Silviase/jgov_v1.4_full_ann huggingface dataset:
nursing homes, respectively.

Table 1 reports basic statistics of major characteristics of both employees and facilities in the survey. This indicates significant differences between for-profit and nonprofit sectors in nursing homes in Japan. Mean daily wages are 4.7 percent higher in proprietary facilities (the mean wage is 9,634 yen in for-profit homes and 10,217 yen in not-for-profit homes). Not-for-profit facilities are more likely to hire male employees in relatively young age groups from 20 to 49 years old. As regards employees' job status and qualifications, nonprofit homes have a higher percentage of workers who are qualified to perform advanced skills (e.g., physicians, staff nurses, social workers, social care workers, and physical therapists) by regular employees, either full-time or part-time. On the other hand, for-profit homes tend to have a higher percentage of home helpers with any degree of skill, care managers, and service managers. For-profit homes currently hiring workers claim greater instability of employees as compared to nonprofit homes for either full-time or part-time status. Also, a higher percentage of for-profit homes are seen to have more difficulties in hiring than nonprofit homes do. Although we do not have such information as experience, education, marital and family status, employee age can be considered as a proxy of years of experience, and their qualification may reflect education.

We also have various facility characteristics. Nursing homes often produce all kinds of community-based long-term care in addition to institutional care, e.g. at-home help, bathing, nursing care, outpatient treatments, guidance to care management at home, short-term stays, and rental-care equipment. For-profit nursing homes are more likely to supply daily needs to the elderly, rather than intensive long-term care such as rehabilitation, nursing care, and medical treatments often performed at special nursing homes that are operated merely by nonprofit organizations due to legal restrictions. Thus, employers in two sectors are demanding workers with different types of jobs; this suggests that nursing facilities in the two sectors are producing different products. Therefore, these variables for service contents are excluded from our regression analysis because they may lead to biased results due to multicollinearity caused by the strong correlation between products facilities provided and employees' qualifications. For-profit facilities tend to hire a
relatively large number of employees compared to not-for-profit, although a higher percentage of homes with the smallest number of employees are for-profit. In addition, non-wage benefits (employment insurance, work-related accident compensation insurance, health insurance, and public pensions) are offered more often by for-profit homes than nonprofit facilities.

On the other hand, nonprofit homes are seen to support employees from different aspects of for-profit homes, e.g., additional pay to basic salary, professional training inside and outside the facility, health examinations, subsidies for uniforms and equipment, and preventive care for work-related back pain and accidents. Our data show that, on average, about 70 percent and 50 percent of full-time employees are offered any type of insurance and public pension by for-profit and nonprofit employers, respectively, while either part-time or short-time employees are much less likely to receive these non-wage benefits in both sectors. We will simultaneously examine the influences of these individual and facility characteristics on both the self-allocation patterns and the quality of care, as well as the impacts of the self-allocation into facilities with specific characteristics as treatment factors on indicators that are seen to be associated with the quality of services.

### 5. Empirical Results and Discussions

To examine differences in quality in the for-profit and nonprofit sectors, we estimate the treatment effect model discussed in the previous section. The results based on the treatment effect model are reported in Table 2 and from Table 3-1 through Table 3-4. Table 2 indicates the effects of an employee's for-profit status on various treatment factors in equation (2) and reports that no coefficients are statistically significant except negative and significant coefficients on public pension to non-regular workers, health examinations, and preventive care for work-related back pain. This finding demonstrates that there is no statistically significant relationship between for-profit status and various treatments including wage rates and provision of many types of insurance and welfare policies $ ^{2} $ but
the for-profit sector, which has recently been allowed to enter into the long-term care market, lags behind in nonwage benefits to employees in some aspects.

Tables 3-1 through 3-4 show the results on equation (3) to assess the influences of both various treatment factors treated as dependent variables in equation (2) and for-profit status on the quality of workers $ ^{3} $ . The figures in these tables are coefficients and standard errors on the dummy variables for for-profit status and treatments.

Panel (1) and (2) in Table 3-1 report the effect of wage rates on various measures of workers' productivity or quality that are alleged to determine the quality of care. As stated above, the wage effects are measured by whether wage rate is larger than the average or whether an additional pay to salary is provided and the for-profit status is not significant in equation (2) in both cases. For-profit dummies are statistically significant from the fourth to sixth columns to imply that nonprofit enterprises enjoy better job stability. This is also the case for labor demand prospect for home-helpers to imply that for-profit enterprises suffer from the shortage in home-helpers in the future. Moreover, for-profit enterprises are disadvantageous in provision of professional training inside or outside, though they are rather advantageous in inside professional training. These estimates demonstrate that nonprofits are better in stable service supply and thus provide higher quality of services.

