Her primary research interests are inequality, poverty, wellbeing, social identities, and migration. Kovesdi received her Bachelors of Science in Politics and Sociology from the University of Bristol and her Masters of Science in Sociology from the University of Oxford. Since joining OPHI in 2018, she has worked on the global Multidimensional Poverty Index and Changes over Time projects, harmonizing global MPI data to analyze trends in poverty for 80 countries. Prior to joining OPHI, she worked on an ESRC-funded research project on dual career couple trajectories and has completed internships at the Hungarian Academy of Sciences and Child Hub for Southeast Europe. Last, the presentation will propose a set of recommendations for improving the availability and coverage of nationally representative household survey data – an essential resource for measuring poverty in all its dimensions, and achieving the overall goal of no poverty.įanni Kovesdi (Oxford Poverty and Human Development Initiative, Department of International Development, University of Oxford) It also proposes a set of measures that could be developed exclusively for high-income countries. The presentation proposes multiple options for a global ‘moderate MPI’ and evaluates each according to a set of common criteria. In addition, the presentation assess the feasibility of a new global moderate multidimensional poverty index with expanded indicator coverage and the inclusion of developed countries, while retaining frequent updates and sub-national dis-aggregation. It presents a comprehensive and detailed overview of the available resources and identifies important gaps in existing survey data.

This presentation reviews the current data landscape with a focus on national and cross-national multi-topic household surveys that might be used to develop a genuinely global multidimensional index covering less acute forms of poverty. While advancements have been made on using administrative or census data for measuring multidimensional poverty, most national MPIs and the global MPI relies on household survey data for a comprehensive and timely assessment of poverty, and its changes over time. But there is a need to measure less acute forms of poverty, as well as to cover high income countries.

In addition, figures on acute multidimensional poverty in over 100 developing countries are published regularly using the internationally comparable global Multidimensional Poverty Index. To this date, over 30 countries launched national Multidimensional Poverty Indices (MPIs) to monitor SDG 1.2.2 and eradicate poverty in all its forms. While the results are promising in terms of government efforts to promote smarter growth that builds 'in and up' instead of out, they also reveal that government could be doing more to facilitate redevelopment and influence its sustainability character, particularly in weaker markets.Timely and disaggregated data are essential for effective policy-making, and achieving the ambitious goals outlined in Agenda 2030. Using property assessment data and cleanup records, the research finds that redevelopment activity has been extensive in both scale and character, particularly in Toronto where the real estate market has been strong. This article examines the character and extent of brownfields infill development that has taken place in three Ontario cities (Toronto, Waterloo, and Kingston) since the provincial policy shift in the early 2000s. In Ontario, Canada's most populous, industrialized, and brownfield-laden province, a suite of progressive policies and programs have been introduced to not only facilitate the assessment and remediation of the brownfields supply, but to also steer development demand away from peripheral greenfields and towards urban brownfields in a manner that considers a wider regional perspective. Abstract : The reuse of brownfields as locations for urban intensification has become a core strategy in government sustainability efforts aimed at remediating pollution, curbing sprawl and prioritizing renewal, regeneration, and retrofitting.

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