JOINT RESEARCH CENTER FOR PANEL STUDIESDISCUSSION PAPER SERIES
DP2012-002
February, 2013

Occupation, Retirement and Cognitive Functioning
Shinya Kajitani, Meisei University,
Kei Sakata, Ritsumeikan University
andColin McKenzie, Keio University【Abstract】
The purpose of this paper is to examine the causal impact of the duration of retirementon cognitive functioning of male elderly workers using data from three waves of the
National Survey of Japanese Elderly (NSJE). We explore the effects of the longest
tenure job (career job) on cognitive functioning. Unlike previous studies, the focusof this paperis on the workersʼ job requirements rather than industry or occupation.
We merge the occupational characteristics in the 3 digit industry code of theDictionary of Occupational Titles (DOT) with 288 NSJE occupations, and investigate
how occupational task requirements such as physical demands, mathematical development,
reasoning development, and language development impact on cognitive functioning after
retirement. The two step estimator we use takes account of the potential endogeneityof the duration of retirement, using the age at which individuals are eligible tostart receiving pension benefits and whether their career job was self-employment
as instruments, and also the left-censoring of the duration of retirement. Our
empirical evidence suggest that the requirements in a perosonʼs career job havestatistically significant impacts on the cognitive functioning after retirement.

Joint Research Center for Panel Studies
Keio University

Occupation, Retirement and Cognitive Functioning
Shinya Kajitani, Meisei University,
Kei Sakata, Ritsumeikan University
andColin McKenzie, Keio University1
Abstract
The purpose of this paper is to examine the causal impact of the duration of retirement on cognitive
functioning of male elderly workers using data from three waves of the National Survey ofJapanese Elderly (NSJE). We explore the effects of the longest tenure job (career job) on cognitive
functioning. Unlike previous studies, the focus of this paper is on the workers’ job requirements
rather than industry or occupation. We merge the occupational characteristics in the 3 digit industrycode of the Dictionary of Occupational Titles (DOT) with 288 NSJE occupations, and investigate
how occupational task requirements such as physical demands, mathematical development,
reasoning development, and language development impact on cognitive functioning after
retirement. The two step estimator we use takes account of the potential endogeneity of the durationof retirement, using the age at which individuals are eligible to start receiving pension benefits andwhether their career job was self-employment as instruments, and also the left-censoring of theduration of retirement. Our empirical evidence suggest that the requirements in a peroson’s career
job have statistically significant impacts on the cognitive functioning after retirement.

Keywords: cognitive functioning, endogeneity, retirement, two stage estimation
JEL Classification Numbers: I10, J2

1

All three authors would like to thank participants at a Workshop on Family Economics at the
University of Bocconi, and would like to gratefully acknowledge the financial assistance provided
by the Japan Society for the Promotion of Science (JSPS) Grant in Aid for Scientific Research (B)No. 24330093 for a project on “Retirement Behavior of the Aged and their Cognitive Ability and
Health”, and the kind permission of the Tokyo Metropolitan Institute of Gerontology, theUniversity of Michigan and the Social Science and the Social Science Japan Data Archive,
Information Center for Social Science Research on Japan, Institute of Social Science, University ofTokyo in making available the data in the "“Longitudinal Study of a National Survey of Japanese
Elderly (NSJE)” (Zenkoku Koureisha no Seikatsu to Kenkou ni kansuru Chouki Jyuudann Chousa)
for use in the analysis in this paper. The third author wishes to gratefully acknowledge the researchsupport provided by the Adolfo Ibanez University, Sciences Po and the University of Bocconi.
1Introduction
An aging population is a common serious issue for many countries. The combination of a low
fertility rate and increases in life expectancy has magnified the problem. An aging population
challenges the maintenance of a pay-as-you-go pension system and also raises concerns that there
may be a shortage of labour supply. A delay in the retirement age is an obvious policy option todeal with some of these problems, and many countries have already raised their retirement ages bydelaying the age for which people are eligible to start receiving pension payments 2 . The
Organization for Economic Co-operation and Development (OECD) (2012) has recently argued
that governments will need to raise the retirement age to ensure that pension systems are both
affordable and adequate.

It has been argued that there may be other externalities of delaying the retirement age because
continued intellectual stimulation can potentially help reduce the deterioration of cognitive
functioning (Potter et al. (2008), Small (2002)). The relationship between retirement and cognitive
skills has attracted much attention (Rohwedder and Willis, (2010) for a survey). These studies test
the so-called ‘use-it-or-lose-it’ hypothesis, that is, they test for a causal effect of retirement on
cognitive performance that suggests that following retirement cognitive performance declines
because retired individuals do not use their cognitive skills as much as when they are working.

One challenge in this area of research is the potential endogeneity of retirement choices as people
with lower cognitive abilities may decide to retire earlier or there may be correlation between the
choice of the age of retirement and unobservable factors such as health. The typical identification
strategy is to use a change in the social security system as an instrument.

Using cross-sectional data from the United States and Europe, Adam et al. (2006) found that there
is a positive causal relationship between retirement and cognition. Moreover, they found that thelonger the retirement period, the lower the cognitive skills. However, Adam et al. (2006) did nottake into account of endogeneity. In order to address endogeneity, the legal age of retirement which
differs across countries has been used as an instrument for retirement in cross-national data (Adamet al. (2006), Coe and Zamarro (2011), Mazzonna and Peracchi (2012), and Rohwedder and Willis,(2010)). After taking account of the endogeneity of retirement, Mazzonna and Peracchi (2012) andSee, for example,
http://www.guardian.co.uk/news/datablog/2010/jun/24/retirement-ages-oecd-countries
22

Rohwedder and Willis (2010) found that there was a significant and negative effect of retirement on
cognitive skills, while Coe and Zamarro (2011) did not find such a causal effect.

Critics of using the legal age of retirement across countries as instruments for retirement argue thatthe approach has a limitation that individuals in different countries face different social and cultural
constraints. Mazzonna and Peracchi (2012) also indicate that the cross-country variation ineligibility ages in early and normal retirement is actually small. Moreover, cross-sectional or crossnational data cannot remove time invariant individual heterogeneity.

Bonsang et al. (2012) use panel data from the Health and Retirement Study (HRS) for the UnitedStates to investigate the causal effect of retirement on cognitive function. Bonsang et al. (2012)
control for time invariant heterogeneity, and examine a single country where all individuals face thesame social and cultural constraints. They find that the effects of retirement on cognitive function
appear with a lag, and conclude that there are positive externalities of a delayed retirement for older
individuals.

Coe et al. (2012) use offers of early retirement windows as instruments for retirement, but they do
not find a clear relationship between retirement and cognitive skills. Coe et al. argue that the choiceof instruments (international differences in retirement age versus nondiscriminatory offering of
retirement windows) and the choice of specification (retirement status versus retirement duration)
may explain some of the contradictory findings in this area.
In neuropsychological research, Stern (2002, p.448) defines the cognitive reserve as “more efficientutilization of brain networks or of enhanced ability to recruit alternate brain networks as needed”.
Stern (2002) distinguishes between passive models and active models for the brain reserve
hypothesis. Passive models indicate that a larger reserve (larger brain) has a better capacity toreplace damaged brain areas, and this enables the brain to maintain higher functioning. In contrast,
active models suggest that the brain actively compensates for damaged area. Higher levels ofintelligence such as educational and occupational attainment are good predictors of which
individuals can sustain greater brain damage before demonstrating functional deficit. This suggests
taking account of the relationship between occupation and cognitive reserves (and functioning).

