Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 5, No. 3, 2022 289 Research on The Inhibitory Effect of Excessive Financialization on Manufacturing Productivity Lingling Huang, Ningxue Fan, Zhuowen Shan and Yuxin Fu Anhui University of Finance and Economics, Bengbu, 233030, China Abstract: This paper takes A-share manufacturing listed companies from 2008 to 2014 as the research object, and investigates the influence of financialization on total factor productivity of manufacturing enterprises. The results show that financialization can significantly inhibit the improvement of total factor productivity in manufacturing industry. The research of this paper provides micro-evidence for the influence of financialization on total factor productivity, and also provides policy enlightenment for guiding funds to "break away from reality to emptiness". Keywords: Enterprise financialization, Total factor productivity. 1. Introduction and Literature Review The report of the 19th National Congress of the Communist Party of China pointed out that China's economy has entered a high-quality development stage from a high-speed growth stage. The foundation of high-quality development lies in the vitality, innovation and competitiveness of the economy. High-quality development should be based on the improvement of production factors, productivity and total factor efficiency. Building a modern economic system is an urgent requirement to cross the threshold and a strategic goal of China's development[1]. To achieve this goal, we must adhere to quality first and benefit first, improve total factor productivity, and constantly enhance China's innovation and competitiveness. Manufacturing is the pillar industry of the national economy, and the real economy represented by manufacturing is the foundation of our country, the instrument of rejuvenating the country and the foundation of strengthening the country. However, there is a certain gap between its production efficiency and the advanced level in the world[2]. Therefore, the decisive role of total factor productivity is becoming more and more important from the perspective of promoting China's economic transformation and upgrading, or from the perspective of moving from a "manufacturing power" to a "manufacturing power". During the 13th collective study in the Political Bureau of the Communist Party of China (CPC) Central Committee, Secretary Xi Jinping emphasized deepening the structural reform of the financial supply side and enhancing the ability of financial services to the real economy. However, under the background of virtual world economy and high return of financial capital, economic financialization has become a major trend of economic development in various countries. In recent years, the development goals of more and more entity enterprises, especially manufacturing enterprises, have shifted to the financial field, which not only weakens the willingness of enterprises to expand reproduction and innovation, but also reduces the investment in equipment renewal and R&D innovation, and is detrimental to the long- term development of enterprises, thus restricting the improvement of their total factor productivity[3]. Total factor productivity (TFP) is an important factor affecting the sustainable development of enterprises, and it is also an important embodiment of the core competitiveness of enterprises. Under this background, is there a close relationship between financialization and TFP of manufacturing enterprises in China? What is the degree and trend of financial influence on total factor productivity? These questions need to be studied and answered urgently. 2. Data Processing and Model Setting 2.1. Samples and Data Sources The research samples of this paper are from A-share manufacturing listed companies in Shanghai and Shenzhen stock markets, and the sample span is from 2008 to 2018. Because the explained variable TFP is the next data in the regression, the data involved in calculating TFP is from 2009 to 2019[4]. After data collection, the samples were screened according to the following steps:①Exclude ST, *ST and PT listed companies; ② Eliminate listed companies that issue H shares, N shares or B shares at the same time; ③ Eliminate the companies listed in the current year, i.e. IPO; (4) Exclude listed companies with major asset restructuring and mergers and acquisitions in that year:⑤Eliminate listed companies with missing data[5]. The sample data comes from three authoritative databases of CSMAR, WIND and CCER in China, and is cross-checked according to different database information to ensure the accuracy of the data. For the missing data, it is supplemented by searching the annual reports of listed companies. 2.2. Variable Measurement (1) Explained variable: the explained variable of this paper is the total factor productivity (TFP) of enterprises. Based on Giannetti et al.