Technological spillover through industrial and regional linkages: Firm-level evidence from China

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Technological spillover through industrial and regional linkages: Firm-level evidence from China☆ Yong Hu a, Karen Fisher-Vanden b, Baozhong Su c, * a b c

Zhejiang Gongshang University, Hangzhou, China Pennsylvania State University, State College, USA China Agricultural University, Beijing, China

A R T I C L E I N F O

A B S T R A C T

JELclassification: O33 R11 R58

This paper provides new insights into the study of technology spillover effects through the interaction between industrial and spatial linkages. We develop a theoretical model that provides a useful modeling framework for spillover research, and then empirically test the model inferences using Chinese firm-level data. Input-output tables and spatial decay measurements are combined to construct the key spillover variables. Using seemingly unrelated regressions, the paper finds that vertical spillover effects are more significant than horizontal spillover effects, both within- and between-regions; regional characteristics have greater impacts on vertical spillovers than on horizontal spillovers; and regional spillover effects vary across different regions. Regional policies and regional endowments, including human capital, transportation infrastructure, and enterprise ownership, are crucial in explaining these heterogeneities in regional technology spillover. Our empirical results provide many policy implications including strengthening the connection between upstream and downstream industries and devoting more R&D to upstream industries.

Keywords: Regional spillover Industrial linkage spillover Internal technology expenditure Imported technology expenditure FDI China

1. Introduction Technology spillovers are “the effects of nonmarket interactions which are realized through processes directly affecting the utility of an individual or the production function of a firm” (Fujita and Thisse, 1996). Non-market interactions typically adopt the form of information exchanges between agents in different locations through certain channels including industrial linkages and regional linkages. The former is referred to as linkage spillover, which mainly depends on the individual industrial structure and industrial chain. The latter is regional spillover, which is mainly determined by factors such as distance, regional endowment and region-specific policies. Most of the existing studies individually investigate these two spillovers, whereas it might be more appropriate to study them as an organic whole since they are correlated with each other. For example, Biel Crystal Manufactory (Shenzhen) Limited, a glass supplier for mobile phone screens that is situated in

Shenzhen, provides intermediate goods for two Chinese cell phone manufacturers, Huawei and Xiaomi, located in Shenzhen and Wuhan, respectively. During the process of Biel supplying intermediate goods to Huawei or Xiaomi, these companies may provide Biel with assistance in building production equipment, provide technical support to Biel, or provide training assistance for Biel’s employees. These behaviors are referred to as backward linkage spillovers. However, because Biel and Huawei are both situated in Shenzhen, whereas Xiaomi is located in Wuhan, these two backward spillovers may be very different. For instance, certain constraints, such as family factors or cultural differences, will be a much smaller impediment to the movement of labor between Biel and Huawei because they are located in the same city. As a result, Huawei employees will be more willing to commute or transfer jobs to Biel than Xiaomi employees. Therefore, in comparison with the backward linkage spillover effect between Biel and Xiaomi, the effect between Biel and Huawei may have a greater chance of intensification

☆ We thank three anonymous referees and the editor, prof. Sushanta Mallick, for their helpful suggestions that have significant improved the paper. This work was supported by Social Science Project of Qianjiang Talents Program of Zhejiang Province (grant number QJC1602003), Modern Business Research Center of Zhejiang Gongshang University (grant number 16YXYP03), Humanity and Social Science of Ministry of Education of China (grant numbers 16YJC790068, 17YJC790157). * Corresponding author. China Agricultural University, Beijing, China. E-mail addresses: [email protected] (Y. Hu), [email protected] (K. Fisher-Vanden), [email protected] (B. Su).

https://doi.org/10.1016/j.econmod.2019.11.018 Received 19 December 2018; Received in revised form 15 November 2019; Accepted 15 November 2019 Available online xxxx 0264-9993/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Hu, Y. et al., Technological spillover through industrial and regional linkages: Firm-level evidence from China, Economic Modelling, https://doi.org/10.1016/j.econmod.2019.11.018

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Fig. 1. Industrial linkage spillover with regional differences. Notes: 1. Upstream firms contain firms that belong to the upstream industries of firm A. 2. Within-region upstream firms contain the firms that belong to the upstream industries of firm A and are located in the same region as firm A. 3. Between-region upstream firms contain firms that belong to the upstream industries of firm A and are located in a different region than firm A. 4. Within-region (between-region) upstream spillover is the externalities of technology innovation of within-region (between-region) upstream firms on firm A through multiple channels.

the differences in within-region spillover effects. Huang and Ge (2012) find that the within-region spillover effect depends on the level of regional innovation. For instance, the spillover effect is positive only when this regional innovation level reaches a minimum threshold. Besides the innovation level, other important factors have been found to influence within-region spillover such as absorptive capacity (Ben Hamida, 2013), industrial agglomeration (Ning et al., 2016; Liang and Goetz, 2018), labor mobility (Tambe and Hitt, 2014), and market structure (Shibata, 2014). Previous studies find evidence of significant positive between-region spillover effects in many regions, for instance, in Chinese regions (Yang et al., 2014), Italian regions (Bronzini and Piselli, 2009), and other European regions (Benos et al., 2015). However, between-region spillover could have a negative impact (Ben Hamida, 2013). Hence, many studies focus on what factors determine the differences for between-region spillover effects. Most of the findings identify distance (Petit et al., 2009; Deltas and Karkalakos, 2013), labor mobility (Breschi and Lissoni, 2009), absorptive ability (Qi et al., 2009; Bengoa et al., 2017), technology similarity (Deltas and Karkalakos, 2013; Drivas et al., 2014), and genetic proximity (Chaudhry and Ikram, 2015) as the main factors. This paper combines industrial linkage and regional spillover studies and uses Chinese firm-level panel data to investigate how within-region and between-region technology spillovers differ across regions; how these different regional spillovers affect the economic disparity in regional growth; and what factors cause these differences in regional spillovers. The basic ideas are illustrated in Fig. 1. We use input-output tables and spatial decay to construct within-region and between-region spillovers. More details can be found in the Appendix regarding the construction of the key variables. Briefly, we first introduce regional differences in Chinese economic development. Since the onset of economic reform in the late 1970s, the Chinese economy has experienced phenomenal economic growth. However, due to disparities in regionspecific endowments and government policies across different regions, the growth in each region shows significant heterogeneity. Using the zoning codes adopted by the National Bureau of Statistics of China

through the talent circulation effect. Moreover, the regional economic development level also has an influence on the backward linkage spillover effect. Compared with Wuhan, Shenzhen has a higher degree of marketization and more talent; these factors are conducive to the occurrence of backward spillover and the absorption and digestion of spillover. Therefore, regional characteristics, including culture, distance, and economic development level, will have an impact on the backward spillover effect. Hence, it is more appropriate to combine linkage spillover with regional spillover when studying technology spillover. Studies based on industrial linkage spillovers generally include horizontal spillover, forward spillover, and backward spillover studies (Aitken and Harrison, 1999; Blalock and Gertler, 2008; Kesavayuth and Zikos, 2012; Newman et al., 2015). Most studies conclude that horizontal spillovers result in negative or insignificant effects (Aitken and Harrison, 1999; Ahmed, 2012; Suyanto and Salim, 2013; Lu et al., 2017). There are also different conclusions in the literature. For instance, local firms may benefit from horizontal spillover (Bin, 2008; Zhang et al., 2014; Sari et al., 2016). Similar to the horizontal spillover, there is no unified conclusion on the empirical findings for forward spillover (Wang, 2008; Xu and Sheng, 2012; Gorodnichenko et al., 2014; Jude, 2016). A vast literature has demonstrated the positive impact of backward spillover (Javorcik, 2004; Blalock and Gertler, 2008; Lin et al., 2013; Mariotti et al., 2015). Previous literature also find that vertical spillovers are influenced by factors such as technological difference (Ni et al., 2017), absorptive capacity (Ben Hassine et al., 2017; Orlic et al., 2018), and openness policy (Wiboonchutikula et al., 2016). Influenced by the idea that “there is a geographic component to the spillover mechanism” (Jaffe and Trajtenberg, 2002), studies on regional spillover (including within-region and between-region spillover) have recently attracted attention. For instance, Ben Hamida (2013) finds that within-region international technology spillovers result in a positive impact for local Chinese firms. However, these results have been challenged by some studies (for instance, Anwar and Nguyen, 2014). Inspired by the controversial results for within-region spillover effects on local firms, many economists have tried to determine what factors account for 2

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Fig. 2. The impact mechanism of regional factors on technology spillovers.

