India is a developing country where even today, the problem of poverty exists as a huge socio-economic issue, posing a threat to national growth. It is disturbing that about 21.9% of Indians cannot accumulate enough monetary resources to fulfil their basic sustenance needs1. Poverty is perhaps the biggest hindrance to the development of our country. It is widely accepted that a prosperous nation is capable enough of dealing with the problem of poverty with effectiveness. But India doesn’t seem to have a very good record on that aspect.
A very prominent proof of the existence of income disparities in India is its ‘Gini coefficient’. The Gini coefficient shows the numeric value of income disparities in a country and it varies from 0 to 1. The country with a value closer to 0 tends to have more income equality, and vice versa2. This shows an important characteristic of India’s growth pattern; a pattern that lacks equity. India’s Gini coefficient is 35.72%, which shows that income inequalities are significantly high, although not as high as South Africa’s (62.5%)3. But considering its huge population, the absolute number of poor in India is whooping!
Well, if poverty is such an important issue, it becomes equally important to acknowledge and analyse the views of various knowledgeable personalities in this regard. Professor Abhijit Banerjee, along with his co-researchers Esther Duflo and Michael Kremer, has spent years in popularizing effective means of radicalizing development economics. The trio has focused on the usage of Randomised Control Trials (RCT) – a technique traditionally used in medical sciences – to scrutinize the best policy alternative towards poverty amelioration.
Policy interventions have far-reaching impacts on low and middle-income groups; hence it becomes extremely important that the right policy interventions are implemented. An experimental approach to development economics – an approach in which randomised experiments are conducted for arriving at research-based outcomes – ensures that the impact of various initiatives is sufficiently evaluated before an efficient policy is devised. RCT is a way to analyze such impact. It has been traditionally undertaken in medical sciences to study the impact of medical intervention on people (participants or subjects). These people are randomly divided into groups that are faced with clinical interventions. The interventions could be in the form of standard practices, placebo (sugar pill) or no intervention at all. After a predetermined period, the outcome of these interventions on various subjects’ groups are measured and compared. This helps in confirming the best way of dealing with a certain ailment or a medical issue. The trial may be blinded, to nullify any biases. This kind of an almost unbiased experimental technique assists in quantifying the reliable effectiveness of a new medical treatment4.
Presently, this technique has not just remained confined to medical sciences. It has spread over a wide arena of academic fields; most prominently in economics. Insights on RCT can be drawn through the book ‘The Design of Experiments (1935)’ by Ronald Fisher5, a famous British statistician and geneticist (thus depicting yet another relationship between biology and statistical economics). Fisher is famously described as “a genius who almost single-handedly created foundations for modern statistical science”6. His method was something like this – select two groups that are similar and then randomly select one of them to receive a treatment being tested, and then compare the outcome of this treatment group with that of the other group – called the control group. If the difference is significant, it is caused by the treatment. Thus, we can conclude whether the treatment is effective or not.
Development economics was based on theoretical approaches and macroeconomics for a long time, but the trio has radicalized it by giving much more importance to experimental approaches and considering the poor and their decision making in policy framing. Conducting highly India-centric research, Abhijit and Esther have deeply studied the nature and causes of poverty in the nation. Staying more reality-based and not following the path of sophisticated theoretical modelling, their cause and effect evaluation has benefited in the process of poverty alleviation. They have popularized RCT through their book ‘Poor Economics- A Radical Rethinking of the Way to Fight Global Poverty’ (2011).
Now that RCT is clearly understood, we can analyze the breakthrough initiative of the Nobel Prize-winning trio. Here is an example of how RCT is practically conducted.
According to J-PAL, a poverty-based research initiative of Abhijit Banerjee and his colleagues, access to primary education has expanded in many developing countries. However, such improvements may not always result in improved learning amongst students if the quality of education is poor. Keeping this in mind, Banerjee and his co-researchers evaluated the Balsakhi programme, a remedial tutoring initiative implemented in Mumbai and Vadodara. This program provides schools with a teacher (known as a Balsakhi, recruited from the local community who has herself finished secondary schooling), to work with children who have been identified as falling behind their peers. The researchers have used statistical methods like standard deviation in order to analyze the quantitative aspects of the intervention.
