External Financing Efficiency of Rural Revitalization Listed Companies in China—Based on Two-Stage DEA and Grey Relational Analysis

Abstract

Rural revitalization is an important strategy to promote sustainable development of rural areas in China. Rural revitalization listed companies play an important role in implementing the rural revitalization strategy and developing the agricultural industry. However, the financing problem has always been a bottleneck problem with Chinese listed companies. This study used a two-stage DEA (data envelopment analysis) method to evaluate the funds raising efficiency, funds using efficiency, and overall financing efficiency of 34 rural revitalization listed companies in 2018. The results show that the financing efficiencies of 34 sample companies were low, only six companies have overall efficient financing, and there was much room for improvement. Financing efficiency varied greatly depending on the nature of the company, the industry, and the listing board. The efficiency of funds using of state-owned enterprises was much lower than that of private companies. The average efficiency value of agricultural company funds raising was lower than that of manufacturing. The efficiency of small and middle-size enterprises (SMEs) was lower than that of main board companies, but the growth enterprise market (GEM) companies achieved higher efficiency in the funds using. Further, by using the grey relational analysis (GRA) method, we found that the key factors affecting the financing efficiency of sample companies included capital structure, debt-paying ability, governance structure, company age, and operating ability. To improve financing efficiency, the companies should not only optimize their capital structure and governance structure but also improve their management and innovation capabilities. At the same time, the state also needs to give different policies support according to the characteristics of the companies.

1. Introduction

China is a big agricultural country, and agriculture is the foundation of the national economy. On 18 October 2017, the report on the 19th National Congress pointed out that it is necessary to always solve the problem of “agriculture, rural areas, and farmers” as the top priority of the work of the whole party, and implement the rural revitalization strategy [1]. The specific development strategies are: (1) by 2020, important progress should be made in rural revitalization, and the institutional framework and policy system should be basically formed; (2) by 2035, rural revitalization will achieve decisive progress, and agricultural modernization will be basically completed; (3) by 2050, rural revitalization will be fully realized, agriculture will be strong, and farmers will be rich [2]. Under this background, the concept of rural revitalization came into being, and rural revitalization listed companies have become the focus of the capital market.

Implementing the strategy of rural revitalization is an important foundation for China to build a modern economic system. The rural revitalization listed companies is an important force in the implementation of the rural revitalization strategy, which is conducive to promoting the shift from agricultural production guidance to quality improvement, increasing the quality of the agricultural supply-side system, enhancing China’s agricultural innovation and competitiveness, and promoting China’s agricultural modernization. Most listed companies in China have problems with low financing efficiency, which is the bottleneck problem with rural revitalization listed companies and restricts the sustainable development of the companies. Financial constraints are often considered to be barriers that prevent enterprises from taking advantage of productive investment opportunities and thus hinder their growth [3]. At present, most Chinese scholars use traditional data envelopment analysis (DEA) to measure financing efficiency. Compared with traditional DEA, the use of two-stage DEA is a new research idea. This paper attempts to use the two-stage DEA method to measure the external financing efficiency of listed companies in the rural revitalization field and uses the gray relational analysis (GRA) to analyze the key factors affecting the financing efficiency. The purpose of this paper is to improve the efficiency of financing by adopting internal management measures and external policy measures, so as to broaden the financing channels of companies, reduce the financing risks and costs, to promote the companies’ long-term sustainable development, and to serve the rural revitalization strategy better.

The remainder of this paper unfolds as follows. Section 2 is a literature review, which summarizes the previous theoretical and empirical research on financing efficiency, and summarizes the main subjects related to the financing efficiency of the previous study. The research objects and method of financing efficiency in this paper are different from previous researches, and its novelty is obvious. In Section 3, we introduce the empirical research method adopted in this paper, expounds the advantages and disadvantages of the traditional DEA model, and explains the reasons for selecting the two-stage DEA model. Then presents the GRA method and its calculation process. In Section 4, we present the data source and index system and discuss the selection of input factors, output factors, and decision units. Section 5 is data processing and empirical analysis. Section 6 is the conclusion, based on the research conclusions, puts forward the policy recommendations, and points out the shortcomings of this paper.

2. Literature Review

Foreign scholars have studied the financing theory and efficiency theory since the 1950s. For example, Modigliani and Miller proposed the MM theory in 1958. The theory proposed that there was no correlation between corporate financing structure and its market value without considering income taxes [4]. Malkiel and Fama (1970) put forward the effective market theory proposed [5], Jensen and Meckling (1976) proposed principal-agent theory [6], Myers and Majiluf (1984) put forward the famous “pecking-order hypothesis”, they believed that companies prioritized when financing in order: Internal financing, debt financing, and equity financing [7]. However, in early Western literature, it was difficult to find the word “financing efficiency”. The main reasons were that Western countries had sophisticated property rights systems, and the company’s financing efficiency was high. Based on theoretical study, some foreign scholars studied the financing efficiency from the empirical aspect. Jain and Kini (1994) selected 682 American enterprises that conducted IPOs (initial public offerings) from 1979 to 1988 as research samples and found that their business performance would decline significantly after the IPO, that was to say, enterprises generally had the problem of low equity financing efficiency [8]. Loughran and Ritter (1995) conducted a study of American companies that issued additional shares from 1970 to 1990 and found that the average yield of companies that issued additional shares in five years after issuance was much lower than that of companies that did not issue additional shares in the same period [9]. Almeida and Wolfenzon (2005) constructed the financing equilibrium model and considered that the external financing demand of enterprises and the supportive attitude of investors were important factors to determine the effectiveness of enterprise resource allocation [10]. Polak et al. (2011) analyzed the financing efficiency from the perspective of agency costs affecting the efficiency of the use of funds and concluded that small and medium-sized enterprises (SMEs) were not easy to obtain funds [11]. JayRaman (2012) advocated that enterprises could improve credit and productivity to meet the bank’s loan requirements, thereby improving financing efficiency [12]. Hovakimian et al. (2013) believed that in order to improve financing efficiency, companies should choose debt financing to support their existing business and choose companies equity financing to ensure their growth needs [13]. For equity financing, Hewlet and Roderic (2011) believed that equity liquidity was positively related to financing efficiency [14]. BalKrishnan et al. (2014) concluded that the amount of equity financing would affect the financing efficiency of American companies [15]. For debt financing, Gomariz and Ballesta (2014) argued that improving financial leverage could help reduce over-investment and improving the debt maturity structure could help improve financing efficiency [16]. In summary, the financing decisions and methods of enterprises would have an impact on the performance of enterprises, and the financing efficiency of sample enterprises in the existing research was generally low.

Chinese scholar Zeng Kanglin (1993) first put forward the term “financing efficiency”. He believed that companies should consider the efficiency and cost of financing methods comprehensively when choosing financing methods [17]. However, he did not define the financing efficiency. Then many scholars had defined the financing efficiency. Gao Xuezhe (2005) believed that corporate financing efficiency was the ability to generate corporate value [18]. Yang Xingquan (2005) defined financing efficiency as the efficiency and effectiveness achieved by the company in financing financial activities [19]. Song Wenbing (1998) and Zhang Haas (2014) described the financing efficiency from the perspective of economics, measured the efficiency by the relationship between cost and benefit. They believed that the financing efficiency was the ratio of the cost invested by the companies in the financing activities to the income generated [20,21]. Ye Wangchun (1999) defined the financing efficiency as the cost, risk, and difficulty of obtaining capital [22]. The concept of financing efficiency varies from scholar to scholar, but its basic connotation is an input–output ratio or cost-benefit ratio. In summary, the definition of corporate financing efficiency in the existing literature is mainly divided into the following two viewpoints: (1) From the perspective of the ability to acquire funds, the definition of financing efficiency is the cost and risk of raising funds. (2) From the perspective of funds using, the financing efficiency is defined as the efficiency of the use efficiency of funds raised by the enterprise, that is, the effective output of the enterprise under fixed investment.