Some research has found that education plays a role in the cognitive reserve (Evans et al., 1993, Le
Carret et al., 2003). Other research has studied the effect of occupation on dementia and
Alzheimer’s disease, and the results suggested that cognitive decline is related to occupation (Evanset al., 1993, Schooler et al., 1999, Stern et al., 1994). Adam et al. (2006) contends that continuing3professional activities can be a protective strategy against cognitive decline. On the other hand, Coeet al. (2012) compares white collar workers to blue collar workers, but they could not find apositive causal association between retirement and cognition among white collar workers.
Interestingly, they found some evidence that there is a positive effect of retirement on the cognitive
skills for blue collar workers.

We use Japanese data to study the impact of retirement on cognitive functioning. There are someadvantages in using Japanese data to study the impact of retirement on cognition. Figure 1 depicts
the old age (65+) dependency ratios3 among some developed countries. Japan is the leader of aging
population in terms of its pace and scale. By 2010, the dependency ratio has reached 35%, and it isestimated that it will reach as high as 70% by 2050 (United Nations 2011). There is anotheradvantage in using Japanese data. Figure 2 shows the labour participation rates of elderly maleworkers (65+) among some developed countries. Although the Japanese labour participation rate
has dropped dramatically over the past 35 years the Japanese elderly males (and females) tend toremain in the labour force longer than in other developed economies, so that investigating theJapanese situation may provide useful information for other countries. This difference may give
more variation in their actual retirement ages in Japan than in other countries. It should be pointed
out that to our knowledge there is no other study examining the impact of retirement on cognitive
functioning for Japanese male (or female) workers.

[Figures 1 and 2 around here]

This paper examines the causal impact of retirement on cognitive functioning for elderly maleworkers using three waves of the “Longitudinal Study of a National Survey of Japanese Elderly”(NSJE), the initial 1987 survey, and two supplementary samples obtained in 1990, and 1996. We
use these samples because they contain information on when the respondents retired, whereas thecontinuing samples in 1990, 1993 and 1996 only contain information on the current status of
retirement, but not when respondent’s retired. This paper’s contribution is its exploration of theeffects of the longest tenure job (career job) on cognitive functioning. In particular, we focus on theworker’s job tasks rather than industry where the worker worked or his/her occupation. We merge
the occupational characteristics in the Dictionary of Occupational Titles (DOT) by 3 digit
occupational code, and examine how occupational task requirements such as physical demands,
mathematical development, reasoning development, and language development impact on
3 The old age dependency rate is defined as the ratio of the population aged 65 years or over to the
population aged 20-64. All ratios are presented as the number of dependents per 100 persons ofworking age (20-64).
4

cognitive functioning after retirement. Unlike Coe et al. (2012) or Adams et al. (2006), we will
examine the effects of the required work tasks in the individual’s career job rather than the effectsof simple occupational category. We argue that our strategy is more beneficial to account for theheterogeneity of work tasks. To our knowledge, this is the first study to examine the effects ofretirement on cognitive functioning in Japan.

In analyzing the causal impact of retirement on cognitive functioning, we will use retirement
duration rather than retirement status as the variable of interest. Some studies such as Bonsang et al.
(2010) and Rohwedder and Wills (2010) use a 0-1 retirement dummy variable which takes 1 if theindividual is retired and takes 0 otherwise, but we argue that this approach is problematic as it
regards retirement as having an immediate constant shift effect on cognition. It is difficult to argue
that two individuals one of whom retires the day of the survey and is treated as retired and one who
retires the day after the survey and who is treated as working would have significant differences intheir cognitive functioning. As Coe et al. (2012) point out, it is more natural to assume that
retirement gradually affects cognition, and modeling the ‘dose-response’ of retirement on cognition
is more appropriate.

Our two stage estimation technique deals with the potential endogeneity of retirement decisions byusing two instruments. In addition to the pension eligibility age, which is the standard instrument inthis area, this paper also uses a self-employed dummy as a second instrument. It is important tonote that the self-employed dummy refers to whether or not the respondent’s career job was
self-employment, not whether or not the respondent’s job immediately prior to retirement was self
-employment.

One potential problem in using retirement duration as the variable of interest is that the retirement
duration is left censored, that is for individuals who are still working retirement duration is treatedas being zero. Previous studies do not take account of 0 values in the retirement period variable.
Here, we apply a Tobit model to deal with this problem in the first stage estimation when we model
retirement duration.

Another estimation issue relates to mixing data from the initial 1987 survey and the supplementary
samples in 1990 and 1996. Mere pooling of these samples significantly changes the age distributionof the overall sample due to the usage of the supplementary samples which are focused on theyoung elderly. In order to adjust the age representation in the sample, we use weights from the
Census when estimating models at both the first and second stages.

5Why should economists pay attention to the relationship between retirement and cognitive
functioning? Mazzonna and Peracchi (2012) contend that cognitive functioning is a crucial factor
for formulating consumption and saving plans. Moreover, it is important to examine whether or notdeclines in cognitive ability is a natural process of aging or whether it can be controlled or affectedby work, education or other behavioral choices (Mazzonna and Peracchi (2012) and McFadden(2008)).

Some studies have examined the effect of job type on health status, and found that workers whose
career job is a blue collar occupation are more likely to show health status deterioration. The results
suggest that physically more demanding blue collar occupations lead to the health deterioration in
the elder age (Case and Deaton (2005), Kajitani (2011), Morefield et al. (2011)). It may also be thecase that the cumulative effects of occupational characteristics may be crucial to cognitive
functioning in later life.

The rest of this paper consists of four sections. Section 2 discusses the identification strategy andthe models to be estimated, while section 3 describes the data. Section 4 reports the results of
estimation and discusses their implications, and section 5 contains a brief conclusion.

2. Empirical Model and Identification Issues
The main goal of this paper is to identify the causal effect of retirement on cognitive functioning
and to examine whether aspects of the career job can have any effects on cognitive functioning
after retirement. The following model is considered:
𝐶𝑂𝐺𝑖 = τ1 𝑅𝐸𝑇𝑖 + τ2 (𝑅𝐸𝑇𝑖 ∙ 𝐷𝑂𝑇𝐽𝑖 ) + 𝜏3 𝐷𝑂𝑇𝐽𝑖 + 𝑋𝑖 β + 𝑢𝑖

(1)

where 𝐶𝑂𝐺𝑖 is the outcome variable (cognitive test score), 𝑅𝐸𝑇𝑖 is the duration of retirement,
𝐷𝑂𝑇𝐽𝑖 is a 0-1 dummy variable relating to the 𝐽th occupational characteristics of the respondent’s
career job, 𝑋𝑖 is a vector of control variables, which includes a constant, the respondent’s age andthe respondent’s years of education, 𝑢𝑖 is an error term, and the subscript 𝑖 refers to the 𝑖th
individual. Following Roberts and Binder’s (2009) suggestion, we also include 1990 and 1996
survey year dummies in 𝑋𝑖 to account for the fact that we are combining information from threesurveys, the 1987, 1990 and 1996 surveys.

