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ISSN : 1598-5504(Print)
ISSN : 2383-8272(Online)
Journal of Agriculture & Life Science Vol.54 No.6 pp.131-139
DOI : https://doi.org/10.14397/jals.2020.54.6.131

Analysis of Operating Efficiency of China’s Agricultural Listed Companies

Zhi-Run Li1, Shi-Yong Piao1, Yu-Cong Sun1, Shuang-Yu HU1, Xuan-You Jin1
1Graduate School, Kangwon National Univ., Chuncheon, 24341, Korea
2Dept. of Agricultural & Resource Economics, Kangwon National Univ., Chuncheon, 24341, Korea
*Corresponding author: Jong-In Lee Tel: +82-33-250-8668 Fax: +82-33-250-8668 E-mail: leejongin@kangwon.ac.kr
June 16, 2020 ; September 22, 2020 ; November 27, 2020

Abstract


In recent years, China's agricultural listed companies have developed rapidly. This paper studies the operating efficiency of listed agricultural companies in China. exploring the factors that affect the operating efficiency of listed agricultural companies, and proposes targeted countermeasures for the development of listed agricultural companies. And give some suggestions to Korean agricultural companies and A share investors. This paper uses the DEA model, selects the 40 best-developed Chinese agricultural listed companies in 2018 as a sample, analyzes the operating efficiency of these companies, and added two new input variables: asset impairment losses (AIL) and business tax and surcharge (BTS), which will also have an impact on operating efficiency. This paper can provide reliable suggestions for the development of agricultural listed companies, and thus guide the healthy operation of agricultural companies.



초록


    Introduction

    This paper introduces the comprehensive efficiency indicator of operating efficiency to evaluate the development of China's agricultural listed companies. The operating efficiency can objectively reflect the problems existing in the costs and benefits of agricultural enterprises in the process of production and operation through financial indicators. By analyzing the different efficiency factors of the production costs and their influencing factors, it is possible to judge the production costs. In this way, we can more specifically find the main aspects of the problems arising in the development of agricultural companies.

    Industries are always concerned about the efficiency and how to reduce operational cost. (Tariq et al., 2019) The level of operating efficiency will directly affect the operating status of the company, especially for the listed companies. (Pang et al., 2007) And the level of operating efficiency may affect the optimal allocation of resources for the entire industry. The advantage of using operating efficiency is that the data is more convenient to obtain. Reliable and accurate data from official channels can be obtained through the financial statements of listed companies. By screening different accounting subjects and selecting the input and output indicators of listed companies, the model of operating efficiency is selected to ensure the scientific predictability of operating efficiency.

    There is a Chinese proverb that says, people take food as their heaven, which means that the most important thing for people is to eat. Agricultural enterprises are important organizations that convert grain into food. It is the basic guarantee for the healthy operation of society. The listed agricultural companies are the part that bears the greatest responsibility. If the operation of listed agricultural enterprises is stagnant, it will directly affect the efficiency of agricultural products entering the market, which will affect people's lives. At this time, if there are no substitutes, it will directly cause food prices to rise. In China, listed agricultural companies often represent monopolists in a certain segment, especially the research objects selected in this study, representing the most advanced productivity of Chinese agricultural companies. It can be said that if there is a problem with the production efficiency of these companies, that will directly cause China's food industry to stop advancing. Therefore, by studying the operating efficiency of listed agricultural companies in China, we can find the reasons that affect the operating efficiency of the company, so as to find out the problems in the company’s development process, at the same time, we can see the gap between excellent enterprises and under-excellent enterprises, and promote the un- der-excellent enterprise progress.

    A large number of scholars have study on agricultural production and efficiency of agricultural listed companies, international research methods widely used mainly focus on the Total Factor Productivity (TFP), the Stochastic Frontier Analysis (SFA), and the Data Envelopment Analysis (DEA). In China, most researches on business operation efficiency depend on the company performance evaluation system promulgated by the government's financial department, use specific methods to determine index weights, and calculate the business performance of agricultural listed companies.

    Tim Coelli (1998)thinks that the advantage of DEA method is, firstly, it can evaluate the efficiency of decision-making units (DMU) with complex production relations. Secondly, the efficiency measured by DEA is not affected by the unit selected for input-output data. Thirdly, the weights of input and output variables in the DEA model are generated by the mathematical planning of the data and do not need to be set in advance, so they are not affected by human subjective factors. These characteristics and advantages determine that this paper chooses the DEA model for research.

    Through the efficiency evaluation of DEA, first, the managers of the enterprise can clearly see the mistakes of the enterprise's input and output, so as to adjust the business strategy and ensure the healthy development of the enterprise. Second, listed companies with high operating income are not necessarily healthy companies. DEA analysis can show investors a more realistic list of companies suitable for A-share investment.

    This paper adopts the DEA model to measure and analyze the operating efficiency of Chinese agricultural listed companies. By selecting the main financial indicators of agricultural listed companies, the DEA model is used for efficiency analysis, the comprehensive efficiency, pure technical efficiency and scale efficiency analysis of Chinese agricultural listed companies were analyzed, and two inefficient companies were selected for specific analysis.

    Materials and Methods

    1. Overview of Operating Efficiency

    The research on the operating efficiency of listed agricultural companies mainly focuses on two aspects: one is to study the influencing factors of operating efficiency, the other is to study the evaluation methods of operating efficiency.

    Research on the factors influencing the operating efficiency of agricultural listed companies. As the world’s largest developing country and a typical emerging market country, China has significant differences from developed countries in terms of social systems and economic development models. The conclusions drawn from the research on operating efficiency of agricultural listed companies in developed countries may not be applicable to China. In addition, the uniqueness of agriculture itself causes its management model and efficiency level to be different from those of other listed companies. Therefore, the factors that affect the operating efficiency of Chinese agricultural listed companies are mostly focused on policy economy and company structure. Zou Caifen et al. (2006) used non-equilibrium panel data to conduct empirical research on tax preferential policies and fiscal support policies, the results shows that adopting preferential tax policies for agricultural listed companies has not had a significant effect on their operating efficiency, and found that the direct financial subsidies have strengthened the company's ability to pay debts, which has led to lazy behavior of managers. Hu Xinghui (2011) also found that the state's fiscal and tax subsidy policies have not had a positive effect on the operating performance of listed agricultural companies, in particular, preferential income tax policies and subsidy income policies have adversely affected the overall performance.

    Research on evaluation method of operating efficiency of agricultural listed companies. Wang & Wang (2014) used the factor analysis method to analyze the financial performance of 42 agricultural listed companies in 2010-2012, the main shortcomings of these companies were found: their own development capabilities, their ability to stabilize capital chains, and their ability to repay foreign debt. Yang & Shaofeng (2010) used the entropy weight method to analyze the operating efficiency of listed agricultural companies from 2006 to 2008, and the results showed that the operating efficiency during the three years was uneven. Meng & Ding (2005) collected two years agriculture listed company data, using the DEA model to analyze their overall level of efficiency, it was found that the average efficiency value of agricultural listed companies was low, and explained that the reason for this phenomenon was the failure of diversification of some agricultural listed companies.

    Although these studies have been carried out in depth, there is still a lack of related research on the operating efficiency of Chinese agricultural listed companies in recent years. The past research on influencing factors is also very limited. The hypothesis puts too much emphasis on the impact of national policies, but weakens the market's impact on companies. Therefore, we selected the agricultural listed companies with the highest operating income in China in 2018 as a sample, selected their operating data, and used the DEA model for analysis.

    2. Empirical Model - DEA

    Data Envelopment Analysis, was first proposed by Charnes, Cooper and Rhodes (1978) on the basis of Farell's envelope idea, used to evaluate the efficiency of the public and non-profit sectors. DEA uses a mathematical programming model to evaluate the efficiency of DMU (Decision Making Unit) from two perspectives: input minimization and output maximization. DEA model is the most commonly used CCR (Charnes Cooper Rhodes), and BCC (Banker Charnes Cooper) model, the former for the overall efficiency of the evaluation and decision unit, which for pure evaluation and decision unit technical efficiency. Pure technical efficiency refers to considering the distance between the company's input-output combination and the production frontier under the condition of variable returns to scale (VRS). Scale efficiency refers to the measurement of the distance between the constant return on scale (CRS) and the variable return on scale (VRS) production frontier under the premise of pure technical efficiency. The relationship between the above three efficiencies is:

    T E = P T E × S E
    Function (1)

    This article uses a CCR model to evaluate the efficiency of decision-making unit technology and scale.

    Suppose there are k companies of the same type, and each company has m types of "inputs" and n types of "outputs", then the m input items of the jth DMU are using X 1 j , X 2 j , , X m j to represent, n output items are represented by Y 1 j , Y 2 j , , Y n j , and all the input and output items are represented by the matrix X, Y.

    Set Vi for the ith indicators X i ( i = 1 , 2 , , m ) 's weight, the Ur are the rth output indicators Y r ( r = 1 , 2 , , n ) 's weight, then the comprehensive value of the jth enterprise's input is i = 1 m V i X i j , the comprehensive value of the output is r = 1 n U r Y r j , and the efficiency evaluation index of each decision unit is defined as:

    h j = r = 1 n U r Y r j i = 1 m V i X i j , j = 1 , 2 , 3 , , k
    Function (2)

    The efficiency evaluation model of the DMU can be obtained as follows:

    M a x h j = r = 1 n U r Y r j i = 1 m V i X i j
    Function (3)

    S.t. h j = r = 1 n U r Y r j i = 1 m V i X i j 1 , j = 1 , 2 , 3 , k
    Function (4)

    V i , U r 0 , i = 1 , 2 , 3 , , m ; r = 1 , 2 , 3 , n
    Function (5)

    In the above model, Xij and Yij are known numbers, Vi and Ur are variables. The meaning of the model is to use the weight coefficients Vi , Ur as variables, the efficiency index hj of all decision-making unit as constraints, and the efficiency index of the jth decision-making unit as a target, that is, for the jth decision-making unit. The effectiveness evaluation of production efficiency is compared with all other decision-making unit. Due to a nonlinear programming (1) formula is not easily obtained, and (2) evaluation formula constraint efficiency values range in [0,1], whereby to obtain a set of optimum weighting values (Vi*, Ur*), can maximize the efficiency value of each DMU. In order to facilitate the calculation and analysis, by converting the parameters, the input-based CCR model can be obtained:

    M i n θ s . t . j = 2 k λ i X i j + S = θ X i k j = 2 k λ i X i j + S + = θ X i k λ i 0 , S 0 , S + 0
    Funtion (6)

    Wherein S-S+ represent the input, output slack variable, the first decision unit composition in the re-create commercially jth relative proportions of agricultural listed companies, P is the kth efficiency values of a decision unit (0 ≤ p ≤ 1). This model is the CCR model. If its optimal solution is: λ * , S * , S + * , θ * , then:

    If θ * = 1 , and S-* = 0, S+* = 0, then DMUk is effective, which represent the overall effectiveness of the decision unit k, that is, to achieve both technical effectiveness and scale effectiveness.

    If θ * = 1 , and S * , S + * at least one of them is not 0, then DMUk is weak valid, it means that the input is redundant and the input resources are not fully utilized, if so S + * 0 , it means that the output is insufficient, that is, between the actual output and the maximum output, there is a gap, so the decision unit k has not achieved both technical effectiveness and scale effectiveness.

    If θ * 1 , DMUk invalid, that is, the decision unit k has neither reached technical validity nor scale effective.

    3. Sample and explanation

    In 2019, China Business Information Network (www.askci.com) released the 2018 list of the top 50 operating income of Chinese agricultural enterprises A-share listed companies, this article uses the top 40 agricultural listed companies as the research sample. China Business Information NetworkIt is an information organization established by China's most professional consulting management theory experts and competitive intelligence companies. It is the first independent third-party service organization in China with the goal of corporate information and data research. The scope of information research involves enterprise management information, enterprise external information, public media information, competitor enterprise information, etc., featuring competitive intelligence research, and mining the internal industry operating rules of data. The financial data of each company comes from NetEase Finance (money.163.com). The accounting subjects used include: total operating income, business taxes and surcharges, selling expenses, management expenses, financial expenses, asset impairment losses.

    China's agricultural listed companies are involved in a wide range of fields, mainly distributed in: animal husbandry, pharmaceutical industry, food industry, fishery, agricultural machinery industry, biological industry, etc. As used herein, selected 40 agricultural listed companies total operating income was 447.75 billion yuan, of which New Hope Group (000876) at 69.06 billion yuan in operating income ranked first, Wen's Shares (300498) to 57.24 billion yuan in operating income ranked the second, the Large Group ranked third, operating income 42.15 billion yuan. It is noteworthy that, 2018 of China's listed companies in agriculture 40 strong in revenue over ten billion yuan company has 14, their total operating income is 35.58 billion yuan, it accounts for 79.4% of the total operating income of 40 companies.

    4. Data collection method

    By comprehensively considering the different listing times of the above agricultural listed companies, changes in abnormal data of the enterprise during the operating period will affect the final efficiency evaluation analysis. This study screens the companies in the sample based on the following principles.

    • (1) Enterprises that have been listed for less than five years will be eliminated (except for those whose annual report has been published for five years).

    • (2) Although the listing time has been over five years, the company that has been ST or between 2012 and 2016 is still in ST status will be eliminated.

    • (3) Enterprises whose main business income accounts less than 50% are excluded.

    • (4) Companies engaged in non-agricultural production industries will be eliminated.

    • (5) The companies with negative values in the selected input- output index items are eliminated.

    After elimination, 40 eligible agricultural listed companies were selected as samples for subsequent analysis.

    5. Index selection

    When using the DEA model to analyze the operating efficiency of agricultural companies, the accuracy lies in the selection of input and output indicators to a certain extent. Therefore, the evaluation index should be able to show the characteristics of the agricultural company's operating efficiency, that is, the selected evaluation index can meet the requirement of the agricultural company to obtain the maximum output through the minimum input. By combining the indicators selected in previous studies, the input indicators selected in this article are: business tax and surcharge (BTS), selling expenses (SE), management expenses (ME), financial expenses (FE), and asset impairment losses (AIL). The output indicator is total operating income (TOI). Based on the results of related studies, in addition to using SE, ME, and FE as explanatory variables, this article also adds BTS and AIL as explanatory variables.

    Business taxes and surcharges (BTS) include consumption tax, resource tax, education surcharges, and urban maintenance and construction tax that the main business of the enterprise should bear. Sales expenses (SE) refer to the various expenses incurred in the process of selling goods and materials and providing labor services. Including travel expenses, depreciation expenses, and office expenses. Management expenses (ME) refer to the various expenses incurred by the administrative department of an enterprise to organize and manage production and operation activities. The specific items included are: company funds, labor union funds, unemployed insurance premiums, labor insurance premiums, board fees, fees for hiring an intermediary agency, and consultations that occur during the operation and management of the company by the board of directors and administrative departments of the company or should be borne by the company Expenses, litigation costs, business entertainment, office expenses, travel expenses, post and telecommunications expenses, greening expenses, management staff salaries and welfare expenses, etc. Enterprises use this account to calculate the occurrence and carry-over of management expenses. Financial expenses (FE) refer to the expenses incurred by enterprises to raise funds needed for production and operation. Items include: net interest expenses (the difference between interest expenses minus interest income), net exchange losses (the difference between exchange losses minus exchange gains), financial institution handling fees, and other expenses incurred in raising production and operating funds. Asset impairment losses (AIL) refers to the loss caused by the recoverable amount of an asset being lower than its book value. The scope of asset impairment mainly refers to the treatment of impairment of fixed assets, intangible assets and other assets except for special regulations. Total operating income (TOI) refers to the total inflow of economic benefits formed by the enterprise in the daily business process of selling goods, providing labor services and transferring the right to use assets. The reason why the six inputs were selected is that they can measure the development status and level of the enterprise from a broader perspective, and more importantly, although the chosen listed agricultural companies have the same business direction, their main business content is very different. The six inputs we selected are subjects included in all research samples, and they are the main financial accounts for measuring the company's development.

