The book that everyone has been so enthusiastic about (well, everyone who supports vastly increased taxes and a much larger government anyway) may rely for much of its power on faulty data:
FT economics editor Chris Giles says he has found serious errors in data used by Thomas Piketty in his best seller Capitalism in the 21st Century, about growing inequality in the Western world.
“Some issues concern sourcing and definitional problems,” Giles writes. “Some numbers appear simply to be constructed out of thin air.”
Correcting for the errors revealed fundamentally different conclusions about rising inequality, Giles said.
“Two of Capital in the 21st Century’s central findings – that wealth inequality has begun to rise over the past 30 years and that the US obviously has a more unequal distribution of wealth than Europe – no longer seem to hold,” he writes.
He continues:
For example, once the FT cleaned up and simplified the data, the European numbers do not show any tendency towards rising wealth inequality after 1970. An independent specialist in measuring inequality shared the FT’s concerns.
Update, 25 May: The concern is not just “fat fingered” data transcription errors, but deliberate falsification of data.
But while the two Harvard professors’ errors seemed to have been unintended, Giles levels a more serious critique: that Piketty actively manipulated his data.
His most damning claim: Piketty altered U.K. data to show that wealth distribution there is worse off than it appears to be.
Piketty says the share of income going to the top 10% never fell lower than 60%, and since the end of the 1970s has returned to 70%, a level not seen in 70 years.
But the data Piketty himself cites shows the top 10% share of wealth is no greater than 50%, and may be as low as 42%.
Giles writes: “This appears to be the result of swapping between data sources, not following the source notes, misinterpreting the more recent data and exaggerating increases in wealth inequality.”
[…]
In a follow-up video on FT.com, Giles shows another example: Piketty appears to have added random numbers to certain formula to bend the data toward his hypothesis. “A 2 is added because the number wasn’t high enough — it didn’t seem to fit what he wanted to show in his charts, so he just added 2 to it,” Giles says. “There was quite a lot of this sort of thing in his spreadsheets.”
Update, 27 May. Nate Silver warns that we should be skeptical of both Piketty and his critics:
Science is messy, and the social sciences are messier than the hard sciences. Research findings based on relatively new and novel data sets (like Piketty’s) are subject to one set of problems — the data itself will have been less well scrutinized and is more likely to contain errors, small and large. Research on well-worn datasets are subject to another. Such data is probably in better shape, but if researchers are coming to some new and novel conclusions from it, that may reflect some flaw in their interpretation or analysis.
The closest thing to a solution is to remain appropriately skeptical, perhaps especially when the research finding is agreeable to you. A lot of apparently damning critiques prove to be less so when you assume from the start that data analysis and empirical research, like other forms of intellectual endeavor, are not free from human error. Nonetheless, once the dust settles, it seems likely that both Piketty and Giles will have moved us toward an improved understanding of wealth inequality and its implications.
