et al 

(2000)

Model Estimated 

Impact for 

Participants 

Assuming zero 

impact on 

nonenrollees

Model Estimated 

Impact for 

Participants 

Without imposing 

zero impact 

assumption

Model Estimated 

Impact for 

Nonenrollees 

All $ 17.70 $ 19.97 $ 25.60 - $ 20.02

16-17 years old 

at application $ 26.00 $ 22.57 $ 20.03 $ 12.24

18-19 years old 

at application - $ 2.40 $ 5.29 $ 16.43 - $ 31.04

20-24 years old 

at application $ 26.20 $ 29.29 $ 30.10 - $ 2.13

The preliminary findings presented in Table D-1 provide one type of specification check for our 

model based approach to estimating impact estimates for applicants with different program experiences. 

 In addition, we also examined a specification check that is based on the conceptual framework 

underlying selection correction models. Specifically, we regressed average earnings per week during 

the 10th quarter after application on an indicator variable for program group members who did not enroll 

in the program, the same set of covariates described above, and the conditional mean sample selection 

adjustment factor for program group nonparticipants using a sample of all control group members and 

program group members who did not enroll in Job Corps. Under the conditions that our model 

adequately accounts for the difference in nonparticipants and a random subset of all eligible applicants, 

the earnings of the nonparticipant program group members should not be significantly different from the 

earnings of all control group members. Although not reported here, preliminary results from this 

specification check also provide mixed evidence regarding the reliability of our statistical models. The 

indicator variable for program group nonparticipants was statistically insignificant overall and for both 
D-12

the 16-17 and 18-19 year old subgroups. In contrast, the preliminary results suggest that the program 

group nonparticipants in the 20-24 year old range at application are significantly different from all 

control group members with the selection correction adjustment. Unfortunately, the model that appears 

best from our first specification check appears to be the most questionable under our second 

specification check.

As all subsequent model-based estimates of the impacts of different program experiences depend 

on the reliability of the models summarizing enrollment in Job Corp, the mixed results from our 

specification checks suggest caution should be exercised in estimating and reporting the results for other 

program experiences. Hence, no additional results were available to report at this time.

D. ESTIMATION OF IMPACTS OF PROGRAM EXPERIENCES AT 48 MONTHS 

AFTER APPLICATION

The preliminary results described above are based on survey information collected at 30 months 

after random assignment. While this time frame provides sufficient time for the program group to enroll 

in Job Corps, it does not necessarily provide ample time to measure the post-program experiences of 

participants who have specific program experiences. For example, the post-program experiences of 

participants who are enrolled in Job Corps at least 270 days are obviously more limited than those who 

do not enroll or are enrolled for less than 30 days. As suggested by the mixed evidence reported 

above, a longer post-program period is needed to adequately assess our model based specifications.

We will more fully examine our model based specifications and, if this assessment suggests these 

models are appropriate, we will estimate the impact of the program experiences listed above using 

information up through 48 months after application. Specifically, we will assess the specification of our 
D-13

models summarizing enrollment using both of the checks described above in Section D.2 using 

information from the longer post-program period. If we judge these models to be reliable, we will 

estimate the impact of participation in vocational program areas, completion of a vocation, attainment of 

a GED, and different lengths of stay in the program using the empirical methods described above.