Co-employment and the Business Register: Impact and Solutions

1. Co-employment and the Business Register:Impact and Solutions Brandy L. Yarbrough brandy.l.yarbrough@census.gov U.S. Census Bureau

2. Overview • Co-employment-- what and why • BR Operational Framework– a brief primer • Impact on the BR– effect of co-employment • Solutions– leveraging data-sharing • Future Direction– toward operational use

3. What is Co-employment? “Business of Employment” “Business of Business”

4. Why Co-employment? Core Competencies Revenue Better Benefits

5. Co-employment Facts & Figures 1 Average of 19 employees ~$80 billion in revenue > 2 million workers co-employed 1 Data obtained from NAPEO website

6. Co-employment: Key to the BR Impact “Employer of record”

7. BR Operational Framework BR Statistical Unit Universes Employers ~7.5 million units Non-Employers ~22.5 million units MU ~25% SU ~75%

8. BR Statistical Unit Maintenance Employers Non-Employers MU SU Survey Data Tax Data

9. BR Tax Data Tax Data Payroll Tax Files Business Income Tax Files EIN EIN SSN OR

10. Payroll Tax Data: SU “Births” The initial presence of payroll attached to an EIN entity results in the creation of a new SU employer unit

11. Payroll Tax Data: SU “Deaths” SU employer units that do not have IRS payroll tax data under their corresponding EIN for a given reference year are effectively removed from the active employer universe. However, if these SU “deaths” have net receipts for the reference year they are eligible for inclusion in the non-employer universe.

12. Identifying Non-Employers • Selected business income tax entities are the basis of the non-employer universe. • Business income tax paying entities (EINs and SSNs) that: • Are not affiliated with an MU company and… • Do not match an active payroll tax entity and… • Are below the net receipts cutoff for their assigned industrial classification (NAICS)… • …are included in the non-employer universe.

13. Non-employer processing flow

14. Co-employment Impact on the BR:Key Elements • As “employer of record”, PEOs may handle payroll tax reporting for their clients under their own EIN. • As a result, client businesses may drop out of the employer universe… • …or they may get improperly counted as non-employers as the clients still file their own business income tax returns • All of the employees of the client businesses are attached to the PEO. Direct impact on the quality of : ... BR statistical unit coverage & accuracy …employment and payroll aggregates at industrial and geographic levels

15. Objectives • Add PEO clients to the employer universe. • Remove them from the non-employer universe. • Distribute employment to client industries and locations.

16. Solutions • Cost • Burden • Low relative impact • Legal uncertainties • Survey? • Inconclusive • Incomplete • Tax data? • BLS data? • Inter-agency data exchange program • Decentralized U.S. statistical agencies/programs • Memorandum-of-understanding (MOU) needed • Implemented in November 2012

17. The Data Exchange • Unemployment Insurance (UI) programs: • Administered at the state level • Each state has its own laws and regulations • Some states require PEOs to list their clients • BLS obtains these data as part of their Quarterly Census of Employment and Wages (QCEW) program. • The PEO client data were included in the interagency agreement • The data provide: • ~ 100,000 PEO clients covering > 1 million employees • The linkage between PEOs and their clients • Name, address, and industrial classification of the client businesses • The EIN of the client businesses • Employment and payroll data for the client businesses

18. Using the BLS Data: Short-Term • 2011 reference year data provided in November of 2012 • Timing: Good and Bad • Bad: • Resource commitments to the 2012 Economic Census (EC) • No time to develop full-scale operational use • Good: • EC infrastructure for collecting/validating the data • Improve EC data products • Experimental approach: • Top ~1000 clients by payroll size were manually added to the BR • Key identifiers and industrial classification, were derived from the BLS-provided data • Cases were mailed EC questionnaires

19. Results & Observations • ~33% response from PEO clients as of July 2013 • No follow-up or reminder notices • Nearly half reported payroll/employment • The remainder had these values imputed using the 2011 data as the basis • The aggregated EC data are provided below: • Payroll and receipts data are in thousands of $ • Adjustments to the payroll/employment data for PEO businesses will be done manually once EC collection and processing have concluded

20. Conclusions & Future Direction • Small-scale exercise but useful starting point • The BLS PEO client data appears to be of sound quality • Useful for improving statistical unit and program coverage • Limitations: • The Census Bureau does not have access to BLS data from all states. • Not all states require PEOs to list their client businesses. • EC and survey instructions for payroll/employment data should be modified if PEO client businesses are to be canvassed. • Full-scale operational system for integration with the BR • Leveraging other opportunities made possible through the data exchange