Behavioural reporting noise in digital tax administration: Compliance signal distortions under the UK making tax digital for income tax regime

Digital tax reforms that increase reporting frequency alter not only the reporting frequency of taxpayers but also the behavioural conditions that produce compliance information. This paper introduces behavioural reporting noise (BRN), defined as systematic distortions of reported compliance signals generated by adaptation to a reporting regime rather than underlying noncompliance. Using Making Tax Digital for Income Tax Self-Assessment (MTD-ITSA) as a motivating example, the paper develops a theoretical framework around three mechanisms: compressed categorisation under deadline pressure, precautionary underclaiming under uncertainty, and misalignment between quarterly submissions and annual declarations. Drawing on research on default effects, cognitive scarcity, and administrative burden, the paper argues that these mechanisms can generate directional and correlated bias in compliance data. The paper also outlines a research design for future empirical testing using HMRC’s pilot population. It contributes to behavioural public administration by identifying BRN as a distinct analytical construct with implications for the design, interpretation, and evaluation of digital governance reforms.