2024-05-02
Decomposing Trust (with Dirk Engelmann, Jana Friedrichsen, Pauline Vorjohann and Joachim Winter).
Room: E0.03, 16:00-17:15.
Trust is an important condition for economic growth and other economic outcomes. Previous studies suggest that the decision to trust is driven by a combination of risk attitudes, distributional preferences, betrayal aversion, and beliefs about the probability of being reciprocated. We compare the results of a binary trust game to the results of a series of control treatments that by design remove the effect of one or more of these components of trust. This allows us to decompose variation in trust behavior into its underlying factors. Our results imply that beliefs are a key driver of trust, and that the additional components only play a role when beliefs about reciprocity are sufficiently optimistic. Our decomposition approach can be applied to other settings where multiple factors that are not mutually independent affect behavior. We discuss its advantages over the more traditional approach of controlling for measures of relevant factors derived from separate tasks in regressions, in particular with respect to measurement error and omitted variable bias.
Decomposing Trust (with Dirk Engelmann, Jana Friedrichsen, Pauline Vorjohann and Joachim Winter).
Room: E0.03, 16:00-17:15.
Trust is an important condition for economic growth and other economic outcomes. Previous studies suggest that the decision to trust is driven by a combination of risk attitudes, distributional preferences, betrayal aversion, and beliefs about the probability of being reciprocated. We compare the results of a binary trust game to the results of a series of control treatments that by design remove the effect of one or more of these components of trust. This allows us to decompose variation in trust behavior into its underlying factors. Our results imply that beliefs are a key driver of trust, and that the additional components only play a role when beliefs about reciprocity are sufficiently optimistic. Our decomposition approach can be applied to other settings where multiple factors that are not mutually independent affect behavior. We discuss its advantages over the more traditional approach of controlling for measures of relevant factors derived from separate tasks in regressions, in particular with respect to measurement error and omitted variable bias.