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Measurement of the Output of Non-Life Insurance

Measurement of the Output of Non-Life Insurance. Brent R. Moulton. Working Party on National Accounts, OECD Paris October 25–28, 2011. SNA 2008 measure of non-life insurance. Only a portion of insurance premium is a payment for services rendered by the insurance carrier.

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Measurement of the Output of Non-Life Insurance

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  1. Measurement of the Output of Non-Life Insurance Brent R. Moulton Working Party on National Accounts, OECD Paris October 25–28, 2011

  2. SNA 2008 measure of non-life insurance • Only a portion of insurance premium is a payment for services rendered by the insurance carrier. • The other portion is needed for paying claims. • Formula in SNA 1993 (premiums plus premium supplements minus claims) very volatile when a catastrophe occurs • Volatility doesn’t reflect change in services provided • SNA 2008 recommends adjusted claims and adjusted premium supplements

  3. Example • Immediately after September 11, 2001 terrorist attacks on World Trade Center and Pentagon, BEA’s SNA 1993-based treatment showed (not at annual rate): • Imports of services reduced by $11 billion • Household final consumption expenditures reduced by $5.5 billion • GDP (current prices) raised by $5.5 billion • Treated as changes in price, not volume • Household final consumption price index: –0.3 percentage point • GDP price index: +0.2 percentage point

  4. Why adjusted claims? • Because insurance premiums provide for both the production of services and the payment of claims, the production of insurance services must be measured indirectly • While insurance carriers must charge premiums that on average cover the cost of claims and the production of services, there is no requirement that they cover these costs period by period • Ex ante, insurance carriers set premiums on the basis of expectations

  5. Example • SNA 1993 measure: Value of insurance services for years 1, 2, 3, and 4 = 45, 65, 55, 25.

  6. Expectation approach • SNA 2008 treatment: • In calculating insurance services, expected (adjusted) claims will be deducted instead of actual losses. • In the example: • In year 4, if expected claims = 60, then value of insurance services = 100 – 60 + 15 = 55. • Approach was developed by the Australian Bureau of Statistics • Presented at OECD meeting in 1999

  7. Formulas used in U.S. national accounts • Loss ratio is ratio of actual losses (claims) to premiums • LRt = Lt/Pt • Normal loss ratio calculated as a geometric weighted moving average of past loss ratios • NLRt = aLRt + a(1-a)LRt–1 + a(1-a)2LRt–2 …, • The weight, a, is assigned a value of 0.3, based on correlation with future values • Normal losses (adjusted claims) calculated as normal loss ratio times premiums • NLt = NLRtPt

  8. U.S. national accounts (continued) • For major catastrophes, the losses were spread equally over a 20-year period. • Geometric-weighted moving averages were also used for premium supplements. • Calculations were applied to each of 22 lines of non-life insurance. • BEA implemented the new output measure in 2003. • See Moulton and Seskin, Survey of Current Business, June 2003, and Chen and Fixler, SCB, October 2003, available at www.bea.gov.

  9. Household insurance – SNA 1993 method

  10. Household insurance – SNA 2008 method

  11. Current vs. capital transfers • SNA 1993 treated all non-life insurance claims as current transfers (D72) in the secondary distribution of income account • For major catastrophes: • Damages to assets are recorded as other changes in volume of assets. • Recording a large value for claims (without an offset for the assets lost) could distort measures such as disposable income and saving.

  12. Example – Hurricanes Katrina & Rita (2005 Q3)

  13. SNA 2008 treatment • SNA 2008 recommends that claims resulting from a major catastrophe should be recorded capital transfers (17.40) • In the United States, insurance industry sources are able to provide source data on claims related to major catastrophes • BEA implemented the capital transfers treatment in 2009

  14. Challenges • Estimates are more robust for sectors paying insurance claims than for recipient sectors • Lack of counterparty data – claims must be allocated by insurance line • For estimating catastrophic losses of fixed assets, assumptions are needed (e.g., uninsured losses, relationship between claims and losses)

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