I have just concluded an interesting conversation with a policeman whose beat includes a premier architectural design school.
What he shared with me could give a pit in the stomach to a Gen-past middle-class parent like me; but, instead of going into a morality tirade, I plan to ponder over it to question appropriateness of the threat perception data we use for policy making.
The policeman originating from rural and conservative Gujarat found it incredibly shocking that young girls indulged in marijuana smoking – a crime that can earn them some interesting time behind bars in an Indian prison – and were completely comfortable in going to shady places hidden behind narrow lanes to buy the stuff.
Though this information can’t be looked at as a dependable dataset, it did strike me because it reminded me of a study done by the very academy that these confident girls came from on threat perception by women in how they look at urban spaces.
This study indicated that women found most of the public spaces, even bus-stands on public roads, to be intimidating.
This poses an interesting question. Are the girls who can comfortably risk going to a drug paddler to buy their fix the same who are scared of standing alone at a bus stand?
Strange though it may sound, the real answer can a YES, and that strikes me as an indicator that we may have been making a gross error in understanding threat perception data that we collect for making policies.
Threat one feels is always based a perceived risk, but a problematic catch is, risk perception can be based on experience, both direct and indirect.
So a girl who has direct experience of safely going and buying drugs from a shady joint but has rarely travelled in public transport and has heard horror stories about it would have a threat perception that allows her to comfortably venture out to buy drugs but feel scared if made to stand alone at a bus stand.
It is this personalised nature of threat perception that can completely mislead policymakers if data is not adjusted for it.
The problem that really needs to be tackled for having right policies is differentiating between threat perceptions that are based on the real and the imaginary. And I see a solution that can help us correct the data, if we adjust it to a simple parameter of wealth.
Indian reality is, there is a massive difference in threat perception between rich and poor.
Rich are cocooned inside a comfort-zone and when asked are offering threat perceptions from hear-say. Poor, on the other hand, have real threats but are too busy fighting to survive and have become fatalistically agnostic to threats.
Unfortunately, as rich are more vocal, they drive the data, so we have always ended up giving priority to issues that they are bothered about, instead of digging in to understand the real threats faced by the poor who are a majority, but silent.
As India is now seriously working on tackling such issues, there is a need to get real if we want to solve real problems, and it can be done by making a dramatic shift in how we collect statistical data.
If we are planning to deploy resources to deal with a problem such as making our cities safe for women, we need to start from the very bottom. Poorest of poor of the society must be asked first about their problems, and only after solving them we should move up.
Let us understand that our nation have a billion voiceless who are suffering silently. Those of us who have a voice can wait for a while, but they have suffered enough. As data is the most powerful weapon today, it is our moral duty to offer it to them first.