#24-001 --Helen Chen, Maura R. Grossman, Anindya Sen, Shu-Feng Tsao
Establishing a FAIR, CARE, and Efficient Synthetic Health Data Sharing Ecosystem for Canada
Abstract
Obtaining access to real-world health data is a significant challenge, mainly due to privacy and security implications. Consequently, researchers and technology innovators ̶ particularly those operating in the health data science and AI technology development spaces – increasingly resort to synthetic health data to bridge the data gap. High-quality synthetic data has the potential to expedite research and development of novel technologies. However, synthetic health datasets in Canada are scarce, and no existing synthetic health datasets conform to the Findable, Accessible, Interoperable, and Reusable (FAIR) standards. Moreover, while federated machine learning offers the advantage of protecting patient privacy by not requiring the exchange of source data across nodes, it has yet to be optimized in Canada’s health research environment, and there is limited use of federated learning with synthetic health data. This paper explores the ethical considerations and value proposition of generating and sharing synthetic health data.
Our goal is to facilitate the development of a reliable and sustainable synthetic data infrastructure that
supports the ethical, responsible, and efficient use of synthetic health data. An important contribution of this research is the establishment of a framework that balances the social benefits of innovation from data sharing with the social costs that occur when individual privacy is compromised. The use of synthetic data significantly reduces the potential for individual harm and is a cost-effective means to lower datasharing costs. We believe that this framework will pave the way for a more robust and secure synthetic data ecosystem, enabling the generation of valuable insights that can drive positive health outcomes for Canadians.
#24-002 --Matthew Doyle, Mikal Skuterud and Christopher Worswick
The Economics of Canadian Immigration Levels
Abstract
In the hope of addressing chronic labour shortages and sluggish economic growth, the Canadian government plans to increase immigration in the coming years to per capita levels not reached since the 1920s. We argue that economic immigration in the Canadian context should aim to boost GDP per capita in the full population including the newcomers. We then examine the potential for increases in Canadian immigration levels to achieve this objective. Our analysis suggests that Canada is not well-positioned to leverage heightened immigration to boost GDP per capita owing primarily to weak capital investment and quantity-quality tradeoffs in immigrant selection. We conclude by providing a framework for identifying the optimal level of economic immigration
#24-003 --Ana Ferrer and Sumeet Singh Dhatt
Canada's racialized immigrant women
Executive Summary
Immigrants have traditionally lagged behind labour outcomes of Canadian born workers, a fact that is more obvious for immigrant women and for recent arrivals (those entering Canada within the last five years). In this report we explore the barriers and challenges faced by racialized newcomer women in the Canadian labour market and how differences in their characteristics are (or aren’t) related to differences in labour market outcomes. We use a specially designed survey to capture the experiences of a sample of racialized newcomer women regarding integration into the labour market and what resources and strategies have been most helpful in achieving career success and improving their quality of life. We follow with an in-depth analysis of the labour market environment of immigrant women to Canada using data from the Labour Force Survey and the O*Net data base. This allows us to quantify to what extent immigrant women may be facing barriers and challenges in the labour market, not only along many standard measures of job quality, such as employment, pay, or type of contract, but also examining other non-standard measures of job quality that are informative of the resilience of the jobs immigrants hold, such as the tasks they perform in their jobs.
We find that significant initial gaps between newcomer and Canadian-born women exist in employment, wages, schedules and tasks. Newcomer women work less hours, are less likely to work full time or have permanent contracts and earn substantially less than their Canadian-born counterparts. They are also less likely to work jobs requiring non-routine cognitive tasks, which are typically associated with quality jobs. However, they also experience significant improvements along all job aspects. For instance, initial wage gaps to between 63% and 68% of their original size over a span of twenty years. This progress is slightly faster for university educated women. More importantly, gaps in non-routine job tasks also diminish substantially over time, at least by 50% if not more. While it is difficult to evaluate whether a given type of job task signals a job as “good“, the general consensus is that nonroutine tasks will be harder to replace by technology, making those tasks – and the jobs that require them - safer (Frank et al., 2021).
Additional analysis also shows that these improvements are far from being homogenous among immigrant women, with significant additional gaps for immigrants more likely to be racialized, particularly those from East and South-east Asia. On this note, we highlight the substantial heterogeneity of experiences regarding the labour market integration of racialized immigrant women, and immigrant women more generally. Education plays a major part in these differences, but also family situation and the ability to validate foreign experience and credentials during the job search