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Description:
Since the term was first coined in studies on the 1990s Japanese crisis, the concept of zombification has been investigated and revived repeatedly when concerns arise about credit misallocation and stagnating productivity growth in an economy. The starting point for these studies nearly always involves trying to identify the so-called 'zombie' firms. This has led in the past years to a proliferation of different definitions and identification methodologies. We survey the most prominent definitions, discussing advantages and limitations of each. We also undertake a comparison of methodologies on a common dataset for euro area firms from 2004-2019, with the exercise revealing limited overlap and low comparability in the firms identified by several prominent studies. In response, we introduce a formalisation of zombie-classifications which helps to make order in the growing number of variations and identification methodologies. Moreover, this formalisation also helps extending the concept of binary identification to that of fuzzy zombie-identification. In particular, we introduce a general procedure to turn arbitrary binary classifications into fuzzy ones showing it successfully increases consistency between zombie definitions.