## Annex I. Data description and sources

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---

### Introduction: scope and framing
- Global temperatures have increased significantly over the past-half century and extreme weather events (cold and heat waves, droughts, floods, storms) have intensified, now dominating the disaster landscape in the 21st century.
- Climate change effects are particularly severe for populations of poor countries and even more so in fragile environments, which may be more vulnerable to humanitarian crisis and instability.
- Sub-Saharan Africa (SSA) is highlighted where climate change is expected to push 39.7 million people into poverty by 2050 if no concrete climate and development action takes place (Jafino et al. 2020).
- Fragile states in SSA:
  - Have much higher vulnerability to climate change and natural disasters than other countries (Mason et al., 2015).
  - Heavily rely on climate-dependent agriculture, amplifying physical and livelihood risks.
  - Face weak governance and conflict that increase vulnerability and reduce capacity to respond to climate shocks.
- Climate shocks can have persistent implications for economic growth by reducing agricultural and industrial output through rising temperatures and altered precipitation patterns.

### Mechanisms linking climate change and fragility
- Climate drivers considered:
  - Direct: rainfall, temperature, CO2 emissions.
  - Indirect: demographics, public services, uneven economic development, arable land area, forest area.
- IPCC (2012) identified pathways by which rising temperatures affect health and livelihoods in Africa:
  - (i) erratic precipitation (more extreme and less predictable rainfall);
  - (ii) extreme events (heat waves, tropical cyclones, extreme rainfall, floods, wildfires, droughts);
  - (iii) rising seas (coastal erosion, floods, water-borne diseases, salinization of drinking water and agricultural land).
- Fragility interacts with climate risk:
  - Structural weaknesses and government failure in fragile states reduce capacity to provide basic services and to address climate- and environment-related threats.
  - Climate-related stress can deepen fragility and increase risk of violent conflict (Navone 2021).
  - Empirical evidence cited: Burke et al. (2009) — a 1ºC increase in temperatures led to a surge in civil conflicts by 4.5 percentage points in Africa.

### Rationale for the empirical approach
- Importance of tail behavior:
  - The tails of distributions can be informative for climate-related variables; climate data often display non-normality and heterogeneity across distributions.
  - Quantile regression is used to detect relationships in different parts of the conditional distribution (upper/lower tails) and is therefore suitable for analyzing climate impacts.
- Research objectives and identification strategy:
  - Investigate the impact of temperature anomalies on income per capita growth using panel fixed effects and panel quantile regression methodologies, focusing on fragile states.
  - Reverse the causality question to analyze the impact of economic conditions on climate change via effects on Green House Gas (GHG) emissions growth rate.
  - Use a dynamic-panel model based on twenty fragile states in sub-Saharan Africa covering 1980 to 2019.
  - Provide scenarios to evaluate potential impacts of mitigation policy responses on fragile states in SSA.

### Major empirical findings on temperature, income, and emissions
- Limiting temperature increase to 0.01°C per annum (Paris Agreement scenario) reduces the loss significantly to about 1 percent.
- Okonjo-Iweala (2020) (using Kompas et al. (2018) modeling data):
  - Climate change resulting in a 3°C temperature will decrease Africa’s GDP by as much as 8.6 percent per year after 2100.
  - If climate change is limited to the 1.5°C agreed to in the Paris Agreement, the decrease in GDP will be only 3.8 percent per year after 2100.
- IMF Regional Economic Outlook (2020) for SSA:
  - Economic activity in a given month can decrease by 1 percent when the average temperature is 0.5°C above that month’s 30-year average.
  - This effect is 60 percent higher than the average for emerging market and developing economies in other regions.
  - Medium-term economic activity can decrease by 1 percentage point when one additional drought occurs; this effect is about eight times that in emerging market and developing economies in other regions.
- One third of the world’s droughts occur in SSA and the frequency of storms and floods is growing fast in this region.
- Burke et al. (2015): a strong negative correlation between income and temperature implies hot, poor countries will probably suffer the largest reduction in growth.

