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Business and Economics > Production and Operations Management

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Cover IMF Working Papers
Public Perceptions of Canada’s Investment Climate: A Natural Language Processing-Based Approach
Flora Lutz
,
Yuanchen Yang
, and
Chengyu Huang
Canada’s muted productivity growth during recent years has sparked concerns about the country’s investment climate. In this study, we develop a new natural language processing (NPL) based indicator, mining the richness of Twitter (now X) accounts to measure trends in the public perceptions of Canada’s investment climate. We find that while the Canadian investment climate appears to be generally favorable, there are signs of slippage in some categories in recent periods, such as with respect to governance and infrastructure. This result is confirmed by both survey-based and NLP-based indicators. We also find that our NLP-based indicators would suggest that perceptions of Canada’s investment climate are similar to perceptions of U.S. investment climate, except with respect to governance, where views of U.S. governance are notably more negative. Comparing our novel indicator relative to traditional survey-based indicators, we find that the NLP-based indicators are statistically significant in helping to predict investment flows, similar to survey-based measures. Meanwhile, the new NLP-based indicator offers insights into the nuances of data, allowing us to identify specific grievances. Finally, we construct a similar indicator for the U.S. and compare trends across countries.
Keywords:
Investment Climate; Canada; Machine Learning; Sentiment Analysis; muted productivity growth; investmest climate; investment flow; NLP-based indicator; Climate finance; Competition; Infrastructure; Productivity; Mining sector; Global