Exporting is a precarious endeavour. In Australia, around 60 percent of all entrants exit in their first year, with more than half of them exiting in the first month alone. These firms start small, but those that survive expand rapidly and drive the country’s total trade in the subsequent years. One explanation in the literature is that exporters are hit with an ex-post shock upon entry; hence, some survive, and others do not. The paper finds that the answer is more nuanced – export market survival is a function of both firms’ ex-ante heterogeneities and is dependent on strategic complementary between exporters. Using the BLADE dataset, the study employs machine learning (ML) to predict exporter survival and applies the Shapley values and regression framework to evaluate each feature. The results shed light on the determinants of exporter survival, capture high-dimensional interaction among predictors, and offer policymakers insights into determining an efficacious way of targeting export promotion activities.

Bio:

Abyaya Neopane is a Ph.D. candidate in Economics at the Australian National University. He has around a decade of experience in research, data analysis, and public policy, working for the government (Nepal), think tanks, and multilateral organizations.

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