This paper analyzes Total Factor Productivity (TFP) in five European countries (France, Germany, Italy, Spain, and UK), the USA and Japan between 1954 and 2017. It uses the common trend– common cycle (CTCC) approach to decompose series in trends and cycles. We find that the seven economies are structurally different and differently affected by similar shocks. We show that trend and cycle innovations are, in most of the cases, negatively correlated as predicted by the ‘opportunity cost’ approach to productivity growth, and that trend innovations are larger than cycle innovations. We provide an interpretation for countries’ differences in TFP performance in recent years that is related to the so-called ‘deep’ determinants in growth literature, such as the presence of efficient markets and institutions. Finally, we present a comparison with the traditional Hodrick and Prescott deterministic filter to highlight the advantages of CTCC methodology that does not require a priori on the nature of the time series.
The productivity gap among major European countries, USA and Japan.
Giorgio Calcagnini;Germana Giombini
;Giuseppe Travaglini
2021
Abstract
This paper analyzes Total Factor Productivity (TFP) in five European countries (France, Germany, Italy, Spain, and UK), the USA and Japan between 1954 and 2017. It uses the common trend– common cycle (CTCC) approach to decompose series in trends and cycles. We find that the seven economies are structurally different and differently affected by similar shocks. We show that trend and cycle innovations are, in most of the cases, negatively correlated as predicted by the ‘opportunity cost’ approach to productivity growth, and that trend innovations are larger than cycle innovations. We provide an interpretation for countries’ differences in TFP performance in recent years that is related to the so-called ‘deep’ determinants in growth literature, such as the presence of efficient markets and institutions. Finally, we present a comparison with the traditional Hodrick and Prescott deterministic filter to highlight the advantages of CTCC methodology that does not require a priori on the nature of the time series.File | Dimensione | Formato | |
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