Rogue Waves: Climate Change and Firm Performance
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Climate change is an existential threat to the global economy and financial markets. There is a large body of literature documenting potential macroeconomic consequences of climate change, but firm-level empirical research on how climate change affects the performance of firms remains scarce. This paper aims to close this gap by empirically investigating the impact of climate change vulnerability on corporate performance using a large panel dataset of more than 3.3 million nonfinancial firms from 24 developing countries over the period 1997–2019. We find that nonfinancial firms operating in countries with greater vulnerability to climate change tend to experience difficulty in access to debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability to climate change. We confirm these findings with alternative measures of climate change vulnerability. Furthermore, partitioning the sample reveals that these effects are significantly greater for smaller firms, especially in high-risk sectors and countries and countries with weaker capacity to adapt to and mitigate the consequences of climate change.

Abstract

Climate change is an existential threat to the global economy and financial markets. There is a large body of literature documenting potential macroeconomic consequences of climate change, but firm-level empirical research on how climate change affects the performance of firms remains scarce. This paper aims to close this gap by empirically investigating the impact of climate change vulnerability on corporate performance using a large panel dataset of more than 3.3 million nonfinancial firms from 24 developing countries over the period 1997–2019. We find that nonfinancial firms operating in countries with greater vulnerability to climate change tend to experience difficulty in access to debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability to climate change. We confirm these findings with alternative measures of climate change vulnerability. Furthermore, partitioning the sample reveals that these effects are significantly greater for smaller firms, especially in high-risk sectors and countries and countries with weaker capacity to adapt to and mitigate the consequences of climate change.

I. Introduction

Climate change already poses one of the most significant systemic risks to the global economy. With the global average surface temperature rising by 1.1 degrees Celsius (°C) compared to the preindustrial average, the frequency and severity of climate shocks have intensified across the world (Figure 1), and these extreme weather events are projected to worsen as the global annual mean temperatures increase by as much as 4°C over the next century (IPCC, 2007; IPCC, 2014; IPCC, 2021; Stern, 2007).2 The socioeconomic consequences of climate change will be felt across the world, but potential vulnerability to weather anomalies depends on the size and composition of economies, the resilience of institutions and physical infrastructure, and the capacity for climate change mitigation and adaption.

Figure 1.
Figure 1.

Weather Anomalies Across the World

Citation: IMF Working Papers 2022, 102; 10.5089/9798400208171.001.A001

Source: NOAA.

There is growing evidence that climate-related shifts in the physical environment have significant macroeconomic consequences (Gallup, Sachs, and Mellinger, 1999; Nordhaus, 2006; Dell, Jones, and Olken, 2012), but research on the firm-level impact of climate change is scarce. Conceptually, there are multiple channels of transmission through which climate change can influence firm performance across different sectors and countries, including economic and financial effects of climate change, economic and financial consequences of climate change adaptation and mitigation policies, as well as effects on political stability. These transmission channels are not necessarily independent of each other, as the impact of climate change may amplify the transmission of risks across all channels. Furthermore, the extent of these effects varies from country to country, depending on the policies implemented—or not taken at all—for climate change adaptation and mitigation. This paper focuses on countries’ exposure to physical risks that correspond to the potential macro-financial losses caused by climate change. However, it should be noted that transition risks related to the process of adjusting toward a low-carbon economy, such as stranded financial asset exposures, can also amount to a sizable burden.

This paper contributes to the literature by analyzing the effects of climate change vulnerability on firm performance in a large panel of more than 3.3 million nonfinancial companies from 24 countries during the period 1997–2019. We take advantage of a new dataset of climate change vulnerability developed by the Notre Dame Global Adaptation Institute (ND-GAIN), while taking into account a broad range of firm characteristics and macroeconomic factors. Empirical results show that climate change vulnerability has a statistically and economically significant impact on various measures of firm performance, including leverage, interest burden, profitability and total factor productivity (TFP). We find that nonfinancial firms operating in countries with greater vulnerability to climate change tend to experience difficulty in access to debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability to climate change. We confirm these findings with alternative measures of climate change vulnerability. Furthermore, partitioning the sample reveals that these effects are significantly greater for smaller firms, especially in high-risk sectors and countries and countries with weaker capacity to adapt to and mitigate the consequences of climate change. The key policy takeaway from this paper is that while climate change is inevitable, policymakers can still strengthen structural and financial resilience to absorb shocks to economic activity and help reduce the financial burden on private firms and create growth opportunities. At the same time, however, it is important to acknowledge the private sector’s critical role in building resilience against and adapting to climate change as well as in efforts aimed at climate change mitigation.

