## wpiea2022103-print-pdf - 2019. We find that an increase in climate change vulnerability is positively associated with

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---

### Key findings
- An increase in climate change vulnerability is positively associated with rising income inequality.
- A one percentage point increase in climate vulnerability is associated with a deterioration of 1.5 percent in income inequality.
- Heterogeneity by country group:
  - In advanced economies, climate change vulnerability has no statistically significant effect on income distribution.
  - In developing countries, the coefficient on climate vulnerability for net income inequality is seven times greater and statistically significant at the 1 percent level.
- Dynamic evidence (panel VAR):
  - A one-standard-deviation shock to climate vulnerability raises income inequality immediately and the positive effect grows over time.
  - A one-standard-deviation shock to climate resilience reduces income inequality immediately and the negative effect grows over time.
- Robustness: results are robust to alternative estimation methods (static panel and panel VAR) and alternative measures of income inequality (gross and net Gini).
- One cited study estimates that climate change could push an additional 68 to 135 million people into poverty.

### Background and context
- The global annual average surface temperature has already increased by about 1.1 degrees Celsius (°C) compared with the preindustrial average during 1850–1900.
- Extreme weather events amplified by this warming are projected to intensify over the next century, with the global mean temperature increase by as much as 4°C over the next century (IPCC 2007, 2014, 2019; 2021; Stern 2007).
- The economic consequences of climate change affect financial and fiscal stability, long-run growth prospects, and income distribution; vulnerability depends on economy size and composition, resilience of institutions and physical infrastructure, and capacity for mitigation and adaptation.

### Data and empirical results
- Sample and period: panel of 158 countries over the period 1995–2019 using an unbalanced annual panel.
- Data sources:
  - Income inequality from the Standardized World Income Inequality Database (SWIID v9.1).
  - Climate vulnerability and resilience from ND-GAIN (GDP-adjusted vulnerability index and resilience index).
- Main empirical result (baseline fixed-effects regressions):
  - "An increase in climate vulnerability is associated with a statistically significant deterioration in income inequality."
- Baseline coefficient estimates (selected):
  - Ln(vulnerability) (t-1): 0.012*** (Gross Gini specification) and 0.015*** (Net Gini specification) in Table 1.
  - Ln(resilience) (t-1): -0.003*** (Gross Gini) and -0.002 (Net Gini) in Table 1.
- Selected regression diagnostics (Table 1):
  - Observations: 1,241 for two specifications; 874 for two other specifications.
  - R-squared: 0.975, 0.987, 0.986, 0.989 (across the four baseline specifications shown).
- Selected subgroup results (Table 2):
  - Advanced and developing country sub-samples reported separately with observations 561 (advanced) and 680 (developing) in some specifications; other columns show 59 and 815 observations for alternate splits.
  - R-squared values in subgroup regressions range from 0.962 to 0.999 in reported columns.

### Conceptual and empirical framework
- Conceptual points:
  - Climate refers to a distribution of weather outcomes for a given location, and climate change describes environmental shifts in the distribution of weather outcomes toward extremes.
  - Climate risks create distributional effects through three channels: differential exposure, larger fractional losses for poorer households/countries, and lower adaptive capacity for poorer households/countries.
  - The relationship between climate change and income distribution is framed as a negative feedback loop where the poor are more exposed and lose a greater fraction of income and wealth.
- Empirical approach:
  - Baseline static model: Gini_it = β * ClimateIndex_it + γ X_it + country and time fixed effects + error, with robust standard errors clustered at the country level.
  - Dynamic model: panel VAR (first-order) with Helmert transformation and GMM estimation; IRFs plotted with 90 percent confidence bands using Cholesky decomposition.
- Controls include: real GDP per capita, real GDP growth, consumer price inflation, terms-of-trade index, trade openness, financial development, population, age dependency, corruption, and other macro-demographic variables.

### Quantitative details and data characteristics
- ND-GAIN indices:
  - Composite indices based on 45 indicators (36 for vulnerability, 9 for resilience).
  - Vulnerability captures exposure, sensitivity, and capacity to adapt across six life-supporting sectors: food, water, health, ecosystem services, human habitat and infrastructure.
  - Resilience (readiness) covers economic, governance and social readiness with nine indicators.
- Sample variation and index statistics:
  - Mean climate resilience: 0.44 over the sample period, varying between minimum 0.24 and maximum 0.70.
  - Climate resilience exhibits variation between minimum 0.12 and maximum 0.81, with a mean value of 40.7 over 1995–2019 (as reported in the text).
- Empirical diagnostics highlighted: high within-sample R-squared values (see Selected regression diagnostics and Selected subgroup results above).

