Focus on corruption

Wednesday 11th February 2015

Yesterday TI (Transparency International) UK, part of a global organisation based in Berlin, "Lifted the Lid on Lobbying." Apposite timing as data mining research by Michal Paulus and Ladislav Kristoufek at Charles University in Prague, Czech Republic, for the first time finds a correlation between the perception of corruption in different countries and their economic development, disclosing a global link between corruption and wealth.

"We face a widely acknowledged crisis of trust in politics and political parties. While the causes of this are complex, there is no doubt that the lobbying environment in the UK and the frequency of lobbying scandals play a major role in damaging public faith in the political and policy process.

‘Lifting the Lid on Lobbying’ describes several risks of corrupt behaviour in lobbying and evaluates, across the UK and devolved nations, the gaps in the rules that permit an environment conducive to corruption to flourish. For the first time, this research compares the wide variations across the UK in the approach to maintaining the integrity and transparency of public institutions and decision making.

The report’s findings indicate that there is much to be done in order to tackle the risks to our institutions, not least the ability of lobbyists to conceal their interests and activity, the role of ‘big money’ in political donations, the ‘revolving door’ between public and private sectors, and the role of external and unaccountable expertise brought in to inform government policy. 

Following an introduction to the report by Transparency International UK, a panel of parliamentarians, researchers, and investigative journalists will discuss the report’s conclusions and their implications for parliamentary standards before opening the discussion to the floor.

Appositely, it cames as Technology Review covers the 2014 global list of countries ranked according to their perceived levels of corruptions.  TI  defines corruption as the misuse of public power for private benefit and publishes a yearly global list of countries ranked according to perceived levels of corruption, a list using at least three sources of information, but that does not directly measure corruption, because of the difficulties in gathering such data.

Instead, it gathers information from sources such as the African Development Bank and the Economist Intelligence Unit. It also places significant weight on expert opinions of assessed corruption levels.

The  resulting Corruption Perceptions Index ranking countries between 0 (highly corrupt) to 100 (very clean). In 2014, Denmark occupied of the top spot as the world’s least corrupt nation while Somalia and North Korea trail as tying for the most corrupt countries.

Paulus and Kristoufek use this data to search and find clusters of countries that share similar properties using a new generation of cluster-searching algorithms. They say the 134 countries they study fall neatly into four groups clearly correlated with the wealth of the nations within them.

The method  behind  this is known as the average linkage clustering approach. It begins by assuming  each country represents a cluster in itself, then looks for the nearest neighbour in the ranking. The pair then become a new cluster and this is placed back into the list as a single entity with the process repeated until it produces a single cluster of all the countries.

A tree diagram then shows how the clusters are related. The researchers connect the ends of the diagram to make it into circle. It is then clear to see how the clusters are grouped. Paulus and Kristoufek say this technique reveals four clear clusters. The first consists of developed countries such as the US, UK, the EU western states, Japan, Australia, New Zealand, Singapore and so on. Their average corruption rating is low at 83.

This cluster is separated by a significant distance from the other clusters. The countries are all rich with an average GDP per capita of $52,138. It contains no African countries and only one from South America in Chile, the region’s most developed nation. 

“In other words, the cluster of the least corrupt countries is composed of the most developed nations across all continents,” they say.

The greater variety is in the second cluster composed of various countries from Europe, as Malta, Slovenia, Cyprus, Spain and Portugal. It has several African countries, as Botswana and a mini-cluster of oil exporting countries as Oman and Kuwait. This cluster has higher corruption levels and  an average rating of 59 and  average GDP per capita at $23,521.

The third cluster is still more corrupt,  ranking 41 and less rich,  average GDP per capita at $9751. Its  mixed list of members including post-communist countries of eastern Europe, as Czech Republic, Poland and Latvia and also most of the countries in North Africa, as Morocco, Egypt and Tunisia.  Greece and Italy also fall into this category.

The largest and final cluster is  the most corrupt, consisting of 60 countries with an average corruption ranking of 24. Countries range from Bulgaria and Romania new EU countries, to Russia, Belarus, Moldova, the Ukraine, to China and India as well many African, Asian and a few Latin American countries,” say the researchers. Authoritarian regimes as Syria, Iran, North Korea are also in this category.

This is the poorest cluster with an average GDP per capita of $3,888.  But hugely diverse  containing Russia GDP per capita of $14,090 to Malawi  GPD per capita of only $287.


Paulus and Kristoufek say the link between the perception of corruption and the wealth of the nation, as measured by its GDP per capita, is clear. “Ranking of countries according to their corruption perfectly copies the ranking according to the economic performance, measured by the gross domestic product per capita.”

That’s an interesting study and important because social scientists have struggled to understand the links between corruption and other measures of performance.  But there are limitations.   Paulus and Kristoufek say the correlation is a clear, but what causes the correlation is not clear.  Are countries poor because they are corrupt or corrupt because they are poor? It is simply a new starting point for further research.

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