metadata: authors: 0: author: Berg,Claudia N.
1: author: Bettarelli,Luca
2: author: Furceri,Davide
3: author: Ganslmeier,Michael Georg
4: author: Grover,Arti Goswami
5: author: Lang,Megan Elizabeth
6: author: Schiffbauer,Marc Tobias
count: World
docna: 0: docna: Firm-Level Climate Change Adaptation : Micro
            Evidence from 134 Nations
docty: Policy Research Working Paper
theme: Financial Infrastructure and Access,Infrastructure Finance,Data Development and Capacity Building,Social Protection,Social Development and Protection,Environment and Natural Resource Management,Finance for Development,Finance,Credit Infrastructure,Public Sector Management,Climate change,Urban and Rural Development,Adaptation,Data production, accessibility and use,Disaster Risk Finance,Flood and Drought Risk Management,Disaster Risk Management,Social Safety Nets
abstracts: cdata!: Are firms adapting to climate change?
            This paper studies this question by combining geocoded World
            Bank Enterprise Survey data with spatially granular weather
            data to estimate temperature response functions for nearly
            160,000 firms in 134 countries over a 15-year period. Our
            results show that market imperfections in low- and
            middle-income countries constrain firms&#x2019; ability
            to adapt. Small and medium-size firms in low- and low-middle
            income countries are most vulnerable, with revenues
            declining by 12 percent in years with temperatures
            0.5&#x25e6;C above historical averages. The impact is
            equally strong for manufacturing and services firms and
            result from declines in labor productivity and wages.
            Heat-sensitive sectors and less resilient firms are more
            severely affected, reinforcing the causal interpretation.
            Unique firm-level information on policy constraints
            including limited financing, burdensome regulations, and
            unsafe conditions suggest that such factors raise adaptation
            costs, undermining economic resilience to climate change.
display_title: Firm-Level Climate Change Adaptation
            : Micro Evidence from 134 Nations
listing_relative_url: /research/2025/03/34462766/firm-level-climate-change-adaptation-micro-evidence-134-nations
topicv3: Private Sector and Climate Change m1326830 1772,Poverty and Climate Change m1327423 587,Climate Change Adaptation Impacts m1327768 1750,Building Collapse m1503703 3362,Investment Behavioural Incentives m1504641 2543
publishtoextweb_dt: 2025-03-10T00:00:00Z
dois: 10.1596/1813-9450-11081
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 61
author_keys: 0: author_key: 647328
1: author_key: 1698934
2: author_key: NA
3: author_key: 2337295
4: author_key: 1066720
5: author_key: 1662677
6: author_key: 1067388
model_extractions: Policy Research Working Paper 11081 Firm-Level Climate Change Adaptation Micro Evidence from 134 Nations _Claudia Berg Luca Bettarelli Davide Furceri Michael Ganslmeier Arti Grover Megan Lang Marc Schiffbauer_ Development Research Group Finance, Competitiveness and Innovation Global Department & International Finance Corporation March 2025
Policy Research Working Paper 11081

# **Abstract**

Are firms adapting to climate change? This paper studies this question by combining geocoded World Bank Enterprise Survey data with spatially granular weather data to estimate temperature response functions for nearly 160,000 firms in 134 countries over a 15-year period. Our results show that market imperfections in low- and middle-income countries constrain firms’ ability to adapt. Small and medium-size firms in low- and low-middle income countries are most vulnerable, with revenues declining by 12 percent in years with temperatures 0.5°C above historical averages. The impact is equally strong for manufacturing and services firms and result from declines in labor productivity and wages. Heat-sensitive sectors and less resilient firms are more severely affected, reinforcing the causal interpretation. Unique firm-level information on policy constraints including limited financing, burdensome regulations, and unsafe conditions suggest that such factors raise adaptation costs, undermining economic resilience to climate change.

This paper is a product of the Development Research Group, Development Economics, the Finance, Competitiveness and Innovation Global Department, and the International Finance Corporation. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mlang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Firm-Level Climate Change Adaptation: Micro Evidence from 134 Nations Claudia Berg<sup>1</sup> , Luca Bettarelli<sup>2</sup> , Davide Furceri<sup>3</sup> , Michael Ganslmeier<sup>4</sup> , Arti Grover<sup>5</sup> , Megan Lang<sup>1</sup> , and Marc Schiffbauer<sup>1</sup>

> 1 _1: World Bank, 2: University of Palermo, 3: International Monetary Fund, 4: University of Exeter, 5: IFC_

_Keywords: Climate adaptation, firms, temperature, resilience JEL Codes: D22, Q56, Q54, O12, O14_ ©2024 The World Bank and International Monetary Fund.
# **1 Introduction**

Empirical studies have documented a robust association between extreme weather and reductions in national economic growth (e.g., Burke, Solomon M Hsiang, and Miguel 2015, Somanathan et al. 2021). Extreme weather may reduce economic activity by affecting the performance of firms. Economic theory predicts that rational, forward-looking firms should make self-protective investments. However, firms operating in low- and middle-income countries face a host of market imperfections that may constrain their ability to adapt. Understanding which types of firms are best able to cope with weather shocks and which features of the business environment support firm-level adaptation is critical for designing well-targeted policies for private sector climate resilience.

To this end, we combine granular satellite weather data with global firm-level data to study the impact of rising temperatures on firms’ performance, ability to adapt, and the policy constraints hindering their adaptation capacity. We use standardized, repeated crosssectional microdata from the World Bank’s Enterprise Survey (WBES, _Enterprise Survey_ 2023) covering 134 countries and more than 160,000 firms—of which more than half operate in low and low-middle income countries—over a 15-year period. We assess the marginal effect of weather deviations from historical trends on corporate revenues and the associated transmission channels such as firmsâ investments, labor productivity, wages, energy intensity, and propensity to innovate. The data also enable us to estimate how firmsâ characteristics and the policy constraints they encounter influence their capacity to adapt to higher temperatures.

This study is among the first to use granular satellite weather data combined with microeconomic data to assess the economic impact of climate change at a global scale, particularly in low- and middle-income countries. The integration of granular climate and firm-level data is critical for identifying the causal economic effects of temperature shocks as it enables us to measure temperature deviations at firmsâ specific locations and account for firm-level heterogeneities, addressing key endogeneity concerns. Our empirical strategy relies on computing location-specific differences between temperatures within firms’ fiscal year and historical 2
average temperatures from 1980–2008. We use these temperature differences to estimate heterogeneous, nonlinear firm responses along multiple dimensions of firm performance. Our identifying assumption is that temperature deviations are as good as random after conditioning on country by sector by year fixed effects, firm size (a general proxy for firm quality), and firms’ location-specific historical mean temperatures.

Another novel feature of our study is that the standardized firm-level data include manufacturing and services firms, as well as a rich set of firm-level characteristics and measures of the business environment. For instance, it includes measures of firms’ main sales markets, the resilience of firms’ production processes, firms’ access to finance, the quality of electricity services, the level of local security and political stability, and the quality of the local regulatory environment. Interacting these measures with location-specific weather data allows us to describe which types of firms and policy reforms are best able to “weather the storm”.