Domestic scholars have also carried out a large number of empirical studies on how to measure the company’s financing efficiency. The main methods are fuzzy evaluation, factor analysis, entropy method, and data envelopment analysis (DEA). The method most commonly used is the DEA, which does not require building a specific function model. Liu Lichang and Feng Genfu (2004) were the early scholars who used DEA to evaluate the financing efficiency in China. They took 47 listed companies that first issued shares in the Shanghai Stock Exchange Market in 1998 as the research objects, and made a comprehensive evaluation of the financing efficiency. Their results show that the financing efficiency in China’s listed companies was generally inefficient [23]. After 2010, there were more and more researches on the evaluation of financing efficiency by DEA in China. The research fields were broader, and the industries were more detailed. For example, the studies were conducted according to the source of funds, the nature of enterprises, industry, and regions. Zhang Yanliang, Hu Chao, and Hu Xiaoyan (2015) used DEA to analyze and evaluate the financing efficiency of the BRICS (Brazil, Russia, India, and China) stock market. The conclusion was that the financing efficiency of China’s stock market was still at a low-level [24]. Feng Zhiyuan and Li Jigang (2016) used DEA to analyze the financing efficiency of 138 A-share listed agricultural enterprises in China. The results show that the financing efficiency level of China’s agricultural listed companies presented great differences, and the internal financing efficiency in different subsectors each are not identical, the overall level of financing efficiency of China’s agricultural listed companies is not high [25]. Shen Chen (2017) used the three-stage DEA model to compare the financing efficiency of small and medium-sized enterprises (SMEs) in the new three-board market. The results show that the main reason for the low financing efficiency of sample enterprises was the low scale efficiency [26]. Recently, Xie Shanshan and Yu Guoxin (2019) used DEA-Malmquist to measure and analyze the financing efficiency of China’s agricultural listed companies. The result was that the financing efficiency of sample companies was low [27]. Xiong Zhengde et al. (2014) used the two-stage DEA model to measure the debt financing efficiency and corresponding efficiency of each stage of the new energy automobile listed companies in China. They thought that the debt financing efficiency of sample companies was low [28]. In general, the results of the research on the financing efficiency of listed companies in China were relatively consistent; that was, the financing efficiency was not high.

Capital is the first and continuing motive force of the company’s economic activities. The business activities and long-term development of companies are inseparable from the capital. Therefore, how to finance has become a significant basic activity of the financial management department of the companies. The rural revitalization strategy is new, and the relevant listed companies cover a wide range of industries including agriculture, manufacturing, wholesale, and retail. At present, no study has been found on the financial efficiency of companies in this field. The financing methods of the company mainly include internal financing and external financing. Companies in western developed countries follow different pecking order financing theories. However, Chinese listed companies prefer equity financing, followed by debt financing, and finally, internal financing. This study combines the two-stage DEA model and the grey relational analysis (GRA) model to study the external financing efficiency of rural revitalization listed companies and divides the financing system into two subsystems: Funds raising and funds using, selecting input indicators, intermediate indicators, and output indicators. The two-stage DEA model is used to analyze the efficiency values of each subsystem and the overall. Then, we further use the GRA method to analyze the key factors affecting the financing efficiency, analyze the reasons for the low financing efficiency, and put forward relevant suggestions.

3. Methods

3.1. Two-Stage DEA

Data envelopment analysis (DEA) is a non-parametric technical efficiency analysis method based on a relative comparison between evaluated objects [29]. The evaluation objects are usually called decision-making units (DMUs). Charnes, Cooper, and Rhodes (1978) proposed the first DEA model, the CCR model [30]. Later, Banker, Charnes, and Cooper (1984) put forward the BCC model of variable return on the scale [31]. The DEA method is a representative and effective efficiency evaluation model [32]. DEA can be used to evaluate the efficiency of multiple inputs and outputs. It does not need to construct a production function to estimate the parameters and is not affected by the dimension of input–output. At the same time, it is not necessary to preset the weight value. Therefore, the evaluation of DMUs is relatively fair. This opened up fertile territory for efficiency evaluation. In the past few decades, DEA has attracted many scholars and has been used in many fields. However, DEA only evaluates the relative efficiency of DMU rather than the absolute efficiency. Although efficiency can be evaluated, the reasons for efficiency or inefficiency need further investigation. Moreover, DEA requires that DMU must have sufficient quantity, and the number of DMU evaluated should be twice or more times the sum of the number of inputs and outputs.

In the traditional DEA model, each DMU is regarded as a “black box”, that is, we do not know how the input is converted into the output, whether the operation itself affects the overall efficiency, etc. [33]. To further reflect the complex interrelationships among DMUs, Fare, and Grosskopf (1996, 2000) proposed the concept of the network DEA and established a variety of concrete DEA models [34]. The two-stage production process is one of the fundamental forms. The idea of modeling is to decompose the production process of the companies into multiple stages of continuous connection through intermediate products. The intermediate products are the outputs of the previous stage and the inputs of the latter stage. By calculating, we could examine the possible impact of each business link on the overall efficiency of the production system. Network DEA evaluates the efficiencies of multidivisional organizations. This model solves for the comparative overall efficiency of the organization along with the divisional efficiencies in a unified framework [35]. Kao and Hwang (2008) established an associated two-stage DEA model based on the relevance of each sub-phase; such a model is more realistic [36]. Rachita Gulati and Sunil Kumar (2017) believed that the main advantage of the network DEA model was that it could more accurately identify the causes of inefficiency and provided further insights into performance evaluation [37]. At present, the two-stage network DEA model mainly includes two basic network structures: Chain and parallel. According to the characteristics of financing efficiency evaluation, this paper adopts the chain network DEA model.

Feng Feng et al. (2011) proposed the two-stage chain DEA system model, as shown in Figure 1. The decision-making process is divided into two stages; each stage corresponds to one decision unit. I₁ is the input variable of sub-decision unit K, I₁∈E^IK; O₁ is the output variable of the sub-decision unit K, and is also the input variable of the sub-decision unit H, O₁ is called the intermediate variable, O₁∈E^OK; I₂ is the input variable of the sub-decision unit H, I₂∈E^IH; and O₂ is the output variable of the sub-decision unit H, O₂∈E^OH [38].

