Jason Jabbari, Yung Chun, Wenrui Huang, Stephen Roll
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引用次数: 0
Abstract
We conduct an impact analysis on a unique technology certificate and apprenticeship program offered by LaunchCode. We merge administrative data containing entrance exam scores with survey data for individuals that were (a) not accepted, (b) accepted but did not complete the course, (c) completed the course but not the apprenticeship, and (d) completed the course and the apprenticeship. By using entrance exam scores as an instrumental variable, we conduct an intent-to-treat model, finding that program acceptance was significantly associated with increased earnings and probabilities of working in a science, technology, engineering, and math (STEM) profession. Then, by using machine learning-generated multinomial propensity score weights, we conduct a treatment-on-treated analysis, finding that these increases appear to be primarily driven by the apprenticeship component.
期刊介绍:
Educational Evaluation and Policy Analysis (EEPA) publishes manuscripts of theoretical or practical interest to those engaged in educational evaluation or policy analysis, including economic, demographic, financial, and political analyses of education policies, and significant meta-analyses or syntheses that address issues of current concern. The journal seeks high-quality research on how reforms and interventions affect educational outcomes; research on how multiple educational policy and reform initiatives support or conflict with each other; and research that informs pending changes in educational policy at the federal, state, and local levels, demonstrating an effect on early childhood through early adulthood.