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Relationship Between Climate Risk and Physical and Organizational Capital

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Abstract

We investigate the relationship between climate risk and corporate investment in physical and organizational capital using an international sample of firms from 39 countries. Our main findings show that climate risk is positively associated with physical capital but is negatively related to organizational capital. We also explore the effects of climate vulnerability on these relationships and find that the positive relationship between climate risk and physical capital is mainly driven by climate-nonvulnerable industries, while the negative relationship between climate risk and organizational capital is principally driven by climate-vulnerable industries. Overall, our findings have significant implications for both domestic and multinational enterprises that engage in long-term investments against the background of climate change.

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Notes

  1. Climate risk is typically categorized into physical risks and transition risks. Physical risks refer to the physical impact of climate change and include catastrophic events such as natural disasters and changing weather patterns. Transition risks, consisting of market, policy and regulation, and reputation risks, refer to the risks related to the transition to a low-carbon economy. We do not differentiate these two types of climate risks in this study.

  2. There is no universally agreed definition of OC. For example, Evenson and Westphal (1995, p. 2237) define OC as “knowledge to combine human skills and physical capital into systems for producing and delivering want-satisfying products”. According to Lev et al. (2009), OC is “the agglomeration of technologies–business practices, processes and designs” that “enables superior operating, investment and innovation performance”. Our definition of organizational capital follows that of Li et al. (2018).

  3. Although we consider both these uncertainties, we do not differentiate between them in our study.

  4. Our study is related to Huang et al. (2018); however, the main difference between our study and Huang et al. (2018) is that Huang et al. (2018) investigate the impact of climate risk on firm performance and financing choices, whereas our study focuses on corporate investment, which has important implications for both firm value and overall economic growth.

  5. We acknowledge that some parts of OC such as R&D expenses are longer term expenditures. However, R&D expenses are more flexible/discretionary than investments in fixed assets and thus likely to be curtailed when managers have difficulty achieving a positive profit growth rate (Baber et al., 1991).

  6. It is important to note that Research and Development (R&D) expenditure is also an important component of firms’ investment. However, we do not consider R&D in our study since missing values account for over 75 percent of all firm-year observations, causing huge estimation bias.

  7. Peters and Taylor (2017) demonstrate that using a different fraction rate of SG&A expenses (\(\theta\)) and depreciation rate (\(\delta\)) do not change their findings.

  8. It is important to note that the score is not simply a vulnerability score. It also stresses the exposure to extreme events, as suggested by Eckstein et al. (2019). In addition, the index score is not all-encompassing but focuses on meteorological events such as storms and floods, and climatological events such as droughts. Some other climate-related events, such as rising sea levels and glacier melting, are not taken into consideration.

  9. The CRI reports each year provide the CRI scores from 2 years prior to the year of the report. For example, the 2019 edition provides the CRI score for year 2017 and the 2018 edition for year 2016.

  10. We do not have access to the 2006 and 2007 editions. There is an increasing trend in coverage of countries, ranging from 130 countries in the 2008 edition to more than 180 countries in the latest editions.

  11. Since both absolute and relative impacts are used to generate the index, we acknowledge that larger countries are likely to have higher climate risk. However, countries with the highest climate risk during the 1998–2017 period based on this index are Puerto Rico, Honduras, and Myanmar. More importantly, to further bolster our findings, we conduct robustness tests based on the relative sub-components of the index, which are not influenced by the size of the economy and the population of a country, and find consistent results.

  12. We also scale the independent variables by the market value of equity and our inferences remain unchanged.

  13. The climate risk measure reported in Table 1 differs from those reported in Huang et al. (2018) in two aspects. First, our measure is reported at the annual level whereas Huang et al. (2018)’s measure is a long-term climate risk measure. Second, our measure covers the period from 2006 to 2017 whereas the long-term measure reported in Huang et al. covers the period from 1993 to 2012. In addition, our sample does not include firms from some of the most affected countries, such as Puerto Rico and Myanmar, due to data unavailability.

