When discussing corruption, a lot of people from less corrupt systems talk about blatantly corrupt systems and you can hear a condescending undercurrent of: “These amoral corrupt people…” in their assessment of the situation. As if it was always the decision of this one corrupt individual to take or give bribes. And, understanding where they come from, in a less corrupt system, that might be broadly true.
My friends in northern Ghana though agreed that a road contractor who wasn’t prepared to pay his 10% to everyone involved would not get any jobs and so, by definition, not be a road contractor. They didn’t judge this as an un-ethical act but, ironically, saw the contractor who distributed part of the contract sum as following the (informal) rules.
When network mapping with a group of people involved in avian flu prevention, we talked about the question: Where in this network do people have the highest incentive to bribe? This helped us to understand that it is not the evil character of cross-border chicken traders but rather the lack of compensation for dead birds and the position of control of the border officials, that makes the cross-border trade a potential corruption hot-spot.
Regina Birner has used Net-Map to visualize and summarize her findings about corruption in the construction of small reservoirs in Ghana and in forestry in Indonesia. We had an interesting discussion this week about how you could use Net-Map as a tool to diagnose potential structural corruption hot-spots without focusing on exposing individuals and thus improve the design of programs and organizations to reduce incentives for corruption. Have a look at her case study.
Can you think of examples that you have experienced or heard about where certain structures invited corruption? What exactly could be the switches to turn for reducing the incentives for corruption in these cases?