2024-05-12-世界银行-利用SWIFT实现发展中国家的高频实时贫困监测_通过即时和频繁跟踪进行的福祉调查（英）_55页_12mb

SWIFT Framework for Poverty Monitoring

Methodology

• Core Function: Provides reliable poverty estimates with reduced cost and time by leveraging machine learning models trained on household surveys.
• Data Requirements: Requires datasets with household welfare aggregates and poverty correlates (e.g., household demographics, asset ownership, labor market status).
• Model Development: Uses stepwise OLS regression and cross-validation to ensure model stability and avoid overfitting.

Key Advantages

• Cost and Time Efficiency: Reduces data collection and analysis costs from years and millions of dollars to minimal additional resources.
• Frequency and Timeliness: Enables frequent (e.g., quarterly or monthly) poverty monitoring, unavailable with traditional methods.
• Flexibility: Applicable in diverse contexts, including crisis situations, geographic disparities, and non-traditional data collection (phone surveys, community-based systems).

Country-Specific Applications

• Paraguay & Botswana: Integrated SWIFT with existing frequent surveys to increase data frequency.
• DRC: Restored comparability between unaligned surveys to continue poverty tracking after data gaps.
• Malawi & Uganda: Utilized community-based and phone surveys to monitor poverty in crisis contexts like climate or refugee emergencies.
• Zambia & Mongolia: Addressed survey design changes to ensure consistent poverty trend analysis.

Global Relevance

• Scalability: Currently used in over 200 projects across 70 countries.
• Innovation Link: Complements advancements in AI and statistical techniques for improved poverty measurement.

Summary

SWIFT is a robust framework for high-frequency poverty monitoring, offering cost-effective and timely alternatives to traditional methods. Its flexibility and proven applications in various contexts position it as a key tool for evidence-based policy-making in low-income countries.