Social Change: Measurement and Theory *

Summary Societal change, which takes a variety of directions and forms and in no way can be assimilated or reduced to a single dimension, is often accompanied by a perception of insufficient understanding and lack of control. There is a frustrated need for mastering complexity and instability, separating the voluntary from the involuntary, the intended from the unintended, opportunities from risks, getting to the real causes and dominating the uncertain implications of social change. Social change catches us unprepared and confused. In this context statistics are generally considered a fundamental instrument of knowledge, but also part of the problem! In the public debate and in the specialized literature, the ability to measure social phenomena through current statistics and indicators is increasingly questioned. Data‐it is claimed‐are lacking, particularly longitudinal data; their quality (accuracy, relevance, timeliness, comparability, etc.) should be improved; indicators do not provide early warning signals, policy performance evaluation, and a precise indication of outcomes. Statistics cannot be used as a reliable and timely basis for decision making by individuals, organizations, governments, and for understanding these decisions. In some cases, statistics have been accused of giving a misleading and false picture of reality: do we measure the real extent of social exclusion and unemployment? Do we fully capture the quality of life and the degradation of the environment? Mismeasurement has been deemed by some commentators as being responsible for the wrong focus in inflation and stabilization policies, science and technology, unemployment and poverty. The productivity paradox, the informal economy, failure to measure welfare and the quality of urban life are instances where statistics do not seem to provide complete and satisfactory answers to the demand for information and knowledge. Our paper illustrates how, quite independently of measurement techniques and data production processes, the inadequacy of the conceptual framework may explain mismeasurement in relation to complex (multidimensional) and dynamic social phenomena. It is then to social theories, explanations and interpretations that statisticians need to turn, in order to come to grips with the new challenges in social measurement. We will develop this thesis looking at a few cases where measurement issues can be connected to both theoretical and empirical difficulties. The statistical gap which reveals itself in the mismeasurement or difficult measurement of social phenomena is closely interconnected with the social science gap. Only close collaboration between statisticians and social scientists can bring about continuous advancement in social science and quality improvement in social statistics.