Edward Conard

Top Ten New York Times Bestselling Author

  • “…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
  • “…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 represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “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
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
Upside of Inequality Oxford Unintended Consequences

Ying Bai and Ruixue Jia National Bureau of Economic Research
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From 1644–1905, Qing China staffed its bureaucracy through exams and patronage. Only 12.6% of exam graduates reached top office, but patronage favored stronger performers, illustrating how merit and connections can be complements rather than substitutes.

Figure 5 studies the interplay between merit and networks by estimating the effects of exam performance and ties to powerful examiners separately for the high- and low-performing categories. The left panel shows the effects of exam performance for the two categories, while the right panel presents those of examiner power. Two patterns stand out. First, exam performance matters more for the high category, consistent with the institutional design that steered this elite group toward more prestigious careers. Second, ties to powerful examiners are also more valuable for the high category, pointing to a complementarity between merit and networks. To understand the implications of our finding on complementarity, it is useful to contrast it with the counterfactual of substitution. When merit and networks are complements (rather than substitutes), those who attained top office tend to exhibit both stronger exam performance and stronger connections. By contrast, under substitution, strong networks would compensate for weaker exam performance, and vice versa. Complementarity implies [a higher degree of meritocracy than substitution, as] successful candidates appear to have had better performance. Second, complementarity makes it harder to identify the influence of networks: their effects can operate under the guise of merit rather than as a visible substitute for it.

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AI Summary. European immigrants who arrived with nearly no wealth converged to similar wealth levels as earlier European settlers within a few generations, while Black, Cuban, Mexican, and Puerto Rican households remained substantially behind, indicating that initial wealth gaps do not uniformly predict long-run inequality across all groups.

Brian Marein Wake Forest University
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Except possibly at the very top of the distribution which SIPP cannot measure, the wealth of “new” Southern and Eastern European immigrants to the US has fully caught up with “old” immigrants. Wealth converges quickly once earnings inequality vanishes.

Does initial wealth explain persistent inequality across immigrant groups?

Core argument: By 1920, 50% of white adults were foreign-born or had foreign-born parents, yet Southern and Eastern European descendants achieved wealth.

Inferring the determinants of long-run inequality from group-level data is complicated by the arrival of 30 million Europeans during the Age of Mass Migration (roughly 1850 to 1924), who are by construction included in average white wealth despite having no direct claim to the wealth accumulated by earlier Americans. By1920, nearly half of white adults were either foreign-born or had foreign-born parents. Using the United States Immigration Commission Reports (commonly known as the Dillingham Commission Reports), I document that immigrants at the turn of the twentieth century arrived with almost no wealth. [Thus] a large share of the white population started from a substantial wealth disadvantage. Northwestern Europeans comprised the overwhelming majority of earlier immigrants, dating back to the initial European settlement of North America, while Southern and Eastern Europeans predominated at the turn of the twentieth century. If initial wealth disadvantages persisted across generations, one would expect households of Southern or Eastern European ancestry to possess less wealth than those of Northwestern European ancestry, since their families arrived later and started with substantially fewer resources. In fact, they do not. On average, they are wealthier.

Takeaways by Macro Roundup® AI

  1. By 1920, 50% of white adults were foreign-born or had foreign-born parents, yet Southern and Eastern European descendants achieved wealth.
  2. European immigrants arrived at the turn of the twentieth century with nearly zero wealth, yet their descendants’ wealth distributions converged.
  3. White ancestry groups exhibit nearly identical wealth distributions by 1980–1990 despite staggered arrival times spanning 270+ years, while Black, Cuban.

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Raj Chetty, Rebecca Diamond, Thomas Foster, Lawrence Katz, et al. Harvard University
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Btw 1993 and 2010, children who moved into revitalized public housing that mixed market-rate and subsidized units, stayed a mean 5 years, and earned ~17% higher income as of their late 20s/early 30s. One key channel was exposure to higher-income peers.

This paper has shown that the largest effort to revitalize high-poverty public housing projects in the United States—the HOPE VI program—succeeded in increasing economic mobility. Each year a child spent growing up in a revitalized public housing project increased their household income in adulthood by 2.77%, implying that growing up from birth in a revitalized public housing project would increase income by 50%. These income gains are substantially larger than the program’s up-front costs to taxpayers, demonstrating that it is possible to create higher-opportunity neighborhoods in a cost-effective manner even without changing broader determinants of outcomes such as labor market conditions or schools. The potential for change in short time frames is particularly notable given the historical roots of differences in opportunity across neighborhoods. The gains from HOPE VI are largely explained by changes in social interaction: children in revitalized projects interact more with peers in surrounding neighborhoods and gain more when they have higher-income peers.

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Oded Galor and Daniel Wainstock National Bureau of Economic Research
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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.

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Oren Danieli, Tanaya Devi and Roland Fryer Tel Aviv University
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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.

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Roland Fryer Wall Street Journal
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Roland Fryer reports on his research that found educational attainment, followed by traits like resilience, self-esteem and conscientiousness were the strongest predictors of intergenerational mobility in the US.

Our work complemented Mr. Chetty’s. His was wide: data on an astonishing number of Americans, but with limited information about each person. Ours was deep: data on a much smaller group of Americans, but with detailed information about each person. From Memphis, Tulsa and New Orleans—cities with higher-than-average poverty rates—we recruited 1,000 adults who self-identified as having grown up poor. By the time we met them in their 40s, some had exited poverty, but most hadn’t. Each took part in a three-hour interview about childhood health, parental income, home environment, lifetime traumas, neighborhood safety, psychological skills, beliefs and current income. The goal was to understand which childhood experiences were most correlated with mobility. We found that human capital was the strongest predictor of mobility. The second most important predictor was noncognitive skills—traits like resilience, self-esteem and conscientiousness. Other factors also mattered: the number of trusted adults in one’s childhood, early encounters with police, mental health and adverse experiences.

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Jared Horvath and Katie Fabricant Acta Psychologica
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“Educationally similar” monozygotic (MZ) twins raised apart [TRA] have a mean IQ delta of 5.8 points, ~ the same as those raised together. “Educationally dissimilar” MZ TRA have a delta of 12.8 points, closer to non-twin siblings than twins reared together.

We gathered data from every available TRA [Twins Raised Apart] case published in the academic literature over the last century that included both individualized IQ and biographical data. This data set (which we believe represents the entirety of the non-amalgamated TRA field; the vast majority embodied in existing studies are amalgamated) consists of 87 pairs. We split these pairs into three groups: similar, somewhat dissimilar, and very dissimilar schooling. Of the 87 pairs, 52 experienced a similar type and duration of schooling within a similar location. In fact, 25 of these pairs attended the same school for some period of time. One would be correct in questioning whether these twins truly meet the criteria of ‘reared apart;’ eyebrows should certainly be raised over how varied researchers have defined the term. Analysis revealed that ‘Educationally Similar’ TRA pairs have an ICC of 0.87 ± 0.02 (n = 52) and an absolute IQ differential of 5.8 points (SD = 6.7). This makes this group nearly indistinguishable from MZ twins reared together. Further analysis of the 35 ‘educationally dissimilar’ TRA pairs shows an ICC of 0.75 ± 0.04 (n = 32) and an absolute IQ differential of 12.8 points (SD = 7.3). The correlation is 0.12 lower than the ‘educationally similar’ TRAs and the IQ differential places this group closer in stature to non-twin siblings than MZ or even dizygotic twins reared together.

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

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