Silviase/jgov_v1.4_full_ann huggingface dataset:
## Sub-index 5: Service convenience

● Can your establishment provide care service in the early morning?

● Can your establishment provide care service in late night?

● Can your establishment deal with emergencies?

● Can your establishment provide services on holidays?

## Sub-index 6: Customer-oriented policies

● Does your establishment always visit customers’ homes and discuss at-home care plans with them?

● Does your establishment regularly communicate with the customer’s families for consulting and receiving claims?

● Does your establishment provide any support to encourage customers to live without help?

● Does your establishment employ knowledge of public health nurses, staff nurses, and doctors for settling at-home care plans?

## Sub-index 7: Information service and complaint processing system

● Does your establishment set up a correspondence for counseling service for customers?

● Does your establishment designate staff to process claims from customers?

● Does your establishment promulgate its service content through brochures or home pages on the web?

● Does a customer have some tentative use of your services?

## Sub-index 8: Protection of customer privacy

● Does your establishment assign a specific administrator to keep records of service use?

● Does your establishment have a storage system in place to keep secure records?

● Does your establishment have any legal requirements against disclosure of secrecy for staff?

● Does your establishment have any instructions for staff about the human rights of clients?

## Sub-index 9: Handling of accidents and emergencies

● Does your establishment reserve some regular doctors or cooperative medical institutions for the customers?

● Does your establishment provide any guiding manuals about accidents and emergencies for staff?
● Does your establishment have liability insurance?

● Does your establishment assign specific staff to deal with accidents and emergencies?

## Sub-index 10: Strategies toward hygiene and infectious disease

● Does your establishment give regular instructions on prevention of infectious disease to the staff?

● Does your establishment set some disciplines about the uniforms of staffs?

● Does your establishment employ any public health nurses or staff nurses to check the health conditions of the customers regularly?

● Does your establishment assign any staff members who are responsible for the management of hygiene and prevention of infectious disease?

## Sub-index 11: Planning and transparency of the business

● Does your establishment clarify the philosophy and strategies of the business in paper documents?

● Does your establishment publicize your business planning in paper documents?

● Does your establishment release your budget planning in paper documents?

● Does your establishment have any outside organizations or individuals who are responsible for the audit of its financial reports?

## Sub-index 12: Clarity of contracting procedure

● Does your establishment make formal contract documents for each deal of service provided?

● Does the document define the contract term and renewal provisions?

● Does the document define the procedure and charges of cancellation?

● Does the document refer to the amount of charge and deliver the receipt to the customers?

In what follows, we compare the average quality of services between public and private providers. Table 3 employs the 12 sub-indexes listed above to present the mean scores by ownership of various types of providers. The ownership of at-home long-term service providers are categorized into the following three groups: for-profit providers, nonprofit providers, and public providers. More concretely, for-profit providers include business corporations, limited companies, private companies and individuals. Nonprofit providers consist of social welfare corporations excluding the social welfare association (“shakyo”), medical corporations, universal partnerships, cooperative associations, civil corporations (“zaidan” or “shadan”) and NPO organizations. Public providers include facilities by local government or the social welfare association (“shakyo”). Although we do not necessarily distinguish public providers from nonprofit providers, we believe it is better to differentiate
these two kinds of providers in the at-home long-term care market in Japan because public providers could act quite differently from nonprofit private providers.

We prepared two sets of scores to measure the quality of care service. The “total score” is calculated by simply adding up the obtained scores of each sub-index, and hence each query has been evaluated evenly. The “principle component score” is estimated by principle component analysis, where each index was evaluated with different weights $ ^{16} $ . “+” indicates that the score is significantly higher than for-profits providers while “-” refers to the reverse.