(NBSC), we group China’s provinces into five regions, i.e., North, Northeast, East, South and Southwest, and compare the annual growth rate of per capita income in each region from 1992–2004.1The per capita income growth rate in the Southwest is 10.2%, which is not far behind that of other regions except for the East region; this is somewhat surprising because the Southwest has the worst infrastructure and lowest per capita income in the early years of economic reform.2 During this time period, the Northeast has the lowest growth rate and the East has the highest growth rate. What factors account for the disparities in income growth rates across different regions? Initial economic status, government preferential policies, natural endowments, and advanced technologies are some of the factors affecting regional growth. However, differences in technology spillover effects also play a critical role in explaining the difference in growth rates among regions. This paper contributes to the existing literature in a number of ways. First, on the one hand, the amount of technology spillover depends mainly on the following three aspects: the subject, the recipient, and the channels of the technology spillover. Regional characteristics have impacts on these aspects (Petit et al., 2009; Deltas and Karkalakos, 2013; Huang and Ge, 2012; Qi et al., 2009; Ben Hamida, 2013). For example, regional R&D investment affects the innovation ability and absorption capability of enterprises in the region. The infrastructure level influences labor mobility and commodity exchange within the region, which will then impact technology spillover. The regional market structure and market size influence technology spillover through commodity transactions and industrial linkages. Preferential policies in a region will encourage more high-tech enterprises to settle within the region and thus generate more technology spillover. On the other hand, horizontal

linkage and vertical linkage spillovers result in different effects (Jeon et al., 2013; Wiboonchutikula et al., 2016; Lu et al., 2017). Fig. 2 illustrates these impacts. Inspired by these considerations, we combine spatial factors and industry linkage structures together and develop a theoretical model to examine how regional differences, including geographical endowments, economic factors, and government policies, affect within-region and between-region spillovers. Second, to the best of our knowledge, there is little literature empirically investigating the interaction between industrial linkages and regional spillover, although there is a huge literature on industrial linkage spillover, regional spillover (including international knowledge spillover) as channels to improve industry-level performance (see for example Bournakis, Christopoulos, and Mallick, 2018). Using a panel dataset containing manufacturing firms in regions of Vietnam from 2000 to 2005, Anwar and Nguyen (2014) find that only backward linkage spillovers in four regions were positive, whereas all of the other spillovers (i.e., horizontal or vertical spillover) in all of the regions are negative or insignificant. Using a dataset on Chinese manufacturing enterprises over 1998–2001, Qi et al. (2008) find that within-region vertical FDI spillover effect and between-region horizontal FDI spillover effect are significant, while within-region horizontal FDI spillover effect is insignificant. Unlike Anwar and Nguyen (2014) and Qi et al. (2008), this paper develops methods to combine linkage spillover and regional spillover together, and then use a dataset containing more industries and enterprises over a longer time period to empirically investigate within-region horizontal and vertical spillover effects (the spillover effects of technology innovation of firms on firms in the same industry or other related industries in the same region) and between-region horizontal and vertical spillover effects (the spillover effects of technology innovation of firms on firms in the same industry or other related industries in other regions). Third, several previous studies find that vertical linkage, especially backward linkage, is more important than horizontal linkage for spillover taking place (Javorcik, 2004; Liu, 2008; Khachoo and Sharma, 2016). For instance, using a panel data containing Chinese manufacturing firms over 1995–1999, Liu (2008) finds that backward linkage is the most important channel through which FDI spillovers take place. Inspired by these findings of previous literature, this paper validates this feature in the case of the within-region spillover and between-region spillover, which extends the empirical finding in Javorcik (2004). Moreover, this paper finds

1 The reason that we choose per capita income instead of GDP as a proxy for the regional economy in each region is that there are huge differences among different regions in terms of population and the number of provinces. For example, the Northeast only consists of three provinces, whereas the East consists of seven provinces. Per capita income is a true indicator of the economic development status in each region. All data are compiled from NBSC. 2 China formally established the socialist market economic system during the year 1992–1993. In 1992, the per capita income was 1435 Yuan in the Southwest, 2026 Yuan in the South, 2564 Yuan in the North, 2668 Yuan in the East, and 2908 Yuan in the Northeast.

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Table 1, the real GDP per capita in Eastern China in 2004 is 19,306 Yuan, a value much higher than that in other regions in China. By contrast, the real GDP per capita in Southwestern China is only 7696 Yuan. Moreover, the Northern and Eastern regions experience rapid growth in their GDP levels from 1995 to 2004, by 221% and 212%, respectively. The economies of the Northeastern and Southwestern regions, by contrast, grow more slowly, by only 177% and 124%, respectively. Many factors contribute to this inequality in regional development in China, such as research and development expenditure (R&D), science and technology activity (S&T), infrastructure, market structure, financing constraint, regional policies, and others. These factors will also be applied to explain our empirical results.

that regional characteristics have greater impacts on vertical spillovers than on horizontal spillovers. Considering the case with two regions (region 1 and 2), two industries (mobile phone industry and glass production industry, which is a supplier for mobile phone industry), three firms (Firm A and B are located in region 1, and belong to mobile industry and glass production industry, respectively. Firm C is located in region 2 and belongs to mobile industry), our results indicate that the spillover effect from firm B to firm C is significant than from firm A to firm C, and the change of endowment (such as infrastructure and market structure) of region 1 has a bigger impact on the former spillover effect than on the latter spillover effect. Fourth, existing studies that focus specifically on Chinese industries usually employ industry-level data, which will suffer from endogeneity and aggregation problems. By employing firm-level data, this study is able to overcome these problems. The paper is organized as follows. The next section introduces the regional development including policy reforms in China. The third section introduces the theoretical model. The fourth section presents the data set used in this analysis and describes our estimation approach. The fifth section discusses the empirical results and offers interpretation. Finally, the sixth section provides concluding remarks.

2.1. R&D expenditure and S&T personnel We use R&D expenditure as a proxy for the technology development and S&T personnel as a proxy for the provincial absorptive capacity in each Chinese province. Fig. 3 reports the total R&D expenditure and S&T personnel in each Chinese province. As shown in Fig. 3, the Northern region and Eastern region have the highest growth rate of R&D expenditure among all five regions. In particular, the R&D expenditures in the North and the East both exceed more than 30 billion Yuan in 2004, whereas the R&D expenditures in the Northeast and Southwest are only 7.8 billion and 8.2 billion respectively. The S&T personnel in the Eastern region ranks the highest among all five regions over 1995–2004 and reaches 1.2 million in 2003. However, the S&T personnel in the Southwestern region and Northeastern region ranks the last among all five regions, hovering around 0.3 million.

2. Regional development in China In the 20 years since the reform and opening up in the late 1970s, GDP differences across regions in China have increased significantly, the characteristics of industrial structure have become localized, and regional divisions have emerged in the market economy. The Eastern region in China has entered the middle stage of industrialization, whereas the Western region is generally still in the initial phase of industrialization, and the deterioration of the old industrial bases in the Northeastern region have continued largely unabated. As indicated in Table 1 Regional summary: Basic statistics for the years 1995 and 2004. Region

Province

Real GDP per capita [Yuan]

Population [Millions]

Real Earnings per employed person [Yuan]

Employment [Millions]

North Northeast East

Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia Liaoning, Jilin, Heilongjiang Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan Chongqing, Sichuan, Guizhou, Yunnan

16,284 (5067) 15,876 (5717) 19,306 (6179)

150.4 (139.9) 107.4 (103.8) 373.3 (348.1)

17,552 (5152) 15,226 (4540) 18,873 (5551)

72.27 (68.8) 46.9 (48.4) 198.74 (198.8)

12,638 (4441)

364.4 (333.9)

16,785 (5413)

191.07 (174.7)

7696 (3425)

201.6 (188.2)

15,660 (4840)

107.63 (102.8)

South Southwest

Notes: Figures not in parenthesis are the statistics for 2004; figures in parenthesis are the statistics for 1995.All figures are based on the China Statistical Yearbook (1996, 2005).

Fig. 3. R&D and S&T of five regions in China. Source: Statistical database of China’s economic and social development, China National Knowledge Infrastructure, 1995–2004. 4

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shown in Fig. 5, the Eastern region presents the lowest financing constraint, whereas the Southern, the Southwestern, and the Northeastern region show the highest values of financing constraint. In particular, Zhejiang province in the Eastern region shows the lowest financing constraint, while Heilongjiang province in the Northeastern region has the highest one among all Chinese provinces.

2.2. Infrastructure As in Zheng et al. (2007), we use transportation infrastructure (highway mileage) and indicators of social urbanization (number of hospital doctors) to measure the infrastructure in each Chinese region. We apply ArcGIS to illustrate the values of these two variables for different Chinese provinces in 2004. As shown in Fig. 4, on average, the highway mileage in the east is higher than that in the Southwest and Northeast. In particular, Heilongjiang and Jilin provinces in the Northeast have one of the lowest highway mileages, whereas Zhejiang and Jiangsu provinces in the East have the highest highway mileage among all Chinese provinces. The Eastern Region also presents a significantly higher number of doctors than that in the Northeastern region and Southwestern region, with Shandong and Jiangsu provinces in the East containing one of the highest numbers of doctors, whereas Guizhou province in the Southwest shows the lowest number among all Chinese provinces.