It would be interesting to observe that higher-than average wage rates contribute to an increase in share of regular or qualified workers and in either regular or non-regular worker's stability for enterprises that currently have no plan to hire additional workers. The coefficients on the wage factor from the labor demand prospect are significantly positive for home helpers, care managers, and staff nurses. These findings imply that many facilities, regardless of profit status, tend to set higher wage rates if they feel that the number of employees will be insufficient in the future. Panel (2) shows that the effects of the treatment factor are somewhat different, resulting in less job instability in facilities.
planning to hire more workers $ ^{4} $ . These enterprises, however, have more optimistic views on the labor shortage in service managers, staff nurses, or staff semi-nurses, probably because they could attract potential workers who will appreciate for additional wage benefits.

Next, Table 3-2 and 3-3 report the estimates on the social insurance effects as treatments. Note that these treatment effects are not significant in equation (2) except public pension for non-regular workers. For-profit dummies are not statistically significant in the shares of regular, qualified or male workers except health insurance for short-term workers, which is in favor of nonprofit enterprises. As observed in Table 3-1, the coefficients on for-profit dummies are statistically significant from the fourth to sixth columns to imply that jobs are more stable in the nonprofit sector. In addition, this is also true that for-profit enterprises suffer from the labor shortage in home-helpers in the future, showed by the positive and significant coefficients on the eighth column. These estimates indicate that, as in Table 3-1, nonprofits are better in stable service supply and thus provide higher quality of services.

As for the treatment effects, which are applicable for both for-profits and nonprofits, the directions of the insurance effect are various. The second columns in Table 3-2 and Table 3-3 show the impacts of social insurance on the share of qualified workers are negative and significant in most cases. This might be attributable to a trade-off between wage and nonwage compensation and probably reflects the fact that facilities that demand qualified labor are more likely to offer additional wage benefits to an already relatively high regular salary, resulting in not being able to afford much nonwage compensation. The remaining columns in Table 3-2 and Table 3-3 indicate that no obviously consistent results are obtained from the coefficients on social insurance for job stability. However, we have two major interesting findings. First, enterprises that plan to increase the number of regular workers in the future may be offering various social insurances to attract potential employees. Second, all these nonwage benefits to non-regular employees are
seen to be significantly contributing the current stability. This is consistent with the results discussed in the wage rate effects $ ^{5} $ . Hence, to fulfill the current and future demands for labor, nursing care facilities might be behaving differently to attract regular and non-regular workers in the long-term care labor market.

It should be noted that the coefficients on the profit dummy are negative and significant in professional training inside or outside for all cases but those variables are rather positive and significant for inside professional training, which are also observed in the wage effects. Moreover, the coefficients on the treatments are large and significantly positive for regular workers in Panel (3), (6), (9) and (12) but they are significantly negative for non-regular workers in Panel (5), (8), (11) and (14). This probably replicates the difference in strategies of human capital investments to regular and non-regular workers among facilities. These facilities might think of professional on-the-job training an investment for improving the skills of stable employees, associated with an increase in their productivity as well as the quality of care they are producing.

Finally, Table 3-4 shows the influences of six types of welfare to care workers on indicators for employees' quality of work; health examinations, subsidies for uniforms and equipment, preventive care for work-related back pain, preventive care for accidents in commuting, professional training for employment managers, and support for mental stress. For-profit dummies contribute job instability and the shortage of home-helpers. As regards the treatment effects, most of these additional supports for workers are seen to have similar effects on workers' quality indicators to social insurance. They have significant positive effects on the share of both regular and qualified workers. In facilities currently
not hiring employees, they are contributing to improve the stability of both regular and non-regular workers, while this is not the case in facilities currently hiring workers. That will lead facilities that offer various supports for employees to be more likely to expect a short supply of labor in the future.

Based on these findings, we have reached the following conclusions. The main results of the sign direction of for-profit and treatment effects are summarized in Table 4. As regards the effects of for-profit status on the quality indicators, we observe similar propensities in most regressions, such that for-profit status tends to discourage the stability of both regular and non-regular employees. Further, for-profit facilities are more likely to anticipate an insufficient supply of home helpers in the future and less likely to offer professional training to employees inside or outside the institutions. They are seen to have no statistically significant influence on the share of either regular or qualified employees. As for the treatment effects, regardless of profit-status, the wage effects have positive and significant effects on the share of regular or qualified workers, job stability, and labor demand prospects. There are few cases in which the effect of treatment indicators of social insurances and additional supports to employees are statistically meaningful.

