Silviase/jgov_v1.4_full_ann huggingface dataset:
Table 6: Decomposition analysis of nonprofit sectoral wage differentials by qualifications

logarithmic value	real value
I. Helper
(1) The predicted wage of not-for-profit facility	9.033	9650.694
(2) The predicted wage of for-profit facility	9.014	8644.670
(3) The predicted wage differential due to attributes	0.027	-33.919
(4) The predicted wage differential due to valuation	-0.009	1039.943
(5) Total predicted wage differential	0.018	1006.024
between for-profit and nonprofit facilities
II. Care manager
(1) The predicted wage of not-for-profit facility	9.422	12889.103
(2) The predicted wage of for-profit facility	9.439	13115.161
(3) The predicted wage differential due to attributes	-0.012	-254.176
(4) The predicted wage differential due to valuation	-0.006	28.118
(5) Total predicted wage differential	-0.017	-226.058
between for-profit and nonprofit facilities
III. Staff nurse
(1) The predicted wage of not-for-profit facility	9.269	11031.572
(2) The predicted wage of for-profit facility	9.230	10147.429
(3) The predicted wage differential due to attributes	-0.010	-181.968
(4) The predicted wage differential due to valuation	0.049	1066.111
(5) Total predicted wage differential	0.039	884.143
between for-profit and nonprofit facilities
IV. Service manager
(1) The predicted wage of not-for-profit facility	9.347	12204.726
(2) The predicted wage of for-profit facility	9.164	10043.299
(3) The predicted wage differential due to attributes	0.122	1959.354
(4) The predicted wage differential due to valuation	0.061	202.073
(5) Total predicted wage differential	0.183	2161.427
between for-profit and nonprofit facilities

Note:

(1) $ \sum\left(\mathbf{X}_{\mathrm{i}}^{2}\delta^{2}+\lambda_{\mathrm{i}}^{2}\theta^{2}\right) $

(2) $ \sum\left(X_{i}^{1}\delta^{1}+\lambda_{i}^{1}\theta^{1}\right) $

(3) $ \left\{\sum\delta^{2}\left(X_{i}^{2}-X_{i}^{1}\right)+\sum\theta^{2}\left(\lambda_{i}^{2}-\lambda_{i}^{1}\right)\right\} $

(4) $ \left\{\sum\left(\delta^{2}-\delta^{1}\right)X_{i}^{1}+\sum\left(\theta^{2}-\theta^{1}\right)\lambda_{i}^{1}\right\} $

(5) (1)-(2)=(3)+(4)
Table 7: Decomposition analysis of nonprofit sectoral wage differentials by factors and qualifications

Due to differentials of attributes b/	Due to differentials of valuations c/	Total wage differentials d/
I. Helper
Male	-12.922	-88.354	-101.275
Age	-28.326	431.128	402.802
Full-time	437.328	302.445	739.773
Part-time, but regular	14.961	-17.913	-2.952
Service contents a/	4.951	14.037	18.988
Size	-37.025	39.608	2.583
Insurance	-29.101	634.702	605.601
Hiring status	40.898	-493.436	-452.538
Professional training	41.592	150.953	192.545
Various support	-7.001	-852.360	-859.361
Other	-55.488	-321.831	-377.319
Prefecture	-283.686	-2171.172	-2454.858
Selectivity	-120.100	-30.030	-150.129
Intercept	0.000	3442.165	3442.165
Total	-33.919	1039.943	1006.024	f/
-2402.222	e/	1156.153
II. Care manager
Male	83.721	-201.170	-117.450
Age	-32.265	1214.937	1182.672
Full-time	202.123	-1609.432	-1407.309
Part-time, but regular	-14.953	-70.414	-85.367
Service contents a/	311.850	2432.948	2744.798
Size	55.136	-121.052	-65.916
Insurance	10.540	-2731.616	-2721.075
Hiring status	27.925	1531.691	1559.616
Professional training	-207.962	-8543.639	-8751.601
Various support	-61.457	-319.258	-380.715
Other	-310.143	1730.144	1420.001
Prefecture	-657.217	8344.325	7687.109
Selectivity	338.526	-17.126	321.400
Intercept	0.000	-1612.220	-1612.220
Total	-254.176	28.118	-226.058	f/
1640.338	e/	-547.458
III. Staff nurse
Male	9.813	20.167	29.979
Age	148.775	991.136	1139.911
Full-time	1320.245	1487.542	2807.787
Part-time, but regular	-156.815	132.287	-24.527
Service contents a/	-90.106	-1285.368	-1375.474
Size	-106.504	-427.493	-533.997
Insurance	-55.860	-110.181	-166.040
Hiring status	33.507	-841.521	-808.014
Professional training	33.305	1443.427	1476.732
Various support	23.563	-171.578	-148.014
Other	-424.201	-8407.284	-8831.486
Prefecture	-717.861	-906.522	-1624.383
Selectivity	-199.829	31.183	-168.645
Intercept	0.000	9110.315	9110.315
Total	-181.968	1066.111	884.143	f/
-8044.204	e/	1052.788
IV. Service manager
Male	608.771	336.670	945.441
Age	325.144	1523.542	1848.686
Full-time	568.890	64.179	633.069
Part-time, but regular	-	-	-
Service contents a/	794.775	6249.839	7044.614
Size	3.882	-1688.030	-1684.149
Insurance	334.049	-891.315	-557.266
Hiring status	-22.023	939.073	917.051
Professional training	37.055	-2500.949	-2463.894
Various support	63.350	-286.133	-222.783
Other	-303.173	2540.824	2237.651
Prefecture	127.053	-1620.627	-1493.574
Selectivity	-578.418	323.230	-255.188
Intercept	0.000	-4788.230	-4788.230
Total	1959.354	202.073	2161.427
4990.303	e/	2416.615	f/

