## wpiea2021193-print-pdf — Appendix 1: Tables

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### I. Purpose and hypotheses
- Objective: investigate how climate shocks affect the impact of external capital inflows (FDI, ODA, remittances) on economic growth in developing countries.
- Key theoretical claims:
  - Financial inflows improve economic growth; effect depends on absorptive capacity.
  - Climate shocks mitigate the positive effect of financial inflows, especially in countries with low absorptive capacity.
- Sample and period: 63–64 low and middle-income countries over 1995-2018 (data divided into non-overlapping five-year periods).

### II. Theoretical framework (neoclassical / MRW-based)
- Production function: Yt = Kt^α Ht^β (At Lt)^{1−α−β}.
- Technology term includes external capital inflow component F and an elasticity θ that is a function of human capital and country characteristics X:
  - At = A0 e^{gt} F^{θ}
  - θ = θ0 + θ1 f(h) + θ2 f(k) + θj f(Xj)
- Climate shocks enter by modifying F and country characteristics:
  - A't = A0 e^{gt} (ω1 F)^{(ω2 θ)} with ω1 > 0 and 0 ≤ ω2 ≤ 1.
- Estimable growth equation (Equation 3.17):
  - log(yi t) − log(yi0) = γ0 + γ1 log(yi0) + γ2 log(ni t + g + δ) + γ3 log(sk it) + γ4 log(hi t) + γ5 log(Flow it) + γ6 log(Flow it)*log(hi t) + γ7 log(Flow it)*log(sk it) + εit.

### III. Empirical strategy and data
- Estimation method: two-step robust System-GMM (Blundell and Bond, 1998) with Windmeijer (2005) finite-sample correction.
- Sample composition: 64 countries (25 LICs, 22 LMICs, 17 UMICs); some regressions use 63 countries for remittances.
- Key data sources:
  - GDP per capita, growth, FDI: IMF World Economic Outlook.
  - Government investment: IMF Investment and Capital Stock Dataset.
  - Human capital: Penn World Table (Groningen Growth and Development Centre) based on Barro and Lee (2013) and Caselli (2005).
  - Population growth, trade, remittances: World Bank WDI.
  - ODA: OECD (ODA total net).
  - Temperature and precipitation: Climate Research Unit (CRU) 1950–2018 monthly series.
  - Natural disasters, drought, flood: EM-DAT (CRED).
- Climate shock construction:
  - Monthly deviation to long-term monthly average, standardized by monthly long-term standard deviation; annual average of absolute monthly deviations is primary indicator (equation 4.4).
  - Alternative measures: weighted monthly deviations, annual maximum and minimum monthly deviations, natural disaster counts/severity.

### IV. Main empirical findings — effect of financial inflows on growth (Table 1)
- System-GMM regression results (dependent variable: GDP per capita growth):
  - Column (1) (ODA): Log ODA per capita coefficient = 2.000 ** (t-stat 2.00). Observations = 310; Countries = 64.
  - Column (2) (FDI): Log FDI per capita coefficient = 1.593 * (t-stat 1.74). Observations = 310; Countries = 64.
  - Column (3) (Remittances): Log Remittances per capita coefficient = 0.459 ** (t-stat 1.97). Observations = 293; Countries = 63.
- Selected control coefficients:
  - Log Fixed Gross Capital Formation: 2.091 ** (column 1), 2.085 ** (column 2).
  - Log Human capital: 6.695 (col 1), 7.953 * (col 2).
- Specification tests (selected):
  - AR(1) p-values: 0.022, 0.022, 0.046.
  - AR(2) p-values: 0.400, 0.401, 0.561.
  - Hansen p-values: 0.481, 0.582, 0.587.
  - Instruments: 24, 28, 44 respectively.

### V. Absorptive capacity conditions (Table 2)
- Absorptive capacity index constructed via principal components from human capital, infrastructure (paved road density, telephone use), and World Governance Indicators.
- Interaction results (System-GMM):
  - ODA: Log ODA per capita = 3.239 * (t-stat 1.70); (Absorptive Capacity)x(ODA) = 1.104 * (t-stat 1.72). Observations = 195; Countries = 61.
  - FDI: Log FDI per capita = 2.358 ** (t-stat 2.03); (Absorptive Capacity)x(FDI) = 1.295 ** (t-stat 2.43). Observations = 195; Countries = 61.
  - Remittances: Log Remittances per capita = 1.149 * (t-stat 1.87); (Absorptive Capacity)x(Remittances) = 0.237 * (t-stat 1.85). Observations = 185; Countries = 60.
- Interpretation: positive and statistically significant interaction coefficients indicate that higher absorptive capacity amplifies the growth effects of ODA, FDI and remittances.

### VI. Climate shocks mitigate financial inflow effectiveness (Table 3 — Average monthly shocks)
- Model includes climate shocks (average monthly precipitation and temperature deviations and their squares) and interactions of their squares with log financial flows.
- Selected coefficients (System-GMM; t-stats and samples preserved):
  - ODA (precipitation): Log ODA per capita = 2.718 * (t-stat 1.70); (Average month precipitation square)x(ODA) = -4.204 * (t-stat 1.94). Observations = 310; Countries = 64.
  - ODA (temperature): Log ODA per capita = 4.949 ** (t-stat 2.21); (Average month temperature square)x(ODA) = -0.611 * (t-stat 1.74). Observations = 302; Countries = 64.
  - FDI (precipitation): Log FDI per capita = 3.392 ** (t-stat 2.17); (Average month precipitation square)x(FDI) = -6.839 * (t-stat 1.83). Observations = 310; Countries = 64.
  - FDI (temperature): Log FDI per capita = 3.014 *** (t-stat 3.30); (Average month temperature square)x(FDI) = -1.450 * (t-stat 1.72). Observations = 310; Countries = 64.
  - Remittances (precipitation): Log Remittances per capita = 1.322 * (t-stat 1.68); (Average month precipitation square)x(Remittances) = -2.420 ** (t-stat 2.52). Observations = 293; Countries = 63.
  - Remittances (temperature): Log Remittances per capita = 0.584 * (t-stat 1.82); (Average month temperature shock square)x(Remittances) = -0.914 ** (t-stat 1.97). Observations = 293; Countries = 63.
- Interpretation: climate shocks (temperature and precipitation deviations) reduce the positive impact of ODA, FDI and remittances on GDP per capita growth; interaction coefficients on financial flows are negative and often statistically significant.

