(2023) 13:28
Okamoto et al. Health Economics Review
https://doi.org/10.1186/s13561-023-00442-x

Health Economics Review

Open Access

RESEARCH

Economic effects of healthy ageing:
functional limitation, forgone wages,
and medical and long‑term care costs
Shohei Okamoto1,2,3* , Haruka Sakamoto4, Kazuki Kamimura3,5, Kohei Komamura3,6, Erika Kobayashi1 andJersey Liang7,8

Abstract
This study aims to estimate the potential economic benefits of healthy ageing by obtaining estimates of the economic losses generated by functional limitations among middle-aged and older people. Utilising two data sources
retrieved from nationally representative samples of the Japanese people, we analysed the association between functional limitation and economic indicators, including labour market outcomes, savings, investment, consumption, andunpaid activities among individuals aged ≥ 60. Using the estimated parameters from our micro-econometric analyses
and the official statistics by the Japanese government and a previous study, we calculated the financial costs thatcan be averted if healthy ageing is achieved as foregone wages and formal medical/long-term care costs incurredby functional limitations. Our micro-econometric analyses found that functional limitation was associated with a 3%
point increase in retirement probability, with a stronger association among those aged 60–69. Moreover, functional
limitation was linked with higher total health spending and less active involvement in domestic work. Foregone
wages generated by functional limitation were estimated to be approximately USD 266.4 million, driven mainly by
individuals in their 60s. Long-term care costs, rather than medical care costs, for older people aged ≥ 85 accounted
for most of the additional costs, indicating that the estimated medical and long-term costs generated by functional
limitations were approximately USD 72.7 billion. Health interventions can yield economic benefits by preventing exits
from the labour market due to health issues and reducing medical and long-term care costs.
Keywords Economic benefits, Disability, Functional health status, Successful ageing, Retirement, Medical care costs,
Long-term care costs
JEL D1, I1, J14, J26*Correspondence:
Shohei Okamoto
shohei@z2.keio.jp
1
Research Team for Social Participation and Healthy Aging, Tokyo
Metropolitan Institute for Geriatrics and Gerontology, 35‑2 Sakae‑ cho,Itabashi‑ku, Tokyo 1730015, Japan
2
Institute of Global Health Policy Research, National Centre for Global
Health and Medicine, Tokyo, Japan
3
Research Center for Financial Gerontology, Keio University, Tokyo, Japan
4
Department of Hygiene and Public Health, Tokyo Women’s Medical
University, Tokyo, Japan
5
Hirao School of Management, Konan University, Hyogo, Japan
6
Faculty of Economics, Keio University, Tokyo, Japan

7
Department of Health Management and Policy, School of Public Health,
University of Michigan, Michigan, USA
8
Department of Health Care Management and Healthy Aging ResearchCenter, Chang Gung University, Taoyuan, Taiwan© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Okamoto et al. Health Economics Review(2023) 13:28

Introduction
Population ageing and disease burden

Worldwide, the ageing rate of people aged ≥ 65 in 2020
was 9.3%, and it may rise to 18% by 2060 and 23% by 2100
[1]. Japan, in particular, had an ageing rate of 28% in 2020,
which will reach its peak of 38% in 2060 and then moderately decrease to 37% in 2100. Population ageing is notalways negative, meaning that countries can achieve longevity due to many successes, such as economic growth,
nutritional and hygiene improvements, medical development, and progress towards universal health coverage.
However, many countries experience a gap between life
expectancy and healthy life expectancy, mainly due tolongevity itself and functional limitations caused by noncommunicable diseases (NCDs) [2]. Despite the disparities across regions in life and healthy life expectancy atbirth, populations in most countries spend around 10
years in poor health [2]. Therefore, reducing the disease
burden arising from functional limitations and othercomorbidities through effective interventions is essential
to ensure that everyone can live healthily.
Economic return on health investment

Many studies, most of which are based on the value ofstatistical life approach, suggest that health interventions, such as immunisation, tobacco and alcohol controls, and nutritional intervention for both infectious
and non-communicable diseases, can produce economic
benefits [3–9]. For instance, the most efficient packageof interventions for NCDs can avert 39 million deaths inall low- and middle-income countries with an additional
cost of USD 140 billion from 2023 to 2030, resulting inan economic benefit of USD 2.7 trillion (benefit-cost
ratio = 19:1) [5]. Delayed ageing, which reduces mortality
and the probability of onset of both chronic conditions
and functional limitation, generates larger economic
values through statistical life and health/social expenditures than merely extending life expectancy without anincrease in healthy life expectancy and combating individual diseases [10, 11].
In many industrialised countries, responding to financial challenges associated with population ageing, particularly healthcare and long-term care costs, is an
important policy target. Some policymakers and scientists believe that this could be addressed by extending
healthy life expectancy and achieving healthy ageing.
However, whether health improvement by intervention isa valuable tool to accomplish this policy target remains
controversial because many of the interventions may becost-effective but not cost-saving [12]. Since healthcare isdesignated to improve or maintain health but not to savemoney, the economic benefits of health investment may

