Ownership, Government R&D, Private R&D, and Productivity in Chinese Industry

Journal of Comparative Economics 29, 136–157 (2001) doi:10.1006/jcec.2000.1704, available online at http://www.idealibrary.com on

Ownership, Government R&D, Private R&D, and Productivity in Chinese Industry1 Albert Guangzhou Hu National University of Singapore, Singapore 119260, Singapore E-mail: [email protected] Received July 29, 1999; revised November 21, 2000 Guangzhou Hu, Albert—Ownership, Government R&D, Private R&D, and Productivity in Chinese Industry This paper examines the relationship between research and development (R&D) expenditure and productivity in China’s enterprises. An empirical model that contains a system of three equations, i.e., the production function, a private R&D equation, and a government R&D equation, is estimated using a cross-sectional data set for Chinese enterprises of various ownership types. We find a strong link between private R&D and firm productivity. Although its direct contribution to firm productivity is insignificant, government R&D contributes indirectly to productivity by promoting private R&D. Hence, providing incentives for enterprises to invest in R&D may be a better alternative than providing R&D grants directly. J. Comp. Econ., March 2001, 29(1), pp. 136–157. National University of Singapore, Singapore 119260, Singapore. °C 2001 Academic Press Key Words: R&D; productivity; ownership; Chinese industry. Journal of Economic Literature Classification Numbers: L00, O31, O33, P31.

1. INTRODUCTION China’s sequential economic reforms have encouraged the flourishing of a variety of firm ownership forms for over a decade. The emergence of these different ownership forms is in part a result of the Chinese government’s effort to restructure state-owned enterprises and in part of deregulation that has allowed the entry of 1 This paper is based on Chapter One of my Ph.D. thesis submitted to the Graduate School of International Economics and Finance, Brandeis University, Waltham, Massachusetts. I am grateful to Professors Gary H. Jefferson and Adam B. Jaffe for their ongoing consul and their many helpful comments on this paper. I have also benefited from the comments of two anonymous referees and seminar participants in the Brandeis Workshop on “Innovation and Technology Transfer in Chinese Industry,” October 7–9, 1999, Brandeis University, (Waltham, MA). The editorial comments of Professor John P. Bonin have led to substantial improvement of the presentation of the paper. All remaining errors are my own. Financial support from the Henry Luce Foundation is gratefully acknowledged.

0147-5967/01 $35.00 C 2001 by Academic Press Copyright ° All rights of reproduction in any form reserved.

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millions of new industrial enterprises. The contrasting performance of firms having different ownership types has prompted economists to examine the implications of ownership for firm performance. Most studies attempt to explain the impact of ownership and other institutional factors on measures of economic performance, such as productivity and profitability. The consensus is that firms with different ownership types do perform differently. What remains unclear is through what channels the ownership effect is transmitted. As Griliches (1979) pointed out, all productivity growth, if measured correctly, is related to expenditures on research and development (R&D). To account for the differences in technical performance across the ownership spectrum in China, this paper investigates how much of the cross-sectional variation of productivity can be attributed to differences in R&D expenditure. The investigation tries to answer three questions. First, what is the link between R&D and productivity at the firm level? Second, how does government R&D affect private expenditure, i.e., are they complements or substitutes? Third, what other factors determine private R&D expenditure? For each question, we will examine how the relationship varies across ownership groups. A large literature investigates the R&D–productivity link (Griliches and Mairesse, 1984; Griliches, 1986). A significant relationship is found between a firm’s productivity and the firm’s investment in R&D, although the link is weaker at the industry level than at the firm level. However, few studies distinguish the different effects of private and government R&D expenditures on productivity. An empirical assessment of this difference will be of particular interest to Chinese policymakers, who are trying to play a proactive role in promoting innovative activity and capabilities within Chinese industry. Investigating the different contributions of private and government R&D to productivity will facilitate a better understanding of the performance disparity among different ownership groups. We estimate the contributions of private and government R&D to firm productivity using a production function in which expenditures on private and government R&D are inputs to the production process. To deal with the endogeneity problem of R&D expenditure and to explore the determinants of private and government R&D and the relationship between the two, we construct an empirical model that consists of three equations, the production function, a private R&D equation, and a government R&D equation. Estimating this system empirically allows an investigation of a number of factors that may constrain or encourage innovative activity in Chinese firms, in addition to providing the returns to R&D activity. Insights into questions concerning the innovative behavior of Chinese firms will also result. Does government R&D crowd out or crowd in private expenditure? Is the driving force of private R&D one of market pull or supply push? Do Chinese firms rely on internal cash flow to finance their technological innovation? To what extent do the answers to these questions vary across the ownership spectrum? With a new and extraordinarily rich data set for a cross-section of Chinese firms, this paper goes beyond the general characterization of ownership, such as