### VII. Robustness and heterogeneity (summary of Appendix tables)
- Alternative climate shock measures and results:
  - Weighted average monthly shocks (Table 4): interaction terms remain negative and statistically significant in most specifications.
  - Annual maximum and minimum monthly deviations (Table A5): both positive and negative deviations reduce the growth impact of financial flows.
  - Natural disaster indicators (EM-DAT) including drought and flood (Table A6): interaction coefficients with ODA, FDI and remittances are negative while main financial flow coefficients remain positive.
  - Different time aggregation: three-year periods (Table A7) produce similar results.
  - Heterogeneity by development level (Table A8): interaction of climate shocks with financial flows and initial development level are positive and significant — higher relative development level mitigates the negative effect of climate shocks.
  - Period-specific interactions (Tables A9 and A10): generally not significant, with two exceptions:
    - Precipitation shock x FDI in 1995-1999: positive and significant (less attenuation in that period).
    - Temperature shock x FDI in 2010-2014: negative and significant (greater attenuation in that period).
  - Low absorptive capacity subgroup (Table A11): coefficients of interactions of financial flows with climate shocks and low absorptive capacity dummy are negative and significant — climate shocks more strongly reduce financial flows’ growth impact in countries with low absorptive capacity.
- Additional robustness notes:
  - Role of aggregate financial flow (Table A4): (Precip. shock square)x(Financial flow) = -20.117* (1.85); (Temp. shock square)x(Financial flow) = -4.841** (2.15).
  - Natural disasters interactions (Table A6) include: (Natural Disasters)x(ODA) = -0.474* (1.74); (Natural Disasters)x(FDI) = -0.539* (1.65).
  - Heterogeneity by development level interactions (Table A8) include: (Precip. shock)x(ODA)x(develop. level) = 1.135** (2.15); (Precip. shock)x(FDI)x(develop. level) = 1.577* (1.86).

### VIII. Policy implications and recommendations
- Strengthen absorptive capacity to preserve and enhance the growth returns of foreign finance:
  - Invest in human capital and skills to raise θ and the productivity effects of inflows.
  - Improve infrastructure (e.g., flood- and storm-resistant construction) and expand access to communications/roads.
  - Strengthen institutions: rule of law, government effectiveness, regulatory quality, control of corruption.
  - Implement sound macroeconomic policies to support sustainable investment and resilience.
- Climate-specific actions:
  - Global mitigation policies (e.g., carbon pricing, green investment) to reduce the incidence/severity of climate shocks.
  - Adaptation and transition policies in developing countries (disaster-resilient public investment, "build back better" practices).
  - Targeted international support: countries more affected by climate shocks need more international assistance aimed at building absorptive capacity rather than a reduction in finance.
- Practical examples cited:
  - Post-cyclone “build back better” campaign in Fiji (after cyclone Winston, 2016).
  - Construction standards improvements: Madagascar, Malawi, Mauritius.
  - Flood-resistant infrastructure: Lesotho, Madagascar, Mozambique.
  - Disaster-resilient public investment examples: Dominica.

### IX. Key numeric highlights (preserved)
- Sample: 64 countries (25 LICs, 22 LMICs, 17 UMICs) for 1995-2018.
- Table 1: Log ODA per capita = 2.000 **; Log FDI per capita = 1.593 *; Log Remittances per capita = 0.459 **.
- Table 2: (Absorptive Capacity)x(ODA) = 1.104 *; (Absorptive Capacity)x(FDI) = 1.295 **; (Absorptive Capacity)x(Remittances) = 0.237 *.
- Table 3 selected interaction coefficients (negative):
  - (Average month precipitation square)x(ODA) = -4.204 *.
  - (Average month temperature square)x(ODA) = -0.611 *.
  - (Average month precipitation square)x(FDI) = -6.839 *.
  - (Average month temperature square)x(FDI) = -1.450 *.
  - (Average month precipitation square)x(Remittances) = -2.420 **.
  - (Average month temperature shock square)x(Remittances) = -0.914 **.

*Source: wpiea2021193-print-pdf - Appendix 1: Tables (https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021193-print-pdf.pdf)*

### References ______________________________________________________________ 27

### wpiea2021193-print-pdf - References ______________________________________________________________ 27

### Figures
- 1. Financial Flows to Developing Countries in 2017 ______________________________ 14
- 2. Financial Flows Trends in Developing Countries in 2017 ________________________ 15
- 3. Financial Flows in Developing Countries by Income Group ______________________ 15
- 4. Financial Flows in Developing Countries by Region ____________________________ 16
- 5. Evolution of Precipitation Shocks Per Income Group ___________________________ 17
- 6. Evolution of Temperature Shocks Per Income Group ___________________________ 17
- 7. Correlation Between Absorptive Capacity and Climate Shocks ____ Error! Bookmark not defined. 

