Divining the level of corruption: A Bayesian state-space approach

This paper outlines a new methodological framework for combining indicators of corruption. The state-space framework extends the methodology of the Worldwide Governance Indicators (WGI) to fully make use of the time-structure present in corruption data. It is estimated using a Bayesian Gibbs sampler algorithm. The state-space framework holds many advantages from a practical, an estimation and a theoretical point of view. Most importantly, it significantly expands the period for which the index can be computed while at the same time addressing the selection bias issues that trouble the Corruption Perceptions Index (CPI). In addition, its estimates are more stable and have smaller confidence intervals than both CPI and WGI. Because the estimation is transparent and data is entered without any manipulations, the estimation procedure is more objective. Journal of Comparative Economics 43 (3) (2015) 782-803. SHERPPA, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium. (C) 2014 Association for Comparative Economic Studies Published by Elsevier Inc. All rights reserved.