## Thomas Blanchet

I am an economist working on income and wealth inequality, public finance and household finance. I obtained my PhD from the Paris School of Economics in 2020. I currently work at the World Inequality Lab as the coordinator for national accounts, statistical tools and methods.

In my work, I develop new tools and methods to measure inequality by combining surveys, tax data and national accounts. I also study the drivers of wealth inequality, and the effects of wealth taxation in the long run.

#### Contact

You can contact me at thomas.blanchet@wid.world. You can also follow me on Twitter at @thomas_blncht.

#### Code and Data

Most of the code I write for my work is hosted on GitHub, and you can access it through my GitHub profile. I mostly work with R and Stata, but also occasionally with Python, Java and C++.

#### Research

You can also find some of my work on my RePEc/IDEAS profile, my CitEc profile, and my Google Scholar profile.

#### Other

I am an occasional photographer and posts some of my work on 500px.

## Research

### The Dynamics of Inequality in the United States: 1962–2100

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.

### Why is Europe Less Unequal than the United States?

with Lucas Chancel and Amory Gethin

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.

### The Weight of the Rich: Correcting Surveys with Tax Data

with Ignacio Flores and Marc Morgan

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.

### Generalized Pareto Curves: Theory and Applications

with Juliette Fournier and Thomas Piketty

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.

## Software

### Stata command “enforce”

Stata module to enforce arbitrary accounting identities between variables.

Type ssc install enforce in Stata to install.

### Stata command “bfmcorr”

Stata module to implement the survey correction method of Blanchet, Flores and Morgan (2019).

Type ssc install bfmcorr in Stata to install.

### Stata command “wid”

Stata module to download data from the World Wealth and Income Database.

Type ssc install wid in Stata to install.

### R package “gpinter”

R package for generalized Pareto interpolation.

### R package “wid”

R package to download data from the World Wealth and Income Database.

## Miscellaneous

### Applying Generalized Pareto Curves to Inequality Analysis (2018)

American Economic Association: Papers & Proceedings, 2018, 108: 114–118

with Bertrand Garbinti, Jonathan Goupille and Clara Martìnez-Toledano

A generalized Pareto curve is defined 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. We present this concept and show how it can be used to better estimate distributions, especially from tax tabulations. By providing a simple decomposition of top shares, we discuss how studying inverted Pareto coefficients can improve the understanding of inequality dynamics. We also show how it helps to better analyze wealth and income concentrations along the distribution, using data for France, Spain, the United States and China.

### Prices and currency conversions in WID.world (2017)

WID.world Methodological Notes Series

This technical note reviews the issues surrounding the use of price indexes and currency conversion factors in WID.world. We explain some of the main issues surrounding the measurement and construction of these data, we describe how the data in WID.world was constructed, and finally we clarify how to properly compare income and wealth levels in WID.world between countries and periods.

### National Accounts Series Methodology (2016)

WID.world Methodological Notes Series

This methodological note presents the methodology followed to construct homogeneous series of national accounts presented on WID.world (i.e. series of net national income, gross domestic product, net foreign income, consumption of fixed capital and population) covering (almost) all countries in the world, from at least 1950 to today.

### Wealth inequality in Europe and in the United States: estimations from surveys, national accounts and wealth rankings (2016)

Master Thesis, Analysis and Policy in Economics, Paris School of Economics

This dissertation studies the distribution of wealth in the United States and six European countries: Austria, France, Germany, Italy, Portugal and Spain. To estimate the top tail of the distribution, I combine survey data with journalist rankings of top wealth holders. I also adjust the distribution for consistency with macroeconomic aggregates. I suggest a method which, unlike previous approaches, does not rely on the Pareto distribution or any other parametric assumption. Instead, I use the properties of order statistics to estimate the quantile function nonparametrically. In the United States, I find that the top 1% owns 40% of the wealth, and the top 0.1% owns 18%. In Europe, wealth inequality is much lower overall, but there are large differences between countries.