Asset Prices and Wealth Inequality (2017)
This paper uses recently available data on the top of the wealth distribution to study the relationship between asset prices and wealth inequality. I document three stylized facts: (1) the share of wealth invested in equity increases sharply in the right tail of the wealth distribution, (2) when stock market returns are high, wealth inequality increases and (3) higher wealth inequality predicts lower future stock returns. These facts correspond to the basic predictions of asset pricing models with heterogeneous agents. Quantitatively, however, standard models with heterogeneous agents cannot fully capture the joint dynamics of asset prices and the wealth distribution. Augmenting the model with additional sources of fluctuations in wealth inequality, namely in the form of time-varying investment opportunities for wealthy households, is crucial to match the observed fluctuations in wealth inequality and in asset prices.
Decomposing the Rise in Top Wealth Inequality (2017)
What drives the recent rise in top wealth inequality? To answer this question, I derive a new formula that expresses the growth of top wealth shares as the sum of three terms: a term due to the wealth growth of top households relative to the economy, a positive term due to idiosyncratic wealth shocks, and a negative term due to population renewal. I propose an accounting framework to identify each term using panel data and I apply this framework to the annual ranking of Forbes Magazine's list of the 400 wealthiest Americans. I find that idiosyncratic shocks account for more than half of the increase in top wealth inequality since 1980. Disruptive forces that change the composition of individuals at the top are therefore central in explaining the recent rise in top wealth inequality.
Bank Exposure to Interest-Rate Risk and the Transmission of Monetary Policy with A. Landier, D. Sraer and D. Thesmar (2016)
We show that the cash-flow exposure of banks to interest rate risk, or income gap, affects the transmission of monetary policy to bank lending and real activity. In the cross-section of banks, income gap predicts the sensitivity of cash-flows and lending to interest rates. The effect of income gap is larger or similar in magnitude to that of previously identified factors, such as leverage, bank size or asset liquidity. To alleviate endogeneity concerns, we build loan-level data to control for firm-level demand shocks. This analysis also allows us to link banks' interest risk exposure to firm investment and employment.