Convergence Accelerator Phase I (RAISE): Unlocking the Power of Data and Science to Empower American Workers

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”第一阶段项目的更广泛影响/潜在效益将是，让普通美国人有能力为成功的职业生涯做出明智的选择，并提高公共资助的劳动培训项目的有效性和影响力。我们汇集了一个由顶尖科学家、州和县政府、非营利组织和私营行业领袖组成的跨部门团队。我们将共同释放行政数据、科学和技术的力量，为美国工人提供有效的再培训项目，以适应不断变化的工作环境。我们的项目依赖于计算机科学、经济学、教育学、公共政策、行为科学和应用金融学的融合和综合优势。一是制定科学有效的公费劳动培训投资回报措施。然后，这些信息将通过公共API和网络工具提供给政府和所有工作人员。该工具将于2022年推出并可用，将帮助政府了解培训项目和高等教育替代方案的回报；赋予员工权力，让他们对自己的未来做出明智的决定；并鼓励培训项目为学员在今天和未来找到有报酬的工作的能力增加价值。“融合加速器”第一阶段项目将解决这样一个问题：尽管未来几十年可能会有数千万工人失业并需要重新培训，但政府目前几乎没有措施来指导培训投资决策，确保培训提供有价值的重新培训和改进的成果，或帮助工人根据未来的投资回报选择项目。为了满足日益增长的需求并成功地支持美国工人从事未来的工作，劳动培训项目必须有效和高效，取得可衡量的成功。回顾性研究使用行政数据来评估孤立的培训项目。然而，他们不能指导目前寻求培训的失业工人，也不能激励计划的改进。我们将通过与州和地方政府合作伙伴合作，将行政数据与因果机器学习和计量经济学方法相结合，为劳动培训项目的成功提供科学有效的衡量标准，促进科学知识的发展，帮助工人为未来的职业生涯做好准备。这些措施将免费提供，支持按成功付费的采购和项目管理模式，便于决策者拥有、实施和扩大规模。他们将赋予工人选择项目所需的信息，为有报酬的就业和成功的职业生涯提供技能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/ potential benefit of this Convergence Accelerator Phase I project will be to empower everyday Americans with the tools to make informed choices for a successful career, and to improve the effectiveness and impact of publicly-funded labor training programs. We have brought together a cross-sector team of leading scientists, state and county governments, and not-for-profit and private industry leaders. Together, we will unlock the power of administrative data, science, and technology to support American workers with effective reskilling programs for a changing work landscape. Our project relies on the convergence and combined strengths of computer science, economics, education, public policy, behavioral science, and applied finance. First, we will create scientifically-valid return on investment measures for publicly-funded labor training programs. This information will then be available to government and all workers through a public API and web tool. The tool, which will be launched and usable by 2022, will help government understand the returns to training programs and alternatives to higher education; empower workers to make informed decisions about their future; and incentivize training programs to add value to their enrollees' ability to find gainful employment today and in the future. This Convergence Accelerator Phase I project will address the fact that, although tens of millions of workers may be displaced and need to reskill in the coming decades, government currently has few measures to guide training investment decisions, ensure that training delivers valuable reskilling and improved outcomes, or help workers choose programs based on return on investment for the future. To meet growing needs and successfully support American workers for the jobs of the future, labor training programs will need to be effective and efficient, delivering measurable success. Retrospective studies have used administrative data to evaluate isolated training programs. However, they cannot guide displaced workers currently seeking training, and do not incentivize program improvement. We will advance scientific knowledge and help workers prepare for careers of the future by combining administrative data with causal machine-learning and econometric methods to deliver scientifically-valid measures of labor training program success in collaboration with state and local government partners. These measures will be freely available, support pay-for-success models of procurement and program management, and be easy for policymakers to own, implement, and scale. They will empower workers with the information needed to select programs that deliver skills for gainful employment and successful careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.