Not all numbers are equal

I’ve started reading Tony Atkinson’s new book, I

and already think it a much better book than the famousĀ 
(which for me was marred by the half-baked r and g business – see for example the Jaume Ventura slides here – as well as the lack of any practical policy suggestions).

[amazon_image id=”0674504763″ link=”true” target=”_blank” size=”medium” ]Inequality[/amazon_image]

Although not far into

, I completely and utterly agree with the following, in a chapter describing carefully the sources and character of the data (something else on which Piketty is actually rather weak – hence the challenge much reported this week from a graduate student at MIT):

In seeking to draw lessons from the statistics on inequality, we have to be confident in the quality of the data we are using. This is why I begin this chapter by describing and evaluation the sources of evidence on which scholars of inequality can draw. Such scrutiny is essential. All too often economists race ahead, drawing conclusions from figures that happen to be there, without asking why the data are suitable.”

Serendipitously, while reading this I’ve also been thinking about a keynote I’m giving soon at an OECD conference onĀ 

and the national accounts statistics in a couple of weeks’ time. All the thousands of studies and political claims resting on GDP growth figures are based on shifting sands, and we economists need to think far more carefully about what they take to be evidence for strong claims. There are some powerful examples in a paper presented by Samuel Williamson and Enrico Berkes recently at the Economic History Society conference.

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