Evaluation and Influence of Chinese Enterprises’ Financial Efficiency Based on the Malmquist Index

-is paper introduces a superefficiency financial efficiency model with undesirable output based on the features that the output of industrial enterprises contains desirable output as well as undesirable output. Furthermore, the Malmquist index model is constructed for financial efficiency dynamic study, and the spatial Durbin model is constructed for evaluation and impact of enterprises. According to the financial data of Chinese enterprises from 2007 to 2019, this paper evaluates the financial efficiency of Chinese interprovincial industrial enterprises dynamically and measures the influence levels of major impacts on the financial efficiency of Chinese interprovincial industrial enterprises quantitatively. As reported by this paper, the conclusions are as follows: (1) In reference to the financial efficiency dynamic study analysis, there is an obvious growth trend in the financial efficiency of Chinese interprovincial industrial enterprises in different years. Based on the horizontal analysis of financial efficiency, there is a relatively large gap in financial efficiency among Chinese interprovincial industrial enterprises. (2) From the separation factors of financial efficiency analysis, the main factor affecting the growth of the financial efficiency of Chinese industrial enterprises is the modification of technology, and the modification of technical efficiency has a minor impact. (3) In accordance with the impacts of enterprise efficiency analysis, several major factors influence the financial efficiency of Chinese industrial enterprises such as major business cost, operating profit, total liabilities, national capital, and the number of R&D personnel.


Introduction
With the world economy getting into the stage of rapid development of postindustrialization, the resource shortage and environmental pollution issues are getting worse and worse, which affect people's lives and the development of society. erefore, the use of scientific methods to analyze and evaluate the financial efficiency of regional industrial enterprises and to dig out their influencing factors and influencing directions is of great practical significance for promoting energy conservation and emission reduction of regional industrial enterprises, improving the ecological environment, and realizing regional sustainable development. Financial efficiency refers to the ratio between input and output of financial resources and other ratio relationships derived therefrom.
ere are multiple methods to evaluate enterprise financial efficiency, and the primary methods are the single index method, DEA, comprehensive index method, principal component analysis, and regression analysis of linear and nonlinear parameters.
Mykola et al. [1] constructed a single index system which includes financial stability, liquidity and solvency, operating activities, profitability, and evaluates the financial efficiency of an industrial enterprise. Güner [2] conducted deep research on infrastructure, operational efficiency, and financial efficiency based on the DEA model to measure the financial efficiency of 13 Turkish seaport enterprises. Deng et al. [3] established a dynamic evaluation system based on data envelopment analysis (DEA), analytic hierarchy process (AHP), and priority ranking enrichment evaluation organization method and evaluated the financial efficiency of China's nuclear power-related enterprises. Charmondusit et al. [4] constructed three sustainable development indicators which are economic indicators, environmental indicators, and social indicators and measured the financial efficiency of the wooden toy industry. Ricardo et al. [5] used a multi-indicator system that included representativeness, participation, and leadership to evaluate the relationship between governance and financial efficiency of Brazilian credit cooperatives. Le et al. [6] used the sample data of 31 Asian countries from 2004 to 2016 to construct a comprehensive index system of three financial dimensions using principal component analysis (PCA) based on standardized variables and conducted research on the financial efficiency of the samples. Mitchell [7] used the linear and nonlinear regression analysis method to evaluate and analyze the financial efficiency of guarantor enterprises. Shu and other scholars [8] combined multilevel dynamic fuzzy evaluation and the BP neural network to establish a financial efficiency evaluation model of private enterprises. Ross [9] used the advantages of the questionnaire and comprehensive interview method to evaluate financial efficiency based on the internal status of the company's management department. Robert [10] proposed a balanced scorecard with distinct advantages to evaluate financial efficiency when implementing the integration of financial and nonfinancial indicators.
From the analysis of the current research data, there are multiple studies on the financial efficiency of an industry or enterprise, but rare on the financial efficiency of macroregional industries. ere is abundant research on the static evaluation of financial efficiency but insufficient research on the dynamic research of financial efficiency.
ere is plenty of analysis on the evaluation of financial efficiency but a lack of analysis on the influencing factors of financial efficiency. erefore, this paper will construct the superefficiency financial efficiency model with undesirable output and the financial efficiency index model and combine them to evaluate the financial efficiency of Chinese interprovincial industrial enterprises dynamically. e evaluation model of influencing factors of enterprise financial efficiency is constructed as well to quantitatively measure the impacts of industrial enterprise financial efficiency. e conclusion will provide a theoretical basis for formulating policies to improve the financial efficiency of Chinese industrial enterprises.

