Dr. Andrea Conte (p), Dr. Anabela Santos

Over the past decades, EU Cohesion Policy has been the Union’s main tool to reduce territorial disparities while supporting economic development. As debates on the post-2027 budget increasingly emphasize competitiveness, innovation, and strategic autonomy, questions arise about whether a stronger focus on growth may conflict with cohesion objectives. This paper examines whether Cohesion Policy generates trade-offs or synergies between competitiveness and territorial convergence.
Using a panel of NUTS3 regions over 2014-2024, the analysis applies a two-step Generalized Method of Moments estimator to address endogeneity in funding allocation. Beyond average regional effects, the study assesses how Cohesion Policy reduce territorial inequalities, while promoting overall competitiveness. Competitiveness is measured through labour productivity and capital deepening, alongside indicators of territorial inequality.
The findings show no evidence of a trade-off between cohesion and competitiveness. Cohesion Policy is associated with labour productivity growth and a reduction in regional disparities, with effects materializing after a time lag. Impacts are non-linear, suggesting diminishing returns at high funding levels and the relevance of absorptive capacity. From a policy perspective, the results challenge the notion that cohesion and competitiveness represent competing objectives. They can be compatible and mutually reinforcing, provided that policy design accounts for regional capacity constraints, timing of effects, and diminishing returns. A more tailored, context-sensitive approach to Cohesion Policy is therefore essential to maximise both growth and convergence outcomes.

Mr Immanuel Satya Pekerti (p)

Achieving meaningful territorial cohesion through public policy intervention requires a careful
evaluation of the impact. This also means addressing the potentially heterogeneous impact within
a region, such as between urban, rural, and remote areas. Advancements in the theoretical and
empirical understanding of heterogeneities between places imply that administering similar
interventions across spaces can yield heterogeneous results, as seen in place-based policies.
Literature (e.g., Fratesi, 2020; Fratesi and Wishlade, 2017) highlights that differences in socioeconomic
context between places can act as a catalyst or dampener in a policy intervention
mechanism that can boost or hinder the achievement of an optimal policy outcome. This
phenomenon is a double-edged sword for the accountability of public budget spending.
This spatial heterogeneity of policy impact is well-known and well-documented at the regional
scale, where data is readily available (e.g., Bachtrögler et al., 2020; Crescenzi and Giua, 2020).
However, this is not the case for the sub-regional scale. A proper policy evaluation should be done
at the corresponding scale where the policy is implemented. Yet most evaluation practices still shy
away from carrying this out due to the paramount challenge of data availability (Gibbons et al.,
2014). The failure to acknowledge this will blur out the important insights on where and how
policy intervention works or fails.
This paper proposes a way to carry out ex-ante policy impact evaluation on a sub-regional or local
scale. This is important in helping countries and regions plan their geographical distribution of
resources to efficiently achieve optimal policy outcomes. To achieve its objective, this research
combines spatial microsimulation, a geographical data disaggregation method, and discrete choice
modelling based on an economic structural model to allow for policy scenario simulations. Spatial
microsimulation is an established method to geographically disaggregate microdata by combining
survey microdata, taking advantage of the extensive information on socio-economic characteristics
of each survey sample, and census data with its complete coverage of the population. This data
disaggregation is done to complement the well-established discrete choice modelling in economics,
which is able to simulate different policy scenarios in accordance with the latest theoretical
economic understanding of labour supply response among working-age mothers.
This paper takes on the case of Italian public investment in formal public childcare capacity
expansion under the Piano Nazionale di Ripresa e Resilienza (PNRR). This policy aims to address
the national gender employment gap problem in order to achieve the Barcelona Target 2030 and
the overarching targets of the European Pillar of Social Rights. This case is chosen given its timely
relevance and the magnitude of public attention, given the amount spent out of the public budget.

Mr Federico Aresu (p), Ms Emanuela Marrocu, Mr Raffaele Paci

The EU Cohesion Policy (CP) is a central instrument of the Union’s strategy to promote balanced development across member states, targeting less developed regions through investments in infrastructure, innovation, and environmental sustainability to reduce economic and social disparities. A large body of literature has examined the impact of CP on regional economic performance using a wide range of empirical approaches. More recently, however, the focus has shifted toward the identification of causal effects, with an increasing number of studies relying on Regression Discontinuity Design (RDD), which is particularly suitable for evaluating the policy since its structure allows for a quasi-experimental design approach.
Recent RDD-based studies, including Percocco (2017) and Bachtrögler et al. (2020), highlight the importance of regional context in shaping the effectiveness of CP. However, most studies relying on this approach fail to account for the spatial heterogeneity in the allocation of funds. Although CP formally distinguishes less developed (treated) from more advanced regions (non-treated), the latter grouping together EU transition and more developed regions, all EU regions receive support, and funding levels vary widely even within the group of less developed regions.
Against this background, Cerqua and Pellegrini (2018) represent the only contribution to date that extended the RDD framework to explicitly incorporate treatment intensity (i.e., different levels of fund allocation) for CP evaluation over the 1994-2006 period. Building on their contribution, our study evaluates the effects of the policy over the 2008–2023 period, which remains largely unexplored in the existing literature, by applying recently developed continuous-treatment RDD approaches, such as that proposed by Dong et al. (2023). The empirical analysis utilizes regionalized and annualized data on CP payments provided by the Directorate-General for Regional and Urban Policy of the European Commission (EC, 2017), enabling us to move beyond the conventional programming-period framework and capture the time-varying intensity of EU support. Preliminary results suggest that, among less developed regions, intermediate levels of funding are associated with negative and statistically significant marginal effects, indicating the presence of allocation and absorption inefficiencies in Southern European regions, particularly in Portugal and Greece.