Modernizing U.S. Labor Statistics: A Strategic Analysis of Real-Time Payroll Data Integration

Executive Summary

The U.S. economy, characterized by rapid technological change and cyclical volatility, requires timely and precise labor market data for effective decision-making. The current primary sources for federal labor statistics, such as the Bureau of Labor Statistics (BLS) monthly reports, are based on traditional survey methodologies that are inherently slow and prone to significant revisions. These limitations create a critical challenge for policymakers, businesses, and researchers, who often make decisions based on a delayed and uncertain view of the economy.

This report conducts a detailed analysis of two core policy proposals for modernizing labor data collection—a mandatory API linking system and a Treasury-led tracking system—and presents a third, more pragmatic and politically viable, hybrid approach. The analysis is grounded in an examination of existing BLS and private-sector payroll systems, drawing on precedents from other federal agencies and analogous real-time data models.

A central finding is that the technical infrastructure to overcome these data lags already exists within the private-sector payroll technology ecosystem. The Social Security Administration’s (SSA) successful implementation of a data exchange program under the Bipartisan Budget Act of 2015 provides a crucial legal and technical precedent. Simultaneously, private payroll APIs from providers like Check, Finch, and ADP demonstrate the technical maturity of real-time data streaming and a growing consensus around standardized data models.

The primary recommendation of this report is to pursue a phased, hybrid implementation strategy. This approach would leverage existing legal precedents and private-sector capabilities while establishing robust governance structures to safeguard privacy and ensure the institutional independence of the BLS. The initial steps would involve the creation of a public-private consortium to define open data standards and the launch of incentivized pilot programs, ultimately leading to a scalable and trustworthy national system for real-time labor statistics.

1. The Current Landscape of U.S. Labor Statistics

1.1. BLS Data Collection and Its Methodological Framework

The Bureau of Labor Statistics (BLS) employs a multi-faceted approach to track the health of the U.S. labor market, primarily through three key programs. The first, and most prominent, is the Current Employment Statistics (CES) program. The CES is an establishment payroll survey of approximately 121,000 businesses and government agencies, covering about 631,000 individual worksites across the nation. It serves as the primary source for monthly employment estimates, as well as data on hours and earnings.¹ The CES program is split into two components: CES National (CES-N), which produces estimates for the entire nation, and CES State and Area (CES-SA), which provides data for states and metropolitan areas. It is important to note that the national estimates are produced independently and are not an aggregation of state-level data.¹ The CES survey is a key economic indicator, with its data being used by a wide range of stakeholders, from the Federal Reserve to college students studying economic concepts like business cycles and productivity.¹ The CES survey counts jobs, which is a different measure than the Current Population Survey (CPS), a household survey that counts people; a worker with two jobs would be counted twice by the CES but only once by the CPS.¹

The second foundational program is the Quarterly Census of Employment and Wages (QCEW), which is based on a universe of Unemployment Insurance (UI) tax accounts covering roughly 11.8 million establishments.² The QCEW is a comprehensive data source that is crucial to the BLS’s methodology. Each year, the CES-National estimates are “benchmarked” to the most recent QCEW data, along with a small amount of employment data not covered by QCEW. This benchmarking process is a standard part of any sample-based survey and is designed to align the CES estimates more closely with the actual population totals.²

Finally, the Employment Cost Index (ECI) measures the change in the hourly labor cost for employers over time. The ECI uses a fixed “basket” of labor to produce a “pure cost change” that is free from the effects of workers shifting between occupations and industries. It includes both wages and salaries as well as the cost of benefits.³ Based on a probability sample of roughly 6,200 private industry establishments and 1,400 state and local government establishments, the ECI is used by private-sector professionals for making pay adjustments and by public-sector entities like the Federal Reserve to gauge the health of the labor market.³

1.2. The Inherent Limitations of Existing Surveys

Despite its rigor, the BLS’s survey-based approach suffers from significant, well-documented limitations that compromise the timeliness and accuracy of its initial reports. The most critical issue is the inherent time lag of the data. Economic data produced by the government is often a lagging indicator, with survey data for the monthly jobs report being collected and analyzed several weeks before its release. The JOLTS report, for example, lags by more than a month.⁵ This delay means that economic policy is frequently based on a backward-looking view of the market, a problem that is exacerbated during periods of economic volatility.⁵