Construction of an Enterprise Financial Efficiency Evaluation Model.
is paper introduces a superefficiency financial efficiency model with undesirable output based on the feature that the output of industrial enterprises contains desirable output as well as undesirable output. e model also comprehensively evaluates the financial efficiency of interprovincial industrial enterprises. Suppose there are "n" types of DMU and "m" types of inputs, then the i th input of k th DMU is recorded as x ik (i � 1, 2, . . . , m); suppose there are q 1 types of desirable output and q 2 types of undesirable output, then the r th undesirable output of k th DMU is recorded as y rk (r � 1, 2, . . . , q 1 ) and the t th undesirable output of k th decision sheet is recorded as b tk (t � 1, 2, . . . , q 2 ); the superefficiency US-DEA model with undesirable output is as follows: 2 Discrete Dynamics in Nature and Society where s − i is the input slack variable; s + r is the desirable output slack variable; s b− t is the undesirable output slack variable; and λ is the combination proportion in effective decisionmaking unit.

Construction of an Enterprise Financial Efficiency Index Model.
A financial efficiency index model is constructed to work on the dynamic financial efficiency of industrial enterprises. In general, indexes include fixed base index and chain base index. A fixed base index for which the base period for calculation remains unchanged. is is different from a chain base index in which the base period for calculation is based on the previous period. is paper will use the fixed base index, which refers to the fixed reference Malmquist index (MI f ) to dynamically compare the financial efficiency of industrial enterprises. e MI f index is divided into technical efficiency change index EC f and technical change index TC f . e following shows the relationship between the three indexes: where E f (x t , y t ), E f (x t+1 , y t+1 ) refers to the t, t + 1 financial efficiency of a fixed period, respectively. e formula of technical efficiency change index, EC f shows as follows: where E t (x t , y t ), E t+1 (x t+1 , y t+1 ) refer to t, t + 1 financial efficiency of a current period, respectively. e formula of technical change index, EC f shows as follows: where E f (x t , y t ), E f (x t+1 , y t+1 ) refer to t, t + 1 financial efficiency of a fixed base period, respectively.

Construction of the Evaluation Model of Influencing
Factors of Enterprise Financial Efficiency. Since the research object of this paper is the financial efficiency of 30 interprovincial enterprises in China, the financial efficiency of these interprovincial enterprises has a spatial correlation. e construction of a regression equation has the feature of solving the spatial correlation of variables. Hence, this paper will construct a spatial Durbin model (SDM) to measure spatial association. e formula of SDM is as follows: where e is the explanatory variable (interprovincial financial efficiency), W is the spatial weight matrix, X is the explanatory variable, λ, β, δ are the coefficients, and ε is the random disturbance.

Index Selection
3.1.1. Input Indicator. Total assets, employment, total water consumption, and total energy consumption of interprovincial industrial enterprises are selected as input indicators to calculate the financial efficiency of Chinese industrial enterprises by using the superefficiency DEA model.

Output
Indicator. e profit of industrial enterprises, total industrial output, industrial wastewater discharge, industrial waste gas discharge, and the total amount of industrial solid waste are selected at the same time as the output indicators. e first two items are regarded as desirable outputs, and the last three items are regarded as undesirable outputs.

Data Source.
is paper selects the data of China's provinces, municipalities directly under the central government and autonomous regions (Hong Kong, Macao, Taiwan, and Tibet are not included in the analysis due to the lack of data) from 2007 to 2019 as the research sample, with a total of 390 observations. All the data are from the Chinese Industrial Statistics Yearbook, Chinese Environment Yearbook, and Chinese Energy Statistics Yearbook from 2008 to 2020.

