Discussion Paper No.368

Empirical Analysis on the Effect of Preventive Care
Programs in Japan Evidence from Municipality-level data

Fengming CHEN and Hiroshi YOSHIDA

May, 2017

TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT TOHOKU UNIVERSITY
27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANEmpirical Analysis on the Effect of Preventive Care Programs in Japan
Evidence from Municipality-level data
Fengming CHENa1, Hiroshi YOSHIDAba

Graduate School of Economics and Management, Tohoku University, 27-1, Kawauchi Aoba-ku, Sendai,
Miyagi 980-8576, JAPAN: cfmdbdx@gmail.com.

bGraduate School of Economics and Management, Tohoku University, 27-1, Kawauchi Aoba-ku, Sendai,
Miyagi 980-8576, JAPAN.

ABSTRACTThis study examines the effects of the number of preventive care programs on thenumber of certified less disabled recipients for long-term care insurance. By constituting
a municipality-level two-period panel data, we find that the total number of preventive
care programs significantly decreases the number of certified less disabled recipients.
When looking at the details of preventive care programs, we find the number ofphysical activities and dining party activities has a negative effect. And we also examine
the combination of preventive care activities and find the effects of physical activities
and dining party activities are relatively robust. We conclude that the design ofpreventive care programs should be considered the characteristics of objects, especiallythe elderly.

Keywords: preventive care, municipality, less disabled, the elderly, long-term care insurance.
JEL classification: I12, I13, I18.

1

Corresponding author, E-mail: cfmdbdx@gmail.com We would like to thank Midori Wakabayashi,

Kentaro Nakajima, Yoko Ibuka and participants of the Japan Health Economics Association meetings of
September 2016 for their helpful advice and comments. Any errors are our own. This paper is a part of theanalysis on the Impact of Population Aging on Economic Society Evidence from China and Japan(doctoral dissertation of Fengming CHEN). This study was also financially supported by the JSPS
KAKENHI[grant number JP26380356].

11. Introduction
It is commonly recognized that family members including spouse and adultchildren provide a vast quantity of long-term care to the disabled elderly in Japan. So it
seems an attracting advice that promoting the increase of the quantity of informal carecan shrink the medical expenditures owing to the fact that the usage of informal carecan substitute for formal care such as home health care use, nursing home care andoutpatient surgery (Van Houtven and Norton, 2004). However, as the number ofdisabled older adults in Japan is growing rapidly, it becomes more and more difficult tosatisfy the demand of frail older adults just through the informal care service provided
by household members. Especially, when the baby boomers start to deteriorate, theywould demand a huge quantity of informal care service. Consequently, the Japanese
government introduced the Long-term Care Insurance (LCI) to support the elderly from2000. Because more and more elderly people become eligible for the long-term careinsurance, the number of insured older adults who apply for the benefits of long-termcare insurance system is increasing rapidly (P. Olivares-Tirado and N. Tamiya, 2014).
Under such a background, policymakers and economists are both interested inwhether or not preventive care programs significantly influences the health status ofprogram participants. The intervention of physical activity is one of the most popular
health interventions used in practice. And some of the previous studies show that theintervention of physical activities strengthens the functional ability of the elderly(Lacroix et al, 1993; Groessl et al, 2016). However, there is little evidence that otherpreventive care behaviors are efficient to maintain mobility in the late life. Although
Yoshida and Chen (2016) find that hobby activities and cognitive disorders preventive
activities are significantly correlated to the decrease of the number of certified support
need level 2 recipients, the results cannot be explained as a causal effect owing to theendogeneity bias of participation behaviors.
The purpose of this study is to conduct an empirical analysis of the effect ofpreventive care programs on the number of certified recipients. We use
municipality-level data to constitute a two-period panel data and apply a standard fixed
effects model. The results show that the total number of preventive care programs,
dining party activity and hobby activity significantly decreases the number of certified
less disabled elderly people, which implies that population-based preventive careprograms are effective means to maintain mobility in the late life for less disabled adults.
However, the results with respect to other preventive care activities do not give any
evidence that they are efficient means to remit the pressure of increasing disabled
adults.
2

