A New Perspective on Regional Development Policies in Europe/Un Regard Nouveau Sur Les Politiques De Developpement Regional En Europe
Notice bibliographique
Résumé
This paper assesses the impact of the European Union's structural funds on the manufacturing sectors of 145 of its regions for the period 1989-2004. Each stage in the EU enlargement process has increased disparities among its regions, threatening European cohesion. Regional development policies were implemented to reduce such inequalities. The examples of Spain, Portugal and Ireland are often quoted when appraising the effectiveness of these policies. Indeed, income in these countries did converge towards the European average after a decade of membership. However, regional policies have also been subject of criticism. From 1989 to 1999,250 billion Euros were spent on structural funds. Some commentators argue that it was too much money for too few results: disparities increased within countries and most of the regions then eligible under objective 1 (the development of the poorest regions) are still eligible today. Others argue that too little was spent on reducing development inequalities given the scale of the disparities and compared with spending on the Common Agricultural Policy (twice the budget for the agricultural sector alone). In this context, much research has been undertaken to evaluate the impact of regional policies on growth, but without ending the debate. Indeed, the results are very varied: some studies report a positive impact, others argue that policy effects are conditional upon other variables; yet others conclude that the impact has been non-significant or even negative. We argue, therefore, that a fresh approach is called for. We challenge the neoclassical theoretical model on which earlier studies rely. Since the advances in economic growth theory and in economic geography indicate that increasing returns to scale affect growth, we introduce such a hypothesis in the context of Verdoorn's law. Furthermore, four main innovations are included. First, we examine the cohesion objective. More specifically, we separate structural fund objectives 1 and 2 (which are the only ones involving the production function) from the other three objectives and we include the additional funds provided by the region or country under EU law on project financing (i.e. the total cost of the project financed is taken into account). Second, we introduce a 5-year time lag to test whether the impact of the funds is deferred. Third, the geographical linkages between regions are explicitly taken into account using spatial econometric techniques that allow for spatial spill-over effects among regions. Fourth, the potential endogeneity of explanatory variables is systematically checked. Two aspects are examined: the potential correlation of the growth rate of output (exploratory variable) with the errors and the endogeneity of two other variables, i.e. the size of structural fund spending and the growth rate of output; because the allocation of structural funds is based on average per capita GDP in the three years before the programme period, endogeneity may occur between the structural funds variable and the growth in output. The results indicate increasing returns and a significant but small negative impact of the structural funds. When these variables are split by objective, the coefficient associated with objective 1 funds (costs) is significant and negative, and also very small, while that associated with objective 2 is not significant. However, these pessimistic results are open to challenge. First, our time lag may not be long enough to show up the funds' full impact on growth. It may still be too early to capture the full impact of the funds. Second, beyond the stated aim of reducing interregional income inequalities, EU aid is not necessarily correlated with the development gap or development potential. In that sense, the European authorities may have tried to achieve too many objectives through regional funding. Third, a significant part of the funds are spent on transport infrastructure. Even if this contributes to the aims of the Single Market by enabling the free movement of goods, services and people, it may not be the right way to reduce disparities among Europe's regions. …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».