  14. For purposes of exposition, throughout this study, we transform CRI so that its estimated coefficient is 1000 times larger.

  15. For brevity, we do not report the results for control variables. In addition, we focus only on the measure of CRI in our cross-sectional analysis. Our results remain unchanged when using the measure of RCRI as the main independent variable.

  16. According to anecdotal evidence as well as Huang et al. (2018), population density is unlikely to correlate with corporate investment, thus satisfying the exclusion criterion. For both instruments, the associated F-statistics derived from the first-stage regressions are well above the suggested critical F-value of 8.96 (for example, the first-stage F-statistic is 486.59 for population density in the PC-climate risk regression model) Therefore, our instrumental variables do not suffer from the weak instrument problem.

  17. Although the relationship between economic growth and urbanization has been well documented in the macroeconomic literature (e.g., Kasman and Durman, 2015), a thorough literature search indicates that the relationship between urbanization and firm-level investment is almost non-existent. In addition, it is important to note that the variations of the urbanization rates in some countries, such as U.S. or Singapore, are so small and negligible over the sample period. Even for developing countries such as India, the change of urbanization rate is rather limited over the sample period.

  18. We test and find that urbanization is not a weak instrument. Again, our instrumental variable does not suffer from the weak instrument problem.

  19. Consistent with our main analysis, we use data from EM-DAT over the period 2006–2017.

  20. The results of sensitivity tests are available upon request.

  21. Our results remain qualitatively unchanged if we use the mean as the cut-off value.

  22. According to Holling (1996), resilience is categorized into engineering resilience and ecological resilience. The former refers to the capability of a system to return to its original state after being disturbed, while the latter stresses the ability to absorb disturbance before it can affect.

  23. The formula to calculate the ND-GAIN score is (Readiness score-Vulnerability score+1)*50. Vulnerability is based on the assessment of six life-supporting sectors to generate six indicators representing the exposure, sensitivity and adaptive capacity for each sector. Readiness consists of nine indictors on economic readiness (1 indicator), governance readiness (4 indicators) and social readiness (4 indicators).

  24. Resilience can also be defined as the opposite of vulnerability. For example, IPCC defines vulnerability as “the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes” (McCarthy et al., 2001). Our results are qualitatively unchanged if we classify countries using the measure of vulnerability.

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Authors and Affiliations

  1. Schulich School of Business, York University, Toronto, Canada

    Kiridaran Kanagaretnam & Lei Zhang

  2. Bauer College of Business, University of Houston, Houston, USA

    Gerald Lobo

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Appendices

Appendix A: Definitions of variables

Variables

Definitions

Measures of climate risk

CRI

Annual Climate Risk Index, which is obtained from Germanwatch’s 2008–2019 editions (for the years 2006–2017), multiplied by (-1). Higher score indicates higher climate risk in the year. Source: Germanwatch (2020)

RCRI

The rank of the CRI. RCRI ranges from 0 to 1. Higher score indicates higher climate risk in the year. Source: Authors’ calculation based on Germanwatch

HighOccur

Indicator variable for the occurrence of natural disasters. Each year HighOccur equals one if a natural disaster that occurs in that year is larger than or equal to the mean value of natural disasters of all countries, and zero otherwise. Source: EM-DAT (2019)

Measures of corporate investment

PC

Physical capital. Measured as capital expenditure (PC) each year divided by total assets (AT). Source: Compustat

OC

Organizational capital. Measured as the stock of organizational capital each year by accumulating a fraction of past SG&A expenses using the following equation:\({OC}_{it}=\left(1-\delta \right){OC}_{it}+\theta \frac{{SG\&A}_{it}}{{CPI}_{jt}}\)

