Underwriting & Rate Making

Underwriting & Rate Making

Chapter 6


  • Premium computations
  • Medical Loss Ratio under the ACA
  • Underwriting
  • Rate Making
  • Community rating
  • Experience rating
  • Self-insuring
  • Combining dissimilar groups
  • Underwriting and the ACA

Premium Computations & Loading Fees

    • Gross premium = Pure premium/(1-loading percentage)
    • Pure premium is the expected loss
    • Loading percentage is the markup to cover objective risk, profit and costs of marketing, adjudicating and processing claims, coordinating benefits and providing access to networks.


    • Pauly & Percy (2000) estimated that the loading percentage was ~ 10% for group coverage and 50% for non-group coverage


  • Karaca-Mandic et al. (2011) used data from the MEPS-IC and concluded that loading fees were:
  • Similar for firms with <100 workers at ~ 34%
  • Firms with 100 to 10,000 workers had loading fees of about 15%
  • Firms with >10,000 workers had loading fees of about 4%

Loading Fees & Loss Ratios

    • Traditionally insurers have used their own approaches to defining which costs were claims and which belonged in the loading fees.
    • Unless there were state insurance regulations to the contrary, it made little difference, the market was usually concerned with the gross premium


  • The ACA changed this with a requirement for a minimum loss ratio

Medical Loss Ratios in the ACA



(Medical Claims + Quality Enhancing Efforts)

(Premiums – Taxes)

ACA requires individual plans and group plans offered to groups up to 100 workers have an MLR of at least 80%


Larger group plans are to have an MLR of at least 85%


If these thresholds are not met, insurers must rebate premiums


Abraham & Karaca-Mandic (2011) estimate that in nine states at least half of individual insurers would likely fail to meet the 80% threshold if the ACA were then in effect.


  • Identifying the determinants of claims experience
  • Establishing risk pools with known expected losses and minimum variance
  • Matching new members to the appropriate risk pool

Objective Risk

Objective Risk = σ/(μ √N)

= variance/(expected loss times square root of covered lives)

o Risk increases with variance

o Risk decreases with the size of the expected loss

o Risk decreases with the number of covered lives

Rate Making

  • Community Rating
  • Manual Rating
  • Adjusted Community Rating
  • Community Rating by Class
  • Experience Rating
  • Prospective Rating
  • Retrospective Rating

Community and Manual Rating

    • Community Rating
    • All covered lives are in the same risk pool


  • Manual Rating
  • Individuals are placed in risk pools based upon common characteristics
  • Age, gender, location, occupation, industry, etc.
  • Health status

Prospective Experience Rating

    • Based upon prior claims of the group


  • Credibility factor
  • Weight of pool vs. group claims experience


  • Risk borne by the insurer
  • Rate is quoted for the forthcoming period

Retrospective Experience Rating

  • Basic premium covers administration, claims adjudication, and any stoploss features.
  • The firm is charged for each dollar of actual claims.
  • Stoploss features limit a firm’s risk:
  • Aggregate stoploss—If total claims exceed a negotiated dollar maximum, the firm pays no additional cost.
  • Specific stoploss—If a specific claim exceeds a negotiated dollar maximum, the firm pays no additional cost.
  • Typically, the firm makes quarterly payments and pays a “retro” fee at year-end to reconcile quarterly payments with actual experience.

Self-Insured Status and the ACA

  • It is relatively rare for smaller firms to be self-insured due to the objective risk issues we discussed earlier.
  • One can buy stoploss coverage to reduce the risk of unexpectedly high claims costs
  • Many have suggested that smaller firms with low claims costs may buy stoploss coverage as a means of avoiding being mixed in with higher cost firms in the exchanges.

Manual Methods Used by HMOs


    • Adjusted Community Rating


  • Community Rating by Class

Adjusted Community Rating

  • Use the claims experience of the entire pool.
  • Use “contract mix” (i.e., proportion of single, couple, family) within each group.
  • Define “contract size” (i.e., people in family) based upon group’s data.
  • Define “charging ratios” (i.e., ratio of couple and family to single rate) based upon group’s data.

Community Rating by Class

  • Premium typically based upon adjusted community rating factors.
  • Plus:
  • Age and gender mix of the group
  • Industry classification of the group

Effect of State Policies on the Market for Private Nongroup Health Insurance


  • Objective:
  • To identify the effects of state nongroup insurance reforms of the 1990s on the purchase of insurance coverage by “more healthy” and “less healthy” individuals.


