People Research on India’s Consumer Economy (PRICE) is a fact-tank and a think tank set up to provide the ‘people-level’ ‘single window’ view on Consumer India and Citizen India. It focuses on how Indians earn, spend, borrow, save, live, think and access public goods.

Despite the good humoured jibes that came our way about the name “People Research”,we stayed with it because we observed that in India macro-economic data and supply side data dominated almost all discussions on the country’s well being and progress, and very little was based on what these numbers translated into in terms of impact on the lives of Indian citizens. Such data, we have seen in our work, has slayed a lot of beautiful hypotheses, in both the business and policy making arenas as well as in the polity, with ugly facts (to borrow the phrase from biologist Thomas Huxley).

We also decided to go the expensive route of being a fact tank that generates robust, pan Indian, people-level primary data that plugs existing data gaps, while also ensuring compatibility for it to be used in a complementary fashion with large government surveys like the NSS and the Census of India.

People-level data that is representative of all Indians is hard to come by. Critiques and debates around the soundness of policy and the performance of policy makers and governments are typically based on ‘supply side’ actions and supply side outcomes of policy (e.g. kilometres of road built, number of students finishing high school) rather than what ‘people impact’ they had (e.g. resultant changes in income earning methods and amount earned). For example we know from macro-economic data that the share of agriculture in India’s GDP and even in India’s rural economy is shrinking; but from this statistic alone, exactly what the extent and nature of financial dependency of rural households is on agriculture can only be conjectured - and often incorrectly is. This is far better understood from knowing how many households have what proportion of their income coming from farm and non-farm activity, how many households have exclusively non-farm income and is this non-farm income urban connected, agriculture connected or neither? Our data shows that there are 17% of rural households which exist solely on farm income and collectively have 18% share of all the income of rural Indian households; 39% of rural households have both farm and non farm sources of income and 40% share of income, and 40% of rural households have only non farm income (including labour that does not work in agriculture) and have 40% share of income. By our estimates, putting all this together, 35% of rural India’s household income is from farm activity at an all India level, obviously varying from state to state.

There are of course the mega data bases of the Census and NSS that do provide the people-level view; but aside from their less than ideal frequency that does not permit a regular feedback loop to business and policy, they also do not have all the measures needed to provide a comprehensive and holistic understanding of what is happening to ‘People India’. To do this, we need data on income, expenditure, occupation, education, borrowings, savings, living conditions and access to public goods data to reside in the same data base from the same (or exactly comparable) respondents, so that linkages between them can be understood and used for, say, business opportunity mapping or for policy design. For example, in rural India, as a result of having an integrated data base, we see very different patterns of debt borrowing in households with different levels of farm and non-farm income - intuitive perhaps, but still needing numbers attached to them to understand performance of the financial services sector more concretely in serving rural India. We also see how much more income is possible as a result of every additional year or additional level of educational attainment, in cities, small towns and villages. Is it any wonder that people drop out of school! We know from the Census of India how many females headed households there are and where they reside; what we additionally know from integrated data base analysis is how many they are by farm (by farm size) /non-farm (by type occupation) source of household income.

Another example of basic and crucial information not available in a robust and rigorous fashion is data pertaining to how the country’s household income is actually distributed among the almost 300 million households - at the most basic level, how much share of India’s income accrues to the poorest 20% and how much to the richest 20% and how much to those in between. Standardised models based on analogous countries can be applied to provide such distribution, but our primary data is often at variance , and with good reason, with these estimates no matter how eminent the source of the estimation. According to our 2016 data, 45% of India’s household income resides with the richest 20% of households, almost 8 percentage points down from 2004-05. The poorest 20% have a 7% share of India’s household income, up a little less than 2 percentage points from 2004-05. What is even more interesting to see is how this income is spread over different states and rural and urban town classes, different occupational and educational groups, different levels of infrastructure development and so on; and to study these not one at a time but taken together.

The census tells us that census towns are increasing in number and hence they harbor an increasing population. However, what we do not know unless we have people-level information is what income people living in these towns have, and how they earn it. Business analysts talk of the increasing consumption importance of so-called tier two and three towns but cannot say much about how much of India’s income lives there or how it has grown or what the nature of occupation or living conditions is of people who live here, and whether it is changing over time. It is such data that we strive to generate so that we can go beyond supply side data of how many crores of potato chips and cell phones people in each town class buy, or even the rate of urbanization, which by itself is of limited utility for business planning or urban planning policy. Finally, there is immense diversity in how the almost 300 million Indian families earn, spend, save, live, think and access public goods. God is found in the detail of the segmented sliced and diced analysis of all India data to uncover patterns and phenomena both known and new.

PRICE has been set up with the mission of providing such macro- consumer or people level data and insights about Consumer India and Citizen India for use in business strategy and public policy. It is a not-for-profit company that is committed to putting more and more of its data in the public domain, as has already been done with the pan Indian ICE 360⁰ Surveys of 2014 and 2016 (ICE stands for India’s Consumer Economy).

Rigorously collected, all India representative data on multiple parameters, that enables disaggregated and insightful analysis is expensive. This is even more so when we seek to get robust estimates of income collected using far more rigorous methodology than relying on claimed income by the respondents, as many income surveys do. The last ICE 3600 survey we did on India’s consumer economy and citizens environment was in 2016, the one prior to that was in 2014. The 2016 survey was made possible thanks to a grant from the Jamsetji Tata Trust, which we gratefully acknowledge. There are significant learnings we have had from mining this data about how India earns, spends, saves, lives, thinks and accesses public goods. We hope we can enthuse more people in worlds of business and public policy to join us and support us in our endeavour to generate more and more people-level, data- driven insights and put in the public domain. We also are seized of the fact that we need to do another round of the ICE 360 survey since a lot of changes have occurred in this period as our estimates for 2018 show in this report. Our spirit in very willing though our flesh is weak! We will, however, continue to strive to make it happen.

Our grateful thanks also to all the intellectuals who inspire and challenge us to do more, and provide us with opportunities to share our work for a larger good. Thank you Bibek Debroy for all this and for the foreword that sets the context for a data based report with clarity and simplicity.