With globalization continuing apace, the world economy continues to experience dramatic change and development. This third edition builds on the popular format of its predecessors to provide the best concise guide to its subject for students of international economics. Since the previous edition, new developments covered include: The book takes the student through the major characteristics of the global economy in jargon-free non-technical language.
Chapter summary diagrams and a wealth of boxes and tables make this an essential introduction for undergraduates and A-level students as well as the casual reader. Published November 8th by Routledge first published January 1st To see what your friends thought of this book, please sign up. To ask other readers questions about Understanding the World Economy , please sign up.
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Be the first to ask a question about Understanding the World Economy. Lists with This Book. This book is not yet featured on Listopia. Aug 03, Deniss Ojastu rated it it was amazing Shelves: A very good book explaining building principles of macroeconomics, focusing on connections and relevant examples rather than dry definitions. Very well-written chapters on command vs. Christopher Eastwood rated it really liked it Jan 20, Martin rated it it was amazing Jun 09, Robin rated it liked it Dec 11, Markus Lustig rated it it was amazing Oct 09, Paris Jordan rated it it was amazing May 19, Lathifatus Syifa rated it it was amazing Apr 07, Unfortunately, the graphical visualization of a network is not trivial at all and the problem becomes even more difficult the larger the network is.
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For this reason, we discuss in the next subsections some visualization approaches that can help in accomplishing this task. For reasons of a practical realizability, we focus on software packages written in R Development Core Team [ ] because these packages are free to use and provide flexibility for a problem specific fine tuning. However, for reasons of completeness we want to mention that there are also stand-alone tools for network exploration e.
For the visualization of general networks we developed an R package called NetBioV [ , ]. NetBioV provides many easy to use functions, layout styles and color schemes to visualize networks. Specifically, NetBioV provides three principle layout styles. These layouts can be either used separately or combined with each other. Briefly, a global layout style treats essentially all nodes of the network in the same way and tries to find a global arrangement according to some algorithm, e.
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A modular layout acknowledges the fact that some nodes are more close to each other forming, e. Finally, a layered layout emphasizes the presence of a hierarchical order in the network, e. Depending on which property one would like to emphasize and highlight, one needs to choose the corresponding layout style. We would like to remark that for the graphical representation of networks, the package NetBioV makes use of the igraph package [ ]. Igraph2 can also directly be used for the visualization of networks but it is more basic and elementary in its usage and requires for this reason more manual fine tuning in order to obtain satisfactory results.
Many of the different networks discussed in the previous sections carry in addition to the economic information geographic information. For instance, banks or firms are located in certain cities which are located in certain countries. That means the nodes of such economic networks, which correspond to banks or firms, can be overlain a geographic map.
In this way the interpretability of these networks is enhanced, see as an example Figure 3. A topological object does not have any geometric properties, e. For this reason, the meaningful connection of networks with geometric information helps 2-fold. First, the visualization of the network is simplified because no algorithm is needed for finding the coordinates of the nodes for drawing them in form of a diagram but this information is directly provided by the geographic information, e.
Second, the interpretation of the networks is enhanced, e. Mapping economic networks with geographic locations by the maps R package. A practical solution to the above problem is given by the R package maps. This package was also used to generate the example in Figure 3 overlaying a network on the world map. Another R package that provides even more functionality is cartography [ ].
In Figure 4 we show an example. This package allows in addition to the visualization of a network, the visualization of additional economic factors like GDP, population size or compound annual growth rate. These additional elements enhance even further the interpretability of the obtained economic networks. In Figure 4 we added some examples showcasing the functionality of this package.
Specifically, information about the GDP per capita is visualized in the color of the country see e. Mapping economic networks with geographic locations by the cartography R package. Another network visualization that is useful for economic networks relates to bipartite networks. The R packages bipartite [ ] provides such a functionality with many options to customize the resulting visualization.
We would like to note that so far the direct analysis of bipartite networks has not received much attention but, usually, projected networks are extracted from bipartite networks which are then analyzed, because these projected networks are ordinary networks which can be analyzed in the conventional way with the standard methods.
However, there are also direct methods available for analyzing bipartite networks [ — ] and it seems that such approaches are generating more and more interest. As we have seen in the previous sections, so far many different economic networks have been studied.
In this section, we discuss some possible extensions that would help in advancing the field. First of all, it is apparent from our discussions that most economic networks are constructed or inferred from one data source only.
This is certainly the easiest way to create such networks but it is not necessarily the best way. It would be interesting to investigate the integration of more than one data source to see in how far the results are changed. Also it seems reasonable to assume that the amount of noise in the networks can be reduced by using multiple data sources.
This is especially true for the financial networks requiring time series data for their inference which are only available for a limited duration. Instead of focusing on only one node type, e. This would require that one uses weighted networks [ ], which allows different node types in the same network. In general, the quality of economic networks is not easy to assess because these are abstract networks and its links are not immediately observable. This is in contrast to a neural network or a gene regulatory network where links connecting pairs of neurons or indicate the regulation of one gene on another [ — ].
In both cases, wet lab experiments can be performed in order to confirm such connections. For financial networks, where nodes correspond to stocks, this is obviously not possible. For this reason the assessment of the quality of the obtained networks needs to be done indirectly, e. This topic relates to the difference between causal networks and association networks [ , ].
Another problem of the current research in economic networks is that there is no database to which constructed networks could be uploaded so that other scientists can re-use them in follow-up studies, e. This would simplify such comparative analyses considerably and also help in reducing errors when reproducing previous results. Also a data repository integrating information from various databases, as discussed in section 5, bringing them into a standardized form, would reduce the obstacles accompanying computational economics studies simplifying the time consuming preprocessing of the data considerably.
In this paper we reviewed studies for estimating and analyzing different types of economic networks. Despite the fact that economic networks are a special form of social networks which having been studied since many decades starting in the s, for economic networks it took much longer to start. Maybe it is no accident that the sparking interest in economic networks in recent years coincides with the emergence of computational social science [ — ] as a general appreciation of phenomena outside the natural sciences.
A further reason is certainly that one global economic crises is followed by another making us as a society realize that only a more thorough understanding of the global economy can help in preventing future meltdowns. Since it is hard to imagine how one would study an interconnected system, such as the world economy, without the usage of networks, we anticipate to see many more studies about economic networks in the years to come and hope that our contribution can help in fostering this process.
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Int J Entrepreneurial Venturing 5: Expanded trade and gdp data. The world economy has been in upheaval with the biggest financial Understanding the World Economy. Why crashes occur What causes some countries to grow and others to stagnate Whether the Euro can survive The economic underpinnings of terrorism The dangers of climate change This book takes the student through the major characteristics of the global economy in jargon-free non-technical language.
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