MétaCan
Menu
Back to cohort
Record W140177387 · doi:10.48713/10336_10838

Poverty impacts of increased openness and fiscal policies in a dollarized economy: a CGE-micro approach for Ecuador

2022· book· en· W140177387 on OpenAlexfundno aff
Ketty Rivera, Sara Wong, Ricardo Argüello

Bibliographic record

VenueRePEc: Research Papers in Economics · 2022
Typebook
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersUniversidad del RosarioInternational Development Research Centre
KeywordsComputable general equilibriumEconomicsLiberian dollarPovertyIncome distributionRevenueTariffOrder (exchange)Fiscal policyDistribution (mathematics)Free tradeCurrencyInternational economicsTax revenueMacroeconomicsInequalityEconomic growthFinance

Abstract

fetched live from OpenAlex

We quantify the effects on poverty and income distribution in Ecuador of bilateral trade liberalization with the US and a budget-neutral value added tax increase which seeks to compensate tariff revenue losses. We stress the study of fiscal policies that the government could tap in order to compensate for tariff revenue loss. This is a very important issue for Ecuador because this country adopted the US dollar as its currency in 2000, forgiving the use of important policy instruments. To study these issues we combine a reduced-form micro household income and occupational choice model (using 2005/6 data from the Ecuadorian LSMS) with a standard single-country computable general equilibrium model (employing a 2004 SAM). We follow a sequential approach that simulates the full distributional impact of trade and tax policies. We find that the impact of these policy changes on extreme poverty and income distribution is small but positive.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.337
Teacher spread0.296 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicIncome, Poverty, and InequalityFrench-language works237,207