Page 2 of 14

be underestimated or mismeasured if emphasis is given
only to healthcare costs as the financial outcomes of theinvestment [13]. With a more comprehensive range of
economic indicators (e.g., labour market outcomes, nonhealth consumption, and unpaid productive activities),
returns on investments in health can be larger.
However, few studies have comprehensively assessed
the association between healthy ageing and these economic indicators. Moreover, the potential economic
benefits of healthy ageing are less known. Only two
studies in the United States estimated the potential economic gains of healthy ageing as the statistical value oflife by a willingness-to-pay approach [10] and using thecosts of major entitlement programs (e.g., Medicare andMedicaid) [11].
The linkage between health and economy among older
adultsHealth is an important element of human capital, which
determines one’s productivity, time available for economic activities, and utility [14, 15]. At a macroeconomic
level, health (i.e., life expectancy) is positively associatedwith countries’ economies and growth [16, 17]. In population ageing, a decline in the labour force will reduce
economic growth [18]; however, this decline can be mitigated if the middle- and old-age population remains ingood health [19]. Therefore, the health status of the population is an important driver of countries’ economies.
At a microeconomic level, labour market and financial outcomes (e.g., consumption, saving, and investment) are keys to connecting health with the country’s
economy (Fig. 1) [20–23]. Many studies have examinedthe effects of health on labour market outcomes, such as
labour productivity and supply in Western countries [24,25], suggesting that deteriorating health reduces labour
productivity and labour force participation. Among older
people, health status works as a push factor that pushes
them out of the labour market [26].
For financial outcomes, it is plausible that healthier
individuals are more active than their unhealthier counterparts in saving for their retirement, expecting that
their life expectancy is long, and investing in non-medical
consumption, including expenditures on leisure activities[20, 21]. Older adults would be subject to weaker constraints imposed by ill-health in their decision-making inthese financial activities by remaining in good health.
The population’s health status can be linked to healthexpenditures. Growth in social expenditures, including
medical and long-term care spending, may be induced by
the ageing of populations. In health economics research,
population ageing itself may not be regarded as one ofthe principal drivers of healthcare expenditure [27];Okamoto et al. Health Economics Review(2023) 13:28

Page 3 of 14

Fig. 1 Healthy ageing and social/economic benefits

however, its contribution to moderate growth in medical
expenditures is still expected [23]. Meanwhile, increasing
long-term care costs incurred by population ageing canbe significant, even with a low base, and potentially larger
when informal care costs are considered [23].
Moreover, older adults, particularly after retirement, play an important role outside the labour market
through unpaid productive activities, such as volunteering, childcare, and domestic work. These activities may
not be regarded as economic activities; however, thecontributions made by the social engagement of older
people, which can be determined by their health status,
are important. Thus, healthy ageing potentially results ineconomic benefits beyond mere health benefits. Therefore, this study aims to analyse the association between
healthy ageing and various economic indicators, expanding the literature by providing more in-depth data andinsights on the linkages than prior studies have done.

Methods
Data collection
Japan Household Panel Survey (JHPS/KHPS)

To assess the potential economic effects of healthy ageing, we retrieved data from the Japan Household Panel
Survey (JHPS/KHPS), which contained information on
economic activities. The JHPS/KHPS comprises a nationwide sample of Japanese individuals aged ≥ 20, whose
data were obtained using a two-stage stratified random
sampling method based on regional classifications andbasic resident registers. The survey began in 2004 andwas conducted annually by using the same procedure.
In this study, we analysed data up to 2019. Information
about the survey (e.g., response rates) is provided ingreater detail on the project’s website [28]. We restricted

our sample to respondents aged 60–95 to provide ananalysis of older individuals. Since not all questions wereasked in all waves, our data consisted of an unbalanced
panel, resulting in approximately 20,000 person-year
observations from 3,000 respondents.
Official macro‑level (aggregated) statistics and datafrom a previous work

We also utilised data sources published by the Japanese
government and a previous study [29] to estimate theeconomic effects of healthy ageing. The data included
statistics on the number of workers, average wages, thepopulation by age, the number of people requiring longterm care, and medical and long-term care costs. The listof statistics is provided in Appendix Table A-1.
Economic indicators

To measure the economic effects, we analysed three
types of economic indicators obtained from the JHPS/
KHPS and identified by previous studies [20–23]: labour
market outcomes, financial outcomes, and unpaid productive activities.
Labour market outcomes were measured by employment status (i.e., retired or not), labour productivity, andwork hours. We operationally defined those not working for pay as retired, excluding individuals with unusual
labour force statuses, such as unemployment, temporaljob leave, job-seeking, and suspension from work, following the idea that retirement refers to the status of beingout of the labour force with no intent to seek employment [30]. Labour productivity was measured as hourly
wages for those in paid work. We obtained hours of workas the hours per day that respondents usually worked.
To exclude outliers from the analysis, we excluded thoseOkamoto et al. Health Economics Review(2023) 13:28

with the top (JPY 31,160.9 per hour or higher) and bottom 1% (JPY 213.5 or lower) of wages. As there were selfemployed individuals, calculated hourly wages were notnecessarily beyond the statutory minimum wages.
Financial outcomes were measured by savings, securities, and consumption. Savings included ordinary
deposits, fixed deposits, instalment savings, gold savings, etc., adjusted by the 2015 consumer price index
in Japan. Securities included stocks, bonds, mutual
funds, etc., adjusted by the 2015 consumer price index
in Japan. To address the high number of missing cases
for savings and securities, we imputed the data by linear interpolation for each respondent across years. Consumption included expenses in the last month of thesurveys, including those for food (including costs foreating out), health (i.e., total health expenditure), culture and recreation, and social relationships. As thesevariables were obtained as household-level variables, weequalised them by household size, dividing them by thesquare root of the number of household members. Furthermore, to address the skewed distributions, we transformed savings, securities, and consumption using theinverse hyperbolic sine transformation [31].
Unpaid productive activities were measured by three
types of activities: volunteering, domestic work, andchildcare (only for respondents who have a child).
These were measured in two ways: (1) hours of activities per week; (2) dichotomised variables that take one
if respondents were engaged in these activities (i.e.,
0 < hours per week of each activity). Hours of activities
were transformed using the inverse hyperbolic sine transformation to address the skewness of their distributions.
To assess whether individuals with functional limitations engage in multiple activities or focus on one primary activity, we additionally analysed the relationshipbetween functional limitations and the number of activities out of four that individuals were engaged in, namely,
employment, volunteer work, domestic work, andchildcare.
Functional limitations

