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Year Up

A training and internship program that helps young people with limited post-secondary education get high-quality jobs by learning to work with technology, developing employment skills, and obtaining internships.

Program Outcomes

  • Employment
  • Post Secondary Education

Program Type

  • Employment - Vocational
  • Skills Training

Program Setting

  • Community

Continuum of Intervention

  • Selective Prevention

Age

  • Adult
  • Early Adulthood (19-22)
  • Late Adolescence (15-18) - High School

Gender

  • Both

Race/Ethnicity

  • All

Endorsements

Blueprints: Promising
Social Programs that Work:Suggestive Tier

Program Information Contact

Roberto Zeledon
Chief Marketing Officer
Year Up
45 Milk Street, 9th Floor
Boston MA 2109
Phone: (855) 305-9995
Email: rzeledon@yearup.org
Website: www.yearup.org

Program Developer/Owner

Roberto Zeledon
Year Up


Brief Description of the Program

Year Up provides six months of full-time training in the IT, business operations, sales and customer support, software development, and financial service sectors followed by six-month internships at major employers. The full-time program provides extensive supports and puts a heavy emphasis on the development of professional and technical skills. Students receive weekly stipends to help cover transportation and other program-related expenses. The program targets young adults with a high school diploma or equivalent who are motivated and who, with assistance, can overcome challenges and successfully enter careers in fast-growing technical occupations.

Outcomes

Study 1 (Fein and Hamadyk, 2018): Compared to the control group, the intervention group showed significantly more

  • employment
  • college enrollment
  • earnings

Risk and Protective Factors

  • health insurance
  • financial hardship

Study 2 (Roder and Elliott, 2011, 2014): Compared to the control group, the intervention group showed significantly more

  • full-time employment and IT and finance jobs
  • earnings and hourly wages

Risk and Protective Factors

  • college financial assistance and interest in attending college

Brief Evaluation Methodology

Study 1 (Fein and Hamadyk, 2018): After recruiting a sample of 2,544 young adults located near eight program offices, local staff randomly assigned participants at a 2:1 ratio to the intervention (N = 1,669) and control groups (N = 875). Data came from a survey of participants at 18 months after randomization and from administrative records that followed participants for up to three years after randomization, as well as official employment and wage records derived from the National Directory of New Hires. The primary measures focused on earnings and employment. Secondary and exploratory measures included college enrollment, financial hardship, marriage and childbearing, and personality traits.

Study 2 (Roder and Elliott, 2011, 2014) recruited 195 youths (ages 18-24) in three Eastern cities in the United States for the study and randomly assigned them to the program group or a no-treatment control group. The youths were followed for three years after the end of the program. The analysis examined self-reported measures of earnings, employment, and college attendance.

Study 1

Fein, D., & Hamadyk, J. (2018). Bridging the opportunity divide for low-income youth: Implementation and early impacts of the Year Up program, OPRE Report #2018-65 (and Appendices). Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.


Race/Ethnicity/Gender Details
Differences in program impacts by gender were not statistically significant, but impacts did vary by race-ethnicity. Results indicated that the intervention increased the earnings of non-Hispanic blacks less than other groups. Overall, however, the program benefitted all groups.

Year Up has standardized training materials, implementation procedures, and playbooks for the delivery of the Year Up program across the network. 

To prepare Year Up staff for their functional roles and to deliver the intervention, Year Up onboards new employees through a collaborative process among the local Operations team, the hiring manager, and Year Up's National office. The manager of each new hire leads all functional onboarding related to organizational or individual responsibilities. A key component to the orientation process is a parallel and continuous self-directed, virtual orientation to train new employees in their specific Year Up role as well as Year Up's organizational practices, program delivery, culture, and more. All new employees participate in a nationally-led, multi-day, new hire in-person training called Baseline In-Person. In addition to this extensive onboarding process, Year Up also offers ongoing training and professional development to staff to ensure staff are equipped in their functional role and are growing professionally. A good deal of this ongoing development is provided by national and regional leadership teams that have long tenure and deep functional expertise in the delivery of different components of the Year Up program (e.g., Admissions and Enrollment, Program Management, Student Support Services, Academic Services and Delivery, Internship Support, Employment Placement Services, Corporate Partner Support and Relationship Management). 

