Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry

Accepted Manuscript Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry

Dayong Liu, Tong Chen, Xiaoyang Liu, Yongze Yu PII:

S0264-9993(18)30157-3

DOI:

10.1016/j.econmod.2018.11.027

Reference:

ECMODE 4783

To appear in:

Economic Modelling

Received Date:

30 January 2018

Accepted Date:

29 November 2018

Please cite this article as: Dayong Liu, Tong Chen, Xiaoyang Liu, Yongze Yu, Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry, Economic Modelling (2018), doi: 10.1016/j.econmod.2018.11.027

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ACCEPTED MANUSCRIPT

Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry Dayong Liua, Tong Chena,*, Xiaoyang Liua, Yongze Yub,* College of Management and Economics, Tianjin University, 92, Wejin Road, Nankai District, Tianjin 300072, China b School of International Economics and Trade, Nanjing University of Finance and Economics, 128, Railway North Street, Nanjing 210003, China * Correspondence. E-mail addresses: [email protected](D.Liu.), [email protected](X.Liu.), [email protected](Y.Yu.), [email protected](T.Chen.). a

Acknowledgments: We are grateful to two anonymous reviewers, the chief editor and guest editor of Economic Modelling for their very helpful comments on the paper. This research was supported by the National Social Science Fund of China (15CJY009) and National Natural Science Foundation of China (71572127, 71672123).

Abstract: Existing research provides contradictory insights about the effect of government subsidies on enterprise technology innovation. By explaining this mechanism with resource allocation, information efficiency and risk control channels, we systematically suggest three effects, leading to an inverted U-shaped relationship between the amount of subsidies and four indicators of technology innovation. Empirical evidence based on dataset of Chinese electronic manufacturing industry confirms that subsidies can promote enterprise technology innovation but it will inhibit innovation when there are too many subsidies. Meanwhile, the impact of subsidies is more significant for non-state-owned enterprises than state-owned ones. Furthermore, the level of regional economic development moderates the impact of government subsidies. The government can take advantage of diverse subsidy policies to drive sustainable technology innovation. JEL codes: O31, O34, O38 Keywords: technology innovation; government subsidy; resource allocation; information efficiency; ownership; electronic manufacturing industry

ACCEPTED MANUSCRIPT

Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry Abstract: Existing research provides contradictory insights about the effect of government subsidies on enterprise technology innovation. By explaining this mechanism with resource allocation, information efficiency and risk control channels, we systematically suggest three effects, leading to an inverted U-shaped relationship between the amount of subsidies and four indicators of technology innovation. Empirical evidence based on dataset of Chinese electronic manufacturing industry confirms that subsidies can promote enterprise technology innovation but it will inhibit innovation when there are too many subsidies. Meanwhile, the impact of subsidies is more significant for non-state-owned enterprises than state-owned ones. Furthermore, the level of regional economic development moderates the impact of government subsidies. The government can take advantage of diverse subsidy policies to drive sustainable technology innovation. JEL codes: O31, O34, O38 Keywords: technology innovation; government subsidy; resource allocation; information efficiency; ownership; electronic manufacturing industry 1. Introduction

Economic development is based on the sustainable development of technology innovation and productivity (Rivera-Batiz and Romer, 1992; Collins, 2015; Batabyal and Beladi, 2016; Mallick and Sousa, 2017; Bournakis et al., 2018), however, innovation requires significant investments and entails uncertain outcomes (Hud and Hussinger, 2015), that is, innovation may fail to bring the expected benefits. Companies always consider that the process is full of risks (Hall, 2002), and people are usually more sensitive to losses than revenues (Kahneman and Tversky, 1979); thus, the actual allocation of innovative resources may be inconsistent with the ideal allocation. This situation may result in underinvestment or misallocation for technological innovation (Arrow, 1972). To avoid this non-ideal situation and to promote innovation and economic growth (Almus and Czarnitzki, 2003), the government may implement policies on creating an incentive to subsidize enterprises (Shin and Kim, 2010). From the existing point of view, government subsidies for promoting enterprise innovation behavior not only have an incentive effect, but can also crowd out an enterprise’s original investment (Atzeni and Carboni, 2006; Wallsten, 2000). The negative impact of subsidies on enterprise innovation may be caused by many factors, including the disclosure of knowledge, the disparity among regional factor markets, product market liberalization, and the intervention of other human factors in the markets (Luo et al., 2016; Jia and Ma, 2017). In addition, several scholars have noticed the contributions of enterprise scale, corporate ownership, and other enterprise characteristics on technology innovation. However, there is still a lack of systematic, in-depth investigation and persuasive empirical research on the relationship between government subsidies and enterprise innovation. The contributions of this study which differs from and complements the existing literature are as follows. First, we describe the characteristics of enterprise technology innovation more comprehensively than existing research (Addessi et al, 2014; Guo et al, 2016; Peng et al, 2014; Jourdan and Kivleniece, 2017; Nemlioglu and Mallick, 2017; 1

ACCEPTED MANUSCRIPT Dhanora et al, 2018). Enterprise technology innovation in this study is depicted from the conduct, performance, structure, and product perspectives, in terms of the indicators R&D, R&D intensity, patent application, and total factor productivity (TFP). It can calibrate the theoretical hypothesis systematically and verify the contradictory insights in existing research. As for the second contribution, we resolve the contradictory problems among views in existing study. Some research points out subsidies positively affect innovation or performance (Afonso and Silva, 2012; Guo et al, 2016; Howell, 2017; Jia and Ma, 2017), however, the literature also potential drawbacks related to resource inefficiencies associated with government intervention in enterprises (Dixit, 1997; Lazzarini, 2015). We summarize the subsidy effect mechanism that government subsidies promote enterprise technology innovation but it will inhibit innovation when there are too many subsidies, and we also clarify the inverse U-shaped relationship between subsidies and technology innovation with different indicators in nonlinear regression models. Furthermore, we examine the moderator variables in the subsidy effect. We employ the variables of ownership and regional economic development to moderate the effect mechanism. Our study finds that non-state-owned enterprises are more sensitive to subsidy than stateowned enterprises, and we verify that economic environment can determine the optimal amount of subsidies for enterprise innovation. Thirdly, in method and sample processing this study avoid the heterogeneity problem from different industries and minimize selection bias more effectively. We choose the whole sample of firms from the same industry, the electronic equipment manufacturing industry in China, which is one of China’s most technologically intensive industries. The whole sample of electronic manufacturing industry means that we observe all the enterprises which survive all the time from 2005 to 2007 in Chinese electronic manufacturing industry. Regardless of whether the enterprise is subsidized and whether the enterprise is innovative or not, no matter how many subsidies it receives, it will be observed. Our empirical research can solve selection bias problems because it does not omit any observation in the same industry. As a result this study provides metrological calibration for theoretical analysis more scientifically. The remainder of the paper is organized as follows. In the next section, we review the literature and propose the research hypotheses. In the third section, we introduce the research methods, data, sample, and variables. In the fourth section, we present the empirical findings, and in the final section, we draw the conclusions, limitations, and direction for future research. 2. Theoretical Background and Hypotheses

