Absence of conflict of interest.
- The study’s objective was to examine the impact of the Linking Innovation, Knowledge, and Employment (@LIKE) program on employment and education outcomes.
- The study used a nonexperimental design to compare the outcomes of youth in the @LIKE program to a matched comparison group. Using administrative data, the authors conducted statistical models to compare outcomes between the groups.
- The study found a significant relationship between @LIKE program participation and increased unsubsidized employment.
- This study receives a low evidence rating. This means we are not confident that the estimated effects are attributable to @LIKE; other factors are likely to have contributed.
Linking Innovation, Knowledge, and Employment (@LIKE) Program
Features of the Intervention
The Linking Innovation, Knowledge, and Employment (@LIKE) program served disconnected youth ages 18–24. The program targeted youth who were unemployed, not in school, and not serving in the armed forces. Eligible youth also had to have a low income, be gang involved, be an ex-offender, be a public assistance recipient or be a recently separated veteran. @LIKE was implemented in Riverside, San Bernardino, and Imperial counties in Southern California. @LIKE was designed to help participants meet educational and employment goals, help maintain employment, and increase earnings. Each county conducted activities to help participants reach these goals. The participants of @LIKE received case management, life coaching, education services and training, soft skills training, career exploration and work readiness preparation (such as apprenticeships and on-the-job training), and supportive services.
Features of the Study
The study used a nonexperimental design to compare the attainment of employment and a high school degree or equivalent between @LIKE program participants and a matched comparison group. The comparison group included youth involved in the Workforce Investment Act (WIA) and Workforce Innovation and Opportunity Act (WIOA) Youth program. The comparison group received case management and access to federally funded education and employment services. The @LIKE sample included 664 youth living in San Bernardino, Riverside, and Imperial counties in Southern California and the comparison group included 6,648 youth living in the same counties. The study sample was 55% male with an average age of 21 years old. The majority were Hispanic (62%), 28% were ex-offenders, 50% were experiencing homelessness, and over half had a high school degree or equivalent (59%). The study used the Virtual OneStop system for @LIKE participant data and county case management systems for comparison group data. Study authors matched participants on demographic characteristics, educational attainment, socioeconomic status, employment, criminal justice involvement, disability status, and location within San Bernardino, Riverside, and Imperial counties. The authors used statistical models to compare differences in outcomes between the two groups.
Education and skills gain
- The study found no statistically significant relationship between @LIKE and attainment of a high school diploma or equivalent.
- The study found that @LIKE members were significantly more likely to obtain unsubsidized employment than comparison group members.
Considerations for Interpreting the Findings
While the study authors matched on age, gender, race, ethnicity, education, and employment, they did not control for baseline outcomes greater than one year before program participation as required by the protocol. These preexisting differences between the groups—and not @LIKE—could explain the observed differences in outcomes. Therefore, the study is not eligible for a moderate causal evidence rating, the highest rating available for nonexperimental designs.
Causal Evidence Rating
The quality of causal evidence presented in this report is low because the authors did not ensure that the groups being compared were similar before the intervention. This means we are not confident that the estimated effects are attributable to @LIKE; other factors are likely to have contributed.