 

Source: Washington State Institute for Public Policy
All benefit-cost ratios are the most recent estimates published by The Washington State Institute for Public Policy for Blueprint programs implemented in Washington State. These ratios are based on a) meta-analysis estimates of effect size and b) monetized benefits and calculated costs for programs as delivered in the State of Washington. Caution is recommended in applying these estimates of the benefit-cost ratio to any other state or local area. They are provided as an illustration of the benefit-cost ratio found in one specific state. When feasible, local costs and monetized benefits should be used to calculate expected local benefit-cost ratios. The formula for this calculation can be found on the WSIPP website.

Start-Up Costs

Initial Training and Technical Assistance

$63 cost-per-student (Staff Training)

Curriculum and Materials

$770 cost-per-student (College Fees, including tuition not covered by financial aid for courses providing technical skills training at our college-based locations)

Licensing

$15 cost-per-student (Licensing & Hosting Fees)

Other Start-Up Costs

Employee Recruiting Fees Startup:  $5,000
Postage & Delivery Startup:  $500
Furniture and Equipment Startup:  $2,500
Printing Design & Photo Startup:  $6,000
Office Supplies Startup:  $200
Building Rental & Building Maintenance & Utilities:  $1,457 cost-per-student (also noted under ongoing costs)

Intervention Implementation Costs

Ongoing Curriculum and Materials

Costs Per Student Per Year
Drug Testing & Screening:  $75
Program Supplies:  $20
Printing Design & Photo:  $13  
Catering:  $50

Costs Per Staff Per Year
Employee Recruiting Fees:  $200
Technology:  $2,700
Office Supplies:  $200
Car Rentals:  $200
Staff Training:  $400
Travel (at 1 Learning Community = 40 students):  $1,250
Travel (at 2 Learning Communities = 80 students):  $1,000
Travel (at 3+ Learning Communities = 120+ students):  $750
Meals:  $650
Lodging:  $750

Costs Per Cohort Per Year
Fellow Service:  $4,500
Postage & Delivery:  $500
Awards:  $1,000

Fixed Costs
Registration, License & Fees:  $500
Management Consultants (if greater than year 2):  $20,000
Printing Design & Photo Fixed:  $5,000
Advertising:  $5,000
Catering (if greater than year 1):  $20,000
CC Processing Fees:  $500
Furniture and Equipment:  $5,000
Miscellaneous:  $500
Membership & Subscriptions:  $7,500

Staffing

To serve 1 cohort of 40 students (called an "LC," or Learning Community), Year Up will staff 13 headcount at $1,215,175 annually. This equated to roughly $13,868 cost-per-student in FY2018 (based upon data from all sites).

We cost out our centralized National departments who support our sites at $2,400 per student and $13,000 per full-time staff headcount.

Other Implementation Costs

Learning & Development Weekly Rate: $50 per student per week
Internship Weekly Rate: $150 per student per week, with the exception of our Bay Area market which is $250 per student per week
Other Student Direct Costs: $100 per student per year

Student Transportation: $250 per student per year

Building Rental and Building Maintenance & Utilities: $1,457 cost-per-student

Implementation Support and Fidelity Monitoring Costs

Ongoing Training and Technical Assistance

Ongoing training, fidelity, technical assistance, and other supports are facilitated by National Program, Human Resources (including a dedicated training team), National Research and Business Intelligence Teams described under administration requirements and cost considerations above. All of these services are included in the centralized cost calculations at $2,400 per student and $13,000 per full-time staff headcount.

Fidelity Monitoring and Evaluation

No information is available

Ongoing License Fees

No information is available

Other Implementation Support and Fidelity Monitoring Costs

No information is available

Other Cost Considerations

Student stipends are $3,600 per student in nearly all locations starting in 2020.

Year One Cost Example

Funding Overview

No information is available

Funding Strategies

Improving the Use of Existing Public Funds

No information is available

Allocating State or Local General Funds

Historically, 1% of all of Year Up's revenue is derived from any form of government funding, federal, state or local.