The use it or lose it hypothesis suggests that as theduration of retirement gets longer, decline in cognitive skills should decline, that is, 𝜏1 < 0.
In equation (1), this paper uses retirement duration rather than the status of retirement since we are6more interested in uncovering any dynamic effects of retirement on cognitive functioning. Whenusing the retirement duration variable, it is important to note that many respondents are still in theworkforce and their value of retirement duration is zero. We argue that there may be non-trivial
differences in cognitive functioning between those who are still in the labour force and those whoare already retired.
The variable 𝐷𝑂𝑇𝐽𝑖 is a 0-1 dummy variable relating to the 𝐽th occupational characteristics of the
respondent’s career job (longest job). We use four occupational characteristics in the Dictionary of
Occupational Titles (DOT) of the respondent’s career job, namely physical demands, mathematicaldevelopment, reasoning development and language development. For the physical demands
characteristic, we create a 0-1 dummy variable which takes the value unity if the score for the
respondent’s characteristic is higher than the median value, and zero otherwise. For othercharacteristics, we create a 0-1 dummy variable which takes the value unity if the score for the
respondent’s characteristic is higher than the median value, and zero otherwise. We label thesevariables DOTP for physical demands, DOTM for mathematical development, DOTR for reasoning,and DOTL for language development. We will include these four DOT variables one by one inequation (1). When the occupational characteristic relates to the physical demands of a job, a valueof the dummy variable equal to 1 (0) relates to high (low) physical demands, so it is expected that
𝜏3 < 0. For the other three variables, where a higher than median value corresponds to a value ofthe dummy variable equal to unity, we also expect that 𝜏3 > 0. For the interaction term 𝑅𝐸𝑇𝑖 ∙𝐷𝑂𝑇𝐽𝑖 , the rate of decline of cognitive function with retirement duration will be higher for job
careers that involve higher than the median physical demand characteristics, so it is also expectedthat 𝜏2 < 0. For the other three variables, it is expected that 𝜏2 > 0.
The possibility that the length of retirement in equation (1) is endogenous is a major obstacle toestimating the causal impact of retirement on cognitive functioning. Individuals whose cognitive
abilities are lower (higher) may retire earlier (later). Moreover, retirement choices may becorrelated with unobservable factors such as health. The typical identification strategy in previous
studies is to use change in the social security system as an instrument for the retirement variable,
and we also adapt this strategy.

We consider the following model to explain the length of retirement:
𝑅𝐸𝑇𝑖∗ = 𝛾1 𝑃𝐸𝑁𝑆𝐼𝑂𝑁𝑖 + 𝛾2 𝑆𝐸𝐿𝐹𝑖 + γ3 𝐷𝑂𝑇𝐽𝑖 + 𝑋𝑖 δ + 𝑤𝑖

(2)

𝑅𝐸𝑇𝑖 = 0 if , 𝑅𝐸𝑇𝑖∗ ≤ 0= 𝑅𝐸𝑇𝑖∗ , if 0 < 𝑅𝐸𝑇𝑖∗ < 10

(3a)
(3b)
7

= 10, if 10 ≤ 𝑅𝐸𝑇𝑖∗(3c)where 𝑅𝐸𝑇𝑖∗ is an unobserved latent variable which is connected to the observed 𝑅𝐸𝑇𝑖 byequation (3), 𝑃𝐸𝑁𝑆𝐼𝑂𝑁𝑖 is the age at which individual 𝑖 is eligible to start receiving pension
benefits, 𝑆𝐸𝐿𝐹𝑖 is a 0-1 dummy variable taking the value unity if the individual 𝑖’s career job is aself-employed job, 𝑋𝑖 is the same vector of control variables as used in equation (1), and 𝑤𝑖 is adisturbance which is assumed to be normally distributed with zero mean and variance 𝜎𝑤2 . Even
though we have information on retirement durations of greater than 10 years, we have purposelychosen to censor this information because we believe that there is a limit to the impact of retirement
duration on cognitive function.4 The combination of (2) and (3), together with the assumption
about 𝑤𝑖 means that this model can be estimated using the Tobit technique (with left and rightcensoring).
By comparing equations (1) and (2), it can be seen that our “instruments” for 𝑅𝐸𝑇𝑖 are the age at
which individuals are eligible to start receiving benefits, 𝑃𝐸𝑁𝑆𝐼𝑂𝑁𝑖 , and the self-employeddummy variable, 𝑆𝐸𝐿𝐹𝑖 . The age at which individuals are eligible to start receiving pension
benefits has changed over the years in Japan. Due to the amendments to the pension law in 1954,
the age at which men are eligible to start receiving pension benefits for men had been graduallyraised by one year in every four years from 55 to 60 over a period of 16 years, 1957 to 19735. As aresult, men born from 2 May 1902 to 1 May 1905 were eligible to start receiving their pension
benefits at the age of 56. Similarly, men born from 2 May 1905 to 1 May 1908 were eligible to start
receiving their pension benefits at the age of 57, and so on. Men born after 2 May 1914 were
eligible to start receiving their pension benefits at the age of 60. We assume that these areexogenous changes to the timing of pension benefits, so that the changes in the eligibility age to
receive pension benefits is only correlated with the retirement variable, but not with cognitive
functioning.

The second instrument is a self-employment dummy which control for whether the respondent’s
career job was self-employment. Seike and Yamada (2004) indicate that the self-employed people
are more likely to stay in the labour force after their mandatory retirement age.

4

Appendix 2 provides details a cross tabulation of of retirement age and the duration ofretirement for our sample.
5 It should be noted that this law did not change the age at which women were eligible to start
receiving their pension benefits. This is another reason why we did not include females in our
analysis.
8In order to estimate the parameters of equation (1) taking account of the endogenity of the durationof retirement, three alternative ways of estimating equations (1)-(3) are maximum likelihood,
instrumental variable estimation, and two step estimation. Here, we employ a two step estimator.
First, we estimate the parameters in equations (2) using a Tobit estimator to obtain estimates of theparameters of 𝛾𝑖 and 𝛿, 𝛾̂i and 𝛿̂ , respectively. From equations (2) and (3), the conditional
expectation of 𝑅𝐸𝑇𝑖 can be computed as
𝐸(𝑅𝐸𝑇𝑖 |𝑍𝑖 ) = 10(1 − Φ𝑈 ) + (Φ𝑈 − Φ𝐿 )𝑍𝑖 𝛼 + 𝜎𝑤 (ϕ𝐿 − ϕ𝑈 )(4)

where 𝑍𝑖 is the vector of regressors in (2), 𝛼 is the vector of parameters in (2),10−𝑍𝑖 𝛼Φ𝑈 = Φ (𝜎𝑤−𝑍 𝛼

10−𝑍𝑖 𝛼𝑤

𝜎𝑤), Φ𝐿 = Φ ( 𝜎 𝑖 ), ϕ𝑈 = ϕ (−𝑍 𝛼), ϕ𝐿 = ϕ ( 𝜎 𝑖 ), Φ(. ) is the cumulative𝑤

distribution function of the standard normal distribution function, and ϕ(. ) is the probability
distribution function. With estimates of the parameters of equation (2), this conditional expectation
can easily be estimated. In the second step, 𝑅𝐸𝑇𝑖 in (1) is replaced by this estimate of theconditional expectation of 𝑅𝐸𝑇𝑖 , and the equation is then estimated by ordinary least squares(OLS). To take account of the generated regressor problem caused by using an estimate of theconditional expectation, standard errors of these OLS estimates are computed using the bootstrap
approach.