### Stylized econometric and modeling results
- Dell et al. (2009) framework: half of the negative short-term effects of temperature are offset in the long run through adaptation.
- Tol (2019): extrapolating short-run weather shock elasticities to long-run climate change is unlikely to yield credible results; climate varies slowly so identification relies on large cross-sections.
- Kompas et al. (2018): CGE modeling rarely captures full global disaggregated intertemporal effects with forward-looking behavior due to computational complexity.

### Key panel estimation results (fragile states in SSA, 1980–2019)
- Panel cointegration (Westerlund): null hypothesis of no cointegration rejected.
- ARDL (long-run, PMG):
  - A 1 percent increase in real GDP per capita increases GHG by .247*** (25 percentage points in text).
  - A 1 percent increase in temperature anomalies increases GHG by .188*** (19 percentage points in text).
  - Technology coefficient: .032***.
  - Error Correction Term: -.199*** (implying a 20 percent speed of adjustment).
- ARDL (long-run, DFE):
  - Real GDP per capita: .849*** (85 percentage points in text).
  - Temperature Anomalies: .295.
  - Error Correction Term: -.145*** (implying a 15 percent speed of adjustment).
- Short-run ARDL coefficients on GHG were generally not significant.
- Panel regression (effects of temperature on income per capita growth):
  - Fixed effects and other specifications: dtemp coefficient = -.024*** (standard error .008).
  - Interpretation reported: a 1°C rise in temperature decreases income per capita growth in fragile states in SSA by −2.4 percentage points (panel regression average estimate).
- Panel quantile regression (distributional effects across 15, 25, 50, 75, 90 percentiles):
  - dtemp coefficients by quantile: 15: -.029***; 25: -.022***; 50: -.02***; 75: -.012***; 90: -.037*** (standard errors reported).
  - Integrating over quantiles: average effect estimate dtemp = -.018*** (standard error .004).
  - Interpretation reported: the average effect across quantiles implies that a 1°C rise in temperature reduces growth of income per capita by −1.8 percentage points.
- Panel quantile regression (effects of income per capita growth on carbon emissions growth):
  - dlrgdp_ca coefficients vary by quantile (examples): 5th: .195***; 50th: .1***; 90th: -.042*.
  - Finding: the impact of GDP per capita growth on emissions is heterogeneous; for the 90th quantile the coefficient is negative and significant, suggesting higher GDP per capita growth could reduce carbon emissions growth for high-emitter countries (consistent with an Environmental Kuznets Curve pattern at high quantiles).

### Robustness checks
- Adding CO2 and interaction terms:
  - Inclusion of dlco2: coefficient .028* (Pooled OLS) with similar dtemp effects (dtemp ≈ -.022*** in robustness specifications).
  - Interaction dtemp_x_dlpop coefficients (robustness): approximately -.632** to -.644** in pooled/fixed/random effect columns (standard errors reported).
- Quantile robustness: inclusion of dtemp_x_dlpop retains negative coefficients at all quantiles (e.g., 15th: -1.048***; 50th: -.242***).

### Carbon tax simulation scenarios and fiscal implications (IMF CPAT and Multiscenario Tool)
- Carbon tax scenarios evaluated: $25, $50, and $75 per ton of GHG (assumed to be reached gradually in 10 years).
- Emission reduction and NDC achievement:
  - For countries with relatively high-emission reduction targets (Central African Republic, São Tomé and Príncipe, Liberia, Mali), a carbon tax of more than $75 per ton would be needed if the carbon tax is the only instrument used.
  - Assuming the 20 countries introduce a carbon tax of $50 and $75 per ton, regional emissions would decrease by 26, and 33 percent on average, respectively.
- Fiscal revenue from carbon taxes (percent of GDP, region average):
  - $25 per ton: additional revenue estimated to be 0.35 percent of GDP.
  - $50 per ton: additional revenue estimated to be 0.66 percent of GDP.
  - $75 per ton: additional revenue estimated to be 0.95 percent of GDP.
  - Marginal revenue gains decline at higher tax rates due to tax base erosion from higher energy prices.
- Uses of carbon tax revenue recommended: compensation for those negatively affected, financing priority spending (health, education, infrastructure, green investment, adaptation), and reducing distortionary taxes.

