By Pulak Ghosh & Soumya Kanti Ghosh
With the second Narendra Modi government being sworn in tomorrow, it’s time India embraces emerging technology and big data in a much bigger way to achieve the goals of the government with precision and swiftness, ensuring that benefits go to the right people and right places, be it economy, health, agriculture or direct benefits. Policymaking must be evidence-based and not on unscientific and outdated surveys.
Take the goods and services tax (GST) that was implemented two years ago and is getting stabilised slowly. We need to use big data and machine-learning tools to derive insights that can be effectively used in successive budgets and economic policymaking.
A proper use of GST data will reveal the sectors that are giving maximum revenue, that are showing month on month increase, and can make predictions of net revenue growth and help in fraud detection. Also, since India is a consumption oriented economy, we could explore measuring GDP using the GST data.
Agriculture has been a key focus of the previous government. More accurate prediction of crop yield is necessary. One needs to combine images from crop-cutting experiments, weather and soil information with satellite data to develop machine-learning algorithm to predict and understand crop yield better. Additionally, the use of supply chain analytics is essential to make farmers and consumers effectively talk to each other.
More insights on the health situation of India are needed to make schemes like Ayushman Bharat more successful. The use of data is essential here to take proactive steps to even address, let alone solve, issues related to malnutrition and under-nutrition. We need to develop a nutritional inequality index across India and find correlation to various disease incidences to help develop a more insightful health policy.
Big data can also be used for developing real-time skilling. Unlike a decade ago, the percentage of people dropping out of learning institutions at an early age is falling. Earlier, most of them would enter the workforce at around the age of 17.
This age has now increased to 23-24 years — thereby explaining why National Sample Survey Office (NSSO) data shows a high unemployment rate among the young.
We need to connect the aspirations of these groups with the demand of the ever-changing market. Every year about 1% of the agricultural labour force is shifting out of agriculture. They need special skilling.
Unlike across the world, India is still lagging behind in its use of data analysis. The current methodology is based mostly on thin surveys and not supported by current modes of data collection that are more comprehensive, less biased and real-time based on digital footprints.
The Centre for Monitoring Indian Economy (CMIE) survey designed for measuring employment, for instance, is a household survey, and its focus is on consumption. Because it’s a household survey, it also captures employment data. The problem with such a data set is that it could be surveying the same households and, thereby, making the sample nonrepresentative after a few years. For a sample to be representative, one must have some way of knowing about the full population. But there is nothing one can do about it, since one can’t have a census every year.
Today, we need to develop an ecosystem that’s high quality, timely and accessible. Big data and artificial intelligence (AI) are key elements in such a process. Big data helps acquire real-time information at a granular level and makes data more accessible, scalable and fine-tuned.
In turn, the availability of such real-time information can help in accelerating the speed and scale at which policymakers can implement change.
Pulak Ghosh is Professor, IIM Bangalore. SK Ghosh is Group Chief Economic Advisor, State Bank of India
DISCLAIMER : Views expressed above are the author’s own.