Economists have long been interested in analyzing entries and exits of establishments. In many countries administrative datasets provide an excellent source for detailed analysis on a fine and disaggregate level. However, administrative datasets are not without problems: restructuring and relabeling of firms is often poorly measured and can potentially create large biases. Information on worker flows between establishments can potentially alleviate these measurement issues, but it is typically hard to judge how well correction algorithms based on this methodology work. This paper evaluates the use of the worker flow methodology using a dataset from Germany, the Establishment History Panel (BHP), merged to information on all worker flows between establishment IDs and survey data. We first document the extent of misclassification that stems from relying solely on the first and last appearance of the establishment identifier (EID) to identify openings and closings. We show that the misclassification bias of using only the EID is very severe: Only about 35 to 40 percent of new and disappearing EIDs with more than 3 employees are likely to correspond to real establishment entries and exits. Among larger establishments misclassification is even more common. We provide 3 pieces of evidence that using a classification system based on worker flows is superior to using EIDs only: First, establishment birth years generated using the worker flow methodology is much higher correlated with establishment birth years from an independent survey. Second, establishment entries and exits which are identified using the worker flow methodology move closely with the business cycle, while events which are identified as simple ID changes are not. Third, establishment exits have a big negative impact on workers' earnings trajectories which is not present for ID changes.
firm dynamics plant closings establishment entry and exit