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Sound policy and decision making in developing countries is often limited bythe lack of timely and reliable data. Crowdsourced data may provide a valuablealternative for data collection and analysis, e. g. in remote and insecureareas or of poor accessibility where traditional methods are difficult orcostly. However, crowdsourced data are not directly usable to draw soundstatistical inference. Indeed, its use involves statistical problems becausedata do not obey any formal sampling design and may also suffer from variousnon-sampling errors. To overcome this, we propose the use of a special form ofpost-stratification with which crowdsourced data are reweighted prior their usein an inferential context. An example in Nigeria illustrates the applicabilityof the method.

Post-sampling crowdsourced data to allow reliable statistical inference: the case of food price indices in Nigeria