(2015)' s estimation method of TFP, this paper estimates the total factor productivity of listed companies in manufacturing industry by using Cobb-Douglas production function through Solo residual method. The specific formula for estimation is: it it it itY A L K   (1) Among them, itY , itL , itK denote the firm's output, labor input and capital input, itA , which is total factor 290 productivity (TFP) in addition to labor and capital, is transformed into a linear form by taking the logarithm of both ends of equation (1) it it it ity l k     (2) In formula (2), ity is the output of enterprise I in the t year, expressed by the natural logarithm of the main business income; itl is the labor input of the enterprise in t years, expressed by the natural logarithm of the total number of employees in the enterprise; itk is the capital investment of the enterprise in t year, that is, the capital expenditure of the enterprise in that year. OLS estimation is performed on formula (2), the resulting residual it is the total factor productivity. As this total factor productivity is estimated by OLS regression, it is expressed by TFP_OLS. Based on the practice of Cheng Chen (2017), this paper uses LP method to estimate TFP_LP, and takes TFP_OLS, which is obtained by conventional methods, as the explained variable of estimation and analysis[6]. (2) Explanatory variable: The explanatory variable in this paper is the degree of enterprise financialization (Fin). Using Demir's (2009) approach for reference, the ratio of financial assets held by enterprises to total assets is taken as an index to characterize the degree of financialization; According to the research ideas of Du Yong et al. (2017), transactional financial assets, net loans and advances, derivative financial assets, net available-for-sale financial assets, and net held-to- maturity investments are all included in the category of financial assets. In addition, in contemporary China, the real estate industry is far away from the real sector and has a high degree of virtualization characteristics (Song Jun and Lu Yang, 2015), so it is included in the measurement of corporate financialization. The specific calculation formula of financial degree (Fin) of enterprises is: Fin  Trading financial assets+net loans and advances+derivative financial assets+net available-for-sale financial assets+net held-to-maturity investment+net investment real estate)/total assets[7]. (3) Control variables: A large number of existing studies believe that factors such as company characteristics will significantly affect total factor productivity. Therefore, this paper considers and sets the following control variables: enterprise Size, expressed by the logarithm of total assets of the enterprise; Enterprise debt ratio (Lev), expressed by the ratio of total liabilities to total assets; Profitability (Roa), expressed by the rate of return on total assets of the enterprise; Growth, expressed by Tobin Q; Cash level, expressed by the ratio of cash flow from operating activities to total assets; Institutional investors' shareholding ratio (Ins), expressed by the proportion of institutional shares to the total share capital; Indep, expressed by the ratio of the number of independent directors to the total number of directors; Age of an enterprise, expressed by the number of years since its listing. In addition, this paper also uses Industry to represent the fixed effect of the industry to control the differences at the industry level; Year represents the fixed effect of time, which is used to control the impact of time factors on TFP of enterprises[8]. See Table 1 for the specific settings of each variable. Table 1. Variable definition and calculation method Variables Variable name Variable code Variable measure Explained variable Total factor productivity (OLS method) TFP_OLS Calculated by OLS method Total factor productivity (LP method) TFP_LP Calculated by LP method Explanatory variable Enterprise financialization degree Fin (Trading financial assets+net loans and advances+derivative financial assets+net available-for- sale financial assets+net held-to-maturity investment+net investment real estate)/total assets Control variable Scale Size Natural logarithmic value of total assets of an enterprise Enterprise debt ratio Lev Total liabilities/total assets Enterprise profitability Roa Rate of return on total assets Enterprise growth ability Growth Tobin q value of enterprise Enterprise cash holding level Cash Operating cash flow/total assets Share holding ratio of institutional investors Ins Number of institutional shares/total share capital Proportion of independent directors Indep Number of independent directors/total number of directors Enterprise listing period Age The difference between the current year and the year of listing 2.3. Measurement Model Setting In order to verify the impact of corporate financialization on total factor productivity, the basic test model of this paper is set as follows: 1 0 1 2 1it it it itTFP Fin Controls Industry Year           (3) Among them, 1itTFP  is the total factor productivity of enterprise i in t+1 year, itFin is the financialization