These results suggest that nonprofits are advantageous in stable service supply proxied by job stability and future labor demand for home helpers, which are supposed to be associated with the quality of workers. In other words, the quality of services is in favor of nonprofit enterprises, not in favor of for-profits which were allowed to enter the long-term care market after 2000.

### 6. Conclusion

This study uses affluent micro-level data on workers and affiliates in Japanese long-term care facilities to evaluate the quality differentials between for-profit versus nonprofit care. Unlike the long-term care market in the United States, the Japanese nursing care market had been legally restricted to be run by the nonprofit sector only. The recent introduction of long-term care insurance allows for-profit facilities to enter only the
home-help market. For-profit and nonprofit firms have just started to compete in the long-term care industry in Japan. The current strong political incentive of Japanese government to restructure administrative and industrial organizations will lead to accelerating the entry of proprietary institutions into the conventional not-for-profit sector and stimulating the competition between different types of enterprises in the same market. Under such far-reaching expansion of the market, where both for-profit and nonprofit sectors coexist, it would be critical to assess differentials in the quality of services provided by various types of facilities in both sectors. We estimate how for-profit status affects the quality of workers measured by the qualifications associated with workers' skills, status or stable service supply that are alleged to determine the level of service quality supplied by each facility. We use the treatment effect model by Barnow, Cain, and Goldberger (1981) to address the sample selection bias caused by nonrandom allocation of workers into facilities with specific characteristics, in company with for-profit and not-for-profit sectors.

Our estimates report that nonprofit enterprises are obviously advantageous in stable service supply and the quality of employees associated with the quality of products are not in favor of for-profit facilities. Therefore, our findings demonstrate that for-profit nursing homes, which were allowed to enter the long-term care market after 2000, have not yet been able to catch up with their nonprofit counterparts, which are still dominant in the long-term care industry, assuming that the quality of services can be measured by the indicators related to worker quality in each facility $ ^{6} $ . This finding might be explained partly by the disadvantage of new entrants to obtain high quality workers. If this is the case, the maturity of the long-term care market would solve the gap between for-profit and
nonprofit. However, if the difference is attributed to cost sensitive behavior of management in for-profit companies to save labor costs, it would be hard to expect the gap in service quality to converge in the future. Further research should address which of these hypothesis is the case to evaluate the causes of the quality difference so that the Japanese long-term care market becomes more competitive.
Barnow, Cain, and Goldberger, (1981), “Issues in the Analysis of Selection Bias.”

## References

Department of Economics, University of Wisconsin, Madison.

Cohen, Joel and Spector, William (1996). “The Effect of Medicaid Reimbursement on Quality of Care in Nursing Homes.” Journal of Health Economics, vol. 15, pp23-48.

Gertler, Paul (1989). “Subsidies, Quality and the Regulation of Nursing Homes,” Journal of Public Economics, vol. 38, pp.33-52.

Gertler, Paul (1992). “Medicaid and the Cost of Improving Access to Nursing Home Care,” Review of Economics and Statistics, vol.74, pp338-345.

Hansman, Henry (1980). “The Role of Nonprofit Enterprise.” Yale Law Journal, vol. 89, pp. 835-901.

Holtmann, A.G, and Idson, Todd L.(1993) "Wage Determination of Registered Nurses in Proprietary and Nonprofit Nursing Homes." Journal of Human Resources, vol. 28, pp55-79.

Nyman, J.A. (1988). “Excess Demand, the Percentage of Medicaid Patients, and the Quality of Nursing Home Care,” Journal of Human Resources, vol. 23, no.1, pp76-92.

O'Brien, J. and Saxberg, B.O. and Smith H.L., (1983). "For-profit or Not-for-profit Nursing Homes: Does it Matter?" Gerontologist vol. 23, pp341-348

Suzuki, Wataru (2002). “Hi-eiri houmon kaigo gyousha ha yuuri ka?” Kikan Shakai Hosho Kenkyuu, vol. 38, no. 1.
Ullmann, S.G. and Holtmann, A.G (1985). “Economies of Scope, Ownership and Nursing Home Cost.” Quarterly Review of Economics and Business, vol. 25 no.4, pp83-94

Weisbrod, Burton A. (1988) The Non-Profit Economy, Cambridge, Harvard University Press.
Table 2 : The Effect of For-Profit Status on the Treatments