This study utilized the two-stage network DEA model but used the slacks-based measure (SBM) approach for evaluating financing efficiency of rural revitalization listed companies and compared the results. The SBM is a non-radial method and is suitable for measuring efficiency when inputs and outputs may change non-proportionally. This model can decompose the overall efficiency into divisional ones. In the network SBM-DEA (NSBM-DEA) model, it is assumed that there are $n$ DMUs (j = 1, 2, …,$n$) consisting of $k$ dimensions ($k$ = 1, 2, …, K). Let $m_k$ and $r_k$ be the number of inputs and outputs of dimension $k$, respectively. Tone and Tsutsui (2009) defined divisional efficiency as follows [39]:

$${\rho }_k=\frac{1-\frac{1}{m_k}\left({{\displaystyle \sum }}_{i=1}^{m_k}\frac{s_i^{k-\ast }}{x_{io}^k}\right)}{1+\frac{1}{r_k}\left({{\displaystyle \sum }}_{r=1}^{r_k}\frac{s_r^{k+\ast }}{y_{ro}^k}\right)}\left(k=1,\cdots ,K\right).$$

(1)

The overall efficiency value is as follows:

$${\rho }_o^{\ast }=\underset{{\mathsf{\lambda }}^k,s^{k-},s^{k+}}{\min }\frac{{{\displaystyle \sum }}_{k=1}^k{w}^k\left[1-\frac{1}{m_k}\left({{\displaystyle \sum }}_{i=1}^{m_k}\frac{s_i^{k-}}{x_{io}^k}\right)\right]}{{{\displaystyle \sum }}_{k=1}^k{w}^k\left[1+\frac{1}{r_k}\left({{\displaystyle \sum }}_{r=1}^{r_k}\frac{s_r^{k+}}{y_{ro}^k}\right)\right]}.$$

(2)

Subject to:

$$\begin{gathered} {{\displaystyle \sum }}_{k=1}^k{w}^k=1, \\ {x}_0^k=X^k{\lambda }^k+s^{k-}\left(k=1,\cdots ,K\right), \\ {y}_0^k=Y^k{\lambda }^k-s^{k+}\left(k=1,\cdots ,K\right), \\ \mathrm{e}{\lambda }^k=1\left(k=1,\cdots ,K\right), \\ {w}^k\ge 0,{\lambda }^k\ge 0,s^{k-}\ge 0,s^{k+}\ge 0,\left(\forall k\right), \\ {Z}^{\left(k,h\right)}{\lambda }^h=Z^{\left(k,h\right)}{\lambda }^k,\left(\forall \left(k,h\right)\right), \end{gathered}$$

where $w$ is the division weight, $s^{k-}$ and $s^{k+}$ are the input and output slacks, and $Z^{\left(k,h\right)}$ is the intermediate link between division $k$ and division $h$.

3.2. Grey Relational Analysis

Grey relational analysis is an important part of the grey system theory. It is a system science theory put forward by the famous Chinese professor Deng Julong (1982) [40]. Here, grey refers to incomplete information, and the grey system is a system with incomplete information [41]. The grey relational analysis measures the degree of correlation among factors according to the similarity or difference of the development trend among factors, i.e., the “grey relational degree”. The basic idea of the grey relational analysis is to judge whether the connection is close according to the degree of similarity with the geometrical curve of the sequence curve. The closer the curve is, the greater the relational degree between the corresponding sequences is, and vice versa. Then, according to the relational degree, the evaluation comparison indicators are sorted, and the advantage analysis is performed to obtain the evaluation results [42,43].

The steps of the grey relational analysis are as follows [44]:

(1) Determine the analytical indicator system according to the purpose of analysis and collect the analytical data.

Arrange $n$ sets of data sequences of $m$ indicators into $m\ast n$ order matrices:

$$\left(X_1^{\prime },X_2^{\prime },\dots ,X_n^{\prime }\right)=\left(\begin{array}{cccc}{x}_1^{\prime }\left(1\right)& x_2^{\prime }\left(1\right)& \dots & x_n^{\prime }\left(1\right)\\ x_1^{\prime }\left(2\right)& x_2^{\prime }\left(2\right)& \dots & x_n^{\prime }\left(2\right)\\ \begin{array}{c}⋮\end{array}& ⋮& ⋮& ⋮\\ x_1^{\prime }\left(m\right)& x_2^{\prime }\left(m\right)& \dots & x_n^{\prime }\left(m\right)\end{array}\right).$$

(3)

(2) Generate the referential series.

The referential series may be composed of the optimal value (or the worst value) of each indicator, or other reference values can be selected according to the evaluation purposes and recorded as:

$$X_0^{\prime }={(x_0^{\prime }\left(1\right),x_0^{\prime }\left(2\right),\dots ,x_0^{\prime }\left(n\right))}^T.$$

(4)

(3) Normalize the data.

The dimensions of the data are not necessarily the same because of the physical meanings of various factors in the system are different. To eliminate the influence of the dimensions, we first should normalize the original data. Commonly normalized methods are the mean method (Formula (5)), the initialization method (Formula (6)), and $\frac{x-\overline{x}}{s}$ transformation.

$$x_i\left(k\right)=\frac{x_i^{\prime }(k)}{\frac{1}{m}{{\displaystyle \sum }}_{k=1}^m{x}_i^{\prime }(k)},$$

(5)

$$\begin{gathered} x_i\left(k\right)=\frac{x_i^{\prime }\left(k\right)}{x_i^{\prime }\left(1\right)}, \\ i=0,1,\cdots ,n,k=1,2,\cdots m. \end{gathered}$$

(6)

The normalized data matrix becomes as shown follows:

$$\left(X_0,X_1,\dots ,X_n\right)=\left(\begin{array}{cccc}{x}_0\left(1\right)& x_1\left(2\right)& \dots & x_n\left(1\right)\\ x_0\left(2\right)& x_1\left(2\right)& \dots & x_n\left(2\right)\\ ⋮& ⋮& ⋮& ⋮\\ x_0\left(n\right)& x_1\left(n\right)& \dots & x_n\left(n\right)\end{array}\right).$$

(7)

(4) Compute the absolute value of the difference between $x_0\left(k\right)$ and $x_i\left(k\right)$, the formula is $\left|x_0\left(k\right)-x_i\left(k\right)\right|$.

(5) Calculate the two-level minimum difference (8) and the two-level maximum difference (9).

$$\Delta min=\underset{i}{min}\underset{k}{min}\left|x_0\left(k\right)-x_i\left(k\right)\right|=\min (\ast ,\ast ,\ast ,\ast ,\ast ,\ast )=\ast ,$$

(8)

$$\Delta max=\underset{i}{max}\underset{k}{max}\left|x_0\left(k\right)-x_i\left(k\right)\right|=\max (\ast ,\ast ,\ast ,\ast ,\ast ,\ast )=\ast .$$

(9)

(6) Apply the grey relational Equation (10) to compute the grey relational coefficient.

$${\zeta }_i\left(k\right)=\frac{\underset{i}{min}\underset{k}{min}\left|x_0\left(k\right)-x_i\left(k\right)\right|+\rho \cdot \underset{i}{max}\underset{k}{max}\left|x_0\left(k\right)-x_i\left(k\right)\right|}{\left|x_0\left(k\right)-x_i\left(k\right)\right|+\rho \cdot \underset{i}{max}\underset{k}{max}\left|x_0\left(k\right)-x_i\left(k\right)\right|},$$

(10)

where $\rho$ is the resolution coefficient, 0 < $\rho$ < 1. The smaller the $\rho$ is, the greater the difference between the correlation coefficients is, and the stronger the discrimination ability is. Usually, $\rho$ is taken as 0.5. The calculated relational coefficient matrix is as follows:

$$\mathsf{\zeta }=\left(\begin{array}{ccc}{\mathsf{\zeta }}_1\left(1\right)& \cdots & {\mathsf{\zeta }}_n\left(1\right)\\ ⋮& \ddots & ⋮\\ {\mathsf{\zeta }}_1\left(n\right)& \cdots & {\mathsf{\zeta }}_n\left(n\right)\end{array}\right).$$

(11)

(7) Calculate the coefficient series $R$.

