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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and use German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from 1 to 9 trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale-or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale-or type-dependence, on one hand, and statistical biases, on the other hand. Yet both economic theory and mathematical formalism indicate that sampling and reporting biases are more plausible explanations for the observed data than scale-or type-dependence.

With the advent of platform economies and the increasing availability of online price comparisons, many empirical markets now select on relative rather than absolute performance. This feature might give rise to the ‘winner takes all/most’ phenomenon, where tiny initial productivity differences amount to large differences in market shares. We study the effect of heterogeneous initial productivities arising from locally segregated markets on aggregate outcomes, e.g., regarding revenue distributions. Several of those firm-level characteristics follow distributional regularities or ‘scaling laws’ (Brock in Ind Corp Change 8(3):409–446, 1999). Among the most prominent are Zipf’s law describing the largest firms‘ extremely concentrated size distribution and the robustly fat-tailed nature of firm size growth rates, indicating a high frequency of extreme growth events. Dosi et al. (Ind Corp Change 26(2):187–210, 2017b) recently proposed a model of evolutionary learning that can simultaneously explain many of these regularities. We propose a parsimonious extension to their model to examine the effect for deviations in market structure from global competition, implicitly assumed in Dosi et al. (2017b). This extension makes it possible to disentangle the effects of two modes of competition: the global competition for sales and the localised competition for market power, giving rise to industry-specific entry productivity. We find that the empirically well-established combination of ‘superstar firms’ and Zipf tail is consistent only with a knife-edge scenario in the neighbourhood of most intensive local competition. Our model also contests the conventional wisdom derived from a general equilibrium setting that maximum competition leads to minimum concentration of revenue (Silvestre in J Econ Lit 31(1):105–141, 1993). We find that most intensive local competition leads to the highest concentration, whilst the lowest concentration appears for a mild degree of (local) oligopoly. Paradoxically, a level playing field in initial conditions might induce extreme concentration in market outcomes.

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Aggregation is one, if not the fundamental challenge of the social sciences. Whenever agents on the micro level interact and are strongly heterogeneous, macro behavior might exhibit emergent properties and is likely irreducible to an ensemble of isolated actions at the micro level. Given the overwhelming complexity of those actions with myriads of agents interacting based on differing beliefs and along heterogeneous dimensions, any explanatory attempt needs to idealize and simplify to reduce this complexity. While the focus of behavioral macroeconomics is to refine behavioral rules, the interaction and heterogeneity of those sophisticated agents can therefore often only be considered to a limited extent. By contrast, the complexity economics approach typically assumes little sophistication and adaptive behavior and focuses on the impact of the intermediate layer between the micro and macro levels on aggregate outcomes, i.e., on distributional regularities and networks. This is the paradigm in which the thesis at hand is situated. Which approach is more appropriate to understand a specific economic phenomenon naturally depends on the network topology and actual heterogeneity of the empirical target system. Rather than substitutes, the central claim this thesis sets out to defend is thus that both perspectives complement each other and that a complexity perspective offers additional interpretations and implications concerning the same phenomenal domain. Instead of a conceptual discussion, it tries to employ the outlined pre-analytic vision and shows that the proposed parsimonious explanations are often observationally equivalent to attempts that attribute most of the observed macro outcomes to micro origins. In these cases, a complexity economics perspective might offer vastly different additional policy implications, e.g., it might help to identify pivotal agents for aggregate outcomes or show how changing social network topologies might influence mental models that more micro-oriented approaches cannot account for by construction.

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