Canada’s Monetary Policy Report: If Text Could Speak, What Would It Say?

Code available at: GitHub

This note analyzes the evolution of the narrative in the Bank of Canada’s Monetary Policy Report (MPR). It presents descriptive statistics on the core text, including length, most frequently used words and readability level—the three Ls. Although each Governor of the Bank of Canada focuses on the macroeconomic events of the day and the mandate of inflation targeting, we observe that the language used in the MPR varies somewhat from one Governor’s tenure to the next. Our analysis also suggests that the MPR has been, on average, slightly more complicated than the average Canadian would be expected to understand. However, recent efforts to simplify the text have been successful. Using word embeddings and applying a well-established distance metric, we examine how the content of the MPR has changed over time. Increased levels of lexical innovation appear to coincide with important macroeconomic events. If substantial changes in economic conditions have been reflected in the MPR, quantifying changes in the text can help assess the perceived level of uncertainty regarding the outlook in the MPR. Lastly, we assess the sentiment (tone) in the MPR. We use a novel deep learning algorithm to measure sentiment (positive or negative) at the sentence level and aggregate the results for each MPR. The exceptionally large impacts of key events, such as 9/11, the global financial crisis and others, are easily recognizable by their significant effect on sentiment. The resulting measure can help assess the implicit balance of risks in the MPR. These measures (lexical innovations and sentiment) could then potentially serve to adjust the probability distributions around the Bank’s outlook by making them more reflective of the current situation.