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Session Overview
Session
J04: The Super Rich
Time:
Friday, 20/Aug/2021:
10:45am - 12:15pm


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Presentations
10:45am - 11:07am

Political Connections and the Super-Rich in Poland

Katarzyna Salach, Michal Brzezinski

University of Warsaw, Poland

We study the impact of political connections of the richest Poles on their wealth level, mobility among the rich and the risk of dropping off the rich list. We use newly collected original panel data (2002-2018) and we find that political connections are not associated with the wealth level of Polish multimillionaires, but that they are linked to the 20-30% lower probability of upward mobility in the ranking of the rich. Moreover, being a former member of the communist party or secret police informant increases the risk of dropping off the rich list by almost 80%. Our results show that, contrary to some other post-socialist countries such as Russia or Ukraine, there is little evidence that the Polish economy suffers from crony capitalism.

Salach-Political Connections and the Super-Rich in Poland-165.pdf


11:07am - 11:30am

Tracking the Super-Rich Using Rich Lists: First-Time Evidence from Switzerland

Enea Baselgia1, Isabel Z. Martinez2

1University of St.Gallen, Switzerland; 2ETH Zurich, Switzerland

We collect, digitize, and supplement the Swiss rich list for the years 1989-2020 published in the “Bilanz” magazine to gain new insights on the structure and dynamics of top wealth in Switzerland. We shed light on the characteristics of the super-rich in Switzerland, which was not possible in prior research based on tax data. We further provide new evidence on the wealth mobility of the super-rich in Switzerland, finding that mobility has declined significantly over the past decade. Inheritances are still the main factor in making it to the very top of wealth distribution. Finally, we estimate the top 0.01% wealth share. Our results suggest that in Switzerland wealth concentration at the top is likely somewhat higher than previously assumed, highlighting the central role of foreigners and, in particular, wealthy expenditure-based taxpayers at the very top of the distribution.

Baselgia-Tracking the Super-Rich Using Rich Lists-466.pdf


11:30am - 11:52am

On Top of the Top: Adjusting Wealth Distributions Using National Rich-Lists

Franziska Disslbacher1,2, Michael Ertl2, Emanuel List1,3, Patrick Mokre2,4, Matthias Schnetzer2

1Vienna University of Economics and Business, Austria; 2Vienna Chamber of Labour; 3Macroeconomic Policy Institute (IMK); 4New School for Social Research

The richest are missing in wealth surveys. Recent attempts to mitigate resulting troubles in the tail combine survey data with rich-lists and estimate a parametric model, most notably the Pareto distribution, for the tail. The literature until now would rely on ad-hoc assumptions or visual inspection for one or the other steps in parameter estimation. This paper combines the most recent contributions to the parametric estimation of heavy tailed distributions into a unified regression approach for the joint estimation of all parameters of the Pareto distribution, as well as the more flexible Generalized Pareto distribution. We apply this transparent and flexible quasi-algorithmic procedure to 14 Eurozone countries and estimate the wealth shares of the top percentiles. This is based on a combination of the most comprehensive collection of journalistic evidence on the richest households from national publications (“rich lists”) and survey data from the Household Finance and Consumption Survey (HFCS).

Disslbacher-On Top of the Top-504.pdf


11:52am - 12:15pm

How Wealthy Are the Rich?

Jan Schulz1,2, Mishael Milakovic1

1Economics Department, University of Bamberg, Germany; 2Bamberg Doctoral Research Group on Behavioral Macroeconomics (BaGBeM)

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and employ German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from one to nine trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale- or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale- or type-dependence on the one hand, and statistical biases on the other.

Schulz-How Wealthy Are the Rich-166.pdf


 
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