QE
Quantitative ethics involves the use of quantitative methods for examining ethics-related issues in human interactions and institutions. Datasets like the Business Bribery Index (BBI) and the World Value Survey (WVS) are examples of the quantitative data that can be utilized by ethicists as a means to test their ethical hypotheses.  Quantitatively-informed ethics can thus be seen as a complement to the traditional method of doing ethics through philosophical reasoning.

Such datasets have various applications in developing theories of ethics. The BBI, for example, includes various characteristics of firms and business managers, which can be used in examining the correlation between the political connections or the gender of managers and their level of corruption.

As a novel approach to understanding and representing human ethical behaviour, quantitative ethics is faced with a number of challenges: the lack of existing datasets, absence of high-quality precedent research, and the limited familiarity of ethics scholars with quantitative methods. Despite these roadblocks, quantitatively informed ethics has the potential to open the doors to a new chapter in the study of ethics. The creative use of data sets presents limitless possibilities for innovation in the way ethics research is conducted.

BBI
The Business Bribery Index (BBI) was developed using data collected in surveying the actual bribe paid by business managers to the governments worldwide. It offers an estimation of national annual bribes paid by the business sector to governments, in each country worldwide, in the currency of that country at the time, and the equivalent amount in US dollars. The data is available for 127 countries mostly in the developing world.

The BBI utilizes estimation and corruption surveys carried out by the World Bank Business Environment and Enterprise Performance Survey (BEEPS) and the World Bank Enterprise Survey (WBES). A number of data sources have also been used to validate the BBI data as a means of ensuring its quality.