As noted in section 3, individuals aged in their early 60s are over-represented in the sample we use.
In order to adjust the age representation in the sample, we compute the proportion of a particularage group to the population from data reported for the National Censuses conducted in 1985, 1990
and 1995, and use them as the weights when estimating the models at both the first and second
stages.

3. Data
Our data are drawn from the 1987 (Wave 1), 1990 (Wave 2), and 1996 (Wave 4) waves of the“Longitudinal Study of a National Survey of Japanese Elderly (NSJE)” (Zenkoku Koureisha no
Seikatsu to Kenkou nikansuru Chouki Jyuudann Chousa). For the purpose of this survey, the“elderly” are defined as people who are 60 years of age or over. These surveys were conducted by
the Tokyo Metropolitan Institute of Gerontology and the University of Michigan. The data wasprovided by the Social Science and the Social Science Japan Data Archive, Information Center forSocial Science Research on Japan, Institute of Social Science, the University of Tokyo. NSJE has
been conducted every three years since 1987. The population of aged 60 and over was extracted by9two-stage stratified random sampling. The surveys include information on the respondent’s
physical health, mental health, family relationships, social relationships, and economic status.
Observations where for health reasons a family member answered the survey on behalf of the
respondent are excluded from our analysis. Kan (2009) and Kajitani (2011) compare the differences
between the sample distributions of the NSJE and the relevant Japanese Census data, and reportthat there is little difference between the two.

The initial sample for the longitudinal study was obtained in 1987, but was supplemented byadditional samples in 1990 and 19966. It is important to note that NSJE only asks respondents about
their year of retirement in Wave 1 in 1987, and for individuals in the supplementary samples in
Wave 2 (1990) and Wave 4 (1996). For the continuing individuals in Waves 2-4, we only know ifthey are currently retired or not. For those continuing respondents who were not retired at the timeof the first survey participated in but who retire sometime later, it is possible by comparing their
responses across two waves to determine that they retired sometime in a three year period, the time
between successive surveys. However, trying to use this information will lead to potentially large
measurement errors in the duration of retirement, so we only use Wave 1 (1987) and the
supplementary samples in Wave 2 (1990) and Wave 4 (1996).

In the supplementary sample in Wave 2 (1990), an additional 580 individuals who are aged 60 to 62
obtained by the stratified random sampling added to those continuing from Wave 1. In Wave 4(1996), an additional 1210 individuals who are aged 60 to 65 obtained by the stratified random
sampling added to those continuing from Wave 3. Table 1 summarises the sample sizes and the
response rates for each wave. As can be seen from Table 1, these four waves of the survey lead toan unbalanced panel, but to date we have not yet exploited the panel nature of the data, but rather
have just pooled data from three of the four waves. As a result, we never observe the same
individual twice in the sample we analyze in this paper.

Using the supplementary samples in Waves 2 and 4 in addition to the sample in Wave 1 means that
individuals in their early 60s are over represented in the sample we use. All the descriptive statistics
and estimation results reported in this paper, weight the data appropriately to take account of thisover representation.

The analysis in this paper is restricted to males. In Japan, women are more likely to quit their jobs

6

Since there was no supplementary sample added in 1993 (Wave 3), we do not use any of the datafrom this wave.
10

after marriage and child birth than in other developed countries. Some women come back to work
after child birth, and others do not. The NSJE survey does not provide information on how longrespondents are away from their jobs and at what age they returned to the workforce. More
importantly, for females in these generations in the sample, in particular, a sizeable number ofindividuals never worked. These women called “Kaji Testudai” in Japanese help with domestic
work in their parental home until they get married and then become a full time housewife after theirmarriage. For these reasons, the analysis of job effects on cognitive functioning for females could
be a little more complicated than the analysis for males.

[Table 1 around here]

All data on the variables used in this paper are drawn from NSJE except the Dictionary of
Occupational Titles (DOT). The definitions of all the variables are summarized in the Appendix 1.

3.1 Cognitive Test Scores
NSJE contains information on the respondents’ answers to questions that test his/her memory. In aninterview, the respondent is asked nine questions: 1) the respondent’s address; 2) the date of theinterview; 3) the day of the interview 4) the respondent’s mother’s maiden name; 5) the name of thecurrent Prime Minister; 6) the name of the previous Prime Minister; 7) the respondent’s date ofbirth; 8) the respondent’s age; and 9) a question that requires the respondent to continuously deduct
3 from 20. We use the accuracy of the respondent’s answers to these questions as a measure ofcognitive functioning. For the question on successively deducting 3 from 20, the answer is recorded
as being correct if the respondent could successfully deduct 3 six times until the number becomes 2.
Over 80 percent of the respondents gave the correct answer to this question.

We create a cognitive score variable based on seven of the nine questions which excludes theaccuracy of answering the questions about the current and the previous Prime Ministers because
these two questions relating to the Prime Minister do not necessarily capture the respondent’s
memory loss. There were eight Prime Ministers within the sample period (1987-1996), and some ofthem did not even survive one quarter (for example, Sosuke Uno for 69 days and Tsutomu Hata for
64 days). It is hard to identify if the wrong answer means a memory loss or a low interest inpolitics.

For each of the remaining seven questions, a correct answer to a question is allocated one point andan incorrect answer is allocated zero points, so the maximum possible score for an individual is 711and the minimum possible score is 0.

3.2 Retirement
Previous research uses two ways to alternative measures of retirement, the self-reported status of
retirement and the retirement period. Previous studies such as Bonsang et al. (2010) andRohwedder and Wills (2010) use a retirement dummy, which takes the value unity if s/he reports
not working, and the value zero if s/he reports to be currently working for pay. However, the effects
of retirement on cognitive functioning may not be instantaneous shift. Bonsang et al. (2012) foundthat the effect of retirement on cognitive functioning is not instantaneous, but appears with a lag.
On the other hand, Coe et al. (2012) prefer to use the duration of retirement as retirement variablearguing that it is more natural to assume that retirement gradually affects cognition. They model the‘dose-response’ of retirement on cognition. In order to capture the gradual effects of retirement, we
will use the duration of retirement as the variable of interest.

The retirement period is computed as follows. As discussed earlier, in the first survey in 1987,
individuals who have retired before the survey are asked when they retired. This information can beused to compute the duration of their retirement at the time of the survey. The same is true forindividuals in the supplementary samples in 1990 and 1996 who have retired before these surveys.
Individuals whose

duration of retirement is greater than

10 years are treated as having aduration of 10 years..

3.4 Dictionary of Occupational Titles (DOT)
One main objective is to investigate how occupational characteristics of a respondent’s career job
are associated with cognitive functioning after retirement. We merge the 3 digit occupation codes inthe Dictionary of Occupational Titles (DOT) with our data set using the 3 digit occupation code
(288 occupations) in the NSJE. We examine four dimensions of an occupational task, namely,
physical demands, mathematical development, reasoning development, and language development.

Following Fletcher et al (2011), in the physical demands category we focus on strength, which ismeasured as one of the five categories: Secondary, Light, Medium, Heavy and Very Heavy. We
compute the physical strength variable by giving scores as follows: Secondary=1, Light=2,
Medium=3, Heavy=4 and Very Heavy=5.

