Category Archives: Socioeconomic

Digital Wages in the Gig Economy

Digital Wages in the Gig Economy

A small research team from the Oxford Internet Institute has recently issued a report based on a three year investigation into the worldwide geographies of the so-called Gig-Economy, online work which allows many talented people in the low and middle income countries of the world to compete on a global stage. From the Executive Summary:

Online gig work is becoming increasingly important to workers living in low- and middle-income countries. Our multi-year and multi-method research project shows that online gig work brings about rewards such as potential higher incomes and increased worker autonomy, but also risks such as social isolation, lack of work–life balance, discrimination, and predatory intermediaries. We also note that online gig work platforms mostly operate outside regulatory and normative frameworks that could benefit workers.

One of the eye-catching and very information rich visualizations comes from a related Blog post by the “Connectivity, Inclusion, and Inequality Group” called “Uneven Geographies of Digital Wages“.


Dollar Inflow and Median Wage by Country

The cartogram depicts each country as a circle and sizes each country according to dollar inflow to each country during March 2013 (on the freelance work platform, rebranded in 2015 to Upwork). The shading of the inner circle indicates the median hourly rate published by digital workers in that country. The graphic broadly reveals that median wages are, perhaps unsurprisingly low in developing countries and are significantly higher in wealthier countries.

Another Blog post on the geographies of online work adds several more visualizations (based on 2013 data, so a bit dated by now). For instance, one world map highlights the relationship between supply and demand. It distinguishes between countries with a positive balance of payment (i.e. countries in which more work is sold than bought) and countries with a negative balance of payment (countries in which more work is bought than is sold). The figure more clearly delineates the geography of supply and demand: with much of the world’s demand coming from only a few places in the Global North.


Balance of payments

Another very interesting and dense visualization is a connectogram (see our previous post on Connectograms and the Circos tool) demonstrating the highly international trade in the online Gig-Economy: 89% of the trade measured by value happened between a client and a contractor who are in different countries. The network therefore attempts to illustrate the entirety of those international flows in one graph. It depicts countries as nodes (i.e. circles) and volumes of transactions between buyers and sellers in those countries as edges (i.e. the lines connecting countries). Country nodes are shaded according to the world region that they are in and sized according to the number of buyer transactions originating in them. Edges are coloured according to the flow of services: with the line shaded as the colour of the originating/selling region. Edges are also weighted according to the total volume of trade.


The Geographic Network of Sales

We see not just a complex many-to-many relationship of international trade, but also the large role that a few geographic relationships take (in particular, India and the Philippines selling to the United States).

Back to the Executive Summary of the above report:

The report’s central question is whether online gig work has any development potentials at the world’s economic margins. Its motive is to help platform operators to improve their positive impact, to help workers to take action to improve their situations, and to prompt policy makers and stakeholders interested in online gig work to revisit regulation as it applies to workers, clients, and platforms in their respective countries.

It is interesting to see these marketplaces evolve, in terms of the international, distributed nature, issues such as taxation, intermediation, opportunities and risks. There are also entirely new forms of social networks forming, based on blockchain powered token systems convertible into crypto-currencies (such as Steem). The core concept here is to eliminate not just geographical distance, but also risks from exchange rate fluctuations and predatory intermediaries. It remains to be seen to what degree this can act as a counterweight to technology-induced increasing inequality.


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Posted by on March 26, 2017 in Industrial, Socioeconomic



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.


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:


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)


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.


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:



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:


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.


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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2012 Election Result Maps

2012 Election Result Maps

The New York Times has covered the 2012 U.S. presidential election in great detail, including the much heralded fivethirtyeight Blog (after the 538 electoral votes) by forecaster Nate Silver. His poll-aggregation model has consistently produced the most accurate forecasts, and called 99 of 100 states correctly in both the 2008 and the 2012 elections.

A popular visualization is the map of the 50 states in colors red (Republican) and blue (Democrat) plus green (Independent). Since most states allocate all their electoral votes to the candidate with the most votes in that state, this state map seems the most important.

2012 Election Result By State (Source:

This map hardly changed from 2008, only Indiana and North Carolina changed color. Hence the electoral vote result in 2012 (332 Dem206 Rep)  is similar to that of 2008 (365 Dem173 Rep). The visual perception of this map, however, is that there is roughly the same amount of red and blue, with slightly more red than blue. This perception becomes even stronger when looking at the results by county.

