In the website opinion survey, hosted by Fund for Peace, that publishes global index of state failure (www.fundforpeace.org), lists down nine factors and asks the respondents to tick one that s/he thinks it to be the most critical cause of state failure. The list included: (1) Corruption, (2) Lack of basic education, (3) Group hatred, (4) Poverty, (5) Lack of social equality, (6) External actors, (7) Natural disasters, (8) Malnutrition and disease, and (9) Environmental damage. As on 22 May 2008, 7053 people ticked their responses. The results, in percentage terms, were as follows: (1) Corruption 42%, (2) Lack of basic education 14%, (3) Group hatred 12%, (4) Poverty 11%, (5) Lack of social equality 10%, (6) External actors 5%, (7) Natural disasters 2%, (8) Malnutrition and disease 2%, and (9) Environmental damage 2 %.
What is interesting in the response pattern is that corruption is single out not just as the top most critical factor; compared to it other listed factors trail far behind in the list. The second top most critical factor, i.e., the lack of basic education has a mere response rate of 14% compared to 42% for the corruption factor. The normal factors that we often like to assume for state failure, namely, external interventions, natural disasters, environmental damages and malnutrition and diseases do not feature prominently. The next interesting feature is that ever since the question was posted in the website last year, as this scribe has been closely observing the response pattern, there has been hardly a significant change in the proportion of responses even when the number of respondents doubled over a period of time.
Using global data on state failure published by the Fund for Peace and corruption data published by Transparency International, in this small write up, an attempt is made to dissect the relationships between corruption and state failure.
Transparency International has recently released its annual Corruption Perception Index (CPI) for 2008 ( www.transparency.org ). CPI is a measure, scaled from 0 to 10 implying higher score to measure better performance in global corruption perception ranking by the country. As for example, with CPI score of 9.3, currently, Denmark is at the top of the list, meaning it to be cleanest country in the world. With CPI score of 1, Somalia is perceived to be the most corrupt country and is at the bottom of the ranking scale. The list is available for 180 countries of the world. Nepal with a score of 2.7 is ranked at 121st position. Last year Nepal’s score was 2.5. There is a slight improvement in Nepal's score and ranking. As a rule of thumb, TI ranks countries with less than CPI score of 3 to be “corruption rampant” countries.
The Failed States Index (FSI) for 2008 is available for 177 countries of the world (www.fundforpeace.org). The index ranges from 0 to 120. Lower the score better is the performance and vice versa. As for example, with FSI of 114.2, Somalia is at the top of the list implying the country to be at the verge of collapse. With FSI of 16.8 Norway is at the bottom of the list. It is the most sustainable country in the world. With FSI 94.2, Nepal is ranked at 23rd position among the list of 177 countries. Last year Nepal’s score was 93.6 and was ranked at 21st position. Compared to last year, there is a slight dis-improvement in FSI score for Nepal.
The failed states indices are color coded: countries scoring 90-120 are colored red implying them to be highly critical zone requiring total alertness. Nepal clearly falls in this group. A total of 35 countries fall in this group. Many of the Asian and African countries fall in this group. Countries scoring 60-89.9 are colored orange implying them to be in a danger zone. Countries in the band of 30-59.9 are coded yellow. These are borderline countries requiring careful monitoring. Finally, countries scoring less than 30 are coded green color. These are highly stable and sustainable countries. There are 15 green countries in the list..
For 2008, paired CPI and FSI data are available only for 170 countries. The regression plot for these two indices is given below:
A couple of interesting points emerge from the plot. First, the relationship between corruption (as measured by CPI score) and state failure (as measured by FSI score) is negative (r = -0.838) implying that countries with high degree of perceived corruption (low CPI scores) also have high chances of state failure (high FSI scores). It may look redundant to compute correlation value as 12 factors used to measure FSI also contain corruption variable. In FSI, corruption is measured in a broad term to mean “criminalization and de-legitimization of the state”. In CPI, corruption is measured as the extent of corruption within the public sector. Since data sources and uses are different, it may be worthwhile to look into their relationships. As for example, the relationship is reverse for Nepal. There is a slight improvement in corruption standing while, at a same time, there is dis-improvement in failed sates index. Does this mean to say that with the arrival of Maoist-led government, there is some gain in corruption rating and loss in failed states ranking? It will be too early to pass a judgement at this point of time. May be we have to keep a watch on next year's publication.
Second, though there are some outliers, top-right corner and bottom-left corners of the graph are, literally, left blank or without dots, indicating there are no countries in the world which are highly clean and highly unstable or which are highly corrupt and highly stable/sustainable. Argentina is one country with a high degree of corruption (CPI 2.9) and a good score on failed sates index (FSI 41.4). Bhutan and Italy stand at the opposite direction having good score on corruption and bad score on failed states index. The paired CPI and FSI scores for Bhutan and Italy are respectively (5.2, 85.4) and (6.0, 83.6).
Third, the co-efficiency of correlation only measures that two variables – corruption and state failure - are highly related. It does not say anything about their causality, meaning which is a cause and which is an effect. Could it be true that the country is highly corrupt because it is a failing state or the other way round? We are basically into a chicken-egg situation here. Though there are now robust statistical techniques to measure the running of the causality, it could run both ways. First a high degree of corruption may invite a country to be unstable and, consequently the situation of instability and un-certainty might invite further corruption thereby completing a vicious circle of corruption-instability-corruption.
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