Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions

TL;DR

This paper demonstrates that integrating high-frequency weather data, specifically daily weather information, into models significantly enhances the accuracy of predicting volatile welfare measures like consumption expenditure, compared to models relying solely on satellite imagery.

Contribution

The study introduces a novel approach of combining high-frequency weather data with satellite imagery to improve consumption expenditure predictions.

Findings

Incorporating daily weather data improves prediction accuracy.

Weather data integration outperforms satellite-only models.

Enhanced predictions benefit welfare analysis in data-sparse regions.

Abstract

Recent efforts have been very successful in accurately mapping welfare in datasparse regions of the world using satellite imagery and other non-traditional data sources. However, the literature to date has focused on predicting a particular class of welfare measures, asset indices, which are relatively insensitive to short term fluctuations in well-being. We suggest that predicting more volatile welfare measures, such as consumption expenditure, substantially benefits from the incorporation of data sources with high temporal resolution. By incorporating daily weather data into training and prediction, we improve consumption prediction accuracy significantly compared to models that only utilize satellite imagery.