2.4. Regional policy The rapid growth in the Eastern region of China is, to a large extent, a beneficial result of preferential policies and geographical advantages. For instance, China established a Special Economic Zone in Fujian in 1980. In 1984, China opened up selected coastal cities in provinces including Shandong, Jiangsu, Shanghai, Zhejiang, and Fujian. Furthermore, Economic and Technological Development Zones were started in Shanghai in 1986 and were followed by the establishment of the Shanghai Pudong New Area in 1990. In 1992, new open economic zones were officially established in major cities along the Yangtze River. Economic and Technological Development Zones were founded in the Anhui, Fujian, and Zhejiang provinces in 1993. These preferential policies help the Eastern region to improve its industrial, port, airport, and urban infrastructures by means of fiscal transfers and banks loans; the policies also give preferential treatment to enterprises in the Eastern region with regard to finance, taxation, credit, investment, and trade, so as to improve the degree of marketization and internationalization within this region. Firms in the Eastern region are allowed to avoid the confiscatory taxation that is required in the planned economy and to import intermediate goods without duty to produce final goods for export. Foreign firms have been encouraged to cooperate in production, R&D, and sales with domestic firms in the Eastern region. These preferential policies have also attracted a great deal of FDI into the Eastern region. As indicated in Table 2, FDI for the Eastern region increases from 16.71 billion dollars in 1995 to 41.98 billion dollars in 2004, an increase of 151%. The FDI in the East accounts for around 44% of the total FDI in China in 1994; this ratio rises to more than 57% in 2004. The acceleration in FDI during the period 1995–2004 in the Eastern region, relative to that in other regions, is most probably the result of preferential policies; foreign investors are convinced of China’s determination to join the world economy because of the policies in the Eastern region.

2.3. Market structure and financing constraint We adopt the market power to evaluate the market structure at each region. As in Peress (2010), we use the Lerner index (defined as one minus costs divided by sales) to represent a firm’s market power. Then we obtained the provincial market power via the weighted average of market power across all firms in the province. We use individual firm’s sale over total sales in the province as the weight. The larger the provincial market power, the stronger would be the market power in the province. Therefore “1-province market power” (a proxy for the market structure) can reflect the degree of market competition in the province. As shown in Fig. 5, the market in the Eastern region is more competitive than that in the Southwestern and Northeastern regions. We also find that the Zhejiang and Jiangsu provinces from the Eastern region have the highest market competition degree among all Chinese provinces. We adopt the approach in Petersen and Rajan (1994) and Li and Yu (2013) to define the index FS as the ratio of interest expense over total fixed assets. We also defined the provincial FS as the weighted average of FS across all firms in the province where the weight is individual firm’s fixed asset over total fixed assets in the province. A larger value of the provincial FS implies smaller financing constraint in the province. As

Fig. 4. Highway mileage (km) and the number of Doctors. 5

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Fig. 5. Market structure and Financing constraints.

Table 2 FDI by region during 1995–2004. Region

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

North Northeast East South Southwest

3.87 2.75 16.71 13.50 0.57

4.99 3.22 18.79 15.00 0.44

5.95 3.95 19.72 15.66 0.46

6.56 3.31 18.78 15.98 0.70

6.69 3.30 18.15 15.39 0.65

6.38 3.72 18.52 15.36 0.59

6.09 4.31 22.00 16.18 0.67

6.90 5.18 29.85 16.92 0.81

5.48 6.93 41.19 20.22 0.79

7.90 7.10 41.98 15.31 0.91

Notes: All figures are based on the China Statistical Yearbook (1996–2005). All values are reported in billions of dollars.

important and advantageous industries and by reforming the SOEs in accordance with the requirements of modern enterprises. In 2016, the Chinese State Council put forward several new policies for the revitalization of old industrial bases in Northeastern China. These policies aim to further cut bureaucracy and delegate power to the lower levels, radically reduce the list of issues that need administrative approval, improve the business environment, and boost the internal vigor of the market in Northeastern China. The new policies also propose pushing forward with the reform of SOEs according to the classification of their functions, regrouping state-owned investment and operation companies, obtaining a value appreciation of state-owned assets, allowing SOEs to transfer some shares, and permitting local governments to sell certain SOE shares.

The regional differences in China’s economic development in recent years can be roughly observed from Table 1. Concerned with social justice and stability, Chinese leaders are committed to fostering economic growth in Western China. The budget for infrastructure in the Western region has constantly been increased over the years, as has been the transfer payment from the Chinese State Council to the West. The State Council has also promulgated comprehensive strategies to facilitate development in backward regions. For example, to help the lagging Western region to catch up with the Eastern region, the Chinese State Council launched the “Grand Western Development Program” in January 2000. Follow-up policies included construction of the “West–East Gas Pipeline” in 2002 and the “Returning Grazing Land to Grass Land” policy in 2003. The “Grand Western Development Program” is divided into three stages: 2000–2010, the foundation stage; 2011–2030, the development stage; and 2031–2050, the modernization stage. The foundation stage focuses on transportation infrastructure to provide close connections between the Western and Eastern regions. Six of the ten key projects in the “Grand Western Development Program” relate to transportation: two railway construction projects (Chongqing-Huaihua Railway and Nanjing-Xi’an Railway), one airport construction project, which plans to build airports in Shanxi, Yunnan, Lanzhou, and Xinjiang, one road construction project, the construction of the “West–East Gas Pipeline”, and the Chongqing Light Rail project. In 2003, to combat the difficulties encountered in the development and reform of the old industrial bases in Northeastern China, the State Council put forward a revitalization strategy. The principle of this policy is to deepen reform and expand the opening up of the region. It focuses on the strategic adjustment of stateowned enterprises (SOEs), by formulating a mechanism to promote the concentration of state-owned capital in the national economic lifelines of

3. A theoretical model We consider a model containing industry H and U, and region 1 and 2. Industry U is the upstream industry of industry H. The model framework, like lots of models in the existing literature, is a variant of the monopolistic competition proposed by Dixit and Stiglitz (1977). Most of the existing models assume a firm’s production as a function of capital, labor and material, while we extend it by adding a variable of technology development expenditure, which, in our paper, is characterized by internal technology development expenditure, imported technology expenditure, and FDI. It is believed in the literature that innovation R&D can enhance the productivity of enterprises, and innovation can reduce the marginal costs of enterprises. Innovation usually includes process innovation and product innovation. One viewpoint suggests that only process innovation can significantly improve enterprise productivity (Lee, 2008), whereas another viewpoint suggests that only product

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inputs, respectively. B1 ; B2 are total factor productivities and adopt the following forms: 0 1ηu 0 1ηu

innovation improves enterprise productivity (Cassiman et al., 2010). However, some empirical findings support the belief that both types of innovation promote productivity, and consequently save production costs (Biesebroeck, 2005; Bournakis and Mallick, 2018). Also, FDI can influence local firm’s productivity through many ways: foreign firms provide technical support or transferred technology to suppliers in order to assist them in technological innovation (Blalock and Gertler, 2008); foreign firms help suppliers diversify by tapping new customers; downstream domestic enterprises buy high-quality intermediate inputs provided by upstream foreign-funded firms to improve their productivity (Liu, 2008; Newman et al., 2015; Jude, 2016); foreign firms provide or help local firms to purchase raw materials and intermediate products. Therefore, we think it is necessary to incorporate technology development expenditure into firm’s production function. Considering a firm located in region 1 and belongs to industry H, we assume its production function as follows: α β

Q ¼ AK L


Z

n1

0

ρ

xj dj þ τ

Z

n1 þn2 n1

ργ xj dj ρ

B1 ¼ Φ1 @R1;U þ τ ρ R2;U A ; B2 ¼ Φ2 @R2;U þ τ ρ R1;U A . R1;U repre1

sents the sum of technology development expenditures of firms that belong to industry U and are located in region 1, R2;U represents the sum of technology development expenditures of firms that belong to industry U and are located in region 2. Φ2 represents region 2’s structural characteristics. ηu < 1, similar to ηh , is the discount factor of industry U. The targeted firm seeks to maximize its profit by choosing capital, labor and intermediate goods: R n þn maxPQ Q  PK K  PL L  0 1 2 Pj xj dj. Applying the variation method for the material j (Hritonenko and Yatsenko, 2012), we obtain:

Pj ¼

(1)

Where α þ β þ γ ¼ 1, 0 < ρ < 1.Q; K; L represent output, capital and labor, respectively. xj is the firm’s demand for differentiated material j from industry U. The differentiated material j is produced by a firm j in industry U, which is a monopolistically competitive market. We assume firm j is located in region 1 when j  n1 , and is located in region 2 when n1 < j  n1 þ n2 . Transportation costs for intermediate goods are taken as

1

xj ¼

:

B2 K αj Lβj M γj

if

γ τAPQ K α Lβ

PK ¼ αAPQ K α1 Lβ

A ¼ Φ1 ðR1 þ τ ρ R2 Þηh , where Φ1 represents region 1’s structural characteristics, such as infrastructure, market structure, regional policies etc. R1 represents the sum of technology development expenditures of firms that belong to industry H and are located in region 1. R2 represents the sum of technology development expenditures of firms that belong to industry H and are located in region 2. The increase in technology development expenditure of other firms in industry H may have different impacts on the targeted firm (the effects can be referred to as horizontal spillover effect). On the one hand, the increased technology development activities of other firms will intensifythe competition level within the industry. This enhanced competition pressure will stimulate the targeted firm to adopt or imitate advanced technology to improve productivity (Kosova, 2010). Moreover, the targeted firm can gain spillovers from other firms’ high-tech products and advanced sales strategies, and thus improve its productivity. These effects are described as competition promoting effect. However, the increased expenditure on technology development of other firms also leads to the loss of existing market share, the increase in fixed cost of the targeted firm, and, ultimately, cause the targeted firm to withdraw from the market or turn to other markets. This effect is referred to as the crowding-out effect. When the promoting effect outweighs the crowding-out effect, the horizontal spillover will have a positive effect on the targeted firm and vice versa. Thus we propose an “industry discount factor” ηh < 1, which depends on the relative magnitude of competition promoting effect and crowding-out effect. The larger the positive horizontal spillover, the closer the discount factor is to 1. Upstream firms located in different regions are influenced by different regional characteristics; so we need to isolate the part of total factor productivity which is affected by regional characteristics. We thus assume the production function of firm j in industry U as follows: i  j 2 0; n1 i  j 2 n1 ; n1 þ n2