DOT also provides information of the General Educational Development (GED) associated with12

occupations. It describes the levels of educational aspects which are required of the worker forsatisfactory job performance. There are three aspects of GED: mathematical development,
reasoning development, and language development. Each of these GED variables is measured on ascale of 1 to 6. We merge this information using the occupational code in NSJE and examine thedepth of white-collar-type work in the worker’s career job.

[Table 2 around here]

Table 2 provides some examples of how we merged the DOT 3 digit code with 288 NSJE
occupational codes. The occupation code 1 in NSJE is “Researcher in Natural Science”, and thecorresponding occupations in this category in the 3 digit code of DOT are 20 to 25, 40, 41 and 45 incorresponding occupations under this category. Similarly, the codes 50, 51, 52, 54 and 55 in the 3
digit code of DOT can be classified in “Researcher in Humanities and Social Science” category in
NSJE. For each NSJE code we compute the average score for physical demands, mathematicaldevelopment, reasoning development, and language development for the DOT codes corresponding
to this category.

We then create a dummy variable for each of the four characteristics which take the value unity if
the score of characteristics in the respondent’s NSJE occupation is higher than the median value,and 0 otherwise. We will include DOT variables one by one in equation (1).

Table 3 shows the descriptive statistics for the occupational characteristics. Although due to thelimitation in the space, we only show the aggregated occupational figures in Table 3, it should be
noted that there are large variations in the score within white collar and within blue collar
occupations. For example, veterinarians who are considered to be professional have a relatively
high score in physical demands (2.357), and this physical demand score is similar to score for blue
collar occupations such as “other logistic workers” (2.35) and “Textile workers” (2.364). On theother hand, “Furniture/wood finishers, other prec. wood workers” have a relative highmathematical development score (4.000), and this is the same score as high as for “judges, publicprosecutors, and attorneys”. Thus, simple blue collar and white collar or professional andnon-professional divisions do not capture such variations within the groups.

[Table 3 around here]

Table 4 shows that there are high correlations between mathematical development, reasoning
development, and language development. Although there are high negative correlations between
13physical demand and GED variables, they do not suggest these skills are substitutes. Considering
these high correlations between job characteristics variables, we put each job characteristic variable
one by one in the equation.

[Table 4 around here]
4. Results and Discussion
Descriptive statistics on all the variables used in the analysis are reported in Table 5. All estimatesin this paper are obtained using STATA version 11.

First, as a benchmark case, we estimated equation (1) by OLS without taking into account theendogeneity of the duration of retirement. To be comparable with earlier research, we estimated amodel which includes a blue collar dummy variable which takes the value unity if the career job isa blue collar occupation, and 0 otherwise. Table 6 reports the estimated results. As found inprevious studies,

before taking into acoount the endogeneity of retirement, the duration ofretirement is statistically significant in some cases (4b, 5b and 6b in Table 6). Moreover, the blue
collar dummy per se is insignificant. However, it is found that the occupations with high physicaldemands reduce the cognitive test score after retirement as the interaction term of the physicaldemand dummy and the duration of retirement is negative and statistically significant. The crossterms of the duration of retirement and the three GED indicators are positive and statisticallysignificant. This means that individuals whose career jobs have alower physical demands scoregroup and or a higher GED score group are more likely to show the deterioration of memory loss.

[Tables 5 and 6 around here]

Table 7 summarises the estimated results when taking into account the endogeneity of retirement
duration but ignoring the left and right censoring of the endogenous variable and the model isestimated by instrumental variable estimation. The first stage estimation results show that our
instruments, the age at which an individual are eligible to start receiving pension benefits and theself-employed dummy are both statistically significant. The models estimated all pass theover-identification tests and the F-tests for weak instruments. The second stage estimates showsthat the duration of retirement is no longer significant in any case, which is consistent with some ofthe previous studies. Furthermore, the interaction term of the blue collar dummy and the duration ofretirement is negative and statistically significant. The cross term variable for physical demand isalso negative and statistically significant, and the cross terms for mathematical development andlanguage development are also positive and statistically significant.
14

Finally, in Table 8 we report the results of estimating our model taking into account both theendogeneity of retirement duration and the left censoring of retirement duration. The first stage
estimations indicate that both instruments are individually and jointly statistically significant. The
results of the effects of occupational characteristics on cognitive test score after retirement are all
statistically significant in their cross term variable.

It should be noted that in none of the estimated results were the survey dummies ever significant in
the first or second stage suggesting that it is legitimate to combine the information from different
surveys with appropriate weights.

Our empirical findings highlight that the requirements in people’s career job have statisticallysignificant impacts on the cognitive functioning after retirement. This is quite different from Coe etal. (2012) where they compare white collar workers to blue collar workers, but they could not finda positive causal association between retirement and cognition among white collar workers. In
addition, they report some evidence that there is a positive effect of retirement on the cognitive
skills for blue collar workers. Due to potential multicollinearity, we could not include all of the job
characteristic variables in one equation. Some may argue that the physical demand dummy merelyreflects the effects of blue collar workers, and that the dummy variables for mathematicaldevelopment, reasoning development and language development are absorbing the effects of whitecollar workers. However, our results are more plausible than Coe et al (2012), and the difference inthe results may be due to how we classify occupations.

The results show that rough division between professional and nonprofessional or between whitecollar and blue collar workers cannot examine what really has impacts on cognitive functioning.
The high physical demand reduces the memory test score after retirement. In contrast, highmathematical development, reasoning development and language development are important inpreventing the decline of cognitive functioning after retirement.

[Tables 7 and 8 around here]

5. Conclusion
This paper examines the causal impact of retirement on cognitive functioning for elderly maleworkers in Japan using data from several waves of the National Survey of Japanese Elderly (NSJE).
We contribute to the literature by exploring the effects of the characteristics of the longest tenured
15

job (career job) on cognitive functioning. This paper focuses on the worker’s job requirements
rather than the industry he/she work in or his/her occupation. After merging the occupational
characteristics in the 3 digit occupational code of the Dictionary of Occupational Titles (DOT) with
288 NSJE occupations, it is found that occupational task requirements such as physical demands,
mathematical development, reasoning development, and language development have some impact
on the cognitive functioning after retirement. Even after taking account of the endogeneity ofretirement duration, and the left censoring of retirement duration, our empirical evidence suggeststhat if the individual’s career job requirement has high mathematical development, reasoning
development, and language development, the memory loss after retirement is slower. On the other
hand, physical task performed in the individual’s career job increases the deterioration of memory
loss after retirement.

There are of course some reservations concerning the interpretation of our results. Although wefound slower deterioration in the memory loss among people who engaged in the job tasks with ahigher level of mathematical development, reasoning development, and language development, thismay not mean such activities stimulate brain and delay the pace of deterioration of memory loss.
One alternative interpretation is that people who worked in such occupations may have somespecific patterns in how they spend time in their after-retirement life. In this case, occupationaltasks may not have direct impacts on cognitive functioning. This issue should be dealt with infurther studies.

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19

Figure 1: Old age (65+) dependency ratios among some developed countries.
80

70

60
Canada50

FranceGermany
40

Italy
Japan

30

UK
US

20

10

0
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Source: United Nation (2011).