We document large, negative impacts of higher-than-average temperatures on revenues for firms in low and low-middle income countries. Small and medium-size firms as well as startups are most vulnerable to rising temperatures. These firms exhibit a 12% decline in revenues when annual average temperatures are 0.5<sup>_◦_</sup> C higher than the historical average. The impact is equally significant for manufacturing and services firms and driven by declines in labor productivity, which also result in lower wages. Firms in heat-sensitive sectors and with less resilient production processes are more affected, supporting our empirical identification. Consistent with the findings in Carleton et al. (2022), we find that income significantly flattens the temperature-revenue curve. In low and low-middle income countries, firms facing limited access to finance, burdensome local business regulations, and unsafe conditions are more vulnerable to temperature increases, weakening economies’ resilience to climate change.

Taken together, our results highlight two novel facts about economic resilience to rising heat. First, the performances of most manufacturing and services firms in low- and lowmiddle income countries has been affected by rising temperatures, with substantial differences in the degree of vulnerabilities depending on firms’ specific characteristics. Second, local 3
policy constraints including finance, business regulation, and the quality of public goods affect firms’ adaptive capacity, undermining developing countries’ economic resilience. Our results underline the importance of designing local context-specific policies to support climate adaptation in the private sector.

# **2 Literature**

In the last decades, deteriorating climate conditions have encouraged scholars to investigate the economic costs of climate change. Studies find that increasing average temperatures by 1<sup>_◦_</sup> C reduces global output and productivity by about 1-3% in hot regions (Nordhaus, 2006; Stern 2008; Bansal and Ochoa 2011; Graff Zivin and Neidell 2012). In contrast, cold countries may benefit from warmer temperatures (Tol, 2021). Along this line, Burke, Solomon M Hsiang, and Miguel (2015) present evidence that the productivity of countries increases along with temperatures until an annual average threshold of 13<sup>_◦_</sup> C, with countries’ productivity declining significantly at higher temperatures. As hotter regions are on average poorer, climate change is also expected to hit developing regions harder (Dell, Jones, and Olken 2012; Letta, Montalbano, and R. Tol 2018; Newell, Prest, and Sexton 2021). In fact, many developing nations face greater natural disaster risks such as typhoons and are exposed to extreme heat and precipitation (Bakkensen and Barrage 2018; Solomon M. Hsiang and Jina 2018). Moreover, climate risks in the developing world are amplified by weak governance capacity (Acemoglu and Robinson 2013; La Porta, Lopez-de-Silanes, and Shleifer 2008; Allcott, Collard-Wexler, and D. O’Connell 2016; Chong et al. 2014).

A recent study by Bilal and Kanzig (2024) suggests that previous evidence based on country-level temperature data could underestimate the real effect of climate on the economy. The authors propose the use of aggregate global temperature—as it accounts for correlated local temperatures and spillover effects across countries—and find that the peak effect of warming temperatures reduces economic activity by about 12% in the medium term, ap4
proximately twelve times larger than what was previously estimated.

While these studies provide important insights, the reliance on cross-country data does not allow to adequately address institutional and other differences across countries that matter for the economic impact of climate change but are typically unobservable, raising endogeneity concerns (see, for example, Durlauf, Johnson, and Temple 2009; Hauk and Wacziarg 2009). Cross-country data also neglect subnational variation in (the impact of) climate change which has been found to be substantial (Bettarelli et al. 2024).

Recent literature has extended the level of granularity of analyses to the sub-national level, leading to more accurate estimates. Kotz, Levermann, and Wenz (2024) use a sample of 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes. They suggest a reduction in global output of 19% by 2050, compared to a scenario without climate impact. Burke and Tanutama (2019) assemble panel data on economic output from over 11,000 districts across 37 countries and find that district-level growth declines for higher average temperatures. Carleton et al. (2022) use subnational data from 40 countries to investigate the age-specific mortality-temperature relationships and suggest a nonlinear effect, whereby extreme cold and hot temperatures increase mortality rates.

We contribute to this literature by using more granular spatial data combined with firmlevel information. The more granular spatial data can help address endogeneity concerns since the effect of climate change can vary at a very granular spatial level such as within provinces or districts due to correlations of local geographies with climate and economic activity—more districts with higher elevations, for example, typically have lower average temperatures and economic activity while coastlines have milder climates and higher activity. Moreover, firmlevel data allow assessing how the impact of climate change differs across firms given their ability to adapt. Young or small firms, for example, often face tighter financial constraints or higher costs to access construction permits needed to invest into more resilient buildings. At the firm-level, Somanathan et al. (2021) find that rising heat days in India between 5
1998 and 2010 have reduced formal manufacturing firms’ revenues by about 2% per year. Severe reductions in manufacturing output due to extreme heat have also been documented among Chinese plants (Zhang et al. 2018) and at the global level (N. Pankratz, Bauer, and Derwall 2023). For the U.S., Addoum, Ng, and Ortiz-Bobea (2020) find no significant impact of temperature exposure on establishment-level sales or productivity using granular climate data from 1990 to 2015. Beyond temperatures, Keiller and Van Reenen (2024) demonstrate that natural disasters adversely affect firm performance. Cevik and Miryugin (2022) find that firms in more climate vulnerable countries have a lower profitability and access to debt financing. A detrimental impact of climate risks on the firm’s financial position is also documented by Venturini (2022), Bansal, Ochoa, and Kiku (2016), Addoum, Ng, and OrtizBobea 2020, and N. Pankratz, Bauer, and Derwall (2023). Huang, Kerstein, and Wang (2018) find that the impact of climate risks on firms is larger in climate vulnerable sectors such as food, healthcare, communications, business services, or transportation. Recent studies also use transcripts from conference calls to quantify the degree of exposure of firms to different climate risks and link it with firm-level outcomes (Li et al. 2020; Sautner et al. 2023). These single-country studies carefully identify local marginal effects but are unable to sketch out the cross-sectional gradient of how firms worldwide are coping with similar shocks.

Several studies also use survey data to measure how rising heat affects labor productivity. Somanathan et al. (2021) collect high-frequency survey data in India. They document that an additional day above 35<sup>_◦_</sup> C in the six preceding days has caused a 2.7% decrease in daily output among textile workers in sites without climate control while worker efficiency has still declined by 1.1% in climate-controlled garment plants. The latter stems from absenteeism, with each heat day raising worker absenteeism by 0.5% and reducing output by 1.4%. Similarly, Graff Zivin and Neidell (2014) find that extreme heat reduces hours worked in heat-sensitive industries in the U.S. Adhvaryu, Kala, and Nyshadham (2020) exploit differences in temperatures across workplaces within the same factory in India due to selected use of lighting technology—low heat-emitting LED bulbs—and find large adverse efficiency 6
effects for indoor temperatures above 28Â°C. Rode et al. (2022) use data from several large economies to show that increasing temperature impacts on workers disutility. And Park, N. Pankratz, and Behrer (2024) find that temperatures above 32<sup>_◦_</sup> C in California, U.S., have increased workplace injuries in construction, indoor manufacturing, and warehousing by 9%.

Rising temperatures have also been estimated to raise firms’ input costs. N. M. C. Pankratz and Schiller (2022) find that exposures to high temperatures at supplier locations in a sample of large multinational firms reduce downstream firms operating income over assets by 0.6%. Using plant-level data from the U.S. Census Bureau, Ponticelli, Xu, and Zeume (2023) find that warmer than average temperatures increase energy costs and decrease productivity in small manufacturing plants (see also Birkie, Trucco, and Campos 2017; Cevik and Miryugin 2022). Moreover, the World Bank (2004) estimates that rising energy demand from additional cooling needs in commercial buildings rises 11-fold by 2037 implying surging energy costs for firms.

