Train to Opportunity: the Effect of Infrastructure on Intergenerational Mobility

Layer 1: Overview

This paper asks whether proximity to transport infrastructure can sever the occupational tie between parents and children — a question with direct bearing on the debate over place-based versus people-based policies. The authors exploit the nineteenth-century expansion of the railroad network across England and Wales, a setting where the First and Second Industrial Revolutions were remaking the occupational structure at the same time that the railroad was knitting together local labor markets and enabling geographic mobility.

The empirical strategy centers on a novel dataset of close to 980,848 father-son pairs constructed from the full digitized population censuses of England and Wales in 1851, 1881, and 1911 (I-CeM project). Individuals are tracked across consecutive censuses using the Abramitzky-Mill-Perez (2019) linking procedure, which achieves match rates of 43–50% for men aged 40–52. Crucially, each individual is geolocated to the street level by matching census addresses to the GB1900 gazetteer, allowing railroad access to be measured as the straight-line distance from the childhood residence to the nearest train station — a finer measure than the district-level presence indicators used in prior work. Sons’ occupations are observed at ages 40–52; fathers’ occupations are measured 30 years earlier when sons were aged 10–22. Occupational mobility uses two complementary scales: HISCO categories (farming, laborer, services, sales, clerical, managerial, professional) and the continuous HISCAM social-interaction-distance ranking (scores 28–99, mean 50, SD 10).

The key endogeneity problem is that railroad companies targeted low-density, cheap land, and that wealthy landowners and local politicians influenced station placement. To isolate exogenous variation, the authors construct a dynamic least-cost path (DLCP) network connecting 53 major towns identified by their 1801 populations (top 10% of the population distribution, threshold 9,172 inhabitants). The DLCP assigns slope costs to 50x50 meter grid cells and finds the minimum-cost path between every town pair. Lines are ranked by betweenness centrality to separate “early” 1851 lines from “late” 1881 lines, giving a time-varying instrument. Proximity to the nearest DLCP line is used as the instrument for proximity to the nearest actual train station, with standard errors clustered at the parish level. Controls include county and census-year fixed effects, distance to the nearest 1801 major town and its population, distance to Roman roads, ancient ports, and navigable waterways, plus household characteristics (number of servants as a wealth proxy, household size, and father’s foreign birth).

Main results (preferred IV specification with full controls): sons who grew up one standard deviation — approximately 5 km, or about one hour’s walk — closer to a train station were 11 percentage points more likely to work in an occupation category different from their father’s. They were 5 percentage points more likely to be upwardly mobile, defined as a son’s HISCAM score exceeding his father’s by more than one standard deviation of the son’s distribution. The downward mobility estimate is 3 percentage points — positive but smaller in magnitude — indicating that railroad access raises occupational churn asymmetrically, predominantly upward. First-stage F-statistics exceed the Staiger-Stock threshold comfortably (135–414 across specifications). OLS estimates are uniformly smaller than IV estimates, consistent with historical evidence that the railroad targeted areas with weaker growth trajectories.

The occupational transitions underlying these results run strongly out of farming and into professional, clerical, sales, and services categories, regardless of the father’s own occupation (Table IV). Sons growing up closer to the railroad were 19 percentage points less likely to work in a declining occupation and 16 percentage points more likely to work in a growing occupation. The distributional pattern shows an inverted-U relationship with father’s occupational decile for occupation-category switching and rank divergence, with the greatest gains concentrated among sons of middle-ranking fathers. For upward mobility specifically, the benefits diminish monotonically as father’s rank rises — sons from blue-collar backgrounds gained more (upward mobility coefficient 0.064) than sons from white-collar backgrounds (0.031).