2012 Election Results By County (Source:

Why is the outcome so strongly in favor of the blue (Democrat) when it looks like the majority of the area is red? The answer is found in very uneven population density of the 50 states. Although roughly the same size, California’s (slightly more blue) population density is about 40x higher than Montana’s (mostly red). On the extreme end of this scale, the most densely populated state New Jersey has about 1000x as many people living per square mile as the least densely populated state Alaska. Urban areas have a much higher density of voters than rural areas. The different demographics are such that urban areas tend to vote more blue (Democrat), rural areas tend to vote more red (Republican). The size of the colored area in the above chart would only be a good indicator if the population density was uniform. A great way to compensate visually for this difference can be seen in the third chart published by the NYTimes.

2012 Election Delta By County (Source:

Now the size of the colored circles is proportional to the number of surplus votes for that color in that county. The few blue circles around most major cities are larger and outweigh the many small red circles in rural areas – both optically intuitive and numerically in total. The original map is interactive, giving tooltips when you hover over the circles. For example, in just Los Angeles county there were about 1 million more blue (Democrat) votes than red (Republican).

2012 Election in Los Angeles County

This optical summation leads to intuitively correct results for the popular votes. The difference in popular vote was about 3.5 million more blue (Democrat) votes or roughly 3%. We see more blue in this delta circle diagram.

Of course, the president is not elected by the popular, but by the electoral votes per state. So no matter how big the Democrat advantage in California may be, there won’t be more than the 55 electoral votes for California. This winner-take-all dynamic of electoral votes by state leads to the outsized influence of swing states which are near the 50%-50% mark on the popular votes. A small lead in the popular vote can lead to a large gain in electoral votes. In extreme cases, a candidate can win the electoral vote and become president despite losing in the popular vote (as happened in 2000 and the very narrow win of Florida by George W. Bush).

Another variation on this theme of visually combining votes and population density information comes from Chris Howard. (This was referenced in an article on by Emily Badger on the spatial divide of urban vs. rural voting preferences which has other election maps as well). The idea is to use shades of blue and red with population density increasing in darker shades of the color, used on a by county map.

2012 Election by county with shading by population density (Source: Chris Howard)

A final visualization comes from Nate Silver’s Blog post on November 8. While the % details of this at the time preliminary result may be slightly off (not all votes had been counted yet), the electoral vote counts remain valid.

2012 Election By State Cumulative (Source: Fivethirtyeight Blog)

It shows which swing state [electoral votes] put the blue ticket over the winning line (Colorado [9]) and which other swing states could have been lost without losing the presidency (Florida [29], Ohio [18], Virginia [13]). It also gives a crude, but somewhat telling indication of where you might want to live if you want to surround yourself by people with blue or red preferences.

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Posted by on November 15, 2012 in Socioeconomic


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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Software continues to eat the world

Software continues to eat the world

One year ago Marc Andreessen, co-founder of Netscape and venture capital firm Andreessen-Horowitz, wrote an essay for the Wall Street Journal titled “Why Software Is Eating The World“. It is interesting to reflect back to this piece and some of the predictions made back at a time when Internet company LinkedIn had just gone public and Groupon was just filing for an IPO.

Andreessen’s observation was simply this: Software has become so powerful and computer infrastructure so cheap and ubiquitous that many industries are being disrupted by new business models enabled by that software. Examples listed were books (Amazon disrupting Borders), movie rental (NetFlix disrupting Blockbuster), music industry (Pandora, iTunes), animation movies (Pixar), photo-sharing services (disrupting Kodak), job recruiting (LinkedIn), telecommunication (Skype), video-gaming (Zynga) and others.

On the infrastructure side one can bolster this argument by pointing at the rapid development of new technologies such as cloud computing or big data analytics. Andreessen gave one example of the cost of running an Internet application in the cloud dropping by a factor of 100x in the last decade (from $150,000 / month in 2000 using LoudCloud to about $1500 / month in 2011 using Amazon Web Services). Microsoft now has infrastructure with Windows Azure where procuring an instance of a modern server at one (or even multiple) data center(s) takes only minutes and costs you less than $1 per CPU hour.

Likewise, the number of Internet users has grown from some 50 million around 2000 to more than 2 billion with broadband access in 2011. This is certainly one aspect fueling the enormous growth of social media companies like Facebook and Twitter. To be sure, not every high-flying startup goes on to be as successful after its IPO. Facebook trades at half the value of opening day after three months. Groupon trades at less than 20% of its IPO value some 9 months ago. But LinkedIn has sustained and even modestly grown its market capitalization. And Google and Apple both trade near or at their all-time high, with Apple today at $621b becoming the most valuable company of all time (non inflation-adjusted).

