Lebanon scores 24/100 and ranks 154 out of 180 countries on Corruption Perception Index (CPI) score; as of the 2021 survey by Transparency International (TI), the score from the World Bank (WB) for Control of Corruption (CC) is at a dismal 12.50 percentile.
The World Economic Forum estimates the cost of Corruption to be more than 5 per cent of global GDP (US $2.6 trillion), and the World Bank believes over $1 trillion is paid in bribes each year (CleanGovBiz 2013).
Why do we need an accurate measure of Corruption?
The World Bank's Worldwide Governance Indicators (WGI) 's and Transparency International CPI scores are the most widely used indexes to measure national Corruption. Hence it is impertinent to understand how these index scores are formulated. What are the factors considered for measuring the scores? An error in this score means we might end up chasing the wrong metric in our fight against Corruption. CPI and CC scores are calculated at a National level, so there is an additional challenge in interpreting these scores and turning them into action at the local and regional levels. If anti-corruption activists develop a deeper understanding of these scores, they can design their anti-corruption strategies more effectively.
There is this perpetual tension between academic researchers on the one hand and policymakers and anti-corruption activists on the other. Policymakers and anti-corruption activists demand actionable insights from the former. The big question is whether these indexes' have resulted in reduced Corruption.
Perceptual and non-perceptual measures of Corruption
The two types of measurement used are perceptual and non-perceptual measures. They are also alternatively referred to as indirect (perceptual) and direct (non-perceptual) measures, respectively. Direct are those measures which are based on hard data and experiences. In contrast, indirect measures are based on perception and not data. Transparency International (TI) and the World Bank use a combination of both direct and indirect measures to develop their corruption indexes. Check this short video by Transparency International (TI) on how they calculate their CPI.
The core problem in both perceptual and non-perceptual approaches arises from the conceptual definition of Corruption and its measurement. How does one define Corruption? How to decide which instance of Corruption is more serious to justify its ranking? How does one distinguish between different types of Corruption? These are all subjective questions, and answers might depend on the context.
Issues with Non-Perceptual Approaches
The critical problem with the non-perceptual approach is developing measures that can be utilised across jurisdictions. Another issue is how reliable the available datasets are, which might itself be inaccurate due to Corruption. There is always the possibility that corrupt officials can develop countermeasures and corrupt the data needed for measurements. (Heywood, 2014). E.g., An authoritarian government might fudge the data to appear less corrupt, or the bureaucracy might misreport the data to avoid being accountable to the authoritarian leadership.
Here are three scenarios on why perceptions-based measurements are confusing.
Suppose different countries have different interpretations of Corruption due to cultural differences. In that case, the surveys from these two countries will give different scores even though both may have the same financial and political Corruption levels. A country may have a relatively low level of financial Corruption coupled with a relatively high level of political Corruption, yet, the extent to which each of these factors is reflected in the final score is ultimately determined by the frequency of each conceptualisation within the constituent surveys.
Another problem is the focus on measuring Corruption and developing rankings at a National level. These scores will always be misleading as Corruption occurs in specific sectors and contexts - local, regional, national and increasingly transnational – and that variation is one of the key reasons that it is so challenging to develop appropriate measures.
Countries with higher levels of income inequality demonstrated a higher degree of corruption perception, which may or may not be accurate. It is true that in Organisation for Economic Co-operation and Development (OCED) countries, income inequality leads to higher Corruption, while in African countries, some of the most unequal countries have lower levels of Corruption.
The current corruption indexes have this Achilles heel of perception; unless the entire process in the United Nations Development Programme (UNDP) guide is revised to include more direct measures, we will not get accurate scores.
Scores designed to reflect administrative Corruption and not state capture
Another drawback of these indexes is that constituent surveys of the CPI overrepresent business-related financial Corruption, which is that the scores reflect administrative Corruption more and significantly less than state capture.
The surveys and assessments used to compile the index include questions relating to bribery of public officials, kickbacks in public procurement, embezzlement of public funds, and questions that probe the strength and effectiveness of public sector anti-corruption efforts ( TI 2010 ).
There is only one state capture measure in the WEF survey. In contrast, Nations in Transit (NIT), International Country Risk Guide (ICRG), Country Policy and Institutional Assessment (CPIA) and Economics Intelligence Unit (EIU) measure administrative Corruption much better than they measure state capture. (Knack, 2006)
Solutions and Remedies to improve the measurement of Corruption.
The processes for direct or non-perceptual measurements need to be improved; this can be done in the following three ways.
1 Existing efforts to collect data should be scaled up, and at the same time, there must be improvements for country-level indicators.
2 More "actionable" data on laws and practices to prevent Corruption should be available for policymakers and anti-corruption activists, creating a feedback loop for improved data collection and actionable insights.
3 Greater public access to the underlying data used to measure CPI and WGI indexes, along with better information on how those underlying data are generated, should be available to the users to customise their own indexes for their purpose. (Knack, 2006)