### Tables
- 1. Effect of ODA, FDI and Remittances on Economic Growth ______________________ 19
- 2. Absorptive Capacity Conditional Effect of ODA, FDI and Remittances on Economic Growth __________________________________________________________________ 20
- 3: Role of Average Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth __________________________________________________________________ 21
- 4. Role of Weighted Average Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth _________________________________________________________ 23

*Source: https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021193-print-pdf.pdf*

### Appendix 1: Tables _______________________________________________________ 32

### Appendix 1: Tables

### I. Introduction — purpose and hypotheses
- Objective: investigate how climate shocks affect the impact of external capital inflows (FDI, ODA, remittances) on economic growth in developing countries.
- Key theoretical claims:
  - Financial inflows improve economic growth; effect depends on absorptive capacity.
  - Climate shocks mitigate the positive effect of financial inflows, especially in countries with low absorptive capacity.
- Sample and period: 63–64 low and middle-income countries over 1995-2018 (data divided into non-overlapping five-year periods).

### II. Theoretical framework (neoclassical / MRW-based)
- Production function: Yt = Kt^α Ht^β (At Lt)^{1−α−β}.
- Technology term includes external capital inflow component F and an elasticity θ that is a function of human capital and country characteristics X:
  - At = A0 e^{gt} F^{θ}
  - θ = θ0 + θ1 f(h) + θ2 f(k) + θj f(Xj)
- Climate shocks enter by modifying F and country characteristics:
  - A't = A0 e^{gt} (ω1 F)^{(ω2 θ)} with ω1 > 0 and 0 ≤ ω2 ≤ 1.
- Steady-state and transition dynamics lead to an estimable growth equation (Equation 3.17):
  - log(yi t) − log(yi0) = γ0 + γ1 log(yi0) + γ2 log(ni t + g + δ) + γ3 log(sk it) + γ4 log(hi t) + γ5 log(Flow it) + γ6 log(Flow it)*log(hi t) + γ7 log(Flow it)*log(sk it) + εit.

### III. Empirical strategy and data
- Estimation method: two-step robust System-GMM (Blundell and Bond, 1998) with Windmeijer (2005) finite-sample correction.
- Sample composition: 64 countries (25 LICs, 22 LMICs, 17 UMICs); some regressions use 63 countries for remittances.
- Key data sources:
  - GDP per capita, growth, FDI: IMF World Economic Outlook.
  - Government investment: IMF Investment and Capital Stock Dataset.
  - Human capital: Penn World Table (Groningen Growth and Development Centre) based on Barro and Lee (2013) and Caselli (2005).
  - Population growth, trade, remittances: World Bank WDI.
  - ODA: OECD (ODA total net).
  - Temperature and precipitation: Climate Research Unit (CRU) 1950–2018 monthly series.
  - Natural disasters, drought, flood: EM-DAT (CRED).
- Climate shock construction:
  - Monthly deviation to long-term monthly average, standardized by monthly long-term standard deviation; annual average of absolute monthly deviations is primary indicator (equation 4.4).
  - Alternative measures: weighted monthly deviations, annual maximum and minimum monthly deviations, natural disaster counts/severity.

### IV. Main empirical findings — effect of financial inflows on growth (Table 1)
- Regression outcomes (System-GMM; dependent variable: GDP per capita growth):
  - Column (1) (ODA): Log ODA per capita coefficient = 2.000 ** (t-stat 2.00). Observations = 310; Countries = 64.
  - Column (2) (FDI): Log FDI per capita coefficient = 1.593 * (t-stat 1.74). Observations = 310; Countries = 64.
  - Column (3) (Remittances): Log Remittances per capita coefficient = 0.459 ** (t-stat 1.97). Observations = 293; Countries = 63.
- Other notable coefficients (selected):
  - Log Fixed Gross Capital Formation: 2.091 ** (column 1), 2.085 ** (column 2).
  - Log Human capital: 6.695 (col 1), 7.953 * (col 2).
- Specification tests:
  - AR(1) p-values: 0.022, 0.022, 0.046.
  - AR(2) p-values: 0.400, 0.401, 0.561.
  - Hansen p-values: 0.481, 0.582, 0.587.
  - Instruments: 24, 28, 44 respectively.

### V. Absorptive capacity conditions (Table 2)
- Absorptive capacity index constructed via principal components from human capital, infrastructure (paved road density, telephone use), and World Governance Indicators.
- Interaction results (System-GMM):
  - ODA: Log ODA per capita = 3.239 * (t-stat 1.70); (Absorptive Capacity)x(ODA) = 1.104 * (t-stat 1.72). Observations = 195; Countries = 61.
  - FDI: Log FDI per capita = 2.358 ** (t-stat 2.03); (Absorptive Capacity)x(FDI) = 1.295 ** (t-stat 2.43). Observations = 195; Countries = 61.
  - Remittances: Log Remittances per capita = 1.149 * (t-stat 1.87); (Absorptive Capacity)x(Remittances) = 0.237 * (t-stat 1.85). Observations = 185; Countries = 60.
- Interpretation: positive and statistically significant interaction coefficients indicate that higher absorptive capacity amplifies the growth effects of ODA, FDI and remittances.

