The effect of government financial investment on the development of traditional medicine: Evidence from public Traditional Chinese Medicine hospitals (PTHs) of Henan Province, China CURRENT STATUS:

Background Traditional medicine has long been used to address relatively common illness, and Chinese government has been continually increasing its investments on Public Traditional Chinese Medicine hospitals (PTHs) in recent years. Objectives This study aimed to assess the scales and structure of investments on PTHs in Henan Province, China, in order to analyze the contribution of Government Financial Investment (GFI) to the revenue growth of PTHs, as well as raising practical investment strategies for decision-makers. Methods This study was a panel data research, conducted in Henan Province, China. By collecting 143 PTHs’ operational data from 2005 to 2017, the authors computed data with Barrow Economic Growth Model (BEG), Stochastic Frontier Analysis (SFA) and Vector Autoregressive Model (VAR), respectively. Results The study observed that the contribution of GFI to PTHs’ revenue growth was positive (average MPG=2.84), which means the scale of GFI hadn’t reached an optimal level. The scales of GFI on Grade III, Grade II A, Grade II B PTHs need to be increased by -5.96%, 4.88% and 11.51% respectively in order to maximize the input-output efficiency. The third year after the investment year may be a more effective period for conducting an effect evaluation of GFI in Henan Province. Conclusions GFI on PTHs usually has a long-term impact on PTHs. Government needs to adjust its GFI policy to maximize the input-output efficiency.


Background
Globally, traditional medicine has long been used to address relatively common illness [1], and Traditional Chinese medicine (TCM) is widely accepted in China as a complement and alternative for medical treatment [2,3,4,5]. But the contemporary private sectors which are the primary traditional medicine providers, appear to seek immediate cash payments that have frequently led to considerable debt and asset sales by traditional medicine users [6]. Government investment intervention is necessary if public traditional medicine hospitals that serve as "essential providers of last resort" tend to survive and to continue fulfilling their mission [7].
In past ten years in China, total government financial investment (GFI) in public TCM hospitals (PTHs) had reached 192.83 billion yuan, and it's currently mainly used for the improvement of house, maintenance of large medical equipment and the salary of staff. Henan Province is an agricultural and emerging industrial province with over 100 million people, a very typical and representative province in central China. In 2017, the number of PTHs in Henan province reached 257, and the open beds were more than 63,100, accounting for seven percent of the national total. The GFI reached 1.60 billion yuan in PTHs, accounting for 6.08% of the total GFI of Henan Province [8,9].
Previous researches suggest that the support of GFI can affect the development of hospitals [10,11].
Woolhandler S et al. found that when experiencing severe cutbacks in funding for public medical facilities, the census at the county hospital, Highland General, has declined by 65 percent and the number of the outpatient visits have decreased by 50 percent [10]. Similarly, Shonick W observed both government health expenditures and hospital revenues had increased sharply, and the hospitals received government subsidies amounting to at least 60 percent of their total revenues [11].
Efficiency analysis in health care has attracted significant interest [12,13] due to escalating health care costs, especially in countries which public involvement in health care provision is high [14].
Increased efficiency is one way to bend the cost curve without compromising quality of care, which means it can get higher returns to fixed investment [15]. Compared with the least efficient hospitals, high-efficiency hospitals tended to have lower average costs, higher labor productivity, and higher profit margins [16]. Rhonda Kerr found that there are opportunities for public policies to support improved efficiency in public hospitals in Australia [17]. Roberto Colomb et al. found that ownership, specialization and size are determinants of all types of efficiency [18]. Chen et al. found that the changes introduced into the health care sector in China during the last thirty years have improved the cost efficiency of the whole system, especially after the public subsidies and medical insurance reforms [19].
This study analyzed the operational status of 143 PTHs in Henan Province from 2005 to 2017 by using the Barrow Economic Growth Model (BEG) to explore the scales and structure of PTHs' GFI based on promoting hospital revenue growth. The current input-output efficiency-maximizing investment strategy of GFI to PTHs was tested by using the stochastic frontier analysis (SFA). At the same time, the most appropriate evaluation period was also estimated based on the long-time impact trend of GFI on PTH's revenue growth by using Vector Autoregressive Model (VAR).