The authors decompose the total railroad effect on intergenerational mobility into three channels using a structural decomposition applied to a sample of 342,715 brothers: (1) changes in local labor-market opportunities, estimated as the effect on mobility for stayers; (2) changes in the returns to spatial mobility, estimated via a within-family comparison of brothers who moved versus stayed; and (3) changes in the rate of spatial mobility itself. Better railroad access raised the probability of moving away from the birth county by 15 percentage points. However, the estimated return to spatial mobility — the extra boost from actually moving — was reduced by railroad access (negative interaction between proximity and mover status), meaning the railroad decreased the relative advantage of leaving. The decomposition (Table C.6) shows that changes in local opportunities account for the great majority of the total mobility effect. Parish-level evidence confirms the local opportunity mechanism: better-connected parishes saw population growth, more industrial chimneys, more entrepreneurs, higher shares of skilled and literate workers, higher Gini coefficients, and higher median occupational ranks — consistent with agglomeration, industrialization, and skill-biased structural change.

The policy implication is that transport infrastructure investment can reduce intergenerational persistence in occupational status, primarily by restructuring the local labor market rather than by enabling workers to exit. The caveat is that these gains were unevenly distributed — middle- and lower-ranking families benefited most, and the railroad simultaneously raised local inequality alongside local mobility.

Layer 2: Deep Dive

What is the core identification strategy and what are the main threats it addresses?

The authors use a ‘dynamic least-cost path’ (DLCP) instrument. They connect 53 major English and Welsh towns (defined as the top 10% of the 1801 population distribution, with at least 9,172 inhabitants) via least-cost routes computed over a 50×50 meter terrain grid that assigns slope-based costs to each cell. The instrument is proximity from the childhood residence to the nearest line in this DLCP network. The logic is that individuals incidentally located near the geographic route between major historical towns are more likely to be near an actual railroad — but the DLCP route is based purely on terrain costs, not on local demand, local resources, or the political lobbying that shaped where stations were actually placed. The strategy addresses: (a) reverse causality from high-growth areas attracting railroad placement; (b) sorting of ambitious or wealthy households toward connected parishes; (c) railroad companies’ demand-driven routing choices. The exclusion restriction could be violated if location along least-cost paths between 1801 major towns is directly correlated with intergenerational mobility for reasons other than the railroad. The paper addresses this by controlling for distance to the nearest 1801 major town and its population (proximity to nodes), proximity to Roman roads, ancient ports, and navigable waterways (pre-existing trade routes), and household wealth proxies.

How is the instrument made dynamic, and why does this matter?

The authors divide the hypothetical network into ’early’ (1851) and ’late’ (1881) lines by ranking lines in decreasing order of betweenness centrality — the number of times a line connects major towns via shortest paths — until the total cost of the 1851 observed network is exhausted. This dynamic structure means the instrument varies across both space and census cohorts (sons measured in 1851-1881 versus 1881-1911). Without the dynamic feature, the instrument could conflate the effects of lines that were built early (and thus had decades to affect local economies) with lines built later. The temporal variation bolsters the plausibility of the exclusion restriction and is shown to be robust in alternative specifications using static least-cost paths and slope-free least-cost paths.

What are the four dependent variables and how is intergenerational mobility defined?

The paper uses four measures: (1) an indicator equal to one if the son works in a different HISCO occupation category than his father; (2) the absolute value of the difference in HISCAM scores between son and father; (3) ‘upward mobility,’ an indicator equal to one if the son’s HISCAM score exceeds his father’s by more than one standard deviation of the son’s score distribution; (4) ‘downward mobility,’ the symmetric indicator for a decline greater than one standard deviation. Sons’ occupations are observed when sons are 40–52 years old; fathers’ occupations are measured 30 years earlier when sons were 10–22. The HISCAM scale is held constant over the period (national GB scale, 1800–1938) so that rankings reflect fixed social stratification positions rather than period-specific prestige. The paper also uses time-varying HISCAM, HISCLASS, Woollard, and Armstrong classifications as robustness checks.

What is the first-stage performance of the instrument?

The first-stage relationship between proximity to the nearest DLCP line and proximity to the nearest actual train station is positive and statistically significant across all specifications. The Sanderson-Windmeijer F-statistic is 414 in the specification without controls, 136 with county and year fixed effects and full controls, and remains well above the conventional threshold of 10. The first-stage coefficient drops from 0.640 to 0.339 when full controls are added, indicating that a portion of the geographic correlation between the DLCP and the actual network reflects the pre-existing economic importance of towns and travel routes — which is precisely what the controls absorb.

What are the main mechanisms and how are they distinguished empirically?

The paper decomposes the total IV effect on intergenerational mobility using a three-part decomposition: (1) Changes in local opportunities, measured as the effect of proximity on mobility for sons who stayed in their birth county (stayers); (2) Changes in the returns to spatial mobility, estimated by comparing brothers who moved with brothers who stayed (using family fixed effects), and interacting this comparison with railroad proximity; (3) Changes in the rate of spatial mobility itself, estimated from the effect of proximity on the probability of county-to-county migration. Table C.6 shows that local opportunities account for the dominant share of the total effect. The railroad raised the migration probability by 15 percentage points (Table VI), so spatial mobility channels exist — but the railroad decreased the relative advantage of actually moving (negative interaction term in Table V), meaning the local opportunity channel more than offsets the spatial channel. Supporting evidence from parish-level regressions (Table VII) shows that better-connected parishes experienced significantly higher population growth, more industrial chimneys, more entrepreneurs per 100 square meters, higher shares of skilled and literate workers, higher Gini coefficients, and higher median occupational ranks — consistent with agglomeration and skill-biased industrialization.

What heterogeneity is documented by father’s occupation and position in the distribution?

The effects are heterogeneous by the father’s occupational position. Figure 6 shows an inverted-U pattern for occupation-category switching and absolute rank divergence: sons of middle-ranking fathers benefit most from railroad access. For upward mobility (Figure 6c), the benefits diminish monotonically from the lower end of the father’s distribution — sons of low-ranking fathers are most likely to move up. Sons of white-collar fathers see smaller (and sometimes statistically insignificant) upward mobility gains (0.031) compared with sons of blue-collar fathers (0.064), while the occupation-category switching benefit is also larger for blue-collar sons (0.108 vs. 0.057) (Table C.1). Separate transition matrices by HISCO category (Table IV) show that railroad access reduces the probability of farming for sons of all father types, and raises probabilities of clerical, sales, and services occupations. Effects on becoming a laborer are heterogeneous: for sons of farmers, proximity raises the probability of becoming a laborer; for sons in service occupations, it decreases it.

What robustness checks are run?

The paper performs an extensive battery. (1) Alternative connectivity measures: distance to the nearest railroad line, indicator variables for train station within 5, 10, and 15 km, and parish-level station presence. (2) Alternative mobility thresholds: 0.5, 1.5, and 2 standard deviations for upward and downward mobility; time-varying HISCAM to account for changing occupational prestige. (3) Removing railroad-specific occupations (train conductors, controllers) to check for mechanical effects. (4) Alternative specifications: second-order polynomials, parish fixed effects (10,419 parishes), and fully nonparametric covariate controls via k-means clustering (500 clusters). (5) Alternative instruments: a slope-free DLCP and a static (non-dynamic) least-cost path. (6) Geolocation robustness: using parish centroids instead of street-level addresses. (7) Linking bias: controlling for the individual probability of being linked using cubic polynomials on linkage probability and surname-frequency dummies; also checking that the railroad network explains little of the share of linked individuals at the parish level. (8) Subsamples: by census year (1851-1881 vs. 1881-1911), by county (leave-one-out), by rural/urban status, by father’s age, by son’s age, by birth order, by native/first-/second-generation immigrant status, by whether the son was born in the same county he grew up in, and by whether the father was in farming. (9) Causal response weighting: the Loken-Mogstad-Wiswall decomposition shows positive IV weights across the entire proximity distribution, consistent with a LATE interpretation. Results are stable across all checks.

How does the paper handle the selection-into-migration problem in estimating returns to spatial mobility?

The authors follow Abramitzky, Boustan, and Eriksson (2012) and use a within-family comparison of brothers — a subsample of 342,715 sons from 157,369 households who grew up in the same household but one moved county while the other stayed. Family fixed effects absorb the shared household characteristics (wealth, motivation, family networks, financial constraints) that jointly determine the propensity to migrate and the baseline mobility trajectory. The railroad-proximity interaction with mover status is instrumented using the interaction of the DLCP instrument with the mover indicator, via a control function approach. The estimated baseline return to spatial mobility (the mover premium) is positive and significant — movers have higher occupation-category divergence and shift more in both directions — but the railroad-induced change in return to mobility is negative, meaning that proximity to the railroad reduced the additional mobility benefit of actually migrating. This finding is the core of the conclusion that local opportunities, not spatial mobility, dominate.

What does the paper document about local labor market changes induced by the railroad?

Parish-level IV regressions (Table VII) show that better proximity to the 1851 network (instrumented by the DLCP) is associated with: significantly higher population growth between 1851 and 1881; a significantly larger number of industrial chimneys (proxying factory concentration, sourced from Heblich-Trew-Zylberberg (2021)); more entrepreneurs per 100 square meters (from the British Business Census of Entrepreneurs); higher shares of high-skilled and literate workers; a higher Gini coefficient over occupational ranks; and a higher median occupational rank. Additionally, sons in better-connected parishes were 19 percentage points less likely to work in a declining occupation and 16 percentage points more likely to work in a growing occupation (Table C.3). Sons were also 3 percentage points more likely to be literate and 7 percentage points more likely to work in a non-manual occupation (Table C.5). These findings collectively point to agglomeration, industrialization, skill-biased technological change, and the creation of a new entrepreneur class as the mechanisms by which the railroad transformed local labor market structure.

What prior work does this paper relate to most closely, and what distinguishes it?

The paper sits at the intersection of the railroad-infrastructure and intergenerational-mobility literatures. In the infrastructure tradition, it relates closely to Donaldson (2018, AER) on railroads in India, Donaldson and Hornbeck (2016, QJE) on US market access, Bogart et al. (2022, JUE) on population and structural change in England and Wales, and Heblich-Redding-Sturm (2020, QJE) on London commuting and urban growth. The closest prior paper is Perez (2017) on nineteenth-century Argentina, who finds railroad access shifted children from agricultural into white-collar and skilled blue-collar occupations; this paper provides similar evidence for England and Wales at individual level and adds a full mechanism decomposition. In the intergenerational mobility tradition it relates to Long and Ferrie (2013, AER) and Long (2013, ERH) on census-based occupational mobility in Victorian Britain. The key methodological advantages of the current paper are: (a) use of the full (not 2%) census for all three years, yielding close to 1 million father-son pairs with match rates of 43–50% versus 15–33% in prior work; (b) street-level geolocation enabling individual-level rather than district-level measurement of railroad access; (c) the explicit three-way mechanism decomposition separating local opportunities, returns to migration, and migration rates; and (d) documenting rich heterogeneity by father’s occupational rank and occupation category.

What are the policy implications and what scope conditions limit their external validity?

The paper’s core policy message is that transport infrastructure investment can be an effective mechanism for reducing intergenerational occupational persistence — primarily by creating new local labor market opportunities rather than by enabling low-income workers to reach distant job centers. This provides historical support for place-based policies of the sort embodied in the Biden ‘Build Back Better’ infrastructure proposals or the UK HS2 high-speed railway project (mentioned in the paper). The main scope conditions limiting generalizability are: (1) The setting is nineteenth-century England and Wales during the Industrial Revolution, when the occupational structure was shifting rapidly from farming to industry and commerce — the railroads arrived at a moment of latent demand for new labor market structures; (2) The benefits were not evenly distributed: middle-ranking families (by father’s occupational rank) gained most in absolute occupational switching and rank divergence, while the lowest-ranked families gained most specifically in upward mobility; (3) The railroad simultaneously raised local inequality alongside local mobility, suggesting infrastructure investment can be inequality-increasing in the cross-sectional distribution of wages even as it reduces intergenerational persistence; (4) The effects are highly localized — even 5 km of additional distance matters — implying that the placement of stations relative to where low-income families actually live is crucial for achieving distributional goals.

What does the paper document about the baseline patterns of intergenerational mobility in the sample?

