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

融合加速器第一阶段 (RAISE)：释放数据和科学的力量，赋予美国工人权力

• 批准号: 1937061
• 负责人: Justine Hastings
• 金额: $ 99.62万
• 依托单位: National Bureau of Economic Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937061&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Unlocking

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.

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目更广泛的影响/潜在好处将是为普通美国人提供工具，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力培训计划的有效性和影响。我们汇集了领先的科学家，州和县政府以及非营利和私营行业领导者的跨部门团队。我们将共同释放管理数据、科学和技术的力量，为美国工人提供有效的再培训计划，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，公共政策，行为科学和应用金融的融合和综合优势。首先，我们将为公共资助的劳动力培训项目制定科学有效的投资回报措施。这些信息将通过公共API和网络工具提供给政府和所有工人。该工具将于2022年推出并投入使用，将帮助政府了解培训计划的回报和高等教育的替代方案;使工人能够对他们的未来做出明智的决定;并激励培训计划增加其注册者在今天和未来找到有酬就业的能力。这个融合加速器第一阶段项目将解决这样一个事实，即尽管未来几十年可能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善成果，或帮助工人根据未来的投资回报选择计划。为了满足不断增长的需求，并成功地支持美国工人的未来工作，劳动力培训计划将需要有效和高效，提供可衡量的成功。回顾性研究使用管理数据来评估孤立的培训计划。然而，他们不能指导目前正在寻求培训的失业工人，也不能激励计划的改进。我们将推进科学知识，并通过将行政数据与因果机器学习和计量经济学方法相结合，帮助工人为未来的职业生涯做好准备，以便与州和地方政府合作伙伴合作，提供科学有效的劳动力培训计划成功措施。这些措施将免费提供，支持采购和项目管理的成功付费模式，并易于政策制定者拥有，实施和扩展。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Unlocking data to improve public policy

解锁数据以改善公共政策

• DOI: 10.1145/3335150
• 发表时间: 2019
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Hastings, Justine S.;Howison, Mark;Lawless, Ted;Ucles, John;White, Preston
• 通讯作者: White, Preston

Delivering Unemployment Assistance in Times of Crisis: Scalable Cloud Solutions Can Keep Essential Government Programs Running and Supporting Those in Need

在危机时期提供失业援助：可扩展的云解决方案可以保持重要的政府项目的运行并为有需要的人提供支持

• DOI: 10.1145/3428125
• 发表时间: 2021
• 期刊: Digital Government: Research and Practice
• 作者: Angell, Mintaka;Gold, Samantha;Howison, Mark;Kidd, Victoria;Molitor, Daniel;Burns, Casey;Johnson, Chris;Kahn, Matthew;Venzke, Stuart;Deneault, Sandra
• 通讯作者: Deneault, Sandra