Bias Modeling for Integrating Digital Data and Conventional Surveys for Migration Estimation

Abstract

Obtaining reliable and timely estimates of migration flows is critical for advancing migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular information on time and space but do not draw from representative samples of the population, thus leading to biased estimates. The thesis proposes a method for combining digital and survey data by modeling the spatial and temporal dependence structure of the biases of digital data. We use simulations to demonstrate the validity of the model, then empirically illustrate our approach by combining geo-located Twitter data with data from the American Community Survey (ACS) to estimate state-level emigration in the United States. We show that our model that combines unbiased and biased data produces predictions that are more accurate than predictions based solely on unbiased data. Our approach demonstrates how digital data can be a complement, rather than a replacement of, representative surveys.