In the full sample of 980,848 father-son pairs covering 1851-1881 and 1881-1911, 80% of sons do not remain in the same HISCO occupation category as their father. The correlation between father’s and son’s HISCAM ranks is 0.28. Among sons, 18% experienced upward mobility (son’s HISCAM rank more than one SD higher than father’s) and 15% experienced downward mobility (more than one SD lower). About 31% of sons moved to a different county from where they grew up, settling on average 100 km away. Sons grew up on average 3.28 km from the nearest train station (SD 5.45 km). These descriptives reveal strong spatial clustering in intergenerational mobility patterns at the parish level.

Does the LATE interpretation hold and what does the weighting function show?

The authors verify the LATE interpretation via two approaches. First, following Loken-Mogstad-Wiswall (2012), they compute the causal response weighting function as the covariance between each discrete proximity indicator and the DLCP instrument, divided by the covariance between the proximity measure and the DLCP instrument. They find positive weights across the entire distribution of proximity to the nearest train station, concentrated most heavily for individuals residing 0.5 to 1.5 proximity units (approximately 2.7 to 8.1 km) from a train station — these are the individuals whose proximity is most affected by incidental location along the DLCP. The absence of negative weights indicates the IV estimate does not mix complier and never/always-taker effects in a sign-reversing way. Second, following Blandhol et al. (2022), a fully nonparametric specification using 500 k-means clusters for covariates yields estimates very close to the parametric baseline, consistent with a LATE interpretation of the linear IV estimator.

Key Concepts

Dynamic Least-Cost Path (DLCP) Network: The paper’s instrument for railroad access. A hypothetical railroad network connecting England and Wales’s 53 largest towns in 1801 via routes that minimize geographic cost (distance plus slope-based terrain costs), ignoring all demand-side factors. Lines are classified as ’early’ (1851) or ’late’ (1881) by betweenness centrality until the cost budget of the actual 1851 network is exhausted. Proximity from childhood residence to the nearest DLCP line instruments proximity to the nearest actual train station.

Intergenerational Occupational Mobility: In this paper, the degree to which a son’s adult occupation differs from his father’s, measured both categorically (same versus different HISCO category) and cardinally (difference in HISCAM scores). Upward (downward) mobility is specifically defined as the son’s HISCAM score exceeding (falling below) the father’s by more than one standard deviation of the son’s HISCAM distribution.

HISCAM Score: A continuous occupational ranking (range 28–99, mean 50, SD 10) derived from the frequency of social interactions — marriages, friendships, parent-child links — between occupations in historical data. Higher scores indicate a more advantageous position in the social stratification structure. The paper uses the national Great Britain scale, held constant for 1800–1938, to make rankings comparable across census years.

Local Opportunities Channel: The mechanism by which railroad access improved intergenerational mobility through restructuring the local labor market — enabling commuting, attracting factories and entrepreneurs, spurring urbanization and industrialization, and creating new occupations requiring new skills — without requiring sons to migrate away from their birth county. Identified empirically as the effect of railroad proximity on mobility outcomes for sons who stayed in their birth county (stayers).

Returns to Spatial Mobility: The additional intergenerational mobility benefit (or penalty) associated with actually migrating to another county, estimated using within-family variation among brothers — one who moved and one who stayed — to net out shared household-level determinants of mobility. The paper finds that railroad access reduced (made more negative) the returns to spatial mobility, meaning that the relative advantage of leaving shrank as local opportunities expanded.

Inconsequential Place IV Approach: An identification strategy (following Chandra-Thompson 2000 and Michaels 2008) in which the instrument for infrastructure access is constructed from the geographic convenience of locations lying between endpoints of a planned network, rather than from demand-side factors at those locations. The DLCP instrument in this paper is a specific implementation: individuals living between 1801 major towns incidentally receive railroad access because the low-cost route between towns passes near their residence.

Occupational Tie (Father-Son): The tendency for sons to remain in the same occupation category or same position in the occupational ranking as their father. In this paper, severing the occupational tie means a son moves to a different HISCO category and/or achieves a HISCAM score meaningfully different from his father’s. The railroad’s main effect is framed as reducing this tie, with upward mobility being the dominant direction of change.