Correct Partitioning of Regional Growth Rates: Improvements in Shift-Share Theory *
Notice bibliographique
Résumé
This paper demonstrates that the Ray-Srinath model resolves, conceptually and mathematically, the issues that are beyond the traditional shift-share model. It does this by establishing the mathematical links between the traditional and the Ray-Srinath models. Ray-Srinath partitions the traditional regional share rate into a region effect rate and a region-industry interaction rate. The traditional industry-mix rate is split into an industry-mix effect rate and an allocation rate. Four is the minimum number of components if they are to be clean ones. L'etude demontre que le modele Ray-Srinath resout les problemes conceptuels et mathematiques inherents au modele shift-share traditionnel en etablissant les liens mathematiques entre les deux modeles. Le modele Ray-Srinath decompose chacune des deux composantes du modele traditionnel en deux composantes plus raffinees et plus justes. La premiere composante traditionnelle, les conditions regionales, est decomposee en effet de region et effet d' interaction region-industrie. La seconde composante traditionnelle, la composition industrielle, est decomposee en effet de structure industrielle et effet d'allocation. Quatre est le nombre minimal requis pour que les composantes soient propres. Introduction Shift-share analysis has become one of the most widely-used partitioning techniques in regional development studies since it was introduced by Prof. J. Harry Jones in The Royal Commission on the Distribution of the Industrial Population published in 1940. Its appeal is that it provides a very simple method of partitioning regional employment growth into two fundamental components: a partitioning that is crucial to understanding regional growth patterns. The model first measures what it purports to be the contribution to employment growth of the regional industrial structure or industry-mix. The residual employment growth is then termed the regional share. Unfortunately they are incorrectly measured. Nevertheless, all analysts followed his lead, and his approach became the basis for the traditional shift-share model (Dunn 1959; Statistics Canada 1973). Regional analysts will not abandon a well-established technique without proof of its deficiencies. This paper seeks to provide that proof. It demonstrates that the results produced by the traditional model are incorrect, by identifying the precise mathematical relationship between his model and the Ray-Srinath model. In essence what this paper does is prove that when the regional growth rates are partitioned correctly the process results in the splitting of each of the two traditional components into two finer components which are correct mathematically in the sense that they measure what they say they do. The problems with the approach used by Jones are, however, conceptual as well as mathematical, and the conceptual limitations cannot be removed without first correcting the mathematical errors. In particular, regional analysts and policy makers no longer accept that regional disparities in employment growth can be understood properly merely in terms of regional industry-mix and regional shifts in industry. An important role is played by other factors, such as regional differences in firm-size distribution and, in an age of increasing globalization, the regional concentrations of foreign multi-national corporations. The Jones model is limited to testing a single assumption: that regional growth rates are largely controlled by industry mix. The Jones model cannot be extended to analyze these multivariate factors simultaneously. Nor can it be used to examine these regional forces two at a rime as they are interwoven and act together simultaneously. Hence in 1990, Ray and Srinath introduced a new multi-factor partitioning model to analyze regional disparities in employment growth (Ray 1990). It was then used to examine the impact on employment growth in Canada of industry-mix, the size-structure of firms, the level of foreign ownership and regional factors, as well as the interactions among them. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».