This issue is compounded by a pronounced decline in survey response rates, which has made the initial data less reliable. While the response rate for the CES survey was approximately 60% in the decade before the pandemic, it has since dropped to less than 45%.⁶ This decline means that the preliminary estimates, which are based on this incomplete data, are now subject to greater uncertainty and larger revisions.⁶

The revision process itself, while a necessary part of the BLS’s methodology to ensure accuracy over time, is a primary source of data uncertainty. The preliminary monthly figures are revised twice in the succeeding two months to incorporate late survey responses and to adjust seasonal factors.⁷ These revisions can be substantial, as demonstrated by the May and June 2025 nonfarm payroll reports. The change in total nonfarm payroll employment for May was revised down by 125,000 jobs, from an initial report of +144,000 to +19,000. Similarly, the June figure was revised down by 133,000 jobs, from +147,000 to +14,000. These revisions resulted in a combined 258,000 fewer jobs for those two months than initially reported.⁷ Annual benchmark revisions, which anchor the estimates to the near-complete QCEW data, can also reveal significant discrepancies. A past benchmark revision found 818,000 fewer jobs than initially reported over a 12-month period, a number that was itself later revised.⁷

The combination of time lags, declining response rates, and substantial revisions creates a self-reinforcing cycle of data uncertainty. This uncertainty can be exploited to undermine trust in federal statistical agencies. When politically inconvenient data is released or revised, as was the case with the weak jobs numbers and the subsequent firing of the BLS Commissioner, the integrity of these institutions is challenged.⁷ The perception that data can be manipulated for political reasons damages public trust and can lead to a less data-driven approach to policy. A system that can provide more timely and accurate initial estimates, less prone to massive revisions, would provide a powerful institutional defense against such attacks and restore confidence in a cornerstone of economic measurement.⁹

1.3. Precedent for Administrative Data Integration

The federal government is not without a model for integrating administrative data from private sources. The Social Security Administration (SSA) has successfully implemented a program that serves as a critical proof-of-concept for the BLS initiative. Under Section 824 of the Bipartisan Budget Act of 2015, the Commissioner of Social Security was granted the authority to enter into information exchanges with payroll data providers.¹¹ This authority, a result of a new legislative act, enabled the creation of the Payroll Information Exchange (PIE) program.

The PIE is an automated, voluntary system where individuals can provide authorization for the SSA to access their wage and employment information from participating payroll data providers.¹³ The SSA’s agreement with a selected provider, such as Equifax, outlines the records to be matched, the procedures for the match, and requirements for data accuracy and security.¹¹ The primary goal of this program is to obtain more timely and complete earnings data to help the SSA administer its programs more efficiently, reduce improper payments, and improve the customer experience.¹¹ Individuals who authorize this data exchange have their own wage reporting responsibilities reduced and are protected from certain penalties for any omissions or errors in the data provided by the payroll provider.¹¹

This program is more than an isolated example of administrative data use; it is a critical precedent that demonstrates the technical and legal feasibility of a government-mandated data integration with the private sector for a sensitive domain like payroll. The SSA’s experience shows that a legal and technical template already exists for a federal agency to successfully partner with payroll providers. This means that an initiative to modernize BLS data collection would not be starting from scratch; it could leverage this existing framework, adapting it to its specific statistical needs. This precedent transforms the policy challenge from a question of “is this possible?” to “how can we scale this proven model?” for the benefit of a broader national purpose.

2. Private-Sector Data Infrastructure and Analogous Models

2.1. The Evolving Ecosystem of Payroll APIs

The private sector has already developed the technical infrastructure necessary to facilitate real-time payroll data exchange. This ecosystem is broadly categorized into two types of APIs, each serving a distinct purpose.

The first type is the Payroll-as-a-Service (PaaS) API, pioneered by companies like Check.¹⁵ These APIs are designed for developers to build and embed payroll products directly into their applications. A PaaS API handles the complex, back-end mechanics of payroll, including calculating taxes for over 13,000 jurisdictions, moving money, and filing tax documentation.¹⁵ This allows a software platform to offer payroll as a seamless, integrated product without having to build the intricate compliance and financial infrastructure from the ground up.¹⁵ A platform using Check’s API, for example, can create a payroll, add employee earnings and hours, and then preview and approve the payroll, with the API handling all the subsequent calculations and filings.¹⁶

The second type of API, the Payroll Data API, is exemplified by companies like Finch. Rather than processing payroll, these APIs are designed to read and write data from a wide network of existing payroll and HR systems, such as ADP and UKG.¹⁵ Finch, for instance, offers a “unified API” that standardizes employment data from over 200 different providers into a single, consistent data model.¹⁷ This functionality allows a company to access real-time data for purposes such as compliance reporting, benefits administration, or financial services, without needing to build and maintain individual integrations for each payroll provider.¹⁷ This API model provides a direct technical blueprint for how a government agency could securely and efficiently access a vast, fragmented ecosystem of payroll data.