First, we look at the total score. For-profit and nonprofit providers have an average score of 28.9 and 29.9 respectively. The average score of nonprofit providers is slightly higher than that of the for-profit ones, but the differential is not statistically significant. This is also the case for the principle component score. Nonprofit providers are dominant in “management and maintenance of service content,” “provision of staff training,” “qualification and experience of staffs,” and “planning and transparency of business,” but for-profits seem to be providing more “convenient” services, as is evident from the other sub-index.

Furthermore, the quality of services supplied by public providers is significantly lower than that of for-profit providers both in the total score and the principle component score. Our results on the differentials in sub-indexes indicate that the services of public providers are worse than those of the for-profit providers in many items, such as “management and maintenance of service content,” “services convenience,” “information service and complaint procession,” “protection of customer privacy,” “handling accidents and emergencies,” “strategies toward hygiene and infectious disease,” and so on. Public providers have higher scores only in “qualification and experience of staffs” and “planning and transparency of business.”

Table 4 presents a comparison of quality of services between newer and older providers. We define newer providers as those that participated in the at-home long term care business after 1999, one year before the launch of the public long-term care insurance system. The results report that older providers have a total score of 28.2, which is slightly lower than the counterpart score of newer providers (29.0). Nevertheless, newer providers have a significantly higher score in the principle component than do the older providers, and better scores in some sub-indexes, including “customers oriented policies,” “information service and complaint processing,” “protection of customer privacy,” “handling accidents and emergencies,” and “strategies toward hygiene and infectious disease.” Meanwhile, old providers have more qualified and experienced staffs.

In sum, we present the following findings: (1) services of public providers are worse
than those of the nonprofits and for-profits; (2) there are no significant differentials in quality of services between nonprofit and for-profit providers; (3) newer providers possibly offer better quality of services than the older ones. However, what we discussed above is based on simple descriptive statistics; it is hard to figure out which factors are responsible for the observed differentials. For instance $ ^{17} $ , newer providers could seemingly supply better services simply because they include for-profit providers.

Next, we compare the quality of services provided by different ownerships and operation length by estimating the following quality function.

$$ Q=\alpha_{_{0}}+\beta S+\sum_{i}\gamma_{_{i}}X_{_{i}}+\delta N+\mathsf{\rho}G+\mathsf{\eta}A+u $$

where Q is the dependent variable that is the scores of each sub-index, the total scores, or the principle component scores. The explanatory variables include a nonprofit dummy (N=1 if nonprofit provider, 0 otherwise), a public provider dummy (G=1 if public provider, 0 otherwise) and a new provider dummy (A=1 if new provider, 0 otherwise). In addition, we also include a number of staff (S) as a proxy of provider's scale and a vector of other attribute variables X such as branch dummy, region dummy $ ^{18} $ , subsidiary business dummy $ ^{19} $ that are likely to influence the quality of services. Equation (1) is estimated by OLS with a Huber-White Sandwich estimator of the variance, and hence the heteroscedasticity is adjusted to some extent.

Table 5 highlights the estimated coefficients on the major explanatory variables. First, public provider dummy (G) is significantly negative for both the total scores and the principle component scores. Second, four public provider dummies in sub-indexes are also significantly negative, with the exception of “qualification and experience of staff.” Hence, our estimates indicate that the quality of services of public providers is relatively lower. On the other hand, the nonprofit dummy (N) is positive and significant both for the total scores and specific indexes such as “provision of staff training” and “planning and transparency of the business,” though it is not statistically significant in the result of the principle components scores. Moreover, the newer provider dummy (A) is positive and significant for the

For-profit	Nonprofit	Public
Newer	71%	56%	29%
Older	29%	44%	71%
principle component scores but it is not significant for the total scores. However, the impact of the new provider dummy on individual sub-indexes is not predictable; we were thus not able to draw any clear conclusions.

