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.
Highlights
Productivity, and its growth, is well recognized by essentially all economists as the key variable to long-run improvement in income andemployment
The dynamics of innovation and economic performance may be shaped by interconnections and complex relationships between the ability of industries to turn out R&D efforts into successful innovations, which in turn lead to high entrepreneurial profits, and the commitment of industries to invest profits in further technological efforts (Bogliacino and Pianta 2013)
This paper aims at analyzing Total Factor Productivity (TFP) in five major European countries (France, Germany, Italy, Spain and UK), USA, and Japan between 1954 and 2017
Summary
Productivity (as measured by output per unit of single input or total inputs), and its growth, is well recognized by essentially all economists as the key variable to long-run improvement in income and (un)employment. We employ annual data and focus on countries that have similar degrees of economic development. The latter choice should provide us with some advantages, with respect to a more heterogeneous sample of countries, when studying the stochastic properties of their productivity. The choice to analyze only the seven countries mentioned above is, pivotal to our analysis These countries account for 45% and 71% of the 2018 world’s and high-income countries’ GDP, respectively.. Increasing the number of countries would likely make the identification of the TFP’s short- and long-run components more difficult, with only a marginal improvement in our understanding of the productivity dynamics in advanced and internationally integrated economies. We aim at separating the secular-nonstationary (trend) component from the cyclical-stationary one without imposing any a priori restriction on their relationship, and at focusing on the so-called ‘deep’ determinants of TFP, which may still be different from one economy to another, even within the European market
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