发表机构
University of Pittsburgh; Stanford University; University of São Paulo（匹兹堡大学; 斯坦福大学; 圣保罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种原则性方法估算美国近18000项任务的时间份额，用于分析AI对美国职业的影响，发现时间权重会改变AI暴露度的测量结果，可作为劳动经济研究的通用基础要素。
AI 中文摘要
经济学中的基于任务的框架将职业建模为任务的组合，这是理解技术如何影响工作的标准视角：新技术会改变每项任务所需的成本或时间，而这些任务层面的影响会汇总为职业层面的影响。我们研究在这种汇总过程中应如何对任务进行加权。现有研究依赖于特殊或缺乏合理论据的任务权重选择。近期研究建议按花费的时间对任务加权，但现有的时间份额要么基于并非为此目的设计的粗略 ONET 数据，要么通过黑箱语言模型估算。我们通过提出一种原则性方法来解决这一缺口，该方法用于估算构成几乎所有美国职业的近 18000 项任务的时间份额。我们的估算将任务时间分解为两个部分：（i）来自 ONET 的任务预期发生频率；（ii）单个任务实例的完成时间。为估算后者，我们基于语言模型提供的关于哪项任务单个实例耗时更长的成对比较，求解一个约束满足问题。我们通过刻画约束满足问题的解空间并收集多个职业的工人数据来验证我们的估算。我们将时间份额应用于分析人工智能对美国职业的影响，发现一些现有结果对时间权重敏感。与现有研究中使用的任务份额相比，考虑暴露于人工智能的工作时间份额会扩大受影响最小和最大职业之间的差距：它降低了大多数职业的测量暴露度，但提高了受影响最大职业的暴露度。按时间重新加权还会在被广泛报道为受人工智能影响最大的 25 个职业中重新调整 11 个职业，使列表顶端从文书工作转向分析角色。时间份额可作为劳动经济学和技术经济学研究及政策制定的通用基础要素。
英文摘要
The task-based framework in economics models occupations as bundles of tasks. It is the standard lens for understanding how technology affects work: a new technology changes the cost or time each task requires and these task-level effects aggregate to occupation-level effects. We study how tasks should be weighted in this aggregation. Prior work has relied on idiosyncratic or ill-justified choices for task weights. While recent work suggests weighting tasks by time spent, existing time shares are either based on coarse ONET data not intended for this purpose or estimated via black-box language models. We address this gap by proposing a principled method for estimating time shares for nearly 18,000 tasks that constitute nearly all U.S. jobs. Our estimates factor a task's time into (i) the expected frequency of the task, derived from ONET, and (ii) the time to complete a single instance of it. To estimate the latter, we solve a constraint satisfaction problem based on pairwise comparisons elicited from language models about which tasks are longer per instance. We validate our estimates by characterizing the solution space of the constraint satisfaction problem and collecting data from workers for multiple occupations. We apply our time shares to analyze how AI exposes U.S. occupations and find that some prior results are sensitive to time weights. Accounting for the share of working time exposed to AI, rather than the share of tasks like prior work, widens the gap between the least and most exposed jobs: it lowers measured exposure for most occupations but raises it for the most exposed. Re-weighting by time also reshuffles 11 of the 25 occupations widely reported as most exposed to AI, shifting the top of the list away from clerical work and toward analytical roles. Time shares can serve as a general primitive for research and policy on the labor economy and the economics of technology.
