An inquiry into the development of science and technology parks in China

Zhang, Haiyang; Sonobe, Tetsushi Working Paper An inquiry into the development of science and technology parks in China Economics Discussion Papers, No. 2010-26 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Zhang, Haiyang; Sonobe, Tetsushi (2010) : An inquiry into the development of science and technology parks in China, Economics Discussion Papers, No. 2010-26, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/41598 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc/2.0/de/deed.en Discussion Paper 2010-26 | November 4, 2010 | No. http://www.economics-ejournal.org/economics/discussionpapers/2010-26 An Inquiry into the Development of Science and Technology Parks in China Haiyang Zhang State Intellectual Property Office of the P.R.C. Tetsushi Sonobe Foundation for Advanced Studies on International Development Abstract In order to investigate the effectiveness of science and technology industrial parks (STIPs), this study examines data on high-tech firms within and outside the STIPs in China, while paying special attention to the issues related to agglomeration and congestion. The main finding is that the negative effect of congestion on productivity is highly likely to outweigh the positive productivity effect of agglomeration economies within the STIPs but not among high-tech firms outside the STIPs. The paper also finds that the productivity of high-tech firms, whether within or outside the STIPs, are positively associated with foreign direct investment and the academic activities of local universities in the same city. JEL O3, O4 Keywords Science and technology parks; agglomeration; congestion; China Correspondence Haiyang Zhang, State Intellectual Property Office, China, 13-2-301, Long Teng Yuan Er Qu, Hui Long Guan, Beijing, China email: [email protected] © Author(s) 2010. Licensed under a Creative Commons License - Attribution-NonCommercial 2.0 Germany An Inquiry into Development of the Science and Technology Parks in China 1． Introduction It is widely recognized that science and technology parks are effective vehicles for promoting new technology-oriented firms, facilitating the commercialization of scientific research, and revitalizing regional economies (Colombo and Delmastro, 2002; Link and Scott, 2003). Since the late 1980s, the Chinese government has been promoting the formation and development of national science and technology industrial parks (STIPs). There has been increasing interest in similar policy in other developing countries. However, the argument that science parks are effective in realizing the previously mentioned roles is not unanimously accepted by all researchers, and some critics in fact consider them to be “high-tech fantasies” (Macdonald, 1987; Massey et al., 1992; Bakouros, Marda, and Varsakelis, 2002). Similar concerns exist in China as well. Cao (2004), Macdonald and Deng (2004), and Hu (2007), for example, question whether the STIPs have successfully fostered the on-park firms’ innovation capability and the development of the regional economy. The on-park firms have been given a variety of preferential treatments by the government. For example, these firms have been provided tax exemptions, which were not given to the high-tech firms outside the STIPs until April 2008. The STIPs occupy large areas in large cities, which are now becoming congested. Questions arise as to whether the STIPs deserve such support and how the STIP policy can be improved. This study uses data on high-tech firms within and outside the STIPs in China to 1 investigate further the effectiveness of the STIPs, while paying special attention to the issues related to agglomeration economies and congestion problems. Concentrating the location of high-tech firms within the STIPs would help the government provide them with physical infrastructure and business support efficiently. According to the spatial economics literature (e.g., Fujita and Thisse 2002), the agglomeration of firms facilitates knowledge spillovers, the development of the division of labor, and the formation of skilled-labor markets. Since the STIPs are agglomerations of high-tech firms, they may well generate and enjoy such agglomeration economies. Moreover, synergies may be created between the STIPs and academic institutions in the same neighborhood and contribute to the development of the high-tech sector of the economy. However, agglomeration tends to be accompanied by congestion, which exerts negative effects on the activities within the agglomeration. If agglomeration economies outweigh congestion effects in the STIPs, preferential treatment and other supports given by the government to the on-park firms are easily justified. If congestion effects prove significant, however, the policy should be reformulated so that the space and infrastructure of the STIPs are used more effectively. For example, efficiency in resource allocation will be improved by replacing the on-park firms benefiting little