EconStor Collection:
https://hdl.handle.net/10419/155
2024-03-29T05:14:35ZForschungsbericht 2023
https://hdl.handle.net/10419/283518
Title: Forschungsbericht 2023
Editors: Universität Lüneburg - Institut für Volkswirtschaftslehre2024-01-01T00:00:00ZRobots and extensive margins of exports: Evidence for manufacturing firms from 27 EU countries
https://hdl.handle.net/10419/283529
Title: Robots and extensive margins of exports: Evidence for manufacturing firms from 27 EU countries
Authors: Wagner, Joachim
Abstract: The use of robots by firms can be expected to go hand in hand with higher productivity, higher product quality and more product innovation, which should be positively related to export activities. This paper uses firm level data from the Flash Eurobarometer 486 survey conducted in February - May 2020 to investigate the link between the use of robots and export activities in manufacturing enterprises from the 27 member countries of the European Union. Applying standard parametric econometric models and a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), we find that firms which use robots do more often export, do more often export to various destinations all over the world, and do export to more different destinations. The estimated robots premium for extensive margins of exports is statistically highly significant after controlling for firm size, firm age, patents, and country. Furthermore, the size of this premium can be considered to be large. Extensive margins of exports and the use of robots are positively related.2024-01-01T00:00:00ZCloud computing and extensive margins of exports: Evidence for manufacturing firms from 27 EU countries
https://hdl.handle.net/10419/283530
Title: Cloud computing and extensive margins of exports: Evidence for manufacturing firms from 27 EU countries
Authors: Wagner, Joachim
Abstract: The use of cloud computing by firms can be expected to go hand in hand with higher productivity, more innovations, and lower costs, and, therefore, should be positively related to export activities. Empirical evidence on the link between cloud computing and exports, however, is missing. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union taken from the Flash Eurobarometer 486 survey conducted in February - May 2020 to investigate this link. Applying standard parametric econometric models and a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), we find that firms which use cloud computing do more often export, do more often export to various destinations all over the world, and do export to more different destinations. The estimated cloud computing premium for extensive margins of exports is statistically highly significant after controlling for firm size, firm age, patents, and country. Furthermore, the size of this premium can be considered to be large. Extensive margins of exports and the use of cloud computing are positively related.2024-01-01T00:00:00ZEstimation of empirical models for margins of exports with unknown non-linear functional forms: A Kernel-Regularized Least Squares (KRLS) approach
https://hdl.handle.net/10419/283517
Title: Estimation of empirical models for margins of exports with unknown non-linear functional forms: A Kernel-Regularized Least Squares (KRLS) approach
Authors: Wagner, Joachim
Abstract: Empirical models for intensive or extensive margins of trade that relate measures of exports to firm characteristics are usually estimated by variants of (generalized) linear models. Usually, the firm characteristics that explain these export margins enter the empirical model in linear form, sometimes augmented by quadratic terms or higher order polynomials, or interaction terms, to take care or test for non-linear relationships. If these non-linear relationships do matter and if they are ignored in the specification of the empirical model this leads to biased results. Researchers, however, can never be sure that all possible non-linear relationships are taken care of in their chosen specifications. This note uses for the first time the Kernel-Regularized Least Squares (KRLS) estimator to deal with this issue in empirical models for margins of exports. KRLS is a machine learning method that learns the functional form from the data. Empirical examples show that it is easy to apply and works well. Therefore, it is considered as a useful addition to the box of tools of empirical trade economists.2024-01-01T00:00:00Z