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Record W3043630898

Development under improved infrastructure, productivity and trade policies : evidence for Guinea-Bissau

2020· article· en· W3043630898 on OpenAlexfundno aff
Júlio Vicente Catéia

Bibliographic record

VenueRepositório Digital Institucional da UFPR (Universidade Federal do Paraná) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
FundersUniversidade Federal de Santa MariaUniversité de Sherbrooke
KeywordsNew guineaProductivityBusinessEconomicsNatural resource economicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Guinea-Bissau has been facing socioeconomic development challenges since it became in the late 1970s.One of these challenges concerns to find a policy option that allows the country to explore its comparative advantages and to reduce poverty.This study uses a dynamic recursive computable general equilibrium model to analysis the long-term effects of three development policies in this extremely poor small country with the agriculturebased economy: trade, productivity, and infrastructure investments.These policies were evaluated in different scenarios.For the trade axis, we have scenario 1 which consists of simulating negative shocks on import tariffs, and scenario 2 representing export taxes reduction.Productivity scenario represents an estimated productivity shock for the selected sectors, while infrastructure investments scenarios is the simulation of new public investments in infrastructure and its funding mechanism.Negative export taxes shocks affected positively the overall output, exports, investment, and real government consumption, while import tariff cuts have opposite effects.Both trade policies increase rural and urban households' income, with stronger impacts for rural poorer ones.Our results suggest the relevance of accumulated wealth in mitigate long-term poverty as it plays an important role in households' consumption.We observe positive impact of productivity shocks and infrastructure investments on the level of economic activity, aggregate productivity, and sectoral spillovers.For the productivity, we found that gains of rural households stemming from increasing production in the agricultural sectors where they find their sources of income.For the infrastructure investments, we find that funding schemes are important in determining these outcomes as they also contribute to increase both urban and rural households' income and consumption.Moreover, although all the evaluated policies show the potential to reduce poverty, it was the productivity policy that provided the best results, because it increased most the households' income and consumption, and further decreased income inequalities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.268
Teacher spread0.192 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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