Geographic Variation in Multigenerational Mobility

Using complete-count register data spanning three generations, we document spatial patterns in inter- and multi-generational mobility in Sweden. Across municipalities, grandfather-child correlations in education or earnings tend to be larger than the square of the parent-child correlations, suggesting that the latter understate status transmission in the long run. Yet, conventional parent-child correlations capture regional differences in long-run transmission and therefore remain useful for comparative purposes. We further find that the within-country association between mobility and income inequality (the “Great Gatsby Curve”) is at least as strong in the multi- as in the inter-generational case. Interpreting those patterns through the lens of a latent factor model, we find that regional differences in mobility primarily reflect variation in the transmission of latent advantages, rather than in how those advantages translate into observed outcomes.

💡 Research Summary

This paper exploits Sweden’s complete administrative registers to map inter‑generational (parent‑child) and multi‑generational (grandparent‑grandchild) mobility across 290 municipalities. Focusing on cohorts born between 1981 and 1989, the authors link three generations—children, their parents, and their grandparents—and observe two outcomes: years of schooling and labor earnings. Education is coded into seven levels (7 to 20 years) and converted to years of schooling; earnings are adjusted for inflation and, because of cohort‑specific labor‑market structures, are predicted at age 40 using a Mincer‑type fixed‑effects model that controls for gender, education, age, and year.

The analysis proceeds in four steps. First, the authors estimate regional correlations for parent‑child (ρ₂) and grandparent‑grandchild (ρ₃) pairs. Across municipalities, ρ₃ is systematically larger than the square of ρ₂, indicating that the simple parent‑child correlation understates long‑run status transmission. Nevertheless, ρ₂ still captures regional differences and remains useful for comparative work. Second, they test the robustness of these patterns to alternative mobility metrics (regression slopes vs. correlations), to the choice of outcome (education vs. earnings), to the time horizon (inter‑ vs. multi‑generational), and to lineage (maternal vs. paternal). Rankings based on education are highly consistent across specifications, whereas earnings‑based rankings are more sensitive to measurement error and sample composition. Third, the paper extends the “Great Gatsby Curve” literature by showing that, within Sweden, municipalities with higher parental‑generation income inequality (Gini) exhibit lower mobility, and this negative association is at least as strong for multi‑generational education mobility as for inter‑generational mobility. The correlation between inequality and multi‑generational education mobility reaches about –0.45, stronger than the –0.32 observed for the inter‑generational counterpart. Fourth, the authors embed the empirical findings in a latent‑factor transmission model. In each municipality, an unobserved “latent advantage” L is transmitted to the next generation with efficiency τ and then mapped into observable education or earnings with coefficients β. Estimating τ and β separately for each municipality reveals that most of the geographic variation in mobility stems from differences in τ (the transferability of latent advantages), while β (the returns of latent advantages to observed outcomes) is relatively uniform. Consequently, regions differ mainly in how effectively parental endowments are passed on, rather than in how those endowments translate into schooling or wages.

Methodologically, the authors address sampling error by weighting regressions by the number of parent‑child pairs per municipality and by conducting bootstrap checks. Sample sizes per municipality range from a few hundred to tens of thousands, and the authors acknowledge that estimates for the smallest areas are less precise, suggesting future work could employ hierarchical Bayesian models to better account for this uncertainty.

The paper makes four substantive contributions: (1) it provides the first nationwide, municipality‑level portrait of multi‑generational mobility; (2) it demonstrates that education‑based mobility measures are robust substitutes for income‑based measures in contexts where income data are scarce; (3) it confirms that the inequality‑mobility trade‑off holds at the sub‑national level and is amplified in the multi‑generational dimension; and (4) it shows that regional mobility gaps are driven primarily by variation in the transmission of latent advantages. Policy implications follow naturally: interventions that improve the “transferability” of parental human and social capital—such as high‑quality universal schooling, early‑childhood programs, and equitable housing policies—could reduce long‑run mobility disparities, especially in municipalities with high income inequality. The authors also call for regular monitoring of municipal mobility indicators using register data to evaluate policy impact over time.

In sum, by leveraging rich Swedish register data, the study deepens our understanding of how geographic context shapes the persistence of socioeconomic status across generations, and it highlights the importance of focusing on the mechanisms of inter‑generational transmission rather than solely on observed outcomes. Future research should extend the latent‑factor framework to incorporate additional local characteristics (school quality, labor market structure, migration flows) and adopt more sophisticated hierarchical estimation techniques to sharpen inference for small municipalities.