A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach

Abstract

Errors in the collection of household finance survey data may proliferate in population estimates, especially when there is oversampling of some population groups. Manual case-by-case revision has been commonly applied in order to identify and correct potential errors and omissions such as omitted or misreported assets, income and debts. We derive a machine learning approach for the purpose of classifying survey data affected by severe errors and omissions in the revision phase. Using data from the Spanish Survey of Household Finances we provide the best-performing supervised classification algorithm for the task of prioritizing cases with substantial errors and omissions. Our results show that a Gradient Boosting Trees classifier outperforms several competing classifiers. We also provide a framework that takes into account the trade-off between precision and recall in the survey agency in order to select the optimal classification threshold.