L'intervento di Mara Giua, del Dipartimento di Economia dell’Università Roma Tre, mette in evidenza l’importanza di utilizzare al meglio le risorse del programma “Next Generation EU”, che condivide con le politiche di coesione obiettivi, governance e territori.
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Mara Giua - The role of Cohesion Policy evaluation for an evidence-based Next Generation EU
1. D E P A R T M E N T O F E C O N O M I C S
The role of Cohesion Policy evaluation
for an evidence-based Next Generation EU
Mara Giua
Department of Economics and Rossi – Doria Centre
Roma Tre University
mara.giua@uniroma3.it
2. The launch of the ‘Next Generation EU’ program (NGEU) has been accompanied by enthusiasm associated to the
potential of such unprecedented amount of resources activated.
However, a scientific debate on how to make these resources successful in fostering resilience and recovery is
essentially missing:
• What types of intervention should be funded?
• In which composition and for whom?
• How to implement them across the different European territories?
• Which governance models should be set up?
5. The European Union
Cohesion Policy
and Next Generation EU
Objectives
Governance multi-level-governance, with horizontal and vertical cooperation to be ensured
Territories
6. The European Union
Cohesion Policy
and Next Generation EU
Objectives
Governance
Territories with Less Developed Regions taking central stage in embracing the frontiers of the
digital and green transitions envisaged by NGEU: local provision of infrastructures,
digital skills enabling digital receptiveness, the degree of remoteness and the
quality of local institutions highly heterogeneously distributed across EU
7. On average Cohesion Policy impact on economic growth and employment is positive (Becker et. al. 2010,
Pellegrini et al. 2013; Giua, 2017; Cerqua and Pellegrini, 2021).
However, it is heterogeneous with a series of factors undermining or corroborating achievements in different
contextual conditions and policy scenario:
• Good-enough government institutions and human capital (Becker et al. 2013; Accetturo et al. 2014).
• Regional productive context (Bachtrögler et al. 2017)
• Sectoral structure of the local economy (Percoco, 2017)
• Coherence with regional needs (Di Cataldo and Monastiriotis (2020)
• Intensity of the treatment (Becker et al. 2012; Cerqua and Pellegrini, 2018)
• Composition of the policy mix (Cristofoletti et al., 2022)
• Macro-economic and institutional characteristics in the different MSs (Crescenzi and Giua, 2020)
8. Crescenzi, R. & Giua, M. (2020)
One or many Cohesion Policies of the European Union? On the differential economic impacts of Cohesion Policy across Member States
On average it has a positive impact on both economic growth and employment
Impact on employment survives the 2008 crisis
But: the average positive impact is concentrated in Germany and the United Kingdom
The impacts in Italy and Spain are limited
9. The same conditioning factors are likely going to matter for NGEU as well…
The long experience of the Cohesion Policy can be leveraged as a laboratory to select
what types of intervention should be funded within NGEU, in which composition and for
whom, and with which governance models.
The role of granular data on past Cohesion Policy is crucial,
as well as the consolidated civic awareness
10. Crescenzi, Giua & Sonzogno, 2021
Mind the Covid-19 crisis: An evidence-based implementation of Next Generation EU
Evidence-based approach to inform and guide the selection of recovery projects which have the
highest likelihood of contributing to the success of NGEU.
We leverage data on all individual projects funded by the EU Cohesion Policy 14−20 in Italy
(source: OpenCoesione)
We select ‘Next Generation EU-style’ projects through a two-step sequential and integrated
procedure based on EU documents (14,242 total projects in our sample).
11. Crescenzi, Giua & Sonzogno, 2021
Mind the Covid-19 crisis: An evidence-based implementation of Next Generation EU
We compute a simple measure of implementation delay (i.e. difference between expected and actual end dates)
and relative delay (i.e. days of delay/expected duration) and classify projects into 3 categories of implementation:
On time - projects that ended within their expected duration;
Light delay - projects that experienced a relative delay lower than the median relative delay experienced by the
projects overall;
Severe delay - projects that experienced a relative delay higher than the median relative delay experienced by the
projects overall.
We relate our implementation measures to four key dimensions of Governance
1. Levels involved in implementation (Levels)
2. Project leadership (Leadership)
3. Administrative complexity (Complexity)
4. Selection of beneficiaries/activation procedure (Activation)
12. Crescenzi, Giua &
Sonzogno, 2021
Mind the Covid-19
crisis: An evidence-
based implementation
of Next Generation EU
13. Crescenzi, Giua & Sonzogno, 2021
Mind the Covid-19 crisis: An evidence-based implementation of Next Generation EU
14. How to ensure evidence-based policies
• Timely Evaluation – prediction and targeting (Machine Learning)
• From does it work to what works, where, for whom
• Actionable policy configurations, thanks to academic (independent) evaluations reconciled with
practiotioners information need, in a plumber economist fashion (Duflo et al)
• Institutional learning from past experience and from evaluation
Rapid and forward-looking learning mechanisms for public policy decision makers
Investment in a culture of rigorous evaluation among public administration staff
Generating new demand for evaluation
Acting as a receptor of the existing one
15. D E P A R T M E N T O F E C O N O M I C S
The role of Cohesion Policy evaluation
for an evidence-based Next Generation EU
Mara Giua
Department of Economics and Rossi – Doria Centre
Roma Tre University
mara.giua@uniroma3.it @mara_giua