Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
研究显示，将卫星图像与家庭调查数据结合，能提高墨西哥市级贫困率估计的准确性和精度，其中基于两阶段家庭模型的预测方法优于其他常见模型。
ABSTRACT Estimates of poverty are an important input into policy formulation in developing countries, making the accurate measurement of poverty rates a first‐order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the accuracy and precision of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that empirical best prediction (EBP) based on a twofold household‐level model outperforms EBPs based on other common small area estimation models. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve poverty estimates in developing countries, even for small subgroups.