Even legacy payroll giants like ADP have developed robust RESTful APIs for developers.²⁰ These APIs use an event-based pattern for resource management, allowing for both the retrieval of resources (e.g., worker data) and the notification of changes.²⁰ ADP’s API Central platform leverages OpenID Connect and OAuth 2.0 for authentication and authorization, ensuring that only authorized users and systems can access data.²¹ Developers can use query parameters like

$filter to retrieve specific data subsets, such as workers with a certain status, and $select to include only specific properties, such as a worker’s legal name.²² The widespread adoption of these technical standards, from startups to legacy providers, demonstrates the industry’s readiness for a more integrated and automated data exchange model.

2.2. A Technical Overview of API Schemas and Data Streams

The private sector’s success in real-time data exchange is not just about the APIs themselves but also the underlying technical standards they employ. Data is typically delivered in structured, platform-agnostic formats like JSON.²³ While a single, universal data schema for payroll does not currently exist across the entire industry, the presence of common data models for

Company, Directory, Individual, Employment, and Payment in APIs like Finch’s suggests a de facto consensus on what constitutes essential payroll information.¹⁷

The use of technologies like WebSockets allows for the real-time streaming of data, a practice common in financial markets to deliver prices, trade information, and market statistics directly from the source to a user’s system.²³ A government-led initiative for real-time labor statistics would need to adopt a similar approach to ensure data timeliness.

2.3. Lessons from Real-Time LMI via Job Postings

The effort to collect real-time labor market information (RT-LMI) from online job postings provides a cautionary tale and a critical lesson. This approach, which involves using automated web crawling to extract data, offers the benefits of timeliness and relatively low cost.²⁴ However, it also suffers from significant weaknesses, including data scraping errors, inconsistent formats, job ad duplication, and a general lack of standardization. The raw data is often “noisy” and requires extensive cleansing and normalization to be useful.²⁴

The experience with job-posting data highlights a fundamental principle: the value of real-time data is only as good as its quality and standardization. Consequently, the first and most critical step for a real-time payroll data initiative is not the construction of a new government API but the collaborative definition of a universal, open Payroll Data Schema. Efforts in the job-posting space, such as the JDX JobSchema+ and the HR Open Standards Consortium, have already demonstrated the value of this approach.²⁷ These initiatives establish a standardized framework for data organization, ensuring consistency and clarity across different platforms and applications.²⁸

A government-led system would face the same challenges of a fragmented industry with different data models. Building a data collection platform without first defining a universal standard would lead to a costly, unwieldy system burdened with constant data cleansing and normalization tasks. Therefore, the foundational step for any successful implementation must be the creation of a public-private consortium to define a standardized Payroll Data Schema that all providers can adopt. This schema would specify the required data fields, formats (e.g., JSON), and validation rules, ensuring data quality at the source and maximizing the statistical utility of the information.

3. Policy and Architectural Proposals for Real-Time Data

3.1. Proposal A: A Mandatory Payroll Data API Linking System

One policy proposal is to enact a legislative mandate requiring all payroll processors and employers to stream payroll data in real time to a centralized government platform, such as the BLS or Treasury. Under this model, companies would be compelled to integrate their systems with a secure government API, using standardized API schemas and secure data pipelines. The API would be designed using modern protocols, such as OAuth 2.0 and OpenID Connect for authentication, and deliver data in a structured format like JSON via real-time streaming technologies.²¹ This approach promises the most comprehensive and timely data coverage, but it also presents significant political, legal, and logistical challenges. It would require a powerful legislative act and place a substantial compliance burden on private companies, particularly small businesses. The implementation would be a multi-year effort requiring a significant budget and a new governance body to manage the platform and enforce compliance.