> > > :


Z

n1

0

xρj dj þ τ

Z

n1 þn2 n1

ργ 1 xρj dj xjρ1 ;

# j2

0; n1 #

j2

n1 ; n1 þ n2

Taking the first order condition (FOC) of the profit function with regard to capital and labor, we have the following equations:

1

if

ργ 1
Z n1 Z n1 þn2 8 ρ ρ α β > γAP K L x dj þ τ x dj xjρ1 ; Q j j > > < 0 n1

(3)

Samuelson’s “iceberg” form, specifically, only a fraction τ ρ < 1 of each unit of intermediate goods shipped from region 2 to region 1 arrives. A is total factor productivity and adopts the following form:

8 α β γ < B1 K j Lj M j

1

PL ¼ βAPQ K α Lβ1


Z

n1 0


Z

Z

n1 þn2

ργ xj ρ dj

(4)

ργ xj ρ dj

(5)

n1

n1 0

xj ρ dj þ τ

xj ρ dj þ τ

Z

n1 þn2 n1

Given the production function form, the dual cost function of upstream firm j can be written as: γ
α
β
8
1 PK;u PL;u PM;u > > x ; j 2 0; n1  > j α γ   < B1 β C PK;u ; PL;u ; PM;u ; xj ¼
> > 1
PK;u α
PL;u β
PM;u γ > : xj ; j 2 n1 ; n1 þ n2  B2 α γ β (6) PK;u ; PL;u ; PM;u are the prices for capital, labor and material, which are used to produce the upstream differentiated output. Firm j in the upstream industry chooses output to maximize its profit:Pj xj  CðPKu ; PLu ; PMu ; xj Þ. Taking the FOC of the profit function and combining with equations (3)–(6), we obtain the price for the intermediate goods: γ β


α
8 1 PK;u PL;u PM;u > > ; j 2 0; n1  > < ρB1 α γ β Pj ¼
> > 1
PK;u α
PL;u β
PM;u γ > : ; j 2 n1 ; n1 þ n2  ρB2 α γ β

(7)

Combining equations (3) and (7), we find that the targeted firm has the same demand for any intermediate goods produced in region 1. We denote this demand as xІ and the corresponding price as PІ . Similarly, for any intermediate goods produced in region 2, the targeted firm has the same demand, which is denoted as xΠ . The corresponding price is denoted asPΠ . We then obtain the ratio of the demands for different types
11 ρ . Substituting this expression into of intermediate inputs: xxΠІ ¼ τBB12

(2)

equation (3), the demand for intermediate inputs can be written as follows:

where α þ β þ γ ¼ 1Kj , Lj and Mj represents capital, labor, and material

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Y. Hu et al.

xІ ¼

Economic Modelling xxx (xxxx) xxx

γPL L

1ρ ρ ! βPІ n1 þ τn2 τBB12

γPK α PL β

γ τ PL L xΠ ¼

1ρ ρ ! βPΠ τn2 þ n1 τBB12 The

production





(8)

(9)

now

can

be

simplified

γ

L¼

αα ββ γ γ ργ A

as：Q ¼



γPK α PL β

(10)

C ¼ PK K þ PL L þ

0

∂C ¼ ∂ R1

Φ1 ηh PK PL

β

PK;u

β


PL;u β

α




γ γ

PM;u γ

∂C ¼ ∂ R2

α

τ Φ1 ηh PK PL

0


β@

PK;u

α

α


PL;u β

PK;u

β
PL;u β

α

β


γ PM;u γ

γ γ

PM;u γ

n1 B1

satisfies the following condition:

þ1

ρ

Bn1 η τ1ρ B1 1ρ ρ n2 η τ11 ρ B2 1ρ ρ C u u C QB @R1;U þτ1ρ R2;U þ R2;U þτ1ρ R1;U A




ρ

1ρ

þ τn2 τB2

n1 n2

@R1 þ τρ R2 A 1ρ ρ !



1ρ ρ ! ρ αα ββ γ γ ργ A2 n1 B1 1ρ þ τn2 τB2

>





1

ρ





1

þτ ρ R

1;U

B1

, then the within-

ρ

1ρ

within-region vertical spillover. The second term is the comparative advantage of outside region characteristics (CAOR): a higher value of comparative advantage will induce a higher between-region vertical spillover relative to the within-region vertical spillover. This effect can be referred to as “outside regional characteristics effect”. nn12 reflects the de-

(12)

gree of relative industrial agglomeration in region 1. Therefore, the above inequality implies that if the relative industrial agglomeration in region 1 is larger than the distance decay effect along with the outside regional characteristics effect, the within-region vertical spillover will be larger than the between-region vertical spillover. Previous studies show that regions with higher industrial agglomeration usually receive larger within-region spillover effects (Ning et al., 2016). A higher nn12 implies that

1ηh 1

γð1ρÞ

þ1

1

Q

A @R1 þ τ1ρ R2 A

ρ

τ ρ ) will increase the between-region vertical spillover relative to the

ρ

1γ 0

γð1ρÞ

iceberg cost. Other things being equal, a lower iceberg cost (i.e., higher

1ηh 1 1

1ρ ρ !

τ ρ R1;U þτ ρ R2;U B2 1ρ

R2;U

0

1

0

region vertical spillover will be bigger than the between-region vertical spillover. In the special case:R1;U ¼ R2;U , the above condition can be
1ρ ρ 1 simplified as:nn12 > τ ρ ΦΦ21 . The first term of the right hand side is

The between-region horizontal spillover is the marginal effect of R2 on the targeted firm’s total cost:

ρ

þ τn2 τB2

1

(11)

γð1ρÞ




ρ

αα ββ γ γ ργ A2 n1 B1 1ρ þ τn2 τB2

1

1ρ ρ !

γð1ρÞ




(15)

Starting from the above total cost function, we calculate the withinregion horizontal spillover as the marginal effect of R1 on the targeted firm’s total cost: α

ρ 1 þ1 C B n1 η B1 1ρ ρ n2 ηu τ1ρ ρ B2 1ρ C u QB @R1;U þτ1ρ R2;U þ R2;U þτ1ρ R1;U A

Comparing equation (14) with 15, we notice that if the ratio of the number of intermediate goods (or firms) in region 1 over that in region 2

γ γ β


α
PK;u PL;u PM;u Q α γ β ¼ γð1ρÞ

1ρ ρ ! ρ ρ αα ββ γ γ ργ A n1 B1 1ρ þ τn2 τB2

α


α


αα ββ γ γ ργ A

Pj xj dj ¼ PK K þ PL L þ n1 PІ xІ þ n2 PΠ xΠ

PK α PL β





ρ

1ρ

n1 B1

∂C ¼ ∂R2;U

Based on equations (8)–(10), the total cost of the targeted firm can be written as: n1 þn2

PM;u γ

1

0

The between-region vertical spillover is the marginal effect of R2;U on the targeted firm’s total cost:

γ

β PK PІ Q

1ρ ρ !γð1ρ ρÞ 1β n1 þ τn2 τBB12 Aαα γ γ PL

Z

PL;u β

α

γ γ

(14)

function

α

PK;u

β


∂C ¼ ∂R1;U

AK α Lβ ðn1 xІ ρ þ τn2 xΠ ρ Þρ . Combing this equation with equations (4), (5), (8) and (9), we obtain the optimal labor demand: 1β

α


Q (13)

ρ

industrial agglomeration in region 1 is rising compared with that in region 2. Therefore, our finding is consistent with previous studies. We then compare the horizontal spillover with vertical spillover, both 1ηh 0

Compared with the within-region horizontal spillover, the betweenregion horizontal spillover is weakened by the iceberg cost, and with the increase in distance, the between-region spillover begins to get smaller. This finding is consistent with several previous studies (Petit et al., 2009; Deltas and Karkalakos, 2013), which identify distance as one of the main factors influencing the between-region spillover. The difference between within-region horizontal spillover and between-region spillover takes place for two reasons: First, the increase in distance will aggravate the inconveniences of commodities transportation and the exchange of talents. Second, different regions possess different infrastructure, human capital, and regional policies, which will influence the between-region horizontal spillovers. We calculate the within-region vertical spillover as the marginal effect of R1;U on the targeted firm’s total cost:

within-region and between-region. If we define A ¼ @R1 þ τ ρ R2 A ; 1

0

0

1

1

B1 ¼ @R1;U þ τ ρ R2;U A; B2 ¼ @R2;U þ τ ρ R1;U A, then 1

1

0

1 ρ

γ ηu @n1 Φ1 1ρ AB1

∂C

∂R1;U ∂C ∂R1

8

¼

ρηu 1 1ρ

þ n2 τ

1 1 1ρþρ

Φ2

ρ

1ρ

AB2

ρηu 1 1ρ

A1 A

0 ρ

ηh @n1 Φ1 1ρ B1

ρηu 1ρ

1 1ρ

þ n2 τ Φ2

ρ

1ρ

B2

ρηu 1ρ

(16)

