Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2017-002
March, 2017

The effect of training on the employment of older workers after compulsory retirementin JapanKazuma Sato＊【Abstract】
Many industrialized countries are facing an ageing population. This threatens thesustainability of the social security system. To overcome this issue, policymakers must
consider measures to encourage older people to work. Job-related training is considered
to be effective for this purpose because it can prevent the deterioration of human capital.
However, studies that examine the relationship between training and employment,
especially in Asia where ageing is advancing rapidly, are still scarce. To fill this gap, thisstudy examines the effect of training on the employment of older workers using theLongitudinal Survey of Middle-aged and Elderly Persons, the largest panel data
available on the elderly in Japan. The key findings can be summarized as follows. First,
the probability of re-employment rises significantly one year and two years after training.
Second, training is effective in the case of re-employment as regular worker. This effectis notable because most re-employed workers are employed as non-regular workers.
These results indicate that training is a useful measure for keeping older workers inwork.
＊

Takushoku University, Faculty of Political Science and Economics, Associate Professor

Panel Data Research Center at Keio University
Keio University

The effect of training on the employment of older workers
after compulsory retirement in Japan¶

Kazuma Sato *Abstract
Many industrialized countries are facing an ageing population. This threatens the sustainability ofthe social security system. To overcome this issue, policymakers must consider measures to encourageolder people to work. Job-related training is considered to be effective for this purpose because it can
prevent the deterioration of human capital. However, studies that examine the relationship between
training and employment, especially in Asia where ageing is advancing rapidly, are still scarce. To fillthis gap, this study examines the effect of training on the employment of older workers using theLongitudinal Survey of Middle-aged and Elderly Persons, the largest panel data available on theelderly in Japan. The key findings can be summarized as follows. First, the probability of reemployment rises significantly one year and two years after training. Second, training is effective inthe case of re-employment as regular worker. This effect is notable because most re-employed workers
are employed as non-regular workers. These results indicate that training is a useful measure forkeeping older workers in work.

JEL Code: J21, J26
Key Word: Older Worker, Training, Matching Method¶ This research was supported by a Health Labour Sciences Research Grant (number H26-Seisaku-

Ippan-003) from the Ministry of Health, Labour, and Welfare in Japan. The permission to use The
Longitudinal Survey of Middle-aged and Elderly Persons was obtained from the Ministry of Health,Labour, and Welfare in Japan. We are grateful to the Ministry of Health, Labour, and Welfare in
Japan.

* Takushoku University

1

1

Introduction

Many industrialized countries are facing an ageing population. This threatens the sustainability ofthe social security system, such as pensions. To overcome this issue, policymakers must consider
measures to encourage older people to work. Job-related training is considered to be valid for thispurpose because it can prevent the deterioration of human capital. Picchio and van Ours (2013)
investigated this issue and show that firm-provided training can enhance the employability of older
workers. Kajitani (2006) also examined the effect of training on employment after compulsory
retirement and shows that training can shorten the period of unemployment. However, studies thatexamine the relationship between training and employment for older workers are still scarce. 1 In
particular, studies that use data for Asia, where ageing is advancing rapidly, are scarce. On the other
hand, there are many studies concerning wages and productivity that show training has a positive effecton wages and productivity (Bartel 1994, 1995; Barret & O’Conell 2001; Booth & Bryan 2005; Conti
2005; Frazis & Loewenstein 2005; Dearden et al. 2006; Zwick 2006; Konings & Vanormelingen 2009;
Almeida-Santos et al. 2010; Görlitz 2011). To fill this gap in the research, we examine the effect oftraining on the employment of older workers by using Japanese panel data.
As a general survey of working conditions conducted by the Ministry of Health, Labour and Welfare
in 2014 shows, the compulsory retirement system is instituted in 93.8% of companies in Japan. Hence,older workers have to retire when they reach the prescribed age. Among Organisation for Economic
Co-operation and Development countries, the elderly in Japan are particularly motivated to work, so
there are many workers who desire re-employment. While some workers find a job soon after
compulsory retirement, others become unemployed for a period of time before starting to look for a

1 Although Ham and Lalonde (1996), Alba-Ramirez (1999), Lee and Lee (2005), and Choi and Kim (2012) also
examined the effect of training on employment, they did not focus on older workers. Kluve (2010) surveyed theliterature on the effect of training on the employment prospects of unemployed workers and clarified that training had
a mild effect on employment, with impacts that changed by targeted age group.

