NITI Needs A NUDGE
To design strategic and long term policy and programme frameworks and initiatives, and monitor their progress and their efficacy…
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In 2010, the newly elected UK government created a 'Nudge unit', more boringly called the 'Behavioral Insights Team'. The objective of the unit, was simple but ambitious: provide behavioral insights to the Government of UK so that existing government policies could be more effective. Nudge revolutionized thinking because it put the focus on design of policies and shifted it from the objectives of policies. It correctly asked the question around 'how' rather than 'what'.
Why does that matter? Here's an example. Most doctors don't wash their hands, not because they don't know. Its because they either forget, or they are in a hurry, or the soap isn't there or they just are dealing with too much. This leads in turn to a lot of hospital-based infections. Handwashing is just not a part of automatic behavior for a lot of doctors. A combined team from Imperial College London and University of Warwick, found that installing hand sanitizers by themselves didn't work. BUT spraying the air with a fresh lemon scent, just before doctors entered the surgery room, changed their behavior. The lemon scent reminded them to sanitize their hands. This in turn reduced a lot of infections and saved lives.
In a now famous example, the Behavioral Insights Team (BIT) found government tax revenue to be inordinately small (sound familiar?) UK tax authorities also spent inordinate amounts trying to recover these taxes, mostly from well-intentioned and otherwise well-meaning citizens who were just lazy. BIT found that a letter with an added line worked well in 'nudging' people to pay their taxes. The line was 'Most people who receive this letter pay their taxes immediately'. The government of UK earned approximately GBP 210 million in additional net tax revenues that year.
So what can the government of India learn from this?
Two years ago the UPA government announced a new and independent office - the Niti Aayog (Niti is an acronym for National Institute for Transforming India). A successor of the planning commission, amongst its many charges are two important ones listed on their website:
'To design strategic and long term policy and programme frameworks and initiatives, and monitor their progress and their efficacy…'
'to actively monitor and evaluate the implementation of programmes and initiatives, including the identification of the needed resources so as to strengthen the probability of success and scope of delivery'
Upfront this is good news. Niti Aayog not only has the mandate to think making policies effective but also evaluating and learning from them. On the other hand, in the more than two years that Niti Aayog we have not witnessed much in terms of action. There are three things that the Aayog can and should do.
First, Niti should first and foremost, evaluate big Government of India programmes. Unfortunately we still don't know whether the government's biggest public works programmes work: NREGA (the National Rural Employment Guarantee scheme), NRLM (National Rural Livelihoods Mission), NRHM (National Rural Health Mission) etc. are amongst the largest public works programmes in the world. Evidence about their effectiveness in reducing poverty and increasing resilience is by turns, patchy and anecdotal at best.
Second the Aayog would do well to focus on implementation research. How can programmes be made more effective? How can we reduce leakages in big government programmes? What can programmes do to reach its target population? How can the uptake of social programmes be encouraged? Unfortunately there is no public agency empowered to answer these questions.
Third, focus on data. Although India prides itself at producing a lot of data unfortunately this does not always mean evidence. Additionally the quality of data in data remains perilously bad. One of the leadership roles that Niti Aayog can take, is setting standards for good data (meta data, training quality and protocols for data collection) while also in the long term thinking about quality assuring data.
We have good ideas. It's time to start implementing them.
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