RDP 2023-06: Firms' Price-setting Behaviour: Insights from Earnings Calls 7. Conclusion

This paper examines firms' price-setting behaviour using insights derived from earnings calls. We develop three sentiment indices (input costs, demand and final prices) using two different methods – one dictionary-based and one using a transformer-based large language model. We show that the signal from these indices about input costs, demand and final prices is contemporaneous with the information the Reserve Bank of Australia receives as part of its business liaison program and leads (in the sense of Granger causality) signals provided by regular firm-level surveys of business conditions. The indices for input costs and final prices also help to predict statistical measures of producer and consumer price inflation in the reference quarter, after considering the past values of both variables.

Our reduced-form analysis, uncovering conditional correlations between the sentiment of final price discussions and the sentiment of discussions about input costs and demand, allows us to draw inferences regarding firms' pricing behaviour that are relevant for understanding the dynamics of the inflation process. Our results are consistent with firms using pricing strategies that focus on a mark-up over costs. They are also consistent with firms being more reactive to rising, rather than falling, input costs. Finally, we document sizable heterogeneity in pricing behaviour across industries.

Overall, this paper shows that techniques in natural language processing can be usefully applied to understand firms' price-setting behaviour and current economic conditions more broadly. This can be a valuable tool for policymakers in assessing the inflation outlook and in understanding the dynamics of the inflation process.

We consider our paper the tip of the iceberg in terms of using large language models to examine earnings calls to better understand firm-level dynamics that are relevant for policy analysis. The capabilities of large language models are advancing rapidly. In this environment, we expect the construction of text-based macroeconomic indices derived from earnings calls to evolve in stride with these advances. For instance, future work could use alternative large language models, such as fine-tuned generative pre-trained transformative models, to annotate earnings call transcripts into topics, tone and tense. Another exciting direction of future research is to combine the firm-level textual insights extracted from the earnings calls with firm-level balance sheet and income statement data, which is readily available. This would provide a much richer panel of information to explore other interesting facets of price-setting behaviour, such as the role of competition and strategic complementarities.