Figure 2: Labour Force Participation Rate of the Aged (65 years or older Men)
45
40

35

Canada30

France25

Germany

20

Italy

15
10
5
0

Source: OECD.Stat Extracts (http://stats.oecd.org/)Japan
United Kingdom
United States

Table 1: Response Rates
Sample Response (valid)

Respnse rate
(excluding the
66.9
82
63.3
83.8
77.7
74.3

Wave 1 (1987) New
3,288
2,200
Continued
2,200
1,671
Wave 2 (1990)
Added
580
336
Wave 3 (1993) Continued
2,441
1,864
Continued
2,226
1,549
Wave 4 (1996)
Added
1,210
898
Notes:(1) The "Continued" sample includes those who did not respond the previous survey.
(2) Cases where a family member answered the survey on behalf of the
respondent due to health reasons of the respondent are excluded from theTable 2: Examples of How to Merge 3 Digit Code in DOT with NSJENSJE OCCUPATIONAL CODE
1 Researcher in Natural Science
2 Researcher in Humanities and Social Science
3 Mining engineer
4 Metallurgical Engineer
5 Mechanic Engineer

DOT 3 DIGIT CODE
20 21 22 23 24 25 40 41 45
50 51 52 54 55
10 11
11
7

Table 3: Characteristics of Career Job
Job Characteristics
Physical demands
Mathematical demands
Reasoning development
With weighting
Career job
Obs Mean Std. Dev. Min Max
Mean Std. Dev. Min Max
Mean Std. Dev. Min Max
Professional, Technical and Managerial
324 1.741
0.232
1
3
3.981
0.490 2.513 5.375
4.783
0.301 3.667
6
Clerical and Sales
198 1.793
0.280 1.417 2.763
2.706
0.365 1.658 3.111
3.604
0.415 2.476 4.333
Service32 2.421
0.583 1.969 3.882
2.039
0.330
1 2.504
3.024
0.483
2 3.614
Agricultural, Fishery, Forestry, and Related
192 2.954
0.267
2 3.5832.073
0.320 1.083 2.207
2.881
0.195
2.25
3.2
Processing
62 2.732
0.121
2.53 2.944
1.633
0.187 1.345 2.292
2.550
0.246 2.101 2.917
Machine Trades
81 2.776
0.249
1.75 3.443
2.322
0.734 1.103 3.889
3.063
0.462 2.121 3.923
Benchwork
85 2.422
0.333 1.839
3
2.071
0.839 1.013
4
2.906
0.570 2.125
4Structual Work
121 3.096
0.182 2.598 3.317
2.203
0.365 1.733 3.313
3.260
0.203 2.733 3.83
Miscellaneous
82 2.819
0.334
2 3.2641.597
0.388 1.193 2.688
2.776
0.326
2
4
Source: Authors calculations using data from NSJE.
Note:
Physical demands are measured on a 1-5 scale, whereas the other characteristics are measured on a 1-6 scale.

Language development
Mean Std. Dev. Min Max
4.440
0.481 3.167
6
3.277
0.631 1.763
4.1
2.691
0.479 1.778 3.268
2.475
0.409
1.25
2.8
1.727
0.183 1.274 2.417
2.370
0.624 1.328 3.615
2.124
0.620 1.138 3.333
2.272
0.235
1.75 3.094
1.822
0.380
1 2.813

Table 4: Coefficients of Correlations between Job Characteristics
With weighting

Physical
DemandReasoning
Development

Physical Demand
1.000
Reasoning Development-0.646
Mathematical Development-0.673
Language Development-0.681
Source: Authors calculations using data from NSJE.
Observations (1,177)

1.000
0.939
0.920

Mathematical
Development

Language
Development

1.000
0.962

1.000

Table 5: Descriptive Statistics
Observations (1,177)
Mean
Std. Dev. Min
Max
Memory test score
6.730
0.586
2
Duration of retirement (year)
2.543
3.735
0
Physical demands (1 if > median)
0.498
0.500
0
Mathematical development (1 if > median)
0.513
0.500
0
Reasoning development (1 if > median)
0.503
0.503
0
Language development (1 if > median)
0.504
0.500
0
Age
67.375
6.456
60
Education (year)
9.889
2.942
0
Age eligible for pension benefit
59.5591.035
55
Self-employed (1 if career job=self-employed)
0.417
0.493
0
year 1987
0.692
0.462
0
year 1990
0.061
0.239
0
Source: Authors calculations using data from NSJE.
With weighting

7
10
1
1
1
1
87
17
60
1
1
1

Table 6: Estimated Results: Without considering endogeneity (With weighting )A: Cognitive equation
RET(1)-0.007[0.006](2a)-0.008[0.006]

RET*BLUE

(2b)
0.008
[0.011]-0.028 *[0.016](3a)-0.008[0.006]

RET*DOTP

(3b)
0.006
[0.009](4a)-0.008[0.006](4b)-0.024 **[0.010](5a)-0.008[0.006](5b)-0.022 **[0.010](6a)-0.008[0.006]-0.031 **[0.014]

RET*DOTM

0.028 *
[0.014]

RET*DOTR

0.026 *
[0.014]

RET*DOTL

0.023 *
[0.014]BLUE-0.055[0.055]0.016[0.053]DOTP-0.055[0.048]0.018[0.046]

DOTM

0.047
[0.052]-0.017[0.047]

DOTR

0.031
[0.055]-0.028[0.049]

DOTL
AGE
EDU
D87
D90
Constant(6b)-0.021 **[0.010]-0.008 **[0.004]0.024 ***
[0.008]-0.004[0.068]-0.128[0.221]7.082 ***
[0.271]
1,177
0.033

-0.008 **[0.004]0.019 **
[0.009]-0.008[0.068]-0.130[0.223]7.167 ***
[0.274]
1,177
0.034

-0.008 **[0.004]0.018 **
[0.009]0.012[0.063]-0.114[0.216]
7.109 ***
[0.263]
1,177
0.041

-0.008 **[0.004]0.020 **
[0.009]-0.011[0.069]-0.131[0.222]
7.149 ***
[0.270]
1,177
0.034

Observations
R-squared
Wald test
H 0 : all coef. of variables except
19.66 ***
21.59 ***
23.86 ***
21.84 ***
cons. in cognitive equation = 0
Notes:
1) standard errors in brackets are estimated using a bootstrap approach based on one thousand replications.
2) *, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively.

