We develop a dynamic model of a platform economy where tokens serve as a means of payment among platform users and are issued to finance investment in platform productivity. Tokens are optimally rewarded to platform owners when token supply (normalized by productivity) is low and burnt to boost franchise value when the normalized supply is high. Although token price is determined in a liquid market, the platform’s financial constraint generates an endogenous token issuance cost that causes underinvestment through the conflict of interest between insiders (owners) and outsiders (users). Blockchain technology mitigates underinvestment by addressing the owners’ time inconsistency problem.

We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users’ transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an -curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users’ transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.

Abstract The rise of centralized mining pools for risk sharing does not necessarily undermine the decentralization required for blockchains: because of miners’ cross-pool diversification and pool managers’ endogenous fee setting, larger pools better internalize their externality on global hash rates, charge higher fees, attract disproportionately fewer miners, and grow more slowly. Instead, mining pools as a financial innovation escalate miners’ arms race and significantly increase the energy consumption of proof-of-work-based blockchains. Empirical evidence from Bitcoin mining supports our model’s predictions. The economic insights inform other consensus protocols and the industrial organization of mainstream sectors with similar characteristics but ambiguous prior findings.

Central banks all over the world are at various stage of developing central bank digital currency (CBDC). Few countries have launched the CBDC or widely used it in the economy. In addition to surveying the literature, which features extremely scarce empirical studies due to data limitations, this paper provides likely the earliest and the most comprehensive empirical documentation of the adoption of China's CBDC (e-CNY) after the central bank launched and actively promoted it in pilot regions using both regulatory power and economic incentives. We find that regions with active promotions for e-CNY plausibly witness more frequent and larger e-CNY transactions, more wallet creations, and greater merchant adoption. However, despite the strong intervention, individual users mostly stick to existing electronic payment Apps and are reluctant to switch to e-CNY. Given the world eagerly learns from China's experiment and China plans to expand the application scope of e-CNY as a general payment tool domestically and internationally with added smart-contract functions, we discuss primary challenges and potential paths forward for the development of e-CNY and CBDCs in general.

Oracle nodes enable smart contracts to access off-chain and cross-chain data, thus bridging information among digital networks and with the real economy. However, reliance on external input creates security and reliability risks in information aggregation and transfer. We describe the general oracle problem, introduce the Oracle Trilemma – highlighting the trade-offs between scalability, decentralization, and truthfulness – and discuss relevant economic issues concerning the role of oracles in applications such as DeFi, supply chain management, gaming, and prediction markets. In particular, we survey off-chain reporting, off-equilibrium alerting, and dynamic incentive design as promising approaches to resolving the trilemma. We further list oracle vulnerabilities and evaluate oracle sustainability through staking and tokenomics programs. Finally, we highlight empirical studies on how oracle integration affects DeFi adoption, token valuation, liquidity, and system risks. We conclude by presenting emerging trends and suggesting open research questions.

We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns , generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean–variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.

Abstract We study credit allocation across firms and its real effects during China’s economic stimulus plan of 2009–2010. We match confidential loan-level data from the nineteen largest Chinese banks with firm-level data on manufacturing firms. We document that the stimulus-driven credit expansion disproportionately favored state-owned firms and firms with a lower average product of capital, reversing the process of capital reallocation toward private firms that characterized China’s high growth before 2008. We argue that implicit government guarantees for state-connected firms become more prominent during recessions and can explain this reversal. Received August 23, 2017; editorial decision November 15, 2018 by Editor Philip Strahan.