Outcomes aside, the Delhi government’s odd-even plan has yielded a rich bounty. It sets the template for citizen engagement with a public policy reform experiment: heightened awareness regarding the core issue, mass participation, intense public scrutiny, and a data-driven discourse. Let’s take these one by one.
Heightened awareness: From the Delhi high court downwards, it is widely acknowledged that this government move has made pollution a talking point and increased general awareness. Odd-even has consistently trended on Twitter (the barometer of our times of how important a topic is) and has sparked numerous prime-time slanging matches. No doubt there is also a haze of misinformation, but mostly, people are now hearing more about air pollution, how it affects their health, and what the various ways to deal with this problem are.
Mass participation: Largely as a result of the above, citizens in Delhi have demonstrated an overwhelming level of compliance with this experiment. I do not believe that just the fear of fines was sufficient to bring about this level of compliance. People have participated in solidarity with the scheme, partially out of their concern for the levels of air pollution, and also possibly, a curiosity to see if the experiment will yield any results. Either way, this experiment would have been a non-starter without this level of mass participation.
Intense public scrutiny: Just as much as commuters in Delhi have participated in the scheme, a wider population has actively dissected this experiment and come out on both sides of the divide. This set of observers and analysts has highlighted implementation challenges and brought out data on current and historical pollution levels gathered and presented from multiple sources—government and non-government. It has been a public policy enthusiast’s dream come true. Heavy public participation, accompanied with this level of public scrutiny, makes for an ideal public policy reform experiment.
Data-driven: The resolution of the debate over the effectiveness of the odd-even plan has to rely on data, and what is available currently is not all robust or scientifically well-informed. But in order to measure impact and arrive at a conclusion regarding the effectiveness of the scheme, such hard data will have to be collected and analysed. One could also look at data on the numerous indirect and unintended benefits—decongestion is a prominent one, for example. To the quantitative data on pollution readings, one can add the qualitative data on people’s perceptions, attitudes and behaviours.
The reality of policy experimentation
Resistance to public policy reform and experimentation ranges from the gentle to vitriolic. Politically, one can argue both sides of the coin—either that this is yet another gimmick by Delhi chief minister Arvind Kejriwal, or that the success in implementing the experiment reveals how strong public support for him continues to be. Reactions to the odd-even policy also reflect entrenched positions, many of which are political and motivated. But importantly, not all of them are so. Understanding this is crucial: a public policy reform will be subject to serious criticism not just motivated by ‘interests’, but stemming from genuinely opposing ideas as well. Those initiating a reform must be prepared to learn—trial, review, tweak, and trial again.
So what next?
It is often said that we get the government we deserve. Citizens in Delhi have participated honestly in a worthwhile public policy experiment. While the final outcomes will be ascertained once more data is collected and analysed, it is clear that problems like pollution can only be tackled with a critical mass of people coming together; collective action that can look beyond personal inconvenience. In return, what they deserve is a government that is committed to finding answers to difficult questions—in this case, a government that is willing to explore all possibilities to devise a set of interventions that can tackle the scourge of air pollution in Delhi.
This is my latest Livemint column