"Minimálbér-paradoxon" versenyzői munkaerőpiacon? ("Minimum-wage paradox" in a competitive labour market? Lessons from an experimental line of thought)

A versenyzői munkaerőpiac hagyományos kereslet-kínálati modellje az egyensúlyi bérszintet meghaladó minimálbér következményeként az egyensúlyi bérszint mellettinél alacsonyabb foglalkoztatást jósol; minél magasabb a minimálbér, annál alacsonyabbat. Empirikus vizsgálatok szerint ugyanakkor a minimálbér-emelés nem feltétlenül csökkenti a foglalkoztatást - ezt nevezik minimálbér-paradoxonnak -, ami legkézenfekvőbben a munkáltatók munkaerő-piaci monopszonerejével látszik magyarázhatónak. Ezzel szemben az a gondolatkísérlet, amelyről ez a cikk beszámol, általánosabb érvényű, versenyzői munkaerőpiacot feltételező magyarázat kidolgozására irányul. / === / In the conventional textbook demand/supply model of competitive labour markets, the introduction of a minimum wage above market-clearing level must reduce employment. Empirical findings suggest, however, that this may not always be the case, a finding most readily explained by monopsonistic competition in the labour market. The experimental line of thought reported here explores an alternative root, interpreting the "minimum-wage paradox" as the outcome of a competitive labour market that displays friction.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.