Our work also links to studies investigating strategies to adapt/respond to climate risks (Burke, Zahid, et al. 2024). Risk reduction is generally pursued through technical measures such as maintenance, materials, facilities, assets and process engineering, risk monitoring and assessment (Berkhout 2012; Hertin et al. 2003; Weinhofer and Busch 2013), or by adopting establishment-level climate controls (Gasbarro and Pinkse 2016; Somanathan et al. 2021; Zhao et al., 2024). Moreover, infrastructure investments such as new roads, sea walls, bridges, water treatment facilities, or power generation plants support firms to cope with climate risks (Hallegatte, Rentschler, and Rozenberg 2019; Hsiao 2023; He et al. 2021). Bettarelli et al. (2024) show that firms facing financial constraints are more exposed to climate uncertainty.

We contribute to the literature by combining highly granular climate data with firm-level data from nearly 160,000 firms in 134 countries from 2006 to 2023 to estimate the economic costs of increasing temperatures at the global level. It is thus one of the first studies to use, at a global scale, granular Satellite weather data combined with rich firm-level data, which is key to address endogeneity concerns. The paper most similar to ours is Kassa and 7
Woldemichael (2024) who also use global WBES data to analyze the effect of heat on firm productivity, finding larger impacts in large firms, manufacturing, and low-income countries. In contrast to previous studies, we make four major contributions. First, we allow for nonlinear temperature response functions and model heterogeneities in country or firm typologies and temperature levels. Second, we assess the impact of yearly deviations of temperatures from locations’ long-term trends and its transmission channels such as investment, labor productivity, labor compensation, or energy costs. Third, we assess which types of firms are more exposed to climate change / less able to adapt accounting for firm characteristics, such as firms’ size, age, main sales market, resilience, and economic activity. Fourth, we assess how policy failures raise firms’ transaction costs to investment in adaptation—including access to finance, security, and business regulations—which is key to design effective policies supporting economic resilience.

# **3 Firm Choices: Location and Adaptation Investments**

The impact of local weather on a firm’s performance depends on where the firm locates and what self-protective measures it takes. Some firms may choose to gamble by investing little in self-protection while others may incur significant costs, for instance purchasing backup power generators or investing in redundant supply chains. In this section, we discuss the joint decision made by firms concerning where to locate and how much to invest in selfprotection. As we discuss below, our empirical work focuses on the set of firms who are operating in a given location at a given point in time.

Within a country, locations differ along many dimensions. Some locations may be close to final consumers while other locations may be close to export ports. Given that firms and workers compete for scarce land, the hedonic compensating differentials literature predicts that areas with productive features will have higher rents and higher wages in equilibrium, while other areas will feature lower wages and higher rents.

8
Each firm knows its own production function and calculates its expected profit in each location. The site selection literature models firms as if they calculate their expected flow profits at each discrete location and then choose the location that offers the highest expected present discounted value of profits. Conditional on selecting a location, the firm has an incentive to make customized investments to self-protect from expected weather realizations. For example, a firm that locates close to the equator will anticipate that on average it will be very hot, so it has an incentive to invest in cooling technologies.

Throughout this paper, we assume that a country/region’s expected weather conditions could play a role in where firms choose to locate. Firms have incentives to use all available information to update their beliefs about short-term weather realizations and longer-term changes in climate. If firms can perfectly anticipate and respond to weather realizations, then firm outcomes like revenue will be uncorrelated with weather realizations. If firms cannot perfectly anticipate or cannot fully respond to weather realizations, then firm outcomes may suffer. For example, if Las Vegas has historically featured a summer average temperature of 94 degrees Fahrenheit then we assume that firms in this area are adapted to 94 degree summers. If in the year 2025, Las Vegas experiences a summer average temperature of 98 degrees Fahrenheit then this is a 4-degree deviation. Our identifying assumption is that this deviation is uncorrelated with unobservable firm attributes affecting corporate performance, conditional on a set of fixed effects and observable firm attributes (see section 5.2). As we discuss below, our empirical work studies how such deviations are associated with firm outcomes (see section 5.1).

Our empirical work will focus on the economic performance of firms in low- and middleincome countries. Such economies often feature market imperfections that undermine firms’ ability to fully adapt to climate change. For instance, credit constraints may prevent firms from relocating to areas with smaller temperature deviations even when relocation would be optimal. Firms that face capital market imperfections will also be less able to finance lumpy adaptation investments. For example, air conditioning equipment may be too costly 9
for some firms to purchase, and credit constraints could prevent them from financing such an investment, difficulties in accessing local construction permits may discourage their investment in more resilient buildings. Even if the firm’s manager perfectly anticipates the adverse impact of climate change on her firm’s performance, she faces a more constrained adaptation menu. In this case, the observer is more likely to estimate a larger negative impact of rising temperatures on firm revenue. To the extent that the degree of credit or other local business environment constraints are correlated with firm performance within a given country, sector, and year, such market imperfections could bias our estimates. We evaluate the extent of the potential bias empirically and examine heterogeneity by measures of market imperfections to document how well firms in different contexts can adapt to rising temperatures.

# **4 Data**

## **4.1 World Bank Enterprise Survey data**

To investigate the economic costs of climate change, we rely on novel, geocoded firm-level data from the World Bank Enterprise Surveys (WBES). The data provide information from nearly 160,000 establishments that are harmonized across 134 countries and are collected about every five years in each country between 2010 to 2023. The WBES is designed to provide internationally comparable establishment-level data that are representative for the formal private sector economy for each country-year survey round. The surveys are stratified by firm size, broad sector (manufacturing versus services), and region. The WBES is the only available dataset that provides comparable firm-level data for a large set of countries, including many low- and middle-income countries. It allows us to estimate the impact of climate change based on microdata at a global scale rather than relying on temperature response functions estimated on a small subset of countries to predict marginal damages for other countries. The data are geocoded, making it possible to exploit rich variation in climate change across detailed locations within countries over time.

10
The data include balance sheet information for each establishment’s latest fiscal year including sales, the number of full-time employees, total labor compensation (wages), investment, and energy consumption. As such, they allow us to assess the impact of climate change on a variety of firm performance measures. They also provide detailed information on establishments’ characteristics such as their year of establishment (age), the main market of their sales (international, domestic, or local), and the main economic activity (4-digit ISIC codes). Moreover, they include detailed information on the business environment, ranging from access to finance, transport, electricity, trade restrictions, business regulation, and corruption. The data are thus well-suited to test the specific microeconomic mechanisms underlying the impact of climate change on firms and to identify policy constraints that may undermine firms’ ability to adapt. Appendix subsection A.1 provides additional details on the survey design and sampling.<sup>1</sup>

# **4.2 Matched firms and climate data**

The climate data comes from the EU Copernicus satellite and ground-based measurement system, which provides, among other, highly granular geospatial information on hourly temperatures (27 x 27 km at the equator) for the entire world, from 1950–2023. The geocoded weather data include hourly temperatures from which we compute the average annual temperature for the detailed location of each firm.

