A New Perspective on Regional Development Policies in Europe/Un Regard Nouveau Sur Les Politiques De Developpement Regional En Europe
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".