    Table 2 shows the descriptive statistics of the 40 companies. By observing the descriptive statistical analysis results of 40 sample enterprise data, we can see that the difference between the maximum value and the minimum value is more obvious whether it is the input index or output index. It shows that there are still big differences in the business and operation methods among these companies. It can be seen that the difference between New Hope (00876), which has the highest total operating income value, and Longli Biological (002604), which has the lowest total operating income value, is 6,820,756. There are significant differences in the business scope of the two companies. New Hope is China's largest feed manufacturer, one of China's largest agricultural and animal husbandry enterprises, has the largest agricultural and animal husbandry industry cluster in China, and is the leader of Chinese agricultural and animal husbandry enterprises, then, Longli Biological is a technology-based enterprise with a relatively single main business, and its main business scope is concentrated in the new energy business. The huge differences between the two enterprises scale and business scope are the main reasons for the difference in TOI. The standard deviation of TOI is 1483081, reflects the uneven development of listed agricultural companies in China. The BTS can accurately reflect the macroeconomic attributes of agricultural companies. It can be seen that the company with the highest BTS is not the company with the highest TOI. At the same time, the company with the lowest BTS is not the company with the lowest TOI, which reflects the internal adjustment ability in the face of the macroeconomic environment. SE, ME, and FE reflect the operational efficiency and management level of the company’s internal team, and are also the top priority for characterizing investment indicators. The companies with the highest SE and ME values are the two companies with the highest TOI values, which reflects that these companies have invested a lot of money in sales and management. The company with the highest FE value is Chuying Farm (002477), and this company's TOI value is only ranked the 25th, which reflects the company's internal management problems. Excessive AIL is a situation that most listed agricultural companies do not want to see. Excessive AIL value is often accompanied by company losses and is also a manifestation of company mismanagement. Coincidentally, the maximum value of AIL also appeared in Chuying Farm (002477). As one of the largest animal husbandry company in China, Chuying Farm (002477) was hit hardly by the bird flu. The company failed to prepare an emergency plan in the face of such emergency epidemic. Realistic experience also shows that after the completion of accounting in 2018, Chuying Farm's overall operating conditions have encountered huge problems, which led to the suspension of all the business of the group.

    Results and Conclusion

    1. Comprehensive efficiency analysis

    Comprehensive efficiency is a comprehensive expression of scale efficiency and pure technical efficiency, and it is the technical efficiency when scale returns are not considered. The comprehensive efficiency in the input angle reflects the ability of listed agricultural companies to use inputs at a certain output level. If the comprehensive efficiency value is 1, the listed agricultural company is considered to be valid for a deterministic DEA. <Table 3> shows that in 2018, 10 agricultural listed companies' technique efficiency is 1, 18 agricultural listed companies' pure technique efficiency is 1. 10 agricultural listed companies' comprehensive efficiency value is 1, achieved DEA effective, accounting for the total number of samples 25%. It shows that the production scale, technical level and operating efficiency of these companies are relatively optimal. The overall efficiency of the other 30 companies is less than 1, which indicates that they have not reached the optimal level in terms of resource allocation and production efficiency, and need to improve production technology or raise management level.

    This shows that these listed agricultural companies still have a lot of room for improvement in their operating structure. Comparing technical efficiency and pure technical efficiency, we can find that companies with poor performance in technical efficiency and pure technical efficiency, such as DKNY (002505), XWSP (000639), HYNY(002321), TKSW (02100), LPGK (000998), CYNM (002477), JFNJ (300022), JXN (002548) and KCGJ (60 0097), have higher scale efficiency, indicating that scale efficiency is not the main reason for the low operating efficiency of these companies.

    There are many factors that affect the efficiency of listed companies, such as the size of listed companies, capital structure, share holding structure, management level, etc. and the main reasons for the low comprehensive efficiency of agricultural listed companies, on the one hand, affected by both natural risks and market risks, it results in low operating income. On the other hand, some listed agricultural companies have blindly pursued diversified development and entered some high-profit industries, but they have not carried out clear strategic positioning, resulting in a reduction in comprehensive efficiency. The results show that most of the listed agricultural companies with excellent overall efficiency are specialized in production investment, with the agricultural industry as the main business, and the advantages of specialization are obvious.

    2. Pure technical efficiency and scale efficiency analysis

    Pure technical efficiency reflects whether listed agricultural companies can effectively use existing production technologies to maximize output. Of the 40 agricultural listed companies, 18 have a pure technology efficiency value of 1, which is at the forefront of pure technology, accounting for 45% of the total sample. This shows that these companies have a relatively high level of operation and management, and the production factors such as assets, manpower, and material resources have been fully utilized to obtain the maximum output. However, there are 22 companies with a pure technical efficiency value of less than 1, accounting for 55%. These companies have relatively low production efficiency, unreasonable combinations of input factors, low output efficiency, and failure to properly utilize production technology capabilities, and should strengthen corporate supervision and improve the company's operation and management level, reasonable allocation of various inputs, reducing production costs and improving production efficiency.

