Miscellany

Winner of Infosys Prize 2018 in Social Sciences: Sendhil Mullainathan

  • Blog Post Date 21 November, 2018
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Ashok Kotwal

Editor-in-Chief, Ideas for India; University of British Columbia

ashok.kotwal@ubc.ca

The Infosys Prize 2018 for Social Sciences has been awarded to Sendhil Mullainathan, Professor of Computation and Behavioral Science, University of Chicago, for his path-breaking work in behavioural economics. In this post, I4I Editor-in-Chief, Prof. Ashok Kotwal discusses the substantial impact of Mullainathan’s work on diverse fields such as development, public finance, corporate governance, and policy design – and relevance to India.



Infosys prizes are meant to celebrate academic achievements by scholars working in India or those of Indian origin working abroad. There are separate prizes for different fields such as Mathematics, Physical Sciences, Life Sciences, Engineering and Computer Science, Social Sciences, and Humanities. The social science prize alternates every year between economics and other social sciences. Abhijit Banerjee, Esther Duflo, Arunava Sen, Raghuram Rajan, Kaivan Munshi have been some of the economists to have won the Social Science prize. This year’s winner is Sendhil Mullainathan who was till this year Robert C. Waggonar Professor of Economics at Harvard. This year he was appointed as University Professor at Booth School of Business, University of Chicago. In 2002, he was awarded a McArthur Fellowship (also known as genius award).

Sendhil has done path-breaking work in behavioural economics. His research has had a significant impact on diverse fields such as development, public finance, corporate governance, and policy design. A significant part of this work is relevant to India. He is presently on a mission to explore the promise of machine learning in conjunction with big data and to lay down the groundwork for the future of applied economics. It may be too early to judge the impact of this work but it clearly has the promise of being foundational. A noteworthy aspect of almost all his work is that it is oriented toward improving policy solutions to real-life problems. Sendhil is also incredibly prolific; I can think of very few economists, Indian or otherwise, who could match Sendhil’s productivity in either quality or quantity.

Many economists focus on an area of research, master the literature in that area, and try to move it forward by extending it. That is not how Sendhil does it. His starting point is often not the academic literature but a real-world puzzle such as ‘why do the poor keep getting indebted?’ or ‘What is the process of petty corruption in India?’ If he gets an insight, he tries to see if it can be applied in other areas. For example, his insight that scarcity could have a negative impact on cognitive ability found many applications in development, public finance, and policy implementation. In pursuing his questions, he does not feel constrained by his toolset. He feels free to choose whatever methodology that suits the problem at hand because he is very much at home with RCTs, econometric analysis of large and small datasets, and theoretical modelling. He is a complete economist.

Let me first summarise Sendhil’s contributions in the area in which I think he has had the biggest impact.

Behavioural economics

His most valuable and also his most original contribution has been in pointing out how poverty (and more generally perceived scarcity in any form) adversely affects ‘mental bandwidth’ (cognitive ability) which, in turn, hampers sound decision-making. This is one of the reasons why seemingly well-designed poverty schemes fail; the poor are often found not to make the most of what is offered. Moreover, they exhibit self-defeating behaviour. They save too little, borrow too much, and fail to enrol in assistance programmes. He explores this theme in several publications with the help of cleverly designed experiments, each pertaining to a different context.

Let me describe just one such experiment carried out in Tamil Nadu (Mani et al. 2013). The point of this exercise was to see if individuals would behave differently if under financial stress. Sugarcane farmers harvest their crop just once a year. Their payment comes only after the harvest. Before the payment for their harvested crop, they are running out of cash and, therefore, are under financial stress. The experiment consisted of testing their cognitive ability through simple IQ tests at two points in time – before and after they have sold their harvest. On average, they found a significant gap (equivalent to 13 IQ points), which is attributed to a drop in cognitive ability after ruling out other possible reasons such as nutrition, labour effort, or anxiety due to price uncertainty.

The insight not only strengthens the rationale for transfers to the poor but also suggests that any transfers should be done in instalments rather than as lump sums. This is a simple but extremely useful insight to policymakers. The insights from a number of experiments that Sendhil and his various co-authors as well as other researchers have performed are nicely summarised in a widely-cited book written with his psychologist colleague – E. Shafir (Shafir and Mullainathan 2013).

Sendhil has done a substantial body of work in behavioural economics that spans many aspects of behaviour: saving, energy Policy, advertising, political economy, and work effort. His 2011 book with Congdon and Kling explore how psychological factors reshape core public finance concepts like moral hazard, deadweight loss, and incidence.

Let me now describe some of his noteworthy papers in other areas:

Discrimination

In a very interesting paper (Bertrand and Mullainathan 2004), they reveal the results of a cleverly constructed experiment to probe racial discrimination by potential employers. Despite the same content, a resume with a name that sounded like an African American was less likely to get an interview call than an application with a White sounding name. The thoroughness with which the experiment was designed made it highly credible.