### 5. Comparison of Efficiency

In this section, we estimate cost functions to assess the efficiency of various types of providers using three types of cost functions: Cobb-Douglas cost function (eq. 2), Log-quadratic cost function (eq. 3) and Translog cost function (eq. 4).

$$ \ln C=\alpha_{_{0}}+\sum_{_{i}}\alpha_{_{i}}\ln P_{_{i}}+\sum_{_{j}}\beta_{_{j}}\ln Y_{_{j}}+\sum_{_{k}}\gamma_{_{k}}X_{_{k}}+\delta Q+\eta N+\upmu G+\upvarphi A+u $$

$$ \begin{aligned}\ln C=\alpha_{_{0}}&+\sum_{i}\alpha_{_{i}}\ln P_{_{i}}+\sum_{i}\beta_{_{i}}(\ln P_{_{i}})^{2}+\sum_{j}\gamma_{_{j}}\ln Y_{_{j}}+\sum_{j}\delta_{_{j}}(\ln Y_{_{j}})^{2}\\&+\sum_{k}\uprho_{_{k}}X_{_{k}}+\upeta Q+\upmu N+\upvarphi G+\upvarnothing A+u\end{aligned} $$

$$ \begin{aligned}\ln C=\alpha_{0}&+\sum_{i}\alpha_{i}\ln P_{i}+\frac{1}{2}\sum_{i}\sum_{j}\beta_{ij}\ln P_{i}\ln P_{j}+\sum_{k}\gamma_{k}\ln Y_{k}+\frac{1}{2}\sum_{k}\sum_{l}\delta_{kl}\ln Y_{k}\ln Y_{l}\\&+\sum_{i}\sum_{k}\rho_{ik}\ln P_{i}\ln Y_{k}+\sum_{m}\eta X_{m}+\mu Q+\mu N+\varphi G+\varsigma A+u\end{aligned} $$

$$ where\sum_{i}\alpha_{i}=1,\quad\sum_{j}\beta_{ij}=\sum_{j}\rho_{jk}=0\quad and\quad\beta_{ij}=\beta_{ji} $$

The dependent variable is the log of total expenditure ( $ \ln C $ ) of individual providers. The explanatory variables include the log of output ( $ \ln Y $ ), which is embodied in the total hours of use of physical nursing care, house work assistance, and multiple services. The explanatory variables also include the log of factor prices ( $ \ln P $ ), which are represented by the employee’s wage (labor cost / overall employment hours), and rental costs (rental costs / overall employment hours) $ ^{20} $ . In addition, other attributes of providers (X) such as branch dummy, region dummy, subsidiary business dummy, and years of operation are also included. Moreover, some variables to measure the quality of services such as the total scores and the principle component scores), nonprofit dummy (N), public provider dummy (G), and new provider dummy (A) are also included. These cost functions are estimated by OLS with
robust standard error, as we have done in the previous section.

The results are reported in Table 6 (the total scores used as an index of quality of services) and Table 7 (the principle component scores used as an index of service quality). In all cases, the coefficients on output $ (lnY) $ and factor prices $ (lnP) $ are statistically significant and have the expected signs. On the other hand, the newer provider dummy is negative and statistically significant. In other words, newer providers are more efficient than older providers even after controlling for ownership effect and quality of services. This result is surprising because both newer and older providers must be able to afford fixed costs such as investing equipment, which should be cheaper or even zero for older providers $ ^{21} $ . Hence, the cost convergence hypothesis presented by Nanbu (2000) is not supported in the at-home long-term care market.

Furthermore, the variable to stand for quality of services is positive, which implies that a tradeoff relationship could exist between quality and cost. Moreover, the dummy on the short-stay business is negative, and this result suggests that the scope of business may have a premium effect. On the contrary, the coefficients on the at-home bathing business dummy, at-home nursing, and the rehabilitation business dummy are positive and significant. As a result, the scope of business seems to not necessarily promote management efficiency.