While there is no consensus on how to measure healthy
ageing, a WHO’s report defines healthy ageing as “theprocess of developing and maintaining the functional
ability that enables well-being in older age” [32]. Following frequently used components of healthy (or successful) ageing [33], we operationally measured the
intrinsic capacity of respondents by assessing difficulties in performing basic activities of daily living (ADL),
instrumental activities of daily living (IADL), and cognitive functioning. In addition to these three components,
to determine functional limitations, we also used thestatus of certification of the need for public long-term

Page 4 of 14care. Under the public long-term care insurance system
in Japan, older people (or their family members) in needof long-term care benefits submit an application to theirmunicipality office. After evaluation by the municipality office, individuals are classified into support needlevel 1–2 or LTC need-level 1–5 (a higher level indicatesmore severe conditions), depending on their level of careneeds. We considered people to have functional limitations if they were certified at care need levels 2, 3, 4, or
5, where individuals were likely to undergo cognitive
decline and/or need partial support from others for theirbasic and/or instrumental daily activities.
Household surveys, including the JHPS/KHPS, tend tolack objective health information and contain only subjective health status, such as self-rated health. Althoughsome studies have demonstrated the validity of thismeasure among people without cognitive impairment[34], using subjective health measures without a healthmodelling approach can cause endogeneity due to measurement error and other potential biases [35]. To avoid
potential endogeneity, we imputed information about
each respondent’s functional limitation status by utilising estimates from the National Survey of the Japanese
Elderly (NSJE). Using common predictors of functional
limitation status between surveys, we obtained the functional limitation equation to predict the functional limitation status among the JHPS/KHPS sample as described
below.
Data: National Survey of the Japanese Elderly

To complement the health indicators lacking in the JHPS/
KHPS, we utilised data derived from the NSJE. The NSJE
consists of a nationally representative sample of Japanese
adults aged 60 or above, extracted from the basic resident
registers using a two-stage stratified random sampling
method. The survey began in 1987 (Wave 1) with followups of participants every 3–6 years, adding new samples
in 1990 (Wave 2), 1996 (Wave 4), 2000 (Wave 5), and2012 (Wave 8). The latest survey currently available wasconducted in 2017 (Wave 9). The project webpage provides more detailed information about the survey [1].
Our analysis only used data obtained from surveys
between Wave 6 (2002) and Wave 9 (2017), which fellwithin a close year range to that of the JHPS/KHPS. The
final sample analysed, with the same age range as the
JHPS/KHPS (i.e., 60–95), contained 8,456 person-wave
observations by 4,148 individuals, comprising an unbalanced panel.
Variables to measure functional limitation

As mentioned in Sects. 2 and 3, we defined the functional limitation status based on ADL), IADL, cognitive

Okamoto et al. Health Economics Review(2023) 13:28

functioning, and the certified status for the need for pulic
long-term care.
Similar to the Katz index [2], ADL was measured using
six items: bathing, dressing, feeding, transferring, goingout, and toileting. IADL was measured using four items:
shopping for personal items, using a telephone, riding thebus or subway alone, and performing light tasks aroundthe house [3]. If a respondent had any difficulties with atleast one of the items in either ADL or IADL, they wereconsidered to have functional limitations.
In addition to ADL and IADL, we used cognitive
functioning measured by the Short Portable Mental Status Questionnaire [4, 5]. The questionnaire
contains nine questions, including the respondent’s home address, interview date, interview day,mother’s maiden name, name of the current prime
minister, name of the previous prime minister, a simple calculation, the respondent’s birthday, and age.
Respondents who provided four or fewer correct
answers out of nine were considered to have moderate cognitive impairment [4] and were defined ashaving functional limitations.
Furthermore, respondents certified for the need for
public long-term care at levels 2, 3, 4, or 5 were also considered to have functional limitations. At these levels,
individuals are considered to undergo cognitive decline
and/or need partial support from others for their ADL
and IADL.
Functional limitation prediction

To predict the binary functional limitation status, we formalised its relationship with covariates as:

Pr yit = 1|xit , uj =exp(xit β + ut )1 + exp(xit β + ut )where yit denotes the functional limitation statusof a respondent i in a cluster (wave) t, xit is a vector
of covariates, and ut represents the random effects.
To build a model to impute functional limitation status from the NSJE into the JHPS/KHPS, it is necessary
to use common predictors included in both surveys.
Among the demographic, socioeconomic, and behavioural determinants of healthy ageing [6, 7], we usedage, age squared, sex, marital status (single or not),the number of household members having at least one
child, employment status, house ownership, educational attainment (i.e., high school graduates definedas 12–15 years of education completed and university graduates with 16 + years of education), self-rated
health, cigarette smoking, alcohol consumption in
addition to controls for years and residential areas byscale (descriptive statistics in Table 1).

Page 5 of 14

Table 1 Descriptive statistics: National Survey of the Japanese
Elderly
VariablesN% or mean

Functional limitation

8,540

21.0%

Female

8,540

56.3%

Employment status

8,540

20.9%

Age

8,540

77.50

Marital status (Single or not)