The remainder of this study is organized as follows. Section II provides an overview of the related literature. Section III describes the data used in the analysis. Section IV introduces the salient features of our econometric strategy and presents the empirical results, including a series of robustness checks. Finally, Section V offers concluding remarks with policy implications.

II. A Brief Overview of the Literature

This paper draws from two major threads of the literature—the macroeconomic impact of climate change and determinants of firm performance. First, there is a growing literature on the economic and financial effects of climate-related shifts in the physical environment.3 Starting with Nordhaus (1991; 1992) and Cline (1992), aggregate damage functions have become a mainstay of analyzing the climate-economy nexus. Although identifying the macroeconomic impact of annual variation in climatic conditions remains a challenging empirical task, Gallup, Sachs, and Mellinger (1999), Nordhaus (2006), and Dell, Jones, and Olken (2012) find that higher temperatures result in a significant reduction in economic growth in developing countries. Burke, Hsiang, and Miguel (2015) confirm this finding and conclude that an increase in temperature would have a greater damage in countries that are concentrated in geographic areas with hotter climates. Using expanded datasets, Acevedo et al. (2018), Burke and Tanutama (2019) and Kahn et al. (2019) show that the long-term macroeconomic impact of weather anomalies is uneven across countries and that economic growth responds nonlinearly to temperature. In a related vein, it is widely documented that climate change by increasing the frequency and severity of natural disasters affects economic development (Loyaza et al., 2012; Noy, 2009; Raddatz, 2009; Skidmore and Toya, 2002; Rasmussen, 2004), reduces the accumulation of human capital (Cuaresma, 2010) and worsens a country´s trade balance (Gassebner et al., 2010).

There is scarce, but growing research in terms quantity and intensity on how risks associated with climate change are priced in financial markets.4 Bansal, Kiku, and Ochoa (2016) and IMF (2020) find that the risk of climate change—as proxied by temperature rises—has a negative effect on asset valuations, while Bernstein, Gustafson, and Lewis (2019) show that real estate exposed to the physical risk of sea level rise sell at a discount relative to otherwise similar unexposed properties. Similarly, focusing on the US, Painter (2019) find that counties more likely to be affected by climate change pay more in underwriting fees and initial yields to issue long-term municipal bonds compared to counties unlikely to be affected by climate change. Finally, from a cross-country perspective, Cevik and Jalles (2020; 2021; 2022) show that climate change vulnerability already has a statistically and economically significant impact on the cost of sovereign borrowing, credit ratings, and the risk of debt default, especially in developing countries.

Climate effects on firm earnings and performance are getting an increasing attention from researchers. Ginglinger and Moreau (2019) find that greater climate risk leads to lower leverage in the post-2015 period, i.e., after the Paris Agreement and show that the reduction in leverage related to climate risk is shared between a demand effect (the firm’s optimal leverage decreases) and a supply effect (lenders increase the spreads when lending to firms with the greatest risk). Addoum et al. (2019) find that extreme temperatures significantly impact earnings in over 40 percent of industries in the U.S. and demonstrate bi-directional effects that harm some industries and bring benefits to others. On the global scale, Pankratz et al. (2019) find that an increasing exposure to extremely high temperatures has negative impact on firms’ revenues and operating income. Focusing on a panel of 55 countries, Huang, Kerstein, and Wang (2018) find that climate risk at the country level is associated with lower corporate earnings and higher earnings volatility. In contrast, in this paper we focus on the developing economies, as these countries bear the most burden of the climate change, which is commonly accepted in the literature. Our paper is most closely related to Huang et al. (2018) and Kling et al. (2021) that explore the impact of climate risks on corporate performance and find that extreme weather events are associated with lower and more volatile earnings and cash flows.5