Maximizing Federal Funds

No information is available

Foundation Grants and Public-Private Partnerships

Year Up raises philanthropic capital from a diverse portfolio of individuals (~30% of all philanthropy), corporate giving entities (~10% of all philanthropy), and foundations (~59% of all philanthropy). Philanthropy accounts for about 40-45% of our total revenue. 

Debt Financing

No information is available

Generating New Revenue

Year Up earns Internship Revenue from our corporate partners, who pay market rates to Year Up to host a Year Up student for the internship phase of the program. This innovative financial model is centered on sustainability and scalability, as revenue from corporate partners covers more than half (nearly 60%) of the organization's direct service expenses.

Year Up raises philanthropic capital from individual donors.

Another funding stream is through Year Up Professional Resources, or YUPRO. YUPRO is a Public Benefit Corporation with the social purpose of providing career opportunities for the alumni of Year Up.

Data Sources

No information is available

Program Developer/Owner

Roberto ZeledonChief Marketing OfficerYear Up45 Milk Street, 9th FloorBoston, MA 2109(855) 305-9995rzeledon@yearup.org www.yearup.org

Program Outcomes

  • Employment
  • Post Secondary Education

Program Specifics

Program Type

  • Employment - Vocational
  • Skills Training

Program Setting

  • Community

Continuum of Intervention

  • Selective Prevention

Program Goals

A training and internship program that helps young people with limited post-secondary education get high-quality jobs by learning to work with technology, developing employment skills, and obtaining internships.

Population Demographics

Young adults aged 18-24 with a high school degree or GED but limited post-secondary education and job prospects.

Target Population

Age

  • Adult
  • Early Adulthood (19-22)
  • Late Adolescence (15-18) - High School

Gender

  • Both

Race/Ethnicity

  • All

Race/Ethnicity/Gender Details

Differences in program impacts by gender were not statistically significant, but impacts did vary by race-ethnicity. Results indicated that the intervention increased the earnings of non-Hispanic blacks less than other groups. Overall, however, the program benefitted all groups.

Other Risk and Protective Factors

Lack of access to economic opportunity.

Risk/Protective Factor Domain

  • Individual

Risk/Protective Factors

Risk Factors

Protective Factors


*Risk/Protective Factor was significantly impacted by the program

See also: Year Up Logic Model (PDF)

Brief Description of the Program

Year Up provides six months of full-time training in the IT, business operations, sales and customer support, software development, and financial service sectors followed by six-month internships at major employers. The full-time program provides extensive supports and puts a heavy emphasis on the development of professional and technical skills. Students receive weekly stipends to help cover transportation and other program-related expenses. The program targets young adults with a high school diploma or equivalent who are motivated and who, with assistance, can overcome challenges and successfully enter careers in fast-growing technical occupations.

Description of the Program

Year Up provides six months of full-time training (e.g., IT and financial operations) followed by six-month internships at major employers. The full-time program provides extensive supports and puts a heavy emphasis on the development of professional and technical skills. Students receive weekly stipends to help cover transportation and other program-related expenses. The program targets young adults with a high school diploma or equivalent who are motivated and who, with assistance, can overcome challenges and successfully enter careers in fast-growing technical occupations.

In the first (Learning and Development) phase, students typically attend classes at Year Up from 8:30 AM until 3:30 PM four days a week, and for a half day on Wednesdays. The training addresses both occupation-specific and general skills. Fields include information technology (the most common emphasis), business operations, financial operations, software development, and sales and customer support. General skills courses focus on professional and business communication skills. Students gain experience in writing, giving presentations, interacting with clients and colleagues, and developing critical thinking skills.

In the second (Internship) phase, which heavily involves employers, the participants maintain full-time schedules, working at internship sites four and a half days a week and returning to Year Up typically on Wednesdays to share and process internship experiences, attend workshops, and plan post-program career transitions. Towards the end of internships, the emphasis on job search and placement intensifies. Active efforts to support job search and placement continue for up to four months after graduation.

