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2399 result(s)

Assessing the effects of higher immigration on the Canadian economy and inflation

We assess the complex macroeconomic implications of Canada’s recent population increases. We find that newcomers significantly boost the non-inflationary, potential growth of the economy, but existing imbalances in the housing sector may be exacerbated. Greater housing supply is needed to complement the long-term economic benefits of population growth.

Testing Collusion and Cooperation in Binary Choice Games

Staff Working Paper 2023-58 Erhao Xie
This paper studies the testable implication of players’ collusive or cooperative behaviour in a binary choice game with complete information. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart.

Immigration and Provision of Public Goods: Evidence at the Local Level in the U.S.

Staff Working Paper 2023-57 Anna Maria Mayda, Mine Z. Senses, Walter Steingress
Using U.S. county-level data from 1990 to 2010, we study the causal impact of immigration on the provision of local public goods. We uncover substantial heterogeneity across immigrants with different skills and immigrants of different generations, which leads to unequal fiscal effects across U.S. counties.

How Far Do Canadians Need to Travel to Access Cash?

Staff Discussion Paper 2023-28 Heng Chen, Daneal O’Habib, Hongyu Xiao
This paper develops a travel-based metric to measure Canadians’ access to cash from automated banking machines (ABMs) and financial institution branches. We find that, overall, access to cash remained stable between 2019 and 2022. The total number of ABMs in Canada increased by 3.7% and the total number of branches decreased by 5.2% during that period.

International Portfolio Rebalancing and Fiscal Policy Spillovers

Staff Working Paper 2023-56 Sami Alpanda, Uluc Aysun, Serdar Kabaca
We evaluate, both empirically and theoretically, the spillover effects that debt-financed fiscal policy interventions of the United States have on other economies. We consider a two-country model with international portfolio rebalancing effects. We show that US fiscal expansions would increase global long-term rates and hinder economic activity in the rest of the world.

Machine learning for economics research: when, what and how

Staff Analytical Note 2023-16 Ajit Desai
This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.

The Heterogeneous Impacts of Job Displacement: Evidence from Canadian Job Separation Records

Staff Working Paper 2023-55 Serdar Birinci, Youngmin Park, Kurt See
When estimating earnings losses upon job separation, existing strategies focus on separations in mass layoffs to distinguish involuntary separations from voluntary separations. We revisit the measurement of the sources and consequences of job displacement using Canadian job separation records.
Content Type(s): Staff research, Staff working papers Topic(s): Labour markets, Potential output, Productivity JEL Code(s): E, E2, E24, E3, E32, J, J3, J31, J6, J63, J65

Borrow Now, Pay Even Later: A Quantitative Analysis of Student Debt Payment Plans

Staff Working Paper 2023-54 Michael Boutros, Nuno Clara, Francisco Gomes
We investigate alternative student debt contracts that defer payments and ease the burden of student loans on US households by preserving disposable income early in borrowers’ lives. Our model shows substantial welfare gains from these contracts relative to existing plans and gains similar to the Biden administration's proposals but with a significantly lower cost.

Identifying Nascent High-Growth Firms Using Machine Learning

Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald
Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.
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