    Scale efficiency measures the ability of an enterprise to use various input resources in proportion to the appropriate scale. When the scale efficiency is 1, the scale is effective, and the amount of input is at the stage of constant returns to scale. 10 company scale efficiency of 1, 14 companies are in a state of diminishing returns to scale, even if the company increases the input, it is unlikely to bring a higher proportion of output. This shows that most of the listed agricultural companies are experiencing extensive growth, blindly pursuing scale expansion, resulting in low efficiency. There are 15 companies in the state of increasing returns to scale. These companies can increase their inputs appropriately, give play to their scale advantages, and increase output. Which means that scale efficiency is not the main reason for the low operating efficiency of these companies. From the analysis of the return to scale, among the listed agricultural companies in 2018, there are 10 companies with constant return to scale, 15 companies in the stage of increasing returns to scale, and 15 companies in the stage of diminishing returns to scale. It shows that the operating scale of most listed agricultural enterprises has reached or exceeded the optimal limit, and the growth of output is relatively slow relative to the growth of input. It is not appropriate to increase output by increasing input, but should adjust the existing resource utilization efficiency.

    For agricultural listed companies, the focus is on how relatively inefficient companies can achieve a relatively effective state by improving resource allocation. Through the above analysis, we can see that the comprehensive efficiency of agricultural listed companies is not good, and more than half of the company are in a relatively ineffective state, so an improvement analysis of these ineffective units is needed. If the sample company is invalid, there must be redundant or insufficient investment.

    First, take the CYNM (002477) as an example. The company's returns to scale are increasing. For output items TOI, Radial Movement is 1023566.557, indicating that the company needed to upgrade 102.36 billion yuan. In revenue for the input items BTS and SE, Radial Movement and Slack Movement are Zero, indicating that neither redundancy nor under investment was invested. For ME, Original the Value of 74769.000, Slack Movement value is 24575.801, it needs to reduce the amount of investment to achieve the Projected Value of 50193.199. For FE, Original Value to 98882.000, Slack Movement is 47453.517, indicating it need to reduce the amount of investment in order to achieve Projected Value of 51428.483. For AIL, Original the Value is 171586.000, Slack Movement value is 171243.729, it need to reduce the amount of investment in order to achieve the Projected Value of 342.271.

    The announcement issued by CYNM (002477) shows that the company is expected to lose 2.9-3.3 billion yuan in 2018, which is a sharp drop compared with the profit of 45.188 million yuan in the same period 2017. Since June 2018, the company has experienced a tight liquidity situation, which has a greater impact on the company's operating performance. Due to tight funds and untimely feed supply, the company's pig breeding mortality rate was higher than expected, resulting in higher than expected pig breeding costs and management expenses; and the live pig market in the fourth quarter was affected by African swine flu, and sales prices were lower than expected. CYNM (002477) also made provision for inventory fall, because the company’s pig breeding cost was higher than the sales price of pigs at the end of 2018, asset impairment provision was made because the industrial funds within the scope of the consolidated statement were mainly invested in the pig breeding industry downstream companies, given that the live pig market was affected by African swine flu in 2018, their profitability and financing capabilities were affected to big extent.

    Take NNTY (000911) as an example. The company's diminishing returns to scale. For the output item TOI, the Radial Movement is 744025.316, which means that the company needs to increase the operating income of 74.40 billion yuan. For the input items SE, ME, FE, both Radial Movement and Slack Movement are zero, indicating that there is neither investment in redundancy nor insufficient investment. For BTS, the Original Value is 74769.000 and the Slack Movement value is -1944.275, which means that the amount of investment needs to be reduced to reach the Projected Value of 1755.725. For AIL, Original Value of 37835.000, Slack Movement value of –36675.920, it needs to reduce the amount of investment in order to achieve the Projected Value of 1159.080.

    NNTY (000911) has lost 1.557 billion yuan in two years (2017 and 2018) and accumulated a large amount of non-performing assets and loss-making business. The financial statements also show that the company’s shareholders’ operating conditions experienced serious losses, resulting in the investment in the entire financial year that cannot drive the positive growth of the company’s business. Eventually led to the major shareholders of the company withdrawing capital in 2019.

    Therefore, the relatively ineffective of agricultural listed companies can adjust the improvement direction according to the DEA efficiency measurement result to make it reach a valid state.

