I. Introduction

Population aging — driven by falling fertility rates and rising longevity — is profoundly impacting economies worldwide. This transformation is occurring rapidly in Asia (Figure 1). Japan has currently one of the highest shares of old-age population (above age 65) in the world, and countries such as Korea and China are projected to be among the most rapidly aging populations in the coming decades, according to the United Nations World Population Projections (UNWPP). As population aging is generally associated with a shrinking labor force, a deeper understanding of its implications for labor markets is key.1

FIGURE 1.

GLOBAL POPULATION AGING TRENDS

Note: This figure plots the average share of individuals aged 65 and over in the total population by decade from 1980 to 2100 using historical data and projections from the UNWPP.

Source: United Nations World Population Prospects (UNWPP), and authors’ calculations.

The traditional view of the macroeconomic implications of population aging is largely gloomy, premised on an inevitable drag on economic growth associated with fiscal strain due to rising fiscal spending on pensions and healthcare, along with reduced labor income tax revenue. However, this view disregards the fact that health improvements have been a key driver of rising longevity. For instance, global life expectancy has increased by around 4.5 years over the last two decades, and the additional years individuals live have been largely free from chronic diseases. In Asia, a number of countries have seen both life expectancy and healthy life expectancy — defined by the World Health Organization as the number of years expected to be lived in full health — rising faster than the world average (Figure 2).2 This ‘healthy aging’ phenomenon has the potential to partly mitigate the adverse economic effects of population aging, particularly in Asia.

FIGURE 2.

ADDITIONAL YEARS OF LIFE EXPECTANCY, 2000-21

Note: HALE denotes health-adjusted life expectancy, defined by the WHO as the average number of years a person is expected to live in full health and free from chronic diseases.

Source: World Health Organization, and authors’ calculations.

This paper studies whether (i) individuals from more recent cohorts in Korea have been aging in better health compared with individuals from earlier birth cohorts, and (ii) whether this healthy aging trend has raised individuals’ ability and willingness to remain attached to the labor market. The focus on Korea stems from two considerations. First, Korea stands out as a country where demographic change has been particularly acute given the notably low fertility rate and the steep rise in life expectancy over the last two decades. Second, Korea produces rich microdata tracking older individuals over time with regularly conducted surveys. To place the findings for Korea in context, we also include other comparator Asian economies in the analysis, in this case China, India, Japan, Malaysia, and Thailand. These countries provide a rich point of comparison given that they are at different stages of their demographic transition. They also have comparable microdata on health indicators and labor market outcomes for older individuals.

This paper makes two contributions. First, we analyze the determinants (correlates) of physical health indicators and whether there is evidence of healthy aging across birth cohorts. We consider different physical health indicators from survey-based micro datasets focused on individuals aged 50 and above. These include a measured indicator of physical strength — grip strength — as well as three self-reported measures: overall health status, the ease of doing activities of daily living (ADLs), and the ease of conducting the instrumental activities of daily living (IADLs).3 The survey-based datasets also include a rich set of indicators on individuals’ socioeconomic characteristics, specifically age, gender, educational level, and household wealth.

Our empirical approach is focused on estimating whether health has been improving over time (i.e., across successive birth cohorts) when controlling for age, other individual socio-economic characteristics, and — when relevant — country fixed effects. This is estimated using (i) a pooled OLS regression, and (ii) a Mundlak (1978) regression — a modified version of the random-effects panel regression model that is suited to exploiting longitudinal data. Our findings reveal a broad-based healthy aging phenomenon in Korea, as evidenced by steady improvements in health indicators over successive birth cohorts. For example, our findings suggest that on average, the grip strength of a 70-year-old individual in 2022 was approximately equivalent to that of a 60-year-old in 2006.

Second, we study how these health indicators relate to labor supply outcomes, specifically the decision of whether to supply labor and when to retire. In addition to studying correlations using a standard OLS regression method, we employ a two-stage least-squares (2SLS) regression approach to identify a causal relationship. In particular, we use exogenous health shocks, proxied by the incidence of certain chronic diseases, to estimate the causal effect of health improvements for older-age populations on their labor supply decisions. Our identification strategy rests on the assumption that at least some incidences of chronic diseases can be considered exogenous to lifestyle factors and socioeconomic characteristics that determine health outcomes. As such, they can be considered as randomly assigned. Given that smoking, poor nutrition, physical inactivity, and excessive alcohol use are key risk factors of most chronic diseases (Hacker, 2024), we control for these lifestyle factors in the estimation.

