发表机构
University of Ottawa（渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出并验证了一个基于设计、结合双重差分与生产函数的框架，用于测量企业层面AI回报的形状，并发现其呈凹性，为加拿大生产力预测提供依据。
AI 中文摘要
关于人工智能（AI）生产力红利的预测相差一个数量级，部分原因是这些预测外推了早期、高暴露度采用者中观察到的平均收益。这些收益是线性扩展、趋于平缓还是被竞争侵蚀，这是一个关于AI回报形状而非其平均值的实证问题。本文开发并验证了一个基于设计的框架，利用关联的企业-工人数据来测量这一形状。该框架结合了预先确定的、基于职业的企业对生成式AI暴露度测量，以及围绕2022年11月ChatGPT发布的连续处理双重差分设计、三个预先指定的凹性检验、将暴露效应分解为采用边际和每个采用者回报、对使用强度的双重稳健分析、对早期AI采用者的交错采用分析，以及一个将AI置于生产力动态规律中的生产函数估计器。我们在一个校准的合成面板上验证了每个组成部分，该面板包含20,000家企业，再现了加拿大统计局关联企业微观数据的结构，并嵌入了一个已知的凹性效应。估计器恢复了真实的剂量-反应曲线并检测到其凹性（斜率差异-0.010，标准误0.003，单侧p=0.002）；主置信区间的蒙特卡洛覆盖率为0.97，凹性检验的检验功效为0.93。验证还揭示了一个实际陷阱：在约40个行业聚类的预趋势检验会过度拒绝真实零假设。该框架以开放代码形式发布，可应用于加拿大企业微观数据，届时将为潜在产出和加拿大生产力差距的持续性估计提供信息。
英文摘要
Forecasts of the productivity dividend from artificial intelligence (AI) differ by an order of magnitude, partly because they extrapolate average gains observed among early, highly exposed adopters. Whether those gains scale linearly, flatten, or are competed away is an empirical question about the shape of the return to AI, not its average. This paper develops and validates a design-based framework for measuring that shape with linked firm-worker data. The framework combines a pre-determined, occupation-based measure of firm exposure to generative AI with a continuous-treatment difference-in-differences design around the release of ChatGPT in November 2022, three pre-specified tests of concavity, a decomposition of exposure effects into an adoption margin and a per-adopter return, a doubly robust analysis of use intensity, a staggered-adoption analysis of earlier AI adopters, and a production-function estimator that places AI in the law of motion of productivity. We validate every component on a calibrated synthetic panel of 20,000 enterprises that reproduces the structure of Statistics Canada's linked business microdata and embeds a known concave effect. The estimators recover the true dose-response curve and detect its concavity (slope difference -0.010, standard error 0.003, one-sided p = 0.002); Monte Carlo coverage of the main confidence interval is 0.97, and the concavity test has power of 0.93. The validation also exposes a practical pitfall: pre-trend tests clustered at roughly 40 industries over-reject a true null. The framework, released as open code, is ready for application to Canadian enterprise microdata, where it will inform estimates of potential output and the persistence of Canada's productivity gap.