### VI. Climate shocks mitigate financial inflow effectiveness (Table 3 — Average monthly shocks)
- Model includes climate shocks (average monthly precipitation and temperature deviations and their squares) and interactions of their squares with log financial flows.
- Key results summary (System-GMM; selected coefficients):
  - ODA (precipitation): Log ODA per capita = 2.718 * (t-stat 1.70); (Average month precipitation square)x(ODA) = -4.204 * (t-stat 1.94). Observations = 310; Countries = 64.
  - ODA (temperature): Log ODA per capita = 4.949 ** (t-stat 2.21); (Average month temperature square)x(ODA) = -0.611 * (t-stat 1.74). Observations = 302; Countries = 64.
  - FDI (precipitation): Log FDI per capita = 3.392 ** (t-stat 2.17); (Average month precipitation square)x(FDI) = -6.839 * (t-stat 1.83). Observations = 310; Countries = 64.
  - FDI (temperature): Log FDI per capita = 3.014 *** (t-stat 3.30); (Average month temperature square)x(FDI) = -1.450 * (t-stat 1.72). Observations = 310; Countries = 64.
  - Remittances (precipitation): Log Remittances per capita = 1.322 * (t-stat 1.68); (Average month precipitation square)x(Remittances) = -2.420 ** (t-stat 2.52). Observations = 293; Countries = 63.
  - Remittances (temperature): Log Remittances per capita = 0.584 * (t-stat 1.82); (Average month temperature shock square)x(Remittances) = -0.914 ** (t-stat 1.97). Observations = 293; Countries = 63.
- Interpretation: climate shocks (temperature and precipitation deviations) reduce the positive impact of ODA, FDI and remittances on GDP per capita growth; interaction coefficients on financial flows are negative and often statistically significant.

### VII. Robustness checks (summary)
- Alternative climate shock measures tested and consistent findings:
  - Weighted average monthly shocks (Table 4): interaction terms remain negative and statistically significant in most specifications.
  - Annual maximum and minimum monthly deviations (Table A5): both positive and negative deviations reduce the growth impact of financial flows.
  - Natural disaster indicators (EM-DAT) including drought and flood (Table A6): interaction coefficients with ODA, FDI and remittances are negative while main financial flow coefficients remain positive.
  - Different time aggregation: three-year periods (Table A7) produce similar results.
  - Heterogeneity by development level (Table A8): interaction of climate shocks with financial flows and initial development level are positive and significant — higher relative development level mitigates the negative effect of climate shocks.
  - Period-specific interactions (Tables A9 and A10): generally not significant, with two exceptions:
    - Precipitation shock x FDI in 1995-1999: positive and significant (less attenuation in that period).
    - Temperature shock x FDI in 2010-2014: negative and significant (greater attenuation in that period).
  - Low absorptive capacity subgroup (Table A11): coefficients of interactions of financial flows with climate shocks and low absorptive capacity dummy are negative and significant — climate shocks more strongly reduce financial flows’ growth impact in countries with low absorptive capacity.

### VIII. Policy implications and recommendations
- Strengthen absorptive capacity to preserve and enhance the growth returns of foreign finance:
  - Invest in human capital and skills to raise θ and the productivity effects of inflows.
  - Improve infrastructure (e.g., flood- and storm-resistant construction) and expand access to communications/roads.
  - Strengthen institutions: rule of law, government effectiveness, regulatory quality, control of corruption.
  - Implement sound macroeconomic policies to support sustainable investment and resilience.
- Climate-specific actions:
  - Global mitigation policies (e.g., carbon pricing, green investment) to reduce the incidence/severity of climate shocks.
  - Adaptation and transition policies in developing countries (disaster-resilient public investment, "build back better" practices).
  - Targeted international support: countries more affected by climate shocks need more international assistance aimed at building absorptive capacity rather than a reduction in finance.
- Practical examples cited:
  - Post-cyclone “build back better” campaign in Fiji (after cyclone Winston, 2016).
  - Construction standards improvements: Madagascar, Malawi, Mauritius.
  - Flood-resistant infrastructure: Lesotho, Madagascar, Mozambique.
  - Disaster-resilient public investment examples: Dominica.

### IX. Key numeric highlights (preserved)
- Sample: 64 countries (25 LICs, 22 LMICs, 17 UMICs) for 1995-2018.
- Table 1: Log ODA per capita = 2.000 **; Log FDI per capita = 1.593 *; Log Remittances per capita = 0.459 **.
- Table 2: (Absorptive Capacity)x(ODA) = 1.104 *; (Absorptive Capacity)x(FDI) = 1.295 **; (Absorptive Capacity)x(Remittances) = 0.237 *.
- Table 3 selected interaction coefficients (negative):
  - (Average month precipitation square)x(ODA) = -4.204 *.
  - (Average month temperature square)x(ODA) = -0.611 *.
  - (Average month precipitation square)x(FDI) = -6.839 *.
  - (Average month temperature square)x(FDI) = -1.450 *.
  - (Average month precipitation square)x(Remittances) = -2.420 **.
  - (Average month temperature shock square)x(Remittances) = -0.914 **.

*Source: wpiea2021193-print-pdf - Appendix 1: Tables (https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021193-print-pdf.pdf)*

### REFERENCES

### REFERENCES

### Themes covered
- Effects of weather shocks, natural disasters, and climate change on economic activity and growth.
- Impact of remittances on growth, savings, capital, exchange rates, volatility, poverty, and nutrition.
- Role of foreign direct investment (FDI) in economic growth, including channels, absorptive capacity, regulation, and sectoral effects.
- Foreign aid/economic development nexus: aid effectiveness, absorptive capacity, firm-level and subnational evidence.
- Methodological and empirical contributions to growth literature: dynamic panel estimators, bias corrections, and data sets.

