Vast empirical evidence underscores that exporting firms are more productive than non-exporters. As governments accordingly pursue export-promoting policies we are interested in the firmness of these conclusions with respect to African small and medium sized enterprises (SMEs) and the influence of the destination of export trade. Using a micro-panel dataset from five African countries we confirm the self-selection. We apply propensity scores to match exporters and use a difference-in-difference methodology to test if African SMEs experience productivity gains because of export participation. Results indicate that African firms significantly learn-by-exporting. Manufacturers obtain significant performance improvements due to internationalization although this effect is moderated by export destination. Firms that export outside Africa become more capital intensive and at the same time hire more workers. In contrast we find evidence that exporters within the African region significantly downsize in capital intensity. Results regarding skill-bias of internationally active firms are mixed, where exporters within the region expand in size and hire more relatively unskilled workers.
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Preprint submitted to Information Processing & Management Tags are a convenient way to label resources on the web. An interesting question is whether one can determine the semantic meaning of tags in the absence of some predefined formal structure like a thesaurus. Many authors have used the usage data for tags to find their emergent semantics. Here, we argue that the semantics of tags can be captured by comparing the contexts in which tags appear. We give an approach to operationalizing this idea by defining what we call paradigmatic similarity: computing co-occurrence distributions of tags with tags in the same context, and comparing tags using information theoretic similarity measures of these distributions, mostly the Jensen-Shannon divergence. In experiments with three different tagged data collections we study its behavior and compare it to other distance measures. For some tasks, like terminology mapping or clustering, the paradigmatic similarity seems to give better results than similarity measures based on the co-occurrence of the documents or other resources that the tags are associated to. We argue that paradigmatic similarity, is superior to other distance measures, if agreement on topics (as opposed to style, register or language etc.), is the most important criterion, and the main differences between the tagged elements in the data set correspond to different topics
