IPSS Discussion Paper Series

(No.2018-E01)

Simplified Projection of the Insurance Premiums in the Greater Area, 2020-2060

Nozomu INOUE (National Institute of Population and Social Security Research)

March 2019

National Institute of Population and Social Security Research Hibiya-Kokusai-Building 6F 2-2-3 Uchisaiwai-Cho Chiyoda-ku Tokyo, 100-0011

IPSS Discussion Paper Series do not reflect the views of IPSS nor the Ministry of Health, Labor

and Welfare. All responsibilities for those

papers go to the author(s).

Simplified Projection of the Insurance Premiums in the , 2020-2060

Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

National Institute of Population and Social Security Research Department of Theoretical Social Security Research Nozomu INOUE

1. Introduction Besides a reduction in the working population, Japan’s aging society and declining birthrate suggest a host of other problems. Among these is the demand for nursing care. Japan’s longevity rate is one of the world’s highest. According to the Ministry of Health, Labor, and Welfare (2017a), the mean life expectancy was over 80 years for both sexes in 2016 (80.98 years for men and 87.14 years for women). However, increased longevity is a primary factor in the aging of society; that is, the prolonging of the mean life expectancy can play a part in accelerating the proportion of people over 65 in the population (the population aging rate). Fig. 1 shows the distribution of the population aging rate of various small areas in the Greater Tokyo Area, created using data from Inoue (2017). The figure makes it clear that the population aging rate is increasing year on year, and in 2060, it will be high in Tokyo’s 23 wards as well. Based on this result, the number of elderly people in the Greater Tokyo Area is expected to increase in the future, which suggests that the number of people in need of nursing care should also increase. Inoue (2018) shows how many people will be in need of nursing care in the Greater Tokyo Area in the future and that the provision of locations for current nursing-care facilities is insufficient, particularly in the city center. Increased demand for nursing care will not just affect locations for nursing-care facilities, but insurance premiums as well. Insurance premiums are determined based on the Insured Long-Term Care Service Plans, which are formulated every three years. They cover approximately 90% of the expenses of nursing-care services for people who qualify as needing nursing care, of which approximately 50% each is covered by public funds and by premiums collected from people with nursing-care insurance. In addition, of the approximately 50% that is covered by this premium, about 22% is the total of the premiums paid by primary insured people. Furthermore, dividing this total of premiums by the number of primary insured people gives the base premium rate. Therefore, an increase in the demand for nursing care may play a part in increasing insurance premiums. Thus, given an increase in the elderly population and consequent increase in people in need of nursing care, it is of utmost importance to examine how much insurance premiums can be expected to increase. This is because if there are areas that see an extreme increase in insurance premiums, this may result in primary insured people who are unable to pay the high premiums. If this happens, it will cause a negative chain reaction, as other resources or other insured people’s premiums will have to be used to compensate, which may cause a further increase in insurance premiums. To that end, the objective of this study is to project the future insurance premiums from 2020 to 2060 in the Greater Tokyo Area, where the elderly population is expected to increase, in order to identify (1) future trends in insurance premiums and (2) which regions are expected to see a particularly sharp increase in insurance premiums. First, Section 2 will describe the data used in this study and the model used to project the future insurance premiums. Section 3 will explain the results of the actual estimation of future insurance premiums. Section 4 will explain what happens when the base premium rate used in the trial calculation is fixed to a uniform value throughout the whole country. Finally, Section 5 will summarize the discussion up to that point and mention future research tasks. Note that “Greater Tokyo Area” in this study refers to the Capital Region (shuto-ken) as defined by the Capital Region Arrangement Act, which is Tokyo and the seven prefectures of Kanagawa, , , Yamanashi, Gunma, Tochigi, and Ibaraki (rather than only Kanagawa, Chiba, and Saitama).