-0.008 **[0.004]0.018 **
[0.009]0.012[0.063]-0.114[0.216]
7.123 ***
[0.269]
1,177
0.044

-0.008 **[0.004]0.020 **
[0.009]-0.008[0.069]-0.126[0.224]7.094 ***
[0.266]
1,177
0.034

-0.008 *[0.004]0.018 *
[0.010]0.008[0.063]-0.118[0.219]7.111 ***
[0.265]
1,177
0.042

-0.008 **[0.004]0.021 **
[0.009]-0.006[0.069]-0.128[0.223]
7.093 ***
[0.268]
1,177
0.033

-0.008 **[0.004]0.020 **
[0.010]0.008[0.063]-0.120[0.217]7.128 ***
[0.265]
1,177
0.040

0.045
[0.055]-0.008 **[0.004]0.021 **
[0.009]-0.008[0.069]-0.127[0.224]7.096 ***
[0.269]
1,177
0.034

27.08 ***

22.55 ***

28.98 ***

21.32 ***

27.36 ***

21.88 ***

-0.009[0.052]-0.008 **[0.004]0.019 **
[0.010]0.006[0.064]-0.120[0.219]7.131 ***
[0.265]
1,177
0.039
26.10 ***

Table 7: Estimated Results: With considering endogeneity (With weighting )A: Cognitive equation (First-stage OLS)
RET (fitted value)

(1)
0.005
[0.025](2a)-0.001[0.025]

RET (fitted value)*BLUE

(2b)
0.039
[0.035]-0.057 *[0.030](3a)
0.001[0.026]

RET (fitted value)*DOTP

(3b)
0.024
[0.028](4a)
0.002[0.026](4b)-0.025[0.031](5a)
0.003[0.027](5b)-0.016[0.031](6a)
0.002[0.026]-0.048 **[0.023]

RET (fitted value)*DOTM

0.053 **
[0.024]

RET (fitted value)*DOTR

0.037[0.024]

RET (fitted value)*DOTL

0.041 *
[0.024]BLUE-0.048[0.049]0.108[0.082]DOTP-0.052[0.047]0.064[0.053]

DOTM

0.043
[0.052]-0.084[0.066]

DOTR

0.024
[0.057]-0.066[0.068]

DOTL
AGE
EDU
D87
D90
Constant(6b)-0.019[0.031]-0.011 *[0.007]0.023 ***
[0.008]-0.004[0.067]-0.121[0.230]7.259 ***
[0.408]
1,177
0.031

-0.010[0.007]0.019 **
[0.009]-0.008[0.067]-0.126[0.229]7.262 ***
[0.415]
1,177
0.032

-0.011[0.007]0.017 *
[0.009]-0.001[0.065]-0.130[0.227]7.229 ***
[0.426]
1,177
0.039

-0.010[0.007]0.019 **
[0.009]-0.010[0.067]-0.126[0.229]7.266 ***
[0.413]
1,177
0.033

-0.010[0.007]0.017 *
[0.009]-0.007[0.066]-0.128[0.227]7.221 ***
[0.427]
1,177
0.038

-0.010[0.007]0.020 **
[0.009]-0.008[0.067]-0.121[0.231]7.226 ***
[0.420]
1,177
0.032

-0.011[0.007]0.017 *
[0.009]-0.006[0.066]-0.131[0.228]7.331 ***
[0.440]
1,177
0.039

-0.011[0.007]0.021 **
[0.009]-0.006[0.067]-0.122[0.230]7.241 ***
[0.423]
1,177
0.031

-0.011[0.007]0.019 **
[0.009]-0.005[0.066]-0.127[0.229]7.304 ***
[0.436]
1,177
0.035

Observations
R-squared
Wald test
H 0 : all coef. of variables except
17.12 ***
18.08 ***
19.76 ***
18.87 ***
20.02 ***
20.25 ***
22.86 ***
18.84 ***
19.40 ***
cons. in cognitive equation = 0
Overidentification test of all
0.38
0.35
0.38
0.37
0.38
instruments: J-test (p-value) 3)
Notes:
1) "Duration of retirement" are calculated from the fitted values of a tobit model, standard errors in brackets are estimated using a bootstrapping technique. ten hundred bootstraps are run.
2) *, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively.
3) We report the test statistics using "ivreg2" in STATA.

0.038[0.055]-0.011[0.007]0.020 **
[0.009]-0.008[0.067]-0.122[0.231]7.233 ***
[0.421]
1,177
0.032

-0.061[0.068]-0.011[0.007]0.018 *
[0.009]-0.006[0.066]-0.130[0.229]7.311 ***
[0.437]
1,177
0.036

19.40 ***

20.10 ***

0.38

B: First-stage equation (OLS)PENSION
SELF(1)0.610 *
[0.367]-1.994 ***[0.342]BLUE(2)0.602 *
[0.364]-1.925 ***[0.314]-0.266[0.342]DOTP(3)0.607 *
[0.365]-1.985 ***[0.326](4)0.606 *
[0.366]-1.971 ***[0.329](5)0.598[0.365]-1.942 ***[0.328]-0.055[0.283]

DOTM

0.136
[0.276]

DOTR

0.275
[0.273]

DOTL
AGE
EDU
D87
D90
Constant(6)0.601 *
[0.365]-1.950 ***[0.327]

0.338 ***
[0.064]0.027[0.083]-0.291[0.728]-0.440[0.560]-55.789 **[25.540]
1,177
-3055
0.245

0.336 ***
[0.062]0.007[0.077]-0.303[0.729]-0.450[0.565]-54.803 **[25.138]
1,177
-3054
0.246

Observations
Log likelihood
R-squared
F-testH 0 : all coef. of variables except
35.31 ***
30.35 ***
cons. = 0
H 0 : all coef. of excluded
17.32 ***
18.97 ***
instruments = 0
Partial R-squared of excluded
0.09
0.07
instruments 3)
Notes:
1) Robust standard errors in brackets2) *, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively.
3) We report the test statistics using "ivreg2" in STATA.

0.338 ***
[0.063]0.024[0.079]-0.296[0.736]-0.444[0.564]-55.532 **[25.270]
1,177
-3055
0.245

0.337 ***
[0.063]0.018[0.081]-0.298[0.728]-0.435[0.559]-55.480 **[25.444]
1,177
-3055
0.246

0.335 ***
[0.063]0.008[0.081]-0.305[0.724]-0.442[0.560]-54.813 **[25.353]
1,177
-3054
0.246

0.259
[0.276]
0.336 ***
[0.063]0.009[0.080]-0.308[0.726]-0.438[0.560]-55.079 **[25.352]
1,177
-3054
0.246

30.47 ***

30.48 ***

30.33 ***

30.37 ***

18.85 ***

18.32 ***

17.92 ***

18.12 ***

0.08

0.08

0.08

0.08

Table 8: Estimated Results: With considering endogeneity (With weighting )A: Cognitive equation (First-stage Tobit)
RET (fitted value)

(1)
0.010
[0.025](2a)
0.008[0.025]

RET (fitted value)*BLUE

(2b)
0.040
[0.033]-0.054 **[0.028](3a)
0.007[0.025]

RET (fitted value)*DOTP

(3b)
0.030
[0.027](4a)
0.010[0.026](4b)-0.025[0.032](5a)
0.011[0.026](5b)-0.015[0.033](6a)
0.010[0.026]-0.052 **[0.024]

RET (fitted value)*DOTM

0.060 **
[0.025]

RET (fitted value)*DOTR

0.042 *
[0.025]

RET (fitted value)*DOTL

0.044 *
[0.026]

BLUE-0.039[0.050]0.103[0.081]DOTP-0.048[0.048]0.076[0.059]

DOTM

0.037
[0.054]-0.103[0.072]

DOTR

0.017
[0.059]-0.080[0.074]

DOTL
AGE
EDU
D87
D90
Constant(6b)-0.017[0.033]-0.012 *[0.007]0.022 ***
[0.008]-0.007[0.067]-0.121[0.225]7.326 ***
[0.399]
1,177
0.031

-0.012 *[0.007]0.019 **
[0.009]-0.009[0.067]-0.123[0.225]7.367 ***
[0.404]
1,177
0.032

-0.012 *[0.007]0.016 *
[0.009]-0.002[0.064]-0.126[0.222]7.301 ***
[0.406]
1,177
0.039

