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Follow the liquidity: Monetary policy, spatial inequality and the Bank of Canada's emergency COVID-19 corporate debt programs

2025· article· en· W4412060205 on OpenAlexafffundabout
Dan Cohen, Martine August, Emily Rosenman, Yun Liu

Bibliographic record

VenueGeoforum · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of WaterlooQueen's University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsCoronavirus disease 2019 (COVID-19)Market liquidityDebtInequalityMonetary policyFinancial system2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EconomicsBusinessMonetary economicsFinanceMedicineVirologyInternal medicine

Abstract

fetched live from OpenAlex

• 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.237
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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