R&D subsidies

To consider these issues, the paper adopts a three-stage framework. The government chooses a subsidy(ies) in the first stage; in the second stage, a monopolist chooses R&D effort which determines the size of the R&D project or the probability of success of the project; in the last stage, the firm chooses its output. It is found that conditional subsidies can yield the same level of innovation and welfare as unconditional subsidies. However, when the probability of success is sufficiently low (be it endogenous or exogenous), conditional subsidies yield suboptimal levels of innovation and welfare. When the firm chooses the probability of success, conditional and unconditional subsidies yield the same levels of innovation and welfare, but conditional subsidies can have the advantage of a lower expected cost of the subsidy to the government. I consider the simultaneous use of conditional and unconditional subsidies, and show that different combinations of the two can lead to the same levels of innovation and welfare as unconditional subsidies alone. Finally, reverse conditional subsidies yield optimal levels of innovation and welfare, except when the probability of success is sufficiently high. Comparing conditional subsidies with reverse conditional subsidies, conditional subsidies yield higher (lower) welfare when the probability of success is high (low). In all, eight different models are considered, allowing for different policy instruments (conditional and/or unconditional subsidies) and different choices of innovation effort by the firm (innovation size versus probability of success); more specifically, for each choice variable by the firm, four combinations of policy instruments are considered: unconditional subsidies only, conditional subsidies only, conditional and unconditional subsidies together, and reverse conditional subsidies only.

resulting from research also implies the emergence of a second type of distortion in the economy: externalities related to R&D activities such as the transmission of knowledge beyond the originator of the innovation (research spillovers). These externalities generally prevent innovators from securing all the rents associated with their innovation, which explains the existence of under-investment in R&D. According to different empirical studies, such investments have been found to be significantly lower than what would be optimal for society (Mansfield (1977), Pakes (1985), Jaffe (1986), Griliches (1992), Nadiri (1993), Jones and Williams (1998)). It would therefore be advisable to correct for this second type of externality as well, e.g. through R&D subsidies. However, by reducing the cost of using alternative energies, such subsidies are also likely to have an impact on the consumption of fossil fuels and thus on carbon emissions.

In the literature, other studies also explore the e¤ects of R&D subsidies in the R&D-based growth model; see for example, Segerstrom (1998), Lin (2002), Zeng and Zhang (2007), Impullitti (2010), Chu and Cozzi (2018), Yang (2018) and Hu, Yang and Zheng (2019). These studies mostly focus on either variety expansion or quality improvement. Only a few studies, such as Segerstrom (2000) and Chu, Furukawa and Ji (2016), explore the e¤ects of R&D subsidies in the Schumpeterian growth model with both dimensions of innovation. However, none of these studies consider how R&D subsides a¤ect the endogenous activation of the two types of innovation.

As the main force behind national innovation, technology innovation is an important contributor to sustainable de- velopment for technological small and medium-sized enterprises (SMEs). However, due to the limitations of scale, lack of resources, and relationships with other en- terprises or government departments that are neither close nor stable, technological SMEs need to leverage the support of government departments in the innovation and development process. Since 2008, technological SME clusters have collapsed in some provinces in China due to the financial crisis. Therefore, the State Ministry of Science and Technology, Department of Small and Me- dium-sized Enterprises, Provincial Department of Sci- ence and Technology, and other relevant government departments have introduced a variety of supporting policies to increase the different levels of innovation funds and technology projects for technological SMEs. Thus, how government R&D subsidies affect the R&D activities of technological SMEs as well as the shift of the technological innovation path is an important issue related to the government-sponsored effect and techno- logical innovation activities.

Turkish industry is appeared as a passive user of transferred technologies. Although this structural characteristic of industry, national engineering industries do not benefit from national patent system to transfer new technologies. Patent system is used to obtain a monopoly power and royality by pharmaceutical and chemistry MNC’s. These MNC’s do not transfer the new strategic technologies such as informations and communications to Turkey by patents. So that, R&D Subsidy System is more useful than the patent system to current accumulation of national technological capability. However, R&D Subsidy system have had to be self-financial systems such as venture capital and it has to be mastered by a large financial capacity. If the structural arrangements in R&D System is realised, it may be reach to a more efficient system for national technology policy in Turkey. In the medium term, there is no another chance for Turkish government to encourage the national industries except to use R&D subsidy systems in the global world.

Behavioural additionalities are expected to result from any R&D policy instrument (Georghiou 2002), and can therefore be applied in several evaluation studies. The concept attempts to capture the impact of public intervention on the innovation process itself and focuses not only on direct effects, but also on indirect impacts on the firm’s behaviour and its strategy. Although behavioural changes can be interpreted as intermediate results of public funding, their impacts are expected to persist beyond the period of the supported activity (OECD 2006). Long-term learning effects may be integrated into the firm’s capabilities and may additionally have structural and institutional impacts on the entire system (Georghiou 2002). Behavioural additionality is therefore positioned between input additionality and output additionality, trying to open the black box and to recognise the underlying forces related to firm innovation processes (OECD 2006, Larosse 2004). By this, it tries to address the questions of how public support interacts with the capabilities and strategies of firms, including issues of competence building, skills in managing R&D processes as well as strategic alliances with other actors (e.g. suppliers, competitors, science organisations) involved in the R&D process.

