Quantifying productivity is a conditio sine qua non for empirical analysis in a number of research fields. The identification of the measure that best fits with the specific goals, as well as being data driven, is currently complicated by the fact that an array of methodologies is available. This paper provides economic researchers with an up-to-date overview of issues and relevant solutions associated with this choice. Methods of productivity measurement are surveyed and classified according to three main criteria: (i) macro/micro; (ii) frontier/non-frontier and (iii) deterministic/econometric

We discuss how standard computable equilibrium models of trade policy can be enriched with selection effects. This is achieved by estimating and simulating a partial equilibrium model that accounts for a number of real world effects of trade liberalisation: richer availability of product varieties; tougher competition and weaker market power of firms; better exploitation of economies of scale; and, of course, efficiency gains via firms selection. The model is estimated on EU data and then simulated in counterfactual scenarios. Gains from trade are much larger in the presence of selection effects with substantial variability across countries and sectors.

We use Italian firm-level data to investigate the impact of trade openness on the distribution of firms across marginal cost levels. In so doing, we implement a procedure that allows us to control not only for the standard transmission bias identified in firm-level TFP regressions but also for the omitted price bias due to imperfect competition. We find that more open industries are characterized by a smaller dispersion of costs across active firms. Moreover, in those industries the average cost is also smaller. * We thank participants to seminars at the Bank of Italy and the University of Cagliari as well as participants to the conference "Empirical methods for the study of economic agglomeration" at Kyoto University, to the 11th EIIT conference (Purdue University, USA) and to the CNR workshop on international trade and development (Villa Mondragone, Italy) for helpful comments. We are especially indebted with Eckhardt Bode, Pierre-Philippe Combes, Gilles Duranton, Thierry Mayer, Tomoya Mori, Henry Overman, David Weinstein and anonymous referees for useful comments. Ottaviano gratefully acknowledges financial support from MIUR and the European Commission. The opinions expressed do not necessarily reflect those of the Bank of Italy.

Productivity has recently slowed down in many economies around the world. A crucial challenge in understanding what lies behind this "productivity puzzle" is the still short time span for which data can be analysed. An exception is Italy where productivity growth started to stagnate 25 years ago. Italy therefore offers an interesting case to investigate in search of broader lessons that may hold beyond local specific cities. We find that resource misallocation has played a sizeable role in slowing down Italian productivity growth. If misallocation had remained at its 1995 level, in 2013 Italy's aggregate productivity would have been 18% higher than its actual level. Misallocation has mainly risen within sectors than between them, increasing more in sectors where the world technological frontier has expanded faster. Relative specialization in those sectors explains the patterns of misallocation across geographical areas and firm size classes. The broader message is that an important part of the explanation of the productivity puzzle may lie in the rising difficulty of reallocating resources between firms in sectors where technology is changing faster rather than between sectors with different speeds of technological change.

We adopt a counterfactual approach to decompose labor productivity growth into growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total Factor Productivity (TFP). We bring the decomposition to the data using international countrysectoral information spanning from the 1960s to the 2000s and a nonparametric generalized kernel method, which enables us to estimate the production function allowing for heterogeneity across all relevant dimensions: countries, sectors and time. As well as documenting substantial heterogeneity across countries and sectors, we find average TEP to account for about 44% of labor productivity growth and TEP gaps with respect to the US to remain almost unchanged, on average, despite an average 1% yearly decrease in the labor productivity gap. The US displays the highest TEP growth rate. We then perform standard convergence regressions finding strong evidence of technological convergence and showing that the effect of a few variables only, among those found significant to explain labor productivity convergence, occurs through the technology channel.

No abstract

We discuss how standard computable equilibrium models of trade policy can be enriched with selection effects. This is achieved by estimating and simulating a partial equilibrium model that accounts for a number of real world effects of trade liberalisation: richer availability of product varieties; tougher competition and weaker market power of firms; better exploitation of economies of scale; and, of course, efficiency gains via firms selection. The model is estimated on EU data and then simulated in counterfactual scenarios. Gains from trade are much larger in the presence of selection effects with substantial variability across countries and sectors.

We use a panel of European firms to investigate the relationship between intangible assets and productivity. We disentangle between tfp and technology adoption, while available studies so far have considered only a notion of productivity conflating the two effects. To this aim, we estimate production function parameters allowing, within each sector, for the existence of multiple technologies. We find that intangible assets both push the firm towards better technologies (technology adoption effects) and allow for a more efficient exploitation of a given technology (tfp effects).