Classification of public TCM institutions
Public TCM institutions with less than 100 beds in China generally refer to community health service centers as primary health care institutions. Institutions with 100-500 beds are set as Grade II hospitals while Grade III hospitals refer institutions with more than 500 beds (5)

Variable index selection
Considering the basic characteristics of PTHs and the effects of avoiding strong linear relationship between input indicators, the variables were selected based on importance and availability [21].
According to expert advice and requirements for the selected analysis model, this study selected the accepted indicators from five aspects of revenue, assets, technology, financial investment, personnel:

The scales and structure analysis method -Barrow Economic Growth Model (BEG)
The BEG was derived from the theory of Endogenous Growth, which introduces government consumption based on the Cobb-Douglas production function [22], and was often used to explain economic growth of increasing economic returns and technical externalizations [23]. The specific form of the model can be expressed as: In this study, the input-output elasticity of GFI to hospital revenue growth is δ, and the marginal output of GFI is Marginal Product of Government (MPG). Following equation was used to get the marginal output and elasticity.

Input-output efficiency analysis method-Stochastic Frontier Analysis (SFA)
The SFA was first proposed by Aigner, Lovell and Schmidt (1977) and was widely used to estimate production functions or cost functions [14]. This study used SFA theory which was proposed by Fried based on traditional Data Envelopment Analysis model to explore the influence of environment and random factors on the evaluation object.
First, an input-oriented BCC model (a Barrow analysis model for variable-scale returns proposed by Banker, Charnes, and Cooper in 1984) [18] was established to measure the overall Input-output efficiency of PTHs under the influence of environmental and stochastic factors [24,25]. Secondly, SFA model was used to decompose the results of the BCC model, using slack variable S ni (refers to the difference between the actual production process and the highest efficiency) of each input including Fixed Assets, Technologies and Clinical Workers as dependent variable and environmental factor variable GFI as independent variable, an SFA regression equation was established for each Sni [26,27,28,29,30]: In the formula:

Evaluation period selection method -Vector Autoregressive Model (VAR)
Based on the results of BEG, the VAR was used to estimate the most appropriate evaluation period of GFI by analyzing the long-term impact trend of Revenue and GFI on others or themselves. By building an impulse response function, the random error terms imposed on an endogenous variable GFI and the impact on PTHs' GFI and Revenue were analyzed, characterizing the response from the variable to a certain error change.

Analysis tool
The initial data was collected and processed by using Excel 2016. DEA and SFA analysis were performed by using DEAP2.1 and FORNT41 software, respectively. BEG and VAR were performed by using Eviews6.0 software. P<0.05 was considered statistically significant.

Operational Information of PTHs
Between 2005 and 2017, the Clinical Workers of total 143 PTHs in Henan Province increased from 32058 to 64669, with an average annual growth rate of 6.02%; the Fixed Assets increased with an average annual growth rate of 15.61%; the Revenue increased from 1.83 billion yuan to 16.37 billion yuan, with an average annual growth rate of 20.02%; while the GFI increased from 0.14 billion yuan to 1.61 billion yuan, with an average annual growth rate of 22.97% (Table 2).

Indicator selection and feasibility test
The Augmented Dickey-Fuller test method (ADF) was selected to perform the unit root test of the time series of each variable in the model. During the test process, variables without time trend term and constant term were selected for testing. Table 4 shows that the ADF value of the second-order difference result (D2) that the statistical value of each variable was smaller than critical value of the critical level (0.05), indicating that the second-order difference sequences of the four variables were stable, that was, all four variables were second-order single-integer sequences. Therefore, the model could be co-integrated.
The main purpose of the co-integration test was to judge the rationality and correctness of model's set, and thus effectively avoided the pseudo-regression of the model. Since the model contains four variables, the Johansen test method was used for co-integration test. In the test form of the model, the test was selected in the form of both trend and intercept terms. It can be seen from Table 5 that at the 5% significance level, both the tracking test and the λ-Max statistic tests display that there were three co-integration vectors between four variables, that is, there was a co-integration relationship between variables. This showed that the linear combination of four variables had longterm stability, and the model can be further analyzed.

Scales and structure analysis
In this study, the regression analysis was performed on the model by Ordinary Least Squares method.
The regression results were as follows: Since the MPG value was greater than 1, GFI scales of PTHs hadn't yet reached the optimal level according to the "Baro Law". In addition, from MPG>0, it can be seen that the GFI had a positive economic impact on promoting the growth of PTHs' revenue.

