Day 8: Frontiers of Poverty Measurement
- Talk 1: Poverty and assets, a gendered perspective (Professor Abena Oduro) View YouTube video
- Talk 2: Using AI and remote sensing for poverty measurement (Dr Adel Daoud) View YouTube video
- Talk 3: Qualitative Comparative Analysis (QCA) (Dr Mary Zhang) View YouTube video
Talk 1: Poverty and assets, a gendered perspective
Professor Abena Oduro (University of Ghana)
Assets are important to households and individuals for several reasons including the potential of some assets to generate income and to be sold or pawned as a coping strategy in response to shocks. This presentation will examine issues surrounding the collection of individual-level asset data and the use of assets in the coping strategies of women and men.
Key resources
Poverty and Assets a Gendered Perspective (Prof. Oduro) (PDF, 581kB)
Prof. Abena Oduro Espanol (PDF, 652kB)
Assets and shocks a gendered analysis of Ecuador Ghana and Karnataka India_CJDS (PDF, 1,548kB)
Measuring Ownership Control and use of Assets (PDF, 403kB)
Key readings and speaker biography
See page 32 of Advanced Poverty Research Methods Online Course - Programme (PDF, 673kB)
Talk 2: Using AI and remote sensing for poverty measurement
Dr Adel Daoud (Linköping University, Sweden)
About 900 million people globally—one-third in Africa and another one-third in India—live in extreme poverty. Operating on the assumption that impoverished communities are trapped in poverty, major global donors have deployed a stream of development programs to break these traps. Despite the scale of programs, scholars have little knowledge about the distribution of global poverty historically and geographically. To address these knowledge gaps, scholars must first tackle a data challenge: the lack of historical and geographical poverty data. The newly founded AI and Global Development Lab is innovating global-poverty research by combining deep-learning, satellite technologies, and knowledge on human development to overcome the data challenge. The Lab is recreating historical and geographical human-development trajectories from satellite images from 1984 to 2022. This new data will measure poverty at unprecedented temporal and spatial granularity. Among other things, this data will enable the Lab (and other scholars) to start examining — with a high precision — the causal effects of foreign aid on poor communities’ chances of breaking poverty. This talk will discuss key scientific challenges and early findings.
The AI Human Development Lab is primarily based at the Institute for Analytical Sociology (IAS), Linköping University, and the Division of Data Science and Artificial Intelligence (DSAI) of the Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden. The Lab is led by Adel Daoud (Primary Investigator), Associate Professor in analytical sociology, IAS, and Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences, DSAI. Other key partners are based at the University of Gothenburg and Harvard University.
The vision of the Lab is to “combine AI and earth observation to estimate sustainable and human development globally.” The Swedish Research Council funds this Lab through a Research Environment Program and a Consolidator Grant. Chalmers AI Research Centre (CHAIR) is supporting the Lab in partnership with IMCG. Google, in partnership with the Group on Earth Observations, provides mentorship and in-kind technical support for the Lab.
Key resources
Using AI and remote sensing for poverty measurement (Dr Adel Daoud) (PDF, 3,608kB)
Adel Doud Espanol (PDF, 3,131kB)
Key readings and speaker biography
See pages 33-34 of Advanced Poverty Research Methods Online Course - Programme (PDF, 673kB)
Talk 3: Qualitative Comparative Analysis (QCA)
Dr Mary Zhang (University of Bristol)
Qualitative comparative analysis (QCA) has been designed to identify patterns of multiple conjectural causation and simplify complex data structures in a logical and holistic manner. This session introduces this mixed-method approach to analysing the necessary and sufficient causalities between variables of interest, with a focus on crisp set QCA. Demonstrations will be performed to guide you through the process from calibrating the crisp dataset to testing and interpreting the causal patterns in the data. It is expected that this session will provide a useful analytic tool for those who are interested in case- or variable-oriented research.
Key resources
Qualitative Comparative Analysis (Dr.Mary Zhang) (PDF, 5,989kB)
Mary Zhang Espanol (PDF, 5,827kB)
Key readings and speaker biography
See page 35 of Advanced Poverty Research Methods Online Course - Programme (PDF, 673kB)