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state- and collective-owned enterprises and enterprises of other ownership types, and takes a closer look at some of the specific forms of ownership in the others category, including stock companies, joint ventures, and foreign enterprises. Section 2 contains a description of the data used in this study. Section 3 sets out an institutional framework and presents some stylized facts to motivate the study. Section 4 develops a three-equation empirical model to investigate the effects of private and government R&D on productivity. Various system estimators are used to estimate the output elasticities and examine the determinants of private R&D and government R&D as well as the relationship between them. Results from the estimation of the empirical model are discussed in Section 5. Section 6 concludes with policy implications. 2. DATA The data used come from a survey of all high-tech firms in the Haidian District of Beijing, China, known as China’s Silicon Valley for the clustering of some of China’s best universities, research institutes, and a large and increasing number of computer, electronics, and other high-tech companies. The survey was conducted by the Beijing Municipal Science and Technology Commission in 1996. After adjusting for missing and nonsensical values, we are left with 813 firms reporting various economic and technological variables for 1995. The original data set contains 10 ownership forms: state-owned enterprises (SOE), collective-owned enterprises (COE), private firms, enterprises jointly owned by two domestic partners (JOW), stock-incorporated companies (STK), joint ventures between a Chinese partner and a foreign (excluding Hong Kong, Macao, and Taiwan) partner, enterprises jointly operated by a Chinese partner and a foreign (excluding Hong Kong, Macao, and Taiwan) partner, joint ventures between a Chinese partner and a partner from Hong Kong, Macao, or Taiwan, enterprises wholly owned by a foreign company, and enterprises wholly owned by a company registered in Hong Kong, Macao, or Taiwan. Because of their similar nature regarding legal implications and institutional characteristics, all joint ventures and jointly operated enterprises are regrouped into one category, joint ventures (JVE), and all foreign wholly owned enterprises into the category of foreign invested enterprises (FIE). Private enterprises are excluded from the analysis, since there are only 5 of them. Therefore, after consolidation, we have six ownership categories. The number of firms in each ownership category is presented in the bottom row of Table 1. SOEs are the largest group in the data set with 316 firms, or 39% of the whole sample. The second largest group is COEs, represented by 26% of the sample or 215 firms. The smallest group is FIEs, with only 34 enterprises, or 4% of the sample. Firms in the data set represent a wide range of industries at the Chinese four-digit SIC level. To facilitate the econometric analysis, we have consolidated industry branches that are represented by fewer than 5 firms into the “other” category and end up with 16 industry branches at the four-digit SIC level. Table 1 provides

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TABLE 1 Industry and Ownership Distribution of the Sample Industry

SOE

COE

JOW

STK

JVE

FIE

Total

Chemicals Pharmaceuticals Nonmetal mineral products Metal products Ordinary machinery Special equipment Electric machinery and equipment Electronics Office equipment, instrument, and apparatus Post and telecommunications Retail commerce Information technology Computer applications and services Research institutes Technology consulting and services Other Total

11 7 6 5 3 15 7 45 17 4 1 3 63 3 84 42 316

6 1 1 0 6 16 5 37 14 3 6 7 42 1 49 21 215

3 2 3 1 1 2 0 6 4 2 1 0 8 0 7 7 47

1 1 0 0 0 2 1 24 1 2 3 2 23 1 25 9 95

4 0 0 3 1 7 3 25 10 1 1 0 19 0 16 16 106

1 0 0 0 0 2 0 12 2 0 0 0 12 0 4 1 34

26 11 10 9 11 44 16 149 48 12 12 12 167 5 185 96 813

Note. SOE, state-owned enterprises; COE, collective-owned enterprises; JOW, domestic jointly owned or operated enterprises; STK, domestic stock incorporated enterprises; JVE, joint ventures; FIE, foreign wholly owned enterprises.

detailed information on industry representation. The largest group is the technology consulting and services group with 185 firms. The computer application and services group is a close second and is represented by 167 firms. There are only 5 firms in the smallest group, i.e., research institutes. Table 1 also provides a crosssection of ownership and industry classification. The heavy representation of JVEs and FIEs in electronics, office equipment, instrument, and apparatus, computer applications and services, and technology consulting suggests that the Chinese government promotes foreign direct investment in technology-intensive sectors. The data have various limitations. First, the cross-sectional nature requires us to assume that the data represent some kind of steady-state equilibrium. This may not be a good assumption, particularly for the Chinese economy, which is undergoing significant structural changes. Second, the sample is restricted to hightechnology firms in the Beijing area only; thus, we have a potential source of sample selection bias. Beijing boasts the largest number of research institutes and universities in China, which are an important source of innovation that no other Chinese city possesses. High-tech enterprises are not in the mainstream of Chinese manufacturing. Therefore, inferences and policy implications must be qualified as they are based on a small and selective sample. Third, since R&D is a pathdependent process, the inability to construct a meaningful measure of knowledge capital and investigate how innovation contributes to productivity growth, not just to the static productivity level, is another shortcoming of the data.

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Having acknowledged the limitations of the data set, we claim that it provides very rich information for the investigation of a number of important innovationrelated issues. This data set has three general categories of variables: firm level economic variables such as sales revenue, output, capital, and labor; firm level R&D related variables such as R&D expenditure; and variables that capture the institutional characteristics of the firm, such as the ownership category, the jurisdiction level, and the industry classification of a firm. 3. OWNERSHIP, INNOVATION, AND PERFORMANCE IN CHINESE INDUSTRY The ownership spectrum in Chinese industry ranges from large- and mediumsize SOEs to subsidiaries of foreign companies. The flourishing of different ownership forms reflects partly the decentralization process in the Chinese economy and partly the still-ambiguous assignment of property rights in China. The legal and institutional structures of these ownership forms have significant implication for firm behavior and performance. Many studies investigate the impact of ownership restructuring on the performance of Chinese industrial enterprises. Jefferson and Singh (1999) provide a comprehensive survey of literature. Labor productivity, total factor productivity (TFP), and profitability are the three performance indicators used most often to measure and compare the performance of Chinese enterprises under different ownership categories. The literature concludes that joint ventures have the highest labor productivity among Chinese firms and that labor productivity in SOEs is higher than in COEs. TFP growth, on the other hand, has been most rapid in the nonstate sector. Jefferson et al. (1996) estimate that TFP growth in COEs has outpaced by far that in the state-owned sector. However, all firms in Chinese industry have experienced a decline in profitability reflecting increasing competition and the dismantling of trade barriers (Naughton, 1992, 1995). In Chinese industry, R&D resources tend to be concentrated in the state sector, even though the nonstate sector has achieved greater productivity growth. Table 2 provides measures of technological performance of sample firms for different ownership categories. JVEs have by far the highest level of labor productivity as well as the highest new product–sales worker ratio. SOEs occupy the lower end of the labor productivity rank. However, labor productivity does not reflect necessarily true technical performance. FIEs and JVEs are generally more capital intensive than domestic enterprises and import a substantial fraction of their intermediate materials, which may carry a quality premium over those produced domestically. Therefore, we compute a relative TFP measure for each type of ownership, using a simple Cobb–Douglas production function and ownership dummy variables. The results are presented in Panel B of Table 2. Total factor productivity is highest in JVEs. COEs have the lowest level of total factor productivity, with SOEs being the second least productive group. Although FIEs do not lead