Theoretical Rationale

Intensive and comprehensive interventions that address both general and occupation-specific skills, use work-based learning, and fully engage employers can help to overcome the barriers that nontraditional students often face. Assistance can give young adults without post-secondary credentials the opportunities to access professional careers and further education.

Theoretical Orientation

  • Skill Oriented

Brief Evaluation Methodology

Study 1 (Fein and Hamadyk, 2018): After recruiting a sample of 2,544 young adults located near eight program offices, local staff randomly assigned participants at a 2:1 ratio to the intervention (N = 1,669) and control groups (N = 875). Data came from a survey of participants at 18 months after randomization and from administrative records that followed participants for up to three years after randomization, as well as official employment and wage records derived from the National Directory of New Hires. The primary measures focused on earnings and employment. Secondary and exploratory measures included college enrollment, financial hardship, marriage and childbearing, and personality traits.

Study 2 (Roder and Elliott, 2011, 2014) recruited 195 youths (ages 18-24) in three Eastern cities in the United States for the study and randomly assigned them to the program group or a no-treatment control group. The youths were followed for three years after the end of the program. The analysis examined self-reported measures of earnings, employment, and college attendance.

Outcomes (Brief, over all studies)

Study 1 (Fein and Hamadyk, 2018): The program significantly improved the primary outcome of earnings plus several additional measures of wages and employment. It also significantly improved measures of college enrollment, health insurance, and financial hardship.

Study 2 (Roder and Elliott, 2011, 2014): The program significantly improved total earnings, hourly wages, full-time employment, and jobs in IT and finance for the intervention group relative to the control group. Overall employment did not differ significantly across conditions, and college attendance was higher for the control group than the intervention group.

Outcomes

Study 1 (Fein and Hamadyk, 2018): Compared to the control group, the intervention group showed significantly more

  • employment
  • college enrollment
  • earnings

Risk and Protective Factors

  • health insurance
  • financial hardship

Study 2 (Roder and Elliott, 2011, 2014): Compared to the control group, the intervention group showed significantly more

  • full-time employment and IT and finance jobs
  • earnings and hourly wages

Risk and Protective Factors

  • college financial assistance and interest in attending college

Mediating Effects

Not examined.

Effect Size

The study reported only a few effects sizes, most notably the values of .28, .18, and .34 for measures of self-assessed career progress.

Generalizability

The study used a large sample from eight major cities across the United States, but the sample was selective in the recruitment of young adults expected to benefit most from the program.

Potential Limitations

Study 1 (Fein and Hamadyk, 2018)

  • Little information on reliability of survey scales and some gaps in measures of employment and college enrollment

Study 2 (Roder and Elliott, 2014)

  • No controls for baseline outcomes
  • No tests for baseline equivalence
  • Evidence of differential attrition
  • Long-term results compromised by entrance into the program of control participants

Endorsements

Blueprints: Promising
Social Programs that Work:Suggestive Tier

Program Information Contact

Roberto Zeledon
Chief Marketing Officer
Year Up
45 Milk Street, 9th Floor
Boston MA 2109
Phone: (855) 305-9995
Email: rzeledon@yearup.org
Website: www.yearup.org

References

Study 1

Certified Fein, D., & Hamadyk, J. (2018). Bridging the opportunity divide for low-income youth: Implementation and early impacts of the Year Up program, OPRE Report #2018-65 (and Appendices). Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.

Study 2

Roder, A., & Elliott, M. (2011). A promising start: Year Up's initial impacts on low-income young adults' careers. Economic Mobility Corporation: New York, NY.

Roder, A., & Elliott, M. (2014). Sustained gains: Year Up's continued impact on young adults' earnings. Economic Mobility Corporation: New York, NY.

Study 1

Evaluation Methodology

Design:

Recruitment: Year Up's eight core offices recruited, screened, and selected 2,544 young adults ages 18-24 for the evaluation. To allow for experimental assignment, local admissions teams recruited three eligible applicants for every two open program seats. Each office enrolled at least two cohorts from January 2013 to August 2014. Study locations included Atlanta, the San Francisco Bay area, Boston, Chicago, Washington DC, New York City, Providence, and the Puget Sound area. The young adults eligible for the program had a high school diploma or equivalent and were judged by local staff as likely to benefit from the program. Many had recently experienced financial hardship and very few were employed or in school.