If the weights ($W_k$) of criteria are equally important, the relational degree $r_{0i}$ is computed as $r_{0i}=\frac{1}{m}{{\displaystyle \sum }}_{k=1}^m{\zeta }_i\left(k\right)\left(k=1,\cdots ,m\right)$. If the weights ($W_k$) of the criteria are different, the $r_{0i}$ is computed as $r_{0i}=\frac{1}{m}{{\displaystyle \sum }}_{k=1}^m{W}_k\cdot {\zeta }_i\left(k\right)(k=1,\cdots ,m)$. After calculation, we can get the correlation series $R$ as follows:

$$R=\left(\begin{array}{cccc}{r}_{01}& r_{02}& r_{03}& \dots \end{array}\right).$$

(12)

(8) Compare and rank the gray relational degree, the larger the value, the more influential the variable.

4. Data Sources and Indicator Selection

4.1. Data Sources

This paper selected the rural revitalization concept listed companies in 2018 on Sina Finance Network as samples. To maintain the consistency of data samples, samples selection followed the following criteria: (1) ST (special treatment) shares were excluded. ST stocks refer to stocks of listed companies that were treated by the stock exchange for special treatment due to abnormal financial or other conditions; (2) excluding listed companies without external liabilities and relevant financial expenses. Finally, 34 listed companies were selected as the final samples. The data comes from Sina Finance Network, the China Stock Market and Accounting Research (CSMAR) Database, and the Global Economic Database Network.

Table 1. Characteristics of 34 listed companies (I2-1).

Stock Code	Company Name	Age (year; by 2018)	Property	Listed Plate	Industry *
000713	Hefei Fengle Seed Co., Ltd.	21.71	state-owned	main board	agricultural
000735	Luoniushan Co., Ltd.	21.57	private	main board	agricultural
000902	Hubei Xinyangfeng Fertiliazer Co., Ltd.	19.75	state-owned	main board	manufacturing
000998	Yuan Longping High-tech Agriculture Co., Ltd.	19.52	state-owned	main board	agricultural
002069	Zhangzidao Group Co., Ltd.	12.27	other	SMEs board	agricultural
002170	Shenzhen Batian Ecological Technique Co., Ltd.	11.29	private	SMEs board	manufacturing
002215	Shenzhen Noposion Pesticide Co., Ltd.	10.87	private	SMEs board	manufacturing
002385	Beijing Dabeinong Technology Group Co., Ltd.	8.73	private	SMEs board	manufacturing
002391	Jiangsu Changqing Agrichemical Co., Ltd.	8.72	private	SMEs board	manufacturing
002505	Hunan Dakang International Food & Agriculture Co., Ltd	8.12	private	SMEs board	wholesale and retail
002506	GCL System Integration Technology Co., Ltd.	8.12	private	SMEs board	manufacturing
002556	Anhui Huilong Agricultural Means of Production Co., Ltd.	7.84	collective	SMEs board	wholesale and retail
002588	Stanley Group Co., Ltd.	7.56	private	SMEs board	manufacturing
300021	DAYU Water-saving Group Co., Ltd	9.18	private	GEM board	manufacturing
300022	Gifore Agricultural Science & Technology Service Co., Ltd.	9.18	private	GEM board	energy, materials, machinery and electronics
300087	Winall Hi-tech Seed Co., Ltd.	8.61	private	GEM board	agricultural
300159	Xinjiang Machinery Research Institute Co., Ltd.	7.99	private	GEM board	manufacturing
300197	Shenzhen Techand Ecology and Environment Co., Ltd.	7.76	private	GEM board	water, environmental and public facilities management

* Industry classifications are classified according to the China Securities Regulatory Commission-level industry names. The agricultural categories in the table also include forestry, animal husbandry, and fisheries.

and

Table 2. Characteristics of 34 listed companies (I2-2).

Stock Code	Company Name	Age (year; by 2018)	Property	Listed Plate	Industry
300275	Chongqing Massci and Tech. Co., Ltd.	7.17	private	GEM board	information transmission, software and information technology services
600108	Gansu Yasheng Industrial (Group) Co., Ltd.	21.38	state-owned	main board	agricultural
600127	JinJian Cereals Industry Co., Ltd.	20.67	state-owned	main board	manufacturing
600313	Zhongnongfa Seed Industry Group Co., Ltd.	17.96	state-owned	main board	agricultural
600354	Gansu Dunhuang Seed Group Co., Ltd.	14.97	state-owned	main board	agricultural
600359	Xinjiang Talimu Agriculture Development Co., Ltd.	19.69	state-owned	main board	agricultural
600389	Nantong Jiangshan Agrochemical and Chemicals Co., Ltd.	17.98	state-owned	main board	manufacturing
600438	Tongwei Co., Ltd.	14.84	private	main board	manufacturing
600486	Jiangsu Yangnong Chemical Co., Ltd.	16.70	state-owned	main board	manufacturing
601012	LONGi Green Energy Technology Co., Ltd.	6.73	state-owned	main board	manufacturing
601038	First Tractor Co., Ltd.	6.40	state-owned	main board	manufacturing
601118	China Hainan Natural Rubber Industry Group Co., Ltd.	7.99	private	main board	agricultural
601952	Jiangsu Agricultural Reclamation and Development Co., Ltd.	1.63	state-owned	main board	manufacturing
603766	Loncin Motor Co., Ltd.	6.39	state-owned	main board	manufacturing
603789	Xingguang Agricultural Machinery Co., Ltd.	3.68	private	main board	manufacturing
603970	Sino-Agrileading Biosciences Co., Ltd.	1.12	private	main board	biopharmaceutical

show the characteristics of 34 listed companies.

4.2. Indicator Selection

The two-stage NSBM-DEA was used to evaluate the external financing efficiency of the companies in two stages. The 1st stage was the funds raising stage, the financing cost was the input, and the obtained financing amount was the output. The 2nd stage was the funds using stage. Taking the amount of financing and the use of funds as inputs and the performance of the companies as outputs to reflect the allocation efficiency of company financing.

4.2.1. Input Indicators for the First Stage

Interest expense. To reflect the cost of debt financing better, this study selected interest expense rather than financial expense because the financial expense in the balance sheet is the balance after the interest income is reduced, it can not reflect the cost of debt.

Equity financing cost. Capital asset pricing model (CAPM) was used to determine the cost of equity financing. CAPM theory holds that the price level of capital assets such as stocks is related to risk, and the model is $R_i=R_f+{\beta }_i\left(R_m-R_f\right)$. Where, $R_i$ is the rate of return of stock $i$, reflecting the cost of equity financing, $R_f$ is the rate of return of risk-free assets, R_m is the rate of return of the market portfolio, and ${\beta }_i$ represents the rate of return on stock $i$ relative to the stock market. In this paper, $R_f$ took the coupon rate of the five-year bond of 2018, and $R_m$ was calculated by average the weighting rate of return and the total market value of the A-shares on the Shanghai Stock Exchange and the Shenzhen Stock Exchange in 2018.