degree of enterprise i in t year, Controls is all control variables, Industry and Year are the fixed effects of industry 291 and time, respectively, 1it  is the random interference items. In order to further verify whether corporate financialization has a "U" or inverted "U" impact on total factor productivity, this paper adds the square term of corporate financialization on the basis of formula (4). The specific model is: 2 1 0 1 2 3 1it it it itTFP Fin Fin Controls Industry Year             (4) 3. Empirical Results and Discussion 3.1. Descriptive Statistics The descriptive statistical results of each control variable are shown in Table 2. Generally speaking, each control variable in the sample index has the characteristics of great differences. Table 2. Descriptive statistics of main variables variable observed value average/mean value median standard deviation maximum minimum value TFP_OLS 6257 0.0021 -0.0083 0.2678 0.7840 -0.6982 TFP_LP 6257 13.1953 13.1261 0.8275 15.4556 11.2675 Fin 6257 0.0151 0.0002 0.0424 0.3263 0 Size 6257 21.6512 21.5335 1.0359 24.8994 19.2439 Lev 6257 0.4157 0.4074 0.2107 0.9454 0.0454 Roa 6257 0.0624 0.0558 0.0611 0.2781 -0.1295 Growth 6257 1.9516 1.5935 1.4530 8.2346 0.2371 Cash 6257 0.0433 0.0413 0.0685 0.2417 -0.1510 Ins 6257 0.0607 0.0342 0.0864 0.5204 0 Indep 6257 0.3686 0.3333 0.0508 0.5714 0.3000 Age 6257 7.5618 six 5.4174 22 one 3.2. Regression Results of Benchmark Model Using Stata15.0 statistical software, OLS mixed regression is carried out on formula (3), and the industry effect and year effect are added to control[9], so that the correlation coefficient between enterprise financialization of explanatory variables and total factor productivity of explained variables can be obtained, and the influence of financialization on TFP can be verified. In order to further investigate whether the Financialization of enterprises has a nonlinear effect on total factor productivity, according to formula (4), the square term of financialization fin is added for OLS mixed regression[10], and the influence of financialization and its square term on TFP is obtained. The specific regression results are shown in Table 3. Table 3. Benchmark regression results of the impact of corporate financialization on total factor productivity variable TFP_OLS TFP_LP (1) (2) (3) (4) (5) (6) Fin -0.5736*** (-6.53) -0.5488*** (-6.50) -0.0301*** (-0.15) -0.2313** (-0.96) -0.1146** (-0.65) -1.1515*** (-3.43) Fin2 / / -2.2082 (-2.61) / / -6.9404 (-3.97) Size / -0.0233*** (-4.94) -0.0232*** (-4.94) / -0.5099*** (-52.49) -0.5102*** (-52.76) Lev / -0.1011*** (-4.32) -0.1027*** (-4.41) / -0.4187*** (-8.53) -0.4233*** (-8.68) Roa / 1.2480*** (15.71) 1.2494*** (15.79) / 2.9692*** (17.22) 2.9738*** (17.31) Growth / -0.0010 (-0.30) -0.0007 (-0.21) / -0.0113 (-1.59) -0.0104 (-1.46) Cash / 0.9250*** (16.51) 0.9215*** (16.49) / 0.3026** (2.44) 0.2917** (2.36) Ins / 0.2237*** (5.76) 0.2255*** (5.82) / 0.0547* (0.66) 0.0603* (0.74) Indep / -0.1788*** (-3.12) -0.1752*** (-3.06) / 0.0645 (0.51) 0.0758 (0.60) Age / 0.0036*** (4.69) 0.0034*** (4.30) / 0.0071*** (4.51) 0.0062*** (3.93) constant term -0.0424** (-1.96) 0.2833*** (2.93) 0.2787*** (2.88) 13.5468*** (155.09) 2.1317*** (10.35) 2.1171*** (10.31) industry control control control control control control time control control control control control control observed value 6257 6257 6257 6257 6257 6257 R2 0.0302 0.2040 0.2051 0.1249 0.6517 0.6529 variance ratio 6.55*** 37.16*** 36.59*** 24.17*** 293.39*** 289.58*** Note: (1)*, * *, * * means passing the test at the significance level of 10%, 5% and 1% respectively; (2) Robust T value in brackets. 292 The coefficients of Fin in column (1) and column (4) are significantly negative at the level of 1% and 5% respectively, indicating that financialization will have a negative effect on the improvement of total factor productivity of enterprises without adding control variables. After adding control variables, the coefficients of Fin in the second and fifth columns are -0.5488 and -0.1146, which are significantly negative at the levels of 1% and 5%, respectively. This means that the higher the degree of financialization of manufacturing enterprises in China, the more restricted the improvement of total factor productivity. Columns (3) and (6) mainly examine whether financialization has a nonlinear impact on TFP of enterprises. Therefore, the square term of financialization degree is introduced into regression equation (5) and tested. Columns (3) and (6) show that the result of financialization Fin is still significantly negative, but the coefficient of its square term Fin2 is not significant, whether it is TFP calculated by OLS method or TFP calculated by LP method. This shows that there is not enough evidence to show that there is a nonlinear relationship between financialization and total factor productivity of listed manufacturing companies in China. 