The growing dominance and ubiquitous reach of software shows in other areas as well. Take automobiles. Software is increasingly been used for comfort and safety in modern cars. In fact, self-driving cars – once the realm of science fiction such as flying hover cars – are now technically feasible and knocking on the door of broad industrial adoption. After driving 300.000 miles in test Google is now deploying its fleet of self-driving cars for the benefit of its employees. Engineers even take self-driving cars to the racetracks, such as up on Pikes Peak or the Thunderhill raceway. Performance is now at the level of very good drivers, with the benefit of not having the human flaws (drinking, falling asleep, texting, showing off, etc.) which cause so many accidents. Expert drivers still outperform the computer-driven cars. (That said, even human experts sometimes make mistakes with terrible consequences, such as this crash on Pikes Peak this year.) The situation is similar to how computers got so proficient at chess in the mid-nineties that finally even the world champion was defeated.

In this post I want to look at some other areas specifically impacting my own life, such as digital photography. I am not a professional photographer, but over the years my wife and I have owned dozens of cameras and have followed the evolution of digital photography and its software for many years. Of course, there is an ongoing development towards chips with higher resolution and lenses with better optic and faster controls. But the major innovation comes from better software. Things like High Dynamic Range (HDR) to compensate for stark contrast in lighting such as a portrait photo against a bright background. Or stitching multiple photos together to a panorama, with Microsoft’s PhotoSynth taking this to a new level by building 3D models from multiple shots of a scene.

One recent innovation comes in the form of the new Sony RX100 camera, which science writer David Pogue raved about in the New York Times as “the best pocket camera ever made”. My wife bought one a few weeks ago and we both have been learning all it can do ever since. Despite the many impressive features and specifications about lens, optics, chip, controls, etc. what I find most interesting is the software running on such a small device. The intelligent Automatic setting will decide most settings for your everyday use, while one can always direct priorities (aperture, shutter, program) or manually override most aspects. There are a great many menus and it is not trivial to get to use all capabilities of this camera, as it’s extremely feature-rich. Some examples of the more creative software come in modes such as ‘water color’ or ‘illustration’. The original image is processed right then and there to generate effects as if it was a painting or a drawing. Both original and processed photo are stored on the mini-SD card.

Flower close-up in ‘illustration’ mode

One interesting effect is to filter to just the main colors (Yellow, Red, Green, Blue). Many of these effects are shown on the display, with the aperture ring serving as a flexible multi-functional dial for more convenient handling with two hands. (Actually, the camera body is so small that it is a challenge to use all dials while holding the device; just like the BlackBerry keyboard made us write with two thumbs instead of ten fingers.) The point of such software features is not so much that they are radically new; you could do so with a good photo editing software for many years. The point is that with the ease and integration of having them at your fingertips you are much more likely to use them.

Example of suppressing all colors except yellow

The camera will allow registering of faces and detect those in images. You can set it up such that it will take a picture only when it detects a small/medium/large smile on the subject being photographed. One setting allows you to take self-portrait, with the timer starting to count down as soon as the camera detects one (or two) faces in the picture! It is an eerie experience when the camera starts to “understand” what is happening in the image!

There is an automatic panorama stitching mode where you just hold the button and swipe the camera left-right or up-down while the camera takes multiple shots. It automatically stitches them into one composite, so no more uploading of the individual photos and stitching on the computer required.

Beach panorama stitched on the camera using swipe-&-shoot

I have been experimenting with panorama photos since 2005 (see my collection or my Panoramas from the Panamerican Peaks adventure). It’s always been somewhat tedious and results were often mixed (lens distortions, lighting changes sun vs. cloud or objects moving during the individual frames, not holding the camera level, skipping a part of the horizon, etc.) despite crafty post-processing on the computer with image software. I have read about special 360 degree lenses to take high-end panoramas, but who wants to go to those lengths just for the occasional panorama photo? From my experience, nothing moves the needle as much as the ease and integration of taking panoramas right in the camera as the RX100 does.

Or take the field of healthcare. Big Data, Mobility and Cloud Computing make possible entirely new business models. Let’s just look at mobility. The smartphone is evolving into a universal healthcare device for measuring, tracking and visualizing medical information. Since many people have their smartphone with them at almost all times, one can start tracking and analyzing personal medical data over time. And for almost any medical measurement, “there is an app for that”. One interesting example is this optical heart-rate monitor app Cardiio for the iPhone. (Cardio + IO ?)

Screenshots of Cardiio iPhone app to optically track heart rate

It is amazing that this app can track your heart rate just by analyzing the changes of light reflected from your face with its built-in camera. Not even a plug-in required!

