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
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 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
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
Time 1
Time 2
Time 3
Time 4
Fully Retire
(n = 721)
Work 35+,
Not
Retired
Work 35+,
Not Retired
Work 0,
Retired
Work 0,
Retired
Stay Full Time
(n = 1,296)
Work 35+,
Not
Retired
Work 35+,
Not Retired
Work 35+,
Not Retired
Work 35+,
Not Retired
Sample is further restricted to persons with cognitive scores in
normal 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 coworkers 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
Item-Test
Correlation
Alpha
Making Decisions and Solving Problems
0.977
0.925
Thinking Creatively
0.957
0.937
Coaching and Developing Others
0.957
0.931
Frequency of Decision-Making
0.880
0.957
Freedom To Make Decisions
0.914
0.947
Intellectual Complexity Items
Test scale
0.952
Item-Test
Correlation
Alpha
Inspecting Equipment, Structures, or Material
0.962
0.943
Handling and Moving Objects
0.956
0.939
Controlling Machines and Processes
0.960
0.936
Operating Vehicles, Mechanized Devices or Equipment
0.920
0.961
Mechanical Complexity Items
Test scale
Occupational Distribution by
Intellectual Complexity Level
Largest Groups Highlighted
Total Sample
Lowest Intellectual Moderate Intellectual Highest Intellectual
N
%
Managerial Specialty
Professional Specialty
Sales
410
432
178
20.33
21.42
8.82
145
Clerical/Administrative Support
407
20.18
5
0.25
26
27
42
72
21
68
61
71
104
55
38
1.29
1.34
2.08
3.57
1.04
3.37
3.02
3.52
5.16
2.73
1.88
Services: Household, cleaning,
and building
Services: Protection
Services: Food preparation
Health services
Personal services
Farming/Forestry/Fishing
Mechanics/Repair
Construction Trade/Extractors
Precision Production
Operators: Machine
Operators: Transportation, etc.
Operators: Handlers, etc.
Total
Score Range
2,017
N
%
%
N
%
18.76
417
12
54.94
1.58
410
15
21
84.54
3.09
4.33
386
49.94
7
0.92
14
2.89
3
0.39
2
0.26
24
1
3
71
20
65
54
68
6
7
2
3.16
0.13
0.4
9.35
2.64
8.56
7.11
8.96
0.79
0.92
0.26
2
0.41
1
1
1
3
7
3
3
2
2
0.21
0.21
0.21
0.62
1.44
0.62
0.62
0.41
0.41
26
38
95
46
34
3.36
4.92
12.29
5.95
4.4
95
46
34
12.29
5.95
4.4
N
773
759
485
2.42 – 3.07
3.09 – 3.59
3.62 – 3.72
Occupational Distribution by
Mechanical Complexity Level
Largest Groups Highlighted
Total Sample
Lowest Mechanical Moderate Mechanical Highest Mechanical
N
%
N
%
N
%
N
%
Managerial Specialty
Professional Specialty
Sales
410
432
178
20.33
21.42
8.82
375
419
31.62
35.33
17
7
170
4.57
1.88
45.7
18
6
8
3.92
1.31
1.74
Clerical/Administrative Support
407
20.18
392
33.05
13
3.49
2
0.44
5
0.25
5
1.34
26
27
42
72
21
68
61
71
104
55
38
1.29
1.34
2.08
3.57
1.04
3.37
3.02
3.52
5.16
2.73
1.88
24
26
42
68
6.45
6.99
11.29
18.28
2
1
0.44
0.22
4
21
68
61
71
104
55
38
0.87
4.58
14.81
13.29
15.47
22.66
11.98
8.28
Services: Household, cleaning,
and building
Services: Protection
Services: Food preparation
Health services
Personal services
Farming/Forestry/Fishing
Mechanics/Repair
Construction Trade/Extractors
Precision Production
Operators: Machine
Operators: Transportation, etc.
Operators: Handlers, etc.
Total
Score Range
2,017
1,186
372
459
0.92 – 1.27
1.33 – 1.94
2.31 – 2.77
Mean Job Complexity and Cognition
Scores
All
Characteristics
Raw Intellectual Complexity
Score
Raw Mechanical Complexity
Score
Cognitive Score
(Time 1)
Cognitive Score
(Time 2)
Cognitive Score
(Time 3)
Cognitive Score
(Time 4)
Full Retiree
Full-Time
Min
Max
3.246
0.389
3.195***
0.399
3.274
0.38
2.417
3.724
1.475
0.591
1.520*
0.626
1.45
0.569
0.916
2.773
18.114
2.992
18.191
2.999
18.07
2.988
12
27
18.014
3.119
17.745**
3.231
18.164
3.046
12
27
17.663
3.412
17.327***
3.503
17.849
3.347
3
27
17.291
3.45
16.928***
3.501
17.492
3.405
4
27
Analytic Approach
• 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
Inverse-probability-weighted
regression adjustment
• 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)
Inverse-probability-weighted
regression adjustment (cont)
• 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
Low
Moderate
High
Robust SE
Robust SE
Robust SE
Retire
-0.759
-0.474
-0.228
0.092
0.104
0.132
Stay Full-Time
-0.292
-0.231
-0.308
0.074
0.071
0.089
ATE
-0.467
-0.244
0.08
(sig)
***
*
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.
Moderate vs.
Low vs. High
Moderate
High
Retire
*
***
Stay Full-Time
Effect of Retirement on Cognitive Decline
by Intellectual Complexity of Job
Low
Moderate
High
Decline ( Standardized Standard Deviations)
0.00
-0.10
-0.12
-0.20
-0.16
-0.12
-0.30
-0.26
*
-0.40
-0.50
-0.41
***
-0.17
Retire
Stay Full-Time
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
Low
Moderate
High
Robust SE
Robust SE
Robust SE
Retire
-0.429
-0.382
-0.796
0.085
0.183
0.163
Stay Full-Time
-0.174
-0.276
-0.296
0.057
0.134
0.118
ATE
-0.255
-0.106
-0.5
(sig)
*
*
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.
Moderate vs.
Low vs. High
Moderate
High
Retire
*
*
Stay Full-Time
Effect of Retirement on Cognitive Decline by
Mechanical Complexity of Job
Low
Moderate
High
Decline ( Standardized Standard Deviations)
0.00
-0.10
-0.09
-0.20
-0.15
-0.30
-0.40
-0.23
-0.16
Stay Full-Time
-0.21
*
-0.50
-0.43
-0.60
Retire
*
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.