Gross domestic product without calibration: How invisible errors undermine governance in fragmented states and the National Product Uncertainty Loss model response

Abstract

Gross Domestic Product (GDP) remains the principal benchmark of economic performance; however, its interpretative value depends fundamentally on the reliability, traceability, and calibration of underlying measurement systems. Despite extensive debates on alternative indicators, limited attention has been given to the reliability of GDP as a measured construct. This study aims to operationalize measurement uncertainty as a governance-relevant variable within macroeconomic interpretation. To address this gap, this study introduces the National Product Uncertainty Loss (NPUL) model, a framework designed to quantify the economic implications of insufficiently calibrated measurement processes embedded within sectoral GDP components, thereby revealing hidden fiscal risks that remain unaccounted for in conventional economic indicators. Using Albania as a pilot case, complemented by comparative insights from Western Balkan economies, the analysis demonstrates how traceability gaps in sectors such as energy, healthcare, hydrocarbons, and construction translate into measurable fiscal exposure. Empirical simulations suggest that systemic measurement inefficiencies may generate baseline deviations of ~0.3–0.4% of GDP, with upper-bound scenarios reaching 1–2.2% under adverse conditions. The findings indicate that strengthening calibration infrastructures and improving uncertainty transparency are not merely technical refinements but core elements of institutional governance reform. By integrating measurement reliability into macroeconomic interpretation, NPUL enhances the policy relevance of GDP and contributes to ongoing debates on data governance, digital sovereignty, and evidence-based policymaking.