Corinne Luu was appointed Policy Advisor in the Canadian Economic Analysis (CEA) department in November 2022. In this capacity she is involved in research and analysis of the Canadian labour market. She also provides intellectual leadership in support of broader analysis and forecasting of Canadian labour market developments.
Ms. Luu first joined the Bank in 2006 as an economist in the International Economic Analysis Department before moving to the Canadian Economic Analysis (CEA) Department as a Senior Economist in 2009. From 2014 to 2017, she worked as an economist for the Organisation for Economic Co-operation and Development (OECD). Upon her return in 2017, Ms. Luu held the position of Principal Economist in the Labour and Inflation division in CEA.
Ms. Luu holds a Master’s in Economics from the University of British Columbia.
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.
We enhance benchmarks for assessing strength in the Canadian labour market. We find the labour market remains tight despite recent strong increases in labour supply, including among prime-working-age women. We also assess the anticipated easing in labour conditions in a context of high population growth.
We propose a range of benchmarks for assessing labour market strength for monetary policy. This work builds on a previous framework that considers how diverse and segmented the labour market is. We apply these benchmarks to the Canadian labour market and find that it has more than recovered from the COVID-19 shock.
Underlying wage growth has fallen short of what would be consistent with an economy operating with little or no slack. While many factors could explain this weakness, the availability of additional labour resources from informal (“gig”) work—not fully captured in standard measures of employment and hours worked—may play a role.
The literature highlights that labour market churn, including job-to-job transitions, is a key element of wage growth. Using microdata from the Labour Force Survey, we compute measures of labour market churn and compare these with pre-crisis averages to assess implications for wage growth.
Because the Bank of Canada has started withdrawing monetary stimulus, monitoring the transmission of these changes to monetary policy will be important. Subcomponents of consumption and housing will likely respond differently to a monetary policy tightening, both in terms of the aggregate effect and timing.
The financial crisis of 2007–09 has highlighted the importance of developments in financial conditions for real economic activity. The authors estimate the effect of current and past shocks to financial variables on U.S. GDP growth by constructing two growthbased financial conditions indexes (FCIs) that measure the contribution to quarterly (annualized) GDP growth from financial conditions.
After 10 years of impressive growth, India is now the fourth largest economy in the world. Yet, to date, India's impact on global commodity markets has been muted. The authors examine how India's domestic and trade policies have distorted and constrained its demand for commodities.
In this paper, the author considers whether fundamentals or other factors can explain the yen's ongoing weakness. In particular, the importance of capital outflows due to the carry trade and longer-term portfolio investment outflows, which may be delaying the adjustment of the yen, are investigated. A simple portfolio model is developed, composed of a speculative […]
This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.