Monthly Archives: April 2014

Getting to Know the Worldwide Governance Indicators

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A while ago I wrote a post suggesting that Ukraine’s propensity for revolution might have something to do with its high level of government corruption in combination with its relatively well-developed civil society. As evidence for this, I showed that Ukraine (together with Kyrgyzstan and Moldova, two countries that have also recently experienced political unrest) was an outlier among post-Soviet states with respect to the relationship between corruption perceptions and authoritarianism. This finding was interesting, but by no means robust enough to warrant broad generalizations about corruption and democracy and revolution.

Since then, a few others chimed in with some ideas. Ben Jones suggested looking at corruption and authoritarianism in countries that experienced revolutions over time. Cavendish McCay looked at corruption and authoritarianism data from the same sources but over the entire globe, and produced a very cool visualization. He also pointed me to the World Bank’s Worldwide Governance Indicators, which contains measures of democracy, corruption, and political stability. Perhaps it would be possible to test my hypothesis empirically using these data. This could be done for individual regions or for the whole world, and could also have a temporal component (the indicators have been published since 1996).

In order to determine if such an analysis is feasible, I decided to take a closer look at the dataset (which is free and downloadable from the website). The Worldwide Governance Indicators (WGI) project is an ambitious one. The authors compile data from 31 different sources (such as think tanks, NGOs, private firms) and produce annual scores for every country for six indicators of the quality of governance. The indicators are:

  • Voice and Accountability
  • Political Stability and Absence of Violence
  • Government Effectiveness
  • Regulatory Quality
  • Rule of Law
  • Control of Corruption

First off, we can look at the data on a map. Fortunately the WGI website has a series of nice Tableau interactive graphics, including maps:

Screen Shot 2014-04-27 at 2.17.49 PM

Looking at the indicators geographically is helpful. But to evaluate whether they can be used to test the hypothesis, I want to see how each indicator is correlated with all the others. For this, we’ll turn to R. Here is a correlation matrix of the six indicators as calculated for 2012. Positive correlations are reflected as positive values. The closer the the number to one, the stronger the correlation. wgi.corrplot As you can see, all the indicators are positively correlated to each other, some very strongly. This is not surprising. We would expect well-governed countries to get high marks for rule of law, regulatory quality, control of corruption, etc. One interesting observation here is that Control of Corruption actually has the lowest correlations of all the indicators. A scatter plot matrix is a good way to look at the data in more detail:
wbi.splom.plot

The idea for this variation on the scatter plot matrix comes from Winston Chang’s R Graphics Cookbook. Its structure is similar to the correlation matrix in that all of the indicators are plotted against each other. The lower panels show scatter plots with LOESS regression lines for each indicator pair. This plot has some extra bells and whistles thrown in – histograms of the distribution of each in indicator in the diagonal panels and correlation coefficients (just like the correlation matrix) in the upper panels. The scatter plots show the strong to moderate correlations that we already saw in the correlation matrix, but allow us to make out some curious features of the data, like the non-linear relationship between Voice and Accountability and many of the other indicators.

The indicator values are in units of a standard normal distribution. A value of zero is the mean, while a value of one is one standard deviation higher than the mean. Given the distributions,  the indicator values range from about -2.5 to 2.5.  Positive values represent better governance, negative represent worse. Because each indicator is measured on the same scale, we can simply sum all six to determine the overall “best governed” country. The top six are:

Country     sum
FINLAND     11.19
SWEDEN      10.94
NEW ZEALAND 10.83
NORWAY      10.67
DENMARK     10.59
SWITZERLAND 10.57

And the bottom six:

SOMALIA              -13.65
CONGO, DEM. REP.     -9.76
SUDAN                -9.74
SYRIAN ARAB REPUBLIC -9.53
AFGHANISTAN          -9.48
KOREA, DEM. REP.     -9.35

I got a bit carried away examining the correlations between the governance indicators, but in a subsequent post I hope to look closer at the democracy – corruption – stability hypothesis. I’m still not quite sure what statistical tests to use and how to apply them, and I’d welcome any ideas. Data and code are posted on Github (github.com/caluchko/wgi)

 

Another Way to Look at Mercury in Seafood

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In the previous post, I used Tableau Public to create a visualization of the Seafood Hg Database. That graphic showed the mean mercury content and number of samples by seafood category. But there are several other dimensions in the database, including the year of the study and the particular species of seafood sampled. I couldn’t resist playing around with the data a little more, this time using the lattice package in R.

The plot below shows the mean mercury concentration (y-axis) in studies of the 12 seafood categories with the highest median mercury concentration. The x axis shows the date of the study. I’ve also plotted a trend line for each panel. This is a nice way to visualize the data, but I wouldn’t read too much into this plot. For one thing, many of the seafood categories contain multiple species, some of which are higher than others in mercury. Also, this plot does not account for the geographical region where the fish were sampled.

fish.hg.latticeplot
We can tease a little more from the dataset by looking at the individual species within a seafood category. Here is a plot of the six tuna species with the greatest number of studies. The larger species, like bluefin, seem have higher mercury contents than the smaller ones, like skipjack. One curious feature of the dataset is also visible here: there were very few studies of mercury in seafood in the 1980s.
fish.hg.tunaplot

How Much Mercury is in Your Favorite Seafood?

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I’ve written before about mercury emissions, mercury as a commodity, and mercury use in artisanal mining. But the reason we pay so much attention to mercury is because of its human health impacts, and these are primarily caused by eating contaminated seafood.

Different types of seafood have different amounts of mercury. Because mercury is bioaccumulative, organisms that are higher on the food chain tend to have greater mercury concentrations. Of course, the particular environment where the organism lives also plays a big part.

Scientists have been interested in the mercury content of seafood for decades. Recently, a group of researchers undertook the herculean task of aggregating data from almost 300 studies. The result is the Seafood Hg Database (and an accompanying paper). The database contains the mean mercury concentrations measured in each study for one or more of 62 seafood categories. Overall, the database represents over 62,000 individual measurements from around the world.

It’s a great dataset to play around with and experiment with visualizations. In the graphic below, I plot mercury concentrations for a subset of common seafood types. Each circle represents the mean concentration measured in one study, and the size of the circle is proportional to the number of samples in that study. I’ve overlaid box plots for each seafood category that show the median of all the means, as well as first and third quartiles (whiskers go to 1.5x the IQR).

I think this is much more instructive than simply plotting the grand mean (average of all the study averages) for each seafood category. For one thing, you lose a lot of information on how much mercury concentration varies within a category. Take tilefish, for example. This is one of the species that EPA and FDA advise pregnant women not to eat. But there are relatively few studies of tilefish, and the mean mercury concentrations they measured vary by an order of magnitude.

Click on the image below to bring up the full interactive Tableau Public visualization:

Hg in seafood

Click on the image to see full version