2. Data and method of analysis

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

As stated in the previous section, this study projects future insurance premiums from 2020 to 2060 in the Greater Tokyo Area, given the increasing demand for nursing care expected in the future, in order to identify (1) future trends in insurance premiums and (2) which regions are expected to see a particularly sharp increase in insurance premiums. Insurance premiums are normally calculated using the Ministry of Health, Labor, and Welfare’s “Worksheet for Insured Long-Term Care Service Plans,” however much data are required to project insurance premiums. Due to constraints in the availability of data, this study will use a simplified model, with reference to Yamamoto (2015), to project future insurance premiums. The features of this model are as follows, suppose that the data on the number of first insured persons, the storage rate of long-term care insurance premiums, the proportion of insured persons by income stage, and other data that is excluding population data are constant throughout the future, the number of data required for calculation was reduced. Therefore, the results of the trial calculation were also examined on the premise that the present and the rest of the population remain unchanged. The data used in the analysis are the number of primary insured people, adjusted number of primary insured people, base-year premiums, rate of receipt of insurance premiums, and charges to be borne by primary insured people per capita. First, the number of primary insured people was taken from data on the number of primary insured people (population aged 65 and older) in the Greater Tokyo Area by small area from 2020 to 2060 in Inoue’s “The Web System of Small Area Population Projection for the Whole Japan” (2017). Note that the insurance premiums are defined for each insured person (i.e., first, second), so data segregated by insured person were used to make the future projects, after totaling the number of primary insured people by small area. Next, the adjusted number of primary insured people was obtained by using the number of primary insured people (by income level) in 2015 from the Ministry of Health, Labor, and Welfare (2017b) data (which is segregated by insured person) to calculate an adjustment factor, which was then applied to the future population as a constant. To give a concrete example, Fig. 2 shows the number of primary insured people in Chiyoda Ward in Tokyo, divided into nine categories by income level. Next, the share of primary insured people at each income level was calculated. These shares would be used to project the future adjusted number of primary insured people. The number of primary insured people at each income level was then multiplied by the premiums defined by the Ministry of Health, Labor, and Welfare for each income level. Finally, the result from multiplying the number of primary insured people at each income level by the corresponding premium was summed for all rates to calculate the adjusted number of primary insured people in Chiyoda in Tokyo, 2015. Similarly, the number of primary insured people in 2020, which is a projected value, was multiplied by the share of primary insured people at each income level that was just calculated. Those values were again multiplied by the rates for each income level, and the total sum of those values is the adjusted number of primary insured people in 2020, as shown in Fig. 3. Moving on, a summary of the data from the Ministry of Health, Labor, and Welfare (2018a) on the base premium rate for each insured person was used as the data for the base-year premiums. This is the base premium rate for each insured person, calculated based on the Phase VII (newest) Insured Long-Term Care Service Plan. The rate of receipt of insurance premiums was calculated by dividing the insurance premiums received by the insurance premiums scheduled, with reference to Matsuoka and Nakazawa (2017). Finally, charges to be borne by primary insured people per capita was treated as an unknown variable and calculated from the equations of the following simplified model. The model for the insurance premiums in Yamamoto (2015) is as in equation (1).

× = ……（1） × × 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖

𝑖𝑖 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 𝑟𝑟𝑖𝑖 12 Then, We set as𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 the number of insurer, as insurance premiums, as charges to be borne by primary insured people per capita, as the number of primary insured people, as 𝑖𝑖 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 adjusted number of primary insured people, as pay rate of long-term care insurance premium. Here, was 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 an unknown variable, and it was estimated with equation (2). 𝑟𝑟 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿

× × × = ……（2） 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 𝑟𝑟𝑖𝑖 12

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿� 𝚤𝚤 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 2

Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Future insurance premiums were projected by using the estimated variable in equation (3). Then, we set as year of projection, and other variables are same as above. Furthermore, the data of the phase VII Insured Long-Term Care Service Plan shall be used assuming that the burden amount of the primary insured person per capita and the𝑥𝑥 pay rate of nursing care insurance premiums are constant in estimating.

