Career Pathways Descriptive and Analytical Project

• Tools You Can Use: DOL's New Career Trajectories and Occupational Transitions Dashboard (Webinar, June 2022)
• A Meta-Analysis of 46 Career Pathways Impact Evaluations (Final Report, January 2022)
• New Insights on Career Pathways: Evidence from a Meta-Analysis (Summary Brief, January 2022)
• Building Better Pathways: An Analysis of Career Trajectories and Occupational Transitions (Final Report, January 2022)
• Appendix: Healthcare, Early Care and Education, Information Technology, and Production/Manufacturing Career Trajectories and Occupational Transition Findings (Expanded Analyses by Occupational Cluster, January 2022)
• Wage Growth Disparities by Gender and Race/Ethnicity Among Entrants to Mid-Level Occupations (Short Paper, January 2022)
• Machine Learning in Workforce Development Research: Lessons and Opportunities (Issue Brief, January 2022)
• Career Pathways: Highlights of Continuous Program Improvement and Evidence Building (One Pager, January 2022)
• Evidence on Career Pathways Strategies: Highlights from a Scan of the Research (Knowledge Development Brief, November 2020)
  • Summary
• Career Pathways Research and Evaluation Matrix (November 2020)

Public Use Data

Career Pathways Meta-Analysis

Codebooks

• CP Meta-Analysis Public Use Dataset Documentation (.pdf)
• CP Meta-Analysis Coding Protocol (.pdf)

Datasets

• CP Meta-Analysis Public Dataset (.dta)
• CP Meta-Analysis Public Dataset (.xlsx)

Career Trajectories and Occupational Transitions Study

Trajectories Data

Codebooks

• Trajectories (3 Years) Codebook (.xlsx)
• Trajectories (5 Years) Codebook (.xlsx)
• Trajectories (10 Years) Codebook (.xlsx)
• Dashboard cluster Codebook (.xlsx)
• Dashboard trajectories Codebook (.xlsx)

CSV Datasets

• Trajectories (3 years) dataset (.csv)
• Trajectories (5 years) dataset (.csv)
• Trajectories (10 years) dataset (.csv)
• Dashboard cluster dataset (.csv)
• Dashboard trajectories dataset (.csv)

Stata Datasets

• Trajectories (3 years) dataset (.dta)
• Trajectories (5 years) dataset (.dta)
• Trajectories (10 years) dataset (.dta)
• Dashboard cluster dataset (.dta)
• Dashboard trajectories dataset (.dta)

Transitions Data

Codebooks

• CPS-SIPP Codebook (.xlsx)
• Emsi Codebook (.xlsx)
• Data Dashboard transitions codebook (.xlsx)

CSV Datasets

• CPS-SIPP dataset (.csv)
• Emsi dataset (.csv)
• Dashboard transitions dataset (.csv)

Stata Datasets

• CPS-SIPP dataset (.dta)
• Emsi dataset (.dta)
• Dashboard transitions dataset (.dta)

• How do workers progress through multiple, progressively higher levels of education and training, and associated jobs, within a pathway over time? How does the demand for workers and credentials map to employer demand? How do these career trajectories vary by business and industry sector and by occupational cluster?
• How can previous research studies on career pathways programs and components be systematically assessed to derive conclusions about that growing and complex body of research?
• Can predictive analytics, machine learning and related approaches to encode and analyze existing data help to identify trends and patterns within existing career pathways study data that were not revealed through traditional analyses? What trends are revealed? How might these approaches improve procedures and the ability to identify themes and data?
• To what extent can we identify: which components of career pathways programs are the strongest drivers of impacts? For what demographic groups? The most effective components at the program level or systems level?

A Meta-Analysis of 46 Career Pathways Impact Evaluations:

• The career pathways approach achieved large gains in participants’ educational progress and employment in the industry trained for. The approach had small impacts on overall employment.
• The career pathways approach increased participants’ short-term earnings by a very small amount but did not meaningfully increase medium or long-term earnings. Researchers noted that few evaluations reported earnings over more than five years, indicating an opportunity for additional research.
• Larger educational progress impacts were associated with a staffing agency serving as a partner, or when employer engagement activities involved employer input on curricula or program design. Programs that had a community or technical college as the lead or partner all had positive educational progress impacts, but those impacts were generally smaller.
• Larger labor market impacts were associated with programs that had a larger share of Black participants. Smaller labor market impacts were associated with programs that offered flexible sequencing of courses or offered tuition or other financial assistance.