We match establishments in the WBES data with their relevant weather data based on firms’ geocodes. This is done in three steps. First, we collapse daily temperatures at the yearly frequency by computing within-year measures of weather-related shocks for each firm, exactly matching each firm’s fiscal year by the day. That is, as firm-level balance sheet data refers to the last fiscal year with respect to the date of the interview, we do not aggregate weather data according to calendar years, but consider each firm’s relevant fiscal

> 1The harmonized WBES data are available online at https://www.enterprisesurveys. org/portal/index.aspx#/library?dataset=Combined. Special permission must be requested to access the masked locations of each firm in the dataset.

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year as reported in the WBES data. Second, we match the annual temperature data to each firm’s geocoded location. Third, we calculate historical averages for each firm’s location by considering the panel of weather data from 1980–2008.

# **5 Empirical framework**

## **5.1 Empirical specification**

Our main metric of temperature shocks is the average annual temperature, although in principle our approach works with any weather metric. For each weather variable, we compute the difference between the variable in the fiscal year in which we observe a firm in the WBES and the historical average of the variable between 1980 and 2008, denoting this difference _CDiff_ . Using differences allows us to identify shocks, or deviations from “business as usual” weather conditions. The deviations remove the impact of fixed factors such as local geography which can affect local temperatures and economic activity—for example, coastlines face milder climates and proximity to ports can imply lower transport costs.

Our core specification includes country by fiscal year by sector fixed effects. As such, we compare the performance of firms in the same sector in the same country over the same fiscal year who face different weather shocks. We control for the historical average of the variable between 1980 and 2008, denoted _CHist_ , so that we draw direct comparisons between locations with similar historical climates.

The WBES provides weights that can be used to construct nationally-representative statistics for firms within a fiscal year. However, as Solon, Haider, and Wooldridge (2015) point out, using survey weights in regressions does not resolve mis-specification stemming from weighted data.<sup>2</sup> The authors show that it is preferable for to model the important sources of heterogeneity instead of using sample weights. We follow their recommendation

> 2The general intuition is that weighting data in regressions resolves issues stemming from over- or undersampling certain groups but cannot resolve bias stemming from differences in the variance of the main explanatory variable between groups.

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and identify two key dimensions of heterogeneity for our core set of results in our sample. The first is GDP per capita (e.g., Carleton et al. (2022)). The second is firm size, measured as the number of permanent, full-time employees ( _Emp_ ), which we consider a stable long-term proxy for firm quality. Modeling these two dimensions of heterogeneity allows us to test the hypotheses that the economic effects of climate change mostly materialize in poorer regions and that small firms may face more constraints in adapting to climate change. Including additional dimensions of heterogeneity beyond GDP and firm size does not change the relationship between temperature differences and firm performance, suggesting that GDP and firm size parsimoniously capture first-order heterogeneity (see Figure A1).

We flexibly model each weather variable using a third-order polynomial, which allows the data to drive the observed relationship between weather deviations and firm performance while preserving statistical power.<sup>3</sup> We fully saturate the model by interacting each term in the polynomial with the our two dimensions of heterogeneity. Following the recommendation of Abadie et al. (2023), we cluster our standard errors at the strata level (i.e., by subnational regions, sectors, and firm size categories). Indexing firms with _i_ , sectors with _j_ , countries with _c_ , fiscal years with _t_ , and strata with _s_ , we estimate Here, _CDiffijcst_ is the difference between the relevant climate variable for firm _i_ in year _t_ (when the Enterprise Survey was conducted) relative to that climate variable in firm _i_ ’s location from 1980–2008. The variable log( _GDPct_ ) is the logarithm of GDP for country _c_ in the year we observe firms in the Enterprise Survey and _Empijcst_ is the number of employees working at firm _i_ in year _t_ . _CHistijcs_ is the historical average of the relevant climate variable from 1980–2008, _γjct_ is a sector by country by fiscal year fixed effect, and _εjsc_ is the error

> 3Figure A2 shows that our results are robust to alternative functional forms.

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term.

# **5.2 Identification**

The granularity of our data allow us to identify causal economic effects of temperature shocks. Our key identifying assumption is that changes in temperatures in a firm’s geocoded location (relative to the location’s long-term mean) are exogenous to the firm’s output other than through the direct impact on its performance, after conditioning on our set of fixed effects and controls. The geographical granularity in our data enables us to account for unobserved heterogeneity across countries, economic activities, and years through country by sector by year fixed effects. Controlling for historical temperatures and firm size further restricts the comparisons we make. We thus exploit within-country, within-sector, within-year fluctuations in temperature, conditional on historical temperature and firm size. In this section, we consider the plausibility of our identifying assumption.

Reverse causality is unlikely to apply in our setting. Although economic activity at more aggregate levels can influence temperature, it is unreasonable to expect that this would happen at a firm’s geocoded location level since an individual firm’s output only has a negligible impact on the climate.

Subnational geography such as proximity to the coast or mountains may influence temperatures as well as economic activity. For instance, coastal regions may have more stable temperatures and potentially more economic opportunities due to their proximity to ports or better transport infrastructure. Comparing the impact of temperatures across firms may thus suffer from omitted variables bias since a location’s fixed geographical characteristics could be correlated with other factors affecting firm performance. Our estimation addresses this by leveraging the impact of temperature changes relative to each location’s long-term mean, which differences out fixed geographical characteristics. Combining granular geocoded climate data with rich firm data is thus key to identifying the economic impact of climate change because it allows us to control for the impact of variation in local geographical char14
acteristics. This is essential since temperatures vary significantly within countries.

Firms may change their locations over time to adapt to the impact of rising temperatures and new firms may choose locations less vulnerable to climate change. This can affect the estimated impact of climate changes on firms if those firms also happen to be more productive, resulting in selection bias. Descriptive evidence from the WBES mitigates concerns about selection bias. First, around 70% of firms in the WBES were established before 2005, when awareness of climate change risks were absent or low. As such, initial firm location decisions for a large majority of firms in our sample were likely not informed by future climate risks. Second, the WBES data in India allow us to track a panel of firms over time. Among the 4,066 panel firms interviewed in 2014 and 2022, only 0.5% changed their location between survey rounds, suggesting that a small share of firms in our sample are relocating over time. Third, Table 1 shows that correlations are low between temperature differences and a set of firm characteristics that are stable over time.<sup>4</sup> However, since some of the covariates are statistically significantly correlated with the temperature shocks we use to identify effects, there is some evidence of selection. We therefore show that our core results are robust to controlling for additional firm characteristics.

Finally, measuring the impact of changes in temperatures from a location’s long-term average may neglect effects of firms’ past adaptive decisions. Specifically, firms in locations with higher average temperatures may have taken measures to reduce their exposure to heat. Neglecting the effects of past adaptive actions biases our estimates away from finding effects of higher than average temperatures on firm performances. This implies that our results, if anything, underestimate the impact of temperature shocks on firms.

> 4In addition to being stable over time, we include only covariates that are nonmissing for at least 95% of our sample.