### Policy implications and recommendations
- Tailored policies recommended for fragile states rather than uniform CO2 controls, taking into account each state’s developmental characteristics and emissions structure.
- Opportunities and challenges for fragile states:
  - Low capacity to propose, legislate, or manage regulations and carbon taxes.
  - If emissions are concentrated in specific sectors or firms, enforcement and collection could be more feasible and politically viable.
- Renewable energy and energy efficiency:
  - Long-run ARDL results suggest that technology and income are positively associated with GHG; increasing renewable energy consumption and higher energy efficiency could potentially reduce GHG emissions in SSA’s fragile states.
- International coordination:
  - Dispersion in needed carbon tax rates reflects cross-country differences in commitment levels and energy sources, indicating a role for stronger international coordination in mitigation targets and support.
- National climate commitments:
  - Fragile states have submitted Nationally Determined Contributions (NDCs); a handful pledged to reduce CO2 emissions by more than 30 percent in 2030 vis-à-vis BAU conditional on international support (Chad, Republic of Congo, Malawi, Zimbabwe).

### Dependent and explanatory variables; control variables and data sources
- Dependent variables:
  - Log of GDP per capita
    - Real GDP at constant 2017 national prices (in mil.2017US$) divided by population
    - Source: PWT 10.0 | Penn World Table | Groningen Growth and Development Centre | University of Groningen (rug.nl)
  - Log of GHG emissions
    - Greenhouse Gas (GHG) Emissions, Total including LUCF- in MtCO2e
    - Source: World | Total including LUCF | Greenhouse Gas (GHG) Emissions | Climate Watch (climatewatchdata.org)
- Explanatory variables:
  - Temperature anomaly
    - The difference from an average, or baseline, temperature. The baseline temperature is computed by averaging 1980-2010 temperature data.
    - Source: Dataset Record: CRU TS4.04: Climatic Research Unit (CRU) Time-Series (TS) version 4.04 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2019) (ceda.ac.uk)
- Other control variables:
  - Log of Population — Number of persons (millions) — Source: PWT 10.0
  - Log of Trade — Imports + Exports of goods and services (current US$) — Source: World Bank Open Data
  - Technology — Mobile cellular subscriptions (per 100 people) — Source: World Bank Open Data
  - Log of Investment — Gross fixed capital formation (current US$) — Source: World Bank Open Data
  - Secondary school — School enrollment, secondary (percent gross) — Source: World Bank Open Data
  - Life expectancy — Life expectancy at birth, total (years) — Source: World Bank Open Data
  - Conflict — 1-armed conflict 0-no armed conflict — Source: UCDP Dataset Download Center (uu.se)
  - Polity_score — from -10 (hereditary monarchy) to +10 (consolidated democracy) — Source: INSCR Data Page (systemicpeace.org)
  - Oil producer — 1- oil producer 0-non-oil producer — Source: World Economic Outlook Databases (imf.org)
- Key dataset characteristics:
  - CRU TS4.04 covers month-by-month variation in climate Jan. 1901- Dec. 2019.
  - Temperature anomaly baseline: average of 1980-2010 temperature data.

### Conclusions (summary)
- Temperature anomalies have a statistically significant negative effect on income per capita growth in SSA fragile states:
  - Panel regression average: a 1°C rise → −2.4 percentage points in income per capita growth.
  - Panel quantile average: a 1°C rise → −1.8 percentage points in income per capita growth.
- Income per capita growth impacts GHG emissions heterogeneously across the emissions distribution; for high emitters higher income growth can be associated with lower emissions growth.
- Carbon pricing (taxes at $25, $50, $75 per ton) can reduce emissions substantially and raise fiscal revenue (0.35, 0.66, 0.95 percent of GDP, respectively, on average), but carbon taxes alone may be insufficient for some countries to meet ambitious NDCs.
- Policy coordination, tailored national strategies (including renewables and energy efficiency), and international support are central to effective mitigation and adaptation in SSA fragile states.

*Source: Annex I. Data description and sources (excerpt) from wpiea2022054-print-pdf.*

### Annex I. Data description and sources ..................................................................................