a/ The sectoral allocation regressions include dummies for service contents as explanatory variables, rather than qualifications.

Note:

$$ \left\{\sum\delta^{2}\left(\mathrm{X}_{\mathrm{i}}^{2}-\mathrm{X}_{\mathrm{i}}^{1}\right)+\sum\theta^{2}\left(\lambda_{\mathrm{i}}^{2}-\lambda_{\mathrm{i}}^{1}\right)\right\} $$

$$ \underset{\mathrm{c}/}{\mathrm{ 饣 }}\quad\left\{\sum\left(\delta^{2}-\delta^{1}\right)\mathbb{X}_{\mathrm{i}}^{1}+\sum\left(\theta^{2}-\theta^{1}\right)\hat{\lambda}_{\mathrm{i}}^{1}\right\} $$

$$ \begin{aligned}\mathrm{_{d/}}\quad\sum\left(\mathrm{X}^{2}_{\mathrm{i}}\delta^{2}+\lambda^{2}_{\mathrm{i}}\theta^{2}\right)-\sum\left(\mathrm{X}^{1}_{\mathrm{i}}\delta^{1}+\lambda^{1}_{\mathrm{i}}\theta^{1}\right)\end{aligned} $$

e/ Net of the intercept

f/ Net of selectivity
# Quality Differentials in For-Profit versus Nonprofit Long-Term Care: Evidence from Japanese micro-level data

## Abstract

This paper aims to evaluate the quality differentials between nonprofit sectors and profit sectors in Japan’s long-term care industry by making use of the Jigyosho Ni Okeru Kaigo Rodo Jittai Chosa (Statistical Survey on Nursing Home Employees) conducted by the Care Worker Support Center Foundation (Kaigo Roudou Antei Center) in September 2000. We investigate the difference in the quality of services represented by workers’ skills (share of qualified workers and provision of professional training), worker’s status (share of regular workers) and stable service supply (job stability and future labor demand) by type of management (for-profit versus nonprofit), which are associated with quality of services. We use the treatment effect model to assess the quality differentials after controlling for various characteristics of each affiliate as well as adjusting for “treatment” effects such as wage rates and non-wage benefits to address the possible selection bias. 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.
### 1. Introduction

Quality control of services is surely one of the most important issues in the long-term care market. A traditional solution to address this problem is to allow only nonprofit enterprises under non-distributional constraints to operate in the market to prevent opportunistic behavior among profit companies, though the effectiveness of the restriction to the market is not deeply investigated.

The introduction of public long-term care insurance in Japan in the spring of 2000, however, poses a major challenge to maintain the quality of services. Under the new insurance system to provide a stable service supply for all insured, for-profit enterprises are also allowed to enter into the home help services market. The empirical questions such as whether the quality of services provided by profit enterprises is higher or lower than that by nonprofit firms or whether the entry of for-profit companies contributed to raise the quality in the overall market are critically important to design the long-term care market. If a gap in quality of services is observed between for-profit and not-for-profit sectors—that is, if for-profit enterprises offer less quality—consumers might tend to use services provided by nonprofit companies, which makes the market less competitive under the same output price and does not support for the entry of for-profit companies into the institutional care market, which is currently prohibited.