The remainder of this study is organized as follows. The background of preventive
care programs is stated in Section 2. Section 3 introduces documented previous review
on the preventive care programs. Then, Section 4 describes the empirical strategy.
Section 5 presents the details of data used in this study. The basic results are presented
in Section 6. At last, we make some conclusions and discussions on our research in
Section 7.
2. Preventive care programs in Japan
2.1 Background
The Japanese government introduced long-term care insurance system as a pillar ofsocial security to help household members relieve the burden of caring for disabled
adults. With the increase of aging population, the number of insured older adults who
apply for the benefits of long-term care insurance system is growing drastically.
According to the Report on Long-term Care Insurance (2013 version), the increasing
speed of less disabled certified recipients (support need level of 1 and 2) is much higher
than that of disabled certified recipients (long-term care need level of 1 to 5).
As to the related social security expenditures, on the basis of estimated results of
MHLW in Japan, the long-term care benefit expenditure in 2012 was about 8.4 trillion
yen and the estimated expenditure associated with long-term care in 2025 will reachabout 19.8 trillion yen. The growth rate of expenditure respect to the long-term care is
one of the highest issues among all of the related social security items. The long-termcare insurance in Japan is very generous for older disabled adults due to the low ratio of
copayment for the expense of long-term care4, which is potential to induce the excess
demand for long-term care service. Although this result is not identified by the
Medicaid generosity, the case of long-term care in Japan may be exist (Grabowski and
Gruber, 2007).
As the number of disabled older adults applying for the long-term care insurance isincreasing dramatically, the focus of decreasing long-term care spending should be
mainly on keeping older adults functional independence. In fact, the Japanese
government started to take some actions including the implementation of preventive
care programs (Revision of Long-term Care Insurance Act in 2005).
2.2 The introduction of preventive care programs
According to the report of “Kaigo Yobo Jigyo oyobi Kaigo Yobo Nichijyo Sekatsu
4

The ratio of copayment was 10% at the beginning of long-term care insurance. There were somemodifications about the ratio of copayment based on the MHLW’s Kaigo Hokenho no Kaisei [Revision of
the Long-term Care Insurance] in 2014.

3

Shien Sogo Jigyo (Chiiki Shien Jigyo) no Jisshi Jyokyo ni Kansuru Tyosha” (A Survey
on Implementation Status of Preventive Long-term Care Projects and Daily Life
Supporting Comprehensive Projects (Regional Supporting Projects)) (2014), the ratio of
municipalities which are implementing the preventive care programs is 73 % and about
10 percentage points higher than that of 2013. Mainly, there is five kinds of preventive
care programs implemented by municipalities including physical activity (PA), tea party
activity (TPA), dining party activity (DPA), cognitive disorders preventive activity(CDPA) and hobby activity. The most popular preventive care program is physicalactivity accounting for 42.8 % in our data. Through promoting radio gymnastics, theelderly are expected to activate their body and keep the independence of functional
ability. The expected effects of tea party activity and dining party activity are supposed
to make a chance for the elderly to communicate with each other. And the DPA has
another purpose to improve the elderly’s nutrition absorption owe to the fact thatprofessional staff are prepared for the food and provide nutritious combinations to theparticipants. The next one is cognitive disorders preventive activity accounts for 7.1% in
our data. Specialists may hold a seminar and pass on knowledge on cognitive disorders
prevention. The last one is hobby activity. There are different kinds of hobby activities
on the basis of municipalities.
Figure 1 shows the number of Kayoi no Ba based on the most disabled degree. We
can see that among the Kayoi no Ba, the percentages of less disabled adults (support
need level 1, support need level 2 and long-term care level 1) are higher than that ofdisabled adults. Especially, with the increment of disabled degree, the composition ratioof certified long-term care need level 5 is close to 0. We should also pay attention to thefact that about half of the number of Kayoi no Ba cannot be categorized by disabled
degrees owing to the missing information on participants or just not suitable forcategories. From figure 1, we see that the effect of preventive care programs is not only
on disabled persons, but also on healthy persons or disabled persons who are notcertified. Taking this situation into account, we use the participant rate of preventive
care programs to adjust the number of preventive care programs.
Figure 1 The number of Kayoi no Ba based on the most disabled degree (Composition Ratios)