### wpiea2022054-print-pdf - Annex I. Data description and sources

### Introduction: scope and framing
- Global temperatures have increased significantly over the past-half century and extreme weather events (cold and heat waves, droughts, floods, storms) have intensified, now dominating the disaster landscape in the 21st century.
- Climate change effects are particularly severe for populations of poor countries and even more so in fragile environments, which may be more vulnerable to humanitarian crisis and instability.
- Sub-Saharan Africa (SSA) is highlighted as a region where climate change is expected to push 39.7 million people into poverty by 2050 if no concrete climate and development action takes place (Jafino et al. 2020).
- Fragile states in SSA:
  - Have much higher vulnerability to climate change and natural disasters than other countries (Mason et al., 2015).
  - Heavily rely on climate-dependent agriculture, amplifying physical and livelihood risks.
  - Face weak governance and conflict that increase vulnerability and reduce capacity to respond to climate shocks.
- Climate shocks can have persistent implications for economic growth by reducing agricultural and industrial output through rising temperatures and altered precipitation patterns.

### Mechanisms linking climate change and fragility
- Climate drivers considered:
  - Direct: rainfall, temperature, CO2 emissions.
  - Indirect: demographics, public services, uneven economic development, arable land area, forest area.
- IPCC (2012) identifies pathways by which rising temperatures affect health and livelihoods in Africa:
  - (i) erratic precipitation (more extreme and less predictable rainfall);
  - (ii) extreme events (heat waves, tropical cyclones, extreme rainfall, floods, wildfires, droughts);
  - (iii) rising seas (coastal erosion, floods, water-borne diseases, salinization of drinking water and agricultural land).
- Fragility interacts with climate risk:
  - Structural weaknesses and government failure in fragile states reduce capacity to provide basic services and to address climate- and environment-related threats.
  - Climate-related stress can deepen fragility and increase risk of violent conflict (Navone 2021).
  - Empirical evidence cited: Burke et al. (2009) — a 1ºC increase in temperatures led to a surge in civil conflicts by 4.5 percentage points in Africa.

### Rationale for the empirical approach
- Importance of tail behavior:
  - The tails of distributions can be informative for climate-related variables; climate data often display non-normality and heterogeneity across distributions.
  - Quantile regression is used to detect relationships in different parts of the conditional distribution (upper/lower tails) and is therefore suitable for analyzing climate impacts.
- Research objectives and identification strategy:
  - Investigate the impact of temperature anomalies on income per capita growth using panel fixed effects and panel quantile regression methodologies, focusing on fragile states.
  - Reverse the causality question to analyze the impact of economic conditions on climate change via effects on Green House Gas (GHG) emissions growth rate.
  - Use a dynamic-panel model based on twenty fragile states in sub-Saharan Africa covering 1980 to 2019.
  - Provide scenarios to evaluate potential impacts of mitigation policy responses on fragile states in SSA.

### Key literature findings cited (preserved verbatim)
- Dell et al. (2012): "a 1◦ C rise in temperature in a given year reduces economic growth by 1.3 percentage points on average" in poor countries; temperature shocks may affect both growth rates and the level of output.
- Burke et al. (2015): extreme temperatures "have significant negative effects in all cases for poor countries."
- Kahn et al. (2021): constant increase in average global temperature by 0.04°C per year, where there is a lack of mitigation policies, "reduces world real GDP per capita by more than 7 percent by" (text truncated in source).

### Data scope and methodological notes (from the text)
- Country sample: twenty fragile states in sub-Saharan Africa.
- Time period: 1980 to 2019.
- Climate variables used as proxies: temperature and precipitation anomalies (temperature and precipitation are considered as proxies for climate change, with the understanding that augmented heating increases evaporation and drought intensity).
- Econometric methods:
  - Panel fixed effects estimations.
  - Panel quantile regressions to capture distributional heterogeneity and effects in tails.
  - Dynamic-panel modeling to analyze GHG emissions growth in relation to economic conditions.
- Paper structure (sections referenced): literature review; data; methodology; panel fixed effects and quantile panel estimations; robustness tests; policy implications; conclusion.

