Life Satisfaction of Employees, Labour Market Tightness and Matching Efficiency
Bibliographic record
Abstract
Di Tella et al. (2001) show that temporary fluctuations in life satisfaction (LS) are correlated with macroeconomic circumstances such as gross domestic product, unemployment, and inflation. In this paper, we bring attention to labour market measures from search and matching models (Pissarides 2000). Our analysis follows the two-stage estimation strategy used in Di Tella et al. (2001) to explore sectoral unemployment levels, labour market tightness, and matching efficiency as LS determinants. In the first stage, we use a large sample of individual data collected from a continuous web survey during the 2007-2014 period in the Netherlands to obtain regression-adjusted measures of LS by quarter and economic sector. In the second-stage, we regress LS measures against the unemployment level, labour market tightness, and matching efficiency. Our results are threefold. First, the negative link between unemployment and an employee’s LS is confirmed at the sectoral level. Second, labour market tightness, measured as the number of vacancies per job-seeker rather than the number of vacancies per unemployed, is shown to be relevant to the LS of workers. Third, labour market matching efficiency affects the LS of workers differently when they are less satisfied with their job and in temporary employment. Our results give support to government interventions aimed at activating demand for labour, improving the matching of job-seekers to vacant jobs, and reducing information frictions by supporting match-making technologies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".