The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Too much of a good thing? On the growth effects of the EU's regional policy
Open accessAbstract
The European Union (EU) provides grants to disadvantaged regions of member states from two pools, the Structural Funds and the Cohesion Fund. The main goal of the associated transfers is to facilitate convergence of poor regions (in terms of per-capita income) to the EU average. We use data at the NUTS3 level from the last two EU budgetary periods (1994–1999 and 2000–2006) and generalized propensity score estimation to analyze to which extent the goal of fostering growth in the target regions was achieved with the funds provided and whether or not more transfers generated stronger growth effects. We find that, overall, EU transfers enable faster growth in the recipient regions as intended, but we estimate that in 36% of the recipient regions the transfer intensity exceeds the aggregate efficiency maximizing level and in 18% of the regions a reduction of transfers would not even reduce their growth. We conclude that some reallocation of the funds across target regions would lead to higher aggregate growth in the EU and could generate even faster convergence than the current scheme does.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997) · The Review of Economic Studies · doi:10.2307/2971733 · Cited by 5,486
Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H., & Wahba, S. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473858 · Cited by 2,395
A Method for Evaluating the Distribution of Power in a Committee System
Shapley, L. S., & Shubik, M. (1954) · American Political Science Review · doi:10.2307/1951053 · Cited by 2,162
The Allocation of Talent: Implications for Growth
Murphy, K. M., Shleifer, A., & Vishny, R. W. (1991) · The Quarterly Journal of Economics · doi:10.2307/2937945 · Cited by 2,092
The role of the propensity score in estimating dose-response functions
Imbens, G. (2000) · Biometrika · doi:10.1093/biomet/87.3.706 · Cited by 1,776
Industrialization and the Big Push
Murphy, K., Shleifer, A., & Vishny, R. (1988) · National Bureau of Economic Research · doi:10.3386/w2708 · Cited by 1,449
The Propensity Score with Continuous Treatments
Hirano, K., & Imbens, G. W. (2004) · Wiley Series in Probability and Statistics · doi:10.1002/0470090456.ch7 · Cited by 1,064
Causal Inference With General Treatment Regimes: Generalizing the Propensity Score
Imai, K., & van Dyk, D. A. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000001187 · Cited by 868
Sachs, J., McArthur, J. W., Schmidt-Traub, G., Kruk, M., Bahadur, C., Faye, M., & McCord, G. (2004) · Brookings Papers on Economic Activity · doi:10.1353/eca.2004.0018 · Cited by 763
Cited by
(2015) · Handbook of Regional and Urban Economics · doi:10.1016/b978-0-444-59531-7.00018-1 · Cited by 597
Some Causal Effects of an Industrial Policy
Criscuolo, C., Martin, R., Overman, H. G., & Van Reenen, J. (2019) · American Economic Review · doi:10.1257/aer.20160034 · Cited by 554
Rodríguez-Pose, A., & Garcilazo, E. (2015) · Regional Studies · doi:10.1080/00343404.2015.1007933 · Cited by 416
Metadata
- Authors
- Becker, S. O., Egger, P. H., & von Ehrlich, M.
- Venue
- European Economic Review
- Publisher
- Elsevier BV
- Published
- 2012-05
- Volume
- 56
- Issue
- 4
- Pages
- 648-668
- ISSN
- 0014-2921 (ISSN-L)
- OpenAlex
- W2014260825
- Cited by
- 260
- References
- 18
- FWCI
- 71.24
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Too much of a good thing? On the growth effects of the EU's regional policy. https://app.cochat.ai/papers/10.1016/j.euroecorev.2012.03.001
- The paper
- Becker, S. O., Egger, P. H., & von Ehrlich, M. (2012). Too much of a good thing? On the growth effects of the EU's regional policy. European Economic Review. https://doi.org/10.1016/j.euroecorev.2012.03.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.