Government subsidies clearly impact corporate innovation behavior, mainly through three channels, as previous studies show. 2.1 Effect of Resource Allocation

Innovation needs capital investment. Government subsidies directly impact innovation by filling the capital gap (Almus and Czarnitzki, 2003; Hall, 2002) and part of the investment needed for innovation activities. However, it is very difficult for smalland medium-sized enterprises to innovate and finance because of the scale or ownership discrimination. Besides, small firms and startups need more money for innovation than their larger competitors (Hall, 2002). Needless to say, government subsidies can effectively alleviate the pressure on such enterprises to finance innovation, thereby 2

ACCEPTED MANUSCRIPT enhancing the enterprises’ innovation initiative (Boeing, 2016; Hussinger, 2008; Lach, 2000). On the other hand, government subsidies are a type of non-operating income that enterprises can obtain without making any effort. When enterprises use government subsidies as capital input for innovation activities, the enterprises do not necessarily expand the scale of innovation investment on the basis of the original investment. The original high-cost innovation financing could be replaced by government subsidies, that is, the capital used for R&D from other funding channels could be crowded out (Almus and Czarnitzki, 2003; Feldman and Kelley, 2006). In addition, the marginal effect of making up for innovation resources gradually decreases as subsidies increase, and the incentive effect of the subsidies has a smaller variation tendency. Besides, exceeding the optimal subsidy level also eliminates the driving force to improve operations and reduce costs (Comanor and Leibenstein, 1969), and business managers may even abandon some of the innovation activities. As a result, firms may rely on external resources from subsidy rather than to increase its own competitiveness in a long term. Even worse, to capture and maintain favorable resources, firms probably choose to seek rent (Murphy et al., 1993) especially when subsidies are very high. Given the factors mentioned above, government subsidies may deviate the market innovation resource allocation from the ideal situation and negatively impact enterprise innovation. 2.2 Information Efficiency Effect

Government subsidies not only directly adjust the allocation of resources, but also convey a guiding signal, such as for future government industrial policy, that tells investors of the greater benefits in certain market areas. For enterprises, the different government subsidies that are allocated to a diversified range of production sectors indirectly reflect the government’s policy orientation, and then attract even more resources to these sectors (Kleer, 2010; Lerner, 2000) and consequently improve the enthusiasm for innovation in related areas. Government subsidies, together with sales promotion, can deliver information to consumers, thus creating greater market demand (Lu et al., 2017). This situation means more potential benefits from and lower average cost of technological innovation, and consequently, a greater willingness of the company to innovate. However, owing to information asymmetry, the direction and intensity of government subsidies may be inconsistent with actual needs. The government’s access to information raises two issues. First, the government may not fully grasp real market information and may face difficulty determining the value of innovation; thus, it may not be able determine the real subsidy demand for the different production sectors. Furthermore, the information itself may incur errors or distortions during the transmission process. Second, some enterprises intentionally conceal or even provide false information to obtain financing (Zhang and Wu, 2014), making it difficult for the government to formulate scientific and reasonable standards. Even given the enterprises’ data, the government still may not be able to identify the enterprise’s actual capacity and willingness to innovate. These two problems lead to an irrational decision of government on the provision of subsidies. Determining the actual use of subsidies encounters many other obstacles. For instance, the probability of management improvement, trade-off between technology licensing and R&D, and learning-by-doing can influence the opportunity cost of innovation. Even if innovation occurs as expected, its profitability can still be unknown 3

ACCEPTED MANUSCRIPT (Lach, 2000). This obscure information further results in undesirable effects on the government’s decision making on the provision of subsidies. Asymmetric information may lead to inappropriate government subsidies, and inappropriate feedback, in turn, conveys ill-conceived government-led signals. When the subsidies increase, this negative effect becomes more obvious, thus exacerbating the distortion of innovation resources. 2.3 Risk Control Effect

When an enterprise performs an innovation activity, it faces risk mainly from the technology, product, and finance markets (Pierrakis and Saridakis, 2017). Subsidies may directly reduce the financial risk, and the guidance signal from the subsidies may also reduce the risk due to the misunderstanding of a new product. Similarly, the technical risks weaken to a certain extent owing to the improvement of the enterprise’s development conditions. The lower risks increase the enterprise’s innovation initiative (David et al., 2000). However, when subsidies flow to one or some production sectors, the market resources are likely to be over-concentrated, the subsidy requirements from other sectors are less likely to be met, and the systemic risk of the technology market, which is difficult to measure but cannot be ignored, rises. Consequently, government subsidies may play a positive role in promoting enterprise innovation and at the same time cause negative effects under certain conditions. Therefore, we propose the following hypothesis: Hypothesis 1. Government subsidies promote enterprise technology innovation but inhibits such innovation when the subsidies exceed a certain level. Innovation is also closely related to the enterprises’ development environment and many external conditions. Because innovation needs sustainable investments in labor and capital, fluctuation in the factors market naturally has a direct impact on the cost of innovation (Haskel and Wallis, 2013; Luo et al., 2016). As a crucial condition, ownership can directly determine the enterprise’s resource access and information endowment (Luo et al., 2016). The policies of regional governments are not the same for non-state-owned and for state-owned enterprises, which also influences corporate behavior and innovation effort (Bronzini and Piselli, 2016). The strategy for the intellectual property rights of non-state-owned enterprises also differs from that of state-owned ones. Facing more competitive market environment private firms may be more active to pay resource and effort into innovation conduct to promote competitiveness. Once the non-state-owned enterprises gain more subsidy they could be more sensitive to innovation input than state-owned enterprises. On contrary, the state-owned enterprises always obtain more sufficient subsidy resource from government and have less pressure or motivation to increase its own innovative ability. Therefore, we propose a second hypothesis: Hypothesis 2. The effect of government subsidies on innovation differs among enterprises with different types of ownership, and non-state-owned enterprises are more sensitive to subsidy than state-owned enterprises.