Y. Hu et al.

Economic Modelling xxx (xxxx) xxx

0

1 ρ

∂C ∂R2;U ∂C ∂R2

1 γ ηu @n1 τρ Φ1 1ρ AB1

¼

ρηu 1 1ρ

1 1ρ

þ n2 τ Φ2

ρ

1ρ

AB2

ρηu 1 1ρ

A

0 ρ

ηh @n1 Φ1 1ρ B1

ρηu 1ρ

ρ

1 1ρ

þ n2 τ Φ2 1ρ B2

size Chinese enterprises during 1995–2004. The data used in our regression is obtained by merging two datasets: the first one consists of economic and financial variables. There are a total of eighty variables. We group into identifying information variables (such as industry and geographic code, ownership), asset variables (such as total asset, fixed asset), output variables (such as sales, value added, output at constant price), cost variables (such as total cost, transportation cost, wage, employee number, training cost), etc. This data covers around 22,000 large and medium-size enterprises (LMEs)3 per year over the ten year period from 1995 to 2004. This data is part of an annual survey performed by the National Bureau of Statistics of China (NBSC), which

A1 (17)

ρηu 1ρ

Equation (16) (17) is the ratio of within-region (between-region) vertical spillover over horizontal spillover. We find that if the ratio of the U industry discount factor over the H industry discount factor satisfies the following condition:

1

0 ρ

ρηu

1

ρ

ρηu

ρ

ρηu

1

ρηu

ρ

C B n1 Φ1 1ρ B1 1ρ þ n2 τ1ρ Φ2 1ρ B2 1ρ n1 Φ1 1ρ B1 1ρ þ n2 τ1ρ Φ2 1ρ B2 1ρ C > maxB ; ρ 1 þ1 ρηu 1 ρηu 1 A @ ρ ρ ρηu 1 ρηu 1 1 1 ρ 1 ρ ρ 1 ρ ηh 1 1 ρ ρ Φ2 A 1ρ Φ 1ρ AB ρ Φ 1ρ AB 1ρ þn2 τ 1ρ n τ þ n τ 1 ρ 1 1 1 2 2 2 n1 Φ1 AB1 B2

γ ηu

carries over the survey after 2004, “but to the best of our knowledge the latest year with reliable data available is 2008” (Brandt et al., 2014). The other dataset consists of science and technology variables. There is a total of 99 variables, which includes total R&D expenditures (internal technology development expenditure, imported technology expenditure), R&D personnel, new product sales, patent application, number of scientific papers, number of technical development institutions of enterprises, etc. After merging these two datasets, we obtain an unbalanced dataset containing around two millions observations, which belong to one of 29 two-digit industries (over 400 four-digit industries), such as food processing industry, textile industry, smelting and pressing of ferrous metals industry, metal products industry, ordinary machinery manufacturing industry, transport equipment manufacturing industry, etc. More details can be found from Table A1 in the Appendix. Our unbalanced dataset includes 23 ownership classifications. We classified each firm into one of the following six classifications: state-owned enterprises (SOE), collective-owned enterprises (COE), enterprises from Hong-Kong, Macao, and Taiwan (HKT), enterprises from all other countries (FIE), privately-owned enterprises (PRI), and others (OTH). In our ten-year sample, the proportions of the six types of ownership are as follows: SOE: 14%, COE: 17%, PRI: 13%, HKT: 15%, FIE: 14%, OTH: 27%. Table 3 provides summary statistics about our unbalanced dataset. Several important changes for China’s large and medium-size enterprises can be observed from Table 3. First, total sale and output both rise steadily over 1995–2004. Specifically, total sale and output roughly triple during these ten years. These statistics partly reflect China’s rapid economic growth during that period. Second, the number of employee decreases during 1995–2001, then rises over 2002–2004, while labor productivity, measured as value added over employment, rises steadily during 1995–2004. This observation may be due to internal technology innovations and technology spillovers in China over that period. Fig. 6 reports the summary statistics of R&D activity. We take the ratio of R&D expenditure over total sales and R&D personnel over total employment as a proxy for the intensity of input of R&D activity, and the ratio of new product sales over total sales and patent applications as a proxy for the intensity of output of R&D activity. Fig. 6 shows the means of these variables. As shown in Fig. 6, the intensity of R&D input rises steadily over 1995–2004, especially the ratio of R&D expenditure over sales quadruples over this period. Although the intensity of R&D output

then spillover effects generated through vertical channel are more significant than the effects generated through horizontal channel, both within- and between-region. That is, if the competition effect in the upstream industry is large enough while its crowding-out effect remains relatively small, the vertical spillover effect will always be larger or more significant than horizontal spillover effect, both within and betweenregion. These results extend the studies of Javorcik (2004) and Ben Hassine et al. (2017), who find strong cost-saving vertical but weak horizontal spillover effects, to the cases of within-region versus between-region. 2 Since B2 τ ρ < B1 < B2 τρ , we also find that ifΦ Φ1 (i.e., CAOR) rises, 1

1

∂C

∂R1;U ∂C ∂R 1

(named as comparative advantage of within-region vertical linkage,

CAWVL) will decline while

∂C ∂R2;U ∂C ∂R2

(18)

(named as comparative advantage of

between-region vertical linkage, CABVL) will rise. This result implies that an increase in the CAOR has a positive impact on the CABVL while a negative impact on the CAWVL. We consider a special case where Φ2 increases and Φ1 decreases. Under this assumption, CAOR will increase; therefore, CABVL will rise while CAWVL will decline. In other words, an increase in Φ2 will induce an increase in CABVL, and a decrease in Φ1 will induce a decrease in CAWVL. This implies that the impact of regional characteristics on vertical spillovers is greater than that of regional characteristics on horizontal spillovers, both within- and between-region. Vertical spillover is the spillover through the industrial chain resulted from a series of market behaviors, such as providing suppliers or consumers with technical support or technology transfer when buying or selling intermediate products (Blalock and Gertler, 2008; Jude, 2016); providing training assistant for local firms’ employee or talent exchange to local firms. These behaviors are more likely affected by regional structural characteristics including transportation infrastructure and absorptive capability. Based on these inferences and observations, we propose the following Hypothesis: Hypothesis. Spillover effects generated through vertical channel are more significant than the effects generated through horizontal channel, both within and between-regions; the impact of regional characteristics on vertical spillovers is greater than that of regional characteristics on horizontal spillovers (both within- and between-region). 4. Data and methodology 4.1. Data

3 NBSC defines large- and medium-size enterprises to be industrial stateowned and non-state owned enterprises with annual sales of over 30 million Yuan, employment over 300 persons, and assets over 40 million Yuan.

Our dataset is firm-level panel data containing large- and medium-

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Table 3 Summary statistics. Year

Number of firms

Sales

Output

Value added

Employment

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

15,592 16,592 14,328 11,246 12,162 13,279 14,269 15,432 17,695 23,267

3.83 4.15 4.47 4.32 4.08 5.31 6.56 7.44 9.45 12.63

4.37 4.39 4.26 4.87 4.53 5.42 6.29 7.59 9.27 12.89

1.13 1.21 1.47 1.36 1.52 1.78 1.94 2.37 2.63 3.89

34.75 31.97 33.59 31.53 29.61 27.23 26.74 25.37 27.12 32.32

Notes: The summary statistics is based on our unbalanced LME dataset. Employment is in millions of workers. Sales, output, and value added are in trillions RMBs.

Fig. 6. Summary statistics of R&D activity. The summary statistics is based on our unbalanced LME dataset. R&D includes internal technology development and imported technology.

rises steadily over 1995–2004, the ratio of new product sales over total sales only increases by a factor less than two. The gap between input and output growth rates of R&D activity implies that the utilization efficiency of R&D inputs needs more improvements in China. Because the size of firms shrinks during this time period which could be due to changes of ownership related to industry reformation, mergers, or changes of address, there are many exiting firms, new entry firms, or changes of firm ID during the ten-year period, along with many firms missing at least one observation during 1995–2004 time period. Moreover, the NBSC changed many firm IDs in year 1998. In order to link the old and new manifestations and maintain the continuity of data, which is necessary for the composition of R&D stock (internal technology development stock, and imported technology stock),4 we have to drop firms that cannot be continuously observed during the 1995–2004 time period, and then track firms using the information of legal person representative, name of main product, industry code for a match, and create a balanced dataset consisting of 2000 firms per year for 1995–2004. In Table 4, we compare our “Balanced-LME sample” data with the data for all industry

Table 4 Shares of LMEs and the Balanced-LME sample in aggregate industry, 20041 (% of total industry). Measures

All industry2

Of which: LME3

Of which: BalancedLME

Sales (100 million Yuan)

181,715 (100%) 6099 (100%)

126,284 (69.5%) 3232 (53%)

20,300 (11.2%)

195,262 (100%) 219,463 (100%)

140,245 (71.8%) 23,267 (10.6%)

26,500 (13.6%)

Employment (10,000 persons) Assets (100 million Yuan) Number of Enterprises

573 (9.3%)

2507 (1.14%)

Notes: 1. Source: National Bureau of Statistics of China, 2005).2. Industrial stateowned and non-state-owned with annual sales of over 5 million. 3. Industrial state-owned and non-state owned enterprises with annual sales of over 30 million Yuan, employment over 300 persons, and assets over 40 million Yuan.

