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Enregistrement W2084558845 · doi:10.1111/j.1748-3131.2008.00091.x

Comment on “Shares of the Rich and the Rest in the World Economy: Income Divergence Between Nations, 1820–2030”

2008· article· en· W2084558845 sur OpenAlexaboutno aff
Marcus Noland

Notice bibliographique

RevueAsian Economic Policy Review · 2008
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueGlobal Economic and Social Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPer capita incomeChinaEconomicsPer capitaRest (music)Development economicsGeographyEconomyDemographic economicsAgricultural economicsPopulationDemography

Résumé

récupéré en direct d'OpenAlex

Maddison (2008) provides a millennial review of national income trends, focusing on the period since 1820, makes forecasts to 2030, roughly 25 years hence, and compares these projections to those obtained from other sources. The forecasts imply a number of outcomes that readers may find interesting, such as China becoming the world's largest economy by 2015, and per capita incomes in the USA exceeding those in Japan and Western Europe by 50% in 2030. When evaluating this sort of exercise, it is important to keep in mind that even over relatively short horizons of say 25 years, the fortunes of individual countries can exhibit significant unexpected divergences. At the turn of the 20th century, per capita incomes were similar in the land-abundant grain producers Argentina and Canada, and incomes in Argentina topped those in Canada as recently as 1934. But 25 years hence, Canada's per capita income exceeded Argentina's by 65 percent, and by 2000 the gap had grown to 160 percent. In the immediate postwar period, Burma and the Philippines were pegged as Asia's rising stars. The Philippines, at least, was not such a bad bet: the country had the world's second highest ratio of human capital to contemporaneous income (trailing Japan but exceeding South Korea). In 1975, per capita incomes in the Philippines exceeded those in Thailand, but 25 years later, Thailand topped the Philippines by nearly 165 percent –despite the 1997 financial crisis that had a disproportionate impact on Thailand. In the 1960s, it is unlikely that anyone contemplating the futures of North and South Korea would have imagined that 25 years hence the poorer South would be preparing to join the Organisation for Economic Cooperation and Development, while the richer North would be on the precipice of one of the worst famines of the 20th century. The titles of two popular books on Japan's economy, Japan As Number One: Lessons for America (Vogel, 1979) and The Sun Also Sets: The Limits to Japan's Economic Power (Emmott, 1989) nicely illustrate this phenomenon with respect to our host's economy. These observations are raised not to belittle to this exercise, but rather to underline how the specific forecasts ought to be taken with the appropriate grains of salt, something that Professor Maddison acknowledges. While the national-level forecasts provide considerable food for thought, much recent attention to inequality has focused on within-country inequality where the relevant policy instruments are better developed. There are multiple hypotheses as to the drivers. These include Stolper–Samuelson effects generated by the effective increase in world labor supply in recent decades associated with some large countries integrating into the global economy, abetted by technological progress which has reduced transactions costs over long distances. Increased cross-border capital mobility has made it more difficult to tax capital relative to labor and may have contributed to a rise in inequality. Technological change may have also both changed relative returns to certain personal skills, aptitudes, or characteristics, reducing the returns to doing routinized tasks relative to interpersonal skills and specific technical aptitude, and even the actual structure of work and management, including the flattening of management, making it possible for a small number of highly placed decision-makers to appropriate the rents generated by large corporate entities. Lastly, increased transborder labor migration is alleged to have deepened inequality within some countries such as the USA. Consideration of the migration issue brings us back full circle to the underlying forecasts. The assumption of constant rates of migration will almost certainly prove incorrect – if cross-country inequality grows as forecast in Table 7, then there will be increasing pressure for migration, much of it illegal. Demographics may emerge as another significant driver as well: rapidly aging countries like Japan and South Korea may face a variety of internal demands and pressures to relax immigration restrictions. Such stresses may prove difficult to resolve politically. It goes without saying that the migration issue is an increasingly contentious one within many countries. But one of the striking characteristics of the world economy today is that while there are reasonably well-developed multilateral norms and institutions with respect to the cross-border flow of goods and capital, there is no equivalent of the World Trade Organization for the movement of people. Uncertainty about policy both at the national and international levels make it difficult to project out to 2030 what the pattern and magnitude of cross-border migration will be, but it is plausible that the divergences from the assumptions underlying this paper's projections may be sufficiently large to alter the forecasts appreciably, at least at the level of some individual countries.

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)0,003
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,050
Score d'incertitude au seuil0,099

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,017
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0040,003
Communication savante0,0030,006
Science ouverte0,0030,002
Intégrité de la recherche0,0290,029
Charge utile insuffisante (le modèle a refusé de juger)0,0120,009

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 Opus0,043
Tête enseignante GPT0,270
Écart entre enseignants0,227 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations1
Publié2008
Routes d'admission1
Résumé présentoui

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