“Chodo kal ki baatein, naye daur mein likheinge hum milkar nayi kahani.” The “Nayi Kahani” of our times, our “Naya Daur”, is “Vikas”, i.e. development. Its main currency is human capital built through high-quality education and health. Data and indices of global and national scope are considered the key tool for developing human capital. It has become a truism that better data leads to better policies and a stronger education and health system. Sadly, this simplistic understanding hides the many ways in which data works against the very goals of development.

Take the example of the health sector. Reproductive, child and maternal health has always been a priority and we measure the state of a health system based on the effectiveness of maternal mortality rate (MMR), infant mortality rate (IMR), vaccination, under-five mortality rate (U5MR) and other related indicators. perform. But we don’t often look at whether improvements in MMR and IMR are due to primary health centers (PHCs) providing important services to reduce MMR and IMR or whether they are bypassing them in favor of hospitals . Are an understaffed frontline health system and overburdened staff the reason we are meeting vaccination targets at the expense of other health goals?

In education, enrollment figures and literacy rates have historically dominated public data sets, masking the poor quality of learning outcomes. Learning outcomes data became public in the early 2000s when Pratham published ASER surveys and published them as part of the National Achievement Surveys (NAS) of the National Research and Training Council in education from 2017. Today, learning outcomes data has taken over other aspects of the education system, which could actually lead to significant improvements in learning. For example, we do not yet have data measuring the quality of teacher training, which we know is the weakest link in the education system.

Then there is the problem of disproportionate attention to quantitative data on outcomes and impact. This is partly because it is easier to collect, analyze and compare. But much of the complex story of social protection can only be told through case studies, qualitative reports, stories of policies and institutions. We have much to learn about why public elementary schools (which are attended by first-generation learners) have young, inexperienced teachers and not experienced teachers. This requires a socio-historical and gender analysis of primary teachers since independence and an analysis of teacher recruitment policies. Or the question of why transfer policies for frontline health workers are incredibly difficult to design.

The larger issue is that data, whether quantitative or qualitative, means nothing on its own if it does not flow from a constantly updated progressive agenda. What we need to question is the political agenda, not just the data. Many Indian states have significantly improved their health indicators and achieved the globally recognized Sustainable Development Goals (SDGs), but our policy agenda must dig deeper into the data and ask at what cost? The data tells us that public school enrollment is decreasing and private school enrollment is increasing. But does the problem come from an excess of school facilities, or from poor infrastructure in these establishments, or are the markets truly offering better primary education facilities? If our public school teachers believe that private schools are preferred because of their superior infrastructure, then our policy action cannot focus solely on literacy rates at the state or even district level. Data makes a problem visible, but it doesn’t tell us what the biggest problem is, how to solve it, who will do it, and at what cost.

Whenever an issue is measured quantitatively and the data is released publicly, it tends to consume a disproportionate amount of political debate. Indexes that compare countries and states further highlight well-being issues for which measurable data is available. This invariably creates winners and losers among countries, states and newspapers over very complex realities. A state ranked well in terms of health indicators and known as socially progressive like Karnataka faces immense challenges in health and education in its northern districts. A state that is well behind the SDG targets for MMR and IMR, like Rajasthan, has made huge progress in maternal and child mortality. Indicators that do not perform well are often removed from public discourse entirely. For example, Rajasthan lifted many people out of intergenerational poverty by reducing out-of-pocket health spending through insurance, but failed to improve primary health care utilization. Patients go to the hospital for basic medical problems like fever and cough, not for primary health care located within a mile or two. The Niti SDG Index 2024 classifies India’s 36 states into achievers, pioneers, achievers and aspirational countries based on the total scores they receive for the SDG indicators. 31 states are ranked as frontrunners for health SDG Goal 3, which is just one tier below the top category. But the experience of health facilities in Karnataka is very different from that in Rajasthan, even though both top the index and vary significantly within Karnataka and Rajasthan.

As visionary policy ideas take root in India, for example the Health and Wellness Center (HWC) or the integration of early childhood education into formal education, we must avoid the pitfalls of a simplistic relationship between social protection data and results. A simple way to do this is to move beyond quantitative data sets and develop other types of knowledge products. We need case studies of policies and projects, oral histories of reform leaders, analyzes of the role of government agencies and bureaucrats. For example, how many of us know what the state level planning department is supposed to do and whether it no longer plays a useful role in the state bureaucracy, impacting the achievement social welfare? When large data sets are released, such as the NAS or National Family Health Survey-5, sponsoring agencies must also produce rigorous, mixed-methods qualitative studies that delve deeper into specific aspects of health/education issues than the sets of data are supposed to resolve. This should also include a discussion on the role of agencies and actors responsible for implementing the data.

The hardest part of getting data to drive us toward real development is anchoring data sets in a progressive policy agenda ambitious enough to make public facilities truly public in nature, not an option of last resort for the poorest of the poor. Should reproductive and child health (RCH) be a higher priority than geriatric care? Is it good to improve learning outcomes even if the condition of public school facilities continues to deteriorate and no one who may read this comment will send their child to a public school?

India is a global leader in statistical analysis and is well placed to lead the way in how and, importantly, when good data produces effective policy. It’s time he claimed this position.

This article is written by Priyadarshini Singh, Fellow, Center for Social and Economic Progress (CSEP), New Delhi.