and loss accounts for private enterprises, positions Orbis as one of the most robust datasets available for analyzing financial performance at the firm level.<sup>67</sup> The Orbis database provided by the Wharton Research Data Services (WRDS) includes latitude and longitude information at the firm level, enabling the precise determination of each firm’s geographic location. This aspect is essential for correlating firms with their respective regional weather data. However, not all firms in our dataset have this geographical data. Almost 20% of firms in our sample do not have the geographical information. To address this, we employed the Google Maps API, using available country, region, and city data for these firms. The methodology for this process is detailed in Appendix A.

For our study, we extracted firm-level information from 2000 to 2019, focusing on medium, large, and very large companies. The primary variables extracted included the firm’s location, fixed assets, and the NACE code for sector identification. While other financial variables were sparsely populated, fixed asset data was more consistently available. Consequently, we used fixed asset growth as a proxy for firm size growth.

Grid-level weather data. In our study, we utilize the temperature gridded data for global domains, which is derived from the Berkeley Earth Foundation dataset (BERKEARTH).<sup>8</sup> This dataset is sourced from the Climate Data Store, as part of the Copernicus Climate Change Service, managed by the European Centre for Medium-Range Weather Forecasts on behalf of the European Union. The BERKEARTH dataset has comprehensive coverage of global temperature changes, encompassing both land and ocean regions and providing historical temperature data spanning over two centuries. The methodology employed in the processing of this dataset is detailed in Rohde & Hausfather (2020).

The BERKEARTH dataset offers data in three distinct grid formats: 1 × 1, 2 × 2, and 3 × 3 degrees. Each ‘1 degree’ in this context refers to a geographical measure, where 1<sup>◦</sup> of latitude corresponds approximately to 111 kilometers (69 miles) on the Earth’s surface. Similarly, 1<sup>◦</sup> of longitude varies in distance, being wider near the equator and converging towards the poles due to the Earth’s spherical shape. For our analysis, we specifically employ the 1 × 1-degree grid format, which provides the most granular weather information. The dataset encompasses three primary variables: mean, maximum, and minimum daily temperatures. In our research, we focus on utilizing the maximum temperature readings.

Income-level data. For the country-level income data, we use the World Bank’s income data.<sup>9</sup> This data is used to investigate whether there are distinct dynamics in the impact of temperature uncertainty on firm behavior across different income levels.<sup>10</sup>

> 6However, Orbis does not provide comprehensive coverage for firms in North America, so the two major countries, the US and Canada, are not fully represented in our sample.

> 7See Lepore & Fernando (2023) for an analysis of physical risks from climate changes at the firm level using Orbis data. 8Similar studies using the grid-level weather data include Nordhaus (2006) and those studies using data from the Climate Research Unit of the University of East Anglia (Harris et al. 2014), such as Auffhammer et al. (2013), Kalkuhl & Wenz (2020).

> 9 `https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-2023`

> 10Some economies, such as Malta, Réunion, Taiwan, and Hong Kong, do not have their income information recorded in the World Bank income data. As a result, they are excluded from the regression analysis that involves income level. However, they are included in the regression analysis when income level is not a factor.

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