Hidden by complexity? Measuring stablecoin, crypto and decentralised finance ecosystems

Decentralised finance data presents a distinctive paradox: while every data point is publicly recorded and accessible, deriving meaningful insights is obscured by the scale, fragmentation and complexity of the ecosystem. Key metrics illustrate that the rapidly evolving DeFi ecosystem introduces unique challenges for economic and financial research in accurately capturing financial activity in DeFi. These challenges stem from protocol architecture and the technical execution of transactions that complicate deriving economic meaning. Leveraging blockchain data for Bitcoin, Ethereum and Tron, the paper illustrates three structural sources of measurement divergences: (1) the economically meaningful aggregation of Bitcoin transaction values, (2) the challenge of programmability and proliferation of spurious smart contracts and (3) comparability of use cases across different chains, exemplified by stablecoins. While the examples relate to specific chains and layers of technical execution, the measurement challenges generalise to all blockchains and DeFi ecosystems relying on a similar technical underpinning. We propose measurement approaches based on granular, data bounded estimates that incorporate explicit assumptions, technical classification and disaggregation to align technical execution with economic meaning. The paper demonstrates that, despite the transparency of public blockchains, widely used indicators of cryptoasset and DeFi activity are highly dependent on methodological choices and underlying assumptions that warrant careful interpretation. The findings imply that on-chain indicators should be treated as noisy approximations rather than direct measures of economic activity.