Nederland kampioen laag loon voor jongeren en flex

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Nieuwe cijfers van Eurostat geven een beeld van een specifieke vorm van ongelijkheid, namelijk het percentage werknemers met een laag loon (hier opgevat als tweederde van het mediane bruto uurloon). Volgens deze cijfers is de ongelijkheid in Nederland kleiner dan in Duitsland, Groot-Brittannië en Ierland, maar groter dan in veel andere West-Europese landen.

Jongeren en flexwerkers hebben veel vaker laagbetaald werk dan andere werknemers. Opvallend is de ‘koppositie’ van Nederland: nergens in Europa hebben zoveel jongeren en flexwerkers een laag uurloon als hier. (Zie ook deze brandbrief over pulpbanen en dit artikel over de lage jeugdlonen in Nederland.)

De grafiek werkt mogelijk niet in oudere versies van internet explorer. Gegevens: jongeren, flex (definitie flex).

UPDATE 31 december - Het ANP maakt nu ook melding van de cijfers van Eurostat.


Low-paid work, here defined as below 2/3 of median gross hourly earnings, as a share of the total workforce (Alle werknemers); under-30s (Jongeren) and workers with fixed-term contracts (Tijdelijk contract). Data from Eurostat (youth, flex).

My first D3 graph

I’m trying to master D3, a javascript library for creating (interactive) web graphics. As an excercise, I redid this graph, which uses Eurostat data on the percentage of the population who have ever written a computer programme.

I can’t say it’s a very good graph: some of the most intriguing aspects of the data have to do with changes over time (decline in some countries, rather large growth in Finland, implausible fluctuations in the Netherlands), which don’t show very well in my graph. Nevertheless, it feels good to have coded my first interactive D3 graph.

P.s. the graph may not be visible in older versions of internet explorer.

ATMs and cycle paths

The habits of cyclists are shaping cities like Amsterdam. “There are many ATMs along the main bicycle path network”, urban planner Marco te Brömmelstroet told Vogelvrije Fietser, the magazine of cyclists’ organisation Fietsersbond.

The map above shows Amsterdam’s main cycle path network (provided by the city as open data) and the location of ATMs. It appears that many ATMs are indeed located near cycle paths. Exceptions include shopping areas such as the Kalverstraat, Gelderlandplein and Bijlmerplein. (I tried to calculate the distance between ATMs and cycle paths but I couldn’t get this to work in QGIS.)

Data viz course assignment: bailout and votes

The fourth assignment of the data visualisation course was to do something with this data on unemployment in US states, published by the Guardian Data Blog. My project could be summarised as ‘It’s the unions, stupid’.

P.S. I didn’t post my work for the third assignment on this blog. I’m afraid it wasn’t any good.

Update - Elsewhere, the impact of the bailout on the election is questioned as well.

Clint Eastwood won the LAUGHTER contest

I’m not sure what this says about the audiences at US national party conventions, but among a sample of 16 speeches, Clint Eastwood’s was the one that elicited the most laughter (Rand Paul’s got most applause). Among the presidential candidates, Obama won the applause contest, while being about equally funny as Romney.

For the second lesson of Alberto Cairo’s online data visualisation course, we were asked to comment on and perhaps redesign this convention word count tool created by the NYT. I wouldn’t be able to do such a cool interactive thing myself (I got stuck in the jQuery part of Codeyear), so I decided to focus on differences between individual speeches instead.

First I needed the transcripts – preferably from one single source to make sure the transcription had been done in a uniform way. As far as I could find, Fox News has the largest collection of transcripts online. As a result, Republican speakers are overrepresented in my sample, but that’s ok because the key Democratic speakers are included as well.

I wrote a script to do the word count (I’m sure this could be done in a more elegant way). One problem with my script was that html-code got included in the total word count. I thought I could correct this by subtracting 1,000 from each word count, but this didn’t work so well, so I had to make some corrections.

This assignment was a bit of a rush job so I hope I didn’t make any stupid mistakes.