Nobel Economics Prize 2019 Development economics has come a full circle

Amar Yumnam *

This year’s Nobel Prize in Economics has been awarded to three Development Economists working in evolving policy interventions for reducing poverty. The three scholars have been working both collaboratively and individually. Of the three, Abhijit Banerjee and Esther Duflo are at the Poverty Action Lab at the Massachusetts Institute of Technology while Michael Kremer at the Harvard University. I feel personally very elated for both Abhijit and Esther are personally known to me.

I have discussed issues with them over lunch and dinner. While the Lunch with Abhijit was at the Faculty Canteen at the University of Southern California, the dinner with Esther was at a Korean restaurant in Los Angeles with one American and one Korean Professors; the first time I had Korean Sake was in the dinner with Esther. Esther is the youngest Economist to win the Nobel. She also happens to be an Economist who had won every Award in Economics in France before she attained the age of 40 years.

With Abhijit, they are the only couple winning Nobel Prize in the subject. Development Economics enjoyed a high tide from the birth in the 1950s and during the 1960s but suffered low credibility and consequent decline in popularity for about a decade and a half from the early 1970s to the mid-1980s. The researches in mid-1980s have turned out to be a robust grounding for recovery with gusto.

This was a period with Chaos Theory in Mathematics and Post-Modernism in Philosophy influencing the rethinking at issues and reality. Further it was also a period when big-data analyses became possible with the availability of powerful computers and accompanying programmes.

In keeping with the methodological changes in Mathematics and Philosophy, in Economics (particularly in Development Economics) the emphasis on contextualisation for understanding Economic Phenomena and evolving Development Interventions gained momentum. The late 1980s (post-1986 in particular) and the early 1990s are landmark years for the robust rebounding of Development Economics.

During this period, various theoretical innovations in thinking about development and emphasising significance of contextual realities emerged. The Endogenous Growth Theorists, Institutional Economists and New Economic Geographers made their contributions during this period. All of them – Robert Lucas, Paul Romer, Douglas North, Oliver Williamson, ElinorOstrom, Paul Krugman – have already been awarded Nobel Prizes in Economics. The contributions of these economists enabled the scholars to identify the areas where we should be looking for realities.

In this context, the Economists started emphasising the primacy of evolving evidence-based policies for development intervention; India’s Professor KaushikBasu (former Chief Economic Advisor of India, former Chief Economist at the World Bank and now back to Cornell University, Ithaca, New York) is one of the main protagonists of this articulation. With the earlier interventions for addressing poverty failing to deliver around the globe, the world felt intense hunger for robust empirical foundations for evolving development interventions. With the traditional field methods manifesting weaknesses, the researchers started looking for more methods for appreciating the reality and establish or otherwise of theories.

Experimental Economics had to be born. “In the field it is difficult to study situations that have not occurred or institutionsthat do not exist because there is no natural experiment. For example, in thelaboratory it is just as easy to study the effects of auction market rules that have neverbeen observed in the economy as to study those that have. When left on her own, naturemay never create a situation that clearly separates the predictions of competing modelsor may never create a situation that allows a clear view of the underlying principlesat work.

Indeed, much of the progress of experimental methods involves the posing ofnew questions or the posing of old questions in a way that experimental methods can beapplied.” It is in this global demand for appreciation of the contextual realities and apprise which policy works or fails to deliver, the approach for randomised experiments for evaluating any intervention before finally adopting as a policy was born.

Abhijit, Esther and Michael are pioneers in this line of research. While Michael has focused his research more on Kenya, the coverage of Abhijit and Esther is more global; the research centre led by the couple have already completed Randomised Evaluations in 83 countries and 978 are undergoing.

On the approach, let me quote straightway from the Toolkit developed by the experts themselves: “Any attempt at drawing a causal inference question such as \What is the causal effect of education on fertility?” or \What is the causal effect of class size on learning?” requires answering essentially counter factual questions: How would individuals who participated in a program havefared in the absence of the program? How would those who were not exposed to the programhave fared in the presence of the program? The difficulty with these questions is immediate.

At a given point in time, an individual is either exposed to the program or not. Comparing the sameindividual over time will not, in most cases, give a reliable estimate of the program’s impactsince other factors that affect outcomes may have changed since the program was introduced.

We cannot, therefore, obtain an estimate of the impact of the program on a given individual. We can, however, obtain the average impact of a program, policy, or variable (we will refer tothis as a treatment, below) on a group of individuals by comparing them to a similar group ofindividuals who were not exposed to the program.To do this, we need a comparison group.

This is a group of people who, in the absence ofthe treatment, would have had outcomes similar to those who received the treatment. In reality,however, those individuals who are exposed to a treatment generally differ from those who arenot. Programs are placed in specific areas (for example, poorer or richer areas), individualsare screened for participation (for example, on the basis of poverty or motivation), and thedecision to participate in a program is often voluntary, creating self-selection.

Families chosewhether to send girls to school. Different regions chose to have women teachers, and differentcountries chose to have the rule of law. For all of these reasons, those who were not exposedto a treatment are often a poor comparison group for those who were. Any difference betweenthe groups can be attributed to both the impact of the program or pre-existing differences (the\selection bias”).

Without a reliable way to estimate the size of this selection bias, one cannot decompose the overall difference into a treatment effect and a bias term. To fix ideas it is useful to introduce the notion of a potential outcome, introduced by Rubin(1974). Suppose we are interested in measuring the impact of textbooks on learning.

Let us callY Ti the average test score of children in a given school i if the school has textbooks and Y Cithe test scores of children in the same school i if the school has no textbooks. Further, define Yias outcome that is actually observed for school i. zWe are interested in the differenceY Ti - Y C i which is the effect of having textbooks for school i. As we explained above, we will not be ableto observe a school i both with and without books at the same time, and we will therefore notbe able to estimate individual treatment effects. While every school has two potential outcomes,only one is observed for each school.”

The website of the MIT writes about the research centre led by Abhijit and Esther thus: “J-PAL was founded in 2003 as the “Poverty Action Lab” by professors Abhijit Banerjee, Esther Duflo and SendhilMullainathan. J-PAL was established to support randomized evaluations measuring interventions against poverty on topics ranging from agriculture and health to governance and education. The Lab was renamed in honour of Sheikh Abdul LatifJameel when his son, MIT alumnus Mohammed Abdul LatifJameel, supported it with three major endowments in 2005.

He further endowed its activities in 2009.”Thusthe wider coverage has been made possible by the funding from this Centre and the winning of the Nobel Prize proves that the expenses have paid off. Knowledge, contextual knowledge at that, has to be the foundation for policy.