4.2.2. Intermediate Indicators

Total debt financing. The total amount of debt financing selected in this paper was the sum of short-term loans, long-term liabilities due within one year, long-term loans, and bonds payable in the financial statements.

Total equity financing. The total amount of equity financing mainly refers to the net cash flow received from the issue of shares, including equity and equity premium. Many scholars choose equity capital to reflect the total amount of equity financing, but equity capital is only the par value of the company’s issued shares. Share premium is the amount that a company actually receives more than its par value when it issues shares at a premium. Therefore, the total equity financing of this paper was the equity plus equity premium.

4.2.3. Additional Input Indicators for the Second Stage

Main business cost. The main business cost refers to the cost of business activities such as selling goods and providing labor services. It reflects the resources consumed by the main business activities of the rural revitalization companies and is expressed in currency. The cost of the main business directly affects the ability of the company to use funds.

Investment in innovation. It reflects the allocation of the company’s funds in innovation, which is expressed by the balance of intangible assets plus the R&D investment in the current period. The core of company innovation lies in the innovation of intangible assets. Intangible asset innovation is one of the core competitiveness of rural vitalization companies, which is helpful to vigorously promote rural supply-side reform.

4.2.4. Output Indicators

Return on the assets ratio (ROA)—this indicator is used to measure how efficiently a company can manage its assets to produce profits during a period. The high ROA indicates that company asset management is efficient. It is an important indicator for measuring the operational efficiency of the company’s assets.

Economic value added (EVA) refers to the income after deducting the total capital cost of the equity and debt from the net operating profit after tax. EVA is a business performance appraisal tool that evaluates the effective use of capital by business operators and the ability to create value for shareholders and reflects the ultimate business objectives of the company.

Weighted average return on equity (ROE) refers to the ratio of net profit to average net assets, which reflects the level of compensation paid for the owner’s equity. It is the most comprehensive financial analysis indicator.

According to Figure 1, the input–output chain of funds raising and using constructed in this paper is shown in Figure 2. The descriptive statistics of the input and output indicators for 34 listed companies are indicated, as in

Table 3. Statistics of input and output indicators.

Variables	Minimum	Maximum	Mean	Std. Deviation
Interest expense	311,025.000	564,524,759.770	119,090,575.806	151,343,344.259
CAPM (Ri)	0.050	0.079	0.066	0.007
Total debt financing	5,000,000.000	9,627,523,321.750	2,136,260,067.531	2,542,063,783.019
Total equity financing	378,308,310.730	9,592,183,172.230	2,746,765,689.724	2,580,678,893.102
Main business cost	134,845,451.830	22,326,898,401.270	5,165,266,721.684	5,551,346,955.979
Investment in innovation	47,746,510.590	2,838,681,638.980	595,466,600.346	659,394,532.669
Return on assets ratio (ROA)	−9.544	12.732	2.256	5.646
Economic value added (EVA)	−1,434,555,762.830	1,880,467,409.990	93,266,796.398	544,593,819.234
Weighted average return on equity (ROE)	−37.540	22.760	3.331	13.622

.

5. Process Data and Empirical Analysis

5.1. Two-Stage DEA Calculation Result Analysis

In this paper, MaxDEA 8 Ultra software was used to calculate the technical efficiency of the sample companies.

Table 4. Calculation result.

Stock Code	Company Name	θ1*	θ2*	θ*
000713	Hefei Fengle Seed Co., Ltd.	0.388	0.445	0.416
000735	Luoniushan Co., Ltd.	0.907	1.000	0.954
000902	Hubei Xinyangfeng Fertiliazer Co., Ltd.	1.000	1.000	1.000
000998	Yuan Longping High-tech Agriculture Co., Ltd.	1.000	1.000	1.000
002069	Zhangzidao Group Co., Ltd.	0.697	0.870	0.783
002170	Shenzhen Batian Ecological Technique Co., Ltd.	0.455	0.336	0.395
002215	Shenzhen Noposion Pesticide Co., Ltd.	0.448	0.727	0.587
002385	Beijing Dabeinong Technology Group Co., Ltd.	0.650	0.259	0.454
002391	Jiangsu Changqing Agrichemical Co., Ltd.	0.648	1.000	0.824
002505	Hunan Dakang International Food & Agriculture Co., Ltd	0.706	0.428	0.567
002506	GCL System Integration Technology Co., Ltd.	1.000	1.000	1.000
002556	Anhui Huilong Agricultural Means of Production Co., Ltd.	0.761	0.517	0.639
002588	Stanley Group Co., Ltd.	0.592	0.216	0.404
300021	DAYU Water-saving Group Co., LTD	0.432	1.000	0.716
300022	Gifore Agricultural Science & Technology Service Co., Ltd.	0.247	1.000	0.623
300087	Winall Hi-tech Seed Co., Ltd.	0.714	1.000	0.857
300159	Xinjiang Machinery Research Institute Co., Ltd.	1.000	1.000	1.000
300197	Shenzhen Techand Ecology and Environment Co., Ltd.	1.000	1.000	1.000
300275	Chongqing Massci and Tech. Co., Ltd.	0.337	0.665	0.501
600108	Gansu Yasheng Industrial (Group) Co., Ltd.	0.626	0.351	0.489
600127	JinJian Cereals Industry Co., Ltd.	1.000	0.322	0.661
600313	Zhongnongfa Seed Industry Group Co., Ltd.	0.491	0.311	0.401
600354	Gansu Dunhuang Seed Group Co., Ltd.	0.620	0.571	0.595
600359	Xinjiang Talimu Agriculture Development Co., Ltd.	0.506	1.000	0.753
600389	Nantong Jiangshan Agrochemical and Chemicals Co., Ltd.	0.275	1.000	0.638
600438	Tongwei Co., Ltd.	1.000	0.618	0.809
600486	Jiangsu Yangnong Chemical Co., Ltd.	0.419	1.000	0.710
601012	LONGi Green Energy Technology Co., Ltd.	0.970	1.000	0.985
601038	First Tractor Co., Ltd.	0.906	0.397	0.652
601118	China Hainan Natural Rubber Industry Group Co., Ltd.	1.000	1.000	1.000
601952	Jiangsu Agricultural Reclamation and Development Co., Ltd.	1.000	1.000	1.000
603766	Loncin Motor Co., Ltd.	0.789	0.450	0.619
603789	Xingguang Agricultural Machinery Co., Ltd.	0.544	0.631	0.588
603970	Sino-Agrileading Biosciences Co., Ltd.	1.000	1.000	1.000
	Average	0.710	0.739	0.724

* θ¹ refers to the funds raising efficiency score in the first stage, θ² refers to the funds using efficiency score in the second stage, θ* refers to the overall financing efficiency score.

and

Table 5. Financing efficiency of 34 rural revitalization listed companies in China in 2018.

