Silviase/jgov_v1.4_full_ann huggingface dataset:
## (5) 研究開発成果の社会展開のための計画

（5～1の5段階評価）：評価4点

## (総論)

実フィールドでの実証実験をふまえて実用化にむけての計画を固め、そのための課題の洗い出しができている点が評価できる。今後は、運用面でのさらなる安定化を目指した機能改善などの実用性の追求、事業と結びついた継続的な研究開発、オープンソフトとして広く使われるようにする努力を期待する。

(コメント)

● 実フィールドでのさらなる安定化を目指した機能改善などの実用性の追求は今後の大きな課題であり、事業と結びついた継続的な研究開発を期待する。

この技術が実用性を発揮するには自治体、デジタルサイネージ事業者などとの連携はもちろんのこと、さらに、コンテンツ生成との密な連携・統合による新たな価値創造の探索が望まれる。

● 実フィールドでの実証実験をふまえて実用化にむけての計画を固め、そのための課題の洗い出しができている点が評価できる。

● 標準化活動の努力も重要であるが、オープンソフトアプリケーションのオープンソフトとして広く使われるようにする努力を期待する。
## (3) 土砂災害対策に当たっての環境配慮

生物多様性を保全しながら土砂災害から住民の安全・財産を守る砂防事業を進めるため、六甲地区等、都市周縁に広がる山麓斜面において、グリーンベルトとして一連の樹林帯を引き続き整備します。また、生物の良好な生息・生育環境を有する渓流や里山等を保全・再生するため、NPO等と連携した山腹工等を引き続き実施します。

## 9 沿岸・海洋

## （1） 沿岸・海洋域の保全

平成23年度に開始した生物多様性の保全上重要度の高い海域の抽出作業を完了し、その成果に基づき海洋保護区の充実とネットワーク化に向けた検討を行います。

ウミガメの産卵地となる海浜については、自然公園法に基づく乗入れ規制地区に指定されている地区においてオフロード車等の進入を禁止するなどにより保護を図ります。

有明海・八代海における海域環境調査、東京湾等における水質等のモニタリング、海洋短波レーダーを活用した流況調査、水産資源に関する調査や海域環境情報システムの運用等を行います。

サンゴ礁生態系保全行動計画の実施を進めるほか、サンゴ礁生態系に対する人為的压力の特定等のための検討を開始します。

## (2) 水産資源の保護管理

漁業法 (昭和24年法律第267号) 及び水産資源保護法 (昭和26年法律第313号) に基づき、採捕制限等の規制を行います。また、海洋生物資源の保存及び管理に関する法律（平成8年法律第77号）に基づき、漁獲可能量や漁獲努力可能量の管理を行うほか、[1] 「資源回復計画」の推進、[2] 外来魚の駆除、環境・生態系と調和した増殖・管理手法の開発、魚道や産卵場の造成等、[3] ミンククジラ等の生態、資源量、回遊等の実態把握及び資源回復手法の解明に資する調査、[4] ウミガメ (ヒメウミガメ等)、鯨類 (シロナガスクジラ等) 及びジュゴンの原則採捕禁止等、[5] 水産資源の持続可能な利用に向けた海洋保護区の検証・推進と希少海洋生物の実態調査、[6] サメ類の保存・管理及び海鳥の偶発的捕獲の対策に関する行動計画の実施促進等、[7] 混獲防止技術の開発等を実施します。

海洋生物の生理機能を解明して革新的な生産につなげる研究開発と生物資源の正確な資源量の変動予測を目的に生態系を総合的に解明する研究開発を実施するとともに、独立行政法人科学技術振興機構の戦略的創造研究推進事業として海洋生物の観測・モニタリング技術の研究開発を実施します。

## (3) 海岸環境の整備

海岸保全施設の設備においては、海岸法の目的である防護・環境・利用の調和に配慮するなど、海岸環境の保全に取り組みます。

## (4) 港湾及び漁港・漁場における環境の整備

良好な海域環境を保全・再生・創出するため、藻場・干潟等の整備を推進するとともに、港の環境保全の重要性を認識・理解し、環境保全のための行動が習慣となるよう、環境保全活動及び環境教育活動を支援します。