### Selected empirical and theoretical findings (by reference)
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- Burke, M., Hsiang, S. M., Miguel, E., (2015). Climate and Conflict. Annual Review of Economics 7, 577–617.
- Dell, M., Jones B. F. and Olken B. A. (2012). Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal of Macroeconomics volume 4(3), pp. 66-95.
- Loayza, N.V., Olaberría, E., Rigolini, J., Christiaensen, L., (2012). Natural disasters and growth: going beyond the averages. World Development, Volume 40, pp.1317–1336.
- Felbermayr, G.J., and Gröschl, J.K. (2013). Natural disasters and the effect of trade on income: A new panel IV approach. European Economic Review volume 58, pp. 18-30.
- Helderop, E., Grubesic, T.H., (2019). Hurricane storm surge in Volusia County, Florida: evidence of a tipping point for infrastructure damage. Disasters volume 43, pp. 157–180.
- Burke, M., and Emerick K. (2016). Adaptation to Climate Change: Evidence from US Agriculture. American Economic Journal: Economic Policy, volume 8(3), pp. 106-40.
- Hendrix, C. S., Salehyan I., (2012). Climate change, rainfall, and social conflict in Africa. Journal of Peace Research Volume 49, pp. 35–50.
- Acosta P. A., Lartey E.K.K. and Mandelman F. S. (2009). Remittances and the Dutch disease. Journal of International Economics Volume 79, Issue 1, September 2009, pp. 102-116.
- Amuedo-Dorantes, C., Pozo, S., (2004). Workers’ remittances and the real exchange rate: a paradox of gifts. World Development, volume, 32 (8), pp. 1407–1417.
- Benhamou Z. A. and Cassin L., (2021). The impact of remittances on savings, capital and economic growth in small emerging countries. Economic Modelling, Volume 94, Pages 789-803.
- Catrinescu N., Ledesma M. L. and Piracha M., (2009). Remittances, Institutions, and Economic Growth. World Development Volume 37, (1), pp. 81–92.
- Imai K. S., Gaihab R., Ali A. and Kaicker N., (2014). Remittances, growth and poverty: New evidence from Asian countries. Journal of Policy Modeling volume 36 pp. 524–538.
- Licuanan, V., Mahmoud, T. O., and Steinmayr, A. (2015). The drivers of diaspora donations for development: Evidence from the Philippines. World Development, volume 65, pp. 94–109.
- Ogunniyi A. Isaiah, M. G., Olagunju K. O., Fadare O. and Adedoyin R., (2020). Governance quality, remittances and their implications for food and nutrition security in Sub-Saharan Africa. World Development, volume 127 pp. 104752.
- Alfaro L., Chanda A., Kalemli-Ozcan S., Sayek S., (2004). FDI and economic growth: the role of local financial markets. Journal of International Economics volume 64, pp. 89–112.
- Durham J. B., (2004). Absorptive capacity and the effects of foreign direct investment and equity foreign portfolio investment on economic growth. European Economic Review volume 48, pp. 285 – 306.
- Herzer D., Klasen S. and Nowak-Lehmann D F., (2008). In search of FDI-led growth in developing countries: The way forward. Economic Modelling, volume 25 pp. 793–810.
- Makiela K. and Ouattara B., (2018). Foreign direct investment and economic growth: Exploring the transmission channels. Economic Modelling volume 72, pp. 296–305.
- Iwasaki I. and Tokunaga M., (2014). Macroeconomic Impacts of FDI in Transition Economies: A Meta-Analysis. World Development Volume 61, pp. 53–69, 2014.
- Gunby P., Jin Y. and Reed W. R., (2017). Did FDI Really Cause Chinese Economic Growth? A Meta-Analysis. World Development, Volume 90, pp. 242–255.
- Arndt C., Jones S. and Tarp F., (2015). Assessing Foreign Aid’s Long-Run Contribution to Growth and Development. World Development, Volume 69, pp. 6–18.
- Dalgaard, C.-J., Hansen, H., Tarp, F., (2004). On the empirics of foreign aid and growth. Economic Journal Volume 114 (496), F191–F216.
- Burnside, C., Dollar, D., (2000). Aid, policies, and growth. American Economic Review volume 37 (6), pp.847–868.
- Easterly, W., (2003). Can foreign aid buy growth. Journal of Economic Perspectives Volume 17 (3), pp. 23–48.
- Chauvet L. and Ehrhart H., (2018). Aid and growth: evidence from firm-level data. Journal of Development Economics volume 135, pp. 461–477.
- Civelli A., Horowitz A. and Teixeira A., (2018). Foreign aid and growth: A Sp P-VAR analysis using satellite sub-national data for Uganda. Journal of Development Economics, volume 134 , pp. 50–67.
- Feenstra Robert, Inklaar Robert, Timmer Marcel, 2015, American Economic Review, 105(10), 3150-3182.
- IPCC, (2014). Summary for Policymakers In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, United Kingdom and New York, NY, USA.
- International Monetary Fund (IMF), (2019), Building Resilience In Developing Countries Vulnerable To Large Natural Disasters, IMF Policy Paper (Washington).
- International Monetary Fund. 2020. World Economic Outlook: A Long and Difficult Ascent. World Economic Outlook Washington, DC, October.
- International Monetary Fund (2016). Sub-Saharan Africa Multispeed Growth? Chapter 3, Regional Economic Outlook: Sub-Saharan Africa, Washington. October 2016.

### Methodology and econometrics references
- Blundell, R. and S. Bond, (1998), Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, volume 87, pp. 115–143.
- Kiviet, J. (1995). On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models, Journal of Econometrics, volume 68, pp. 53–78.
- Nickell, S.,(1981). Biases in Dynamic Models With Fixed Effects, Econometrica, volume 49, pp. 1417–1426.
- Windmeijer, F., (2005), A finite sample correction for the variance of linear efficient two-step GMM estimators, Journal of Econometrics, volume 126, pp. 25–51.
- Feeny, S. and de Silva A. (2012). Measuring absorptive capacity constraints to foreign aid. Economic Modelling, Volume 29, pp. 725-733.
- Feenstra Robert, Inklaar Robert, Timmer Marcel, 2015, American Economic Review, 105(10), 3150-3182.
- Caselli, F;. (2005). Accounting for Cross-Country Income Differences. In Phillipe Aghion and Steven N. Durlauf, editors, Handbook of Economic Growth, Volume 1A, pp. 679-741.
- Mankiw, N. G;, Romer D., and Weil D., A Contribution to the Empirics of Economic Growth, Quarterly Journal of Economics, volume 107 (2), pp. 407-438.
- Romer, P. M., (1990). Endogenous Technological Change, Journal of Political Economy, volume 98 (5), S71.