from agglomeration economies with those which would benefit more. Data on individual high-tech firms within and outside the national STIPs are unavailable. The data used in this study are aggregated to the STIP level for the on-park firms and to the city level for the off-park high-tech firms. For this reason, our empirical analysis falls short of the identification of the agglomeration economies and congestion effects. Suggestive evidence, however, is obtained by estimating the production elasticities of private capital and labor inputs as well as the productivity 2 effects of past R&D expenditures and spillovers from universities and foreign ventures in the same city, separately for on- and off-park firms. The main finding is that congestion effects are highly likely to be stronger than agglomeration economies within the STIPs, whereas there is no evidence for congestion effects or agglomeration economies among high-tech firms outside the STIPs. Hu (2007) uses the data on 53 national STIPs and finds among other things that agglomeration has no dynamic effects contributing to productivity growth in the STIPs. Our study reinforces Hu’s study with a comparison of the STIPs and the high-tech sector outside the STIPs and with an investigation into the congestion effects and static agglomeration economies. The next section describes the development process of the STIPs in China. Based on the literature on agglomeration economies and congestion issues, Section 3 develops a conceptual framework that guides the empirical inquiry, which is presented in Section 4. A summary of the findings and the policy implications are contained in Section 5. 2. Development of STIPs The first national science and technology industrial park in China is the Beijing Zhongguancun STIP, which was approved by the Chinese State Council in 1988, followed by 26 national STIPs in 1991 and by 25 in 1992. The establishment of the Yangling STIP in Shannxi province in 1997 and the recent approval of the Ningbo STIP in Zhejiang Province in 2007 brought the total number of national STIPs to 54. Four of them are located in the municipalities supervised by the central government, i.e., Beijing, Shanghai, Tianjin, and Chongqing. The 23 provincial capitals also host 3 national STIPs. The remaining 27 national STIPs are located in generally developed cities along the coast, like Shenzhen and Qingdao or specialized cities such as Yangling, which is known for its modern agriculture. Figure 1 shows the geographic location of the national STIPs in China in 2006. Geographically, the distribution of the national STIPs is biased toward the eastern regions, followed by the central and western regions. This spatial pattern seems to reflect the distribution of industrial resources and technological capabilities across China. For a firm to gain entry into the STIPs, it is required to be qualified as a high-tech firm. In China, there are certain criteria for qualifying as a high-tech firm. First, a high-tech firm is required to develop or use technology in the new and high-tech products or services listed in the Catalog for High and New Technology Products published by the Ministry of Science and Technology, such as electronics and information technology, aerospace technology, and biotechnology. Second, a high-tech firm is required to spend at least 3% of its annual gross revenue on Research and Development (R&D) to develop products or services. Third, of the high-tech firm’s employees, 30% or more must have at least a college degree, and at least 10% must be engaged in R&D. Finally, a high-tech firm must be certified every year by a provincial-level government agency in charge of science and technology issues. Failure to meet these conditions disqualifies the firm from enjoying various policy incentives given to high-tech firms. Note that a high-tech firm does not have to be research-oriented. High-tech firms are mostly manufacturers. High-tech firms are not necessarily located in the national STIPs. Many of them are located outside the national STIPs. In this paper, we refer to those high-tech firms in the national STIPs as on-park firms and those outside the STIPs as 4 off-park firms. According to the Statistics Report of the China Torch High Technology Industry Development Center (hereinafter the Torch Center), there were 43,249 high-tech firms in China in 2006, and 27,293 were on-park and 15,956 were off-park. While the on-park firms are clustered in STIPs, the off-park firms are scattered. Another important difference is that on-park firms are more favorably treated by the government than off-park firms. For example, on-park firms are exempted from corporate income tax for the first two years and enjoy a favorable tax rate of 15% from the third year on, whereas the normal corporate income tax rate is 25%. Their revenues generated by the use of newly transferred technology are only taxable beyond the first 300,000 yuan (or about US$ 45,000). Import licenses are not demanded by the customs office when