and for large- and medium-size enterprises in three dimensions: sales revenue, employment, and fixed assets. We find that, although our sample only contains slightly over one percent of China’s industrial enterprises with annual sales of over CNY 5 million, it captures 11.2 percent of industrial sales, 9.3 percent of industrial employment, and 13.6 percent of industrial assets. Over fifty percent of the firms in our balanced dataset are located in the Eastern region. In all five regions, the machinery, equipment, and instruments industry contains the largest number of firms. The balanced dataset also shows that firms in the Eastern region have the largest stock of technology development expenditure, with 51.4 percent of internal technology development, and 48.7 percent of imported technology expenditure. The Northeastern region has the lowest ratio of total technology development expenditure, that is, the sum of internal technology development expenditure and imported technology expenditure related

4 The R&D process for each firm is a long-term process. It will take significant time from the initial R&D investment to the successful innovation of new technology, productivity gains and production cost savings. Therefore, the impact of the current year’s R&D investment on enterprise technology innovation will usually not be effective for several years. In consideration of this feature, the investigation of the impact of R&D activities on enterprises’ productivity and production costs should be traced back to R&D investment in the previous years. Moreover, the annual R&D investment of enterprises varies year by year. Only the stock of the R&D investment instead of the flow can truly represent the strength of the R&D investment of enterprises. Given these considerations, it is more reasonable to adopt R&D stock rather than R&D flow when investigating the impact of R&D activities on firm production cost. A similar explanation applies to the choice of stock rather than flow for imported technology.

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to the value of industrial output at constant price among the five regions. The balanced dataset also shows that the East is the region with the highest foreign capital intensity, that is, foreign capital over total capital, followed by the Southwest. More summary statistics on the dataset can be found in Table A2-A4 in the Appendix.

Table 5 Description of variables.

4.2. Methodology

PL PM

0

0

Y V ; lnRtl

Y V

VM 0 ¼ αM þ βKM lnPK þ βLM lnPL þ βMM lnPM þ βQM ln Q þ lnR βRM VC 0 0 þ F βFM þ T βTM þ εM

(21)

VL is the value of labor expenditures; VM is the value of material; VC is the value of total cost. We assume that the disturbance vectors ðε; εL ; εM Þare multivariate normally distributed with zero mean and constant covariance matrix. Labor value share, material value share, and capital value share add up to one. According to Berndt (1991), the estimated coefficients of value share equations are independent with the choice of which specific value share equation we drop. Therefore, we drop the capital share equation and construct a system equation only consisting of equations (19)–(21). To ensure that the coefficients exhibit the usual properties of symmetry and homogeneity of degree one in prices, we impose the following constraints: 0

βa;b ¼ βb;a ; i αZ ¼ 1; βZZ i ¼ 0; βRZ i ¼ 0; βFZ i ¼ 0; βTZ i ¼ 0; βQZ i ¼ 0

(22)

where i is a vector of ones. To impose these cross-equation constraints, it is necessary to use a systems estimator. Seemingly unrelated regression (SUR) estimator is an approximate estimator. Besides the cross-equation constraints, one would still expect that the SUR estimator obtains different parameter estimates with those using equation-by-equation OLS estimator for two reasons. First, one would expect error terms across equations to be contemporaneously correlated, implying that the disturbance covariance matrix would be non-diagonal.5 Second, equations (20) and (21) contain different regressors. For both these reasons, SUR estimator would provide more efficient estimators of parameters than equation-by-equation OLS. Given these reasons, we choose SUR rather than equation-by equation OLS estimator.

0

BðR; F; T; ZÞ ¼ βRZ lnR lnZ þ βFZ F lnZ þ βTZ T lnZ; lnR  ðlnRtl ; lnRtm ; RX

Fkn

(20)

NðR; F; TÞ ¼ αR lnR þ αF F þ αT T; 0

Y V

VL 0 0 ¼ αL þ βLL lnPL þ βLK lnPK þ βLM lnPM þ βQL ln Q þ lnR βRL þ F βFL VC 0 þ T βTL þ εL

0

0

RX

Note: Xinternal (tl) or imported(tm); Y horizontal (h), upstream (up), or downstream (down); and Vwithin-region (ig) or between-region (bg).

C is total cost of production, NðR; F; TÞ and BðR; F; T; ZÞ represent the neutral productivity effect and the factor-biased productivity effect of R, F, and T, respectively. lnΖ represents prices of capital, labor, and mate0 rial. Q is gross value of industrial output in constant prices. lnR repre0 0 sents firm R&D related activities, F represents FDI related activities. T represents year dummy variables (Year95  Year04 ). Table 5 provides the definitions of these variables.

Y V ; Fkn

Stock of internal technology development expenditures, which includes regular R&D spending and the expenditure for process innovation and quality improvement of existing products Stock of imported technology, which is the purchase of advance technology from foreign countries Foreign capital stock intensity, which is calculated as the ratio of foreign capital over total capital Price of fixed assets, which is calculated as (value added – wage bills – welfare payments)/(net value of fixed assets) Price of labor, which is calculated as (wage bills þ welfare payments)/ (number of employed persons) Price of materials, which is calculated as the weighted average of industrial prices by using the input–output shares Weighted average stock of internal technology development expenditure (or imported technology expenditure) in a firm’s three-digit horizontal standard industrial classification (SIC) industry (or two-digit SIC upstream or downstream industries) within (or between) regions Weighted average foreign capital stock intensity of a firm’s three-digit horizontal SIC industry (or two-digit SIC upstream or downstream industries) within (or between) regions

PK

(19)

0

Rtl

Fkn

1 0 0 ln C ¼ α0 þ NðR; F; TÞ þ BðR; F; T; ZÞ þ αZ lnZ þ αQ ln Q þ lnZ βZZ lnZ 2 0 þ ln QβQZ lnZ þ αQ2 ðln QÞ2 þ ε

F  ðFkn ; Fkn

Definition

Rtm

The standard approach for measuring the neutral and factor-biased effects of FDI and technology development involves the estimation of production functions or dual cost functions (Berndt, 1991). The theoretical connection between production or cost functions and factor demands makes this approach appropriate for the measurement of factor bias. The choice of whether to use the production function approach or the cost function approach depends on the relevant set of exogeneity assumptions. For the production function formulation – which incorporates quantities of output and inputs – input quantities are assumed to be exogenous, whereas input prices are assumed to be exogenous in the cost function. In highly aggregated data sets, input prices are likely to be endogenous and therefore a production function may be more appropriate. At the firm level, however, choices of factor inputs are likely to be endogenous, whereas factor prices are more likely to be set in the market and are therefore plausibly exogenous. Because our data set allows us to impute factor input prices for the individual firms, we use the cost function approach. As in the theoretical model, the production function is given as: Q ¼ QðA; K; L; xj Þ. A is total factor productivity, which depends on the technology development expenditure. Therefore, the dual cost function should be: C ¼ CðA; PK ; PL ; PM ; QÞ. Based on equation (9.42) of Berndt (1991), incorporating the log of three stocks of technology development expenditure and pairwise interactive terms, we obtain our translog cost function as equation (19):

0

Name

lnRtm Þ;

lnRtl ; Fkn lnRtm Þ;

0

lnΖ  ðlnPK ; lnPL ; lnPM Þ: RX Y V and Fkn X Y are the key variables for our investigation. We will explain the construction of these variables in great depth in the Appendix. Table A6 in the Appendix presents summary statistics for our key variables by region. Using Shephard’s Lemma, we derive the cost share equation associated with each factor input by taking the derivative of the cost function with respect to the relevant input price: ∂∂lnlnPCi ¼ PCi Xi ; i ¼ K; L; M. Specif-

5

We use Stata command “corr” to test the simultaneous correlation of error terms of different equations. The P-value of the test is 0.0001. Hence we reject the original Hypothesis at 1% significant level, which means that the error terms are correlated across equations.

ically, by taking the derivative of equation (19) with respect to each input price, we obtain the following cost share equations:

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The reason that we do not adopt the random effects approach6 is that the unobserved effect may be correlated with some of the independent variables. An unobserved difference in leadership ability at a firm is one situation in which simultaneity may arise. An effective manager can reduce the production costs in the following ways: First, an effective manager can increase workers’ productivity through creating a friendly working environment, assigning different talented workers to the suitable roles, highlighting good performance, and creating events to increase employee motivation (Nemlioglu and Mallick, 2017). These efforts will raise workers’ morale and increase efficiency. Second, an effective manager will make appropriate financial decisions including budget control, balance between facility maintenance and substitution, and future employment plans. Third, an effective manager will be aware of market fashions and produce timely product upgrades, which will help to reduce the possibility of overstocked and out-dated products. All these advantages of great leadership will contribute to low production cost and are included in the firm’s unobserved error term. On the other hand, an effective manager will be more likely to make policies or regulations to use R&D or FDI more effectively. For example, Elkins and Keller (2003) examine past empirical literature investigating the impact of leader quality on the likelihood of success for R&D organizations. The results indicate that leaders who can encourage project members with intellectual incentives, communicate effectively and motivate workers are more likely to successfully guide an organization. However, leadership is unobserved in our dataset and regression error terms. This will induce a counterfeit association between the low production cost and the use of R&D and FDI. In order to overcome this problem, we adopt the fixed effect estimation method. For each firm in our panel dataset, we create a dummy variable and incorporate these variables in our regression equations. Even with truthful reporting, the values of R&D expenditure, imported technology, and FDI collected in our dataset are still only an approximation. Classical assumptions assume that measurement errors (the true value minus the observed value) are irrelevant to either the observed or true values of the variables. If the covariance of the measurement error with the observed value is zero, our estimators using SUR are unbiased consistent estimators of the true value of the parameters. When the measurement error is independent from the true value of the variable, which is referred to as the classical errors-in-variables (CEV), we have attenuation bias in our regression coefficients (Wooldridge, 2009). The absolute value of the estimated coefficients will tend to underestimate the coefficients. However, this attenuation bias, or downward bias, will reinforce our results. For example, if the estimated coefficient of R&D turns out to be negative, then the true value of the parameter should also be negative with a larger magnitude after we have incorporated the downward bias. To be more specific, we assume that x1 is the observed value of R&D, x1 * is the true value of R&D, and e1 ¼ x1  x1 * is the measurement error. The CEV can be represented as covðx1 * ;e1 Þ ¼ 0. Under this assumption, as in Wooldridge (2009), the relationship between the probability limit of our OLS estimates (βe1 ) and the true value ! σ 2* x of coefficient (β1 ) is plimðβe1 Þ ¼ β1 2 1 2 . Based on this equation, we σ x1 þσ

