Health and Retirement StudyDemonstrating 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

Themes of the Study• Resources for successful aging (51+)

Economic, public, familial, physical, psychological

• Behaviors and choicesWork and retirement, savings and wealth, physical and mental health, residence, transfers, use of programs, management of resources

• Events and transitionsHealth, cognition, retirement, widowhood, institutionalization

• 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

How Big is HRS?

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

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”

AGE

92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

YEAR

70

65

60

AHEAD

CODA

HRS

90

85

80

75

55

50

War Babies

Early Boomers

Mid Boomers

HRS Longitudinal Sample Design

2500

3000

3500

4000

4500

Tot

al B

irth

s (1

000's

)

1900 1920 1940 1960 1980year

Figure 2. Size of Birth Cohorts Represented in HRS

AHEAD1890-1923

CO

DA

1924-1930

HRS1931-1941

War B

aby1942-46

Early B

oomer

1948-53

Mid-B

oomer

1954-60

Now enteringRetirement

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

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 amentally stimulating life helps maintain one’s Cognitive ability, and

(b) The hypothesis that work provides a more stimulating environment than retired life

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

Mental Retirement, Susann Rohwedder and Robert J. WillisJournal of Economic Perspectives, 2010

Cross-Country Correlation of Retirement and Cognitive

Performance

Employment rate and cognitive performance Relative difference between 60-64 and 50-54 years old men

United States

DenmarkGreece

SwedenSwitzerland

United KingdomSpain

Germany

ItalyThe Netherlands

Belgium

Austria

France

-25%

-20%

-15%

-10%

-5%

0%

-100% -90% -80% -70% -60% -50% -40% -30% -20% -10% 0%

Employment rate (relative difference)

Cog

nitiv

e pe

rfo

rman

ce (

rela

tive

diff

ere

nce)

Source: S. Adam, E. Bonsang, S. Germain and S. Perelman (2007), “Retirement and cognitive reserve: A stochastic frontier approach applied to survey data”, CREPP DP 2007/04, University of Liège.

Earlier retirement

DecreasingCognition

Retirement Policy Shapes Retirement Behavior

Source: J. Gruber and D. Wise, Social Security and Retirement Around the World (NBER, 1999)

Pe

rce

nt

Ear

ly R

eti

rem

en

t

Percent Penalty for Continued Work

20 40 60 80 10030

40

50

60

70

USSweden

CanadaSpain

Germany

UK

FranceHolland

Belgium

Italy

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

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

Journal of Health Economics, 2012

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(a)

(b)

Note that this strategy may pickup “local averagetreatment effect”on those most prone to retirementat age 62

Instrumental Variable Estimates of

Retirement Effect on Memory Score

Simulated Effect of Early vs. Late Retirement

ConclusionAs 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