RDP 2023-06: Firms' Price-setting Behaviour: Insights from Earnings Calls 1. Introduction

As observed in other advanced economies, consumer price inflation in Australia has been unusually high in recent years. While demand conditions have played an important role, supply-side factors have been the biggest driver of recent inflation outcomes and have been front of mind for company executives and policymakers (Figure 1).^[1] The impact of upstream supply shocks on prices are generally not well captured by inflation models, so alternative and timely sources of information have become more important in assessing the inflation outlook. For this reason, policymakers have been closely monitoring firms' price-setting behaviour using insights derived from business liaisons and surveys of firms.

In this paper, we examine firms' price-setting behaviour by listening into what companies are saying about input costs, demand and final prices during their earnings calls. To do this, we use modern techniques in natural language processing (NLP) applied to listed Australian firms' earnings call transcripts. This approach allows us to systematically analyse firms' first-hand experiences with cost pressures and the effect this is having on their price-setting decisions.

Before the fact, it is unclear whether earnings calls, business liaison information or survey indicators reveal the most information about the pricing behaviour of firms. At the outset, our view is that each source has its own strengths and weaknesses, and it is the accretion of information from all three that is most useful to policymakers. Earnings calls provide consistent firm-level information over time, track a large number of firms and – using the new methodology outlined in this paper – allow analysts to construct a huge variety of different indices to monitor over time. However, the sample is restricted to larger listed companies and the information is only updated during earnings season. Business liaisons are timelier, but the composition of firms changes from period to period and responses are influenced by the types of questions being asked. Finally, existing business survey indicators provide consistent information over time, but sample sizes tend to be smaller, firm-level information is unavailable and policymakers are restricted to analysing a small number of pre-existing indicators.

To build our firm-level indices, we mine the text of listed Australian firms' earnings calls, starting from 2007. Sentiment indices are developed from the transcripts, covering various input costs, demand and final prices. This is done using two techniques. The first is a simple dictionary-based approach with each word coming from new dictionaries developed in consultation with policy experts from the Reserve Bank of Australia's Economic Analysis Department and supplemented using word embeddings. These dictionaries are available for download in the online Supplementary Information. The second, and preferred approach, draws on state-of-the-art machine learning models (zero-shot text classifiers) to uncover semantic meaning and identify when company executives are talking about our topics of interest. A subset of forward-looking indices is also developed. We experiment with two techniques to do this. First, we use recently developed algorithms to identify the tense being used in various sections of the earnings calls and then restrict our analysis to the parts of speech that are forward looking. Second, we use transformer-based machine learning classifiers to identify sections of the transcripts that are delivered in the future tense.

Using our newly constructed indices, this paper contributes to our analysis of current economic conditions in several ways. 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 extensive business liaison program and can help predict (in the sense of Granger causality) signals provided by regular firm-level surveys of business conditions. These results are consistent with a simple conceptual framework we use to explain why there is real-time information in earnings calls. Moreover, past information in our sentiment indices for input costs and final prices can help predict (again, in the sense of Granger causality) official statistics for producer and consumer price inflation in the reference period – that is, ignoring lags in the publication of these official statistics. Establishing that our new indicators track current economic conditions is helpful because, using the flexible methodology outlined in this paper, earnings calls can be used to construct a host of firm-level indicators that may not be available from other sources.

Using firm-level regressions we also document several facts about firms' price-setting behaviour that are relevant for policymakers in understanding the dynamics of the inflation process. We do not establish causal relationships but uncover interesting conditional correlations. In particular, we have four key findings related to the sensitivity, or elasticity, of sentiment about final prices to input costs and demand, after controlling for the effects of shocks that are common to all firms, including global supply shocks:

• First, final price sentiment has a stronger association with sentiment about input costs compared to sentiment about demand. This association is consistent with survey-based findings that the predominant pricing strategy of firms is to set prices as a mark-up over costs (Park, Rayner and D'Arcy 2010).
• Second, we find that firms react more to increasing input costs compared to decreasing input costs, suggesting that rising prices are likely to remain front of mind for company executives even as supply pressures moderate. This asymmetry is consistent with the type of firm-level behaviour reported in Peltzman (2000) and Pitschner (2020), who show that cost shocks matter much more for price increases.

• Third, we show that discussions around final prices have become more sensitive to sentiment regarding import costs in the post-COVID-19 environment but seem less sensitive to rising labour costs.
• Finally, we show the association between price-setting sentiment and input cost/demand sentiment differs significantly across industries, highlighting significant heterogeneity in firms' price-setting intentions.

Our paper also makes a methodological contribution to the literature. There is a growing body of research using earnings calls for macroeconomic analysis. For example, quarterly earnings calls have been used to analyse firm-level climate exposures (Sautner et al 2023), cyber risk exposures (Jamilov, Rey and Tahoun 2021), political risk exposures (Hassan et al 2019), sources of country risk (Hassan et al 2021) and the diffusion of disruptive technologies (Bloom et al 2021). All of these papers rely on matching documents to curated lists of keywords in order to identify topics of interest in the transcripts. Instead, ours is the first paper (to our knowledge) that uses a class of transformer-based machine learning models called zero-shot classifiers to classify earnings calls into our topics of interest. The classifier we use can be used to classify documents into any arbitrary set of themes. These classifiers are based on a powerful transformer-based neural machine translation architecture which has swept through the NLP landscape (O'Neill et al 2021; Ash and Hansen 2023), most prominently through the recent release of ChatGPT.

Finally, we make an economic contribution. The significant heterogeneity in pricing behaviour that we document has implications for macroeconomic models. Our findings suggest that the tone of firms' discussions about final prices depends on the source of the shocks firms face (demand or cost driven) as well as the direction of the shock, with firms' appearing to react more to cost increases relative to decreases. The latter finding suggests the assumption of symmetry in some price-setting models used in applied macroeconomics could be revisited. Our results also show that some industries seem much more responsive to changes in input costs and demand conditions relative to other industries. This underscores the importance of continuing to develop rich multisector models of the economy to better understand firms' reactions to different types of shocks, such as that developed by Rees (2020).

Footnote

See Beckers, Hambur and Williams (2023) for an estimate of the relative contribution of supply- and demand-side drivers to inflation in Australia. [1]