8,540

40.4%

Household size

8,540

1.88

Having a child

8,540

94.1%

House ownership

8,540

88.9%

SRH: Very bad

8,540

2.6%

SRH: Bad

8,540

15.5%

SRH: Fair

8,540

42.5%

SRH: Good

8,540

24.8%

SRH: Very good

8,540

14.5%

Education: High school

8,540

0.1%

Education: University or higher

8,540

26.1%

Cigarette smoking

8,540

6.8%

Alcohol consumption

8,540

11.0%

SD

7.19
1.67

Note: SD represents standard deviation; SRH represents self-rated health

The result of the analysis on the functional limitation status is shown in Table 2, indicating that many ofthe demographic and health-related variables are usefulpredictors of functional limitation status. Based on thisestimation result, we obtained a linear prediction of theprobability of the functional limitation status among the
NSJE sample. We defined respondents with a predictedprobability larger than 0.5 as having functional limitations; our predictive values were very high (Table 3).
Before applying the estimated parameters in the JHPS/
KHPS sample, we also estimated the function of the random effects ut as a linear function:

ut = c + δxi + ei
where c is a constant term, xi is a vector of the aforementioned covariates at baseline (or the closest information from the baseline if a variable at baseline is missing)with parameters δ, and ei denoting a stochastic disturbance. To account for the prediction error from this linear prediction, we obtained random numbers following
the normal distribution with the same mean and standard
deviation as the residual, specifying a random-number
seed on Stata generated by Microsoft Excel for Mac 2021.
Using these parameters and random numbers, we
imputed the functional limitation status in the JHPS/KHPS
sample, defining an imputed probability larger than 0.5 ashaving functional limitations. To test the robustness, we
conducted the analysis by using the continuous probability.

Okamoto et al. Health Economics Review(2023) 13:28

Page 6 of 14

Table 2 Results: Functional limitation function
Covariates

beta (SE) Covariates

FemaleAgebeta (SE)0.32*

SRH: Bad-2.69**(0.14)(Ref: Very bad)

(0.30)-0.56**

SRH: Fair-4.51**(0.15)
Age2(0.32)

0.00**

SRH: Good-5.48**(0.00)
Marital status (single or
not)

0.30*

Household size

0.15**(0.35)(0.40)

Yit = 𝛽0 + 𝛽1 Functional limitationsit + 𝛽2 xit + 𝛾tl + 𝜇i + 𝜖it

SRH: DK-3.42**

where Yit denotes the economic indicators of our interest for individual i in year t, Functionallimitationsit indicates the imputed functional limitation status, taking thevalue one if a person has functional limitations. xit is avector of control variables, β0 is a constant term, β1 is aparameter of our interest, β2 are coefficients of covariates, and ǫit represents a stochastic disturbance.
The control variables include demographic and socioeconomic variables that can be confounders of the associations between functional limitation and economic
indicators: age, age squared, eligibility for employees’
pension for the flat benefits and wage-proportional benefits (judged by a respondent’s birth year), marital status(i.e., single or not), having at least one child, the numberof household members, house ownership, employment
status, contract type (full- or part-time), and equalised
household income. We used different controls for eachcategory of dependent variables because confounders of
the association between health and labour market outcomes/savings, investment, and consumption/unpaid
activities would not be identical. Specifically, when analysing labour market outcomes, pension eligibility affects
retirement decision-making [36], so variables indicating
pension eligibility were included in the analysis of retirement status. Meanwhile, to analyse productivity andhours of work, we included the contract type because
contracts for employed individuals may largely determine
wage rates and working hours. Furthermore, as productivity and work hours were observable only among workers, the models for these dependent variables include
inverse mills ratios, estimated from an employment status function (Appendix Table A-2), to address sample
selection. The analysis of labour market outcomes was
restricted to individuals aged 60–89 since no respondent worked beyond this age range. In contrast, to analysefinancial outcomes (i.e., savings, investment, and consumption) and unpaid activities, we controlled for retirement status and income, as these inevitably affect timeand budget constraints.
Moreover, we controlled for prefecture-by-city-scaleby-year fixed effects (γtl ) and individual fixed effects (µi )(1.21)
Cigarette smoking

0.19

Alcohol consumption-0.69**

Constant

14.54*(0.24)
Employment status-1.27**

House ownership-0.33(0.19)(0.19)

Education: High school(0.13)(0.17)(5.92)-0.29

Var(_cons[Individuals]) 3.57**(0.15)
Education: University+(0.51)

-0.45

Year

Yes

(0.28)

Area-by-scaleYes

Age range

60–95

Observations

8,456

Individuals

4,148

Note: Estimated using a multilevel mixed-effects generalised linear model witha binomial distribution of the dependent variable and the logit link function;
Weighted by population weights estimated by national statistics of population
estimates and censuses based on age, sex, and regions at baseline for samples
extracted in 1987, 1999, and 2012; Values represent coefficients, with robust
standard errors reported in parentheses; ** p<0.01, * p<0.05

Table 3 Prediction of functional limitation status in the NSJE
sampleActual functional
limitation

Predicted functional
limitation

0

1

Total

6,637

488

6,472

93.15%

6.85%

100%

1

112

1,303

1,984

7.92%

92.08%

100%

Total

6,749

1,669

8,540

0

Utilising the panel data structure of the JHPS/KHPS, we
expressed the association between health and economic
indicators as follows:-6.48**(0.03)-0.39

Empirical strategy
Micro‑econometric analysis on the linkage
between functional limitations and economic indicators

SRH: Very good

(0.12)

Having a childfunctional limitations score to assess the relationship withthe outcomes of our interest. The results are available
upon request.

Note: Actual functional limitation is defined as the status where a respondent
has any difficulties in ADL or IADL, their cognitive functioning score being lessthan four (Moderate decline), or is certified for long-term care needs at levels
2–5

For the sake of the additional robustness test, we estimated the number of functional limitations comprising a
total of 19 items from ADL, IADL, and cognitive functioning in the same procedure. We also used this continuousOkamoto et al. Health Economics Review(2023) 13:28

to analyse all outcomes. The city scale includes three
categories of ordinance-designated cites, other cities,
and towns and villages. Intuitively, prefecture-by-scaleby-year fixed effects control for unobserved factors
varying across time and locations (e.g., economic conditions and policy changes). In addition, time-invariant
individual heterogeneity, such as personality and genes,
is controlled by individual fixed effects. To control forhigh-dimensional fixed effects, we conducted estimations
using a Stata command: reghdfe [37].
To partially correct for biases due to non-response, weused two types of weights for all the analyses: cross-sectional and longitudinal. These approaches are similar tomultiple imputations grounded in missing at random or
ignorable non-response, consistent with previous studies [38, 39]. To mitigate the non-response bias at baseline, we estimated cross-sectional weights by factors (i.e.,age, sex, marital status, education, employment status,
and residential regions) using the closest census andnational surveys conducted by the Japanese government.
Moreover, to mitigate biases caused by sample attrition
during follow-ups, we calculated weights estimated as
the inverse probabilities of responding to each wave by alogit model conditional on factors at baseline, which were
the same as those used for cross-sectional weights. Ethical approval for the JHPS/KHPS was not required as thedata were publicly available. For the NSJE, all the study
procedures were approved by the Institutional Review
Board of the Tokyo Metropolitan Institute of Gerontology (R21-008).
Economic costs of functional limitations

