12. The Inflation-Unemployment Trade-Off
Bibliographic record
Abstract
Recent estimates of prices, production, and employment show the worst of all possible worlds—high inflation, declining production, and rising unemployment. • The GNP deflator increased at an annual rate of 9.3 percent in the first half of the year. • Real GNP declined at an annual rate of 2.3 percent in the second quarter, after an increase of only 1.1 percent in the first. • The unemployment rate eased up to 6.0 percent in August, after months at or near 5.6 percent, and is expected to rise further. These figures call into question one of the basic assumptions underlying decades of policy discussion—that there is an exploitable tradeoff between inflation and production (or unemployment). Policymakers long took for granted that unemployment could be reduced if the country was willing to accept a higher rate of inflation. It was common through the early 1970s to hear policy discussed in terms of this tradeoff. That some people still talk in these terms while others deny that such a tradeoff exists is not hard to explain. To some extent, this contrast reflects differences in the interpretation of data that are far from conclusive. But to a greater extent, it reflects differences in the time frames the two groups are considering. Effects of a change in policy (fiscal or monetary) on production are felt quickly—in weeks, months, or quarters. Full effects on the price level, however, take at least two years, and it may take longer for the effects to work through the system. People looking at near horizons, therefore, emphasize the effects on production and employment. Those taking a longer view emphasize the effects on prices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".