Assignment: Local program staff used an online lottery tool developed by Abt Associates to randomly assign participants within each local site at a 2:1 ratio to the intervention and control groups. Intervention group members (N = 1,669) had the opportunity to participate in Year Up but were not required to enroll. Control group members (N = 875) could not participate in Year Up but received a list of other services available in the community.

Assessments/Attrition: Participants completed a baseline survey and an 18-month follow-up survey (about 6 months after the end of the year-long program). The response rate for the follow-up survey was 78% for the treatment group and 73% for the control group. Item non-response among survey responders was under 4%, except for parental college attendance (6.0%), typical high school grades (7.2%), family income (9.5%), and expected near-term future work hours (6.0%).

Additional data from administrative records extended the follow-up period to more than one year after the program end. Data on new hires (from the National Directory of New Hires) accessed in April 2018 covered a period extending from "two quarters before random assignment to three years (12 quarters) after random assignment" (p. 35). Data on college enrollment accessed in April 2017 covered "nearly three years (11 quarters) after random assignment" (p. 35). Data from the administrative records were complete for 98% of the sample.

Sample:

A majority of sample members were black (54%) or Hispanic (31%). Men (59%) outnumbered women (41%), though women were well-represented for a tech-focused training program. Most sample members (68%) lived with their parents, and few (9%) had children. Many struggled in high school: 40% reported usual grades of C or below, and only 10% reported usually receiving As. About half had attended some college. Nearly two thirds (63%) were in families with annual incomes below $30,000.

Measures:

The outcome measures, 45 in total, came from two independent sources, survey self-reports and administrative records. Data on new hires came from extracting records of the National Directory of New Hires. The database covered most employment but missed self-employment and independent contracting. Data on college enrollment came from the National Student Clearinghouse. The database included nearly all private and public 4-year schools and public 2-year schools. Coverage was less complete for private 2-year schools and for-profit 2-year and 4-year schools. The study provided little information on the validity of the survey measures, but most appeared straightforward.

Earnings and Employment: Most measures of earnings came from administrative records. The single measure identified by the researchers as primary was the average quarterly earnings in the 6th and 7th quarters after random assignment. Four secondary measures included the annual earnings in years 1, 2, and 3 and employment. A total of 17 measures came from the survey. There were three indicators of career pathways employment (working for $15/hour or more, in a job requiring mid-level skills, and in a program-targeted occupation) and three measures of self-assessed career progress (perceived career progress, confidence in career knowledge, and access to career networks). The surveys also provided 11 measures of earnings, hours worked, and hourly wages.

Post-Secondary Education: The seven measures from administrative records covered quarterly college enrollment, quarterly cumulative college enrollment, and yearly college enrollment. The five measures from the survey included credits received and credentials awarded from college, other institution, licensing, and any source. These measures were described as exploratory because they were not expected to show effects until later.

Other Outcomes: The 11 measures from surveys included dependence on public assistance, financial hardship, access to health insurance, marriage and childbearing, grit, savvy, and core self-evaluation. These measures were described as exploratory because they were not expected to show effects until later, may have effects in unknown directions, or were of unknown validity.

Analysis:

Given the 28 baseline covariates, the analysis used a multi-step control strategy. It first regressed each outcome on a set of baseline variables for the control group. It next used the regression results to calculate predicted outcome values for the two conditions. The final steps calculated the average difference between actual and predicted values for each condition and, to assess the program impact, subtracted the average condition differences from one another. It appears from Appendix A.2 that the covariates included baseline outcomes.

The tests relied on different significance levels, depending on the outcomes. The analyses of the researcher-identified primary and secondary outcomes, called the confirmatory and secondary analyses, used one-tailed tests but were limited in number to minimize the problem of multiple comparisons. The more extensive exploratory analyses used two-tailed tests. But the tables present sufficient information to apply two-tailed tests at the .05 to all results.