they import materials and parts from abroad if the materials and parts are used to produce exports. The government has given such privileges to on-park firms primarily because when the government started the STIPs, it gave the top priority of the STIP policy to the growth of national STIPs. Indeed, the national STIPs have grown at an astonishing speed. For the 14 years from 1992 to 2006, the annual growth rate of real output value per STIP was more than 40%, average labor productivity grew more than sevenfold, and the number of firms in the STIPs also grew more than seven times. Table 1 presents the data on the number of on-park firms in the 53 STIPs in 2001 and 2006. The number of on-park firms per national STIP increased from 458 in 2001 to 865 in 2006. During the same period, the real output per worker also grew from 88,000 yuan to 153,000 yuan. Table 1 also presents the data on the five largest STIPs in terms of the number of on-park firms in 2006, and the five fastest growing parks in terms of labor productivity measured by the value added per worker from 5 2001 to 2006. The largest STIP is the Beijing Zhongguancun Park, which had 18,096 firms in 2006. The five parks that experienced the fastest growth in labor productivity are located in economically less developed regions. This observation suggests that labor productivity has been converging among the STIPs, consistent with the result of the growth regression by Hu (2007). In Beijing and Tianjin, the number of on-park firms more than doubled in the five years from 2001 to 2006. A question arises as to how the STIPs could manage to accommodate such a rapidly increasing number of firms. As mentioned earlier, the STIPs are located in large cities, where the ever-increasing scale and diversity of economic and cultural activities are taking place. It is difficult to imagine that the space and infrastructure for the STIPs can be increased without limit. According to the statistics provided by the Torch Center, the land areas of the national STIPs as a whole increased by 36.1 square kilometers, which is about 5% of their total land area, from 2001 to 2006. This should be regarded as a very small increase relative to the rapid growth in the number of on-park firms and their rapid expansion of production. This study uses data on input and output of high-tech firms taken from the Torch Center’s statistics report. In this data set, information on the on-park firms is aggregated to the STIP level and that on the off-park firms is aggregated to the city level. Because of missing data, we use the data of 49 STIPs and 41 non-STIPs covering the period from 2002 to 2006. Table 2 compares the on- and off-park firms in size and other respects. The first three rows of Table 2 indicate that while the number of on-park firms is larger than that of the off-park firms, the on-park firms have much smaller employment sizes than the off-park firms. These observations suggest that there is congestion in the 6 national STIPs. Note, however, that the congestion, if any, does not result from free access. On the contrary, the entry into the national STIPs is strictly controlled by the STIP authority, and so is the land allocation to the on-park firms. The on-park firms are smaller also in terms of revenues, value added, and export value than the off-park firms. But the on-park firms tend to have higher labor productivity than the off-park firms.1 There seem to be several reasons for the relatively high labor productivity of the on-park firms. Among them is that the on-park firms are more high-tech than the off-park firms, which is reflected in the on-park firms’ relatively large R&D expenditure. Another possible reason is that the on-park firms tend to employ highly educated workers, whose salaries are likely to be high, compared with the off-park firms, as shown toward the bottom of Table 2. There is more to say about the reasons why the on-park firms tend to have higher labor productivity and smaller sizes, as will be discussed in detail below. 3. Framework of empirical inquiry 3.1. Agglomeration economies Our analysis begins by formulating a production function that can accommodate agglomeration economies, congestion, and other possible sources of productivity changes. Jacobs (1969) argues that the scale and diversity of large cities allow firms in different sectors to benefit from the cross-fertilization of ideas. Following her lead, Glaeser et al. (1992) and Henderson et al. (1995) distinguish dynamic agglomeration economies from static agglomeration economies. The former contribute to productivity growth, whereas the latter contribute to productivity 1 The labor productivity, which appears in Table 2, is the mean of the real value added divided by the number of workers. 