A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax

We developed a non-parametric technique to measure Total Factor Productivity (TFP). Our paper has two major novelties in estimating the production function. First, we propose a productivity modelling with both idiosyncratic firm factors and aggregate shocks within the same framework. Second, we apply Bayesian Markov Chain Monte Carlo (MCMC) estimation techniques to overcome restrictions associated with monotonicity between productivity and variable inputs and moment conditions in identifying input parameters. We implemented our methodology in a group of 4286 manufacturing firms from France, Germany, Italy, and the United Kingdom (2001-2014). The results show that: (i) aggregate shocks matter for firm TFP evolution. The global financial crisis of 2008 caused severe adverse effects on TFP albeit short in duration; (ii) there is substantial heterogeneity across countries in the way firms react to changes in R&D and taxation. German and U.K. firms are more sensitive to fiscal changes than R\&D, while Italian firms are the opposite. R\&D and taxation effects are symmetrical for French firms; (iii) the U.K. productivity handicap continued for years after the financial crisis; (iv) industrial clusters promote knowledge diffusion among German and Italian firms.