A method to measure the efficiency of industry finance integration of manufacturing enterprises based on SFA model

Abstract

This study aims to analyze the efficiency and influencing factors of participating in financial institutions in China’s listed manufacturing enterprises, and uses the stochastic frontier model (SFA) to carry out empirical research. Firstly, with the help of SFA, combining with the condition of integration of industry and finance of manufacturing enterprises, the indexes of output, input and influencing factors of the manufacturing enterprises were screened out, and the calculation model of integration of production and financing efficiency was established. Then, the integration of production and financing of manufacturing enterprises in 2006–2016 was used as the research case. Using the Frontier 4.1 and applying the CD production function as the production function of SFA in this study to calculate the efficiency of its panel data. The results show that the efficiency of the integration of production and financing is basically on the rise in 2006–2016. The scale of enterprises, the proportion of shares, the financial risk, the government support and the financial support all significantly affect the efficiency, and the property right has little effect on the efficiency of the integration.

Keywords

SFA manufacturing enterprises integration of production and finance efficiency

1 Introduction

Integration of production and financing refers to the internal integration and combination of industrial capital and financial institutions through mutual participation, holding and holding controlling interest modes and so on. It is the requirement of increasing the level of capital operation after the development of industrial capital to a certain extent [1, 2]. The intensification of economic globalization makes the competitive environment dynamic and industrial boundary fuzzy, some enterprises through the implementation of industrial capital and financial capital credit relationship between internal external to gain competitive advantage, has become an important strategy to adapt to the global competition and the boundless development. The combination of industry and finance can be divided into two types: production and financing (industrial enterprises and financial institutions) and financial and financial products (financial capital equity enterprises) two forms. From the practice of our country, due to the constraints of laws and regulations and economic system, the path of combining production and financing is mainly the entry of financial enterprises into the financial field, that is, by the production and financing, especially in the form of equity participation in financial activities is the most common. Generally speaking, the combination of industry and finance can be divided into two forms: production and financing (industrial enterprises participating in financial institutions) and finance and production (financial institutions participating in industrial enterprises). From our practice of our country, due to the restriction of legal rules and economic system, the path of combining production and financing is mainly the entry of financial enterprises into the financial field, namely the production and financing, especially participating in financial activities in the form of equity is most common capability, a large number of private manufacturing enterprises’ financing difficulties, costly financing and other issues, coupled with the implementation stage of Europe and the United States on the manufacturing back-flow plan, to further suppress the healthy development of China’s manufacturing industry, contributing to the manufacturing industry lower and lower profits. In recent years, China’s manufacturing industry is facing the problem of overcapacity, low added value of the products, lagged behind independent innovation ability, a large number of private manufacturing enterprises financing difficulties, financing expensive, coupled with the implementation stage of Europe and the United States on the manufacturing back-flow plan, to further suppress the healthy development of China’s manufacturing industry, contributing to the manufacturing industry lower and lower profits. To break through the above difficulties, a large number of manufacturing enterprises had tried to expend the development of space by integrating production and financing, and thus have formed a large number of combinations of enterprises, having a profound impact on enterprise, industry and regional economy. Research on the above problems, can not only guide the integration of industry and finance of manufacturing Enterprises to avoid the low efficiency of operation, but also is conducive to the relevant government departments to grasp the status quo of integration of production and finance of enterprises, provide a scientific basis for making relevant policies which promote the transformation and upgrading of the manufacturing industry and financial system reformation, but also will enrich the technical efficiency research in the field of integration of production and financing.

2 Literature review

For the following research, the author first defines the efficiency of integration of production and financing. In economics, efficiency mainly involves technical efficiency, scale efficiency, pure technical efficiency and allocative efficiency, among which technical efficiency is the most common. Technical efficiency refers to the capacity of achieving maximum output on the condition of fixed production input, or the capacity of using minimum input on the condition of fixed output [3]. Yi and Du believed that the integration of production and financing efficiency refers to the output caused by the integration of production and financing enterprises, reflects the resource allocation level of this kind of enterprises, is an important symbol of measuring the comprehensive competitiveness and input-output ability of integration of production and financing enterprises [4, 5]. Combining with the above research, the author believes that the efficiency of integration of production and financing refers to the technical efficiency of enterprises combining production with finance. Below if no special instructions, efficiency refers to technical efficiency mentioned.

