What Factors Influence Firm Perceptions of Labour Market Constraints to Growth in the MENA Region
Bibliographic record
Abstract
Labour market constraints constitute prominent obstacles to firm development and economic growth of countries located in the Middle East and North Africa (MENA) region. This paper aims at examining the implications of firm characteristics, national locations, and sectoral associations for the perceptions of firms concerning two basic labour market constraints: labour regulations and labour skill shortages. The empirical analysis is carried out using firm-level dataset sourced from the World Bank's Enterprise Surveys database. A bivariate probit estimator is used to account for potential correlations between the errors in the two labour market constraints' equations. We implement overall estimations and comparative cross-country and cross-sector analyses, and use alternative estimation models. The empirical results reveal some important implications of firm characteristics (e.g., firm size, labour compositions) for firm perceptions of labour regulations and labour skill shortages. They also delineate important cross-country and cross-sector variations. We also find significant heterogeneity in the factors' implications for the perceptions of firms belonging to different sectors and located in different MENA countries. This paper provides policy-makers with information needed in the design of labour policies that attenuate the impacts of labour market constraints and enhance the performance of firms and the long-run economic growth.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 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".