In column (1), the dependent variable is the updating on inflation expectations. In column (2), the dependent variable is the updating on the expected exchange rate depreciation. These results differ from the simpler binned scatterplots in Figure 7 in that they include additional control variables and allow for cross-learning. Table 4 reports the coefficients of the two key independent variables, corresponding to the interactions between the treatment assignments and the size of the information shock. For simplicity, we refer to these variables as information shocks.

The first coefficient from column (1) of Table 4 indicates that information about inflation has a significant effect on inflation expectations: a 1 pp increase in inflation shock increases inflation expectations by 0.237 pp (p-value<0.001). The second coefficient from column (1) of Table 4 is close to zero (-0.031) and statistically insignificant (p-value=0.189), indicating that the information shock about the exchange rate does not have a significant effect on inflation expectations. In other words, individuals use the feedback in a compartmentalized manner.

The magnitude of the pass-through from the inflation feedback to the inflation expectations is in the same order of magnitude as the pass-through estimated in other information experiments. For example, Bottan and Perez-Truglia (2020b) shows that a 1 pp increase in feedback about future home prices increases the home price expectations by 0.205 pp. However, the degree to which subjects incorporate the information is lower than that reported in other studies. For example, Cavallo, Cruces, and Perez-Truglia (2017) find that, when forming inflation expectations, the average Argentine respondent assigns a weight of 0.432 to the feedback and the remaining 0.568 to their prior beliefs (coefficient _α_ -statistics reported in Panel B, column (1) of Table 1). The fact that individuals are less prone to incorporating information in our context may reflect a more educated and financially savvy population that has more confidence in their prior beliefs. However, this difference in rates of learning could be attributed to differences in the survey methods. For example, other studies provide information and elicit beliefs on a computer screen, whereas our study uses phone surveys, which could arguably make the information less salient. Also, other studies where subjects are paid to fill out the survey could generate experimenter demand effects. Subjects in our survey were not paid for their participation.

The second coefficient in Table 4, column (2), indicates that information about the exchange rate has a significant effect on exchange rate expectations: a 1 pp information shock increases expectations of a nominal exchange rate depreciation by about 0.065 pp (p-value=0.036). Again, we find compartmentalized learning about the exchange rate: the first coefficient in column (1) is close to zero (0.033) and statistically insignificant (p-value=0.206), indicating that information about the inflation rate does not have a significant effect on participants’ exchange rate expectations. We find that the magnitude of the learning effects for the exchange rate (coefficient of 0.065) is lower than the magnitude of learning effects for inflation (0.237), and the difference between the two is statistically significant (p-value<0.001). Following a Bayesian 22