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

13.1%

16.7%

13.8%

46.3%

6.6%

16.5%

13.8%

44.6%

7.4%
0.9%

4

long-term care 1

long-term care 3

2.4% 0.3%
14.0%

support need 1

long-term care 2

1.0%

2014

100.0%

support need 22.0% 0.4%
2013

90.0%

long-term care 4
long-term care 5
othersSource: The results of “survey on Implementation Status of Preventive Long-term
Care Projects and Daily Life Supporting Comprehensive Projects (Regional Supporting
Projects)” (2013&2014) are rearranged by the authors.
3. Review of Previous Studies
3.1 Physical programs
A vast quantity of previous studies on the effect of physical activity programs is
documented. And this is consistent with the situation that physical activity is most held.
Promoting physical activity in diverse settings and utilizing multiple channels ofcommunication can help to alleviate some barriers for older adults who accompany
some functional limitations or physical disabilities. Although keeping the motivation ofregular physical activity and self-management are very important to accomplish therecommended goals, environmental barriers including weather, lack of equipment for
the elderly to have a rest may prevent the elderly from taking part in physical activities(Brawley et al., 2003). As to the effect of physical activity, it seems that there is aconsistent conclusion that physical activity intervention significantly affects thefunctional independence of the older adults. As the benefits of frequent activity in termsof maintaining mobility appeared to be approximately equal for walking, gardening, andvigorous exercise, we should design activities programs for the whole populations ofolder adults with a variety of capabilities and health concerns (Lacroix et al, 1993).
Certainly, strict screening or supervising is necessary for the implementation of physicalactivity program (Inagaki et al, 2012; Groessl et al, 2016).
3.2 The determinants of preventive care
According to Grossman’s (1972) model, health can be considered as capital stock.
On the basis of the definition of this health production function, medical care or healthy
behaviors can be considered as inputs. And health information can be also treated as
input (Wagner et al, 2001). In their research they find that the intervention on healthinformation is associated with a decreased reliance on health professionals for
information. It implies that the increase of health information mainly induced byeducation attainments may promote the investment on preventive care service. For
example, by applying a standard logistic model, Zheng and Kuroda (2010) find that the
decline rate of the instrumental activities of daily living (IADL) is high for people withlow educational backgrounds, which suggests that in order to prevent the functional
5decline among the elderly, it is important to spread knowledge about health to people
with low educational backgrounds among developing countries such as China.
There is also a big difference on the use of preventive care between immigrants
and natives. Especially, among the insured with continuous private coverage, citizen andnon-citizen immigrants are significantly less likely to use preventive care than natives in
the United States (Pylypchuk and Hudson, 2009).
3.3 The Limitations of Previous Review and Contribution of this Study
Many previous studies use a small-scale micro data to discuss the effects of
preventive care programs. For example, Inagaki et al (2012) develop a care prevention
exercise called “Ogenki Shan-Shan Taisou” (OSST) with the cooperation of the local
government of Oita-city. Community-dwelling people aged 64 to 90 years old (N=130)
participated in this program. As the results, more than 80% of subjects continue OSSTat home almost every day. Significant improvements are observed in body weight,
isometric knee extension strength, shoulder flexibility, 10-m walking time, maximal
step length, and stepping. However the estimated results are difficult to show theexternal validity. At the same time, under strict screening or supervising, the effects of
preventive care programs may be overestimated.
In this study, instead of using small-scale micro level data, we use aggregate
municipality-level data to constitute a two-period panel data and apply a standard fixed
effect model to estimate the causal effect of preventive care programs. There are three
merits to use municipality-level data. Firstly, as preventive care programs are mainly
implemented by municipalities in Japan, we are interested in the effect of preventive
care programs on municipalities, not on individuals. So using municipality-level data isreasonable. Secondly, we can easily obtain a panel data set and control unobservablevariables which are time invariant (e.g. the motivation of municipalities) and get causal
effects of preventive care programs. At last, using municipality-level data allows us toobtain relative external validity when compared with some previous studies.
4. Empirical strategy
The basic model specification for our estimation is as follows:
Y i,t=βPrograms i,t +δX i,t +ηi + λt + εi,t ,(1)
where i represents municipalities, t is year, Y represents the number of long-term carerecipients per 100,000 population of 65 years old and above by disabled degrees,
Programs is the number of preventive care programs per 100,000 population of 65 years
old and above weighted by the participant rate (The number of participants divided by
6