7 firms’ R&D activities to high-tech firms. Similarly, Hu (2007) finds that the productivity growth of the STIP responds positively to the foreign direct investment that its host city receives. Following Griliches (1979, 1988), we consider that a weighted sum of real R&D investment in the past, which we refer to as R&D stock hereafter, is likely to be correlated with productivity A. Jacobs (1969), Glaeser et al. (1992), and Henderson (2003) among others argue that productivity is improved by the cross-fertilization of diverse ideas, which is particularly active in large and diverse cities. Thus, variables that measure the urban scale and the diversity of industrial structure of the host city for high-tech firms may serve as Z variables. 4. Regression Analysis 4.1. Specification Using panel data of 49 STIPs and 41 non-STIPs for five years from 2002 to 2006, we estimate the following functions for the STIPs and the non-STIPs separately:3 ln(Y/L) = a ln(K/L) + (a + b – 1)lnL + Z c + u + λ + e, (11) it it it it it it YYi Yt Yit ln(L/N) = Z c + u+ λ + e, (12) it it it LLi Lt Lit ln(N) = Z c + u+ λ + e, (13) it it NNi Nt Nit 3 See Bhide and Kalirajan (2004) for a general discussion of the advantages of this kind of specification, in which lnY is decomposed into ln(Y/L), ln(L/N), and lnN, and each is regressed on a same set of controls Z. 14 where subscript i indicates the i-th group of high-tech firms (i.e., STIP or non-STIP), subscript t indicates the t-th year, and N is the number of firms in the group. Y, L, K, and Z denote the same variables as discussed in the previous section.4 Their detailed definitions, means, and standard deviations are provided in Table 4. Variables u, λ , and e are the unobserved group effect, year effect, and random error, respectively. We use the per capita form in equation (11) because the estimated coefficient on the second term tells us whether the sum of the production elasticities a + b is greater than unity. In the estimation of equation (11), we are concerned with the endogeneity problem arising from the facts that Γ and ρ , which appear in production functions (9) and (10), are unobservable, and that these unobservable variables are likely to influence employment L. No valid instrumental variable, however, is found in the available data. We hope that the use of the panel-data model estimation method mitigates the estimation bias substantially. As another approach to this issue, we will remove the first two terms on the right-hand side of equation (11) and focus on the question of how Z variables are correlated with Y/L, employment size L/N, and the number of firms N. In this approach, we cannot see if there are agglomeration economies (i.e., if ε is greater than unity), but we can infer whether congestion is severe. In the previous section, we discussed the effects of Z variables on employment L. In equations (11) and (13), however, the dependent variables are ln(L/N) and lnN. We choose this specification because the estimation of the effects of Z on ln(L/N) and lnN gives at least the same information as that on lnL and probably more. As mentioned earlier, vector Z includes five variables. The first is R&D stock, which is a weighted 4 Precisely speaking, Z in equations (9) to (11) is a vector and it includes 1 to accommodate the intercept. 15 sum of the real R&D investment in the past. R&D investment is likely to have lagged effects, but its effects are subject to obsolescence. Thus, the weight is smaller for the investment in the more remote past as follows: R&Dit = (1 – δ )Iit-1 + (1 – δ )2Iit-2 + ··· + (1 – δ )nIit-n , (14) where I is the annual real R&D investment of all the firms in group i, δ is the annual depreciation rate, and n refers to the number of years for which R&D outcomes remain usable. According to Nadiri and Pruch (1996), an arbitrary depreciation rate between 10% and 15% is often used to construct R&D stock. Griliches (1979) finds that the lag structure of the productivity effect of R&D reaches a peak at about the third year. Data on annual R&D investment of the high-tech firms are available only from 1999. In view of this data constraint, our main specification of regression uses the R&D stock variable that includes the lagged R&D investments up to n = 3 and depreciates them at δ = 15%, and the alternative specification for the robustness check uses the stock variable including R&D investments up to n = 5 with an annual depreciation rate of 10%. The second variable included in vector Z is the stock of the past foreign direct investments that the host city for the high-tech firms in group i received. This variable, denoted by FDI, is constructed by assuming that the productivity effect of the past investment wears off at 15% per year for the first three years and disappears at the end of the third year. We also constructed an alternative FDI measure by applying a depreciation rate of 10% and the truncation at the end of the fifth year. The third variable included in vector Z is the number of university teachers, UTit, in 16 the host city of group i. This variable is intended to capture the knowledge spillovers from local universities. The fourth and fifth variables included in vector Z are intended to capture the so-called urbanization economies, which