We find that better old-age health is associated with decisions to increase labor supply, both in terms of participating in the labor force and postponing retirement. Next, using chronic diseases as an instrument for health shocks, we confirm that exogenous improvements in health lead to increases in labor supply. The estimated impact using our IV approach is substantially larger than that implied by OLS regression estimates, pointing to the importance of tackling potential endogeneity using our instrumental variable. Our results are robust even when considering different variants of the chronic disease instrument and when controlling for year fixed effects capturing changes in labor markets and retirement policies over time.

Taken together, our results imply that as healthy aging continues, older-age individuals are expected to increasingly contribute to the labor market, including on a voluntary basis. The healthy aging trends and the effects of health improvements on labor market outcomes of older individuals are also found to be qualitatively comparable for the sample of Asian peer economies. While there are differences across health metrics, the results for Korea are generally somewhat stronger.

Our paper relates closely to several strands of literature. First, it relates to papers that have studied evidence of healthy aging trends across birth cohorts (Abeliansky and Strulik (2019) for Europe; Abeliansky, Erel, and Strulik (2020) for the US; and Old and Scott (2023) for the UK). Second, it relates to papers looking at the effects of healthy aging on the labor force and economic growth (Kotschy and Bloom, 2023). More broadly, it also relates to a larger body of literature studying the impact of health on economic outcomes. For instance, previous studies have demonstrated that sudden changes in health induce retirement decisions or labor supply reductions for the older work population (Disney et al., 2006; Au et al., 2004; Bound et al., 1999; Riphahn, 1999), force younger individuals into inactivity (García-Gomez et al., 2010; García-Gómez and López-Nicolás, 2006), and that the employment effects may persist over many years (García-Gómez et al., 2013).

Primarily, we differentiate from this literature by (i) providing new empirical evidence on the healthy aging phenomenon considering a variety of measures of health for Korea and selected Asian peer countries, and (ii) seeking to identify the causal impact of healthy aging on the decision of whether to participate in the labor market, by using a novel instrument for health shocks — being diagnosed with a chronic disease.

Finally, this paper relates closely to concurrent work in which we analyze healthy aging trends for a wider sample of 41 advanced economies and developing countries, across an expanded set of health metrics, and estimating the impact on a broader set of labor market outcomes, including also the intensive margin of labor supply, labor earnings, and productivity (Gruss et al., 2025; IMF, 2025).

The remainder of this paper is organized as follows. Section 2 describes the data used in the analysis, while section 3 presents the empirical strategy. Sections 4 and 5, respectively, present our estimates of healthy aging trends and their impact on labor supply. Section 6 concludes.

II. Literature

This section describes the data used in our analysis, including the sample of countries, health indicators, and the labor force status data considered.

A. Sample Coverage

While our analysis focuses on Korea during the time period of 2006-20, our full sample also includes five other countries in the Asia and Pacific region, specifically China, India, Japan, Malaysia, and Thailand, for the years up to 2022 (Figure 3). In total, we have around 200,000 observations for individuals aged 50-90 based on household surveys focused on older populations (Table A1), with about one-third of the sample drawn from Korea. We use pre-harmonized data from the Gateway for Global Aging for all countries except Thailand, for which we harmonized corresponding microdata. The presence of multiple survey waves enables a longitudinal analysis — tracking a subset of individuals over time — in all countries except for India and Malaysia, where data exist for only one wave.4

FIGURE 3.

SAMPLE COVERAGE

Source: CHARLS, HART, JSTAR, KLoSA, LASI, MARS and authors’ calculations.

B. Health Indicators

We consider a variety of physical health indicators that are comparable across different surveys, including both measured and self-reported metrics.5 For measured indicators, we use respondents’ maximum grip strength (in kilograms). For self-reported metrics, we rely on responses regarding overall health status (reported on a 1-5 scale), the ease of performing activities of daily living (ADLs) — such as getting in and out of bed, bathing, dressing, eating, and walking across the room — and instrumental activities of daily living (IADLs) — such as managing money, taking medications, shopping for groceries, and preparing a hot meal.6 We also summarize the self-reported health measures into a first principal component.