### Policy implications and recommendations
- Overarching implication: climate vulnerability raises income inequality, particularly in developing countries with weaker adaptive capacity; climate resilience can reduce inequality.
- Policy recommendations (explicitly listed in the paper):
  - (i) Implement inclusive development policies consistent with climate mitigation and adaptation objectives.
  - (ii) Improve social safety nets and access to healthcare to increase the poor’s ability to cope with climate shocks.
  - (iii) Enhance physical resilience through smart infrastructure investments.
  - (iv) Strengthen financial resilience with better insurance and financial products.
  - (v) Expand the economy’s production frontier through reforms aimed at higher productivity growth and greater economic diversification.
- Distributional design considerations for climate policy:
  - Account explicitly for impacts on income inequality when designing mitigation and adaptation policies.
  - Avoid relying solely on traditional cost-benefit calculations for adaptation investments as these may favor the wealthy.
  - Design mitigation policies (e.g., carbon taxes, removal of fossil-fuel subsidies) equitably and compensate poor households for energy price increases through direct cash transfers.
  - Use well-targeted cash transfer assistance to vulnerable segments when natural disasters occur.

### Conclusion
- Climate change vulnerability has adverse effects on income inequality after controlling for standard economic and demographic factors.
- The effect is quantitatively meaningful (one percentage point increase in vulnerability → 1.5 percent increase in income inequality) and concentrated in developing countries.
- Building climate resilience and designing equitable mitigation/adaptation policies can mitigate distributional impacts.

### Appendix: List of Countries (selected by region)
- Africa: South Africa, Angola, Botswana, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Congo, Rep., Congo, Dem. Rep., Benin, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea-Bissau, Guinea, Cote d'Ivoire, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Niger, Nigeria, Zimbabwe, Rwanda, Sao Tome and Principe, Seychelles, Senegal, Sierra Leone, Namibia, Eswatini, Tanzania, Togo, Uganda, Burkina Faso, Zambia.
- Americas: United States, Canada, Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Venezuela, RB, Antigua and Barbuda, Bahamas, The, Barbados, Dominica, Grenada, Guyana, Belize, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago.
- Asia: Bangladesh, Bhutan, Brunei Darussalam, Myanmar, Cambodia, Sri Lanka, India, Indonesia, Timor-Leste, Lao PDR, Malaysia, Maldives, Nepal, Palau, Philippines, Thailand, Vietnam, Solomon Islands, Fiji, Kiribati, Vanuatu, Papua New Guinea, Samoa, Tonga, Marshall Islands, Micronesia, Tuvalu, China, Mongolia.
- Europe: United Kingdom, Austria, Belgium, Denmark, France, Germany, San Marino, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, Finland, Greece, Iceland, Ireland, Malta, Portugal, Spain, Turkey, Cyprus, Israel, Belarus, Albania, Bulgaria, Moldova, Russian Federation, Ukraine, Czech Republic, Slovak Republic, Estonia, Latvia, Serbia, Montenegro, Hungary, Lithuania, Croatia, Slovenia, North Macedonia, Bosnia and Herzegovina, Poland, Romania.
- Middle East and Central Asia: Bahrain, Iran, Islamic Rep., Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, Egypt, Arab Rep., Yemen, Rep., Afghanistan, Pakistan, Djibouti, Algeria, Libya, Mauritania, Morocco, Sudan, Tunisia, Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, Uzbekistan.

*Source: wpiea2022103-print-pdf (IMF working paper content, empirical analysis covering 158 countries during 1995–2019 using ND-GAIN and SWIID data).*

### 2019. We find that an increase in climate change vulnerability is positively associated with

### wpiea2022103-print-pdf - 2019. We find that an increase in climate change vulnerability is positively associated with

### Key findings
- An increase in climate change vulnerability is positively associated with rising income inequality.
- Splitting the sample into country groups reveals a considerable contrast:
  - In advanced economies, climate change vulnerability has no statistically significant effect on income distribution.
  - In developing countries, the coefficient on climate change vulnerability is seven times greater and statistically highly significant, due largely to weaker capacity for climate change adaptation and mitigation.
- Climate change could undermine poverty eradication efforts, disproportionately hit the poorest regions, and worsen income inequality within countries.
- One study cited estimates that climate change could push an additional 68 to 135 million people into poverty.