arise from the scale and diversity of urban activities. We use the number of non-agricultural working population, WPit, in the host city of group i as a proxy for city size. To measure the industrial diversity in a city, we use an urban industrial diversity index, following the lead of Henderson, Kuncoro and Turner (1995). This index is defined by UID = 1 – it ∑∑ = = ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ M mM mmit mit cE E 1 2 1 , (15) where E is the number of employees in a two-digit industry m in the host city for group i in year t, and M is the total number of two-digit industries. There are 19 two-digit industries in total, including agriculture, manufacturing, mining, public utility, wholesale and retail, real estate, construction, finance, and education. UID takes a value between zero and unity. A greater value indicates the greater diversity of the city. The data on FDI, UT, WP, and UID are taken from mit Chinese Statistics Yearbook and China Urban Statistics Yearbook. 4.2. Estimation results Table 5 presents the estimated labor productivity function (11). The first three columns report the results based on the fixed-effects models, while the next three columns show the results based on the random-effects models. The sample consists 17 of the 49 STIPs in columns (1) and (4), the 41 non-STIP data in columns (2) and (5), and the pooled sample in columns (3) and (6). The pooled sample is used to examine whether the STIPs and non-STIPs differ much in the coefficients, especially the coefficient on L. For this purpose, we add interaction terms to the regressors, multiplying each variable in equation (11) by the dummy variable that is unity for STIPs and zero for non-STIPs. Columns (3) and (6) report the coefficients on the interaction terms, i.e., the difference in the coefficients. The estimated sum of the production elasticities with respect to the capital and labor a + b is significantly smaller than unity in columns (1) and (4), but it is almost equal to unity in columns (2) and (5). These results suggest that there is severe congestion in the STIPs but not outside the STIPs. The results of the Hausman specification results are shown toward the bottom of the table in columns (4) to (6). According to the results, the random-effects model is inconsistent in the case of the STIP sample, but it is consistent in the case of the non-STIP sample and the pooled sample. These results indicate that K/L, L, or some variables in Z are correlated with the group effect uYi in the STIP sample, and that the correlation is weak in the non-STIP sample. If there is severe congestion in the STIPs, it is expected that L is influenced by the unobservable, land/infrastructure variable Γ . To the extent that the effect of Γ is reflected in the group effect uYi, L is expected to be correlated with uYi in the case of congestion. Thus, the results of the Hausman test are also consistent with the view that congestion is occurring in the STIPs but not in the non-STIPs. Turning to the coefficients on the Z variables, we focus on the fixed-effects estimates for the STIPs in column (1) since the random-effect estimates are inconsistent in the STIP sample, but for the non-STIPs, we will discuss the 18 random-effects estimates in column (5) as they are consistent according to the result of the Hausman test and more efficient than the fixed-effects estimates. In both columns (1) and (5), R&D and foreign direct investment are positively associated with the productivity of high-tech firms. The number of local university teachers is positively associated with the productivity of the on-park firms, as shown in column (1). The two variables related to urbanization economies, i.e., lnWP and lnUID, do not have significant coefficients in any column. The positive association with FDI and labor productivity is consistent with the results of the growth regression analysis conducted by Hu (2007) as well as the other studies on the spillover effects of FDI in China. Nonetheless, our results concerning FDI need to be interpreted with caution. A large inflow of foreign direct investment into a city may not necessarily be a cause of the relatively high productivity in the city, but the former may be a result of the latter. It is conceivable that the agglomeration of highly productive firms in a city attracts a large inflow of FDI to the city. With our data and specification, it is difficult to establish a causal relationship between FDI and productivity. The STIP dummy has a positive and highly significant coefficient in column (6), which indicates that the STIPs have higher labor productivity than the non-STIPs with the effects of all the other regressors being controlled for. A possible reason for this result is that the STIP authority is selective in admitting high-tech firms. Because of the preferential policies in favor of the on-park firms, high-tech firms are attracted to the national STIPs. If the STIP authorities admit high-performing firms into the STIPs selectively, it