This indicates that our estimates underestimate the coefficients; this is the attenuation bias. Hence, the attenuation bias will reinforce our results, rather than weaken them. However, some types of measurement errors do not satisfy the above classical assumptions, which may cause the endogeneity problem. We use instrumental variables to fix this problem. More details can be found in the section about the robustness check, where we adopt the GMM method (R&D lagged one period as instruments). 5. Results and interpretation 5.1. Vertical spillover versus horizontal spillover We divide our dataset into five subsets according to firm location (North, Northeast, East, South, and Southwest), then run the SUR regression including equations (19)–(21) under the constraints of equation (22) on each of the subsets and obtain the estimated coefficients. In order to save space, we only list part of the regression results in Table A7, and the rest of the results are available upon request. We then adopt the growth accounting method to evaluate the contribution of three types of technology development to the change in total cost. Specifically, using the coefficients from Table A7 in the Appendix and mean values of each variable in our equation (19), we subtract the corresponding terms in the right hand side of equation (19) evaluated in year 2004 by the values evaluated for the year 1995. These differences account for the percentage change in total cost contributed by each type of technology development expenditure. The specific formula is available in Table 6 footnotes. The results are listed in Tables 6–9, consisting of the neutral effect, the factorbias effect and the total effect, for three channels through which three technology developments have spillover effects in five regions. More explanations on the values in Tables 6–9 can be found in the Appendix. The values in the fourth column of Table 6 (focus on the horizontal total effect, upstream total effect, and downstream total effect) indicate that spillover effects generated through vertical channels have larger magnitude–either cost-saving or cost-increasing–than effects generated through horizontal channel, both for within the Eastern region and outside the Eastern region. This pattern also holds for the Northeastern, Northern, Southern, and Southwestern regions (this pattern can be more easily observed from Table A8, which combines the source of capital–internal technology development, imported technology, foreign capital intensity–together and only lists the horizontal and vertical spillover effects). Moreover, according to the values in the fourth column of Table 7, the spillover effects of within-region horizontal internal technology development expenditure, imported technology and foreign capital intensity, and between-region horizontal internal technology development expenditure in the Southwest are all insignificant. According to the values in the fourth column of Table 9, the spillover effects of within-region horizontal imported technology and foreign capital intensity in the Northeast are insignificant. The spillover effects of between-region horizontal internal technology development expenditure in the Northeast are also insignificant. Previous studies have provided several reasons for this feature: In order to obtain high-quality intermediate products, downstream enterprises are willing to provide upstream enterprises with technical support including technology transfer and training of labor forces (Keller, 2010). Higher quality requirements for intermediate products by downstream firms will stimulate upstream firms to pursue technological and managerial innovation (Javorcik and Spatareanu, 2005). Downstream firms can achieve independent innovation through imitative improvements when they purchase from upstream foreign firms, which usually have advanced technology and management practices. These empirical results extend the studies of Javorcik (2004) who finds strong cost-saving vertical but weak horizontal spillover evidence to the cases of within versus between regions. Therefore, even with the factor of distance, which can influence the spillover effect through transportation costs, technology similarities, etc., spillovers from upstream or downstream industries have bigger impacts on the individual

x* 1

find that, although the magnitude of the estimated coefficient is less than the true value of the coefficient, they have the same sign. Moreover, if β1 is positive, then our estimate βe1 is positive but with a smaller magnitude.

6

In addition to the logical inference in the text, the Hausman test is also applied to verify whether fixed effect or random effect is more appropriate for our estimation. The P-value of the Hausman test is 0.0000. Therefore, the original Hypothesis is strongly rejected, which implies that we should adopt fixed effect in our estimation. In the case of heteroscedasticity, for which the Hausman test is not suitable, we add a cluster robust standard error into SUR and run the Stata command “xtoverid”. The P-value is still 0.0000. Hence, we adopt the fixed effect method. 12

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Table 6 Contribution to the change in total cost, 1995–2004 (East).

Within-region

Internal technology Stock

Imported technology

Foreign capital intensity

between-region

Total Internal technology Stock

Imported technology

Foreign capital intensity

Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total

Total

Total effect

Neutral effect

0.0199*** 0.3543*** 1.0575*** 0.6834*** 0.0146*** 1.4776*** 0.3527* 1.1103*** 0.0048*** 0.0771*** 0.0760*** 0.0037*** 1.7973*** 0.0647** 0.9547*** 1.1429*** 0.1234*** 0.0197*** 0.9175*** 0.7981*** 0.1390*** 0.0056*** 0.0482*** 0.0064 0.0602*** 0.2022***

0.0229 0.5703** 0.1807 0.4124** 0.0419*** 0.273*** 0.2067*** 0.0244 0.0044 0.0084 0.0396 0.036*** 0.472*** 0.0185 0.2707 0.3241 0.072*** 0.0266*** 0.0668*** 0.0162 0.0773 0.0040 0.0252** 0.0095 0.0387* 0.033***

Factor-biased effect Total

Capital-biased

Labor-biased

Material-biased

0.0427*** 0.9246*** 1.238*** 0.271*** 0.027*** 1.205*** 0.1460 1.086*** 0.001*** 0.0687*** 0.037*** 0.0319*** 1.325*** 0.0462** 1.225*** 1.4670*** 0.1954*** 0.007*** 0.8507*** 0.782*** 0.0618*** 0.002*** 0.023*** 0.0030 0.022*** 0.2356***

0.014***a 0.0087 0.048

0.035 1.0614*** 1.4077***

0.0217*** 0.1281*** 0.1214***

0.0008*** 0.2113*** 0.0234

0.011 1.0217*** 0.1342

0.0155**** 0.0284*** 0.0116

0.0003*** 0.0024** 0.0027**

0.0055** 0.1665*** 0.1107***

0.0048*** 0.0954*** 0.0715***

0.0012 0.0446 0.0538

0.0521*** 1.3735*** 1.6546***

0.0047 0.1035*** 0.1338***

0.0017*** 0.0508** 0.1131***

0.0033 0.8805*** 0.884***

0.012*** 0.021*** 0.0111**

0.0007** 0.0037 0.002

0.0029 0.0095** 0.0037

0.002*** 0.0099*** 0.0026

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at the 1% level. a Here we explain how we start from the regression results to obtain individual contributions to the change in total cost. The value 0.014 is obtained by employing βlnPK  lnRtl h ig Δ lnPK  lnRtl h ig . Where βlnPK  lnRtl h ig is the coefficient of the variable lnPK  lnRtl h ig in our regression results, Δ lnPK  lnRtl h ig is the the following formula: Δ ln C change of mean of variable lnPK  lnRtl h ig over 1995–2004, and Δln C is the change of the log of total cost over 1995–2004. Other values in the fifth, seventh, eighth, and ninth columns are calculated in similar ways. The values in the fourth column are the sum of the corresponding values in the fifth and sixth columns. The values in the sixth column are the sum of the corresponding values in the seventh, eighth, and ninth columns. Table 7 Contribution to the change in total cost, 1995–2004 (Southwest). Total effect

Within-region

Internal technology stock

Imported technology

Foreign capital intensity

Between-region

Total Internal technology Stock

Imported technology

Foreign capital intensity

Total

Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total

0.0277 1.318*** 0.5570*** 0.733*** 0.0364 1.143*** 0.322*** 1.429*** 0.0031 0.1048*** 0.126*** 0.025*** 2.187*** 0.0412 3.314*** 1.5658*** 1.707*** 0.0862* 1.0410*** 0.5095 0.4453*** 0.0132*** 0.1199** 0.0462 0.1793*** 1.083***

Neutral effect

0.0430 2.898*** 2.5122*** 0.3426** 0.0265 0.0887 0.0659 0.0038 0.0040 0.0082 0.0278 0.0320 0.3069** 0.1135 1.2630 0.8480 1.9975** 0.0191 0.2495** 0.0191* 0.2495 0.0155 0.0347 0.1080 0.1582** 1.5899*

Factor-biased effect Total

Capital-biased

Labor-biased

Material-biased

0.0153 1.5801** 1.955*** 0.39*** 0.0099 1.055*** 0.388*** 1.433*** 0.0009 0.0966*** 0.154*** 0.057*** 1.88*** 0.0723 2.051*** 2.4139*** 0.2904*** 0.0670* 0.7915*** 0.5287 0.1958*** 0.0023** 0.0853** 0.0618 0.0211*** 0.5073***

0.0004 0.4568* 0.1551

0.0075 2.0287** 2.302***

0.0232 0.0081 0.1917*

0.0035 0.0509 0.2277**

0.0082 1.0385*** 0.6218***

0.0147 0.0672*** 0.0061

0.0005 0.003 0.0035

0.0002* 0.0984*** 0.2103***

0.0016** 0.0048*** 0.0527***

0.0101 0.6555*** 0.327**

0.0872 2.0239*** 2.6174***

0.0048 0.6283*** 0.5306***

0.0106* 0.2862** 0.2982*

0.0752** 1.077*** 0.8226**

0.0025 0.0007 0.0042

0.0017*** 0.0034 0.0007

0.0176*** 0.0937** 0.0588

0.0136*** 0.0118** 0.0037

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at 1% level.