### Background and context
- The global annual average surface temperature has already increased by about 1.1 degrees Celsius (°C) compared with the preindustrial average during 1850–1900.
- Extreme weather events amplified by this warming are projected to intensify over the next century, with the global mean temperature increase by as much as 4°C over the next century (IPCC 2007, 2014, 2019; 2021; Stern 2007).
- The economic consequences of climate change affect financial and fiscal stability, long-run growth prospects, and income distribution; vulnerability depends on economy size and composition, resilience of institutions and physical infrastructure, and capacity for mitigation and adaptation.

### Conceptual note
- Climate refers to a distribution of weather outcomes for a given location, and climate change describes environmental shifts in the distribution of weather outcomes toward extremes.

### Data and indices referenced
- Sources mentioned include SWIID and ND-GAIN (used for climate vulnerability and resilience indices not adjusted for the level of real GDP per capita).

### Academic and metadata
- JEL Classification Numbers: C30; D30; E60; O10; Q54
- Keywords: Income inequality; climate change; vulnerability; resilience
- Author’s E-Mail Address: scevik@imf.org; joaojalles@gmail.com
- Acknowledgment: The authors would like to thank Alfredo Cuevas and the participants of a seminar at the European Department of the International Monetary Fund (IMF) for helpful comments and suggestions.

*Source: wpiea2022103-print-pdf - 2019. We find that an increase in climate change vulnerability is positively associated with*

### 2030. These projections are consistent with evidence from household-level studies showing that Hurricane Mitch

### wpiea2022103-print-pdf - 2030. These projections are consistent with evidence from household-level studies showing that Hurricane Mitch

### Key findings on climate change and income inequality
- Sample and period: panel of 158 countries over the period 1995–2019 using an unbalanced annual panel.
- Data sources: income inequality from the Standardized World Income Inequality Database (SWIID v9.1); climate vulnerability and resilience from ND-GAIN (GDP-adjusted vulnerability index and resilience index).
- Main empirical result (baseline fixed-effects regressions):
  - "An increase in climate vulnerability is associated with a statistically significant deterioration in income inequality."
  - A one percentage point increase in climate vulnerability is associated with a deterioration of 1.5 percent in income inequality.
- Baseline coefficient estimates (selected):
  - Ln(vulnerability) (t-1): 0.012*** (Gross Gini specification) and 0.015*** (Net Gini specification) in Table 1.
  - Ln(resilience) (t-1): -0.003*** (Gross Gini) and -0.002 (Net Gini) in Table 1.
- Heterogeneity by country group:
  - Climate vulnerability has no statistically significant effect on income distribution in advanced economies.
  - For developing countries, the coefficient on climate vulnerability for net income inequality is seven times greater and statistically significant at the 1 percent level.
- Dynamic evidence (panel VAR):
  - A one-standard-deviation shock to climate vulnerability raises income inequality immediately and the positive effect grows over time.
  - A one-standard-deviation shock to climate resilience reduces income inequality immediately and the negative effect grows over time.
- Robustness: results are robust to alternative estimation methods (static panel and panel VAR) and alternative measures of income inequality (gross and net Gini).

### Conceptual and empirical framework
- Conceptual points:
  - Climate risks create distributional effects through three channels: differential exposure, larger fractional losses for poorer households/countries, and lower adaptive capacity for poorer households/countries.
  - The relationship between climate change and income distribution is framed as a negative feedback loop where the poor are more exposed and lose a greater fraction of income and wealth.
- Empirical approach:
  - Baseline static model: Gini_it = β * ClimateIndex_it + γ X_it + country and time fixed effects + error, with robust standard errors clustered at the country level.
  - Dynamic model: panel VAR (first-order) with Helmert transformation and GMM estimation; IRFs plotted with 90 percent confidence bands using Cholesky decomposition.
- Controls include: real GDP per capita, real GDP growth, consumer price inflation, terms-of-trade index, trade openness, financial development, population, age dependency, corruption, and other macro-demographic variables.