Score	Stage 1		Stage 2		Overall
	Effective Number	The Proportion	Effective Number	The Proportion	Effective Number	The Proportion
Efficient	10	29.41%	17	50.00%	8	23.53%
Inefficient	24	70.59%	17	50.00%	26	76.47%

report the technical efficiency scores of 34 rural revitalization companies. Companies with a score of 1 were efficient, while companies with a score of less than 1 were inefficient. In the 1st stage, the funds raising efficiency score (θ¹) was between 0.247 and 1.000, with an average of 0.710. Only ten companies were efficient in funds raising, accounting for 29.41% of the total, and nine out of the remaining 24 inefficient companies had efficiency scores below 0.500. In the 2nd stage, the funds using efficiency score (θ²) was between 0.216 and 1.000, and the average was 0.739. Among these companies, 17 companies were efficient in funds using, accounting for 50% of the total. Ten of the remaining 17 inefficient companies had efficiency scores below 0.500.

From Table 5 we could also see that the overall financing efficiency score of rural revitalization listed companies was between 0.395 and 1.000, with an average of 0.724. Only eight companies have an overall financing efficiency of 1, which is technically efficient, accounting for 23.53% of the total. The remaining 26 companies could not be efficient in two stages at the same time, financing was not efficient, accounting for 76.47% of the total, and 19 companies had an efficiency score lower than the average of 0.724, accounting for 55.88% of the total.

Table 6. Efficiency of different types of companies.

	Types	Property			Industry			Listed Plate
Average Score		State-Owned Enterprise (14) **	Private Enterprise (18)	Others (2)	Agricultural Enterprise (10)	Manufacturing Enterprise (18)	Others (6)	Main Board (19)	SMEs Board (9)	GEM Board (6)
θ1 *		0.714	0.704	0.729	0.695	0.729	0.675	0.760	0.662	0.622
θ2 *		0.703	0.771	0.693	0.755	0.720	0.768	0.742	0.595	0.944
θ*		0.708	0.738	0.711	0.725	0.725	0.722	0.751	0.628	0.783

* θ¹ refers to the funds raising efficiency score in the first stage, θ² refers to the funds using efficiency score in the second stage, θ* refers to the overall financing efficiency score. ** represents the number of companies.

statistics on the efficiency according to the types of the sample companies. From the perspective of the property rights of the sample companies. The state-owned holding companies’ overall financing efficiency (0.708) and funds using efficiency (0.703) were lower than those of private companies. However, the efficiency of funds raising of state-owned holding companies (0.714) was higher than that of private companies (0.704), which is consistent with the current situation that private companies pay more attention to efficiency and more policies support to state-owned companies and easier access to financing.

From the perspective of the industry, the average efficiency value of different industries was similar. The efficiency score of funds using in the manufacturing industry (0.720) was the lowest, while the efficiency scores of funds raising in agriculture and other industries were lower. From the perspective of the listed plate, the average efficiency score of the main board listed companies (0.760) was significantly higher than that of the SMEs and GEM companies, which was more likely to be related to the easier access to equity financing in Chinese main board market. The average efficiency score of funds using of GEM companies was much higher than that of companies in the other two plates, while the SMEs had low-efficiency scores because SMEs were faced with some problems, such as difficulties in financing, high financing costs, and poor profitability.

In general, the financing efficiency of rural revitalization companies was low. Companies that performed well during the funds raising stage did not necessarily perform well on funds using, and vice versa. These companies could improve overall financing efficiency by improving the efficiency of funds raising or the efficiency of funds using, or both. We also note that the average efficiency scores of funds raising in the 1st stage were lower than those of funds using in the 2nd stage. Therefore, to improve the overall financing efficiency more effectively, the companies should focus on improving the efficiencies of funds raising that is reducing the financing costs.

5.2. Grey Relational Analysis of Factors Affecting Financing Efficiency

Financing efficiency is affected by many factors of the internal and external environment. It is difficult to control the influence of external factors and measure it. Therefore, this paper only analyzed internal influencing factors. Combined with the characteristics that listed companies of rural revitalization in China were mainly the agricultural and manufacturing industries, the study was conducted from the perspective of company size, company age, governance structure, capital structure, debt-paying ability, development ability, operation ability, and profitability.

(1) Company size, company age, and governance structure. In general, the greater the size of a company’s assets are, or the stronger its development ability and growth potential are, the lower the financing interest rate can be obtained in the bond market. According to the life cycle theory, at different stages of enterprise growth, with the constraints of information constraints, enterprise size, and capital requirements, the financing structure of the enterprise also changes, the ability to obtain external financing and the cost is also different. In this paper, the governance structure was represented by the first largest equity concentration. The equity concentration refers to the concentration of equity expressed by all shareholders due to the difference in shareholding ratio or the quantitative indicator of the dispersion of equity. The equity concentration is the main indicator to measure the company’s equity distribution, and it is also an important indicator to measure the company’s stability.

(2) Capital structure and debt-paying ability. The capital structure refers to the proportional relationship between the owner’s equity and the creditor’s rights, which is the result of the fundraising portfolio of the enterprise for a certain period. It determines the debt repayment and refinancing ability of the enterprise to a large extent, and determines the future profitability of the enterprise. It is also an important indicator of a company’s financial situation. A reasonable financing structure can reduce the financing cost and play the role of financial leverage. The debt paying ability is an important indicator of the company’s financial status and operational capabilities. Whether the company can pay cash and pay debts is the key to its survival and healthy development of the company. The capital structure and debt-paying ability of this paper were expressed by the asset-liability ratio indicator and the quick ratio, respectively.

(3) Development capacity and operational capacity. Development capacity is the potential ability of enterprises to expand their scale and improve their strength. In this paper, development capacity was expressed by the revenue growth rate indicator. The higher the value of the indicator, the faster the growth of the company’s operating income, the better and stronger the market prospects of the company. The operation ability reflects the company’s business strategy ability, decision-making ability, and management ability. The strength of the operation ability indicates the degree of utilization of the company assets and the efficiency of use. The continuous improvement of operation capability is the basis of company survival. This paper uses the total asset turnover rate to reflect the company’s operating capacity.

(4) Company value and profitability. The company value is the present value of the company’s expected free cash flow discounted by its weighted average cost of capital as a discount rate. It is closely related to the company’s financial decisions, reflecting the time value of the company’s funds, risk, and sustainable development. Profitability is the ability of a company to make a profit. The profitability will directly affect the interests of investors and creditors and have an impact on the refinancing of the company.

The indicators are shown in

Table 7. Relational analysis indicators.

Indicator Types	Indicators	Formula	Code
Company size	Total assets (yuan)	Total liabilities + total owner’s equity	A1
Company age	Company age (year)	Company listing time by the end of 2018	A2
Governance structure	First largest equity concentration (%)	Capital contribution/registered capital × 100%	A3
Capital structure	Asset-liability ratio (%)	Total liabilities/total assets × 100%	A4
Debt paying ability	Quick ratio (%)	Quick-moving assets/current liabilities × 100%	A5
Development ability	The revenue growth rate (%)	(Main business income of this year-main business income of the previous year)/main business income of the previous year × 100%	A6
Operation ability	Total assets turnover (%)	Sales revenue/average total assets × 100%	A7
Company value	Free cash flow (yuan)	Net operating profit after tax-net investment	A8
Profitability	Earnings per share (EPS) (yuan)	Current net profit attributable to common shareholders/Weighted average numbers of common shares outstanding for the period	A9

, and the descriptive statistics of grey relational analysis indicators are indicated, as in

Table 8. Descriptive statistics of the grey relational analysis indicators.