4

ACCEPTED MANUSCRIPT At the same time, the regional education level, natural resource conditions, and policy environment are directly related to the factor market, which has a significant impact on the production elements’ prices. Therefore, regional characteristics cannot be overlooked in the study of enterprise innovation behavior. In developed regions, unlike in more economically backward areas, enterprises learn from each other more often and compete with each other more intensely. Because of the combination of these factors, enterprises in developed areas generally have a higher rate of innovation (Audretsch and Feldman, 1996). Given the same resources, the resource conversion efficiency of the enterprises in the developed regions may be higher. At the same time, in the more economically developed regions, business clusters, such as enterprise alliances, are more likely to form and usually obtain government subsidies and support more easily (Broekel et al., 2015; Nieto and Santamaría, 2007). In addition, because of spillover effects (Czarnitzki et al., 2007) and competition, those business clusters have a higher rate of innovation (Broekel et al., 2015; Rui and Swann, 1998). When examining the effect of government subsidies on enterprise innovation, the influence of adjustment and restriction based on regional economic differences cannot be neglected. The following hypothesis incorporates this expectation: Hypothesis 3. The level of regional economic development moderates the effect of government subsidies, and the optimal amount of subsidies for enterprise innovation is larger in a developed area than in a non-developed area. 3. Data and Variables 3.1 Data and Sample of Electronic Manufacturing Industry

The electronic equipment manufacturing industry has a standard industry code (SIC) of C40. It is the foremost high-tech and technologically intensive industry. Advanced technologies are vital to electronic manufacturing companies, and thus, the industry is bustling with innovation activities. Focusing on this industry avoids the heterogeneity in the different industries. The data for the electronic manufacturing enterprises are from the China Industrial Enterprise Database (Annual Surveys of Industrial Production) from 2005 to 2007, collected by the Chinese government’s National Bureau of Statistics. This Annual Survey of Industrial Production is a census of all non-state firms with more than 5 million RMB in revenue (about $600,000) plus all state-owned firms (Hsieh and Klenow, 2009). The patent data of the enterprises are from the IncoPat patent database. We obtain the whole sample of electronic manufacturing industry from this dataset. That means we observe all the enterprises which survive all the time from 2005 to 2007 in this industry. Regardless of whether the enterprise is subsidized and whether the enterprise is innovative or not, no matter how many subsidies it receives, it will be observed in the sample and considered. It can prevent selection bias and endogenous problem, and produces effective metrological regression results. We form a balanced panel with the sample data for three consecutive years. This balanced panel preserves the complete production and technology information and includes all firms with an annual revenue of above 5 million RMB from 2005 to 2007. In addition, we filter the sample data according to the following conditions: (1) the total output value of the enterprise is negative; (2) the input of the enterprise is negative, including the number of employees, the middle investment, and the original value of fixed assets and current value of the fixed assets; and (3) a large amount of enterprise 5

ACCEPTED MANUSCRIPT data is missing, such as the values of many important indicators. The final sample contains 3,888 observations from 1,296 companies. 3.2 Variables

This study mainly examines the effect of government subsidies on enterprise innovation, so government subsidies is the core explanatory variable. At the same time, we select four core explanatory variables to measure the firm’s innovative behavior and performance, and we introduce a number of control variables that have a high correlation with enterprise innovation to verify the robustness of the results. 3.2.1 Independent Variable

Government Subsidies (sub): We use the indicator of enterprise subsidies incomes from the government as the measurement of subsidy variable. The data come from the China Industrial Enterprise Database (Annual Surveys of Industrial Production) from 2005 to 2007, collected by the Chinese government’s National Bureau of Statistics. 3.2.2 Dependent Variables

R&D (rd): As the general index total R&D input is the value of all factors that an enterprise utilizes to drive innovation, including funds and human resources. This indicator ignores the difference of enterprises scale and just reflects total innovation expenses. Hence, innovation activity is further measured by the firm’s R&D intensity. R&D Intensity (rdintensity): We use the proportion of R&D in overall assets to represent the innovation activities of enterprise. It is total expense on R&D divided by assets of firm. R&D intensity indicator shows degree of innovation effort and it is more precise to describe the innovation conduct than total R&D expenses because it considers enterprise scale. Patent Progress (patprogess): Patents are regarded as the most reasonable indicators for measuring innovation performance. Acs and Audretsch (1989) pointed out that patents, such as for new technologies, processes, and products, are the most appropriate exponents of enterprise innovation outcomes. With the changes in some innovation indicators, such as R&D and scientific and technical personnel, patents as innovation outputs have the same variation tendency. We employ an enterprise’s annual patent application number as an indicator of innovation performance. Total Factor Productivity (TFP). This study chooses an enterprise’s TFP to describe the enterprise’s overall technological progress which can measure to a certain extent the achievement or output of innovation. There are many common methods for calculating the TFP, including the ordinary least squares, fixed-effect, Olley-Pakes, and LevinsohnPetrin methods. In this study, we improve the method of Olley and Pakes’ (1992), the calculation method is introduced in appendix A. 3.2.3 Control Variables

Patent Stock (patstock): The innovation is marginal innovative progress and constrained by original technology base or knowledge stock. Enterprise patent stock may influence its future innovation. Hence we use patent application stock as the indicator of technical advantage in the regression. Market Share (ms): The market share of an enterprise is one of the most important factors of its future development strategy. It describes the industrial structure and affects 6

ACCEPTED MANUSCRIPT the conduct and performance of enterprise innovation. We use the ratio of the enterprise’s output to overall industrial output as a representation of the market share. Enterprise Scale (scale): The relationship between firm size and enterprise innovation is an ongoing research topic. Enterprises of different scales clearly have different levels of economic strength and anti-risk abilities, which obviously influence business innovation. There is a classical debate about whether smaller enterprises or larger ones are more inclined to carry out innovative activities (Jeng and Pak, 2016; Sulistyo and Siyamtinah, 2016). Morgan et al. (2009) argued that firm size certainly impacts firm innovation. To ensure the robustness and reliability of our research results, in the regression model, the total number of employees in the enterprise is used as a proxy for enterprise scale. Enterprise Growth (growth): Innovation strategy planning can be accompanied by estimation of the enterprise architecture and growth process (Lee, 2009). We use the number of years since the enterprise’s founding as a proxy for business growth variables. Export (export): Companies that participate in export trade face more intense competition with overseas competitors, which means they need to adapt to a broader range of consumer preferences. In this process, enterprises may experience the export learning effect, in which they learn from their overseas competitors’ advanced management experience and their innovative development direction (Alarcón and Sánchez, 2016). These learning outcomes may also play a crucial role in promoting business innovation. We use the export delivery value to directly measure the export behavior of the enterprise and make the model explanation more reasonable. Operating Profit (profit): A firm’s income and expenditures are closely linked. If the enterprise is lucrative, it has more capital to invest in innovation. Companies with stronger profitability are also better equipped to transform innovation outcomes into actual outputs and create more wealth under the same conditions (Jeng and Pak, 2016). We use business profit as an indicator of corporate profitability. Human Capital Investment (trainingfee): Corporate human capital investment clearly impacts innovation activities and performance. Human capital has always been regarded as the basis of an enterprise’s intellectual capital and the structure of its various parts (Shang and Lin, 2010). Thus, employee education fee as a representative of the enterprise’s human capital investment should be included among control variables. Ownership of Enterprise (ownership): We calculates the proportion of state capital to the firm’s paid-up capital. The proportion of state capital, which represents the nature of the composition of the firm, is used as a control variable to improve the accuracy of the regression model. The variables are defined in Table 1. Table 1. Variable definitions. Variable Dependent variable Innovation activity

