Thomas Blanchet

I decompose the dynamics of the wealth distribution using a simple dynamic stochastic model that separates the effects of consumption, labor income, rates of return, growth, demographics and inheritance. Based on two results of stochastic calculus, I show that this model is nonparametrically identified and can be estimated using only repeated cross-sections of the data. I estimate it using distributional national accounts for the United States since 1962. I find that, out of the 15 pp. increase in the top 1% wealth share observed since 1980, about 7 pp. can be attributed to rising labor income inequality, 6 pp. to rising returns on wealth (mostly in the form of capital gains), and 2 pp. to lower growth. Under current parameters, the top 1% wealth share would reach its steady-state value of roughly 45% by the 2040s, a level similar to that of the beginning of the 20th century. These conclusions apply to a wide class of models of the wealth distribution, regardless of the exact primitives they use to account for, say, consumption or the labor market. I then use the model to analyze the effect of progressive wealth taxation at the top of the distribution.

with Lucas Chancel and Amory Gethin

Conditionally accepted, American Economic Journal: Applied Economics

We combine all available household surveys, income tax and national accounts data in a systematic manner to produce comparable pretax and posttax income inequality series in 38 European countries between 1980 and 2017. Our estimates are consistent with macroeconomic growth rates and comparable with US Distributional National Accounts. We find that inequalities rose in most European countries since 1980 both before and after taxes, but much less than in the US. Between 1980 and 2017, the European top 1% pretax income share rose from 8% to 11% while it rose from 10.5% to 21% in the US. Europe's lower inequality levels are mainly explained by a more equal distribution of pretax incomes rather than by more equalizing taxes and transfers systems. “Predistribution” is found to play a much larger role in explaining Europe's relative resistance to inequality than “redistribution”: it accounts for between two-thirds and ninety percent of the current inequality gap between the two regions.

with Ignacio Flores and Marc Morgan

Forthcoming, Journal of Economic Inequality

Household surveys fail to capture the top tail of income and wealth distributions, as evidenced by studies based on tax data. Yet to date there is no consensus on how to best reconcile both sources of information. This paper presents a novel method, rooted in calibration theory, which helps to solve the problem under reasonable assumptions. It has the advantage of endogenously determining a “merging point” between the datasets before modifying weights along the entire distribution and replacing new observations beyond the survey's original support. We provide simulations of the method and applications to real data. The former demonstrate that our method improves the accuracy and precision of distributional estimates, even under extreme assumptions, and in comparison to other survey correction methods using external data. The empirical applications provide useful and coherent illustrations in a wide variety of contexts. Results show that not only can income inequality levels change, but also trends. Given that our method preserves the multivariate distributions of survey variables, it provides a more representative framework for researchers to explore the socio-economic dimensions of inequality, as well as to study other related topics, such as fiscal incidence.

with Juliette Fournier and Thomas Piketty

Forthcoming, Review of Income and Wealth

We define generalized Pareto curves as the curve of inverted Pareto coefficients $b(p)$, where $b(p)$ is the ratio between average income or wealth above rank $p$ and the $p$-th quantile $Q(p)$ (i.e. $b(p)=\mathbb{E}[X|X>Q(p)]/Q(p)$). We use them to characterize entire distributions, including places like the top where power laws are a good description, and places further down where they are not. We develop a method to flexibly recover the entire distribution based on tabulated income or wealth data as is generally available from tax authorities, which produces smooth and realistic shapes of generalized Pareto curves. Using detailed tabulations from quasi-exhaustive tax data, we demonstrate the precision of our method both empirically and analytically. It gives better results than the most commonly used interpolation techniques.