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We develop a production-network model to capture how decentralized finance (DeFi) has evolved across different sectors of financial services. The model allows us to measure the value added by different DeFi sectors and to study how the connections across the sectors influence token prices.

This paper studies the effects of financial development, taking into account both formal and informal financing. Using cross-country firm-level data, we document that informal financing is utilized more by rich countries than poor countries.

We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant.

We study competition for consumer attention, in which platforms can sacrifice service quality for attention. A platform can choose the “addictiveness” of its service.

We develop a model with firm heterogeneity in importing and cross-border shopping among consumers. Exchange-rate appreciations lower the cost of imported goods, but also lead to more cross-border shopping; hence, the net impact on aggregate retail prices and sales is ambiguous.

The present paper shows that, everything else equal, some transactions to transfer portfolio credit risk to third-party investors increase the insolvency risk of banks. This is particularly likely if a bank sells the senior tranche and retains a sufficiently large first-loss position.

Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.

ToTEM III is the most recent generation of the Bank of Canada’s main dynamic stochastic general equilibrium model for projection and policy analysis. The model helps Bank staff tell clear and coherent stories about the Canadian economy’s current state and future evolution.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

Positive commodity price shocks reduce labor income inequality in commodity-exporting economies by compressing the skill premium, as unskilled-labor-intensive sectors such as construction expand faster than skill-intensive services. A multisector small-open-economy model shows how saving or spending commodity windfalls can reinforce or moderate this decline in inequality.