At present, domestic and foreign scholars have done a lot of research on the integration of production and financing which mainly focus on the causes and effects of the integration of production and financing. First of all, in the aspect of the integration of production and financing motivation, scholars divided their motivation into three aspects: first, to reduce transaction costs. The transaction cost of enterprises and financial institutions mainly focuses on collecting costs and expenses for the negotiation information, especially in the areas whose financial market is undeveloped, the cost of financing is larger, the integration of production and financing make the enterprises and financial institutions establish a relatively stable trading relationship. Second, optimizing the allocation of resources, and reducing enterprise risk. With the help of integration of production and financing, enterprises obtain huge financial resources for support, which can broaden the financing channels for enterprises and get professional talent guidance from financial institutions, reducing the financial risk and business risk in the rapid expansion of enterprises. Third, to achieve coordination effect, after the integration of production and financing, enterprises and financial institutions realized complementary business, reduced unnecessary posts and related expenses, reduced management costs, and achieved management synergy. Next, in the aspect of the effect of the integration of production and financing, scholars at home and abroad have a lot of controversy in this field, but there is consensus among them. The reason is that the development and consummate degree of capital market in each country is discrepant, the legal system the integration of production and financing should be based on the concrete analysis of concrete problems [6, 7]. Therefore, the economic consequences of mainly reflected in the three aspects of financing constraints, investment efficiency and corporate performance. These three aspects also mainly explain the effectiveness of the integration of production and financing. Scholars at home and abroad generally believe that the integration of production and financing can ease the financing constraints of enterprises, especially for the private enterprises. At the same time, the integration of production and financing enables enterprises to have more convenient financing channels, and can grasp the investment opportunities and improve the efficiency of investment better.

In the aspect of the view that the integration of production and financing can improve enterprise performance, scholars each one sticks to his argument [8]. Scholar Lin Yuan thinks that the integration level is not deep, and will make the business performance index fell [9]. In addition, when the integration of production and financing alleviates the financing constraints, it may aggravate the excessive investment of enterprises, enlarge the operational risks of enterprises, and the increase of risk factors will reduce the efficiency of enterprises integrating of production and financing operations. The literature review shows that the efficiency of the integration of production and financing is undoubtedly, but has the following shortcomings: first, the researches on the integration of production and financing are mainly based on theoretical analysis, and lack of quantitative empirical research. Secondly, lack of researches on the efficiency and influencing factors of the integration of production and financing. In view of this, the author will make improvement from the following aspects of: first, to take A-share manufacturing enterprises as a case of participating in financial institutions, using stochastic frontier method to measure the efficiency of equity participation in financial institutions in manufacturing enterprises. Secondly, the influence factors of the efficiency of the manufacturing industry are extracted.

3 Selection of variables and construction of SFA model

3.1 Variables selection

3.1.1 Input and output indicators

Whether the input and output indicators are suitable or not will directly affect the authenticity of the measurement of the efficiency of the manufacturing enterprises in the subsequent empirical part.

On the basis of relevant scholars’ research, according to the characteristics of manufacturing enterprises, the author choose fixed assets as capital input, deal with staff salaries and bonus benefits as human input, and select the net profit as the output index.

3.1.2 Influencing factors index

The efficiency of the combination of manufacturing industry and finance enterprises is affected by many factors. Combining with the research of related scholars, the influencing factors can be divided into the internal factors and external environmental factors [10 –12]. The specific influencing factors are:

(1) Enterprise scale

There is a significant correlation between enterprise size and technological efficiency. Larger enterprises are more likely to obtain financial institutions’ approval, and then obtain more financial support to achieve greater output profits. While small and medium-sized enterprises possess flexible response, are good at capturing market opportunities and other advantages, and the payment of the labor price is lower than that of large enterprises, also, they do not undertake social responsibility and target imposed by the government like large enterprises. Therefore, how the scale of an enterprise affects the efficiency of enterprise’s production and financial integration needs to be illustrated by empirical analysis.

