champagne anarchist | armchair activist

Data

Wikinews is a great idea, but is it viable?

I’ve decided to start reposting relevant Dutch-language articles from Wikinews on News from Amsterdam. Wikinews is a sister project of Wikipedia. Its contributors describe themselves as follows:

We are a group of volunteers whose mission is to present reliable, unbiased and relevant news. All our content is released under a free license. By making our content perpetually available for free redistribution and use, we hope to contribute to a global digital commons. Wikinews stories are written from a neutral point of view to ensure fair and unbiased reporting.

It would seem a bit naive to simply claim that your writing is neutral and unbiased. The strength of Wikinews rather lies in providing a place were the merits of a story and its sourcing can be discussed on the basis of arguments. In times of clickbait, hoaxes and fake news, that’s an interesting concept.

Wikinews operates without ads (which matters). Their website may look a bit austere, but since it’s all open source, anyone can resuse the content with a different layout.

For now, Wikinews mainly consists of syntheses of news published by other media, but the site also invites other types of stories such as investigative reporting; (photo) reports and opinion articles. This could also provide room for another goal of Wikinews, which is to cover stories that are underreported in other media.

But is Wikinews viable? The consensus seems to be that it’s not. As Jonathan Dee of the New York Times put it in July 2007: «Wikinews … has sunk into a kind of torpor; lately it generates just 8 to 10 articles a day on a grab bag of topics that happen to capture the interest of its fewer than 26,000 users worldwide …».

That was ten years ago. Since, activity on the English-language version of Wikinews has further declined, as the chart below shows. Meanwhile, there’s a remarkable rise in the number of articles on Dutch-language Wikinews.

Perhaps this is a temporary boost of enthusiasm, that will fade out after a while. Then again, maybe it’ll last and Wikinews will reinvent itself. It’ll be interesting to see how this develops.

Charts on mobile screens - always tricky

The chart shown here is from Dutch national statistics office CBS and it was created with Highcharts (or at least that name appears a few dozen times in the source code). It shows working people with second jobs and it’s from this page.

There’s a problem with the labels on the x-axis: they have been abbreviated and only show the first digit of the years. This could easily have been avoided by showing fewer labels when the chart is displayed on a narrow screen. In fact, that solution is used in the other graphs in the same article. Apparently, Highcharts doesn’t adjust the number of labels automatically, or it doesn’t do so consistently.

It appears that CBS has chosen Highcharts because it’s an easy way to create charts with added functionality. And some of that functionality seems to make sense: I can imagine people using the option of downloading a PNG to use it in a report or share it on social media.

However, it may not always be a good idea to rely on standard solutions. Here’s another example where the labels have been abbreviated. It’s a bit of a challenge to figure out what they mean.

I know it can be a pain to get charts to work well on different types of screens (let alone network graphs). Apparently, you cannot simply rely on Highcharts to get it right. CBS should probably assign someone to edit each chart individually, making sure they are displayed properly on different screen types.

As for the contents of the charts: here’s why it’s not OK that more and more Dutch workers need to take a second job to make ends meet (in Dutch).

How to make a d3js force layout stay within the chart area - even with multiple components

For a post on analysing networks of corporate control, I wanted to create some network graphs with d3.js. The new edition of Scott Murray’s great book on d3.js, which is updated to version 4, contains a good example to get you started. However, I was still struggling with some practical issues, as the chart below illustrates (reload the page to see the problem develop).

A large part of the graph drifts out of the chart area, and the problem only gets worse on a mobile screen. But I figured out some sort of solution.

As Murray explains, you can vary the strength value of the force layout. Positive values attract, negative values repel. The default value is –30. You could set d3.forceManyBody().strength(-3) to create a more compact graph.

Of course, the ideal setting will depend on screen size. You could vary the strength value according to screen width. While you’re at it, you may also want to vary the radius of nodes and the stroke-width of edges. For example with something like this:

if(w > 380){
    var strength = -3;
    var r = 3;
    var sw = 0.3;
}
else{
    var strength = -1;
    var r = 3;
    var sw = 0.15;
}

Now this may make the graph more compact, but it doesn’t solve one specific problem: components not connected to the rest of the chart will still drift out of the chart area. In my example, there are four components: a large one, and three pairs of nodes that are only connected to each other and not to the rest of the graph.

The way in which I dealt with this was to create four different graphs and attach the small components to a forceCenter at the margin of the chart area. For example, d3.forceCenter().x(0.1 * w).y(0.9 * h)) will put one of them in the bottom left corner. Here’s the result:

It’s still a lot of code - I can’t help feeling there should be a more efficient way to do this. Also, it’s slightly weird that the small components immediately freeze, whereas the large one takes its time to develop into its final shape. And the text labels could be improved. But at least it seems to work.

