We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net‐zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.

Why do seemingly similar firms show such different productivity? We argue that unresolved measurement problems, i.e., the persistent incongruity between economic transactions and their accounting representation, affect resource allocation. Our metric quantifies technical accounting terminology in firm disclosures to capture these unresolved measurement problems, UMPs, using over 90,000 10-Ks. A one standard deviation increase in UMP is associated with lower capital investment (6 percent), R&D (5 percent), and hiring growth (30 percent). We also find a reduction in total factor productivity (5 percent) and Tobin’s q (4 percent). Further, CEO compensation sensitivity to accounting performance decreases with UMP, whereas stock-based sensitivity remains unaffected. Our inferences continue to hold when we use a Bartik instrument, which exploits differential exposure to GAAP changes to isolate accounting-driven variation from the firm’s underlying economics. The results suggest unresolved measurement problems are a significant friction in resource allocation. Data Availability: The UMP dataset is available upon request. Please contact the first author.