In India, data and public policy communities speak two different languages. We have to find ways to demystify data, and convince the establishment that it isn’t complicated, and that it can help in making better decisions, says OP Agarwal, CEO, WRI India.
The impact of COVID-19 pandemic on the global economy is for everyone to see. The Indian economy has also taken a hit, though it’s too early to predict the extent of damage, as we still haven’t touched the peak of the crises. When we will get there is anybody’s guess. The economic recovery, to a great extent, will depend on how long this pandemic, and the consequent restrictions, last. If it happens in three-four months — like in the case of China — then the recovery will be quick and stable, but if it takes a longer time — say six-seven months — we will take a long time to get to the pre-COVID stage.
In India, migrant labor and daily-wage workers have been very badly hit. The government’s initial efforts have, very rightly, focused on the health front. As a result, the attention to the plight of the migrant labor got delayed. It has been a challenging task, especially since there is little clarity on the exact number of such migrant and daily wage/informal workeers. However, now that the action on the health front is under control, things will hopefully get better for migrant workers, daily-wage laborers and other vulnerable sections.
India is known for its swift recovery from economic crises. We recovered faster than others from the 2008 crises. Even now, IMF has projected a positive growth of 1.9% for India in 2020. It is only one out of two countries that the IMF thinks will still have a positive growth. Next year, the economy is estimated to have a growth rate of 7.9%. India has been able to recover fast from such economic crises primarily due to a large domestic market for its products and relatively lower dependence on the global market.
This transitory phase — until the process of recovery begins — is going to be crucial. While most white-collar workers will continue to get salaries, even if it means taking home less due to cuts, the gig workers, or those employed in the unorganized sector, will have to endure hardships for some time. It will take a large amount of public spending for these people to get back on their feet. In some cases, the psychological impact will be deeper than the economic impact, particularly for sectors like tourism and transport, for whom the picture will stay grim until people start going out and resume pre-COVID activities.
Geospatial data for policymaking
Geospatial data is fundamental to all forms of policymaking and policy action. The COVID-19 global pandemic has in fact highlighted the importance of such data in dealing with a massive health crises. Moving forward, this data will play a very important role in social and economic recovery.
WRI has mapped electricity access to economic potential in two states in India. This helps take decisions on how to locate new solar energy facilities. We can suggest places where more solar plants/facilities should be installed, to help boost economic activities in those areas. This can further help in improving healthcare systems, especially in remotely located rural areas. This is an example of how geospatial data and information can be used for rebuilding after the Coronavirus crises.
In the absence of such data, decisions are taken in an unscientific manner and often on the basis of who makes the loudest demand. Another problem is that the data and public policy communities speak two different languages. We have to find ways to demystify data and convince policymakers that it isn’t complicated, and that it can help in making better decisions. The key is to bring these two communities together. Today, there is no dearth of data, but still we see public policy being led by personal perceptions, rather than concrete data
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Embedding data into the system
As far as the government is concerned, every department has its own sets of data. Good decision-making requires these datasets to be properly overlaid so that they offer suggestions for management. Institutionally, governance is too fragmented for this to happen. This must get corrected. I feel that having data teams across ministries/departments — both at central and state levels — can be a solution. We need data management and use to become a part of the larger system.
For example, WRI works a lot on Climate Change. Unless the concerns about Climate Change get embedded into the planning process in each department, be it urban affairs, infrastructure development, or rural development, you will have officials thinking that Climate Change is someone else’s baby. This is not good. Climate Change has to be everyone’s baby – just like data driven decision-making has to be everyone’s baby.
Obtaining data, making sense of it, managing it and eventually using it for taking decisions is a process that needs to be embedded at all levels. That requires a massive capacity building initiative and educating people that data is crucial, and it’s not difficult to deal with.
Enabling sustainable development post COVID-19
Of the 17 diverse Sustainable Development Goals, those around poverty alleviation and removal of hunger are fundamental to a decent life. So, that is our absolute priority. A large section of the population is, and will, suffer due to the pandemic. We must use this as an opportunity to realize that we need data to do scientific work and frame public policies.
We must start building systems that give us the right information to guide our actions. We must collectively look at people who have lost their jobs and see how we can help them get out of the poverty trap. We must focus on the migrant problem and work towards building an environment in which they feel safe and comfortable. These workers are critical for city development and contribute to our habitats.
So, we must understand their difficulties and put in place systems that can enable them to come back, or continue working in cities, without fear or stress. In this case, psychological comfort is as important as economic support, and so we must be empathetic towards the needs of these workers. Since the Novel Coronavirus pandemic has not been uniformly spread across the country, we need to have a granular approach to figure out what all we can start and where. A graded approach to economic activities can be a viable situation. But doing this right needs good data and the use of such data. The current approach of classifying areas into red, orange and green is an example of how data is being used to deal with an extremely difficult situation.
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