To do this, we employ a unique dataset that merges firm-level financial data with meteorological information. Our analysis hinges on the concept of location-specific weather uncertainty for firms, measured via grid-level temperature uncertainty. We utilize financial records from the Orbis database, covering 30 million firms across 64 countries, alongside daily surface temperature data on a 1x1 grid from the Berkeley Earth Foundation dataset (BERKEARTH). The merging of these two datasets creates an extensive grid-level dataset, covering around 4,000 economically meaningful grids with firm presence from 2000 to 2019. Grid-specific temperature uncertainty is measured based on the maximum daily temperatures over a year. We examine whether this grid-specific temperature uncertainty is associated with the growth of firms located in the grid.

Our analysis yields three key findings. First, there is a strong negative correlation between weather uncertainty and firm growth. Our results indicate that for every one-degree increase in temperature uncertainty, measured by the standard deviation of deviations from expected temperatures, firms’ growth rate of fixed assets decreases by 0.77 percentage points.

Second, the impact of weather uncertainty on firm growth varies across industries with different degrees of investment irreversibility. Industries with higher investment irreversibility experience a more pronounced negative impact on firm size growth. Doubling capital intensity intensifies the negative effect of temperature uncertainty on firm size growth by roughly 0.72 percentage points.

Third, the impact of weather uncertainty on firm growth varies across countries with different income levels. Higher-income countries are more adversely affected by weather uncertainties in terms of firm behavior. As income levels double, the negative impact of temperature uncertainty on fixed asset growth becomes more severe by 0.63 percentage points.

This paper is among the first to examine firms’ behavior in response to location-specific temperature uncertainty at the grid level. While climate-related uncertainty is often studied using indirect, non-weather measures like the Climate Change News Index (Engle et al. 2020) and in climate policy uncertainty literature (Gulen & Ion 2016), recent studies have increasingly focused on direct measures of weather variability, as highlighted by Kahn et al. (2021), Kotz et al. (2021), Donadelli et al. (2022), Sheng et al. (2022). Our approach builds on these recent efforts by emphasizing weather variability as a key factor influencing economic activity. In doing so, we also draw on the established tradition of using gridded output data, particularly Nordhaus (2006) and later studies employing grid-level or sub-national geographic data, such as Kalkuhl & Wenz (2020), Wenz et al. (2023), Gortan et al. (2024).

Our main contribution is the construction of grid-level economic data based on firm-level activities. Unlike regional GDP data, firm-level data shows how the private sector, driven by profit motives, responds to climate change and the risks it poses. This firm-level data enables us not only to capture the overall relationship between weather variability and economic activity but also to test important hypotheses, such as the moderating effects of investment irreversibility in this relationship—something that is difficult to achieve without detailed firm-level data. By doing so, we bridge the climate change impact literature, particularly studies using grid-level output data (Nordhaus 2006, Kalkuhl & Wenz 2020), with research on firms’ behavior under uncertainty (Myers 1977).

The paper is structured as follows: Section 2 reviews the real options theory. Section 3 discusses the main data sources, cleaning process, and key descriptive statistics. Section 4 outlines the empirical methodology and presents our findings. Section 5 concludes the paper.

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