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Monetary theory says that money is essential if it helps to achieve better incentive-feasible outcomes. We test this in the laboratory.

We study the competition between a welfare-maximizing public payment platform (e.g., CBDC or fast payment system) and a profit-maximizing private platform in a two-sided market, deriving optimal public pricing and showing how network effects, fragmentation, and policy mandates like zero fees or cost recovery shape welfare, usage, and fee incidence.

Would a shift in trading in fixed-income markets—from over the counter (bilateral trading) to a centralized electronic platform—improve welfare? We use trade-level data on the secondary market for Government of Canada debt to answer this question.

We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes.

The Canadian Financial Monitor (CFM) survey uses non-probability sampling for data collection, so selection bias is likely. We outline methods for obtaining survey weights and discuss the conditions necessary for these weights to eliminate selection bias. We obtain calibration weights for the 2018 and 2019 online CFM samples.

This paper studies optimal need-based financial aid when parental transfers—unobserved by policymakers—vary across and within families of similar means. Using data on U.S. college students, I document substantial inequality in parental transfers, especially among wealthier families. I then analyze how this affects aid design aimed at reducing inefficiencies from borrowing constraints and the aid itself.

Since the autumn of 1997, the regional offices of the Bank of Canada have conducted quarterly consultations with businesses across Canada. Timed to feed into the process that precedes the Bank's fixed dates for announcing monetary policy decisions, the consultations (now referred to as the Business Outlook Survey) are structured around a questionnaire which is sent to 100 firms that reflect the Canadian economy in terms of region, type of business activity, and firm size. Martin describes both the consultation process and the questionnaire and makes an initial assessment of the data gathered during the business interviews. The article includes charts and correlation tables that illustrate the responses to the key questions included in the survey.

Uncertainty about the level of future inflation adversely affects the economy because it distorts the savings and investment decisions of households and businesses. Since these decisions typically involve planning horizons of many years, the adverse effects from inflation uncertainty can be reduced by adopting a policy framework that makes future inflation more predictable over long time horizons. When the inflation-control target was renewed in May 2001, the agreement affirmed that monetary policy will be directed at moving inflation to the 2 per cent midpoint of the target range over a six-to-eight-quarter horizon. The author describes how this policy commitment increases the predictability of average inflation over periods longer than one year. This relationship is illustrated using the Canadian experience from the inflation-targeting period.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

We examine loan insurance—credit risk transfer upon origination—in a model in which lenders can screen, learn loan quality over time, and can sell loans. Some lenders with low screening ability insure, benefiting from higher market liquidity of insured loans while forgoing the option to exploit future information about loan quality.