Growth Sectors in Morocco and Investment Potential: A Quantitative Analysis
Bibliographic record
Abstract
During the 1990s Morocco implemented a series of major institutional and economic reforms that made the country politically stable and helped it to withstand the destabilizing effects of the Arab Spring. Political reforms resulted in the adoption of a new constitution in 2011, was followed by initiatives to improve justice, public administration, the fight against corruption, and to strengthen governance, transparency, and ethics in public life. The country also embarked on a regionalization of public policies and decentralization of administration to ensure an integrated and durable regional development. This reform momentum was further emphasized by the King of Morocco when in his 2019 throne speech he stressed that “… the stake is thus to rebuild a strong and competitive economy, by encouraging the private initiative, while launching new productive investment plans and by creating new job opportunities…” During two last decades Morocco recorded relatively solid economic and social results due to significant public investments and structural reforms aiming to: (i) stabilize the macroeconomic framework by reducing domestic and external vulnerabilities, in particular through the gradual suppression of subsidies for energy products and some foodstuffs; (ii) improve the framework of management of public finance through the adoption of a new Organic Law of Finance in 2015; and (iii) support the diversification and the competitiveness of the national economy. Morocco also reinforced its sectorial policies through plans for sector development aiming at enhancing the economic growth potential and the creation of jobs, including in the manufacturing sectors with significant added value in sectors such as the automotive, aeronautics and pharmaceutical products. The Moroccan economy has demonstrated an appreciable resilience in the face of an international context characterized by a succession of crises. The rate of growth of real GDP improved on average annually from 3.1% during the 1990s to nearly 4.2% on average annually between 2007 and 2018, sustained by the tertiary sector’s dynamism which posted an increase in its value added of 4.2%, contributing of 2.1 points in the GDP (Figure 1). The secondary sector also showed a similar tendency with a 3.3% increase in added value, carrying with it 0.9 percentage points contribution in economic growth, while the primary sector added value grew by 4.4% for a contribution to the growth of the GDP of 0.6 point (DEPF, 2019).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.005 | 0.001 |
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".