the total population of municipality), X is the set of control variables, ηi and λt aremunicipality and year fixed effects. εi,t represents error term. The X includes thefollowing variables: aging rate (the number of 65 years old and above accounting for the
total population of each municipality), the square of aging rate, the logarithmic form of
total population, intervention conditions of the settings of specialist (Specialist) andintervention conditions of the settings of financial support (Financial). We consider thatthe increase of preventive care programs will significantly decrease the number ofcertified support need recipients and certified long-term care level 1 recipients on
averagely. As the data used are municipality level data, the assumption of constantvariance may fail in this case. To address this concern, we correct the standard errors forheteroscedasticity by using the cluster robust estimator of variance clustering at themunicipality level.
5. Data
5.1 Data
The data used in this study incorporate three categories: 1.municipality-levellong-term care insurance related data (Source A), 2.municipality-level preventive careprogram related data (Source B), 3.municipality-level population related data. Respect
to municipality-level long-term care insurance related data (Source C), data by insurer
from the “Kaigo Hoken Jigyo Jokyo Hokoku Geppo” (Monthly Report on the Conditionof Long-Term Care Insurers) published by MHLW in Japan are used5.
Regard to municipality-level preventive care program related data, we mainly
obtain the information from “Kaigo Yobo Jigyo oyobi Kaigo Yobo Nichijyo Sekatsu
Shien Sogo Jigyo (Chiiki Shien Jigyo) no Jisshi Jyokyo ni Kansuru Tyosha” (A Survey
on Implementation Status of Preventive Long-term Care Projects and Daily Life
Supporting Comprehensive Projects (Regional Supporting Projects)), which isbelonging to MHLW in Japan. As our focus is on the effect of preventive care programs,
we mainly use the related information from the result sheet of “Kaigo Yobo ni Shisuru
Jyumin Unei no Kayoi no Ba no Tenkai Jyokyo (Shikuchosonbetsu)” (the Situation of
Public Space for Preventive Care Programs Administrated by Residents)6 including thetimes of total preventive care programs, the times of physical activity, dining party
activity, tea party activity, cognitive disorders preventive activity and hobby activity, the5

Available on the website of the Ministry of Health, Labor and Welfare:< http://www.mhlw.go.jp/topics/0103/tp0329-1.html>.
6 Available on the website of the Ministry of Health, Labor and Welfare:2013 version< http://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000075280.html>
2014 version< http://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000096350.html>

7intervention on the settings of specialist 7 and the intervention on the settings offinancial support. Since municipality-level data are available only for 2013 and 2014,
we obtain the two years’ data and constitute a two-period panel data.
In terms of population related data, data from “Jumin Kihon Daityo Nenrei
Kaikyu Betsu Jinko (Shikutyosonbetsu)” (Basic Resident Register Age-Specific
Population) published by Ministry of Internal Affairs and Communications (MIAC) areused8. In order to take demographic characteristics into account, we include aging rate(the proportion of the elderly who are aged 65 and over) and logarithmic form of
population for each municipality into account. We also include the square of aging rate
to identify the nonlinear effect. In order to match the information of preventive careprograms, we choose to use the population related data of years 2013 and 2014.
Since some of municipalities are administrated by long-term care insurance
unions, we can only obtain the total number of certified recipients without the detail ofeach member. We have to exclude the municipalities which are administrated by unions.
And we get a final data set with 3080 valid observations.
5.2 Dependent variables
Preventive care program is most likely to affect long-term care expenditurethrough its effect on the certification of less disabled adults. On the basis of figuresassociated with the number of certified long-term care recipients, we calculate certified
support need level 1, support need level 2 and long-term care need level 1 recipients per
100,000 population of 65 years old and older (population related data) of eachmunicipality as dependent variables. The definitions of dependent variables are as
follows:
Support 1 =Number of certified support need level 1 recipients / (Number of persons
aged 65 and over)*100,000
Support 2 = Number of certified support need level 2 recipients / (Number of persons
aged 65 and over)*100,000
Longterm 1 = Number of certified long-term care need level 1 recipients / (Number of
persons aged 65 and over)*100,000
As shown in table 1, the average number of certified support need level 2 per
100,000 population of 65 years old and over is similar to that of certified support need7