III. Data Overview

We obtain harmonized firm-level financial data from the Orbis database in 24 countries for the period from 1997 to 2019, which provides a comparable coverage of both public (listed) and private (non-listed) firms including small and medium-sized enterprises. However, similar to any other large-scale micro dataset, the Orbis data require careful management to ensure consistency and comparability across firms and countries and over time. First, we select countries with sufficient number of observations by setting a threshold of 10,000 annual observations per country. Second, following the data cleaning principles suggested by Gal (2013) and Kalemli-Özcan et al. (2015), we drop observations where total assets, tangible fixed assets, employment, operating revenue, sales and short-term loans and long-term debt in any given year are missing or negative, and where total assets do not equal to total liabilities and equity. Third, we winsorize the firm-level variables at the 1st and 99th percentile of the distribution in order to minimize the effect of possibly spurious outliers.6 After these steps, we obtain an unbalanced panel of 3,357,471 firms with a total of 20,880,384 firm-year observations from 24 countries during the period 1997–2019.7

Appendix Table A1 and Appendix Table A2 display the distribution of nonfinancial firms across 24 countries and 11 nonfinancial sectors grouped according to the statistical classification of economic activities based on the Nomenclature des Activités Économiques dans la Communauté Européenne (NACE). The majority is concentrated in Russia, Hungary and Romania, accounting for 31.7, 11.3 and 11 percent of nonfinancial firms covered in our sample, respectively. It is important to note that the number of firms covered in the Orbis database varies from one year to another, increasing from less than half percent in 1997 to 10 percent in 2016 onwards (Appendix Table A3). In terms of sectoral coverage, the dataset is based on the NACE classification of economic activities and covers nonfinancial sectors excluding agriculture, public administration and defense, activities of extraterritorial organizations and bodies, and activities of households as employers and for own use. Most of the firms in the sample operate in the retail and wholesale trade sector, accounting for over a third of the sample size, followed by administrative and professional activities with 13.9 percent and manufacturing with 13.3 percent.

Descriptive statistics of all variables for the entire sample are presented in Appendix Table A4. Our firm-level dependent variables are (i) corporate leverage (measured by the ratio of total debt to total assets in the previous period), (ii) interest burden (measured by the ratio of interest payments in the current period scaled by total debt at the end of the previous year), (iii) profitability (measured by the ratio of profit before taxes to total assets in the preceding period), and (iv) TFP (estimated using the Levinsohn and Petrin (2003) approach). We include several key firm characteristics, such as firm age (measured by the log of years since establishment), firm size (measured as the logarithm of total assets), cash flow (measured by the ratio of cash flow to total assets), and asset tangibility (measured by tangible fixed assets to total assets).

The main explanatory variable of interest is climate change vulnerability as measured by the ND-GAIN index, which capture a country’s overall susceptibility to climate-related disruptions.8 To assess a country’s vulnerability to climate change, the ND-GAIN index takes into account six life-supporting sectors including food, water, health, ecosystem services, human habitat, and infrastructure. Within each sector, six indicators are evaluated from three components: the exposure of the sector to climate-related or climate-exacerbated hazards, the sensitivity of that sector to the impacts of the hazard, and the adaptive capacity of the sector to cope or adapt to these impacts. An important advantage of the ND-GAIN climate change vulnerability index is that it not only considers the physical factors of a country, such as geographic locations and physical climate impact that contribute to vulnerability externally, but also accounts for a country’s degree of dependency on sectors that are climate sensitive, as well as the ability of the economy to mitigate potential damages during and after those negative climate shocks.

