UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

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Recent Submissions

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    Environmental bonds and public liability for resource extraction site cleanup
    (University of Waterloo, 2024) Insley, Margaret
    Governments have been left with large liabilities for cleanup at natural resource extraction sites after firms have declared bankruptcy. This research studies the impact of different forms of financial assurance on a firm's optimal actions over the full life cycle of a hypothetical natural gas well, in a world of uncertain natural gas prices, when firm bankruptcy may shift cleanup costs to the government. A firm's stochastic optimal control problem is described by an HJB equation with the natural gas price modelled as a stochastic differential equation. The impact of financial assurance is examined in relation to firm investment decisions, the cleanup liability imposed on government and resource taxation revenue. A Cash Deposit and Surety Bond are contrasted with the case of no financial assurance requirement. The "fair" fee (assuming the absence of arbitrage opportunities) is determined for the Surety Bond issued by a third party. Numerical results demonstrate that in the presence of distortionary taxes, there is a trade off between indemnifying the government against cleanup costs versus maintaining government tax and royalty revenues. A numerically plausible case is presented in which the total value of the natural gas well (to the firm and the government) is not increased by the imposition of a strict form of financial surety.
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    Canada's racialized immigrant women
    (University of Waterloo, 2024) Ferrer, Ana; Dhatt, Sumeet Singh
    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 database. 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 or 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.
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    The economics of Canadian immigration levels
    (University of Waterloo, 2024) Doyle, Matthew; Skuterud, Mikal; Worswick, Christopher
    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.
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    Establishing a FAIR, CARE, and efficient synthetic health data sharing ecosystem for Canada
    (University of Waterloo, 2024) Chen, Helen; Grossman, Maura R.; Sen, Anindya; Tsao, Shu-Feng
    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 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 data-sharing 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.
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    Implicit incentive provision with misspecified learning
    (University of Waterloo, 2025) Echenique, Federico; Li, Anqi
    We study misspecified Bayesian learning in principal-agent relationships, where an agent is assessed by an evaluator and rewarded by the market. The agent's outcome depends on their innate ability, costly effort - whose effectiveness is governed by a productivity parameter - and noise. The market infers the agent's ability from observed outcomes and rewards them accordingly. The evaluator conducts costly assessments to reduce outcome noise, which shapes the market's inferences and provide implicit incentives for effort. Society - including the evaluator and the market - holds dogmatic, inaccurate beliefs about ability, which distort learning about effort productivity and effort choice. This, in turn, shapes the evaluator's choice of assessment. We describe a feedback loop linking misspecified ability, biased learning about effort, and distorted assessment. We characterize outcomes that arise in stable steady states and analyze their robust comparative statistics and learning foundations. Applications to education and labor market reveal how stereotypes can reinforce across domains - sometimes disguised as narrowing or even reversals of outcome gaps - and how policy interventions targeting assessment can help.