The terrestrial Arctic is often treated as a uniform biome of low biological diversity. It is true that species richness as well as the diversity of functional groups declines with increasing latitude within most organism groups (Matveyeva and Chernov 2000). However, trends in other less studied aspects of biological diversity, such as genetic diversity within species, might well turn out to be comparable to other biomes (Callaghan et al. 2004, The ACIA report 2005), at least on certain spatial scales.
When exploring patterns of diversity at different levels of biological organisation it is important to consider how they vary at different scales in time and space in relation to variability of the environmental factors that affect diversity. Typical for the Arctic is the overriding role of abiotic factors in shaping the environment experienced by the organisms in most habitats, and the large topographic impact on theses factors, thereby creating large environmental heterogeneity at various spatial scales (Jónsdóttir 2005).
A significant amount of research in international macroeconomics has been devoted to answer the question of what are the channels through which business cycles are transmitted across countries. Unfortunately and despite all this work, there is still no consensus on what determines business cycle comovement.
There are also important discrepancies between the data and what standard models predict regarding the international comovement of macroeconomic aggregates. These discrepancies were first identified by Backus, Kehoe and Kydland (1992) for the OECD countries. They have been labelled “anomalies” when proved robust to various changes to parameter values and model structures. The discrepancies are two. First, in the data, correlations of output across countries are larger than analogous correlations for consumption. With only a few exceptions, previous work obtains consumption cross-country correlations that significantly exceed output correlations. This inconsistency has been labelled the “consumption/output anomaly” or the “quantity anomaly”. Second, investment and employment comove across countries in the data, while most models predict negative values for their cross-country correlation. Many candidates have been suggested to propose a solution to these puzzles, but no agreement has been reached on what is the best way to solve them.
A prominent feature in many network industries is the existence of switching costs: consumers can switch between networks but it is costly (in terms of money and/or effort) for them to do so. Examples include PC operating systems, wireless phone services, DVD players, etc. In fact, according to Shy (2001), switching costs are one of the main characteristics of network industries that distinguish them from other types of industries. While there have been many studies that analyze network effects and switching costs separately, few have looked at them jointly, and it is unclear whether the findings thus far can be applied to markets in which those two factors coexist.
This paper focuses on the effects of switching costs in network industries. I build an oligopolistic model of price competition that incorporates both network effects and switching costs. Firms dynamically optimize. A Markov perfect equilibrium is numerically solved for, and I assess the effects of switching costs on the frequency with which market dominance occurs. The effects of switching costs on prices and welfare are also investigated.
