NSS: Not Just Statistics for Nerds, but an Arsenal of Data for Business and Policy

When strategy or marketing teams consider how best to reach the Indian consumer, the question arises: which entity to consider? Panchayat, city, constituency, district or state? The same question arises when planning investment allocations or developing program budgets. The answer is often the opposite: Why do we have data?

I argue that the regional NSS framework, as defined by the National Sample Survey Organisation (NSSO) – which divides India into 85 regions – offers a very useful tool for both business strategies and policy planning.

I raise this issue in the context of the birth anniversary of PC Mahalanobis on June 29, also celebrated in India as “Statistics Day”.

The National Sample Survey (NSS), established in 1950 under his pioneering leadership, was intended as a series of large-scale nationwide surveys to collect information on all aspects of the economic life of citizens. Over the years, it has provided detailed data on various economic, social and demographic indicators in numerous regions of India.

The NSSO, for its all-India survey, divides each state into regions, each of which is a group of districts. As a result, the 28 states of India have been divided into 85 regions.

How many people actually know about NSS?: Although everyone knows about the Census, it seems that the NSS is mainly mentioned only among statisticians and researchers, and we hear about it when reports are published or when the veracity of public datasets is questioned.

More than the data itself, let me draw your attention to the regional framework of the NSS, which is rarely talked about. Since 1950, expert statisticians at the NSSO — now part of the Ministry of Statistics and Programme Implementation — have been working to establish a set of regions based on homogeneity related to agro-climatic conditions, demography, geographical proximity, etc. This not only provides a sensible sampling framework for research, but can also be considered for wider use to understand how Indian economic and social life is organised. Which begs the question, why, when so much thought (and investment) has gone into developing it, why is it relegated to the realm of statistics and reporting?

Let’s make the most of what NSS has to offer:I propose to expand the scope of application of the NSS framework and actively use it for business development, strategic planning and government policy.

Let me dispel the notion that NSS is for geeks. For example, companies should consider organizing sales teams from an NSS perspective and conducting reviews at that level (a scientifically defined set of constituencies), especially for products and services that have significant consumer penetration at the sub-state level. Companies such as Unilever and other large FMCG companies have benefited from developing robust regional strategies based on dividing the market in a way that more targeted sales. However, not every company may have the resources to invest in this institution.

Of course, the NSS approach is not the only form of region classification, but it remains a well-developed framework that is available for wider application.

Similarly, strategic teams – both in large companies and new ventures – can benefit from analyzing the economic potential of these regions and see which ones are better suited for sales markets, sources of raw materials or paths for growth and investment, and then plan where to create channels distribution, where to locate the next factory, etc., and develop region-specific strategies.

For example, Madhya Pradesh is divided into seven NSS regions, Assam into four, and Punjab into two, with each region having distinct characteristics and specific consumption spending patterns.

NSS is versatile: From a government perspective, it will be important for states to use the NSS lens to identify regional strengths to develop structural incentives and fiscal allocations tailored to specific regions within states that can help increase both output and jobs. This also applies to philanthropic organizations, civil society, and all those working on grassroots development and service delivery. Designing and fine-tuning outreach through the NSS lens can provide a targeted approach that takes into account the needs, aspirations, and strengths of a locality and its people.

Steps can be taken to identify regions and clusters requiring focused attention, for example to strengthen access to health care and education or reduce multidimensional poverty.

If we look at the data itself or the information architecture, periodic NSS surveys provide regional estimates of poverty, employment, consumption, and spending, to name just a few indicators. Of course, there have been debates, but the political will must join forces with measures to ensure timely availability of data at the individual level with appropriate aggregates to enable use not only by academics but also by the private sector.

Further, all other sub-state level datasets, whether generated by the government or by companies like Ola, Oyo, Swiggy etc. should be aggregated at the NSS level and given NSS region tags. Triangulating scattered information has its share of known challenges. In fact, a designated NSS code should be mandatory for all sub-state level datasets collected by any agency, public or private.

Combining this with the Indian data stack, as well as newer datasets such as GST network data or satellite imagery, can give us local insights, but at a manageable level of aggregation.

India’s 28 states were too many and the districts too many (806 were eventually counted). This set of 85 NSS regions may not be perfect, but it is practical and representative in terms of planning, management, review and operation.

Today, India has much to be grateful to Mahalanobis, the statistician we fondly remember as “The Professor.” Keeping his legacy alive means not only celebrating his work and the institutions he created, but also rethinking how the building blocks, such as the regional NSS framework, can be further applied.

I recognize the challenges involved, but I argue that by adopting newer tools and strengthening our digital public infrastructure, we can further accelerate this process by adapting, investing in, and building on well-researched data frameworks that are (in some sense) public goods that exist but are underutilized. The wisdom of this great man – as it should be ours – was in realizing that the collection of robust national statistics is not an end in itself, but a tool to enable proactive planning not only by central and state governments, but also by social enterprises and businesses too.

The author is an impact advisor, former director of Tata Trusts and author of Regional Economic Diversity: Lessons from Emerging India