The role of the wealth distribution on output volatility

We explore the link between wealth inequality and business cycle fluctuations in a two-sector neoclassical growth model with endogenous labor and heterogeneous agents. Assuming that wealth inequality is described by the distribution of shares of capital, we show that in the most plausible situations wealth equality is a stabilizing factor. In particular, when wealth is Pareto distributed and preferences generate non-linear absolute risk tolerance indices, a rise in the Gini index may only be associated to a rise in volatility. When individual preferences are such that the individual absolute risk tolerance indices are linear, as with HARA utility, even a low level of taste heterogeneity ensures that a rise in inequality may not reduce volatility, and this independently of the wealth distribution. Finally, we note that such a clear result is at odd with the existing related literature.

The role of the wealth distribution on output volatility

We explore the link between wealth inequality and business cycle fluctuations in a two-sector neoclassical growth model with endogenous labor and heterogeneous agents. Assuming that wealth inequality is described by the distribution of shares of capital, we show that in the most plausible situations wealth equality is a stabilizing factor. In particular, when wealth is Pareto distributed and preferences generate non linear absolute risk tolerance indices, a rise in the Gini index may only be associated to a rise in volatility.When individual preferences are such that the individual absolute risk tolerance indices are linear, as with HARA utility, even a low level of taste heterogeneity ensures that a rise in inequality may not reduce volatility, and this independently of the wealth distribution.Finally, we note that such a clear result is at odd with the existing related literature.Wealth Inequality, Pareto distribution, Gini index, Elastic Labor Supply, Macroeconomic Volatility, Endogenous Equilibrium Business Cycles.

Why is productivity procyclical? Why do we care?

Productivity rises in booms and falls in recessions. There are four main explanations for this procyclical productivity: (i) procyclical technology shocks, (ii) widespread imperfect competition and increasing returns, (iii) variable utilization of inputs over the cycle, and (iv) resource reallocations. Recent macroeconomic literature views this stylized fact of procyclical productivity as an essential feature of business cycles because each explanation has important implications for macroeconomic modeling. In this paper, we discuss empirical methods for assessing the importance of these four explanations. We provide microfoundations for our preferred approach of estimating an explicitly first-order approximation to the production function, using a theoretically motivated proxy for utilization. When we implement this approach, we find that variable utilization and resource reallocations are particularly important in explaining procyclical productivity. We also argue that the reallocation effects that we identify are not "biases" -- they reflect changes in an economy’s ability to produce goods and services for final consumption from given primary inputs of capital and labor. Thus, from a normative viewpoint, reallocations are significant for welfare; from a positive viewpoint, they constitute potentially important amplification and propagation mechanisms for macroeconomic modeling.Productivity ; Business cycles

Structure of Triadic Relations in Multiplex Networks

Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different "layers" that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.Comment: Main text + Supplementary Material included in a single file. Published in New Journal of Physic