“Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
“…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
“Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
“Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
“…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
“…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
“…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
“Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
“…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
“…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts

For whatever topics you select (currently: Mobility/Assortative Mating):

Include articles that mention this topic 32

Only articles primarily about this topic 12

Why Is Geographic Mobility Declining?

Urvi Neelakantan, John Bailey Jones and Yue Li Federal Reserve Bank of Richmond

Date Posted:: May 16, 2025
Is Database:: Database

Btw 1996 and 2024, geographic mobility as measured by interstate migration declined for all age groups, particularly the 25–34 cohort. A FRBR note argues the decline is driven by the rise of dual earning couples and aging.

As the earnings and full-time employment rates of women approach those of men, we should expect geographic mobility to fall for a few reasons. First, dual-earner couples with similar incomes would become more common [and] couples with similar earnings are less likely to relocate than those with unequal earnings. The convergence of earnings and employment between men and women may also increase the need for child care. This in turn could affect geographic mobility by encouraging individuals or couples with children to locate near relatives who can help with child care.

Takeaways by Macro Roundup^® AI

As women's earnings and full-time employment rates approach those of men, geographic mobility would be expected to fall, in part because dual-earner couples with similar incomes become more common.
Couples with similar earnings are less likely to relocate than couples with unequal earnings.
The convergence of earnings and employment between men and women may increase the need for child care, which could encourage individuals or couples with children to locate near relatives who can help.

Anatomy of US Inequality

Oded Galor and Daniel Wainstock National Bureau of Economic Research

Date Posted:: December 26, 2025
Is Database:: Database
Is Important:: Important

Inequality within 186 distinct US ethnic groups accounts for 96% of total income variation, while between-group inequality contributes only 4%, largely constant across time and regions. For 6 broad ethnic categories, within variation is 97% of the total.

This paper examines inequality within the U.S. population, exploring variations between and within ethnic groups. Leveraging the ancestral origins of a representative sample of the U.S. population, consisting of millions of U.S.-born, working-age individuals, the study decomposes income inequality into within-group and between-group components, distinguishing disparities among those sharing a common ancestry from inequality between groups. As indicated in Figure 1, inequality within the 186 distinct ancestral groups accounts for 96% of the variation in overall income inequality in the U.S., while between-group inequality accounts for only 4%. Similarly, inequality within six broad ethnic categories (Asian, Black, Hispanic, Native American, Pacific Islander, and White) accounts for 97% of the variation, while between-group inequality accounts for just 3%. When restricting the sample to individuals with the same educational attainment and demographic characteristics, within-ethnic-group inequality still accounts for the principal share of income dispersion. Spatial decomposition reveals that the predominance of within-group inequality holds across local micro-areas throughout the U.S. The South exhibits a modestly smaller share of within-group inequality—a pattern consistent with the region’s greater ethnic fragmentation and enduring legacy of discrimination.

Takeaways by Macro Roundup^® AI

Inequality within 186 distinct ancestral groups accounts for 96% of the variation in overall U.S. income inequality, while between-group inequality accounts for 4%, based on a representative sample of millions of U.S.-born, working-age individuals.
Within six broad ethnic categories — Asian, Black, Hispanic, Native American, Pacific Islander, and White — within-group inequality accounts for 97% of the variation in income inequality, versus 3% between groups.
Among individuals with the same educational attainment and demographic characteristics, within-ethnic-group inequality still accounts for the principal share of income dispersion, and its predominance holds across local micro-areas, with the South showing a modestly smaller within-group share.

Getting Beneath the Veil of Intergenerational Mobility: Evidence from Three Cities

Oren Danieli, Tanaya Devi and Roland Fryer Tel Aviv University

Date Posted:: November 20, 2025
Is Database:: Database

A data-driven method to select cost-effective policy interventions, applied to survey responses of ~1,000 people reared in poverty in 3 US cities, confirms the primacy of education, but finds an ~= role for childhood determinants of noncognitive skills.

We develop a new descriptive statistical method to assist the design of future experiments, whose goal is to improve some outcome variable. Most descriptive methods ignore the potential difficulty of changing the covariates in an intervention. Our method uses information from the joint distribution of the covariates, and instead of recommending experiments that would extrapolate into cases rarely seen in the data, it focuses on the way in which the outcome variable (in our case, escaping poverty) is higher in real life. Panel A in Figure 3 shows, using non-parametric estimation methods, that the most important correlate of income mobility is education. A close second– and statistically indistinguishable– is resilience: the ability to bounce back from stressful situations,measured by responses to questions such as “It does not take me long to recover from a stressful event.” Half of the significant correlates of intergenerational income mobility are psychological skills: resilience, Big 5 [personality traits from psychological test], self-esteem, self control, locus of control and grit. Mental health problems are also significant in this specification, [as are] whether the respondent was ever in trouble with the police in their youth, had adverse childhood experiences, and the existence of adult relationships they trusted.

