The Efficiency Costs of Information Assurance in AI-Enabled Labor Markets: Evidence from LinkedIn's Policy Changes

Generative artificial intelligence (GenAI) systems rely heavily on user-generated data for training. As governments and platforms impose increasing restrictions on the use of personal data, an important question is whether limiting access to user data for AI training affects the performance of AI-enabled economic systems. We examine this question in the context of labor-market matching. Our setting exploits a unique sequence of LinkedIn policy changes: the quiet introduction of user data collection for AI training in August 2024, the restriction of Hong Kong user data from AI training in October 2024, and the subsequent restoration of data access in November 2025. Using employment and job-posting data from Revelio Labs and a Difference-in-Differences design comparing Hong Kong and Singapore, we find that the restriction significantly increased labor-market frictions: employee turnover increased and tenure declined, vacancies remained open longer, job-posting match rates fell, and wages decreased. To strengthen the causal interpretation, we examine the full policy lifecycle: labor market outcomes improved following LinkedIn's initial AI rollout, deteriorated after the restriction, and recovered after the restoration. Similar patterns emerge in an independent cross-country comparison between regions with and without access to LinkedIn's user data for AI training. The effects are strongest among firms that rely more heavily on platform-mediated recruitment. Our findings show that restricting access to user data for AI training can affect labor-market outcomes through AI-enabled matching systems and contribute to ongoing discussions regarding AI governance, privacy regulation, and the economic consequences of data access.