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03-13: Drawing policy advice from land data analysis
2:00pm - 3:30pm
Session Chair: Hamady Diop, NEPAD, South Africa
Predicting deprivations in housing and basic services from space in slums of Dhaka
Amit Patel1, Christian Borja-Vega2, Luisa Mimmi3, Tomas Soukup4, Jan Kolomaznik4, Marcia Mundt1, Hyunjung Lee1, Tanushree Bhan1
1University of Massachusetts Boston, United States of America; 2World Bank, United States of America; 3Inter American Development Bank, United States of America; 4GiSAT, Czech Republic
This paper develops a novel approach to identify and enumerate housing deprivations in slums of Dhaka using Earth Observation data. We integrate household survey data with very high resolution remote sensing data to build a robust econometric model to estimate housing and basic infrastructure deprivation such as water and sanitation in the slums of Dhaka, Bangladesh. Such a model could be used to predict housing and basic services deprivation in areas where household surveys are not available. Identification of most deprived areas from space could be used to inform policymaking and targeting beneficiaries of such policies. We argue that spatial data, which have become increasingly available and affordable, could answer the following questions: ii) How to identify and delineate slums spatially in a metropolitan area using Earth Observation data? ii) How to detect and predict deprivations in housing and basic services as a function of factors from Earth Observation data?
International collaboration: capturing the impact of emerging trends
Columbia University, United States of America
The surge in global population, over the past decade, has fueled technological innovation. Many advancements are developed to manage and forecast population growth rates while complexities in the political and economic landscape increases. With this in mind, the research how international collaboration can strengthen our ability to achieve the poverty reduction targets in the 2030 Agenda. The research methodology used to explore the potential impact of data collaboration among the World Bank, United Nations, and Internal Monetary Fund is a case study. In the case, the research captures how data collaboration can strengthen the Partnership Framework for Crisis-Affected Situations between the World Bank, IMF and the United Nations. The research examines the mandate and goals of the framework then explores how data collaboration strengthens or weakens the group’s ability to achieve the desired goals. The primary sustainable development goal highlighted in the case is goal 1.A.
The effects of agricultural income on Internally Displaced Persons: Evidence from Colombia
Paola Palacios, Miguel Angel Perez-Uribe
Universidad Icesi, Colombia
Colombia has the largest population of Internal Displaced Persons (IDPs) in the world. Not only IDPs suffer a significant welfare loss suffered after migrating, they also generate an enormous cost to the Colombian society in several respects. The purpose of this study is to estimate the impact of agricultural income on the number of IDPs expelled from Colombian municipalities. To address the possible endogeneity and omitted variables bias, we use an instrumental variables’ approach. The standardized deviation of precipitation from its mean serves as an instrument for municipal agricultural income. Our main result indicates that agricultural income has a negative and statistically significant impact on forced displacement: an increase in agricultural income of one percent reduces forced displacement in the municipality by 1.2%. As a robustness check, we use alternative definitions of economic activity at the municipality level such as agricultural loans, GDP, and energy consumption finding similar results.
The consequences of increasing block tariffs, magnitude and distribution of electricity and water subsidies for households in Addis Ababa, Ethiopia
Helena Cardenas, Dale Whittington
University of North Carolina at Chapel Hill, United States of America
In Addis Ababa the increasing block tariff (IBT) is used to calculate households’ monthly bills for both electricity and water services. We estimate the combined water and electricity subsidies received by households with private connections to both the electricity grid and the piped network water in 2016, and evaluate the distribution of these subsidies among wealth groups. We use customer-billing data and match those data with socioeconomic information collected from a household survey. Results show that the combined subsidies are large. The average household receives a subsidy of US$26 per month, about 6% of household income. Also we find that both the electricity and the water IBT subsidies disproportionately target richer households, with even worse poor targeting outcomes when both sectors are considered jointly. The poorest quintile receives 12% of the cumulative subsidies provided by both electricity and water services, while the richest quintile receives 31% of the cumulative subsidies.