Salonanarchist | Leunstoelactivist

Efforts to raise turnout in elections may increase turnout inequality

Just the other day I posted something about unequal voter turnout in Amsterdam (higher turnout in neoliberal-voting neighbourhoods; lower turnout in left-voting neighbourhoods). The conclusion would seem obvious: raise turnout, and election outcomes will likely become more representative of the preferences of Amsterdammers.

Now it turns out things may not be that simple. Based on a smart analysis (via), Ryan Enos, Anthony Fowler and Lynn Vavreck find that «get out the vote» efforts may raise turnout disproportionally among people who are more likely to vote in the first place, thus exacerbating turnout inequality.

This is not inconsequential, for these «high-propensity» citizens are far from representative of the general population. They are:

wealthier, more educated, more likely to attend church, more likely to be employed, more likely to approve of Bush, more conservative, and more Republican. They are more supportive of abortion rights and less supportive of withdrawing troops from Iraq, domestic spending, affirmative action, minimum wage, gay marriage, federal housing assistance, and taxes on wealthy famiilies.

All in all, it seems that in many respects, people who are likely to vote lean to the right compared to the general population; and that this right-wing bias may be exacerbated by efforts to raise turnout.

This is pretty sobering, but it doesn’t mean that the whole idea of raising turnout should be thrown out of the window. First of all, Enos et al. point out that their method can be used to gain a better understanding of the impact of interventions. Hopefully this will help develop interventions that reduce inequality instead of increasing it.

Second, it appears that the experiments analysed by Enos et al. randomly assigned people to treatment or control groups (I checked this for the largest experiments - the ones done by Gerber, Green & Larimer and Nickerson & Rogers). Of course, this is good practice from a research point of view.

However, it might still make sense to do voter mobilisations that specifically target a group of unlikely voters (instead of a randomly selected treatment group). For example, one might target a neighbourhood that normally has very low turnout. If I understand the findings of Enos et al. correctly, it’s conceivable that this would increase turnout inequality within the targeted neighbourhood, while at the same time reducing turnout inequality across the entire city.

Then again, perhaps we should consider compulsory voting after all (I’ll admit I used to be pretty sceptical of that idea). In a previous study, one of the authors (Anthony Fowler) analysed the impact of the introduction of compulsory voting in Australia in the first half of the 20th century. «When near-universal turnout was achieved, elections and policy shifted in favor of the working-class citizens who had previously failed to participate.» (pdf)

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High turnout in liberal-voting neighbourhoods, low turnout in left-voting neighbourhoods

A ‘prominent civil servant with a social-democrat background’ gets to hand out 400,000 euros in subsidies to turn out ethnic minorities to vote, the Telegraaf newspaper reported last week. «It’s not difficult to guess which parties will benefit the most from a turnout campaign among hard to reach groups of voters.»

Ok, so they’re hyping it a bit, but the story is more or less accurate. Last year, the city council almost unanimously asked for a campaign that should result in «a turnout of at least 65% across Amsterdam and a substantial increase in turnout in districts that have a low turnout and among specific groups».

Turnout in elections is uneven, as the charts below illustrate. In neighbourhoods where many people voted economic left (SP or PvdA), turnout was low in 2010. By contrast, in neighbourhoods that tend to vote (neo) liberal (pro-market parties VVD and D66), turnout was high. On the one hand there’s Bijlmer Centrum: 57% voted economic left in 2010, but turnout was only 34%. At the other end of the spectrum, there’s for example the Apollobuurt: 57% voted liberal and turnout was 65%. A similar pattern occured in previous elections.

What causes this correlation between political outcome and turnout? A possible explanation: high educated, well-paid, white home owners have more confidence that politicians will take their interests into account. Therefore, they’re more inclined to think it makes sense to vote. And they often vote liberal.

Interestingly, turnout isn’t always that unequal, as a comparison of the 2002 and 2006 elections serves to illustrate.

The boxplot to the left shows that turnout tended to be higher in 2006 than in 2002. At least as interesting is the fact that inequality in turnout has decreased. The chart to the right shows how this happened. In allmost all neighbourhoods, turnout rose relative to 2002, but it rose most in neighbourhoods that had low turnout in 2002. Examples include the Kolenkit in West, the Vogelbuurt in Noord and Bijlmer Centrum. Incidentally, turnout inequality rose again in 2010.