Dependent Variable: Treatments	Coefficients (S.E.)
Daily Wage>Average	-0.040
(0.144)
Additional Pay to Basic Salary	-0.073
(0.140)
Employment Insurance
(Regular Workers)	0.098
(0.150)
Employment Insurance
(Short-term Workers)	0.133
(0.145)
Employment Insurance
(Non-Regular Workers)	0.117
(0.175)
Work-Related Accident Compensation Insurance
(Regular Workers)	0.012
(0.150)
Work-Related Accident Compensation Insurance
(Short-term Workers)	0.028
(0.141)
Work-Related Accident Compensation Insurance
(Non-Regular Workers)	-0.066
(0.142)
Health Insurance
(Regular Workers)	-0.145
(0.142)
Health Insurance
(Short-term Workers)	0.006
(0.179)
Health Insurance
(Non-Regular Workers)	-0.169
(0.223)
Public Pension
(Regular Workers)	-0.200
(0.143)
Public Pension
(Short-term Workers)	-0.061
(0.195)
Public Pension
(Non-Regular Workers)	-0.441
(0.236)	*
Welfare to Care Workers:
Additional Pay to Basic Salary	-0.073
(0.140)
Welfare to Care Workers:
Health Examination	-0.532
(0.131)	**
Welfare to Care Workers:
Subsidy for Uniform and Equipments	-0.141
(0.151)
Welfare to Care Workers:
Preventive Care for Work-Related Back Pain	-0.446
(0.142)	**
Welfare to Care Workers:
Preventive Care for Accident in Commuting	0.015
(0.142)
Welfare to Care Workers:
Professional Training for Employment Managers	-0.173
(0.144)
Welfare to Care Workers:
Support or Abbreviation for Mental Stress	0.225
(0.137)

(Note) The estimated coefficients on a dummy variable for for-profit status in Equation (2).

The numbers in the parenthesis are standard errors.

* refers to 10% level significance and ** does to 5% level in the last column.
Table 3-1: The Effects of For-Profit Status and the Treatments on Quality of Workers (Wage Rate Effect)

Share of Regular Workers	Share of Qualified Workers	Share of Male Workers	Job Instability (Regular) (With Plan to Hire Additional Workers)	Job Stability (Regular) (No Plan to Hire Additional Workers)	Job Instability (Non-Regular) (With Plan to Hire Additional Workers)	Job Stability (Non-Regular) (No Plan to Hire Additional Workers)	Insufficient in Future (Home Helper)	Insufficient in Future (Care Manager)	Insufficient in Future (Services Manager)	Insufficient in Future (Staff Nurse)	Insufficient in Future (Staff Semi-Nurse)	Insufficient Training (inside or outside)	Professional Training (inside only)	Professional Training (outside only)
Panel (1) Daily Wage>Average
Profit Dummy	-0.005(0.021)	0.004(0.017)	0.013(0.011)	0.053(0.024)	**	-0.045(0.027)	*	0.070(0.032)	**	-0.018(0.019)	0.172(0.045)	**	0.056(0.049)	0.044(0.028)	0.035(0.044)	-0.001(0.030)
Treatments	0.163(0.078)	**	0.105(0.044)	**	-0.029(0.083)	0.317(0.025)	**	-0.025(0.148)	0.211(0.020)	**	0.525(0.050)	**	-0.036(0.157)	0.549(0.049)	**	0.026(0.021)
Log likelihood	-361.290	-118.129	296.417	-590.388	-511.103	-891.650	-207.788	-1044.45	-1198.219	-754.5785	-1037.017	-829.141	-1104.434	-580.0105	-852.8843
Panel (2) Additional Pay to Basic Salary
Profit Dummy	-0.006(0.020)	0.005(0.018)	0.012(0.011)	0.047(0.028)	*	-0.054(0.025)	**	0.068(0.032)	**	-0.024(0.018)	0.162(0.037)	0.048(0.042)	0.038(0.033)	0.014(0.044)	-0.010(0.036)	-0.149(0.038)
Treatments	0.046(0.049)	0.211(0.023)	**	-0.019(0.029)	-0.367(0.021)	**	-0.305(0.033)	**	-0.016(0.024)	**	0.092(0.054)	0.040(0.093)	-0.417(0.134)	**	-0.484(0.040)	0.044(0.027)
Log likelihood	-311.633	-58.940	339.843	-490.141	-471.972	-845.300	-166.795	-1013.137	-1151.153	-690.6989	-976.3279	-734.9483	-1055.468	-529.7631	-818.4257

(Note) The estimated coefficients on a dummy variable for for-profit status and estimated treatment effects in Equation (1). The numbers in the parenthesis are standard errors.