漁港・漁場では、水産資源の持続的な利用と豊かな自然環境の創造を図るため、海水交換機能を有する防波堤、水産動植物の生息・繁殖に配慮した護岸等の整備及び砂浜の再生に資する施設の整備など、自然調和・活用型の漁港漁場づくりを積極的に展開します。また、藻場・干潟の保全等を推進するとともに、漁場環境を保全するための森林整備に取り組みます。さらに、木材利用率が高い増殖礁の開発や漁場機能を強化する
ESRI Discussion Paper Series No.24

The Determinants of Nursing Home Exit and the Price Elasticity of Institutional Care: Evidence from Japanese Micro-level Data

By

Haruko Noguchi and Satoshi Shimizutani

December 2002

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
The Determinants of Nursing Home Exit and the Price Elasticity of Institutional Care: Evidence from Japanese Micro-level Data $ ^{1} $

By

Haruko Noguchi and Satoshi Shimizutani

December 2002

Please address correspondence to:

Haruko Noguchi, Toyo-Eiwa University, Yokohama, Japan

Email: hnoguchi@newage3.stanford.edu

Satoshi Shimizutani, Cabinet Office, Tokyo, Japan

Email: satoshi.shimizutani@esri.cao.go.jp
## Abstract

This paper uses a unique census data on Japanese nursing homes to evaluate the determinants of nursing home exit and the price elasticity of the institutional care. We take two approaches to address these by making use of micro-level data from Kaigo Service Shisetsu Zigyousho Chousa (Survey on Care Service Providers) conducted by the Ministry of Health, Labor and Welfare in September 2000. First, we use probit estimates to examine the choice decision to exit a nursing facility to home by making use of clients who left their facilities in September 2000. Our results show that one percentage point increase in self-burden to use institutional care will lead to an increase in probability to return to home by 0.04 percentage points for long-term care welfare facilities, 3.7 percentage points for long-term care health facilities, respectively. On the contrary, the price elasticity with respect to exit to re-hospitalization is minus 3.3 percentage points (health care facility) and minus 1.9 percentage points (medical care facility). These results suggest that at-home care is a substitute for institutional care but care services in medical institution are complement for services at nursing homes. Especially, the elderly with relatively lower care levels and thus higher self-burden to use long-term care tends to be re-hospitalized if they do not have any family members to live together. Therefore, policies to address deduction of the “socially hospitalized” elderly should focus on these types of the elderly. Then, we apply a duration model to the sample that consists of all clients who stayed in or left from a nursing home in September 2000 in order to evaluate the price effect on how long a client stays in a nursing home. Our results report that price elasticity with respect to duration staying in nursing homes is 1.7 percentage points (95% confidence interval: 0.4-3.0 percentage points) for long-term care welfare facilities; and 1.8 percentage points (95% confidence interval: 0.0-3.8 percentage points) for health facilities. Our findings demonstrate that price policy might be useful for allocating clients from institutional care to at-home care, though the effect is relatively small for long-term care welfare facilities compared to health facilities.
### 1. Introduction

The purpose of this paper is to examine what determines exiting behavior from institutional care in Japan and how the exit is price sensitive. This study is motivated by surely urgent and difficult challenges Japan struggles with an unprecedented speed of aging; restructuring the whole social security system by improvement of the efficiency of medical care and deduction of an enormous amount of medical and care costs. The tremendous costs of medical care in Japan are partly attributed to a large number of “socially hospitalized” patients, who no longer need medical care but are obliged to stay in hospitals due to non-medical factors. Most of European countries, especially North European countries, which have also suffered the rapid increase in medical care costs, have been pursuing transfer from medical institution to care institution and further, from institutional care to at-home care to deduce care costs. The introduction of the long-term care insurance $ ^{2} $ in Japan in spring of 2000 echoed the trend in these developed countries suffering from huge medical and care costs.