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# **6 Results**

## **6.1 The economic impact of rising temperatures**

Figure 1 and the first row of Table 2 show the baseline results from Equation 1 measuring the impact of temperature differences on firms’ revenues. To ease the visualization of results, we report nine charts in Figure 1, based on World Bank income classifications (high income, upper-middle income, and combining low income and lower-middle income, denoted LMCs) and firm size categories (small, medium, and large). For instance, the bottom-left panel reports the effect of temperature differences on firms’ revenues for small firms in low and lower-middle income countries; the top-right panel shows the effect on large firms in highincome countries. Each chart in Figure 1 plots the predicted response functions of the logarithm of firms’ revenues to a N-degree Celsius difference between the historical average temperature and the current fiscal year temperature at the mean value of each income group and size category, based on the coefficients from the estimation of Equation 1. In Table 2, we instead present the estimated effect of a 1 standard deviation increase in temperature, relative to a location’s historical average temperature, at the mean value of each income group and size category, based on the coefficients from the estimation of Equation 1. Each row shows estimated effects of a 1 standard deviation increase in temperature differences for a different measure of firm performance: revenue, revenue per worker, average wages, whether a firm makes any investments in the current fiscal year, the value of investments made in the current fiscal year, whether the firm has engaged in innovation, and the firm’s energy intensity.

Figure 1 shows that the economic costs of climate change from rising temperatures are large and significant for all firms in low and lower-middle income countries, which include the majority of firms (more than 80,000) in our sample. For small and medium-size firms, a 1 standard deviation increase in temperatures relative to the historical average in firms’ locations—corresponding to 0.75<sup>_◦_</sup> C—reduces corporate revenues by approximately 20-22% 16
(Table 2, first row). The negative effect of climate change is lower for large firms in low and lower-middle income countries but still sizable and statistically significant at 14%, suggesting that large firms in LMCs are better able to adapt but cannot fully mitigate the adverse impact of higher temperatures on firm revenues.

The negative effects of higher temperatures disappear among firms in upper-middle income (UMCs) and high-income countries (HCs). The result may reflect that countries in both income groups face substantially lower average temperature levels. That is, while the average temperature is 23<sup>_◦_</sup> C in LMCs, it is only 17<sup>_◦_</sup> C in UMCs and 15<sup>_◦_</sup> C in HCs. In other words, although it is possible that firms in higher income countries face fewer constraints to adapt to rising temperatures, the results may also indicate that the costs are larger in hotter regions, in line with earlier studies using macroeconomic data (Dell, Jones, and Olken 2012; Burke, Solomon M Hsiang, and Miguel 2015). Notably, all sizes of firms in high income countries—which face relatively low average temperatures of 15<sup>_◦_</sup> C in our sample— benefit from positive temperature deviations, which is consistent with the finding of R. S. Tol (2021). Our results remain stable when we include all covariates that displayed statistically significant correlations with temperature differences as additional controls (see Table A1).

The baseline results establish that temperatures exceeding location-specific historical means significantly slow private sector growth in LMCs, disproportionally affecting small and medium firms. The findings thus highlight substantial heterogeneity in the effects of rising temperatures depending on firm-level and location-specific characteristics, emphasizing the importance of using granular data to assess the economic impact of climate change.

To isolate the effect of high temperatures, we estimate the impact of extremely hot days on firm performance. This also serves as a robustness check for our baseline results, using an alternative measure of heat. We re-estimate Equation 1 using an alternative climate shock reflecting the impact of hot days, i.e., the difference in the total number of days in a fiscal year with temperatures reaching at least 35<sup>_◦_</sup> C for at least 1 hour in the fiscal year in the WBES compared to the historical mean. As with average temperatures, there is a significant 17
difference in the number of extreme heat days across country income groups, ranging from 6 in HCs to 32 in LMCs.

The shapes of the response functions for firm revenues to differences in days with extreme heat mirror those in Figure 1 for average temperature (see Figure A3). Specifically, a 1 standard deviation increase in the number of extreme heat days within a fiscal year relative to the locations’ long-term mean— corresponding to around 10 additional hot days—reduces corporate revenues by about 6-12% in LMCs.<sup>5</sup> Smaller firms in UMCs also tend to be negatively affected by more extreme heat with 10 additional heat days reducing corporate revenues by about 3%. Firms in HCs are not adversely affected by additional extreme heat days in our sample which points to a higher resilience of firms to rising temperatures possibly supported by the small average number of such days in in HCs.

# **6.2 Impact channels**

What drives the effect of high temperatures on firm revenues? We examine the channels through which rising temperatures impact corporate revenues by estimating the effects of temperature differences on firms’ production factors (Table 2). We find that temperature shocks reduce the labor productivity in firms of all sizes in LMCs. Consistent with the impact on revenues, the adverse effect of higher-than-expected temperatures on labor productivity in LMCs is about 50% larger for small and medium-size firms than for large firms. In line with the impact on corporate revenues, years with positive temperature deviations raise labor productivity in firms of all sizes in HCs, which are relatively cold in our sample.

The lower productivity of workers among firms in LMCs also translate into lower wages. A 1 standard deviation increase in temperatures relative to historical trends in firms’ locations and fiscal years reduces wages, measured by average labor compensation per worker, by approximately 9% (Table 2, third row). The reduction in labor productivity is consistent with previous firm-level studies for individual countries such as India (see Somanathan et al.

> 5The results for the point estimates for extreme heat days are not reported but available from the authors upon requests.

18
2021 and Section 2).

Although higher temperatures reduce workers’ productivity, they do not seem to affect firm investments. We measure investments as a binary variable, if the firm invested in the corresponding fiscal year or not, and as the log of total investment value. In both cases, we do not find systematic evidence that higher-than-expected temperatures reduce firms’ quantity of investments in LMCs (Table 2, rows 4-5). In fact, small and medium firms exhibit strikingly little heterogeneity by income level in both measures of investment relative to the patterns observed in revenues, labor productivity, and wages. Consistent with the decline in labor productivity, however, we find that higher temperature deviations tend to reduce the innovative capacity of small and medium-size firms in LMCs who are less likely to introduce new products or processes in those years (Table 2, row 6).

Notably, temperature deviations do not affect the energy intensity of small or medium firms in LMCs—defined as total costs of energy inputs over sales (Table 2, row 7). The absence of temperature effects on energy intensity despite their negative impact on labor productivity suggests that either many small and medium-size firms in LMCs have not invested in energy-consuming cooling solutions such as air conditioning or that their ability to use them effectively is constrained by the lack of reliable electricity access. However, higher than usual temperatures raise the energy intensity of large firms in LMCs. This is consistent with the weaker impact of heat on large firms’ labor productivity and suggests that large firms in LMCs tend to invest in energy-consuming adaptation solutions such as air conditioning. Similarly, large firms in UMCs and HCs are more energy intensive when they face higher temperatures relative to long-term trends while small and medium firms do not, potentially pointing to the importance of firm-level constraints to adaptation.

Taken together, the results suggest that higher than average temperatures lead to an economically large decline in labor productivity in LMCs causing revenue losses for firms. The decline in labor productivity also leads to cuts in labor compensation and lower willingness or ability to experiment with new products and production processes. Large firms in LMCs 19
seem to be able to reduce the adverse impact on heat on their workers’ productivity at the cost of higher energy intensity, but such adaptation is insufficient to completely mitigate the negative effects of high temperatures.