(2) Property right nature

As for enterprises with different property rights, their Incentive, supervision and restraint mechanism and management mode and governance structure are different. In the industry access and exit, historical burden, as well as investment and financing, the treatment they enjoy is also different, so the state-owned enterprises are usually closely related to monopoly and lack of competitive market environment, while the highly competitive market structure is associated with non-state-owned property rights. In order to reflect this risk, the author introduces financial risk as the influencing factor of the efficiency of the combination of industry and finance.

(3) Financial risk

Shares of financial institutions in manufacturing enterprises can bring many positive effects for the enterprise, but there are still some risks, including financial risk, risk transactions, conflict of interest risk, portfolio risk and regulatory risk. The combination of Finance and industry will reduce the efficiency of the combination of Finance and industry. In order to reflect this risk, the author introduces financial risk as the influencing factor of the efficiency of the combination of industry and finance.

(4) Share the proportion of financial institutions

The proportion of different sizes that Manufacturing enterprises share financial institutions will affect the use of financial advantages of enterprises to achieve all kinds of performance improvements. According to the relevant research when enterprise shares of financial institutions there is a certain critical value, when the equity ratio is higher than the critical value, shareholding enterprises will get significant positive return; if the equity ratio is lower than the critical value, the combination is short-term speculation, there is no significant correlation with performance. Therefore, the proportion of manufacturing enterprises participating in financial institutions will directly affect the efficiency of the combination of industry and finance.

(5) Types of equity financial institutions

The differences of different financial institutions (banks, insurance companies, securities companies) mode of operation, capital requirements, risk intensity and levels of development, will give the participating parties, the efficiency of the combination of industry and finance of manufacturing enterprises different effects.

(6) Government support

Policy factors have a special impact on the combination of industry and finance in our country, external supervision of all levels of government, industry access, tax relief, and system construction support has been the important powerful guarantee for enterprise shares of financial institutions. Therefore, the government’s support for the financial and manufacturing enterprises will be the external factors that affect the efficiency of the integration of industry and finance.

(7) Financial support

One of the other financial institutions that combine finance with Finance can use its financial resources (funds, information, and talents) to provide financial support for manufacturing enterprises, and enhance the performance of enterprises. Therefore, the support financial institutions provide for the manufacturing enterprises can be regarded as another external factor of the industry combination efficiency.

3.2 SFA model

The technical efficiency measurement methods are mainly nonparametric and parametric methods, represented by DEA and SFA respectively. Seeing that the article not only calculate the efficiency of combining industry and finance in different enterprises and different years, but also analyze the different factors of efficiency of combining production and finance. While the DEA model can only calculate the efficiency, it can’t analyze the impact of various factors on efficiency. The SFA model can do, but also can examine the parameter from the stochastic frontier production function model and technical parameters of non-efficiency function in the model, and can also judge the fitting degree of the model, which is the DEA model, is unable to do [13 –15]. Accordingly, the author decided to adopt SFA model as the research method of this study.

The functional form of the SFA model can be expressed as follows: $y_{it} = f (x_{it}, β) exp (ν_{it} - μ_{it})$ (1)

In the formula (1), y_it says unit i’s output scalar during t, x_it says input factor of production factor, β is parameter to be estimated, error term exp(ν_it - μ_it) is the composite structure which consists of two parts, the first part ν_it is random disturbance, which says statistical noise, and includes measurement error and other random factors, υ_it ∈ N (0σ_{υ
²}). The second part: μ_it says non negative technology non efficiency terms, which is used to indicate the impact only on each unit, μ_it ∈ N (0σ_{υ
²}). ν_it and μ_it are independent of each other. The model is interpreted as: due to stochastic perturbation and technical inefficiency, individual unit producers can’t reach the frontier of production function.

These two factors, ν_it and μ_it can’t be observed, but the proper definition of random disturbance is a white noise, the average number of multiple observation value is zero, so the technical efficiency of individual producers can be determined by the expected ratio of the expected output of the producer in the sample to the stochastic frontier: ${TE}_{it} = \frac{E [f (x_{it}, β) exp (υ_{it} - μ_{it})]}{E [f (x_{it}, β) exp (υ_{it}) | μ_{it} = 0]} = exp (- μ_{it})$ (2)

Determine whether or not the production status of the production unit is located at or below the production boundary criteria, and it depends on μ_it. If μ_it = 0, the production status of the production unit is on the frontier of production, TEit = 1, if μ_it > 0, it is lower than the frontier production frontier, TEit < 1, at this point, the unit is technical inefficient.

