Home > Research > Publications & Outputs > A Non-parametric Estimation of Productivity wit...


Text available via DOI:

View graph of relations

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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>12/01/2024
<mark>Journal</mark>Oxford Bulletin of Economics and Statistics
Publication StatusE-pub ahead of print
<mark>Original language</mark>English


We develop a non-parametric technique framework for estimating firm-level Total Factor Productivity (TFP). Our paper has two major novelties: first, we propose a modelling of productivity with both firm-idiosyncratic factors and aggregate shocks. Second, we apply the Bayesian Markov Chain Monte Carlo (MCMC) technique that offers a numerical integration of productivity outside the posterior overcoming the restrictive assumptions about the relationship between productivity and variable production inputs. We implement our methodology in a group of 4,286 manufacturing firms from France, Germany, Italy, and the UK (2001–14). The results show that: (i) aggregate shocks matter for firm TFP evolution. The global financial crisis of 2008 caused severe, albeit short, adverse effects on TFP; (ii) there is substantial heterogeneity across countries in the way firms react to changes in R&D and taxation. German and UK firms are more sensitive to fiscal changes than R&D, while the opposite is true for Italian firms. R&D and taxation effects are symmetrical for French firms; (iii) the UK productivity handicap continues for years after the financial crisis; and (iv) there are substantial knowledge spillovers among German and Italian firms.