# **6.3 Differences in impacts depending on firm characteristics and sectors**

Although firm size empirically accounts for significant differences in firm responses to temperature, a firm’s size is the result of a host of other firm characteristics. Studying these underlying characteristics allows us to better understand which factors may drive firm responses to rising temperatures. For instance, studies like Bettarelli et al. (2024) suggest that young firms can be more exposed to climate change as they may face tighter credit constraints hindering their capacity to invest in adaption. Export-orientation further signals firms’ ability to compete in international markets and makes them less dependent on domestic demand shocks due to climate change. We replace firm size with these measures of underlying firm characteristics in Equation 1 to better understand which characteristics are most strongly associated with flatter temperature response functions for firms in different income categories.

The top panel of Figure 2 shows that young firms (blue) in LMCs are more affected by higher-than-expected temperatures than older firms, especially for relatively smaller temperature differences. A one standard deviation increase in temperature differences reduces revenues by 31% for firms in the bottom decile of age in LMCs while only reducing revenues by 15% for firms in the top decile of age in LMCs (Table 3, row 1). In UMcs and HCs, there are no significant differences in temperature response functions by firm age.

Temperature shocks in LMCs also disproportionately affect the performance of firms that export less relative to those that export most of their output. Qualitatively, firms in the bottom decile of exports in LMCs are less resilient to local temperature shocks than those in the top decile, although differences are not statistically significant (Figure 2, bottom 20
panel). However, even firms in LMCs that sell almost all their output in international markets experience substantial negative effects, with a one standard deviation increase in temperature differences reducing revenues by 17.1%, labor productivity by 12.3%, and wages by 8.4% (Table 4). The results imply that higher temperatures disrupt the production process of firms (supply side) rather than the (local) demand for their products given that firms selling their product in international markets are not affected by demand swings from local temperature variations in their home country. The finding is consistent with our documented impact of heat on labor productivity.

Next, we explore differences across economic activities in the impact of temperature shocks, using information on the economic sector (ISIC rev. 3.1) in which firms operate. First, we distinguish between firms operating in manufacturing and services. The top panel of Figure 3 shows no significant differences in the impact of temperature differences beteween manufacturing and services firms in LMCs: revenues for both decline significantly as a result from higher-than-expected-temperatures within a fiscal year. Among UMCs and HCs, manufacturing firms respond better to temperature shocks. For high-income countries which have relatively low average temperatures, Figure 3 reveals that the positive effect of temperature shocks is driven entirely by manufacturing firms, which could be explained by the adverse impact of cold days on manufacturing in some countries (see Table A2 for results on other firm performance metrics).

We also use classifications in the literature on heat-sensitive sectors such as transport, construction, or utilities given the intensity of specific tasks more exposed to heat (see Graff Zivin and Neidell (2014) and Addoum, Ng, and Ortiz-Bobea (2020), for more details). We follow this classification to test the effect of temperature shocks on corporate revenues in heat- and non-heat-sensitive sectors across country income groups (Figure 3, bottom panel). We find that a 1 standard deviation increase in temperature differences in LMCs reduces firms’ revenues by 43% in heat-sensitive sectors while the effect is negative (12.5%) but not statistically significant at for firms in non-heat-sensitive sectors (Table 5). Large temperature 21
deviations exceeding 1 standard deviation also reduce firms’ performances in heat-sensitive sectors in UMCs whereby firms in both types of sectors in HCs are not affected. The stronger impact of temperature deviations on firms in heat-sensitive sectors corroborates the robustness of our causal interpretation of the results.

Overall, the results highlight that—consistent with recent microeconomic studies—the effect of climate change is not limited to specific aggregate sectors, such as agriculture, construction, or specific manufacturing sectors such as food or textiles. Instead, the impact of temperature changes seems to primarily depend on task-specific exposure to heat across all sectors. Moreover, small, young, or non-exporting firms in developing countries often face stronger constraints to adaptation, making them more vulnerable to the impact of rising temperatures.

# **6.4 How a favorable business environment enhances firms’ resilience to climate change**

The stronger impact of rising temperatures on small and young firms points to binding constraints for those firms to adapt.<sup>6</sup> For example, limited access to affordable finance, high transaction costs, or long waiting times due to cumbersome procedures to get construction permits can limit firms’ investment in resilient buildings or cooling technologies. Deficiencies in labor, health, and safety regulations such as indoor temperature limits or in the implementation/inspections to enforce these can further undermine firms’ adoption of more resilient technology or practices.

The WBES is unique in that it provides information on the business environment, allowing us to directly estimate how it impacts the vulnerability of firms to climate change. We therefore construct four business environment indices measuring (i) access to finance, (ii) business regulations, (iii) the quality of electricity services, and (iv) security. The indices

> 6Burke et al. (2024), for example, suggests that the ability of firms to cope with climate shocks is strongly mediated by the business environment in which a firm operates.

22
combine questions on managers’ assessment of different business environment obstacles, use of informal sources to finance working capital, lack of an account or line of credit, power outages, and losses due to theft or vandalism, among others (see Appendix A2 for a complete description of the variables and the construction of the indices).

The results suggest that favorable business environments enhance economic resilience to rising temperatures in LMCs. Specifically, a one standard deviation temperature shock reduces the revenues of firms with limited access to finance in LMCs by 30%, compared to just 23% for firms with high access to finance (Table 6). Firms with access to more affordable financing only see revenues decline for large shocks beyond 1.5 degrees Celsius above historical averages (Figure 4, top panel). Similarly, a one standard deviation temperature shock reduces firms by 32% if they face cumbersome business regulations and only by 21% when facing streamlined regulations (Table 7). The difference is statistically significant and widens for larger temperature shocks, as shown in the bottom panel of Figure 4.

Certain public goods also matter in LMCs and UMCs. The bottom panel of Figure 5 shows that firms reporting lower security in LMCs and UMCs, meaning that they operate under unsafe conditions and are more exposed to theft or vandalism, are disproportionally more vulnerable to temperature shocks. A one standard deviation temperature difference reduces firm revenues by 35% for firms in the bottom decile of our security index in LMCs relative to just 17% for firms in the top decile of the security index (Table 8). By contrast, we do not find evidence that firms reporting less reliable electricity services are more severely impacted by temperature shocks in any income category (Figure 5, top panel and **??** ).

As countries’ income increases, the differences between business environment regimes (e.g., low vs. high access to finance) generally become less clear-cut and sometimes only emerge among large temperature shocks. Overall, however, temperature response functions suggest that a favorable business environments are associated with economies that are more resilient to climate shocks.

23
# **7 Conclusion**

We merge granular satellite weather data with unique global firm-level data to examine the impact of rising temperatures on firms’ performance, adaptation capacity, and the policy constraints limiting their ability to adapt. Using standardized microdata from the World Bank’s Enterprise Survey covering 134 countries from 2008 to 2023 and over 160,000 firms— more than half of which are located in low and lower-middle income countries—we evaluate the marginal effects of weather deviations from historical trends on firm revenues. We explore transmission channels such as firm investments, labor productivity, wages, energy intensity, and innovation, and assess how firms’ characteristics and the policy constraints they face affect their ability to adapt to rising temperatures.

We find significant negative impacts of higher-than-average temperatures on corporate revenues among firms in low- and lower-middle-income countries. Small and medium-sized firms are especially vulnerable to rising temperatures, with revenues dropping by 12% when annual average temperatures exceed the historical average by 0.5C. The effect is driven by lower labor productivity, also reducing the compensation of labor. Younger firms and firms in heat-sensitive sectors are more severely affected, supporting our causal interpretation. In low and lower-middle income countries, firms with limited access to finance, burdensome regulations, and lower security are more susceptible to temperature increases, further undermining the economy’s resilience to climate change.