For each variable in ND-GAIN data, raw data are scaled into scores ranging from 0 to 1 to facilitate the comparison among countries. Scaling is based on reference points using a formula for the vulnerability indicator: the vulnerability score is then calculated by first taking the arithmetic mean of 6 constituent indicators for each sector, and then equally weighting across 6 sectors. Since the ND-GAIN climate change vulnerability index tends to be correlated with macroeconomic variables, such as real gross domestic product (GDP) or the human development index (HDI), we use a version of the climate change vulnerability index adjusted for the level of income. This version of the climate change vulnerability index is calculated by subtracting a country’s measured climate change vulnerability from its expected value based on the regression of climate change vulnerability and real GDP. As a result, the correlation between the GDP-adjusted climate change vulnerability index and real GDP or the HDI becomes statistically insignificant.9

Figure 2 shows the time profile and box-whisker plots for the vulnerability index for the entire sample and income group, respectively. We can observe that vulnerability to climate change shocks has been declining, particularly since the early 2000s. It is also clear from the data that this decline is primarily driven by advanced economies that have been becoming less vulnerable to climate change over time. Developing countries have demonstrated limited improvement, and the median value of climate change vulnerability has slightly increased over the studied period.

Figure 2.
Figure 2.

Climate Change Vulnerability

Citation: IMF Working Papers 2022, 102; 10.5089/9798400208171.001.A001

Source: ND-GAIN; authors’ calculations.

Aggregate pictures, however, hide marked heterogeneity across countries that should not go unnoticed. Figure 3 compares the GDP-adjusted climate change vulnerability index in 1995 with that in 2019. We can see that the situation in North America, Europe Russia, Australia has improved, while South Asia and South America experienced an increase in vulnerability over the past two decades. Sub-Saharan Africa remained relatively unchanged over the studied period. It is important to highlight that the time-series variation in the ND-GAIN indices reflect the changes in countries’ levels of vulnerability (which are not necessarily forward looking), not from the changes in the projected vulnerability to physical risks associated with climate change.

Figure 3.
Figure 3.

Climate Change Vulnerability Across the World in 1995 vs. 2019

Citation: IMF Working Papers 2022, 102; 10.5089/9798400208171.001.A001

Note: Values of the GDP-adjusted ND-GAIN climate change vulnerability index are plotted. Light colors correspond to improvement in the vulnerability index, darker – to its deterioration. Source: ND-GAIN.

Firm-level data extend over a long period, covering economic booms and downturns. This coverage of different stages of the business cycle enriches the empirical analysis, but also necessitates the inclusion of country-specific information. Following the literature, we introduce a set of control variables, including the Human Development Index (HDI), real GDP growth, financial development as measured by domestic credit to the private sector as a share of GDP, trade openness as measured by the share of international trade in GDP, inflation as measured by the consumer price index (CPI), and average surface temperature in a given year. To bettercapture the level of economic development, we prefer the multidimensional HDI, which also includes per capita income in PPP-adjusted US dollars, instead of the commonly used standalone value of real GDP per capita.10 These statistics are assembled from the World Bank’s World Development Indicators (WDI) and the United Nations Development Program (UNDP) database.

There are large variations in firm performance—as measured by leverage, profitability, interest burden, and productivity—and key firm characteristics used in the analysis across sectors and type of firms, as well as in macroeconomic, financial and institutional conditions across countries and over time. It is therefore essential to analyze the time-series properties of the data to avoid spurious results by conducting panel unit root tests. We check the stationarity of all variables by applying the Im-Pesaran-Shin (2003) procedure, which is widely used in the empirical literature to conduct a panel unit root test. The results, available upon request, indicate that the variables used in the analysis are stationary after logarithmic transformation or upon first differencing.

IV. Empirical Strategy and Results

In our empirical analysis, we focus on the determinants of corporate performance according to the following specification:

yisct=α1vulct+α2firmisct1+α3macroct+ηi+ηst+ηcs+εisct(1)

in which the subscripts i, s, c, and t denote firm, sector, country, and time, respectively. The dependent variable, y, is leverage, profitability, interest burden and productivity as defined in the previous section. vul is the measure of climate change vulnerability. vul denotes climate change vulnerability—the main variable of interest in our empirical analysis. The term firm is a vector of company-specific control variables, including total assets, cash flow, asset tangibility, and age. The term macro denotes a set of country-specific, including real GDP growth, the HDI, consumer price inflation, trade openness, and financial development.

