Person:
Rentschler, Jun
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Fields of Specialization
Economics of Development, Environment, and Climate
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Last updated
May 3, 2023
Biography
Jun Rentschler is a Senior Economist at the Office of the Chief Economist for Sustainable Development, working at the intersection of climate change and sustainable resilient development. Prior to joining The World Bank in 2012, he served as an Economic Adviser at the German Foreign Ministry. He also spent two years at the European Bank for Reconstruction and Development (EBRD) working on private sector investment projects in resource efficiency and climate change. Before that he worked on projects with Grameen Microfinance Bank in Bangladesh and the Partners for Financial Stability Program by USAID in Poland. He is a Visiting Fellow at the Payne Institute for Public Policy, following previous affiliations with the Oxford Institute for Energy Studies and the Graduate Institute for Policy Studies in Tokyo. Jun holds a PhD in Economics from University College London (UCL), specializing in development, climate, and energy.
Publication Search Results
1 - 3 of 3
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Publication
People in Harm's Way: Flood Exposure and Poverty in 189 Countries
(World Bank, Washington, DC, 2020-10)Flooding is among the most prevalent natural hazards affecting people around the world. This study provides a global estimate of the number of people who face the risk of intense fluvial, pluvial, or coastal flooding. The findings suggest that 1.47 billion people, or 19 percent of the world population, are directly exposed to substantial risks during 1-in-100 year flood events. The majority of flood exposed people, about 1.36 billion, are located in South and East Asia; China (329 million) and India (225 million) account for over a third of global exposure. Of the 1.47 billion people who are exposed to flood risk, 89 percent live in low- and middle-income countries. Of the 132 million people who are estimated to live in both extreme poverty (under $1.9 per day) and in high flood risk areas, 55 percent are in Sub-Saharan Africa. About 587 million people face high flood risk, while living on less than $5.5 per day. These findings are based on high-resolution flood hazard and population maps that enable global coverage, as well as poverty estimates from the World Bank's Global Monitoring Database of harmonized household surveys. -
Publication
The RISE Framework
(World Bank, Washington, DC, 2022-01-17)The world has witnessed unparalleled economic progress in the last three decades. But success is not preordained, and several headwinds threaten this hard fought progress. Inequality is leaving many people and subgroups behind and excluding them from enjoying the benefits of this great economic expansion. More recently, the world has awakened to the reality of a new type of risk. The coronavirus disease 2019 (COVID-19) struck at a time when the world was healthier and wealthier than ever before. There is little disagreement over the need to enable a recovery that is fairer, safer, and more sustainable. This report describes how these ambitious objectives can be achieved by providing evidence based tools and information to guide countries to spend better and improve policies. It is in this context that this document presents policy guidance to identify and diagnose key development challenges and develop solutions to help countries build better. -
Publication
Where Are All the Jobs ?: A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries
(World Bank, Washington, DC, 2022-03)Globally, both people and economic activity are increasingly concentrated in urban areas. Yet, for the vast majority of developing country cities, little is known about the granular spatial organization of such activity despite its key importance to policy and urban planning. This paper adapts a machine learning based algorithm to predict the spatial distribution of employment using input data from open access sources such as Open Street Map and Google Earth Engine. The algorithm is trained on 14 test cities, ranging from Buenos Aires in Argentina to Dakar in Senegal. A spatial adaptation of the random forest algorithm is used to predict within-city cells in the 14 test cities with extremely high accuracy (R- squared greater than 95 percent), and cells in out-of-sample ”unseen” cities with high accuracy (mean R-squared of 63 percent). This approach uses open data to produce high resolution estimates of the distribution of urban employment for cities where such information does not exist, making evidence-based planning more accessible than ever before.