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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“.

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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 oDesk.com 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.

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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.

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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

 

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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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Olympic Medal Charts

Olympic Medal Charts

The 2012 London Olympic Games ended this weekend with a colorful closing ceremony. Media coverage was unprecedented, with other forms of competition around who had the most social media presence or which website had the best online coverage of the games.

In this post I’m looking at the medal counts over the history of the Olympic Games (summer games only, 27 events over the last 116 years, no games in 1916, 1940, and 1944). Nearly 11.000 athletes from 205 countries competed for more than 900 medals in 302 events. The New York Times has an interactive chart of the medal counts on their London 2012 Results page:

Bubble size represents the number of medals won by the country, bubble position is roughly based on a world map and bubble color indicates the continent. Moving the slider to a different year changes the bubbles, which gives a dynamic grow or shrink effect.

Below this chart is a table listing all gold, silver, bronze winners for each sport in that year, grouped by type of sport such as Gymnastics, Rowing or Swimming. Selecting a bubble will filter this to entries where the respective country won a medal. This shows the domination of some sports by certain countries, such as Diving (8 events, China won 6 gold and 10 total medals) or Cycling – Track (10 events, Great Britain won 7 gold and 9 total medals). In two sports, domination by one country was 100%: Badminton (5 events, China won 5 gold and 8 total medals), Table Tennis (4 events, China won 4 gold and 6 total medals).

There is also a summary table ranking the countries by total medals. For 2012, the United States clearly won that competition, winning more gold medals (46) than all but 3 other countries (China, Russia, Britain) won total medals.

Top 10 countries for medal count in 2012

Of course countries vary greatly by population size. It is remarkable that a relatively small nations such as Jamaica (~2.7 million) won 12 medals (4, 4, 4), while India (~1.25 billion) won only 6 medals (0, 2, 4). In that sense, Jamaica is about 1000x more medal-decorated per population size than India! In another New York Times graphic there is an option to compare medal count adjusted for population size, i.e. with the medal count normalized to a standard population size of say 100 million.

Directed graph comparing medal performance adjusted for country size

Selecting any node in this graph will highlight countries with better, worse or comparable relative medal performance. (There are different ways to rank based on how different medals are weighted.)

The Guardian Data Blog has taken this a step further and written a piece called “alternative medals table“. This post not only discusses multiple factors like population, GDP, or number of athletes and how to deal with them statistically; it also provides all the data and many charts in a Google Docs spreadsheet. One article combines GDP adjustment with cartographical mapping across Europe:

Medals GDP Adjusted and mapped for Europe

If you want to do your own analysis, you can get the data in shared spreadsheets. To do a somewhat more historic analysis, I used a different source, namely Wolfram’s curated data source accessible from within Mathematica. Of course, once you have all that data, you can examine it in many different directions. Did you know that 14853 Olympic medals were awarded so far in 27 summer Olympiads? The average was 550 medals, growing about 29 medals per event with nearly 1000 awarded in 2008 and 2012.

A lot of attention was paid to who would win the most medals in London. China seemed in contention for the top spot, but in the end the United States won the most medals, as it did in the last 5 Olympiads. Only 7 countries won the most medals at any Olympiad. Greece (1896), France (1900), the United Kingdom (1908), Sweden (1912), and Germany (1936) did so just once. The Soviet Union (which no longer exists) did it 8 times. And the United States did it 14 times. China, which is only participating since 1984, has yet to win the most medals of any Olympiad.

Aside from the top rank, I was curious about the distribution of medals over all countries. Both nations and events have increased, as is shown in the following paired bar chart:

Number of participating nations and total medals per Summer Games

The number of nations grew steadily with only two exceptions during the thirties and the seventies; presumably due to economic hardship many nations didn’t want to afford participation. 1980 also saw the Boycott of the Moscow Games by the United States and several other delegations over geopolitical disagreements. At just over 200 the number of nations seems to have stabilized.