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TABLE 2 Comparison Statistics of Sample Firms for 1995 SOE A. Labor productivity Gross value of industrial output per worker New product sales per worker B. TFP ranka Ownership dummies C. Innovation inputs R&D intensity (R&D/sales) Technician–labor ratio D. Government R&D supportb Share of R&D from the government Share of R&D from the banks Share of R&D from the firm E. Quality of inputs Quality of capital equipment (CAPQL) Quality of labor (LABQL)

COE

126.16 155.45 (187.73) (393.84) 47.71 70.78 (105.44) (324.99)

JOW

143.36 (314.12) 42.21 (96.07)

STK

JVE

FIE

110.99 313.27 216.90 (120.93) (881.92) (356.72) 52.35 174.55 108.59 (97.18) (823.30) (323.39)

0.22 (0.26)

0.12 Reference group 0.40 (0.27) (0.30)

0.89 (0.29)

0.33 (0.38)

0.07 (0.13) 0.49 (0.29)

0.07 (0.15) 0.41 (0.26)

0.10 (0.17) 0.39 (0.24)

0.07 (0.15) 0.44 (0.27)

0.06 (0.12) 0.42 (0.26)

0.06 (0.11) 0.36 (0.24)

0.05 (0.18) 0.05 (0.20) 0.54 (0.48)

0.03 (0.15) 0.06 (0.21) 0.64 (0.47)

0.03 (0.10) 0.05 (0.17) 0.51 (0.47)

0.03 (0.15) 0.06 (0.20) 0.63 (0.47)

0.00 (0.01) 0.04 (0.19) 0.69 (0.46)

0.00 (0.00) 0.00 (0.00) 0.50 (0.51)

2.60 (0.58) 0.63 (0.26)

2.56 (0.57) 0.60 (0.25)

2.50 (0.46) 0.59 (0.23)

2.66 (0.58) 0.69 (0.23)

2.96 (0.54) 0.63 (0.27)

3.08 (0.60) 0.74 (0.26)

Note. Standard deviations are in parentheses. The coefficients of the ownership dummies are obtained from estimating a Cobb–Douglas production function including ownership and industry dummies. The estimation results excluding the ownership and industry dummies are as follows: a

Y = 3.01 + 0.13 ∗ K + 0.74 ∗ L (0.35) (0.04) N = 813, b

(0.07)

R = 0.28. 2

Innovation funds that firms receive from other sources are not listed.

in technological performance, as we would expect given their technological and managerial sophistication, the small size of our sample leaves open the question of representativeness. A different rank arises in Panel C of Table 2, where the R&D intensity of different ownership categories is compared. SOEs have the second highest R&D intensity, while JVEs and FIEs have the lowest, i.e., 6%. However, the difference among the groups is not significant. SOEs also have the highest technician–labor ratio (0.49). Again, JVEs and FIEs occupy the lower ranks. Therefore, with the

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exception of JOWs, most of which are former SOEs, SOEs are the most R&D intensive measured by R&D inputs, especially R&D personnel.2 The Chinese government’s R&D support is biased toward state and other domestic ownership groups, as indicated in the comparison of sources of innovation financing for different ownership groups in Panel D of Table 2. SOEs receive the largest fraction of R&D expenditure from the government, but not by as much as we would expect.3 JVEs and FIEs receive little or no financial assistance from the government but rely heavily on their own funds to finance R&D activity. JVEs have the largest share of innovation expenditure accounted for by internal sources. Since the Chinese government controls the banking sector and uses bank lending as a policy instrument, the share of R&D expenditure coming from banks can also be viewed as a measure of indirect governmental support. On this measure, JVEs receive less support from the government than SOEs. From our data set, it is obvious that the government’s R&D support is biased toward firms with state and domestic ownership. These simple statistics reinforce the stylized fact that firms possessing more innovation resources and government support are not the ones performing better technically. Jefferson et al. (1999) were the first, to analyze the relationship between innovation and ownership in the Chinese economy. Using the innovation ladder paradigm for Chinese enterprises developed in Jefferson and Rawski (1995), they find evidence supporting the hypothesis that the competition between SOEs at the core and COEs at the periphery drives innovation activity in Chinese firms. In another related theoretical study, Qian and Xu (1998) show that bureaucracy might hinder innovation under a soft budget constraint in a centralized economy. One of the first studies to investigate determinants of R&D expenditure in China, Lin (1992) analyzes the driving force of China’s agricultural R&D using hybrid rice as an example. 4. THE MODEL The empirical model consists of three equations. The production function assesses the relative contribution of private and government R&D to firm productivity, controlling for quality differences in capital and labor. The other two equations, the private and government R&D equations, serve two purposes. They provide instruments to deal with the endogeneity of private and government R&D in the production function. More importantly, these two structural equations explore the determinants of private and government R&D and their interrelationship.

2 This result is subject to the qualification that joint ventures and foreign enterprises may draw on results of R&D conducted in the enterprise’s foreign affiliate or parent company. Therefore, R&D expenditures of these enterprises in China may in fact represent a lower bound on their R&D effort. 3 The way we calculate government support may underestimate the support SOEs received from the government, since most SOE loans from the state banks are essentially policy loans with very lax, and mostly unenforceable, terms.