According to a survey done in 2005, about 44% of Indian children within the age group of 7 to 12 could not read a basic paragraph and 50% could not do simple subtraction even though most of them were enrolled in schools7. J-PAL, in association with Pratham, an NGO, evaluated the effectiveness of the Balsakhi programme in improving this situation. Under this programme, certain public schools were randomly chosen. Tutors were assigned to each of these schools: teaching grades two, three and four. Students who had problems with a basic understanding of concepts that were supposed to be covered in their previous class- for instance, if a grade three student couldn’t understand concepts taught in class one and two- he was to be tutored. Groups of fifteen to twenty students were formed and a tutor would teach them concepts of the previous class. In a short span of about forty days; the ‘tutored’ students showed wonderful improvements in academics and were able to perform better than before. Such improvements were not visible in the group that was exposed to classroom learning only. Such a trial helped determine the efficiency of the Balsakhi programme. The difference in both the groups showed that the ‘treatment’ was in fact viable8.
RCT has been lauded across the world for its efficiency and viability. Yet, some people within and outside the policy framing domain hold certain reservations against this method. One of its major criticisms, which holds true for any other statistical method as well, is the problem of generalization of an outcome. Let us take an example – suppose the attendance of students in schools of two different places Sitapur in Uttar Pradesh and Aurangabad in Maharashtra is very low. An important point to notice here is that Sitapur is a flood-prone district, while Aurangabad has no such problem. Now, suppose an RCT is conducted in both the districts to determine factors that would raise attendance in schools. The outcome in Aurangabad is that good infrastructure would attract more students towards schools, thus reducing absenteeism. The outcome in Sitapur, however, is completely different. According to the results, good teachers should attract more students to the schools, since there is a continuous threat of destruction of infrastructure due to floods and therefore good infrastructure may not motivate students to go to school.
Thus the outcome at one place cannot be generalized for the entire country. Moreover, if experiments have to be done across the country, then it would be extremely costly in terms of money and time, thereby not always a viable option. Randomization in itself has a major issue. The result of a randomized experiment may deviate from the actual result. Now, since policy may directly affect the superset of a random sample, depending just on outcomes of RCT may sometimes make the policy ineffective and may also have negative impacts. For instance, suppose the outcome of an RCT carried out using a certain population set shows that some policy initiative might be beneficial, but in reality, it may not be beneficial for the whole country. The point here is also that RCT reveals ‘what works’ but doesn’t largely reflect ‘why does it work’. So unless we assume ceteris paribus, i.e. no factor other than the prescribed one affects the policy, there is no way we can assure ourselves of the reliability of the outcome of RCT. And ‘ceteris paribus’ is rather a hypothetical assumption9.
Whatever the criticisms may be, RCT has been effective enough in helping the trio achieve a Nobel Prize. As of now, it is considered a very good method of developing and evaluating a policy solution. But, in my opinion, the problem of implementation remains to be a major constraint in its success. We could have some of the best policies in the basket, but they are good only on paper until they can be implemented efficiently. In fact, major policies fail only due to implementation problems; and hence it’d be interesting to know if innovations are made in the methods of implementation. But, one thing can surely be concluded from the experiments of the trio – these trials have the potential to improve millions of lives!
By Amogh Sangewar,1st year undergraduate student, SRCC.
References
1.Asian Development Bank(n.d.) India and ADB. Retrieved from:
https://www.adb.org/countries/india/poverty
2.BBC News(2015) Who, What, Why; What is the Gini coefficient. Retrieved from:
https://www.bbc.com/news/blogs-magazine-monitor-31847943
3.World Population Review(2019) Gini Coefficient By Country 2019. Retrieved from:
http://worldpopulationreview.com/countries/gini-coefficient-by-country/
4.MedicalNewsToday(2018) What is a Randomized controlled trial? Retrieved from:
https://www.medicalnewstoday.com/articles/280574
5.Julian C.Stanley(1966) The influence of Fischer’s ‘The design of experiments ‘ on educational research thirty years later. Retrieved from: https://www.jstor.org/stable/1161806?seq=1#metadata_info_tab_contents
6.The Royal Society(n.d.) Ronald Fisher-Science in the making. Retrieved from: https://makingscience.royalsociety.org/s/rs/people/fst00034451
7.J-PAL, Balsakhi Remedial Tutoring in Vadodara and Mumbai, India. Retrieved from:
https://www.povertyactionlab.org/evaluation/balsakhi-remedial-tutoring-vadodara-and-mumbai-india
8.J-PAL, Balsakhi Remedial Tutoring in Vadodara and Mumbai, India. Retrieve