Gattone, Tulia

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Tulia Gattone specializes in Development Economics and Applied Econometrics. She is currently a postdoctoral researcher at the University of Florence, on leave from the Italian public administration. She has worked as a private sector development analyst at the World Bank Group in Washington DC. She also gained professional experience at the Implementation Support Unit of the Anti-Personnel Mine Ban Convention in Geneva and the Center for European Policy Analysis in Washington DC. Professor Gattone has also worked as a teaching, graduate, and research assistant at Syracuse University in Syracuse, NY. She holds a Ph.D. in Development Economics from Sapienza University of Rome and an M.A. in International Relations from Roma Tre University. Before working on her doctorate, Professor Gattone received a M.Sc. in Economics and Commerce from D’Annunzio University in Pescara, an M.A. in International Relations from the Maxwell School of Citizenship and Public Affairs in Syracuse, NY, and a B.Sc. in International Economics, Management, and Finance from Bocconi University in Milan.

Publication Search Results

Now showing 1 - 5 of 5
  • Publication
    Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator
    (2024) Gattone, Tulia
    This study presents a micro-level indicator of farmers’ positioning in the market chain, based on the conceptual framework outlined by Antràs and Chor (2013, 2018). The indicator considers the selling location of a farming household and its crop buyers. Using panel data from the World Bank’s ‘Living Standards Measure-ment Study: Integrated Surveys on Agriculture’ for Ethiopia and Nigeria, this paper applies the proposed indicator empirically and showcases its superior performance in comparison to existing alternatives at the micro-level. Furthermore, by analyzing the dynamics of farmers’ food and total consumption over time and controlling for vari-ous household and production characteristics, as well as potential confounding factors, this study shows that moving towards a downstream position in the market chain has a positive impact on farmers’ food and total consumption levels. The results are validated through sensitivity analysis and robustness checks.
  • Publication
    Economic and Financial Development as Determinants of Crypto Adoption
    (2025) Magazzino, Cosimo; Gattone, Tulia; Horky, Florian
    This research investigates the macroeconomic determinants of crypto adoption, illuminating the potentials of cryptocurrencies to accelerate financial inclusion. By exploiting an extensive dataset from 165 countries between 2019 and 2021, this study employs various econometric methodologies, including Panel Feasible Generalized Least Squares (PFGLS), Robust Least Squares (RLS), and Quantile Regressions (QR). These classic econometric techniques are complemented by several machine learning techniques such as Bagging, Boosting, and Support Vector Machine (SVM) regressions, as well as Artificial Neural Networks (ANNs) and Naïve Bayes (NB) classification algorithms. The results show an interesting trend: cryptocurrency adoption is more prevalent in countries with robust financial markets and higher education levels. This suggests that crypto adoption is primarily a byproduct of sophisticated financial environments and an educated population, rather than a direct facilitator of financial inclusion.
  • Publication
    Unleashing the power of innovation and sustainability: Transforming cereal production in the BRICS countries
    (Elsevier, 2024) Magazzino, Cosimo; Gattone, Tulia; Usman, Muhammad; Valente, Donatella
    Amidst escalating food insecurity and climate change threats, which exacerbate food shortages and increase agricultural emissions, this paper explores transformative strategies in cereal production within the BRICS countries from 1990 to 2021. The uncontrolled growth of intensive agriculture, aimed at satisfying the growing global demand for food in a context already threatened by climate change, has led to a uniformity of crops with devastating impacts on biodiversity and ecosystem functioning, resulting in a transformation of soil and its capacity to implement ecosystem services, such as food, fiber, and raw material production, nutrient recycling, carbon sequestration, clean water availability, and the regulation of water regimes and local temperatures. These changes have had negative consequences on agricultural production. Thus, sustainable agriculture faces three closely related challenges: reducing environmental impact, in-creasing productivity, and adapting to and mitigating climate change. This analysis utilizes advanced econometric tools such as panel second-generation unit root tests, Westerlund’s cointegration test, second-generation long-run estimators, and the Dumitrescu-Hurlin causality test, together with several machine learning algorithms, to investigate the influence of technological innovations and improved land management on cereal yields. The findings demonstrate a positive correlation between technological advancements, enhanced land management for cereal cultivation, and the food production index with increased cereal output. At the same time, emissions from agriculture significantly reduce yields over time. Furthermore, an interaction analysis reveals that the comprehensive integration of these factors significantly boosts cereal productivity. The study also identifies directional causal relationships between technological and emission factors and cereal production, suggesting a complex interplay with land use. Sustainable land use is one of the key conditions for ensuring the ecological resilience of agricultural practices in terms of providing ecosystem services. Implementing these strategies calls for a collaborative approach among governments, policymakers, farmers, researchers, and other stakeholders, considering each BRICS nation’s unique environmental, socio-economic, and local contexts, and fostering regional cooperation to promote sustainable agricultural practices.
  • Publication
    Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses
    (Springer Nature, 2024) Magazzino, Cosimo; Giolli, Lorenzo; Gattone, Tulia
    This study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises.
  • Publication
    Greenhouse gas emissions and road infrastructure in Europe: A machine learning analysis
    (2025) Magazzino, Cosimo; Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo; Gattone, Tulia
    This paper explores the determinants of greenhouse gas (GHG) emissions in Europe, focusing on transportation-related variables. By combining classical econometric models with Machine Learning (ML) techniques, we analyze data spanning from 2013 to 2021. The empirical findings highlight the complex relationship between newer passenger cars and GHG emissions, noting the significant impact of their production and increased usage. Conversely, the adoption of alternative fuel vehicles is found to significantly reduce emissions. This is further supported by ML models, which emphasize the critical role of car density and alternative fuel vehicles in determining emissions. Policy implications suggest the need for targeted interventions, including the promotion of electric and hybrid vehicles, enhancements in transportation infrastructure, and the implementation of economic incentives for clean technologies.