Does universal long-term care insurance boost female labor force participation? Macro-level evidence

In 2014, the aging rate (population ages 65 and above, % of total) among OECD nations reached the range of between 6.9% (Mexico) and 27.0% (Japan), and the averages of the aging rates among the OECD and EU members were 16.8% and 19.8%, respectively. All of these numbers are unprecedentedly high (World Development Indicators, 2017). Faced with a situation in which their societies are aging, OECD nations have introduced and developed LTC systems that are based on their own institutional and historical backgrounds (Colombo et al., 2011; Swartz, 2013; Costa-font et al., 2015).).

Table 1 summarizes the characteristics of LTC systems for the elderly among OECD countries in terms of coverage, benefits, and sources of funding based on Colombo et al. (2011). We do not consider LTC systems that target only non-elderly people with a disability. As can be seen in this table, LTC systems are quite diverse among OECD nations, but there are some clusters. First, Nordic countries, which are often considered as the leading welfare states, finance LTC costs through tax revenues. In addition, these countries provide LTC services to people with a disability without specific age-related criteria. The United Kingdom, Spain, and the Czech Republic are also categorized in this cluster. Second, many continental European countries such as France, Italy, and Austria adopt more mixed financing systems, but also provide LTC services without strict age-related criteria. Third, public LTCI has been adopted by only a few continental European and Asian countries such as Germany, Luxembourg, the Netherlands, Japan, and South Korea, where public health insurance systems had already been adopted before the introduction of LTCI.

Japan has had LTCI since 2000. One important feature of the Japanese LTCI is that its introduction caused sharp, but not incremental increases in LTC financing and spending. This provides us with a good opportunity to identify the impact of a large-scale LTCI introduction.

Before LTCI was introduced in 2000, public LTC services in Japan were mainly means-tested programs for the low-income elderly. Under the means-tested programs, the elderly people in need of LTC but ineligible for public LTC benefits were often admitted to hospitals and stayed there for a long time even after necessary medical treatment had concluded (Campbell and Ikegami, 2000, 2003). This is called “social hospitalization” of the elderly, which was (and still is) considered a notorious social phenomenon in Japan's aging society. This problem was exacerbated by the introduction of a new healthcare scheme for the elderly in 1983, which had a relatively generous payment system for elderly hospital admission. In order to minimize such “social hospitalization” and to cope with both increasing medical costs for the elderly and the expanding need for LTC services caused by a rapidly aging population, in the 1990s the Japanese government implemented several reforms that were financed by national and local taxes. Due to several limitations of the tax-financing LTC system, the Long Term Care Insurance Law was enacted in 1998 and enforced in April 2000.

In what follows, we explain the institutional setting of LTCI in Japan. First, when it comes to financing, LTCI in Japan is managed as a uniform and independent social insurance system that is, however, financed by both insurance premiums and taxes. This mixed financing system is not peculiar to LTCI; the Japanese public health insurance system is also financed by both insurance premiums and taxes. Insurers of LTCI are in principle local municipalities and the sources of their revenues are insurance premiums and local and national taxes along with fiscal adjustment systems.

Second, regarding eligibility, Japan's LTCI is a universal program that does not require means-testing for eligibility for LTC services. That is, all people aged 65 and above are covered by Japan's LTCI, but people aged 40–64 years are only eligible for LTCI benefits if they have age-related diseases. LTC benefits for younger people with a disability are mostly provided by local governments and financed by tax revenues.

Third, the Japanese LTCI is a centralized system. That is, the institutional settings of financing, care needs assessments, and eligibility criteria and the types and schemes of service provision are mostly determined by the central government. At the same time, the insurers are local municipalities and have responsibility for the implementation of care needs assessments and the planning of local LTC service provision. In most areas, LTC services are publicly funded by LTCI but often privately provided by firms and nonprofit organizations.

Fourth and finally, Japan's LTCI provides only in-kind benefits. The Japanese government has not included cash benefits for informal caregivers in LTCI, probably because of concern that cash benefits could strengthen female gender roles in informal caregiving and could prevent women from joining or staying in the labor market (Campbell, 2002). Hieda (2012) also indicates that the Ministry of Health and Welfare excluded the option of cash benefits in the early stages of the policymaking process due to fiscal reasons.