The logarithm type stochastic frontier model can be obtained by taking the logarithm of both sides of the formula (1): $In (y_{it}) = Inf (x_{it}, β) + υ_{it} - μ_{it}$ (3)

Battese & Coell used variance parameter γ to test the proportion of technical inefficiency in compound disturbance, so as to judge whether there are technical inefficiency factors in SFA model: $γ = \frac{σ_{μ}^{2}}{σ_{μ}^{2} + σ_{υ}^{2}}$ (4)γ ∈ [0, 1], σμ says technical non efficiency term, σν says a random disturbance term. If γ tends to 0, the technical inefficiency is not obvious. At this point, there is no need to use the SFA method, and the OLS method can be used directly. If γ tends to 1, it shows that the error term is mainly due to technical inefficiency, and the SFA method is appropriate.

In order to find out the reasons for the differences of technical efficiency among different production units, Battese & Coell proposed the technical non efficiency function (BC model), which can analyze the factors that affect the technical inefficiency of production units: $μ_{it} = δ_{0} + Z_{it} δ + ω_{it}$ (5)

δ₀ is the constant item, Z_it is factor variables affecting technical inefficiency, δ is coefficient of if δ is minus, there is a positive influence relationship between Z_it and the technical inefficiency; on the contrary, there is a negative influence; ω_it is a random error term.

SFA method is used to calculate the efficiency of manufacturing enterprises’ combination of Finance and industry, and the appropriate production function should be established. According to the above analysis, the investment in this research is capital input, payable to staff salaries and bonus benefits, and the output is net profit for manufacturing enterprises. At present, there are two commonly used production functions:

First, ran slog production function, its expression is:

$\begin{matrix} In (y_{it}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) \\ + 1 / 2 β_{kk} ({InK}_{it})^{2} + 1 / 2 β_{ll} ({InL}_{it})^{2} \\ + β_{kl} {InL}_{it} {InK}_{it} + υ_{it} - μ_{it} \end{matrix}$ (6)

Yit, Kit, and Lit respectively indicate the enterprise i’s net profit, capital input, payable staff salaries and bonus benefits during t. β is the coefficient of regression.

Second, Cobb-Douglas production function. In the upper case, if β_kk = β_ll = β_kl = 0, the Cobb-Douglas production function can be obtained: $In (y_{it}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) + υ_{it} - μ_{it}$ (7)

And what kind of production function is established needs to be verified by LR statistic in the following empirical analysis with Frontier 4.1. LR test value: $LR = - 2 In [L (H_{0}) - L (H_{1})]$ (8)

In the formula, LR obeys the mixed chi square distribution, L (H₀), L (H₁) is the corresponding log likelihood rate of the formula (6) and (7) respectively. If the LR value is greater than the critical value of the mixed chi square distribution, this study will apply the logarithmic production function. Instead, it applies to the C-D production function.

Referring to the BC model, and combining the input variables, factors affecting efficiency variables, and output variables mentioned above, a technical inefficiency model of this study is constructed:

$\begin{matrix} μ_{it} = δ_{0} + δ_{1} In (X_{1 it}) + δ_{2} In (X_{2 it}) + \dots + \\ δ_{6} In (X_{6 it}) + ω_{it} \end{matrix}$ (9)

In formula (9), X_1it … X_6it respectively says efficiency influencing factor index of enterprise i in T. δ₁ … δ₆ says the estimated coefficients of the factors influencing the efficiency of variables, which can reflect the enterprise scale, the nature of property rights, financial risk, the proportion of the shares of financial institutions, government support, influence degree of financial support for the manufacturing enterprise combination efficiency.

