Following the money by following debt: tracking Ecuador’s sovereign bonds with financial data platforms
Bibliographic record
Abstract
• Financial platforms like Bloomberg Terminals are valuable data sources for studying global capitalism. • Following bonds is a multiscalar method tracing financial actors, practices, and processes. • Details how creditors shaped Ecuador’s 2020 debt restructuring. • Reveals asset managers’ role in sovereign debt markets for developing and emerging economies. • Sheds light on patterns of uneven development in the global financial system. By 2024, trading in emerging market (EM) bonds had surpassed US$6.116 trillion, as the sovereign debt of low- and middle-income countries (LMICs) reached record levels. Credit instruments multiplied during a period of financialization, while the centralization of capital ownership by asset management firms marked a later phase in this process. These transformations have heightened concerns about creditor power and the opacity of bond ownership, particularly as the lack of transparency complicates sovereign debt restructurings for developing and emerging economies (DEEs) during moments of crisis. Indeed, few researchers have directly tracked financial instruments through capital markets, constrained by the volume and complexity of financial transactions, despite calls to “follow the money”. This paper uses the Bloomberg Terminal as a novel data source for tracking changes in bond ownership, pricing, and market activity. It also critically engages with how these financial data platforms mediate the ways global finance knows and values the world. Influenced by historical materialism and informed by critical geographies of accounting and debates around debt audits, I combine commodity chain analyses and social studies of finance. As an example, I follow a 2017 Ecuadorian bond which shows how concentrated ownership in major asset managers shaped restructuring in the wake of a global market shock, how minority creditors leveraged ESG demands as a negotiating tool, and how index-driven portfolio strategies and offshore financial hubs reinforced global capital hierarchies. Beyond sovereign bonds, this method of repurposing data platforms can be applied to other financial instruments, exposing the infrastructures and practices that reproduce uneven development and concentrate financial power.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".