One aspect of the currently prevailing view on corruption is the emphasis on quantifications of corruption, which have been used to research its causes and effects, and to gauge progress of anti-corruption reforms around the world. This chapter is dedicated to these measures. Corruption country scores are an example of so-called Global Performance Indicators and assume that by taking the right initiatives, countries can improve their ranking in a given Global Performance Indicator. However, the available measures of corruption are not well suited to assess changes of corruption over time. A more general conclusion also emerges from this chapter. In studying social phenomena using quantitative techniques of analysis, it is considered to be important to draw a sharp line between the definition of a concept, which should come first, and attempts at measuring it, which should be conditional on the chosen definition. However, when measures of social phenomena are successful, they take on a life of their own and contribute to an ossification of the concept they refer to. Consideration of the extent to which the prevailing concept of corruption and its most popular measures have shaped each other also provides a good angle to discuss corruption more generally.

The definition, measurement, and scale for the Transparency International corruption T-index are discussed. It increases when corruption falls. A large cross-country sample exists, but long time-series are based on anecdotes. They seem to confirm equivalence. The theories connecting development and corruption are surveyed. There are the demand theory seeing honesty as an intangible good, the transaction theory, and the sand vs. grease theories. The T-index has a beautiful transition curve that is very robust. The causality tests show that the main direction of causality is from income to corruption, but there is some simultaneity. While the T-variable is positively correlated to the levels of income, democracy, and economic freedom, it is negatively correlated to the first difference of these development variables. It is also a problem that the four variables are strongly confluent, as they all have transitions, but the transition of corruption is later than the transitions to democracy and a modern economic system. A set of examples show that institutional uncertainty gives corruption.

Do immigrants undermine culture in a way that destroys productivity in destination countries? Some scholars have argued that because immigrants come from countries with dysfunctional social capital – norms and institutions – they will import it and pollute the social capital in destination countries. One potential channel through which this could occur is corruption. We examine stocks and flows of immigrants over a twenty-year time period to see if corruption increased in destination countries. We generally find that immigration is not associated with increases in corruption. Additionally, we find that immigration tends to decrease corruption in destination countries with low levels of corruption or high levels of economic freedom.