Automation Accelerators: Do Pandemic Shocks Permanently Reshape Task Allocation and Regional Inequality?

Construction (Ogunrinde et al. 2022) and healthcare (Khavandi et al. 2023) illustrate how pandemic constraints pushed automation forward. Ramirez Lizardi et al. (2025) show health shocks lead to short-lived labor absences; firms may respond by automating to insure against such volatility. We assemble a sector-region panel linking (i) robot shipments, RPA/AI SaaS adoption, and digital-twin/IoT deployment logs (as suggested by Lăzăroiu et al. 2024), (ii) COVID stringency and local infection waves, and (iii) regional specialization profiles and unemployment (Mariš & Marišová 2015). Event-study and shift-share designs estimate the causal effect of shock intensity on automation, and its persistence in task composition and local labor outcomes. We compare adjustment dynamics to historical mechanization (Feigenbaum et al. 2022 on telephone operators) to test whether modern general-purpose AI yields different wage and mobility paths than past, more task-specific automation. This synthesis is novel by explicitly modeling “automation accelerators” and their spatial distribution, answering bibliometric calls (Samatova et al. 2025) for sector-specific and regional analyses of transitions. The payoff is evidence on whether shocks merely pull adoption forward (with fade-out) or lock in new task allocations that widen regional inequality—guiding targeted reskilling and place-based policy.

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