Phillips Curve Is a Particular Case that Economists Misinterpret the Correlation between Two Dependent Variables for Causal Relation
Bibliographic record
Abstract
Since labor supply and labor demand determines both employment and wage in labor market endogenously, both wage and employment, which we observe, are dependent variable in the sense of ex-post. Since unemployment is equal to labor supply minus labor demand, unemployment is dependent variable in the sense of ex-post, too. There is no causal relation between two dependent variables because independent variable explains dependent variable. Thus, the relationship between ex-post unemployment and ex-post wage in Phillips (1958) is not causal relation but correlation as meaningless as the strong correlation between ice cream sale and drowning rate. Similarly, both price and quantity we observe are determined by supply and demand endogenously in the sense of ex-post. Thus, inflation rate is dependent variable because inflation rate is the change in price level (i.e., change in the average price of all goods in the sense of ex-post). Hence, we are not permitted to explain unemployment rate by inflation rate and vice versa. As Friedman (1977) conjectured that Phillips curve is correlation which is misinterpreted for causal relation (i.e., trade-off) by economists, I conclude that Phillips curve is meaningless correlation between two dependent variables so that economic policy based on Phillips curve is invalid.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".