* refers to 10% level significance and ** does to 5% level in the last column.
Table 4 Signs of For-Profit and Treatment Effects (Summary)

Treatments	For-Profit Dummy
Wage Rates	negative (insignificant)
Social Insurance	various (Public Pension = negative & significant)
Welfare Policy	various (Health Exam & Care for Back Pain = negative & significant)

Quality of Workers	Treatment
Wage Rates	Social Insurance	Welfare Policy
Table 3-1	Table 3-2, 3-3	Table 3-4
Worker's Skills	+ (significant)	insignificant [regular] - (significant) [qualified]	various
(For-Profit Dummy)	insignificant	insignificant	insignificant
Job Stability	insignificant [Plan to hire more] + (significant) [No plan to hire more]	zero or + (significant) [Plan to hire more] zero or + (significant) [No plan to hire more]	+ (significant) [Plan to hire more] + (significant) [Plan to hire more]
(For-Profit Dummy)	+ (significant) [Plan to hire more] insignificant	+ (significant) [Plan to hire more] zero or - (significant) [No plan to hire more]	zero or + (significant) [Plan to hire more] zero or - (significant) [No plan to hire more]
Labor Demand	+ (significant)	various	various
(For-Profit Dummy)	+ (insignificant) + (significant) [helper]	zero or + (significant) [helper]	zero or + (significant) [helper]
Professional Training (inside or outside) (For-Profit Dummy)	+ (insignificant)	various	various
- (significant)	- (significant)	- (significant)
# 平成２６年度医薬関係予算概算要求の概要

## 平成26年度概算要求額

うち、要

10,665百万円

8，289百万円

新しい日本のための優先課題推進枠

2,377百万円

平成25年度予算額

9,006百万円

对前年度增减额

1,659百万円

(△ 7 1 7 百万円)

对前年度

118.4%

( 92.0%)

※括弧内は「新しい日本のための優先課題推進枠」を除いた計数である。

※ 計数については、整理上、変更があり得る。

## 《主要事项》

I 医療関連イノベーションの一体的推進（医療関連産業の活性化）

【推進枠】

Ⅱ 予防・健康管理の推進等【推進枠】

Ⅲ 医薬品・医療機器・再生医療等製品の承認審査の迅速化

Ⅳ 医薬品・医療機器・再生医療等製品の安全対策の推進

V 適切な承認審査や安全対策の在り方等に関する研究（レギュラトリーサイエンス研究等）の推進【一部推進枠】

VI 違法ドラッグなどの薬物乱用対策の推進

VII 血液製剤対策の推進
Ⅰ	医療関連イノベーションの一体的推進（医療関連産業の活性化）
【推進枠】	5 3 7	→ 1, 5 7 5

医療分野の研究開発から実用化につなげる体制を整備すること等により、医療関連産業の国際競争力を向上させるための取組を推進する。

## ○革新的な製品の実用化を促進するための審査・安全対策の充実・強化

## （１）審査基準の明確化

1 3 4 → 4 1 5

アカデミアやベンチャーなどによる革新的医薬品・医療機器・再生医療等製品の開発や実用化を促進するため、PMDA-WESTの体制整備を含め、薬事戦略相談を充実する。

○ 革新的医薬品・医療機器・再生医療等製品の実用化を推進するため、PMDAの審査員と研究者が一体となり、最先端の技術の有効性と安全性を評価するためのガイドライン整備を拡充する。

☐ 希少疾病用医薬品等の開発・審査の迅速化及び高度化に資するデータベースを整備する。

## （２）医療機器・再生医療等製品の特性を踏まえた制度

19 → 403

○ 医療機器の審査の迅速化・合理化を図るため、高度の管理を要する医療機器のうち後発医療機器等を対象として、登録認証機関を活用した認証制度の拡充を行う。

そのための環境整備として、登録認証機関による後発医療機器の審査に必要な基準を作成するとともに、PMDAにおいて登録認証機関からの照会に迅速に対応するため、既に承認された医療機器との性能等の比較を行うことができるデータベース整備等を行う。

## (3) 安全対策の強化

3 8 5 → 4 7 8

○ 市販後安全対策を強化するため、電子カルテなどの医療情報を活用した、疫学的手法による副作用情報等の収集・分析のためのデータベースを整備するとともに、再生医療等製品等の患者登録システムを開発する。

☐ 化粧品の安全対策の強化のため、化粧品成分の安全性確認指針や安全性プロファイルの公開方法の検討を行うとともに、医師からの健康被害の効率的な収集方法の検討などを行う研究を実施する。