Intent-to-Treat: The study included participants assigned to the intervention group but who did not enroll in the program. For those lost to follow-up, the analyses applied weights to adjust for differential nonresponse. In addition, missing data for item non-response was imputed using a weighted "hot deck" matching procedure (Appendix A.1).

Outcomes

Implementation Fidelity:

Nearly all treatment group members (96%) accepted the offer to enroll and began the program. About 81% of the sample and 85% of enrollees completed the six-month learning and development phase. Nearly all those completing the learning and development phase received internships, and 75% of the treatment group members finished the program.

A detailed implementation analysis found that Year Up generally implemented its services with high fidelity to the program model. Overall, treatment group members received substantially higher levels of training and support than control group members. However, the eight offices varied in completion of the program components (range = 61% to 82%).

Baseline Equivalence:

Using all 2,544 randomized participants, tests for condition means on 28 baseline measures of sociodemographic characteristics, outcomes, and covariates produced no significant differences (p < .05).

Differential Attrition:

The conditions had roughly similar response rates of 78% for the treatment group and 73% for the control group. Exhibit D-3 compared the condition means for the baseline measures using all participants and survey respondents. There were no significant differences at the .05 level for the 28 baseline measures in either the full or the analysis sample. The analyses still used a weighting procedure to adjust for nonresponse (see Appendix D.3). Further, checks in Exhibit D-4 demonstrated that, for outcome measures available for the full sample, analysis of the subsample of survey respondents produced results similar to those for the full sample.

Posttest:

Overall, approximately 30 of the 45 outcomes showed significant effects, including the primary and secondary measures of earnings and employment.

Earnings and Employment: The primary outcome - average quarterly earnings in the 6th and 7th quarters after random assignment - was significantly higher for the intervention group than the control group (p < .05, two-tailed test). More detail in Exhibit 6-2 revealed significantly lower earnings in year 1, when the program was ongoing and intervention participants were receiving stipends, but significantly higher earnings in years 2 and 3. The same pattern emerged for employment, with significant positive effects in later years. Tests for the survey measures showed significant intervention effects in 16 of 17 outcomes. The authors noted (p. 7) that the increase in earnings came from more hours worked and higher wages rather than from higher rates of employment.

Post-Secondary Education: Tests using the seven administrative measures showed generally positive effects. The intervention group had significantly higher college enrollment in year 1, when the program was ongoing, and significantly lower enrollment in year 2. However, when measuring cumulative months of fulltime enrollment (Exhibit 6-7), the intervention group had significantly higher scores than the control group in all years. Tests for the five survey measures found three significant effects on credits received, a licensing credential received, and a credential received from any source.

Other Outcomes: Tests for 11 self-reported outcomes found five significant effects of the intervention: having health insurance, receiving less cash or in-kind support, experiencing less financial hardship, perceived savvy, and core self-evaluation.

Long-Term: The effects of the program on earnings and cumulative college enrollment were maintained in years 2 and 3, well after the 1-year follow-up period.

Study 2

Evaluation Methodology

Design:

Recruitment: Program staff in three cities, Boston, New York City, and Providence, Rhode Island, invited eligible young people to take part in the program. The recruitment in 2007 took place shortly before the recession of 2008. Eligible youths came from low-income backgrounds, were ages 18-24, had a high school degree or GED, and showed strong motivation to improve their opportunities with an intensive, full-year training. A total of 195 agreed to join the study.

Assignment: The research team randomly assigned the participants based on the capacity of the programs in the three cities. Of the 195 youths in the sample, 135 were randomly selected for the intervention group and 60 were randomly selected for the control group. Control participants were told that they were being placed on a waiting list, could re-apply to the program after ten months, and were allowed to pursue employment or postsecondary education or training elsewhere. However, 29% of the control group members eventually participated in the program, which affected the long-term comparisons.

Assessments/Attrition: Roder and Elliott (2011) mentioned that the last survey occurred between 24 and 30 months after random assignment (or 12 and 18 months after the program end). Figures 2 and 3, however, reported quarterly data over two years, suggesting either ongoing surveys after randomization or retrospective reports. The follow-up survey included 164 youths, with completion rates of 84% overall, 89% for the intervention group, and 73% for the control group.