Nonetheless, it is not an easy challenge to evaluate the quality of services provided in the home help services since there is no consensus on which variable is an appropriate proxy to measure quality. As discussed below, some researchers use disclosure to family or happiness of family members. Others use staff intensity or worker experience. Another popular way to evaluate the quality of services in the long-term care market is to calculate the total scores of “indicators” assigned to detailed items on various characteristics of each facility. Although there are no absolute variables to measure the quality of services, it is widely accepted that quality of workers measured by the length of experience, education, skills, qualifications, and job stability of employees, all of which are plausibly associated with worker productivity. This approach could be justified because
the nursing home industry is labor intensive.

This paper aims to evaluate the quality difference in services between for-profit and nonprofit enterprises in Japan's long-term care industry, focusing on the different quality of workers. We do not use a single or a couple of variables a priori to assess differences in quality, but rather the comprehensive characteristics of workers in each facility. For this purpose, we take advantage of the “Statistical Survey on Nursing Home Employees” conducted by the Care Worker Support Center Foundation (Kaigo Roudou Antei Center), which provides rich and overall characteristics of individual employees. More concretely, we investigate the difference in workers' skills (e.g. share of qualified workers, professional training), worker's status (e.g. share of regular workers) and stable service supply (e.g. job stability, labor demand) by type of management (for-profit versus nonprofit), which are associated with the quality of services. We use the treatment effect model to control for various characteristics of each affiliate as well as adjusting the “treatment” effects such as wage rates and non-wage benefits to address the possible selection bias.

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.

This paper is organized as follows. The next section briefly reviews some theory and previous research on the quality differentials between for-profit and nonprofit. Section 3 presents an empirical analysis. Section 4 describes the data set we use in this study, and Section 5 provides specification and discusses the results. The last section concludes.

### 2. Previous Research

There have been many studies on quality of services in for-profit and nonprofit
enterprises in the United States. Most of these studies start from the discussion of the “contract failure” theory motivated by Hansmann (1980). He provides the general theory of nonprofit enterprises. First, he emphasizes asymmetry of information between providers and consumers, especially in the market where consumers are unable to judge the quality of services provided. Second, he argues that, due to “the non-distributional constraints” under which the nonprofit firms are prohibited from distributing net earnings, the nonprofit managers are keen on raising the quality of services, and the kind of opportunistic behavior often observed in the profit sector could be avoided. As a result, consumers tend to use services provided by not-for-profit enterprises rather than for-profit counterparts.

Many researchers have assessed quantitatively differentials in the quality between nonprofit and for-profit sectors, especially focusing on nursing homes in the United States. However, the findings are inconclusive $ ^{1} $ . Weisbrod (1988), Ullmann and Holtman (1985), Cohen and Spector (1996), Holtman and Idson (1991), and Gertler (1989) find better quality of services in the nonprofit sector, while Gertler (1984) and Suzuki (2002), looking at Japanese home help services, observe better quality in for-profit, not in nonprofit. Other studies such as Nyman (1988) and O'Brien (1983) report no significant differences between for-profit and nonprofit sectors.

This inconclusiveness could be partly attributed to the lack of consensus on which variable is an appropriate proxy to measure the quality. A proxy to measure the “quality” depends on each study. Some researchers use disclosure to family, use of sedatives, happiness of family members (Weisbrod (1988)) and other use staff intensity (Cohen and Spector (1996)) or workers’ experience (Holtman and Idson (1993)). Another well-known way to evaluate the quality of services in the long-term care market is to calculate total scores of “indicators” assigned detailed items on various characteristics of each facility and employees. This approach also cannot escape from being criticized for using insufficient measure to address the quality of services because the evaluation items are still subjectively chosen and collecting subjective indicators does not lead to objective evaluation.
Although there are no absolute variables to measure the quality of services, it is widely accepted that quality of workers is measured by the length of experience, education, skills, qualifications and job stability of employees, and is probably related to workers' productivity, which may affect the quality of care in the labor intensive market. This study takes advantage of a rich data set on Japanese nursing homes and employees to evaluate the service quality, focusing on workers' quality.

We have three major categories of variables to use in evaluating the quality of employees. The first category is workers' skills, such as the share of qualified or professional workers and professional training inside and outside the facility. The second category is workers' status proxied by the ratio of regular workers. The third category is job stability in each facility and demand for workers, both of which support stable service supply. We investigate the difference in the quality of workers measured by these variables by type of management (for-profit versus not-for-profit), after controlling various characteristics of each affiliate. Moreover, as discussed shortly after, we perform the treatment effect model to adjust the "treatment" effects such as whether wage rates are greater than average and whether either non-wage benefits including various insurance or welfare are provided to employees specifically by each facility.