For the historical, institutional, and political backgrounds of LTCI introduction in Japan, see also Campbell and Ikegami (2000, 2003), Campbell (2002), Campbell et al. (2010), and Rhee et al. (2015). In addition, Tables A1 and A2 in Appendix A1, which are based on Exhibit 3 and Exhibit 2 in Campbell et al. (2010), respectively, compare LTCIs in Japan and Germany based on the institutional settings in 2008.

In this paper, we focus on LTCI's macrolevel impact on public expenditure and female labor participation. The advantage of using country-level data is that we can examine the nation-level general-equilibrium impact of LTCI, which is rarely investigated in the literature.

Previous individual-level studies of LTCI effects on female labor supply (mostly in Germany and Japan) present mixed results (Geyer and Korfhage, 2015, 2018; Shimizutani et al., 2008; Tamiya et al., 2011; Sugawara and Nakamura, 2014; Fukahori et al., 2015; Yamada and Shimizutani, 2015; Kondo, 2017; Fu et al., 2017). As a whole, however, these studies imply that LTCI with in-kind benefits may have some positive effect on female labor supply, whereas LTCI with cash benefits seems to have a negative effect, although evidence is still insufficient to draw a strong conclusion. If these implications based on individual-level studies can be straightforwardly applied to a macrolevel analysis, we expect Japan, where only in-kind benefits are available, to have experienced a positive LTCI impact on female labor supply.

The findings of the above microlevel studies are important, but there are some limitations. Several previous studies argue that they utilize a difference-in-differences (DID) method as their identification strategies (Geyer and Korfhage, 2018; Shimizutani et al., 2008; Tamiya et al., 2011; Fukahori et al., 2015; Fu et al., 2017). Treatment and control groups in these studies, however, are not defined based on an exogenous group-level exposure to LTCI introduction as a standard DID framework implies. This is because in Germany and Japan, LTCI programs were uniformly introduced nationwide and their coverage is universal (for all generations in Germany and for the elderly in Japan). Hence it is impossible to compare informal caregivers who are affected by LTCI introduction with informal caregivers who are not.

Most of the previous studies therefore compare changes in the labor supply before and after LTCI introduction between “informal caregivers” and “others”, without directly comparing LTCI-affected with non-LTCI-affected caregivers. This empirical strategy may be plausible in some circumstances, but cannot take into account the fact that the introduction of a universal LTCI scheme should also affect the decision-making behind “being a caregiver or not”. This may result in possible endogeneity bias, because “informal caregivers” consist of different subgroups before and after LTCI introduction.

In addition, large-scale LTCI introduction can also affect female labor force participation through the creation of employment opportunities for middle-aged women. For example, some empirical welfare-state studies such as Mandel and Semyonov (2006) emphasize the role of the welfare state as a provider of employment opportunities for women. This employment effect of LTCI introduction on the female labor market may lead to the violation of SUTVA (Stable Unit Treatment Value Assumption) in microlevel studies.

One alternative way to identify the causal effect of an LTCI introduction that takes into account these problems is to exploit some regional variation in the intensity of the LTCI introduction. For example, Løken et al. (2016) utilize the differential increase in the availability of federal funds in municipalities caused by a national LTC reform for the elderly in Norway, and Hollingsworth et al. (2017) utilize an LTC policy reform in Scotland using England and Wales as the control regions.

It is, however, difficult to find such an exogenous variation in the introduction of a universal and uniform LTCI, which may explain why previous studies in Japan and Germany utilize the different identification strategies described above.

We therefore shift our focus from a microlevel or municipality-level variation to a country-level variation to examine the aggregate impact of LTCI introduction. One advantage of cross-country analysis is that we can directly investigate the nation-level aggregate impact of LTCI introduction by comparing Japan with other countries that have not experienced LTCI introduction.

While it may be difficult to construct a valid SC unit using country-level data because of the large heterogeneity among countries, there is now an increasing number of studies that investigate the aggregate impact of nation-level policy reforms on relevant outcomes using country-level panel data and the SC method (Ryan et al., 2016; Restrepo and Rieger, 2016; Rieger et al., 2017; Arnold and Stadelmann-Steen, 2017; Podestà, 2017; Barlow, 2018; Olper et al., 2018; Tanndal and Waldenström 2018; Andersson, 2019; Rubolino and Waldenström, 2020; Geloso and Pavlik, 2020; Absher et al., 2020).

To address the inherent vulnerability of constructing an SC unit using country-level data, we provide sensitivity and placebo analyses based on methods proposed in Abadie et al. (2010) and Abadie et al. (2015) and demeaned-outcome analysis based on Ferman and Pinto (2019). In addition, we implement more extensive placebo analyses by combining permuted treatment assignment and leave-one-out estimation (Appendix A5). Finally, we also provide additional robustness checks by limiting donor pool countries and conducting in-time placebo tests (Appendix A6). All of these additional analyses are meant to cope with some drawbacks in exploiting cross-country variation for causal inference and also address some concerns about the uniqueness or incomparability of Japanese demographic conditions.

Because our study focuses on the specific nationwide event of LTCI introduction in Japan using country panel data, we have only one “treated” unit in our sample for analysis. The SC method proposed by Abadie and Gardeazabal (2003) and Abadie et al. (2010) is a suitable method to investigate the impact of such a single but noticeable event. We will now briefly explain how the SC method achieves the identification of aggregated LTCI effects.

First, let us define the aggregate effect of the LTCI introduction as α_it on some outcome variable Y_it, where i and t indicate a country and a year, respectively. This implies we assume that the effect of the LTCI introduction varies across countries and years. Next, we consider the situation in which an LTCI program is introduced in country i = J (i.e. Japan) in year T₀ and assume that the LTCI introduction is fully implemented and irreversible. In this case, we can define the treatment effect α_Jt as follows: (1)αJt=YJt(1)−YJt(0), for t>T0{\alpha _{Jt}} = {Y_{Jt}}\left( 1 \right) - {Y_{Jt}}\left( 0 \right),\;\;\;{\rm{for}}\;t > {T_0} where Y_Jt(D_J) is a potential outcome with the intervention status D_J, where D_J = 1 indicates the LTCI introduction and D_J = 0 represents no LTCI introduction. Thus Y_Jt(1) is identical to an observed outcome YJtobsY_{Jt}^{obs} and Y_Jt(0) is a “counterfactual” outcome that would be realized if country J did not introduce LTCI in t ≥ T₀ + 1. In order to estimate α_Jt, we need to estimate Y_Jt(0) in t ≥ T₀ + 1.

Abadie and Gardeazabal (2003) and Abadie et al. (2010) proposed a novel method to estimate Y_Jt(0) by utilizing the weighted average of outcome variables of control units i (i = 1,2...,N), that is, Σk≠Jwk*Yktobs{\Sigma _{k \ne J}}w_k^*Y_{kt}^{obs} . An optimal time-invariant weight wk*w_k^* for each control unit k is determined so that the vector of optimal weights W*=(w1*,w2*,…,wk*)′{W^*} = \left( {w_1^*,w_2^*, \ldots ,w_k^*} \right)' minimizes the difference between the pre-intervention outcomes and characteristics (called predictors) of the treated unit and the weighted average of predictors of the control units, given that 0≤wk*≤10 \le w_k^* \le 1 and Σk≠Jwk*=1{\Sigma _{k \ne J}}w_k^* = 1 . A single fictional control unit constructed by the optimal weights W* is called synthetic control.

Thus, SC has pre-intervention outcomes and characteristics which are set as similarly as possible to those of the treated unit in terms of observed predictors, but it does not receive a treatment in the post-intervention period. Therefore the outcome of the SC in the post-intervention period is meant to represent the counterfactual status of the treated unit Y_It(0).

Given that the SC can provide unbiased estimates of the counterfactual status of the treated unit Y_It(0), α_It is estimated as follows: (2)α^Jt=YJtobs−∑k≠Jwk*Yktobs.{\hat \alpha _{Jt}} = Y_{Jt}^{obs} - \sum\limits_{k \ne J} {w_k^*Y_{kt}^{obs}.}

Building on some parametric assumptions but allowing for time-varying unobserved confounders, Abadie et al. (2010) proved that the above SC estimator is unbiased if the treated unit and the SC are well-matched in observed predictors and outcome variables in long pre-intervention periods.

In a subsequent study, Abadie et al. (2015) recommended that the SC method should be applied in cases where a sizable number of pre-intervention periods are available, in order to construct a credible SC. We examine the effects of Japanese LTCI introduction since 2000 on the fiscal outcomes and female labor force participation. Our pre-intervention periods are in most cases about 20 years (1980–1999).

One weakness of the SC method is that it does not provide a formal statistical test for the null hypothesis. As a complement to formal statistical hypothesis testing, Abadie et al. (2010) provides an alternative, informal, placebo test akin to a permutation or randomization test in which a researcher calculates and collects “placebo” SC estimates by assigning the “label” of the intervention status to each control unit and then compares a true SC estimate with these placebo values. Most of the previous studies using the SC method show the results of this kind of placebo test, and we also present the results of this conventional test in Section 5.5. In Appendix A5, we further explore the placebo analysis in the SC method and provide extended placebo trials that are still informal but more rigorous and we hope more informative.

One important issue in SC analysis is how to select the candidates for control countries, which are called “donor pool” countries. Due to data availability, we first limit our sample, including Japan, to 24 nations that had joined the OECD before 1980. This sample mostly consists of developed countries in Western Europe, North America (the United States and Canada) and Oceania (Australia and New Zealand), as well as Japan. This sample restriction is justifiable from an econometric perspective, because it is preferable to have relatively homogeneous control units in a donor pool that are reasonably comparable to the treated unit in terms of socioeconomic characteristics (Abadie et al., 2010, 2015).

In addition, we exclude Germany, the Netherlands, and Luxembourg from the donor pool because these countries adopted LTCI during the sample period. This means that we do not allow these countries to be included in the synthetic Japan.

Finally, Iceland, Greece and Turkey, which are original OECD members, are also excluded from the donor pool because of lack of data for Iceland, the unusual budgetary situation in the late 2000s for Greece, and significant socioeconomic differences with Japan for Turkey. Note that relatively new OECD members such as Eastern European countries and South Korea are also not included in the donor pool because, along with lack of data, they were developing countries with significantly different political regimes in the 1980s and 1990s.

As a result of these sample selection procedures, 17 OCED countries are selected as primary donor pool countries. In some SC estimations, a few more countries are further dropped from the donor pool due to lack of data. In robustness checks, we further limit our donor pool countries based on several additional criteria.

Figure 1 provides the results of SC estimation for in-kind benefits for the elderly. Thick solid lines are realized in-kind benefits as percentage of GDP for panel A and as per elderly person for panel B. The other three lines are the counterpart values of three SCs.

Figure 1

SC 1 in the graph is constructed from the original donor pool and therefore its values are regarded as baseline counterfactual outcomes in the post-intervention period; that is, they represent the levels of in-kind benefits for the elderly if LTCI had not been introduced in Japan. As discussed in Section 3, SC estimates are the gaps between the outcomes of a treated unit and an SC. If an SC is validly constructed based on pre-intervention outcomes and predictors, SC estimates are expected to be around zero in the pre-intervention period and can be interpreted as causal effects in the post-intervention period.

SC 2 is constructed using SC estimation in which the country that receives the highest weight in the first SC estimation is excluded from the donor pool. SC 3 is constructed from a donor pool that additionally excludes the country that receives the highest weight in the second SC estimation. These robustness checks using SCs 2 and 3 are particularly important in our cross-country comparison, where there is a risk that some specific countries receive higher weights and idiosyncratic shocks in these countries may undermine the validity of the SC estimation. See also Abadie et al. (2015) for further discussion of this type of sensitivity checks. In Appendix A3, we provide the weights and the predetermined covariate values used for constructing SCs 1–3.

The results of SC estimation in Figure 1 indicate sharp increases in in-kind benefits for the elderly in Japan just after the introduction of LTCI. The gaps between the actual benefit level and those of the SCs persist and increase during the sample period and the size of the gaps reaches around one percentage point of GDP in panel A and about US$1,000 (in terms of PPP) in panel B in 2010, 10 years after LTCI introduction.

We argue that the expenditure increase by one percentage point of GDP within 10 years is not negligible and its aggregate-level impact on the social and economic outcomes such as female labor force participation is worth investigating.

We then examine whether LTCI introduction crowds out closely related public expenditure, that is, other public health expenditures. Figure 2 provides the results of SC estimation for public health expenditure. When we compare the actual outcomes with those of SCs 1–3 in panels A and B, the gaps between the outcomes in Japan and synthetic Japan are negative in the early 2000s, indicating that LTCI introduction might have led to the suppression of public health expenditure in this period. This suppression, however, appears to be small in terms of effect size, and we will further examine the significance of the observed suppression using placebo tests in Section 5.5. Overall, there is no clear evidence that LTCI introduction has caused a large public-expenditure shift from healthcare to LTC and the persistent suppression of public health expenditure.

Figure 2

Moving on from fiscal outcomes, Figure 3 provides our SC estimation results for female labor force participation (LFP) rates by age cohort. Despite the large fiscal expansion for LTCI, there is no sign of positive LTCI effects on the LFP rates in any of the cohorts. In fact, the female LFP rates for ages 50–54 and 55–59 appear to have been even suppressed after LTCI introduction, compared with those of all of the SCs.

Figure 3

The overall tendency of Japan's stagnated LFP rates in the post-intervention period suggests that there may exist a Japan-specific trend in the female LFP rates that is not taken into account by the SCs. This implies that SC estimates (i.e. outcome gaps between Japan and an SC) may not properly capture the causal effects of the LTCI introduction.

In order to eliminate this possible Japan-specific trend in the female LTF rates, we subtract the female LFP rate of ages 45–49 from the female LFP rate of ages 50–54 and ages 55–59 and then use these differenced variables as outcomes. The idea behind this procedure is that women aged 45–49, whose LFP rate is the highest among the four age cohorts in the post-reform period, are likely to be less affected by LTCI introduction, because their parents and parents-in-law tend to still have no need of LTC, whereas women aged 50–54 and 55–59 are likely to be more affected by the LTCI introduction because of a higher need for LTC for their parents or parents-in-law.

Thus, subtracting the female LFP rate of age cohort 45–49 from that of an older cohort may effectively eliminate the Japan-specific trend of female LFP, leaving a change in the older cohort LFP rate caused by LTCI introduction. This estimation strategy is akin to the triple-difference or difference-in-difference-in-difference (DDD) strategy, although we use the SC method after differencing the outcomes of “more affected” and “less affected” cohorts in both treated and control (or donor pool) countries.

The estimation results based on this strategy are shown in Figure 4. The left graph shows the trend of LFP-rate differences between the age cohorts 50–54 and 45–49 in Japan (bold solid lines) and its SCs. The right graph presents the counterpart trends of LFP-rate differences between the age cohorts 55–59 and 45–49. The results also do not indicate any positive impact of LTCI introduction on the female LFP rates for these two age cohorts. In fact, the right-hand graph again shows that the female LFP rates for the age cohort 55–59 seem to be suppressed after 2000 even after eliminating the trend of female LFP for the age cohort 45–49.

Figure 4

To complement the above analyses, we also conduct additional SC estimation. That is, to alleviate imperfect pre-intervention fit, we implement SC estimation using demeaned outcomes in which pre-intervention outcome means are subtracted from outcome values (i.e. Y˜it=Yit−(1/T0)Σt=1T0Yit{\tilde Y_{it}} = {Y_{it}} - \left( {1/{T_0}} \right)\Sigma _{t = 1}^{{T_0}}{Y_{it}} ). Ferman and Pinto (2019) shows that the demeaned SC estimator has better properties than the original SC estimator under some conditions, in particular when the imperfect pre-intervention fit exists in the conventional SC estimation. Note that we use only pre-intervention demeaned outcomes as predictors.

Figure 5 shows that the results of demeaned SC estimates are in line with those of the original estimates, while pre-intervention fits are improved for some LFP outcomes. Overall, we again find no sign of positive LTCI effects and female LFP rates for ages 55–59 (and possibly ages 50–54) appear to have been suppressed after LTCI introduction.

Figure 5

Figure 6 shows estimation results for placebo trials on all of the outcomes except for the results of demeaned SC estimation. On one hand, the first and second graphs in this figure indicate that the SC estimates for in-kind benefits for the elderly seem to be higher than most of the placebo estimates just after 2000, indicating that we can unambiguously conclude there was a fiscal impact of LTCI introduction (around a one percentage point increase in 2014). On the other hand, we do not find any clear effect on the public health expenditure, although negative SC estimates are relatively large around 2005 in particular, for per capita public health expenditure.

Figure 6

Negative SC estimates for female LFP rates are sometimes clearly larger in size than most placebo estimates. In particular, the female LFP rate for ages 55–59 decreases after 2000 and the magnitude is larger than any placebo estimates. This tendency is mitigated if we subtract the female LFP for ages 45–49 from female LFP for ages 55–59 (the last two graphs). The last graph, nonetheless, indicates that the female LFP rate for ages 55–59 stagnated after 2000 and the magnitude is larger than most of the placebo estimates, although pre-intervention fits are poor for many placebo trials.

In the Appendices, we also provide the results of placebo tests for demeaned SC estimation (Appendix A4) and extended placebo tests based on permuted treatment assignment, resampled donor pools, and several test statistics (Appendix A5) to verity the above findings.