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Recent efforts have been very successful in accurately mapping welfare indatasparse regions of the world using satellite imagery and othernon-traditional data sources. However, the literature to date has focused onpredicting a particular class of welfare measures, asset indices, which arerelatively insensitive to short term fluctuations in well-being. We suggestthat predicting more volatile welfare measures, such as consumptionexpenditure, substantially benefits from the incorporation of data sources withhigh temporal resolution. By incorporating daily weather data into training andprediction, we improve consumption prediction accuracy significantly comparedto models that only utilize satellite imagery.

Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions