In the first quarter of 2024, 51.8 percent of the total wealth in the United States was owned by members of the baby boomer generation. In comparison, millennials own around 9.4 percent of total wealth in the U.S. In terms of population distribution, there is almost an equal share of millennials and baby boomers in the United States.

We use Danish Register Data to examine intergenerational rank-rank correlations in net wealth and gross housing wealth by child age and parental income. Our results indicate that gross housing wealth correlations are more stable by child age than are net wealth correlations, which we argue is due to a downward bias in net wealth correlations from transitory debt. Intergenerational housing wealth correlations also are larger for lower-income families, while net wealth correlations do not vary much across the income distribution. Finally, we show that intergenerational net wealth and gross housing wealth correlations move in opposite directions across the income distribution.

ITW01 - Intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Intergenerational wealth transfers...

ITW08 - Net wealth and intergenerational wealth transfer values of households. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Net wealth and intergenerational wealth transfer values of households...

Future expectation of intergenerational wealth transfers

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

ITW05 - Intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Intergenerational wealth transfers...

R-Workspace containing two customized functions for plotting,

- grid_arrange_shared_legend taken from: https://github.com/tidyverse/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs

- multiplot taken from: http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_%28ggplot2%29/

#

and three data.frames:

- mdat: Raw data of mothers, includimng founder generation (N = 15961, extracted from the Krummhörn pedigree containing 74639 individuals)

- mdat0: Study sample including only mothers with minimum pedigree depth of 4 (N = 6447)

- mdat1: Data set of mothers for which all grandparents of them and their partner(s) are known (N = 2321)

with the variables:

$ momid : num ID of mother

$ incl : num 1 if included in data set of known mothers, otherwise 0

$ SES : Factor for socioeconomic status (4 levels: "Unknown", "Landless", "small to medium scale farmers", "large scale farmers")

$ minByear : num Birth year of 1st born child

$ maxByear : num Birth year of last born child

$ minDepth : num Pedigree depth as the generation number of known ancestors

$ Fmean : num Inbreeding coefficient F (as calculated by pedigree::calcInbreeding())

$ cons : num 1 if consanguineous (i.e. F>=0.0156), otherwise 0

$ cousMarr : num 1 if in 1st cousin marriage (i.e. F~0.0625), otherwise 0

$ consanguinity : Factor for consanguinity (3 levels: "low (<0.0156)", "medium (>=0.0156)", "high (1st cousins)")

$ docGrasenMean : num Mean number of grasen within a mother's founded families

$ eltnrfGrasenMean : num Number of grasen within the mother's family-of-origin

$ eltnrmGrasenMean : num Mean number of grasen within the mother's partner(s)' family/ies-of-origin

$ mlocal : chr Code of the Kirchspiel, in which the mother was first married

$ decade : Factor fot the decade in which the mother's first marriage occured (28 levels)

$ cross : num 1 if identified as cross-cousin marriage, otherwise 0

$ paral : num 1 if identified as parallel cousin marriage, otherwise 0

ITW03 - Intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Intergenerational wealth transfers...

The folder vomlehn_winberry_full_replication_packet contains data and programs replicating tables and figures from "The Investment Network, Sectoral Comovement, and the Changing U.S. Business Cycle", by vom Lehn and Winberry. Please see the "README full" file for additional details. The folder vomlehn_winberry_networks contains a subset of data and code to construct the Investment Network data. Please see the "README investment network" file for additional details.

A correlation curve is introduced as a tool to study the degree of intergenerational income mobility, i.e. how income status is related between parents and adult child. The method overcomes the shortcomings of the elasticity of children’s income with respect to parents’ income (i.e. its sensitiveness to different dispersion among the generations) and the correlation coefficient (i.e. its inability to capture nonlinearities). The method is particularly suitable for comparative studies and in this study labour earnings are compared to disposable income. The correlation between the parental income and the child’s adult disposable income becomes stronger for higher percentiles in the income distribution of the parents. Above the median the correlation is found to be stronger than for labour earnings. Interestingly, the elasticity is higher for labour earnings for most part of the distribution and complementing the elasticity with correlation curves provides a much more complete picture of the intergenerational income mobility.

