Knowledge and Ignorance in a Secondary Insurance Market

1. Knowledge and Ignorance in a Secondary Insurance Market Jay Bhattacharya Stanford University September 2008

2. Knowledge Aggregation in Markets • Many economists have stressed the ability of markets to aggregate local knowledge. • e.g. Hayek’s famous AER essay • Recent interest in ability of markets to predict the future: • Political betting markets • Terrorism insurance markets • Life insurance markets (e.g. Mullin and Philipson)

3. Can Decentralized Knowledge Fail? • The behavioral economics literature emphasizes misperceptions and cognitive errors. • There is limited evidence (except perhaps savings behavior) whether such errors are important in real market settings with large stakes. • What if getting prices right depends upon knowledge that no one has?

4. Financial Times 9/8/08 • “United Airlines temporarily lost most of its market value on Monday after a false report the carrier had returned to bankruptcy court surfaced on the internet.” • “A six-year-old Chicago Tribune story on United’s 2002 bankruptcy filing – spotted on a Google search by an investment newsletter – triggered a sell-off of the carrier’s shares that ended when trading was halted. The stock reached a low of $3, then rebounded once trading resumed to close down 11 per cent.” • “Investors accepted the article as news that the Chicago-based airline had once again sought protection from creditors, a scenario that had grown more feasible in the past year as jet fuel prices skyrocketed.”

5. Research Aims • Develop evidence from the secondary life insurance market on: • The extent to which market participants have mistaken perceptions regarding their own mortality risks. • The extent to which the market anticipates medical technological breakthroughs.

6. Why Secondary Life Insurance Markets? • This market is a good setting to test for the presence of cognitive errors. • It requires participants to make complicated evaluations involving their own mortality. • This market is a good setting to test for whether markets are good at predicting the future. • Firms need to know whether technological advances will turn a good deal sour.

7. Background on the Secondary Life Insurance Market

8. The Secondary Life Insurance Market • The basic transaction: • “Cash out” a life insurance policy before death. • The buyer of the policy (typically a 3rd party or the life insurance firm itself) becomes the beneficiary. • Variations on the market: • Viatical settlements market: the market arose in the late 1980s in response to the AIDS epidemic. • Life settlements: transactions are similar to the viatical settlement market, except for the patient population consists of the chronically ill. • Accelerated death benefits: the life insurance company itself becomes the beneficiary.

9. Tracking the Viatical Settlement Market • Thirty-eight states regulate transactions in the viatical settlement market in some form. • Several states require any viatical settlement firms doing business in the state to report on all transactions nationwide. • Through FOIA requests, we have collected all available information on viatical settlement transactions from state agencies in California, Connecticut, Kentucky, NY, Texas, North Carolina, and Oregon. • Because nearly all large firms sell in those states, we have data on (nearly) the universe of VS transactions from 1995 to 2001. • We have done a lot of work to cull out duplicate entries.

10. Breakthroughs in Treatment of HIV • Protease Inhibitors introduced in late 1995 • Protease Inhibitors combined with other ARVs (HAART) have been shown to reduce mortality in: • Clinical trials (Hammer et al., 1997; Staszewski et al., 1999 ) • Observational studies (Detels et al., 1998; Palella et al., 1998; Lucas, Chaisson, and Moore, 1999; Vittinghoff et al., 1999; Lucas, Chaisson, and Moore, 2003 )

11. Death rates declined initially but reached a plateau in 1998 Source: Centers for Disease Control

12. Average Life Expectancy of Viators from 1995-2001

13. Nominal Price of a Viatical Settlement, by Life Expectancy and Year

14. Size of Viatical Settlement Market 1995-2001

15. Secondary Life Insurance Market Grew in the 90s Size of Secondary Life Insurance Market $1000 million New HIV Treatments Introduced $500 million $50 million

16. Life Insurance Companies Offering ADB products Total Life Insurance in Force in 1998 $13.2 trillion Total held by companies offering ADB $10.3 trillion Secondary Life Insurance Markets are Expanding beyond HIV

17. Evidence of Mistaken Consumer Perceptions

18. Explaining the Empirical Patterns of Viatication • Two models to explain who sells their life insurance policy. • A model where sellers correctly perceive their mortality risk • A model of mistaken mortality risk (MMR) • The latter model is motivated by evidence from the HRS that suggests that: • Individuals early in the course of a chronic disease are more pessimistic about their probability of death than warranted • Individuals late in the course of a chronic disease are more optimistic than warranted.