A similar development has taken place at the national level. In elections for the Lower Chamber, liberal-voting municipalities tend to have higher turnout than left-voting ones. Again, turnout inequality was lower in 2006 than in 2002 and 2003. (If you want to check the calculations: data and code for the analysis at both the local and the national level can be found here.)

2006 was a year in which left-wing parties got relatively many votes. For example, PvdA, GroenLinks, SP and AADG jointly got 33 seats in the Amsterdam council, compared to 26 in 2002. Since inequality was less uneven in 2006, it’s conceivable that the 2006 election result better reflected the preferences of Amsterdammers than the election result of 2002.

In any case: if we want a fairer election outcome, it’s important to get more people to vote, especially in neighbourhoods that tend to have low turnout. Whether the municipal turnout campaign will be effective is difficult to say on the basis of the plans, but it is possible to raise turnout. For example, by organising local elections on the same day as national elections.

About those weird Netflix genres

The hippest story on Twitter right now is how Alexis Madrigal of the Atlantic discovered the 76,897 genres Netflix uses to classify its movie offering. Some examples of these weirdly specific genres include Critically-acclaimed Cerebral Independent Films; Feel-good Movies starring Elvis Presley and Coming-of-age Animal Tales.

Madrigal explains how straightforward it is to navigate all the genre pages on the Netflix website by incrementing the id in the url. But then he mentions that he retrieved the genres using «an expensive piece of software called UBot Studio that lets you easily write scripts for automating things on the web». Surely a few lines of Python code could’ve done the job? In fact, I guess you could probably extract the subgenre structure and the genre elements - region, adjectives, time period etc - with nltk and regex.

Never mind that, though. Madrigal’s article is an interesting read. Here it is if you haven’t read it yet. And here’s a critique of Netflix’s algorithms by Felix Salmon of Reuters, who argues that its recommendations are no longer about quality but about offering more of the same. You watched one Dark Political Movie from the 1980s? Then we’ll show you some more Dark Political Movies from the 1980s.

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Just 13% of my Linkedin connections use buzzwords

Linkedin recently released it’s newest analysis of overused buzzwords in members’ profiles. Of course, this is just a ploy to get you to volunteer more personal details («Update your profile today!»), but never mind that.

Just in case, I checked whether any of my connections engage in overusing buzzwords. Reassuringly, the majority can’t even be bothered to fill out «summaries» and «specialties» in the first place. Those who do, seldom use the top-ten buzzwords for the Netherlands, as the table below shows.

Term Percentage Term Percentage
Verantwoordelijk 0.0 Responsible 0.5
Strategisch 1.8 Strategic 3.2
Expert 3.7 Expert 3.7
Creatief 0.9 Creative 1.8
Innovatief 0.0 Innovative 1.4
Dynamisch 0.0 Dynamic 0.5
Gedreven 0.5 Motivated 0.9
Duurzaam 0.0 Sustainable 1.4
Effectief 0.0 Effective 0.5
Analytisch 0.9 Analytic 0.5

Only two Dutch buzzwords are used by more than one percent of my connections. Interestingly, their English equivalents are slightly more prevalent. 87% of my connections are completely buzzword-free. For what it’s worth, people who use buzzwords also tend to have more connections.

Full disclosure: I may have used the word «strategic» in my own profile.

Update 21 Dec - Don’t push it, Linkedin.

Incidentally, 436,567 people, that’s less than 0.17% of all the 259m Linkedin users. Not that impressive.

Method

I used these scripts (in part adapted from Matthew Russell’s Mining the Social Web) to get the «summaries» and «specialties» of connections from the Linkedin Api and process them.

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Mijn Facebookvrienden vinden FNV Schoongenoeg leuk. En Hans Spekman

Viz3

Eigenlijk ben ik niet zo’n fan van Facebook, maar nu ik doorkrijg wat voor analyses je ermee kan doen begin ik er ook wel een beetje de lol van in te zien. Hierboven zie je m’n Facebooknetwerk. Ik heb opgezocht welke pagina’s mensen leuk vinden. Het populairst is FNV Schoongenoeg, de pagina van de schoonmakerscampagne; deze is door 45 mensen in m’n netwerk geliked. Terecht.