is no wonder that the STIPs have higher productivity than the non-STIPs if other things are equal. It is likely that the strong negative effect of 19 congestion, as represented by the large negative coefficient on lnL in Table 5, is made up for by this selection effect, so that the STIPs and the non-STIPs differ only by 21% in the sample mean of the average labor productivity, as shown in Table 2. To check the robustness of these estimation results, the same regressions are run for the two overlapping three-year periods 2002-2004 and 2004-2006. The results are reported in Tables 6 and 7. Not only the qualitative results but also the magnitudes of the estimated coefficients are generally similar among Tables 5 to 7. Thus, we find no evidence for any structural change over time. A relatively prominent difference is found in the coefficient on lnUT (i.e., the number of local university teachers), which is positive and highly significant in 2002-2004 but insignificant in 2004-2006. This result suggests that the local universities tend to lose importance as a source of knowledge spillovers. As another robustness check, the depreciation rate and the number of lags of R&D and FDI are changed from 15% to 10% and from 3 years to 5 years, respectively. The estimation results remain qualitatively the same, and are thus not reported in this paper. Table 8 presents the estimated function that explains the labor productivity with the Z variables and without capital and labor inputs. The period under study is the entire sample period 2002-2006. The results of the Hausman specification test show the same pattern as before; i.e., the random-effects estimates are inconsistent for the STIPs and consistent for the non-STIPs and the pooled data. Labor productivity is correlated with the Z variables in qualitatively the same way as in the previous regression tables. R&D and FDI are positively associated with productivity in both the STIPs and the non-STIPs. The number of local university teachers has a positive association with the productivity of the on-park firms but not the off-park firms. The 20 two variables representing urbanization economies do not have significant coefficients in any column. Keeping these results in mind, we turn now to the results of the regressions of employment size L/N and the number of firms N on the Z variables, which are presented in Tables 9 and 10. These tables look very different from Table 8. The random-effects model is inconsistent for the STIPs and consistent for the non-STIPs according to Table 8, but it is consistent in Table 9 for both the STIPs and non-STIPs and inconsistent in Table 10 for both samples. The STIPs with high levels of R&D, FDI, and UP tend to have high productivity according to Table 8, but they have neither large employment sizes nor a large number of on-park firms according to Tables 9 and 10. These contrasting results are consistent with the view that because of congestion, the STIPs cannot take advantage of productivity gains from R&D and knowledge spillovers by increasing firm sizes and the number of on-park firms. According to columns (2) and (5) of Table 9 and column (2) of Table 10, the R&D and FDI are positively correlated with neither the employment size nor the number of high-tech firms outside the STIPs. These variables have positive and significant coefficients in the labor productivity function, as shown in column (5) of Tables 5 to 8. Moreover, the coefficient on the UID is negative and significant in columns (1), (2), (4), and (5) of Table 9, whereas it is insignificant in all the columns of Tables 5 to 8. These contrasting results suggest that not only the on-park firms but also the high-tech firms outside the STIPs are faced with congestion, and that congestion is even more severe in cities with higher UID, i.e., more diverse industries. These results concerning UID, together with the absence of correlation between UID and productivity, reinforce Hu’s (2007) finding that there is no evidence for dynamic 21 urbanization economies. Still it is not clear whether these results indicate that the STIPs do not need to be located in large cities, since the positive effects of urbanized economies might be reflected in the effects of foreign ventures and universities, which tend to concentrate in large cities. This issue is left to future studies. 