Centralization of wage bargaining and the unemployment rate: revisiting the hump-shape hypothesis

Is there a relation between wage bargaining institutions and unemployment? The humpshape hypothesis”, first introduced by Calmfors and Driffill (1988), states that countries with highly centralized and highly decentralized wage bargaining processes have a superior performance in terms of unemployment than countries with an intermediate degree of centralization. Calmfors and Driffill’s results were obtained on a sample including data from 1962 up to 1985. This paper shows that the claimed superiority in terms of unemployment of centralized countries over intermediate ones during the ’60s and the ’70s depended upon their high levels of government expenditure and public sector employment. The evidence shows that from the beginning of the ’80s the expansion of the public sector in centralized countries slowed down considerably and, at the same time, the correlation between the degree of centralization and unemployment weakened. This evidence helps reconcile recent findings of poor correlations between measures of economic performance and indexes of bargaining systems with Calmfors and Driffill’s original results. The paper concludes by questioning the compatibility of the reported evidence with the theoretical framework proposed by CD to explain the hump-shape hypothesis.wage negotiations, unemployment rate, public employment

On the cohomological equation for nilflows

Let X be a vector field on a compact connected manifold M. An important question in dynamical systems is to know when a function g:M -> R is a coboundary for the flow generated by X, i.e. when there exists a function f: M->R such that Xf=g. In this article we investigate this question for nilflows on nilmanifolds. We show that there exists countably many independent Schwartz distributions D_n such that any sufficiently smooth function g is a coboundary iff it belongs to the kernel of all the distributions D_n.Comment: 27 page

Searching for non-gaussianity: Statistical tests

Non-gaussianity represents the statistical signature of physical processes such as turbulence. It can also be used as a powerful tool to discriminate between competing cosmological scenarios. A canonical analysis of non-gaussianity is based on the study of the distribution of the signal in the real (or direct) space (e.g. brightness, temperature). This work presents an image processing method in which we propose statistical tests to indicate and quantify the non-gaussian nature of a signal. Our method is based on a wavelet analysis of a signal. Because the temperature or brightness distribution is a rather weak discriminator, the search for the statistical signature of non-gaussianity relies on the study of the coefficient distribution of an image in the wavelet decomposition basis which is much more sensitive. We develop two statistical tests for non-gaussianity. In order to test their reliability, we apply them to sets of test maps representing a combination of gaussian and non-gaussian signals. We deliberately choose a signal with a weak non-gaussian signature and we find that such a non-gaussian signature is easily detected using our statistical discriminators. In a second paper, we apply the tests in a cosmological context.Comment: 14 pages, 7 figures, in press in Astronomy & Astrophysics Supplement Serie