After detecting economic indicators associated withfunctional limitations, we estimated the economic costsof functional limitations in Japan (i.e., foregone wages
and medical and long-term care costs). We described theapproaches later. All analyses were conducted using Stata
MP, version 17.0 (StataCorp LLC, College Station, USA).

Results
Table 4 presents descriptive statistics of the JHPS/KHPS
sample. The sample contained people with an averageage of 68.25 years. Among them, the proportion of individuals estimated to have functional limitations was 3.6%.
Approximately half the respondents were employed during the study period.
Labour market outcomes

Table 5 presents the estimated parameters of the linkages between functional limitations, labour market outcomes, savings and investment, consumption, and unpaid
productive activities. As shown in the table, having a

Page 7 of 14

functional limitation is associated with a 3-percentagepoint increase in the probability of retirement (standard
error [SE]: 0.01). However, we did not find a significant
association between functional limitation and productivity/hours of work. To assess heterogeneity among younger
individuals whose employment rates were higher thanolder people, we restricted the age range between 60 and
69, as the associations may become stronger among thisgroup. We observed identical results qualitatively, findinga larger increase in the probability of retirement due tofunctional limitation in this group (Appendix Table A-3).
Savings, investment, and consumption

As for savings, investment, and consumption, we foundthat functional limitation was only associated withincreased health expenditure (β: 0.29, SE: 0.07).
Unpaid productive activities

Regarding unpaid activities, we found that individuals with functional limitations were less likely to engage
in domestic work. This was measured by both time (β:-0.16, SE: 0.06) and engagement (β: -0.07, SE: 0.03). We
also found that those with functional limitations tended
to engage in a smaller number of paid or unpaid activities(β: -0.13, SE: 0.04).
For all the economic indicators, instead of using abinary variable to indicate functional limitations, we conducted additional analyses using the continuous probability of having functional limitations and the number offunctional limitations. As shown in Appendix Table A-4
and A-5, similar results to those obtained with the binary
indicator were obtained when using the continuous
probability. Moreover, as for the number of functional
limitations, we observed clearer relationships between
functional limitations and economic indicators andunpaid productive activities: those with a larger number of functional limitations tended to retire, less invest
in securities, and be inactive in volunteer and domestic
works (Appendix Table A-6 and A-7).
Foregone wages

From the estimates on the labour market outcome, weestimated foregone wages of labour market exits due tofunctional limitations, which could have been saved byachieving healthy ageing. In this study, annual forgone
wages were estimated as follows:[]
Forgone wages =Σ Pr(retirement of individuals with functional limitation)][∗ % of individuals with functional limitationj][][∗ N of workersj ∗ Yearly wagej

Okamoto et al. Health Economics Review(2023) 13:28

Page 8 of 14

Table 4 Descriptive statistics: JHPS/KHPS
Variables

With functional limitations

Without functional limitations

N(Person-year)

N(Person-year)% or mean(SD)% or mean
(SD)

Age

20,39567.99 (5.51)

754

75.36 (6.38)

Women

20,395

50.5%

754

66.0%

Retire20,395

52.3%

754

93.5%

Self-employment

20,395

17.3%

754

4.1%

Marital status (Single or note)

20,395

20.7%

754

37.4%

House ownership

20,395

88.6%

754

76.9%

Having a child

20,395

60.2%

754

54.8%

Household size (N of household members)

20,3952.6 (1.28)

754

2.38 (1.32)

Hourly wage (JPY)

7,919

3,228.04 (3,873.23)

28

3,779.53 (4,751.61)

Hours of work (Hours/day)

8,640

4.82 (2.75)

30

4.75 (3.18)

Contract type: Full time

8,640

19.7%

30

23.3%

Contract type: Part time

8,640

37.7%

30

13.3%

Consumption: Total (JPY1,000)

18,738186.76 (178.51)

654

157.98 (105.14)

Consumption: Food (JPY1,000)

18,98054.54 (31.08)

668

49.03 (32.46)

Consumption: Health (JPY1,000)

18,50710.42 (21.01)

643

14.12 (22.37)

Consumption: Entertainment (JPY1,000)

17,90310.31 (33.62)

612

5.84 (14.69)

Consumption: Social (JPY1,000)

18,37323.01 (30.08)

635

17.52 (36.56)

Equalised household income (JPY10,000)

18,980355.01 (306.83)

668

281.15 (275.48)

Equalised household savings (JPY10,000)

18,612926.6 (1285.03)

656

844.21 (1286.41)

Time use: Volunteer (Hours per week)

10,3920.39 (1.61)

321

0.06 (0.39)

Time use: Domestic work (Hours per week)

9,992

2.33 (2.48)

311

2.22 (2.47)

Time use: Childcare (Hours per week)

10,4490.2 (1.45)

322

0.09 (0.56)