5. Conclusions Congestion is a common problem in cities across the developing world, especially those cities with industrial clusters that were formed spontaneously by firms and have been growing (e.g., Otsuka and Sonobe, 2008). The industrial park is usually a solution to the congestion problem. In China, for example, local governments have developed numerous industrial parks to reduce the congestion caused by industries in their townships, cities, or provinces. The national STIPs are the highest grade of industrial parks in China. The analysis of this paper, however, has offered suggestive evidence that the high-tech firms in the STIPs now suffer from the negative effect of congestion on productivity. The paper has also found that the productivity of high-tech firms, whether within or outside the STIPs, is positively associated with the foreign direct investment and the academic activities of local universities in the same city. In the presence of congestion that outweighs agglomeration economies, preferential treatment in favor of the on-park firms leads to inefficient resource allocation. In China, the preferential treatment has contributed to the growth of the STIPs by attracting a large number of firms to the STIPs. As the STIPs become overcrowded with firms, however, such a policy gives firms the wrong incentive. To alleviate the efficiency loss due to congestion, the STIPs should expel the firms that hardly 22 generate synergistic effects and benefit little from agglomeration economies. Recently, the Chinese government has reformed the STIP policy and begun giving tax exemptions to every high-tech firm, whether within or outside the STIPs. This should be a good move if congestion outweighs agglomeration economies in the STIPs. 23 Table 3 Returns to private capital and labor inputs With agglomeration economies Without agglomeration economies If congested ε β α − + 1 1<+ β α If not congested 1> −+ + εβα β α 1 30 Table 4 Definition, mean, and standard deviation of variables Variable Definition Group Mean S.D. On-park 117.5 58.7 Y/L Average labor productivity in terms of output value added per labor (1,000 yuan) Off-park 96.6 71.4 On-park 273.3 130.3 K/L Capital stock per labor (1,000 yuan) Off-park 248.4 175.1 On-park 81.2 80.0 L Number of total employees within an STIP or outside it in the same city (1,000 workers) Off-park 118.1 150.3 On-park 666 1,845 N Number of total firms within an STIP or outside it in the same city Off-park 294 492 On-park 124 83 L/N Average firm size in terms of average number of workers per high-tech firm Off-park 403 212 On-park 2.9 6.9 R&D R&D capital stock, which is constructed by using the perpetual inventory method with an assumed depreciation rate of 15% and three period lags (million yuan) Off-park 2.3 4.8 WP Non-agricultural working population in an STIP-host city (1,000 persons) City level 290 268 UID Urban Industrial Diversity Index City level 0.79 0.09 FDI FDI capital stock, which is constructed by using the perpetual inventory method with an assumed depreciation rate of 15% and three period lags (million yuan) City level 972 1,309 UT Number of university teachers in an STIP-host city (1,000 persons) City level 10.2 10.1 31 Table 5 Estimated Labor Productivity Function, 2002-2006 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data ln(K/L) 0.39*** (5.34) 0.65*** (7.80) -0.26** (-2.03) 0.48*** (7.77) 0.65*** (11.39) -0.17* (-1.83) lnL -0.32*** (-3.85) -0.06 (-0.64) -0.26** (-2.44) -0.25*** (-3.98) 0.03 (0.52) -0.27*** (-3.10) lnR&D 0.07* (1.87) 0.03 (0.52) 0.04 (0.72) 0.10*** (2.93) 0.06** (1.99) 0.04 (0.96) lnFDI 0.10** (2.24) 0.09 (0.98) 0.01 (0.10) 0.14*** (6.92) 0.08*** (2.73) 0.06* (1.72) lnUT 0.12** (1.93) -0.05 (-0.24) 0.17 (0.92) -0.03 (-0.66) -0.06 (-0.14) 0.03 (0.30) lnWP -0.20 (-1.10) -0.11 (-0.33) -0.09 (-0.35) -0.11 (-0.76) -0.02 (-0.18) -0.09 (-0.83) lnUID 0.06 (0.20) 1.61 (1.36) -1.55 (-1.50) -0.27 (-0.97) 0.60 (1.17) -0.87 (-0.83) STIP dummy 2.96*** (3.26) Hausman specification test (Chi 2) 31.62*** d.o.f. =11 6.17 d.o.f. =11 28.53 d.o.f. =22 Sample size 245 205 450 245 205 450 Dependent variable is log(Yit/Lit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in the table, but they will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parentheses are t statistics in the fixed-effects models and z statistics in the random-effects models. *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 32 Table 6 Estimated Labor Productivity Function, 2002-2004 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data ln(K/L) 0.37*** (3.82) 0.66*** (6.51) -0.29 (-1.10) 0.50*** (5.93) 0.63*** (8.18) -0.13 (-1.25) lnL -0.30*** (-3.25) -0.07 (-0.79) -0.23* (-1.95) -0.26*** (-3.18) 0.05 (0.48) -0.31*** (-3.75) lnR&D 0.06* (1.64) 0.10** (2.22) 0.04 (0.77) 0.09* (1.99) 0.08** (2.04) 0.00 (0.06) lnFDI 0.06* (1.72) -0.09 (-0.47) 0.15 (0.65) 0.15*** (5.12) 0.08*** (2.94) 0.07* (1.82) lnUT 0.17*** (2.73) -0.08 (-0.35) 0.25 (0.86) -0.04 (-0.55) -0.06 (-0.65) 0.02 (0.18) lnWP 0.05 (-0.08) -0.25 (-0.74) 0.30 (0.91) -0.29 (-1.06) -0.16 (-0.54) -0.13 (-0.43) lnUID 0.03 (0.12) 1.19 (0.81) -1.16 (-0.69) -0.54 (-0.97) 0.59 (0.86) -1.03 (-1.27) STIP dummy 2.05** (2.16) Hausman specification test (Chi 2) 42.00*** d.o.f. = 9 9.46 d.o.f. = 9 18.67 d.o.f. =18 Sample size 147 123 270 147 123 270 Dependent variable is log(Yit/Lit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in the table but will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parentheses are t statistics in the fixed-effects models and z statistics in the random-effects models. *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 33 Table 7 Estimated Labor Productivity Function, 2004-2006 