What factors influence firm perceptions of labour market constraints to growth in the MENA region?
Why this work is in the frame
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Bibliographic record
Abstract
Purpose – Labour market constraints constitute prominent obstacles to firm development and economic growth of countries located in the Middle East and North Africa (MENA) region. The purpose of this paper is to examine 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. Design/methodology/approach – The empirical analysis is carried out using firm-level data set 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. The authors implement overall estimations and comparative cross-country and cross-sector analyses, and use alternative estimation models. Findings – 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. The authors also find significant heterogeneity in the factors’ implications for the perceptions of firms belonging to different sectors and located in different MENA countries. Originality/value – Reforms in labour regulations and investment in human capital are important governmental policy interventions for promoting firm development and economic growth in the MENA region. This paper contributes to the empirical literature by analysing the factors influencing the perceptions of firms located in the MENA region concerning labour regulations and labour skill shortages. It 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it