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Outside Sweden’s cities, occupations exposed to automation risk saw weaker job and pay growth from 2011–2021, but bigger local markets and closely related local skills softened the hit; urban occupations — richer in non-automatable tasks — showed no such decline.

How local labour market skill relatedness and size moderate the impacts of automation
Peter Njekwa Ryberg · January 07, 2026 · Regional Studies
openalex correlational medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Across Swedish local labour markets from 2011–2021, higher spatially-adjusted automation risk is associated with lower employment and wage growth in non-metropolitan occupations, but larger labour markets and greater local skill relatedness mitigate these negative associations, while metropolitan occupations show resilience.

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This paper examines how local labour market skill relatedness and size moderate the impacts of automation on occupations across Swedish local labour markets. Using administrative data and a spatially explicit risk of automation measure that accounts for regional differences in occupational task contents, it finds a negative association between automation and employment growth and wage income growth for non-metropolitan occupations between 2011 and 2021. Skill relatedness and labour market size mitigate these negative relationships. In contrast, no negative associations are found for metropolitan occupations. Due to their higher shares of non-automatable tasks, they are more resilient to adverse automation effects.

Summary

Main Finding

Automation is associated with lower employment growth and wage income growth for occupations in non‑metropolitan Swedish local labour markets (2011–2021). Higher local labour market skill relatedness and larger market size attenuate these negative effects. Metropolitan occupations show no such negative associations, likely because they contain a higher share of tasks that are less automatable.

Key Points

  • The negative impact of automation on employment and wages is heterogeneous across regions: present in non‑metro areas, absent in metro areas.
  • Skill relatedness — the extent to which an occupation’s skill set is similar to other local occupations — mitigates automation’s adverse effects.
  • Larger local labour markets also buffer occupations from automation‑driven declines.
  • Metropolitan areas are more resilient because their occupational task mixes include more non‑automatable tasks.
  • Results highlight the importance of regional task composition and agglomeration in shaping automation outcomes.

Data & Methods

  • Geographic and temporal scope: Sweden, 2011–2021, analysis at the occupation × local labour market level.
  • Data: Swedish administrative employment and wage records (administrative microdata) combined with task/occupation content information.
  • Automation exposure: a spatially explicit risk-of-automation measure that accounts for regional differences in occupational task contents (i.e., the same occupation can face different automation risk depending on local task mixes).
  • Empirical approach: regression analyses linking local automation risk to subsequent employment growth and wage income growth, with tests for moderation by (a) local skill relatedness and (b) labour market size; comparisons between metropolitan and non‑metropolitan labour markets.
  • Controls and identification: standard covariates and fixed-effects strategies implied to isolate associations (paper emphasizes robustness across specifications).

Implications for AI Economics

  • Heterogeneity matters: aggregate estimates of automation impact can mask strong regional variation; models and forecasts should incorporate local task composition and urban/rural differences.
  • Local complementarities and skill networks are protective: policies that foster skill relatedness (retraining that builds on existing local skills) may reduce displacement costs.
  • Agglomeration economies moderate automation risk: larger labour markets and metropolitan agglomerations provide more non‑automatable task opportunities and occupational diversity, suggesting urbanization changes the distributional effects of AI.
  • Policy targeting: rural and smaller labour markets may require more active labor‑market interventions (reskilling, mobility support, industry diversification) to cope with automation.
  • Research agenda: incorporate spatially varying task content into structural models of automation; evaluate the causal role of reallocation, job creation, and local spillovers in mediating AI impacts.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Uses high-quality administrative panel data and a regionally-adjusted (task-based) automation-risk measure with heterogeneity tests, providing credible descriptive evidence of differential impacts; however, it does not exploit a clear exogenous shock or instrument, so causal claims are limited by potential omitted confounders and reverse causation. Methods Rigormedium — Methodologically careful in constructing a spatially-explicit risk measure and in exploring moderators (skill relatedness, market size) on a decade-long administrative panel, but the absence of a well-identified quasi-experimental strategy (instrument, difference-in-differences with plausibly exogenous timing, or regression discontinuity) and incomplete information about controls or robustness checks reduces rigor relative to a high-standard causal design. SampleAdministrative microdata from Sweden covering occupations within local labour markets (occupation × local market panel) between 2011 and 2021, with measures of employment counts and wage income growth; includes a spatially-explicit task-based risk-of-automation indicator that varies by occupation and region and variables capturing local skill relatedness and labour-market size; analysis contrasts metropolitan versus non-metropolitan localities. Themeslabor_markets adoption IdentificationAssociational regression analysis exploiting cross-occupation and cross-local-labour-market variation in a spatially-explicit risk-of-automation measure over 2011–2021; the study estimates associations between local automation risk and subsequent employment and wage-income growth and tests interactions with local skill relatedness and labour-market size. Identification therefore relies on conditional correlations (observed covariates and variation across places and occupations) rather than an exogenous instrument or randomized variation. GeneralizabilitySingle-country (Sweden) context — results may not generalize to countries with different labour market institutions, industrial structure, or social safety nets., Time period 2011–2021 — may not fully capture effects of more recent generative-AI advances after 2021., Uses a task-based risk proxy for automation, not direct measures of technology adoption or firm-level implementation., Findings at occupation × local-market aggregation may mask within-occupation heterogeneity (firm, worker-level) and industry-specific dynamics., Definitions of metropolitan vs non-metropolitan and local labour market boundaries may affect results when applied elsewhere.

Claims (7)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The analysis uses administrative data and a spatially explicit risk of automation measure that accounts for regional differences in occupational task contents. Other positive spatially explicit risk of automation / measurement approach
Reading fidelity high
Study strength high
not reported
0.5
There is a negative association between automation and employment growth for non-metropolitan occupations between 2011 and 2021. Employment negative employment growth
Reading fidelity high
Study strength medium
not reported
0.3
There is a negative association between automation and wage income growth for non-metropolitan occupations between 2011 and 2021. Wages negative wage income growth
Reading fidelity high
Study strength medium
not reported
0.3
Skill relatedness mitigates the negative relationships between automation and labour-market outcomes (employment and wage growth). Skill Acquisition positive moderation of automation effects on employment and wage growth
Reading fidelity high
Study strength medium
not reported
0.3
Labour market size mitigates the negative relationships between automation and labour-market outcomes (employment and wage growth). Labor Share positive moderation of automation effects on employment and wage growth
Reading fidelity high
Study strength medium
not reported
0.3
No negative associations between automation and employment or wage growth are found for metropolitan occupations over the study period. Employment null_result employment growth and wage income growth
Reading fidelity high
Study strength medium
not reported
0.3
Metropolitan occupations have higher shares of non-automatable tasks and are therefore more resilient to adverse automation effects. Automation Exposure positive share of non-automatable tasks / resilience to automation
Reading fidelity high
Study strength medium
not reported
0.3

Notes