1,721,063 research outputs found
Political favouritism and inefficient management: Policy-makers’ birth town bias in EU quality certifications
No full textIn the current era of EU-driven strategies for sustainable local development, the EU Commission has
designated the geographical indication (GI) policy as a flagship initiative in 2024. The certification
procedure has been simplified, with increased involvement from national and local authorities. This
study explores the potential impact of reforms on GIs in Italy, focusing on whether the birthplaces of
regional council members receive preferential acknowledgment. Analysing municipal-level data, we
employ a Difference in Differences approach and machine learning for counterfactual analysis.
Results indicate a higher likelihood of GIs for councillors' birth municipalities, particularly in areas
with lower ex-ante institutional quality. These findings underscore the potential consequences of EU
reform, warning against political favouritism and inefficient policy management
Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications
No full textGeographical Indications (GIs), as Protected Designation of Origin (PDO)and Protected Geographical Indication (PGI), offer a unique protection scheme to preserve high-quality agri-food productions and support sustainable rural development at the territorial level. However, not all the areas with traditional agri-food products are acknowledged with a GI. Examining the Italian wine sector by a geo-referenced database and a machine learning framework, we show that municipalities which obtain a GI within the subsequent 10 year period (2002–2011) can be predicted using a large set of (lagged) municipality-level data (1981–2001). We find that the Random Forest algorithm is the best model to make out-of-sample predictions of municipalities which obtain GIs. Results show that there is a sort of optimal territorial condition characterized by the successful matching of wine-growing profession (vineyards), local actors involved (number of farmers), and physical dimension of farms (middle farms). Being in a vital economic system and the distance from major urban centers also emerges among the main relevant features in predicting the success of GIs. The methodology adopted and the evidence provided lead to policy reflections, in the light of the future Common Agricultural Policy (CAP) programming period and the scheduled reform of the GI’s quality scheme
Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications
No full textGeographical Indications (GIs) offer a unique protection scheme to preserve
high-quality agri-food productions and support sustainable rural development
at the territorial level. However, not all the areas with traditional agri-food
products are acknowledge with a GI. Examining the Italian wine sector by a
geo-referenced database and a machine learning framework, this paper shows
that municipalities which obtain a GI within the following 10 years (2002-2011)
can be predicted using a large set of (lagged) municipality-level data (1981-
2001). Results point out that local wine growing tradition, participation and
education rates as well as the engagement in other GI quality schemes (food
and spirits) are determinant in the prediction of GI certifications. This evidence can support policy makers and stakeholders to target rural development
policies and investment allocation, offering strong highlights for the future reforms of GIs quality scheme
Regional Well-Being and Its Inequality in the OECD Member Countries
No full textThis paper analyses the inequality between the regions of the Organization for Economic 13 Co-operation and Development (OECD) member countries by using stochastic multi- 14 objective acceptability analysis and the associated multivariate Gini index. By consider- 15 ing a large number of possible combinations of weights, the distribution of the potential 16 rankings for each region is used to measure multidimensional inequality both within and 17 between countries. Our results show that beyond the expected two clubs of rich and poor 18 countries, a third group of countries emerges that belongs neither to the top nor to the 19 bottom of the ranking, an outcome that can be attributed to the presence of significant 20 economic differences among regions within those countries. Most of the inequality lies 21 between countries, but regional well-being also significantly varies within the same 22 countries and we find an inverse U-shape connection between regional well-being and 23 its inequality within the OECD member countries
The Impact of Italy's Strategy for Inner Areas on Depopulation and Industrial Growth: A Staggered Diff-in-Diff Analysis with Spatial Spillover Effects
No full textFiscal decentralization and income (re)distribution in OECD countries' regions
Full text linkCross-country income inequality has declined in the last decades, but this trend has been paralleled by an increase in within-countries inequality. At the same time, many governments have implemented fiscal decentralization policies, devolving increasing decisionmaking powers on fiscal matters to sub-national levels of government. In this paper, we
provide empirical evidence on the relationship between fiscal decentralization and intraregional income redistribution, based on regional level data on inequality and government revenues for 187 regions of 15 OECD countries. Our results how that within region income redistribution is negatively associated with fiscal decentralization, especially when it takes the form of revenue decentralization
Place-Based Policies and the location of economic activity:evidence from the Italian Strategy for Inner areas
No full textAssessing the effectiveness of coordination among public authorities in cohesion expenditure
No full text
Co-payment exemption and healthcare consumption: quasi-experimental evidence from Italy
Full text linkThis paper investigates the causal effect of co-payment exemption on the number of specialist visits in the Italian National Health System. Exploiting a discontinuity in the multiple eligibility criteria, we apply multiple regression discontinuity in a quasi-experimental setting, considering both age and income requirements. Differently from the standard regression discontinuity, this twofold discontinuity allows to identify the effect of co-payment on a particularly needy sub-population of less wealthy people and how it changes according to the eligibility criteria. We find positive effects of co-payment exemption and the effects are stronger for less wealthy and older individuals. The result may be useful to the policy maker to tailor ad-hoc policies aimed at disadvantaged sub-populations
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