It includes public health nurse, nursing staff, physical therapist, occupational therapist,
speech-language-hearing therapist, nutritionist, registered dietitian and dental hygienist.
8
Available on the website of the Ministry of Internal Affairs and Communications:2014.1.1 http://www.soumu.go.jp/menu_news/s-news/01gyosei02_02000062.html
2015.1.1<http://www.soumu.go.jp/menu_news/s-news/01gyosei02_03000062.html>

8level 1 (Support 1: 2,207.412 VS Support 2: 2,299.727) and smaller than that of thenumber of certified long-term care need level 1.
5.3 Key independent variables
The most important explanatory variable is the number of preventive careprograms per 100,000 population of 65 years old and over (Programs weighted byparticipant rate). The average number of the programs executed in one municipality is
12.605 with a big standard deviation of 91.164. There are five categories of preventive
care programs in our data set including physical activities (PA), dining party activities(DPA), tea party activities (TPA), cognitive disorders preventive activities (CDPA) andhobby activities (HA). In terms of the means of different preventive care programindicators, we find that PA has the highest mean (5.238) as the most popular preventive
care program. The next one is HA (2.644). And the TPA has a very similar mean like
HA. As the limitation of specialists, municipalities are unlikely to hold CDPA as oftenwith a small mean (0.890).
We also control the demographic factors including the ratio of the number of 65
years old or older occupying the number of total population (Aging Rate1), the squareof aging rate (Aging Rate2), and logarithmic form of the total number of population(Lnpop). As shown in Table 1, the average of Aging rate is 29.9%, which is slightly
higher than that of official statistics. By introducing a binary variable representing theintervention conditions about the settings of specialists (Specialist), we find that 55.8
percentages of the regions are associated with the intervention about specialists.
Similarly, we also use a binary variable to describe the intervention conditions about
financial support (Financial support). And about half of the municipalities implementing
preventive care programs receive some kinds of financial supports from the central
government, local government or other sponsors. In order to control the year effect, we
also constitute Year2014 dummy variable (If the data are from 2014, 1 is assigned,otherwise 0).

Table1. Descriptive statistics
Variables

Observations

Means

Std. Dev

Min

Max

Support1

3,080

2,207.412

1,076.276-1,036.269

9,708,738

Support2

3,080

2,299.727

784.4160.000

7,407,407

Longterm1

3,080

3,488.312

884.1990.000

8,664.260

SourceDependent variable

9A&CIndependent variable
Lnpop

3,080

10.187

1.480

5.106

15.109

Aging

3,080

0.299

0.067

0.131

0.580

Aging2

3,080

0.094

0.043

0.017

0.336

Programs

3,080

12.60591.1640.000

2,616.293

PA

3,080

5.238

49.3690.000

1,744.195

DPA

3,080

1.192

18.9640.000

712.524

TPA

3,080

2.641

14.8010.000

437.670

CDPA

3,080

0.890

20.7300.000

1,132.429

HA

3,080

2.644

27.8140.000

875.824

Specialist

3,080

0.558

0.497

0.000

1.000

Financial

3,080

0.497

0.500

0.000

1.000

Year2014

3,080

0.500

0.500

0.000

1.000

C

B&C

B

Note: The details of Source A, B and C are as follows.
A: “Monthly Report on the Condition of Long-Term Care Insurers”
B: “the Situation of Public Space for Preventive Care Programs Administrated by Residents”
C: “Basic Resident Register Age-Specific Population”

6. Estimated results
6.1 The Results of Preventive Care Programs
Firstly, we estimate the effect of the total number of preventive care programs
Reg1 is shown in table 2 representing OLS and Fixed effect model by using Support 1,
Support 2, Longterm 1 and Longterm 2 data, respectively. We mainly focus on theestimated results of Fixed effect model and use the results of OLS as comparisons.
According to the estimated result of Reg1, we find that the results of OLS1 and OLS2
are significantly positive. The increment of total number of preventive care programs
will augment the certified support need level 1 and the certified support need level 2
recipients. However, taking the unobserved variables by using fixed effect model into
account, we find the coefficient for support2 turns to be significantly negative indicating
the increase of total preventive care programs significantly decreases the certified
support level 2 recipients. Because the results of Longterm1 and Longterm2 are
insignificantly, we believe that the decreases of the certified support level 2 recipientsare unlikely to shift to higher disabled degrees.
According to the results of “Kaigo Yobo Jigyo oyobi Kaigo Yobo Nichijyo Sekatsu
Shien Sogo Jigyo (Chiiki Shien Jigyo) no Jisshi Jyokyo ni Kansuru Tyosha” (A Survey
on Implementation Status of Preventive Long-term Care Projects and Daily Life
Supporting Comprehensive Projects (Regional Supporting Projects)) (2014), there are10