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4.1. The Production Function Griliches (1979) was the first to model the contribution of R&D to productivity in a micro production function framework. In his model, knowledge capital, defined as the sum of discounted past R&D expenditures, enters the production function as an input along with capital and labor. Returns on R&D are obtained by estimating empirically certain forms of the production function. The cross-sectional data used in this paper do not allow us to construct a knowledge capital stock. Instead, we use contemporaneous R&D4 as a proxy. To focus on the key issues, we assume the following simple Cobb–Douglas form of production technology: γ

Y = C α L β RP RGδ eε ,

(1)

where Y is a value-added measure of industrial output. The usual inputs to the production function include capital (C) and labor (L). The firm’s privately generated knowledge capital is approximated by two kinds of investment in innovation, i.e., private R&D expenditure (RP ) and government R&D grant (RG ). These are treated as two endogenous variables in the three-equation system. The endogeneity issue is dealt with by including two other equations in the system: one for private R&D and one for government R&D. Measurement error plagues this type of productivity analysis. The popular TFP measure is likely to be biased if production inputs are measured with errors. Jefferson et al. (1996) discuss the measurement problems and spend considerable effort constructing a series of investment good deflators. Our data set does not provide as much information as a panel data set, but measurement problems are restricted to the cross-section dimension. Quality and quantity are the two dimensions of an accurate measure of capital service. Jorgenson (1989) provides an analytical framework to construct the capital input measure by modeling the different vintages of capital goods. There is substantial heterogeneity in the vintage of capital equipment that Chinese firms use in production so that an accurate measure of capital stock that explicitly takes into account this quality difference is important.5 To derive such a measure, we assume that the true measure of capital stock, C, equals c∗ qC , where qC is a measure of capital quality and c is the accounting measure of net capital stock. The direct implication of measurement error is that the TFP measure may be overestimated for enterprises with more advanced capital equipment. In Chinese industry, JVEs and FIEs usually employ capital equipment that is more advanced than that used by domestic enterprises, including SOEs and COEs. 4 Using a panel data set for a sample of SOEs in Beijing for the period from 1991 to 1995, we did construct the knowledge capital stock measure for various values of the depreciation rate. The correlation between contemporary R&D and the knowledge stock measures is consistently greater than 0.85. 5 Other issues such as capacity utilization have similar distorting effect on estimating total factor productivity. Without the necessary data, we can only control for one of these factors.

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This stylized fact is borne out by the capital modernity variable in our data set. The survey questionnaire asks the firms to assess the modernity of their capital equipment by reporting the percentage of their capital equipment at four levels, internationally advanced, domestically advanced, domestically average, and domestically backward. To derive an index measure to characterize the overall modernity of a firm’s capital, we assign values ranging from 4 (highest) to 1 (lowest). Then modernity values are weighted by the share of each level to arrive at an index measure of the modernity of a firm’s capital equipment. In Panel E of Table 2, the capital quality index (CAPQL) indicates that capital equipment in JVEs and FIEs is more advanced than in domestic enterprises. The true labor input is the actual amount of homogeneous labor involved in the production process. The conventional measure of number of workers suffers two problems. First, the number of hours worked per worker may vary from firm to firm. If firms are not employing the optimal number of workers, which is clearly the case with Chinese SOEs, additional noise is added to the number-of-workers measure. Second, workers are not homogeneous across firms. The quality of labor is likely to affect a firm’s productivity. Although Chinese SOEs and large COEs have been the traditional employers of high-quality workers and engineers, JVEs and FIEs are luring talent away from SOEs with lucrative compensation packages and promising career development prospects. Although we are unable to account for the first issue, the data used in this paper do provide a proxy for labor quality. We assume that the true measure of labor input is l ∗ qL , where qL is a measure of labor quality and l is the number of workers. We calculate qL as the percentage of the total labor force that has received college or above education in each firm of the sample. Panel E of Table 2 compares the differences in labor quality (LABQL) among different ownership groups. The variation is small, with FIEs hiring the best educated labor force. Government R&D consists of grants extended to firms by the government and subsidized loans extended by state-owned banks to firms under special government science and technology plans. Private R&D mainly includes the outlays of a firm’s own cash and bank loans on commercial terms6 from state-owned banks. Using the definitions of C and L, we write the production function in Eq. (1) in log form as log(Y ) = α log(c) + α log(qC ) + β log(l) + β log(qL ) + γ log(RG ) + δ log(Rp ) + ε.

(2)

Equation (2) is the first equation of the three-equation empirical model. 6 These loans are regarded as commercial, in the sense that their terms are different from those of the special government directed loans under various government plans. The Chinese government is pushing the state-owned banks to operate on a commercial basis rather than as government treasurers. However, as any transaction between an SOE and a state-owned bank, these loans probably involve terms that are hard to enforce.

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4.2. R&D Expenditure There has been a long debate over what drives a firm’s R&D effort. Schmookler (1966) proposed the demand–pull hypothesis under which firms are pulled by market demand to undertake technological innovation in order to generate new products or reduce costs. The supply–push hypothesis (Rosenberg, 1974) emphasizes that the opportunity to carry out innovation in different technical areas differs and therefore leads to variations in R&D effort. These two hypotheses are tested empirically in Scherer (1982), Pakes and Schankerman (1984), and Jaffe (1986, 1988), either directly or indirectly with mixed evidence. Lin (1992) finds evidence in support of the demand–pull hypothesis by analyzing determinants of investment in the innovation of hybrid rice in China. Many studies have also tried to answer the question of whether government R&D and private R&D are complements or substitutes.7 David et al. (1999) provides an up-to-date and comprehensive summary of the literature. Most of the studies (Levy and Terleckyj, 1983; Levin and Reiss, 1984; Lichtenberg, 1984, 1987) find evidence of a complementary relationship. Leyden and Link (1991) provide a theoretical argument for this complementarity based on technical complementarity at the production level among funding, infratechnology, and knowledge sharing. Government R&D may increase the marginal product of private R&D, through knowledge spillover or cost sharing, and, therefore, it should lead to greater private expenditure. However, when government R&D is spent in areas where private return to R&D is already high, it may displace private R&D expenditures. We assume the following semi-reduced form private R&D equation: log(Rp ) = φ0 + φ1 log(S) + φ2 log(RG ) + φ3 EIRGT + φ4 QUASD + φ5 TPROF + φ6 T + φ7 OWN + µ.

