Ibrahim Index

OS - Overall Score
SRL - Safety and Rule of Law
PHR - Participation and Human Rights
SEO - Sustainable Economic Opportunity
HD - Human Development

Rank	Country	OS	SRL	PHR	SEO	HD
1	Mauritius	83	90	77	84	80
2	Seychelles	77	81	69	66	99
3	Botswana	76	88	69	70	77
4	Cape Verde	75	84	80	67	72
5	South Africa	71	72	73	65	75
6	Namibia	67	80	66	63	60
7	Ghana	65	75	68	53	62
8	Tunisia	62	64	38	66	81
9	Egypt	60	67	35	66	73
10	Lesotho	60	70	68	52	51
11	São Tomé and Príncipe	58	68	65	44	56
12	Benin	57	69	62	51	45
13	Morocco	57	63	56	49	40
14	Senegal	56	63	60	53	49
15	Algeria	55	54	37	55	75
16	Tanzania	55	62	56	53	49
17	Zambia	55	64	56	49	51
18	Gambia	53	50	49	51	62
19	Mali	53	62	58	50	42
20	Mozambique	52	63	56	49	40
21	Burkina Faso	52	64	57	53	35
22	Malawi	52	65	51	45	45
23	Libya	51	52	19	50	85
24	Uganda	51	54	51	49	49
25	Swaziland	51	64	27	52	60
26	Kenya	51	50	55	45	51
27	Gabon	50	57	42	43	59
28	Madagascar	49	51	51	50	43
29	Comoros	49	54	57	29	53
30	Djibouti	49	60	33	45	55
31	Rwanda	47	47	38	55	49
32	Sierra Leone	46	52	52	42	39
33	Burundi	45	50	49	42	39
34	Cameroon	44	49	32	46	50
35	Ethiopia	44	49	35	49	42
36	Mauritania	44	49	35	49	42
37	Nigeria	43	48	38	43	43
38	Liberia	43	50	51	29	42
39	Togo	43	57	40	34	40
40	Niger	42	56	38	43	33
41	Congo, Republic of	42	44	41	39	44
42	Angola	39	44	44	36	33
43	Guinea- Bissau	39	46	46	33	31
44	Côte d’Ivoire	37	38	29	39	42
45	Guinea	36	39	33	34	36
46	Equatorial Guinea	35	46	19	35	39
47	Sudan	33	22	23	38	48
48	Central African Republic	33	35	42	28	26
49	Zimbabwe	33	35	31	19	46
50	Eritrea	32	40	20	25	43
51	Congo, Democratic Republic of	31	33	36	26	35
52	Chad	29	38	23	32	23
53	Somalia	8	8	12	4	8

Methodology

Statistically, our challenges in compiling this index were many, including choosing the most appropriate statistical method to aggregate the data into one composite Index, and at a more basic level, finding the most “suitable” set of indicators that sufficiently capture governance as defined by the Board of the Foundation, its Founder, and its Advisory Council and Technical Committee members. The 2009 Ibrahim Index of African Governance is a work in progress, and is both a continuation and an improvement on the work of the first two years. We continue to put together a number of official data indicators that capture governance in the way that the Foundation and its partners perceive it. In addition, this year, largely as a result of direct input from our African partners and interlocutors, we have introduced a considerable number of qualitative indicators (expert assessment). We believe that these add substantial value and nuance in capturing aspects of governance, particularly in relation to assessing current governance performance.

This is especially the case given the highly lagged nature of a large number of official data, some of which had to be excluded because it reflected little of a government’s recent performance.  However, while the worst of those legacy indicators were excluded, some indicators with a more limited legacy effect were still included.

At the practical level, we found that many official data indicators that we would have liked to include did not have sufficient data coverage and were not released or updated periodically enough, to warrant inclusion. This factor led us to exclude what could arguably be considered the most important indicators on governance: poverty indicators.  On another front, and similar to what was done previously, for many indicators that we include the lack of a full dataset for the period 2000-2008 meant that we had to estimate missing values. For this, we used mean substitution and extrapolation. In addition, indicators which had only one year’s data were excluded.

The Ibrahim Index is a composite. Following the gathering of the raw data on all the indicators, a method was chosen to put the raw data on to a common scale, that is to say, to re-scale or normalise the raw data, so that it can be usefully combined to produce an overall score for each country.  There are a number of statistical methods and data aggregation techniques to choose from. The IIAG uses the same method as in the past, namely, the “min-max” method. However, this year, a statistical technique was used to deal with outliers, given the high degree of sensitivity of the min-max method to outliers.

Fundamentally, the min-max method involves re-scaling the raw data values to a scale of 0-100, for every indicator, for every country, and for every year. This re- scaling is done using the formula:

[xt – Min(X)]/[Max (X) – Min (X)]*100

Where xt is the raw value for that indicator for a particular country in year t, and the Min(X) and Max (X) are the minimum and maximum values for that indicator over the whole period and for all countries. The final result was subtracted from 100 where necessary, so that a higher number always indicated a better performance.

The method used and the nature of the data mean that it is more instructive to look at scores and ranks in more recent years, rather than in the early years. A key reason for this is that data in the early years are patchy but data availability improves substantially over time. Comparisons of scores across sub-categories and categories are misleading and so should be avoided.Moreover, comparisons across countries (for the same period) should be governed by the non-trivial and considerable margins of error, which are present in any Governance Index or indicator. Those margins of error mean that score or rank comparisons when differences across countries are small should be avoided, since they would reflect a statistical “tie”.

Finally, this year, in order to render the Ibrahim Index more reflective of recent performance, we decided to use the latest available data for every indicator where it was available. This means that for the year marked 2007/2008, for example, 2008 data was used if available, and 2007 if not.