The outbreak of the COVID-19 pandemic and the associated lockdown measures have been a shock to market systems worldwide, affecting both the supply and demand of labor. Intensified by this pandemic-driven recession, online labor markets are in many ways at the core of the economic and policy debates about their technological innovation, which could be used as a way of economic reform and recovery. In this work, we focus on crowdsourcing, which is a specific type of online labor. We apply a unique dataset of labor data to investigate the effects of online training, a policy that was provided to requesters by the platform during the COVID-19 period. Our findings suggest that workers indirectly finance on-the-job online training by accepting lower wages during the pandemic. By utilizing a difference-in-difference research design, we also provide causal evidence that online training results in lower job completion time and the probability of being discontinued. Our findings show that both employers and employees in our online labor context reacted to the pandemic by participating in online labor procedures with different risk strategies and labor approaches. Our findings provide key insights for several groups of crowdsourcing stakeholders, including policy-makers, platform owners, hiring managers, and workers. Managerial and practical implications in relation to how online labor markets react to external shocks are discussed.