Predicting Economic Welfare with Images on Wealth

TL;DR

This study demonstrates that household wealth and poverty levels can be reliably predicted using only images from Dollar Street with CNN models, eliminating the need for traditional survey data.

Contribution

Introduces a novel image-based approach using CNNs to predict household economic welfare across multiple countries without traditional survey data.

Findings

CNN accurately predicts consumption levels with RMSE of 0.66 and R-squared of 0.80.

Model classifies extreme poverty with 87% accuracy.

Higher performance in poverty classification when adjusting thresholds by income group.

Abstract

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.