There are advantages and drawbacks when using either measured or self-reported indicators of individuals’ health. Measured indicators such as grip strength are objective and arguably more reliable, but they capture only a narrow dimension of physical health, and no other measured indicators are available for Korea. In contrast, self-reported measures capture individuals’ perceptions of their physical condition and their ability to perform basic daily tasks — factors that are likely to be of first-order importance to their willingness and ability to work. To provide a more comprehensive picture, we use both types of indicators in the analysis.

For comparability, all health indicators are standardized to have a mean of zero and a standard deviation of one, with higher values indicating better health. As shown in Figure 4, and perhaps unsurprisingly, health outcomes tend to deteriorate with age. We also observe important differences across countries. For instance, average grip strength is substantially higher in China than in India and Malaysia, while average self-rated health is significantly higher in Thailand, Japan and Malaysia when compared with China and Korea. In contrast, cross-country variation is considerably smaller for ADLs and IADLs. Notably, the availability of health indicators is unbalanced across countries in our sample, with data on grip strength missing for Japan and Thailand, while data on IADLs and hence also our constructed principal component are missing for Malaysia and Thailand; see Table A1.

FIGURE 4.

HEALTH INDICATOR AVERAGES BY AGE AND COUNTRY

Source: CHARLS, HART, JSTAR, KLoSA, LASI, MARS and authors’ calculations.

We also draw on self-reported data on health behaviors and the incidence of 17 chronic diseases.7 Figure A1 illustrates average scores on health behaviors across countries, revealing substantial differences that highlight the potential role of national health policies in promoting healthy lifestyles.8 It also shows the average incidence of chronic diseases, ranging from over one-third of individuals reporting high blood pressure to a much smaller fraction reporting conditions such as Parkinson’s disease. To account for reporting differences for chronic diseases across individuals and surveys, we construct our instrument — chronic disease incidence — as the number of chronic diseases an individual reports having, expressed as a share of the total number of chronic diseases listed in the survey.

C. Labor Force Status

To examine whether better health causes individuals to increase their labor supply, we also utilize survey data on labor force status, specifically (i) whether the individual participates in the labor market, and (ii) whether the individual is retired. While related, these two measures are distinct. Retirement is only one form of labor market inactivity and is typically only applicable beyond a certain age. For example, Figure 5 shows labor force status by age group, illustrating the tendency for individuals in Korea to transition from activity into retirement as they age, notably from 60 years old onward, with only a small fraction moving into non-retirement inactivity. In contrast, in Japan, individuals are more likely to shift into non-retirement inactivity, especially before 65.9

FIGURE 5.

LABOR FORCE STATUS BY AGE, KOREA VS. JAPAN

Source: JSTAR, KLoSA, and authors’ calculations.

Another notable difference arises from gender gaps in labor force participation (Figure 6), which are particularly pronounced in Korea, even at relatively younger ages (50-59), leading to a lower aggregate labor force participation rate. Given these differences in the labor force status composition and transitions over the age spectrum, both margins — participation and retirement — are of interest.

FIGURE 6.

LABOR FORCE PARTICIPATION GENDER GAPS, KOREA VS. JAPAN

Source: JSTAR, KLoSA, and authors’ calculations.

III. Empirical Strategy

To investigate whether health has improved across successive birth cohorts, we regress various health indicators ( H_i,t ), one at a time, on individuals’ year of birth ( YOB_i ),10 controlling for a vector ( X_i,t ) of individual socioeconomic characteristics, in this case age, gender, education, and log household wealth. We estimate this specification separately for Korea and for a pooled sample of Asian peer countries, where we additionally control for country fixed effects. Standard errors are clustered at the year-of-birth level. The pooled ordinary least squares (OLS) specification is given by:

For robustness, and following the literature (Abeliansky and Strulik, 2019; Abeliansky et al., 2020; Old and Scott, 2023), we also leverage the longitudinal nature of the data and estimate a Mundlak (1978) regression. This regression addresses concerns that unobserved individual effects may be correlated with explanatory variables, potentially biasing the OLS and random-effects estimates. Unlike a fixed-effects model, which discards between-individual variation, the Mundlak regression augments a random-effects model with the within-individual averages of time-varying explanatory variables ( X_i ):

This method treats individual-specific effects as partially explained by the means of explanatory variables over time, effectively providing a middle ground between fixed effects and random-effects models. It allows for the estimation of the coefficients on both time-varying (e.g., age, wealth) and time-invariant (e.g., year of birth, gender, education) explanatory variables at the individual level.

