An important corollary of Digital India is the sheer amount of data that gets generated. Such data can power analysis offering insights for (a) a granular understanding of the patterns of economic and social activity in the country, and (b) designing appropriate and data-based policy interventions.
As the Covid pandemic eased and the economic activity went back to normal, economic analysts looked out for data sets that offered a deeper understanding of the Indian economy. Tables with high-frequency indicators (HFIs) became all the rage during Covid so that one could see at a glance how things were shaping up during and post the lockdowns. While HFI tables continue to be useful, there were limited “signals” emerging from them as the situation returned to normalcy across almost all parameters of demand, supply, trade, logistics, tax collected and spent, among others.
As part of the HFI, the value and volume of FasTag monthly swipes formed a core part of our understanding of the logistics movement in the country. FasTag is now largely mandatory for tolling on national and state highways. However, these devices are now also used for other payments, like parking and fuel. As more state highways adopt toll collections via FasTag, the monthly numbers increase due to economic activity, toll rates, and higher adoption.
Dig deeper
Enter data from the Indian Highway Management Corporation Limited (IHMCL). IHMCL has, over the last many months, given granular data on the tolls collected at each tolling booth. This information set contains daily volume and value of toll collected from a wide variety of vehicles. Since toll plazas are neatly mapped to their latitude-longitude, such data can also be converted into visually intuitive map representations. Given all-India coverage of this data, across more than 800 toll booths, a map representation offers immediate perspective on the economic activity in various parts of the country.
An example of what is possible is shown in the accompanying map. The circles represent location of the toll booths, relative sizes are the relative collections at each booth (or, in case of volume charts, traffic), and the colour represents change over a corresponding period, in this case, year-on-year.
Given that last year saw elevated Wholesale Price Inflation (WPI), the toll collected increased across the country. This shows up in the preponderance of “green” in the map, showing good growth in value. Many other economic takeaways that can be built upon this basic dataset.
Data power
Such disaggregated, granular data sets, put out in public domain, create their own utility. Every user has a different perspective, and availability of such data sets allows them to explore analyses closest to their line of work and requirement. The same data set, in the hands of an economic analyst and a roads technical planner can yield different insights. Such different perspectives generate ideas on more robust design and development.
The spatial and temporal dimensions of disaggregated data offer insights to local governments and cities which can be catered to their unique situation. Cross pollination of ideas can happen only when different constituents realize the unique positions that they are in. Such analyses create opportunities for learning and improving the efficiency and effectiveness of the system. The temporal data sets offer a perspective on evolution—interventions can be planned for engendering or managing growth, as required.
Cross sector linkage of the data and analysis can help identify the intervention that needs to be planned. For example, if a similar dataset from Railways can be paired with the roads freight data, many new types of insights can de detailed (last mile connectivity, time taken, sectors covered, etc.) As more data sets come in the public domain, each can learn from the other to design better outcomes.
PM GatiShakti
The PM GatiShakti initiative which covers a wide swathe of Indian infrastructure aims to create such interlinkages between development. As more data sets get released in public domain, this will require a cadre of data analysts who can help define, analyse, and interpret the treasure trove of digital data. These new analysts and analyses can help design effective policy interventions.
Data-based analysis and development plans can bring out more effectively how the economy and society is structured and operating. These can then be weaved into designing India’s infrastructure. An integrated infrastructure strategy offers investors confidence in the offtake and commercial viability of their investments.
Policy interventions which reduce volatility can help bring down the cost of capital. Similarly, large datasets, regularly released, can offer investors a chance to finely price their risks—bringing down the risk premium attached to projects. Such lowering of risk perception, driven by a deeper understanding of the data, can feed into lower cost of capital.
Digital India should form the base of, and usher in an era of, Analytical India (AI).
The author is with National Investment and Infrastructure Fund Limited. With research assistance from Akshata Kalloor.
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