The number of medals depends primarily on the number of events at each Olympiad. This year there were 302 events in 26 types of Sports. Total medal count isn’t necessarily exactly triple that since in some events there could be more than 1 Bronze (such as in Judo, Taekwondo, and Wrestling). Case in point, in 2012 there were 968 medals awarded, 62 more than 3 * 302 events.

What is the distribution of those medals over the participating nations? One measure would be the percentage of nations winning at least some medals. Another measure showing the degree of inequality in a distribution is the Gini index. Here I plotted the percentage of nations medaling and the Gini index of the medal distribution over all participating nations for every Olympiad:

Percentage and Gini-Index of medal distribution by nations

Up until 1932 3 out of 4 nations won at least some medals. Then the percentage dropped down to levels around 40% and lower since the sixties. That means 6 of 10 nations go home without any medals. During the same time period the inequality grew from Gini of about .65 to near .90 One exception were the Third Games in 1904 in St. Louis. With only 13 nations competing the United States dominated so many sports to yield an extreme Gini of .92 All of the last five Games resulted in a Gini of about .86, so this still very large amount of medal winning inequality seems to have stabilized.

It would be interesting to extend this to the level of participating athletes. Of course we know which athlete ranks at the top as the most decorated Olympic athlete of all time: Michael Phelps with 22 medals.

 
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Posted by on August 15, 2012 in Recreational

 

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Underestimating Wealth Inequality

Underestimating Wealth Inequality

What are people’s perceptions about estimated, desirable and actual levels of economic inequality? Behavioral economist Dan Ariely from Duke University and Michael Norton from Harvard Business School conducted a survey of ~5,500 respondents across the United States to find out. Their survey asked questions about wealth inequality (as compared to income inequality), also known as net worth, essentially the value of all things owned minus all things owed (assets minus debt).

Addendum 3/9/2013: A recently posted 6min video illustrating these findings went viral (4 million+ views). It is worth watching:

The authors published the paper here and Dan Ariely blogged about it here in Sep 2010. One of the striking results is summarized in this chart of the wealth distribution across five quintiles:

From their Legend:

The actual United States wealth distribution plotted against the estimated and ideal distributions across all respondents. Because of their small percentage share of total wealth, both the ‘‘4th 20%’’ value (0.2%) and the ‘‘Bottom 20%’’ value (0.1%) are not visible in the ‘‘Actual’’ distribution.

It turned out that most respondents described a fairly equal distribution as the ideal – something similar to the wealth distribution in a country like Sweden. They estimated – correctly – that the U.S. has higher levels of wealth inequality. However, they nevertheless grossly underestimated the actual inequality, which is far higher still. Especially the bottom two quintiles are almost non-existent in the actual distribution. There was much more consensus than disagreement across groups from different sides of the political spectrum about this. From the current policy debates one would not have expected that. They go on to ask the question:

Given the consensus among disparate groups on the gap between an ideal distribution of wealth and the actual level of wealth inequality, why are more Americans, especially those with low income, not advocating for greater redistribution of wealth?

In the last chapter of their paper the authors offer several explanations of this phenomenon. One of them is the observation that the apparent drastic under-estimation of the degree of inequality seems to reveal a lack of awareness of the size of the gap. This is something that Data Visualization and interactive charts can help address. For example, Catherine Mulbrandon’s Blog Visualizing Economics does a great job in that regard.

The authors go on to look at other aspects from the perspective of psychology and behavioral economics. While fascinating in its own right, this excursion is beyond the scope of my Data Visualization Blog. They conclude their paper with general observations

…suggesting that even given increased awareness of the gap between ideal and actual wealth distributions, Americans may remain unlikely to advocate for policies that would narrow this gap.