2

job. We focus on the latter and verify whether training during the period of unemployment is able toenhance the probability of re-employment. Since compulsory retirement can be regarded as an
exogenous job loss, it is possible to control for the heterogeneity of factors that have fallen into
unemployment.
In estimating the effect of training, we must pay attention to the self-selection for participation intraining. If more able workers carry out the training, the effect of training will be overestimated dueto the selection. On the other hand, if less able workers tend to do the training, the effect of training
will be underestimated. Therefore, taking into account the selection is key for estimating the causal
effect of training. To overcome this issue, Heckman et al. (1997) employ a matching method. We also
exploit a matching method, entropy balancing, which was developed recently by Hainmueller (2011,2012). Entropy balancing is a matching method that creates a sample weight to control for thedifferences in covariates among workers who carry out training and workers who do not. The
advantage of using entropy balancing is that it can control for the individual heterogeneity among
workers more accurately than any other matching method. In the model of entropy balancing, we
control not only for individual attributes, work-related variables before retirement, and current healthbut also for the intention to work, which jointly determines the participation of training and reemployment. This makes it possible to examine the causal effect of training.
The key findings can be summarized as follows. First, the probability of re-employment rises
significantly one year and two years after training. Second, training is effective in the case of reemployment a regular worker. This effect is notable because most re-employed workers are employed
as non-regular workers. These results indicate that training is a useful measure for keeping older
workers in work.
The remainder of this paper is organized as follows. The next section describes the data, and Section
3 explains the empirical strategy. Section 4 discusses the estimation results, and Section 5 provides
3

concluding remarks.

2

Data
2.1

Data description

The data used in this analysis is from the Longitudinal Survey of Middle-aged and Elderly Persons
conducted by Ministry of Health, Labour and Welfare in Japan. This is the largest panel survey of
elderly people in Japan. The survey was first implemented in 2005 with 33,815 male and female
respondents aged 50–59 years. The survey is conducted annually, and we use the data for 2005–2009
because the questionnaire on training is available until 2009. The data investigates families, income,
employment, well-being, and type of residence.
In this analysis, we limit the sample to men and women who were employed and experienced
compulsory retirement. Of the 3,130 individuals that experienced compulsory retirement, 1,365 werere-employed immediately after retirement, and 1,765 were unemployed after retirement. We focus on
the latter to clarify the effect of training on re-employment. After deleting the missing values of the
explanatory variables, the total number of individual-year observations becomes 1,716. The averageretirement age from the questionnaire is 60 years old, which is almost the same as in the 2014 generalsurvey of working conditions of the Ministry of Health, Labour and Welfare.
Before entering the econometric specification, we briefly check the relationship between training
and re-employment for older workers by using descriptive statistics. Training is defined as thedevelopment of skills for work or self-enlightenment during the last year of employment before
retirement, and training conducted after retirement. The employment rate is defined as the percentageof employed workers. Figure 1 shows the employment rate up to three years after the training at periodt. The figure clearly shows that the employment rate in each period is higher for those who received
4training. This result implies the potential of training to enhance the employability of older workers.
However, it should be noted that as this casual observation does not take into account self-selection,
the effect of training may be overestimated.

2.2

Transition of employment status, occupation, and firm size before andafter compulsory retirement

How do employment status, occupation, and firm size change before and after compulsory
retirement? Since these changes have a great influence on the working conditions of older workers,
we briefly check the transitions. Table 1 shows the changes in employment status. The results indicatethat while most of the workers who worked in regular employment before retirement changed to nonregular employment after re-employment, workers who worked in non-regular employment before
retirement stayed in non-regular employment after re-employment. In particular, 92.31% of part-timeworkers before retirement worked in the same employment status after re-employment. These results
indicate that regardless of employment status before retirement, many workers work as non-regular
employees after re-employment.
Table 2 indicates the changes in occupation. The results show that the percentage of workers withthe same occupation before and after re-employment is low, except for agriculture, fishery, forestry,and other work, implying that many workers experience a change in occupation. This implies thepossibility that older workers cannot make effective use of their occupational experience gained before
retirement.
Table 3 indicates the changes in firm size. It shows that in many cases, company size becomes
smaller after re-employment, and there are few cases where the company size becomes larger.