The literature on intergenerational income mobility uses a diverse set of measures and there is limited knowledge about whether these measures provide similar information and yield similar conclusions. We provide a framework to highlight the key concepts and properties of the different estimators. We then show how these measures relate to one another empirically. Our main analysis uses income tax data from Australia to produce a comprehensive set of empirical estimates for each of 19 different mobility measures at both the national and regional level. We supplement this analysis with other data that uses either within or between country variation in mobility measures. A key finding is that there is a clear distinction between relative and absolute measures both conceptually and empirically. A region may be high with respect to absolute mobility but could be low with respect to relative mobility. However, within broad categories, the different mobility measures tend to be highly correlated. For rank-based estimators, we highlight the importance of how the choice of the distribution used for calculating ranks can play a critical role in determining its properties as well as affect empirical findings. These patterns of results are important for policy makers whose local economy might fare well according to some mobility indicators but not others.

This dataset contains replication files for "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States" by Raj Chetty, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez. For more information, see https://opportunityinsights.org/paper/land-of-opportunity/. A summary of the related publication follows. We use administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States. First, we characterize the joint distribution of parent and child income at the national level. The conditional expectation of child income given parent income is linear in percentile ranks. On average, a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child’s income. Second, intergenerational mobility varies substantially across areas within the U.S. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose. Third, we explore the factors correlated with upward mobility. High mobility areas have (1) less residential segregation, (2) less income inequality, (3) better primary schools, (4) greater social capital, and (5) greater family stability. While our descriptive analysis does not identify the causal mechanisms that determine upward mobility, the publicly available statistics on intergenerational mobility developed here can facilitate research on such mechanisms. The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department. This work is a component of a larger project examining the effects of tax expenditures on the budget deficit and economic activity. All results based on tax data in this paper are constructed using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures: Evidence from Spatial Variation across the U.S.,” approved under IRS contract TIRNO-12-P-00374 and presented at the National Tax Association meeting on November 22, 2013.

We estimate intergenerational income mobility in Italy using administrative data from tax returns. Our estimates of mobility in Italy are higher than prior work using survey data and other indirect methods. The rank-rank slope of parent-child income in Italy is 0.22, compared to 0.18 in Denmark and 0.34 in the United States. The probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 0.11. Upward mobility is higher for sons and first-born children. We uncover substantial geographical variation: upward mobility rates are much higher in Northern Italy, where provinces have higher measured school quality, more stable families, and more favorable labor market conditions.

Introduction

Millionaire Statistics: The global definition of success has long included crossing the coveted million-dollar mark. In 2025, becoming a millionaire is no longer just a dream, you know, for the ultra-elite. With the digital economy booming, global investments rising, and intergenerational wealth transfers accelerating, the number of millionaires around the world is climbing at a really impressive pace.

In this article, I’ll dive deep into all the millionaire statistics 2025, highlighting where millionaires live, how they earn and spend, their demographics, migration patterns, and what the future holds, literally everything. Backed by data from trusted financial sources like UBS, Henley & Partners, and Empower, this is your only statistical guide to the world’s millionaires. Let’s completely break it down.

This dataset contains replication files for "Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility" by Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/recentintergenerationalmobility/. A summary of the related publication follows. We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US Treasury Department or the Internal Revenue Service or the National Bureau of Economic Research.

Review of Economics and Statistics: Forthcoming.

Household low-income status using low-income measures (before and after tax) by household type (multigenerational, couple, lone parent, with and without children), age of members, number of earners, and year.

Ionchas maidir le haistrithe rachmais idirghlúine amach anseo

The programs replicate tables from "Household Time Use Among Older Couples: Evidence and Implications for Labor Supply Parameters", by Rogerson and Wallenius. Please see the Readme file for additional details.