Indicator

Unit

rd

Thousand yuan

Structure of innovation input

rdintensity

%

Innovation performance Productivity Independent variable subsidy Control variable Technical advantages Market

patprogress tfp

Piece 1

Total expense on R&D. Total expense on R&D divided by assets of firm. (R&D / Assets) Annual number of patent applications Total factor productivity based on OP method

sub

Thousand yuan

Total amount of government subsidies

patstock ms

Piece 1

Openness

export

Thousand yuan

Number of patents stocked in the last term Ratio of total product revenue to total industrial output Export delivery value

7

Definition

ACCEPTED MANUSCRIPT Scale Profitability Growth Human capital investment Ownership

scale profit growth trainingfee ownership

People Thousand yuan Years Thousand yuan 1

Number of employees in an enterprise Current business operating profit Number of years since enterprise' founding Total investment in human capital Ratio of state capital to total paid-up capital

4. The Model

The sample data in this study are panel data for 2005 to 2007. When dealing with panel data, we need to decide between a fixed-effect and a random effect model. If we cannot determine an individual effect on the model’s explanatory variables, the random effect model should be chosen; otherwise, the fixed-effect model is more appropriate. The data are tested using the Hausman test for every regression model. To characterize the nonlinear relationship between the variables in the hypothesis, we set the first and second terms as the core explanatory variables (government subsidies) to perform the regression analysis. The basic regression equations are as follows: yit = Constant + 1 subit +  2 subit2 +  3 patstock1tit +  4 msit +  5 scale1it + 6 growthit

+7 exportit +  8 profitit +  9 trainingfeeit + 10 ownershipit +  i +  it

Eq.(1)

where yit represents rd, rdintensity, patprogess, and tfp, respectively, in equations (1)-(3), subit represents government subsidies, subit2 is a quadratic term of subsidies, a fixed or random effects term, and

 i is

 it is a stochastic error term.

To test hypothesis 2, this study also use the variable ownership of enterprise (ownership) to moderate the effect of subsidy on innovation. We divide enterprises sample into non-state-owned and state-owned ones, it compares the impact of subsidies on technology innovation for non-state-owned and state-owned enterprises. Equation (2) is used to estimate the moderating effect of ownership (non-state-owned and stateowned enterprises) as follows: state-holding yit |non-state-own = Constant + 1 subit +  2 subit2 +  3 patstock1tit +  4 msit +  5 scale1it

+6 growthit + 7 exportit +  8 profitit +  9 trainingfeeit +  i +  it

Eq.(2)

To test hypothesis 3, we choose per capita GDP (pgdp) as a variable to describe the level of regional economic development for moderator effect calibration. Based on the World Bank’s global income divisions, namely high income, low income, and middle income, from 2005 to 2007, we group the enterprises according to the per capita GDP of the provinces in which they are located. A province with a per capita GDP greater than or equal to 50,000 RMB is regarded as a developed area, and a province with a per capita GDP lower than 50,000 RMB as a non-developed area. A regression analysis of the two sets of data is carried out to verify the effect of the regional economic development level on government subsidy effectiveness as follows: group1 yit |group2 = Constant + 1 subit +  2 subit2 +  3 patstock1tit +  4 msit +  5 scale1it + 6 growthit

+7 exportit +  8 profitit +  9 trainingfeeit + 10 ownershipit +  i +  it

Eq. (3)

In equation (3), we divide the sample enterprises into two groups based on the per capita GDP of the provinces in which they are located, that is, a group comprised of enterprises in developed areas (provinces with a per capita GDP of or above 50,000 8

ACCEPTED MANUSCRIPT RMB), and another group comprised of enterprises in non-developed ones (provinces with a per capita income below 50,000 RMB). 5. Results

Table 2 shows the descriptive statistics of the variables in different models. Table 2. Descriptive statistics. Variable patprogress rd

Mean 1.435 5,331

Std. Dev. 18.02 47,511

Min 0 0

Max 785 1.800e+06

rdintensity tfp sub patstock

0.0161 5.576 508.5 2.967

0.0501 1.218 6323 35.93

0 0.517 0 0

1 9.522 272,653 1,433

ms export

0.0759 392,531

0.510 3.157e+06

0 0

10.88 7.100e+07

scale profit growth trainingfee ownership

455.8 18,450 9.850 94.38 0.0596

1,112 239,835 33.11 550.5 0.215

0 -1.600e+06 0 0 0

16,987 1.000e+07 2,005 13,670 1

Table 3 shows the basic regression results for the effect of subsidies on total R&D expenses. The coefficient of the government subsidy square term is negative and the coefficient of subsidies is positive, which show that R&D has an inverted U-shaped relationship with government subsidies. Meanwhile, regarding the estimated coefficients of the control variables, there is positive relationship between enterprise scale (total number of employees) and innovation, and human capital investment (trainingfee) can also drive enterprise innovation. Table 3. Impact of government subsidies on enterprise R&D. Variables

Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

rd

rd

rd

rd

rd

rd

sub

0.983*** (3.6902)

0.986*** (3.6883)

0.983*** (3.6802)

0.966*** (3.6338)

0.791*** (2.9830)

0.811*** (3.0607)

sub2

-5.55e-06*** (-5.3257)

-5.57e-06*** (-5.2968) -2.624 (-0.1154)

-5.54e-06*** (-5.2665) -0.707 (-0.0310) 9,036* (1.8341)