Dynamic Analysis of the Financial Efficiency of Industrial
Enterprises. According to the input and output index data of Chinese interprovincial industrial enterprises from 2007 to 2019, the financial efficiency of Chinese industrial Discrete Dynamics in Nature and Society enterprises in 2007 as a fixed reference can be calculated by using MaxDEA7 software and formulas (1) and (4). e detailed data are shown in Table 1. e following can be seen from the interprovincial financial efficiency data in Table 1

Analysis on the Total Change of Financial Efficiency of Industrial Enterprises.
e financial efficiency of industrial enterprises in different periods is calculated above. Based on this, the absolute amount of financial efficiency change (ΔE) in different years can be calculated and decomposed into the absolute amount caused by technical efficiency modification (ΔE EC ) and the absolute amount caused by technical modification (ΔE TC ). e absolute relationship between the three can be expressed as follows: where E(x t+1 , y t+1 ) is the financial efficiency calculated in period t + 1 and E(x t , y t ) is the financial efficiency calculated in period t. According to formula (6) and the data in Table 1, the absolute change of financial efficiency (ΔE) of Chinese industrial enterprises in different years can be calculated. is refers to the difference between the financial efficiency of the previous year and the financial efficiency of the next year. If the difference is greater than 0, it indicates that the financial efficiency will increase in the next year, and if the difference is less than 0, it indicates that the financial efficiency will decrease in the next year. e detail data can be seen in Table 2. It is worth knowing from Table 2 : (1) the financial efficiency of dynamic industrial enterprises in most provinces has increased. Of 30 provinces, 27 provinces increased, accounting for 90%. (2) e financial efficiency of dynamic industrial enterprises is decreasing in only a few provinces, such as Heilongjiang, Ningxia, and Xinjiang, accounting for 10%.

Analysis on the Difference of Financial Efficiency of Industrial Enterprises Caused by the Technical Efficiency
Modification. According to formula (6), the difference in financial efficiency in different years can be decomposed into absolute quantity (ΔE EC ) caused by technical efficiency modification and absolute quantity (ΔE TC ) caused by technical modification. e data of the financial efficiency differences caused by technical efficiency modification are shown in Table 3. e following conclusions are drawn from the data in Table 3 : (1) From the analysis of the cumulative differences in value caused by technical efficiency modification in different years among provinces, there are 18 interprovinces greater than 0, accounting for 60% of all interprovinces; there are 12 interprovinces less than 0, accounting for 40% of all interprovinces. It reflects that most interprovincial differences caused by technical efficiency modifications are increasing. (2) From the analysis of the average difference value caused by technical efficiency modification in different years of all provinces, it was 0.032 in 2007 and -0.036 in 2019. It is surprisingly a decrease, reflecting that the differences in overall financial efficiency caused by technical efficiency modification did not increase.

Analysis on the Differences of Financial Efficiency of Industrial Enterprises Caused by Technological Modification.
Similarly, the data of financial efficiency change caused by technical modification is decomposed according to formula (6) and the detailed data can be seen in Table 4. e following conclusions are drawn: (1) From the analysis of the cumulative differences in value caused by technical modification in different years among provinces, all of them are greater than 0, reflecting that the main reason that caused the increase of financial efficiency among provinces is technical modification. Simultaneously, there is a large index differences among provinces, with the highest being 5.285 in Shandong and the lowest being 0.380 in Hainan.

Determination of Influencing Factors of Enterprise Financial Efficiency.
is paper selects the following influencing factors to conduct research on the influence levels of different impacts on the financial efficiency of industrial enterprises: main business cost (cost), operating profit (prof ), total liabilities (liab), national capital (scap), foreign capital (fcap), the number of patents applied (pate), and the number of R&D personnel (R&D). e abovementioned data of indicators are from 2007 to 2019, and the data source is the same as in Section 3.2.