*Source: Annex I. Data description and sources (excerpt) from wpiea2022054-print-pdf.*

### 2100. On the other hand, when adhering to the Paris Agreement, limiting the temperature increase to 0.01°C

### 2100. On the other hand, when adhering to the Paris Agreement, limiting the temperature increase to 0.01°C

### Major empirical findings on temperature, income, and emissions
- Limiting temperature increase to 0.01°C per annum (Paris Agreement scenario) reduces the loss significantly to about 1 percent.
- Okonjo-Iweala (2020) (using Kompas et al. (2018) modeling data):
  - Climate change resulting in a 3°C temperature will decrease Africa’s GDP by as much as 8.6 percent per year after 2100.
  - If climate change is limited to the 1.5°C agreed to in the Paris Agreement, the decrease in GDP will be only 3.8 percent per year after 2100.
- IMF Regional Economic Outlook (2020) for SSA:
  - Economic activity in a given month can decrease by 1 percent when the average temperature is 0.5°C above that month’s 30-year average.
  - This effect is 60 percent higher than the average for emerging market and developing economies in other regions.
  - Medium-term economic activity can decrease by 1 percentage point when one additional drought occurs; this effect is about eight times that in emerging market and developing economies in other regions.
- One third of the world’s droughts occur in SSA and the frequency of storms and floods is growing fast in this region.
- Burke et al. (2015): a strong negative correlation between income and temperature implies hot, poor countries will probably suffer the largest reduction in growth.

### Stylized econometric and modeling results
- Dell et al. (2009) framework: half of the negative short-term effects of temperature are offset in the long run through adaptation.
- Tol (2019): extrapolating short-run weather shock elasticities to long-run climate change is unlikely to yield credible results; climate varies slowly so identification relies on large cross-sections.
- Kompas et al. (2018): CGE modeling rarely captures full global disaggregated intertemporal effects with forward-looking behavior due to computational complexity.

### Key panel estimation results (fragile states in SSA, 1980–2019)
- Panel cointegration (Westerlund): null hypothesis of no cointegration rejected.
- ARDL (long-run, PMG):
  - A 1 percent increase in real GDP per capita increases GHG by .247*** (25 percentage points in text).
  - A 1 percent increase in temperature anomalies increases GHG by .188*** (19 percentage points in text).
  - Technology coefficient: .032***.
  - Error Correction Term: -.199*** (implying a 20 percent speed of adjustment).
- ARDL (long-run, DFE):
  - Real GDP per capita: .849*** (85 percentage points in text).
  - Temperature Anomalies: .295.
  - Error Correction Term: -.145*** (implying a 15 percent speed of adjustment).
- Short-run ARDL coefficients on GHG were generally not significant.
- Panel regression (effects of temperature on income per capita growth):
  - Fixed effects and other specifications: dtemp coefficient = -.024*** (standard error .008).
  - Interpretation reported: a 1°C rise in temperature decreases income per capita growth in fragile states in SSA by −2.4 percentage points (panel regression average estimate).
- Panel quantile regression (distributional effects across 15, 25, 50, 75, 90 percentiles):
  - dtemp coefficients by quantile: 15: -.029***; 25: -.022***; 50: -.02***; 75: -.012***; 90: -.037*** (standard errors reported).
  - Integrating over quantiles: average effect estimate dtemp = -.018*** (standard error .004).
  - Interpretation reported: the average effect across quantiles implies that a 1°C rise in temperature reduces growth of income per capita by −1.8 percentage points.
- Panel quantile regression (effects of income per capita growth on carbon emissions growth):
  - dlrgdp_ca coefficients vary by quantile (examples): 5th: .195***; 50th: .1***; 90th: -.042*.
  - Finding: the impact of GDP per capita growth on emissions is heterogeneous; for the 90th quantile the coefficient is negative and significant, suggesting higher GDP per capita growth could reduce carbon emissions growth for high-emitter countries (consistent with an Environmental Kuznets Curve pattern at high quantiles).

### Robustness checks
- Adding CO2 and interaction terms:
  - Inclusion of dlco2: coefficient .028* (Pooled OLS) with similar dtemp effects (dtemp ≈ -.022*** in robustness specifications).
  - Interaction dtemp_x_dlpop coefficients (robustness): approximately -.632** to -.644** in pooled/fixed/random effect columns (standard errors reported).
- Quantile robustness: inclusion of dtemp_x_dlpop retains negative coefficients at all quantiles (e.g., 15th: -1.048***; 50th: -.242***).

