2016年-世界发展银行全球_Socioeconomic_Resilience___Multi-Hazard_Estimates_in_117_Countries_47页_1mb

Socioeconomic Resilience: Multi-Hazard Estimates in 117 Countries

Core Content

This working paper introduces a model to assess socioeconomic resilience to natural disasters, defined as the ability of an economy to mitigate the impact of disaster-related asset losses on welfare. The model is applied to 117 countries, considering riverine and storm surge floods, earthquakes, windstorms, and tsunamis. It provides country-level estimates of socioeconomic resilience and evaluates policy options to reduce welfare losses.

Main Viewpoints

• Socioeconomic resilience is quantified as the ratio of asset losses to welfare losses, indicating how effectively an economy can protect its population from the negative impacts of disasters.
• The impact of natural disasters on welfare is not only determined by asset losses but also by distributional effects (who is affected) and coping mechanisms (such as insurance and social protection).
• Post-disaster transfers are shown to be highly effective, with an estimated benefit of at least $1.30 per dollar disbursed, and their efficiency is not very sensitive to targeting errors.
• Adaptive social protection is a particularly promising policy to improve resilience, as it can reduce welfare losses even when it does not directly reduce asset losses.
• Poverty reduction can increase resilience, as it leads to more equitable distribution of assets and income, thereby reducing the impact of consumption losses on the poor.

Key Information

Socioeconomic Resilience Formula

$$
\text {Socioeconomic Resilience} = \frac {\text {Asset Losses}}{\text {Welfare Losses}}
$$

This ratio is used to assess the risk to wellbeing:

$$
\text {Risk} = \frac {(\text {Hazard}) \cdot (\text {Exposure}) \cdot (\text {Asset Vulnerability})}{\text {Socioeconomic Resilience}}
$$

Welfare Function

The paper uses a constant relative risk aversion welfare function:

$$
w (\tilde{c}) = \frac {\tilde{c}^{1 - \eta} - 1}{1 - \eta}
$$

Where:

• $\tilde{c}$ is the net present value of consumption.
• $\eta$ measures risk aversion and aversion to inequality, and is set at 1.5 for this analysis.

Distributional Impacts

• The poverty-exposure bias ($PE$) measures how much more the poor are affected compared to the rest of the population.
• The poor are disproportionately affected by natural disasters due to their higher asset vulnerability and lower income.
• In some countries, every $1 of asset loss leads to $1.6 of welfare loss, emphasizing the greater impact on the poor.

Policy Levers

• The model identifies policy levers to reduce welfare losses, including:
  • Adaptive social protection (especially for the poor).
  • Insurance (especially for wealthier households).
  • Early warning systems and better land-use planning.
• Uniform transfers may be more effective than proportional transfers, as the latter may benefit wealthier households more, leaving less support for the most vulnerable.

Data and Methodology

• The model uses data on natural hazards, population, asset location, and socioeconomic characteristics.
• It incorporates insights from natural and social sciences to estimate expected asset and welfare losses.
• Vulnerability estimates are derived from housing data, assuming that poor people live in more vulnerable buildings.
• The model is calibrated using the Global Assessment Report (UN-ISDR 2015) and includes new countries and hazards beyond the earlier study.

Findings and Implications

• Socioeconomic resilience is a key driver of disaster risk and welfare loss.
• Ignoring distributional impacts can lead to an overly pessimistic view of progress in disaster risk management.
• Reducing inequality and improving access to social protection can significantly reduce welfare losses.
• The model and data are publicly available, supporting replicability and further analysis.

Conclusion

This paper provides a comprehensive framework to assess socioeconomic resilience to natural disasters, emphasizing the importance of distributional effects and ex-post support mechanisms in policy design. It highlights the need for holistic risk management strategies that combine hard measures (e.g., infrastructure) with soft measures (e.g., social protection) to improve resilience and reduce welfare losses.