Angus Deaton’s classic, The Analysis of Household Surveys: A Microeconometric Approach to Development Policy has been re-issued with a new preface by the author. The original publication has been cited more than 6,700 times. When it was first issued, one reviewer characterized it as “a rugged tour through a broad swath of author Angus Deaton’s intellectual countryside… Deaton’s prose and reasoning are uniformly sharp, and I found myself quite willing to study at length whatever he was willing to teach.”
Here are a few of Deaton’s reflections, twenty years after he first published this work.
On how it came about:
The origins of the book go back to the early days of the Living Standards Measurement Study (LSMS), which was set up in the World Bank around 1980. As its name suggests, the original idea was to promote household surveys that would enable the better measurement of poverty and of living standards around the world, something that was difficult to do with the data then available. As time went on, and people came and went, the LSMS surveys evolved into multi-purpose tools that would permit not only measurement, but also analysis, permitting a better understanding of how people's lives work, what makes them tick, and why they are as well off or as poorly off as they are.
On instrumental variables:
As I taught the material over the years, it became clear that many of the uses of instrumental variables and natural experiments that had seemed so compelling at first lost a good deal of their luster with time… Some of the studies using natural experiments and instruments have worn well, but that is more the exception than the rule. Twenty years later, I now find myself very much more skeptical about instruments in almost any situation.
On natural experiments:
They often give a clean answer, eliminating effects that otherwise would cloud the analysis. Yet the "clean" answer is not always the answer that we want for policy or understanding. This is one aspect of the familiar trade-off between internal and external validity; a natural experiment is like a laboratory experiment where many factors are held constant, but where we have little idea whether the effect will be replicated in settings that may be more relevant for policy.
On randomized controlled trials:
RCTs often yield new insights and unexpected findings. Yet they also have more problems than we anticipated, both in theory and practice. They are not magic tools, any more than panel data or instrumental variables were magic tools.
And finally, on descriptive analysis:
The trick is to use the data to tell us something that we didn't know before, and that can help us change our minds, or see things differently. Sometimes this can be done from simple descriptive statistics… That almost half of all children in India were severely malnourished was a finding that then Prime Minister Manmohan Singh described as "a national shame." Such straightforward descriptions can have huge effects on policy, as can correlations and reg.
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