One point caught my eye in a recent post by Bob Murphy about the Giles analysis. I know I can count on him to tell me if I am getting any of this wrong, but at least I can tell him I invite the criticism.
Bob suggests that the ONS data discrepancy is a red herring. I find that a little odd because it's what most of the critics seem to think is the major point, so if it is a red herring then it's one people are concerned about because it's what the critics are interested in. Regardless, Bob contends that there are bigger issues with more frequent data, such as the Inland Revenue Service (IRS) data for the top 10%'s share of wealth that is circled in black, below. Piketty's series is in blue.
"You can see the huge gulf between that raw data set, and Piketty’s blue line above it. You can see the Inland Revenue Series (IRS) data here; note that the figures for wealth held by the top 10% at the end of the series in the year 2005 is 54%, not the nearly 70% value through which Piketty’s trend line moves in that year[...] So it should be crystal clear to anyone who actually wants to see if Giles has a point–and went through his work carefully–that Giles’ case doesn’t rest or fall on our opinion of the ONS data. Rather, Piketty’s blue line in the shot above is well above the IRS data for the middle-2000s. The only raw data source Piketty can use to get such a high figure for wealth held by the 10% (at 70.5%) in the year 2010 is to rely on the “HMRC Top 10%” data, but the HMRC report itself proclaims that it is not suitable for such purposes (according to Giles in his FT critique, but I could not personally track down this claim and independently verify it)."I think we need to be very careful about claims like this, and this is precisely why I'd rather wait for more details on what went into the data processing. In this case what leaps out at me is the discontinuity in the "LATEST IRS Top 10%" data and the data that come before and after it, particularly since the early data points of this series overlap with other data series in the same year that are much higher. Generally things don't move discontinuously like that in nature, so when you see that in the data there is very likely to be either an abrupt policy change or (more likely in this case) something different about how the data is collected. Maybe some things are left out of the new series that were in the old series. If they are just different series maybe the sampling frame is a little different or the variables are a little different. Maybe definitions differ across two surveys or within one survey over time.
A good example in the work I'm doing for the NAE is a very simple series on employment levels for engineers and engineering technicians and technologists. I use the CPS for this because it's best for long term labor market information. Every once in a while the federal government slightly changes occupational definitions, so tracking this over decades means that I am looking at slightly different populations. Some of this is just accounting for natural changes in jobs over time (as you can imagine, an engineering technician's job today is very different from what it was forty years ago), and certain changes are bigger than others. One big change to occupational categories occurred in 2002, largely to account for the rise of computer and information technology occupations (ironically, it makes studying computer and information technology labor markets during this period a real pain). Some engineers and engineering technicians got pushed into other categories (in a lot of cases IT) at this time and what you have in the data is relatively sharp break. That's what you see in my figure below.
Now I could have just continued the data series and made the break look like it was an actual drop but that would be misleading. I knew much of the change was not a drop in employment at all - it was a change in the way the data was collected. I didn't do that. Instead I disconnected the pre- and post-2002 series and used a dashed line in the post- section to make sure it was distinct, and I discussed the issue in my text.
I could have done what Piketty did - reconstruct my own series from the various data sources I had (I am working with many other data sources by the 2000s besides the CPS), and using what I know about the changes in the occupational categories. Indeed the BLS provides documentation for what adjustment factors to apply, and in this case of course it could mean a difference of a couple hundred thousand workers.
Figure 5. Employment of engineers and engineering technicians and technologists, 1971-2013
Source: Author’s calculation from the 1971-2013 March CPS
For this project it was completely unnecessary to do all that. I am just giving a taste of levels and trends in these fields (particularly technicians, which are less commonly studied) to a group of (mostly) non-economists and moving on to other analyses. If I was doing work where the trend itself was very important to my discussion I would have reconstructed it instead of just separating the series. If I had done that then my reconstructed series would have been well above the actual data reported in the CPS (or well below, if I traced the new definitions backwards), just like Piketty.
The thing is, it would have been a perfectly legitimate thing for me to do. And I have a suspicion Piketty's adjustments here were perfectly legitimate. They seem to jive with the results everyone else is getting and it strikes me as more probable that Giles, a journalist, is slightly confused about what kind of data processing went into the book than that Piketty, an economist, is making this stuff up.
But I just don't know. I really can't repeat enough that I really don't know what was done and why and I can't form a firm opinion - beyond general suspicions - until I do.
The income tax and minimum wage stuff is shockingly sloppy, but I don't see the nefariousness in it that some people do. People that want a global wealth tax are sufficiently to the left that they are likely not trying to score points by making George H.W. Bush look bad and Bill Clinton look good. Those two seem pretty close from an American's perspective, much less from a French leftist's perspective. I think it's more likely that ideological biases passively contributed to the sloppiness (sort of a "well that sounds right" sort of thing) than that he was trying score ideological points by manipulating it.
It's like Tom Woods and the old 1920-21 depression errors I wrote about a while ago with the timing of Wilson and Harding's fiscal policy and the various monetary policy decisions. I don't think Woods made the mistakes because he was trying to boost Harding's reputation. I think he was being sloppy, took a look at it, figured it sounded right - it fit his narrative - and called it a day.