19. A Vanilla Model with Correct Mortality Predictions • People maximize discounted expected utility (including utility from bequests). • Assets include: • (Exogenous) income in each time period • A non-liquid asset that can be used to secure a loan (such as a house) • Zero premium life insurance note that pays off at death. • Income can be moved around different times and states by borrowing/lending against the house and by selling/viaticating the life insurance policy.

20. Why Treat Actuarially Fair Life Insurance as Valuable Asset? • The unit price of life insurance depends on health status at the time of purchase. • For patients who suffer unexpected health shocks, the actuarially fair unit price of life insurance exceeds the original unit price. • Thus, unexpected health shocks generate a valuable new asset for the chronically ill with life insurance.

21. Trade-offs in Cashing Out Life Insurance • Patients have three options to finance current consumption: • Spend liquid assets. • Borrow against non-liquid assets such as housing—i.e. credit market. • Viaticate. • All of these potentially reduce bequests.

22. Complete Markets in This Context • Viatical settlements and credit markets are complementary in distributing income across time and across different states of the world (uncertain time of death). • Given an arbitrary initial allocation of income in time and in mortality-state space, it is impossible to replicate the time-pattern of consumption achievable with viatical settlements and credit markets combined using only one of these instruments. • Actually, in this setting, any mortality contingent commodity combined with any certain credit note will complete the market.

23. Mortality Risk and Prices in the Vanilla Model • Given a mortality risk profile, the expected net present value of the stream of returns from purchasing a viatical settlement must equal the n.p.v. of secured lending. • This is true regardless of the mortality risk of the policy holder. • Healthier patients receive higher discount to the face value of life insurance since they are more likely to die later. • This does not mean that changes in mortality risk profiles leave unchanged the incentive to viaticate rather than borrow.

24. Vanilla Comparative Statics • In the simplest versions of this model: • Relative to healthy consumers, unhealthy consumers are more likely to sell life insurance • Healthy and unhealthy consumers with more non-liquid assets are more likely to viaticate. • Both of these comparative statics are driven by wealth effects. • Increased mortality risk, increases the equity in life insurance holdings. • Unless the consumer’s portfolio is reorganized, all of the increase in wealth would go to increased bequests. • Increased wealth lead to increased consumption, which increases both optimal viatication and borrowing.

25. A Model of Mistaken Mortality Risk • The true price of selling insurance is the same for both healthy and unhealthy consumers. • What if sick consumers do not correctly perceive their mortality risk? • Relatively unhealthy consumers (late in the course of disease) think they are getting a “good deal” at actuarially fair prices • Relatively healthy consumers (early in the course of disease) think they are getting a “bad deal.”

26. No Arbitrage Opportunity • The misperception in price that this model posits does not generate any arbitrage opportunities for third parties • Misperception does not imply mispricing • Competition prevents VS firms from “taking advantage” of the misperception. • Prices are right  no free lunch

27. Favorable Perceived Terms of Trade • Let be some cut-off mortality risk. • Patients with that risk perceive the same price in both credit and viatical settlement markets. • Terms favor the credit market for patients with mortality risk (healthy patients). • Terms favor the viatical settlements market for patients with risk (unhealthy patients).

28. Budget Constraint for the Unhealthy—Terms Favor Viatical Settlements

29. First Prediction • Health status is negatively correlated with the decision to viaticate. • Terms of trade favor credit markets for healthier consumers. • Terms of trade favor viatical settlements markets for unhealthier consumers. • Unlike the economic model, this prediction is not motivated by the wealth effect alone (though that is present in the model).

30. Changes in Non-Liquid Assets for the Healthy

31. Changes in Non-Liquid Assets for the Unhealthy

32. Second Prediction • For the healthiest consumers, the decision to viaticate is negatively correlated with non-liquid assets. • Terms favor credit markets, so the healthy substitute new borrowing for viatical settlements. • For the sickest, the decision to viaticate is positively correlated with non-liquid assets. • Terms favor viatical settlement markets, so the unhealthy increase cashing out.

33. Changes in Liquid Assets • Increasing liquid assets allows both healthy and unhealthy patients to substitute liquid assets for borrowing, viatication, or both. • Thus, increases in liquid assets reduces or leaves unchanged life insurance supply, as long as consumption and bequests are normal goods.

