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 - 2 of 2
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Publication
Infrastructure Disruptions: How Instability Breeds Household Vulnerability
(World Bank, Washington, DC, 2019-06)This review examines the literature on the welfare impacts of infrastructure disruptions. There is widespread evidence that households suffer from the consequences of a lack of infrastructure reliability, and that being connected to the grid is not sufficient to close the infrastructure gap. Disruptions and irregular service have adverse effects on household welfare, due to missed work and education opportunities, and negative impact on health. Calibrating costs of unreliable infrastructure on existing willingness to pay assessments, we estimate the welfare losses associated with blackouts and water outages. Overall, between 0.1 and 0.2 percent of GDP would be lost each year because of unreliable infrastructure -- electricity, water and transport. -
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.