Research

We study the shape of the global top wealth distribution, using the *Forbes List of Billionaires*. By constructing test statistics from ratios of log‑wealth moments, we assess whether the upper tail follows a Pareto distribution. We reject the Pareto null in all region‑year combinations with sufficient observations. We then compare log‑normal and Weibull alternatives; our moment‑ratio test results are compatible with both. Introducing a hazard‑rate–based test for log‑wealth distributions reveals pronounced right‑tail differences: the log‑normal distribution is rejected in favor of Weibull. The Weibull distribution accurately predicts regional averages of billionaire wealth, where Pareto performs poorly. Extending the analysis, we find that U.S. city‑size and firm‑size distributions are also Weibull‑distributed.

Estimating the market value of private businesses is essential for understanding both aggregate firm dynamics and top wealth inequality, yet these values are inherently unobservable. This paper introduces an econometric approach that treats the gap between true market values and initial estimates as measurement error. I employ time‑series restrictions on these errors as moment conditions within a GMM framework, and use the fitted values from these estimations as error‑free estimates of private business wealth and capital stocks. Applying this method to Dutch administrative data linking the universe of firms to their owners, I find that aggregate private business wealth increases by 30 % of GDP initially, and is more stable than the unadjusted series. Top 1 % and 0.1 % wealth shares increase by 3–5 percentage points, peaking at 38 % and 20 %, respectively. Adjusted returns to firm wealth exhibit a steeper gradient across the wealth distribution than unadjusted returns, consistent with models of return heterogeneity.

We study the fourfold increase in the number of billionaires since 2001, and its regional variation. We develop a model where wealth is proportional to the length of a Self‑Avoiding Walk on a random network, which rationalizes the Gompertz distribution of log wealth in our data. The model predicts the elasticity of top inequality to depend solely on population size and the lower thresholds for wealth to depend solely and one‑for‑one on regional GDP per‑capita and a global wealth‑income ratio. All predictions hold in our data. We find strong evidence that inequality, measured by the hazard rate of log wealth, is increasing in population size. Time fixed effects do not significantly improve the model fit, but regional effects do. Counterfactual exercises closely predict observed mean (log) wealth and billionaire numbers. The increases in billionaire numbers and mean (log) wealth are almost entirely driven by increases in GDP per‑capita. We interpret our results in the context of models where market size shapes firm (and hence wealth) concentration.

We construct and analyze household wealth and its composition for The Netherlands since 1854. The household wealth‑income ratio followed the familiar U‑shaped pattern over the 20th century. The wealth‑income ratio increased in the 19th century, driven by industrialisation and booming private foreign investments, to a peak of 700 % around 1880. In contrast to other countries, the wealth‑income ratio remained high up until 1929. We construct the first series on colonial wealth in the literature and show that colonial and other foreign investment account for most of the gap with other countries in the pre‑WWII period. The initial post‑war decline of the ratio is driven by rapid income growth. The increase in the ratio since the 1970s has been mainly driven by the large capital‑funded pension system. Housing plays only a secondary role in net‑wealth accumulation due to significant mortgage debt.

The purpose of this note is twofold. First, we want to clarify the conceptual and methodological differences between the Dutch wealth inequality series in Toussaint, de Vicq, Moatsos, and van der Valk (2022) and the Dutch wealth inequality series available on wid.world. Second, we want to compare the Dutch wealth distribution series with and without pension wealth.