5

3

Econometric model
3.1

Entropy balancing

Taking the self-selection bias into account is key to estimating the pure training effect on the reemployment of older workers. Propensity score matching and propensity score weighting are usefulfor reaching this goal. However, we employ entropy balancing because it has two advantages(Hainmueller & Xu 2013). First, entropy balancing is more effective for reducing the imbalances ofindividual heterogeneity than other matching methods. Second, it is easier with entropy balancing todo the balance check, which confirms whether imbalances in individual attributes between workers
who carry out training and workers who do not still exist after matching. We briefly explain the method
to estimate the average treatment effect on the treated (ATT) using entropy balancing. 2
When estimating the effects of training on re-employment, the ATT is as follows.

ATT = E[𝑌𝑌1𝑖𝑖 − 𝑌𝑌0𝑖𝑖 |𝐷𝐷𝑖𝑖 = 1] = E[𝑌𝑌1𝑖𝑖 |𝐷𝐷𝑖𝑖 = 1] − E[𝑌𝑌0𝑖𝑖 |𝐷𝐷𝑖𝑖 = 1]

(1)

In equation (1), 𝑌𝑌𝑖𝑖 indicates the re-employment dummy, where 𝑌𝑌1 indicates the value at the time

when workers engaged in training, and 𝑌𝑌0 is the value when workers did not. D indicates thetraining dummy. D = 1 indicates workers who engaged in training (treatment group), and D = 0
indicates workers who did not engage in training (control group). In equation (1), E[𝑌𝑌0𝑖𝑖 |𝐷𝐷𝑖𝑖 = 1] isthe value of re-employment of workers who did not engage in training had they engaged in training.

This value cannot be observed because it is counterfactual. To solve this issue, entropy balancing

2 There are still few analyses that use entropy balancing; representative studies in economics are Marcus (2013) and

Freier et al. (2015). Marcus (2013) uses entropy balancing to estimate the effect of job displacement on the mental
health of spouses. Freier et al. (2015) use entropy balancing to estimate the effect of graduating from university withan honours degree on later income.

6

replaces E[𝑌𝑌0𝑖𝑖 |𝐷𝐷𝑖𝑖 = 1] by using a weighted control group:E[𝑌𝑌0𝚤𝚤 |𝐷𝐷�
𝚤𝚤 = 1] =∑{𝑖𝑖|𝐷𝐷=0} 𝑌𝑌0𝑖𝑖 𝑤𝑤𝑖𝑖∑{𝑖𝑖|𝐷𝐷=0} 𝑤𝑤𝑖𝑖

(2)

In equation (2), 𝑤𝑤𝑖𝑖 is the sample weight for the control group. This sample weight is calculatedby the constraint equations, which satisfy an exact balance between the first and second moments of
the individual attributes in the treatment and control groups. This is the most important feature ofentropy balancing. By satisfying the first and second individual attribute moments, we can obtain
similar means and variances for the individual attributes between the treatment and control groups.
Thus, most differences in the individual attributes between the treatment and control groups are
removed. In the analysis, the first and second moments are employed to equate the mean andvariance among groups.
We conduct the estimation through two steps. First, the sample weight for the control group isestimated by entropy balancing. Second, the probit model is estimated with the sample weight. The
mean differences and the probit model without the sample weight are also estimated to check theextent of the self-selection bias. In addition, we also estimate propensity score matching by applying
kernel matching for the robustness check.
The dependent variable is the re-employment dummy. The re-employment dummy takes a valueof 1 if unemployed workers in period t were employed in period t+1, and takes a value of 0 if
unemployed workers in period t stayed unemployed in period t+1. The re-employment dummies atperiods t+2 and t+3 are also used to confirm the persistence of the training effect. The variable that
identifies the treatment and control groups takes a value of 1 if workers engaged in job-related
training in period t, and takes a value 0 if workers did not. In the analysis, we treat the training after
retirement.
7