× = ……（3） 2018 × 𝑥𝑥× 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝚤𝚤 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖 𝑥𝑥 𝑥𝑥 2018 � 𝚤𝚤 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑜𝑜𝑜𝑜𝑖𝑖 𝑟𝑟𝑖𝑖 12 3. Simplified Projec𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿tion of the Insurance Premiums Section 2 described the data necessary to make simplified projects of the future insurance premiums in the Greater Tokyo Area and the data analysis method. This section will verify those projects to identify (1) future trends in insurance premiums and (2) which regions are expected to see a particularly sharp increase in insurance premiums. First, Fig. 4 shows the mean insurance premium rates by prefecture up to the year 2060. While insurance premiums in nearly all prefectures will be at their peak in 2040 (Phase XIV), and Tokyo both exhibit different changes. The mean rate in Gunma Prefecture will be at its peak in 2020 (Phase VII), and the mean rate in Tokyo will be at its peak in 2050. Of particular note is that the peak Tokyo rate exceeds ¥7500 according to the trial calculation. This difference in peaks can be attributed to the difference in the rate at which the population ages from prefecture to prefecture. That is, Gunma Prefecture is expected to age sooner than other prefectures, while Tokyo is expected to age later than other prefectures. Table 1 shows the trends in the over-65 populations of the Greater Tokyo Area. As this table makes clear, there will be almost no change in Gunma Prefecture from 2020 to 2040, and this phase is its peak. The table also confirms that Tokyo will reach its peak in 2050. As the population of each prefecture will age at a different rate, insurance premiums will not peak uniformly but will exhibit different changes from one prefecture to the next. Next is a more detailed projection. Fig. 5 shows the distribution of the index of increases in premiums by insured person across the entire Greater Tokyo Area from 2018 to 2060. This index of increase was calculated with the Phase V premium rates as 100. Note that the Phase VII premium rates announced by the Ministry of Health, Labor, and Welfare are written alongside the rates calculated using the projected population data in this study. Over the entire duration, the kurtosis of the distribution tends to decrease gradually, while its variance tends to increase. The mean values are 19.9 in Phase VII (2018), 29.1 in Phase VII (2020), 29.8 in Phase XI (2030), 32.1 in Phase XIV (2040), 27.7 in Phase XVII (2050), and 14.2 in Phase XXI (2060), which matches the results in Fig. 4, where the peak occurs in Phase XIV. It can also be observed that there is an increase in the number of regions where the index becomes negative—that is, the rates of their premiums fall below those of Phase V. Meanwhile, after 2020, there are also some regions where the indices become unusually high. It is supposed that this occurs in regions where the number of insured people was extremely small to begin with. The reason for this is that in such regions, a decrease by even one person could result in a large rate of decrease, so insurance premiums projected from the number of insured people are likewise influenced similarly. After that, we confirmed the distribution of the index of increase in premiums by insured person by prefecture. Figs. 6 through 10 show the distribution of the index of increase in premiums by insured person by prefecture from 2020 to 2060. Due to the small sample size, the distributions have an irregular form, unlike that of the entire Greater Tokyo Area, but the variance in each prefecture’s distribution will still increase in the same way. It can also be seen that Tokyo, Saitama Prefecture, and have the highest number of regions where the index becomes unusually high. Next, we identified the regions with an unusually high index of increase in premiums by insured person. Ordinarily, a Smirnov-Grubbs test should be used to calculate statistically significant anomalies, but for this study, anomalies were simply defined as cases where the index exceeded 150; that is, the rates had increased to 1.5 times the Phase V premium rates. Then, this definition of anomaly was used to confirm the anomalous regions by fiscal year. Note that in no region did the index ever go below -100. Table 2 presents an overview of the number of regions

