arxiv: 2604.24336 · v1 · submitted 2026-04-27 · 💰 econ.GN · q-fin.EC

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Effects of Genetic Propensity for Education on Labor Market and Health Trajectories across the Working Life

Stefano Lombardi , Nurfatima Jandarova , Kristina Zguro , Jarkko Harju , Aldo Rustichini , Andrea Ganna

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Pith reviewed 2026-05-07 17:30 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC

keywords polygenic indexeducational attainmentincome trajectoriesjob mobilityintergenerational transmissionlabor marketgenetic effects

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The pith

Genetic propensity for education drives steeper lifetime income growth only for tertiary-educated workers through increased job mobility, with most of the link tracing to fathers rather than the individual's own genes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses long-term Finnish registry data on over 50,000 graduates to show how a polygenic index for educational attainment shapes economic outcomes differently by education level. Higher values of this index forecast faster income growth and higher cumulative earnings exclusively among those who complete tertiary education, operating through more frequent shifts to higher-paying employers rather than advantages at labor-market entry or differences in health. When parental indices are accounted for in family trios, the income gap shrinks sharply, indicating that indirect genetic effects transmitted from fathers explain the bulk of the observed pattern. This matters for understanding why education-linked genetic factors amplify inequality in mid-career earnings even after schooling is completed.

Core claim

In a sample of 51,056 Finnish graduates tracked annually for up to 25 years after leaving school, higher EA-PGI predicts substantially larger discounted lifetime income only among tertiary-educated individuals (EUR 45,392 or 13.1 percent higher at the 90th versus 10th percentile), with no such gradient among secondary-educated workers. The divergence arises from greater job-to-job mobility into higher-quality firms rather than initial wages or employer quality. Controlling for parental EA-PGI in 12,871 trios reduces the gap by 71 percent, and the paternal index exerts a stronger effect on offspring income than the offspring's own index.

What carries the argument

The EA-PGI (polygenic index for educational attainment), which aggregates genetic variants associated with years of schooling and acts via accelerated movement across employers rather than health or entry conditions.

If this is right

Income advantages from higher EA-PGI accumulate gradually through repeated employer switches rather than appearing at labor-market entry.
The income gradient tied to EA-PGI is absent below the tertiary-education threshold.
Accounting for parental EA-PGI, especially the father's, accounts for most of the raw association with offspring earnings.
Health trajectories do not mediate the EA-PGI to income link within education groups.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Interventions that improve job mobility for lower-EA-PGI graduates might narrow long-run earnings gaps even without changing genetic endowments.
The stronger paternal than maternal transmission suggests targeted study of father-specific channels such as occupational networks or role modeling in career progression.
Similar registry linkages in other countries could test whether the education-level specificity of the PGI-income link generalizes beyond the Finnish context.

Load-bearing premise

That differences in EA-PGI largely capture genetic influences on education and labor outcomes without major remaining bias from shared family environment, population stratification, or assortative mating.

What would settle it

Observing that high- and low-EA-PGI individuals within the same education group show identical rates of job changes to higher-paying firms over the career would falsify the proposed mobility mechanism.

Figures

Figures reproduced from arXiv: 2604.24336 by Aldo Rustichini, Andrea Ganna, Jarkko Harju, Kristina Zguro, Nurfatima Jandarova, Stefano Lombardi.

**Figure 1.** Figure 1: Average annual income by EA-PGI level, over time and by education view at source ↗

**Figure 2.** Figure 2: Worker productivity index and EA-PGI level by education view at source ↗

**Figure 3.** Figure 3: Employer mobility and quality of tertiary-educated individuals by EA view at source ↗

**Figure 6.** Figure 6: Average annual income by EA-PGI level, over time and by education, un view at source ↗

read the original abstract

Education is a major source of inequality in income and health. Polygenic indices for educational attainment (EA-PGI) capture both direct and indirect genetic influences on education, but their effects on income and health remain unclear. Using Finnish registry data on 51,056 graduates followed annually since graduation for up to 25 years, we report three findings. First, higher EA-PGI strongly predicts income growth, but only among higher educated people: tertiary-educated graduates at the 90th percentile earn EUR 45,392 (13.1 percent) higher discounted lifetime income than those at the 10th percentile. This effect is not mediated by overall health and is entirely absent for the secondary (high school)-educated workers, who do not benefit from higher EA-PGI levels. Second, EA-PGI does not predict income differences at labor market entry or the quality of the first employer, but rather higher job-to-job mobility toward higher-quality firms that drives the long-run income divergence. Third, controlling for parental EA-PGI in 12,871 parent-offspring trios reduces the discounted lifetime income gap by 71 percent, and the effect of paternal (but not maternal) EA-PGI on offspring income exceeds that of the offspring's own EA-PGI. These findings suggest that genetic factors associated with educational attainment predict income trajectories primarily through faster and more frequent changes to higher-paying employers. However, much of this association reflects indirect paternal genetic effects, consistent with enduring paternal patterns of intergenerational job and income transmission.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

EA-PGI raises lifetime income 13% only for tertiary grads via job mobility, but parental trio controls cut the gap 71% with paternal effects dominating.

read the letter

The one thing to know is that this paper uses 25 years of Finnish registry data on 51k graduates to show higher EA-PGI predicts faster income growth only among the college-educated, operating through repeated job switches to higher-quality employers rather than entry-level placement or health, while parental PGI controls in 12k trios shrink the income gap by 71% and give paternal scores more weight than the offspring's own score. The long panel and the mobility channel are the clearest additions. They track how the earnings divergence accumulates over time instead of appearing at the start, and the health mediation test rules out one simple alternative. The trio results add concrete numbers on intergenerational transmission that go beyond most prior cross-sectional genetic studies. The null for secondary-educated workers also stands out as a boundary condition worth testing further. The main soft spot is the interpretation of the parental controls. The 71% reduction and the paternal coefficient excess are presented as evidence of indirect genetic effects, but without sibling fixed effects, transmission disequilibrium tests, or other within-family designs, residual assortative mating or subtle stratification in the PGI could still explain part of the pattern. The abstract does not detail robustness checks on that specification, so the claim that much of the association is indirect paternal genetic effects rests on a regression that may not fully isolate the channel. This is for labor economists and behavioral geneticists working on gene-environment interactions and mobility. A reader who wants empirical detail on how genetic scores translate into career trajectories will find usable numbers here. It deserves a serious referee because the data horizon and the mechanism test are real strengths, even if the trio identification needs more scrutiny to support the stronger causal language.

Referee Report

2 major / 2 minor

Summary. The paper analyzes Finnish registry data on 51,056 graduates followed for up to 25 years to study how polygenic indices for educational attainment (EA-PGI) shape income and health trajectories. Key claims are that higher EA-PGI predicts substantially higher discounted lifetime income (EUR 45,392 or 13.1% gap between 90th and 10th percentiles) only among tertiary-educated workers via faster job-to-job mobility to higher-quality firms, with no effect among secondary-educated workers and no mediation by health; additionally, in 12,871 parent-offspring trios, controlling for parental EA-PGI reduces the own-PGI income gap by 71% and paternal EA-PGI shows a larger coefficient than the offspring's own EA-PGI, implying that much of the association operates through indirect paternal genetic effects.

Significance. If the core results hold after addressing confounding concerns, the paper would provide valuable evidence on the mechanisms through which genetic propensity for education influences long-run labor market outcomes and intergenerational transmission, particularly the role of job mobility and paternal channels. The large sample, long panel, registry linkage, and quantitative estimates of lifetime income differences represent strengths for an empirical contribution in genetic economics.

major comments (2)

[parent-offspring trios analysis] Trio analysis (third main finding): The claim that 'much of this association reflects indirect paternal genetic effects' rests on the regression of offspring income on own EA-PGI plus parental EA-PGI in the 12,871 trios, which produces a 71% drop in the own-PGI coefficient and a larger paternal than own coefficient. This interpretation requires that parental EA-PGI fully blocks transmitted genetic and correlated environmental factors, but the analysis does not report within-family fixed effects, sibling comparisons, or transmission disequilibrium tests that would address residual population stratification or assortative mating on education (and thus on PGI). This is load-bearing for the indirect-effects conclusion.
[mechanisms and mediation] Health mediation test: The paper states that the EA-PGI income effect among tertiary graduates 'is not mediated by overall health,' but the abstract and available description provide no details on the specific health measures, the statistical mediation approach (e.g., Baron-Kenny, product-of-coefficients, or counterfactual), or robustness to different health proxies. This matters for the claim that mobility (rather than health) drives the income divergence.