The covariates have three categories. The first category is the individual attributes and variables
related to work before retirement; the second category is a variable relating to employment
willingness past the age of 60 years old; and the third category is a health variable. In the analysis,
these variables are used step by step as covariates to verify how the effect of training on reemployment changes. Individual attributes include dummy variables for gender, education, age, thenumber of family members, home ownership, years, and earnings from public pensions, employment
insurance, social security benefits, and private pensions. Work-related variables before retirement
include job tenure, employment status, occupation, and firm size.
The variables concerning the employment intentions past 60 years of age are constructed from thequestion, “Do you want to carry out work and receive income after the age of 60?” 3 We created adummy variable that equals 1 if respondents answered they wanted to work as long as possible forthis question, and 0 otherwise. We also created a dummy variable that equals 1 if respondents
answered they wanted to work until a certain age over 60, and 0 otherwise. Finally, we created adummy variable equalling 1 if respondents answered that they did not want to work after 60 years
old, and 0 otherwise. In the analysis, the last dummy variable is used as a reference group. As
Kajitani (2006) points out, to control for these intentions is crucial because they jointly determine
training and re-employment.
The health-related variables include dummy variables for good health and the number of serious
diseases of the respondent. The dummy for good health indicates whether respondents have goodsubjective rated health or not. The dummy for serious diseases indicates the number of diseases the
respondent suffers from, including diabetes, heart disease, stroke, hypertension, hyperlipidemia, andcancer.

3 This question exists only in the survey for the first year, and we assume that the value does not change over thewhole period.

8

3.2

Basic statistics before and after matching

Entropy balancing controls for the differences in individual attributes between the treatment andcontrol groups. Basic statistics before and after matching, shown in Table 4, are used to check theextent of such control measures. The variables before matching show significant differences in themeans for education, age, home ownership, earning from public pension, earning from employment
insurance, occupation and firm size before compulsory retirement, and intention to work. These results
show that while workers who engage in training tend to have higher educational attainment and havehigher percentages for receiving employment insurance, working at professional and technical work,
and intention to work as long as possible after retirement, they have a lower average age and lower
percentages of home ownership, reception of employment insurance, and working in production andlabour work. On the other hand, the basic statistics after matching indicate that the mean difference
for all variables becomes 0.00, implying that differences in individual attributes disappear throughentropy balancing.

4

Empirical results

Table 5 shows the results for the effect of training on the re-employment of older workers. Panel(A) shows the results for re-employment one year after training. 4 All coefficients of the meandifferences, probit model, entropy balancing, and propensity score matching for panel (A) are
positively significant. This indicates that training increases the probability of re-employment after one
year. Although the size of the coefficients decreases when the individual attributes, employment

4

The values of the probit model represent the marginal effects.

9motivation, and health are controlled step by step, the variables are significant in any cases, so thetraining effect on employment is robust. Comparing the sizes of the coefficients of the probit modeland entropy balancing, those for entropy balancing are larger. This indicates a negative bias of selfselection, implying that less able older workers tend to engage in training. Panel (B) shows the results
for re-employment two years after training. Also for these results, even if individual attributes,
intention to work, and health are controlled for, all coefficients are positively significant. These results
indicate that training increases the probability of re-employment after two years. Panel (C) shows theresults for re-employment three years after training. Unlike the previous results, most of thecoefficients, except for the mean difference, probit, and propensity score matching, are not significant.
This indicates that training does not have an effect on the probability of re-employment after three
years.
To summarize the results so far, training significantly increases the probability of re-employment
after one and two years. Training is promising for the employment of older workers. This result is
consistent with Picchio and van Ours (2013) and Kajitani (2006). However, the result for the selection
bias is different from previous studies. Picchio and van Ours (2013) point out the existence of apositive selection bias, while Kajitani (2006) points out there is no selection bias. On the other hand,
our study shows the existence of a negative selection bias. This is because our study focuses on
workers who become unemployed after compulsory retirement. While able workers become employedsoon after retirement, less able workers become unemployed after retirement. Hence, it can beconsidered that the analysed samples consist of workers with relatively low abilities. 5
Whether subjects are unemployed are re-employed with regular employment or non-regular
employment has a big impact on income and working hours. Determining whether job-related training

5 We check the differences in the work-related variables between workers who were re-employed immediately after

retirement and workers who were not. Workers who were re-employed after retirement have a higher ratio of regular
employment, and their occupations and company sizes did not change much at re-employment.

