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Let's start with the inequality data.
National and industrial inequality data for Taiwan and Turkey.
This "consumption smoothing" renders year-to-year income inequality data all but meaningless, some say.
National and regional inequality data for Argentina, Brazil, China, Cuba, Europe, India, Russia, and the United States.
In Asian countries where inequality data are available on both measures, the income coefficient is typically a fifth higher than that based on expenditure.
In this Datablog post, I re-examine the wealth inequality data for the UK, since this is the country where Chris Giles's adjustments make the most difference to Piketty's original data.
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I refer to this property as the data-information inequality: information ≤ data.
But judging by the income-inequality data, it seems to me that the Occupy Wall Street movement, which to the extent that it has a central issue is basically about protesting inequality and spectacular concentrations of wealth, picked the right spot for its first camp-out.
In addition to the four socio-demographic variables which were used to explore inequalities, data on comorbidities were used to take case-mix into account.
Analysis of this model under various environmental conditions has shown that the non-uniform distribution of substrates that was due to either initial inequalities data or high diffusion rate along with the chemotaxis resulted in emergent community dynamics evident in a movement of cells to a better place of living, into the nodes with higher concentrations of necessary substrates.
Research has repeatedly suggested that Silicon Valley tech firms have worsened inequality, and data shows the area has lost affordable units at alarming rates.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com