-5.47e-06*** (-5.2309) -3.633 (-0.1603) 1,220 (0.2372) 8.845*** (5.0523) 0.717 (0.0389)

-4.78e-06*** (-4.5775) 10.94 (0.4849) 73,864*** (6.3744) 9.049*** (4.6637) 1.164 (0.0636) -0.00701*** (-6.3822) -0.0256*** (-4.9615)

5,054*** (10.1534) 3,888 0.012

5,062*** (10.0854) 3,888 0.012

4,371*** (6.9658) 3,888 0.013

939.0 (1.0004) 3,888 0.023

-1,427 (-1.3941) 3,888 0.041

-5.22e-06*** (-4.9572) 7.856 (0.3483) 80,681*** (6.8617) 9.508*** (4.8948) 1.264 (0.0692) -0.00775*** (-6.9175) -0.0257*** (-4.9944) 5.117*** (3.1215) -5180 (-0.9867) -2,019* (-1.8293) 3,888 0.045

patstock ms scale growth export profit trainingfee ownership Constant Observations R-squared

9

Model (6)

ACCEPTED MANUSCRIPT F Number of id

15.39 1,296

10.26 1,296

8.545 1,296

10.00 1,296

13.74 1,296

12.11 1,296

Note: t-statistics are in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1.

To further explore enterprise R&D more precisely, we carry out a regression using equation (1) for R&D intensity. Table 4 shows the regression results with R&D intensity as the dependent variable. As can be seen from the results, the coefficient of government subsidies is positive and the coefficients of the subsidy square term are negative, which means that subsidies and R&D intensity continue to have an inverted U-shaped relationship. From the structural point of view, as government subsidies increase, corporate innovation becomes more aggressive (i.e., R&D takes up a bigger portion of the total assets), but after a certain level, the government subsidies inhibit the firm’s innovation (proportion of R&D in total assets). The results support hypothesis 1, that is, government subsidies can stimulate an enterprise’s effort in innovation and improve its output but inhibit such effect on innovation when the subsidies reach a certain level. This is because, although subsidies can directly fill the gap in corporate innovation funds and reduce the risk of innovation, a certain level of government subsidies can cause excess enterprise profits and crowd out some of the enterprise’s innovation investment, eliminating the motivating force for enterprise innovation. Table 4. Impact of government subsidies on enterprise R&D intensity. Variable

Model (1) rdintensity

sub sub2

Model (2) rdintensity

Model (3)

Model (4)

Model (5)

rdintensity

rdintensity

rdintensity

Model (6) rdintensity

5.21e-07*

5.15e-07*

5.15e-07*

5.11e-07*

5.07e-07

(1.7032)

(1.6805)

(1.6789)

(1.6668)

(1.6405)

(2.3503)

-1.62e-12

-1.58e-12

-1.58e-12

-1.56e-12

-1.54e-12

-2.09e-12*

(-1.3534)

(-1.3105)

(-1.3059)

(-1.2945)

(-1.2703)

(-1.8140)

6.60e-06

6.89e-06

6.26e-06

6.93e-06

2.11e-05

(0.2530)

(0.2637)

(0.2394)

(0.2635)

(0.9618)

0.00136

-0.000313

0.00459

0.00406

(0.2408)

(-0.0528)

(0.3404)

(0.6316)

1.90e-06

2.29e-06

-1.97e-06

(0.9387)

(1.0131)

(-1.6183)

1.93e-06

1.95e-06

-4.37e-06

patstock ms scale growth

(0.0907)

6.30e-07**

(0.0917)

(-0.2188)

export

-6.86e-10

-4.66e-10

(-0.5360)

(-0.5329)

profit

-2.07e-10

-1.42e-09

(-0.0345)

(-0.3234)

trainingfee

1.41e-06

ownership

-0.00859**

(0.8967) (-2.0239) Constant

0.0159***

0.0159***

0.0158***

0.0150***

0.0147***

0.0170***

(27.7919)

(27.5346)

(21.8666)

(13.8515)

(12.3513)

(13.3579)

Observations

3,888

3,888

3,888

3,888

3,888

3,888

R-squared

0.001

0.001

0.001

0.002

0.002

0.007

F

1.465

0.998

0.763

0.657

0.532

--

1,296

1,296

1,296

1,296

1,296

1,296

Number of id

Note: t-statistics are in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1.

10

ACCEPTED MANUSCRIPT Tables 5 and 6 show further results on the influence of subsidies on enterprise innovation output (the level of patent progress; patprogress) and productivity (TFP). For both the variables patent progress and TFP, the coefficients of subsidies and its square term are positive and negative, respectively, similar to those for R&D and R&D intensity, at least at the five percent significance level. Along with Figure 1, these tables clearly show that the inverted U-shaped relationship exists, which once again confirms hypothesis 1. Some interesting results can also be seen with regard to a control variable in the regression. As shown in tables 5 and 6, the impact of firm scale is always positive at a significance level of one percent, which means there may be more innovation incentives for large companies. Besides, the coefficients of the control variables, including profit, training fee, and ownership, are all positive for the regression results for both patprogress and TFP. Although some of the coefficients do not pass the 10 percent significance level, they still support some findings of previous research and can serve as references for further theoretical studies. To further explore how the ownership of enterprises can moderate the effect of government subsidies, we divide our sample data into two groups according to whether state assets exist. Figure 2 illustrates the kernel density graphs of subsidies, R&D investment, R&D intensity, and TFP for non-state-owned and state-owned enterprises, and Table 7 shows the regression results. The significance of the coefficients related to non-state-owned enterprises is clearly higher than that of the coefficients related to stateowned enterprises. Some of the quadratic term coefficients of state-owned enterprises are positive, which is slightly different from the previous results in tables 5 and 6. These outcomes could be persuasive evidence that subsidies are more effective incentives for non-state-owned enterprises; in other words, non-state-owned enterprises are more sensitive to subsidy . Interestingly, the coefficients of training fee also indicate that human resource investment for non-state-owned corporations always has a positive effect on innovation activities. Table 5. Impact of government subsidies on enterprise patent progress. Variable sub sub2

Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

Model (6)

patprogress

patprogress

patprogress

patprogress

patprogress

patprogress

4.17e-06*

4.74e-06*

4.75e-06*

4.62e-06*

5.42e-06**

5.49e-06**

(1.6640)

(1.8894)

(1.8918)

(1.8461)

(2.1506)

(2.1771)

-1.34e-11

-1.73e-11*

-1.74e-11*

-1.69e-11*

-2.01e-11**

-2.11e-11**

(-1.3606) patstock ms

(-1.7528)

