Unemployment insurance and the distribution of workers between labour force states
Bibliographic record
Abstract
In this study, I examine the influence of unemployment insurance benefits on labour force participation, employment, and unemployment. Conclusions are developed concerning the consequences of the 1971 revision of the Canadian unemployment insurance programme, which differ from those of earlier writings in this field. My model estimates the proportions of the population in each labour force state (employment, unemployment, and "not in the labour force"). Each labour force state proportion is modelled as a function of the gross flows between the labour force states. This model resembles a Markov model and is similar to the model developed by Toikka (197 6). The decisions by employers and employees that generate the gross flows between labour force states are modelled as behavioural functions of economic variables. Unlike other studies, this study imposes strict consistency between equations due to the conservation of the population in the gross flows. Other studies have tended to be single equation models and the specification of the equations between studies and in one case, within a study, is not consistent. The model is estimated for ten age-sex populations. It is estimated using monthly' data for the period 1961 to 1975. The estimation method is Full Information Maximum Likelihood. Because the system of three equations is singular, one equation is redundant and may be dropped during estimation. Estimation is independent of which equation is dropped. This study brings evidence to support the position that different groups respond in different ways to changes in unemployment insurance. According to the model, prime age men are unresponsive to short-term fluctuations in incentives. Young and old men appear to reduce their labour supply when unemployment insurance benefits are increased. This is the net effect of changes in the gross flows between labour force states. The model suggests that the net labour force participation of women increases in response to increases in unemployment Insurance benefits. Men and women differ in their response to unemployment insurance in two additional ways. First estimated responses for women are generally greater than those for men. While women respond seasonally and non-seasonally to unemployment insurance, the response by men tends to be restricted to seasonal behaviour. These findings are consistent with earlier findings in that they suggest a general increase in unemployment and labour force participation due to increases In unemployment insurance. Although my findings suggest some unemployment insurance—induced quit behaviour, they do not suggest a decline in the aggregate level of employment. The dominant result in this study is that unemployment insurance Induces labour force participation, which places upward pressure on employment and unemployment.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".