Bibliographic record
Abstract
THE DEBT INDEX There are both short run and long run issues involving the problem in the United States and elsewhere. In the short run, the deficit represents a problem for policy makers while in the long term, the national is an issue that must be addressed. To construct a debt I combine the absolute value of the annual federal budget deficit divided by federal government spending with the national divided by nominal GDP. Put simply: Deficit/Spending + Debt/GDP = Debt Index This combines the temporal aspects of our short and long term problems into one measure. The Relation of the Debt Index to Macroeconomic Variables To determine if the index had any meaningful statistical relationship with various macroeconomic variables I generated a series of correlation coefficients comparing the index with these variables on an annual basis since 1980. A major finding was that the index was highly and negatively correlated with private investment as a percent of GDP. The coefficient was -.831 and it was significant at the 99 percent confidence level. It might be expected that other measures of might be highly and/or more so correlated with investment as a percent of GDP so I ran correlations with these as well. The results are in Table I. Table I demonstrates that the relationship of the index to private investment is greater than any individual component of the index. A simple regression was run to suggest the explanatory power of the relationship between the index and investment spending. The results were: Regression Analysis: I/GDP versus Debt Index The regression equation is I/GDP = 23.8 - 6.28 Debt Index Predictor Coef SE Coef T P Constant 23.8483 0.5935 40.18 0.000 Debt Index -6.2818 0.7675 -8.19 0.000 S = 0.979482 R-Sq = 69.1% R-Sq(adj) = 68.0% While this is a fairly simplistic method of determining explanatory power it does show, according to the adjusted R-Square, that the index explains 68 percent of the movement in private investment. To determine how robust this relationship might be I used IMF data, going back to 1980, when possible, to construct a index for 15 major global economies of vary characteristics. The results can be seen in Table II. Accept where noted, all coefficients are significant at the 1 percent level in Table II as well as all of the following tables. This would indicate that the index is highly and negatively correlated with most western-style economies. This would include Sweden, where a large percentage of GDP flows through the public sector. France and New Zealand are the only economies where the relationship does not hold. Another variable exhibiting a significant relationship with the index is the unemployment rate. For the United States, the correlation coefficient is .479 and it is significant at the 1 percent level. This implies that higher levels of short term and long term correspond to higher levels of unemployment. However, in this case the deficit and deficit to government spending ratio are more highly correlated with unemployment than the index, with coefficients of .626 and .824, respectively. In contrast the national and the to GDP ratio are not significantly correlated to the unemployment rate, indicating that unemployment is related much more closely to short run difficulties in the United States. Table III shows the correlation coefficients among the various countries with respect to the index and unemployment. Unlike in the case of the USA, none of the countries that show a significant correlation coefficient have higher coefficients on the relationship with the short term deficit measures. Some, such as Greece, Canada, and Australia, show a more significant correlation with the to GDP ratio. …
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".