five kinds of preventive care programs implemented by municipalities. The mostpopular preventive care program is physical activity (PA) accounting for 42.8 % among
the preventive care programs. One possible explanation for the popularity of physicalactivity is that the implementation of physical activity is easier than any otherpreventive care programs no matter in public space or at home. For example, according
to Inagaki et al (2012), they develop a care prevention exercise called “Ogenki
Shan-Shan Taisou” (OSST) with the cooperation of the local government of Oita-city.
As the results, more than 80% of subjects continued OSST at home almost every day.
According to the result of Reg2 in Table 2, we find that after controlling
unobserved variables (E.g. the motivation of municipalities) only the result of Support 2
is significantly negative. In the previous studies, PA is described as an efficient means toextend the functional ability of the elderly. However, the effect of PA needs
perseverance (Brawley et al., 2003). The older adults do not obtain obvious effect justby attending PA one time or few times. Consequently, efficient screening also plays animportant role in improving the effect of PA. If there is no screening or supervising, theolder adults gradually will give up attending PA owing to the shortage of perseverance.
The typical adverse selection may happen among PA.
Next, we will discuss the estimated results of DPA shown in Reg3 of Table 2. The
DPA is supposed to have two potential channels for influencing the health status ofparticipants. One aspect is by providing an opportunity for communication. The otherone is by improving nutrition conditions of the participants. Because these two potential
channels are simultaneously influencing the elderly, it is impossible to separate andvalue each channel. According to the results of Reg3 in Table 2, we find the coefficientof Longterm1 in FIX3 is significantly negative. The increase of DPA significantly
decreases the number of certified long-term care need level 1 recipients. However, theeffect of TPA is similar to the first potential channel of DPA to promote thecommunication among participants, there is little evidence for TPA influencing thenumber of certified recipients from Reg4 in Table 2. It may suggest that the
improvement of nutrition condition is more important or the combination ofcommunication and the improvement of nutrition are more efficient for preventive care.
The result in Reg5 of Table 2 show that the CDPA decreases the number of certified
long-term care need level 1 and 2 recipients. One possible explanation is that lessdisabled people may prefer to stay away from available health information, fearing theimpact that a change of belief could have on their behavior, which is called “strategic
ignorance” Carrillo and Mariotti, 2010). According to “strategic ignorance”, people may
be reluctant to obtain related information and give up attending CDPA.
11According to the results of Yoshida and Chen (2016), they find that hobby
activities are significantly correlated to the decrease of the number of certified support
need level 2 recipients. However, the results cannot be explained as a causal effectowing to the endogenous bias of participation rates. The estimated results are shown inreg6 of Table 2. We find little evidence for supporting HA influencing the number ofcertified recipients.
Table 2 the Results of Total Preventive Care Programs
OLS1

FIX1

OLS2

FIX2

OLS3

FIX3

OLS4

FIX4

Support1

Support1

Support2

Support2

Longterm1

Longterm1

Longterm2

Longterm2

0.511*

0.0162

0.553***-0.0831*0.08300.00746-0.264-0.159(0.304)(0.0829)(0.205)(0.0465)(0.180)(0.121)(0.235)(0.142)

YES

YES

YES

YES

YES

YES

YES

YES

0.851*

0.03811.070***-0.205**

0.109

0.218

-0.426-0.248(0.509)(0.183)(0.282)(0.0916)(0.356)(0.211)(0.485)(0.330)

YES

YES

YES

YES

YES

YES

YES

YES

2.808***

1.677

0.724*-0.2930.943**-2.623***-1.848-2.399(0.340)(1.273)(0.414)(1.212)(0.425)(0.992)(1.527)(1.502)

YES

YES

YES

YES

YES

YES

YES

YES

0.377

0.319

0.738

0.106

-1.276-0.938-0.220-0.243(0.943)(0.517)(1.070)(0.413)(1.080)(0.700)(0.609)(0.439)

