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

Intellectual Complexity Items 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

Test scale 0.952

Mechanical Complexity Items 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

Test scale

Occupational Distribution by Intellectual Complexity Level

  Total Sample Lowest Intellectual Moderate Intellectual Highest Intellectual

  N % N % N % N %

Managerial Specialty 410 20.33     410 84.54

Professional Specialty 432 21.42   417 54.94 15 3.09

Sales 178 8.82 145 18.76 12 1.58 21 4.33

Clerical/Administrative Support 407 20.18 386 49.94 7 0.92 14 2.89

Services: Household, cleaning, and building

5 0.25 3 0.39 2 0.26 

Services: Protection 26 1.29 24 3.16 2 0.41

Services: Food preparation 27 1.34 26 3.36 1 0.13  Health services 42 2.08 38 4.92 3 0.4 1 0.21Personal services 72 3.57 95 12.29 71 9.35 1 0.21Farming/Forestry/Fishing 21 1.04 46 5.95 20 2.64 1 0.21Mechanics/Repair 68 3.37 34 4.4 65 8.56 3 0.62Construction Trade/Extractors 61 3.02   54 7.11 7 1.44Precision Production 71 3.52   68 8.96 3 0.62Operato rs: Machine 104 5.16 95 12.29 6 0.79 3 0.62Operators: Transportation, etc. 55 2.73 46 5.95 7 0.92 2 0.41

Operators: Handlers, etc. 38 1.88 34 4.4 2 0.26 2 0.41

Total 2,017 773 759 485

Score Range   2.42 – 3.07 3.09 – 3.59 3.62 – 3.72

Largest Groups Highlighted

Occupational Distribution by Mechanical Complexity Level

  Total Sample Lowest Mechanical Moderate Mechanical Highest Mechanical

  N % N % N % N %

Managerial Specialty 410 20.33 375 31.62 17 4.57 18 3.92

Professional Specialty 432 21.42 419 35.33 7 1.88 6 1.31Sales 178 8.82 170 45.7 8 1.74

Clerical/Administrative Support 407 20.18 392 33.05 13 3.49 2 0.44

Services: Household, cleaning, and building

5 0.25   5 1.34  

Services: Protection 26 1.29   24 6.45 2 0.44

Services: Food preparation 27 1.34   26 6.99 1 0.22Health services 42 2.08   42 11.29  Personal services 72 3.57   68 18.28 4 0.87Farming/Forestry/Fishing 21 1.04     21 4.58Mechanics/Repair 68 3.37     68 14.81Construction Trade/Extractors 61 3.02     61 13.29Precision Production 71 3.52     71 15.47Operato rs: Machine 104 5.16     104 22.66Operators: Transportation, etc. 55 2.73     55 11.98

Operators: Handlers, etc. 38 1.88         38 8.28

Total 2,017 1,186 372 459

Score Range   0.92 – 1.27 1.33 – 1.94 2.31 – 2.77

Largest Groups Highlighted

Mean Job Complexity and Cognition Scores

  All Full Retiree Full-Time Min MaxCharacteristics                Raw Intellectual Complexity Score

3.246 0.389 3.195*** 0.399 3.274 0.38 2.417 3.724

Raw Mechanical Complexity Score

1.475 0.591 1.520* 0.626 1.45 0.569 0.916 2.773

Cognitive Score 18.114 2.992 18.191 2.999 18.07 2.988 12 27

(Time 1)Cognitive Score

18.014 3.119 17.745** 3.231 18.164 3.046 12 27(Time 2)Cognitive Score

17.663 3.412 17.327*** 3.503 17.849 3.347 3 27(Time 3)Cognitive Score

17.291 3.45 16.928*** 3.501 17.492 3.405 4 27(Time 4)

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 SERetire -0.759 -0.474 -0.228  0.092 0.104 0.132Stay Full-Time -0.292 -0.231 -0.308  0.074 0.071 0.089ATE -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.

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

 Low Moderate High

Robust SE Robust SE Robust SERetire -0.429 -0.382 -0.796  0.085 0.183 0.163Stay Full-Time -0.174 -0.276 -0.296  0.057 0.134 0.118ATE -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.

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.