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Professor Daniel Franks is Director of the Global Centre for Mineral Security at the University of Queensland’s Sustainable Minerals Institute and is an Australian Research Council Future Fellow. Professor Franks is known internationally for his work on the interconnections between minerals, materials and sustainable development, with a particular focus on the role of minerals in poverty reduction. He has introduced a number of key concepts in development studies including ‘mineral poverty’, ‘mineral security,’ and ‘development minerals;’ and has worked with a wide range of public and private sector partners to implement breakthrough sustainability innovations, such as OreSand to drastically reduce mine waste, and ‘social impact management plans,’ a regulatory tool now adopted throughout the world.

He is the author of more than 160 publications, including more than 40 publications for the United Nations. His research has appeared in journals such as Nature Sustainability and the Proceedings of the National Academy of Sciences, and is available in 11 languages. He is an Editorial Board Member of the International Journal of Minerals Policy & Economics, as well as Impact Assessment and Project Appraisal and has field experience at more than 100 mining and energy sites and 40 countries.

My research and teaching interests include development, applied microeconometrics, agriculture, poverty, and the environment. I am particularly interested in using new data sources and new empirical methods to improve our understanding of a wide range of outcomes, especially the SDGs.

My current work focuses on three separate strands of literature: labor allocation, poverty, and methodologies to improve estimates of statistics of interest to economists and policymakers. Ongoing work studies how households allocate their labor in developing countries, especially across sectors. I work on the effects of pollution on agricultural productivity as well as how a changing environment affects labor allocation decisions. I also have ongoing work studying poverty and the best ways to improve poverty estimates, whether across time or across space. Relatedly, in work with regular collaborators, we are studying the best options to estimate different statistics – including poverty and labor force statistics – in developing countries, where data infrastructure may not allow the types of methods used in developed countries. For example, we are studying whether small area estimation methods or machine learning methods offer more accurate predictions of different outcomes and at different levels of aggregation. As part of my small area estimation and poverty work, I take part in capacity building with the United Nations Statistics Division and the World Bank to improve the understanding of small area estimation across the world. Current work revolves around disseminating working guidelines as well as in-person workshops for officials from national statistics offices.