Exposure to floods in Bangladesh

Exposure to abnormal floods is believed to have negative shortand long-term consequences for welfare and health in poor countries, and such impacts are likely to grow worse with continued anthropogenic climate change. However, two common proxies for flood exposure, self-reported exposure and rainfall, are problematic. This paper describes a method for constructing objective measures of flood exposure using satellite data. Using the case of Bangladesh in the period 2002-2011, we show that (a) self-reported exposure has an important, non-random bias in that it responds much more strongly to actual exposure in areas where floods are relatively rare, and (b) rainfall is at best weakly correlated with floods. Satellites, Self-reports, and Submersion: Exposure to Floods in Bangladesh Raymond Guiteras, Amir Jina, and A. Mushfiq Mobarak January 13, 2015 A burgeoning “Climate-Economy” literature (Dell, Jones and Olken, 2014) attempts to understand and project the economic impacts of anthropogenic climate change. This literature has largely focused on uncovering the effects of changes in temperature and precipitation on economic activity. Important papers have documented both shortrun effects—for example, on agriculture (e.g., Schlenker and Roberts, 2009), health (e.g., Deschênes and Greenstone, 2011), and labor (e.g., Graff Zivin and Neidell, 2013)—and long-run effects—for example, on economic growth (Dell, Jones and Olken, 2012), and education (e.g., Maccini and Yang, 2009). This has been made possible by the availability of temperature and precipitation data with reasonable spatial and temporal resolution. Global climate change is likely to cause rising sea levels, more powerful cyclones and greater coastal storm surges, and increased frequency and severity of flooding (IPCC, 2014). The economics literature has made less progress in modeling the socio-economic effects of these other phenomena expected to be associated with climate change, and which may have more intense, deleterious effects in the short run. A handful of recent papers have used physical science models to create such data (e.g., Anttila-Hughes and Hsiang, 2012; Hsiang and Jina, 2014). However, no similar effort has been made for flooding, a class of disaster that affects more people than any other (EM-DAT, 2012). We describe the progress we have made in creating a time series of flood exposure derived from a new analysis of satellite data. We focus on the lower Ganges Delta, and the nation of Bangladesh in particular, one of the countries historically most affected by floods, and predicted to experience increasing flood severity due to climate change (Mirza, 2010). Guiteras: Department of Economics, University of Maryland, 3115 Tydings Hall, College Park, MD 20742, [email protected]. Jina: Department of Economics, University of Chicago, 5757 S University Ave., Chicago, IL 60637, [email protected]. Mobarak: School of Management, Yale University, Evans 3502, 165 Whitney Avenue, PO Box 208200, New Haven, CT 06520, [email protected]. We thank Jesse Anttila-Hughes, Anja Tolonen and participants at the ASSA session on High Stakes Energy and Environmental Problems in Developing Countries for their comments, Kristian López and Claudia Vargas for excellent research assistance, and the International Growth Centre and the University of Maryland Department of Economics for financial support. The authors declare no conflict of interest.