Andere voorbeelden van populaire pagina’s zijn FNV Supermarkt (34), Hans Spekman (22, daar keek ik van op) en de campagne om van 1 mei een nationale feestdag te maken (13).

Dit betekent niet dat mensen in m’n netwerk alleen maar pagina’s leuk vinden die met vakbonden of politiek te maken hebben. Zo vinden ze samen 631 pagina’s leuk die met muziek te maken hebben, maar ze vinden meestal niet dezelfde muziekpagina’s leuk. Wie FNV Schoongenoeg leuk vindt deelt deze like met 44 anderen; degene die Mark E. Smith (The Fall) leuk vindt deelt deze like met niemand anders. (Dezelfde persoon blijkt ook Iggy And The Stooges leuk te vinden. Mooi zo.)

De grafiek hierboven laat zien dat hier een patroon in zit. Lichtblauwe cirkels zijn mensen die hun voorkeuren gemiddeld met weinig anderen delen (althans, weinig anderen binnen m’n netwerk). Donkerblauwe cirkels zijn mensen die juist vaak pagina’s leuk vinden die anderen in m’n netwerk ook leuk vinden. Daar zitten veel mensen tussen met een achtergrond in de vakbeweging. Daar wordt flink campagne gevoerd; wellicht zorgt dat ervoor dat bepaalde pagina’s door veel mensen worden geliked.

Als je wil weten welke cirkel je bent in de grafiek hierboven, laat het dan even weten.

Methode

Een flink deel van de analyse is gebaseerd op de cursus Social Network Analysis van Lada Adamic en hoofdstuk 2 van Mining the Social Web van Matthew Russel. Allebei aanbevolen. Ik heb Python gebruikt om gegevens te ontfutselen aan de Facebook Graph API en om de gegevens te verwerken (mensen die in de privacysettings hun likes hebben afgeschermd heb ik bij de analyse buiten beschouwing gelaten). De scripts zijn hier te vinden. De grafiek heb ik gemaakt met Gephi.

Overigens leert een zogenaamde modulariteitsanalyse dat de groep die ik had aangemerkt als mensen met een vakbondsachtergrond in feite uit twee clusters (zie deze grafiek) bestaat: één met vooral mensen die betrokken zijn bij mijn eigen bond en één met mensen die bij andere bonden en sociale bewegingen betrokken zijn. De eerste van deze clusters heeft het meeste gedeelde likes.

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Chart junk

Twitter has fallen in love with a new study on data visualization. Not surprisingly, for Michelle Borkin and her co-authors promise to throw some light on the great controversy of this field: pro or against chart junk.

So what’s the controversy about? On the one hand, there are those who think it’s OK to add non-functional embellishments to graphs, because this may make them more engaging and memorable. Usually, Nigel Holmes is quoted as a proponent of this view and this graph is often offered as an illustration.

On the other hand, there are those who dismiss such embellishments as chart junk, which distracts from the content of the graph. The main name here is Edward Tufte, who argues for a high «data-to-ink ratio». The foremost example is his minimalistic but effective slope graph.

Both sides may have a point, but my sympathy lies with Tufte. I’ll admit that some of Holmes’ infographics are actually quite funny, but many embellished graphs you’ll find in the media (Dutch examples here and here) are just silly.

Of course, that’s a matter of taste, but what’s the scientific verdict? Borkin et al. had subjects look at visualizations for a second and tested whether they would recognize them when the same image was shown again. They found that:

visualizations with low data-to-ink ratios and high visual densities (i.e., more chart junk and ‘clutter’) were more memorable than minimal, ‘clean’ visualizations.

So does this settle the matter? Not quite. Borkin and her co-authors say that their findings are just a «first step to understanding how to create effective data presentations». Stephen Few, a well-known critic of chart junk, goes one step further and calls their study «useless» for that purpose. His main point is that the subjects got to look at the examples for just one second:

Visualizations cannot be read and understood in a second. Flashing a graph in front of someone’s eyes for a second tells us nothing useful about the graphical communication, with one possible exception: the ability to grab attention.