 
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Posted by on December 12, 2011 in Socioeconomic

 

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Inequality on Twitter

Inequality on Twitter

A lot has been written about economic inequality as measured by distribution of income, wealth, capital gains, etc. In previous posts such as Inequality, Lorenz-Curves and Gini-Index or Visualizing Inequality we looked at various market inequalities (market share and capitalization, donations, etc.) and their respective Gini coefficients.

With the recent rise of social media we have other forms of economy, in particular the economy of time and attention. And we have at least some measures of this economy in the form of people’s activities, subscriptions, etc. Whether it’s Connections on LinkedIn, Friends on FaceBook, Followers on Twitter – all of the social media platforms have some social currencies for attention. (Influence is different from attention, and measuring influence is more difficult and controversial – see for example the discussions about Klout-scores.)

Another interesting aspect of online communities is that of participation inequality. Jakob Nielsen did some research on this and coined the well-known 90-9-1 rule:

“In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.”

The above linked article has two nice graphics illustrating this point:

Illustration of participation inequality in online communities (Source: Jakob Nielsen)

As a user of Twitter for about 3 years now I decided to do some simple analysis, wondering about the degrees of inequality I would find there. Imagine you want to spread the word about some new event and send out a tweet. How many people you reach depends on how many followers you have, how many of those retweet your message, how many followers they have, how many other messages they send out and so on. Let’s look at my first twitter account (“tlausser”); here are some basic numbers of my followers and their respective followers:

Followers of tlausser Followers on Twitter

Some of my followers have no followers themselves, one has nearly 100,000. On average, they have about 3600 followers; however, the total of about 385,000 followers is extremely unequally distributed. Here are three charts visualizing this astonishing degree of inequality:

Of 107 followers, the top 5 have ~75% of all followers that can be reached in two steps. The corresponding Gini index of 0.90 is an example of extreme inequality. From an advertising perspective, you would want to focus mostly on getting these 5% to react to your message (i.e. retweet). In a chart with linear scale the bottom half does barely register.

Most of my followers have between 100-1000 followers themselves, as can be seen from this log-scale Histogram.

What kind of distribution is the number of followers? It seems that Log[x] is roughly normal distributed.

As for participation inequality, let’s look at the number of tweets that those (107) followers send out.

Some of them have not tweeted anything, the chattiest has sent more than 16,000 tweets. On average, each follower has 1280 tweets; the total of 137,000 tweets is again highly unequally distributed for a Gini index of 0.77.

The top 10 make up about 2/3 of the entire conversation.

Again the bottom half hardly contributes to the number of tweets; however, the ramp in the top half is longer and not quite as steep as with the number of followers. Here is the log-scale Histogram:

I did the same type of analysis for several other Twitter Users in the central range (between 100-1000 follower). The results are similar, but certainly not yet robust enough to statistical sampling errors. (A larger scale analysis would require a higher twitter API limit than my free 350 per hour.)

These preliminary results indicate that there are high degrees of inequality regarding the number of tweets people send out and even more so regarding the number of followers they accumulate. How many tweets Twitter users send out over time is more evenly distributed. How many followers they get is less evenly distributed and thus leads to extremely high degrees of inequality. I presume this is caused in part due to preferential attachment as described in Barabasi’s book “Linked: The new science of networks“. Like with all forms of attention, who people follow depends a lot on who others are following. There is a very long tail of small numbers of followers for the vast majority of Twitter users.

That said, the degree of participation inequality I found was lower than the 90-9-1 rule, which corresponds to an extreme Gini index of about 0.96. Perhaps that’s a sign of the Twitter community having evolved over time? Or perhaps just a sign of my analysis sample being too small and not representative of the larger Twitterverse.

In some way these new media are refreshing as they allow almost anyone to publish their thoughts. However, it’s also true that almost all of those users remain in relative obscurity and only a very small minority gets the lion share of all attention. If you think economic inequality is too high, keep in mind that attention inequality is far higher. Both are impacting the policy debate in interesting ways.

