RESUMEN
As the climate changes, human livelihoods will increasingly be threatened by extreme weather events. To provide adequate disaster relief, states extensively rely on multilateral institutions, in particular the United Nations (UN). However, the determinants of this multilateral disaster aid channeled through the UN are poorly understood. To fill this gap, we examine the determinants of UN disaster aid using a dataset on UN aid covering almost 2,000 climate-related disasters occurring between 2006 and 2017. We make two principal contributions. First, we add to research on disaster impacts by linking existing disaster data from the Emergency Events Database (EM-DAT) to a meteorological reanalysis. We generate a uniquely global hazard severity measure that is comparable across different climate-related disaster types, and assess and bolster measurement validity of EM-DAT climate-related disasters. Second, by combining these data with social data on aid and its correlates, we contribute to the literature on aid disbursements. We show that UN disaster aid is primarily shaped by humanitarian considerations, rather than by strategic donor interests. These results are supported by a series of regression and out-of-sample prediction analyses and appear consistent with the view that multilateral institutions are able to shield aid allocation decisions from particular state interests to ensure that aid is motivated by need.
RESUMEN
In this article, we present and describe a new dataset of non-state actor participation in seven regional fisheries management organizations (RFMOs). The dataset contains institutional, economic and ecological variables relevant for non-state actor participation in RFMOs and for RFMO effectiveness. To code non-state actor participation and institutional factors, we quantify information from publicly available RFMO reports as well as data from the Policy IV dataset. We pair these data with existing datasets on ecological and economic factors from the RAM Legacy and the Sea Around Us databases. This article describes the data collection process and the coded variables in detail.