### Carbon tax simulation scenarios and fiscal implications (IMF CPAT and Multiscenario Tool)
- Carbon tax scenarios evaluated: $25, $50, and $75 per ton of GHG (assumed to be reached gradually in 10 years).
- Emission reduction and NDC achievement:
  - For countries with relatively high-emission reduction targets (Central African Republic, São Tomé and Príncipe, Liberia, Mali), a carbon tax of more than $75 per ton would be needed if the carbon tax is the only instrument used.
  - Assuming the 20 countries introduce a carbon tax of $50 and $75 per ton, regional emissions would decrease by 26, and 33 percent on average, respectively.
- Fiscal revenue from carbon taxes (percent of GDP, region average):
  - $25 per ton: additional revenue estimated to be 0.35 percent of GDP.
  - $50 per ton: additional revenue estimated to be 0.66 percent of GDP.
  - $75 per ton: additional revenue estimated to be 0.95 percent of GDP.
  - Marginal revenue gains decline at higher tax rates due to tax base erosion from higher energy prices.
- Uses of carbon tax revenue recommended: compensation for those negatively affected, financing priority spending (health, education, infrastructure, green investment, adaptation), and reducing distortionary taxes.

### Policy implications and recommendations
- Tailored policies recommended for fragile states rather than uniform CO2 controls, taking into account each state’s developmental characteristics and emissions structure.
- Opportunities and challenges for fragile states:
  - Low capacity to propose, legislate, or manage regulations and carbon taxes.
  - If emissions are concentrated in specific sectors or firms, enforcement and collection could be more feasible and politically viable.
- Renewable energy and energy efficiency:
  - Long-run ARDL results suggest that technology and income are positively associated with GHG; increasing renewable energy consumption and higher energy efficiency could potentially reduce GHG emissions in SSA’s fragile states.
- International coordination:
  - Dispersion in needed carbon tax rates reflects cross-country differences in commitment levels and energy sources, indicating a role for stronger international coordination in mitigation targets and support.
- National climate commitments:
  - Fragile states have submitted Nationally Determined Contributions (NDCs); a handful pledged to reduce CO2 emissions by more than 30 percent in 2030 vis-à-vis BAU conditional on international support (Chad, Republic of Congo, Malawi, Zimbabwe).

### Conclusions (summary)
- Temperature anomalies have a statistically significant negative effect on income per capita growth in SSA fragile states:
  - Panel regression average: a 1°C rise → −2.4 percentage points in income per capita growth.
  - Panel quantile average: a 1°C rise → −1.8 percentage points in income per capita growth.
- Income per capita growth impacts GHG emissions heterogeneously across the emissions distribution; for high emitters higher income growth can be associated with lower emissions growth.
- Carbon pricing (taxes at $25, $50, $75 per ton) can reduce emissions substantially and raise fiscal revenue (0.35, 0.66, 0.95 percent of GDP, respectively, on average), but carbon taxes alone may be insufficient for some countries to meet ambitious NDCs.
- Policy coordination, tailored national strategies (including renewables and energy efficiency), and international support are central to effective mitigation and adaptation in SSA fragile states.

*Source: wpiea2022054-print-pdf (IMF).*

### Annex I. Data description and sources

### Annex I. Data description and sources

### Dependent variables
- Log of GDP per capita
  - Real GDP at constant 2017 national prices (in mil.2017US$) divided by population
  - Source: PWT 10.0 | Penn World Table | Groningen Growth and Development Centre | University of Groningen (rug.nl)
- Log of GHG emissions
  - Greenhouse Gas (GHG) Emissions, Total including LUCF- in MtCO2e
  - Source: World | Total including LUCF | Greenhouse Gas (GHG) Emissions | Climate Watch (climatewatchdata.org)

### Explanatory variables
- Temperature anomaly
  - The difference from an average, or baseline, temperature. The baseline temperature is computed by averaging 1980-2010 temperature data.
  - Source: Dataset Record: CRU TS4.04: Climatic Research Unit (CRU) Time-Series (TS) version 4.04 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2019) (ceda.ac.uk)