The initial stock of organizational capital is estimated as: \({OC}_{i0}=\theta \frac{{SG\&A}_{i1}}{g+\delta }\)

where \({OC}_{it}\) denotes organizational capital of firm i at time t. \(\delta\) is the depreciation rate of organizational capital. g is the growth rate of firm-level SG&A expenses. \(\theta\) denotes the fraction of SG&A expenditure invested in OC. CPI is consumer price index in country j. OC is scaled by total assets (AT). Source: Authors’ calculation

Firm level variables

AT

Total assets (AT) Source: Compustat

Sgrowth

Percentage change of Sales (SALE). Source: Compustat

CF

Cash flows (OANCF) divided by lagged total assets. Source: Compustat

TQ

Tobin’s q, calculated as the market value of equity plus the book value of assets minus book value of equity plus deferred taxes, all divided by book value of assets. Source: Compustat

Industry level variables

Vulnerability

Indicator variable that equals one for Agriculture (Fama–French Industry Code 1), Business Services (Code 34), Communication (Code 32), Energy [Mines (code 28), Coal (Code 29), and Oil (Code 30)], Food Products (Code 2), Health Care (Code 11), and Transportation (Code 40), and zero otherwise. Source: Compustat

Country-level variables

Common

An indicator variable that equals one if the legal origin is common law, and zero otherwise. Sources: Djankov et al. (2007)

Credit

Measure of creditor right, which is formed by adding (1) whether the country imposes restrictions, such as creditor’s consent or minimum dividends to file for reorganization; (2) whether secured creditors are able to gain possession of their security once the reorganization petition has been approved (no automatic stay); (3) whether secured creditors are ranked first in the distribution of the proceeds that result from the disposition of the assets of a bankrupt firm; and (4) whether the debtor does not retain the administration of its property pending the resolution of the reorganization. The index ranges from 0 to 4. Sources: La Porta et al. (1998) and Djankov et al. (2007)

Legal

Legal is the law enforcement index, which ranges from 0 to 10, with higher values indicating stronger law enforcement. Sources: World Economic Forum (2017)

Investorpro

Measure of strength of investor protection that ranges from 0 (worst) to 10 (best)

Sources: World Economic Forum (2017)

ABF

Measure of easy access to financing from World Economic Forum 2017 Global competitiveness index. It is constructed based on response to the following question: “In your country, how easy is it to obtain a bank loan with only a good business plan and no collateral? [1 = extremely difficult; 7 = extremely easy]” Sources: World Economic Forum (2017)

UA

Measure of Uncertainty Avoidance. Source: House et al. (2004)

FO

Measure of Future Orientation. Source: House et al. (2004)

Inflation

Measure of inflation. Source: The World Bank (2019)

GDPgrowth

Annual growth of total GDP. Source: The World Bank (2019)

GDPpercap

Natural logarithm of GDP per capita. Source: The World Bank (2019)

PopDensity

Population density. Number of people (in 1000) per km2 of land area. Source: The World Bank (2019)

Urbanization

The ratio of urban population to total population. Source: The World Bank (2019)

Awareness

Country-level climate risk awareness. Source: Pugliese and Ray (2009)

Resilience

Country-level resilience, proxied by ND-GAIN score, which is calculated as (Readiness score-Vulnerability score+1)*50. Source: Notre Dame Global Adaptation Initiative (ND-GAIN) (2019)

Appendix B: Sample attrition table

 

Number of Countries

Number of Observations

Starting with all countries and regions in the world

247

 

Dropping countries with no climate risk data

−63

 

Dropping countries with no coverage in Compustat Global

−94

340,376

Dropping firms in regulated industries

 

−57,772

Dropping countries with missing data on country-level variables

−48

 

Dropping firms with missing variables for baseline regression model

 

−107,142

Dropping countries with less than 100 observations

−3

−102

Final sample

39

175,360

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Kanagaretnam, K., Lobo, G. & Zhang, L. Relationship Between Climate Risk and Physical and Organizational Capital. Manag Int Rev 62, 245–283 (2022). https://doi.org/10.1007/s11575-022-00467-0

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  • DOI: https://doi.org/10.1007/s11575-022-00467-0

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