There is little research on the nonprofit quality differentials in Japan except Suzuki (2002), which is partly explained by the unavailability of micro-level data on wages and employee characteristics in Japanese nursing homes. To our knowledge, this study is the first attempt in Japan to assess quantitatively the quality differentials between for-profit and nonprofit sectors, focusing on worker-level micro-level data.

### 3. Empirical Specification and Econometric Issues

This study utilizes the treatment effect model (Barnow, Cain and Goldberger (1981)) to assess the difference in the quality of services between the nonprofit and profit sectors. When evaluating sectoral differentials on the quality of services, our major empirical challenge is to obtain consistent estimates controlling for misspecification bias attributable
to unobserved or omitted variables and sample selection bias caused by the employment nonrandom allocation process. Some might be tempted to use simple linear estimation to regress various proxies to stand in for service quality on a sector allocation dummy variable as well as other control variables. But it is possible that this approach will yield unreliable biased estimates, owing to the endogeneity problem such that the sector affiliation of workers are influenced by unobservable various characteristics that also would affect worker preferences. Namely, the nature of characteristics of workers who decide to work in either a firm with a specific treatment or without it is highly alleged to be systematically different and managers in these two kinds of firms tend to hire workers with specific types of characteristics. For example, some workers place higher value on wage rates; others are more attracted to non-wage benefits such as insurance and welfare.

Suppose that the $ i^{th} $ worker chooses to work in a firm with a specific characteristic or treatment to maximize utility defined as:

$$ \mathbf{U}_{\mathrm{i}}^{\mathrm{j}}=\omega\mathbf{T}_{\mathrm{i}}^{\mathrm{j}}+\gamma^{\mathrm{j}\prime}\mathbf{V}_{\mathrm{i}}+\mathbf{w}_{\mathrm{i}}^{\mathrm{j}}\quad\mathrm{~w h e r e~}\mathrm{j}=1,0 $$

$ T_{i}^{1}(=I) $ and $ T_{i}^{0}(=0) $ are dichotomous indicators whether or not the firm the $ i^{th} $ worker chooses treats employees in a specific way and thus, the superscript I stands for the presence of a treatment and O stands for no treatment. $ U_{i}^{1} $ and $ U_{i}^{0} $ indicate the level of utility if the $ i^{th} $ worker chooses to be self-selected into the firm with a treatment or without it. $ V_{i} $ represents various measurable characteristics attributed to both individual workers and facilities such as human capital indicators, for-profit or nonprofit facility, the number of employees, and demand for labor policy in each facility. $ w_{i}^{1} $ and $ w_{i}^{0} $ include unobserved factors determining the $ i^{th} $ worker's utility. And $ \omega $ , $ \gamma^{1} $ , and $ \gamma^{0} $ represent parameters. The $ i^{th} $ worker is expected to be self-allocated in the sector where he or she achieves greater utility, along with the selection rule as follows (Goddeeris (1988)):
$$ \begin{aligned}&Z_{\mathrm{i}}^{1^{*}}=\mathrm{U}_{\mathrm{i}}^{1}-\mathrm{U}_{\mathrm{i}}^{0}=\boldsymbol{\omega}(\mathrm{T}_{\mathrm{i}}^{1}-\mathrm{T}_{\mathrm{i}}^{0})+(\boldsymbol{\gamma}^{1}-\boldsymbol{\gamma}^{0})^{\prime}\mathrm{Y}_{\mathrm{i}}+\mathrm{w}_{\mathrm{i}}^{1}-\mathrm{w}_{\mathrm{i}}^{0}=\boldsymbol{\omega}+\boldsymbol{\gamma}^{\prime}\mathrm{V}_{\mathrm{i}}+\mathrm{w}_{\mathrm{i}}\\ &\\ &\mathrm{Z}_{\mathrm{i}}^{1}=1\ \mathrm{if}\ \mathrm{Z}_{\mathrm{i}}^{1^{*}}\geq0\ \left(-\mathrm{w}_{\mathrm{i}}\leq\boldsymbol{\gamma}^{\prime}\mathrm{V}_{\mathrm{i}}\right)\mathrm{or}\ \mathrm{U}_{\mathrm{i}}^{1}\geq\mathrm{U}_{\mathrm{i}}^{0}\\ &\\ &\mathrm{Z}_{\mathrm{i}}^{1}=0\ \mathrm{if}\ \mathrm{Z}_{\mathrm{i}}^{1^{*}}<0\ \left(-\mathrm{w}_{\mathrm{i}}>\boldsymbol{\gamma}^{\prime}\mathrm{V}_{\mathrm{i}}\right)\mathrm{or}\ \mathrm{U}_{\mathrm{i}}^{1}<\mathrm{U}_{\mathrm{i}}^{0}\qquad\mathrm{where}\ \mathrm{l}=1,2,3\\ \end{aligned} $$

