论文标题
通过有关财富的图像来预测经济福利
Predicting Economic Welfare with Images on Wealth
论文作者
论文摘要
使用包含有关财富信息的图像，这项研究调查了图片能够可靠地预测家庭的经济繁荣。如果没有关于传统基于财富的方法的与财富相关信息和人工制造的财富质量标准的调查，则这种新颖的方法仅利用在Dollar Street上发布的图像作为66个国家 /地区家庭财富的输入数据，并使用卷积神经网络（CNN）方法预测每个家庭的消费或收入水平。最佳结果可预测CNN回归问题中的均方根误差为0.66的均方根误差，R平方为0.80。此外，这个简单的模型在对极端贫困的分类中也表现良好，精度为0.87，F-beta得分为0.86。由于该模型通过其收入群体将贫困线的不同门槛应用到国家时，在极端贫困分类中表现出更高的表现，因此建议世界银行决定以收入群体不同地定义贫困线的决定是有效的。
Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of wealth quality that the traditional wealth-based approach relied on, this novel approach makes use of only images posted on Dollar Street as input data on household wealth across 66 countries and predicts the consumption or income level of each household using the Convolutional Neural Network (CNN) method. The best result predicts the log of consumption level with root mean squared error of 0.66 and R-squared of 0.80 in CNN regression problem. In addition, this simple model also performs well in classifying extreme poverty with an accuracy of 0.87 and F-beta score of 0.86. Since the model shows a higher performance in the extreme poverty classification when I applied the different threshold of poverty lines to countries by their income group, it is suggested that the decision of the World Bank to define poverty lines differently by income group was valid.
