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Author: demetriodor

Debt and the nature of money

When I wrote that David Graeber’s book opens your eyes, that was not just a figure of speech. First, consider this: In recent weeks, Theodoros Mavridis has bought fresh eggs, tsipourou (the local brandy: beware), fruit, olives, olive oil, jam, and soap. He has also had some legal advice, and enjoyed the services of an accountant to help fill in his tax return. None of it has cost him a euro, because he had previously done a spot of electrical work – repairing a TV, sorting out a dodgy light – for some of the 800-odd members of a fast-growing exchange network in the port town of Volos, midway between Athens and Thessaloniki. In return for his expert labour, Mavridis received a number of Local Alternative Units (known as tems in Greek) in his online network account. In return for the eggs, olive oil, tax advice and the rest, he transferred tems into other people’s accounts. Fascinating, but also completely understandable according to a token theory of money. Whether you call it euro, drahma, or tems doesn’t really matter. It is just a unit of exchange. For the community within which it functions, it doesn’t need the backing of the state (or a central bank) or a gold standard. Of course, the limitation is that the system can only function within a relatively small community: “It’s also a way of showing practical solidarity – of building relationships.” This quote directly links to Graeber’s argument about the intimate association between debt and community: the tems…

Hyperlinks

Chocolate can help you stay slim – gotta love statistics for bringing such insights to the world Abuses of statistics in psychological research [paper discussed by W. Briggs] A nice map of media popularity in the US states at Forbes Policy experiments in Sweden [at the wonderful Development Impact blog] The Strait of Gibraltar – photo by ESA

What to do about improving governance?

Here is a link to my interview for Gulan Magazine discussing how to establish good governance and the role of formal institutions. “Even if institutions are working in Western Europe or in the US and they are imported to the developing countries, there is no guarantee that they would produce the same outcome because institutions are embedded (they function) in a certain environment and when they are transferred to other countries there is no guarantee that they would work. I have some experience observing from very close the institutional transfer recommended by the western advisers to the countries of Central and Eastern Europe – these countries were consolidating their democracies and when they were building their market economies – some of the imported institutions worked and some of the institutions that were suggested by western experts were actually counterproductive. Again, importing institutions can only do that much – it really depends on the local people to make them work. There are some institutions that obviously benefit the society in many different contexts no matter the country, developing or developed. For example stable civil service.” Here is a link to Mark Twain on why you shouldn’t give interviews 😉

Visualizing left-right government positions

How does the political landscape of Europe change over time? One way to approach this question is to map the socio-economic left-right positions of the governments in power. So let’s plot the changing ideological  positions of the governments using data from the Manifesto project! As you will see below, this proved to be a more challenging task than I imagined, but the preliminary results are worth sharing nonetheless. First, we need to extract the left-right positions from the Manifesto dataset. Using the function described here, this is straightforward: lr2000<-manifesto.position(‘rile’, start=2000, end=2000) This compiles the (weighted) cabinet positions for the European countries for the year 2000. Next, let’s generate a static map. We can use the new package rworldmap for this purpose. Let’s also build a custom palette that maps colors to left-right values. Since in Europe red traditionally is the color of the political left (the socialists), the palette ranges from dark red to gray to dark blue (for the right-wing governments). library (rworldmap) op <- palette(c(‘red4′,’red3′,’red2′,’red1′,’grey’,’blue1′, ‘blue2′,’blue3’, ‘blue4’)) After recoding the name of the UK, we are ready to bind our data and plot the map. You can save the map as a png file. library(car) lr2000$State<-recode(lr$State, “‘Great Britain’=’United Kingdom'”) lrmapdata <- joinCountryData2Map( lr2000,joinCode = “NAME”, nameJoinColumn = “State”, mapResolution=’medium’) par(mai=c(0,0,0.2,0),xaxs=”i”,yaxs=”i”) png(file=’LR2000map.png’, width=640,height=480) mapCountryData( lrmapdata, nameColumnToPlot=”position”,colourPalette=op, xlim=c(-9,31), ylim=c(36,68), mapTitle=’2000′, aspect=1.25,addLegend=T ) dev.off() The limits on on the x- and y-axes center the map on Europe. It is a process of trial and error till you get it right, and…

Torture and game theory

The latest issue of Political Research Quarterly has an interesting and important exchange about the use of game theory to understand the effectiveness of torture for eliciting truthful information. In this post I summarize the discussion, which is quite instructive for illustrating the prejudices and misunderstandings people have about the role and utility of game theory as a tool to gain insights into the social world. In the original article, Schiemann builds a strategic incomplete-information game between a detainee (who can either posses valuable information or not, and be either ‘strong’ or ‘weak’) and a state which can be either ‘pragmatic’ (using torture only for valuable information) or ‘sadistic’ (torturing in all circumstances). There are two additional parameters capturing uncertainty about the value and completeness of the information provided by the detainee, and two styles of interrogation (providing leading evidence or not). The article then proceeds to identify the equilibria of the game, which turn out to be quite a few (six), and quite different – in some, truthful information is provided while in others, not; in some, torture is applied while in others, not; etc…. At this point you will be excused for wondering what’s the point of the formal modeling if it only shows that, depending on the parameters, different things are possible. Schiemann, however, makes a brilliant move by comparing each of these equilibria to some minimal normative standards that proponents of torture claim to uphold – namely, that torture should not be used on detainees who have provided all their information, that transmitted information should be generally reliable, and that in all cases only…

Compiling government positions from the Manifesto Project data with R

****N.B. I have updated the functions in February 2019 to makes use of the latest Manifesto data. See for details here.*** The Manifesto Project (former Manifesto Research Group, Comparative Manifestos Project) has assembled a database of ‘quantitative content analyses of parties’ election programs from more than 50 countries covering all free, democratic elections since 1945’ and is freely accessible online. The data, however, is available only at the party, and not at the government (cabinet) level. In order to automate  the process of extracting government positions from the Manifesto data, I wrote a simple R function which combines the party-level Manifesto data with the data on government compositions from the ParlGov database. The function manifesto.position() produces a data frame with the country, the time period, the government position of interest, and an index (id) variable. You can get the data either at a monthly or yearly period of aggregation, specify the start and the end dates, and get the data in ‘long’ or ‘wide’ format. Here is how it works: First, you would need R up and running (with the ‘ggplot2‘ library installed). Second, you need the original data on party positions and on government compositions, and this script to merge them. Alternatively, you can download (or source) directly the resulting merged dataset here. Third, you need to source the file containing the functions. Here are a few examples of the function in action: #### ### 1. Load the data file from the working directory or from the URL (default)…