The Effect of a Long-Term Care Insurance Program on Subjective Well-Being of Older Adults with a Disability: Quasi-Experimental Evidence from China

Abstract

China launched its long-term care insurance (LTCI) program for older adults in 2016. Although the scheme has shown some promising outcomes, little is known about whether it improves subjective well-being. This study explored this topic among older persons with a disability and identified the underlying mechanisms associated with the channel of this effect using data from a national survey. The LTCI program was shown to improve the subjective well-being among older persons with a disability and this effect increased over time. The LTCI program has great positive effect among women and those who lived alone compared to their counterparts. Mechanism analysis revealed that the main channel by which the LTCI program has positive effect occurred through the satisfaction of long-term care needs and improved self-reported health. This study suggests promising benefits of the LTCI program for older Chinese adults.

Keywords

long-term care insurance quality of life disability aging

What this paper adds

• This study assessed the effects of both the introduction and duration of the LTCI program on the subjective well-being of older adults with a disability.

• This study provided insight into the differential effects of the LTCI policy across various population groups.

• This study explored potential mechanisms through which the LTCI program may affect subjective well-being.

Applications of study findings

• Evaluating the effect of the LTCI program in the pilot cities contributes to evidence-informed design and implementation of a national LTCI program in China.

• The findings offer evidence-based implications for the Chinese government on the further implementation, expansion, and evaluation of the LTCI program.

• This study supports the implementation and broader diffusion of the LTCI program to other countries, especially low- to middle-income countries.

Introduction

With a dramatic increase in life expectancy, population aging has become an increasing concern in China (Chang et al., 2020). The number of people aged 60 and older reached 267 million in 2021 and accounted for 18.9% of the total population of China (Wang, 2022). This demographic change presents the older adults with a disability, their families, and the government with a challenge to satisfy the demand for long-term care (LTC). Although there has been an expansion in LTC services, its growth has lagged behind other areas of activity (Feng et al., 2020; Ma et al., 2019). The growing occurrence of unmet need for LTC has led to “bed blocking” in the acute care hospital sector and growth in medical expenditures (Forder, 2009; Gaughan et al., 2015). Expansion in the provision of more appropriate LTC services in China remains an essential component of overall efforts to maintain and improve the health and well-being of older adults (Jeon & Kendig, 2017).

To promote health and well-being of older adults by providing affordable LTC services, China introduced a long-term care insurance (LTCI) program in Qingdao in 2012 and expanded it to 15 piloted cities in 2016. The LTCI program is mainly financed by public health insurance scheme funds. Individuals covered by Urban Employees Basic Medical Insurance (UEBMI) or Urban-Rural Residents Basic Medical Insurance (URRBMI) schemes are automatically enrolled in the LTCI program. A comprehensive disability assessment tool (such as the Barthel ADL index) is used to determine eligibility for LTCI, and those assessed above a certain degree of disability are deemed LTCI beneficiaries. It covers the expenses of institutional, hospital, and community care for qualified beneficiaries with certain disabilities. The details of LTCI are presented in Supplementary Table A1. The design of the LTCI program varies across the pilot cities, and there are still challenges in the establishment of a national LTCI program. Evaluating the effect of the LTCI program in the pilot cities contributes to the evidence-informed design and implementation of a national LTCI program.

Extensive efforts have been made to assess the effects of LTCI programs in Korea, Japan, and Germany (Chen & Xu, 2020). Some studies examined the effects of LTCI on the choice of formal or informal care (Courbage et al., 2020; Li & Jensen, 2011); and on the supply of informal caregivers (Fu et al., 2017; Shinya & Nakamura, 2014). There is growing evidence that the adoption of the LTCI program was associated with an improvement in the health status of older adults (Lee et al., 2014; Lei et al., 2022; Liu & Hu, 2022; Sohn et al., 2020). Some studies have explored the effects of the LTCI program on the cost of hospitalization, and on government health budgets and expenditures (Choi et al., 2018; Feng et al., 2020; Moon et al., 2021; Yu et al., 2019). However, there has been limited research on how this program affects subjective well-being. Furthermore, the strength of association between the adoption of the LTCI program and subjective well-being has been debated. A German study found that the LTCI program improved subjective well-being (Lee, 2015), while research conducted in South Korea demonstrated the LTCI program had no significant effect on life satisfaction (Jung et al., 2011; Lee, 2015; Lee & Wolf, 2014).

The main purpose of active aging is to improve quality of life (World Health Organization, 2002). This has been increasingly assessed by subjective perceptions of well-being (Lee & Wolf, 2014). While the LTCI program has been on the Chinese policy agenda for more than 5 years, none of research in China has examined how the LTCI program affects subjective well-being. Overall, this paper adds to the existing body of knowledge by evaluating the effects of the LTCI program on the subjective well-being of older adults with a disability. The causal effect is estimated using propensity score matching combined with difference-in-difference (PSM-DID) methods. The application of the PSM-DID method may reduce potential self-selection biases due to both observed factors and unobserved heterogeneity. Use of these methods assists in offering more precise causal effects (Duong & Thanh, 2019). To gain further understanding, we explore the different group effects depending on the living arrangements and gender. We further examine the mechanisms through which the LTCI program affects subjective well-being. The LTCI program may affect subjective well-being via improved health and reduced likelihood of having unmet LTC need (Berg et al., 2006; Lei et al., 2022). The findings are of direct relevance for policy makers and practitioners in China concerned with the implementation, expansion, and evaluation of the LTCI program in aging society.

Methods

Data

The study was based on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a nationally representative, population-based, and longitudinal survey aiming at understanding the determinants of healthy longevity among older adults aged 65 and over in China. The survey was conducted in 23 provinces, covering about 85% of the total population of China. The baseline survey was conducted in 1998, and the follow-up surveys were conducted in 2000, 2002, 2005, 2008, 2011, 2014, and 2018. Face-to-face interviews were conducted to collect data on life quality, health status, demographics, and socioeconomic characteristics both at the individual and household level. Detailed information of the survey has been published, and survey data was reported to be of high quality (Zeng et al., 2017).

Our analysis used the 2011, 2014, and 2018 waves of the CLHLS, covering the period before and after the implementation of the LTCI program. The original sample consisted of respondents over 65 years old, and only older persons with a disability (the policy relevant group) were as our research participants. Individuals who had limitations in more than one activity of daily living (ADLs, including dressing, bathing, eating, getting in and out of bed, toileting, and control over urination and defecation) were identified as disabled (Chang et al., 2020; Chen & Xu, 2020). We obtained a total of 2544 observations after we dropped observations with missing values to dependent variable and other control variables (8.6%). We created a balanced panel analysis dataset over the course of the three survey waves to better sidestep the sample attrition.

Measurement

Dependent Variable

The outcome variable of our interest is subjective well-being. It was measured by responses to a single universal self-rated life satisfaction question with responses coded on a 5-point Likert scale (1 = Very unsatisfied, 2 = Unsatisfied; 3 = General; 4 = Satisfied; 5 = Very satisfied). This measurement approach has been widely accepted as a measure of subjective well-being among older adults (Fan et al., 2022; Han & Gao, 2020).

Independent Variable

The implementation of the LTCI program was included as a dummy independent variable, with 1 representing individuals living in the pilot cities in the years when the program was operational and 0 otherwise. We restricted the treated group to individuals covered by LTCI in the pilot cities, while the respondents who did not live in pilot cities as the control group.

Mediating Variables

The mediators, including self-reported health and unmet LTC need, were collected in mechanism analysis. Self-reported health was coded as 1 if the respondent reported having “good” or “very good” health and 0 otherwise. Unmet LTC need was measured by a question asking respondents to rate whether they had received adequate care services in daily living activities to meet their needs (1 = Fully meet; 2 = Basically meet; 3 = Unmet). This type of subjective self-assessment of unmet LTC needs has been widely used in the literature (García-Gómez et al., 2015; Lei et al., 2022).

Covariates

We controlled for a set of covariates that have been shown in the literature to be determinants of subjective well-being (Han & Gao, 2020; Xu & Simpson, 2022): (1) individual-level characteristics, including age in years, gender (female or male), marital status (married or divorced/widowed/never married), ethnicity (Han nationality or others), education (years), enrollment in pension insurance, whether having chronic diseases, whether smoking, whether drinking, and whether taking physical exercise regularly; and (2) household-level characteristics, including the number of children, whether living with family members or not and annual household incomes. The specific definitions and descriptive statistics of variables are given in Table 1.

Table 1.

Descriptive Statistics.

Variables	Description	Mean	SD
Dependent variable
Life satisfaction	1 = very unsatisfied; 2 = unsatisfied	3.792	0.824
Life satisfaction	3 = general; 4 = satisfied; 5 = very satisfied	3.792	0.824
Independent variables
Age	Age of respondent in years	82.991	9.142
Gender	1 = female, 0 = male	0.579	0.494
Married	1 = married, 0 = divorced, widowed, or never married	0.433	0.496
Ethnic group	1 = Han nationality, 0 = otherwise	0.740	0.439
Education	Years of education	2.047	3.345
Pension insurance	1 = enroll in pension insurance, 0 = otherwise	0.241	0.428
Chronic diseases	1 = suffer from chronic diseases, 0 = otherwise	0.678	0.467
Smoke	1 = smoke cigarettes, 0 = otherwise	0.154	0.361
Drink	1= drink alcohol, 0 = otherwise	0.139	0.346
Physical activity	1 = take physical exercise regularly, 0 = otherwise	0.310	0.463
Children number	The number of children	4.296	1.941
Co-resident	1 = live alone, 0 = otherwise	0.232	0.422
Ln (household income)	Log (household income)	9.519	1.670
Mediating variables
Self-reported health	1 = good, 0 = otherwise	0.398	0.490
Unmet LTC need	1= fully meet; 2 = basically meet; 3 = unmet	1.157	0.417
N			2544

Note. SD means standard deviation.

Statistical Analysis

PSM-DID

We used the propensity score matching and difference-in-difference (PSM-DID) method to conduct the analysis. The PSM-DID method can address possible selection bias (Fu et al., 2017). The Gaussian kernel matching algorithm with a bandwidth of 0.06 was employed to take advantage of the large sample size and to reduce estimation bias (Chen et al., 2020; Lei & Lin, 2009). Furthermore, considering the characteristics of the phased launch and implementation of LTCI pilot policies, we adopted the time-varying DID approach with individual and time fixed effects to analyze the effect of LTCI policy and the effect over time. Specifically, our model controlled for both the effects of the time-invariant unobserved individual- and period-specific factors. The model was specified as follows

S W B_{i t} = β_{0} + β_{1} L T C I_{i t} + X_{i t}^{'} β + λ_{i} + θ_{t} + ε_{i t}

(1)

where the

S W B_{i t}

indicates the life satisfaction of sample i in year t;

L T C I_{i t}

is a dummy variable, and it takes value 1 when individual i lives in the pilot cities in the post-implementation period and 0 in the pre-implementation period;

X_{i t}

is a vector of observable individual and household level factors; The

λ_{i}

and

θ_{t}

represent individual and time fixed effects, respectively;

ε_{i t}

is the random error term. The key parameter of interest is

β_{1}

, which measures the effects of the LTCI program on subjective well-being. Standard errors are clustered at the city level to account for possible correlation in outcomes between older adults in the same city.

Mediating Effect Model

We constructed a mediating effect model following the prior research of Baron and Kenny (1986) to explore potential mechanisms through which the LTCI program might affect subjective well-being. The mediating effect model was described as

M_{i t} = α_{0} + α_{1} L T C I_{i t} + X_{i t}^{'} α + λ_{i} + θ_{t} + ε_{i t}

(2)

S W B_{i t} = γ_{0} + γ_{1} L T C I_{i t} + γ_{2} M_{i t} + X_{i t}^{'} γ + λ_{i} + θ_{t} + ε_{i t}

(3)

where

M_{i t}

represents the mediating variables, including self-reported health and unmet LTC need. A statistically significant, coefficient

α_{1}

means that the LTCI program has a significant effect on the mediating variable

M_{i t}

in model (2). Finally, we carried out a regression to model (3), mediating effects were demonstrated by a fall in or loss of significance in coefficients on the

L T C I_{i t}

for life satisfaction (

γ_{1}

) when controlling for mediator

M_{i t}

Results

Covariates Balancing Test for PSM

We performed the balancing test of covariates. Detailed results are displayed in Supplementary Table A2. The common support hypothesis of performing PSM was satisfied, as 2524 samples were included, and only 20 samples were excluded in the matched sample. The covariate balance test showed that all the covariates were balanced after matching, indicating that the PSM method achieved the randomization requirement.

Validity of the Identification Assumptions

The key assumption underlying the DID approach is known as the common trends assumption. It assumes that the outcome variables have similar trends over time for both treatment and control groups in the absence of the LTCI program. We employed the approach developed by Moser et al. (2014) to test for the parallel trend assumption (Feng et al., 2020). As reported in Figure 1, the coefficients were statistically non-significant before the implementation of the LTCI program, implying that the common trend assumption was satisfied.

Figure 1.

Common trend test. Note: The X-axis indicates a particular wave relative to the benchmark wave. The dashed line is a 95% confidence interval.

Main Results

Table 2 reports the effect of the LCTI program on subjective well-being for older adults with a disability. We found that the LTCI program improved subjective well-being by 0.212 (p < .01) in Model 2. With the gradual implementation of the LTCI program, the scheme’s generosity for LTC services tended to increase. Consequently, the effect of the LTCI may differ over time and, in particular, in the years since reform. We tried to examine the effect of LTCI program duration on subjective well-being. Specifically, program implementation length was measured as the number of years since program implementation, and it ranged from 0 to 6. The positive effect of program duration on subjective well-being increased over time (see Table 2, Model 3).

Table 2.

Effect of LTCI on Life Satisfaction.

Variables	Model 1	Model 2	Model 3
Variables	β (SE)	β (SE)	β (SE)
LTCI	0.2142*** (0.0601)	0.2121** (0.0671)	0.1514 (0.0942)
LTCI × Duration			0.0722** (0.0263)
Control variables	No	Yes	Yes
Individual-year fixed effects	Yes	Yes	Yes
R ²	0.3551	0.3867	0.3867
N	2524	2524	2524

Notes: *p < 0.05, **p < 0.01, ***p < 0.001. Cluster-robust standard errors are in parentheses. The other control variables are the same as shown in Table 1.

Sub-Sample Analysis

In this section, we analyzed whether the effects vary across subgroups by adding interaction terms to the basic model. The results are reported in Table 3. We first examined whether there were different effects by living arrangements. The LTCI program was found to have a larger positive effect on subjective well-being for those who lived alone (β = 0.347, p < .05) (see Model 1). We then explored heterogeneous effects by gender. The findings are presented in Model 2, and show that the positive effect of LTCI on well-being appeared to be greater among women compared to their male counterparts (β = 0.309, p < .05). Additionally, the positive effect of the LTCI program on well-being in Model 3 was reported to be larger among the women living alone (β = 0.376, p < .001) in comparison to their counterparts.

Table 3.

Heterogeneity Analysis.

Variables	Model 1: Co-resident	Model 2: Gender	Model 3: Co-resident and gender
Variables	β (SE)	β (SE)	β (SE)
LTCI × Co-resident	0.3474* (0.1654)
LTCI × Gender		0.3086* (0.1462)
LTCI × Co-resident × Gender			0.3763*** (0.0689)
LTCI	0.0095 (0.1627)	0.1357 (0.0751)	0.1470 (0.0754)
Control variables	Yes	Yes	Yes
Individual-year fixed effects	Yes	Yes	Yes
R ²	0.3934	0.3926	0.3941
N	2524	2524	2524

Notes: *p < 0.05, **p < 0.01, ***p < 0.001. Cluster-robust standard errors are in parentheses. The other control variables are the same as shown in Table 1.

Mechanism Analysis

We explored the potential mechanisms by performing a mediation analysis. Specifically, we first examined the effects of the LTCI program on the two mediators (self-reported health and unmet LTC need). The estimation results are presented in Models 1 and 3 of Table 4. The LTCI program substantially improved self-reported health and decreased the likelihood of having unmet LTC need.

Table 4.

Mediation Analysis.

Variable	Model 1	Model 2	Model 3	Model 4
	Self-reported health	Life satisfaction	Unmet LTC need	Life satisfaction
	β (SE)	β (SE)	β (SE)	β (SE)
LTCI	0.1389* (0.0707)	0.1503** (0.0537)	−0.1071* (0.0502)	0.1808** (0.0554)
Self-reported health		0.4513*** (0.0761)
Unmet LTC need				−0.3005*** (0.0429)
Control variables	Yes	Yes	Yes	Yes
Individual-year fixed effects	Yes	Yes	Yes	Yes
R ²	0.2864	0.5213	0.1699	0.4832
N	2524	2524	2524	2524

Notes: *p < 0.05, **p < 0.01, ***p < 0.001. Cluster-robust standard errors are in parentheses. The other control variables are the same as shown in Table 1.

We then explored the mechanisms after controlling for the mediating indicators. As shown in Models 2 and 4 in Table 4, the positive effects of the LTCI program on subjective well-being fell by 29.1% and 14.8% after including the two mediators of self-reported health status and LTC utilization, respectively. These findings demonstrated that the LTCI program improved health and reduced unmet LTC needs, which in turn improved subjective well-being.

Robustness and Sensitivity Checks

We conducted a series of sensitivity analyses to test the robustness of our results. First, we conducted a placebo test to reduce the effects of other policies. Older adults with a disability who were not covered by the LTCI program in the pilot city were assigned to the placebo treatment group, while the control group was the same with the control group specified in our main analysis. As neither the treatment nor control group in this exercise was exposed to the LTCI scheme, there should be no significant difference in subjective well-being between these groups. The results are reported in Model 1 in Supplementary Table A3. The coefficient on the LTCI program was not statistically significant, implying that the estimated positive effect of the LTCI program on subjective well-being was not affected by other policies.

Second, we conducted a counterfactual test to verify whether life satisfaction was associated with the implementation of the LTCI program. To explore whether other unobserved external factors affected the estimation results, we assumed that the LTCI program was implemented in other waves rather than its real implementation wave. As shown in Model 2 in Supplementary Table A3, the counterfactual LTCI program was found to have no significant effect on subjective well-being, which suggests that the LTCI program indeed has a positive effect on subjective well-being as reported in the main analysis.

Third, to deal with the possible association between the unobserved omitted variables and subjective well-being, we adopted another placebo test by randomly assigning LTCI program experiment to the non-pilot cities in which the quantity is equal to the number of real pilot cases (Cai et al., 2016; La Ferrara et al., 2012). We repeated this random data generating process 500 times to avoid contamination of rare events. Supplementary Figure A1 compares the distribution of estimates from the repeated process with the benchmark estimate (Table 2 Model 2). We found that the mean value of estimates was 0.003. The distribution of estimates was clearly centered around zero, and p > .1 for most of the estimates. These results suggest that our estimates are not severely biased due to any unobserved omitted factors.

Finally, we checked whether variation in the implementation of the LTCI program across cities and time periods affected our main study findings. We used an event study estimator developed by De Chaisemartin and D’Haultfoeuille (2020), De Chaisemartin et al. (2021) to consolidate our findings, which is a valid and robust estimator when the treatment effect is heterogeneous or dynamic over time (Garrouste & Zaiem, 2020; Lindgren et al., 2021). In our analysis, we performed these treatment and placebo tests using Stata’s did_multiplegt module. As shown in Supplementary Figure A2, the estimated result was 0.183 (p < .05), which is slightly smaller than the estimate in Table 2. This suggests that our estimation results are robust and not driven by any particular time-period.

Discussion

This study examined the effect of the LTCI programs on subjective well-being for older adults with a disability in China. We took advantage of the quasi-experimental nature of the way in which the LTCI program was introduced to the pilot cities in China by constructing a PSM-DID model. Overall, our statistical analysis has revealed that the implementation of the LTCI program improved subjective well-being, and the positive effect increased over time. The results of these robustness tests strengthened the credibility of our findings concerning the positive effect of the LTCI program on subjective well-being. Our findings were consistent with previous studies. Jeon (2017) used data from South Korea and found that the national LTCI had a positive effect on the life satisfaction of older adults, and the effect became stronger over time. The evidence from our study may inspire policymakers to implement the LTCI program throughout China.

Sub-sample disparities were identified in this study. The positive effect of the LTCI program on subjective well-being was more pronounced among older adults who lived alone. This may be attributed to the fact that the formal care services and psychological comfort in the LTCI benefit package satisfied the care needs of older adults who lived alone, as the services may be considered as substitutes for informal care services provided by family members (Bakx et al., 2015; Weaver et al., 2009). With regard to diversity within the gender group, our findings suggest that the positive effect of the LTCI program appeared to be more substantial among female than their male counterpart. One possible explanation is that older women are more likely to have their remaining years spent with disability compared to their male counterpart as women have longer life expectancy than men (He & Chou, 2020; Wheaton & Crimmins, 2016). As such, older women are more likely to use formal LTC services than older men (He & Chou, 2020). The LTCI can provide care services to satisfy the care needs of female older adults, and accordingly, improve their subjective well-being to a greater extent.

Additionally, our analysis revealed that the LTCI program has a positive effect on subjective well-being through improved self-reported health and reduced the likelihood of older adults having unmet needs. This finding was consistent with previous literature. The implementation of LTCI program significantly increased the use of LTC services for older adults, and also improved the quantity of care they receive (Lei et al., 2022; Tamiya et al., 2011). Similarly, the LTCI scheme significantly improved health outcomes for older adults and relieved their physical pain (Lei et al., 2022; Ma et al., 2019). Improvement in health and increased access to LTC services for persons with a disability, probably accounts for the improvement reported in subjective well-being (Berg et al., 2006).

The empirical findings have several important implications for policy decision makers charged with the design and implementation of a national LTCI program. First, our study recommends expanding coverage, and the pilot cities warrant strengthening in order to further advance the LTCI program on an existing basis. Second, compared with older adults living with their families, older adults living alone have a higher rate of unmet LTC need, and thus, more support would go a long way in meeting unmet needs for LTC for them. Third, the LTCI program may be extended to include appropriate (and timely) clinical services for older adults with a disability started by their level of disability in order to advance their health.

Study Strengths and Limitations

There are several strengths of our study. First, this study used a nationally representative data and performed a longitudinal analysis to infer the causal effects of the LTCI program on the subjective well-being among older adults with a disability. We also assessed the effects of both the introduction and duration of the LTCI program on subjective well-being, with offering insight into the heterogeneous policy effects. Furthermore, we explored the potential mechanisms through which the LTCI program may affect subjective well-being. This study has the potential to provide a comparatively comprehensive picture of the effect of the LTCI program on the subjective well-being of older adults with a disability.

Nevertheless, several limitations should be recognized. First, there is no information on individuals’ qualification for receiving LTCI benefits in CLHLS survey. The intent-to-treat effect estimated in this study included the direct effect for LTCI beneficiaries and the spillover effect for nonusers covered by the program, so that the effect for the beneficiaries may underestimate the effect of the program. Second, by the end of 2020, the LTCI program had expanded coverage to 49 cities, so future studies are planned to explore when data is available. Third, this paper focused on examining the effect of the LTCI program on subjective well-being, but there is plenty of room to explore the effects of LTCI on other physical and mental health outcomes. Moreover, due to data limitation, we could not examine the effect among individuals under 65 years old. This is also left as one of the critical agendas for future research.

Conclusions

The study demonstrated that the LTCI program improved long-term subjective well-being among older persons with a disability. This positive effect of the program was more pronounced among women and those who lived alone compared to their counterparts. We further find that subjective well-being was improved by meeting the long-term care needs of older adults with a disability and thereby enhancing their physical health. Hence, it should further expand the coverage of the LTCI program on an existing basis to better improve the well-being of older adults with a disability.

Supplemental Material

Supplemental Material - The Effect of a Long-Term Care Insurance Program on Subjective Well-Being of Older Adults with a Disability: Quasi-Experimental Evidence from China

Supplemental Material for The Effect of a Long-Term Care Insurance Program on Subjective Well-Being of Older Adults with a Disability: Quasi-Experimental Evidence from China by Hongli Fan, Yingcheng Wang, Jinyan Gao, Zixuan Peng, and Peter C. Coyte in Journal of Applied Gerontology

Footnotes

Acknowledgments

All authors thank the Center for Healthy Aging and Development Studies, Peking University, for supporting the data.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Foundation of China (No. 21BRK003, No. 21BJY064), the National Natural Science Foundation of China (No. 72171133), and the National Natural Science Foundation of Shandong Province (No. ZR2021QG017).

Ethical Approval

The authors used only secondary datasets for this present study and data analyses. Therefore, approvals from Institutional Review Board (IRB) were not required.

ORCID iD

Hongli Fan

Supplemental Material

Supplemental material for this article is available online.

References

Bakx

De Meijer

Schut

Van Doorslaer

(2015). Going formal or informal, who cares? The influence of public long-term care insurance. Health Economics, 24(6), 631–643. https://doi.org/10.1002/hec.3050

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Berg

A. I.

Hassing

L. B.

McClearn

G. E.

Johansson

(2006). What matters for life satisfaction in the oldest-old? Aging & Mental Health, 10(3), 257–264. https://doi.org/10.1080/13607860500409435

Cai

(2016). Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China. Journal of Development Economics, 123, 73–85. https://doi.org/10.1016/j.jdeveco.2016.08.003

Chang

Yang

Deguchi

(2020). Care providers, access to care, and the long-term care nursing insurance in China: An agent-based simulation. Social Science & Medicine, 244, 112267. https://doi.org/10.1016/j.socscimed.2019.112667

Chen

Fan

Chu

(2020). The double-burden effect: Does the combination of informal care and work cause adverse health outcomes among females in China? Journal of Aging and Health, 32(2), 1–11. https://doi.org/10.1177/0898264320910916

Chen

(2020). Effect evaluation of the long-term care insurance (LTCI) system on the health care of the elderly: A review. Journal of Multidisciplinary Healthcare, 13, 863–875. https://doi.org/10.2147/JMDH.S270454

Choi

J. W.

Park

E-C.

Lee

S. G.

Park

Ryu

H. G.

Kim

T. H.

(2018). Does long-term care insurance reduce the burden of medical costs? A retrospective elderly cohort study. Geriatrics & Gerontology International, 18(12), 1641–1646. https://doi.org/10.1111/ggi.13536

Courbage

Montoliu-Montes

Wagner

(2020). The effect of long-term care public benefits and insurance on informal care from outside the household: Empirical evidence from Italy and Spain. European Journal of Health Economics, 21(8), 1131–1147. https://doi.org/10.1007/s10198-020-01215-7

10.

De Chaisemartin

D’Haultfœuille

Guyonvarch

(2021). DID_MULTIPLEGT: Stata module to estimate sharp difference‐in‐difference designs with multiple groups and periods. In Statistical software components S458643. Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s458643.html

11.

De Chaisemartin

D’Haultfoeuille

(2020). Two‐way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996. https://doi.org/10.1257/aer.20181169

12.

Duong

Thanh

(2019). Adoption and effects of modern rice varieties in Vietnam: Micro-econometric analysis of household surveys. Economic Analysis and Policy, 64, 282–292. https://doi.org/10.1016/j.eap.2019.09.006

13.

Fan

Peng

Zhang

Coyte

P. C.

(2022). The contribution of alternative types of care on life satisfaction among disabled older persons in China. Ageing and Society, 1–17, https://doi.org/10.1017/S0144686X22000101

14.

Feng

Wang

(2020). Does long-term care insurance reduce hospital utilization and medical expenditures? Evidence from China. Social Science & Medicine, 258(4), 113081. https://doi.org/10.1016/j.socscimed.2020.113081

15.

Forder

(2009). Long-term care and hospital utilization by older people: An analysis of substitution rates. Health Economics, 18(11), 1322–1338. https://doi.org/10.1002/hec.1438

16.

Noguchi

Kawamura

Takahashi

Tamiya

(2017). Spillover effect of Japanese long-term care insurance as an employment promotion policy for family caregivers. Journal of Health Economics, 56, 103–112. https://doi.org/10.1016/j.jhealeco.2017.09.011

17.

García-Gómez

Hernández-Quevedo

Jiménez-Rubio

Oliva-Moreno

(2015). Inequity in long-term care use and unmet need: Two sides of the same coin. Journal of Health Economics, 39, 147–158. https://doi.org/10.1016/j.jhealeco.2014.11.004

18.

Garrouste

Zaiem

(2020). School supply constraints in track choices: A French study using high school openings. Economics of Education Review, 78, 102041. https://doi.org/10.1016/j.econedurev.2020.102041

19.

Gaughan

Gravelle

Siciliani

(2015). Testing the bed-blocking hypothesis: Does nursing and care home supply reduce delayed hospital discharges? Health Economics, 24(S1), 32–44. https://doi.org/10.1002/hec.3150

20.

Han

Gao

(2020). Does welfare participation improve life satisfaction? Evidence from panel data in rural China. Journal of Happiness Study, 21(5), 1795–1822. https://doi.org/10.1007/s10902-019-00157-z

21.

A. J.

Chou

(2020) What affects the demand for long-term care insurance? A study of middle-aged and older adults in Hong Kong. Journal of Applied Gerontology, 39(4), 413–422. https://doi.org/10.1177/0733464818766598

22.

Jeon

H. S.

(2017). Exploratory analyses on the effects of the national long-term care insurance: Using the Korea welfare panel study (2006–2015). Health and Social Welfare Review, 37(1), 307–331. https://doi.org/10.15709/hswr.2017.37.1.307

23.

Jeon

Y. H.

Kendig

(2017). Care and support for older people. In Ageing in Australia (pp. 239–259). Springer. https://doi.org/10.1007/978-1-4939-6466-6_14

24.

Jung

K. H.

Yong-Un

Ji-Young

(2011). The effects of long-term care insurance on the life satisfaction and satisfaction in family relationships: The DD method combined with propensity score matching. Korean Journal of Social Welfare, 63(4), 301–326, https://www.koreascience.or.kr/article/JAKO201109649110063.page

25.

La Ferrara

Chong

Duryea

(2012). Soap operas and fertility: Evidence from Brazil. American Economic Journal-Applied Economics, 4(4), 1–31. https://doi.org/10.1257/app.4.4.1

26.

Lee

H. S.

(2015). Better off after the introduction of universal long-term care insurance systems? A comparetive study of Germany, Japan, and Korea. [Doctoral dissertation, Syracuse University]. https://surface.syr.edu/etd/306

27.

Lee

H. S.

Wolf

D. A.

(2014). An evaluation of recent old-age policy innovations in South Korea. Risk Analysis: An Official Publication of the Society for Risk Analysis, 36(6), 707–730. https://doi.org/10.1177/0164027513519112

28.

Lee

T. W.

Yim

Cho

Chung

(2014). Cognitive function, behavioral problems, and physical function in long-term care insurance beneficiaries with dementia in South Korea: Comparison of home care and institutional care services. Journal of the American Geriatrics Society, 62(8), 1467–1475, https://doi.org/10.1111/jgs.12944

29.

Lei

Bai

Hong

Liu

(2022). Long-term care insurance and the well-being of older adults and their families: Evidence from China. Social Science & Medicine, 296(2019), 114745. https://doi.org/10.1016/j.socscimed.2022.114745

30.

Lei

Lin

(2009). The new cooperative medical scheme in rural China: Does more coverage mean more service and better health? Health Economics, 18(S2), S25–S46. https://doi.org/10.1002/hec.1501

31.

Jensen

G. A.

(2011). The impact of private long-term care insurance on the use of long-term care. INQUIRY: The Journal of Health Care Organization, 48(1), 34–50. https://doi.org/10.5034/inquiryjrnl_48.01.05

32.

Lindgren

Pettersson-Lidbom

Tyrefors

(2021). The causal effect of transport infrastructure: Evidence from a new historical database (No. 1407). IFN Working Paper. https://arxiv.org/abs/2106.00348

33.

Liu

(2022). Evaluating the long-term care insurance policy from medical expenses and health security equity perspective: Evidence from China. Archives of Public Health, 80(1), 1–15. https://doi.org/10.1186/s13690-021-00761-7

34.

Song

Chen

(2019). Long-term care insurance, the control of medical expenses and “Value-Based Health Care”. China Industry Economics, 12, 42–59. https://doi.org/10.19581/j.cnki.ciejournal.2019.12.003

35.

Moon

Park

H. J.

Sohn

(2021). The impact of long-term care service on total lifetime medical expenditure among older adults with dementia. Social Science & Medicine, 280, 114072. https://doi.org/10.1016/j.socscimed.2021.114072

36.

Moser

Voena

Waldinger

(2014). German Jewish Émigrés and US invention. American Economic Review, 104(10), 3222–3255. https://doi.org/10.1257/aer.104.10.3222

37.

Shinya

Nakamura

(2014). Can formal elderly care stimulate female labor supply? The Japanese experience. Journal of the Japanese and International Economies, 34, 98–115. https://doi.org/10.1016/j.jjie.2014.05.006

38.

Sohn

O’Campo

Muntaner

Chung

Choi

(2020). Has the long-term care insurance resolved disparities in mortality for older Koreans? Examination of service type and income level. Social Science & Medicine, 247(1), 112812. https://doi.org/10.1016/j.socscimed.2020.112812

39.

Tamiya

Noguchi

Nishi

Reich

M. R.

Ikegami

Hashimoto

Shibuya

Kawachi

Campbell

J. C.

(2011). Population ageing and wellbeing: Lessons from Japan’s long-term care insurance policy. Lancet, 378(9797), 1183–1192. https://doi.org/10.1016/s0140-6736(11)61176-8

40.

Wang

P. P.

(2022). The total population keeps growing, and the urbanization level steadily improves. National Bureau of Statistics of China. http://www.stats.gov.cn/xxgk/jd/sjjd2020/202201/t20220118_1826609.html

41.

Weaver

Stearns

S. C.

Norton

E. C.

Spector

(2009). Proximity to death and participation in the long-term care market. Health Economics, 18(8), 867–883. https://doi.org/10.1002/hec.1409

42.

Wheaton

F. V

Crimmins

E. M.

(2016). Female disability disadvantage: A global perspective on sex differences in physical function and disability. Ageing & Society, 36(6), 1136–1156. https://doi.org/10.1017/S0144686X15000227

43.

World Health Organization . (2002). Active ageing: A policy framework. A contribution of the world health organization to the second united nations world assembly on ageing.

44.

Simpson

V. L.

(2022). Subjective well-being, depression, and delays in care among older adults: Dual-eligible versus Medicare-only beneficiaries. Journal of Applied Gerontology, 41(1), 158–166. https://doi.org/10.1177/07334648211000920

45.

Liu

Yang

(2019). Long-term care insurance’s impacts on medical expenditure-based on synthetic control research on Qingdao case. Insurance Study, 2, 114–127. https://doi.org/10.13497/j.cnki.is.2019.02.010

46.

Zeng

Feng

Vaupel

J. W.

(2017). Demographics, phenotypic health characteristics and genetic analysis of centenarians in China. Mechanisms of Ageing and Development, 165(Pt B), 86–97. https://doi.org/10.1016/j.mad.2016.12.010

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