Tunisia COVID-19 Country Case Study
Bibliographic record
Abstract
The Tunisian economy can be described as an open economy in terms of its trade openness2, which is high relative to other countries in MENA (World Bank 2021). Tunisia’s trade openness and the large role of services in the economy have contributed to the sizeable pandemic era economic contractions (World Bank 2021). Moreover, the tourism sector, which played a key role in Tunisia’s economy, was the most affected by the pandemic, as well as international trade (World Bank 2020). Moreover, Tunisia experienced substantial political instability leading to the president dismissing the prime minister, suspending parliament, and ruling by decree in late July 2021. Although our data predate these political events, they are important to keep in mind in understanding the policy responses and way forward. The Tunisian economy, which was already in a bad shape in 2019, endured a harsh blow with the pandemic. Tunisian GDP contracted by 8.8 per cent in 2020 after modest growth of 0.9 per cent in 2019. The most affected sectors were “hotels and restaurants”, which contracted by 77.3 per cent in the second quarter of 2020 relative to the same quarter a year earlier, and “transport”, which sank by 51.4 per cent in the same period (Institut National de la Statistique (INS) 2021a; Krafft, Assaad, and Marouani 2021b). There was a slight recovery in the third and fourth quarters of 2020, but most sectors’ growth remained negative relative to the same quarter in the previous year. The government adopted a series of economic support and social protection policies to alleviate the effects of the crisis on firms and households. The emergency response cost 2.6 billion Tunisian dinar (TND), which represents 2.3 per cent of GDP (IMF 2021; Krafft, Assaad, and Marouani 2021c). The response included several measures to ease the burden on firms by postponing tax payments, social insurance contributions and loan reimbursements (IMF 2021; Krafft, Assaad, and Marouani 2021b). In addition, the government introduced a state guarantee for new credit that was extended in the 2021 budget law. The Central Bank also eased monetary policies and the regulatory standards for the banking sector and was allowed by the parliament to directly finance the government budget with TND 2.6 billion (IMF 2021). Several vulnerable groups of people received emergency cash transfers to cope with the crisis. Support relied on regular social protection schemes by targeting households enrolled in the national anti-poverty cash transfer program (PNAFN) and in subsidised health insurance schemes (AMGII) and some received one-off transfers (mainly during the full lockdown period) (Hassen, Marouani, and Wojcieszynski 2021). The Central Bank also postponed household loan payments for three to six months in Spring 2020 in order to support middle-class workers who did not benefit from cash transfers (Hassen, Marouani, and Wojcieszynski 2021).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".