Variables	Minimum	Maximum	Mean	Std. Deviation
A1	0.400	1.000	0.724	0.212
A2	299,455,444.680	39,659,244,130.930	9,294,769,999.371	9,599,041,878.701
A3	1.120	21.710	11.590	6.046
A4	9.130	67.840	31.486	14.476
A5	0.190	0.880	0.497	0.179
A6	0.320	3.180	0.982	0.513
A7	−0.420	0.580	0.058	0.221
A8	0.180	2.120	0.740	0.441
A9	−2,618,387,655.560	8,340,311,793.310	1,036,921,643.745	1,993,925,363.649
A10	−1.385	3.031	0.261	0.686

.

For gray relational analysis, reference series must be determined first. This paper used the overall financing efficiency $\theta$ reference series: A′₀ = (a′₀(1), $a$′₀(2),…, a′₀(34)), each factor indicator A_j was used as a comparison series: A′_j = (a′_j(1), a′_j(2),…, a′_j(34)), (j = 1,2,…,9). To eliminate the influence of the dimension, the paper used the mean method to normalize the original data. The standardized data is shown in

Table 9. Normalized data.

Stock Code.	A	A1	A2	A3	A4	A5	A6	A7	A8	A9
007133	0.575	0.263	1.873	1.083	0.674	0.903	5.686	1.124	−0.029	0.735
000735	1.317	0.679	1.861	0.544	0.708	0.813	−2.333	0.243	0.539	1.299
000902	1.381	0.968	1.704	1.510	0.586	1.027	1.892	1.528	1.030	2.432
000998	1.381	1.653	1.684	0.290	1.031	0.963	2.092	0.341	0.328	2.626
002069	1.082	0.382	1.058	0.977	1.762	0.327	−2.178	1.009	1.482	0.183
002170	0.546	0.380	0.974	0.924	0.922	0.458	1.741	0.761	1.204	0.034
002215	0.811	0.594	0.938	0.858	1.018	1.005	7.184	1.076	−0.227	1.435
002385	0.627	1.947	0.754	1.310	0.820	0.734	0.512	1.397	4.082	0.439
002391	1.138	0.438	0.752	0.972	0.393	1.199	5.762	1.027	0.544	3.416
002505	0.783	1.481	0.701	0.621	1.224	0.735	1.407	1.233	1.790	−0.430
002506	1.381	2.025	0.701	0.893	1.553	0.829	−3.859	0.773	6.271	0.043
002556	0.882	0.887	0.676	1.288	1.382	0.731	2.985	2.859	−0.443	0.851
002588	0.558	0.790	0.653	0.726	0.795	0.839	1.381	1.045	0.114	0.629
300021	0.989	0.411	0.792	0.740	1.193	1.089	6.601	0.710	0.151	0.526
300022	0.861	0.204	0.792	0.417	1.580	0.768	−0.688	2.044	0.015	0.323
300087	1.184	0.205	0.742	0.683	0.898	1.013	−0.671	0.704	0.076	0.824
300159	1.381	1.051	0.689	0.477	0.666	1.422	0.238	0.271	0.337	0.740
300197	1.381	2.656	0.670	1.168	1.456	0.371	−0.918	0.466	2.705	0.503
300275	0.692	0.107	0.618	0.949	0.687	1.187	−3.213	0.317	0.213	−1.270
600108	0.675	0.921	1.845	0.494	0.883	1.010	3.656	0.408	0.813	0.162
600127	0.913	0.236	1.783	0.709	1.303	0.438	1.561	1.924	−0.098	−0.324
600313	0.554	0.335	1.549	0.734	0.657	1.664	−1.861	1.399	0.321	0.195
600354	0.822	0.229	1.292	0.410	1.206	0.811	9.963	0.424	0.561	−1.921
600359	1.040	0.230	1.699	1.281	1.578	0.749	−7.232	0.361	0.492	−2.220
600389	0.881	0.345	1.552	0.927	0.868	0.804	1.316	1.721	0.361	5.055
600438	1.117	4.140	1.280	1.635	1.216	0.389	0.949	1.163	8.043	2.003
600486	0.980	0.800	1.440	1.149	0.706	1.724	3.289	0.985	0.327	11.606
601012	1.360	0.032	0.580	0.477	1.158	1.274	5.887	0.819	2.222	3.521
601038	0.900	0.858	0.552	1.323	1.299	0.865	−3.981	0.550	0.145	−5.301
601118	1.381	4.267	0.689	2.015	0.664	1.335	−6.432	0.645	−2.525	0.198
601952	1.381	1.414	0.141	2.155	0.375	3.236	2.254	0.971	0.566	1.705
603766	0.855	1.607	0.552	1.617	0.827	1.051	1.023	1.296	2.392	1.863
603789	0.811	1.289	0.318	1.047	0.645	1.446	−1.142	0.523	0.055	−0.636
603970	1.381	0.174	0.097	1.596	1.267	0.791	1.130	1.881	0.143	2.756

. The companies’ ages were calculated, and the data of other indicators were derived from the CSMAR database.

When calculating the grey relational degree, $\rho$ was taken as 0.5. The results of the GRA are shown in

Table 10. Results of the grey relational analysis (GRA).

Code	Relational Degree	Rank
A1	0.894	6
A2	0.916	4
A3	0.933	3
A4	0.940	1
A5	0.938	2
A6	0.704	9
A7	0.908	5
A8	0.836	7
A9	0.819	8

.

The ranking of relational degrees shows that: R₄ > R₅ > R₃ > R₂ > R₇ > R₁ > R₈ > R₉ > R₆.

The relational degrees of the asset-liability ratio, quick ratio, first largest equity concentration, company age, and total assets turnover rate were all over 0.900. These show that capital structure, debt-paying ability, governance structure, company age, and operation ability were the five most important factors affecting the financing efficiency of rural revitalization listed companies. The asset-liability ratio had the highest relational degree, 0.940. The asset-liability ratio not only reflects the capital structure of the company but also reflects the repayment ability of the company’s long-term liabilities. The excessive asset-liability ratio will affect debt financing, and on the contrary, it will affect equity financing, which is the key factor affecting the financing efficiency. The relational degree of the quick ratio was 0.938. The higher the quick ratio, the stronger the short-term repayment ability of the company, the easier it is to obtain external short-term loans. However, the high quick ratio easily leads to the inefficiency of the funds using. The impact on the first largest equity concentration (0.933) on financing efficiency was second only to the quick ratio, which is an important indicator for measuring the company’s structure. Demsetz and Lehn (1985) argued that when the shareholding ratio of large shareholders was high enough, it would have an “encroachment effect” on minority shareholders, thus reducing company performance [45]. The relational degree of the company age was also as high as 0.916, which indicates that the financing costs and capital allocation efficiency of listed companies in different stages of development were quite different. In general, for companies with a high total assets turnover rate (0.908), their funds using efficiencies were also high, which could improve the financing efficiency of companies.

The relational degrees of company size, company value, and profitability were 0.894, 0.836, and 0.819, respectively, which have significant impacts on the financing efficiency of the rural revitalization listed companies. The listed companies with high total assets (0.894) had abundant resources and strong financial resources, which could disperse certain operational risks. The total assets indicator is an essential factor for creditors and investors to consider. Free cash flow (0.836) is the cash that the company holds to return to shareholders actually. The higher the free cash flow, the easier it is for the company to obtain equity, and the higher the financing efficiency. Earnings per share (EPS; 0.819) was one of the most important financial indicators for investors and other information users to evaluate the companies’ profitability, predict the company’s growth potential, and then make relevant economic decisions.

