Heterogeneous Risk Exposure and the Dynamics of Wealth Inequality

In this paper I argue that the dynamics of wealth inequality are largely driven by heterogeneous exposure to aggregate risk in asset returns. I propose a quantitative model of households' optimal portfolio choice that builds on evidence that housing is a necessary good. The model replicates households' portfolio heterogeneity along the wealth distribution: just like in the data, as households get wealthier they shift their portfolios away from safe assets, first towards housing, and then towards equity. Because households in different parts of the wealth distribution are exposed to different sources of aggregate risk, the model has strong implications for the evolution of inequality. In particular, temporary shocks in equity returns have large and persistent effects on top wealth shares. A key implication is that the observed rise in U.S. wealth inequality was mostly due to abnormal equity returns and it is therefore expected to revert back to lower levels.

When Does Wealth Inequality Matter for Asset Pricing?

In this paper I show that, contrary to conventional wisdom, the wealth distribution does matter for the determination of asset prices. I do so by showing that, in a model in which households' equity share is increasing in wealth, approximate aggregation does not hold and households make systematic errors when trying to forecast prices ignoring wealth inequality. In order to understand the effect of inequality on asset prices, I solve a two-assets general equilibrium model of wealth inequality and use recent advances from scientific machine learning to extend the algorithm in Villaverde et al. (2020) to solve systems of neural stochastic differential equations for the aggregate states. Finally, I look at how the introduction of such GE feedback between wealth inequality and asset prices changes our understanding of the effects of government policy.

Wealth Inequality at the Top: Down to the Roots

(joint with M. Vogler and G. Sorg-Langhans)

Multiple theories of inequality compete to explain U.S. wealth inequality and the share of wealth held by the top one percent. To what extent does it matter which of these models we rely on? In this paper we analyze the responses of the different theories to a host of policy experiments. To this end, we form a quantitative model that nests the competing channels and assesses the effects of policy experiments by sequentially shutting off all but one of these model mechanisms. Our model is directly calibrated on the wealth distribution which allows us to starkly contrast the different theories and clearly understand the mechanisms at work. We find stark differences in predictions across channels for a given policy experiment, indicating that, by choosing a particular mechanism, researchers might already predetermine the outcome of their policy experiments.