### Quantitative details and data characteristics
- ND-GAIN indices:
  - Composite indices based on 45 indicators (36 for vulnerability, 9 for resilience).
  - Vulnerability captures exposure, sensitivity, and capacity to adapt across six life-supporting sectors: food, water, health, ecosystem services, human habitat and infrastructure.
  - Resilience (readiness) covers economic, governance and social readiness with nine indicators.
- Sample variation:
  - Mean climate resilience: 0.44 over the sample period, varying between minimum 0.24 and maximum 0.70.
  - Climate resilience exhibits variation between minimum 0.12 and maximum 0.81, with a mean value of 40.7 over 1995–2019 (as reported in the text).
- Selected regression diagnostics (Table 1):
  - Observations: 1,241 for two specifications; 874 for two other specifications.
  - R-squared: 0.975, 0.987, 0.986, 0.989 (across the four baseline specifications shown).
- Selected subgroup results (Table 2):
  - Advanced and developing country sub-samples reported separately with observations 561 (advanced) and 680 (developing) in some specifications; other columns show 59 and 815 observations for alternate splits.
  - R-squared values in subgroup regressions range from 0.962 to 0.999 in reported columns.

### Policy implications and recommendations
- Overarching implication: climate vulnerability raises income inequality, particularly in developing countries with weaker adaptive capacity; climate resilience can reduce inequality.
- Policy recommendations (explicitly listed in the paper):
  - (i) Implement inclusive development policies consistent with climate mitigation and adaptation objectives.
  - (ii) Improve social safety nets and access to healthcare to increase the poor’s ability to cope with climate shocks.
  - (iii) Enhance physical resilience through smart infrastructure investments.
  - (iv) Strengthen financial resilience with better insurance and financial products.
  - (v) Expand the economy’s production frontier through reforms aimed at higher productivity growth and greater economic diversification.
- Distributional design considerations for climate policy:
  - Account explicitly for impacts on income inequality when designing mitigation and adaptation policies.
  - Avoid relying solely on traditional cost-benefit calculations for adaptation investments as these may favor the wealthy.
  - Design mitigation policies (e.g., carbon taxes, removal of fossil-fuel subsidies) equitably and compensate poor households for energy price increases through direct cash transfers.
  - Use well-targeted cash transfer assistance to vulnerable segments when natural disasters occur.

### Conclusion summary
- Climate change vulnerability has adverse effects on income inequality after controlling for standard economic and demographic factors.
- The effect is quantitatively meaningful (one percentage point increase in vulnerability → 1.5 percent increase in income inequality) and concentrated in developing countries.
- Building climate resilience and designing equitable mitigation/adaptation policies can mitigate distributional impacts.

*Source: IMF working paper content (wpiea2022103-print-pdf), empirical analysis covering 158 countries during 1995–2019 using ND-GAIN and SWIID data.*

### Appendix Table A1. List of Countries

### Appendix Table A1. List of Countries

### Africa
- South Africa, Angola, Botswana, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Congo, Rep., Congo, Dem. Rep., Benin, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea-Bissau, Guinea, Cote d'Ivoire, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali,  Mauritius, Mozambique, Niger, Nigeria, Zimbabwe,  Rwanda, Sao Tome and Principe, Seychelles, Senegal, Sierra Leone, Namibia, Eswatini, Tanzania, Togo, Uganda, Burkina Faso, Zambia

### Americas
- United States, Canada, Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Venezuela, RB, Antigua and Barbuda, Bahamas, The, Barbados, Dominica, Grenada, Guyana, , Belize, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the , Grenadines, Suriname, Trinidad and Tobago

### Asia
- Bangladesh ,Bhutan, Brunei Darussalam, Myanmar, Cambodia, Sri Lanka, India, Indonesia, Timor-Leste, Lao PDR, Malaysia, Maldives, Nepal, Palau, Philippines, Thailand, Vietnam, Solomon , Islands, Fiji, Kiribati, Vanuatu, Papua New Guinea, Samoa, Tonga, Marshall Islands, Micronesia, Tuvalu, China, Mongolia

### Europe
- United Kingdom, Austria, Belgium, Denmark, France, Germany, San Marino, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, Finland, Greece, Iceland, Ireland, Malta, Portugal, Spain, Turkey, Cyprus, Israel, Belarus, Albania, Bulgaria, , Moldova, Russian Federation, Ukraine, Czech Republic, Slovak Republic, Estonia, Latvia, Serbia, Montenegro, Hungary, Lithuania, Croatia, Slovenia, North Macedonia, Bosnia and Herzegovina, , Poland, Romania

### Middle East and Central Asia
- Bahrain, Iran, Islamic Rep., Iraq, Jordan, Kuwait,  Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, Egypt, Arab Rep., Yemen, Rep., Afghanistan, Pakistan, Djibouti, Algeria, Libya, Mauritania, Morocco, Sudan, Tunisia, Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic,  Tajikistan, Turkmenistan, Uzbekistan

*Source: Appendix Table A1. List of Countries (from the supplied PDF content).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022103-print-pdf.pdf_