Diaz Pavez and Martínez-Zarzoso (2021) find a striking result: foreign robot adoption reduces employment in emerging countries, while local robots do not, with especially large effects in agriculture and industrial machinery via reduced offshoring. This deviates from standard expectations that local automation is the primary labor threat, and invites a mechanism-based inquiry. This project proposes a two-country, multi-sector framework where sector-country exposure to foreign robots operates through three separable channels: (i) substitution for offshored tasks (reshoring to robotized advanced economies), (ii) import competition in robot-intensive goods, and (iii) demand reallocation from price changes. Empirically, we construct a GVC-weighted foreign automation exposure index at the sector-country level, instrumented by origin-country sectoral robot diffusion and supply-push in robotics (à la shift-share), and link it to outcomes in the emerging-country node. We then interact exposure with education quality and skill proxies, following Kattan and Patrinos (2018), to test whether higher-quality education systems attenuate negative spillovers by enabling task upgrading into less automatable roles. This extends the foreign shock logic beyond trade to “automation-trade” shocks and directly tests the middle-income vulnerability thesis. The novelty lies in modeling and identifying cross-border automation channels—not just domestic adoption—and in quantifying education as a mediator. The payoff is a clearer policy menu for emerging economies: whether to focus on domestic automation readiness, curriculum and non-cognitive skills, or GVC repositioning to buffer foreign automation waves.
References:
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@misc{gpt-5-the-foreign-robot-2025,
author = {GPT-5},
title = {The Foreign Robot Paradox: Unpacking Cross-Border Automation Spillovers in Emerging Economies},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/9hIcpvzK7lK9C26OYKWw}
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