    Compared with the listed agricultural companies in South Korea, Chinese listed agricultural companies occupy more raw materials, have a broader market, and the company's scale and output value are larger, so the problems displayed are more obvious. South Korea’s agricultural enterprises operate in more subdivided areas, with higher operating costs, and a higher degree of refined cooperation in each operating link. During the development process, they may pay more likely pay attention to the operating profit of the enterprise and ignore the operating efficiency. Therefore, by studying the operating efficiency of Chinese listed agricultural companies, it is possible to clearly raise the problems that may exist in the development process of Korean agricultural enterprises, especially for operating efficiency, how the enterprise should increase or reduce its own operating investment, and to maximize the output, this will be a very meaningful study. This article studies the outstanding A-share listed companies in China. It is a good guide for investors in Korea or the worldwide. By observing the operating efficiency of the company, you can judge whether the company’s development is healthy. Generally speaking, it is unscientific for investors to judge the company's development based solely on business performance and reputation.

    From the perspective of overall operating efficiency, the number of agricultural listed companies achieving technical efficiency, pure technical efficiency, and scale efficiency was 10, 18, and 10 respectively. Among them, there are a large number of companies that have achieved pure technical effectiveness, but not many companies that have achieved technical effectiveness and effective scale. It can be seen that there is still a lot of room for improvement in the scale of operation of Chinese agricultural listed companies. Comparing technical efficiency and pure technical efficiency, it can be found that companies with poor performance in technical efficiency and pure technical efficiency, such as Dabei Nong (002385), Tangrenshen (002567), Dongfang Group (600811), etc., have higher scale efficiency. This shows that scale efficiency is not the main reason for the low operating efficiency of these companies. From the analysis of scale returns, the number of companies in China's agricultural listed companies that have constant scale returns is 10, which is relatively low.

    This article calculates the operating efficiency of Chinese agricultural listed companies by adding new explanatory variables. It proves that asset impairment losses (AIL) will also have an impact on operating efficiency, which can prompt more agricultural listed companies to pay attention to this financial indicator. At the same time, the business tax and surcharge (BTS) variables are added, which makes this article think differently when measuring the relationship between companies and taxes, which can provide a new possibility for future research.

    Figures

    Tables

    DEA analysis of China ’s agricultural listed companies indicators

    Descriptive statistics of China ’s agricultural listed companies data

    Evaluation of the operating efficiency of listed agricultural companies

    Inefficient companies factor table-CYNM (002477)

    Inefficient companies factor table-NNTY(000911)

    References

    1. Charnes A , Cooper WW and Rhodes E. 1978. Measuring the efficiency of decision making units. European J. of Operational Research 2: 429-444.
    2. Hu XH. 2011. Research on comprehensive performance of Chinese agricultural listed companies. Huazhong Agricultural University, China.
    3. Meng LJ and Ding Z. 2005. Efficiency analysis of agricultural listed companies based on DEA. Journal of Nanjing Agricultural University(Social Science Edition) 2: 39-43.
    4. Pang RZ , Zhang Y and Xue W. 2007. Factors influencing the operating efficiency of Chinese listed banks-A two-stage analysis based on Tobit regression model. Financial Forum 10: 29-35.
    5. Tariq N , Asim M , Al-Obeidat F , Zubair Farooqi M , Baker T , Hammoudeh M and Ghafir I. 2019. The security of big data in fog-enabled IoT applications including block chain: A survey. Sensors 19: 1788.
    6. Tim C. 1998. A multi-stage methodology for the solution of orientated DEA models. Operations Research Letters 23: 143-149.
    7. Wang RC and Wang JZ. 2014. Empirical analysis of financial performance of Shanghai and Shenzhen agricultural listed companies based on factor analysis. J. of Shandong Agricultural University(Natural Science Edition) 3: 449-453.
    8. Yang JF and Zheng SF. 2010. Evaluation and comparison of operating performance of listed agricultural companies from 2006 to 2008. J. of Harbin Institute of Technology (Social Science Edition) 3: 74-81.
    9. Zou CF , Xu JL and Wang YP. 2006. Empirical analysis on the impact of government fiscal and tax subsidy policy on the performance of agricultural listed companies. Industrial Economics Research 3: 53-59.
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