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Scholarship code CU2.07

Water-in-food, conflicts, and migrations

  • Reference person
    Massimo
    Riccaboni
    massimo.riccaboni@imtlucca.it
  • Host University/Institute
    IMT School for Advanced Studies Lucca
  • Internship
    Y
  • Research Keywords
    Virtual Water
    Conflicts
    Migrations
  • Reference ERCs
    SH1_2
    SH7_2
    SH7_6
  • Reference SDGs
    GOAL 2: Zero Hunger
    GOAL 10: Reduced Inequality
    GOAL 12: Responsible Consumption and Production
  • Studente
  • Supervisor
  • Co-Supervisor

Description

This is an interdisciplinary research project that analyzes the causes and consequences of the water-in-food trade and the socio-economic impact of water scarcity. Water is virtually embodied in many commodities, especially food and beverages. Therefore, international trade represents a way to transfer water across borders. In principle, the water-in-food trade could enhance water efficiency by saving water in water-scarce countries which can import virtual water. Also, human migrations might be beneficial to the water endowments of origin countries for reducing the pressure on local resources. In previous studies, we have found that this vision is over-simplistic since trade and migration patterns depend on complex economic, political, social, demographic, and environmental drivers. For instance, in Metulini et al. (2016) we show that migrants strengthen the commercial links between countries, triggering trade fluxes caused by food consumption habits persisting after migration. Sometimes, when the water suitcase of migrants exceeds the water footprint of inhabitants, migration flows turn out to be detrimental to the water endowments of origin countries. On the other hand, we find that water-in-food imports in water-scarce countries help in reducing conflicts and refugee movements (Metulini, Riccaboni, and Serti, 2020). Water availability is tightly linked to Food security, intended as the ability to meet the energy needs of the world population. At present, however, there is limited knowledge of how virtual water trade affects food security. The main goal of this project is to analyze real-world data (remote sensing, trade, migration, wars, and conflicts) to simulate future scenarios of virtual water trade and its impact on sustainability. More specifically, the main objectives are (a) a better understanding of the global dynamics of water-in-food flows and (b) the evaluation of the impact of such flows on food safety and sustainability. The project aims to inform the policy agenda of international agencies (e.G., FAO, WTO, OECD) as well as regional and national authorities to reduce the water footprint of trade and to enhance sustainable trade.

Suggested skills:

The ideal candidate has a background in economics, social or political sciences with an interest in international studies and sustainability. Good command of statistics, econometrics, or numerical methods will be a plus.

Research team and environment

The research will be conducted in an interdisciplinary environment at IMT School of Advanced Studies in Lucca. The research team is made up of the members of the AxES research unit at IMT Lucca (axes.Imtlucca.It) led by Prof. Riccaboni. The Laboratory for the Analysis of CompleX Economic Systems (AXES) is a research unit whose work spans different fields of economics: from economic theory to applied econometrics, from international economics to political economy, from spatial and urban economics to industrial organization and business economics. We all share a common interest in original socio-economic research that provides information critical to policy-making with a problem-solving approach. In our research, we incorporate skills and tools from different disciplines, including network theory, the physics of complex systems, data science, decision science, or political science. In fact, we believe that a modern approach in economics requires considering the solution of economic problems more important than sticking to academic disciplines. Under such a multidisciplinary perspective, we strive to utilize the most recent developments in big data and machine learning, seeking to combine them with more traditional econometric approaches in our research.