(3)

Sales revenue, S, is used as a proxy for the expected market demand for a firm’s products or services, so that the demand–pull hypothesis would suggest a positive and significant φ1 . The relationship between government and private R&D is captured by the coefficient of the log of RG . The coefficient of T , or technology opportunity, controls for the effect of supply–push and industry dummies are used as proxies. The ownership dummies capture any ownership-specific pattern left in private R&D expenditures after the other factors have been considered. The substantial information asymmetry inherent in an R&D project makes external financing of R&D very expensive. Therefore, firms may have to rely on internal funds. A number of studies, e.g., Hall (1992), find a positive impact of internal cash flow on private R&D expenditure. In the absence of such a measure, we use total profit (TPROF) as a proxy for the availability of internal funds. Two other 7 They are complements (substitutes) in that an increase of government R&D leads to an increase (decrease) of private R&D.

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institutional variables may also affect private R&D expenditure. The dummy, EIRGT, indicates whether the firm has the right to engage in foreign trade without the intermediation of state trading companies. When a firm has this right, the firm may have a greater incentive to undertake R&D, since it has access to a larger and potentially more lucrative market. The subjective assessment of the overall quality level of a firm’s products (QUASD) is also considered. The lower the quality level, i.e., the greater the QUASD, the greater incentive the firm may have to undertake R&D. The potential wedge between private and social returns to innovation driven by knowledge spillover (Griliches, 1992) has prompted public policy concern about insufficient incentive for private R&D expenditure. Government support of R&D in various forms, such as a subsidy or tax rebate, is deemed necessary to bring its level to the socially efficient amount. The Chinese government uses direct funding of R&D projects through various science and technology development plans. Most previous studies have treated government R&D as exogenous. However, the allocation of government funds is not independent of private R&D spending because the latter is indicative of the R&D effort and technological sophistication of a firm. The government may choose firms with the greatest capacity to carry out a project especially in Chinese industry, where the government is playing an active role in directing public resources to technological innovation. Various government science and technology plans, which are designed to promote innovation in Chinese enterprises and facilitate the diffusion of technology, attest to this statement (Yuan et al., 1992). The Chinese government’s expenditure on R&D exhibits ownership and industry bias. SOEs and COEs receive more support than do nonstate enterprises; domestic enterprises receive more support than do foreign invested enterprises (Panel C of Table 2). The Chinese government has set priorities to develop certain hightechnology industries and different industries exhibit different probabilities for successful innovation. Hence, some industries receive more state funding than others. The following government R&D equation incorporates these determinants: log(RG ) = ψ0 + ψ1 log(Rp ) + ψ2 log(TECH) + ψ3 PATNT + ψ4 T + ψ5 OWN + τ.

(4)

The government is assumed to judge a firm’s capability to carry out an innovation project successfully on the basis of two variables. The number of technicians engaged in R&D (TECHR) is a measure of a firm’s current capability to conduct innovation successfully. The number of patents a firm has applied for or purchased in the past 3 years (PATNT) indicates the firm’s track record on innovation. The industry dummies (T ) and the ownership dummies (OWN) capture any industry bias and ownership bias that the government may have in allocating R&D support.

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Equations (3) and (4) provide the instruments for a consistent estimation of the coefficients of private and government R&D in the overidentified three-equation system. For example, the log of the number of technicians, LOGTECHR, and the number of patents purchased or granted, PATNT, are used as instruments for government R&D. Identification of the three-equation model comes from the exclusion assumptions in specifying the three equations. Although the specification of the two R&D equations may be considered somewhat ad hoc, the specification is defensible given the constraint of the data set and is based on our understanding of China’s innovation procedures. 5. RESULTS AND DISCUSSION 5.1. The R&D–Productivity Link We assess the R&D–productivity link within a single equation framework by estimating variants of Eq. (2) using OLS and instrumental variable (IV) estimators. The results presented in Table 3 suggest a positive and significant link between a firm’s own R&D expenditure and its productivity. In the OLS estimation in column (2), the coefficient of LOGPRIRD, or the log of a firm’s own expenditure on R&D, suggests that a 1% increase in private R&D can lead to a 0.08% increase in productivity and its impact is highly statistically significant. However, the contribution of government R&D to productivity is insignificant and of ambiguous sign. In column (2), the impact of government R&D on productivity is about half the size of private expenditure and statistically insignificant. Comparing columns (1) and (2), the inclusion of the two R&D variables in column (2) raises the adjusted R 2 by only 0.01 to 0.28, implying that there is still a large amount of the variation in productivity not explained by these factors. A potential problem with the models in columns (2) and (3) of Table 3 is that both private R&D and government R&D are conscious choices made by the firm and the government and are likely to be correlated with the disturbance term in Eq. (2). In column (4) of Table 3, we estimate the production function using an IV estimator to correct for the potential simultaneity bias. The specifications of Eqs. (3) and (4) provide the basis for choosing instruments. Compared with the OLS results, the impact of private R&D on firm productivity has become three times larger and remains highly statistically significant. Government R&D’s contribution to productivity has become negative but is still statistically insignificant. The new coefficient for LOGPRIRD implies that a 1% increase in a firm’s private R&D expenditure leads to a 0.32% increase in productivity, whereas that of LOGGOVRD suggests an insignificantly negative impact of government R&D on productivity. The sharp differences between the OLS estimators and IV estimators underscore the simultaneity of government and private R&D. Government R&D may have an insignificant or even negative direct impact on productivity, but it may stimulate further private expenditure by firms, leading to

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ALBERT GUANGZHOU HU TABLE 3 Production Function Estimation Variables

Constant LOGNVFAA LOGEMPLY LOGCAPQL LOGLABQL

OLS (1)

OLS (2)

0.09 (1.44) 0.15∗∗∗ (0.03) 0.75∗∗∗ (0.07) 0.59∗∗ (0.26) 0.19 (0.12)

0.27 (1.43) 0.14∗∗∗ (0.03) 0.69∗∗∗ (0.08) 0.57∗∗ (0.26) 0.16 (0.12) 0.04 (0.04) 0.08∗∗∗ (0.02)

Some sig. 813 0.27

Some sig. 813 0.28

LOGGOVRD LOGPRIRD SOE COE JVE FIE STK Industry dummies Number of obs. Adjusted R 2

OLS (3)

IV (4)

1.21 (1.47) 0.12∗∗∗ (0.04) 0.70∗∗∗ (0.08) 0.35 (0.27) 0.15 (0.12) 0.05 (0.04) 0.08∗∗∗ (0.02) 0.23 (0.26) 0.13 (0.27) 0.87 (0.30) 0.32 (0.38) 0.37 (0.30) Some sig. 813 0.29

1.17 (1.59) 0.13∗∗∗ (0.04) 0.65∗∗∗ (0.10) 0.35 (0.29) 0.21 (0.14) −0.35 (0.38) 0.32∗∗∗ (0.09)

Some sig. 813 0.25

Note. The dependent variable is the log of value added. Significant at the 1% significance level. ∗∗ Significant at the 5% significance level. ∗ Significant at the 10% significance level.

