NBER Working Papers · 2026 · Nicholas Bloom、Gideon Moore、Lisa K. Simon、Caelan Wilkie-Rogers
中文摘要
经济学家日益使用职业档案数据来重构就业史并衡量技能供给。我们表明，这些记录并非固定的历史快照，而是工作者随时间修订的可变账户。使用2020–2026年Revelio Labs数据的月度批次，我们记录到19.7%的美国资深LinkedIn用户会追溯性地编辑他们已经离职的职位头衔或描述。这些“时间旅行”式编辑与劳动力市场转换紧密相关：在此类编辑发生前后，与较晚编辑的用户相比，工作者更换雇主的可能性要大得多。这种可变性会使技能的历史衡量产生偏误，但它也揭示了工作者关于哪些技能具有需求的信念。追溯性编辑显示出2022年后AI相关措辞的急剧增加，以及近期远程办公和DEI措辞的减少。最后，与LLM相关的书写特征在ChatGPT之后激增，尤其是在受教育程度较低的群体和来自较低排名项目的MBA中，揭示了异质性的AI辅助档案编辑。
Abstract
Economists increasingly use professional profile data to reconstruct employment histories and measure skill supply. We show that these records are not fixed historical snapshots, but mutable accounts that workers revise over time. Using monthly vintages of Revelio Labs data from 2020–2026, we document that 19.7 percent of established U.S. LinkedIn users retroactively edit the title or description of a job they have already left. These “time-travel” edits are closely tied to labor market transitions: around such edits, workers are much more likely to change employers as compared to later-editing users. This mutability can bias historical measures of skills, but it also reveals workers’ beliefs about which skills are in demand. Retroactive edits show sharp post-2022 increases in AI-related language and recent reductions in work-from-home and DEI language. Finally, LLM-associated writing markers surge after ChatGPT, especially among less-educated groups and MBAs from lower-ranked programs, revealing heterogeneous AI-assisted profile editing.