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060 where the index of increase in the insurance premiums was anomalous. The number was five regions in Phase VII (2020); six regions in Phase XI (2030); six regions in Phase XIV (2040); seven regions, the most, in Phase XVII (2050); and four regions, the fewest, in Phase XXI (2060). In each of these phases, the anomalous regions were always in Saitama Prefecture, Chiba Prefecture, or Tokyo. The only anomalies in Tokyo were the villages of Aogashima and Mikurashima; there were none on the mainland. The highest index in all phases was 346 in Aogashima during Phase XIV (2040), and the regions that were anomalous for the entire duration were Tōnoshō, Chiba Prefecture; Higashi-chichibu, Saitama Prefecture; and Kamikawa, Saitama Prefecture. The highest number of insured people was 65,663 in Urayasu, Chiba Prefecture in Phase XVII (2050), and the lowest was 43 in Aogashima, Tokyo in Phase XXI (2060), when there were few insured people in every region. Finally, the last step was to see if there were any geographical characteristics in the insurance premiums for each insured person. Fig. 11 shows the distribution of the insurance premiums in the Greater Tokyo Area by insured person. Whereas Table 2 highlights the regions where the index of increase is unusually high, Fig. 11 is a choropleth that represents regions with high premiums as actual numbers. Normally, actual population numbers would be represented with graduated symbols rather than a choropleth map, but a choropleth map is used here as the numbers are the per capita premiums. First, by Phase VII (2020), there is already a scattering of regions with high premiums, such as northwestern Gunma Prefecture; Odawara, ; Isumi, Chiba Prefecture; and Higashi-chichibu, Saitama Prefecture. This trend is the same in 2030, except in the 23 wards of Tokyo, where there are significant changes. The premiums in nearly all of the 23 wards of Tokyo increase, and this trend continues until 2060. By 2040, premiums can also be seen to increase in regions such as , ; Kawasaki, Kanagawa Prefecture; Chiba, Chiba Prefecture; and Funabashi, Chiba Prefecture—a remarkable trend in the so-called city center environs. Meanwhile, by 2060, the insurance premiums have decreased significantly in almost all regions of Gunma and Yamanashi Prefectures, indicating an increasing bipolarization of insurance premiums.

4. Conclusion This study projected the future insurance premiums from 2020 to 2060 in the Greater Tokyo Area, given the increasing demand for nursing care expected in the future. This was done in order to identify (1) future trends in insurance premiums and (2) which regions are expected to see a particularly sharp increase in insurance premiums. Insurance premiums are normally calculated using the Ministry of Health, Labor, and Welfare’s “Worksheet for Insured Long-Term Care Service Plans,” but due to constraints in the availability of data, this study used a simplified model, with reference to Yamamoto (2015), to project future insurance premiums. Estimating the change in the mean insurance premium rates by prefecture showed that in almost all prefectures, insurance premiums will reach their peak in Phase XIV (2040); however, in Gunma Prefecture, they will reach their peak in Phase VII (2020), and in Tokyo, they will reach their peak in 2050. Next, verifying the index of increase in premiums by insured person across the entire Greater Tokyo Area from 2018 to 2060, calculated with the Phase V premium rates as 100, showed that over the entire duration, the kurtosis of the distribution tended to decrease gradually, while its variance tended to increase. It also showed that there was an increase in the number of regions where the index became negative. Meanwhile, after 2020, there were also some regions where the indices became unusually high. In addition, verifying the distribution of the index of increase in premiums by insured person by prefecture showed that the variance of each prefecture’s distribution increased, just like that of the distribution across the entire Greater Tokyo Area. Moving on, the regions with an unusually high index of increase in premiums by insured person were identified. The number of regions where the index was anomalous was five regions in Phase VII (2020); six regions in Phase XI (2030); six regions in Phase XIV (2040); seven regions, the most, in Phase XVII (2050); and four regions, the fewest, in Phase XXI (2060). The highest index in all phases was 346 in Aogashima during Phase XIV (2040), and the regions that were anomalous for the entire duration were Tōnoshō, Chiba Prefecture; Higashi-chichibu,

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Saitama Prefecture; and Kanagawa, Saitama Prefecture. Here, the insurance premiums for each insured person were tested for any geographical characteristics. First, by Phase VII (2020), there was already a scattering of regions with high premiums, such as northwestern Gunma Prefecture; Odawara, Tochigi Prefecture; Isumi, Chiba Prefecture; and Higashi-chichibu, Saitama Prefecture. This trend was the same in 2030, except in the 23 wards of Tokyo, where there were significant changes. The premiums in nearly all of the 23 wards of Tokyo increased, and this trend continued until 2060. By 2040, premiums could also be seen to increase in regions such as Yokohama, Kanagawa Prefecture; Kawasaki, Kanagawa Prefecture; and Chiba, Chiba Prefecture—a remarkable trend in the so-called city center environs. Meanwhile, by 2060, insurance premiums had decreased significantly in almost all regions of Gunma and Yamanashi Prefectures, indicating an increasing bipolarization of insurance premiums. Those were the results of analyzing the simplified projects of future insurance premiums for the Greater Tokyo Area. This paper will end by mentioning some tasks for future research. Although anomalously high indices of increase in premiums by insured person were uniformly defined as values exceeding 150 throughout the entire duration of this study, statistical methods need to be used to calculate the anomalies. In comparison to Fig. 4, the change in the mean insurance premium rates by prefecture should form a smoother curve when anomalies are excluded from the calculations of those mean rates. Also, due to constraints in the availability of data, a simplified model was used in this study to project the insurance premiums, but using the Ministry of Health, Labor, and Welfare’s “Worksheet for Insured Long-Term Care Service Plans” should yield more accurate results. Furthermore, in this study, insurance premiums were only calculated for the Greater Tokyo Area, but the aging of society is not a phenomenon limited to the Greater Tokyo Area; it affects Japan as a whole, which suggests the need to expand the target region. Doing so will make it possible to use cluster analysis to distinguish regional patterns in changes in insurance premiums. Finally, we should consider the current nursing care insurance premium policy, and then decide whether it is appropriate to estimate future long-term care insurance premiums with constant variables. This is because the current nursing care insurance premium set the amount after decreasing the regional differences as much as possible and therefore we have to assume that the regional differences are constant throughout the future.

References

Inoue, T., 2017, The Web System of Small Area Population Projection for the Whole Japan (regular version 2.0), http://arcg.is/1GkdZTX. Inoue, N., 2018, “GIS Analysis of Nursing Faculties in the Tokyo Metropolitan Area, 2010-2060,” The Aoyama Journal of Economics, Vol.70, No.1, pp.1-10. Matsuoka, H. and Nakazawa, K., 2017, “Study on the effect of price revision of long-term care insurance premium on storage rate (Kaigo Hoken Ryo Kakaku Kaitei Ga Shunou Ritsu Ni Ataeru Eikyou),” RIEB Discussion paper series, DP2017-J05. Ministry of Health, Labor, and Welfare, 2017a, Life Tables for 2016, https://www.mhlw.go.jp/toukei/saikin/hw/life/ life16/index.html. Ministry of Health, Labor, and Welfare, 2017b, Fact-finding Survey on Project of Long-term Care for 2017, https://www.mhlw.go.jp/topics/kaigo/osirase/jigyo/15/index.html. Ministry of Health, Labor, and Welfare, 2018a, The First Premium and Projected Service Amount of Nursing-care Insurance in the Seventh Term Planning Period, https://www.mhlw.go.jp/stf/houdou/0000207410.html. Ministry of Health, Labor, and Welfare, 2018b, Fact-finding Survey on Project of Long-term Care for March 2018, https://www.mhlw.go.jp/topics/kaigo/osirase/jigyo/m18/1803.html. Yamamoto, K., 2015, The Simulation of the Long Term Care Insurance Premiums in JA PAN , The 10th IAGG Asia/Oceania Regional Congress, 19-22 October 2015.

