Pension Expense (both GAAP & IFRS) for the Income Statement

Pension Expense = Increase in the DBO/PBO during the accounting period.

Five Components of Company Pension Expense

  1. Current Service Cost = Amount by which a company’s defined benefit obligation increases as a result of employee service during the accounting period.  The current service cost is fully and immediately recognized for the accounting period.
  2. Interest Cost (same as the discount rate discussed later) = Amount by which a company’s existing defined benefit obligation increases as a result of the passage of time.  The interest cost is fully and immediately recognized for the accounting period.
  3. Return on Plan Assets = Amount of returns generated by plan assets during the accounting period.  Typically, companies apply EXPECTED return on plan assets when calculating pension expense.  Long-term expected return will better reflect the plan’s investment strategy and reduce year to year volatility in the pension expense.  The use of expected returns is allowed by GAAP and IFRS.  Since this is an asset return, the return on plan assets component acts as a contra expense, offsetting other costs.
  4. Amortization of Past Service Cost = The difference in the DBO after a plan amendment has been adopted and the DBO before the plan amendment.  The plan amendment could reduce costs, creating a benefit that reduces the pension expense.
  • GAAP: This is recorded as a direct to equity adjustment outside of net income, as part of other comprehensive income for the accounting period in which the amendment took place.  A periodic past service cost expense is then amortized to the pension expense over the remaining service lives of the employees covered by the amendment.
  • IFRS: If the amendment affects any vested obligations, then the vested percentage of the past service cost is incorporated into the pension expense for the accounting period of the amendment and the remaining past service cost for unvested obligations is amortized to future pension expense calculations over the course of the related vesting period.

5Amortization of Actuarial Gains and Losses.

  • Actuarial gains and losses arise from:

  • Differences between expected plan returns and actual plan returns (see #2 of 5); and

  • Changes in actuarial assumptions that impact the current service cost (see #1 of 5). Examples: employee life expectancy, salary growth forecasts, interest cost component assumptions, retirement dates, etc.

  • GAAP: Actuarial gains and losses are recognized as part of other comprehensive income during the period of gain or loss, on the company’s statement of changes in shareholder’s equity.

  • IFRS: Actuarial gains and losses do not flow to equity, but are applied to assets or liabilities and are incorporated in the calculation of a net asset or liability on the balance sheet.  A net pension asset is reported as pre-paid pension expense; a net liability is accrued pension expense.

  • 10% Amortization Expense “Rule” – Companies will not begin to incorporate an amortization gain/loss into its calculation of pension expense until the gain/loss from asset return differences or the benefit/cost from changes to the plan exceeds the greater of 10% of the value of plan assets or 10% of the DBO.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.