Visualizing 60 years of International Commerce

Commercial ties between countries

Recent trends in Comparative Political Analysis include the addition of spatial techniques. By means of spatial techniques the factors that help explaining a feature of countries go beyond the usual “internal” factors and include the interaction of countries between them.

The economic links between countries seem to be amongst the most used indicators of relationship between countries. Other measures include the distances, or whether the countries share regional borders.

Kristian Skrede Gleditsch has worked in providing estimates of trade flows between independent states (1948-2000). The dataset presents the amount of imports and exports between all independent states in the post Second World War period, using different sources.
Unfortunately, the dataset stops at 2000, but it is still a valuable resource for social researchers.

Although the information aggregated and compiled by Skrede Gleditsch is very valuable, it is of low practical use due to the large amount of data, which makes it difficult to be represented in a meaningful way. In order to be able to get a sense of the relationships between countries it is not sufficient to have the raw amount of trade between countries.

Using matrices to represent the data

The most convenient way to work with such data is by using a Weighting matrix. That is, a row-normalized square matrix with zeros in the diagonal.

  • squared, because it must include all possible dyadic (country-to-country) relationships. The best way to represent it is by using a matrix with the same number of rows and columns, where each cell represent the value of the relationship between the country of origin (row) and the country of destination (column).
  • row normalized, because the goal is to have a measure of the connection between each country and the rest of the units. So that the values in a single row have to be normalized. That is, they must represent a percentage of relationship between 0 and 1, with the sum of all of them being equal to 1.
  • zeros in the diagonal, because the relationship between the country and itself is not relevant. Since the diagonal cells are those representing the same country in the origin and destination, they must be fixed at 0.

The following figure represents the weighting matrix of imports for all the countries in the sample for the year 2000. The color of the squares represents the strength of the relationship in commercial terms of country in the row versus all other countries (in the columns). A darker color means that the country in the row imports a great percentage of its total imports from the country in the column. By contrast, lighter squares represent weak commercial ties (from the perspective of row countries). Consult the list of abbreviations of the country names.

Historical perspective on commercial relationships

In addition to the fixed photo for a single year, the dataset provides historical perspective. In this case, a concatenation of images is necessary. The following video shows 60 years of imports between the 25 countries with higher amount of commercial activity in 2000. The weights in this case are calculated only between them.

There are many things to comment out about this video.

  • Immediately after the War, most of the countries concentrate their imports in a single partner, the USA. This is why the first column is dark. All countries in the row (origin countries) have a great percentage of imports with the USA.
  • The imports become less concentrated in the USA, except for Mexico and Canada, where they still have the USA as the main market to buy.
  • Japan becomes a relevant commercial partner in the 70s.
  • Although there are many variations, the main picture remains pretty stable.

The importance of weighting matrices

The weighting matrices not only help visualizing the raw data, but also are the intermediate value needed in many research designs that employ spatial regression techniques.

Code to generate the plots and the video

The plots have been generated in R using the “color2Dmatplot” function created by Jim Lemon, in the plotrix package. Ideally, a Hinton Diagram would be the best option to represent weighting matrices, but it is not yet available in R.

## -- Imports, Global, 25 biggest countries
xc for (t in 1:length(dimnames(imp)$year)) {
  svg(file=paste("plots-weights/import-25countries-", dimnames(imp)$year[t], ".svg", sep=""),
    width=10, height=10)
  cl   color2D.matplot(W.imp[t,xc,xc],
    border="white", cellcolors=cl,
    show.legend=FALSE, axes=FALSE, xlab="", ylab="")
  axis(1, at=0.5:length(Nc[xc]), labels=namc[xc], las=2, cex.axis=1)
  axis(2, at=rev(0.5:length(Nc[xc])), labels=namc[xc], las=1, cex.axis=1)
  title(paste("Imports of row from column", dimnames(imp)$year[t], sep=" - "))

The “W.imp” object is the weighting matrix. In fact, it is three dimensions array, with the years in the first dimension, the origin country in the second and the destination countries in the third.

