Exploring unemployment persistence: a probabilistic analysis in 20 OECD countries to understand its social implications
Bibliographic record
Abstract
Purpose This study assesses the probability of an OECD member country exhibiting high persistence in unemployment duration, considering income inequality, productivity, accumulation of human capital and labor income share in Gross Domestic Product (GDP) between the years 2013–2019. Design/methodology/approach To achieve the purpose of the study, a probabilistic analysis with panel data is employed, focusing on 20 OECD countries segmented into two groups: those with high persistence and low persistence in unemployment duration. Probit and Logit models are estimated, marginal changes are analyzed and the models are evaluated in terms of their classification accuracy. Finally, trends in probabilities over time are examined. Findings This paper exhibits that countries with higher human capital index, greater labor income share in GDP, and more relevant productivity for well-being reduce their probabilities of experiencing high persistence in unemployment duration. It is observed that Mexico (MEX), Greece (GRC), Italy (ITA), and Turkey (TUR) have elevated probabilities of experiencing high persistence in unemployment duration in the future, while Costa Rica (CRI), Estonia (EST), Slovakia (SVK), Czech Republic (CZE), Lithuania (LTU), Poland (POL), and Israel (ISR) show a marked downward trend in these probabilities. Lastly, countries like the United Kingdom (GBR), Denmark (DNK), Sweden (SWE), Norway (NOR), Netherlands (NLD), Germany (DEU), United States (USA), and Canada (CAN) present minimal risk of experiencing high persistence in unemployment duration in the future. Research limitations/implications The measurement of the relationship between development outcomes and persistence in unemployment duration has been scarce. Generally, the literature has focused on the analysis of development and unemployment without delving into the duration of unemployment, let alone persistence in duration. Practical implications This paper provides a solid foundation for the formulation of policies aimed at promoting sustainable employment and inclusive economic growth. Social implications Based on the findings of the study, two key development policies are proposed. Firstly, the implementation of investment programs in Human Capital to increase productivity is recommended. Resources should be directed towards initiatives that improve the necessary skills and competencies in the labor markets of OECD countries, especially in strategic economic sectors with higher production linkages. Additionally, incentivizing the application of active labor policies is proposed. This entails prioritizing policies aimed at increasing the labor income share in GDP through progressive fiscal reforms that strengthen social safety nets and ensure fair labor standards. Implementing employment programs targeted at vulnerable groups, such as long-term unemployed individuals, youth, female heads of households and marginalized communities, is also recommended to eliminate structural barriers to labor market participation and reduce disparities in unemployment persistence. Adopting these policies can help mitigate the risk of high unemployment duration persistence and foster sustainable and inclusive long-term economic growth. Originality/value This is the first study to analyze the probabilities of both developing and developed countries experiencing high persistence in unemployment duration. It specifically evaluates these probabilities over a period of time and also estimates potential outcomes if real investments were made to enhance their human capital, productivity and employability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".