This study focuses on an empirical assessment on the determinants of the reallocation between institutional care and at-home care in Japan. Especially, we quantitatively evaluate the price effect on clients' allocation among various factors since a price mechanism was lastly introduced under the long-term care insurance and thus there is a room for a proper price policy to work for transferring more clients from institutional care to at-home care $ ^{3} $ . We take two approaches to the question on how price mechanism works for reallocation of the elderly who need care. First, we use a probit estimation to examine the price effect on the decision making of the exiting behavior from institutional care. We use the data set on clients who left their care-offering institutions in September 2000 and where they moved. We estimate the price elasticity with respect to returning to home, after controlling for clients' characteristics including age as well as facilities' attributes. Second, we apply a
duration model to evaluate the price effect on how long a client stays in a nursing home. We use the sample used in the first approach in addition to those who stayed in their nursing facilities, whose duration is truncated.

We take advantage of a unique and rich micro-level data from Kaigo Service Shisetsu Zigyousho Chousa (Survey on Care Service Providers) conducted by the Ministry of Health, Labor and Welfare in September 2000. The survey is a census and thus we use the first comprehensive data set on care providers at micro-level. The sample is all nursing homes in Japan, more than 10,000. This survey contains detailed information on care service supply as well as characteristics of each facility. In addition to the information on facilities, the survey provides data on half of all clients as of October 1 $ ^{st} $ and all those who left their institutions in September 2000.

Our main conclusion is as follows. First, our probit estimates on the determinants of exiting behavior report that one percentage point increase in self-burden to use institutional care will lead to an increase in probability to return to home by 0.04 percentage points for long-term care welfare facilities, 3.7 percentage points for long-term care health facilities, respectively. Our findings demonstrate that price policy might be useful for allocating clients from institutional care to at-home care but the effect is very small for long-term care welfare facilities. On the other hand, our duration model estimates on clients' staying period show that price elasticity with respect to duration to stay in nursing homes is 1.7 percentage points (95% confidence interval: 0.4-3.0 percentage points) for long-term care welfare facilities; and 1.8 percentage points (95% confidence interval: 0.0-3.8 percentage points) for health facilities, but the result for medical facilities is not statistically significant. The difference of price elasticity between long-term care welfare and health facilities based on probit estimates, it becomes negligibly small for the duration model, because we do not distinguish locations after nursing home exit, excluding those who died to exit, for duration estimates.

This paper is organized as follows. The next section briefly reviews related previous research. Section 3 describes the data set we use in this study. Section 4 uses probit model to examine the determinants of exiting behavior and choosing place after leaving. Section 5 uses a duration model to the determinants of timing of exit and price elasticity. The last section aggregates and discusses our findings and concludes.
### 2. Previous research

As far as we know, there has been no empirical research to investigate the determinants of exiting behavior of clients in Japanese nursing homes. Thus we must owe previous studies to those in the U.S.. Although many researchers tackled analysis on demand for long-term care in the U.S. and entry and exit of nursing homes, surprisingly, there are relatively fewer studies focusing on economic factors such as income and price of nursing home use (Headen (1993)).

Chiswick (1976) used aggregate cross-section data to estimate price elasticity of nursing home demand and concludes that one percentage increase in prices reduces use by 2.3 percent. Scanlon (1980) utilized state-level data to measure price elasticity and reports that one percentage increase in prices reduces nursing home use by 1.12 percent. Although they do not address price elasticity of nursing home use, Garber and McCurdy (1989) addressed transition from community to nursing homes using a transition probability model. Their finding is that the risk to nursing homes admission is reduced by home ownership and co-residence with children but the effect of income is not significant. Ingram and Kleinman (1989) used a discrete-time hazard function approach to estimate some factors for entry and exit of nursing homes and found that home ownership decreases entry risk and living alone increased it but other factors such as income, marital status, informal care did not have statistically significant effects.

Header (1993) tackles price elasticity of nursing home entry using micro-level data. He used a Cox proportional hazards model to estimate economics determinants of first nursing home entry. He found that wealth significantly reduced the hazard of nursing home entry and it is positively related with the opportunity cost of informal caregiver time. He estimated the price elasticity of the hazard of nursing home entry of 0.7.