Building Better Pathways: An Analysis of Career Trajectories and Occupational Transitions:

• There is meaningful variation in wage growth trajectories among workers starting in mid-level occupations. Assuming a starting wage of $20 per hour, entrants to launchpad occupations earn about $7.20 more per hour after 10 years compared to those who enter lower-wage-growth occupations.
• Several characteristics distinguish launchpad occupations. Though launchpad occupations can be found across occupational clusters, workers who enter occupations in “Knowledge” clusters such as Information Technology, Management/Finance, and Engineering/Science/Architecture see the highest average wage growth. Launchpad occupations emphasize transferrable skills such as problem solving and two-way communication. Problem solving is not just foundational for advancement, but also a skill that becomes more important as workers advance occupationally. Skills in managing people also become more important as workers advance to higher paying occupations.
• Distinct worker experiences are associated with wage growth. Frequent job changes (seven or more over a 10-year period) are associated with lower wage growth. Workers that experience lower wage growth also spend fewer months working on average. In contrast, leaving a starting occupational cluster is associated with greater wage growth.
• Wage growth varies for workers with different backgrounds or experiences who start in the same mid-level occupation. For example, women tend to experience lower wage growth than men, and similarly, Hispanic and Black workers tend to experience lower wage growth than White non-Hispanic workers. In addition, workers with higher levels of education tend to experience higher wage growth than those without a high school diploma or equivalent. This trend also holds for workers who have a parent with a college degree: these workers experience higher wage growth than those whose parents do not have a college degree.
• When they make an occupational transition, women and workers of color are less likely to advance to higher-level jobs, and more likely to stay in the same occupational cluster.
• The Career Trajectories and Occupational Transitions Dashboard is a new source of labor market information that, together with other data sources, can be used to develop programs and policies that are responsive to the real-world experience of workers. The dashboard offers a way for users to interact with the data used in the project to explore: (1) how workers in mid-level occupations move through specific jobs and occupational clusters, and (2) how they experience wage growth over time. This information can be used by policymakers and practitioners to identify “launchpad” occupations associated with higher wage growth, look for clusters or occupational characteristics associated with wage growth, and understand where disparities exist and could be addressed.

Machine Learning in Workforce Development Research: Lessons and Opportunities:

• Machine learning can be a powerful tool in the right context. Machine learning approaches and tools can be used to analyze large volumes of data to answer clear research questions that cannot feasibly be answered using traditional methods, though users should be cognizant of the risks and limitations involved (such as the approach’s evolving legal, computing, and cost environment).
• Conducting a “discovery” phase would help identify if particular research questions are good candidates for workforce-focused machine learning projects instead of traditional methods. A framework of goals and activities is offered to help yield promising opportunities to use machine learning.

Career Pathways Meta-Analysis Final Report

Peck, L.R., Schwartz, D., Strawn, J., Weiss, C. C., Juras, R., de la Rosa, S. M., Greenstein, N., Morris, T., Durham, G., Lloyd, C. (2021). Abt Associates. A Meta-Analysis Of 46 Career Pathways Impact Evaluations: Final Report. Chief Evaluation Office, U.S. Department of Labor.

Career Pathways Meta-Analysis Summary Brief

Strawn, J., Peck, L. R., Schwartz, D. (2021). Abt Associates. New Insights on Career Pathways: Evidence from a Meta-Analysis. Chief Evaluation Office, U.S. Department of Labor.

Career Trajectories and Occupational Transitions Final Report

Schwartz, D., Clarkwest, A., Hashizume, M., Kappil, T., Strawn, J. (2021). Abt Associates. Building Better Pathways: An Analysis of Career Trajectories and Occupational Transitions. Chief Evaluation Office, U.S. Department of Labor.

Appendix: Career Trajectories and Occupational Transition Findings

Abt Associates. (2021). Appendix: Healthcare, Early Care and Education, Information Technology, and Production/Manufacturing Career Trajectories and Occupational Transition Findings. Chief Evaluation Office, U.S. Department of Labor.

Wage Growth Disparities Short Paper

Clarkwest, A., Kappil, T., Schwartz, D., Hashizume, M., Martinson, K. (2021). Abt Associates. Wage Growth Disparities by Gender and Race/Ethnicity Among Entrants to Mid-Level Occupations in the United States: Findings from the Career Trajectories and Occupational Transitions Study. Chief Evaluation Office, U.S. Department of Labor.

Machine Learning Issue Brief

De La Rosa, S. M., Greenstein, N., Schwartz, D., Lloyd, C. (2021). Abt Associates. Machine Learning in Workforce Development Research: Lessons and Opportunities. Chief Evaluation Office, U.S. Department of Labor.

Career Pathways One-Pager

Abt Associates. (2021). Career Pathways: Highlights of Continuous Program Improvement and Evidence-Building. Chief Evaluation Office, U.S. Department of Labor.

Career Pathways Knowledge Development Brief

Sarna, M., Adam, T. (2020). Abt Associates. Evidence on Career Pathways Strategies: Highlights from a Scan of the Research. Chief Evaluation Office, U.S. Department of Labor.

Career Pathways Research and Evaluation Matrix

Sarna, M., Adam, T. (2020). Abt Associates. Expanded Career Pathways Research and Evaluation Matrix. Chief Evaluation Office, U.S. Department of Labor.