We are grateful to UN-Habitat for funding the work on the initial version of the indicators, including 20 city profiles, and GIZ through their 'Operator Models' project which funded an intermediate version of the indicators and a further 5 city profiles. We acknowledge all the profilers of the individual cities – many are named in Table S2, and others in reference (Scheinberg et al. 2010). We thank past MSc students under the authors' supervision for offering preliminary partial data clearing and commentary: Henry Hickman (MSc dissertation at University of Leeds, supervised by C.A.V and D.C.W.) and Margaux Fargier (Final year MEng dissertation at Imperial College London, supervised by S.M.G, D.C.W and C.A.V.). We are grateful to Dr Josh Cottom and Mr Ed Cook at the University of Leeds for input on the GDP version selection. We acknowledge the support of Dr Ljiljana Rodic for contributing in data quality control.

This is the input dataset for the research publication "Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning". It features Metadata info used by R codes Full data set for the WABI, used by the R codes Data required for plotting the map in Figure 1 The independent variables data set refers to specific indicators of the WABI methodology (https://www.sciencedirect.com/science/article/pii/S0956053X14004905) which generates solid waste management and resource recovery profiles for cities. It is applied here for 40 cities around the world. The data set contains also values for a series of explanatory variables, which are measures of the level of socioeconomic development at country level.