### 3. Data

The data utilized in this study is the micro-level data from the Kaigo Service Shisetsu Zigyousho Chousa (Survey on Care Service Providers) conducted by the Ministry of Health, Labor and Welfare in September 2000. The survey is performed
annually from 2000 when the public long-term care insurance was commenced. The data is a census and thus we use the first comprehensive data set on care providers at micro-level data. The sample is all nursing homes in Japan, more than 10,000. This survey contains detailed information on care service supply as well as characteristics of each facility. In addition to the information on facilities, the survey provides data on half of all clients as of October 1 $ ^{st} $ and all those who left their institutions in September 2000.

First, we focus on the behavior of those who exited from nursing homes in September 2000 to investigate the determinants of exit behavior. The basic statistics reported in Tables 1-1, 1-2 and 1-3 shows how many clients remained in their facilities and how many clients exited from three different types of nursing homes and where they moved after exiting. First, Table 1-1 shows that 1,439 people left from 4,463 long-term care welfare facilities for the elderly $ ^{4} $ (henceforth, “welfare care facilities”) and 58% of those people died. Thirty-five percent of elderly persons was re-hospitalized and only 4% moved to at-home care and 3% transferred to other nursing homes. Second, Table 1-2 reports that 13,743 clients exited from 2,667 long-term care health facilities for the elderly $ ^{5} $ (henceforth, “health care facilities”) and 45% of these clients moved to at-home care, both of the figures are much larger than those in the special nursing homes, while the share of re-hospitalized clients is almost same. Only 2% died and 14% was transferred to other nursing homes. Third, Table 1-3 shows that 2,669 people left from 3,930 long-term care medical facilities for the elderly $ ^{6} $ (henceforth, “medical care facilities”). Out of these clients, 23% moved to in-home care receiver, 19% changed their nursing homes, 33% was re-hospitalized and 24% died.

The difference in the shares of where a client moved after leaving from nursing home is basically accounted for the different functions of these three types of nursing homes. The “welfare care facilities” are designed to provide institutional care service for those who always need care but cannot receive at-home care services. Medical care is not provided in the welfare care homes and clients in this type of facility often.
live in a nursing home as long as they live. This function explains the higher share of those who left from the facilities due to death. On the contrary, the remaining two types of nursing home offer medical treatments. The “health care facilities” aim to offer institutional care for clients in transition from hospitalization to in-home care. These patients are in stable health condition and do not need hospitalization but rehabilitation, long-term care or medical care $ ^{7} $ . This role of the health care home accounts for the higher share of those who moved to home. The “medical care facility”, as manifest in the name, offers care in service home for patients who need medical management all the time such as catheterization. The shares of clients who returned to home or died are approximately in the middle of those figures in the welfare care and health care facilities.

It should be noted that hospitals and clinics contribute greatly to absorb the leavers from any types of nursing homes. The share of clients who were transferred to those institutions is greater than 30% in all the three types of nursing homes.

### 4. The determinants of exiting behavior and choosing place after leaving

Suppose that the $ i^{th} $ elderly person's optimal health stock is $ H_{i}^{*} $ , which can be described as a function of his or her investments to nursing home care ( $ C_{i}^{N} $ ), formal community-based care ( $ C_{i}^{F} $ ), and informal home care ( $ C_{i}^{I} $ ) as follows:

$$ \mathbf{H}_{\mathrm{i}}^{*}=\mathbf{H}_{\mathrm{i}}^{*}(\mathbf{C}_{\mathrm{i}}^{\mathrm{N}},\mathbf{C}_{\mathrm{i}}^{\mathrm{F}},\mathbf{C}_{\mathrm{i}}^{\mathrm{I}}) $$

