An overview of the SDGs
Francesca Perucci
This session aims to provide an overview and introduction to the process by the global statistical community to address the new data requirements to fulfill the ambitions and principles of the 2030 sustainable development agenda. In the new agenda, Member States recognize the great importance of quality, accessible, timely and reliable disaggregated data that will be needed to inform policies and interventions and for the measurement of progress, ensuring that no one is left behind. The wide range of topics of the new agenda requires an unprecedented amount of data and will pose an enormous challenge especially in the developing countries.
In the face of these challenges, the global data community needs to identify ways to ensure the collection and use of high-quality, relevant, timely and disaggregated data. We are witnessing an exponential growth in data sources—from mobile phones to administrative records, from satellite imagery to citizen-generated data—and a continuous innovation in data technologies and methods. Today, policy makers and citizens alike have unprecedented opportunities to produce and utilize the information they need to make better decisions and improve people’s lives.
In this session, presentations will cover the process for the development of the SDG Indicator framework, including global, regional, national and thematic indicators and the establishment of national SDG platforms. The session will also discuss mechanisms to allow the integration of different data sources from both within and outside the official statistical system. Issues of interoperability, including open data and new data principles will also be reviewed.
Finally, the session will provide an overview of issues of capacity building to ensure that countries can address the new data needs, including the Cape Town Global Action Plan and other processes within the UN Statistical Commission.
Monitoring and promoting the SDGs through the (Big) Data revolution
Emmanuel Letouzé
Two years after their formal adoption, there is an urgent need to mobilize the “Data Revolution” in general, and its core component Big Data in particular, to reach the Sustainable Development Goals (SDGs). What can and should be done to seed and support a virtuous data-enabled process of social transformation through innovation and inclusion – a transformation in which the SDGs are much more systematically monitored and promoted?
This overarching question raises two more. First, what is the theory of societal change explicitly or implicitly put forth by the strongest advocates of the SDGs? And how valid is that theory? In other words: what are we saying or selling about the causal impact of measurement and evidence more largely?
Second, how can the instrumental role of measurement—via greater transparency, accountability and efficiency — be magnified? Despite the hope that Big Data could help fill “data gaps” and perhaps even fix the “statistical tragedy” in the poorest countries, there is still no body of stable, scalable methodologies to ‘leverage’ Big Data to make a significant contribution to measuring the SDGs in the next decade. Why is that? Can this be changed and if so how?
These are some of the questions this session will seek to address in proposing a theoretical and methodological framework for monitoring and promoting the SDGs through the (Big) Data Revolution, based on the work and vision of Data-Pop Alliance.
Inequalities in Latin America
Mireia Fernandez-Ardèvol
Inequality is a global problem that requires global solutions. It is on the rise, with the richest 10% population earning up to 40% of total global income, and the poorest 10% between 2 and 7%. Beyond income, other forms of inequality, as the digital divide, are also relevant.
The first two parts of the course focus on income inequality. First, we analyze the SDG’s Goal 10, aimed at reducing inequalities within and among countries. We revise its targets and their associated indicators and reflect about their scope, which is wider than the traditional economic focus. Second, we examine the available indicators of income distribution in Latin America and the way they are built to discuss their meaning and their reach critically.
The last part focuses on the digital divide, particularly on data regarding diffusion and use of ICTs in Latin America. Again, we critically discuss the information reported, with a specific interest in mobile communications and the Internet. We examine the evolution of the digital divide and the way the phenomenon has been conceptualized and measured, and the limitations of data availability when it comes to studying weaker social groups.
Quantitative reasoning on sustainable development indicators
Bruno Cautrès
The fourth day aims to provide an introductory overview of the main issues related to the quantitative analysis of sustainable development indicators. The constitution of large data sets of empirical measures in the different domains of sustainable development requires different quantitative and statistical techniques to create synthetic indexes as well as to study their relationships. The methodological questions will be illustrated from examples coming from sustainable development problems and research questions. Since the session is an introductory and overview one, it will not require previous statistical skills from participants.
This session will address the following issues in a non (too) technical approach but in an integrated perspective, underlying the links between the different issues and points: the logics of quantitative measures and indicators (from observations to data, from small data sets to big ones and their methodological issues), measurement issues (types of variables, levels of measurement, issues in aggregate-level measures versus individual-level ones, multi-level indicators), multivariate analysis of quantitative indicators (how the study the dimensionality of indicators and how to reduce it to create synthetic indicators? Describing and correlating indicators, data reduction techniques and their main objectives) and causal analysis (how to establish a relationship between X and Y? The relationships between socio-economic and socio-political indicators, from correlation and regression to causation).