How Do Pre-Retirement Job Characteristics Shape One’s Post-Retirement Cognitive Performance? Dawn C. Carr, PhD Stanford University Melissa Castora-Binkley, PhD University of South Florida Ben Lennox Kail, PhD Georgia State University Robert J. Willis, PhD University of Michigan Laura Carstensen, PhD Stanford University We are grateful to the “Working Longer” Program of the Sloan Foundation for support of this research
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How Do Pre-Retirement Job Characteristics Shape One’s Post-Retirement Cognitive Performance? Dawn C. Carr, PhD Stanford University Melissa Castora-Binkley,
Mental Retirement Rohwedder and Willis, Journal of Economic Perspectives (2010) Cross-country correlation of cognition Retirement (country means) Estimate of causal effect of retirement on episodic memory using policy variation across countries as IV IV Estimate of Retirement Effect: -.011/.228=-4.82 Data Sources: HRS: Heath and Retirement Study (U.S.) ELSA: English Longitudinal Study of Ageing SHARE: Survey of Health, Ageing and Retirement in Europe
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How Do Pre-Retirement Job Characteristics Shape One’s Post-Retirement Cognitive Performance?
Dawn C. Carr, PhDStanford University
Melissa Castora-Binkley, PhD
University of South Florida
Ben Lennox Kail, PhDGeorgia State University
Robert J. Willis, PhD
University of Michigan
Laura Carstensen, PhDStanford University
We are grateful to the “Working Longer” Program of the Sloan Foundation for support of this research
Motivation
• Many components of cognitive ability decline with age, beginning around age 20 and continuing through the rest of life– Fluid intelligence, working memory, episodic memory, etc
• “Use it or lose it” hypothesis suggests that mental exercise may stave off decline
• While lab evidence for hypothesis is weak, a growing body of research using population data finds that delay of retirement has causal effect on improving cognitive performance on memory
• This evidence suggests that the work environment is more mentally stimulating than the home environment or, possibly, that the expectation of retirement reduces the incentive of workers to exert the mental effort to maintain their skills and cope with other challenges at work
Mental RetirementRohwedder and Willis, Journal of Economic Perspectives (2010)
Cross-country correlation of cognitionRetirement (country means)
Estimate of causal effect of retirementon episodic memory using policy variationacross countries as IV
IV Estimate of Retirement Effect: -.011/.228=-4.82
Data Sources:HRS: Heath and Retirement Study (U.S.) ELSA: English Longitudinal Study of Ageing SHARE: Survey of Health, Ageing and Retirement in Europe
Motivation (cont.)• Growing body of literature supporting mental retirement effect
Bongsang, Adam & Perelman (2012); Mazzonna and Perachi, 2012; Celidoni, et. al., 2015).
• Need for better understanding of mechanism underlying effect. • Recent studies examining complexity of work provide promising leads
– Finkel, et al. (2009) Swedish Adoption/Twin Study of Aging found people in occupations with high engagement with people build up verbal skills faster during work, but lost them faster once they retired. Suggest that “taking away work from one’s a life style” is a key element in changing mental exercise.