10

promotes employment in regular employment can provide useful policy information. Therefore, we
examine the effect of training on employment status at the time of re-employment with a multinomial
logit model. The dependent variable is 1 for regular employment, 2 for non-regular employment, and3 for continuing unemployment at period t. All workers are unemployed in period t-1. We use the same
explanatory variables as those in Table 5.
Table 6 shows the results of the effect of training on re-employment by employment status. All
values in Table 6 are marginal effects. Panel (A) shows the results of re-employment one year after
training. While all coefficients for regular workers in panel (A) are positively significant, those for
non-regular workers are not significant. This result indicates that although training enhances theprobability of re-employment by regular workers after one year, it does not affect the re-employmentof non-regular workers.
Panel (B) shows the results for re-employment two years after training. Most of the coefficients inpanel (B) are not statistically significant. This indicates that training has no effect on re-employment
after two years. On the other hand, panel (C), which shows the results for re-employment three years
after training, shows all coefficients for regular workers to be positively significant. This result
indicates that training increases the probability of re-employment by regular workers after three years.
Considering the coefficients for non-regular employment are not significant, training appears to beeffective for the re-employment of regular workers.

5

Conclusion

The purpose of this study is to clarify the effect of job-related training on the re-employment ofolder workers. Compared with previous studies, there are two advantages to this study. First, we use
11the largest available panel data for older workers in Japan, which is ageing rapidly among Asian
countries. As most studies in this field use data for the United States or Europe, this study contributes
to the accumulation of empirical analysis for other regions. Second, we use entropy balancing toaccount for the self-selection bias of training. We control for the bias by including the intention towork past 60 years old in the covariates for entropy balancing. The key findings can be summarized
as follows. First, the probability of re-employment rises significantly one year and two years after
training. Second, training is effective in the case of re-employment a regular worker. This effect isnotable because most re-employed workers are employed as non-regular workers. These results
indicate that training is a useful measure for keeping older workers in work.
The findings show that active labour market policies can be effective for promoting the employmentof older workers. Considering the trend of ageing in the future, it is essential to implement support
measures to promote the development of capacity for the elderly. While support measures for young
and middle-aged workers are being expanded in Japan, capacity development for the elderly is not
sufficient, and future improvement is needed.
Finally, an outstanding issue should be noted. In this study, we analyzed the relationship between
training and the employment of older workers in Japan. However, as the employment of elderly people
will become an issue in other Asian countries experiencing ageing populations, it is necessary to carryout analysis using data for countries other than Japan. This will be a future research topic.

12References
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15

Figure 1. Employment rate after training
30.00

(%)

26.95

26.03

24.82

25.00

20.00

15.00

14.48

14.32

employment rate
(after t+2 year)

employment rate
(after t+3 year)

13.21

10.00

5.00

0.00
employment rate
(after t+1 year)

training at period t

no training at period t

Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

16Table 1. Change in employment status before and after compulsory retirement(%)
Regular employee
Employment status before retirement

Regularemployee

Full-time employee- manager
Full-time employee- under manager
Part-time worker

Non-regular Subcontracted worker
employee
Contract employee /Specialized contract employee
Total

Full-time employee Full-time employee- manager- under manager

Employment status after re-employment
Non-regular employee
Contract employee
Subcontracted
Part-time worker/Specialized
workercontract employee

Total

25.00

0.00

50.00

0.00

25.00

100

0.00

14.29

60.00

6.67

19.05

100

0.00

7.69

92.31

0.00

0.00

100

0.00

0.00

0.00

100.00

0.00

100

0.00

0.00

71.43

0.00

28.57

100

0.77

12.31

63.08

6.15

17.69

100

Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

17Table 2. Change in occupation before and after compulsory retirement(%)
Occupation after re-employment
Occupation before retirement

