The State's Politics of "Fake Data"

TL;DR

This paper explores how state data, often labeled as 'fake,' serve institutional functions by balancing accuracy with organizational needs, emphasizing their relational, processual, and performative nature.

Contribution

It introduces a nuanced understanding of 'fake' state data as relational, processual, and performative, shifting focus from accuracy to organizational utility and governance.

Findings

State data often serve organizational interests over representational accuracy.

Fakeness in data is relational, context-dependent, and emerges through workflows.

Recognizing the politics of data fictions can improve governance and accountability.

Abstract

Data have power. As such, most discussions of data presume that records should mirror some idealized ground truth. Deviations are viewed as failure. Drawing on two ethnographic studies of state data-making in a Chinese street-level bureaucrat agency and at the US Census Bureau we show how seemingly "fake" state data perform institutional work. We map four moments in which actors negotiate between representational accuracy and organizational imperatives: creation, correction, collusion, and augmentation. Bureaucrats routinely privilege what data do over what they represent, creating fictions that serve civil servants' self-interest and enable constrained administrations. We argue that "fakeness" of state data is relational (context dependent), processual (emerging through workflows), and performative (brought into being through labeling and practice). We urge practitioners to center fitness-for-purpose in assessments of data and contextual governance. Rather than chasing impossible representational accuracy, sociotechnical systems should render the politics of useful fictions visible, contestable, and accountable.