Updated WGI methodology makes use of the strong time-dependence in corruption. * Doubles the available corruption perception estimates. * Increases the precision of the estimates. * Allows the detection of the significance of changes in the level of corruption. * More transparent and fewer ad hoc manipulations increasing the objectivity. 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.