Source: LoSasso and Lurie (2009)


    • Between 1993 and 1996, eight states enacted legislation that limited the factors that insurers could use in setting premiums in the nongroup (i.e., individual) insurance market.


    • These states also had “guaranteed issue” provisions.


  • Kentucky, Massachusetts, Maine, New Hampshire, New York, New Jersey, Vermont, and Washington

Enacted “Community Rating”

  • The states all disallowed the use of health status or medical underwriting.
  • New Jersey: Pure community rating
  • New York: Geographic differentials allowed
  • Vermont: +/– 20 percent for demographics (except Blue Cross and HMOs)
  • New Hampshire: 3 to 1 ratio for age
  • Others: age, geography, family composition, gender

Data and Methods

  • Survey of Income and Program Participation (SIPP)
  • Nationally representative panel surveys of individuals
  • 1990 to 2000
  • Those age 18 to 64 in 41 identifiable states
  • Approximately 35,750 observations per year
  • Probit regression:

Non-group Coverage = f(law, individual characteristics, state, and year fixed effects)

Results for “Healthy”

  • “Healthy” are men age 22 to 35 with self-reported health status of excellent or very good.
  • Adoption of community rating resulted in:
  • 2.0 percent decline in nongroup coverage
  • 37 percent decline relative to the mean
  • 3.9 percent increase in probability of being uninsured
  • 13 percent increase relative to the mean

Results for “Unhealthy”

  • “Unhealthy” are all those age 40 to 64 with self-reported health status of poor
  • Adoption of community rating resulted in:
  • 4.5 percent increase in nongroup coverage
  • 50 percent increase relative to the mean
  • 7.4 percent decline in probability of being uninsured
  • 50 percent decrease relative to the mean


  • Community rating had the effect of lumping dissimilar risks into the same pool
  • Raised the premium for low-risk folks relative to what they used to pay
  • Some of whom now dropped out of the health plan
  • Lowered the premium for high-risk folks relative to what they used to pay
  • Some of whom now joined the health plan

Underwriting in the ACA

    • Under the ACA insurers may not use health status in establishing rates


    • The may not use gender


  • Age rating is limited to a 1 to 3 band

Society of Actuaries Study

  • MEPS data on use and cost of services


  • Suppose there are an equal number of men and women in each age group.


Moving the ACA from 1 to 3 rating band to 1 to 5

    • Suppose the age range of health care spending is closer to 1 to 5 rather than 1 to 3


    • The ACA 1 to 3 rule raises premiums to young adults and encourages them to forgo coverage


  • What would happen to premiums if the rule was changed to allow a 1 to 5 band?

Discussion Questions

    • Suppose your insurance competitor begins to use information from genetic testing to set insurance rates in the nongroup market. Suppose further that these tests do identify meaningful differences in claims experience. You choose not to implement such a model. Discuss what is likely to happen to your enrollee mix and to the premiums you charge. Suppose federal law prevents the use of generic data to set rates (as the Genetic Information Nondisclosure Act of 2008 does). Does this mean that as an insurer you can ignore the effects of generics data?











Discussion Questions

  • Those who frequently watch late-night TV have undoubtedly seen commercials that say “no health questions.” In what way is this phrase relevant to the purchase of insurance? Who is likely to be attracted to the policies being offered? What do you expect about the premiums of these policies relative to policies that do ask health questions?

Discussion Questions

  • Suppose your small, general medicine physician group is offered a capitated managed care contract. Through this contract, the group will be paid on a capitated basis. This means that the group will be responsible for all of the costs of each patient’s care. What does the concept of objective risk tell you about the desirability of this contract?

Discussion Questions

  • Terhune (2002) described “reunderwriting” in the nongroup market. In this process, individuals are reclassified into a higher-risk group once they have a significant illness or claim. Discuss the effects of such a model on premiums and enrollment. If healthcare claims were essentially random over time in each of an insurer’s established risk pools, how would this affect your conclusions?

Discussion Questions

  • Given the discussion of self-insured health insurance plans, under what conditions would you expect a small employer to become self-insured?

Discussion Questions

  • The available data suggest that experience rating (together with self-insurance) is by far the most common underwriting method used for large employers. The avoidance of state insurance regulations and premium taxes may explain why firms tend to self-insure, but why do you think experience-rated approaches are more common than manual rating?

Discussion Questions

The ACA prohibits medical underwriting in the non-group market. Other things equal, what effect will this prohibition have on the premiums of healthy and unhealthy enrollees? Who are likely to be the healthy? The unhealthy? The ACA also prohibits gender differences in premiums in the exchanges. What effect is this likely to have on premiums?

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