Establishment of a model for calculating the efficiency of combining production with finance

Based on the above analysis, the model of the efficiency of combining efficiency of manufacturing enterprises is given: ${\begin{matrix} In (y_{it}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) + 1 / 2 β_{kk} {({InK}_{it})}^{2} \\ \begin{matrix} \end{matrix} + 1 / 2 β_{ll} {(In L_{it})}^{2} + β_{kl} {InL}_{it} {InK}_{it} + υ_{it} - μ_{it} \\ In (y_{it}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) + υ_{it} - μ_{it} \\ μ_{it} = δ_{0} + δ_{1} In ({Scale}_{1 it}) + δ_{2} (Pr {operty}_{2 it}) \\ \begin{matrix} \end{matrix} + δ_{3} In ({Risk}_{3 it}) + δ_{4} In ({Ratio}_{4 it}) + δ_{5} In ({Gov}_{5 it}) \\ \begin{matrix} \end{matrix} + δ_{6} In ({Bank}_{6 it}) + ω_{it} \\ {TE}_{it} = exp (- μ_{it}) \end{matrix}$ (10) In formula (10), TE_it represents the combined efficiency of manufacturing enterprises i in t. Scale, Property, Risk, Ratio, Gov and Bank respectively, the scale of enterprises, expressed with total assets of enterprises at the end of the year; the nature of property rights, the value is 1, if the enterprise is state-owned holding, or 0; financial risk, represented by the rate of corporate assets and liabilities; the proportion of the shares of financial institutions, represented by the proportion of shares held by the enterprise financial institutions; government support, represented by the proportion of government subsidies accounting for the net profit of enterprises at the end of the year; financial support, the proportion of bank loans accounting for total assets. Whether the SFA method is appropriate or not and what kind of production function should be established is checked by panel data of case studies.

3.3 Data sources and processing

In this study, the panel data of China’s manufacturing listed companies participating in financial institutions from 2006 to 2016 are used to study the efficiency of the integration of production and financing and influencing factors. Share information is from the Shenzhen and Shanghai stock exchange website and Cninf, and statistics calibers are the consolidated financial statements. Initial data roots in annual reports on the participation of financial institutions by listed manufacturing companies from 2007 to 2017 published by Cninf, then are calculated and processed. In order to eliminate the heteroscedasticity problem in the application of SFA method, all the indexes are in the form of natural logarithms except for the property rights. In order to ensure the continuity of the effects of equity participation on the efficiency of the integration of industry and finance, delete the sample of listed companies which delist halfway or withdraw from the participation of financial institutions. because the empirical part will analyze the impact of share ratio on the efficiency of the combination of finance and industry, we eliminate samples without complete financial data and lack of equity participation; in addition, in order to make it convenient to analyze the effects of equity ratio and type of financial institution on the efficiency of the combination of Finance and industry, we eliminate companies that share a number of financial institutions, that is, retaining enterprises which only share of one financial institution. The final valid sample includes 74 panel data.

4 Analysis results

4.1 Determine the production function

As mentioned in the previous study, the appropriate production function should be chosen when the SFA method is used to estimate the efficiency of the manufacturing industry. The selection process for the production function is as follows:

–Alternative hypothesis H1:

This study uses the logarithmic production function:

$\begin{matrix} In (Y_{1}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) \\ + 1 / 2 β_{kk} ({InK}_{it})^{2} + 1 / 2 β_{ll} ({InL}_{it})^{2} \\ + β_{kl} {InL}_{it} {InK}_{it} + υ_{it} - μ_{it} \end{matrix}$ (11)

–Original hypothesis H0:

This study uses C-D production function: $In (Y_{1}) = β_{0} + β_{k} In (K_{it}) + β_{l} In (L_{it}) + υ_{it} - μ_{it}$ (12)

Frontier 4.1 is used to make hypothesis test on the two production functions of the above hypothesis in the manufacturing and financial integration panel data of manufacturing listed companies. The results are shown in Table 1.

Table 1

Applicability test of SFA model production function

Parameter	Value
L(H0)	–59.763
L(H1)	–53.024
LR	13.478
The critical value	24.049
Result	Accepting null hypothesis

Note: The critical value is mixed chi square test when significance level is 0.01.

As can be seen from Table 1, both the two stage LR is less than the critical value, so we accept the original hypothesis. Compared with the logarithmic production function, Cobb-Douglas production function is more suitable for the fitting of the panel data. Therefore, the Cobb-Douglas production function is used as the production function in this study SFA.