∗∗∗

productivity gains indirectly. The following equation helps illustrate this argument: ∂Y ∂ Rp ∂Y dY = + . d RG ∂ Rp ∂ RG ∂ RG

(5)

Equation (5) states that the total effect of government R&D on productivity consists of two partial effects: a direct effect, captured by the second term on the right-hand side of Eq. (5), and an indirect effect, illustrated by the first term on the right-hand side of Eq. (5). The indirect effect captures the impact on firm productivity of the amount of private R&D that is crowded in or crowded out by government spending. The distinction of the two effects is important in drawing policy implications and interpreting the empirical results. We consider the possible interaction between government and private R&D in the next section.

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Throughout Table 3, we have controlled for the quality of capital and labor by including LOGCAPQL and LOGLABQL in the regression. Both variables appear positive, although only LOGCAPQL is significant and in only two cases. The inclusion of the ownership dummies in column (3) of Table 3 reduces significantly both the magnitude and the statistical significance of the capital quality variable compared with those in column (2), which may suggest that the cross-ownership productivity difference is related partly to the difference in capital quality. We tried to explore this point further by including interactive terms of ownership and input quality in the regression, but none of the terms was statistically significant. To examine the implication of ownership for the R&D–productivity link, the IV estimation of the production function in column (4) of Table 3 is replicated for four ownership groups in Table 4. First, the contribution of private R&D to firm productivity remains economically and statistically significant in SOEs, STKs, and JVEs. However, the magnitude of the contribution varies substantially across ownership groups. Private R&D expenditure in STKs has an output elasticity of 0.46, whereas it is only 0.26 in SOEs. Since R&D intensity varies little across ownership groups (Table 2), the variation in output elasticity suggests that returns to R&D differ across the ownership spectrum. The contribution of government R&D to productivity is negative and insignificant within each ownership group except for JVEs. Second, the production technology seems to vary considerably across

TABLE 4 IV Estimates of Production Function for Selected Ownership Groups Variables Constant LOGNVFAA LOGEMPLY LOGCAPQL LOGLABQL LOGGOVRD LOGPRIRD Industry dummies Number of obs. Adjusted R 2

SOE

COE

3.04 (2.45) 0.11∗ (0.06) 0.76∗∗∗ (0.13) −0.03 (0.45) 0.15 (0.21) −0.35 (0.36) 0.26∗∗∗ (0.10) Some sig. 316 0.25

4.59 (3.09) 0.22∗∗ (0.08) 0.26 (0.19) −0.21 (0.56) 0.14 (0.28) −0.33 (1.41) 0.39 (0.25) Not sig. 215 0.17

Note. The dependent variable is the log of value added. Significant at the 1% significance level. ∗∗ Significant at the 5% significance level. ∗ Significant at the 10% significance level.

∗∗∗

STK −0.28 (5.25) −0.04 (0.12) 0.86∗∗∗ (0.29) 0.67 (0.99) 0.63 (0.53) −0.37 (0.60) 0.46∗ (0.28) Not sig. 95 0.38

JVE 1.79 (5.09) 0.29∗∗ (0.13) 0.18 (0.26) 0.47 (0.92) 0.30 (0.36) 0.17 (0.48) 0.29∗∗ (0.14) Some sig. 106 0.21

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ownership groups. The output elasticity of capital ranges from an insignificant −0.04 for STKs to a highly significant 0.29 for JVEs. The output elasticity of labor also varies considerably. The labor quality and capital quality variables are not statistically significant in Table 4. Answering the first question we raised in the Introduction, our results indicate an economically and statistically significant link between private R&D and firm productivity. This link varies in magnitude across ownership groups. In contrast, the direct contribution of government R&D is insignificant. 5.2. Private R&D and Government R&D: Complements or Substitutes? Government R&D could have an indirect positive impact on firm productivity if it stimulates further private expenditure, given that private R&D enhances firm productivity. We examine the relationship between and the determinants of private and government R&D by estimating Eqs. (3) and (4), using both an OLS estimator and a two-stage least squares estimator in Table 5. The results indicate a strong complementary relationship between private and government R&D. The coefficient of LOGGOVRD in column (3) of Table 5 implies that a 1% increase in government grants induces a 1.75% expansion in private R&D expenditure. In the government R&D equation in column (4), the government rewards each 1% increase in private R&D expenditure with a 0.14% increase in government funding. Estimates of these relationships differ sharply between the OLS model and the two-stage least squares model, suggesting the existence of considerable simultaneity between the two variables. The most noticeable case is the coefficient of government R&D, which increases from 0.1 in the OLS model to 1.75 in the two-stage least squares model. The two-stage least squares estimate also suggests a much stronger impact of private R&D expenditure on government funding than the OLS estimate. The interaction between private and government R&D is examined further within individual ownership groups in Table 6. We exclude FIEs because they do not receive government funding. The complementary relationship between private and government R&D remains, although it is only statistically significant in the case of SOEs. When the government increases R&D support by 1%, it induces SOEs to spend 1.04% more from their own funds. For the government R&D equation, private R&D has a significant influence on government funding only in the cases of COEs and STKs. The government increases funding to STKs by 0.58% for a 1% increase in private R&D expenditure. In JVEs, government funding is virtually unrelated to private R&D expenditure. In summary, the results in Table 5 and 6 suggest a complementary relationship between private and government R&D, providing an answer to the second question we asked in the beginning. Given that private R&D has a significant and positive impact on firm productivity and that government R&D induces further private expenditure, the results discussed in this subsection substantiate the indirect impact of government R&D on firm productivity.