Another system comes from Withings, this one designed to turn the iPhone into a blood pressure monitor. A velcro sleeve with battery mount and cable plugs into the iPhone and an app controls the inflation of the sleeve, the measurement and some simple statistics.

Blood pressure monitor system from Withings for iPhone

Again, it’s fairly simple to just put the sleeve around one upper arm and push the button on the iPhone app. The results are systolic and diastolic blood pressure readings and heart rate.

Sample blood pressure and pulse measurement

Like many other monitoring apps this one also keeps track of the readings and does some simple form of visual plotting and averaging.

Plot of several blood pressure readings

There is also a separate app which will allow you to upload your data and create a more comprehensive record of your own health over time. Withings provides a few other medical devices such as scales to add body weight and body fat readings. The company tagline is “smart and connected things”.

One final example is an award-winning contribution from a student team from Australia called Stethocloud. This system is aimed at diagnosing pneumonia. It is comprised of an app for the iPhone, a simple stethoscope plug-in for the iPhone and on the back-end some server-based software analyzing the measurements in the Windows Azure cloud according to standards defined by the World Health Organization. The winning team (in Microsoft’s 2012 Imagine Cup) built a prototype in only 2 weeks and had only minimal upfront investments.

StethoCloud system for iPhone to diagnose pneumonia

This last example perhaps illustrates best the opportunities of new software technologies to bring unprecedented advances to healthcare – and to many other fields and industries. I think Marc Andreessen was spot on with his observation that software is eating the world. It certainly does in my world.

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Posted by on August 20, 2012 in Industrial, Medical, Socioeconomic


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Faceplant with Facebook?

With the Facebook IPO coming up this Friday there is a lot of attention around its business model and financials. I’m not an expert in this area, but my hunch is that a lot of people will lose a lot of money by chasing after Facebook shares. Why?

I think there are two types of answers. One from reasoning and one from intuition.

For reasoning one needs to look at a more technical assessment of the business model and financials. Some have written extensively about the comparative lack of innovation in Facebook’s business model and core product. Some have compared Facebook’s performance in advertising to Google – the estimates are that Google’s ad performance is 100x better than that of Facebook. Some have pointed out that many of Facebook’s core metrics such as visits/person, pages/visit or Click-Through-Rates have been declining for two years and go as far as calling this the Facebook ad scam. One can question the wisdom of the Instagram acquisition, buying a company with 12 employees and zero revenues for $1B. One can question the notion that the 28 year old founder will have 57% of the voting rights of the public company. One could look at stories about companies discontinuing their ad Facebook efforts such as the Forbes article about GM pulling a $10m account because they found it ineffective. The list goes on.

Here is a more positive leaning infographic from an article looking at “Facebook: Business Model, Hardware Patents and IPO“:

Analysis Infographic of pre-IPO Facebook (source: Gina Smith,

To value a startup at 100x last year’s income seems just extremely high – but then Amazon’s valuation is in similarly lofty territory. As for reasoning and predicting the financial success of Facebook’s IPO, people can cite numbers to justify their beliefs both ways. At the end of the day, it’s unpredictable and nobody can know for sure.

The other answer to why I am not buying into the hype is more intuitive and comes from my personal experience. Here is a little thought experiment as to how valuable a company is for your personal life: Imagine for a moment if the company with all its products and services would disappear overnight. How much of an impact would it have for you as an individual? If I think about companies like Apple, Google, Microsoft, or Amazon the impact for me would be huge. I use their products and services every day. Think about it:

No Apple = no iPhone, no iPad, no iTunes music on the iPod or via AppleTV on our home stereo. That would be a dramatic setback.

No Google = no Google search, no GMail, no YouTube, no Google maps, no Google Earth. Again, very significant impact for me personally. Not to mention the exciting research at Google in very different areas such as self-driving vehicles.

No Facebook = no problem (at least for me). I deactivated my own Facebook account months ago simply because it cost me a lot of time and I got very little value out of it. In fact, I got annoyed with compulsively looking at updates from mere acquaintances about mundane details of their lives. Why would I care? I finally got around to actually deleting my account, although Facebook makes that somewhat cumbersome (which probably inflates the account numbers somewhat).

I’m not saying Facebook isn’t valuable to some people. Having nearly 1B user accounts is very impressive. Hosting by far the largest photo collection on the planet is extraordinary. Facebook exploded because it satisfied our basic need of sharing, just like Google did with search, Amazon did with shopping or eBay did with selling. But the entry barrier to sharing is small (see LinkedIn, Twitter or Pinterest) and Facebook doesn’t seem to be particularly well positioned for mobile.

