EVIDENCE-BASED POLICY MAKING- CHALLENGES AND OPPORTUNITIES

THE CONTEXT: 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: how it is coll­ected, interpreted, and constructed into an index is 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.

EVIDENCE-BASED POLICY

• 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 the 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.

DATA TO DATA POLITICS

• Information and communication technologies (ICTs) have had a defining impact on how data is currently viewed 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.
• 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

• Amassing large, granular data about the citizens by the state through census, periodic surveys, etc. 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 governance process is often messy and 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 combine 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, the number of women participants (outcome) needs to be captured and 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.

WHY DATA CENTRIC GOVERNANCE (EVIDENCE-BASED POLICY MAKING) IS IMPERATIVE FOR DEVELOPING NATIONS

Evidence from randomised evaluations can yield insights and conclusions into questions at the heart of controversial policy debates. Since the past decade or so, evidence-based policy-making has gained traction, with some governments and NGOs having institutionalised processes for rigorously evaluating innovations and incorporating evidence into decision-making.

CASE STUDY

• The seminal and pioneering work of Noble Prize winner of Abhijit Banerjee, Esther Duflo and Michael Kremer in development economics using randomised evaluations to test the effectiveness of social programmes and policies with the objective of reducing poverty marks a definite shift in discerning development from an entirely theoretical perspective.
• The path-breaking approach that they follow is popularly known as randomised control trial (RCT), which is used to test the effect of small interventions on individual behaviour. The lab has transformed the field of development economics from being mainly theoretical to empirical with high-quality evidence that has influenced piloting, testing, and scaling of effective policies around the globe. For example, with support from Jameel Poverty Action Lab (J-PAL), the Ministry of Education in Peru formed a dedicated unit to identify, test and scale low-cost interventions to improve educational outcomes.
• J-PAL is promoting the scale-up and replication of effective programmes. Randomised evaluations allow researchers to learn not only about the impact of a particular programme but also to draw out the mechanisms behind its success to help derive general lessons that can be applied in the same context.

IMPACT OF THE STUDY

• From randomised evaluations in India, Ghana and Kenya, researchers learnt why children are behind in school and thereby built a range of cost-effective strategies based on the insight of regrouping students by their current learning level rather than by their age group.
• On the other hand, the Government of Zambia has been able to apply the general idea of “Teaching at the Right Level” (TaRL, an approach developed by Indian NGO Pratham) to inform the design of its own remedial programs. This has significantly improved the learning opportunities in both India and Africa.

IMPORTANCE OF THE STUDY

Cases that highlight the value of EBP in developing nations: one where evidence-based policies transformed lives and the other where the lack of an evidence-based response has caused widespread death.

• First, the Government of Tanzania has implemented various health service reforms informed by the results of household disease surveys. This EBP contributed to over 40% reductions in infant mortality in two pilot districts.
• Second, the AIDS/HIV crisis has aggravated in some countries because respective governments have ignored the evidence of what causes the disease and how to prevent it from spreading further.

EXAMPLES OF EVIDENCE POLICYMAKING IN INDIA

• CENSUS BY MINISTRY OF HOME AFFAIRS
• SWACHCH SARVEKSHAD BY MINISTRY OF HOUSING AND URBAN AFFAIRS
• NATIONAL FAMILY HEALTH SURVEY BY MINISTRY OF HEALTH AND FAMILY WELFARE, GOVERNMENT OF INDIA
• MULTI-DYNAMIC POVERTY INDEX BY NITI AAYOG
• SDG RANING OF STATES BY NITI AAYOG
• ASER REPORT BY PRATHAM NGO

CHALLENGES OF POLICYMAKING

• 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.
• 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, 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.

WHY DATA-CENTRIC GOVERNANCE IS THE RIGHT STEP TO CHOOSE AND HOW INDIA CAN ACHIEVE IT?

• India is 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 good, robust and reliable database system. 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 scholars (like Drèze) 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 those policy decisions are based on robust and rigorous data.
• Finally, it is equally essential to ack­nowledge that policymaking is a contested space that is interactive, discursive and, therefore, a negotiated process.
• In the global South, the rigorous, constant implementation of data-based governance or policymaking is likely to be challenging. The government 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.

THE WAY FORWARD:

• Data-driven governance is being touted globally as a new approach to governance, one where data is used to drive policy decisions, set goals, measure performance, and increase government transparency.
• Evidence-based policymaking (EBP) assists in making decisions about projects, programmes and policies by placing the best available evidence from research conducted at the heart of policy development and implementation. It also makes explicit what is known through scientific evidence.
• In contrast to opinion-based policymaking, evidence-based policymaking needs an evidence base at all stages in the policy cycle to define issues, shape agendas, make active choices, identify options, deliver them, and monitor their impact and outcomes. Basically, it is a set of methods that informs the policy process, rather than aiming to directly affect the eventual objectives of the policy directly. Thereby, EBP advocates a more systematic, rational and rigorous approach to produce better outcomes.
• The pre-requisite for evidence-based policy is that the data must be trustworthy, and it depends upon the quality of the data and the quality of the professional statisticians.
• Credible statistics is important for good governance and decision-making in all sectors of society. Therefore, policy-makers are more likely to use evidence in decision-making if that evidence is unbiased, rigorous, substantive, relevant, timely, actionable, easy to understand, cumulative and easy to explain to constituents. EBP approaches can dramatically help reduce poverty and improve economic performance in developing nations.
• Impact evaluations of social programmes have emerged as an important tool to guide social policy in developing polities as they allow for accurate measurement and attribution of impact can help policy-makers identify programmes that work and those that do not work so that effective and performing programmes can be promoted and ineffective ones can be discontinued.

THE CONCLUSION: The EBP has the potential for high impact change that India shouldn’t ignore. Thereby, systemic institutionalisation of EBP is the way forward in eradicating poverty and improving economic performance, education, health care, and social assistance for millions of people. But, if governance decisions are to be data-centric, there is a need to ensure a decentralised, robust, reliable database system. 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.