4.2 Calculation of the efficiency of combining production and finance and analysis of its influencing factors

According to the above established the combination efficiency model: formula (10), the Cobb-Douglas production function is used as the production function in this study SFA. We use Frontier 4.1 to calculate the efficiency of the financial and industrial combination panel data of manufacturing listed companies, the results are shown in Tables 2, 3, 4.

Table 2
2006–2016 manufacturing listed companies with melting efficiency value

Year Maximum Minimum Mean

2006 0.945 0.004 0.704

2007 0.942 0.002 0.701

2008 0.806 0.006 0.688

2009 0.800 0.008 0.682

2010 0.853 0.013 0.701

2011 0.861 0.018 0.709

2012 0.899 0.022 0.713

2013 0.921 0.025 0.736

2014 0.952 0.030 0.768

2015 0.965 0.050 0.790

2016 0.979 0.060 0.824

Year	Maximum	Minimum	Mean
2006	0.945	0.004	0.704
2007	0.942	0.002	0.701
2008	0.806	0.006	0.688
2009	0.800	0.008	0.682
2010	0.853	0.013	0.701
2011	0.861	0.018	0.709
2012	0.899	0.022	0.713
2013	0.921	0.025	0.736
2014	0.952	0.030	0.768
2015	0.965	0.050	0.790
2016	0.979	0.060	0.824

Table 3

Manufacturing listed companies with financial efficiency in different types of financial institutions

	Bank	Securities institutions	Guarantee company	Insurance company	Trust company	Other
Mean	0.838	0.791	0.920	0.901	0.722	0.855
S.D	0.274	0.313	0.177	0.178	0.418	0.263

Table 4

Stochastic frontier model to estimate the result

Parameter
Frontier production	Coefficient values β	t-value
β ₀	5.492^***	4.005
β _K	0.191^***	2.687
β _L	0.564^**	6.241
Technical inefficiency	Coefficient values δ	t-value
δ ₀	20.130^***	3.932
δ ₁	–5.321^***	–7.865
δ ₂	–1.406	–0.876
δ ₃	17.010^***	7.978
δ ₄	3.008^***	2.402
δ ₅	–2.282^*	–1.781
δ ₆	114.077^***	7.003
δ ²	210.528^***	8.836
γs	0.669^***	1058.115
Logarithmic likelihood values	–959.763

Note: ^*, ^**, ^*** expresses significant at 10%, 5%, and 1% levels respectively, the numerical value is negative, indicating that the BC model has a positive effect on efficiency.

As can be seen from Table 2, the combination efficiency of China’s manufacturing listed companies is 0.824, according to the ten years’ trend of 2006 to 2016, the whole is on the rise, but in 2008 there was a substantial decline, then gradually recovered. The reason is that in 2008 the global financial crisis makes China listed manufacturing enterprises difficult to manage, as the economy continues to recover, manufacturing industry listed companies operating performance is reversed, promoting the production of financial efficiency to been improved. In addition, the maximum efficiency and the minimum value of the combination of financial and industrial efficiency can be seen that the gap between the two is larger, indicating that the efficiency of the financial integration of the listed manufacturing enterprises is uneven.

Table 3 shows that the situation where the listed companies in the manufacturing sector share the Guarantee Corporation and the insurance companies have higher efficiency of combining industry and finance, while the participation of other financial institutions is inefficient. It is worth mentioning that the efficiency of joint stock banks is not high, the reason is: first, the bank’s management behavior aims at maximizing its own value rather than maximizing the value of the enterprise, and there is a certain rent-seeking phenomenon. The bank may also transfer its financial risk through the bank shares held by the enterprise to the enterprise, and financial risk reduces the efficiency of combining production with Finance; second, the bank did not play the positive role of supervision, and the degree of information asymmetry and contract execution cost between banks and enterprises is higher, leading banks not to providing scientific and reasonable advice and guidance for business operations, at the same time, enterprises are also not good use of financial resources to optimize industrial entities.

