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Record W7043870129

Tunisia COVID-19 Country Case Study

2022· article· en· W7043870129 on OpenAlexaboutno aff

Bibliographic record

VenueRePEc: Research Papers in Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Government (linguistics)PoliticsOpenness to experienceTourismCommonwealthDecreeTerms of tradeExchange rateLiberalization
DOInot available

Abstract

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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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.090
GPT teacher head0.356
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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