India, July 1 -- For most of human history, one's life trajectory was determined right at birth. If you were born in a royal family, you led an opulent life. If you were born in a serf's family, you led a life of misery. The industrial revolution and the spread of democracy helped create a more dynamic world. In the modern era, a cab driver's daughter can dream about becoming a billionaire. A billionaire's son can have nightmares about going bankrupt one day. Yet, even in the modern world, the distribution of wealth and power is far more unequal than the distribution of talent. A 2018 research paper by the Italian physicists A Pluchino and A Rapisarda and the economist AE Biondo shows how randomness (or luck) could help explain this puzzle. Using a simple simulation, the trio show how mediocre-but-lucky individuals are likely to reach much greater heights of success than their more talented peers. Several studies on stock market investors reach a similar verdict: luck plays a big role in generating outsized stock market fortunes for a few. The rest have to be content with average or below-average returns. In socially stratified countries such as India, it is not just randomness that generates unequal opportunities or outcomes. The place of your birth, the language(s) you speak, your parents' level of education, your caste, and your gender determine the life you lead. If one's peers from the same socio-economic background are doing well in life and one isn't, one can blame it on luck. When most people from one's community are struggling in life, one usually ends up blaming the "system". In a 1973 research paper, the American economists Albert Hirschman and Michael Rothschild had noted that people's tolerance for inequality depended on the pace of upward mobility for people around them. Hirschman and Rothschild used the analogy of a traffic jam on a two-lane highway to drive home this point. If the right lane of the highway begins moving, those on the left lane might initially feel optimistic, and wait for their turn to move. But if the left lane remains stuck while the right lane keeps moving, optimism will make way for frustration soon enough. If some communities get left behind as others progress, the former are bound to feel frustrated. Such resentments are socially corrosive and politically hazardous. Over the past seven decades, the Indian State has used various tools to address such resentments. It has provided scholarships to students from deprived communities. It has funded a number of welfare and livelihood schemes to give a leg up to the poor. Most importantly, it has created a system of quotas to ensure better representation of marginalised caste groups in educational institutions, public sector jobs, and elected bodies (panchayats, state assemblies, and parliament). Both supporters and critics of such affirmative action policies believe that the Indian State can do better in this regard. The Union government's decision to include additional questions on caste in the upcoming census is a welcome step in this direction. By providing more up-to-date data on caste groups and their living conditions, it can help fine-tune affirmative action policies. However, there is only so much evidence a one-time census can collect. What India needs is a dynamic and granular database on socio-economic inequalities. This will need significant statistical investments, and can be best achieved if census operations are brought within the ambit of India's statistics ministry. Each census can then be followed up with more detailed district-level surveys on specific aspects of caste-based and other forms of deprivations. Taken together, the data from censuses and surveys can be used to design improved affirmative action policies. For instance, within one broad caste group, upward mobility may differ considerably across sub-castes or jatis. A one-time census can't be expected to track shifts in social group dynamics over time. However, repeated representative surveys can help collect such data on an ongoing basis. How deprived or "backward" one social group is with respect to others can be tracked over time to enlarge State support for some groups, and scale down such support for others. Such data could also help us reimagine the social justice agenda. The political scientist Suhas Palshikar has argued that the State should construct a multi-dimensional index of backwardness to determine the targets of affirmative action. The index could, for instance, take into account factors such as caste, occupational category, asset-ownership, and location. Such an index could be used to generate a "backwardness score" for each social group, and focus policy attention on the most disadvantaged. The State may not be very effective in countering the role of luck in one's life. However, it can be quite effective in tackling structural inequalities. A new affirmative action toolkit built upon a revitalised statistical system would allow the Indian State to tilt the playing field in favour of the most deprived....