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TABLE 5 Private R&D and Government R&D OLS Variables CONSTANT LOGSALES

Private R&D (1) 1.16∗∗ (0.48) 0.35∗∗∗ (0.02)

LOGTECHR LOGGOVRD

0.10∗ (0.06)

LOGPRIRD TPROF

QUASD SOE COE JVE FIE STK Industry dummies Number of obs. Adjusted R 2

Gov’t R&D (2) −0.05 (0.23) 0.22∗∗∗ (0.04) 0.04∗∗ (0.02)

Private R&D (3) 0.84 (0.70) 0.23∗∗∗ (0.04) 1.75∗∗∗ (0.49)

0.03 (0.02) 0.31 (0.29) −0.02 (0.07) −0.18 (0.35) −0.35 (0.36) −0.74 (0.44) −0.96 (0.55) −0.41 (0.40) Some sig. 813 0.30

Gov’t R&D (4) −0.18 (0.24) 0.15∗∗∗ (0.05) 0.14∗∗∗ (0.04)

−0.01 (0.02)

0.01 (0.01)

PATNT EIRGT

2SLS

−0.03 (0.21) −0.19 (0.22) −0.37 (0.24) −0.36 (0.30) 0.16 (0.24) Not sig. 813 0.07

0.03 (0.02) 0.16 (0.42) 0.00 (0.10) −0.12 (0.49) 0.11 (0.53) 0.23 (0.68) −0.06 (0.82) −0.55 (0.57) Not sig. 813 0.17

−0.01 (0.21) −0.19 (0.22) −0.38 (0.24) −0.32 (0.31) 0.17 (0.25) Not sig. 813 0.08

Note. The dependent variables are the logs of private and government R&D. Significant at the 1% significance level. ∗∗ Significant at the 5% significance level. ∗ Significant at the 10% significance level.

∗∗∗

5.3. Other Determinants of R&D The results in Tables 5 and 6 also help to answer the third question on our list, i.e., what other factors determine private R&D expenditure? First, we examine the impact of sales revenue on private R&D expenditure. The results in column (3) of Table 5 show that if sales revenue increases by 1%, a firm increases private

−0.90 (0.62) −0.11 (0.16) Not sig. 316 0.26

0.04∗ (0.02)

1.04∗∗ (0.47)

1.32∗ (0.82) 0.28∗∗∗ (0.05)

Private R&D

Not sig. 316 0.07

−0.01 (0.03)

0.09 (0.07)

0.25∗∗∗ (0.08)

−0.16 (0.23)

Gov’t R&D

1.04 (6.17) 1.03 (1.99) Not sig. 215 −0.07

−0.07 (0.23)

11.88 (18.94)

−2.64 (7.71) −0.26 (0.94)

Private R&D

Gov’t R&D

Some sig. 215 0.04

0.00 (0.03)

0.19∗∗ (0.09)

−0.05 (0.11)

−0.17 (0.25)

COE

0.35 (2.53) −0.45 (0.37) Not sig. 47 0.47

−0.02 (0.15)

0.04 (1.12)

1.75 (1.69) 0.46∗∗∗ (0.09)

Private R&D

JOW

Not sig. 47 0.16

0.25 (0.31)

0.16 (0.12)

0.18 (0.22)

−0.63 (0.59)

Gov’t R&D

Note. The dependent variables are the logs of private R&D expenditure and government R&D. ∗∗∗ Significant at the 1% significance level. ∗∗ Significant at the 5% significance level. ∗ Significant at the 10% significance level.

Industry dummies Number of obs. Adjusted R 2

QUASD

EIRGT

PATNT

TPROF

LOGPRIRD

LOGGOVRD

LOGTECHR

LOGSALES

CONSTANT

Variables

SOE

0.06 (2.42) 0.21 (0.28) Not sig. 95 0.08

−0.04 (0.08)

1.28 (1.60)

−0.14 (1.65) 0.08 (0.21)

Private R&D

TABLE 6 Private R&D and Government R&D by Ownership Group (2SLS)

−0.42 (0.41)

0.58∗ (0.32)

−0.14 (0.29)

−0.27 (0.72)

Gov’t R&D

Not sig. 95 0.11

STK

1.28 (1.01) −0.47 (0.54) Not sig. 106 0.03

−0.03 (0.03)

2.25 (1.91)

1.16 (2.31) 0.27∗∗ (0.13)

Private R&D

JVE

Not sig. 106 0.28

0.04 (0.11)

0.02 (0.12)

−0.03 (0.16)

−0.03 (0.37)

