Data based governance – learning and lessons

Data based governance – learning and lessons

Data based governance – learning and lessons- Today Current Affairs

The new currency driving governance today is data. Whether it is the debate on the hunger index or the arguments regarding the caste census, data is at the centre of these controversies: the manner in which it is coll­ected, interpreted, and constructed into an index are being vociferously debated by everyone, including those who have only a rudimentary understanding of data. The pandemic management that relies heavily on numbers in terms of testing, vaccinating or tracking recoveries and deaths has only heightened this fascination with data.

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The reason for this obsession with data is because evidence-based policy (EBP) making or data-based governance has been touted as a rational form of governance that bases its decisions not on populist pressures but on objective data.This requires evidence-based data at all stages of policymaking. EBP is viewed as especially important for deve­loping countries where public resources are often scarce or limited. It req­uires both data and the process of data collection to be scientific, rigorous and validated both in the process of coll­ection as well as analysis. However, the entire process of data collection and its interpretation often tends to be imbued with political economy issues in deve­loping countries and as Head explains “policy decisions are not deduced primarily from facts and empirical models, but from politics, judgment and debate.”

Data to Data Politics: The Hindu Analysis

Tracing the history of the process of data collection, some European analysts illustrate how European countries amassed vast volumes of data in the early 19th century on a range of variables that often did not lead to any meaningful analysis. His layered analysis of Foucault’s “bio­politics” into “overt” and “subversive biopolitics” in the Canadian context where the very first census of the population took place in 1666 shows how the overt bio­political agenda (tax incentive for larger families in the Canadian context) did not have the intended effect but the subversive biopolitics in the form of “categorisation” of the population that took roots persists till date 

Information and communication tech­no­logies (ICTs) have had a defining impact on the way data is viewed currently as “it rec­onfigures relationships between states, subjects, and citizens” . Today, big data, machine learning and algorithms are the frameworks within which citizens operate—oblivious to the manner in which this digital interface is converting them into data to be used by unknown entities. It is in this context that some analysts make the distinction bet­ween politics of data and “data politics” where they view “data as a force that is generative of politics” and as “a lang­uage with performative force.” In this age of data politics, new players like transnational corporations that control ICTs and social media domains are becoming more important forces than the state. This is alarming as unlike the checks and bala­nces that limit the state’s influence, these large, transnational corporations are not constrained or held accountable by any such mechanisms. This merits a deeper inquiry.

Data-based Governance: The Hindu Analysis

Amassing of large, granular data about the citizens by the state through census, periodic surveys, etc, and now through digital convergence has continued unabated and gained further traction in the context of EBP. Data-based governance aims to facilitate the use of research and evidence to inform programmatic funding decisions. The goal is to further ­invest in what works to improve outcomes for citizens based on prior evidence. In general, data-based governance assumes the existence of a system of reliable,

rigorous and validated data with associated infrastructure. However, in reality the process of governance is often messy and at times riddled with political compulsions as governance invo­lves both formal and informal dom­ains, rules and actors. This makes governance outcomes even more challen­ging to measure. This is especially bec­ause governance outcomes are a combination of tangible outputs and intangible processes. Measuring only tangible outputs without capturing the intangible processes is likely to provide misleading inferences. For example, if one is trying to assess women’s participation in a gram sabha, not only the number of women participants (outcome) needs to be captured but also the nature of parti­cipation (process) should be documented. Often, quantitative data collections focus only on quantifiable measures, thus omi­tting qualitative processes that give mea­ning to those numbers.

