Leveraging Reverse Regressions for Bias Diagnosis in the Digital Economy Datasets

Abstract

This paper evaluates reverse regression in simulations and applications motivated by the digital economy data. Data from digital platforms ranging from e-commerce transactions to user-generated content offers vast potential for economic analysis, yet it frequently suffers from measurement errors and endogeneity problems. With digital platforms producing vast amounts of data that are frequently user-created, collected, or compiled, researchers encounter growing difficulties in validating data reliability. The reverse regression provides a unique diagnostic tool set for identifying and correcting biases when the standard assumptions of Ordinary Least Squares (OLS) are not satisfied. This is particularly true in contexts like gig work income reports, online advertising, and consumer trends inferred from internet activities. Based on the simulated digital data of a medium enterprise business digital sales data associated with advertising expenditure reported via Google or Meta dashboards, this study finds that the forward regressions are biased or attenuated. The study therefore recommends that reverse regression involving the digital platform data be applied as a diagnostic and corrective tool set in early-stage econometric diagnostics, especially when robust instrumental variables are unavailable.