Bibliographic record
Abstract
Final demand determines output in commercial non agricultural industries ( Q ). Employment is linked to Q by a distributed lag mechanism. In turn employment has an impact on other parts of model. In particular employment appears as a determinant of the labour supply and of the distribution of income. The latter is closely related to the structure of final demand. Hence, employment is a key-variable when one uses the model to make a forecasting exercise. Some of the principles behind the specification and estimation of the model are discussed. For example, for forecasting purposes one must limit the size of the model. Although the specification of each equation is derived from the body of accepted macro-economic theory, the limited size of the model and the necessity to obtain data at the time a forecast is made impose some constraint on the specification of the equations. In the short run, actual employment is not immediately adjusted to the level of desired employment, i.e., employment level that could minimize production costs. The quarterly changes in employment are then made proportional to the gap between desired and actual employment. Desired employment is obtained by using an inverse production function. However, the adjustment mechanism is not stable over the cycle. Various proxy variables could be used to unbody that phenomenon into the equation. The ratio of actual to potential output and the relative change in the gap between actual and potential output are combined to make an indicator of the degree of slackness or tension in the economy over the cycle. This indicator is introduced into the final specification of the equation. The equation estimated is consistent with economic theory and gives excellent forecasting results. However the severe recession of 1974 was accompanied by an exceptional drop in productivity that could be entirely captured by the equation and some adjustments were needed to forecast employment for that particular period.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".