Follow the liquidity: Monetary policy, spatial inequality and the Bank of Canada's emergency COVID-19 corporate debt programs
Bibliographic record
Abstract
• Canada’s central bank implemented novel monetary programs in response to COVID-19. • These programs disproportionately benefited certain regions and sectors. • Central bank asset purchases impact corporate financial actions significantly. • Tracking financial liquidity reveals ties between the public and private sectors. • Monetary policy’s effects are spatially and politically uneven – not neutral. In a financial crisis, maintaining liquidity in the economy is a central concern of monetary policymakers seeking to stave off frozen financial markets. In such moments, central bank actions influence private markets in particularly visible ways. In this paper we analyze how the public sector provides liquidity to the financial sector in moments of crisis, arguing that liquidity provision and risk backstopping are crucial dynamics in the public sector's support of private markets, and can reproduce patterns of spatialized inequality. We use the case study of the Bank of Canada’s (BoC) response to the COVID-19 crisis to examine the geographical impacts of the the BoC's asset purchase programs, which helped entrench an unequal status quo in the Canadian economy. We analyze two emergency response programs, the Corporate Bond Purchase Program and Commercial Paper Purchase Program, and find disproportionate support for certain regions (Alberta, Quebec, Ontario) and sectors (finance, and fossil fuel firms). Through a futher analysis of the balance sheets of Daimler Canada Finance Inc., whose debt the BoC disproportionately purchased, we demonstrate how relational methods of financial analysis can allow geographers to better understand the functioning of power in the financial system beyond what can be revealed by following distinct sums of money. A close read of these balance sheets reveals that “neutral” monetary policy hides distributional effects of liquidity provisions and illustrates profoundly spatial dynamics that contribute to the entrenchment of financial power and rentiership in the Canadian economy and maintain geographical inequalities in the name of supporting the economy through crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".