The ηi coefficient denotes the firm-specific fixed effects capturing time-invariant unobservable factors. The ηst coefficient denotes the set of sector-year fixed effects capturing unobserved time-invariant heterogeneity among firms across sectors, and common shocks to firms belonging to the same sector in a given year. This helps control for aggregate and sectoral demand or policy-induced shocks, as well as cross-sectional dependence among firms in our sample. Furthermore, including sector-year fixed effects allows us to interpret the coefficient on, for example, the leverage ratio as the effect of higher indebtedness relative to a firm’s sector peers at time t. This is an important consideration since some sectors are more highly leveraged than others, with differing investment patterns. The ηcs coefficient does the same for country-sector groups. As a result, without sector-country and sector-year fixed effects, the results would only reflect average investment patterns in more leveraged sectors. Finally, εisct is an idiosyncratic error term. Robust standard errors are clustered at the firm level to account for the fact that observations pertaining to a firm are correlated and thus do not contain as much information as un-clustered errors.

We begin the empirical analysis with the standard fixed effects model, but endogeneity concerns arising from omitted variables and reverse causality prevent making causal statements. We address potential endogeneity concerns due to omitted variables by estimating panel models with firm, sector, country and time fixed effects and by controlling for a plethora of firm and country characteristics. Table 1 presents the results of our baseline estimations with corporate leverage, interest burden, profitability and total factor productivity. We estimate the equations using the standard fixed effects model and include the set of macroeconomic controls, such as HDI, real GDP growth, financial development, trade openness, inflation and average surface temperature in all specifications.

Table 1.

Climate Change and Firm Performance —Baseline Estimations

article image
Note: Robust standard errors clustered at the firm level are reported in brackets. Firm as well sector-country and sector-year fixed effects are included in all specifications. *** p<0.01, ** p<0.05, * p<0.1

Our baseline empirical findings show that the estimated coefficients on firm- and country-level control variables have the expected signs and are also statistically significant. With regards to the main variable of interest, we find that climate change vulnerability has a statistically significant, but economically small effect on corporate leverage, with an estimated coefficient of -0.064, after controlling for macroeconomic factors and average temperature. That means, an increase of 0.01 unit in the climate change vulnerability index is associated with a decline of about 0.06 percentage points in corporate leverage. This may be reflecting that nonfinancial firms in countries with greater vulnerability to climate change experience more constrained access to debt financing due to the reluctance of lenders to supply credit to firms exposed to climate-related risks, especially in sectors with greater vulnerability. Of course, such firms may also proactively limit debt accumulation that could become an operational burden, especially during adverse shocks, and rely more on internal resources and equity financing for new investment projects. This is consistent with our estimations showing a statistically significant and economically large positive coefficient on the interest burden. An increase of 0.01 unit in the climate change vulnerability index is associated with an increase of about 0.57 percent in the cost of borrowing. Similar to findings for the impact of climate change vulnerability on sovereign bonds presented in Cevik and Jalles (2022), nonfinancial firms in countries with greater vulnerability to climate change are also subject to higher intertest rates on average. We also find strong evidence that climate change vulnerability is associated with a stifling effect on corporate profitability and firm-specific total factor productivity. An increase of 0.01 unit in the climate change vulnerability index is associated with a decline of approximately 0.7 percentage points in profitability and about 2.6 percentage points in productivity, which indicate that nonfinancial firms in countries with greater vulnerability to climate change are at a significant disadvantage compared to counties unlikely to be affected substantially by climate change.