Participation: Volunteer

10,392

12.5%

321

3.1%

Participation: Domestic work

9,992

73.5%

311

66.2%

Participation: Childcare

10,449

4.9%

322

3.4%

Note: SD represents standard deviations. Descriptive statistics are calculated by the available case basis. Thus, sample size is not always consistent with the ones usedin the analyses

where j denotes six age ranges: 60–64, 65–69, 70–74,
75–79, 80–84, and 85–89. Although the incidence rateof functional limitation among workers should ideally be
used, we could not do so due to data unavailability. Eachparameter was obtained from the estimates in this study
and official statistics of the Japanese government (Appendix Table A- 8). Our estimates suggest that functional
limitation generates an annual economic loss of approximately JPY 32.3 billion (≈ USD 266.4 million; USD 1 iscalculated as JPY 120), with 95% confidence intervals
ranging from JPY 8.8 billion (≈USD 72.8 million) to JPY
55.9 billion (≈USD 460.1 million). As younger age groups
had higher employment rates, the economic losses werelarger in these groups (Fig. 2). Particularly, most economic losses were driven by individuals in their 60s(Appendix Figure A- 1).
We further calculated the lifetime foregone wages
among those aged between 60 and 68 as follows, considering that the average effective age of labour force exit inJapan is around 68 years old for men [40]. The assumptions made in Appendix Table A-8 about average yearlywages, employment rates, and functional limitation rates
were kept the same. As the detailed statistics by each age
were not available, it was assumed that these values linearly changed from 60 to 68 years based on available statistics. The estimated retirement probability of people withfunctional limitation was 0.08, which was obtained in therelevant estimate in Appendix A-3. It was also assumed
that individuals with functional limitations would not
return to a job post-retirement, and retirement occurredat the beginning of the year. By this calculation, the lifetime forgone wage among those aged 60–68 was estimated to be about JPY 175.6 billion (≈ USD 1.5 billion).
Medical and long‑term care costs

We found that functional limitation was associatedwith an increase in total health expenditure. The total
health expenditure in the JHPS/KHPS was obtained as

18,746

2,923

Observations

Individuals

2,848

18,362

60–95(0.06)-0.16**

2,110

10,771

60–95(0.03)

-0.00

2,923

18,74660–95(0.01)

-0.02

2,968

19,64860–95(0.04)

-0.03

Food

60–95

(0.07)

-0.01

2,943

2,889

19,142 18,466

60–95(0.07)

0.29**

Health Culture andentertainment

Consumption

2,930

18,991

60–95(0.08)

0.10

2,848

18,362

60–95(0.03)-0.07*

2,110

10,771

60–95(0.02)

0.01

60–69

(0.14)

2,113

1,525

10,771 6,706

60–89(0.04)-0.13** -0.10

2,948

19,401

60–95(0.03)-0.01

Social relationships Total

Number of activities
Domestic work Childcare (Paid and unpaid activities)

2,976

19,87460–95(0.09)-0.04

Securities

Note: Estimated by a linear probability model for a binary variable and linear model for continuous variables; Productivity and hours of work are log-transformed; Savings, securities, consumption, and times for unpaid
activities are transformed by the inverse hyperbolic sine transformation; The number of activities is a total count of activities that individuals are engaged in, which includes employment; Values represent coefficientswith individual-level cluster robust standard errors in parentheses; For labour market outcomes, models include controls for age, age squared, employees’ pension eligibility for the flat benefits and the wage-proportional
benefits (only for retirement), self-employment, contract type (only for productivity and hours of work), marital status, house ownership, having a child, household size, prefecture-by-scale-by-year-fixed-effects, andindividual-fixed-effects; The analyses for productivity and hours of work include an additional control of the inverse mills ratios predicted in Appendix Table B-2; For financial outcomes and unpaid activities, models
include controls for age, age squared, employment status, marital status, house ownership, having a child, household size, equalised household income transformed by the inverse hyperbolic sine transformation,
prefecture-by-scale-by-year-fixed-effects, and individual-fixed-effects; Weighted by cross-sectional and longitudinal weights; ** p<0.01, * p<0.05

60–95

Age range(0.01)

Functional limitation -0.02

Childcare

2,945
VolunteerDomestic work

1,639

19,609Volunteer

1,555

8,670

60–95

(0.11)-0.03

Engagement (Yes/No)

3,113

Individuals

7,853

60–89

(0.21)-0.24

Time (Hours per week)

21,149Observations

60–89(0.16)

60–89

-0.11

(0.01)

Age range

Savings and investment
Productivity (Hourly wage) Hours of work Savings

Functional limitation 0.03**

Retirement

Labour market outcome

Table 5 Functional limitation and economic indicators: Labour market and financial outcomesOkamoto et al. Health Economics Review(2023) 13:28
Page 9 of 14

Okamoto et al. Health Economics Review(2023) 13:28

Page 10 of 14

Fig. 2 Estimated foregone wages due to functional limitations (Unit of currency: USD 1,000). Note: % of people with functional limitation denotes
the proportion of the population with certification of levels 2–5 long-term care needs; Based on the estimate in Table 5, lines represent foregone
wages calculated by the point estimate for the probability of retirement due to functional limitations, with the shaded area representing 95%
confidence intervals

costs at a household level, and the information about
long-term care costs was not available. Therefore, theparameter from our analysis may not reflect the actual
costs incurred. With the official statistics and an estimate by a previous study [29], we estimated costs forformal medical and long-term care used by people
with functional limitation (i.e., certified for long-termcare needs at 2–5 levels). The additional total costs forformal medical and long-term care generated by functional limitation were estimated as follows:
Medical and long − term care costs[(= Σ Per − capita medical and long − term care costs of those) (with functional limitationk − Per − capita medical and long)]−term care cos ts of those with no functional limitationk[]∗ % of individuals with functional limitationk[]∗ N of populationk

where k denotes six age categories of 60–64, 65–69,
70–74, 75–79, 80–84, 85–89, and 90 or over. As percapita medical care costs for individuals with functional limitation by age groups were not available, weused values from a previous study in Japan [29]. Parameters used for the estimate are presented in Appendix
Table A-9.
Figure 3 and Appendix Table A-10 show the estimatedformal medical and long-term care costs, additionallygenerated by functional limitations, that can be saved if
healthy ageing is achieved. In contrast to foregone wages,

long-term care costs, rather than medical care costs forolder people aged ≥ 85, accounted for a large part of thetotal costs due to functional limitation. The total additional medical and long-term care costs generated byfunctional limitations are approximately JPY 8.8 trillion(≈ USD 72.7 billion).