– Fisher, et al., 2014 find those in more mentally demanding jobs in HRS had higher cognitive function prior to retirement, and experienced less decline in cognitive performance following retirement. Suggest that cognitive complexity generate cognitive resiliance
– Kajitani, et al. (2013) found men in careers that require high mathematical, reasoning and language development experience less decline in memory following retirement
Goals of this paper
• Use counterfactual econometric framework to estimate the effect on cognitive change over a 6 year time span of full retirement vs continued full time work for workers whose jobs differ in intellectual and mechanical complexity
• Interpret our results in terms of a new psychological theory, STAC
STAC-Scaffolding Theory of Aging and Cognition
• Many components of cognition decline with age– Working memory, ability to learn and recall new information, fluid
intelligence
• Why, then, are most older adults able to continue functioning quite well despite these declines ? (Park & Reuter-Lorenz, 2009)
• Compensatory scaffolding, defined as recruitment of additional circuitry in the brain, shores up deteriorating components
– Scaffolding occurs in new learning and also in less novel or practiced behaviors• Sustained (3 mo.) in engagement in cognitively demanding activities
enhances episodic memory function (Park, et al., 2013) • Limited cognitive benefit of sustained engagement in social activities
STAC (continued)
• Left pre-frontal cortex of young people lights up when solving novel problems– Suggests fluid intelligence (i.e., reasoning) is primarily involved
• For older people, both left and right lobes light up – Suggests memory processes also involved
• Higher performing older adults show more bi-lateral activity than lower performing adults– Suggests higher cognitive ability leads to more scaffolding
STAC: Scaffolding Theory of Cognitive Aging
Link between STAC and Human Capital Theory
• Human capital theory suggests that an individual’s productivity in a given activity depends on – reasoning ability (Gf: fluid intelligence)– knowledge relevant for that task (Gc: crystallized
intelligence)
• Gf and Gc tend to be complementary– Early in life, Gf increases the productivity of people in
acquiring knowledge through schooling, job experience and other activities (e.g., managing finances, rearing children)
– Later in life, accumulated knowledge increases productivity of person whose reasoning ability has declined
Link between STAC and Human Capital Theory
• STAC suggests that neural circuitry connects Gf and Gc. – Cognitively complex jobs plausibly require more mental
exercise to maintain skills and perform challenging tasks – Plausible that complex circuitry leads to less domain-specific
capabilities and less difference in the degree to which work and home environments provide mental stimulation.• Higher fluid intelligence: allows faster linkage of relevant pieces of
knowledge needed to accomplish a given task• Likely to be network economies of scale that are realized in more
cognitively complex jobs that provide neural links between knowledge acquired in various domains at work and non-work environments
This Paper
• Use Data from HRS to Examine effects of Occupational Complexity on Cognitive Change
Measurement of Cognitive Change
27-point cognitive scale. It is the sum of– Episodic memory: immediate and delayed word
recall (0-20 pts.)– Working memory: timed serial 7s, (0-5 points)– Processing speed: backward counting (0-2
points)
Our dependent variable is the change in the cognition score between time 1 and time 4
Sample Definition
Work 35+, Not
Retired
Work 35+,Not Retired
Work 35+,Not Retired
Work 35+,Not Retired
Work 0,Retired
Work 0,Retired
Stay Full Time(n = 1,296)
Fully Retire(n = 721)
Work 35+, Not
Retired
Work 35+,Not Retired
Time 1 Time 2 Time 3 Time 4
Sample is further restricted to persons with cognitive scores innormal range at baseline.
Job Complexity
• Characteristics of jobs have been coded by the U.S. Department of Labor O*Net.
• We have used a cross-walk between HRS Census based occupation codes and O*NET standard occupation codes kindly provided to us by Peter Hudomiet in order to link the O*NET and HRS data.
Aspects of Occupations Coded by O*Net
A. Abilities:• Deductive
reasoning • Inductive
reasoning • Mathematical
reasoning• Arm-hand
steadiness • Finger dexterity • Multi-limb
coordination
B. Activities:• Getting information • Inspecting equipment, structures, or material • Processing information• Analyzing data or information• Making decisions and solving problems • Thinking creatively • Developing objectives and strategies • Handling and moving objects • Controlling machines and processes • Operating vehicles, mechanized devices or
equipment • Interacting with computers • Repairing and maintaining mechanical equipment • Repairing and maintaining electronic equipment • Documenting/recording information• Establishing and maintaining interpersonal
relationships • Assisting and caring for others• Performing for or working directly with the public• Coaching and developing others
C. Contexts:• Face-to-face discussions• Coordinate or lead others• Responsibility for outcomes and
results• Spend time making repetitive
motions• Impact of decisions on co-
workers or company results• Frequency of decision-making• Freedom to make decisions• Degree of automation• Importance of being exact or
accurate • Importance of repeating same
tasks• Structured versus unstructured
work• Pace determined by speed of
equipment
Components of Intellectual and Mechanical Complexity of Occupation
• We seek to answer the counterfactual question: – What would happen to the cognitive trajectory of persons of a given degree
of complexity who retire fully compared to the trajectory they would have experienced had they continued working full time?• Clearly, it is impossible to answer this question at the level of the individual since
any given person in our sample either continues to work full time or to retire fully.• Put differently, this question inherently involves treating the outcome variable as
missing for the counterfactual condition.
