We construct a novel text dataset to measure the sentiment component of communications for 23 central banks over the 2002-2017 period. Our analysis yields three results. First, comovement in sentiment across central banks is not reducible to trade or financial flow exposures. Second, sentiment shocks generate cross-country spillovers in sentiment, policy rates, and macroeconomic variables; and the Fed appears to be a uniquely influential generator of such spillovers, even among prominent central banks. And third, geographic distance is a robust and economically significant determinant of comovement in central bank sentiment, while shared language and colonial ties have weaker predictive power. (JEL E52, E58, F42)

We use text analysis and a novel dataset to measure the sentiment component of central bank communications in 23 countries over the 2002-2017 period. Our analysis yields three key results. First, using directed networks, we show that comovement in sentiment across central banks is not reducible to trade or financial flow exposure. Second, we find that geographic distance is a robust and economically significant determinant of comovement in central bank sentiment, while shared language and colonial ties are economically significant, but less robust. Third, we use structural VARs to show that sentiment shocks generate cross-country spillovers in sentiment, policy rates, and macroeconomic variables. We also find that the Fed plays a uniquely influential role in generating such sentiment spillovers, while the ECB is primarily influenced by other central banks. Overall, our results suggest that central bank communication contains systematic biases that could lead to suboptimal policy outcomes. (JEL E52, E58, F42)