*References listed in "REFERENCES" (wpiea2021193-print-pdf).*

### APPENDIX 1: TABLES

### APPENDIX 1: TABLES

### List of Countries (Table A1)
- Countries included (alphabetical blocks as presented): Argentina; Bangladesh; Benin; Bolivia; Botswana; Burkina Faso; Burundi; Cambodia; Cameroon; Central African Republic; Colombia; Congo, Dem. Rep.; Costa Rica; Cote d'Ivoire; Dominican Republic; Egypt, Arab Rep.; El Salvador; Eswatini; Ethiopia; Gambia, The; Ghana; Guatemala; Haiti; Honduras; Jordan; Kenya; Lao PDR; Lesotho; Liberia; Madagascar; Malawi; Mali; Mauritania; Mauritius; Mexico; Mongolia; Morocco; Mozambique; Myanmar; Namibia; Nepal; Nicaragua; Niger; Nigeria is not listed; Pakistan; Panama; Paraguay; Peru; Philippines; Rwanda; Senegal; Serbia; Sierra Leone; South Africa; Sri Lanka; Tanzania; Thailand; Togo; Tunisia; Turkey; Uganda; Vietnam; Zambia; Zimbabwe.

### Data Sources (Table A2)
- GDP per capita: IMF WEO
- Foreign direct investment: IMF WEO
- Investment: IMF Investment and Capital Stock Dataset
- Human capital index: Penn World Table
- Infrastructure: World Bank World Development Indicator (WDI)
- Population growth: World Bank World Development Indicator (WDI)
- Remittances: World Bank World Development Indicator (WDI)
- Official development assistance (ODA): OECD
- Temperature: Climate Research Unit (CRU)
- Precipitations: Climate Research Unit (CRU)
- Natural disasters, flood, drought: EM DAT
- Institutions quality: World Governance Indicators (WGI)

### Role of Climate Shocks on the Effect of Financial Flow on Economic Growth Without Human Capital (Table A3)
- Dependent variable: GDP per capita growth. Observations: 325 for columns (1)–(4); 307 for (5)–(6). Countries: 67 for (1)–(4); 66 for (5)–(6).
- Selected coefficient estimates (robust t statistics in parentheses; significance indicated):
  - Log initial GDP per capita: -1.570 (0.65) in (1); -6.094* (1.73) in (2); 0.959 (1.44) in (3); 2.941* (1.88) in (4); -0.111 (0.08) in (5); 1.159 (1.19) in (6).
  - Log population growth: -0.060 (0.00) in (1); 17.547*** (2.86) in (2); 5.128 (0.95) in (3); 17.166 (1.38) in (4); 2.343 (0.22) in (5); 3.465 (0.42) in (6).
  - Log Fixed Gross Capital Formation: 4.889** (2.06) in (1); 1.180 (0.91) in (2); 0.790 (1.31) in (3); 3.219*** (3.44) in (4); 1.628 (1.31) in (5); 1.323 (1.45) in (6).
  - Average month temperature shock: -21.285** (2.45) in (1); -7.766 (1.54) in (3); -14.535*** (2.91) in (4).
  - Average month temperature shock square: 14.353*** (3.09) in (1); 9.766** (2.41) in (3); 10.685*** (2.79) in (4).
  - Log ODA per capita: 5.857* (1.70) in (1); 3.509 (1.22) in (2).
  - Interaction (Average month precipitation shock square)x(ODA): -12.346* (1.81) in (1).
  - Log FDI per capita: 2.350** (2.20) in (2); 2.276* (1.82) in (3).
  - Interaction (Average month precipitation shock square)x(FDI): -5.152** (2.22) in (3).
  - Log Remittances per capita: 2.051** (2.24) in (3); 0.789** (2.09) in (4).
  - Interaction (Average month precipitation shock square)x(Remittances): -3.362** (2.33) in (4).
  - AR(1): p-values range from 0.005 to 0.051 across columns. Hansen: p-values range from 0.365 to 0.958.

### Role of Climate Shocks on the Effect of Aggregate Financial Flow on Economic Growth (Table A4)
- Dependent variable: GDP per capita growth. Observations: 293; Countries: 63.
- Selected estimates:
  - Log initial GDP per capita: -2.698** (2.54) in (1); -0.311 (0.21) in (2); -2.451 (1.07) in (3).
  - Log Human capital: 7.897** (2.23) in (1); 1.009 (0.22) in (2); 10.039* (1.78) in (3).
  - Trade of goods and services: -1.568** (2.23) in (1); 0.388 (0.30) in (2); -1.086*** (2.74) in (3).
  - Financial flow: 5.159** (2.35) in (1); 10.120* (1.66) in (2); 6.379** (2.31) in (3).
  - (Precip. shock square)x(Financial flow): -20.117* (1.85) in (1).
  - Average month temperature shock: -12.347** (2.53) in (3).
  - Average month temperature shock square: 17.055*** (2.73) in (3).
  - (Temp. shock square)x(Financial flow): -4.841** (2.15) in (3).
  - AR(1): p-values 0.059–0.061; Hansen:p-values 0.224–0.596.

