The ‘statistical zero’ in India can be bridged with decentralization and if states build their own quality databases
The new show in town is the game of numbers. Running for quite some time now, it keeps the audience engaged with its volatile and changing rules. If one season negated the entire body of data while disappointing viewers, the next brought some quarters of delight with its data reinterpretation. The latest season has shocked viewers as an entire body of data has gone missing. articles in Hindu (Editorial page, August 19, 2021), Significance of ‘There is no data’ answer, which is an in-depth review of the latest season of Missing Data, is enlightening.
data on politics
Data politics is nothing new. The interconnectedness of power and knowledge and its use by states to control populations has long been explained by Foucault, Bourdieu, and others. The issue is further complicated by rapid technological innovations in information and communication technologies, where through Internet connectivity, both the subject and object of data are now inextricably linked. The move to evidence-based policy making or evidence-based budgeting by governments refers to the collection of large, fine-grained data about citizens by states.
The purpose of data-based policy making or budgeting is to facilitate the use of evidence to inform programmatic funding decisions. The goal is to invest further in actions that work to improve outcomes for citizens. Data-based decisions can address inter-district disparities through targeted resource allocation. However, data-based governance pre-supposes the existence of reliable, rigorous and valid data, with or without performance impact or outcomes. If governance decisions are to be data centric, there is a need to ensure a good, robust and reliable database.
data based policy making
States collect huge amounts of administrative data. However, these administrative data are often not valid. For example, it is well known that the flow of funds below the block level is often opaque and the data submitted by local bodies is generally not valid. Attempting to match funds with functions at the panchayat level is rather challenging. While there is a significant need to link databases of different departments, it is not easy as regional jurisdiction and household level identifiers may differ from department to department. Some mechanism needs to be brought in to harmonize these different data sets with a single identifier; But more importantly, these data sets need to be validated through urban local bodies and rural local bodies.
Accurate collection, measurement and interpretation of data is critical for data-based decision making to be successful. However, this is fraught with challenges as more data is used, it is misused, misused or even manipulated. For example, lack of data in some areas does not necessarily indicate better governance. During the novel coronavirus pandemic, some states were not testing enough. Consequently, the interpretation of the data on COVID-19 positive cases appears to be that some states, particularly in South India, were unable to control COVID-19 cases as compared to their North Indian counterparts; With some very poor health indicators as well as infrastructure. In such cases, allocation of resources and decision making based on data is likely to be adversely affected.
Similarly, a 2012 academic study on assessing the quality of governance in states had an indicator under a ‘law and order’ variable intended to measure police behaviour, and the indicator was “complaints against police behaviour”. How should such an indicator be measured? What would be an ideal score – more or less? The answer is not simple: it is complex and context-specific and therefore should not be interpreted in isolation.
A low score in a poor, backward state does not indicate that police behavior is exemplary; This may indicate that people are afraid to complain against police behavior for fear of retribution. A high score in a state with high literacy and Human Development Index (HDI) may mean that people have enough faith in the judiciary and the state to complain against police behavior, thus indicating better quality of governance. becomes. Similarly, issues like mental health, which comes with heavy social stigma in India, need careful measurement as higher incidences of mental health (from institutional sources) indicate better access to institutional care as well as a social context can give which is the least. stigma
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Education Data of Tamil Nadu
Similarly, the recently released data on education released by the central government, which shows about 27 educationally backward districts in Tamil Nadu, is shocking. Despite these figures, the same report elsewhere places Tamil Nadu in fourth place in educational attainment. The literacy rate in Tamil Nadu in the 2011 census was 80.1% higher than the national figure of 73%. While there were inter-district variations in literacy, Dharmapuri district, with a literacy rate lower than the national average, still had 68.5% literacy in 2011. It is problematic to imagine that such a decline has occurred over the past 10 years. Some recent state level studies have shown further improvement in literacy in districts as compared to 2011. Clearly, in this case, the measurement of district-level educational backwardness needs to be closely examined. Such interpretations also highlight the need to supplement quantitative data with smaller qualitative studies to capture processes, subjectivity and relevant factors.
As the data game shows, we are in a data-driven world. While on the one hand, there is a move towards data-based governance and decision-making, on the other hand, many are concerned about the ‘statistical void’ of the national statistical base being destroyed either due to delays or data suppression, e.g. Scholars Jean Dreze and others are calling for decentralized systems of data collection processes, with states building their own databases. This requires states to invest heavily in both human and technical infrastructure with built-in quality control measures to ensure an interesting turn in the data game.
Kripa Anantha Pur is Associate Professor at Madras Institute of Development Studies, Chennai. Views expressed are personal
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