YES

YES

YES

YES

YES

YES

YES

YES

-0.582***0.0648-0.0393-0.0200-0.166-0.470***-0.181*-0.476***(0.164)(0.0469)(0.158)(0.0437)(0.199)(0.0573)(0.0964)(0.0641)

YES

YES

YES

YES

YES

YES

YES

YES

1.657*-0.3351.979***-0.320

0.544

0.458

-0.440-0.281(0.926)(0.619)(0.542)(0.216)(0.624)(0.344)(0.797)(0.578)

YES

YES

YES

YES

YES

YES

YES

YES

Reg1
Programs

Covariates
Reg2
PA

Covariates
Reg3
DPA

Covariates
Reg4
TPA

Covariates
Reg5
CDPA

Covariates
Reg6
HA

CovariatesNote:*, **, *** denotes statistical significance at the 10%, 5%, 1%, respectively. Robust cluster
standard errors (clustering at the municipality level) are shown in parentheses.
126.2 The Results of Combinations of Preventive Care Programs
Table 3 shows the combinations of preventive care programs. We find about 8.4%of the municipalities implement all of the five kinds of preventive care programs. As thefinancial resource is limited, executing all kinds of preventive care programs is difficult
for most municipalities. Among the 4 activities combinations, the most popular
combination of pa, dpa, tpa and ha occupies 12.8%. And among the 3 activities
combinations, 20.2% of the municipalities execute the combination of pa, tpa and ha.
Since the municipalities can freely choose the combination of preventive care programs,
the effect of different combination of preventive care programs is necessary to examine.
Table 3 The combinations of preventive care programs (distribution)
5 activities combination
pa & dpa & tpa & cdpa & ha
4 activities combinations
pa & dpa & tpa & cdpa
pa & dpa & tpa & ha
pa & dpa & cdpa & ha
pa & tpa & cdpa & ha
dpa & tpa & cdpa & ha
3 activities combinations
pa & dpa & tpa
pa & dpa & cdpa
pa & dpa & ha
pa & tpa & cdpa
pa & tpa & ha
pa & cdpa & ha
dpa & tpa & cdpa
dpa & tpa & ha
dpa & cdpa & ha
tpa & cdpa & ha

%

Obs

0.084

258

0.091
0.128
0.088
0.111
0.086

280
395
270
342
266

0.146
0.099
0.138
0.130
0.202
0.122
0.094
0.135
0.091
0.116

449
305
426
401
623
376
289
417
280
357

Note: pa, dpa, tpa, cdpa and ha are binary variables (if the number of preventive careprograms is positive, 1 is assigned; otherwise 0).
Here, we discuss the main combinations of preventive care programs used inpractice. The first one is the combination of all five programs. On the basis of estimatedresults in 6.1, in this part we mainly focus on the effect of PA, DPA and CDPA. Basedon column 6 of Table 4, we find PA can reduce the number of certified long-term carerecipients. However, as the estimated result of column 8 is significantly positive
indicating that the number of certified long-term care need level 1 recipients may shift
to long-term care need level 2. As to the effect of DPA, we find the estimated coefficientof column 4 and 8 are significantly negative indicating the implementation of DPA will
decrease the number of certified support need level 2 and long-term care need level 2
recipients. Regarding to the results of CDPA, we find the preventive effect appears
among less disabled elderly, which is different from the previous results of CDPA. One
13

possible explanation is that the diversity of preventive care programs may promote theparticipant rate of CDPA.
Next, let’s have a look at the results of 4 activities combination including PA, DPA,
TPA and HA. The result of PA is similar to that of 5 activities combination decreasing
the number of support 2 and long-term care1 recipients. However, because the result ofcolumn 8 is nonsignificant, certified long-term care need level 1 recipients may not shift
to the higher level certified recipients.
When estimating the combination of 3 preventive care activities, the effect of PA
again is proved. Although the result appears only among certified support need level 2
recipients.
Table 4 The estimated results of preventive care programs combinations
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

OLS1

Fix1

OLS2

Fix2

OLS3

Fix3

OLS4

Fix4

Support1

Support1

Support2

Support2

Longterm1

Longterm1

Longterm2

Longterm2

5 Activities Combination
PA-1.9332.841-7.580***2.780-3.726-10.605**-4.369*15.57***(3.405)(4.232)(2.535)(3.370)(2.682)(4.726)(2.449)(2.875)

