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Learning-by-exporting vs. self-selection in Ecuadorian manufacturing firms: Evidence from different industry classifications

Abstract:

This paper analyzes the differences in productivity performance between exporting and non-exporting manufacturing firms in Ecuador during the period 2007–2018, using two underexplored industry classifications in this analysis: the Pavitt Taxonomy and the OECD technological intensity classification. We estimate total factor productivity (TFP) at the firm level with a modified (Gandhi, Navarro, and Rivers 2020) approach that allows us to use an additional state variable and endogenize the law of motion for productivity, which allows the past decision about exporting to affect future productivity. This approach captures the static and dynamic gains (learning-by-exporting) in productivity from exporting. Moreover, we test self-selection on the entry and exit sides of the market, and show a robustness check of the learning-by-exporting hypothesis by using a difference-in-difference matching estimator. Our results indicate that exporters have better productivity performance than non-exporters. We find evidence in favor of the self-selection on the entry and exit sides of the market. Finally, we show there are dynamic gains in productivity from exporting, supporting the learning-by-exporting hypothesis.