June 30, 2025
In this post, Max Longmuir reflects on his time as a Stone Center postdoctoral scholar, his work on the GC Wealth Project and other studies, and why some initial ideas wound up in his "project cemetery."
By Max Longmuir
"Think twice whether you want to conduct research on topics on wealth." That was the advice an early mentor gave me before I began my Ph.D. in economics, focusing on household saving behavior and wealth inequality. It wasn't meant as a threat, nor did he think the topic was irrelevant. His concern was more pragmatic: the kind of high-quality data needed for this type of research is limited, and while contributions in this field are certainly necessary, they are more difficult to carry out empirically.
Well, I still pursued a research career as an applied microeconomist, working on wealth inequality and intergenerational mobility. And in hindsight, I'm still grateful for this advice, mainly for two reasons:
First, he was right. The data situation for wealth is indeed challenging. While other resources, such as income, are regularly collected in representative household surveys, or can be obtained from tax records in some countries, microeconomic data on wealth are harder to come by. Surveys are less likely to ask about households' wealth holdings, or they do so less frequently. Comparable administrative data tend to exist only in countries with a wealth tax, limiting access to a small group of countries.
Second, his comment helped me approach research ideas more carefully, especially in terms of their feasibility. Many initial project ideas now rest in my "project cemetery" because they simply couldn't be pursued with the data available to me. But having realistic expectations from the start helped me cope better with these dead ends.
And here's the good news for researchers everywhere: the data situation is steadily improving. Panel household datasets like the Panel Study of Income Dynamics (PSID) in the U.S., the Socio-Economic Panel (SOEP) in Germany, the Household, Income and Labour Dynamics in Australia (HILDA) Survey in Australia, and many others provide increasingly longer periods of panel data to analyze dynamic wealth processes over time. There is also more and more potential to study intergenerational wealth correlations — that is, how the wealth, or the wealth rank, of children relates to that of their parents.
The Luxembourg Wealth Study Database (LWS) provides extensive wealth modules that enable cross-national comparisons using harmonized household-level survey data across many countries. In addition, more institutions are publishing detailed inequality and macroeconomic statistics, including long-run series, for a broad range of countries. This includes the World Inequality Database (WID), distributional national accounts published by several central banks, and, last but not least, the GC Wealth Project.
The latter is administered by an international team based at the Stone Center and Roma Tre University, and it provides a comprehensive database of wealth levels, wealth inequality, and wealth taxation across countries and over time. I contribute to this project by coordinating the Wealth Topography section, which compiles and visualizes cross-country data on household asset portfolios and debt structures.
What can be done with this kind of data? In one of my projects, I use the Dutch National Bank Household Survey (DNB), another wealth panel, to examine heterogeneity in investment behavior across the wealth distribution. I find that, for most people, investment returns are not correlated with their wealth endowments. This contrasts with findings from other countries, where returns tend to rise with wealth. This suggests that heterogeneous returns contribute differently to wealth inequality across countries.
In another project, my coauthors and I use SOEP data to study how the rank of parents in the wealth distribution correlates with that of their children in Germany. This rank-rank correlation, a measure of intergenerational persistence, was around 0.26 in our estimates for Germany, which is noticeably lower than the around 0.4 often found for the United States. We also show that these cross-country differences are driven almost entirely by housing wealth and are amplified by declining homeownership rates among younger cohorts.
Despite recent advances, there remains much to understand about the distribution and transmission of wealth. Let me highlight a few areas where I believe more empirical and theoretical work is both possible and urgently needed.
One crucial question is how individuals perceive wealth inequality, and how these perceptions relate to actual data. The evolution of traditional measures like the Gini index or the top 1% wealth share are important, but to which extent are they salient to the public? Moreover, do such indicators adequately capture the economic and social transformations that matter most to people? A further dimension concerns which types of wealth individuals focus on. Do people care equally about the distribution of different asset classes, such as housing, financial, or business wealth, or are some more prominent in shaping perceptions? For example, business wealth tends to be highly concentrated and primarily held by those at the very top of the distribution. Yet for the majority of households, inequality in housing wealth may be more immediately relevant, especially when facing barriers to entering the housing market or attempting to relocate to more desirable neighborhoods.
Closely linked is the normative question: What kind of wealth inequality do we consider problematic? Should we worry most about the concentration at the very top, or, for instance, about wealth gaps between the middle and the lower end of the distribution? These considerations feed into policy debates, especially around taxation. For example, should wealth be taxed at all? And if so, should it be done through direct taxes on net wealth or through better taxation of capital income, inheritance, and unrealized gains?
A growing body of research, including my own, has focused on intergenerational wealth correlations: how closely the wealth rank of children mirrors that of their parents. But even here, key conceptual choices remain open. Should we compare parents' and children's wealth at the same age or at the same point in time? Should we focus on relative ranks, absolute levels, or even subjective expectations? These choices matter both for measurement and for interpreting what intergenerational mobility really means.
Reflecting on the advice I received from my supervisor, I think he was totally right. The data can be messy, the access restricted, and the empirical challenges considerable. But the topic is more relevant than ever, and the opportunities for meaningful contributions are growing rapidly.
So, what would I tell a first-year Ph.D. student today? Think twice before working on wealth, but if you do, embrace the complexity and enjoy every bit of the ride. There's still so much we don't know. And that's what makes it exciting.
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