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Record W2606240556 · doi:10.1111/roiw.12309

Energy Subsidy Reform and Poverty in Arab Countries: A Comparative CGE‐Microsimulation Analysis of Egypt and Jordan

2017· article· en· W2606240556 on OpenAlexfundno aff
John Cockburn, Véronique Robichaud, Luca Tiberti

Bibliographic record

VenueReview of Income and Wealth · 2017
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
FundersDepartment for International Development, UK GovernmentInternational Development Research Centre
KeywordsComputable general equilibriumEconomicsMicrosimulationSubsidyPovertyMacroeconomicsEnergy subsidiesFiscal policyInvestment (military)International economicsDevelopment economicsEnergy policyEconomic growthMarket economy

Abstract

fetched live from OpenAlex

This study simulates the macroeconomic and distributive impacts of real proposed (by local policy makers) energy subsidy reforms in Egypt and Jordan. To do that, we develop a dynamic CGE‐microsimulation model that is able to reconcile the general equilibrium effects of the reform and the individual‐ and household‐specific distributive effects. While the nature of the proposed reforms differs in the two countries, the study underscores the need, in both countries, for reform to generate fiscal savings to boost private investment and increase economic growth. It also shows that the reform alone would further exacerbate poverty through increased consumer prices. However, a modest reinvestment of fiscal savings into cash transfers creates a win‐win scenario of reduced poverty without significantly sacrificing the fiscal and growth benefits from the reform. Impacts (prices, growth, fiscal savings, poverty) are greater in Egypt due to the extent of proposed reforms and the fact that a larger share of the energy products concerned are consumed directly by households, while in Jordan the major effects come from the increase in intermediate input costs which generate a fall in the aggregate demand and, so, in labor demand.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.304
Teacher spread0.287 · 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.

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

Citations36
Published2017
Admission routes1
Has abstractyes

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