Issue 1 (March)

This study documents the relationship between economic freedom and unauthorized immigration to the U.S.A. and tries to answer a still-in-debate question, what motivates people to unauthorizedly migrate? Using the generated instrument variables methodology, and a data panel of 15 countries and 16 years, the endogenous variable is the gap in economic freedom between the country of origin and the U.S., and the dependent variable is the unauthorized immigrant’s growth rate. The results show that for each 10% improvement in economic freedom in the country-of-origin unauthorized immigration decreases around 7%. Estimations are robust even after using other covariates and external instruments. The main conclusion is that economic and social factors play a significant role in shaping migration patterns and that the difference in economic freedom between the country of origin and the U.S. plays a fundamental role in deciding to unauthorizedly migrate

We propose an open-access innovation index for the U.S. states. This index was built by classifying into four core areas the twelve more important indicators, inputs, and outputs, that are widely accepted in the literature as the most relevant for measuring innovation. We consider the indicators aggregated in the areas of human capital and business environment as the inputs that create knowledge and influence the promotion of innovation, while those in areas of engagement and efficiency as the outputs that are all those outcomes and improvements related to the innovation process. As an example of potential uses of our index, we ran a basic econometric experiment using the economic growth rate as the dependent variable. Despite the results being qualitatively consistent with the literature, we suggest further research using our index and other methodologies. We included in the appendix the overall index score and ranking from 2000 to 2015.

The use of digital technologies like artificial intelligence, robotics, or smart devices can be expected to go hand in hand with higher productivity and lower trade costs, and, therefore, to be positively related to export activities. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union to shed further light on this issue by investigating the link between the digitalization intensity of a firm and extensive margins of exports. We use a new machine-learning based regression method, Kernel-Regularized Least Squares (KRLS), which effectively handles non-linear relationships in models and does not impose any restrictive assumptions for the functional form of the relation between margins of exports, digitalization intensity, and any control variables. We find that firms which use more digital technologies do more often export, do more often export to various destinations all over the world, and do export to more different destinations.