In this paper we examine the consistency in the timing of crisis events of the most prominent databases of banking and fiscal crises. We calculate Cohen's kappa measure, utilize signaling approach and panel logit models with random effects on selected early warning indicators and identify the most influential crisis observations using the Pregibon's delta‐beta influence statistics. Our results confirm that the degree of commonality across databases is indeed relatively high, especially if introducing a 1‐year lag. However, there is still a significant role played by a few influential observations. This problem is more pronounced in the banking crisis literature.