Recent research in behavioural science has highlighted the importance of patient complaints in the UK National Health Service (NHS) as an indicator of the level of care and patient safety within the hospitals studied. Using information from patients receiving treatment in their job and work conditions, compliance with patient safety standards can be determined, and an evidence base created for patient safety advocacy groups. A similar approach to the NHS – using citizen science as a foundation for improving policy and the enforcement of standards – is also relevant for anti-corruption. With the rapid proliferation of technology and the increase in wireless information networks vis-à-vis wired networks, the smartphone could possibly be the data scientists’ sharpest tool in the fight against corruption.
The focus of our research is transnational corruption in international business. Policies such as the US Foreign Corrupt Practices Act and the UK Bribery Act have extraterritorial reach, whilst countries like Australia have intimated that they plan to raise the maximum penalties for corruption tenfold. These pieces of legislation are designed to reduce corporations’ appetite to engage in corrupt behaviour, i.e., to reduce the ‘supply’ of bribes in international business, and yet very little is known about the impact (if any) of such policies on either potential recipients or potential payers of bribes in international business transactions.
Let’s take the hypothetical example of bribing a public official. Suppose the public official is situated in a country with tough anti-bribery laws, but the foreign investor comes from a country ranked in the middle of the 2018 Corruption Perceptions Index. How do perceptions about the behaviour of others and of law enforcement agencies affect an individual’s willingness to engage in corruption? To evaluate and improve the impact of anti-bribery laws, we need to better understand the levers of control that reduce the acceptance and offering of bribes in international transactions.
To do this, we gather insights into how local social norms, beliefs, and expectations about others’ behavioural integrity are affected by, and interact with, the international legal architecture aimed at combatting corruption and bribery. We harness the power of citizen-centred methods of data collection, just as with the NHS patients, to do so.
Our project will push the boundaries of citizen science by employing a derivation of Class-EX (an experimental software platform developed by Co-Investigator Marcus Giamattei). This software has previously been used to capture data relating to the conditional cooperation of football fans during the 2014 World Cup, as well as that of team reasoning, with 238,700 decisions across 50 countries captured already using the platform.
Weaponising software like this in the fight against corruption will allow us to better understand how behaviours and beliefs are affected when we vary the country of origin of the potential bribe-receiver, bribe-payer, and enforcement officer. Only then can we understand whether the assumptions on which extraterritorial policies such as the US Foreign Corrupt Practices Act and the UK Bribery Act are based are realistic, and hence, how far their reach really extends.