### Other control variables
- Log of Population
  - Number of persons (millions)
  - Source: PWT 10.0 | Penn World Table | Groningen Growth and Development Centre | University of Groningen (rug.nl)
- Log of Trade
  - Imports + Exports of goods and services (current US$)
  - Source: World Bank Open Data | Data
- Technology
  - Mobile cellular subscriptions (per 100 people)
  - Source: World Bank Open Data | Data
- Log of Investment
  - Gross fixed capital formation (current US$)
  - Source: World Bank Open Data | Data
- Secondary school
  - School enrollment, secondary (percent gross)
  - Source: World Bank Open Data | Data
- Life expectancy
  - Life expectancy at birth, total (years)
  - Source: World Bank Open Data | Data
- Conflict
  - 1-armed conflict 0-no armed conflict
  - Source: UCDP Dataset Download Center (uu.se)
- Polity_score
  - from -10 (hereditary monarchy) to +10 (consolidated democracy)
  - Source: INSCR Data Page (systemicpeace.org)
- Oil producer
  - 1- oil producer 0-non-oil producer
  - Source: World Economic Outlook Databases (imf.org)

### Key dataset characteristics and coverage (as described)
- CRU TS4.04 covers month-by-month variation in climate Jan. 1901- Dec. 2019.
- Temperature anomaly baseline: average of 1980-2010 temperature data.

### References (selection as listed in source)
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- Alkhathlan, K., & Javid, M. (2013). Energy consumption, carbon emissions and economic growth in Saudi Arabia: An aggregate and disaggregate analysis. Energy Policy, 62, 1525–1532. https://doi.org/10.1016/j.enpol.2013.07.068
- Barrios, S., Bertinelli, L., & Strobl, E. (2010). Trends in rainfall and economic growth in Africa: a neglected cause of the African growth tragedy. The Review of Economics and Statistics, 92(2), 350–366. https://www.jstor.org/stable/27867541
- Blanc, E. (2012). The Impact of Climate Change on Crop Yields in Sub-Saharan Africa. American Journal of Climate Change, 01(01), 1–13. https://doi.org/10.4236/ajcc.2012.11001
- Brini, R. (2021). Renewable and non-renewable electricity consumption, economic growth and climate change: Evidence from a panel of selected African countries. Energy, 223, 120064. https://doi.org/10.1016/j.energy.2021.120064
- Brückner, M., Ciccone, A. (2011). Rain and the Democratic Window of Opportunity. Econometrica, 79(3), 923–947. https://doi.org/10.3982/ecta8183
- Burke, M. B., Miguel, E., Satyanath, S., Dykema, J. A., & Lobell, D. B. (2009). Warming increases the risk of civil war in Africa. Proceedings of the National Academy of Sciences, 106(49), 20670–20674. https://doi.org/10.1073/pnas.0907998106
- Burke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527(7577), 235–239. https://doi.org/10.1038/nature15725
- Cashin, P., Mohaddes, K., & Raissi, M. (2017). Fair weather or foul? The macroeconomic effects of El Niño. Journal of International Economics, 106, 37–54. https://doi.org/10.1016/j.jinteco.2017.01.010
- Dasgupta, S., Laplante, B., Wang, H., & Wheeler, D. (2002). Confronting the Environmental Kuznets Curve. Journal of Economic Perspectives, 16(1), 147–168. https://doi.org/10.1257/0895330027157
- Dell, M., Jones, B. F., & Olken, B. A. (2009). Temperature and Income: Reconciling New Cross-Sectional and Panel Estimates. American Economic Review, 99(2), 198–204. https://doi.org/10.1257/aer.99.2.198
- Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature Shocks and Economic Growth: Evidence from the Last Half Century. American Economic Journal: Macroeconomics, 4(3), 66–95. https://doi.org/10.1257/mac.4.3.66
- Dell, M., Jones, B. F., & Olken, B. A. (2014). What Do We Learn from the Weather? The New Climate-Economy Literature†. Journal of Economic Literature, 52(3), 740–798. https://doi.org/10.1257/jel.52.3.740
- Frimpong, J. M., & Oteng-Abayie, E. F. (2006). Aggregate Import demand and Expenditure Components in Ghana: An Econometric Analysis. MPRA Paper No. 599. https://mpra.ub.uni-muenchen.de/599/
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*Climate Change in Sub-Saharan Africa’s Fragile States: Evidence from Panel Estimations — Working Paper No. WP/22/54*

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