Towards a Multidimensional Measure of Governance

1. Towards a Multidimensional Measure of Governance Shabana Singh Vanderbilt University April 2011

2. “Government is not reason; it is not eloquence; it is force! It is a dangerous servant and a terrible master.” • – George Washington, 1st President of USA. • What is governance? • UNDP: "comprises the mechanism and process for citizens and groups to articulates their interests, mediate their differences and exercise their legal rights and obligations.” • European Commission: "the way public functions are carried out, public resources are managed and public regulatory powers are exercised". • World Bank: "the state's institutional arrangements; the processes for formulating policy, decision making, and implementation; information flows within government; and the overall relationship between citizens and government."

3. Introduction • The available indices: • Broad focus indices: • Political Freedom Index (HDR, 1992) • World Governance Indices by World Bank • Mo Ibrahim’s Index of African Governance • Narrow focus indices: • Corruption Perception Index by Transparency International

4. Introduction Governance is a multidimensional phenomena • Worldwide Governance Indicators • Voice and Accountability • Political Stability and Absence of Violence • Government Effectiveness • Regulatory Quality • Rule of Law • Control of Corruption • Political Freedom Index: • Personal Security • Rule of Law • Freedom of expression • Political Participation • Equality of Opportunity • Mo Ibrahim’s Index of African Governance • Safety and Security • Rule of law, Transparency Corruption. • Participation and Human Rights • Human Development • Sustainable Economic Opportunity

5. Introduction • Broad concerns about governance indicators: • What is governance and what indicators should be incorporated? • (Arndt and Oman 2006), (Thomas 2010) • Quality of data and cross-country comparisons • (Kaufmann and Kraay 2007), (Julius Court and Mease 2002) and (Knack, Kugler, and Manning 2003) • The sensitivity of the measures to the scaling of data • This issue is the main focus of this paper

6. Mo Ibrahim’s IAG • The data: • 57 indicators of Governance for 48 countries in Africa • Multiples sources for data, like Transparency International, CIRI Human Rights Data • 18 of the 57 indicators are ordinal variables • Three tier structure to the index. • 57 indicators are divided into 15 sub-categories. • 15 sub-categories are aggregated up to five dimension indices. • Five dimensions of Governance are aggregated to get a single measure of Governance

7. Mo Ibrahim’s IAG

8. Mo Ibrahim’s IAG

9. Mo Ibrahim’s IAG • Aggregation Methodology: • Rescaling of raw data (both ordinal and cardinal) • Three methods used for choosing the min and max: • 1st method allows for inter-temporal comparisons for each country • 2nd method allows for cross country comparisons for each year • 3rd method : the “benchmark” case similar to the 2nd but uses values from 2000 • Scores can be below 0 and above 100

10. Main Issue • The IAG has 18 of its 57 indicators as ordinal variables. IAG imputes cardinal values to ordinal data • Problematic: Choice of scale can affect the rankings of different nations Example: • 4 nations and 4 dimensions. • We use a simple average across the 4 dimensional values to arrive at an overall measure. • Observations can take values on a scale of 0-10 in each dimension. Higher values indicate better performance.

11. Example- Ordinal data Nation A has the lowest score and Nation D has the highest. Undertake the following exercise, with the data above: • Rescale the data to a 0-100 scale. • Square the value in each dimension to get the new rescaled values. • Compute the composite index, as done previously (simple average of the scores in each dimension).

12. Example- Ordinal data Nation A now has the highest score! Note: The ranking of the nations in each specific dimension are preserved. The aggregation (simple average) results in a new scheme for the composite index.

13. Revisiting Alkire-foster methodology • Identifcation stage: Dual cut-off • Deprivation cutoffs idenfity whether deprived in the that index • Dimension cutoff: No. of indicators one has to be deprived in to be considered poor • Aggregation stage : FGT based measure: • For ordinal dimensions use the multidimensional adjusted headcount (M0) • For cardinal dimensions use the any of the class of measures

14. Revisit: Example Original data: With a scale of 0-10 Using the AF methodology: Note: Lower scores imply better performance

15. Revisit: Example Rescaled data: Scale 0-100 Using AF Methodology on the new data: Notice: No change in rankings!

16. A new governance index • Indicators are aggregated using AF to give dimension specific governance indices • Average of these dimension specific indices gives overall level of governance for the nation • Do not aggregate across nations. • For the IAG the dimensions: Safety and Security, Rule of law, Transparency Corruption & Participation and Human Rights • Treat them as ordinal , use M0 • For IAG dimensions: Human Development & Sustainable Economic Opportunity • Treat them as cardinal, use M1

17. A new governance index • For ordinal dimensions • For cardinal dimensions • Overall governance is

18. Main Results

19. Results (Strict lines)

20. Results (Softer Lines)

21. IAG Methodology Scaling of all indicators is necessary Gives value interpretation to ordinal variables Retain information on depth of deprivation within a dimension (indicator). New Methodology No scaling required Can be used with ordinal variables Gain information on depth of deprivation in governance (in terms of k) across the indicators Focus on deprived nations and dimensions (with the indicator-specific cutoffs) Comparison

22. Report Cards • The new index is a counting approach where zero implies not governance poor and one implies maximum deprivation. • This allows a very convenient representation a nation’s performance. • Governance Report Card • Report card for Rwanda: