What works for employment records: International practices and implications for the United States

Executive summary

The modernization of employment records (ERs) has become central to 21st-century labor market policy, economic mobility, and social inclusion. ERs are structured administrative data documenting an individual’s employment history, including who did what, for whom, where, for how long, and with what outcomes. Around the world, peer countries have built integrated, real-time, and worker-centric ER systems that reduce administrative burden and unlock transformative insights for policy and practice.

This report benchmarks a wide range of international practices to inform U.S. stakeholders pursuing modernization efforts. While these initiatives necessarily build on the unemployment insurance wage record system, true modernization requires evolving toward a public-domain, worker-centered employment records infrastructure that serves broader economic and inclusion goals.

The United States maintains a wage records ecosystem primarily centered around state unemployment insurance (UI) systems. This system exhibits fragmentation and inconsistency across states, with limited scope and coverage that often excludes key data fields such as occupation, hours worked, work location, and employment tenure. These limitations impede labor market analysis and restrict the effectiveness of job matching, workforce training, education, and equity-focused interventions. Without occupation codes, for example, states cannot identify regional skills gaps or match training investments to employer needs. Without data on hours worked, policy evaluations of workforce training programs cannot distinguish between a program that lands a participant in a stable full-time job rather than precarious part-time work. The data reporting lag of six to nine months also hinders timely policy responses.

A more modern ER system will become increasingly important as artificial intelligence (AI) spreads through the economy. As routine cognitive tasks become automated through AI, verified experience—documented through comprehensive ERs—will increase in value. Workers who can demonstrate successful adaptation, problem-solving, and skill application across multiple roles and contexts will have significant advantages in an AI-augmented economy.

This report examines global practices in modernizing employment records systems, offering insights that may inform U.S. discussions.¹ Drawing on international cases—including real-time payroll integration models (U.K.’s RTI, Australia’s STP), social security-linked systems (Germany IEB), tax-based employment databases (New Zealand LEED), digital labor registries (Brazil’s eSocial), skills planning systems (South Africa), digital identity-enabled platforms (Estonia, India), and distributed credential frameworks (EU initiatives)—these examples show how modern systems can improve labor market transparency, reduce employer burden, and empower workers through better data. For workers, improved ER systems can streamline access to services, increase control over employment histories, and enhance career and education navigation. For business, they can reduce duplicate reporting and improve workforce planning through more precise, comprehensive, and timely labor market insights.

Although governance models vary, international experience shows that standards, interoperability, and institutional capacity can be achieved through different routes, from strong national coordination (as in Germany or the U.K.) to distributed architectures and trust frameworks (as in the EU).

The path ahead

This report aims to complement and inform important U.S. efforts already underway, including those led by the Jobs and Employment Data Exchange (JEDx), the National Governors Association (NGA), and the National Association of State Workforce Agencies (NASWA), as well as state UI modernization initiatives.

While creating a standalone, public-domain ER platform would eventually require new federal legislation and resources, the most viable short-term path involves concerted state-led innovations supported by voluntary collaboratives and federal technical guidance. Successful international models suggest that incremental adoption of shared data standards and interoperable technology, piloted at the state or regional level, can both generate early wins and provide a scalable template for possible future national infrastructure.

Whether modernization results from state-led efforts or by way of federal legislation, the international examples described in this report elucidate common components of modern ER systems that are key to ensuring broad-based implementation. Across diverse systems, six core practices emerge.

Key findings

• Set standards nationally, implement locally: National technical standards, like those used by Germany’s Federal Employment Agency, ensure consistency while preserving operational autonomy. EU systems show how common frameworks can achieve alignment across jurisdictions.
• Pilot first, improve continuously: Phased rollouts and iterative refinement (such as U.K.’s RTI) help manage complexity and build stakeholder support.
• Build around unique identifiers and protect privacy: Stable, personal, and firm identifiers (India’s Universal Account Number, New Zealand’s tax identifiers) enable interoperability² and integration across systems. EU wallet standards show how interoperability can coexist with selective disclosure and privacy- by-design.³
• Design for timeliness and reuse: Embedding reporting in payroll flows (Australia’s STP, Brazil’s eSocial, Estonia’s Employment Register) improves data timeliness and reduces burden. Verifiable-credential models (EU) allow information to be issued once and reused securely. Reusing records means, for example, that a single employer submission can be used across government agencies.
• Empower workers through access and transparency: Tools like Brazil’s Digital Labor Card enable individuals to verify employment histories, boosting data quality and trust.
• Align incentives, avoid unintended consequences: Policy design must consider behavioral responses, as well-meaning initiatives can create unintended consequences when incentives are misaligned. In Brazil, for example, an initiative intended to formalize self-employment inadvertently allowed employers to reduce labor costs by allowing them to misclassify employees as contractors (see Section 3.1.5.).

These lessons indicate that any approach depends on focused investments in a common architecture, shared data definitions, and the connective infrastructure needed to link data systems. Ultimately, modernizing employment records should enable every worker to securely access, verify, and share their employment history—a public good for an AI-ready economy.

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