Discussion
This study aimed to evaluate the linkage between healthy
ageing and economic indicators to determine the potential economic benefits of healthy ageing. We found thatfunctional limitation, used as an indicator for healthy
ageing, was associated with a higher likelihood of retirement, a higher level of total health expenditures, anda lower probability of engaging in unpaid productive
work. We estimated foregone wages and additional
medical and long-term care costs, which could be savedby achieving healthy ageing. Our estimates indicatethat foregone wages and additional medical/long-termcare costs were approximately JPY 32.3 billion (≈ USD
266.4 million) and JPY 8.8 trillion (≈ USD 72.7 billion),
respectively. Although it is difficult to contrast these values with estimates from previous studies [10, 11] due tothe differences in measurements of economic gains, ourfindings are qualitatively consistent with them in that
healthy ageing can generate economic gains.
In line with existing findings [24–26], poor health was
associated with retirement in our study, working as apush factor from the labour market. However, our estimates did not suggest that functional limitation wasOkamoto et al. Health Economics Review(2023) 13:28

Page 11 of 14

Fig. 3 Medical and long-term care costs, additionally generated by functional limitations (Unit of currency: USD 1 million). Note: % of people withfunctional limitation denotes the proportion of the population with certification of levels 2–5 long-term care needs

linked to productivity and work hours among workers, potentially because one’s later-life health status canlargely affect whether they work or not, rather than variabilities in productivity among workers. Averting theretirement of people with functional limitations, particularly of the working-age population, is essential not
only for the health of the population but for the country’s
economy. Furthermore, we found that functional limitation was linked to total health expenditure, indicatingthat those with functional limitations spent more on
health expenditure.
In our further estimates of additional medical andlong-term care costs, we found that long-term care costs,
rather than medical care costs, were a significant driverof incurred costs. This is likely because people may use
healthcare services even if they do not have functional limitations. While we did not provide an economic value forvolunteer and domestic works, engaging in these activities
generates non-monetary values and may allow older people to live independent lives without requiring intensive

long-term care, which is well in line with our estimates that
long-term care costs are largely saved by healthy ageing.
In this study, we did not cover crucial indicators. For
instance, due to data unavailability, we could not estimate
the costs of informal care that fall on individuals otherthan those with functional limitations. Family membersof people with functional limitations usually bear theburdens of caregiving, compelling them to reduce theirworking hours or quit their job. A previous study identified 99,100 annual job leaves due to caregiving in Japan,
with 64.3% remaining out of the labour market even two
years after their leaves [41]. The study estimated annual
foregone wages induced by this to be JPY 182.1 billion (≈USD 1.4 billion), which is higher than our estimates ofthe foregone wages of people with functional limitations
themselves. In addition to the financial costs of informal care, gender inequality is also a concern. In many
countries, women tend to bear the burden of caregiving
more than men [42]. Consequently, they may be required
to refrain from participating in social and economic
activities. Also, we could not include costs generated byOkamoto et al. Health Economics Review(2023) 13:28

premature death since the JHPS/KHPS did not collectthis information. Therefore, the actual economic benefits
that can be produced by achieving healthy ageing may bemuch larger than our estimates.
Potential interventions to achieve healthy ageing

To achieve healthy ageing and reduce the burden of disease, clinical and behavioural interventions, as well asintersectoral policies, are necessary [5]. Previous studies suggest that modifiable factors, including smoking,alcohol consumption, physical activity, and abnormal
bodyweight, account for healthy ageing [43, 44]. Therefore, addressing these behavioural determinants ofhealth, which requires a health-in-all-policies approach,is essential to promote progress towards healthy ageing. Furthermore, the life course approach must be recognised as the health status of older adults is not only
determined by current determinants but also by pastones [45]. This indicates the need for interventions forchildren, adolescents, adults, and older adults to achieve
healthy ageing [46].
Interventions can still be effective in preventing anddelaying functional limitations, even for older adults.
Existing studies suggest that interventions targeting
modifiable factors, such as social interactions, smoking, physical activity, nutritional status, cognitive training, and occupational and physical therapy sessions withhome modifications, are effective [47–49]. To provide
effective interventions for older adults, multidisciplinary and multifactorial interventions with individualised
programmes, case management, long-term follow-up,
physical exercise component, and technology can be key
to success [46, 50, 51]. Moreover, some studies suggestthat health promotion among older adults is cost-effective [52–54]. Although we solely evaluated the potential
effects of healthy ageing, investment in healthy ageing
can be cost-effective by improving the health of older
people, generating additional economic and financial
returns.
Policy implications

Our estimates point to three policy and research concerns that policymakers and researchers should understand: labour policies (e.g., retirement policy andoccupational safety), employment opportunities in thehealth and long-term care sectors, and data collection for
healthy ageing.
First, there is a need for labour policies that incentivise
older people to work. Despite good health and willingness to work, retirement policies may discourage older
people from being employed, such as early pension/
retirement age and a large wage decline after reaching a

