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We propose a new approach to detect and quantify informal employment resulting from irregular migration shocks. Focusing on a largely informal sector, agriculture, and on the exogenous variation from the Arab Spring wave on southern Italian coasts, we use machine-learning techniques to document abnormal increases in reported (versus predicted) labour productivity on vineyards hit by the shock. Misreporting is largely heterogeneous across farms depending, e.g., on size and grape quality. The shock resulted in a 6 percent increase in informal employment, equivalent to one undeclared worker for every three farms on average and 23,000 workers in total over 2011-2012. Misreporting causes significant increases in farm profits through lower labour costs, while having no impact on grape sales, prices, or wages of formal workers.