Along the same lines, they did an experiment in Delhi along caste lines in its software and business services sector (Banerjee et al. 2008). They found mixed results. They wrote another related paper with R. Hanna (Bertrand et al. 2010); they find that the affirmative action policy favouring lower castes also favours lower income applicants, but it ends up discriminating against other disadvantaged groups like women.

Corruption

I do not know a better paper on corruption in India than Bertrand et al. 2007. Sendhil and his co-authors recruited 822 applicants for driver’s license in Delhi. They study the allocation of driver’s licenses in India by randomly assigning applicants to one of three groups: bonus (offered a bonus for obtaining a license quickly), lesson (offered free driving lessons), or a control group that was left to its own devices. Both the bonus and lesson groups are more likely to obtain licenses. However, bonus group members are more likely to make extralegal payments and to obtain licenses without knowing how to drive. All extralegal payments happen through private intermediaries (‘agents’). An audit study of agents reveals that they can circumvent procedures such as the driving test. Overall, the results support the view that corruption does not merely reflect transfers from citizens to bureaucrats but distorts allocation. Sendhil also has a Handbook chapter on corruption with Banerjee and Hanna.

Corporate governance

A few papers by Sendhil and his frequent co-author M. Bertrand have had a big impact in this area. (1) Mullainathan et al. 2002. It develops a general empirical methodology to test for ‘tunneling’ (whereby majority shareholders move resources from firms to other firms in their business groups where they have greater cash flow rights.) (2) Bertrand and Mullainathan 2003. This paper uses variation in corporate governance generated by State adoption of anti-takeover laws to empirically map out managerial preferences. (3) Bertrand and Mullainathan 2001. It shows that CEO compensation depends not just on performance but significantly on luck (a random factor outside the CEO’s control), for example, the price of oil for a CEO of an oil company.

Machine learning and big data

Economics as a field is changing fast. Machine learning technology and the compilation of big data are progressing fast and simultaneously. It is inevitable that economics will be dominated by this upcoming technology. Sendhil is one of the pioneers to lay down the groundwork for the appropriate methodology for the use of this technology. More recently, Sendhil has focused on how machine learning and big data can be used for prediction in economics (Mullainathan and Spiess 2017).

This is an amazing record for any economist of any age. Sendhil is 46 years old and yet he has had a huge impact on several sub-fields of economics and even other fields such as psychology, public health, and data science. He has asked fundamental questions and done justice to the gravity of those questions in the way he has tackled them. His work has generated useful guidelines for policymaking. A significant amount of his work is relevant to India. The goal of Infosys prizes is to identify role models for budding, aspiring Indian scholars and Sendhil certainly fulfills this role.

Further Reading

  • Banerjee, Abhijit, Marianne Bertrand, Saugato Datta and Sendhil Mullainathan (2008), “Labor market discrimination in Delhi: Evidence from a field experiment”, Journal of Comparative Economics, 37 (2009):14–27. Available here.
  • Bertrand, Marianne, Rema Hanna and Sendhil Mullainathan (2010), “Affirmative action in education: Evidence from engineering college admissions in India”, 94 (1–2): 16–29.
  • Bertrand, Marianne, Simeon Djankov, Rema Hanna and Sendhil Mullainathan (2007), “Obtaining a Driver's License in India: An Experimental Approach to Studying Corruption”, Quarterly Journal of Economics, 122(4): 1639–1676.
  • Bertrand, Marianne and Sendhil Mullainathan (2003), “Enjoying the Quiet Life? Corporate Governance and Managerial Preferences”, Journal of Political Economy, 111(5): 1043–1075. Available here.
  • Bertrand, Marianne and Sendhil Mullainathan (2004), “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination”, American Economic Review, 94(4): 991–1013. Available here.
  • Bertrand, Marianne and Sendhil Mullainathan (2001), “Are CEOs Rewarded for Luck? The Ones Without Principals Are”, The Quarterly Journal of Economics, 116(3): 901–932. Available here.
  • Mani, Anandi, Sendhil Mullainathan, Eldar Shafir and Jiaying Zhao (2013), “Poverty Impedes Cognitive Function”, Science, 341(6149): 976–980.
  • Mullainathan, Sendhil and Jann Spiess (2017), “Machine Learning: An Applied
  • Econometric Approach”, Journal of Economic Perspectives, 31(2): 87–106. Available here.
  • Mullainathan, Sendhil, Marianne Bertrand and Paras Mehta (2002), “Ferreting Out Tunneling: An Application to Indian Business Groups”, The Quarterly Journal of Economics, 117(1): 121–148.
  • Shafir, E and S Mullainathan (2013), Scarcity: Why Having Too Little Means So Much, Times Books.
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