Page 12 of 14

certain age. Work environments, including hours/daysof work and occupational safety, are important so thatolder people can work healthily, even if their physical or
cognitive functioning mildly declines compared to their
younger ages.
Second, the reduction of healthcare and long-term carecosts should not be automatically regarded as ‘benefits’,as they create employment opportunities in the healthand long-term care or other sectors, contributing to thecountry’s economy. However, a shortage of labour force
and financial sustainability of public health and longterm care systems is common in many countries. Thus,
the reduction of financial costs and burdens borne byworkers through health improvement may mitigate thesechallenges.
Third, data collection and analysis are necessary tobetter understand and evaluate the economic benefitsof healthy ageing in each country. Our framework highlights the need to collect data on health indicators (e.g.,
ADL, IADL, and cognitive functioning), labour market
outcomes (e.g., employment status, days/hours of work,
and wage), expenditures (e.g., health and long-term carespending and leisure expenditures), financial activities(e.g., savings, consumption, and investment), and social
activities (e.g., volunteer, domestic work, and group
activities) to properly assess the benefits of healthy ageing. Depending on the country’s context, such as size,
socio-economic development, or cultural context, policymakers should tailor their policies to reflect the social or
economic values of the country and the sections involvedin necessary policies for achieving healthy ageing.
Limitations

This study has several limitations. First, the sample of ahousehold survey may not be representative of the general population, even though we attempted to address
this issue by using cross-sectional and longitudinalweights. People willing to respond to complex questions(e.g., financial assets) may be biased (e.g., healthier andwealthier than the average population). As self-reported
measures have a subjective element, responses to complex questions, including one’s wage, may not always
be accurate. Therefore, future research should consider
obtaining data from public records (e.g., tax and medical
and long-term care records) in combination with survey
data from people of various ages, especially older people.
Second, the JHPS/KHPS does not have a large sample of older individuals as it focuses on education andemployment, primarily collecting data from younger
individuals. Although we imputed the functional limitation status in the JHPS/KHPS using data from the NSJE,
which seemed to work well, the different age distribution
between the surveys (i.e., NSJE contained more older

Okamoto et al. Health Economics Review(2023) 13:28

respondents than JHPS/KHPS) could lead to imprecise
estimates if the determinants of functional limitation varied significantly across age groups in the samples.
Third, the JHPS/KHPS does not collect information
about deaths. By further analysing foregone wages due
to premature death, more accurate estimates could befeasible.
Fourth, our estimate of additional medical care costs
due to functional limitations used data from one city inJapan, as detailed information about medical care costs
by long-term care need level was not available. Although
we found that long-term care costs were a significant
driver of additional costs rather than medical care costs,
relying on medical care costs data from a specific city
may lead to discrepant estimates from genuine costs. Disparities in healthcare resources across regions can impact
medical care costs. Therefore, future studies should consider nationwide data linked to public records.
Fifth, the definition of healthy ageing in this study wassolely based on one’s health status (i.e., functional limitation), while excluding other factors that contribute tohealthy ageing, such as equity, cohesion, and well-being[55]. If a broader concept of healthy ageing is adopted, its
benefits may also expand.
Sixth, while we partly addressed the issue of endogeneity by avoiding self-reported measures and adopting a high-dimensional fixed-effect model, the linkages
between health and economic indicators can still be
endogenous due to factors such as reverse causality andunobserved time-varying confounding. Therefore, better evidence using causal inference is required.
By analysing data from Japan, we suggest that
achieving healthy ageing can produce economic benefits by preventing exits from the labour market due
to health issues and reducing medical and long-termcare costs. Also, healthy older people can make contributions through nonpaid productive activities.
Policymakers and researchers should understand thelinkages between healthy ageing and the economy toaddress a wide range of health determinants throughmultisectoral involvement from both health and nonhealth sectors to provide cost-effective interventions
for older people.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s13561-​023-​00442-x.
Additional file 1: Appendix Table A-1. List of the data sources. Appen‑
dix Table A-2. Predictors of employment status. Appendix Table A-3.
Functional limitation and economic indicators: Labour market outcomesamong people aged 60-69. Appendix Table A-4. Continuous probabilityof having functional limitations and economic indicators: Labour market
and financial outcomes. Appendix Table A-5. Continuous probability of
having functional limitations and economic indicators: Unpaid activities.

Page 13 of 14

Appendix Table A-6. Number of functional limitations and economic
indicators: Labour market and financial outcomes. Appendix Table A-7.
Number of having functional limitations and economic indicators: Unpaid
activities and number of activities. Appendix Table A-8. Parameters andestimates of foregone wages due to functional limitation. Appendix
Table A-9. Per-capita annual costs for health and long-term care. Appen‑
dix Table A-10. Additional medical and long-term care costs. Appendix
Figure A-1. Estimated foregone wages due to functional limitation
among people in their 60s.
Acknowledgements
We thank the Panel Data Research Center of Keio University for providing the data,
the Japan Household Panel Survey, for a secondary analysis in this study. We are also
grateful for the helpful technical inputs from Dr Hiromasa Okayasu and Ms April
Siwon Lee at the World Health Organization Regional Office for the Western Pacific.
Authors’ contributions
SO conceptualised the study, conducted the statistical analysis, and prepareda draft of the paper, which was refined by HS, KKa, KKo, EK, and JL. All authors
approved the final version of the paper and provide their consent for submission and publication.
Funding
This research is supported by a Grant-in-Aid for JSPS Fellows (No. 20J00394)
and Grants-in-Aid for Scientific Research (No. 20H00091 and 23H00063).
However, the founder was not involved in conceptualisation, design, data collection, analysis, the decision to publish, or the preparation of the manuscript.
Availability of data and materials
The authors do not have the right to share the data.

Declarations
Ethics approval and consent to participate
Ethical approval for the JHPS/KHPS was not required as the data were publicly
available. For the NSJE, all the study procedures were approved by theInstitutional Review Board of the Tokyo Metropolitan Institute of Gerontology(R21-008). Each respondent provided oral consent to participate in the survey.
Consent for publication
Not applicable.
Competing interests
We declare no conflicts of interest associated with this study.
Received: 15 November 2022 Accepted: 22 April 2023

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