4. Conclusion and Enlightenment Based on the panel data of China's A-share manufacturing listed companies from 2008 to 2018, this paper empirically studies the impact of corporate financialization on total factor productivity. It is found that there is no nonlinear relationship between financialization of manufacturing enterprises and total factor productivity. Whether or not the square term is added, the impact of financialization on total factor productivity is significantly negative, that is, the higher the degree of financialization, the more unfavorable it is to the improvement of total factor productivity. According to the research conclusion of this paper, it can be found that although financialization has inhibited the total factor productivity of manufacturing industry, it does not mean that the allocation of financial assets by entity enterprises should be completely denied[11]. Through policy guidance, manufacturing enterprises should abandon the concept of market arbitrage, position the allocation of financial assets in terms of capital reserves, and truly regard financialization as an important guarantee for R&D innovation and technological progress. Specifically, we should start from the following three points: First, curb asset bubbles and reduce the excess rate of return of the virtual economy[12]. In the final analysis, the reason why enterprises apply a large amount of capital to financial operations is that the virtual economy is attracted by the excess returns, and the distortion of returns among different industries is the key factor of over-financialization. The government should crack down on speculation by strengthening the degree and increasing the frequency of financial supervision. At the same time, house prices should be strictly controlled to avoid a large amount of physical capital entering the real estate sector. By reducing the rate of return of the virtual economy, a large number of manufacturing enterprises can "pull out" from the allocation of financial assets arbitrage in the market. Second, strengthen the innovation atmosphere and improve the return rate of physical investment. Governments at all levels should create a good atmosphere and environment for innovation, guide enterprises to increase market competitiveness through technological innovation, improve total factor productivity, and reduce the attractiveness of financial investment to enterprises. Acknowledgment This work is supported by Innovation and Entrepreneurship Training Project for College Students of Anhui University of Finance and Economics in 2021, Project number: S202110378044. References [1] Ma Guangqi,Wang R. A study on the impact of preventing excessive financialization of firms on reducing agency costs-- empirical evidence from Chinese A-share listed manufacturing companies[J]. Price Theory and Practice, 2022(05):134- 137+207. DOI:10.19851/j.cnki.cn11-1010/f.2022.05.217. [2] Zhang Shigen, Chen Xintong, Wang Xinping. Overconfidence of management and overfinancialization of real enterprises--a study sample of non-financial listed companies in Shanghai and Shenzhen A-shares in China[J]. Journal of Fujian Jiangxia College,2022,12(04):45-57. [3] Tian Qi. Research on the risk of over-financialization of Aoma Electric and its countermeasures[D]. Jiangxi Normal University, 2022. [4] Xu Zhaohui,Wang Mansi. Research on the governance effect of digital transformation on excessive financialization of real enterprises[J]. Securities Market Herald,2022(07):23-35. [5] Zou Bingxiu. A study on the extrusion effect of excessive financialization on R&D expenditures of China's listed manufacturing companies[D]. Hunan University, 2021. DOI:10.27135/d.cnki.ghudu.2021.003026. [6] Xiao Zongyi,Lin Lin,Xu Mingyuan. A study of the theoretical mechanism of excessive financialization affecting sustainable corporate innovation[J]. Journal of Finance Quarterly,2022,16(01):192-217. [7] Xiang, Weimin, Xie, Jing, Li, Jiao. Excessive financialization of real estate in binary equilibrium: mechanism, measurement and influencing factors[J]. Jianghuai Forum,2022(01):57- 63.DOI:10.16064/j.cnki.cn34-1003/g0.2022.01.006. [8] Deng Y. Research on preventing excessive financialization and the effective path of financial services to the real economy [J]. Southwest Finance,2022(03):19-32. [9] Zhao J, Wang JX. Excessive financialization, global financial crisis and modernization of China's financial governance system[J]. Economic Research Reference, 2022(02):53-71. doi:10.16110/j.cnki.issn2095-3151.2022.02.008. [10] Liu, L.F., Du, J.M.. The impact of corporate financialization on corporate value--and the identification and governance of excessive financialization[J]. Southern Economy,2021(10):122-136.DOI:10.19592/j.cnki.scje.390693. [11] Yao Xiaoju. Excessive financialization, direction of earnings forecast revisions and stock price synchronization[J]. Finance and Accounting Newsletter,2021(13):75-79. doi:10.16144/j.cnki.issn1002-8072.2021.13.015. [12] Wang Yaping. Research on the cohort effect of excessive financialization of real enterprises [D]. Henan University, 2021. doi:10.27114/d.cnki.ghnau.2021.000749.