Unexpectedly, we observe that the public provider dummy is negative and significant in most cases in Tables 6 and 7. It is well known that public providers generally lack incentives to minimize costs; what we found is thus puzzling. We try to investigate further the differential of the cost structure between public and private providers. To figure out the inherent factors accounting for the public-private efficiency gap, Table 8 tabulates the share of “customers living nearby services centers,” proportion of part time helpers, rate of reliance on government subsidy $ ^{22} $ , and net utilization rate of long term care facilities respectively. The “customers living nearby service centers” refers to customers who can reach a service center within 30 minutes from their own homes or companies. Net utilization rate of long-term care facilities equals the share of hours to spend on total long-term care hours to total working hours of helpers. Table 8 reports that (1) public providers have a higher ratio of “customers living nearby service centers” (91.7 percent) than those of the for-profit providers (66.4 percent) and nonprofit providers (78.7 percent); (2) public providers have a higher rate of reliance on government subsidy and a lower proportion of part-time helpers than do the private providers; and (3) public providers have a higher net utilization rate of facilities than for-profit providers do.

Now, we add these variables to additional explanatory variables. Table 9 reports selected parts of the new estimation results. The major findings are summarized as follows.
First, the proportion of “customers living nearby service centers” significantly reduces costs and improves efficiency of management accordingly. Second, contrary to the prevailing perception, the effect of the higher proportion of part-time helpers is positive and significant $ ^{23} $ . We have two hypotheses to explain this surprising result. The first hypothesis proposes that the cost-saving effect by employing more part-timers is already reflected in the coefficients on wage variables $ (lw, lw^{2}) $ . The second hypothesis suggests that employment of full-time helpers rather than part-timers could be more effective in saving labor costs because full time helpers are more capable to deal with accidents and emergencies. Third, the effect of the net utilization rate of facilities is positive and significant. This could be attributed to the fact that providers with a higher utilization rate offer services for customers who live far from service centers more frequently, which costs more. Finally, the rate of reliance on government subsidy seems to have no significant effect on cost differentials. It should be noted that the coefficient on the public provider dummy is no longer statistically significant in the new estimations reported in Table 9. As a result, the positive and significant effect of the public provider dummy on efficiency observed in Tables 6 and 7 should be misleading. In other words, it is not public ownership but the factors we examined above potentially lead to a significant effect on efficiency. In particular, two factors contribute to the seemingly efficiency of public providers: customers living nearby service centers and the proportion of part-time helpers. Public providers on average have a higher proportion of customers living nearby service centers essentially because they have acquired such customers before the start of the public long-term care insurance system. In short, public providers are not necessarily more efficient than other providers; what seems to be their relative efficiency may be simply be the result of their acquired benefits as earlier participants in the market.

### 6. Conclusion

This paper evaluates the quality and efficiency of the at-home long-term care market in Japan, a market in which for-profit enterprises were allowed to enter after the introduction of the long-term care insurance in Spring of 2000. We take advantage of data from a unique self-conducted survey to compare the quality of services and efficiency of various types of providers with different ownerships, including for-profits, nonprofits and public-owned providers, and different lengths of operation.

We present two major findings. First, contrary to the prevailing perception, there are no statistically significant differentials in the quality of services between for-profit and nonprofit providers. Although the non-profits have the advantages in qualification and experience of staff and provision of professional training, the quality of services provided by
nonprofits is worse than that of the for-profit counterparts from a couple of perspectives. Second, our estimates on the cost function after controlling for quality of services demonstrate that the management of newer providers is more efficient than that of the older providers. We find that public-owned providers could not necessarily be more advantageous in efficiency than other providers under an “equal fitting” market and that there is no reason to believe that the for-profit providers were behaving opportunistically. In other words, our results indicate that the competition mechanism works effectively in the at-home long term care market and that free market policy contributes significantly toward improving the quality and the efficiency of the at-home long term care market in Japan.

Further research should address such topics as how to promote equal fitting between public and private providers in the long-term care market, and whether to allow complete and unfettered entry of for-profits into the institutional care market, which is still prohibited in Japan.
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