The availability of PC-level data can help elected representatives identify unmet needs in their constituencies and subsequently work with the administration and civil society to bridge the gap.
Authors: Mayank Mishra and Swathi Rao
Published: April 26, 2023 in Times of India
Earlier this year, a Lok Sabha MP sought data from the Ministry of Labour and Employment on the number of agricultural labourers registered as beneficiaries under a particular scheme. Specifically, the MP sought data for her parliamentary constituency (PC). However, this PC-level data, she was told, is neither available nor collected for the scheme.
The PC, a geographical unit of administration, is representative of the needs and aspirations of its people. Therefore, for parliamentarians to effectively address the needs of their constituents, it is imperative to understand and prioritise PC level issues for policy focus and intervention.
Historically, the district has been the smallest administrative unit for which data pertaining to government schemes and campaigns has been collected; however there has been a dearth of data at the PC level. While district-level data is necessary, the boundaries of districts and PCs rarely, if ever, coincide. Although 391 of the 543 PCs share their names with districts, their population and demographics are different. For example, Udaipur PC which is homonymous with Udaipur district, intersects parts of Udaipur, Pratapgarh, and Dungarpur districts. This is further complicated by the constantly evolving geometry and number of districts; Rajasthan, for example, recently announced the creation of 19 new districts. The incongruous nature of PCs and districts could eventually prevent elected representatives such as MPs from gauging the well-being and progress of their constituency.
Evaluating the health of a PC using NFHS data
The UK, for instance, has created interactive dashboards across several indicators – like broadband coverage, household profiles, universal credit rollout, health conditions, etc. for parliamentary constituencies. Although the Government of India has also made significant strides in collecting and disseminating data across sectors, especially with its data.gov.in platform, not all data that is available to the public is user-friendly; and the data that is available, is mapped to administrative boundaries. However, more recently, a UK-like data tracker has been developed at Harvard for Indian PCs, using publicly available data from the National Family Health Survey (NFHS).
The NFHS, first conducted in 1992-93, is a periodic survey that provides information on several health, nutrition and population indicators such as fertility, family planning, maternal and child health, nutrition, clean fuel usage, etc., at the district, state and national level. The survey covers approximately 610,000 households across the 707 districts in the country. Since 1992-93, five surveys have been conducted, with the most recent one in 2019-21.
Given the sample size, scale and periodicity of the NFHS surveys and the indicators covered under it, NFHS data is a treasure-trove for policymakers, researchers, media, and other stakeholders in the public policy and research ecosystem. Furthermore, the availability of health, nutrition and population indicators at the PC-level in an interactive and user-friendly format could be transformative for policymakers. It would provide them with the tools to assess the impact of schemes in their constituency and identify unmet needs.
To elucidate this further, let us look at school attendance rates among girls. The NFHS measures this as a percentage of girls aged 6 and above who ever attended school. Over the years, many governments at the centre and states have envisioned and implemented several schemes to improve female school attendance. In 2009, the Government of India also codified the right to free and compulsory elementary education. PC-level data shows that these schemes have resulted in a significant improvement in girls’ school attendance over the years, with several PCs recording over 90% attendance. However, despite the tremendous progress registered, some PCs in the country still reported attendance rates below 50%, while some others saw a decline from the NFHS-4 levels. The availability of PC-level data can help elected representatives identify unmet needs in their constituencies and subsequently work with the administration and civil society to bridge the gap.
Linking Data to Governance
To improve coordination between district administration and elected representatives, the Ministry of Rural Development created the District Coordination and Monitoring Committees (DDMC) in 2016, chaired by MPs from the districts, to oversee the implementation and monitoring of central schemes. Access to and ready availability of PC level data could be crucial for these committees for their deliberations and decision-making.
States like Andhra Pradesh, in an attempt to reduce inefficiencies, have taken streamlining a step further by reorganising districts to match PCs; AP now has 26 districts and 25 PCs. While this is a move in the right direction, reorganising districts may not be a feasible solution in all contexts and therefore, ensuring the availability of PC-level data would better align MPs with the needs of their constituents.
Prime Minister Narendra Modi, in 2019, announced the government’s intention to move towards evidence based policy making in 2022. The launch of the National Data and Analytics Platform in 2022 is an acknowledgement of this intent and marks a milestone in democratising data. Having got the ball rolling, the government now has the opportunity to incentivise the collection mapping of data to the parliamentary and assembly constituency levels and disseminate it in more user-friendly and interactive formats. The availability of such data will not only aid elected representatives in allocating resources better, but also elevate the policy discourse in the country by making it more participative.
Kofi Annan, the former UN Secretary General wrote on the role of good data in ending malnutrition in Africa, “Without good data, we’re flying blind. If you can’t see it, you can’t solve it.”