### Role of Maximum and Minimum Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth (Table A5)
- Dependent variable: GDP per capita growth. Observations: range 274–310 across columns; Countries: 63–64.
- Selected coefficients and interactions (significance noted):
  - Log initial GDP per capita: -1.614 (1.28) in (1); -7.142** (2.46) in (2); -7.994** (2.41) in (3).
  - Log population growth: 27.008*** (4.87) in (7); 23.146*** (5.06) in (12).
  - Log Fixed Gross Capital Formation: 2.769*** (2.71) in (7); 2.559*** (2.97) in (8); 4.078*** (3.71) in (10).
  - Log Human capital: 7.395* (1.91) in (1); 22.481** (2.33) in (2).
  - Trade of goods and services: -1.085*** (3.04) in (1); -1.159** (2.23) in (3); -1.434*** (2.71) in (8); -1.574*** (2.67) in (10); -1.441** (2.47) in (12).
  - Log ODA per capita: 1.401* (1.78) in (1); 5.264*** (2.83) in (3); 6.062* (1.86) in (4).
  - (Max month precipitation shock square)x(ODA): -1.317* (1.72) in (1).
  - (Max month temperature shock square)x(ODA): -0.183* (1.67) in (1).
  - (Min month temperature shock square)x(ODA): -5.108* (1.85) in (1).
  - Log FDI per capita: 2.240** (2.01) in (4); 3.102* (1.85) in (5); 2.703*** (3.35) in (6); 1.259* (1.72) in (8).
  - Various interaction terms with FDI and Remittances show negative and significant coefficients, e.g., (Max month precipitation shock square)x(FDI): -1.815* (1.83) in (5); (Min month precipitation shock square)x(FDI): -3.332* (1.81) in (6); (Max month precipitation shock square)x(Remittances): -1.584*** (3.25) in (9); (Min month precipitation shock square)x(Remittances): -1.136* (1.94) in (10).
  - AR(1): p-values often < 0.05. Hansen:p-values vary across columns.

### Role of Natural Disasters on the Effect of ODA, FDI and Remittances on Economic Growth (Table A6)
- Dependent variable: GDP per capita growth. Observations: 276–299 across columns; Countries: 63–64.
- Selected estimates:
  - Log initial GDP per capita: -12.106*** (3.86) in (1).
  - Log Fixed Gross Capital Formation: 2.516** (2.56) in (4); 2.417*** (3.15) in (7).
  - Log Human capital: 4.492* (1.86) in (5); 5.660* (1.74) in (7).
  - Trade of goods and services: -1.925*** (2.74) in (2); 1.502** (2.41) in (4); -1.809* (1.76) in (8).
  - Log ODA per capita: 7.423** (2.17) in (1).
  - (Natural Disasters)x(ODA): -0.474* (1.74) in (1).
  - Log FDI per capita: 2.437* (1.84) in (2); 1.672** (2.20) in (7).
  - (Natural Disasters)x(FDI): -0.539* (1.65) in (2).
  - Log Remittances per capita: 0.620* (1.70) in (2).
  - Interactions with Drought and Flood: DroughtxODA: -1.955* (1.70) in (3); DroughtxFDI: -0.925* (1.73) in (3); DroughtxRemittances: -0.973* (1.69) in (3); FloodxODA: -0.662* (1.78) in (3); FloodxFDI: -1.020* (1.95) in (3); FloodxRemittances: -1.269* (1.68) in (3).
  - AR(1): p-values often < 0.05. Hansen:p-values vary.

### Role of Average Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth Using Three-year Time Periods (Table A7)
- Dependent variable: GDP per capita growth. Observations: 496 (or 488) depending on column; Countries: 64 (63 for some).
- Selected estimates:
  - Log initial GDP per capita: -7.232** (2.25) in (1); -10.300* (1.69) in (2); -9.243** (2.44) in (3).
  - Log Fixed Gross Capital Formation: 2.436*** (3.83) in (1); 3.357*** (3.53) in (4); 2.317** (2.44) in (6).
  - Log Human capital: 17.293** (2.35) in (1); 33.408*** (2.94) in (3).
  - Average month temperature shock: -12.739*** (2.89) in (1); -12.662*** (3.33) in (3).
  - Average month temperature shock square: 11.699*** (3.06) in (1); 9.533*** (3.58) in (3).
  - Log ODA per capita: 4.166* (1.78) in (1); 3.927** (1.97) in (2).
  - (Average month precipitation shock square)x(ODA): -6.847* (1.71) in (1).
  - Log FDI per capita: 5.233** (2.03) in (2); 1.659* (1.82) in (3).
  - (Average month precipitation shock square)x(FDI): -6.015** (2.16) in (3).
  - Log Remittances per capita: 2.486** (2.20) in (3); 0.803** (2.35) in (4).
  - (Average month precipitation shock square)x(Remittances): -3.219** (2.25) in (4).
  - AR(1): p-values 0.001–0.005. Hansen:p-values reported.

### Role of Average Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth According to Development Level (Table A8)
- Dependent variable: GDP per capita growth. Observations: 310; Countries: 64 (63 in some columns).
- Selected estimates:
  - Log initial GDP per capita: -11.240*** (3.37) in (1); -9.083*** (3.00) in (2).
  - Log Fixed Gross Capital Formation: 2.425*** (2.72) in (3); 2.255** (2.13) in (5); 2.041** (2.09) in (6).
  - Log Human capital: 23.270*** (2.86) in (1); 21.066*** (2.64) in (2); 23.457*** (3.37) in (4); 25.986** (2.47) in (5).
  - (Average month precipitation shock square)x(ODA): -10.073** (2.33) in (1).
  - (Precip. shock)x(ODA)x(develop. level): 1.135** (2.15) in (1).
  - (Average month precipitation shock square)x(FDI): -18.938** (2.16) in (2).
  - (Precip. shock)x(FDI)x(develop. level): 1.577* (1.86) in (2).
  - (Average month precipitation shock square)x(Remittances): -7.584*** (2.77) in (3).
  - (Precip. shock)x(Remit.)x(develop. level): 0.692** (2.10) in (3).
  - (Average month temperature shock square)x(FDI): -4.586** (2.07) in (2).
  - (Average month temperature shock square)x(Remittances): -3.379*** (2.63) in (3).
  - AR(1): p-values range from 0.014 to 0.085. Hansen:p-values reported.

