Automation anxiety, fairness perceptions, and redistribution: Past experiences condition the response to future job loss

Abstract

Do beliefs about fairness interact with past experiences of labor market shocks to condition redistributive preferences? In a large-scale survey experiment, we investigate the effect of informing individuals that growth in automation could disrupt labor markets in ways that are (possibly) viewed as unfair. We then exploit the COVID-19-induced labor market shock to test for an interaction between treatments and shock exposure. We find that beliefs interact with shock exposure to increase redistributive preferences and commitments to donate potential prize winnings. Our findings suggest this may be motivated by respondents' concerns about their vulnerability to future labor market shocks.