minor comments (2)

[income trajectory calculations] Clarify the exact construction of the discounted lifetime income measure, including the discount rate, assumed retirement age, and handling of right-censoring for shorter careers.
[labor market mobility analysis] Provide more detail on how 'employer quality' is measured and how job-to-job mobility is identified in the registry data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We respond to each major comment below, indicating where we will revise the manuscript and where we provide additional clarification or caveats.

read point-by-point responses

Referee: [parent-offspring trios analysis] Trio analysis (third main finding): The claim that 'much of this association reflects indirect paternal genetic effects' rests on the regression of offspring income on own EA-PGI plus parental EA-PGI in the 12,871 trios, which produces a 71% drop in the own-PGI coefficient and a larger paternal than own coefficient. This interpretation requires that parental EA-PGI fully blocks transmitted genetic and correlated environmental factors, but the analysis does not report within-family fixed effects, sibling comparisons, or transmission disequilibrium tests that would address residual population stratification or assortative mating on education (and thus on PGI). This is load-bearing for the indirect-effects conclusion.

Authors: We agree that parental EA-PGI controls do not fully eliminate residual population stratification or assortative mating, and that methods such as transmission disequilibrium tests or sibling fixed effects would provide stronger identification. Our sample of 12,871 trios does not contain the additional genotyped siblings required for those tests. The 71% attenuation and the larger paternal coefficient are nevertheless consistent with indirect paternal channels, which is the standard interpretation in the existing literature using parental PGI. We will revise the text to qualify the claim more explicitly, add a limitations paragraph discussing these identification issues, and note that the results should be viewed as suggestive rather than definitive evidence of indirect effects. revision: partial
Referee: [mechanisms and mediation] Health mediation test: The paper states that the EA-PGI income effect among tertiary graduates 'is not mediated by overall health,' but the abstract and available description provide no details on the specific health measures, the statistical mediation approach (e.g., Baron-Kenny, product-of-coefficients, or counterfactual), or robustness to different health proxies. This matters for the claim that mobility (rather than health) drives the income divergence.

Authors: We apologize for the brevity in the abstract and main text. The full manuscript employs a product-of-coefficients mediation approach using two registry-based health proxies: annual days of sickness absence and counts of hospital inpatient days. We will expand the methods section with a detailed description of these measures, the exact mediation procedure, and additional robustness checks that substitute alternative health indicators (e.g., outpatient visits and disability pension receipt). These revisions will make clear that health does not mediate the EA-PGI–income relationship among tertiary graduates, thereby reinforcing the job-mobility mechanism. revision: yes

Circularity Check

0 steps flagged

No significant circularity in regression-based empirical claims

full rationale

The paper's derivation consists of OLS and related regressions of lifetime income trajectories on externally constructed EA-PGI (from prior GWAS) using independent Finnish registry data on 51,056 graduates and 12,871 trios. The key results—PGI-income gradient only among tertiary-educated, mediation via job mobility, and 71% attenuation when adding parental PGI—are direct statistical outputs from these models rather than algebraic identities or self-fitted predictions. No equations reduce by construction to inputs, no uniqueness theorems are imported from self-citations, and the parental-control step relies on additional trio data rather than re-labeling a fit. The chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only abstract available; full methods section not accessible. Central claim rests on standard assumptions in polygenic score research and registry data validity.

axioms (2)

domain assumption Polygenic indices for educational attainment validly capture direct and indirect genetic influences without substantial confounding from population structure or pleiotropy
Invoked when interpreting EA-PGI as genetic propensity for education and its effects on income
domain assumption The sample of Finnish graduates is representative and free of major selection bias for studying labor market and health trajectories
Used to generalize the conditional effects and mobility findings

pith-pipeline@v0.9.0 · 5598 in / 1563 out tokens · 152101 ms · 2026-05-07T17:30:15.506061+00:00 · methodology

discussion (0)

Reference graph

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