The Structuralist Theory of Employment
Bibliographic record
Abstract
The postwar era showed us that we knew much less about employment determination than we thought. In this country, I remember, economists estimated in the mid-1950's that unemployment could fall to 4.5 percent or less without bringing an inflation problem. Later, it took an unemployment rate around 5 percent to keep inflation stable, as 1964 and 1973 illustrated. By the mid-1980's, it apparently took an unemployment rate of more than 6 percent: witness the turnaround of the inflation rate early in 1987. In Western European economies with few exceptions and in Canada, the rise of unemployment has been much greater, reaching a higher level from a generally lower starting point. Among the OECD countries, the secular increase in joblessness typically exceeds the increase found in the average recession, and it is far more destructive, since its social ill-effects are somewhat cumulative. I began to try to understand this sea change in unemployment ten years ago. My first effort, on the slump in Western Europe, with Jean-Paul Fitoussi (Fitoussi and Phelps, 1988), invoked wage stickiness nominal or real, to show how the overseas shock to real interest rates that began in 1981 could drive Europe's unemployment rate above the natural rate. We did not explore how such an external shock might alter the natural unemployment rate itself. I soon sensed, though, that there was a permanent component to the rise in Europe's unemployment. If I was to rescue the concept of the natural rate from the growing discontent with it, I needed to understand how events had driven up the natural rate in Europe and elsewhere in the West. To model the natural rate I decided, naturally enough, to return to the road I started
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".