1. Health and Retirement Study
Demonstrating the Value of a Longitudinal Design
Robert Willis, PhD
Amanda Sonnega, PhD
Institute for Social Research
University of Michigan

2. Public Use Data for Research and Policy
The primary objective of the HRS is to provide data for a community of scientific and policy researchers from around the world who study individual aging processes and the impact of population aging.

3. Study Overview
Created in 1990 by an act of U.S. Congress to provide data for the study of health and retirement
Nationally representative longitudinal survey of more than 26,000 individuals over age 50 in the United States (U.S.)
PI: David Weir, PhD (Juster ; Willis )
13 Co-Investigators from different disciplines
14,700 registered user worldwide.
More than 2,000 publications used HRS data

4. Themes of the Study Resources for successful aging (51+)
Economic, public, familial, physical, psychological
Behaviors and choices
Work and retirement, savings and wealth, physical and mental health, residence, transfers, use of programs, management of resources
Events and transitions
Health, cognition, retirement, widowhood, institutionalization

5. How Big is HRS? 37,500 people have participated
200,000 interviews completed
350,000 person-years of observation
10,000 retirements
4,500 cases of incident dementia
12,000 deaths

6. Longitudinal Design
Begun in 1992 with about 12,000 individuals ages 51 – 61
“Core” interview takes place every 2 years
Additional birth cohorts added over time
Sample is refreshed every 6 years
Sample is now nationally representative of individuals over age 50

7. Why do we need longitudinal data?
To study processes that change and unfold over time
For example, life-cycle saving, cognitive trajectories, health and mortality
To study temporal relationships
antecedents to retirement
consequences of retirement
To help sort out causal relationships that are important for policymakers to understand
Implication of health insurance reform for costs
Implications of social security reform for savings and welfare in retirement
To study cohort differences
Implications of the “Fiscal Cliff”

8. HRS Longitudinal Sample Design

9. Now entering Retirement War Baby 1942-46 AHEAD 1890-1923 HRS 1931-1941
CODA
Early Boomer
Mid-Boomer

10. Scientific Productivity of the HRS
HRS Rate of production ( ) = 2.5 papers per week

11. Why do we need longitudinal data?
In short, to answer many of the questions
addressed in the sessions we are hearing today

12. In the remainder of this talk, I focus in detail on
one example to show how cross-national and
longitudinal data can be used as alternative means
to address a given research question about the
(joint) validity of
(a) the “Use it or lose it” hypothesis that living a
mentally stimulating life helps maintain one’s
Cognitive ability, and
(b) The hypothesis that work provides a more
stimulating environment than retired life

13. Mental Retirement, Susann Rohwedder and Robert J
Mental Retirement, Susann Rohwedder and Robert J. Willis Journal of Economic Perspectives, 2010
Use cross-sectional data from HRS, ELSA, and SHARE to compare cognition in retired and non-retired individuals, using national policies as instrumental variables
Uses measure of episodic memory (immediate and delayed word recall) that is measured in same way in HRS, ELSA, SHARE,
Finds large negative “causal effect “ of retirement on cognition.
Consistent with hypothesis that work is more mentally stimulating than retirement and that memory capacity is malleable

14. Cross-Country Correlation of Retirement and Cognitive Performance
Decreasing
Cognition
Earlier retirement

15. Retirement Policy Shapes Retirement Behavior70
Belgium 60
France
Italy
Holland UK 50
Germany
Percent Early Retirement
Spain
Canada 40 US
Sweden 30 20 40 60 80
100
Percent Penalty for Continued Work
Source: J. Gruber and D. Wise, Social Security and Retirement Around the World (NBER, 1999)

16. Cross-Country Results on Causal Effect of Retirement on Cognition
Rohwedder-Willis use country-specific policies on age of retirement to estimate causal effect of retirement
found large effect: 40% drop in memory score
Bingley and Martello (2012) find effects 1/3 as large for women and 2/3 as large for men as the R-W results when education is controlled
But this research question can also be addressed with longitudinal data

17. Reaching age 62 or 65 is exogenous to cognition
Journal of Health Economics, 2012
Uses six waves of HRS data ( ) with respondents: under age 76, had worked at least to age 50,
Uses retirement spikes at age 62 and 65 as instrumental variables to identify causal effect of retirement on cognition.
Argues that cognition should change smoothly with age in the absence of a change in environment caused by retirement
Reaching age 62 or 65 is exogenous to cognition
Uses fixed regression (i.e., dummy variable for each person) to
control for time-invariant observed and unobserved variables that are correlated with cognition

18. Age Profile of Memory Score based on Age Dummies from Fixed Effect Regression
Note drop at age 62

19. Change in (a) Retirement Probability and (b) Cognition By Age

20. Change in (a) Retirement Probability and (b) Cognition By Age
Note that this
strategy may pick
up “local average
treatment effect”
on those most
prone to retirement
at age 62
(a)
(b)

21. Instrumental Variable Estimates of Retirement Effect on Memory Score

22. Simulated Effect of Early vs. Late Retirement

23. Conclusion
As the HRS-sister studies around the world develop into long panels, the synergies of cross-national and longitudinal analysis will create an extremely powerful tool to address important scientific and policy questions associated with individual and population aging.
To achieve this promise, it is critical that these panels receive support from their governments
through thick and thin. I applaud the Israeli
government for its recent expression of support for the long term continuation of Israel-SHARE

24.
Thank you