...

The United States Enters the Payroll Complexity Global Top Ten 2025 in Strada’s Global Payroll Complexity Index

Global Payroll Complexity Index

Strada, a leader in international workforce management, has released its 2025 Global Payroll Complexity Index (GPCI). The seventh edition of this benchmark reveals that global payroll complexity increased by 5% on average. Consequently, the report highlights a major shift in North America.

The United States Joins the Top Ten

Therefore, the United States has entered the payroll complexity global top ten 2025 for the first time, securing the 6th position globally. This follows a substantial 17% rise in complexity since 2023. The root of this complexity lies in the widening variation within long-established state and jurisdictional frameworks. Managing compliance across 51 jurisdictions has become a constant challenge for US employers. The difficulty is further compounded by the growth of hybrid and remote work. This makes accurate multi-state tax compliance increasingly challenging.

The top ten most complex US states for payroll in 2025 include:

  • California
  • New York
  • Massachusetts
  • Oregon
  • New Jersey
  • Connecticut
  • Washington
  • Delaware
  • District of Columbia
  • Rhode Island

Besides​‍​‌‍​‍‌​‍​‌‍​‍‌ that, California and New York are still the most complicated ones, mainly due to the intricately structured wage rules and the combination of city/state taxes. The states of Massachusetts and Oregon added to the complexity due to their spread of new paid-leave programs and high-earner surtaxes. Kristi Jones, SVP at Strada, emphasized that automation should be combined with thorough payroll knowledge to achieve a clear understanding of the complexity. In short, the GPCI confirms the growing burden on US employers as the country officially secures its position in the global top ten by 2025.

Explore HR Tech News for the latest advancements in Human Resources Technologies and insightful updates from industry experts!

News Source: Businesswire.com