NONLINEARITY IN THE CANADIAN AND U.S. LABOR MARKETS: UNIVARIATE AND MULTIVARIATE EVIDENCE FROM A BATTERY OF TESTS
Bibliographic record
Abstract
The nonlinearity of macroeconomic processes is becoming an increasingly important issue at both the theoretical and empirical levels. This trend holds for labor market variables as well. The reallocation theory of unemployment relies on nonlinearities. At the same time there is mounting empirical evidence of business cycles asymmetries. Thus the assumption of linearity/nonlinearity becomes crucial for the corroboration of labor market theories. This paper turns the microscope on the assumption of linearity and investigates the presence of asymmetries in aggregate and disaggregate labor market variables. The assumption of linearity is tested using five statistical tests for U.S. and Canadian unemployment rates and growth rates of the employment sectoral shares of construction, finance, manufacturing, and trade. An AR(p) model was used to remove any linear structure from the series. Evidence of nonlinearity is found for the sectoral shares with all five statistical tests in the U.S. case but not at the aggregate level. The results for Canada are not clear-cut. Evidence of unspecified nonlinearity is found in the unemployment rate and in the sectoral shares. Overall, important asymmetries are found in disaggregated labor market variables in the univariate setting. The linearity hypothesis was also examined in a multivariate framework. Evidence is provided that important asymmetries exist and a linear VAR cannot capture the dynamics of employment reallocation.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".