Health shocks limit individuals’ participation in the labor market and pose a major risk to household welfare. In this paper, we derive two novel health shock indicators using machine learning based on sick days and hospitalizations: one for transitory and one for persistent shocks. In an event study framework, we show their respective effects on employment, yearly working hours, and labor earnings, but also partner earnings and household net income. Persistent shocks induce large negative employment effects that end up impacting household net incomes. In contrast, transitory shocks induce only minor employment effects that leave household net incomes unaffected.
Keywords: Labor supply, health shocks, machine learning, event study