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Challenges of Policymaking: The Hindu Analysis

In addition, states routinely gather vast quantities of administrative data. However, large proportions of these data rem­ain unutilised or are unusable as ­often these administrative data are not validated or updated. At times, the same data is collected by different agencies with different identifiers making integration or consolidation of data difficult. To avert duplication of data, which is costly both in terms of human as well as financial resources, it is essential to stan­dardise data collection across departments. In addition, data starts to become scarce and variable at lower tiers of governance, for instance, the devolution of funds at the sub-block level is often opaque and self-reported without external validation. This makes matching of funds, particularly untied grants with specific functions at the sub-block level chall­enging as funds are often fungible. Admi­nistrative data is generally inaccessible to the public and researchers for scrutiny or analysis. Citing the example of Denmark, where opening up of admi­nistrative data on tax collection gave significant insights that led to key tax ref­orms, Blum and Pande (2015) advocate encouraging and incentivising governments to share the administrative data especially in the context of Sustainable Development Goals (SDGs).

Measuring governance is a challenging proposition. This is particularly true in the domain of law and order which is an essential aspect of governance. Two studies aiming to measure governance across states in India by developing a composite governance index lay bare the challenges of choosing appropriate indicators and their measurement and interpretations. One of them uses estimates of crime rates (ECR) as one of the indicators which looks at the total number of criminal cases reported and defl­ated with the total population figures for each state to get a ratio. Today Current Affairs.

Another indicator relates to estimates of industrial disputes and strikes (EIDS) to assess worker satisfaction. Similarly, another acade­mic study assessing the quality of governance across states uses an indicator under the “Law and Order” section that attempts to measure police behavior as “Complaints against Police Behaviour.” While these are important indicators, they also present challenges of measurement—how should such indicators be measured? Is a low score deemed ideal or a high score? The answer is not simple and is context-specific. For instance, a low score either for crime rates or complaints against ­the police behavior in a poor, backward state does not necessarily denote that the crime rate is less or that the police behavior is exemplary; on the contrary, it may indicate that people are scared to report crimes or complain against the police behavior in fear of reprisals. Similarly, a high score in a state with high literacy and human development indicators (HDIs) can be

interpreted to mean that people have enough confidence in the judiciary and the state to file reports against crimes and also register complaints against negative police behaviour, thus becoming an indicator of better governance. Likewise, more ind­ustrial strikes in a highly unionised state are not necessarily a sign of bad governance but could indicate that the workers’ rights are being protected. 

Similarly, the union government’s edu­cation data ranks TN fourth in educational attainment, but elsewhere shows that 27 of TN districts as being educationally backward . The 2011 Census data shows the literacy rate in TN as being higher (80.33%) than the national literacy rate (74%). Further, the National Family Health Survey (NFHS) which was dep­loyed in 2015–16 indicates that 79.4% of women and 89.1% men were literate in the state showing an improvement in literacy as compared to Census 2011. Hence, it is inconceivable that such a severe slide could have taken place in the last five years. Clearly, in this ins­tance, the measurement of district-level educational backwardness needs closer scrutiny 

Today, we are mired in a data-driven world. Governance is increasingly being pushed to become data centric. Data-centric governance or policymaking is a step in the right direction. However, the paradox of data-centric governance in India right now is that it is caught between two countervailing forces—a rel­entless churning of digital and other forms of data that are often unreliable and/or prone to errors on the one hand and a steady erosion of credible, scientific sources of data on the other. If governance decisions are to be data centric, there is a need to ensure a system of good, robust and reliable database. With several national statistical bases, such as the National Sample Surveys, that provide an interim glimpse into the trajectory of the economy in between the decadal census counts, getting eroded either through delays or data suppression, the danger of a “statistical vacuum” has been raised by some of the analysts and others who have advocated a decentralised system of data collection process where states take the lead in building their own bottom-up databases. This requires individual states to invest heavily in both human and technical infrastructure with built-in quality control measures to ensure that policy decisions are based on robust and rigorous data. Finally, it is equally essential to ack­nowledge that policymaking is a contested space which is interactive, discursive and, therefore, a negotiated process. In the global South, rigorous constant implementation of data-based governance or policymaking is likely to be challenging as often discretionary policy decisions need to be taken by the government by prioritising one group over the other to redress historical inequa­lities. Thus, data-based governance req­uires not just validated and scientific data but also requires the policymakers to use it wisely by contextualising it to ensure equality and equity.


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