Not all Technologies are Equal: Measuring and Disentangling the Labor Effects of Technology Change in Manufacturing

美国制造业受到全球化和技术变革等强大经济力量的影响，这些力量极大地改变了工人的劳动力市场结果。尽管美国制造业的价值在增长，但美国制造业工人的数量却在下降，中等收入工作岗位减少，失业工人的收入大幅下降。利用新的经验数据和方法，我们准确地预测当前和新兴技术对劳动力结果的影响，为培训计划和劳动力政策提供有意义的见解，以缓解相关的劳动力市场失灵。关于技术变化及其对就业、工资和生产率的影响，存在重要的文献。这篇文献表明，技术变革导致高技能工人从常规工作转向不成比例的工资增长。然而，到目前为止，这项研究在很大程度上无法区分不同类型的技术变化，以及与工资和就业的可能关系。此外，将政策和技术变化联系起来的经济方法主要限于历史数据和汇总数据(例如，区域或国家)。这些方法无法描述新兴技术变化在单个制造步骤中的后果，以及在所需技能、培训和能力的非常精细水平上对劳动力的影响。建模方面的这种粗制滥造给设计有效的培训政策带来了挑战，并限制了理解新兴技术对工人所需具体技能的影响的能力。我们提出了新的方法并提供了新的经验数据来解决这些问题，并预测了新兴技术对劳动力市场的影响。具体地说，我们试图预测(I)新兴技术如何影响生产率、工资和就业；(Ii)对最先进的经济生产功能的影响；以及(Iii)在出现颠覆性技术变化时可能特别严重的劳动力市场故障。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Manufacturing in the US is affected by powerful economic forces, such as globalization and technological change, that have dramatically changed labor market outcomes for workers. Despite growth in the value of U.S. manufacturing, the number of manufacturing workers in the U.S. has declined, with fewer middle-income jobs and displaced workers experiencing large losses in earnings. Leveraging new empirical data and methods, we anticipate the effects of current and emerging technologies on labor outcomes with a precision that provides meaningful insights for training programs and for workforce policies that can mitigate related labor market failures.Significant literature exists on technological change and its influence on employment, wages, and productivity. This literature suggests that technology change causes a shift away from routine tasks and towards disproportionate wage gains for workers with higher skills. The research to-date, however, has largely been unable to distinguish between different types of technology change, and the possible relation to wages and employment. Further, the economic approach linking policy and technical change has been mostly limited to historical and aggregated (e.g., regional or national) data. These approaches are unable to characterize the consequences of emerging technology changes at individual manufacturing steps and the consequences for labor at very fine levels of required skills, training, and competencies. This coarseness in modelling provides a challenge to the design of effective training policies and limits the ability to understand the impact of emerging technologies on the specific skills required for workers. We propose new methods and present novel empirical data to address these issues and anticipate the labor market effects of emerging technologies. Specifically, we seek to anticipate (i) how emerging technologies affect productivity, wages, and employment; (ii) the implications for state-of-the-art economic production functions, and (iii) labor market failures that may be particularly severe in the presence of disruptive technological change.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.