A data-driven network model can be used to study occupational mobility and automation by analyzing how workers transition into new jobs in response to automation scenarios. The model takes into account the network structure of occupational mobility and its impact on unemployment levels [1] [2] [3] . At a macro level, the model reproduces the Beveridge curve, a key stylized fact in the labor market, providing insights into its counter-clockwise cyclicality [4] [5] . At a micro level, the model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to automation shocks . The network structure plays […]
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