生成式人工智慧對北京城市內部不平等與技能溢價的影響

摘要

生成式人工智慧（GenAI）是首波大規模觸及高認知任務的自動化浪潮，但其對城市內部不平等的影響仍 largely 未知。我們利用北京2018至2024年間的500萬則職位公告，匯集五個主流大型語言模型的任務層級評估，建構出鄰里層級的生成式AI暴露指數。我們檢視此衝擊的空間、結構與因果機制。研究發現：生成式AI暴露高度集中於城市核心區域，加深了城市內部的AI鴻溝。自2023年起，高暴露鄰里即便持續吸引高技能勞動者，卻出現薪資停滯——形成「高技能陷阱」。此薪資懲罰源於任務去技能化與勞動市場擁擠的加劇。以ChatGPT發布為基準的雙重差分設計支持因果解讀。這些發現挑戰了既有的技能偏向技術變革理論，並為全球科技樞紐的包容性AI治理提供了基礎。

English

Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.

生成式人工智慧對北京城市內部不平等與技能溢價的影響

Generative AI impacts on intra-urban inequality and skill premium in Beijing

摘要

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