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In this article, the authors build on the framework used in the Bank of Canada's Financial System Review to assess the evolution of household indebtedness and financial vulnerabilities in response to changing economic conditions. To achieve this, they first compare two microdata sets generated by Ipsos Reid's Canadian Financial Monitor and Statistics Canada's Survey of Financial Security. They find that the surveys are broadly comparable, despite methodological differences. This enables them to use the combined information content for the identification of the threshold value of the debt-service ratio (DSR). The article then presents an innovative framework that uses household-level microdata to simulate changes in the distribution of the DSR under various stress scenarios. The authors show how this framework can be used by analyzing the effects of two different scenarios on the distribution of the debt-service ratio and the impact on vulnerable households. This tool will enable researchers to refine their analyses of current risks to the financial health of Canadian households. The article concludes with comments on future directions for refining the Bank's analyses of household sector risk.

Deputy Governor Lawrence Schembri speaks before the Economics Society of Northern Alberta (ESNA).

We simulate introducing a central bank digital currency (CBDC) and consider consumer adoption, merchant acceptance and usage at the point of sale. Modest adoption frictions significantly inhibit CBDC market penetration along all three dimensions. Incumbent responses to restore pre-CBDC market shares are moderate to small and further reduce the impact of a CBDC.

This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure.

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes.

We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs.

We analyze the value proposition and limitations of decentralized finance (DeFi). Based on a distributed ledger and smart contracts, DeFi can guarantee the execution of financial contracts, potentially lowering the costs of intermediation and improving financial inclusion.

Many reports and analyses interpret the release of new economic data based on the headline surprise—for instance, total inflation, real GDP growth and the unemployment rate. However, we find that headline news alone cannot adequately explain the responses of market prices to new information. Rather, market prices react more strongly, on average, to non-headline news such as the composition of GDP growth, quality of jobs created and revisions to past data. Thus, tracking the impact of non-headline information released on the news day is crucial in analyzing how markets interpret and react to new economic data.

The cost of borrowing Government of Canada treasury bills (t-bills) in the repurchase (repo) market is mainly explained by the relationship between the parties involved. Some pairs of parties conduct most of their repos for t-bills rather than bonds, and at relatively high borrowing costs. We speculate that these pairs have formed a mutually beneficial service relationship in which one party consistently receives t-bills, while the other receives cash at a relatively cheap rate.