(-1.7609)

(-1.7178)

(-2.0242)

(-2.1105)

-0.000679***

-0.000684***

-0.000707***

-0.000763***

-0.000769***

(-3.1794)

(-3.1985)

(-3.3122)

(-3.5590)

(-3.5856)

-0.0231

-0.0830*

-0.304***

-0.291***

(-1.7144)

(-2.7595)

(-2.6017)

scale

(-0.4991)

6.78e-05***

8.08e-05***

8.18e-05***

(4.1115)

(4.3856)

(4.4267)

growth

3.62e-05

3.45e-05

3.43e-05

(0.1986)

(0.1972)

export

(0.2080)

1.37e-08

1.22e-08

(1.3113)

(1.1492)

profit

1.32e-07***

1.32e-07***

(2.7014) trainingfee

(2.7044) 1.14e-05 (0.7291)

ownership

0.0324 (0.6482)

11

ACCEPTED MANUSCRIPT Constant

0.135***

0.137***

0.139***

0.112***

0.115***

0.111***

(28.7901)

(29.0116)

(23.4926)

(12.6679)

(11.8250)

(10.5808)

Observations

3,888

3,888

3,888

3,888

3,888

3,888

R-squared

0.001

0.018

0.018

0.021

0.025

0.031

F

1.389

4.299

3.285

5.029

4.708

3.859

Number of id

1296

1296

1296

1296

1296

1296

Note: We applied the logarithmic process to the variable patent progress in the panel data regression. t-statistics are in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1.

Table 6. Impact of government subsidies on enterprise TFP. Variable sub sub2 patstock

Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

Model (6)

tfp

tfp

tfp

tfp

tfp

tfp

2.72e-05*** (3.0983) -7.48e-11*** (-2.6611) 0.000921 (1.2765)

2.80e-05*** (3.2468) -7.89e-11*** (-2.8378) 0.00100 (1.4361) 6.55e-07*** (6.1369)

2.33e-05*** (2.7403) -6.65e-11** (-2.4051) 0.000822 (1.2206) 2.19e-07 (1.2692) 0.000153*** (3.9100) 0.273** (2.1585)

2.28e-05*** (2.7080) -6.63e-11** (-2.4141) 0.000909 (1.3650) 3.01e-07* (1.7412) 0.000157*** (4.0811) 0.612*** (3.4166) -0.0181*** (-3.1436) -7.85e-08*** (-2.7250)

2.01e-05** (2.3790) -7.14e-11*** (-2.6074) 0.000688 (1.0359) 7.46e-08 (0.4023) 0.000158*** (4.1320) 0.698*** (3.8771) -0.0191*** (-3.3497) -8.29e-08*** (-2.8857) 0.000128*** (3.1682)

5.471*** (89.0283) 0.046 869 365

5.438*** (92.2128) 0.125 869 365

5.306*** (86.7752) 0.207 869 365

5.553*** (55.1398) 0.236 869 365

5.549*** (55.6144) 0.251 869 365

2.01e-05** (2.3793) -7.15e-11*** (-2.6079) 0.000691 (1.0396) 7.53e-08 (0.4058) 0.000158*** (4.1368) 0.695*** (3.8508) -0.0196*** (-3.2606) -8.25e-08*** (-2.8674) 0.000128*** (3.1730) 0.0448 (0.2652) 5.552*** (55.2343) 0.251 869 365

profit scale1 ms growth export trainingfee ownership Constant R-squared Observations Number of id

Note: t-statistics are in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1.

12

0

.02

200000

.04

400000

rd

rdintensity

600000

.06

.08

800000 1000000

ACCEPTED MANUSCRIPT

100000

0

100000

sub

200000

300000

200000

300000

0

100000

0

100000

sub

200000

300000

200000

300000

5

0

6

1

7

2

tfp

8

patprogress

3

9

4

10

0

sub

sub

Figure 1. Quadratic regression lines of subsidies and enterprise technology innovation. Note: We applied the logarithmic process to the variable patent progress.

Table 7. Impact of subsidies on non-state-owned and state-owned enterprises. Variables Sub Sub2 patstock ms scale growth export profit trainingfee Constant Observations R-squared F Number of id

rd

rdintensity

patprogress

tfp

Group 1

Group 2

Group 1

Group 2

Group 1

Group 2

Group 1

Group 2

0.858*** (3.1249) -5.47e-06*** (-5.0307) -57.68** (-2.4143) 92,997*** (7.2491) 10.41*** (5.0720) 1.212 (0.0648) -0.00892*** (-7.3676) -0.0283*** (-5.2240) 3.940** (2.1771) -2,519** (-2.2520) 3,530 0.0533 14.40 1,219

-1.752 (-1.0032) 0.000762*** (4.4293) 934.8*** (29.1363) 157,433*** (4.8544) 3.992 (1.5681) 51.10 (0.2444) -0.0206*** (-4.9041) 0.000167 (0.0102) 0.549 (0.4096) -8,973* (-1.9155) 358 0.8647 127.9 169

6.31e-07** (2.2667) -2.13e-12* (-1.7767) 1.75e-05 (0.7566) 0.00385 (0.5691) -1.86e-06 (-1.3579) -2.48e-06 (-0.1202) -4.56e-10 (-0.4932) -1.24e-09 (-0.2710) 1.60e-06 (0.9224) 0.0170*** (12.6987) 3,530 0.0062 -1,219

-5.17e-07 (-0.1515) 1.03e-10 (0.2886) 9.89e-05 (1.5393) -0.000312 (-0.0155) -2.78e-06 (-1.0411) -0.000442*** (-3.2106) -6.50e-10 (-0.1350) -1.97e-08 (-0.6396) 1.03e-06 (0.3494) 0.0231*** (6.5998) 358 0.0683 -169

4.82e-06** (1.9874) -1.96e-11** (-2.0394) -0.000470** (-2.2238) -0.336*** (-2.9611) 8.53e-05*** (4.6995) 5.50e-06 (0.0333) 1.51e-08 (1.4091) 1.64e-07*** (3.4227) 3.27e-05** (2.0415) 0.106*** (10.7662) 3,530 0.0169 4.386 1,219

0.000186** (2.4832) -1.68e-08** (-2.2681) -0.00636*** (-4.6110) -1.446 (-1.0372) -0.000175 (-1.5956) 0.0494*** (5.4996) 2.35e-07 (1.3009) -2.52e-06*** (-3.5785) -3.00e-05 (-0.5210) -0.617*** (-3.0641) 358 0.2939 8.323 169

2.04e-05** (2.3336) -7.32e-11** (-2.5679) 0.000671 (0.9472) 0.768*** (4.0624) 0.000170*** (3.9160) -0.0306*** (-3.6568) -9.26e-08*** (-3.0597) -4.63e-08 (-0.2445) 0.000137*** (3.1166) 5.670*** (46.0491) 763 0.0485 -326

7.72e-05 (0.5526) -5.58e-09 (-0.4578) -5.87e-05 (-0.0412) 1.255** (2.1929) 0.000154* (1.7270) -0.0227*** (-3.0535) -1.44e-07 (-1.1204) 3.88e-06*** (4.0260) 0.000234** (2.0479) 5.609*** (26.3378) 106 0.2515 -59

Note: We applied the logarithmic process to the variable patent progress in the panel data regression. Group 1 refers to non-state-owned enterprises, and Group 2 refers to state-owned enterprises. t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, and * p < 0.1.

