Mechanisms of institutional change in central and eastern European welfare state restructuring

Abstract

The study of Central and Eastern European (CEE) social policy has now reached the attention of the international academic community, but despite an increasing number of publications on the topic, the mechanisms of institutional change occurring in these welfare states in transition have, so far, remained partly unexplored and this in spite of the clear importance that such an investigation might have for future reform proposals.1 How can better social policy reforms be implemented in the region if the paths and mechanisms of institutional change are not fully understood? In order to achieve this objective, this chapter investigates the mechanisms of institutional change in CEE welfare state restructuring adopting a mechanism-based explanation approach. Here, the focus is given not on relationships between variables, but on actors and the intended and unintended outcomes of their actions. Following Peter Hedström (2008), the explanatory power of a mechanism-based approach lies, in fact, in the explanation of an event, giving account of why it happened, not only by citing earlier events, but also providing (or suggesting) causal mechanisms (see also Hedström and Swedberg, 1998; Elster, 1998). For Jon Elster (1998), a mechanism explains by opening ‘the black box’ and showing the ‘cogs and wheels’ of the internal machinery. It also provides a continuous chain of causal intentional links between the explanans and the explanandum. As correctly emphasized by Hedström (2008), identifying the details of the mechanisms not only produces explanations that are more precise and intelligible, since it permits identification of structural similarities between processes that at first glance could seem completely dissimilar, but it also avoids an unnecessary proliferation of concepts. Moreover, the investigation of the mechanisms involved in an outcome can also allow the identification of a genuine causal relationship and not simply a correlation between variables. This ultimately allows the detection of models that otherwise would remain unknown.