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Table.1 Trends in the over-65 population of the Greater Tokyo, 2010-2060

（unit：1,000 people）

2010 2020 2030 2040 2050 2060

All areas 9,114 11,457 11,867 13,201 13,507 12,247 Tokyo 2,684 3,195 3,370 3,986 4,349 4,045 Kanagawa 1,829 2,324 2,453 2,799 2,842 2,605 Chiba 1,338 1,788 1,856 2,038 2,060 1,846 Saitama 1,469 1,930 1,963 2,141 2,127 1,913 Ibaraki 667 843 847 852 819 713 Tochigi 442 557 566 570 549 474 Gunma 473 575 566 571 537 459 Yamanashi 213 245 245 244 223 190

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Table.2 Overview of the Number of Regions where the Index of Increase in the Insurance Premiums was Anomalous

Phase VII (2020) Phase XI (2030)

Index of Index of The number of Insurance The number of Insurance Pref. City Pref. City Increase care insured Premiums Increase care insured Premiums

Saitama Higashi-chichibu 302 3,478 22,003 Chiba Tōnoshō 319 16,701 16,977

Chiba Tōnoshō 294 15,682 15,941 Saitama Higashi-chichibu 304 3,489 22,073

Chiba Kōzaki 214 5,377 12,574 Tokyo Aogashima 266 60 20,154

Chiba Isumi 182 28,163 11,507 Chiba Kōzaki 211 5,318 12,436

Saitama Kamikawa 179 8,111 12,675 Saitama Kamikawa 194 8,573 13,397

Chiba Isumi 178 27,761 11,343

Phase XIV (2040) Phase XVII (2050)

Index of Index of The number of Insurance The number of Insurance Pref. City Pref. City Increase care insured Premiums Increase care insured Premiums

Tokyo Aogashima 346 73 24,521 Tokyo Aogashima 285 63 21,162

Chiba Tōnoshō 304 16,089 16,355 Chiba Tōnoshō 266 14,587 14,828

Saitama Higashi-chichibu 278 3,266 20,662 Saitama Higashi-chichibu 241 2,950 18,663

Saitama Kamikawa 207 8,926 13,948 Saitama Kamikawa 202 8,797 13,747

Chiba Kōzaki 179 4,768 11,150 Chiba Urayasu 169 65,663 11,041

Chiba Isumi 155 25,462 10,404 Tokyo Mikurashima 157 123 10,468

Saitama Toda 151 41,956 11,119

Phase XXI (2060)

Index of The number of Insurance Pref. City Increase care insured Premiums

Chiba Tōnoshō 245 13,744 13,971

Saitama Higashi-chichibu 223 2,795 17,682

Saitama Kamikawa 178 8,103 12,662

Tokyo Aogashima 163 43 14,444

Note: An abnormal value was set when the increase index of the insurance premium exceeded 150.

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.1 Distribution of the Population Aging Rate of Various Small Area in the Greater Tokyo Area, 2020-2060

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.2 How to adjust the number of insured people of Chiyoda in Tokyo, 2018

First Insured People, 2018 10,835

①

Level1 Level2 Level3 Level4 Level5 Level6 Level7 Level8 Level9 1,573 502 548 1,067 695 1,048 995 1,021 3,386

②

Level1 Level2 Level3 Level4 Level5 Level6 Level7 Level8 Level9 15% 5% 5% 10% 6% 10% 9% 9% 31%

0.45 0.75 0.75 0.9 1.0 1.2 1.3 1.5 1.7 ③ Level1 Level2 Level3 Level4 Level5 Level6 Level7 Level8 Level9 708 377 411 960 695 1,258 1,294 1,532 5,756

④ Adjusted First Insured People,2018 12,989

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.3 How to adjust the number of insured people of Chiyoda in Tokyo, 2020

Projected First Insured People, 2020 10,571 15% 5% 5% 9% 9% 31% 10% 6% 10%

Level1 Level2 Level3 Level4 Level5 Level6 Level7 Level8 Level9 1,535 490 535 1,041 678 1,022 971 996 3,303

0.45 0.75 0.75 0.9 1.0 1.2 1.3 1.5 1.7

Level1 Level2 Level3 Level4 Level5 Level6 Level7 Level8 Level9 690 367 401 937 678 1,227 1,262 1,494 5,616

Adjusted First Insured People,2020 12,673

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020- 2060

Fig.4 Trends in Average Insurance Premiums by Prefecture, 2012-2060

8,000

7,500 Average Insurance Premiums by Prefecture(unit:Yen) 7,000

6,500

6,000

5,500

5,000 4,500

4,000 - (2018 (2050) XVII Phase Phase XI (2030) XI Phase （ （ (2020) VII Phase (2040) XIV Phase (2060) XXI Phase Phase VII VII Phase - 2012 - 2015 PhaseVI PhaseV 2020) 2014 2017

） ）

All areas Tokyo Kanagawa Chiba Saitama Ibaraki Tochigi Gunma Yamanashi

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020- 2060

Fig.5 Distribution of the Index of Increases in Premiums by Insured Person Across the Entire Greater Tokyo Area, 2018-2060

Note: Phase 5 nursing care insurance premium amount was set to 100.

