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Visualizing Global Risks 2013

Visualizing Global Risks 2013

A year ago we looked at Global Trends 2025, a 2008 report by the National Intelligence Commission. The 120 page document made surprisingly little use of data visualization, given the well-funded and otherwise very detailed report.

By contrast, at the recent World Economic Forum 2013 in Davos, the Risk Response Network published the eighth edition of its annual Global Risks 2013 report. Its focus on national resilience fits well into the “Resilient Dynamism” theme of this year’s WEF Davos. Here is a good 2 min synopsis of the Global Risks 2013 report.

We will look at the abundant use of data visualization in this work, which is published in print as an 80-page .pdf file. The report links back to the companion website, which offers lots of additional materials (such as videos) and a much more interactive experience (such as the Data Explorer). The website is a great example of the benefits of modern layout, with annotations, footnotes, references and figures broken out in a second column next to the main text.

RiskCategories

One of the main ways to understand risks is to quantify it in two dimensions, namely its likelihood and its impact, say on a scale from 1 (min) to 5 (max). Each risk can then be visualized by its position in the square spanned by those two dimensions. Often risk mitigation is prioritized by the product of these two factors. In other words, the further right and/or top a risk, the more important it becomes to prepare for or mitigate it.

This work is based on a comprehensive survey of more than 1000 experts worldwide on a range of 50 risks across 5 broad categories. Each of these categories is assigned a color, which is then used consistently throughout the report. Based on the survey results the report uses some basic visualizations, such as a list of the top 5 risks by likelihood and impact, respectively.

Source for all figures: World Economic Forum (except where noted otherwise)

Source for all figures: World Economic Forum (except where noted otherwise)

When comparing the position of a particular risk in the quadrant with the previous year(s), one can highlight the change. This is similar to what we have done with highlighting position changes in Gartner’s Magic Quadrant on Business Intelligence. Applied to this risk quadrant the report includes a picture like this for each of the five risk categories:

EconomicRisksChange

This vector field shows at a glance how many and which risks have grown by how much. The fact that a majority of the 50 risks show sizable moves to the top right is of course a big concern. Note that the graphic does not show the entire square from 1 through 5, just a sub-section, essentially the top-right quadrant.

On a more methodical note, I am not sure whether surveys are a very reliable instrument in identifying the actual risks, probably more the perception of risks. It is quite possible that some unknown risks – such as the unprecedented terrorist attacks in the US on 9/11 – outweigh the ones covered here. That said, the wisdom of crowds tends to be a good instrument at identifying the perception of known risks.

Note the “Severe income disparity” risk near the top-right, related to the phenomenon of economic inequality we have looked at in various posts on this Blog (Inequality and the World Economy or Underestimating Wealth Inequality).

A tabular form of showing the top 5 risks over the last seven consecutive years is given as well: (Click on chart for full-resolution image)

Top5RisksChanges

This format provides a feel for the dominance of risk categories (frequency of colors, such as impact of blue = economic risks) and for year over year changes (little change 2012 to 2013). The 2011 column on likelihood marks a bit of an outlier with four of five risks being green (= environmental) after four years without any green risk in the Top 5. I suspect that this was the result of the broad global media coverage after the April 2011 earthquake off the coast of Japan, with the resulting tsunami inflicting massive damage and loss of lives as well as the Fukushima nuclear reactor catastrophe. Again, this reinforces my belief that we are looking at perception of risk rather than actual risk.

Another aggregate visualization of the risk landscape comes in the form of a matrix of heat-maps indicating the distribution of survey responses.

SurveyResponseDistribution

The darker the color of the tile, the more often that particular likelihood/impact combination was chosen in the survey. There is a clear positive correlation between likelihood and impact as perceived by the majority of the experts in the survey. From the report:

Still it is interesting to observe how for some risks, particularly technological risks such as critical systems failure, the answers are more distributed than for others – chronic fiscal imbalances are a good example. It appears that there is less agreement among experts over the former and stronger consensus over the latter.

The report includes many more variations on this theme, such as scatterplots of risk perception by year, gender, age, region of residence etc. Another line of analysis concerns the center of gravity, i.e. the degree of systemic connectivity between risks within each category, as well as the movement of those centers year over year.

Another set of interesting visualizations comes from the connections between risks. From the report:

Top5Connections

Top10ConnectedRisks

Finally, the survey asked respondents to choose pairs of risks which they think are strongly interconnected. They were asked to pick a minimum of three and maximum of ten such connections.

Putting together all chosen paired connections from all respondents leads to the network diagram presented in Figure 37 – the Risk Interconnection Map. The diagram is constructed so that more connected risks are closer to the centre, while weakly connected risks are further out. The strength of the line depends on how many people had selected that particular combination.