Roder and Elliott (2014) followed the participants for an additional two years, three years after the end of the program. The response rates of the follow-up survey were 73% overall, 76% for the intervention group, and 68% for the control group.

Sample:

Of the 164 young people in the analysis sample, 50% were African American and 34% were Latino. More than half (57%) were male, 81% lived with a parent or guardian, and 18% lived in public housing. All had at least a GED or high school degree, but 8% had a criminal conviction and English was a second language for 15%.

Measures:

For the one-year follow-up (Roder and Elliott, 2011), program staff collected data on students who graduated from the program, while a survey firm conducted interviews with members of the control group and with members of the intervention group who dropped out of the program. For the long-term follow-up (Roder and Elliott, 2014), a single survey firm collected data on all available participants. The study provided no details on the reliability or validity of the self-reports from participants, but the measures were straightforward. The outcomes included:

  • Total earnings
  • Employment, including full-time employment and number of jobs
  • Hourly wages
  • Job type (e.g., IT and finance)
  • Employer-provided benefits
  • College attendance

Analysis:

The analyses simply compared condition means and percentages, without any controls.

Intent-to-Treat: The analyses included all participants with follow-up data, regardless of whether or not they ever attended or graduated from the program. For the long-term data, after many of those initially assigned to the control group had entered and completed the program, treatment-on-the-treated results were reported along with ITT results.

Outcomes

Implementation Fidelity:

The study did not measure the quality of implementation but reported that, of the 120 intervention group members, 90% attended part of the program and 64% graduated on time in July 2008.

Baseline Equivalence:

The study did not examine baseline equivalence of the conditions.

Differential Attrition:

For the one-year follow-up (Roder and Elliott, 2011), the conditions differed substantially in attrition rates - 11% for the intervention group and 27% for the control group. Footnote 4 examined the significance of differences in the attrition rates between the intervention and control group members by baseline characteristics. There were significant differences on three factors. Attrition was higher among control group members than among intervention group members for those ages 18-19, African Americans, and those with English as the primary language.

For the long-term follow-up (Roder and Elliott, 2014), the conditions differed moderately in attrition rates - 24% for the intervention group and 32% for the control group. The appendix examined the significance of differences in the attrition rates between the intervention and control group members by baseline characteristics. There were significant differences on two factors. Attrition was higher among control group members than among intervention group members for those ages 18 to 19 and those with a GED rather than a high school diploma.

Posttest:

The results in Roder and Elliott (2011) covered the quarterly periods during the one-year program and the one-year post-program period. During the program period, when the intervention group was going through the training, the control group had higher employment and earnings. However, during the second year after random assignment, relative to the control group, the intervention group showed significantly more:

  • fulltime employment (but not overall employment),
  • annual earnings in the second and third quarter,
  • earnings per hour, and
  • more jobs in information technology and investment operations.

Long-Term:

The results in Roder and Elliott (2014) covered all four years of the study but focused on the three years after the program end. The comparisons were limited by the entrance of 29% of control participants into the program, which likely lowered employment and earnings of the control group during the training and raised employment and earnings afterward. As a point of comparison, intervention participants made approximately $2000 less per quarter, compared to control participants, while completing the intervention.

Figure A1 in the appendix lists the ITT effects for 25 outcomes, with only three reaching significance at the .05 level, while the text listed some additional significant effects. Relative to the control group, the intervention group showed significantly more:

  • holders of a single job (but not higher employment rates or hours worked),
  • earnings in years 2-3 after the program but not year 4,
  • jobs in information technology and investment operations,
  • recipients of educational financial assistance, such as loans, grants, or scholarships, and
  • interest in attending college (among those not attending college).

Contact

Blueprints for Healthy Youth Development
University of Colorado Boulder
Institute of Behavioral Science
UCB 483, Boulder, CO 80309

Email: blueprints@colorado.edu

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Blueprints for Healthy Youth Development is
currently funded by Arnold Ventures (formerly the Laura and John Arnold Foundation) and historically has received funding from the Annie E. Casey Foundation and the Office of Juvenile Justice and Delinquency Prevention.