Gov’t R&D

152 ALBERT GUANGZHOU HU

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R&D expenditure by 0.23%, which is consistent with the demand–pull hypothesis. This same impact remains highly significant in SOEs, JOWs, and JVEs. Private R&D expenditure in JOWs is most sensitive to variation in sales revenue. A 1% fall in sales revenue results in a 0.46% decrease in R&D expenditure. Second, a supply factor that may affect a firm’s investment in R&D is the availability of financing. The private R&D equation includes total profits (TPROF) as a proxy for internal funds, but we do not find much evidence of its effect on a firm’s R&D expenditure in Table 5 or 6, except for SOEs. Third, the two institutional variables, EIRGT and QUASD, do not appear with any statistical significance, although the import and export right does have a positive but insignificant impact on private R&D both in the whole sample regressions (Table 5) and within ownership group regressions (Table 6, except for SOEs). Therefore, beside government grants, sales revenue appears to be the most important determinant of private R&D expenditure. The number of technicians turns out to be an important factor influencing the government’s decision to distribute a R&D grant. For the two-stage least squares regression in Table 5, if a firm expands its technical work force by 1%, it will receive 0.15% more funding from the government. However, this effect disappears in the within-ownership group regressions (Table 6). The patent counts variable exerts an insignificant impact on the government’s R&D expenditure in both tables. 5.4. The Overall Picture We can improve the efficiency of the estimates by using a three-stage least squares estimator if the error terms in the three equations are correlated. The results from the three-stage least squares estimation in Table 7 are very similar to those from the two-stage least squares exercise. The private R&D–productivity link remains highly significant, both economically and statistically. The output elasticity of R&D at 0.33 is little changed from that in column (4) of Table 3. The direct contribution of government R&D is negative and insignificant, with an output elasticity of −0.42. However, government spending provides a strong inducement to private spending, suggesting a strong complementary relationship between private and government R&D. The three-stage least squares results indicate that a 1% increase in government funding induces firms to spend 1.58% on R&D from internal sources. This pattern is consistent with that found in many studies on OECD economies (David et al., 1999). Therefore, the net total effect of government R&D on firm productivity is the sum of the direct contribution to productivity and the indirect inducement effect, as Eq. (5) showed. In theory, Eq. (5) provides the formula to evaluate the net impact of government R&D. We do not calculate the net effect because the coefficient of government R&D has a large standard error so that any inference based on it would be misleading. The three-stage least squares results also confirm the significant role of sales revenue in determining private R&D expenditure.

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TABLE 7 A Three-Equation System Estimate: Three-Stage Least Squares

CONSTANT LOGNVFAA LOGEMPLY LOGCAPQL LOGLABQL

Production function

Government R&D

1.95 (1.44) 0.15∗∗∗ (0.03) 0.66∗∗∗ (0.09) 0.19 (0.26) 0.28 (0.12)

−0.07 (0.23)

LOGSALES 0.14∗∗∗ (0.05)

LOGTECHR LOGGOVRD LOGPRIRD

−0.42 (0.36) 0.33∗∗∗ (0.09)

0.15∗∗∗ (0.04)

TPROF

Some sig.

−0.09 (0.20) −0.24 (0.21) −0.60∗∗∗ (0.23) −0.44 (0.29) 0.04 (0.23) Some sig.

813

813

813

SOE COE JVE FIE STK

at the 1% significance level. at the 5% significance level. ∗ Significant at the 10% significance level.

∗∗ Significant

1.58∗∗∗ (0.41)

0.57 (0.23) −0.04 (0.05) −0.05 (0.43) −0.23 (0.44) −0.12 (0.50) −0.69 (0.64) −0.13 (0.49) Some sig.

QUASD

∗∗∗ Significant

0.24∗∗∗ (0.04)

0.02 (0.01)

EIRGT

Number of obs.

1.03∗∗ (0.52)

0.00 (0.01)

PATNT

Industry dummies

Private R&D

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155

6. CONCLUSIONS AND POLICY IMPLICATIONS An empirical model consisting of a three-equation system, a Cobb–Douglas production function, a private R&D equation, and a government R&D equation, is used to examine the R&D–productivity link in Chinese industry. We estimate the model using a multiownership cross-sectional data set of Chinese enterprises in the Haidian District of Beijing, China. We find a significant and robust link between private R&D and firm productivity. The output elasticity of private R&D is estimated to be 0.33 and is highly significant in the whole sample regression. However, the direct impact of government R&D on firm productivity is insignificant and negative. This pattern is robust in the regressions for each ownership group. We find a statistiscally and economically significant complementary relationship between private and government R&D. Our results suggest that a 1% increase in government R&D can induce the firm to expand its own R&D spending by 1.58%. This complementary relationship loses statistical significance in the regressions with individual ownership groups. Our results support the demand–pull hypothesis in that current sales revenue of the firm provides an important driving force for private R&D expenditure. However, the availability of internal funds, as proxied by total profit, does not seem to affect private R&D spending in any significant way. Two important policy implications follow from our study. First, since government R&D grants have little direct impact on a firm’s total factor productivity, policymakers could conclude that free R&D grants are not an optimal policy instrument to promote technological innovation in China’s enterprises. Other instruments that directly boost the private incentive to innovate would be preferable. Second, the Chinese government’s science and technology policy bias in favor of SOEs is not justifiable. SOEs are less efficient in transforming R&D into productivity than are certain nonstate firms. Hence, reallocation from the state sector to the nonstate sector of innovation resources, both financial and human, may yield social welfare gains. This paper is a modest step toward a complete understanding of the R&D– productivity link and the private–public R&D interaction in China’s enterprises. Our study has been constrained by various data limitations, such as the inability to construct the R&D capital stock and the exclusively high-tech nature of the sample firms. Given the speed and importance of ongoing enterprise restructuring in Chinese industry and the paramount importance of technological innovation, more studies using larger and more representative data sets to investigate the contribution of technological innovation to firm performance, the role of government in promoting R&D, and the implications firm ownership has for technological innovation in Chinese industrial enterprises are recommended. APPENDIX The definitions and the summary statistics of the variables are presented in Table 8.

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ALBERT GUANGZHOU HU TABLE 8 Variable Information and Summary Statistics

Variable name

Unit

VALAD NVFAA EMPLY CAPQL LABQL GOVRD PRIRD TECHR TPROF SALES PATNT

TYa TY Person Index Index TY TY Person TY TY Count

EIRGT QUASD

a

Definition

Mean

Std dev

Min

Max

Value added 4013 13233 1 189593 Net value of fixed asset 2147 9736 1 172506 The number of workers 62 139 1 1970 Quality of capital 2.65 0.59 1 4 Quality of labor 0.63 0.26 0 1 Government R&D grant 40 331 0 5474 Private R&D expenditure 315 1527 0 32075 The number of technicians 12.43 39.10 0 568 Total profit 1364.8 7113.16 −14429 100514 Sales revenue 10745.6 58370 0 878350 The number of patents 0.45 2.14 0 37 purchased or applied for Dummy Export-import right 0.17 0.38 0 1 dummy variable Discrete Quality level of major products 4.23 1.10 1 5 (=1, 2, 3, 4, 5, with 5 being the lowest quality)

TY, thousand yuan.

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