13

0

0

.05

.05

.1

.1

R&D

.15

subsidy

.15

.2

.2

.25

ACCEPTED MANUSCRIPT

0

5

x

10

15

0

5

State-holding

10

x

Non-state-owned

15

State-holding

0

0

.1

.1

.2

tfp

rdintensity

.2

.3

.3

.4

Non-state-owned

-15

-10

x

Non-state-owned

-5

0

0

2

4

x

6

Non-state-owned

State-holding

8

10

State-holding

Figure 2. Kernel density graphs of subsidies, R&D, R&D intensity, and patent progress. Note: The dotted line represents state-owned enterprises, and the solid line represents non-state-owned enterprises. We applied the logarithmic process to the variables subsidies, R&D, and R&D intensity.

To verify hypothesis 3, which states that the level of regional economic development moderates the effect of government subsidies, we perform a regression analysis with the data grouped according to per capita GDP The dependent variables in Table 8 are the four variables related to enterprise innovation (i.e., R&D, R&D intensity, patent progress, and TFP). First, the inverted U-shaped relationship is similarly found in both developed area and non-developed areas as clearly illustrated in Figure 1. These results can be considered to further support hypothesis 1. As shown in Figure 3, the same amount of government subsidies had completely different effects on enterprise innovation for areas with different levels of economic development. The optimal amount of government subsidies for an enterprise in a non-developed area is smaller than that for an enterprise in a developed area, and the same amount of subsidies produces a greater incentive for the enterprise in a developed area because such subsidies are more likely to be supplemented by the area’s greater regional resources. These results strongly support hypothesis 3. Moreover, from the results in Table 8, we can conclude that in developed areas, enterprise scale and profitability can boost technology innovation more than in non-developed areas. These results could serve as a reference for policy decision and evaluation. Table 8. Impact of subsidies on enterprise technology innovation in developed and non-developed areas. Variable sub sub2

rd

rdintensity

patprogress

tfp

Group A

Group B

Group A

Group B

Group A

Group B

Group A

Group B

-0.566*** (-3.1426) 1.44e-05***

-1.191 (-1.3834) -8.94e-07

1.15e-06** (1.9818) -6.30e-12

4.75e-07 (1.4467) -1.38e-12

8.29e-06* (1.8249) -4.71e-11

1.30e-05** (2.1719) -4.10e-11**

0.000154** (2.5611) -3.85e-09**

1.71e-05* (1.9120) -7.00e-11**

14

ACCEPTED MANUSCRIPT

patstock ms scale growth export profit trainingfee ownership Constant Observations R-squared F Number of id

(11.6002) 581.5*** (20.0305) 17,555***

(-0.3642) -56.95* (-1.6627) 177,085***

(-1.5852) 0.000208** (2.2307) -0.00110

(-1.4077) 5.38e-06 (0.3713) 0.00844

(-1.6200) -0.00211*** (-3.0699) -0.0637

(-2.4123) -0.000640*** (-2.6959) -0.495**

(-2.5227) 3.28e-05 (0.0136) -0.0240

(-2.4162) 0.000942 (1.5199) 1.076***

(5.7713) 4.374*** (8.0006) -1.894 (-0.2656) -0.00206*** (-3.5048) 0.00229 (1.2947) 7.918*** (9.9367) -1,741 (-1.0677) 92.68 (0.1750) 2,149 0.4712 -836

(6.3641) 34.92*** (6.4251) 923.6 (1.0286) -0.0215*** (-7.3402) -0.116*** (-4.1300) 1.563 (0.4212) -1,884 (-0.1328) -20569** (-2.4278) 1,739 0.1272 13.26 819

(-0.1125) -3.44e-06* (-1.9460) -1.12e-05 (-0.4884) 4.06e-10 (0.2152) -3.73e-10 (-0.0657) 9.29e-07 (0.3630) -0.00779 (-1.4794) 0.0206*** (11.9999) 2,149 0.0140 -836

(1.0542) 1.63e-06 (0.9386) 0.000108 (0.4195) -1.32e-09 (-1.3785) -7.47e-09 (-0.7879) 9.60e-07 (0.6526) -0.0200*** (-3.4193) 0.0217*** (6.4935) 1,739 0.0016 -819

(-0.3808) 4.84e-06 (0.1636) 7.63e-06 (0.0445) 9.54e-09 (0.5881) -1.37e-08 (-0.1951) -3.56e-05 (-1.4506) 0.00579 (0.1004) 0.100*** (6.6230) 2,149 0.0124 1.630 836

(-2.5642) 0.000151*** (4.0127) 0.0354*** (5.6824) 1.82e-08 (0.8952) 2.59e-07 (1.3311) 4.91e-05* (1.9099) -0.0343 (-0.3482) -0.187*** (-3.1834) 1,739 0.0854 8.494 819

(-0.0397) 0.000207 (1.4302) 0.0195 (1.0717) -4.46e-08 (-0.5711) 1.62e-06 (1.6501) -0.000200 (-1.1507) 0.444 (1.4148) 4.869*** (16.3132) 437 0.0678 1.556 213

(3.5260) 0.000257*** (4.6352) -0.0360*** (-3.9376) -2.69e-07*** (-5.0341) 2.36e-06*** (4.6181) 9.58e-05* (1.9272) 0.214 (0.6336) 5.790*** (42.1663) 432 0.3132 -208

Note: We applied the logarithmic process to the variable patent progress in the panel data regression. Group A has

0

0

.02

.04

rd

rdintensity

.06

.08

200000 400000 600000 800000 1000000

pgdp < 50,000, and Group B has pgdp > 50,000. t-statistics are in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1.