### Role of Average Precipitation Shocks on the Effect of ODA, FDI and Remittances on Economic Growth According to the Period (Table A9)
- Dependent variable: GDP per capita growth. Observations: 310, 310, and 293 across columns; Countries: 64, 64, and 63.
- Selected estimates:
  - Log Fixed Gross Capital Formation: 3.326 (1.49) in (1); 2.078** (2.26) in (2); 2.029* (1.81) in (3).
  - Log Human capital: 13.702** (2.07) in (1); 15.430 (1.40) in (3).
  - Average month precipitation shock square: 27.864 (1.21) in (1); 12.532 (1.60) in (2); 9.313 (1.10) in (3).
  - Log ODA per capita: 3.948* (1.95) in (1).
  - (Average month precipitation shock square)x(ODA): -6.029* (1.68) in (1).
  - (Average month precipitation shock square)x(FDI): -5.261** (2.14) in (2).
  - Log Remittances per capita: 1.932** (2.16) in (3).
  - (Average month precipitation shock square)x(Remittances): -2.613* (1.71) in (3).
  - Period-interaction terms with Precip. shock reported for 1995-1999, 2000-2004, 2005-2009, 2010-2014 dummies (coefficients and t-statistics as listed).

### Role of Average Temperature Shocks on the Effect of ODA, FDI and Remittances on Economic Growth According to the Period (Table A10)
- Dependent variable: GDP per capita growth. Observations: 310, 301, and 293; Countries: 64, 64, and 63.
- Selected estimates:
  - Log initial GDP per capita: -4.809** (1.97) in (1); -11.597*** (2.81) in (2).
  - Log population growth: 12.415*** (3.25) in (2); 12.091 (1.36) in (3).
  - Average month temperature shock square: 12.438 (1.18) in (1); 6.880* (1.66) in (3).
  - Log ODA per capita: 4.861* (1.92) in (1).
  - (Average month temperature shock square)x(ODA): -5.732** (2.27) in (1).
  - Log FDI per capita: 14.669 (1.49) in (2).
  - (Average month temperature shock square)x(FDI): -1.057* (1.89) in (2).
  - Log Remittances per capita: 1.342* (1.74) in (1).
  - (Average month temperature shock square)x(Remittances): -0.982* (1.82) in (1).
  - Period-interaction terms with Temp. shock for 1995-1999, 2000-2004, 2005-2009, 2010-2014 dummies are reported with coefficients and t-statistics.

### Impact of Climate Shocks on the Effect of ODA, FDI and Remittances on Economic Growth According to the Period: Role of Absorptive Capacity (Table A11)
- Dependent variable: GDP per capita growth. Observations: 325 for columns (1)–(4); 307 for (5)–(6). Countries: 67 for (1)–(4); 66 for (5)–(6).
- Selected estimates:
  - Log initial GDP per capita: -4.285*** (3.27) in (1); -2.409*** (3.70) in (2).
  - Log population growth: -14.530* (1.86) in (1); -8.920** (2.48) in (2).
  - Log Fixed Gross Capital Formation: 2.119** (2.55) in (1); 0.707** (1.96) in (2); 1.696*** (6.72) in (3); 2.613*** (4.50) in (4).
  - Trade of goods and services: 1.189 (1.53) in (1); 1.081*** (4.22) in (3); 2.348*** (4.23) in (5).
  - Average month precipitation shock: 14.369 (1.29) in (1); -3.903 (1.44) in (2); -7.170** (2.43) in (3).
  - Average month precipitation shock square: 40.192** (2.42) in (1); 13.756*** (5.33) in (2); 12.837*** (4.03) in (3).
  - Average month temperature shock: -4.883 (1.31) in (1); -5.267** (2.04) in (2); -4.464*** (2.67) in (3).
  - Average month temperature shock square: 4.239** (2.20) in (1); 4.839*** (3.28) in (2); 2.677** (2.36) in (3).
  - Absorptive Capacity dummy: coefficients reported (e.g., 1.242 (1.19) in (1); 1.408 (1.46) in (2)).
  - Log ODA per capita: 5.451** (2.23) in (1); -0.017 (0.03) in (2).
  - (Average month precipitation shock square)x(ODA): -9.381** (2.26) in (1).
  - (Precip. shock)x(ODA)x(absorptive Capacity): -0.686* (1.77) in (1).
  - (Average month temperature shock square)x(ODA): -0.254** (2.02) in (1).
  - (Temp. shock)x(ODA)x(absorptive Capacity): -0.603* (1.96) in (1).
  - Log FDI per capita: 0.771* (1.66) in (2).
  - (Average month precipitation shock square)x(FDI): -2.468** (2.46) in (2).
  - (Precip. shock)x(FDI)x(absorptive Capacity): -0.473* (1.73) in (2).
  - (Average month temperature shock square)x(FDI): -0.518*** (2.63) in (2).
  - (Temp. shock)x(FDI)x(absorptive Capacity): -0.570* (1.81) in (2).
  - Log Remittances per capita: 0.598** (2.02) in (3); 0.658*** (4.84) in (4).
  - (Average month precipitation shock square)x(Remittances): -1.417*** (2.62) in (3).
  - (Precip. shock)x(Remit.)x(absorptive Capacity): -0.804** (2.17) in (3).
  - (Temp. shock)x(Remit.)x(absorptive Capacity): -0.373*** (3.77) in (3).
  - AR(1): p-values reported (e.g., 0.011–0.054); Hansen:p-values reported.

*Appendix tables and notes: Robust t statistics in parentheses. Significance codes: * p < 0.10, ** p < 0.05, *** p < 0.01. Period dummies are included in the equations. Instrument counts and AR/Hansen p-values are reported for each specification.*

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