  I introduce a new way of decomposing the evolution of the wealth distribution
using a simple continuous time stochastic model, which separates the effects of
mobility, savings, labor income, rates of return, demography, inheritance, and
assortative mating. Based on two results from stochastic calculus, I show that
this decomposition is nonparametrically identified and can be estimated based
solely on repeated cross-sections of the data. I estimate it in the United
States since 1962 using historical data on income, wealth, and demography. I
find that the main drivers of the rise of the top 1% wealth share since the
1980s have been, in decreasing level of importance, higher savings at the top,
higher rates of return on wealth (essentially in the form of capital gains),
and higher labor income inequality. I then use the model to study the effects
of wealth taxation. I derive simple formulas for how the tax base reacts to the
net-of-tax rate in the long run, which nest insights from several existing
models, and can be calibrated using estimable elasticities. In the benchmark
calibration, the revenue-maximizing wealth tax rate at the top is high (around
12%), but the revenue collected from the tax is much lower than in the static
case.