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.6 Distribution of the Index of Increase in Premiums by Insured Person by Prefecture, PhaseVII (2020)

1.Tokyo 2.Kanagawa 3.Chiba 0 1 5 7 0 5

23 5 18 2 15 15 12 7 7 7 7 4 4 5 1 1 2 1 2 1 1 1 1 1 1 2 3 1 1 1 1 1 1 1 0 4.Saitama 5.Ibaraki 6.Tochigi 0 1 y c 5 7 n e 0 u 5 q 5 r e 17 2 14 F 10 12 9 7 7 7 6 5 5 4 6 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 0 7.Gunma 8.Yamanashi All areas 97 0 1

76 5 7

56 0 5

28 28 5

2 15 9 9 9 7 6 6 6 5 7 5 2 1 2 2 3 2 2 2 1 3 1 3 2 2 1 1 1 1 1 1 0 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.7 Distribution of the Index of Increase in Premiums by Insured Person by Prefecture, Phase XI (2030)

1.Tokyo 2.Kanagawa 3.Chiba 0 1 0 7 5 5 0

21

2 5 15 11 9 910 8 4 5 5 6 5 6 4 3 2 1 1 1 1 2 3 1 1 1 1 2 3 2 2 1 1 1 1 0 4.Saitama 5.Ibaraki 6.Tochigi 0 1 0 y c 7 5 n e u 5 0 q r e

2 5 15 14

F 11 9 8 7 9 4 5 5 6 6 1 1 2 2 2 1 2 1 1 1 1 1 3 1 1 3 1 3 2 1 0 7.Gunma 8.Yamanashi All areas 0 1 0 83 7 5 58 59

44 5 0

31

2 5 14 14 7 8 7 8 5 5 4 4 6 4 6 5 2 1 1 2 1 1 1 3 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 0 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.8 Distribution of the Index of Increase in Premiums by Insured Person by Prefecture, Phase XIV (2040)

1.Tokyo 2.Kanagawa 3.Chiba 0 0 1 5 7 0 5 5 2 1110 8 8 8 7 5 5 6 5 5 5 6 4 4 4 5 4 4 1 1 1 1 3 3 1 1 1 2 1 2 1 1 1 2 3 3 1 1 1 1 1 1 0 4.Saitama 5.Ibaraki 6.Tochigi 0 0 1 y c 5 7 n e 0 u 5 q 5 r e

2 14 F 10 10 7 9 7 7 6 5 6 4 1 1 2 1 2 2 3 3 1 3 1 1 1 1 1 3 1 1 1 1 1 3 2 3 2 2 1 0 7.Gunma 8.Yamanashi All areas 0 0 1 5 7

53 49

0 47 5 33 34 27

5 22

2 1315 10 9 7 5 4 6 6 5 6 5 2 2 1 3 2 3 1 1 1 3 2 3 3 1 1 1 1 1 1 1 3 1 1 2 1 1 1 1 1 1 0 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.9 Distribution of the Index of Increase in Premiums by Insured Person by Prefecture, Phase XVII (2050)

1.Tokyo 2.Kanagawa 3.Chiba 0 0 1 5 7 0 5 5 2 9 7 6 6 5 6 4 5 4 5 6 4 5 4 5 4 5 5 5 1 1 1 2 3 2 3 1 1 1 1 1 1 3 3 2 2 1 1 1 1 2 3 3 2 1 1 1 1 1 1 0 4.Saitama 5.Ibaraki 6.Tochigi 0 0 1 y c 5 7 n e 0 u 5 q 5 r e