Turning social media attention into income is another story altogether. In his recent Blog post “Turning social media attention into income“, author Srininvas Rao muses:

“The low barrier to entry created by social media has flooded the market with aspiring entrepreneurs, freelancers, and people trying to make it on their own. Standing out in it is only half the battle. You have to figure out how to turn social media attention into social media income. Have you successfully evolved from blogger to entrepreneur? What steps should I take next?”

 
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Posted by on December 6, 2011 in Industrial, Scientific, Socioeconomic

 

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Share and Inequality of Mobile Phone Revenues and Volumes

Share and Inequality of Mobile Phone Revenues and Volumes

The analyst website Asymco.com visualizes various financial indicators of mobile phone companies in this interactive vendor bubble chart (follow link, select “Vendor Charts”). It covers the following 8 companies: Apple, HTC, LG, Motorola, Nokia, RIM, Samsung, Sony Ericsson. From the “vendor data” tab I downloaded the data and looked at the revenue and volume distributions for the last 4 years.

Revenue Share of Mobile Phones and corresponding Gini Index

Note the sharp reduction in inequality of revenue distribution in the 9/1/08 quarter, when Apple achieved nearly 10x in revenue (and volume) compared to the year before. While the iPhone 1 was introduced a year earlier in 2007, in commercial terms the iPhone 3G started to have strong market impact when introduced in the second half of 2008.

Volume Share of Mobile Phones and Gini Index

Volume inequality is considerably higher (average Gini = 0.61) than Revenue inequality (0.43) due to two dominant shippers (Nokia and Samsung), which continue to lead the peer group in volume. Only recently has the inequality been reduced, i.e. the volumes are distributed more evenly. Apple’s growth in volume share has come at the expense of other players (mainly Motorola and Sony Ericsson).

Volume share is a lagging indicator regarding a company’s innovation and success. It can be dominated for a long time by players who are past their prime and in financial distress (like Nokia). Revenue is more useful to predict a company’s future growth and success. But the real story is told when comparing Profit. Apple’s (Smart Phone) Profit dwarfs that of the other 7 competitors:

Profit Comparison between 8 Mobile Phone Vendors (Source: Asymco.com)

Click on the image to go to Asymco’s interactive chart (requires Flash). The bubble chart display over time is very revealing regarding Apple’s meteoric rise.

 
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Posted by on October 22, 2011 in Financial, Industrial

 

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Market Capitalization Inequality in the Steve Jobs era

The excellent analyst website asymco.com recently published a post titled Visualizing the Steve Jobs era. In it they display an area chart of the relative size of market capitalization of about 15 companies they have tracked for the last 15 years.

Since I had looked at the Gini index of a similar set of companies in an earlier post on Visualizing Inequality I contacted the author Dirk Schmidt. Thankfully he shared the underlying data. From that I calculated the Gini index for every quarter and overlaid a line chart with their area chart.

Share of Market Capitalization Area Chart overlaid with Gini Index

Dirk elaborated in his post and identified three distinct periods in his post:

  • Restructuring of Apple 1997-2000 – Gini remains very high near 0.85 due to MSFT dominance
  • iTunes era 2001-2006 – Gini decreases to ~ 0.55 due to AAPL increase and taking share from other established players
  • Mobile devices era 2007-2011 – Gini increases again to 0.65 due to increasing dominance of AAPL and irrelevance of smaller players

Regardless of the absolute value of the Gini index – note the caveat from the earlier post that it is very sensitive to the number of contributors – the trend in the Gini can be an interesting signal. One company dwarfing every other like a monopoly corresponds to high Gini (here 0.85 due to MSFT dominance). A return to lower Gini values (here down to ~0.5) signals stronger competition with multiple entrants. The recent reversal of the Gini trend (up to 0.65 due to AAPL dominance) is a sign that investors see less choices when it comes to buying shares in those tech companies. Whether that’s a leading indicator for consumers seeing less choices in the marketplace is another question…

 
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Posted by on September 29, 2011 in Financial, Industrial

 

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