I’ll have to agree with Few: Borkin et al. may have demonstrated that chart junk is effective at grabbing someone’s attention, but not that it’s effective at helping people understand data. Apart from that, I maintain that embellished visualizations may sometimes be fun, but will often be silly and/or pretentious.

De papieren OEK

OEK
De ruim 4.500 Amsterdamse leden van de Fietsersbond krijgen drie keer per jaar het ledenblad OEK in de bus (bezorgd door vrijwilligers, waarvoor dank). De bond vraagt zich af:

of er tegenwoordig meer mensen zijn die het eigenlijk wel prima vinden om de OEK voortaan alleen digitaal te lezen en de papieren versie niet meer hoeven te ontvangen […] U kunt dan uit de distributielijst worden gehaald. Ook als u fervent voorstander bent van de papieren OEK, mag u dit laten weten.

Nou, bij deze dan. Ik lees zoveel mogelijk digitaal - boeken, kranten, rapporten. Veel praktischer. Maar voor de OEK maak ik graag een uitzondering. Je ziet dat het blad met enthousiasme in elkaar is gezet. Echt papier, niet van dat glimmende. Flink veel tekst per pagina, maar zonder dat het onleesbaar wordt. Een fijn blad om door te bladeren en te lezen.

Uiteraard ligt dat niet alleen aan het uiterlijk, maar ook aan de inhoud. In het laatste nummer bijvoorbeeld een goede analyse over de onzin van bewustwordingscampagnes, zoals de smileyborden («Wacht op groen!») die een tijdje bij stoplichten hebben gehangen. Een inventarisatie van in het asfalt gereden fietsbeldoppen op het Leidseplein. En nog veel meer (pdf).

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Script to look up the gender of Dutch first names


This script determines the gender of Dutch persons by looking up their first name in a database of the Meertens Institute. The database indicates how often the name occurred as a first name for men and women in 2010. If the name is used for women substantially more often than for men, the name will be interpreted as female – and vice versa.

The reason I wrote the script has to to with this article on how the performance of women professional road cyclists is improving. I wanted to check whether a similar trend is going on among amateur riders, more specifically, participants in the Gerrie Knetemann Classic (incidentally, the script would take Knetemann for a woman – it’s not foolproof). The results of the ride are available online, but pre-2012 editions lack information on the gender of participants. So that’s what the script was for.

Speed of participants in Knetemann Classic

The results of the analysis aren’t exactly clearcut. The number of women participants in the 150km ride varied from 36 to 46, or 5 to 8% of the participants whose gender could be determined (the percentage for 2013 was 6%). The (median) speed of women participants rose in 2013, and more so than for men, but this rather thin to speak of a trend.

Slovenians seem to buy more bicycles than the Dutch or even Danes

Mona Chalabi of the Guardian has collected data on car and bicycle sales and concludes that bicycle sales not only outnumber car sales, but that the gap has widened. The title of the article suggests the recession might play a role, but this article by Fabian Küster of the European Cyclists’ Federation - who uses the same sources - suggests it’s «an idle hope to believe that as soon as Europe’s economy recovers, car sales will go up again to pre-crisis levels».

If you look at car sales per 1,000 population, it turns out the Slovenians are Europe’s most enthusiastic bicycle buyers (that’s assuming the bicycle sale data for Slovenia are correct - this article quotes a lower number but gives no source). If you look at the bicycle sales to car sales ratio the picture changes considerably - likely because fewer cars are sold in poorer countries.

Embed code for the graph (the graph probably doesn’t work in older versions of IE):

<iframe src = "http://www.dirkmjk.nl//2013/bikeSales/bikeSales2.html" frameborder=0 width = 510 height=610 scrolling='no'></iframe>

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«Tweet this» link (using jQuery)

Creating a «tweet this» link with jQuery turns out to be quite simple - that is, once you know how it works...

<span class='tweetThis'></span>

<script> jQuery(".tweetThis").append("<a href=\'https://twitter.com/intent/tweet?text="+jQuery('h1')[0].innerHTML+"&url=http%3A%2F%2Fdirkmjk.nl"+location.pathname+"\'>Tweet this<\/a>") </script>

Update - Apparently the code messes up the url if you have puncutation in it (the original url of this article - automatically generated from the title - had the quotation marks in it)

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