Input-output efficiency analysis
The SFA model was used to analyze the impact of GIF on the input-output efficiency of input factors: Fixed Assets, Clinical Workers and Technologies. In three types of PTHs, management factor () was the main factor affecting Fixed Assets and Clinical Workers (γ≈1, P<0.05). γ values in the input relaxation equations of Technologies in Grade III PTHs were approximately equal to 1 but not pass the t-test, indicating that difference between the management factor and the random error() had little impact on the efficiency, but it had an impact on the direction of efficiency.
Further analysis found increasing the GFI will reduce the efficiency of Grade III PTHs (β>0, P<0.05), but will improve the efficiency of Grade II B PTHs (β<0, P<0.05). In Grade II A PTHs, increasing the GFI will reduce the waste of Fixed Assets and Clinical Workers (β<0, P<0.05), but it will intensify the waste of Technologies (β>0, P<0.05). See Table 6 and Table 7 for details.

The efficiency-maximizing investment strategy of GFI
Based on the results of  (Table 8).
This study created a VAR model with four variables including: Revenue, Fixed Assets, Clinical Workers and GFI of PTHs, and analyzed the impulse response function based on this. The specific results show that the change of GFIs' scale has a cyclical decrease trend in its long-term impact, and its overall cumulative impact was almost zero. Generally speaking, in China, the current changes in GFI will not affect the scale of future GFI, which makes it simple and feasible to adjust the current GFI according to the efficiency-maximizing investment strategy ( Figure 1).

The most appropriate evaluation period of GFI
The study also analyzed the most appropriate evaluation period based on the long-time impact trend of GFI on PTH's revenue growth by using VAR. As shown in Figure 2, firstly, GIF had a positive impact on PTHs' revenue, and the impact will gradually decline after the third period (one year as a period in PTHs, generally). Secondly, this positive economic impact began to zero after the 17th period. In short, the GFI had a significant role in promoting the revenue growth of PTHs, and the third year after the investment year may be a more effective period for conducting an effect evaluation of GFI in Henan Province.

Discussions
This study found that the MPG value of GFI to PTHs' revenue was greater than 1 between 2005 and 2017, indicating the optimal GFI scales hasn't yet been reached. Schlenker T found that the traditional medical providers were included in the Medicaid Waivers to help achieving the "Triple Aim" goals in the United States Texas [31]. It's suggested that the government should carry out sustainable strategic design when designing and arranging hospital investment, and allocate sufficient resources which will help to effectively address patient needs, technological changes and increased acumen [32,33]. The growth of fixed assets also has a positive impact on PTHs' revenue growth [16]. Simultaneously, the impact of GFI on PTHs' revenue have been steadily weakening, lasting about 17 th period, and the overall impact was positive. If the impact on the current GFI is to be evaluated, the impact evaluation within a single investment period (one year in China) is suitable for the next third period to obtain the maximum value. And the overall impact evaluation should be conducted after the 17 th period. Governments and hospitals should carefully formulate GFI impact evaluation plan and select appropriate evaluation cycles to accurately assess the effectiveness of GFI.
This study attempted to estimate the current state and the efficiency-maximizing investment strategy of PTH's GFI from one of the most populated provinces of China. We recommend future studies to stress on the classification criteria of hospital categories based on the size of the beds rather than the official classification criteria. And the time span of data collection can be further extended to improve the accuracy and credibility of the analysis results. Furthermore, there is also room for optimization in the selection of indicators.

Conclusions
The GFI for TCM has experienced a period of rapid growth of the past 12 years, but there is no strong evidence that its actual impact is consistent with the expected target. Although our analysis was limited to PTHs, it provided important evidence that GFI can impact the input-output efficiency of traditional hospitals and promote its development, and governments need to make evaluation period decision carefully. Our findings can provide new evidence and decision-making basis for governments to develop policies that promote the development of traditional medicine and increase the supply of traditional medicine services.       Grade II B + + + a "+" means that the increase in GFI will increase the efficiency of input utilization; b "-" means that the increase in GFI will increase the waste of input input required to achieve maximum efficiency to the current amount; c Eliminate the ratio of inputs affected by GFI to the current amount