Publications

The article investigates the effects of Employment Protection Legislation (EPL) on capital and skills according to the intensity of international competition. Grounded on a panel data sample for 14 OECD countries and 18 industries from 1988 to 2007, and a difference-in-difference approach, we find that strengthening EPL: (i) leads to a capital-labour substitution in favour of non ICT non R&D capital to the detriment of employment, this effect being mitigated in industries highly exposed to international competition; (ii) lowers ICT capital and, even more severely, R&D capital relatively to other capital components; and (iii) works at the relative disadvantage of low-skilled workers. Strengthening EPL can therefore be an impediment to organizational and so technological change and risk taking on globalized markets. An illustrative simulation suggests that structural reforms weakening EPL could have a significant favorable impact on firms’ ICT and R&D investment and on hiring low-skilled workers.

On the face of it, econometrics and machine learning share a common goal: to build a predictive model, for a variable of interest, using explanatory variables (or features). However, the two fields have developed in parallel, thus creating two different cultures. Econometrics set out to build probabilistic models designed to describe economic phenomena, while machine learning uses algorithms capable of learning from their mistakes, generally for classification purposes (sounds, images, etc.). Yet in recent years, learning models have been found to be more effective than traditional econometric methods (the price to pay being lower explanatory power) and are, above all, capable of handling much larger datasets. Given this, econometricians need to understand what the two cultures are, what differentiates them and, above all, what they have in common in order to draw on tools developed by the statistical learning community with a view to incorporating them into econometric models.