Coin Open Days → more info

Date of event: Friday, April 20, 2018 - 09:30 to 17:00

Where: Ispra, Italy

Aim:

This workshop brings together the JRC-Seville, with scientists of the European Commission’s Competence Centre on Composite Indicators and Scoreboards (COIN) with a view to discuss together how to further improve the indicators and to ensure that they are developed using the highest standards in composite indicator methodology.

1) Impact Indicator of Quarantine Pests

Description:

“The Impact Indicator of Quarantine Pests (IIQP) is a tool to rank plant pests according to the severity of the economic, social and environmental impact that they can cause in the Union territory. The indicators try to summarize and reflect the recitals and articles of Regulation (EU) 2016/2031. The PIQP should be applied to all plant pests that can potentially affect EU crop, horticulture and forestry activities.”

Societal impact:

“The increasing threat of plant pests is a worldwide phenomenon mainly due to the globalization of the plant trade and the effects of climate change. In the last decade, the EU has been confronted with several large scale outbreaks of new plant pests. These pests, of particular importance for the Union territory, deserve better preparedness both at EU and Member State level, with more prevention, detection and control. Resources for pest prevention, detection and control are scarce and have to be allocated. The indicators are therefore a tool for supporting plant health policy-making based on the soundest scientific evidence and data by aiming at identifying those pests to which resources would be best allocated based on their potential economic, social and environmental impact.”

2) Food Price Vulnerability indicator

Description:

“The definition of food security suggests a conceptual framework of four dimensions to analyze and target food security: Availability, access, utilization and stability. The concept of food security was initially focused on availability aspects. The FAO’s 1996 definition brought an increased emphasis on nutrition which led to the term Food and Nutrition Security (FNS). The refinement of FAO’s definition in 2002 gave increased importance to the demand side, people’s consumption and the issues of access to food by vulnerable individuals and households. Food access from a physical and economic perspective is a key pillar of food security. There is a strong link between food availability and access. Local food availability depends mainly on food production and trade systems while food access depends on food affordability (i.e. cost of food relative to people’s earnings) and physical aspects of accessibility (e.g. roads density, market distances). We aim at developing an indicator to rank geographic areas according to people’s vulnerability to food price changes. The indidicator takes into account the different components of the access dimension of food security.”

Societal impact:

“Food and nutrition security is a top priority in the international and individual governments’ political agendas. The 2030 Agenda for Sustainable Development builds on the Millennium Development Goals (MDGs) and call on all countries and stakeholders to act together to end hunger and prevent malnutrition by 2030. Food security indicators and statistics are crucial to assess and monitor food security at national and sub-national levels and inform food security programs and policy intervention. In developing countries, in particular in Africa, poor households allocate more than 60% of their budgets to food. Food prices are a key indicator of changes in food supply and demand, as they signal the availability and affordability of food. They are key determinants of households’ access to food. According to this view of food security, policy makers and different type of organizations aim at monitoring local food prices to anticipate and react to potential food crises. But this analysis needs to connect the prices with population's earnings and accessibility issues. Timely access to data and analysis becomes a key issue for policy decisions to understand where vulnerable populations are mostly affected.”

3) Quality indicator of Crowdsourced Food Price Data

Description:

“A crowdsourcing technique, i.e. citizens’ data-sharing implies a collection of data on a purely voluntary basis with no control on the sampling design, on the data collection strategy and on its quality. The traditional body of validated statistical methods may not be adequate when dealing with crowdsourcing methods. In our project we are developing a robust statistical spatial methodology to validate and aggregate food price data collected through crowdsourcing. The aim is to produce up-to-date food price indices at the most local level, possibly at two stages of the food chain (farm gate and market). As part of this methodological approach a set of indicators is being developed to measure different aspects of the quality of those crowdsourced datasets. The idea is to develop a global indicator of quality to rank the crowdsourced food price datasets.”

Societal impact:

“In developing countries, in particular in Africa, poor households allocate in average more than 60% of their budgets to food expenditure. Food prices are a key determinant of people’s access to affordable and sufficient food. However, in these countries there is often a lack of timely & reliable access to food price data. Close to real-time food prices are important for several purposes: early warning systems related to the assessment of food security highlighting areas that require attention by enhancing the ‘access’ component of food security; reducing information asymmetries thus improving market efficiency across the value chain; and ex ante and ex post economic analysis for supporting evidence-based decision and policy making. In recent years a number of initiatives have explored the use of new technologies and crowdsourcing methods to overcome the challenge of lack of timely data. While the contribution of mobile technologies to data collection systems is widely acknowledged, crowdsourcing approaches require further attention with regard to data quality”.