Next, to estimate the effect of better health on labor market outcomes among older individuals — such as the decision to participate in the labor force or to retire — we take the following two-step approach:

Correlations. We begin by estimating the association (correlation) between each health indicator ( H_i,t ) and each labor market indicator ( LMI_i,t ), one at a time, by means of an OLS regression. The specification controls for a vector ( Z_i,t ) of individual socioeconomic characteristics (age, gender, education, log household wealth) and a time trend (or time fixed effects as a robustness check):

In addition to studying this relationship for Korea, we also conduct a separate analysis for the pooled sample of peer countries for comparison, in which case we also control for country fixed effects.

Causal effects. To address potential endogeneity concerns — such as reverse causality, where labor market participation may influence individuals’ health — and attempt to capture causal effects, we employ a two-stage least-squares (2SLS) regression approach. We instrument health using exogenous health shocks, proxied by the incidence of chronic diseases.11 Considering that most chronic diseases are caused by a short list of risk factors — smoking, poor nutrition, physical inactivity, and excessive alcohol use (Hacker, 2024), we control for these lifestyle factors in the regression.12 The identification strategy rests on the assumption that at least some chronic diseases — those not explained by individuals’ socioeconomic or behavioral factors — can be considered exogenous. It is also unlikely that chronic diseases affect labor supply directly, rather than through their impact on health, thereby supporting the exclusion restriction.13

IV. Evidence of Healthy Aging

Our results provide broad-based evidence of healthy aging in Korea. After controlling for age and other individual socioeconomic characteristics, later-born cohorts exhibit better physical health than earlier-born cohorts.

In particular, OLS regression estimates for Korea reveal significant improvements in the health of subsequent birth cohorts for all of the health indicators considered (Figure 7 and Table A2). The most pronounced gains are observed for grip strength, a measured metric. However, positive and statistically significant trends are also found for the principal component summary of self-reported health indicators as well as for its subcomponents — overall health status, ADLs, and IADLs.

FIGURE 7.

HEALTHY AGING – TIME TREND COEFFICIENTS, KOREA VS. PEERS

Note: This figure shows estimates from OLS and Mundlak regressions of health indicators on year of birth, with individuals’ age, gender, education, wealth, and country of residence controlled for. Asterisks (***, **, and *) respectively denote significance of the year-of-birth coefficients at the 1, 5, and 10 percent level.

Source: CHARLS, HART, JSTAR, KLoSA, LASI, MARS and authors’ calculations.

The results using the Mundlak regression specifications are qualitatively similar in terms of sign and significance, with positive and significant annual improvements across all health indicators (Figure 8 and Table A4). Compared to OLS, the Mundlak estimates suggest somewhat stronger healthy aging trends.

FIGURE 8.

HEALTHY AGING IN KOREA: AGE 70 IS THE NEW…

Note: This figure shows age-equivalent gains, measured as the coefficient on year of birth (trend improvement effect) multiplied by the number of years in our sample (17 years) and divided by (coefficient on age minus coefficient on year of birth).

Source: KLoSA, and authors’ calculations.

For comparison, we also examine the pooled sample of the five Asian peer countries (China, India, Japan, Malaysia, and Thailand) while excluding Korea. On average, we find no significant evidence of healthy aging for the grip strength metric (Figure 7 and Table A3). However, self-reported indicators show significant improvements over time for the principal component of health indicators, as well as two of its components: overall health status and IADLs. These results are qualitatively and quantitatively consistent across both the OLS and Mundlak specifications (Figure 7 and Table A5). That said, we caution that the limited time coverage of health data in Korea’s peer countries (often spanning only a few survey waves) may lead to imprecise trend estimates.

Overall, Korea appears to have experienced larger improvements in measured health metrics (grip strength) than its regional peers, based on both OLS and Mundlak estimates. For self-reported health measures, the comparison is less clear and depends on the estimation approach. For example, healthy aging trends in Korea based on self-reported health appear substantially smaller than in peer countries according to OLS estimates; however, the Mundlak regression estimates suggest stronger trends, particularly for ADLs and IADLs.14

To illustrate the magnitude of healthy aging gains in Korea, we use the estimated coefficients on year of birth and age to calculate age-equivalent health gains.15 Our estimates suggest that by 2022, the average 70-year-old in Korea had grip strength comparable to that of a 60-year-old in 2006 (Figure 8). Similarly, self-reported health for a 70-year-old individual in 2022 was equivalent to that of someone aged 59-64 in 2006 — depending on the regression specification used. Among the subcomponents, the largest gains are observed for ADLs and IADLs, with age-equivalent improvements of 8-13 years. The smallest gains are found for self-reported overall health status, where a 70-year-old person in 2022 is comparable to someone aged 64-68 in 2006. Altogether, these findings suggest that “the 70s are the new 60s” in Korea, with roughly a decade of improvement in age-equivalent health over the sample period.

In addition to cohort trends, our regressions highlight other important socio-economic determinants of health. As expected, health tends to deteriorate with age and is generally better among individuals with higher education and greater household wealth. Gender differences are also notable: men tend to have higher grip strength and overall self-rated health, while women in Korea report better outcomes in ADLs — contrasting with the regional average, where men tend to fare better.16

By interacting cohort trends with these socio-economic determinants, we can investigate whether healthy aging trends are similar across subgroups of the population. The results shown in Appendix Table 6 indicate that within-country health disparities have persisted in Korea. In particular, health gaps have persisted across household wealth quintiles (given equal trends), widened by gender (given stronger healthy aging trends for males relative to females), and narrowed across education groups (given stronger healthy aging trends for individuals with lower levels of education).

Finally, we also observe important cross-country differences in average health outcomes. Figure 9 shows country fixed effects from regressions using the full sample, benchmarked against China. Even after controlling for individuals’ age, gender, education, and wealth, significant variation remains. Self-rated overall health status is notably higher in Thailand, Japan, and Malaysia. Meanwhile, grip strength is notably lower in India and Malaysia. Cross-country differences in ADLs and IADLs are smaller but still meaningful, with most countries scoring slightly higher than China — except for India. The results are robust across both the OLS and Mundlak specifications.

FIGURE 9.

CROSS-COUNTRY HEALTH HETEROGENEITY

Note: This figure shows estimates from regressions of health indicators on country fixed effects, with individuals’ age, year of birth, gender, education, and wealth controlled for. The left chart shows OLS estimates while the right chart shows Mundlak estimates that further control for individuals’ mean age and wealth. Asterisks (***, **, and *) respectively denote significance of the country fixed effect coefficients at the 1, 5, and 10 percent level.

Source: CHARLS, HART, JSTAR, KLoSA, LASI, MARS and authors’ calculations.

Next, we examine how better health outcomes influence older individuals’ labor supply decisions.

V. Effect of Health on Labor Market Status

Our OLS estimates indicate that better physical health — both measured and selfreported — is associated with a higher likelihood of labor force participation and a lower probability of retirement among older workers in Korea (Table 1). These estimates should be interpreted as correlations, given the potential endogeneity between health and labor market outcomes that could bias the OLS estimates.

The results from our 2SLS regression, aimed at estimating causal effects, are consistent with the OLS estimates. We instrument health using the incidence of chronic diseases, which we assume to be exogenous to labor market behavior after controlling for socio-economic characteristics and health-related behaviors. Our first-stage regressions confirm that the incidence of chronic diseases is a strong instrument for physical health across all measures, with F-statistics exceeding the Stock and Yogo (2005) rule-of-thumb critical value of 10 and thereby ruling out weak instruments (Table 1). The second-stage regressions show that exogenous improvements in health significantly increase labor force participation and reduce retirement rates. Notably, the estimated effects from the IV approach are substantially larger than those from the OLS regressions, stressing the importance of tackling potential endogeneity.

TABLE 1

EFFECTS OF HEALTH ON DECISION TO PARTICIPATE AND/OR RETIRE — OLS VS. 2SLS, KOREA

Note: This table shows estimates from the OLS and 2SLS regressions of labor force participation and retirement dummy indicators on health indicators (one at a time), with individuals’ socio-economic characteristics (age, gender, education, household wealth), lifestyle factors (dummy indicators for having ever smoked, being over/underweight, or infrequent moderate physical activity), and the survey year controlled for. Asterisks (***, **, and *) respectively denote significance of coefficients at the 1, 5, and 10 percent level.

Source: KLoSA, and authors’ calculations.

Combining these results with the earlier evidence of healthy aging allows us to estimate the effects of healthy aging on labor market outcomes. We normalize the results to reflect the effect of a one-decade improvement in health. The largest effects are found for grip strength: a ten-year gain in this measure increases the probability of labor force participation by about 19 percentage points and decreases the probability of retirement by about 18 percentage points (Table 2).17 In turn, improvements in self-reported health observed over one decade are found to increase labor force participation by about 2.5 percentage points.

TABLE 2

EFFECTS OF ONE-DECADE IMPROVEMENTS IN HEALTH ON THE PROBABILITY OF WORKING AND/OR RETIRING, KOREA

Note: This table shows estimates from two-stage least-squares regressions of labor market outcomes on health indicators (one at a time), rescaled to represent the effects of a one-decade improvement in health (dividing health measures by ten times the coefficient on the year of birth). Asterisks (***, **, and *) respectively denote significance of coefficients at the 1, 5, and 10 percent level.

Source: KLoSA, and authors’ calculations.

We also explore heterogeneity in the effects of health on labor force participation across socio-economic characteristics.18 This also allows for an assessment of the external validity of our findings. We find somewhat larger effects for relatively younger age groups, males, and lower-education and lower-wealth individuals (Table 3).19 The impact of better health on labor force participation is strongest among individuals in their 50s and 60s, with smaller effects for older age groups. This suggests that other factors, such as skill obsolescence, the incentives of pension plans, and age discrimination, increasingly constrain attachment to the labor market at older ages. Turning to gender, the effects for men are almost twice as large as those for women, suggesting that men’s labor supply decisions are more responsive to their health status. The results also show a stronger likelihood of healthier older individuals with lower education levels remaining in the labor market relative to those with tertiary education. Finally, the effect of health on the probability of remaining in the labor market is higher for individuals from lower-wealth households.

TABLE 3

HETEROGENEOUS EFFECTS OF HEALTH ON LABOR FORCE PARTICIPATION, KOREA

Note: This table shows estimates from the 2SLS regressions of labor force participation on health indicators (one at a time), with individuals’ socioeconomic characteristics (age, gender, education, household wealth), lifestyle factors (dummy indicators for having ever smoked, being over/underweight, or infrequent moderate physical activity), and the survey year controlled for. Separate regressions are estimated for subsamples based on individuals’ socioeconomic characteristics relating to age group, gender, education, and household wealth. Asterisks (***, **, and *) respectively denote significance of coefficients at the 1, 5, and 10 percent level.

Source: KLoSA, and authors’ calculations.

Our results remain robust to a range of sensitivity checks, including alternative specifications of the chronic disease instrument. Importantly, our baseline estimates are robust to alternative instruments that restrict our instrumental variable to consider only a subset of chronic diseases that are most plausibly exogenous to work or occupational factors20 (Table A7). The results are also robust when accounting for differing chronic disease severity by using alternative instruments that (i) weigh diseases by their severity (measured as their estimated impact on the principal component of self-reported health),21 and (ii) consider a dummy indicator for the top ten chronic diseases with most severe impacts on health according to the ranking by Gruss et al. (2025) — specifically Parkinson’s, Alzheimer’s, psychological disorders, stroke, kidney disease, lung disease, osteoporosis, arthritis, asthma, and urinary incontinence22 (Table A7). Moreover, the results are robust to alternative instrument definitions such as the total number of chronic diseases and a dummy indicator for whether the respondent reports having any of the 17 chronic diseases.23

In addition, we show that positive health effects remain robust even after controlling for year fixed effects (Table A8). This suggests that the observed increases in labor supply are not driven by time variations in policies at the country level, such as changes in statutory retirement ages or other labor market reforms. Relatedly, we also find that health — instrumented by chronic diseases — has no significant impact on older individuals’ probability of being unemployed, conditional on their decision to participate in the labor market (Table A9).24 Altogether, the positive effect of health on labor supply seems to reflect stronger voluntary attachment to the labor market by healthier older individuals, potentially supported by appropriate incentives, rather than a byproduct of labor market reforms or firms’ decisions.

Finally, we also study results for Korea’s peer countries, as summarized in Table 4.25 OLS estimates are qualitatively similar across most health indicators, except for grip strength and self-reported overall health status, which are not statistically significant in the comparator group.26 However, when using 2SLS with the incidence of chronic disease as an instrument, we find significant effects for all health indicators — including those that were insignificant in the OLS regressions. Notably, the magnitudes of the causal effects are substantially larger in Korea than in the comparator countries, nearly twice as large for most indicators with the exception of self-rated overall health, where the effects are similar. These findings suggest that objectively measured health is a particularly strong determinant of labor supply in Korea.

TABLE 4

EFFECTS OF HEALTH ON THE DECISION TO PARTICIPATE IN THE LABOR MARKET AND/OR RETIRE — OLS VS. 2SLS, ASIA-5 (EXCL. KOREA)

Note: This table shows estimates from OLS and 2SLS regressions of labor force participation and retirement dummy indicators on health indicators (one at a time), with individuals’ socio-economic characteristics (age, gender, education, household wealth), lifestyle factors (dummy indicators for having ever smoked and being over/underweight), survey year, and country of residence controlled for. The sample includes China, India, Japan, Malaysia, and Thailand unless otherwise specified in the regression column. Asterisks (***, **, and *) respectively denote significance of coefficients at the 1, 5, and 10 percent level.

Source: CHARLS, HART, JSTAR, LASI, MARS and authors’ calculations.

Our estimates imply that healthy aging has accounted for a significant share of the rise of labor force participation in Korea during the period of 2006-20 (Figure 10). Healthy aging, measured by gains in grip strength, more than accounted for the total rise in elderly labor force participation — suggesting that other factors may have played a counteracting negative role. Meanwhile, healthy aging when measured by the self-reported health principal component contributed approximately 40 percent of the total rise in labor force participation by older workers.

FIGURE 10.

KOREA’S CHANGE IN LABOR FORCE PARTICIPATION, 2006-20

Note: This figure shows the estimated contributions of healthy aging vis-à-vis other (residual) factors in explaining the change in Korea’s labor force participation during the period 2006-2020. Dots denote the change in labor force participating during 2006-2020 implied by estimating a linear annual time trend by age group.

Source: KLoSA, and authors’ calculations.

Overall, these results suggest that further boosting healthy aging can help to raise the voluntary labor supply among the older population. Health prevention and promotion policies can reduce the incidence of chronic disease and reduce health inequalities (Liu et al., 2016; Rashbrook, 2019; Lee, Park, and Lee, 2020; Hacker, 2024). Policy tools range from expanding preventative healthcare (e.g., immunization and health screenings) and mental health services to targeting healthy behaviors through substance-abuse prevention, tobacco and junk food taxes, smoke-free regulations, and promoting physical exercise. Many of these measures do not necessarily carry a high implementation cost and have often been found to be cost-effective by lowering future health expenditures (McDaid, Sassi, and Merkur, 2015; OECD, 2015).

To extend working lives in line with longer life expectancy, health policies may need to be paired with other measures. Policies that promote higher effective retirement ages — through incentives to postpone retirement, phased retirement, and reduced early-retirement benefits — must balance fiscal sustainability with protection against old-age poverty. Lifelong reskilling, age-friendly workplaces, flexible work, and combating age discrimination are also crucial, particularly as artificial intelligence reshapes labor markets.

Demand-side factors may also influence the labor market outcomes of older individuals. For instance, anecdotal evidence suggests that structural labor market rigidities in Korea — particularly seniority-based wage and promotion systems — create incentives for firms to encourage early retirement and discourage the hiring of older workers.27 This may partly explain the large number of retirements before age 55, well before the statutory retirement age of 60 and pension eligibility (OECD, 2022; Lee and Cho, 2022), despite improvements in health and increases in the statutory retirement age.

VI. Concluding Remarks

Our findings provide micro-level evidence of a healthy aging phenomenon in Korea, where individuals are aging in better physical health than previously. Gains in the physical capacities of older individuals across subsequent cohorts are observed consistently across multiple dimensions of health, including objective measures such as grip strength and self-reported indicators of overall health status and the ease of performing a variety of daily activities. The most pronounced improvement is observed for grip strength: by 2022 and based on this metric, the average 70-year-old in Korea had physical health comparable to that of an average 60-year-old in 2006.

In turn, better health across cohorts leads to an increased labor supply among older individuals. Specifically, a decade’s worth of healthy aging — measured through improvements in grip strength — is estimated to have increased the probability of labor force participation and reduced the likelihood of retirement by nearly 20 percentage points. These results are qualitatively comparable to, and somewhat stronger than, those observed in other Asian countries with available data.

Our findings suggest that policies to promote health improvements and reduce the incidence of chronic diseases could help expand the effective labor supply of older individuals as they live longer, healthier lives. Together with workplace adaptations, adequate training, and incentives to postpone retirement and combat age discrimination, healthy aging policies could help ease the economic pressures typically associated with population aging in Asia — particularly the shrinking labor force that dampens potential growth and strains public finances.