$ Z_{i}^{l^{*}} $ refers to the $ i^{th} $ worker’s unobserved propensity to choose a treatment (the binomial “treatment” effect), which indicates whether or not the $ i^{th} $ worker’s wage rate is greater than the average (superscript=1); whether non-wage benefits such as various insurances are provided to employees (superscript=2); and whether employee welfare is concerned in the facility (superscript=3). We define $ Z_{i}^{l}=1 $ which implies the $ i^{th} $ worker actually chooses a treatment if $ Z_{i}^{l^{*}}\geq0 $ and $ Z_{i}^{l}=0 $ which indicates that the $ i^{th} $ worker does not choose a treatment, otherwise.

The treatment effect model uses the full maximum likelihood method to estimate simultaneously the treatment partly based on binomial choice variables ((2) in the above specification) and the regression of service quality variables, continuous or binomial, on independent variables including the treatment part ((3) in the following specification). Unlike Heckman's two-stage approach, the treatment effect model will evaluate the total gross effects of sectoral differences in quality of care, including all workers in the same regression analysis. Also, this model will allow us to observe directly the impact of choosing to work in facilities with specific characteristics as treatment factors on the quality of services, using these factors themselves as explanatory indicators.

$$ \mathbf{Y}_{\mathrm{i}}=\mathbf{a}^{\prime}\mathbf{X}_{\mathrm{i}}+\boldsymbol{\beta}^{\prime}\mathbf{Z}_{\mathrm{i}}^{\mathrm{l}}+\boldsymbol{\delta}^{\prime}\mathbf{D}+\boldsymbol{\varepsilon}_{i} $$

where

$$ \boldsymbol{\varepsilon}_{\mathrm{i}},\mathrm{\boldmath~w}_{\mathrm{i}}\sim\mathrm{N}\big(0,0,\delta_{\varepsilon}^{2},\delta_{\mathrm{w}}^{2},\boldsymbol{\uprho}\big) $$

This is an application of a classical regression method, assuming that $ \varepsilon_{i} $ in equation (3) and $ w_{i} $ in equation (2) are bivariate normal distribution with mean zero and
correlation, $ \rho $ . Estimating this model based on ordinary least squares (OLS) will be biased unless the sample is collected randomly from the binomial treatment part (2). To remedy this bias, then, we estimate both regressions simultaneously, assuming the bivariate distribution between the observed binomial treatment effect and dependent variables to measure the quality of services. As discussed in the second section, we have three major categories of the dependent variable $ Y_{i} $ ; workers' skills (the share of qualified or professional workers and professional training inside and outside the facility); workers' status (the ratio of regular workers); and job stability in each facility and demand for workers indicating a stable supply of services. D refers to a dummy variable to indicate for-profit status. $ X_{i} $ includes factors to determine the $ i^{th} $ workers' skills, qualifications, and job stability. All the factors that determine workers' choices in selecting a treatment do not necessarily influence worker quality. In other words, $ Y_{i} $ and $ V_{i} $ are not essentially the same, but we will control for both equations using the same explanatory variables, due to the availability of the data set.

### 4. Data

The empirical analysis of this study uses the Jigyosho Ni Okeru Kaigo Rodo Jittai Chosa (Statistical Survey on Nursing Home Employees), conducted by the Care Worker Support Center Foundation (Kaigo Roudou Antei Center) in September 2000. This survey is not based on a random sample of the population, but, rather, contains data on various characteristics of as many as 39,261 employees in 1,347 nursing facilities all over Japan, which are self-enumerated by the nursing home's administrator. In this study, we choose a sample of 24,967 employees from nursing homes, due to the lack of information on ownership and various indicators used in our empirical analysis. There are seven major types of ownership in the industry: for-profit and six not-for-profit facilities; social welfare cooperation, medical cooperation, authorized Nonprofit Organization (NPO), Co-op, agricultural cooperative and other charitable corporation. In this survey, 2,823 (11.3 percent) and 22,144 (88.7 percent) employees are working at proprietary and nonprofit