34. Third Prediction • For all consumers, a small increase in liquid assets will either reduce or leave unchanged the incentive to participate in the viatical settlements market.

35. Three Predictions for the MMR Model • Prediction 1: Health status is negatively correlated with the decision to viaticate. • Prediction 2: Effect of non-liquid assets. • For the healthiest, viaticating is negatively correlated with non-liquid assets. • For the sickest, viaticating is positively correlated with non-liquid assets. • Prediction 3: Increases in liquid assets will weakly reduce the supply of life insurance.

36. Data • HIV Cost and Services Utilization Study (HCSUS) • Longitudinal sample of 2,864 HIV patients in care. • 3 Waves-wave 0 (1996), wave 1 (1997), wave 2 (1998) • Information on life insurance holdings and sales, health status,income and demographics and state of residence • 1,009 patients report life insurance holdings. • 165 patients (16.4%) sold policies. • 886 patients in states without minimum price regulation on viatical settlement sales

37. Summary Statistics • Patients who viaticate are more likely to: • Be male • Be white • Have a college degree • Have income > $2,000 per month • Own a house • Have AIDS and low CD4+ T-cell levels.

38. Empirical Model (1) • Let be the hazard of not selling life insurance (t=0 at the inception of the viatical settlements market or at the date of HIV diagnosis (whichever is later)).

39. Empirical Model (2) • We model the hazard of not selling life insurance as: • Xit is the vector of covariates measured at time t • β is the vector of regression coefficients • is the baseline logit hazard rate

40. Asset Measurement • House ownership is the only measure of non-liquid assets that is reliably measured in each wave of HCSUS. • In waves where other assets are measured, house ownership is strongly correlated with other wealth • Income is a good measure of liquid assets.

41. Health Measurement • Health status is measured using predicted one-year mortality rates. • Probit incorporates demographic and health status measures, including CD4 T-cell counts and clinical stage. • The health measure binary (whether predicted mortality exceeds an arbitrary cutoff). • Makes interpretation of results easier. • Results are not sensitive to the cutoff (within reason).

42. Predicted Viatication Probabilities

43. Alternative Theories • Viatical settlements and Medicaid program participation • Viatical settlements and taxes • Adverse selection in viatical settlement markets • Differential transactions costs of life insurance sales for healthy vs. unhealthy consumers

44. Viatical settlements and Medicaid • In most states, funds from a viatical settlement count against Medicaid asset limits, while life insurance holdings do not. • This provides a disincentive to sell life insurance that applies to healthy and unhealthy alike. • Typically HIV patients apply for Medicaid late in the course of their disease. • Medicaid asset accounting rules most likely deter the relatively unhealthy from selling insurance more than the relative healthy

45. Viatical settlements and taxes • The 1996 Health Insurance Portability and Accountability Act exempts viatical settlements from federal taxes as long as the seller has a life expectancy of 24 months or less or chronically ill. • This fact might explain the relative desirability of viatical settlements for the unhealthy, but cannot explain the pattern of observed interactions between health and non-liquid assets on the hazard of selling insurance.

46. Asymmetric Information • What if viatical settlement firms cannot observe mortality risk? • Separating equilibria may exist with welfare loss for low risk types (relative to symmetric information). • High risk types (low mortality) impose a negative externality on low risk types (high mortality). • This may make credit markets more attractive for low risk (high mortality) types. • This is inconsistent with the evidence which indicates that the healthy are less likely to viaticate. • This is a reasonable result given that good measures of life expectancy are available for HIV patients, and patients undergo a thorough medical evaluation before viatication. • Also, there is no evidence that prices change with the face value of the policy.

47. Differential Transaction Costs • What if costs of borrowing are higher for the relatively unhealthy • As banks anticipate transaction costs of liquidating estates of the relatively unhealthy to collect loan payments? • This is consistent with the evidence which indicates that the unhealthy are more likely to viaticate. • But this is an unlikely explanation as • Standard credit applications do not ask for health status and mortality risks • It might be illegal to discriminate (charge different loan processing fees) based on mortality risk • Search costs of finding a viatical company and negotiating a transaction might be higher for the relatively unhealthy who only have a few more months to live.

48. How Well Does the Market Anticipate Technological Shocks?

49. Nominal Price of a Viatical Settlement, by Life Expectancy and Year

50. Number of Viatical Firms by State from 1995 - 2001