Independent plot files have been glued using mencoder.

mencoder mf://import-25countries-*.png \
-mf fps=1:type=png \
-ovc lavc \
-lavcopts vcodec=mpeg4 \
-oac copy \
-o video-commerce_imports_25.avi

The Ogg Theora version of the video has been obtained by using ffmpeg2theora.

ffmpeg2theora video-commerce_imports_25.avi
Posted in Spatial Models | Tagged | 1 Comment

From the Chicago Boys to the Goldman Sachs Men

It is a well known story that in the 50’s some Chilean Economists were trained at the University of Chicago. After they returned to Chile, they began pushing for economic reforms, including the liberalization of the economy, the privatization of the copper mines (and any other public properties), the reluctance towards the social role of the state and other policies that have now became known as the “neoliberal agenda”.

After the military takeover of general Pinochet against Allende they were appointed to the main economic institutions of the country. Their ideas of economic orthodoxy were used by the dictatorship, along with the implementation of their policy recommendations.

Juan Gabriel Valdés, in a book entitled “Pinochet’s Economists: the Chicago School in Chile” argues that this was a part of a transfer of ideology from the United States to Latin America: “traits that make Chicago a subculture were personalized in a handful of individuals who transmitted their vision of the world and their science inside and outside of Chicago to their disciplines” (52).

Ideology Diffusion

This process of ideology diffusion is somewhat similar to the current situation nowadays in the economic institutions of Europe. The difference, however, is that the boys have grown up, and they do not come from a university, but from a investment firm. More concretely, they come from Goldman Sachs, the third investment bank by total fees in 2010.

This map is pretty clear of the role of the Goldman Sachs Men in the governance of European economic institutions:

Source: The Independent

Moreover, this picture also helps clarifying that the markets are not abstract illusions that by the invisible hand distribute with justice the resources. But instead they have nouns and faces, and are real individuals.

I don’t know what Goldman Sachs has in order to be able to spread so efficiently its ideas. As a scientific I have to consider the endogeneity mechanism that states that it may be that they are able to recruit the most brilliant minds in the world to work with them. And after some time, those very brilliant minds are so brilliant that apparently are the single options to govern Italy or Greece. Yes, it may be that the recruitment processes at Goldman Sachs work really well. But since I do not have evidence that the Goldman Sachs personnel department is managed by an even smarter mind, I do not discard the fact that it is the other way round.

Non Majoritarian institutions

The disembarkment of the Goldman Sachs Man in Europe started in non-majoritarian institutions. That is, those bodies that act on behalf of some sort of delegation. Bodies that were created by democratic institutions (generally, Parliaments) in order to carry out specific tasks of economic governance or market surveillance. The most visible institutions of this kinds are the Central Banks.

The problem, however, is that there exists a tension between democratic legitimacy used to create nonmajoritarian institutions and the duties for which they were created. And apparently, nowadays the best Central Banker is not the one that is more capable of getting out of the current financial crisis, but the one that deviates minimally from the economic orthodoxy. In this case, the economic orthodoxy says that the single duty of a Central Bank is the control of the inflation rate. And so, a reasonable duty such as having to keep a low inflation rate becomes extremism when it is the only and single reason of its existence, when they want to demonstrate to everyone that they are immune to political pressures (ugh, politics is always something so dirty!). When, to sum up, they do not realize the overall picture, that their duty can not be maintained against democratic will, against common sense in the very exceptional circumstances in which we are living.


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Forecasting elections by polling the polls

Democratic institutions are filled with individuals choosen on free elections. Although forecasting behaviour of individuals may not be the first and single purpose of social science, this blog will start with an entry about the uses of Bayesian tools to pool knowledge from different sources and provide a forecast for the Spanish legislative elections that will be held next november 20th, 2011.

I have adapted Simon’s Jackman model on “polling the polls” in order to account for a multiple party system, with many different poll houses which publish relatively few polls. This contrasts with the usual environments of two-party systems, where there are less poll houses but they take lots of snapshots at different points in time.

Less than three weeks before the elections the forecast still predicts a bipartisan system. Conservative (PP) and Socialists (PSOE, in power) account for 78 of the support, with the former being ahead by 14 percentage points (46% vs 32%). IU (Leftists) and UPyD with 5.9% and 3.2%. The rest of the parties are territorially centered (CiU with 3.3% and ERC with 0.9% in Catalonia; PNV with 1.2% in the Basque Country; BNG with 0.9% in Galicia and CC in the Canary Islands with 0.7%).

Prediction for Spanish elections at 111101

Consult all the results at Forecasting Spanish elections 2011 and subscribe to the blog via RSS in order to get the latest updates.

Posted in Applied models, Bayes, Elections | Tagged , , | Leave a comment