Taken together, our results highlight two novel facts about economic resilience to rising heat. First, the performances of most manufacturing and services firms in low- and low-middle income countries has been affected by rising temperatures, with substantial differences in the degree of vulnerabilities depending on firms’ specific characteristics. Second, local policy constraints including finance, business regulation, and the quality of public goods are associated with firms’ ability to adapt to climate change. This suggests that poor business environments may be undermining developing countries’ economic resilience. Our results underline the importance of designing local context-specific policies to support climate adaptation in 24
the private sector. Moving forward, it seems critical to better understand how such constraints affect specific adaptation technologies or organizational practices that enable firms to adjust to rising temperatures which may be pursued by future research once better data on firm technology adoption become available.

25
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# **9 Tables**

Table 1: Correlations between Temperature Differences and Firm Characteristics

||(1)<br>Temp. Diference|
|---|---|
|Age|-0.000<br>(0.000)|
|Part of Larger Firm|0.010***<br>(0.003)|
|Managerial Experience|-0.000<br>(0.000)|
|Pct. Owned Private Domestic|0.000<br>(0.000)|
|Pct. Owned Private Foreign|0.000<br>(0.000)|
|Pct. Owned Govt.|-0.000<br>(0.000)|
|Pct. Owned Other|0.000<br>(0.000)|
|Female Top Manager|-0.006**<br>(0.003)|
|No. Employees|-0.000<br>(0.000)|
|Long-term Mean Temperature|-0.009***<br>(0.002)|
|_N_|137033|

_Note:_ Correlations come from a regression of average annual temperature differences on the listed firm characteristics. We report standard errors clustered at the WBES strata level in parenthe* ** ses. _p <_ 0 _._ 10, _p <_ 0 _._ 05, ***, _p <_ 0 _._ 01.

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Table 2: Effect of Average Temperature Difference on Firm Performance, Accounting for Heterogeneity in GDP and Firm Size

||Small|Small|Small|Medium|Medium|Medium|Large|Large|Large|
|---|---|---|---|---|---|---|---|---|---|
||LIC+LMC|UMC|HIC|LIC+LMC|UMC|HIC|LIC+LMC|UMC|HIC|
|Log(Revenue)|-0.226<sup>_∗∗_</sup>|-0.028|0.123<sup>_∗∗_</sup>|-0.202<sup>_∗∗_</sup>|-0.015|0.141<sup>_∗∗_</sup>|-0.137<sup>_∗_</sup>|0.083<sup>_∗_</sup>|0.245<sup>_∗∗_</sup>|
||(0.065)|(0.037)|(0.055)|(0.06)|(0.036)|(0.054)|(0.072)|(0.049)|(0.061)|
|Log(Rev. per worker)|-0.174<sup>_∗∗_</sup>|0.004|0.139<sup>_∗∗_</sup>|-0.153<sup>_∗∗_</sup>|0.005|0.136<sup>_∗∗_</sup>|-0.113<sup>_∗∗_</sup>|0|0.101<sup>_∗∗_</sup>|
||(0.046)|(0.026)|(0.039)|(0.043)|(0.026)|(0.039)|(0.048)|(0.032)|(0.041)|
|Log(Avg. Wage)|-0.091<sup>_∗∗_</sup>|-0.002|0.065<sup>_∗∗_</sup>|-0.084<sup>_∗∗_</sup>|-0.005|0.061<sup>_∗_</sup>|-0.091<sup>_∗∗_</sup>|-0.032|0.025|
||(0.037)|(0.021)|(0.033)|(0.034)|(0.021)|(0.033)|(0.04)|(0.027)|(0.035)|
|Invest (Binary)|-0.004<sup>_∗∗_</sup>|-0.002|-0.002|-0.003|-0.003|-0.002|-0.004|-0.004<sup>_∗∗_</sup>|-0.003|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|
|Log(Investment)|-0.094|-0.107<sup>_∗_</sup>|-0.117|-0.104|-0.092|-0.083|-0.161|0.03|0.147<sup>_∗_</sup>|
||(0.117)|(0.061)|(0.087)|(0.109)|(0.06)|(0.086)|(0.113)|(0.07)|(0.089)|
|Innovation|-0.024<sup>_∗_</sup>|-0.012<sup>_∗_</sup>|-0.004|-0.021<sup>_∗_</sup>|-0.011|-0.004|-0.008|-0.004|-0.002|
||(0.013)|(0.007)|(0.011)|(0.012)|(0.007)|(0.011)|(0.013)|(0.009)|(0.012)|
|Log(Energy Intensity)|0.023|-0.003|-0.023|0.036|0.015|-0.005|0.165<sup>_∗∗_</sup>|0.161<sup>_∗∗_</sup>|0.126<sup>_∗∗_</sup>|
||(0.053)|(0.034)|(0.054)|(0.049)|(0.034)|(0.053)|(0.058)|(0.044)|(0.058)|

Notes: Each row represents a different regression of a given firm performance variable on the model specified in equation (1). Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. We evaluate the estimated model at average firm size and GDP per capita within each group of firms listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs and LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.<sup>_∗_</sup> (p<0.10),<sup>_∗∗_</sup> (p<0.05)
Table 3: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Age

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.309|-0.149|0.011|-0.066|0.268|0|
||(0.076)|(0.084)|(0.047)|(0.052)|(0.069)|(0.07)|
|Log(Rev. per worker)|-0.154|-0.151|0.033|-0.047|0.184|0.037|
||(0.046)|(0.049)|(0.028)|(0.029)|(0.04)|(0.039)|
|Invest (binary)|-0.004|-0.002|-0.004|-0.001|-0.003|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.197|0.02|-0.049|-0.082|0.069|-0.165|
||(0.124)|(0.14)|(0.07)|(0.08)|(0.097)|(0.099)|
|Log(Avg. wage)|-0.109|-0.048|-0.008|-0.008|0.074|0.025|
||(0.037)|(0.039)|(0.022)|(0.025)|(0.033)|(0.034)|
|Log(Capacity Util)|0.009|0.023|-0.008|0.016|-0.022|0.01|
||(0.018)|(0.019)|(0.011)|(0.012)|(0.016)|(0.016)|
|Log(Energy Intensity)|0.034|0.104|-0.027|0.116|-0.076|0.125|
||(0.053)|(0.057)|(0.037)|(0.038)|(0.056)|(0.055)|
|Innovation|-0.033|-0.001|-0.014|-0.004|0.001|-0.007|
||(0.013)|(0.014)|(0.008)|(0.008)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and age. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of firm age and columns labeled “high” evaluate the estimated model at the 90th percentile of firm age within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

