This is something we thought about a lot and couldn’t come to any firm conclusions on. In part, that’s because WID by necessity aggregates up to the region level very early on when putting together a global distribution, making it hard to see where countries end up.
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Thanks. Ok, so to your original point, omission of the top 1% in the countries big enough to matter couldn't really be affecting the middle of that graph. So... I'm back to Maya's default bias in favor of trusting survey data more.
Maybe this chart shows an elephant morphing into a loch ness monster? @wid_inequality has distributed nat accounts for individual countries in blue, red, green lines. Getting to yellow involves lots of merging & rescaling across regions @gabriel_zucman @ChancelLucas @BrankoMilanpic.twitter.com/ganEmur1X8
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Informal sector is included in surveys. Most (I think practically all) of the difference btw WID and L-K data comes not from the top 1% but from the assumption on how are undistributed corporate profits allocated across the distribution.
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Actually, it is the use of tax data at the top (rather than assumptions on undistributed profits) that explain the bulk of the difference between WID data and survey data, at least in China, India, Brazil. For details see for instance http://wir2018.wid.world/part-1.html (below box 1.1)
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People also forget (or pay no attention) to the fact that the fiscal top in China includes only 0.5% of _urban_ households, in India and Russia about 1%. So 99% of data comes from the surveys (which are then "augmented" through other assumptions).
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In India now about 7% of the adult population files tax returns. That said for emerging countries
@BrankoMilan is right, WID data largely relies on surveys for the bottom 90% or 99%. Using tax data for the remaining 10% or 1% yields estimates more coherent with national accounts.
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