0

100000

sub

0

300000

100000

sub

non-developed

developed

200000

300000

developed

5

-2

6

0

7

2

tfp

8

patprogress

4

9

6

non-developed

200000

0

100000

sub

non-developed

200000

300000

0

100000

sub

non-developed

developed

200000

300000

developed

Figure 3 Quadratic regression lines of subsidies and enterprise technology innovation in developed and non-developed areas. Note: The dotted line represents the developed areas, and the solid line represents the non-developed areas. We applied the logarithmic process to the variable patent progress.

We further have robustness analysis with another regression model. Because the patent application number is not continuous, we also use count data model to calibrate 15

ACCEPTED MANUSCRIPT our hypothesis about inverted U-shaped relationship between the amount of subsidies and technology innovation. We take advantage of poisson regression and negative binomial regression for patent application number respectively. The results are shown in Table 9. We find that coefficients of subsidy square term keep negative all the time in Model (1)-(6). We claim that the effect of subsidy on innovation is significant and our results are stable. The estimates included in Table 9 support the inverted U-shaped relationship again and confirm the previous results. Table 9. Robustness test with count data model. Variables Model (1) sub

sub2

Poisson regression Model (2) Model (3)

Negative binomial regression Model (4) Model (5) Model (6)

1.10e-05***

1.44e-05***

1.15e-05***

(4.0724)

(5.1367)

(3.8242)

(2.1100)

(2.2433)

(2.2114)

-2.96e-11***

-2.95e-11***

-2.55e-11***

-3.18e-10*

-4.46e-10**

-8.16e-10***

(-3.9267)

(-3.7197)

(-2.9629)

(-1.7238)

(-2.2083)

(-3.8166)

-0.000729***

-0.000807***

0.0583***

0.0609***

(-8.4886)

(-9.0746)

(5.6478)

(5.8800)

-0.747***

-2.778***

1.953***

1.907

(-2.6332)

(-5.1962)

(3.4086)

(1.1493)

0.00160***

0.00143***

0.000413**

0.000437***

(18.5860)

(14.1251)

(2.2592)

(2.6159)

0.143***

0.112***

-0.000833

3.78e-05

(5.0110)

(-0.1966)

patstock

ms

scale

growth

(6.3655) export

profit

trainningfee

9.94e-05**

0.000120**

-3.01e-07*

(4.7333)

(-1.6579)

5.33e-07***

-2.07e-08

(4.0383)

(-0.0236)

2.95e-05

0.00272***

(0.7866)

(4.9167)

0.171

-0.472

(1.0759) Constant lnalpha 687 229

(0.0071)

3.46e-07***

ownership

Observations Number of id

0.000113**

687 229

687 229

(-1.1384) 0.236** (2.4040) 3.570*** (61.6330) 3,888 1296

-1.300*** (-11.0098) 3.094*** (49.1999) 3,888 1296

-1.519*** (-12.6361) 3.036*** (47.9838) 3,888 1296

Note: We applied the variable patent progress as dependant variable in the count data model regression. z-statistics are in parentheses. *** p < 0.01, ** p < 0.05, and * p < 0.1.

6. Conclusions

This study examines the impact of government subsidies on technology innovation by performing an empirical analysis of firm data from the China Industrial Database (Annual Surveys of Industrial Production) and incoPat patent database. We describe the effect of government subsidies in terms of four characteristics of enterprise innovation 16

ACCEPTED MANUSCRIPT and drew some valuable conclusions that can serve as a significant reference for policy formulation and practical solution. Scholars have explored the impact on corporate innovation behavior from different perspectives, such as the innovation behavior of enterprises under different economical environments. However, few scholars have performed systematic empirical research on the relationship between government subsidies and enterprise innovation. Our study attempts to verify some theoretical points. Based on existing research, we point out that government subsidies influence enterprise innovation behavior mainly through the three channels of resource allocation, information efficiency, and risk control. Three hypotheses are put forward following a theoretical review. Our results show that government subsidies promote enterprise technology innovation but it will inhibit innovation when there are too many subsidies. Meanwhile, ownership and level of regional economic development moderate the effect of government subsidies. The optimal amount of government subsidies for an enterprise is larger in a developed area than in a non-developed area because subsidies are more likely to be supplemented by the developed area’s greater resources. Our study has produced insightful results, but it also has limitations. We investigate the impact of general subsidies on enterprise innovation and do not classify the subsidy along to the purpose because of the data limitation. We may collect more specific data sample and explore the impact of different kind subsidies in further research. Although our analysis examines the different effects of subsidies on enterprise technology innovation in developed and non-developed areas, further in-depth investigations about the mechanisms of the subsidies’ different effects on innovation in different areas are needed. Future studies can also include more moderating variables and examine the interaction effect of subsidies with the environment using this study’s econometric model.

Appendix A. Calculation method of TFP

In line with a summary of existing studies (Hsieh and Klenow, 2009), this study argues that Olley and Pakes’ (1992) method effectively estimates an enterprise’s TFP. They establish a structural equation model that maximizes the firm’s profit as a behavioral equation for the firm’s investment and market exit decision. Existing literatures mainly use Cobb-Douglas production function to calculate TFP as follow:

Yit = Ait M it M K it K LitL

(A.1)

Where Yit , M it , K it , Lit is firm i’s output, intermediate input, capital and labor at year t,respectively. Ait is productivity or technology progress of firm. Basic regression equation taking the logarithm transformation of Cobb-Douglas production function is as follow: (A.2) ln Yit =  0   M ln M it M   K ln K it K   L ln LitL   it TFP can be measured by estimating the Solow residual between real output and fitted value as follow: 

TFPit  ln Yit  ln Yit

(A.3)

17

ACCEPTED MANUSCRIPT 

Where ln Yit , ln Yit is logarithm of real output and fitted value respectively. And we use the deflated price on the industrial level in this measurement.

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ACCEPTED MANUSCRIPT Zhang, X., Wu, J., 2014. Research on effectiveness of the government R&D subsidies: Evidence from large and medium Enterprises in China. American Journal of Industrial & Business Management 4, 503– 513. DOI: 10.4236/ajibm.2014.49056.

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Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry

Highlights 

Existing research provides contradictory insights about the effect of subsidies.

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We systematically explain the effect mechanism of subsidies on innovation.

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We prove that there is an inverse U-shaped relationship between subsidies and innovation.

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Subsidies have a greater impact on non-state-owned enterprises than otherwise.

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Optimal amount of subsidies for developed areas is larger than non-developed areas.