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The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena. [ABSTRACT FROM AUTHOR]

• Aggregated composite indexes (Cis) are likely to be affected by sampling variability. • We show how uncertainty about indicators due to sampling error affects final scores. • Our method is applied to real indicators of a CI for environmental performance. • Our method could be used for non-response issues or other non-sampling errors. In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multidimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When indicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index. This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data. To our knowledge, this is the first attempt to measure the impact of indicators' sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non-response issues, or other kinds of non-sampling errors. [ABSTRACT FROM AUTHOR]