Then, the total costs of long-term care use can be defined as:

$$ \mathbf{H}_{\mathrm{i}}\boldsymbol{\pi}=\mathbf{C}_{\mathrm{i}}^{\mathrm{N}}\mathbf{P}^{\mathrm{N}}+\mathbf{C}_{\mathrm{i}}^{\mathrm{F}}\mathbf{P}^{\mathrm{F}}+\mathbf{C}_{\mathrm{i}}^{\mathrm{I}}\mathbf{P}^{\mathrm{I}} $$

where $ \pi $ represents the shadow price of health, $ P^{N} $ and $ P^{F} $ are the prices of nursing
home care and community-based home care, and $ P^{1} $ is an opportunity cost to informal care givers’ working hours or wage rates. Under the $ i^{th} $ elderly person’s optimal demand for health, the demand for each type of long-term care input can be derived by minimizing the total cost function, (2). Therefore, the Lagrangian is:

$$ \mathbf{L}_{\mathrm{i}}=\mathbf{C}_{\mathrm{i}}^{\mathrm{N}}\mathbf{P}^{\mathrm{N}}+\mathbf{C}_{\mathrm{i}}^{\mathrm{F}}\mathbf{P}^{\mathrm{F}}+\mathbf{C}_{\mathrm{i}}^{\mathrm{I}}\mathbf{P}^{\mathrm{I}}+\lambda[\mathbf{H}_{\mathrm{i}}^{*}-\mathbf{H}_{\mathrm{i}}^{*}(\mathbf{C}_{\mathrm{i}}^{\mathrm{N}},\mathbf{C}_{\mathrm{i}}^{\mathrm{F}},\mathbf{C}_{\mathrm{i}}^{\mathrm{I}})] $$

The first-order conditions are given by $ P^{N}=\lambda H_{i_{N}}^{*} $ , $ P^{F}=\lambda H_{i_{F}}^{*} $ , $ P^{I}=\lambda H_{i_{I}}^{*} $ , and $ \mathrm{H_{i}^{*}=H_{i}^{*}(C_{i}^{N},C_{i}^{F},C_{i}^{I})} $ . Solving these equations, we obtain the following three optimal conditions on relative prices:

$$ \mathbf{P}^{\mathrm{N}}/\mathbf{P}^{\mathrm{F}}=\mathbf{H}_{\mathrm{i N}}^{*}/\mathbf{H}_{\mathrm{i F}}^{*} $$

$$ \mathbf{P}^{\mathrm{N}}/\mathbf{P}^{\mathrm{I}}=\mathbf{H}_{\mathrm{i}_{\mathrm{N}}}^{*}/\mathbf{H}_{\mathrm{i}_{\mathrm{I}}}^{*} $$

$$ \mathbf{P}^{\mathrm{F}}/\mathbf{P}^{\mathrm{I}}=\mathbf{H}_{\mathrm{i}_{\mathrm{F}}}^{*}/\mathbf{H}_{\mathrm{i}_{\mathrm{I}}}^{*} $$

Substituting these into equation (1), the three conditional input demand functions under the optimal demand for health can be derived as follows:

$$ \mathbf{C}_{\mathrm{i}}^{\mathrm{N}}=\mathbf{C}_{\mathrm{i}}^{\mathrm{N}}(\mathbf{P}^{\mathrm{N}},\mathbf{P}^{\mathrm{F}},\mathbf{P}^{\mathrm{I}}\mid\mathbf{H}_{\mathrm{i}}^{*}) $$

$$ \mathbf{C}_{\mathrm{i}}^{\mathrm{F}}=\mathbf{C}_{\mathrm{i}}^{\mathrm{F}}(\mathbf{P}^{\mathrm{N}},\mathbf{P}^{\mathrm{F}},\mathbf{P}^{\mathrm{I}}\mid\mathbf{H}_{\mathrm{i}}^{*}) $$

$$ \mathbf{C}_{\mathrm{i}}^{\mathrm{I}}=\mathbf{C}_{\mathrm{i}}^{\mathrm{I}}(\mathbf{P}^{\mathrm{N}},\mathbf{P}^{\mathrm{F}},\mathbf{P}^{\mathrm{I}}\mid\mathbf{H}_{\mathrm{i}}^{*}) $$

where $ \partialC_{i}^{N}/\partialP^{N}<0 $ , $ \partialC_{i}^{F}/\partialP^{F}<0 $ , and $ \partialC_{i}^{I}/\partialP^{I}<0 $ . This shows that the demand for each type of long-term care input depends on both its own and relative input prices. In this study, we will focus on the demand for nursing care facility, $ C_{i}^{N} $ .

