Recent research has documented a significant rise in the volatility (e.g.,
expected squared change) of individual incomes in the U.S. since the 1970s.
Existing measures of this trend abstract from individual heterogeneity,
effectively estimating an increase in average volatility. We decompose this
increase in average volatility and find that it is far from representative of
the experience of most people: there has been no systematic rise in volatility
for the vast majority of individuals. The rise in average volatility has been
driven almost entirely by a sharp rise in the income volatility of those
expected to have the most volatile incomes, identified ex-ante by large income
changes in the past. We document that the self-employed and those who
self-identify as risk-tolerant are much more likely to have such volatile
incomes; these groups have experienced much larger increases in income
volatility than the population at large. These results color the policy
implications one might draw from the rise in average volatility. While the
basic results are apparent from PSID summary statistics, providing a complete
characterization of the dynamics of the volatility distribution is a
methodological challenge. We resolve these difficulties with a Markovian
hierarchical Dirichlet process that builds on work from the non-parametric
Bayesian statistics literature
