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Discussion of Out-of-Pocket Health Care Expenditures by Edward Norton , Hua Wang, Sally C. Stearns Michael Lechner University of St. Gallen (SIAW) Mannheim , October 2005. Set-up. Research question

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  1. DiscussionofOut-of-Pocket Health Care ExpendituresbyEdward Norton, Hua Wang, Sally C. StearnsMichael LechnerUniversity of St. Gallen (SIAW)Mannheim, October 2005

  2. Set-up Research question • Understand the distributional consequences of out-of pocket expenditures for certain demographic groups • Understand the behavioural consequences, if OFPHCE have distributional ones (health care expenditures are the largest potential risk faced by elderly) More precise formulation of research question 1) Who are the people who face the highest expenditure risk ? • What are distributional and behavioural implications of this risk? Methods used To answer 1): Pooled random effects binary choice panel model with random attrition ‘to explain’ a certain threshold for OFPHCE based on large and informative monthly micro data aggregated to yearly data To answer 2): Deduction from 1) based on implicit behavioural economic model

  3. Results – part 1 Who faces the highest expenditure risks? Key findings of paper. • There is no distributional issue for non-long-term care. Why? Gender, Race, and martial status are significant ... low marginal R2 informative? • LTC: Women (because they live longer), oldest old (they are older)have higher expenditure risk My interpretation: The longer you survive the higher the probability that you get chronically ill.Die rich or survive poor.Does this really point to an economic inefficiency or an unintentional redistribution (from whom to whom? From the living old to those already dead?). I do not see any ex-ante problem .. Die rich or survive poor – I guess that is a principle accepted by economistsA provocative thought: Suppose that people in LTC are hardly able to consume. Insuring them using the tax system would imply a redistribution of resources from the younger poor to the older rich thus enabling them to make some inheritance ... Is this what we want? • LTC: Married persons face lower expenditure risk Because you have to have a spouse who is alive (which is more likely if you are younger)  same issue as before although the spousal impoverishment act may play some role Part of 2) and 3) depend entirely on having controlled for age, health and death correctly.

  4. Key assumption for empirical results Health is a strictly exogenous dynamic process that cannot be influenced by out-of-pocket HC expenditure If yes: 1) Health status, time to death, and martial status (widowed or not) are exogenous variables and can be used as regressors. 2) Implicit assumption of random attrition from panel is justified. If not true: Coefficient would also contain effect of expenditure on health, death ... True? Unlikely. People can control to some extend the amount of services they buy (that ought to be effective). What can we do if this is assumption is wrong? Very, very difficult question ...

  5. Other issues concerning the estimation results • Age and genderSeems to be reasonable to expect different age profile for men and women because of different life expectancy  fully interact age dummies and gender • The strong effects for Racemay suggest that some important socio-economic variable is missing (wealth? consumption?) • Why has education a negative effect for LTC, and a positive effect for LTC? • Exogeneity of income ... and martial status (as already discussed ...) Econometric issues • Why aggregate monthly data to yearly data?  let the panel data estimator do this optimally ... • Functional form correct? Chosen method fairly robust although it does not exploit all information in the data; other functional forms like tobits or two part models have their problems as well • R2 (which one?) may not be an optimal measure in a nonlinear model (depends on threshold?) to compare models – chose threshold such that mean(y) are the same in both estimations

  6. Results – part 2Behavioural findings (implications of who is who question) • Undersaving, because insurance provides safety net • Increases in longevity increase LTC cost because age is significant conditional on time to death(seems to be a little overstretched: depends on the reason of the increase ... suppose people live longer because of technical progress in health care technology) • If increases in longevity are unanticipated, then the elderly do not save enough. yes, but this always happens when there are surprises which are not insurable • Low marginal benefit of health care expenditure ...I missed that the connection to the empirical findings • Older may transfer money to their kids before they die in exchange for care (and let the public insurance pay the rest after the kids stop caring ...) ok  tax these transfers if you do not like them! Are they bad?

  7. Alternative What would an applied labour economist do?This is a challenging ( interesting!) econometric problem! • Would not believe in the exogeneity of age (=observability) / health / death and try to find an instrument (distance to nursery home?) • Would like to have a more flexible specification ... • Define a causal effect that is interesting and define corresponding treatment variables • Define treatment, outcome and control variable - outcome: whether to have some amount of OPE - here treatment seems to be all the demographic variables - control variables seem to be regions, time to death, time dummies, ... - define background variables that may be related to treatment and outcome - combine treatment variables in something discrete and do some matching analysis (robust, no functional forms, heterogeneous effects) - would control for the probability to die explicitly (but not use the observed outcome!) - would not control for age! Age would be the treatment!- would she use the panel structure? and if so, how?

  8. Conclusion • A nice, straightforward paper • Very interesting descriptive results • Doubts about the forceful causal interpretation remain with this discussant • Interesting and fun to read. Good! • I learned a lot. Even better.

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