“Chodo kal ki baatein, naye daur mein likheinge hum milkar nayi kahani.” The ‘Nayi Kahani’ of our era, our “Naya Daur’, is ‘Vikas’ i.e. development. Its key currency is human capital built through high-quality education and health. Data as well as indices of global and national scope are regarded as the key tool to develop human capital. It’s become a truism that better data leads to better policies and a stronger education and health system. Alas, this simplistic understanding hides the many ways data defeats the very purposes of development.
Take the example of the health sector. Reproductive, child and maternal health has historically been prioritised and we measure the state of a health system based on how well Maternal Mortality Rate (MMR), Infant Mortality Rate (IMR), immunisation, Under Five Mortality Rate (U5MR) and other related indicators perform. But we don’t often explore whether the improvements in MMR and IMR are due to Primary Health Centres (PHCs) which provide important services for reducing of MMR and IMR or by bypassing them for hospitals. Is a poorly staffed frontline health system and an over-burdened staff the reason why we are hitting the targets for immunisation at the cost of attention to other health objectives?
In education, enrolment figures and literacy rates historically dominated public data sets, which obscured the poor quality of learning outcomes. Learning outcome data came in public prominence in the early 2000s when Pratham came out with ASER surveys and post that, by National Council of Educational Research and Training’s National Achievement Surveys (NAS) 2017 onwards. Now, learning outcome data has taken over the discourse over other aspects about the education system which may actually lead to significant improvements in learning. For example, we don’t yet have data that measures the quality of teacher training, which we know is the weakest link in the education system.
Then there is the problem of the disproportionate attention to quantitative data on outcomes and impact. This is partly because it’s easier to collect, analyse, and compare. But, much of the complex story of social welfare can only be told through case studies, qualitative reports, histories of policies and institutions. We do have much to learn about why government primary schools (that are attended by first generation learners) have young inexperience teachers and not the senior experienced ones. This requires a socio-historical and gender analysis of primary school teachers since Independence and an analysis of teacher recruitment policies. Or the question why transfer policies for frontline health staff are incredibly difficult to design.
The larger question is that data, whether quantitative or qualitative, by itself, means nothing if it is not derived from a constantly updated progressive agenda. What we need to question is the policy agenda, not just the data. Many states in India have dramatically improved health indicators, and met globally acclaimed Sustainable Development Goal (SDG) targets, but our policy agenda needs to push the data further and ask, at what cost? Data tells us that enrolments to government school facilities are falling and those to private schools are increasing. But, is the problem one of excess of school facilities, or poor infrastructure in these facilities, or do markets genuinely provide better primary schooling facilities? If our government school teachers think that private schools are preferred due to superior infrastructure facilities, then our policy action can’t just focus on state or even district level literacy rates. Data makes a problem visible, but it does not tell us what the most important problem is, how to solve it, who will do so and at what cost.
Whenever an issue is quantitatively measured and the data is publicly disseminated, it tends to hog a disproportionate amount of policy discussion. Indices which compare countries and states further cast a spotlight on those welfare problems that have measurable data. This invariably creates winners and losers among countries and states and papers over very complex realities. A state ranking high on health indicators and known as a socially progressive such as Karnataka has immense health and education challenges in its northern districts. A state that is way behind SDG goals on MMR and IMR like Rajasthan has made huge improvements in both maternal and child mortality. Indicators which don’t perform well often get entirely removed from public discourse. For example, Rajasthan has pulled many from inter-generational poverty by reducing out of pocket health expenditure through insurance but has not managed to enhance utilisation of PHCs. Patients head to hospitals for basic medical issues like fever and coughs and not the PHC located within a kilometre or two. The 2024 Niti SDG index, categorises the 36 Indian states, into achievers, frontrunners, performer and aspirant based on the total scores they receive across SDG indicators. 31 states are categorised as front runners for SDG goal 3 on health, which is just one level below the top category. But the experience of health facilities in a Karnataka is very different from a Rajasthan, even though both are front runners in the index and varies significantly within Karnataka and Rajasthan.
As visionary policy ideas take root in India, for example, the Health and Wellness Centre (HWC) or the integration of pre-primary education in formal schooling, we need to avoid the pitfalls of a simplistic relationship between data and welfare outcomes. An easy way to do this is to move beyond quantitative datasets and develop other types of knowledge products. We need case studies on policies and schemes, oral histories of reform leaders, role analysis of government bodies and bureaucrats. For example, how many of us know what the planning department at the state-level is supposed to do and whether it’s no longer playing any useful role in the state bureaucracy which in turn impacts the achievement of social welfare? When large datasets are released, such as NAS or National Family Health Survey-5, commissioning agencies must also produce rigorous, mixed-methods qualitative studies that deep dive into specific aspects of health/education problems the datasets are meant to address. This should also include a discussion on the role of the agencies and actors which are tasked with implementing the data.
The more challenging part of making data take us towards real development is anchoring datasets into a progressive policy agenda which is ambitious enough to make public facilities truly public in nature, not one of option of last resort for the poorest of the poor. Should Reproductive and Child Health (RCH) be a higher focus than geriatric care? Is it good to improve learning outcomes even though the state of public-school facilities continues to falter and no one who can read this commentary will send their child to a government school?
India has been a global leader in statistical analysis and is well placed to lead the way in how and more importantly when good data produces effective policy. It’s time it claims this position.
This article is authored by Priyadarshini Singh, fellow, Centre for Social and Economic Progress (CSEP), New Delhi.