First, we use a probit model to investigate the determinants of the exiting behavior using clients' micro-level data from all nursing homes in Japan in September 2000, available in the survey discussed in the previous section. There are some candidates which determine the place after exiting from a nursing home such as
physical or health condition of individual clients, family situation, price to use facility care services (the self burden to use each long-term care service), which is a proxy for price of institutional care in facilities, and price to use at-home long-term care, which might serve as a substitute or complement. Unfortunately, the data set we use in this study does not contain any exact proxy for at-home services, which makes it difficult to estimate the price elasticity between at-home care services and facility provided care services. However, we could address the problem using some proxies for price of at-home care services such as care levels of clients or the number of family members who live with them at home. This approach could be justified by the fact that the limits for benefits paid for long-term care services depends care levels $ ^{8} $ and co-resident family members plausibly provide some at-home care services to affect outside care service demand, both of which are considered to be input cost for health production function of a client.

Our estimates on the determinants of exiting behavior are reported in Table 2-1 through 2-3. The estimates are appeared in Table 2-1 for the welfare care facilities, Table 2-2 for the health care facilities and Table 2-3 for the medical care facilities. In this section, we focus on the price effect on the behavior to exit. We summarize the marginal effects in these tables as follows.

Summary Table 1: The marginal effect of cost to use institutional care (self burden) on exits

Type of facility	Welfare care facility	Health care facility	Medical care facility
To home	0.0004 (0.0002) $ ^{**} $	0.0365 (0.0043) $ ^{**} $	0.0003 (0.0035)
To hospitals, clinics	-0.0044 (0.0045)	-0.0326 (0.0043) $ ^{**} $	-0.0191 (0.0044) $ ^{**} $

(note) $ * $ * refers to 5% level significance and $ * $ does to 10% level. Due to the small size of the sample, we did not perform a probit estimation for “To other care institution” and “death”.

The figures in this small table report probabilities in exiting from each type of
facility to home or hospitals and clinics with respect to one percentage point change in cost to use facility care. The price effect on exit to home is statistically significant for both welfare and health care facilities. One percentage point price increase lead to an increase in probability to return to home of 0.04 percentage points for the welfare care institution and 3.7 percentage points for the health care facility $ ^{9} $ . This finding suggests that price mechanism works for exit from a nursing facility to in-home, except medical care facility, though the elasticity is very small for welfare care facilities. On the contrary, the price effect on exit to re-hospitalization is negative and statistically significant not for welfare care facilities but for health or medical facilities. The estimated elasticity is minus 3.3 percentage points in health care and minus 1.9 percentage points in medical care facilities, respectively. This finding indicates that an increase in price to use institutional care results in decrease in both institutional care and care services in hospitals and clinics. Both estimated price effects are statistically significant but opposite for the price effect on exit to home in health care facilities. This result suggests that at-home care is a substitute for institutional care but care services in medical institution are complement for services at nursing homes.

Next, the following small tables summarize the effect of care levels and number of co-resident family members both of which are captured as input cost to a client's health production function $ ^{10} $ .

Summary Table 2-1: The marginal effect of cost by care levels on exits: Welfare care facility

Care levels, co-residence	To home	To hospitals, clinics
Care Level 2	-0.0012 (0.0271)	-0.0164 (0.0695)
Care Level 3	-0.0004 (0.0260)	-0.1081 (0.0664)
Care Level 4	-0.0009 (0.0269)	-0.1228 (0.0685)
Care Level 5	-0.0013 (0.0273)	-0.0842 (0.0696)
No co-residence	-0.0022 (0.0222)	-0.0872 (0.0571)