2 13 F 10 9 8 5 7 5 6 5 7 6 7 1 1 1 2 2 1 1 1 1 1 1 1 2 2 3 1 1 1 1 1 3 1 3 1 3 3 1 1 0 7.Gunma 8.Yamanashi All areas 0 1 5 7

0 45

5 39 3434 30 29 5 17 19 2 16 1012 11 7 9 8 8 5 4 5 4 6 5 1 3 1 1 2 2 1 2 1 1 1 2 3 2 2 1 1 1 2 2 2 2 2 1 2 1 1 1 1 1 0 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.10 Distribution of the Index of Increase in Premiums by Insured Person by Prefecture, Phase XXI (2060)

1.Tokyo 2.Kanagawa 3.Chiba 0 1 5 7 0 5 2 5 10 109 10 5 5 6 5 4 6 5 6 4 5 4 5 1 1 1 2 3 3 3 1 1 1 2 2 1 3 3 1 1 1 2 2 2 2 3 1 2 1 1 1 1 1 0 4.Saitama 5.Ibaraki 6.Tochigi 0 1 0 y c 5 7 n e 0 u 5 q 5 r e

2 13 F 11 10 10 9 7 8 4 5 4 4 6 4 4 4 1 1 1 3 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 0 7.Gunma 8.Yamanashi All areas 0 1 5 7

0 47 42 5 3840 29

5 22 19 21 2 14 10 12 12 8 8 7 8 5 4 4 4 5 6 1 1 3 2 1 1 2 1 1 1 1 3 2 2 3 3 1 2 1 1 1 1 1 1 1 0 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350 -100-50 0 50 100150200250300350

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Simplified Projection of the Insurance Premiums in the Greater Tokyo Area, 2020-2060

Fig.11 The Distribution of the Insurance Premiums in the Greater Tokyo Area by Insured Person

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IPSS Discussion Paper Series（English）

No Author Title Date

2013-E02 Ryotaro Fukahori, The Effects of Providing Informal Care on Labor Oct. 2013 Kazuma Sato,and Force Participation, Subjective Health, and Life Tadashi Satisfaction among Middle-aged Family Members 2013-E01 Akiko S. Oishi Child Support and the Poverty of Single-Mother Aug. 2013 Households in Japan 2011-E04 Ryo Nakajima and Estimating the Effects of Pronatal Policies on Jul. 2012 Ryuichi Tanaka Residential Choice and Fertility 2011-E03 Masayoshi Hayashi Forecasting Welfare Caseloads: Apr. 2012 The Case of the Japanese Public Assistance Program 2011-E02 Wataru Kureishi and Precautionary Wealth and Single Women Mar. 2012 Midori Wakabayashi in Japan 2011-E01 Yuka Uzuki The Effects of Childhood Poverty on Sep. 2011 Unemployment in Early Working Life: Evidence from British Work History Data 2010-E01 Tadashi Sakai and Who values the family-friendly aspects of a job? Jul. 2011 Naomi Miyazato Evidence from the Japanese labor market 2009-E01 Kazumasa Oguro, Child Benefit and Fiscal Burden: OLG Model Jul. 2009 Junichiro Takahata with Endogenous Fertility and Manabu Shimasawa 2008-E02 Junya Hamaaki The effects of the 1999 pension reform on Dec. 2008 household asset accumulation in Japan: A test of the Life-Cycle Hypothesis 2008-E01 Takanobu Kyogoku Introduction to the theories of social market Jul. 2008 2007-E02 Tetsuo Fukawa Household projection 2006/07 in Japan using a Oct. 2007 micro-simulation model 2007-E01 Takanobu Kyogoku In Search of New Socio-Economic Theory May 2007 on Social Security 2005-07 Aya Abe Empirical Analysis of Relative Deprivation Mar. 2006 and Poverty in Japan 2005-04 Takashi Oshio and The impact of social security on income, poverty, Oct. 2005 Satoshi Shimizutani and health of the elderly in Japan