Trade and productivity: an industry perspective

We use a sample of 14 OECD countries and 15 manufacturing industries to test for the effect of trade on productivity. Endogeneity concerns are accounted for using the geographical component of trade as instrument as suggested by Frankel and Romer (Am Econ Rev 89(3):279–399, 1999). We find that trade, measured in terms of the export ratio, increases productivity, even if country-fixed effects such as the quality of institutions are controlled for, though results are less robust for imports. Estimates at the aggregate manufacturing level turn out much larger, emphasizing the role of inter-industry spillovers.

Creative Disasters? Flooding Effects on Capital,Labour and Productivity Within European Firms

This paper examines the impact of floods on the firms’ capital accumulation, employment growth and productivity by using a difference-in-difference (DID) approach and considering the firms’ asset structure. We find evidence that, in the short run, companies in regions hit by a flood show on average higher growth of total assets and employment than firms in regions unaffected by flooding. The positive effect prevails for companies with larger shares of intangible assets. Regarding the firms’ productivity a negative flood effect is observable which declines with an increasing share of intangible assets.      

Choice is suffering: A Focused Information Criterion for model selection

In contrast to conventional measures, the Focused Information Criterion (FIC) allows the purpose-specific selection of models, thereby reflecting the idea that one kind of model might be appropriate for inferences on a parameter of interest, but not for another. Ever since its invention, the FIC has been increasingly applied in the realm of statistics, but this concept appears to be virtually unknown in the economic literature. Using a straightforward analytical example, this paper provides for a didactic illustration of the FIC and demonstrates its usefulness in economic applications.     

On the composition of elementary errors

Analysis of statistical distributions.

1. Let m and σ denote the mean and the standard deviation of a statistical variable X, and let W(x) be the probability function of that variable as defined in the first paper ¹ This journal, 1928, p. 13. We shall refer to that paper by the letter I. — The sense in which the words probability function and frequency function are used here must be carefully observed, If the probability that a certain variable lies between x and x+dx is f(x) dx, then f(x) is the frequency function of the variable. The probability function is, in cases where a finite frequency function ex-ists, equal to the integral of the latter, taken over the interval from -∞ to x — The notations of the present paper will, as a rule, correspond to those of I, the most important exception being the symbol n , which will here always denote the number of observations in a statistical series and not, as in I, the number of elementary components. , Art. 1. If we put (cf. I, formula (3)) F(x) is the probability function of the variable , with the mean value 0 and the standard deviation 1. Denoting by µ₂, µ₃, ... the moments of W(x) , taken about the mean (cf. I, Art. 7, where m is supposed to be zero), we put, following Charlier,      

Insurance business described by a mathematical model

At the end of November 1972 the Filip Lundberg Foundation arranged a two days seminar in Saltsjöbaden, Sweden. The purpose of the seminar was to have a discussion about Management Control of Insurance Business. Twenty participants from the four Scandinavian countries took part in the discussions. It was thought that Management Control of Insurance Business must be based on a mathematical model of insurance business where all relevant factors can act together. It was generally agreed that this approach to the problem was useful and important and that this is a problem area where much research work needs to be done in the future. During the seminar the problem area was divided into the following six topics for discussion:     

What has determined the rapid post-war growth of intra-EU trade?

Based on the gravity model by Baier and Bergstrand (2001), we use a static and dynamic panel approach to estimate the determinants of the growth of intra-EU trade from 1960 to 2000. The results suggest that income growth was the major force, accounting for approximately two-thirds of total growth. Trade liberalization still had a sizeable effect, while other factors had only a little impact (income convergence, real effective exchange rate changes) or played no role (trade costs). Our estimation results for intra-EU trade, using a variety of extensions, underline the robustness of the Baier-Bergstrand cross-section findings for world trade. JEL no. C23, F12, F14, F15