Public Debt, Ownership and Power The Political Economy of Distribution and Redistribution
Bibliographic record
Abstract
This dissertation offers the first comprehensive historical examination of the political economy of US public debt ownership. Specifically, the study addresses the following questions: Who owns the US public debt? Is the distribution of federal government bonds concentrated in the hands of a specific group or is it widely held? And what if the identities of those who receive interest payments on government bonds are distinct from those who pay the taxes that finance the interest payments on the public debt? Does this mean that the public debt redistributes income from taxpayers to public creditors? Who ultimately bears the burden of financing the public debt? \n \nDespite centuries of debate, political economists have failed to come to any consensus on even the most basic facts concerning ownership of the US public debt and its potential redistributive effects. Some claim that the public debt is heavily concentrated and that interest payments on government bonds redistribute income regressively from poor to rich. Others insist that the public debt has become very widely held and instead redistributes income progressively. The lack of consensus, I argue, boils down to both the empirical and theoretical problems that plague existing studies. \n \nEmpirically, only a handful of studies have attempted to map the ownership pattern of US federal government bonds, and even fewer have made efforts to measure the redistributive effects associated with a given ownership pattern. And to make matters worse, those few studies that do attempt to map the pattern of US public debt ownership make little effort to theorize in any systematic way the distributive and redistributive dimensions of the public debt. \n \nAnchored within a ‘capital as power’ theoretical framework, my purpose in this is to shed some much-needed light on the dynamics of distribution and redistribution that lie at the heart of the public debt. I show for the household and corporate sectors how over the past three decades, and especially in the context of the current crisis, the ownership of federal bonds and federal interest has become rapidly concentrated in the hands of dominant owners, the top 1% of households and the 2,500 largest corporations. Over the same period the federal income tax system has done little to progressively redistribute the federal interest income received by dominant owners. In this way, this dissertation argues that, since the early 1980s, the public debt has come to reinforce and augment the power of those at the very top of the hierarchy of social power. \n \n[This thesis was nominated for the York University Dissertation Prize.]
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".