HFCS-OeNB Freitagsseminar with Arthur Kennickell and Isabel Z. Martinez
Chasing the Tail: A Generalized Pareto Distribution Approach to Estimating Wealth Inequality
Arthur Kennickell, Stone Center, City University of New York
Since the work reported in Vermeulen , a literature has developed on using the simple Pareto distribution along with “rich list” information to make improved estimates of the upper tail of the wealth distribution measured in surveys. Because the construction of such external data is typically opaque and subject to potentially serious measurement error, it may be best not to depend exclusively on this approach. This paper develops an alternative approach, using the generalized Pareto distribution (GPD), of which the simple Pareto is a subset, extending an estimation strategy developed by Castillo and Hadi . The greater flexibility of the GPD allows the possibility of modeling the tail of the wealth distribution, using a larger set of data for support than is typically the case with the simple Pareto. Moreover, the elaboration of the estimation method presented here allows explicitly for the possibility that the extreme of the observed upper tail is measured with error or that it is not captured at all. The approach also allows the incorporation of external data on total wealth as a constraint on the estimation. For the applications considered here using Austrian and U.S. micro data, the model relies on an estimate of total household wealth from national accounts, rather than rich-list information. The results suggest that where sufficiently comparable and reliable estimates of aggregate wealth are available, this approach can provide a useful way of mitigating problems in comparing distributional estimates across surveys that differ meaningfully in their effective coverage of the upper tail of the wealth distribution. The approach may be particularly useful in the construction of distributional national accounts.
Tracking and Taxing the Super-Rich: Insights from Swiss Rich Lists
Isabel Z. Martinez, ETH Zürich
We collect, digitize, and supplement the Swiss rich list for the years 1989–2020 published in the “BILANZ” business magazine to gain new insights on the structure and dynamics of top wealth in Switzerland. Using this data allows us study the super-rich in Switzerland in ways that were not possible in previous research based largely on tax data. In addition to presenting this valuable data source, and also discussing its limitations, we make three distinctive contributions to the literature. First, we present a number of new facts on the wealth elite in Switzerland. We show that about 60% of the super-rich are heirs—a much larger fraction than in the United States where many of the super-rich are self-made—and that five in ten super-rich residing in Switzerland are foreign-born. Second, we estimate the sensitivity of the location-decision of super-rich foreigners to a preferential tax scheme that offers wealthy foreigners to be taxed on their expenses rather than on their true income and wealth. We are the first to evaluate this policy—similar to “nondom” taxation that exists in other countries like the UK or Italy—and show that when some of the Swiss cantons abolished this practice, they lost about 30% of their stock of super-rich taxpayers. Third, we use the wealth series compiled in our BILANZ dataset to estimate the wealth shares of the top 0.01% in Switzerland and show how they compare to earlier estimates by Föllmi and Martínez (2017) based on wealth tax data. We find that top wealth concentration is higher than previously assumed, an conclude that top wealth shares based on tax data constitute a lower bound, while the estimates based on our BILANZ data are upper bounds.
Friday, 11 November 2022, 09:30 a.m. to 12:30 p.m.
The event is planned both, online via Webex and onsite with limited attendance by invitation only at the Oesterreichische Nationalbank, Otto-Wagner-Platz 3, 1090 Vienna.