The network of Dutch firms

One of the ways in which firms are linked is through board members who also sit on the boards of other firms. Researchers use these board interlocks to determine which firms occupy a central position in the corporate network. This «is widely considered as an indication of a powerful or at least advantageous position», Frank Takes and Eelke Heemskerk explain in an interesting paper on the subject.

Two Dutch newspapers, de Volkskrant and NRC Handelsblad, have published visualisations of the Dutch (corporate) elite and their board memberships. You can use the data from those visualisations to create board interlock networks. Below is an example using data from NRC Handelsblad from 2017:

Darker nodes represent organisations with a more central position in the network, as measured by their betweenness centrality. Below is another example, using data from de Volkskrant from 2013:

The most obvious difference is that the second graph contains far more nodes (organisations) and edges (shared board members) than the 2017 chart. But there’s more. The 2017 dataset contains only nodes that have at least two edges - probably the result of a selection criterion used by NRC Handelsblad because of the type of visualisation they wanted to make. Further, the 2013 dataset consists of multiple components: three sets of organisations only share board members with each other; not with the rest of the network.

Given the differences between the datasets, would it still be meaningful to make comparisons between the two? The table below shows the top 10 of organisations with the highest centrality scores, for 2013 and 2017. The comparison is limited to organisations that are included in both datasets.

2013 2017
VNO-NCW VNO-NCW
DNB Ahold
Concertgebouw Concertgebouw
KLM KLM
ABN Amro Schiphol
Aegon NV FrieslandCampina
Concertgebouw Fonds Philips
DSM DNB
Philips Rabobank Groep
Heineken NV Vopak

Organisations like employers’ organisation VNO-NCW, the Concertgebouw concert hall and airline KLM seem to occupy a pretty stable position at the centre of the network. VNO-NCW has a huge non-executive board with representatives from a wide range of industries. The Concertgebouw has been described years ago as the living room of the [Dutch] elite.

Aside from these stable elements, there are substantial differences between the two rankings. The rank correlation is only 0.33 and not statistically significant. This may be due to differences in the way the datasets were created; the small size of the overlap between them (only 34 organisations) and other data quality issues.

On the other hand, some changes in the ranking appear to reflect genuine changes in the position firms occupy. Two examples:

  • One of the fastest risers is Ahold. Ahold merged with Belgian retailer Delhaize in 2016. It would seem plausible that this has strengthened their position in the corporate network.
  • ABN Amro disappeared from the top 10. The bank used to have a board with well-connected members like Gerrit Zalm and Joop Wijn (both have gone through the revolving door between government and the corporate world), Peter Wakkie and Marjan Oudeman (one of the most influential Dutch women according to various rankings). In 2015, chairman Wakkie stepped down over a commotion caused by excessive executive board remunerations (the bank was still state-owned after having been bailed out with public money in 2008). Subsequently, Oudeman, Zalm and Wijn also left the bank, for reasons partly related to its upcoming flotation. It appears the current board has a lower profile.

This type of analyses could benefit enormously from having a larger dataset available. This is yet another reason why the Dutch Company Register should be opened up as open data: this will allow for better understanding of the networks of corporate control.

Method and data

Both de Volkskrant (2013, 2014) and NRC Handelsblad (2017) have published visualisations of the Dutch (corporate) elite and their board memberships. Note that these board memberships not only include companies, but also employers’ organisations, cultural institutions and other types of organisations the collectors of the data deemed relevant for analysing corporate elite networks.

Before comparisons can be made, the names of the organisations need to be cleaned up. Beyond correcting typos and dealing with additions like N.V. (plc) and B.V. (ltd), this involves deciding when to consider units as part of the same organisation. Pragmatically, I decided to treat businesses that are part of the same corporate structure as identical. This may not always be the ideal approach; on the other hand, it’s not always possible to determine what unit a name refers to (e.g. ING could refer to the holding or to one of its subsidiaries). I did treat foundations (e.g. charities linked to a company) as separate from the company.

There are different ways to measure the centrality of a node in a network. Taking my cue from Takes and Heemskerk, I used betweenness centrality, which is based on how often a node is on the shortest path between two other nodes. I calculated centrality for the entire network, that is, before taking a subgraph. I included endpoints to prevent many nodes having a score of zero.

I used the Python library networkx to analyse the graphs (here’s the code and here’s the accompanying text file for cleaning up organisation names). I used d3.js to visualise the network graphs - here’s a description of the problems I ran into and how I dealt with them.