Takeaways by Macro Roundup^® AI

Using non-parametric estimation, the authors find education is the most important correlate of intergenerational income mobility, with resilience — the ability to bounce back from stressful situations — a close and statistically indistinguishable second.
Half of the significant correlates of intergenerational income mobility are psychological skills: resilience, the Big 5 personality traits, self-esteem, self-control, locus of control, and grit.
Mental health problems, youth trouble with the police, adverse childhood experiences, and the existence of trusted adult relationships are also significant in the authors' specification.

A Chip Off the Old Block? Genetics and the Intergenerational Transmission of Socioeconomic Status

Sjoerd van Alten, Silvia Barcellos, Leandro Carvalho, Titus Galama, et al. National Bureau of Economic Research

Date Posted:: September 19, 2025
Is Database:: Database

A 10-percentile increase in a person’s polygenic index (PGI) raises that person’s income by 0.9 percentiles and that of their child by 0.7 percentiles; ~1/2 is direct genetic transmission, and ~ 1/2 the effect of parent’s genes on nurturing capacity.

We examine how the genetics of one generation influences the SES [Socioeconomic Status] of the next by linking genetic data from the Dutch Lifelines Cohort to tax records for 2006-2022. Figure 5 plots the next-generation genetic effect against the same-generation genetic effect for each SES measure. The 22.5-degree line represents a benchmark where the next-generation genetic effects are half as large as the same-generation genetic effects. The figure indicates a high degree of persistence in the effects of the reference’s genetics across generations: e.g. moving a reference 10 percentiles higher in the PGI distribution increases that reference’s own income by 0.9 percentiles, and their offspring’s income by 0.7 percentiles. For most outcomes, the markers lie closer to the 45-degree line than to the 22.5-degree line, suggesting that genetic transmission is not the sole mechanism through which one generation’s genetics affects the next. Genetic transmission explains about 50% of the total effect, while genotypic assortative mating contributes little. This implies that the remaining 50% must be attributed to genetic nurture—the influence of the reference’s genetics operating through environmental pathways, even when these genetic markers are not transmitted to the offspring.

Takeaways by Macro Roundup^® AI

Linking Dutch Lifelines Cohort genetic data to 2006–2022 tax records, the authors find that moving a reference 10 percentiles higher in the polygenic index distribution raises that person's own income by 0.9 percentiles and their offspring's by 0.7 percentiles.
Genetic transmission explains about 50% of the total effect of one generation's genetics on the next generation's socioeconomic status, with genotypic assortative mating contributing little.
The authors attribute the remaining 50% to genetic nurture, the influence of the reference's genetics operating through environmental pathways even when those genetic markers are not transmitted to the offspring.

The Effects of Parental Income and Family Structure on Intergenerational Mobility: A Trajectories-Based Approach

Yoosoon Chang, Steven Durlauf, Bo Hu, and Joon Park National Bureau of Economic Research

Date Posted:: September 17, 2025
Is Database:: Database

Chang et al find that in the PSID, higher parental income and living in an intact family during middle childhood and adolescence are more predictive of favorable child outcomes than income and family structure during early childhood.

[The two panels] capture the age-specific effects of parental income and family structure, respectively, on a child’s latent permanent income in the Panel Study of Income Dynamics [PSID]. Parental income during middle childhood and adolescence exerts a significantly stronger influence on a child’s future income than parental income during early childhood, a finding [that] contrasts with the early childhood development literature. The sensitivity of adult outcomes to adolescent incomes suggests that the uses of this income have distinct effects from early childhood investment. One reason why this finding is credible is that incomes in later childhood and adolescence influence the schools and neighborhoods, determinants that are not operational for younger years. Children raised in two-parent households tend to achieve higher adult income status than those raised in single-parent households. While this advantage is relatively modest during early childhood, it becomes pronounced in middle childhood and adolescence, peaking during the late high school years at around age 17. [Editor’s note: This may reflect the likelihood that a family that is still intact when the child is 17 was more functional throughout].

Takeaways by Macro Roundup^® AI

Parental income during middle childhood and adolescence exerts a significantly stronger influence on a child's latent permanent income in the Panel Study of Income Dynamics than parental income during early childhood, contrasting with the early childhood development literature.
Children raised in two-parent households tend to reach higher adult income status than those raised in single-parent households, an advantage that is modest in early childhood but pronounced in middle childhood and adolescence, peaking around age 17.
The sensitivity of adult outcomes to adolescent parental income suggests that this income has uses distinct from early childhood investment, since later-childhood income influences schools and neighborhoods, determinants not operational in younger years.

ZIP Code Is Destiny? Turns Out That’s Bunk

James Heckman and Sadegh Eshaghnia Wall Street Journal

Date Posted:: September 5, 2025
Is Database:: Database
Is Important:: Important

Heckman argues that contrary to Chetty’s research, local geography has little causal effect on children’s adult outcomes in the US. Studies that find otherwise reflect parental self-selection.

