Economic indicators and the blockchain : an employment level data feed system

Abstract

Accurate and timely economic data collection is essential for informed decisionmaking, yet traditional methods suffer from delays and limited sample sizes. This thesis explores how blockchain, smart contracts, and oracles can enhance the speed, accuracy, and decentralization of economic data collection, addressing concerns raised by Wassily Leontief in 1971. A theoretical use case demonstrates how employment events, such as hiring and firing, can be validated in real-time using blockchain, reducing reliance on outdated reporting systems. Beyond employment data, the study highlights broader applications, including tracking negative externalities, inputoutput economics, and virtual economies. Challenges such as scalability, data privacy, and regulatory compliance are examined, with recommendations for future research. The findings suggest that blockchain-based systems have the potential to transform economic modeling by enabling more responsive, data-driven decision-making.