Giles describes several categories of issues that he found with Piketty’s data:
a) Fat fingers
Prof. Piketty helpfully provides sources for the data he uses in his work. Frequently, however, the source material is not the same as the numbers he publishes. …
b) Tweaks
On a number of occasions, Prof. Piketty modifies the figures in his sources. This might not be a problem if these changes were explained in the technical appendix. But, with a few exceptions, they are not, raising questions about the validity of these tweaks. …
c) Averaging
Prof. Piketty constructs time-series of wealth inequality relative for three European countries: France, Sweden and the UK. He then combines them to obtain a single European estimate. To do so, he uses a simple average. This decision (shown in the screen grab below) is questionable, as it gives every Swedish person roughly seven times the weight of every French or British person. …
d) Constructed data
Because the sources are sketchy, Prof. Piketty often constructs his own data. One example is the data for the top 10 per cent wealth share in the US between 1910 and 1950. None of the sources Prof. Piketty uses contain these numbers, hence he assumes the top 10 per cent wealth share is his estimate for the top 1 per cent share plus 36 percentage points. However, there is no explanation for this number, nor why it should stay constant over time.
There are more such examples. …
e) Picking the wrong year for comparison
There is no doubt that Prof Piketty’s source data is sketchy. It is difficult to find data that relates to the start of each decade as his graphs demand. So it is only natural that he might say 1908 is a reasonable data point for 1910 on the graph.
It becomes less reasonable when, for example, Prof. Piketty uses data from 1935 Sweden for his 1930 datapoint, when 1930 data exists in his original source material. …
f) Problems with definitions
There are different ways to compute wealth data ranging from estimates based on records at death to surveys of the living. These methods are not always comparable.
In the source notes to his spreadsheets, Prof. Piketty says that the wealth data for the countries included in his study are all obtained using the same method. …
But this does not seem to be true.
g) Cherry-picking data sources
There is little consistency in the way that Prof. Piketty combines different data sources.
Sometimes, as in the US, he appears to favour cross-sectional surveys of living households rather than estate tax records. For the UK, he tends to avoid cross sectional surveys of living people.
Prof Piketty’s choices are not always the best possible ones. A glaring example is his decision relative to the UK in 2010. The estate tax data Prof. Piketty favours comes with the following health warning.
“[The data] is not a suitable data source for estimating total wealth in the UK, or wealth inequality across the whole of the wealth population; the Wealth and Asset survey is more suitable for those purposes”.
These choices matter: in both the UK and US cases, his decision of which type of data to use has the effect of showing wealth inequality rising, rather than staying constant (US) or falling (UK).
So what happens if you correct Piketty’s errors? This appears to be the key chart, particularly the two graphs on the bottom. It is apparent that the superficial plausibility of Piketty’s account derives from his own “tweaks” and misrepresentations, not from the underlying data:
Of course, Giles and Giugliano will not have the last word. The debate over Piketty’s sources and the integrity of his conclusions is just beginning. One wonders, though: why didn’t any economists take the trouble to do what Giles and Giugliano, two reporters, did? And why did so many liberals leap to endorse Piketty’s data when they obviously had no idea whether it was valid or not? Paul Krugman, for example:
[I]f you think you’ve found an obvious hole, empirical or logical, in Piketty, you’re very probably wrong. He’s done his homework!
Well, we know the answer to that one.
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