Advocates of [relocating struggling families to residential areas with better conditions] ignore serious flaws in the research. The methods used in this literature falsely link the disparity of outcomes across locations to the causal effect of neighborhoods. In reality, the difference is due to the parents who self-sort into neighborhoods. Those who move to better neighborhoods early on tend to be more affluent, more educated and more likely to have intact families. They move to better neighborhoods with people similar to themselves, just as later movers, who are less affluent and less educated, move to less affluent neighborhoods. Focusing on the family rather than the neighborhood changes the conversation around intergenerational mobility. In our 2022 study on lessons for America from Denmark, we analyzed data collected by Denmark’s government statistics agencies for the entire population, similar to census data in the U.S. As with Americans, more-affluent and better-educated Danish are more likely to move to good neighborhoods before children are born. Less-educated and less-affluent families that relocate later in their children’s lives tend to be less stable than early movers, who are less likely to experience divorce or a change in household composition.

Takeaways by Macro Roundup^® AI

The authors argue that neighborhood-effects research falsely attributes disparities in outcomes across locations to the causal effect of neighborhoods, when the differences stem from the parents who self-sort into those neighborhoods.
Families who move to better neighborhoods early tend to be more affluent, more educated and more likely to have intact families than those who relocate later in their children's lives.
A 2022 study of Danish government statistics covering the entire population found that, as in the U.S., more-affluent and better-educated Danes are likelier to move to good neighborhoods before children are born, while later movers are less stable.

Inter-State Labor Mobility and the U.S. Economy

Andrea Foschi, Christopher House, Christian Proebsting, and Linda Tesar Federal Reserve Bank of Kansas City

Date Posted:: August 26, 2025
Is Database:: Database

U.S. gross migration rates have fallen modestly over the past 50 years, but net migration has hardly budged. “On average, only 10–12 out of 100 moves reflect a net flow from one location to another.”

The decline in gross inter-state migration over the last 50 years is relatively modest and has been essentially stable since the early 2000’s. The net migration rate, which is one of the main equilibrating mechanisms between locations, exhibits no trend. There is little evidence that demographic changes explain the decline in aggregate gross migration. Figure 2 plots gross migration and its decomposition into the contributions of absolute net migration and offsetting migration– since 1975 using the IRS data. We see again the slight decline in the gross migration rate, matched by a similar trend in the Offsetting Migration Rate. We also see that the majority of gross migration is given by flows that offset each other, and only a small fraction of flows actually induce a change in a given state’s population. On average, only 10-12 out of 100 moves reflect a net flow from one location to another. Net flows are a small fraction of overall labor migration and have remained fairly constant.

Takeaways by Macro Roundup^® AI

Only 10–12 of every 100 U.S. inter-state moves reflect a net flow from one location to another, with the majority of gross migration consisting of flows that offset each other.
The decline in gross inter-state migration over the past 50 years is relatively modest and has been essentially stable since the early 2000s, with the net migration rate showing no trend.
IRS data since 1975 show the slight decline in the gross migration rate matched by a similar trend in the offsetting migration rate, and there is little evidence that demographic changes explain the decline.

Btw 1996 and 2024, geographic mobility as measured by interstate migration declined for all age groups, particularly the 25–34 cohort. A FRBR note argues the decline is driven by the rise of dual earning couples and aging.

Does the convergence of men's and women's earnings reduce geographic mobility?

Takeaways by Macro Roundup® AI

Related Articles:

Inequality within 186 distinct US ethnic groups accounts for 96% of total income variation, while between-group inequality contributes only 4%, largely constant across time and regions. For 6 broad ethnic categories, within variation is 97% of the total.

How much of US income inequality occurs within ethnic groups?

Takeaways by Macro Roundup® AI

Related Articles:

A data-driven method to select cost-effective policy interventions, applied to survey responses of ~1,000 people reared in poverty in 3 US cities, confirms the primacy of education, but finds an ~= role for childhood determinants of noncognitive skills.

What factors correlate most strongly with intergenerational income mobility?

Takeaways by Macro Roundup® AI

Related Articles:

A 10-percentile increase in a person’s polygenic index (PGI) raises that person’s income by 0.9 percentiles and that of their child by 0.7 percentiles; ~1/2 is direct genetic transmission, and ~ 1/2 the effect of parent’s genes on nurturing capacity.

How much of genetic influence on children's income comes through environment?

Takeaways by Macro Roundup® AI

Related Articles:

Chang et al find that in the PSID, higher parental income and living in an intact family during middle childhood and adolescence are more predictive of favorable child outcomes than income and family structure during early childhood.

Does parental income matter more in adolescence than early childhood?

Takeaways by Macro Roundup® AI

Related Articles:

Heckman argues that contrary to Chetty’s research, local geography has little causal effect on children’s adult outcomes in the US. Studies that find otherwise reflect parental self-selection.

Do neighborhoods cause better child outcomes, or do families sort themselves?

Takeaways by Macro Roundup® AI

Related Articles:

U.S. gross migration rates have fallen modestly over the past 50 years, but net migration has hardly budged. “On average, only 10–12 out of 100 moves reflect a net flow from one location to another.”

Has migration between U.S. states declined significantly?

Takeaways by Macro Roundup® AI

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