Predicting poverty trends by survey-to-survey imputation: the challenge of comparability
验证了一种基于统计模型的调查间插补方法，利用消费调查和简易调查更频繁地测量贫困率变化，以马拉维十年数据为例，发现该方法在控制家庭人口构成和季节性覆盖后有效，但不同调查的可比性是主要挑战。
Abstract Poverty in low-income countries is usually measured using large and infrequent household consumption surveys. The challenge is to find methods to measure poverty rates more frequently. This study validates a survey-to-survey imputation method, based on a statistical model utilizing consumption surveys and light surveys to measure changes in poverty rates over time. A decade of poverty predictions and regular poverty estimates in Malawi provides a unique case study. The analysis suggests that this modelling approach works within the same context given that households’ demographic composition is included in the model. Predicting poverty using different surveys is challenging because of different aspects of comparability. A new way to account for seasonal coverage strengthens the model when imputing for surveys covering different seasons. It is important for national statistics offices and supporting agencies to prioritize maintaining consistency in the way data are collected in surveys to provide comparable trends over time.