38
Table 4: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Percent Direct Exports

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.249|-0.171|-0.011|0.031|0.18|0.193|
||(0.075)|(0.086)|(0.045)|(0.054)|(0.068)|(0.072)|
|Log(Rev. per worker)|-0.156|-0.123|-0.002|0.017|0.121|0.13|
||(0.044)|(0.049)|(0.026)|(0.03)|(0.038)|(0.04)|
|Invest (binary)|-0.003|-0.005|-0.003|-0.004|-0.003|-0.004|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.098|-0.158|-0.076|-0.084|-0.058|-0.024|
||(0.122)|(0.131)|(0.067)|(0.074)|(0.095)|(0.098)|
|Log(Avg. wage)|-0.099|-0.084|-0.02|-0.005|0.043|0.059|
||(0.035)|(0.04)|(0.021)|(0.024)|(0.033)|(0.034)|
|Log(Capacity Util)|0.007|0.043|-0.001|0.021|-0.008|0.002|
||(0.017)|(0.018)|(0.01)|(0.011)|(0.015)|(0.016)|
|Log(Energy Intensity)|0.053|0.093|0.029|0.06|0.01|0.033|
||(0.051)|(0.057)|(0.035)|(0.039)|(0.054)|(0.056)|
|Innovation|-0.022|-0.013|-0.01|-0.001|0|0.009|
||(0.013)|(0.014)|(0.007)|(0.008)|(0.011)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and the percentage of sales that are direct exports. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the percentage of direct exports and columns labeled “high” evaluate the estimated model at the 90th percentile of the percentage of direct exports within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

39
Table 5: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Sector Heat Sensitivity

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||High|Low|High|Low|High|Low|
|Log(Revenue)|-0.429|-0.125|-0.106|0.061|0.154|0.21|
||(0.101)|(0.104)|(0.065)|(0.06)|(0.097)|(0.092)|
|Log(Rev. per worker)|-0.249|-0.065|-0.047|0.046|0.116|0.136|
||(0.062)|(0.057)|(0.038)|(0.033)|(0.056)|(0.049)|
|Invest (binary)|-0.004|-0.002|-0.001|-0.005|0.001|-0.007|
||(0.002)|(0.003)|(0.002)|(0.002)|(0.004)|(0.004)|
|Log(Investment)|-0.328|0.058|-0.044|-0.077|0.184|-0.186|
||(0.186)|(0.159)|(0.104)|(0.086)|(0.152)|(0.118)|
|Log(Avg. wage)|-0.145|-0.037|-0.024|0.005|0.074|0.038|
||(0.045)|(0.053)|(0.029)|(0.028)|(0.049)|(0.042)|
|Log(Capacity Util)|0.06|-0.025|0.037|-0.02|0.018|-0.016|
||(0.027)|(0.022)|(0.018)|(0.012)|(0.029)|(0.018)|
|Log(Energy Intensity)|0.15|-0.025|0.116|-0.047|0.089|-0.065|
||(0.072)|(0.067)|(0.049)|(0.046)|(0.077)|(0.072)|
|Innovation|-0.003|-0.043|-0.004|-0.018|-0.005|0.002|
||(0.016)|(0.018)|(0.009)|(0.01)|(0.016)|(0.016)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and whether a firm is in a heat-sensitive industry. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model for firms not in heat sensitive sectors and columns labeled “high” evaluate the estimated model for firms in heat sensitive sectors within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

40
Table 6: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Access to Finance

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.295|-0.229|0.009|-0.035|0.253|0.122|
||(0.078)|(0.086)|(0.049)|(0.052)|(0.072)|(0.073)|
|Log(Rev. per worker)|-0.171|-0.138|0.032|-0.023|0.196|0.069|
||(0.047)|(0.05)|(0.029)|(0.029)|(0.042)|(0.04)|
|Invest (binary)|-0.002|-0.006|-0.001|-0.005|-0.001|-0.004|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.13|-0.141|-0.023|-0.116|0.063|-0.096|
||(0.134)|(0.136)|(0.08)|(0.078)|(0.107)|(0.1)|
|Log(Avg. wage)|-0.068|-0.125|0.024|-0.048|0.099|0.014|
||(0.038)|(0.041)|(0.024)|(0.025)|(0.036)|(0.035)|
|Log(Capacity Util)|-0.013|0.051|-0.019|0.028|-0.024|0.009|
||(0.019)|(0.021)|(0.012)|(0.012)|(0.017)|(0.016)|
|Log(Energy Intensity)|0.082|0.037|0.027|0.047|-0.018|0.054|
||(0.054)|(0.058)|(0.038)|(0.039)|(0.058)|(0.056)|
|Innovation|-0.013|-0.031|-0.004|-0.017|0.003|-0.006|
||(0.013)|(0.014)|(0.008)|(0.009)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of access to finance. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the access to finance index and columns labeled “high” evaluate the estimated model at the 90th percentile of the access to finance index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

41
Table 7: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Regulatory Index

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.323|-0.211|-0.052|-0.001|0.166|0.168|
||(0.08)|(0.084)|(0.05)|(0.051)|(0.073)|(0.072)|
|Log(Rev. per worker)|-0.169|-0.143|-0.009|0.004|0.119|0.123|
||(0.046)|(0.05)|(0.029)|(0.029)|(0.041)|(0.04)|
|Invest (binary)|-0.004|-0.003|-0.004|-0.001|-0.005|0|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.049|-0.214|0.033|-0.164|0.098|-0.124|
||(0.131)|(0.14)|(0.076)|(0.079)|(0.105)|(0.102)|
|Log(Avg. wage)|-0.098|-0.08|-0.022|-0.002|0.039|0.061|
||(0.039)|(0.039)|(0.025)|(0.024)|(0.036)|(0.034)|
|Log(Capacity Util)|0.03|-0.004|0.009|-0.002|-0.008|0|
||(0.019)|(0.02)|(0.012)|(0.012)|(0.017)|(0.017)|
|Log(Energy Intensity)|0.055|0.071|0.036|0.043|0.021|0.021|
||(0.056)|(0.057)|(0.039)|(0.038)|(0.057)|(0.056)|
|Innovation|-0.026|-0.018|-0.016|-0.008|-0.008|0.001|
||(0.013)|(0.014)|(0.008)|(0.008)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of regulation. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the regulatory index and columns labeled “high” evaluate the estimated model at the 90th percentile of the regulatory index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

42
Table 8: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Security Index

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.174|-0.348|0.054|-0.102|0.238|0.096|
||(0.085)|(0.084)|(0.053)|(0.051)|(0.074)|(0.072)|
|Log(Rev. per worker)|-0.07|-0.23|0.058|-0.06|0.162|0.076|
||(0.051)|(0.048)|(0.031)|(0.029)|(0.042)|(0.04)|
|Invest (binary)|0|-0.006|0|-0.006|0.001|-0.005|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.175|-0.083|-0.062|-0.075|0.029|-0.069|
||(0.14)|(0.132)|(0.083)|(0.075)|(0.108)|(0.1)|
|Log(Avg. wage)|-0.017|-0.154|0.029|-0.054|0.067|0.026|
||(0.041)|(0.04)|(0.026)|(0.024)|(0.036)|(0.034)|
|Log(Capacity Util)|-0.002|0.025|-0.007|0.014|-0.011|0.004|
||(0.021)|(0.02)|(0.013)|(0.012)|(0.018)|(0.017)|
|Log(Energy Intensity)|-0.022|0.135|-0.016|0.104|-0.012|0.08|
||(0.06)|(0.057)|(0.04)|(0.039)|(0.058)|(0.056)|
|Innovation|-0.029|-0.014|-0.016|-0.008|-0.007|-0.003|
||(0.015)|(0.014)|(0.009)|(0.009)|(0.013)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of security. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the security index and columns labeled “high” evaluate the estimated model at the 90th percentile of the security index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

43