Just sharing two really great quotes on why inequality is more than just a hip-of-the-moment.
First: Milanovic, Branko. Global Inequality (p. 234-235). Harvard University Press.
"First, there are methodological advances in economics, thanks to the reintroduction of inequality into economists’ way of thinking, which will be difficult to forget about or ignore. Economics is moving from an almost single-minded concern with representative agents and averages to a concern with heterogeneity. And as soon as one enters the territory of heterogeneity, she is dealing with inequality. It need not be inequality only in wealth and income; it could be inequality in education, health status, IQ or SAT scores, trust, corruption, or anything. But once you no longer think only in terms of averages, your outlook on the world changes dramatically. It can be likened to going from a two-dimensional to a three-dimensional world. By now, these concerns are fairly deeply implanted among the new generations of economists and social scientists. Economists are including them in their dissertations, research projects, and empirical papers, and as these long-term projects become completed, and as the new generation begins to fill academic and research positions, the paradigm will gradually change. Replacing an old paradigm takes a long time; it sometimes requires an important economic event to reveal the discrepancy between what a paradigm teaches and how the world really functions. (This is precisely what the Great Recession did for the paradigm of the representative infinitely lived income-maximizing agent with perfect information.) The new heterogeneity-and-inequality-based paradigm that is being created now will take some time to impose itself, but it, in turn, will not be easy to displace."
And a second, to my opinion even a better one, Borgatti and Everett (best known and quoted authors in network analysis), 1999, p. 394:
"As a final point for reflection, it is interesting to consider that to fit a core/periphery model is to reduce a complex dyadic variable — a network — to a single attribute of actors. Network researchers tend to disdain "attribute data" (Wellman, 1988, p. 31). The complaint is not that we compute from the pattern of network relations a single summary value that describes each actor’s position. This is what any centrality measure does and is completely unremarkable. Rather, the core/periphery model says that all ties in the network error aside. are the result of a single attribute. In effect, this denies the necessity for having collected complex relational data a matrix., since much simpler data a vector. contains the same information content. This goes against the grain for network analysts, who like to think that relational data are richer and reveal emergent properties that mere attributes of actors simply cannot capture (e.g., see Wellman, 1988). When the core/periphery model fits, it means that to a certain extent, we do not need to know who is connected to whom. All we need is a single actor attribute. It is the same thing as when we fit the model of independence on a contingency table and find that it fits. As good scientists and structuralists we should be happy to find such a parsimonious description of our data. But, more likely, we are disappointed that nothing more "interesting" is going on."
Piše: Andrej Srakar.