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data ln(K/L) 0.43*** (3.82) 0.63*** (6.14) -0.20* (-1.69) 0.46*** (5.93) 0.66*** (7.10) -0.20* (-1.89) lnL -0.35*** (-3.85) 0.03 (0.21) -0.38** (-2.17) -0.23** (-2.48) -0.02 (-0.30) -0.21* (-1.71) lnR&D 0.03 (0.12) 0.08** (1.98) -0.05 (-0.60) 0.16*** (3.94) 0.10** (2.57) 0.06 (1.06) lnFDI 0.10*** (2.82) 0.12*** (2.70) -0.02 (-0.34) 0.13*** (4.48) 0.08** (2.33) 0.05 (1.28) lnUT 0.09 (1.55) 0.08 (1.11) 0.05 (0.79) 0.01 (0.05) 0.12 (1.25) -0.11 (-0.98) lnWP -0.31 (-0.44) 0.11 (0.21) -0.42 (-0.76) 0.11 (1.06) 0.14 (1.21) -0.03 (-0.23) lnUID 0.38 (0.72) -0.93 (-0.51) 1.31 (0.64) -0.20 (-0.54) -0.83 (-0.39) 0.63 (0.27) STIP dummy 3.28*** (3.36) Hausman specification test (Chi 2) 12.02 d.o.f. = 9 5.76 d.o.f. = 9 29.72 d.o.f. =18 Sample size 147 123 270 147 123 270 Dependent variable is log(Yit/Lit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in the table but will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parentheses are t statistics in the fixed-effects models and z statistics in the random-effects models, and *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 34 Table 8 Estimated Labor Productivity Function without K and L, 2002-2006 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data lnR&D 0.07** (2.05) 0.06* (1.88) 0.01 (0.13) 0.08*** (2.65) 0.07** (2.44) 0.01 (0.27) lnFDI 0.08* (1.81) 0.16** (2.02) -0.08 (-0.76) 0.15*** (5.30) 0.13*** (4.20) 0.02 (0.51) lnUT 0.17*** (2.58) -0.01 (-0.04) 0.18 (1.25) 0.02 (0.59) -0.08 (-0.86) 0.10 (0.95) lnWP -0.05 (-0.59) -0.20 (-0.64) 0.15 (0.47) 0.04 (0.61) -0.08 (-0.75) -0.13 (-1.08) lnUID 1.22 (1.24) 1.49 (1.35) -0.27 (-0.22) 0.62 (0.47) 1.32 (1.14) -0.70 (-1.00) STIP dummy -0.41 (-0.61) Hausman specification test (Chi 2) 22.44** d.o.f. = 9 10.87 d.o.f. = 9 16.95 d.o.f.= 18 Sample size 245 205 450 245 205 450 Dependent variable is log(Yit/Lit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in this table but will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parentheses are t statistics in the fixed-effects models and z statistics in the random-effects models, and *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 35 Table 9 Estimated Function of Average Employment Size, 2002-2006 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data lnR&D 0.01 (0.49) -0.06 (-1.49) 0.07 (1.57) 0.02 (0.95) -0.02 (-0.72) 0.05 (1.24) lnFDI 0.01 (0.29) -0.07 (-1.21) 0.08 (1.15) -0.00 (-0.00) -0.10*** (-2.70) 0.10** (2.01) lnUT -0.08 (-1.21) -0.07 (-0.48) -0.01 (-0.08) -0.13 (-1.59) -0.09 (-0.91) -0.05 (-0.46) lnWP 0.07 (1.09) 0.26 (1.21) -0.19 (-0.90) 0.04 (0.61) 0.17 (1.49) -0.14 (-1.06) lnUID -0.58* (-1.94) -2.47*** (-3.20) 1.89** (2.44) -0.68** (-2.59) -2.69*** (-4.53) 1.99*** (3.04) STIP dummy -0.74 (-0.81) Hausman specification test (Chi 2) 4.88 d.o.f. = 9 10.27 d.o.f. = 9 13.60 d.o.f.= 18 Sample size 245 205 450 245 205 450 Dependent variable is log(Lit/Nit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in the table but will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parenthesis are t statistics in the fixed-effects models and z statistics in the random-effects models, and *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 36 Table 10 Estimated Function of Number of Firms, 2002-2006 Fixed-effects model Random-effects model (1) (2) (3) (4) (5) (6) STIPs non-STIPs interactio n terms in pooled data STIPs non-STIPs interaction terms in pooled data lnR&D 0.01 (0.15) 0.07 (1.50) -0.07 (-1.38) 0.01 (0.38) 0.14*** (3.94) -0.13*** (-2.74) lnFDI 0.00 (0.04) -0.07 (-1.09) 0.07 (0.99) 0.04 (1.08) 0.15*** (3.43) -0.10* (-1.78) lnUT -0.06 (-1.06) 0.13 (0.81) -0.19 (-1.26) 0.12 (1.38) 0.04 (0.38) 0.08 (0.64) lnWP 0.08 (1.33) -0.22 (-0.86) 0.30 (1.34) 0.22*** (2.96) 0.40*** (2.77) -0.18 (-1.12) lnUID -0.65 (-0.94) 0.56 (0.62) -1.21 (-1.52) -0.79 (-1.59) -0.24 (-0.34) -0.55 (-0.72) STIP dummy 4.27*** (3.73) Hausman specification test (Chi 2) 23.48*** d.o.f. = 9 120.45*** d.o.f. = 9 51.49** d.o.f.= 18 Sample size 245 205 450 245 205 450 Dependent variable is log(Nit). Year dummies and an intercept are included in the regression. The results concerning them are not reported in the table but will be provided upon request. Columns (3) and (6) report the estimated coefficients on the interaction of the STIP dummy and each regressor. Numbers in parenthesis are t statistics in the fixed-effects models and z statistics in the random-effects models, and *, **, and *** indicate the 10 percent, 5 percent, and 1 percent significance levels, respectively. 37 Figure 1: Geographic Distribution of the National STIPs in China by 2006 Source: The Annual Report of the Torch Center, 2007. 38 Please note: You are most sincerely encouraged to participate in the open assessment of this discussion paper. You can do so by either recommending the paper or by posting your comments. Please go to: http://www.economics-ejournal.org/economics/discussionpapers/2010-26 The Editor © Author(s) 2010. Licensed under a Creative Commons License - Attribution-NonCommercial 2.0 Germany