What Happened to the Phillips Curve in the 1990s in Canada
Bibliographic record
Abstract
This paper begins by reviewing the empirical properties of the Phillips Curve in both Canada and the U.S over the last forty years. In particular, we document the extent to which the slope of the Phillips Curve has declined in both countries over the nineties. Then, building upon a commonly used macro model, we attempt to explain this decline. The framework we develop focuses on the nature of the Phillips Curve when monetary authorities are imperfectly informed about real developments in the economy but nevertheless try to set monetary policy optimally. Our model explicitly recognizes two distinct activities performed by the central bank. On one hand, the central bank tries to provide sufficient liquidity to help private agents exploit gains from trade during periods in which prices are pre-set. On the other hand, the central bank also performs an information-gathering role as it continuously tries to infer the state of the economy. We show how this dual role gives rise to a Phillips Curve relationship that both exhibits causality running from output to prices and justifies a feedback from prices to the setting of monetary instruments. Based on this model, we argue that the observed flattening of the Phillips Curve may be the result of improvements in the manner in which central banks gather information regarding real forces affecting the economy, and that the flattening is not a reflection of a change in the output-inflation tradeoff faced by the central bank. Finally, we compare our proposed explanation of the flattening of the Phillips curve with leading alternative hypotheses.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".