To obtain a granular analysis, we split the sample into (i) small and large firms by classifying companies with total assets in the lowest quartile as small and those in the highest quartile as large, and (ii) young and old if its age falls into the bottom or top quarter of the age distribution of all firms operating in the same industry in that year. These results are presented in Appendix Table A8 and A9, respectively. First, with regard to size, we find that the impact of climate change vulnerability is greater on smaller nonfinancial firms than larger enterprises and relative to the baseline coefficient estimate. Second, with regards to age, the magnitude and size of coefficient on climate change vulnerability varies with measures of corporate performance, but younger firms appear to be more adaptive. Therefore, even though some of these specifications suffer from a significant reduction in the sample size due to limited availability, the impact of climate change vulnerability on firm performance is still clear across all specifications.

Some sectors are more exposed and vulnerable to climate change, mostly due to a greater risk of physical damage and severe disruptions to business operations. Following the literature, we define agriculture, mining, construction and transportation as climate vulnerable sectors (Fleming, Kirby, and Ostdiek, 2006; Hsiang, 2010; and Challinor et al., 2014, Huang, Kerstein, and Wang, 2018), and estimate the baseline regression models on a subset of firms operating in these sectors. The results, presented in Table 2, reveal an interesting pattern between climate change vulnerability and corporate performance as measured by leverage and interest burden. In these highly vulnerable sectors, an increase in climate change vulnerability is associated with significantly higher interest burden, even though firms appear to have lower levels of debt. This intriguing pattern, in our view, reflects fewer debt financing opportunities that may be available for firms operating in sectors with greater susceptibility to the threats associated with climate changes. The impact of climate change vulnerability on profitability and productivity, however, appears to be comparable to the benchmark results. We also estimate regressions separately for each sector and present these results in Appendix Table A6, which confirm significant heterogeneity in the effect of climate change vulnerability across nonfinancial sectors.

Table 2.

Climate Change and Firm Performance—Vulnerable Sectors

article image
Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects are included in all regressions as well as sector-year and sector-country fixed effects. *** p<0.01, ** p<0.05, * p<0.1

Following the approach used by Ginglinger and Moreau (2019), we introduce a post-2015 dummy in an effort to explore whether the Paris Climate Accord has reshaped firm behavior with regards to climate change vulnerability. These results, presented in Table 3, suggest that greater climate change vulnerability results in lower corporate leverage after 2015, while the overall effect of climate vulnerability on leverage moves slightly below zero. This could reflect firms’ anticipation of significant costs associated with technological innovations required to meet carbon emission targets and other environmental commitments. Higher levels of debt accumulation, caused by measures for climate change adaptation and mitigation, are likely to result in excessive interest payments in the future, as already indicated by a larger coefficient we find for the period after the Paris Agreement. This also implies that firms operating in more vulnerable countries may need to shift more resources away from production to deal with business disruptions caused by climate change and to undertake additional investments necessary for climate change mitigation. These changes, in turn, could lead to lower firm-level productivity and, combined with additional costs, in lower profitability.

Table 3.

Climate Change and Firm Performance—After the Paris Agreement

article image
Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects are included in all regressions as well as sector-year and sector-country fixed effects. *** p<0.01, ** p<0.05, * p<0.1

We confirm the robustness of our empirical findings by using an alternative measure of climate vulnerability based on the INFORM Global Risk Index that identifies countries at risk of emergencies including climate-related natural disasters (De Groove, Poljansek, and Vernaccini, 2015; UN OCHA, 2020). These results, presented in Appendix Table A10, remain consistent with our baseline estimations and show that the climate risk is strongly related with various measures of firm performance. We estimate our models with the ND-GAIN climate change resilience index, which measures a country’s capacity for climate change adaptation and covers three areas— economic, governance and social readiness—with nine indicators.11 These results, presented in Appendix Table A11, confirm that climate change resilience is associated with measures of firm performance as expected. For example, an improvement in climate change resilience is found to have a positive effect on productivity and profitability at the firm level, while firms operating in more resilient countries have easier access to debt financing at lower interest rates. This is consistent with our findings using the climate change vulnerability index, which indicate constrained access to debt financing at a higher cost of borrowing in countries with greater vulnerability to climate change. Finally, we estimate the models excluding Russia, which accounts for about one-third of nonfinancial firms in the sample. These results, presented in Appendix Table A12, confirm that the baseline results remain unchanged.

