Data merging. A key aspect of our research methodology is the merging of the Orbis and BERKEARTH datasets using latitude and longitude information. This common element allows us to associate firm-level financial performance data from Orbis with the corresponding weather data from BERKEARTH, both mapped to the same geographic region.

Given that the weather data is gridded with one degree of latitude and longitude, we first assign a unique index to each grid. For example, Index 1 is assigned to the upper leftmost grid (longitude −180 to −179 and latitude −90 to −89). Subsequently, we assign the same index to the location of each firm. For instance, a firm located at longitude −179 _._ 3 and latitude −89 _._ 3 would also be assigned Index 1.

These indices are then used to merge the two datasets. As a result, the final firm-level data includes annual observations of the firm, its location, sector, and fixed assets, as well as the mean, variance and maximum of the temperature for the gridded location of the firm.

# 3.2 Data pre-processing

We carry out several cleaning procedures to enhance the quality and consistency of the data. First, we exclude firms with non-positive assets from our dataset. This exclusion is based on the accounting principle that fixed assets should always be non-negative. While it is technically possible to have zero fixed assets, these cases are excluded to enable the calculation of the growth rate of fixed assets. Second, to avoid potential double counting, our analysis is limited to unconsolidated data. We select U1 and U2 as the consolidation codes in Orbis data.<sup>11</sup> This choice ensures that the data reflects each firm’s independent operations, preventing distortion from inter-company transactions or accounts. This is crucial for examining the impact of location-specific factors, such as weather, on firms’ behavior, which would be limited with consolidated data. Finally, We also focus on countries with more than 500 observations and more than single observation for country-year-grid group.

All nominal variables (fixed & total assets) are deflated using the GDP deflator at the country level, sourced from World Bank data.<sup>12</sup>

# 3.3 Construction of variables

Firm-level variable From Orbis data, we construct the variable that indicates the growth rate of the firm’s size. We use the fixed assets as the main size variable. For each firm _i_ in country _c_ and year _t_ , we define the size growth as the log return on the fixed asset _Ki,t_ : where _κ_ is the nominal fixed capital and _pc,t_<sup>_k_is the deflator of country</sup><sup>_c_at time</sup><sup>_t_.</sup> Furthermore, to construct the proxy for investment irreversibility, we use the ratio of tangible fixed assets to total assets, following Gulen & Ion (2016), who argues that companies with significant tangible fixed capital investment often undertake projects requiring substantial initial expenditures, and thus

> 11U1 represents the unconsolidated record without the accompanying consolidated record, while U2 represents the unconsolidated record with the accompanying consolidated record.

> 12 `https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG`

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