<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tsz-Nga Wong | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/tsz-nga-wong/</link><description>Tsz-Nga Wong</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/tsz-nga-wong/index.xml" rel="self" type="application/rss+xml"/><item><title>Artificial intelligence and technological unemployment</title><link>https://macropaperwarehouse.com/papers/artificial-intelligence-and-technological-unemployment/</link><guid>https://macropaperwarehouse.com/papers/artificial-intelligence-and-technological-unemployment/</guid><description>&lt;p&gt;Wang and Wong develop a continuous-time labor-search model to assess the dynamic effects of generative AI (GenAI) on labor productivity and unemployment. The paper is motivated by conflicting empirical evidence: micro studies find productivity gains of 14% (Brynjolfsson, Li, and Raymond 2025) and 55.8% faster coding (Peng et al. 2023), while macro estimates suggest modest TFP gains of at most 0.064% annually (Acemoglu 2024), and occupation-level evidence shows a 13% relative employment decline in AI-exposed jobs (Brynjolfsson, Chandar, and Chen 2025).&lt;/p&gt;</description></item></channel></rss>