Labour Productivity Dynamics in Europe: Alternative Explanations for a Well Known Problem

The study of regional dynamics in employment productivity has been the basis for a large body of literature; the use in cross-section applications of the well known linear relationship between changes in productivity and Output growth first proposed in 1949 by P.J. Verdoorn has allowed greater insights into the dynamic nature of economic systems. Furthermore, it has been shown that a positive Verdoorn coefficient represents the existence of localized increasing returns, which contradicts Neoclassical orthodoxy. In this paper, a dynamic analysis of labour productivity in manufacturing is performed for a sample of 211 European Union regions. Three hypothesis are tested: first, following recent work by Bernard Fingleton (1999 & 2000), two components related to Growth Theory are added to the original Verdoorn relation, the productivity gap between each spatial unit and the leader in the first period and two proxies for human capital. Second, the importance of the Marshallian type externality as well as economies of urbanization is tested. These factors are calculated according to the weighted density of each variable at the NUTS3 level for each of the 211 NUTS2 regions, following the work of Ciccone and Hall (1996). Finally, and following the ideas presented in the seminal paper by Chinitz (1961), the importance of the industrial mix and the level of regional specialization/diversity is taken into account through the use of a spatially weighted specialization measure. Spatial Econometrics methods are used and alternative forms for the spatial weights matrix are tested, based on time distances calculated using a network model built with the existing road network. Legal speed limits permit an accurate calculation of distance between each node.

Evolution of Helping and Harming in Viscous Populations When Group Size Varies

Funding: Balliol College and the Royal Society.Recent years have seen huge interest in understanding how demographic factors mediate the evolution of social behavior in viscous populations. Here we study the impact of variation in group size on the evolution of helping and harming behavior. Although variation in group size influences the degree of relatedness and the degree of competition between groupmates, we find that these effects often exactly cancel, so as to give no net impact of variation in group size on the evolution of helping and harming. Specifically, (1) obligate helping and harming are never mediated by variation in group size, (2) facultative helping and harming are not mediated by variation in group size when this variation is spatial only, (3) facultative helping and harming are mediated by variation in group size only when this variation is temporal or both spatial and temporal, and (4) when there is an effect of variation in group size, facultative helping is favored in big groups and facultative harming is favored in little groups. Moreover, we find that spatial and temporal heterogeneity in individual fecundity may interact with patch-size heterogeneity to change these predictions, promoting the evolution of harming in big patches and of helping in little patches.Publisher PDFPeer reviewe

The Effects of Additive Outliers and Measurement Errors when Testing for Structural Breaks in Variance

This paper discusses the asymptotic and finite-sample properties of CUSUM-based tests for detecting structural breaks in volatility in the presence of stochastic contamination, such as additive outliers or measurement errors. This analysis is particularly relevant for financial data, on which these tests are commonly used to detect variance breaks. In particular, we focus on the tests by Inclán and Tiao [IT] (1994) and Kokoszka and Leipus [KL] (1998, 2000), which have been intensively used in the applied literature. Our results are extensible to related procedures. We show that the asymptotic distribution of the IT test can largely be affected by sample contamination, whereas the distribution of the KL test remains invariant. Furthermore, the break-point estimator of the KL test renders consistent estimates. In spite of the good large-sample properties of this test, large additive outliers tend to generate power distortions or wrong break-date estimates in small samples.

ON THE SMALL SAMPLE PROPERTIES OF DICKEY FULLER AND MAXIMUM LIKELIHOOD UNIT ROOT TESTS ON DISCRETE-SAMPLED SHORT-TERM INTEREST RATES

Testing for unit roots in short-term interest rates plays a key role in the empirical modelling of these series. It is widely assumed that the volatility of interest rates follows some time-varying function which is dependent of the level of the series. This may cause distortions in the performance of conventional tests for unit root nonstationarity since these are typically derived under the assumption of homoskedasticity. Given the relative unfamiliarity on the issue, we conducted an extensive Monte Carlo investigation in order to assess the performance of the DF unit root tests, and examined the effects on the limiting distributions of test procedures (t- and likelihood ratio tests) based on maximum likelihood estimation of models for short-term rates with a linear drift.Unit root, interest rates, CKLS model.

On the Small Sample Properties of Dickey Fuller and Maximum Likelihood Unit Root Tests on Discrete-Sampled Short-Term Interest Rates

Testing for unit roots in short-term interest rates plays a key role in the empirical modelling of these series. It is widely assumed that the volatility of interest rates follows some time-varying function which is dependent of the level of the series. This may cause distortions in the performance of conventional tests for unit root nonstationarity since these are typically derived under the assumption of homoskedasticity. Given the relative unfamiliarity on the issue, we conducted an extensive Monte Carlo investigation in order to assess the performance of the DF unit root tests, and examined the effects on the limiting distributions of test procedures (t- and likelihood ratio tests) based on maximum likelihood estimation of models for short-term rates with a linear drift.Unit root, interest rates, CKLS model.