Using Natural Language Processing to Measure COVID-19-Induced Economic Policy Uncertainty for Canada and the USA

In this paper, we develop an economic policy uncertainty (EPU) index for the USA and Canada using natural language processing (NLP) methods. Our EPU-NLP index is based on an application of several algorithms, including the rapid automatic keyword extraction (RAKE) algorithm, a combination of the RoBERTa and the Sentence-BERT algorithms, a PyLucene search engine, and the GrapeNLP local grammar engine. For comparison purposes, we also develop an index based on a strictly Boolean method. We find that the EPU-NLP index captures COVID-19-related uncertainty better than the Boolean index. Using a structural VAR approach, we find that a one-standard deviation (SD) economic policy uncertainty shock with EPU-NLP leads, both for Canada and the USA, to larger declines in key macroeconomic variables than a one SD EPU-Boolean shock. In line with the COVID-19 impact, the SVAR model shows an abrupt contraction in economic variables both in Canada and the USA. Moreover, an uncertainty shock with the EPU-NLP caused a much larger contraction for the period including the COVID-19 pandemic than for the pre-COVID-19 period.