For years, governments, policymakers, and philanthropists have contributed funds for the delivery of social programs to achieve specific goals and development outcomes. These funds have been used to tackle poverty, hunger, malnutrition, and other critical policy issues. But while necessary, they have met varying degrees of success. Each year the government spends crores on social service programs, but mostly without focused outcome assessments. It has, therefore, become impossible to assess the effectiveness of this spending. Measurements tend to focus on inputs and access, rather than on the achievement of output and outcomes. This makes it challenging for governments and private players to make informed, evidence-based choices about their investments and spending.
Take education, for example. Despite the Indian government’s commitment to education and a right to education act, India has some of the worst education indicators in the world. According to the ASER 2018 report, only slightly over half of all children enrolled in Standard 5 could read at least a Standard 2 level text and only 28.1% of Standard 3 children could subtract. In rural parts of certain states, Rajasthan for example, a girl is more than twice as likely to be out of school compared to a boy.
Policies and programmes in India are often riddled with inefficiencies and neither established institutional norms nor public discourse have sufficiently demanded evidentiary support for decisions that have wide-ranging consequences. This enables governments and bureaucratic organisations to carry on with the status quo. Hence, demands for sound policies backed by evidence are the need of the hour.
Cross-Match Multiple Data Platforms
Going forward, high-quality data and information management before, throughout, and after a development program or intervention will prove to be crucial. Building this data infrastructure – whether from the top-down or the bottom-up — will be a fundamental requirement for a sustainable and responsive policy framework. Data availability instead of data existence also appears to be the great challenge in India, where a variety of government institutions – such as the Ministry of Statistics and Program Implementation and others – collect vast amounts of data but there is limited coordination or alignment on data-access policies. In addition, quality and reliability of data appears to be a challenge, in particular regarding individual-level information. One of the greatest challenges continues to be different data systems and platforms within the government, which are not set up to be cross-referenced. This creates data silos and significantly impacts the reliability of information, as government institutions apply different assumptions and models to a social problem, cutting across various agencies. A possible remedy is hiring data analysts and technical coders who are able to cross-match multiple platforms. Faster cross-matching can move the needle from collection to analysis.
In addition, Indian government institutions engage only in a limited way with the private or the non-profit sector in terms of data collection despite the massive scale and reach of civil society in India. Social service providers and private research institutions gather large volumes of proprietary information (for example, through surveys or focus groups) that have great potential to complement government databases. The institutionalisation of data-sharing policies can contribute to creating a more open data culture, in which both public and private actors share information top-down as well as bottom-up.
Leverage Private Actors for Impact Evaluations
Bridging the gap between sound data analysis and the operational, legal, ethical and political issues that bureaucrats and politicians are confronted with on a daily basis requires critical analysis – this is where research meets policy. Ultimately, what matters in evidence-based policy is not just evidence but understanding. This becomes the most important use of collected data, to cumulatively understand and combine evidence with context to make informed choices.
On one hand, there remains a vast amount of meaningful research which can directly inform and enrich policymaking and implementation, but researchers often do not tailor their work to answer or solve particular policy questions. On the other hand, there is a dearth of ideas and analysis on niche and sector-specific issues, where policy planners and bureaucrats who implement crucial programs daily could use help.
While it is important to link evidence to policy, investments must be made in building the capacity of policy planners to leverage what is already available. Going forward, data and evidence must be clearly analysed, as this lays the ground for future impact modelling. Knowledge and resources of academic institutions must leverage the process of building impact evaluation capacity.
As governments continue to search for the best ways to achieve real impact, certain provider participant relationships can be incentivised such that they inform the design of government programs. These include Pay-For-Success programs and instruments such as Social and Development Bonds. When implemented effectively, payment structures based on successfully meeting agreed social outcomes can increase efficiency, lower costs, and have a profound impact on program success.
Given the early stage development of such techniques, measurement and evaluation must be given importance. This will prove effective in not only establishing a data culture within governments, but in highlighting the importance of informed policymaking based on rigorous quantification.