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Discrete Dynamics in Nature and Society

Analysis of Influencing Factors of Financial Efficiency
Based on the Spatial Dobbin Model. StataSE-64 software is applied for the research on the influence levels of various influencing factors on interprovincial financial efficiency. e spatial Durbin model is used for regression according to the dynamic data of the financial efficiency of interprovincial industrial enterprises in Table 1. e regression results are shown in Table 5: (1) As shown in Table 5, the data in column coef. of the Min project reflects the impact of various influencing factors on the financial efficiency of their own provinces. e necessary factors that impact on financial efficiency are operating profit (prof ), national capital (scap), and the number of R&D personnel (R&D), and the credibility is 99%. Operating profit (prof ) and national capital (scap) have positive coefficients, and this indicates that these two factors have a positive correlation on the financial efficiency of their own province. e coefficient of other factors is negative, indicating that these factors have a negative correlation with the financial efficiency of the province. (2) In Table 5, the data in column coef. of the Wx project reflects the impact of various influencing factors on the financial efficiency of neighboring provinces. e major factors that impact on the financial efficiency of neighboring provinces are the main business cost (cost), total liabilities (liab), national capital (scap),       Table 5 reflects the direct impact of various influencing factors on the financial efficiency of the province. e main factors that have an impact on the financial efficiency are operating profit (prof ), national capital (scap), and the number of R&D personnel (R&D), and the credibility is 99%. e positive coefficients are operating profit (prof ) and national capital (scap), meaning that these factors have a direct impact on the financial efficiency of the province. e coefficient of other factors is negative, meaning that these factors have a negative correlation with the direct impact on the financial efficiency of the province. (4) As shown in Table 5, the data in the coef. column of the LR-Indirect project reflects the indirect impact of various influencing factors on the financial efficiency of the province, and the indirect impact is not significant. (5) As shown in Table 5, the data in the coef. column of the LR-Total project reflects the overall impact of various influencing factors on the financial efficiency of the province, and the overall impact is not significant.

Conclusion
is paper constructs a superefficiency financial efficiency model and a financial efficiency index model with undesirable output to dynamically evaluate the financial efficiency of Chinese interprovincial industrial enterprises. A spatial Durbin model is constructed as well to quantitatively measure the influencing factors of the financial efficiency of industrial enterprises. e conclusions of this paper are as follows: (1) According to the dynamic analysis of the average values of financial efficiency of Chinese interprovincial industrial enterprises in different years, the dynamic growth trend of financial efficiency is noticeable. Based on the horizontal analysis of the average value of the financial efficiency of different Chinese interprovincial industrial enterprises, there is a large financial efficiency disparity among provincial industrial enterprises, indicating that the financial efficiency of some interprovincial enterprises can be enhanced. (2) On the basis of the analysis of the financial efficiency of Chinese interprovincial industrial enterprises, the main factor affecting the growth of the financial efficiency of Chinese industrial enterprises is the modification of technology, and the modification of technical efficiency has a minor impact. (3) From the influence factors analysis of the financial efficiency of Chinese interprovincial industrial enterprises, the factors that have a significant impact on the financial efficiency of industrial enterprises are as follows: main business cost, operating profit, total liabilities, national capital, and the number of R&D personnel, and the credibility is up to 99%. It is necessary to start from these factors if the financial efficiency of industrial enterprises would like to be improved.

Data Availability
is paper selects the data of China's provinces and municipalities directly under the central government and autonomous regions (Hong Kong, Macao, Taiwan, and Tibet are not included in the analysis due to the lack of data) from 2007 to 2019 as the research sample, with a total of 390 Note. e data with * , * * , and * * * in the table indicate, respectively, p < 0.10, p < 0.05, and p < 0.01. 8 Discrete Dynamics in Nature and Society observations. All the data are from the Chinese Industrial Statistics Yearbook, Chinese Environment Yearbook, and Chinese Energy Statistics Yearbook from 2008 to 2020.

Conflicts of Interest
e authors declare that they have no conflicts of interest.