529 different connections were identified by survey respondents out of the theoretical maximum of 1,225 combinations possible. The top selected combinations are shown in Figure 38.

It is also interesting to see which are the most connected risks (see Figure 39) and where the five centres of gravity are located in the network (see Figure 40).

One such center of gravity graph (for geopolitical risks) is shown here:RiskInterconnections

The Risk Interconnection Map puts it all together:

RiskInterconnectionMap

Such fairly complex graphs are more intuitively understood in an interactive format. This is where the online Data Explorer comes in. It is a very powerful instrument to better understand the risk landscape, risk interconnections, risk rankings and national resilience analysis. There are panels to filter, the graphs respond to mouse-overs with more detail and there are ample details to explain the ideas behind the graphs.

DataExplorer

There are many more aspects to this report, including the appendices with survey results, national resilience rankings, three global risk scenarios, five X-factor risks, etc. For our purposes here suffice it to say that the use of advanced data visualizations together with online exploration of the data set is a welcome evolution of such public reports. A decade ago no amount of money could have bought the kind of interactive report and analysis tools which are now available for free. The clarity of the risk landscape picture that’s emerging is exciting, although the landscape itself is rather concerning.

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Posted by on January 31, 2013 in Industrial, Socioeconomic

 

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Inequality and the World Economy

Inequality and the World Economy

The last edition of The Economist featured a 25-page special report on “The new politics of capitalism and inequality” headlined “True Progressivism“. It is the most recommended and commented story on The Economist this week.

We have looked at various forms of economic inequality on this Blog before, as well as other manifestations (market share, capitalization, online attention) and various ways to measure and visualize inequality (Gini-index). Hence I was curious about any new trends and perhaps ways to visualize global economic inequality. That said, I don’t intend to enter the socio-political debate about the virtues of inequality and (re-)distribution policies.

In the segment titled “For richer, for poorer” The Economist explains.

The level of inequality differs widely around the world. Emerging economies are more unequal than rich ones. Scandinavian countries have the smallest income disparities, with a Gini coefficient for disposable income of around 0.25. At the other end of the spectrum the world’s most unequal, such as South Africa, register Ginis of around 0.6.

Many studies have found that economic inequality has been rising over the last 30 years in many industrial and developing nations around the world. One interesting phenomenon is that while the Gini index of many countries has increased, the Gini index of world inequality has fallen. This is shown in the following image from The Economist.

Global and national inequality levels (Source: The Economist)

This is somewhat non-intuitive. Of course the countries differ widely in terms of population size and level of economic development. At a minimum it means that a measure like the Gini index is not simply additive when aggregated over a collection of countries.

Another interesting chart displays a world map with color coding the changes in inequality of the respective country.

Changes in economic inequality over the last 30 years (Source: The Economist)

It’s a bit difficult to read this map without proper knowledge of the absolute levels of inequality, such as we displayed in the post on Inequality, Lorenz-Curves and Gini-Index. For example, a look at a country like Namibia in South Africa indicates a trend (light-blue) towards less inequality. However, Namibia used to be for many years the country with the world’s largest Gini (1994: 0.7; 2004: 0.63; 2010: 0.58 according to iNamibia) and hence still has much larger inequality than most developed countries.

World Map of national Gini values (Source: Wikipedia)

So global Gini is declining, while in many large industrial countries Gini is rising. One region where regional Gini is declining as well is Latin-America. Between 1980-2000 Latin America’s Gini has grown, but in the last decade Gini has declined back to 1980 levels (~0.5), despite the strong economic growth throughout the region (Mexico, Brazil).

Gini of Latin America over the last 30 years (Source: The Economist)

Much of the coverage in The Economist tackles the policy debate and the questions of distribution vs. dynamism. On the one hand reducing Gini from very large inequality contributes to social stability and welfare. On the other hand, further reducing already low Gini diminishes incentives and thus potentially slows down economic growth.

In theory, inequality has an ambiguous relationship with prosperity. It can boost growth, because richer folk save and invest more and because people work harder in response to incentives. But big income gaps can also be inefficient, because they can bar talented poor people from access to education or feed resentment that results in growth-destroying populist policies.

In other words: Some inequality is desirable, too much of it is problematic. After growing over the last 30 years, economic inequality in the United States has perhaps reached a worrisome level as the pendulum has swung too far. How to find the optimal amount of inequality and how to get there seem like fascinating policy debates to have. Certainly an example where data visualization can help an otherwise dry subject.

 
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Posted by on October 15, 2012 in Socioeconomic

 

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