Gilesova kritika Pikettyjevega Kapitala temelji na eni sami časovni podatkovni točki

Chris Giles, ki je v petek popoldne v Financial Timesu razburkal ekonomsko blogosfero s kritiko Pikettyjevega Kapitala v 21. stoletju, je svoj “dokaz” o nenaraščanju koncentracije bogastva v V. Britaniji utemeljil na eni sami časovni točki. Natančneje kot dokaz je vzel anketo britanskega statističnega urada o porazdelitvi premoženja (ki je, kot pravijo na uradu, še “v eksperimentalni fazi) in sicer za leto 2006. Problem tega pristopa je najmanj dvojen. Prvič, z uporabo ene same časovne točke seveda ni mogoče analizirati dolgoročnega trenda (kar Piketty naredi z analizo trenda skozi 200 let). In drugič, podatki iz anket ne odražajo realnega stanja, saj anketiranci tendirajo k poročanju nižjih vrednosti premoženja, hkrati pa anketa zajema samo vzorec gospodinjstev. Boljši način, čeprav ne idealen, je uporaba individualnih davčnih napovedi in “kapitalizacija” dohodkov iz kapitala, kot sta nedavno za ZDA naredila Saez in Zucman.

Giles’ claims attracted substantial attention in no small part because his attack strikes at the Paris School of Economics professor’s data, which have been nearly universally praised for thoroughness—even by critics who disagree with the conclusions that he draws from them. While it is clear that Giles spent some time looking over Piketty’s spreadsheets, he jumps to conclusions that are not supported by the points he raises.

Indeed, my examination of Giles’ analysis and the spreadsheets that Piketty provided to the public indicate that perhaps the key claim by Giles is erroneous. Giles bases his argument that there was not an increase in wealth concentration in the United Kingdom but rather a decrease on a single data point from a 2010 wealth survey in the UK. Because that survey did not exist in 2000, it cannot be directly compared to other time series data without harmonization. The entirety of the drop Giles claims is occurring can be explained by switching from one survey to another.

In contrast, Piketty went through the different surveys and sources to stitch together a coherent data set that is presumably free of these discontinuities. In order to do his comprehensive analysis of the change in wealth inequality over time, Piketty had to look at disparate data sources, harmonized them (so that he could compare apples to apples), and draw conclusions. Wealth is notoriously difficult to measure, which makes working with wealth data especially tricky. Piketty has been exceptionally transparent with the data sets used in his book (the data can be found here).

Giles uses the raw, non-harmonized wealth data to claim that wealth inequality in the United States has been flat and that it has been decreasing in the United Kingdom. Yet by combining these non-harmonized data sets, Giles is comparing apples to oranges. To say that this deviates from best data practices would be an understatement. In addition, as Piketty notes in his response to the article, recent work by Emmanuel Saez and Gabriel Zucman using better data and more sophisticated methods show an increase in wealth concentration in the United States. I am not aware of comparable analysis for the United Kingdom to confirm or refute those claims made by Giles.

In še Newsweek:

The centerpiece of Giles’s argument may also need closer examination, though. In the piece, Giles states that Piketty cited a figure showing the top 10 percent of British people held 71 percent of total national wealth, but that the U.K.’s Office of National Statistics put that figure at only 44 percent.

According to the Office of National Statistics, however, the Wealth and Assets Survey that Giles believes Piketty should have used as the basis for his research on wealth inequality in the U.K., has collected data since 2006 and is still in what it tells Newsweek is an “experimental” stage.

In other words, these figures, according to the office doing the survey, are not yet ready for prime time.

Giles told Newsweek he did not think the U.K. data should be assumed to be of lesser quality just because it is new and experimental—but he also did not appear to know this fact about the data and said he would “look into it.”

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