Professional andtechnical work

Management

Office work

Sales

Services

Security

Professional and technical work
Management
Office work
Sales
Services
Security
Agriculture,fishery, forestry
Transportation,communication
Production process, labor work
Other work
Total

46.43
7.14
0.00
0.00
9.09
0.00
0.00
16.67
6.67
0.00
13.85

14.29
14.29
6.67
0.00
9.09
0.00
0.00
16.67
0.00
12.50
7.69

0.00
28.57
20.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
5.38

0.00
14.29
6.67
50.00
9.09
0.00
0.00
0.00
0.00
0.00
7.69

7.14
7.14
0.00
16.67
36.36
75.00
0.00
16.67
33.33
25.00
19.23

0.00
0.00
6.67
8.33
0.00
0.00
0.00
0.00
6.67
0.00
3.08

Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

18

Agriculture,fishe Transportation,ry, forestry
communication
3.57
7.14
0.00
0.00
0.00
0.00
100.00
0.00
6.67
0.00
4.62

0.00
0.00
13.33
0.00
9.09
0.00
0.00
33.33
3.33
0.00
4.62

Production
process,labor work
21.43
21.43
20.00
16.67
9.09
25.00
0.00
0.00
30.00
0.00
19.23

Other work

Total

7.14
0.00
26.67
8.33
18.18
0.00
0.00
16.67
13.33
62.50
14.62

100
100
100
100
100
100
100
100
100
100
100

Table 3. Change in firm size before and after compulsory retirement(%)
Firm size before retirement
Less than 99
100-999
1000 or more
Public worker
Total

Less than 99
84.44
53.66
45.71
50.00
62.40

Firm size after re-employment
100-999
1000 or more Public worker
11.11
2.22
2.22
39.02
2.44
4.88
22.86
22.86
8.57
50.00
0.00
0.00
24.80
8.00
4.80

Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

19

Total
100
100
100
100
100

Table 4. Basic statistics before and after matchingtreatment group(training=1)

before matching
control group(training=0)

treatment group(training=1)

after matching
control group(training=0)

meanvariancemean

variancemeandifferencemeanvariancemean

variancemeandifference

individual attributes
male
education: vocational college / junior college
education: university/graduate schoolage
number of family members
marriedhaving own homeearning from public pension
earning from employment insurance
earning from social security benefit
earning from private pension
work related variables before compulsory retirement
job tenure
regular worker
professional and technical work
management
sales
services, security
transportation,communication
production process, labor work
other work
firm size: 100-999
firm size: 1000 or more
firm size: public worker