As shown in Table 4, γ is 0.669, greater than 0, and both have passed the 1% significant test. The results show that the SFA method is suitable for the calculation of the efficiency of the manufacturing enterprises’ efficiency, and the efficiency is greatly affected by the inefficiency of the technology. The capital output elastic coefficient, β _K = 0.191, is significantly less than the output elasticity coefficient of human β _L = 0.564, meaning the labor of the manufacturing enterprise net profit contribution is much greater than the capital, and its reason lies in: first, China’s manufacturing enterprises are labor-intensive industries in itself, the nature of the industry determines that the contribution of the labor force is greater than that of the capital; second, because the mechanisms of China’s capital market is not perfect, causing the enterprise financing channel is not smooth and the information disclosure mechanism is not perfect, which makes when manufacturing enterprises participate in financial institutions, the role of capital is inadequate. In addition, from the influence of factors on the efficiency of the combination of production and finance, excepting for δ ₂ , other factors all passed the significance test. Specifically, δ ₁ = –5.321 which expresses that the size of enterprises and the combination efficiency are significantly related. The scale of the enterprise is large and the development trend is good, making the source of profit stable, and it is easy to obtain bank loans, combining finance and industry to achieve financial returns. δ ₂ = –1.406, has not passed the significance test, which shows that the property right attribute has little effect on the technical efficiency of the manufacturing and financial integration listed companies in our country. δ ₃ = 17.010 shows that the financial risk is negatively related to the efficiency of the combination of industry and Finance, although the combination of industry and finance can bring high returns to manufacturing enterprises, But the risks which arise in this process, especially enterprise’s capital and the proportion of financial leverage resulted by financial risk have gone up, making the enterprise industry and finance combination efficiency descend. δ₄ = 3.008, shows that the ratio of financial institutions to the financial efficiency of the listed companies in the manufacturing industry is negatively correlated with the efficiency of the combination of the financial industry and the listed companies, at present our country listed manufacturing company is still in the primary stage of the combination, far away from achieving the depth of integration even strategic integration, once suffering financial risks, financial institutions will make decisions based on maximizing their own interests, ignoring the participating parties interests, then the risk will be transferred to the participating parties, ultimately affecting the leading business of equity partners. δ ₅ = –2.282 shows that the government subsidy will promote the efficiency of the integration of industry and finance, there is no need to explain this much, and the financial aid to the combination of manufacturing enterprises and listed companies is more grater [16], it is more conducive to enhance the performance of enterprises. δ ₆ = 114.077, shows that the financial support and the combination efficiency is significantly negatively related to financial institutions, only in order to obtain interest, bank - based financial institutions provide loans to manufacturers, therefore, lending behavior prefer shorter and less risky projects, while the time of enterprises sharing financial institution is longer, and the risk is higher, the contradiction between the two makes the enterprise prefer short-term debt long-term investment, leading to greater risk, but if the investment return is not enough to cover the loan interest or loss, the enterprise investment is not only difficult to recover but also faces urged the bank loans loan pumping pressure.

5 Conclusions

We had used the SFA to estimate the efficiency of China’s manufacturing listed companies participating in financial institutions’ combination of production and Finance during 2006–2016, and had made further study on the influence factors of efficiency of integration of industry, and we obtained the following conclusions: first, except for the impact of the global financial crisis in 2008, the combination of efficiency is in the overall upward trend during ten years, but the efficiency level of the financial integration of manufacturing listed enterprises is uneven. Secondly, the situation where the listed companies in the manufacturing sector share the Guarantee Corporation and the insurance companies have higher efficiency of combining industry and finance, while the participation of other financial institutions is inefficient. Third, compared to labor input, capital investment has little influence on the output of Listed Companies in manufacturing industry; therefore, we should give full play to the capital effect and improve the current environment of the combination of industry and finance through institutional arrangements, effectively improving the efficiency of capital operation. Fourth, enterprise scale, equity participation ratio, financial risk, government support and financial support are the main factors that influence the efficiency of the combination of Finance and industry, while the property right has no significant influence on the efficiency of the combination of industry and finance. Among them, enterprise size and government support have a positive impact on the efficiency of the financial and industrial integration. Financial risk, equity participation and financial support have a negative impact on the efficiency of the integration of industry and finance. The conclusions enable us to have a more comprehensive understanding of the efficiency and influencing factors of the efficiency of China’s manufacturing listed companies, and provide guidance and direction for improving the efficiency of the combination of industry and finance.

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