MACROECONOMIC DETERMINANTS OF OUWARD REMITTENCES FROM CANADA
Bibliographic record
Abstract
This paper investigates the influence of Canada’s macroeconomic environment on outward remittance flows over the period 1990–2022. Using the Ordinary Least Squares (OLS) method, the analysis evaluates the role of GDP per capita, exchange rate, inflation, and labor force participation rate in shaping remittance dynamics. The results suggest that the exchange rate is the only statistically significant determinant, while inflation is the least influential variable. These findings highlight the central role of currency fluctuations in determining the scale of remittances sent abroad from Canada and raise questions about the relative weight of broader macroeconomic factors compared to labor market and policy conditions. Beyond the econometric assessment, the paper situates remittance flows within the context of three major global events: the 2008 global financial crisis, the 2014 oil price collapse, and the COVID-19 pandemic. Evidence shows that while the financial crisis did not substantially curtail immigrants’ ability to remit, both the oil price crash and the pandemic led to marked declines in outward transfers. The 2014 episode is particularly noteworthy, as it coincided with restrictive government policies on temporary foreign worker admissions. This overlap illustrates that policy interventions may exert more pronounced and longer-lasting effects on remittance behavior than macroeconomic shocks alone, by directly shaping migrant inflows and employment opportunities. The study concludes by emphasizing the need for a supportive and inclusive regulatory environment for remittances in Canada. Policy recommendations include further reducing transfer costs in line with the United Nations Sustainable Development Goals, ensuring transparency to eliminate hidden costs such as exchange rate markups, and promoting financial access for migrants. Strengthening remittance regulation not only enhances household welfare abroad but also reinforces Canada’s commitment to equitable migration policy and sustainable economic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".