Het netwerk van Nederlandse ondernemingen

Een van de manieren waarop bedrijven met elkaar verbonden zijn, is door gedeelde bestuurders: een commissaris bij een bepaald bedrijf zal soms ook commissaris zijn bij andere bedrijven. Onderzoekers gebruiken deze ‘board interlocks’ om te analyseren welke bedrijven een centrale plek innemen in het ondernemingennetwerk. Dit «wordt veelal beschouwd als indicatie voor een machtige of tenminste gunstige positie», aldus Frank Takes en Eelke Heemskerk in een interessant artikel over dit onderwerp.

Zowel de Volkskrant als NRC Handelsblad hebben visualisaties gemaakt van de (zakelijke) elite en hun commissariaten en andere functies. De gegevens van die visualisaties kan je weer gebruiken om netwerken te maken op basis van board interlocks. Hieronder een voorbeeld met gegevens van NRC Handelsblad uit 2017:

Donkere nodes verwijzen naar organisaties met een centrale positie in het netwerk, afgemeten aan hun betweenness centrality. Hieronder nog een voorbeeld, op basis van gegevens van de Volkskrant uit 2013:

Het duidelijkste verschil is dat de tweede grafiek veel meer nodes (organisaties) en edges (gedeelde bestuurders) bevat dan de grafiek over 2017. Maar er zijn nog meer verschillen. De dataset uit 2017 bevat alleen nodes met tenminste twee edges - waarschijnlijk het gevolg van een selectiecriterium dat NRC Handelsblad heeft gebruikt vanwege het type visualisatie dat ze wilden maken. De dataset uit 2013 bestaat verder uit meerdere componenten: drie sets van organisaties zijn alleen onderling verbonden en niet met de rest van het netwerk.

Er zijn dus flinke verschillen tussen de datasets. Is het desondanks zinvol om ze onderling te vergelijken? De tabel hieronder laat voor beide jaren de top–10 van organisaties met de hoogste betweenness centrality zien. De vergelijking beperkt zich tot organisaties die in beide datasets voorkomen.

2013 2017
VNO-NCW VNO-NCW
DNB Ahold
Concertgebouw Concertgebouw
KLM KLM
ABN Amro Schiphol
Aegon NV FrieslandCampina
Concertgebouw Fonds Philips
DSM DNB
Philips Rabobank Groep
Heineken NV Vopak

Organisaties als VNO-NCW, het Concertgebouw en KLM lijken een vrij stabiele centrale positie in te nemen. VNO-NCW heeft een omvangrijke Raad van Commissarissen met vertegenwoordigers uit uiteenlopende sectoren. Het Concertgebouw werd jaren geleden al omschreven als de huiskamer van de elite.

Naast deze constante factoren zijn er flinke verschillen tussen de twee ranglijsten. De rangcorrelatie is slechts 0.33 en niet statistische significant. Dit kan te maken hebben met verschillen in de manier waarop de datasets zijn samengesteld; de kleine omvang van de overlap tussen de twee dataset (slechts 34 organisaties) en andere problemen die te maken hebben met de kwaliteit van de gegevens.

Aan de andere kant, sommige verschillen tussen de ranglijstjes lijken wel degelijk overeen te komen met daadwerkelijke veranderingen in de positie van ondernemingen. Twee voorbeelden:

  • Een van de snelste stijgers is Ahold. Ahold is in 2016 met Delhaize gefuseerd. Het lijkt plausibel dat dit hun positie in het bedrijvennetwerk heeft versterkt.
  • ABN Amro is verdwenen uit de top–10. Vroeger had de bank commissarissen met veel connecties, zoals Gerrit Zalm en Joop Wijn (die allebei door de draaideur tussen overheid en bedrijfsleven zijn gegaan), Peter Wakkie en Marjan Oudeman (volgens verschillende lijstjes één van de meest invloedrijke Nederlandse vrouwen). In 2015 stapte voorzitter Wakkie op na een rel over buitensporige beloningen (de bank was nog altijd in handen van de staat nadat hij in 2008 met publiek geld was gered). Vervolgens verlieten ook Oudeman, Zalm en Wijn de bank, deels vanwege de beursgang die eraan kwam. Het huidige bestuur lijkt een bescheidener profiel te hebben.

Bij dit soort analyses zou het enorm helpen om meer gegevens te hebben. Dat is nog een extra reden waarom het Handelsregister beschikbaar zou moeten komen als open data: dat biedt meer inzicht in de onderlinge verhoudingen in zakelijk Nederland.

Methode en gegevens

Zie de Engelstalige versie van dit artikel.

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