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Q. In recent times, Randomised Control Trials (RCTs) have acquired salience in policy debates. In the context of poverty alleviation programmes in India, discuss the role RCTs can play in policy formulation.

Approach:

• Introduce by giving a brief background on Randomized Control Trials (RCTs) and its recent context.
• Discuss how RCTs can be used in policy formulation especially that of poverty alleviation programs.
• Discuss the limitations associated with the method in Indian context.
• Conclude accordingly.

Answer:

A Randomized Control Trial (RCT) is an experiment designed to understand the efficacy of a policy  intervention on an outcome or event. It involves selecting two sets of individuals at random,  wherein one of the two is exposed to a policy intervention – ‘experimental group’ and the other  group is not exposed to it – ‘control group’. RCT experiment examines the impact of policy  interventions, often over long periods of time, to gauge the impact of policy, and whether it justifies  the associated costs. It also involves breaking larger questions about policy interventions into  smaller, easier-to-test studies.

RCTs have acquired salience in policy debates in recent times. The Nobel Prize in Economics (2019)  was awarded to Abhijit Banerjee, Esther Duflo and Michael Kremer for their experimental approach to alleviate global poverty by using RCTs.

RCTs can play an important role in policy formulation in India in the context of poverty alleviation as:

• They help to break down grand development issues into specific questions about how poor people see and respond to certain interventions in their real life conditions. For example, when  conducting studies, ‘poverty’ can be broken down into several dimensions such as income, poor  health, inadequate education etc.
• RCT is a bottom-up approach as it looks at questions of development through the lens of the poor.
• RCTs enable rigorous testing of propositions about specific cause-effect relationships. For instance, a study on health subsidy using RCTs revealed that poor people are extremely price sensitive regarding investments in preventive healthcare.
• There is a systematic way to evaluate the effectiveness of the funds spent on development aid each year using randomized controlled trials. Thus, the focus remains on optimizing aid
• These experiments may be used to nudge irrational/ineffective behavior away to get better outcomes. For instance, bad health outcomes can be broken into smaller factors like  absenteeism of medical staff, poor quality medicines etc. and experiments on each factor can be  conducted to understand what works better.

Therefore, RCTs provide a firm basis for evidence-based policymaking. There is evidence of this  method contributing to improvements in various government programs such as the ‘Chunauti  programme’ (an education reform scheme) in Delhi.

However, there are various limitations of this approach to poverty alleviation in India, including:

The RCT finding in education, health or sanitation interventions in a few villages in Bengal may  not be applicable to villages in Bihar or Arunachal Pradesh. This is because local conditions  vary and there is no certain way to know which results are applicable to which places.

The conclusion of an RCT may fail to scale up as the population size increases.  These trials are heavily controlled and the scenarios they create are invented, like in a lab,  whereas the real world is complex and dynamic.

Choosing samples for an RCT experiment in a random manner does not make them identical as  the members of the control and experimental groups are rarely similar on social, political and institutional aspects, despite the best possible design and implementation.

Hence, bottom-up approaches led by RCTs can be a supplemental means to improve program effectiveness. These can play a role in building scientific knowledge and useful predictions but they  can only do so as part of a cumulative program, combining with other methods, including  conceptual and theoretical frameworks