The Link between Individual Expectations and Savings: Do nursing home expectations matter?

1. The Link between Individual Expectations and Savings: Do nursing home expectations matter? Kristin J. Kleinjans, University of Aarhus & RAND Jinkook Lee, Ohio State University Preliminary – comments and suggestions appreciated

2. Research Questions • Are expectations about future nursing home entry linked to actual entry? • Is there a relation between these expectations and savings behavior?

3. Why do we care? • In the US : • 40% chance of entering a nursing home for those who reach age 65 • 10% of those stay for at least 5 years • high cost of stay per year (average): US-$60,000 for semi-private room US-$70,000 for private room

4. Why do we care? • How are nursing home stays financed? • Medicaid (means tested) • (1/3 of individuals when admitted, • + 1/3 after savings depleted) • long-term care insurance (3% of total cost) • individual savings

5. Then… • Do individuals form sensitive expectations about stay in NH? • Do individuals adjust their savings behavior in response to (change in) expectation?

6. Why use Individual Expectations? • Give additional information otherwise not available • Have been found to have explanatory power (e. g. Hurd/ McGarry 2002, subjective survival prob.) • Have not yet really been linked to economic behavior (Hurd/ Smith/Zissimopoulus 2004, survival prob. and retirement found only small effects)

7. Our (preliminary) Results • Are expectations about future nursing home entry linked to actual entry? • yes • 2. Is there a relation between these expectations and savings behavior? positive for singles with (very) low positive wealth

8. Data • HRS 1992-2002 (6 waves) • Age of initial cohort: 51- 61 + spouses • Sample Size: 7,600 households in 1992 • AHEAD 1993-2002 (5 waves) • Age of initial cohort: 70 and older + spouses • Working sample: • 15, 089 respondents

9. Survey Question:Probability of Entering Nursing Home • "What is the percentage chance that you will move to a nursing home in the next five years?“ • Possible answers: • 0 to 100 • refuse • don’t know

10. Survey Question:Probability of Entering Nursing Home • "Nursing homes are institutions primarily for people who need constant nursing supervision or are incapable of living independently. Nursing supervision must be provided on a continuous basis for the institution to qualify as a nursing home. Please don't include stays in adult foster care facilities or other short-term stays in a hospital.“ • (HRS respondents, and 1996 onwards) • "Of course, nobody wants to go to a nursing home, but sometimes it becomes necessary.“ • (AHEAD 1993 and 1995)

11. What influences the probability of entering a nursing home? • (geriatric, medical literature) • Age (+), gender (women + ) • Health, ADL, IADL • Marital status, children (-), living siblings (-) • Race (being white +) • Education (+) • Low income (+), net worth (-)

12. Self-reported prob. of NH Entry • Low non-response rates: • Refusal: < 1% in all waves • ”Dont know”: < 10% in all waves • rounding to the nearest 5% between 15% and 95% • bunching of answers: 0% and 50% • Evidence for rounding: health

13. Subjective Prob. of NH Entry

14. Subjective Prob. of NH entry

15. How to measure the outcome • Currently living in NH • Having been in NH since last interview • Including short-term stays (< 30 days) • Excluding short-term stays

16. How do expectations relate to outcomes* ?Mean Subjective NH Prob. by Entry *Measured as having been in NH since last interview (currently living in NH)

17. How do expectations relate to outcomes ?Random-Effect Probit of Actual Entry (2 waves later) Measure: Currently living in NH. Additional covariates included.

18. Possible Effects of Expectations on Savings • Differs by (non-housing) wealth: • Low wealth: No effect (Medicaid) • In the lower middle: Negative effect (spend down) • In the upper middle: None or Positive effect (Too late for saving enough?) • High wealth: No effect

19. Median Savings Rates by Wealth Range Singles (2000) * Median Savings Average age: 74

20. Measurement of Savings • difference in (non-housing) wealth • measured as difference of logs of wealth in period t and t+1 • Log of wealth = log(wealth+1) if wealth >= 0 • - log(1-wealth) if wealth < 0

21. Other Factors Affecting Savings (Singles) • Age, gender, race • SES - Permanent income: use predicted income given • real income, age, age2, marital status, race, gender, education, region of residence • Health status, health insurance(s), LTC insurance • Bequest motive: use # of children • (endogeneity of bequest intention)

22. Fixed Effect Regression for SavingsSingles, only if NH prob. changed All wealth groups included. Additional covariates included.

23. Fixed Effect Regression for SavingsSingles, only if NH prob. changed Separate regressions by wealth groups: neg., zero, 10 (positive non-hous. wealth) deciles. Shown: regression with stat. sign. coefficient on NH prob. Additional covariates included.

24. Results • no effect for most wealth groups • exception: lowest positive wealth decile (+) • Sensitivity analysis: • Negative effect for low wealth ranges (2. and 3. pos. wealth decile) for HRS sample and with wave dummies

25. (Preliminary) Conclusions • Expectations related to risk of nursing home entry and actual entry • Positive effect on savings for those with very low but positive wealth • Some evidence for dissaving for those with slightly higher wealth

26. Potential Problems • Endogenous non-random sample selection through deaths and NH entry Should bias coefficients downwards (1. decile) • Endogeneity of wealth Should be less of a problem since wealth was accumulated during work life

27. Next Steps: Address Sample Selection Issues • Include deceased in sample for Part 1 (expectations versus outcomes) • Use IV estimation for Part II (effect on savings) Potential candidate: Number of living children works if death of child affects NH exp but not savings rate