-0.012 *[0.007]0.019 **
[0.009]-0.012[0.067]-0.124[0.225]7.349 ***
[0.399]
1,177
0.033

-0.012 *[0.007]0.016 *
[0.009]-0.005[0.064]-0.125[0.223]7.300 ***
[0.409]
1,177
0.039

-0.012 *[0.007]0.019 **
[0.009]-0.009[0.067]-0.120[0.227]7.331 ***
[0.408]
1,177
0.032

-0.012 *[0.007]0.016 *
[0.009]-0.004[0.064]-0.129[0.224]7.424 ***
[0.432]
1,177
0.041

-0.012 *[0.007]0.021 **
[0.009]-0.008[0.067]-0.121[0.226]7.340 ***
[0.404]
1,177
0.032

-0.012 *[0.007]0.019 **
[0.009]-0.005[0.064]-0.125[0.224]7.382 ***
[0.422]
1,177
0.036

Observations
R-squared
Wald test
H 0 : all coef. of variables except
17.12 ***
18.37 ***
19.88 ***
18.95 ***
19.70 ***
20.32 ***
22.61 ***
18.88 ***
19.01 ***
cons. in cognitive equation = 0
Notes:
1) "Duration of retirement" are calculated from the fitted values of a tobit model, standard errors in brackets are estimated using a bootstrap approach based on one thousand replications.
2) *, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively.

0.031[0.058]-0.012 *[0.007]0.020 **
[0.009]-0.009[0.067]-0.121[0.227]7.341 ***
[0.405]
1,177
0.032

-0.071[0.075]-0.012 *[0.007]0.018 *
[0.009]-0.005[0.064]-0.127[0.224]
7.396 ***
[0.424]
1,177
0.037

19.44 ***

19.55 ***

B: First-stage equation (Tobit)
PENSION
SELF(1)
2.536 **
[1.186]-8.474 ***[1.503]BLUE(2)
2.502 **
[1.176]-8.189 ***[1.428]-1.112[1.125]DOTP(3)2.520 **
[1.179]-8.422 ***[1.461](4)
2.502 **
[1.184]-8.296 ***[1.459](5)2.463 **
[1.181]-8.207 ***[1.450]-0.313[1.007]

DOTM

1.151
[0.997]

DOTR

1.453
[0.988]

DOTL
AGE
EDU
D87
D90
Constant(6)
2.478 **
[1.180]-8.240 ***[1.449]1.174 ***
[0.257]0.077[0.270]-0.578[2.778]-2.039[2.639]-230.034 ***[85.716]
1,177
805
90
-1207

1.164 ***
[0.252]-0.011[0.256]-0.590[2.754]-2.034[2.669]-225.772 ***[84.524]
1,177
805
90
-1206

Observations
Left-censored observations
Right-censored observations
Log likelihood
F-testH 0 : all coef. of variables except
20.38 ***
17.65 ***
cons. = 0
H 0 : all coef. of excluded
15.99 ***
16.50 ***
instruments = 0
Notes:
1) Robust standard errors in brackets2) *, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively.

1.171 ***
[0.255]0.055[0.263]-0.600[2.785]-2.045[2.650]-228.483 ***[85.018]
1,177
805
90
-1207

1.167 ***
[0.255]-0.004[0.265]-0.608[2.760]-1.961[2.671]-227.352 ***[85.467]
1,177
805
90
-1206

1.157 ***
[0.255]-0.027[0.265]-0.613[2.753]-2.008[2.680]-224.300 ***[85.236]
1,177
805
90
-1206

1.505
[1.000]1.161 ***
[0.255]-0.033[0.262]-0.639[2.753]-1.995[2.680]-225.403 ***[85.163]
1,177
805
90
-1206

17.50 ***

17.58 ***

17.69 ***

17.73 ***

16.72 ***

16.28 ***

16.15 ***

16.30 ***

Appendix I: Definitions of Variables

AGE

Defintion
Number of years between when the respondent reports retiring and the survey
0-1 dummy variable taking the value unity if the NSJE occupational code of the
respondent's career job is 64～288，and 0 if the occupational code is 1～63.
Physical demands (1 if > median)
0-1 dummy variable taking the value unity if the physical demand score for the
respondent's NSJE occupational code is higher than the median value, and 0
otherwise.
Mathematical development (1 if > median) 0-1 dummy variable taking the value unity if the mathematical development score
for the respondent's NSJE occupational code is higher than the median value, and 0
otherwise.
Reasoning development (1 if > median)
0-1 dummy variable taking the value unity if the reasoning development score forthe respondent's NSJE occupational code is higher than the median value, and 0
otherwise.
Language development (1 if > median)
0-1 dummy variable taking the value unity if the language development score forthe respondent's NSJE occupational code is higher than the median value, and 0
otherwise.
Age
Respondent's age in years at the time of the survey

EDU

Education (year)

Number of years of schooling

PENSIONAge eligible for pension benefit

The age at which the respondent is eligible to start receiving pension benefits

SELF

Self-employed

D87year 1987

D90

year 1990

0-1 dummy variable taking the value unit if the career job is a self-employedoccupation, and 0 otherwise.
0-1 dummy variable taking the value unity if the respondent is surveyed in Wave 1(1987), and 0 otherwise. (Reference year is Wave 4 (1996))
0-1 dummy variable taking the value unity if the respondent is surveyed in Wave 2(1990), and 0 otherwise. (Reference year is Wave 4 (1996))

Short Name Long Name
RET
Duration of retirement (year)
BLUE
Blue-collar
DOTP

DOTM

DOTR

DOTL

Appendix II: The Sample Distribution of Age and Duration of Retirement
Duration of Retirement
Age at Retirement/
Current Age for the

Employed
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
87

Total

0
0
0
0
0
0
0
0
0
0
119
141
141
86
82
67
15
20
18
10
15
9
12
15
15
13
5
4
2
5
1
3
2
1
2
1
1
805

1

2
0
0
0
0
0
0
0
0
0
6
13
2
4
1
4
1
0
1
0
0
1
1
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
36

3
0
0
0
0
0
0
0
0
3
4
17
5
4
3
0
1
0
1
1
1
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
44

4
0
0
0
0
0
0
0
2
7
3
7
1
4
3
0
2
1
4
0
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
37

5
0
0
0
0
0
0
1
4
2
4
11
3
1
0
2
1
0
3
1
1
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37

6
0
0
0
0
0
4
0
3
5
3
11
2
1
1
1
3
1
0
0
1
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
39

7
0
0
0
0
4
3
2
2
0
5
7
0
1
1
2
2
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
31

8
0
0
0
1
1
4
2
0
4
1
1
2
1
1
2
1
1
2
3
1
2
0
0
1
1
1
0
0
0
1
0
0
0
0
0
0
0
34

9
0
0
0
0
1
1
2
2
2
0
3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12

10 yearsor more
0
3
0
2
0
3
1
1
0
2
1
1
0
0
2
4
0
2
0
7
1
2
0
6
0
5
0
11
2
8
1
3
0
4
0
4
2
5
1
3
1
3
0
0
0
4
0
2
0
3
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
90

Total
3
2
3
3
8
14
7
19
25
33
192
163
162
107
103
82
22
35
30
19
26
13
18
19
22
16
6
4
3
6
1
3
2
1
2
2
1
1,177