The development capability had little impact on the financing efficiency of rural revitalization listed companies. The relational degree of the revenue growth rate was only 0.704. In theory, the stronger the company’s development capabilities, the higher the company’s value, and the easier it is to raise funds. However, we found that 11 of the 34 listed companies had negative operating profit, while only five companies had negative total profits. Five of the remaining 23 companies had operating profits of less than 80 percent of the total profit. This shows that many rural revitalization companies had invested more in non-main business, and the qualities of operating profit were not high, which had no important impact on the company’s financing efficiency.

6. Conclusions and Discussion

6.1. Conclusions

To better measure the financing efficiency of Chinese rural revitalization of listed companies, this paper divided the financing process into two related stages: Funds raising and funds using. We used the two-stage DEA model to measure the comprehensive technical efficiency of sample company financing, and then analyzed the influencing factors with GRA.

The results were:

(1) The financing efficiencies of 34 sample companies were low; only six companies had overall efficient financing. Financing efficiency varied greatly depending on the nature of the company, the industry, and the listing board. The efficiency of funds using of state-owned enterprises was much lower than that of private companies. The average efficiency value of agricultural company funds raising was lower than that of manufacturing. The efficiency of SMEs was lower than that of the main board companies, but the GEM companies had achieved relatively higher efficiency in the funds using. This is similar to the findings of other Chinese scholars. Such as, Wang Xiaoning, Du Ling, and Wang Qizheng (2016) believed that the insufficient of investment caused by financing constraints makes the scale of enterprises generally small, unable to obtain optimal scale benefits, resulting in lower overall financing efficiency of SMEs. Private companies had the highest overall technical efficiency and scale efficiency [46]. China’s gem listed enterprises’ overall financing efficiency was low (Liu Lixia and Zhu Yuchao, 2009) [47]. The financing efficiency of most agricultural listed companies was in an ineffective state, indicating that the most agricultural funds had not been utilized effectively, and there were redundancy in capital input and allocation, and enterprise financing efficiency was low (Xie Shanshan, Yu Guoxin, 2019) [27].

(2) Capital structure, debt-paying ability, governance structure, company age, and operating ability were the five most important factors in the financing efficiency of rural revitalization listed companies. Company size, company value, and profitability also had a significant impact on financing efficiency, but development capacity had less of an impact on financing efficiency. This is basically consistent with the research results of most scholars on the factors influencing the financing efficiency of listed companies in China. For example, Yan Aoxue and Meng Fengping (2015) analyzed the factors affecting the equity financing efficiency of agricultural listed companies by GRA. They concluded that the size, capital structure, and profitability of enterprises were the most important factors affecting the equity financing efficiency of agricultural listed companies in China. The operation ability had a crucial influence on the equity financing efficiency of agricultural listed companies [48]. Qi Zhiting (2013) believed that the size of the company would affect the financing efficiency of listed companies. The larger the company, the smaller the financing cost, which could reduce the financing cost and improve the operating efficiency. Finally, it would increase the value of the enterprise, improve the financing efficiency, and make rational use. Financial leverage could improve the business performance of the company and thus improve the financing efficiency [49].

6.2. Discussions

Implementing the rural revitalization strategy is an urgent requirement to solve the imbalanced and insufficient development of urban and rural areas in China, and is a great strategy for promoting sustainable development in rural areas. The implementation of the rural revitalization strategy puts forward the goals of achieving industrial prosperity, improving the quality of the agricultural supply system, and accelerating the modernization of agriculture and rural areas. The realization of these goals is inseparable from the participation of relevant companies, and the rural revitalization listed companies play an important role. The financing provides a financial guarantee for the normal operation and sustainable development of the companies and is the source of the companies’ vitality. The overall financing efficiencies of rural revitalization listed companies are not high and need to be further improved.

(1) Optimize capital structure and corporate governance structure. The financing channels of listed companies in China are not reasonable enough. Debt financing mainly comes from commercial loans of financial institutions such as banks, while commercial loans generally tend to be large or state-owned enterprises, which cannot alleviate the financial pressure of SMEs and private companies. Compared with the development speed of the stock market, the development of the Chinese bond market is somewhat slow. Contrary to the “funding priority” theory, the listed companies in China prefer equity financing and have a higher degree of equity concentration. The asset-liability ratios of rural revitalization listed companies were generally low, which does not play a good role in financial leverage. Therefore, it is necessary to improve the companies’ financing channels, diversify the financing methods, pay attention to the development of the bond market, choose the optimal financing method, maintain a moderate debt financing ratio, optimize the shareholding structure, and avoid dominant share.

(2) Improve the companies’ management level and innovation capabilities. The companies’ management level constrains its own development. A company with strong debt-paying ability is naturally more likely to be favored by external debt funds, and the increase in profitability will increase the value of the company and attract more investors. At the same time, the company’s continuous development will also help the company to expand its scale. These will help reduce the company’s financing costs and improve the efficiency of funds raising. Also, the improvement in the company’s management level can reduce operating costs and improve the efficiency of funds using. In particular, state-owned enterprises and SMEs must effectively improve the efficiency of funds using. The improvement in the quality of the rural revitalization strategic agricultural supply system depends on the enhancement of the innovative ability of the companies. From the perspective of innovation input efficiency, half of the companies have redundant innovation input, indicating insufficient innovation capability. The companies should pay attention to the introduction and cultivation of innovative talents to maximize the innovation benefits.

(3) Increase state policy support. The shortage of funds and the lack of financial support tools have restricted the revitalization and long-term development of the rural economy. The government can adopt differentiated assistance policy according to the characteristics of financing efficiency of the companies. Among 34 rural revitalization listed companies, private enterprises, SMEs and agricultural companies all had a considerable proportion. These companies had higher financing costs and were less efficient in funds raising than other companies. The government should improve the credit guarantee system, reasonably reduce the guarantee fee rate, increase the financing support for these companies, broaden the financing channels, and reduce the financing difficulty and cost of these companies as much as possible. Banks should also increase their support for SMEs and private companies, change their attitudes toward these companies in the past, pay attention to the financing services, and provide tailor-made services and products for the financing of these companies. For state-owned companies, the government should further to streamline government agencies, delegate more management power to enterprises, and enhance the vitality of state-owned companies in general. For agricultural companies, the government should improve policy financing tools and financing models, and give more financial tilt, such as increasing financial subsidies for agricultural projects undertaken by the companies, and taking measures to encourage the innovative development of agricultural companies.

6.3. Shortcomings

(1) There are many types of classifications for rural revitalization listed companies. The samples selected in this paper were from the classification of Sina Finance Network. There may be many other relevant companies that were not included in the research. In addition, considering that the rural revitalization strategy is a new strategy that was proposed just in 2017, this paper only studied the cross-sectional data of 2018.

(2) This paper only measured the external financing efficiency without analyzing the internal financing efficiency.

(3) This paper only analyzed the internal factors of financing efficiency but did not consider external factors, such as company location, local GDP, and other factors.

(4) The above shortcomings will hopefully be further addressed in future research, such as increasing the sample size, expanding the study period, increasing the research on internal financing efficiency, and comprehensively considering internal and external influencing factors.