Why Tight Labour Markets Do Not Close Gender Pay Gaps: Evidence from a 20-Country Eurostat Panel

Soura Vamseekar, Marti (2026): Why Tight Labour Markets Do Not Close Gender Pay Gaps: Evidence from a 20-Country Eurostat Panel.

Abstract

EU Pay Transparency Directive 2023/970/EU requires mandatory gender pay gap reporting and joint pay assessments across all member states by June 2026. The Directive’s implicit premise — that structured disclosure will close gaps that competitive labour markets have failed to address — has not been empirically tested against cross-country tightness data. We test it using a 20-country, 11-sector, 6-year (2019–2024) Eurostat panel covering the EU27. We compute four composite workforce intelligence indices — Hiring Pressure Index (HPI), Labour Resilience (LR), Equity Risk Score (ERS), and Transition Readiness (TR, in development) — and find that labour market tightness and gender pay equity risk are structurally misaligned. The Pearson correlation between employment rates and gender pay gaps across the 20-country sample is weakly positive (r ≈ +0.41; p ≈ 0.07, n = 20), contradicting competitive equalisation theory. This cross-sectional correlation is treated as indicative; the panel dimension of the dataset provides the stronger basis for inference. The five tightest labour markets (Netherlands HPI=100, Germany HPI=99, Czech Republic HPI=95, Hungary HPI=88, Estonia HPI=67) record all-sector gaps of 11.4%, 16.8%, 17.5%, 16.9%, and 16.3% respectively — all above the EU27 average of 11.1%. A novel Combined Risk Quadrant, plotting HPI against ERS for all 20 countries, identifies Germany, Czech Republic, Hungary, and Latvia as Priority intervention cases: maximum hiring pressure coexisting with near-maximum equity risk. The Finance sector (EU27 average gap 24.28%) is the highest-risk sector in virtually every country, followed by ICT (19.68%). Construction’s negative EU27 average gap (−3.49%) is a statistical artefact of extreme occupational segregation, not evidence of equality. Apparently low gaps in Italy (3.3%) and Spain (8.9%) reflect positive selection of women into employment rather than genuine pay equity. We present WorkforceGuard, an open-source analytics system that implements these indices over a DuckDB/dbt pipeline ingesting live Eurostat data, with provenance metadata on very output, evidence-bounded LLM analysis, and a SHA-256 hash-chained governance log meeting the Directive’s audit requirements. The system and all data are publicly available.

• Item Type: MPRA Paper
• Language: English
• Keywords: gender pay gap; pay transparency; EU Pay Transparency Directive; labour market tightness; composite indicators; hiring pressure; equity risk; Eurostat; open-source analytics JEL codes: J31, J16, J21, J71, J58, C43, K31
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J21 - Labor Force and Employment, Size, and Structure; J - Labor and Demographic Economics > J3 - Wages, Compensation, and Labor Costs > J31 - Wage Level and Structure ; Wage Differentials; J - Labor and Demographic Economics > J5 - Labor-Management Relations, Trade Unions, and Collective Bargaining > J58 - Public Policy; J - Labor and Demographic Economics > J7 - Labor Discrimination > J71 - Discrimination; K - Law and Economics > K3 - Other Substantive Areas of Law > K31 - Labor Law

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