An Empirical Analysis of the Effects of Budget Deficits (Total and Primary) and Personal Income Tax Rates on the Ex Post Real Interest Rate Yield on Long-Term U.S. Treasury Bonds
Bibliographic record
Abstract
This empirical study adopts an open-economy loanable funds model to investigate the impact of post-Bretton Woods U.S. federal government budget deficits and personal income tax rates on the ex post real interest rate yield on thirty-year Treasury bonds. In this study, the budget deficit is measured in two different ways, the total (“unified”) budget deficit and the primary deficit (the total/unified deficit minus net interest payments). Two different estimation techniques, autoregressive two stage least squares estimation and the ARCH (Autoregressive Conditional Heteroscedasticity) Method, for the 1973-2016 study period provide evidence that the ex post real interest rate yield on thirty-year Treasury bonds has been an increasing function of both federal budget deficit measures (expressed as a percent of GDP) and the maximum marginal federal personal income tax rate. The estimations all imply that elevating either the total/unified or primary federal budget deficit appears to raise the cost of borrowing in the U.S., whereas reducing the maximum marginal personal income tax rate appears to reduce the cost of borrowing. Given the potential effects of longer-term real interest rates on investment in new plant and equipment and overall economic growth, policy-makers should not overlook these findings.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".