A Nobel Prize for measurement: Congratulating Angus in the Indian Express
— Blog Post — 4 min read
In the preface to his magisterial 2013 book The Great Escape, Angus Deaton thanks his teachers. “Richard Stone was perhaps my most profound influence,” Deaton writes, “from him I learned about measurement – how little we can say without it and how important it is to get it right.”
Important, indeed. And difficult – but well worth doing. On Monday afternoon, the Nobel committee recognized the importance of measurement when they awarded Professor Deaton the 2015 Nobel Prize in economics.
We know what it feels like to owe much to a special teacher. Professor Deaton passed Stone’s lesson on to his own students, including us. Deaton’s career has been wide ranging. Most recently, he has helped the world understand its ongoing, incomplete, but nonetheless dramatic great escape from poverty and early death. When Angus was born in Scotland, around 6 percent of babies there died before their first birthday; today, this infant mortality is under half of one percent. But over 4 percent of babies born in India still die in their first year, and the infant mortality rate among the many babies born in the northern plains states exceeds that of Scotland 69 years ago. Fully 8 percent of children die in infancy who are born in Sitapur district of Uttar Pradesh, where we have worked since 2011 and where the population is 85 percent as large as Scotland’s today.
One important part of Deaton’s research over the last decades has focused on poverty and well-being in India. In this work, he has been resolutely empirical: he focuses on what the data can tell us, and what they sometimes cannot. The paradoxes of development in India have been fertile ground for Deaton’s data-minded approach. India is a place where good things do not always come together, and where conventional development stories are often inverted.
Despite rapid economic growth, and significant but slower improvements in poverty, children in India are shorter, on average, than children in sub-Saharan Africa, who are poorer. As Indian households have become richer over the past decades, they have eaten fewer and fewer calories, on average – a puzzle seemingly at odds with the basic rules of household economics. In some of his most recent work, Deaton has shown that poor people in Africa, where health outcomes are very bad, do not tend to see improving health as a policy priority – a puzzle that may be reflected in Delhi’s apathy to its lethal air pollution. Such paradoxes, Deaton taught us, are opportunities to learn something important.
This all adds up to a central message of Deaton’s work: that becoming richer is not necessarily the same thing as becoming better off. This, too, is an important lesson for India, where infant mortality and other basic measures of human development are considerably worse than what other countries with similar levels of GDP per capita experience. Indeed, India’s “excess” neonatal mortality – above what its per capita GDP would predict in international comparison – is greater than total neonatal mortality in China.
Professor Deaton was working on what is now called “evidence-based policy” before it was a hot topic: in the 1990s he published definitive work helping researchers sort out how to use household surveys to measure consumption in poverty. But notice what he includes in “evidence”: careful, statistical descriptions based on survey data designed to be informative about populations. Angus reminds economists – and, still, us – that there is no substitute for such careful thinking.
Nor is there a substitute for usefully designed, representative survey data. Perhaps such data were part of what drew Deaton’s focus to India. India historically had a long tradition of outstanding sample surveys, which Deaton has drawn upon again and again to teach India and the world about the well-being of individuals in the country that represents a large fraction of any international statistic.
Some of our own work built upon Angus’ surprising observation that GDP is uncorrelated with height across developing countries, in Demographic and Health Survey (DHS) data.But Deaton, the 2015 Nobel laureate, has never written a paper about the height of people in India using data collected since 2005. We are sure that he would love to, and that it would teach the world important things about well-being in India, but no new DHS has been released for India in the past decade. The government has not collected it. Yet, in this same time period, Bangladesh has released two DHSs. The meager data that are eventually released in today’s India, such as the Rapid Survey of Children, are often made publicly available only as summary statistics of averages, not household level data; Deaton helped economists realize that such aggregated numbers overlook inequality and obscure details of household behavior.
India has slowly transformed from a world leader in the availability of survey data to a place where meaningful statistics simply are not available. These days, nobody really knows the height of India’s children, or how much weight the average woman gains in pregnancy, or what fraction of people in rural India defecate in the open. The dimming of the historical light of Indian statistics matters for the world: one-fifth of all humans are born here.
As Deaton’s most recent work in development reminds us, development economics has political implications. Credible, independent data are critical not only for research, but for democracy. As Deaton noted in his Princeton University press conference on Monday afternoon, government choices not to collect or release data often reflect vested interests.
Deaton’s work shows that useful measurement of well-being in India is not only possible – it is practical and informative. It is indispensible. As we congratulate Professor Deaton on this deserved honor, we should remember that somewhere an economics student in India is now beginning a career in which she could make Nobel prize-worthy contributions to the well-being of the next generation of Indians. India should make sure that she has the data she will need to do this work. She, too, can say little without measurement.