3.2. Proposal B: A Treasury-Led Payroll Tracking System

A second proposal involves leveraging the existing authority and infrastructure of the U.S. Treasury. Under this model, payroll data would be routed through Treasury or IRS systems, which already handle tax filings. Payroll providers could submit data via an API using unique, anonymized employee identifiers, and the Treasury would then aggregate and anonymize the data before relaying it to the BLS for statistical analysis.²⁹ The Treasury’s HRConnect system, which already has a robust bi-directional interface with the National Finance Center (NFC) for processing federal employee payroll, provides a precedent for this kind of internal government data exchange.²⁹

While this model appears to leverage existing infrastructure, it introduces a profound institutional risk. A Treasury-led system would place the collection and initial aggregation of a primary economic indicator under the control of a politically sensitive executive department. The BLS, by its design, is an independent and non-partisan agency responsible for producing objective data on jobs, wages, and prices.¹⁰ Recent events, such as the firing of the BLS Commissioner following the release of weak jobs numbers, have shown how easily this independence can be challenged when data is politically inconvenient.⁹ Placing the raw data stream under the control of the Treasury or IRS, even with a plan to share aggregated data with the BLS, creates the potential for political influence, data suppression, or the perception of such influence. This institutional compromise could damage the credibility of federal data and “destroy trust in core American institutions” ⁹, a risk that far outweighs the potential efficiency gains. A successful model for modernizing labor statistics must prioritize the institutional integrity and independence of the BLS above all else.

3.3. Proposal C: A Hybrid, Opt-in Architecture with Strong Incentives

This report proposes a third, hybrid approach that is the most pragmatic and politically viable. This model would be a voluntary, incentive-based system where private payroll providers could opt-in to a secure government-sponsored API, potentially hosted on a platform like api.data.gov.³⁰ This API would be built to an open, standardized schema developed by a public-private consortium.

This approach avoids the political hurdles of a mandatory system and the institutional risks of a Treasury-led one. It leverages the private sector’s existing technical capabilities and a growing industry-wide enthusiasm for streamlined integrations.¹⁵ The primary challenge would be achieving comprehensive data coverage without a mandate, which would require significant incentives. The SSA’s PIE program provides a blueprint for these incentives, where participating businesses receive reduced reporting requirements.¹¹

The governance of this system would be shared but with a clear separation of duties. A public-private consortium, potentially funded by Congress, would be responsible for defining the data schema and API standards. The BLS would manage the data lake and the public-facing API, ensuring that it remains the sole authority for data aggregation, analysis, and publication, thus preserving its independence.

4. Critical Considerations: Legal, Privacy, and Institutional

4.1. The Legal Authority and Framework

The implementation of a real-time labor statistics system would require specific legal authority from Congress. The most viable legal path is to expand upon the precedent set by the SSA’s program under Section 824 of the Bipartisan Budget Act of 2015.¹¹ This act authorized the SSA Commissioner to enter into data exchanges with payroll providers. A new legislative act would be needed to grant similar authority to the BLS, specifying the agency’s ability to engage in these exchanges for the purpose of producing labor statistics. The legislation would also need to clearly define employer obligations, compliance timelines, and potential exemptions for small businesses to avoid placing an undue burden on the private sector.

4.2. Privacy, Data Protection, and Anonymization

Any system that collects and processes payroll data must adhere to the highest standards of privacy and data protection. The Department of the Treasury defines personally identifiable information (PII) as any information that is “linked” or “linkable” to a particular individual.³² The foundational principle for this initiative must be

data minimization, where the BLS only receives the minimal data necessary for statistical purposes, not raw, personally identifiable records. The system’s design must be guided by this principle, ensuring that individual identities are safeguarded at every step.

A single anonymization technique is insufficient for the diverse nature of payroll data. A multi-layered strategy is required, where the choice of technique is carefully matched to the specific data element to maximize privacy protection without sacrificing statistical utility. The research material details a number of relevant techniques.³⁴ For example,

pseudonymization could be used for direct identifiers like Social Security numbers or names, replacing them with unique, non-reversible tokens. This allows for the tracking of a single employee’s data over time without revealing their identity. Generalization could be applied to geographic data, replacing a street address with a more general location, such as a zip code or a Census block. This protects individual privacy while retaining valuable geographic context for statistical analysis. Finally, for highly sensitive numerical data like wages or hours, data perturbation could be used. This involves adding a small, controlled amount of random noise to the data, protecting individual privacy while preserving the overall statistical distribution and trends. This multi-layered approach demonstrates a sophisticated understanding of both data privacy and statistical requirements, ensuring the final output is robust, credible, and worthy of public trust.

4.3. Ensuring Institutional Independence and Trust

The political independence of federal statistical agencies is paramount. As the ITEP report highlights, the chronic underfunding of these agencies and recent incidents of political interference, such as the firing of the BLS Commissioner, are part of a broader erosion of the federal data infrastructure.⁹ A new system must be designed to insulate the BLS from such pressures.