V. Conclusion

Climate change already poses one of the most significant systemic risks to the global economy, and extreme weather events are projected to worsen as the global annual mean temperatures increase by as much as 4°C over the next century. There is growing evidence that climate-related shifts in the physical environment have significant macroeconomic consequences—from economic growth to sovereign bonds. This paper contributes to the literature by analyzing the effects of climate change vulnerability on firm-level performance in a large panel of more than 3.3 million nonfinancial companies from 24 countries during the period 1997–2019.

The empirical analysis shows that climate change vulnerability is strongly associated with constrained access to debt financing at a higher cost of borrowing and lower levels of productivity and profitability at the firm level. That is, nonfinancial firms operating in countries with greater vulnerability to climate change tend to experience difficulty in access to debt financing even at higher interest rates, while being less productive and profitable relative to firms in countries with lower vulnerability to climate change. We confirm these findings with alternative measures of climate change vulnerability. Furthermore, partitioning the sample reveals that these effects are significantly greater for smaller firms, especially in high-risk sectors and countries with weaker capacity to adapt to and mitigate the consequences of climate change.

Econometric evidence presented in this paper has clear policy implications to better prepare for and cope with the consequences of climate change, especially in high-risk developing countries. Policymakers need to strengthen structural and financial resilience to absorb shocks to economic activity and help alleviate the financial burden of climate change adaptation and mitigation on private firms. At the same time, however, it is important to acknowledge the private sector’s critical role in building resilience against and adapting to climate change as well as in efforts aimed at climate change mitigation. Accounting for 85 percent of all investments worldwide, private firms also have a significant control over most of the climate-related investment needs in adaptation (such as the location and design of buildings and infrastructure) and mitigation (such as the transition to clean energy and technological solutions for carbon emission reduction).

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Appendices

Appendix Table A1.

Firm Distribution by Sector

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Note: AGR – Agribusiness, MIN – Mining, MFG – Manufacturing, UTI – Utilities, CON – Construction, IT – Information technology, OTH – Other service activities, households, extra territorial bodies, TRD – Wholesale and retail trade, accommodation, TRA – Transport and storage, EST – Real estate, ADM – Professional and administrative activities.
Appendix Table A2.

Firm Distribution by Country

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Appendix Table A3.

Sample Breakdown by Year

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Appendix Table A4.

Definition of Variables

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Appendix Table A5.

Summary Statistics

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Appendix Table A6.

Correlation among Macroeconomic Indicators

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Appendix Table A7.

Climate Change and Firm Performance—By Sector

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Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects as well as year and country fixed effects are included in all regressions. Firm-level and other controls used in the baseline specification are included but not reported for ease of exposition. AGR – Agribusiness, MIN – Mining, MFG – Manufacturing, UTI – Utilities, CON – Construction, IT – Information technology, OTH – Other service activities, households, extra territorial bodies, TRD – Wholesale and retail trade, accommodation, TRA – Transport and storage, EST – Real estate, ADM – Professional and administrative activities. v indicates a vulnerable sector. *** p<0.01, ** p<0.05, * p<0.1
Appendix Table A8.

Climate Change and Firm Performance—By Size

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Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects are included in all regressions as well as sector-year and sector-country fixed effects. Negative adjusted R-squared is a result of low number of observations and a large number of explanatory variables, including the fixed effects. *** p<0.01, ** p<0.05, * p<0.1
Appendix Table A9.

Climate Change and Firm Performance—By Age

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Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects are included in all regressions as well as sector-year and sector-country fixed effects. *** p<0.01, ** p<0.05, * p<0.1
Appendix Table A10.

Climate Change and Firm Performance—Alternative Indicators

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Note: Robust standard errors clustered at the firm level are reported in brackets. Firm fixed effects are included in all regressions as well as sector-year and sector-country fixed effects. *** p<0.01, ** p<0.05, * p<0.1