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“Leave the talks of yesterday, together we will write a new story in the new era.” The ‘new story’ of our era, our ‘new era’, is ‘development’ i.e. development. Its key currency is human capital created through high quality education and health. Data as well as indices of global and national scope are considered key tools for developing human capital. It has become a truism that better data leads to better policies and stronger education and health systems. Alas, this simple understanding hides the many ways in which data defeats development objectives.
Take the example of the health sector. Reproductive, child and maternal health have historically been prioritized and we focus on maternal mortality rate (MMR), infant mortality rate (IMR), immunization, under-five mortality rate (U5MR) and other relevant indicators. Measures the health of the health system. perform. But we often do not explore whether improvements in MMR and IMR are due to primary health centers (PHCs) providing critical services to reduce MMR and IMR or to hospitals bypassing them. Are poorly staffed frontline health systems and overburdened staff preventing us from meeting vaccination targets at the expense of other health objectives?
In education, enrollment figures and literacy rates historically dominated public data sets, which obscured the poor quality of learning outcomes. Learning outcome data came into public prominence in the early 2000s with the first ASER surveys and, subsequently, the National Council of Educational Research and Training’s National Achievement Survey (NAS) 2017. Now, learning outcomes data has taken over the discussion about other aspects about the education system, which can 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 the unbalanced focus on quantitative data on outcomes and impact. This is partly because it is easy to collect, analyze 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 have a lot to learn about why government primary schools (which cater to first-generation learners) have inexperienced young teachers and not senior, experienced teachers. This requires a socio-historical and gender analysis of primary school teachers and analysis of teacher recruitment policies since independence. Or the question is why it is incredibly difficult to design relocation policies for frontline health workers.
The bigger question is whether data, whether quantitative or qualitative, means nothing in itself if it is not derived from a continuously updated progressive agenda. We need to question the policy agenda, not just the data. Many states in India have dramatically improved health indicators, and have met globally acclaimed Sustainable Development Goal (SDG) targets, but our policy agenda needs to take data further and ask which At cost? The data tells us that enrollments in public school facilities are falling and enrollments in private schools are increasing. But, is the problem one of overabundance of school facilities, or poor infrastructure in these facilities, or do markets actually provide better primary schooling facilities? If our government school teachers think that private schools are preferred because of better infrastructure, then our policy action cannot focus only on state or even district level literacy rates. Data makes a problem visible, but it doesn’t tell us what the most important problem is, how to solve it, who will do it, and at what cost.
Whenever an issue is measured quantitatively and the data is publicly disseminated, it influences a disproportionate amount of the policy discussion. Indices that compare countries and states further shed light on welfare problems that have measurable data. This always creates winners and losers among countries, states and newspapers over extremely complex realities. The northern districts of a state like Karnataka, which ranks high on health indicators and is known as a socially progressive state, face immense health and education challenges. States like Rajasthan, which is far behind the SDG targets on MMR and IMR, have made huge improvements in both maternal and infant mortality rates. Indicators that do not perform well are often omitted from public discussion altogether. For example, Rajasthan has lifted many people out of intergenerational poverty by reducing out-of-pocket health expenditures through insurance, but has not managed to increase PHC use. Patients turn to hospitals for basic medical problems like fever and cough and not to the PHCs located within a kilometer or two. The 2024 Policy SDG Index categorises 36 Indian states into achievers, leaders, performers and aspirers based on the total scores obtained across SDG indicators. 31 states are classified as front runners for SDG Target 3 on health, just one level below the top category. But the experience of healthcare facilities in Karnataka is very different from that of Rajasthan, even though both are leaders in the index and vary considerably within Karnataka and Rajasthan.
As visionary policy ideas take root in India, for example, health and wellness centers (HWCs) or integration of pre-primary education into formal schooling, we need to avoid the pitfalls of a simplistic relationship between data and wellness outcomes. Is required. 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 plans, 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 is it no longer playing any useful role in the state bureaucracy which in turn affects the achievement of social welfare? When large datasets are released, such as the NAS or the National Family Health Survey-5, commissioning agencies should also prepare rigorous, mixed-methods qualitative studies that examine in depth specific aspects of the health/education problems that need to be addressed. Want to address the dataset. It should also include a discussion of the role of agencies and actors who are tasked with implementing the data.
The more challenging part of making the data move us toward real development is incorporating the datasets into a progressive policy agenda that is ambitious enough to make public amenities truly public, not just a last resort for the poorest. In. Should reproductive and child health (RCH) be given more attention than geriatric care? Is it any good to improve learning outcomes, even if the state of public-school facilities continues to deteriorate and no one who may read this comment will send their child to a government school?
India has been a global leader in statistical analysis and is well positioned to lead in how and, more importantly, when good data informs effective policy. Now the time has come for him to claim this position.
This article is written by Priyadarshini Singh, Fellow, Center for Social and Economic Progress (CSEP), New Delhi.
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