To achieve this, the report proposes establishing legal firewalls, clear memoranda of understanding (MOUs), and a politically independent governance body, such as a Board of Statistical Oversight, to oversee the real-time data system. This body would be responsible for defining data standards and access protocols. It would also ensure that the BLS remains the sole authority for data aggregation, analysis, and publication, preserving its institutional integrity and credibility.

5. Implementation Roadmap, Costs, and Benefits

5.1. Cost-Benefit Analysis

The costs of implementing a real-time data system would be significant for both the government and the private sector. The government would need to invest in building and securing a new API platform, establishing a data lake, and funding the public-private consortium. The private sector, particularly payroll providers, would face one-time costs for developing and maintaining the API integrations and ongoing costs for data transmission.

However, the benefits would far outweigh these costs. Real-time data would provide policymakers, including the Federal Reserve, with a more precise and timely understanding of the labor market, leading to better-informed monetary policy decisions.⁵ It would enable more responsive fiscal policy and better-targeted labor market interventions during economic crises. By reducing the “noise” and uncertainty of the current system, it would allow businesses to make more strategic decisions about hiring and investment. Ultimately, the move to a real-time data system would foster a more resilient and dynamic economy by providing a truer reflection of its state.

5.2. A Phased Implementation Strategy

A phased approach is the most prudent path to implementation.

• Phase I: Legal Foundation and Consortium. The initial step is to secure the necessary legislative authority, drawing on the SSA’s Section 824 as a model.¹¹ Concurrently, a public-private consortium should be established, funded by Congress, to define the open
  Payroll Data Schema and API standards. This ensures that the technical foundation is in place when the legal authority is granted.
• Phase II: Pilot Programs. Launch voluntary pilot programs with a select group of large payroll providers (e.g., ADP, Paychex) and major employers. These pilots would test the data collection, aggregation, and anonymization processes in a controlled environment, similar to the SSA’s partnership with Equifax for the PIE program.¹³
• Phase III: Incentivized Scaling. Based on the pilot’s success, a nationwide system would be rolled out. Incentives, such as reduced reporting requirements for participating businesses, would be offered to encourage widespread adoption, eventually leading to near-universal coverage.

5.3. Validation and Quality Control Protocols

A real-time data stream would require a new approach to validation and quality control. The system would need robust, continuous validation checks for data completeness, accuracy, and consistency.¹⁹ To ensure the data’s integrity, the real-time stream would be regularly reconciled against the comprehensive QCEW data, which would continue to serve as the benchmark.² This reconciliation process would allow for timely, preliminary estimates that are far less prone to the massive revisions that plague the current system.² Finally, the system would need a robust mechanism for handling data stream failures from private providers and maintaining a secure, immutable audit trail of all data transmissions to ensure transparency and accountability.

Conclusion and Recommendations

The U.S. economy’s reliance on a survey-based system for labor statistics has created a critical vulnerability, characterized by significant time lags, data uncertainty, and a susceptibility to political interference. The analysis in this report demonstrates that the technical solutions to this problem are not theoretical; they are already mature and operational within the private-sector payroll and API ecosystem. Furthermore, a successful legal precedent for public-private data integration has been established by the Social Security Administration.

This report concludes that the most effective and responsible path forward is the adoption of a hybrid, opt-in policy and architectural proposal. This approach mitigates the political risks of a mandatory system and the institutional compromise of a Treasury-led system while leveraging private-sector innovation to create a more resilient and trustworthy federal data infrastructure.

The following immediate next steps are recommended:

1. Legal Action: Congress, in partnership with the BLS, should initiate a legal review of the SSA’s Section 824 and draft new legislation to expand similar authority to the BLS. This legislation should specifically authorize the agency to engage in data exchanges with private payroll providers for statistical purposes, with clear mandates for privacy and data security.
2. Consortium Establishment: The BLS should convene a public-private consortium comprising leading payroll technology providers and data privacy experts. The immediate task of this consortium would be to collaboratively define a universal, open Payroll Data Schema and API standards that will form the technical foundation of a new system. This crucial step ensures that the government is prepared to build a system that is interoperable and robust from day one, avoiding the data quality and standardization issues that plague other real-time data efforts.

By taking these steps, the nation can transition from a backward-looking, uncertain data model to a forward-looking, real-time system that provides a more accurate and responsive picture of the American labor market.

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