1. A Discussion of: Using Subjective Conditional Expectations to Estimate the Effect of Health on Retirement Guistinelli & Shapiro Debra Sabatini Dwyer Assistant Dean Research, Strategic Planning CAS Professor of Economics, Tech and Society Stony Brook University Prepared for the 19th Annual Meeting of the Retirement Research Consortium, Washington DC August 3, 2017

2. Potential Contribution To the Literature on Health, Expected Retirement, and Social Security Policy Background/Motivation
Who Cares? Policy Relevance of Predicting Retirement Ages
Solvency and Sustainability
Social Security Fact Sheet 2017
Life expectancy is significantly higher at 85
48 million Americans age 65+ today; over 79 million by 2035
Dependency ratio will drop from 2.8 to 2.2
Average retirement age around 63 (Munnell, 2015; Dixon, 2017)
Does not match life expectancy gains
People are costing more in retirement. Need them to work more for solvency
Will financial incentives work?
Need to understand the role of health to answer that

3. Potential Contribution To the Literature on Health, Expected Retirement, and Social Security Policy What we know about health and retirement
- There is general consensus that healthier individuals are more likely to continue working, and individuals in poor health are more likely to retire early, as work becomes increasingly difficult with declining health. However, the relation between health and retirement timing is not linear, as good health is also related to early retirement, particularly among individuals at higher SES levels who can afford to retire. [Fisher et al 2016]
Disentangling the effects of health and wealth on retirement, as well as the impact of retirement on health (the health-work nexus) lead to a vast literature
General consensus comes from robust results regarding a strong connection between health, wealth, and work
Identifying the causal relationship of health on work is difficult when we don’t observe counterfactuals on uncertain factors like health
This work seeks to measure counterfactuals to tease out the effect of health on work
We typically only observe the path taken, not what an individual would have done if a factor went a different way
In this case, health is the uncertain variable, where the authors collect hypothetical health state specific work planning data

4. Potential Contribution To the Literature on Health, Expected Retirement, and Social Security Policy
To uncover the propensity for work among a healthy population of older workers based on expectations about future health as well as preferences for work under given hypothetical scenarios of future health and given existing wealth status

5. Merit of the Potential Contribution
Policies using financial incentives may not realize desired outcomes if health drives behavior
May also cause undue burden if financial penalties for earlier retirement grow
Critical for Policy Design to understand independent effect of health
Health-Work Nexis: Sensitivity Analyses and Robustness
No one study can identify relationship
It takes a body of work and there are still unanswered questions
This work reveals counterfactuals in health states given preferences for work and wealth to predict work life expectancies
Addresses the issue of how being sick affects work and retirement given their preferences and wealth situation
Provides an estimate of who plans to retire in two and four years and how those plans change under different health scenarios
Methods to examine responses to hypothetical outcomes provides counterfactuals that can be used for other policy simulations

6. Comments Sample Selection Two levels of it:
First: The VRI population is not representative so the authors need be careful about saying they have population estimates – be more specific about which population.
Second: Only 29% for 2 year analysis and 25% for 4 year analysis of the VRI sample is included in the analyses and they are not random
Two types left out:
Those who could afford earlier retirement
Those who are in poor health or unable to work – who are also more likely to be older and have experienced the health shock already
Need to see comparison of attributes between those included and those excluded

7. Comments Sample Selection Continued
The Authors say on page 6: This sample therefore represents a subpopulation of particular interest for our analysis, as it is made of individuals who in principle have the capacity to work longer but for whom assessing the causal link between health and retirement is particularly challenging as they have not experienced the health shocks used for identification in the standard approach.
Those who are not in the sample may also not have experienced a health shock but could afford earlier retirement. So oversampling those for whom wealth may be a more binding constraint, sicker at the margin, and/or with a greater taste for work
Would those people omitted still be working under different policy circumstances? Can we tell by leaving them out and can we generalize results?
How does it affect interpretation of results?

8. Period 1 working and healthy; wealth given Period 2 both can change
Comments
Sample Selection Continued
Provide a little structure to thinking about who is in the sample, who is not, and how it affects the behavior you are modeling
Simple 2 period model
Period 1 working and healthy; wealth given
Period 2 both can change
In this framework think about those not in the sample. Are they the same as those included but differ only in that we observe both periods for them? You can examine attribute differences between the two groups to draw inferences.
Providing this type of structure will help to organize the empirical results more efficiently as well
We seek to uncover work transition probabilities holding all fixed and varying health

9. Example of Policy Implications of Potential Sample Selection Bias
What can we learn from the behavior of a selectively more affluent group?
Increasing Early Retirement Benefit Eligibility Age: If they are willing to work despite poor health, then no need to provide financial incentives
Avoids undue burden on those who need to retire earlier given health and occupational attributes
Increasing Early Retirement Benefit Eligibility Age: If they are NOT willing to work with poor health at the margin, then may need to provide financial incentives
Creates social welfare concerns given tradeoffs – and magnitude unknown on less affluent population
Target health promotion and management policies?
They find that 30% of this population are not responsive to health changes. Whether or not that is sufficient is normative –but useful for policy. Sample selection concern only affects interpretation.

10. Bridge Job Choice – Endogenous?
Bridge jobs may be the outcome of an anticipated decline in health
Implications?
Would underestimate effects of health

11. Significance of Findings for Policy
Innovative Approach to uncovering treatment effects or individual level health effects on work and aggregate them to population parameters
As authors acknowledge, the approach lends itself to policy simulations so we can assess outcomes based on both efficiency and equity criteria
The authors are able to uncover estimates of transition probabilities using the SATE approach
Ex. They find a population transition probability estimate of 42% - working despite a transition from good to poor health.

12. Key Punchlines for Policy
People are optimistic about future health outcomes
Variation predictable by age and SES
Health does drive earlier retirement on average
Response to poor health is significant
Self-Reported future work propensity is influenced heavily by age when health is omitted
Sensitive to interaction with health
Not clear how useful this is in terms of predicting causality of health and work but does suggest the relationship is strong [Table 5]
When examining counterfactuals for true treatment effects Authors subtract (SATE)
I calculated a percentage reduction in the probability of working (transition probability)
Going from H to h
2 year – 68%
4 year – 78%