I strongly suspect that Facebook’s valuation is both initially inflated – the $50 per account estimate of early social networks doesn’t scale up with the demographics of the massive user base – as well as lately hyped up by greedy investors who sense an opportunity to make a quick buck. My hunch is that FB will trade below its IPO price within the first year, possibly well below. But then again, I have been surprised before…

I’m not buying the hype. What am I missing? Let me know what you think!

UPDATE 8/16/2012: Well, here we are after one quarter, and Facebook’s stock valuation hasn’t done so well. Look at the first 3 month chart of FB:

First 3 month of Facebook stock price (Screenshot of StockTouch on iPad)

What started as a $100b market valuation is now at $43b. One has to hand it to Mark Zuckerberg, he really extracted maximum value out of those shares. It turns out sitting on the sidelines was the right move for investors in this case.

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Posted by on May 16, 2012 in Financial, Socioeconomic


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Inequality Comparison

Inequality Comparison

In previous posts on this Blog we have looked at various inequalities as measured by their respective Gini Index values. Examples are the posts on Under-estimating Wealth Inequality, Inequality on Twitter, Inequality of Mobile Phone Revenue, and how to visualize as well as measure inequality.

Here is a bubble chart comparison of 14 different inequalities:

Comparison of various Inequalities



  • P1: Committee donations to 2012 presidential candidates (2011, Federal Election Commission)
  • P2: US political donations to members of congress and senate (2010, US Center for Responsive Politics)
  • A1: Twitter Followers (of my tlausser account) (2011, Visualign)
  • A2: Twitter Tweets (of my tlausser account) (2011, Visualign)
  • I1: Global Share of Tablet shipment by Operating System (2011,
  • I2: Mobile Phone Shipments (revenue) (2009,
  • I3: US Car Sales (revenue) (2011,
  • I4: Market Cap of Top-20 Nasdaq companies (2011, Nasdaq)

    The x-axis shows the size of the population in logarithmic scale. The y-axis is the Gini value. The “80-20 rule” corresponds to a Gini value of 0.75. Bubble size is proportional to the log(size), i.e. redundant with the x-axis.


    Most of the industrial inequalities studied have a small population (10-20); this is usually due to the small number of competitors studied or a focus on the Top-10 or Top-20 (for example in market capitalization). With small populations the Gini value can vary more as one outlier will have a disproportionately larger effect. For example, the Congressional Net Worth analysis (top-left bubble) was taken from a set of 25 congressional members representing Florida (Jan-22, 2012 article in the Palm Beach Post on net worth of congress). Of those 25, one (Vern Buchanan, owner of car dealerships and other investments) has a net worth of $136.2 million, with the next highest at $6.4 million. Excluding this one outlier would reduce the average net worth from $6.9 to $1.55 million and the Gini index from 0.91 (as shown in the Bubble Chart) to 0.66. Hence, Gini values of small sets should be taken with a grain of salt.

    The studied cases in attention inequality have very high Gini values, especially for the traffic to websites (top-right bubble), which given the very large numbers (Gini = 0.985, Size = 1 billion) is the most extreme type of inequality I have found. Attention in social media (like Twitter) is extremely unevenly distributed, with most of it going to very few alternatives and the vast number of alternatives getting practically no attention at all.

    Political donations are also very unevenly distributed, considerably above the 80-20 rule. The problem from a political perspective is that donations buy influence and such influence is very unevenly distributed, which does not seem to be following the democratic ideals of the one-person, one-vote principle of equal representation.

    Lastly, economic inequalities (wealth, income, capital gains, etc.) are perhaps the most discussed forms of inequality in the US. Inequalities at the level of all US households or citizens measure large populations (100 – 300 million). One obvious observation from this Bubble Chart is that capital gains inequality is far, far higher than income inequality.

    Tool comment: I have used Excel 2007 to collect the data and create this chart. Even though it is natively supported in Excel, the Bubble Chart has a few restrictions which make it cumbersome. For example, I haven’t found a way to use Data Point labels from the spread-sheet; hence a lot of manual editing is required. I also don’t know of a way to create animated Bubble-Charts (to follow the evolution of the bubbles over time) similar to those at GapMinder. Maybe I need to study the ExcelCharts Blog a bit more… If you know of additional tips or tweaks for BubbleCharts in Excel please post a comment or drop me a note. Same if you are interested in the Excel spread-sheet.

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    Posted by on February 3, 2012 in Industrial, Socioeconomic



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