0.63
0.15
0.21
60.12
1.78
0.83
0.88
0.52
0.15
0.01
0.13

0.24
0.13
0.17
4.28
1.07
0.14
0.11
0.25
0.13
0.01
0.12

0.59
0.09
0.14
60.82
1.88
0.87
0.93
0.67
0.10
0.01
0.14

0.24
0.08
0.12
2.76
1.67
0.11
0.06
0.22
0.09
0.01
0.12

0.04
0.06***
0.06**-0.70***
-0.11
-0.04
-0.05***-0.15***
0.05**
0.01
-0.01

0.63
0.15
0.21
60.12
1.78
0.83
0.88
0.52
0.15
0.01
0.13

0.24
0.13
0.17
4.28
1.07
0.14
0.11
0.25
0.13
0.01
0.12

0.63
0.15
0.21
60.12
1.78
0.83
0.88
0.52
0.15
0.01
0.13

0.23
0.13
0.17
4.39
1.42
0.14
0.11
0.25
0.13
0.01
0.12

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

26.03
0.81
0.31
0.12
0.08
0.11
0.03
0.13
0.05
0.33
0.29
0.09

204.20
0.15
0.22
0.11
0.08
0.10
0.03
0.11
0.05
0.22
0.21
0.09

27.68
0.79
0.18
0.12
0.06
0.09
0.04
0.24
0.05
0.35
0.27
0.06

177.20
0.17
0.15
0.11
0.05
0.08
0.04
0.18
0.04
0.23
0.20
0.06

-1.65
0.02
0.13***
0.00
0.03
0.03
-0.01
-0.11***
0.01
-0.02
0.02
0.03*

26.03
0.81
0.31
0.12
0.08
0.11
0.03
0.13
0.05
0.33
0.29
0.09

204.20
0.15
0.22
0.11
0.08
0.10
0.03
0.11
0.05
0.22
0.21
0.09

26.03
0.81
0.31
0.12
0.08
0.11
0.03
0.13
0.05
0.33
0.29
0.09

188.60
0.15
0.22
0.11
0.08
0.10
0.03
0.11
0.05
0.22
0.21
0.09

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

intention to work over 60
want to work as long as possible
want to work even if over 60

0.42
0.28

0.25
0.20

0.27
0.31

0.20
0.21

0.15***
-0.03

0.42
0.28

0.25
0.20

0.42
0.28

0.24
0.20

0.00
0.00

0.45
0.64

0.25
0.69

0.39
0.68

0.24
0.76

0.06
-0.05

0.45
0.64

0.25
0.69

0.45
0.64

0.25
0.69

0.00
0.00

health related variables
good healthnumber of serious disease
sample size

201

1515

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.
Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

20

201

1515

Table 5. Effect of training on re-employment(A) 1 year after training
Individual attributes
Individual attributes+intention to work

Mean difference

0.128***(0.031)

Individual attributes+intention to work+health variables(B) 2 year after training
Individual attributes
Individual attributes+intention to work

Mean difference

0.125***(0.032)

Individual attributes+intention to work+health variables(B) 3 year after training
Individual attributes
Individual attributes+intention to work
Individual attributes+intention to work+health variables

Mean difference

0.105***(0.033)

Probit
0.064**(0.025)0.050**(0.025)0.048*(0.025)

Entropy balancing
0.080**(0.033)0.058*(0.033)0.055*(0.033)PSM
0.089**(0.036)0.072*(0.037)0.068*(0.036)

NTreatment
145

NControl
1,257

145

1,257

145

1,257

Probit0.068**(0.027)0.055**(0.026)
0.051*(0.027)

Entropy balancing
0.078**(0.032)
0.061*(0.032)0.054*(0.032)PSM
0.093**(0.038)0.080**(0.040)0.074*(0.041)

NTreatment
141

NControl
1,217

141

1,217

141

1,217

Probit0.047*(0.028)0.027(0.027)0.025(0.027)

Entropy balancing
0.047(0.033)0.022(0.032)0.018(0.031)PSM
0.061*(0.037)0.050(0.038)0.047(0.036)

NTreatment
136

NControl
1,171

136

1,171

136

1,171

Notes: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The matching method of propensity score matching is kernel matching. The kernel type used is
Gaussian, and the kernel bandwidth is 0.06.
Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

21

Table 6. Effect of training on re-employment by employment status
Multinomial logit(A) 1 year after training

Regular worker
0.019**(0.008)0.019**(0.008)0.018**(0.007)

Individual attributes
Individual attributes+intention to work
Individual attributes+intention to work+health variablesMultinomial logit(B) 2 year after training

Regular worker
0.013(0.008)0.013(0.008)0.012(0.008)

Individual attributes
Individual attributes+intention to work
Individual attributes+intention to work+health variables

Regular worker
0.019**(0.008)0.018**(0.008)0.018**(0.008)

Individual attributes
Individual attributes+intention to work
Individual attributes+intention to work+health variables

1402
1402
1402

Sample size

Non-regular worker
0.051*(0.027)0.038(0.027)0.034(0.027)

Multinomial logit(C) 3 year after training

Sample size

Non-regular worker
0.037(0.026)0.022(0.026)0.020(0.026)

1358
1358
1358

Sample size

Non-regular worker
0.020(0.028)0.003(0.028)0.001(0.028)Notes: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The estimated values represent the marginal effects.
Source: Author’s calculations by using Longitudinal Survey of Middle-aged and Elderly Persons.

22

1307
1307
1307