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Enregistrement W3146042552 · doi:10.25904/1912/1832

Trade Liberalisation and Poverty in a Computable General Equilibrium (CGE) Model: The Sri Lankan Case

2005· dissertation· en· W3146042552 sur OpenAlexfundno aff
Athula Naranpanawa

Notice bibliographique

RevueGriffith Research Online (Griffith University, Queensland, Australia) · 2005
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueGlobal trade and economics
Établissements canadiensnon disponible
Organismes subventionnairesInternational Development Research CentreInternational Fine Particle Research Institute
Mots-clésOpenness to experienceEconomicsComputable general equilibriumGlobalizationPovertyLiberalizationFree tradeInternational economicsLiberian dollarGains from tradeCommercial policyInternational tradeDevelopment economicsMacroeconomicsEconomic growthMarket economy

Résumé

récupéré en direct d'OpenAlex

Many trade and development economists, policy makers and policy analysts around the world believe that globalisation promotes growth and reduces poverty. There exists a large body of theoretical and empirical literature on how trade liberalisation helps to promote growth and reduce poverty. However, critics of globalisation argue that, in developing countries, integration into the world economy makes the poor poorer and the rich richer. The most common criticism of globalisation is that it increases poverty and inequality. Much of the research related to the link between openness, growth and poverty has been based on cross-country regressions. Dollar and Kraay (2000; 2001), using regression analysis, argue that growth is pro poor. Moreover, their study suggests that growth does not affect distribution and poor as well as rich could benefit from it. Later, they demonstrate that openness to international trade stimulates rapid growth, thus linking trade liberalisation with improvements in wellbeing of the poor. Several other cross-country studies demonstrate a positive relationship between trade openness and economic growth (see for example Dollar, 1992; Sach and Warner, 1995 and Edward, 1998). In contrast, Rodriguez and Rodrik (2001) question the measurements related to trade openness in economic models, and suggest that generalisations cannot be made regarding the relationship between trade openness and growth. Several other studies also criticise the pro poor growth argument based upon the claim of weak econometrics and place more focus on the distributional aspect (see, for example, Rodrik, 2000). Ultimately, openness and growth have therefore become an empirical matter, and so has the relationship between trade and poverty. These weaknesses of cross-country studies have led to a need to provide evidence from case studies. Systematic case studies related to individual countries will at least complement cross-country studies such as that of Dollar and Kraay. As Chen and Ravallion (2004, p.30) argue, 'aggregate inequality or poverty may not change with trade reform even though there are gainers and losers at all levels of living'. They further argue that policy analysis which simply averages across diversities may miss important matters that are critical to the policy debate. In this study, Sri Lanka is used as a case study and a computable general equilibrium (CGE) approach is adopted as an analytical framework. Sri Lanka was selected as an interesting case in point to investigate this linkage for the following reasons: although Sri Lanka was the first country in the South Asian region to liberalise its trade substantially in the late seventies, it still experiences an incidence of poverty of a sizeable proportion that cannot be totally attributed to the long-standing civil conflict. Moreover, trade poverty linkage within the Sri Lankan context has hardly received any attention, while multi-sectoral general equilibrium poverty analysis within the Social Accounting Matrix (SAM) based CGE model has never been attempted. In order to examine the link between globalisation and poverty, a poverty focussed CGE model for the Sri Lankan economy has been developed in this study. As a requirement for the development of such a model, a SAM of the Sri Lankan economy for the year 1995 has been constructed. Moreover, in order to estimate the intra group income distribution in addition to the inter group income distribution, income distribution functional forms for different household groups have been empirically estimated and linked to the CGE model in 'top down' mode: this will compute a wide range of household level poverty and inequality measurements. This is a significant departure from the traditional representative agent hypothesis used to specifying household income distributions. Furthermore, as the general equilibrium framework permits endogenised prices, an attempt was made to endogenise the change in money metric poverty line within the CGE model. Finally, a set of simulation experiments was conducted to identify the impacts of trade liberalisation in manufacturing and agricultural industries on absolute and relative poverty at household level. The results show that, in the short run, trade liberalisation of manufacturing industries increases economic growth and reduces absolute poverty in low-income household groups. However, it is observed that the potential benefits accruing to the rural low-income group are relatively low compared to other two low-income groups. Reduction in the flow of government transfers to households following the loss of tariff revenue may be blamed for this trend. In contrast, long run results indicate that trade liberalisation reduces absolute poverty in substantial proportion in all groups. It further reveals that, in the long run, liberalisation of the manufacturing industries is more pro poor than that of the agricultural industries. Overall simulation results suggest that trade reforms may widen the income gap between the rich and the poor, thus promoting relative poverty. This may warrant active interventions with respect to poverty alleviation activities following trade policy reforms.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,003
Communication savante0,0040,002
Science ouverte0,0020,004
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,210
Tête enseignante GPT0,332
Écart entre enseignants0,121 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2005
Routes d'admission1
Résumé présentoui

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