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The heterogeneous causal effects of the EU's Cohesion Fund

Angelos Alexopoulos, Ilias Kostarakos, Christos Mylonakis, Petros Varthalitis

TL;DR

This paper tackles the challenge of evaluating the EU Cohesion Fund (CF) by employing a matrix-completion causal-inference framework within a factor-model structure to recover time-varying, region-specific treatment effects. By moving beyond local average treatment effects, it reveals substantial heterogeneity and distributional dynamics: CF effects are front-loaded, larger in poorer regions, and exhibit non-linear returns as transfer intensity rises, with strong reductions in regional income dispersion. The approach yields robust average effects on regional $GVA$ and $GFCF$ and depthful insights into who benefits and where, informing targeted, place-based policy design. Practically, the findings imply that moderate, well-timed CF allocations can meaningfully promote convergence, while excessive funding may yield diminishing or even counterproductive returns, suggesting room for redistribution to optimize growth impacts across the EU.

Abstract

This paper estimates the causal effect of EU cohesion policy on regional output and investment, focusing on the Cohesion Fund (CF), a comparatively understudied instrument. Departing from standard approaches such as regression discontinuity (RDD) and instrumental variables (IV), we use a recently developed causal inference method based on matrix completion within a factor model framework. This yields a new framework to evaluate the CF and to characterize the time-varying distribution of its causal effects across EU regions, along with distributional metrics relevant for policy assessment. Our results show that average treatment effects conceal substantial heterogeneity and may lead to misleading conclusions about policy effectiveness. The CF's impact is front-loaded, peaking within the first seven years after a region's initial inclusion. During this first seven-year funding cycle, the distribution of effects is right-skewed with relatively thick tails, indicating generally positive but uneven gains across regions. Effects are larger for regions that are relatively poorer at baseline, and we find a non-linear, diminishing-returns relationship: beyond a threshold, the impact declines as the ratio of CF receipts to regional gross value added (GVA) increases.

The heterogeneous causal effects of the EU's Cohesion Fund

TL;DR

This paper tackles the challenge of evaluating the EU Cohesion Fund (CF) by employing a matrix-completion causal-inference framework within a factor-model structure to recover time-varying, region-specific treatment effects. By moving beyond local average treatment effects, it reveals substantial heterogeneity and distributional dynamics: CF effects are front-loaded, larger in poorer regions, and exhibit non-linear returns as transfer intensity rises, with strong reductions in regional income dispersion. The approach yields robust average effects on regional and and depthful insights into who benefits and where, informing targeted, place-based policy design. Practically, the findings imply that moderate, well-timed CF allocations can meaningfully promote convergence, while excessive funding may yield diminishing or even counterproductive returns, suggesting room for redistribution to optimize growth impacts across the EU.

Abstract

This paper estimates the causal effect of EU cohesion policy on regional output and investment, focusing on the Cohesion Fund (CF), a comparatively understudied instrument. Departing from standard approaches such as regression discontinuity (RDD) and instrumental variables (IV), we use a recently developed causal inference method based on matrix completion within a factor model framework. This yields a new framework to evaluate the CF and to characterize the time-varying distribution of its causal effects across EU regions, along with distributional metrics relevant for policy assessment. Our results show that average treatment effects conceal substantial heterogeneity and may lead to misleading conclusions about policy effectiveness. The CF's impact is front-loaded, peaking within the first seven years after a region's initial inclusion. During this first seven-year funding cycle, the distribution of effects is right-skewed with relatively thick tails, indicating generally positive but uneven gains across regions. Effects are larger for regions that are relatively poorer at baseline, and we find a non-linear, diminishing-returns relationship: beyond a threshold, the impact declines as the ratio of CF receipts to regional gross value added (GVA) increases.

Paper Structure

This paper contains 23 sections, 17 equations, 18 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related literature
  3. Background: the Cohesion Fund
  4. Data and Descriptive Statistics
  5. Empirical Strategy
  6. Notation and preliminaries
  7. Causal inference by using matrix completion
  8. Estimation of causal effects
  9. Results
  10. Average Treatment Effect
  11. Heterogeneous effects
  12. Quantitative analysis of the heterogeneous effects
  13. Geographical analysis of the heterogeneous level effects
  14. Heterogeneous impact of the Cohesion Fund by income group
  15. Regional heterogeneity over time
  16. ...and 8 more sections

Figures (18)

  • Figure 1: Yearly Cohesion Fund payments
  • Figure 2: Share of Cohesion Fund payments
  • Figure 3: $\widehat{ATT}_t$ for GVA
  • Figure 4: $\widehat{ATT}_t$ for GFCF
  • Figure 5: Distribution of GVA effects
  • ...and 13 more figures