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gsr-19

Improving Global Development Statistics

Title

Using Advertising Audience Estimates to Improve Global Developments Statistics

Abstract

The United Nations Sustainable Development Goals (SDGs) are a key instrument in setting the agenda around global development until 2030. These goals come with a set of 232 indicators against which countries should monitor their progress with respect to the SDGs. Existing data sources to measure progress on the SDGs and global population trends however are often (i) outdated, (ii) lacking international comparability, (iii) lacking appropriate disaggregation, or (iv) missing completely. These problems are often especially acute among less developed countries. In this paper we describe how anonymous, aggregate data from the online advertising platforms of Facebook, LinkedIn and other services can be used in combination with existing data sources to improve global development statistics. We illustrate the process of using and validating such non-representative data through two case studies looking at (i) Internet access gender gaps, and (ii) international migration statistics.

Keywords

Facebook, global development statistics, online advertising, SDG monitoring

Authors

Ingmar Weber
(Qatar Computing Research Institute, Qatar)

Ingmar Weber is the Research Director for Social Computing at the Qatar Computing Research Institute (QCRI) where he leads a team of 16 researchers and engineers working on projects of both local and global societal relevance. He obtained his BA and MA in mathematics from Cambridge University before pursuing a PhD in computer science at the Max Planck for Computer Science in Saarbrücken. He subsequently held posts at the École polytechnique fédérale de Lausanne in Switzerland and Yahoo Research Barcelona in Spain before joining QCRI in 2012. Throughout his academic career he has collaborated with over 100 experts with a wide range of expertise, publishing over 140 peer-reviewed articles on topics ranging from efficient data structures, to game theory, to political polarization. Ingmar is an ACM, AAAI and IEEE senior member. He currently serves as an ACM Distinguished Speaker. In his interdisciplinary research, he analyses large amounts of data to study population behaviour at scale. Topics of current interest include Data for Development in general and (i) monitoring international migration, and (ii) filling gender data gaps in particular. He is a pioneer in the innovative use of advertising data from Facebook, LinkedIn and other platforms to study these and other topics.
              

 
Ridhi Kashyap
(University of Oxford, UK)

Ridhi Kashyap is associate professor of social demography and fellow of Nuffield College at the University of Oxford. Dr Kashyap grew up in Delhi, India, and completed her undergraduate degree in Social Studies at Harvard University in 2010, a master’s degree in Demography at the European Doctoral School of Demography and Autonomous University of Barcelona, and her DPhil (PhD) in Sociology and Demography jointly affiliated with the University of Oxford and the Max Planck Institute for Demographic Research in 2017. Her research spans different topics in demography and sociology, including gender, mortality and health, marriage, and ethnicity and migration. She is currently working on projects about gender inequalities in internet and mobile use and their implications for other types of social inequalities, son preference, prenatal sex selection and gender gaps in health and mortality, and the effects of educational expansion on marriage and family change. She uses both quantitative social data sources such as surveys, official statistics and censuses, as well as digital trace data sources in her research. Her methodological interests focus on how computational innovations both in terms of modelling approaches such as agent-based models and digital data from web and social media platforms can be applied to study social and demographic processes. In an ongoing project supported by a Data2X and UN Foundation “Big Data for Gender Challenge Award” she and her collaborators are working on how big data from the web, in particular large-scale online advertising data that provide information on the aggregate numbers of users of online platforms by demographic characteristics, can be leveraged to measure sustainable development and gender inequality indicators.
 

 
Emilio Zagheni
(Max Planck​ Institute for Demographic Research, Germany)

Emilio Zagheni (PhD in Demography, UC Berkeley 2010; MA in Statistics, UC Berkeley 2008) is Director of the Max Planck Institute for Demographic Research in Rostock, Germany and Affiliate Associate Professor of Sociology at the University of Washington, Seattle, where he is also a Data Science Fellow of the eScience Institute. Zagheni is a demographer who uses mathematical, statistical and computationally-intensive approaches to study the causes and consequences of population dynamics. Motivated by the ambition to improve people's lives through the scientific study of our societies, he is consolidating a portfolio that leverages interdisciplinary approaches to monitor demographic change, to explain population processes, and to predict future demographic outcomes. He is best known for his pioneering work on using Web and social media data for studying migration processes. In 2016, he received the Trailblazer Award from the European Association for Population Studies for his pivotal role in developing the field of Digital and Computational Demography. Emilio Zagheni has published in top journals in Demography (e.g.Demography, Population and Development Review, Population Research and Policy Review) and Statistics (e.g., Journal of the American Statistical Association, Biostatistics) as well as in ACM (Association of Computing Machinery) conference proceedings (e.g., WebSci, WWW, WSDM). He co-chairs the IUSSP (International Union for the Scientific Study of Population) Panel on Big Data and Population Processes.