This paper selects the 2007-2015 Shanghai Stock Exchange and Shenzhen Stock Exchange listed companies as a research sample, deletes financial companies, companies with asset-liability ratios greater than 1, and companies which expla- natory variables or explained variables are missing. As mentioned earlier, in Chinese R&D activities, companies account for the vast majority. In the R&D activities carried out by enterprises, enterprises above designated size have played an important role. In 2015, for example, in all R&D investment of enter- prises, enterprises above designated size accounted for 90%. At the same time, the information disclosure of listed companies is relatively complete and the da- ta availability is good. Therefore, based on representativeness and data availabil- ity, we selected Chinese listed company data as a research sample. The reason for choosing data from 2007 is because the Ministry of Finance promulgated new accounting standards in 2006. It was implemented in listed companies on Janu- ary 1, 2007. The new standard revised the accounting for R&D expenses of en- terprises. As a result, the statistics of the R&D expenditure data after 2007 and before have changed, so a unified analysis cannot be performed. And the reason why the research data is as of 2015 is because the database update is slow; it is only updated to 2015, so there is no way to do further research. In summary, we have selected data for 2007-2015. The equity nature data of this paper comes from the RESSET database, and the others are from the CSMAR database. In order to eliminate the influence of extreme values, we have processed winsorize for continuous variables at the upper and lower 1% level.

Second, the dynamic effects of public R&D programs may also be related to the existence of the certification effects of such programs (Lerner, 2000; Meuleman and De Maeseneire, 2008). Projects backed by government subsidies have precise duration and are assessed by administrative agencies once the granting period expires. Failing this assessment may send negative signals to potential external financiers; hence, firms may have to prove their performance within the duration of the government granting period in addition to the concerns related to the adjustment costs. Therefore, we should expect the short-run effects of such government R&D programs to be strong, especially when external finance is important for firms. Indeed, public R&D programs exert significant positive short-term influences (within one to three years after the infusion of public R&D subsidies), as per empirical analysis (e.g., Levy and Terleckyj, 1983; Mansfield and Switzer, 1984; Lichtenberg, 1984; Guellec and Van Pottelsberghe, 2000; Callejón and García-Quevedo, 2005).

To cope with the fast-changing business environment, Chinese government makes every effort to increase business R&D subsidies. The aim of this paper is to examine the efficiency of Chinese government R&D subsidies on innovative performance and the moderating role of a firm’s R&D capacity. Based on the data from Chinese Large and Medium Firms during 1997-2012, we analyze whether government R&D subsidies affect a firm’s innovative performance, as well as how this ef- fect works. The findings suggest that the firms that received increasing government R&D subsidies will have a better innovative performance, yet up to a point. Beyond this threshold, a greater share of government R&D subsidy will reduce a firm’s innovative performance. And such substitution effect is larger for firms with greater R&D capacity. Also, the firm’s own R&D capacity, size, indus- try technical levels have varying degrees of impact on the efficiency of government R&D subsidies. The findings of this paper may have practical value and help governments to develop relative reg- ulations and policies.

The existing studies on subsidized mixed oligopolies have focused mainly on the e ff ect of output subsidies on production allocation. In particular, they have assumed that public and private firms have a given identical production technology. However, R&D e ff orts could work to improve firms’ technologies, thereby a ff ecting production allocation. Inevitably, social benefits depend not only on the production allocation but also on an allocation of firms’ R&D investments. As such, adjusting the allocations of production and R&D is required to achieve the first-best outcome.

This paper analyzes the growth effect of subsidy policies in a modified R&D-based growth model of Romer (1990), in which both innovation and capital accumulation are engines of long-run economic growth. We show that, under certain conditions, subsidizing the R&D sector may be growth-impeding.

This study explores the e¤ects of patent protection and R&D subsidies on innova- tion and income inequality using a Schumpeterian growth model with heterogeneous households. We …nd that although strengthening patent protection and raising R&D subsidies have the same macroeconomic e¤ects of stimulating innovation and economic growth, they have drastically di¤erent microeconomic implications on income inequal- ity. Speci…cally, strengthening patent protection increases income inequality whereas raising R&D subsidies decreases (increases) it if the quality step size is su¢ciently small (large). An empirically realistic quality step size is smaller than the threshold, implying a negative e¤ect of R&D subsidies on income inequality. We also calibrate the model to provide a quantitative analysis and …nd that strengthening patent protection causes a moderate increase in income inequality and a negligible increase in consump- tion inequality whereas raising R&D subsidies causes a relatively large decrease in both income inequality and consumption inequality.

The results in the above proposition are to some extent unexpected. Indeed, consider the case where the free spillover is zero, l is close to zero, and the marginal damage cost of pollution is high. In this case, where free spillover is absent and spillover bene…ts are nearly absent, we expect the regulator to subsidize original research at a higher rate to prevent environmental damage. The result in item (ii) is actually showing the reverse. This result is very interesting from the environmental point of view. To summarize, the subsidy policy of the regulator consists in trying to induce a minimum level of R&D externalities. Indeed, when the free spillover is high enough, he supports original research, and when it is low enough and the marginal damage of pollution is su¢ciently high, he supports absorptive research.

