Is China’s GDP undervalued? A digital economy model

Abstract

Abstract Transactions in a digital economy generate data that firms accumulate but national accounts do not price. Within the model developed here, that omission implies measured GDP understates output. We adapt the recursive data-economy framework of Farboodi and Veldkamp (2026) to China and construct a bottom-up measure of digital investment from the itemized asset disclosures of 5,120 listed firms. The model does not identify a unique asset perimeter, so we carry two measurement boundaries throughout rather than designating one as the headline. For 2024 the adjustment is 1.60% of GDP, or 2.16 trillion RMB, under a broad boundary that includes telecommunications network assets, and 0.60%, or 0.82 trillion RMB, under a narrow boundary that excludes them. These are alternative measurement boundaries, not statistical bounds. On listed firms alone, before any extrapolation to unlisted firms, the broad-boundary figure is 0.91%. Because the knowledge stock is not identified without forecast-error data, the broad-boundary figure carries a calibrated scenario range of 0.77–4.07%, which reflects alternative calibrations rather than sampling uncertainty. Every figure is conditional on a normalization of the mapping between measured digital investment and the value of data. A validation audit of the classifier uncovers measurement defects that are not specific to China.