10,026 research outputs found
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
- …