European countries have for years used subsidies for stimulation of new technologies, assistance to disadvantaged regions, R&D, "bail outs," and help to small and medium firms. Article 92 of the Treaty of Rome forbids any subsidy "that distorts or threatens to distort competition by favoring certain undertakings or the production of certain goods." At the beginning of the implementation of the 1992 Internal Market Program (EC 1992), the Commission made it clear that many of the industrial subsidies that had been granted by member states in the past were inconsistent with economic integration. Subsidies designed to prop up inefficient "national champions," the Commission argued, delayed the opening of European industry to the benefits of competition. R&D subsidies by

To assess whether an increase in s is likely to have a positive long-run e¤ect on economic growth, we provide a simple calibration exercise here. Our analysis involves the following parameters f ; ; ; s; ; F g. We follow Acemoglu and Akcigit (2012) to set the discount rate to a standard value of 0.05. We set the markup ratio to 1.30 that implies a 30% markup, which is within the range of empirical estimates summarized in Jones and Williams (2000). For the degree of spillovers 1 , we consider a value of 0.10. As for the R&D subsidy rate s, we consider a common value of 15% in developed countries. We consider a range of values for the entry cost parameter F 2 (0; F ), where the upper bound F (1 )( 1)=[ (1 s)] is imposed to ensure the global stability of the dynamics of N t . For each value of F , we

This research is on the one hand of practical relevance, since it discovers how governmental subsidies practically change the course and the process of innovation projects of the case company. This knowledge provides the reader with useful information about how, not only the input and output sides of R&D projects are influenced, but also how the particular project behavior is affected. It gives insight on how those incentives influence the speed, the scope, the complexity, the collaboration and other project attributes. This knowledge is particularly valuable to project managers of the case company, who are applying for subsidies for the first time, since it supports the managers to better anticipate how their innovation projects are influenced by the policy. Further, the project managers can better estimate the input, output and the behavioral changes the subsidy can potentially have on their project. This is especially of interest for the project managers and also for the general management of the case company, since innovation is naturally an important topic and also since the interest in public funding within the organization has increased significantly over the last years.

DOI: 10.4236/ajibm.2018.85078 1129 American Journal of Industrial and Business Management The types of R & D activities in higher education institutions are mainly basic research and applied research. Compared with scientific research institutes, the cooperative relationship between higher education institutions and enterprises is closer. Enterprises and institutions of higher learning are in the upstream and downstream relations in the process of knowledge production and application, and they are also complementary relationships. Fundamental research and ap- plied research conducted by institutions of higher learning have provided new knowledge reserves for corporate R & D activities. The spillover of knowledge generated to companies will gradually improve the corporate R & D environ- ment and reduce R & D risks. Enterprises can use basic knowledge to conduct further technical research and development, thus increasing R & D investment. In addition to the role of “knowledge spillovers,” universities and colleges have a role to foster and deliver high-quality R & D professionals for companies. With the increase in research and development spending of universities and colleges, it will enable them to train more R & D professionals. When these R & D profes- sionals flow from schools to enterprises, they will increase their R & D efficiency, reduce the risk of R & D activities, and increase the R & D expenditure of enter- prises. The study by Van Pottelsberghe de la Potterie (2008) shows that the gov- ernment’s R & D grants for colleges and universities has prompted companies to increase R & D spending [19].

A DEA model was used to evaluate the performance of these 1408 DMUs. DEA models for a given DMU use ratios based on the amount of output factors per given set of input factors. Most basic DEA models, such as CCR model and BCC model, don‘t have any means to rank efficient DMUs because all efficient DMUs have same value of one. Thus, we can‘t compare the performance of efficient subprojects using these basic DEA models. This paper utilized a modified DEA model which Andersen & Peterson [14] suggested to obtain super-efficiency measure for ranking efficient DMUs. This super-efficiency model was utilized to find the rank of projects supported by the 21 st Century Frontier R&D Program. Let us denote Y j ={yij} to an output vector in which yij

technological profile, as the chance to apply increases when the propensity to perform R&D projects is higher. 5 The available information allows us to consider several variables. The first one is internal R&D intensity, which we compute as the ratio of internal R&D expenditures over total employment. We also define total R&D intensity as total (internal plus external) R&D expenditures per employee and the percentage of R&D employment (over total em- ployment), as a proxy of skilled labour. In our sample, the means of these variables are greater in firms that have been awarded a European subsidy than in firms with a national subsidy, and superior in these nationally-subsidized firms than in firms with a CDTI loan (see Table 2). In addition, we introduce an indicator reflecting whether the firm has technological coopera- tive agreements. We can distinguish between the kinds of partners, which can be clients, pro- viders, competitors, consultants and laboratories, other firms of the group, universities, public research centres (PRCs) and technological centres. As can be seen in Table 2, the sample mean of these indicators is higher for participants in public R&D programmes than for non- awarded firms.

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