Health and Retirement Study

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

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.

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 1992-1998; Willis 1995-2007) • 13 Co-Investigators from different disciplines • 14,700 registered user worldwide. • More than 2,000 publications used HRS data

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 Themes of the Study

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

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 Longitudinal Design

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” Why do we need longitudinal data?

HRS Longitudinal Sample Design

Now entering Retirement War Baby 1942-46 AHEAD 1890-1923 HRS 1931-1941 CODA 1924-1930 Mid-Boomer 1954-60 Early Boomer 1948-53

Scientific Productivity of the HRS HRS Rate of production (2008-2011) = 2.5 papers per week

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

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

Mental Retirement, Susann Rohwedder and Robert J. WillisJournal 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

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

Retirement Policy Shapes Retirement Behavior 70 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)

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 Cross-Country Results on Causal Effect of Retirement on Cognition

Journal of Health Economics, 2012 • Uses six waves of HRS data (1998-2008) 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

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

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

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)

Instrumental Variable Estimates of Retirement Effect on Memory Score

Simulated Effect of Early vs. Late Retirement

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

http://hrsonline.isr.umich.edu Thank you

Health and Retirement Study