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To punish or not to punish: Scientific model agrees with legalizing giving bribe

  • Who is to blame?

Photo: Siddharth Kankaria/Research Matters

Have you ever been asked to pay extra money to register your new property? Have you found yourself repeatedly a victim of bribery, being forced to pay surreptitiously for something that you are legally entitled to? Then you have fallen prey to ‘harassment bribery’. While the law imposes strict laws against both the bribe giver and the receiver, the problem is so widespread that an estimated 70% of cases registered with India’s Central Bureau of Investigation (CBI) in 2014 were related to bribery and corruption. 

Taking note of this pervasive menace in our society, the then Chief Economic Adviser to the Govt. of India, Mr. Kaushik Basu, recommended legalization of giving a bribe. In a paper that he presented on this “small but radical idea”, he argued that if giving bribe is legalized, then nothing prevents the victim of bribery from blowing the whistle on corrupt officials. Now, in a recent paper published in the journal Scientific Reports, Prof. Supratim Sengupta along with Prateek Verma and Anjan Nandi at the Indian Institute of Science Education and Research (IISER), Kolkata, have put this proposal to test.

In the last two years, the researchers have extensively analysed the process of harassment bribery in a quantitative framework under evolutionary game theory, and have published several publications in this regard, the first of its kind in the country. Modelled on game theory, a mathematical framework to study strategy and decision-making, the researchers have studied the effect of ‘asymmetric penalties’, a system where the victim is not punished, but corrupt official is, on the prevalence of harassment bribery. Their studies have been conducted in both ‘unstructured’ populations, where each member is equally likely to interact with any other member of the population, and ‘structured’ populations where interactions are usually more restricted.

The researchers used computer simulations to study how populations change their behaviour when faced with varying bribe demands and the ‘cost’ associated with complaining against a corrupt official. Under ‘unstructured’ populations, the study concluded that just changing policies might not suffice in preventing the menace of bribery. However, the study involving ‘structured’ populations (resembling the real world), indicates that reducing the penalty on giving bribe is more likely to force corrupt officials to change their behaviour and become honest under circumstances characterized by relatively low cost of complaining. Intriguingly, they find that the spread of honest officers depends on the number of connections each citizen has in the network which signify the extent to which a citizen can influence other fellow citizens in the network. They find that an optimal range of connections facilitates the spread of honest officers, but too few or too many connections is counter-productive to the reduction of corruption. 

So have we found a panacea to the society’s foremost evil? Prof. Sengupta disagrees and favours a more measured approach.

“It is essential to take into account how individuals change their behaviour in response to the outcome of an interaction. There is no magic bullet for reducing corruption and our work does not (and cannot) suggest any”, he says.

For starters however, the evolutionary models that Prof. Sengupta’s group has employed, suggest that reducing penalties for the victims will have an impact on the level of corruption.

In the fight against corruption, a collective effort and participation from all sections of society is essential for victory. The contribution of scientists towards scientific analyses of socio-economic strategies will provide an impetus to new and improved policies that combat corruption. As Prof. Sengupta and his team train their sights on more complex populations, we move one step closer to being able to predict the outcomes of policies before implementation, and hopefully, towards efficient and effective governance.