– The best we can do is to estimate the mean trajectory of a group of people who did retire compared to the trajectory of similar people who continued to work.• An obvious challenge is that people choose their occupation and also choose
whether or not to retire. Because of self-selection, comparisons of mean outcomes may be biased because the comparison groups differ
• One approach to this challenge is to use IV methods. Unfortunately, we do not have plausible instruments for occupational choice and retirement
• The approach we use is drawn from the treatment effects literature, implemented as one of the of the estimators in Stata’s teffect command.– We classify occupational complexity as low, medium or high for
intellectual complexity or mechanical complexity– This yields 2x3=6 treatments (work, low)…etc for each complexity type
• The ipwra model with multiple treatments contains two equations– Potential Outcome Means (POMs)
• Change in Cognition = F(Covariates|Occ, Fully Retired in Times 3 and 4)• Change in Cognition = F(Covariates|Occ, Working Full Time in Times 3 and 4)
– Multinomial Logit Propensity Model• Probability individual in treatment j = G(Covariates)
– Average Treatment Effects (ATE)• ATE(retire) = POM(retired|complexity) – POM(work|complexity)
• The ipwra model yields unbiased estimates of the POMs and ATEs of the treatments assuming selection on observables (aka ignorability or unconfoundedness)– We have attempted to include a set of covariates that
make this assumption plausible– Example of violation of this assumption
• An individual develops a sleep disorder that reduces his cognition and also increases the disutility of work, leading him to retire. Clearly, his decline in cognition has not been caused by retirement
Covariates
Adjusted Potential Outcome Measures by Cognitive Complexity of Occupation
Table 4A: Adjusted POM Estimates for Changes in Cognitive Performance, Time 1 to Time 4 by Level of Intellectual Complexity Level of Cognitive Complexity of Job
Table 4B: Significant Within-Group Differences in Cognitive Decline: Level of Intellectual Complexity of One’s Job By Work Transition Group Level of Cognitive Complexity of Job
Low vs.
ModerateLow vs. High
Moderate vs. High
Retire * *** Stay Full-Time
Effect of Retirement on Cognitive Decline by Intellectual Complexity of Job
Adjusted Potential Outcome Measures by Mechanical Complexity of Occupation
Table 4A: Adjusted POM Estimates for Changes in Cognitive Performance, Time 1 to Time 4 by Level of Mechanical Complexity Level of Cognitive Complexity of Job
Table 4B: Significant Within-Group Differences in Cognitive Decline: Level of Mechanical Complexity of One’s Job By Work Transition Group Level of Cognitive Complexity of Job
Low vs.
ModerateLow vs. High
Moderate vs. High
Retire * *Stay Full-Time
Effect of Retirement on Cognitive Decline by Mechanical Complexity of Job
Summary and Conclusion
• As suggested by the STAC theory, the people in intellectually complex jobs seem to suffer relatively small losses in cognition when they retire, perhaps due to the development of extensive scaffolding from work that is transferable to the retirement environment where they remain intellectually active.
• Conversely, people in jobs with low intellectual complexity appear to suffer substantial losses in cognition, perhaps because they did not build much scaffolding during their work career and do not maintain mental exercise during retirement.
• The results for the effects of mechanical complexity show small and less significant differentials. However, in this dimension, those in highly complex jobs suffer a larger loss in cognition than those in jobs with lower mechanical complexity. This result might arise because the scaffolding developed in the workplace is less relevant for retired life.
Summary and Conclusion (cont)
• Under the assumption of selection on observables, our results can be interpreted as causal.
• However, there are good reasons to worry that this assumption may not hold. – In particular, the basic hypothesis that maintaining mental exercise is a key
to maintaining cognitive ability means that we need explore the complexity of the home environment and how people change their non-market activities after retirement.
– The HRS has measures of time allocation (e.g. time spent watching TV) and mental state (e.g., boredom) that people experience both before and after retirement that we have begun to look at
• There is also much scope for further integration of both theory and measurements of economists and psychologists to advance our understanding of the determinants of cognitive aging.