1 / 59

Knowledge and Ignorance in a Secondary Insurance Market

Knowledge and Ignorance in a Secondary Insurance Market. Jay Bhattacharya Stanford University September 2008. Knowledge Aggregation in Markets. Many economists have stressed the ability of markets to aggregate local knowledge. e.g. Hayek’s famous AER essay

melia
Download Presentation

Knowledge and Ignorance in a Secondary Insurance Market

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related