1.932-25.785

5.347**-41.355***

0.272-2.946-3.860-57.687***(5.392)(16.685)(2.271)(10.46)(2.096)(15.524)(3.881)(9.476)-3.226

2.729

2.038

-0.890-3.298-0.9830.702

12.495***(7.243)(3.977)(4.532)(2.758)(4.887)(3.495)(4.765)(3.363)5.133

28.55***

4.730-9.257

3.561

3.871

0.684

-22.91**(3.627)(8.457)(2.865)(10.096)(5.037)(7.469)(2.659)(9.247)0.038-56.203***

4.007**

6.343

2.171

32.557*3.372**-2.371(2.309)(18.777)(1.670)(16.865)(1.722)(19.635)(1.655)(17.901)

Covariates

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

258

258

258

258

258

258

258

258

R-squared

0.221

0.323

0.175

0.299

0.149

0.549

0.129

0.555

DPA

TPA

HA

CDPA

4 Activities Combination
PA

DPA

TPA

HA

-2.516***0.104-2.823**-5.268**-1.618-3.764**-1.2180.233(0.856)(1.716)(1.111)(2.038)(1.157)(1.855)(0.914)(2.809)8.265-3.776

7.511**

0.312

1.561

-5.1990.827-17.32*(5.282)(6.086)(3.010)(6.855)(3.912)(8.996)(3.313)(9.618)-3.7043.285-1.1841.160-2.728

1.395

0.727

10.35*(2.986)(3.630)(2.202)(4.098)(2.514)(5.396)(2.153)(5.787)4.744***-0.7965.351***

3.402

3.510*

7.102***

0.528-1.866(1.497)(2.696)(1.683)(3.056)(2.001)(2.597)(1.269)(4.044)14

Covariates

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

395

395

395

395

395

395

395

395

R-squared

0.177

0.283

0.133

0.172

0.141

0.328

0.111

0.152

3 Activities Combination
PA-1.1921.750-1.734*-4.243***-1.2200.589-1.646*-2.105(0.842)(1.593)(1.012)(1.328)(0.846)(1.625)(0.941)(2.244)-0.0991.105**

1.809

0.913*-1.897-2.369***

0.753

0.075

(1.299)(0.447)(1.174)(0.473)(1.427)(0.424)(1.153)(0.538)3.297**-1.0664.557***

2.054***

3.120**

0.714

1.538

0.001

(1.459)(1.435)(1.669)(0.721)(1.575)(0.949)(1.393)(1.382)

Covariates

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

623

623

623

623

623

623

623

623

R-squared

0.163

0.147

0.134

0.169

0.151

0.279

0.115

0.127

TPA

HA

*, **, *** denotes statistical significance at the 10%, 5%, 1%, respectively. Robust cluster
standard errors (clustering at the municipality level) are shown in parentheses.

7. Conclusions and Discussions
Long-term care expenditure has been growing explosively, exerting huge pressure
on local and central governments’ budgets. Extending the functional ability of theelderly not only improves personal welfare, but also reduces the burden of long-termcare expenditure for government. Consequently, using preventive care programs torelieve the expenditure is an attracting option for policymakers.
In this study, we use a two-period panel data and apply a standard fixed effects
model to examine the effect of preventive care programs including physical activities,
dining activities, tea party activities and cognitive disorders preventive activities on thenumber of support need level 1 and support need level 2 recipients. We find that thecoefficients of Programs, DPA and HA are statistically significant. One possible
explanation is that people may prefer to obey “strategic ignorance” (Carrillo andMariotti, 2010). For example, if older disabled adults attend the cognitive disorders
preventive activities, they will learn much knowledge about cognitive disorders, which
may disturb their daily life. According to “strategic ignorance”, people may be reluctant
to obtain related information and give up attending the cognitive disorders preventive
activities. We also examine the combinations of preventive care programs. And we find
PA and DPA are relatively robust and efficient means of preventive care programs.
We hope that the results of this study can provide some evidence for designingefficient preventive care programs in the future. As the data used are macro level data, it
15

is difficult to obtain the exact information for individuals to examine the possibility ofshift. In the future, it is necessary for to examine the effect of preventive care programson the number of disabled older adults by using individual level data.

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