Therefore, the change of targeted firm’s location from the Eastern region to be the Southwestern region will induce an increase in CAOR (specifically, Φ2 increases while Φ1 decreases). On the other hand, Table 6

firm than spillovers generated from horizontal industries. As shown in section 2, the Eastern region has a comparative advantage in regional characteristics compared with the Southwestern region. 13

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Table 8 Contribution to the change in total cost (Southwest, 1995–1999 versus 2000–2004). 1995–1999

Within-region

Internal technology Stock

Imported technology

Foreign capital intensity

Between-region

Total Internal technology Stock

Imported technology

Foreign capital intensity

Total

H U D Total H U D Total H U D Total H U D Total H U D Total H U D Total

2000–2004

Total effect

Neutral effect

Factor-biased effect

Total effect

Neutral effect

Factor-biased effect

0.291 2.668 2.187*** 0.19*** 0.107 4.291** 2.416*** 6.6*** 0.021 0.064 0.403** 0.36** 6.43*** 0.624 3.345*** 10.415*** 6.445*** 0.438 5.042** 2.94*** 1.662 0.0169 0.313* 0.119 0.416*** 7.691***

0.812* 11.212* 8.815** 1.583** 0.129 0.583 1.631* 1.176 0.017 0.357 1.175* 0.835 0.429*** 0.331 9.102* 10.216 0.782 0.029 0.123 0.828* 0.733 0.001 0.532** 0.078 0.452*** 1.063***

0.521* 8.543 6.628*** 1.393*** 0.022 3.707** 4.047** 7.777*** 0.003 0.293* 0.772** 0.475*** 6.859*** 0.292 5.756*** 0.198*** 5.663*** 0.468 5.166** 3.768** 0.929* 0.015 0.218 0.198 0.036** 6.628***

0.096 0.724 2.223*** 3.044*** 0.009* 1.219*** 0.331 0.897*** 0.01* 0.018* 0.199*** 0.191*** 1.955*** 0.095 7.009*** 1.867*** 5.237*** 0.078 2.816*** 2.256*** 0.638*** 0.015*** 0.234*** 0.199 0.203*** 4.394***

0.008** 0.52 0.111 0.417* 0.01* 0.005 0.035 0.019** 0.014*** 0.003 0.0004 0.018** 0.379*** 0.029 1.042** 0.153 1.225*** 0.011 0.055 0.03 0.013 0.0004 0.038** 0.0004 0.037** 1.173***

0.088 0.204 2.334*** 2.627*** 0.02** 1.225*** 0.367 0.878*** 0.003** 0.022** 0.198*** 0.172*** 1.575*** 0.066 5.966*** 2.021*** 4.011*** 0.09 2.761*** 2.226*** 0.624*** 0.015*** 0.195*** 0.198 0.165*** 3.221***

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at 1% level. H represents horizontal spillover effects, U represents downstream spillover effects, and D represents downstream spillover effects. We simplify the table by omitting the capital-biased, labor-biased, and material-biased effects.

Table 9 Contribution to the change in total cost, 1995–2004 (Northeast).

Within-region

Internal technology stock

Imported technology

Foreign capital intensity

Between-region

Total Internal technology stock

Imported technology

Foreign capital intensity

Total

Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total

Total effect

Neutral effect

0.098** 1.58*** 2.731 1.243*** 0.027 1.198 0.818** 0.352 0.009 0.035 0.958*** 0.932*** 0.042*** 0.313 11.859*** 6.865*** 4.681*** 0.065* 0.899 0.202 1.166 0.047** 0.275** 0.788* 1.111*** 4.626***

0.182* 3.141 1.719* 1.239*** 0.026 0.385 0.451 0.091 0.011 0.11 0.99*** 0.891*** 2.038*** 0.341 12.089*** 4.295 7.452*** 0.022 0.864 0.764 1.606 0.032 0.289 0.787** 1.109*** 6.955***

Factor-biased effects Total

Capital-biased

Labor-biased

Material-biased

0.084 1.556*** 1.011 2.482* 0.001 0.813 0.368** 0.444 0.001 0.074 0.031 0.042 1.996** 0.028 0.229 2.57*** 2.771*** 0.086** 0.034 0.561 0.439 0.014** 0.014** 0.001 0.002 2.32***

0.002 0.025 0.054

0.104 1.739 1.126

0.022* 0.158 0.061

0.005 0.032 0.227*

0.039 0.811 0.142

0.033 0.029* 0.002***

0.001 0.032 0.01

0.003 0.069 0.019

0.002 0.111** 0.041

0.001 0.01 0.22

0.091 0.248 3.151

0.063* 0.008 0.801***

0.009 0.023 0.112

0.162 0.016 0.445

0.066*** 0.005 0.005

0.003 0.005 0.007

0.005 0.027 0.064

0.017** 0.018 0.055

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at 1% level.

shows that comparative advantage of within-region vertical linkage

region vertical linkage (CABVL) in the Eastern region (i.e.,

∂C

(CAWVL) in the Eastern region (i.e.,

∂R1;U ∂C ∂R 1

, as we introduce in section 3) is

∂C ∂R2;U ∂C ∂R 2

as we

¼ 5 (the numerator is the sum of betweenintroduce in section 3) is region vertical spillovers of three stocks; the denominator is the sum of between-region horizontal spillovers of three stocks). Similarly, Table 7 shows that the CAWVL in the Southwestern region is 2:2472 0:061 ¼ 36:83; the 0:253 0:0506

¼ 61:5(the numerator is the sum of within-region vertical spillovers of three stocks; the denominator is the sum of within-region horizontal spillovers of three stocks). The comparative advantage of between-

1:827 0:0297

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CABVL in the Southwestern region is 1:0506 0:0318 ¼ 33. Therefore, if the location of the targeted firm changes from the Eastern region to be the Southwestern region, a decline in the CAWVL and a rise in the CABVL occur, which indicate that an increase of regional endowment will have a bigger impact on vertical spillover than on horizontal spillover. These results confirm our Hypothesis in section 3.

government formulated a series of coastal development strategies including many preferential policies. The coastal development strategies, especially the preferential policies, bring many benefits to the development of the economy in the Eastern region. For instance, Alder et al. (2016) find that special economic zones improve local economic development through the accumulation of physical capital. Mendoza (2016) also finds that cities with preferential policy treatment have higher income growth. These preferential policies also induce a huge influx of FDI into the Eastern region. As listed in Table 2, FDI in the Eastern region steadily grows from 16.71 billion dollars in 1995 to around 42 billion dollars in 2004. During the period 1995–2000, FDI in the Eastern region accounts for around 40% of the total FDI in China; after 2000, this ratio became more than 50%. Geographical advantage and convenient transportation facilities lower the cost of the exchange of inputs and final production, provide higher freedom of movement for laborers, and speed up the spread of technology and managerial skills. Firms’ high absorptive abilities help them to better recognize, understand, and apply newly acquired technologies. Regional preferential policies spur the introduction of advanced technology and growth of the regional economy. With advantages in these three aspects, large cost-saving within-region spillovers should be expected within the Eastern region. This is consistent with our empirical results.

5.2. Spillover effects by different regions In this section, we present the main empirical results on spillover effects of the representative regions, and investigate how the structural characteristics of the various regions, such as R&D, infrastructure, enterprises ownership and provincial policies, influence these spillover effects. 5.2.1. Preferential policies and rapid growth in the Eastern region For the Eastern region, all three types of technology stock have significant spillover effects on local firms. Table 6 shows that the withinregion total spillover effect reduces the average firm’s production cost by 179% (4th column and 14th row of Table 6). Therefore, on average, total technology innovations in the Eastern region have a positive impact on local individual firm. Previous studies indicate that within-region spillover is influenced by regional characteristics such as infrastructure, absorptive capacity, industrial agglomeration, and governmental policies (Huang and Ge, 2012; Ben Hamida, 2013; Tambe and Hitt, 2014; Ning et al., 2016). Therefore, we will investigate whether these factors explain the result that the Eastern region receives greater within-region spillover than most other regions. Physical infrastructure, especially transportation infrastructure, is an important factor in determining the degree of spillover. Higher transportation costs will force a firm to choose suppliers and consumers that are close to their location. Therefore, vertical spillover is more likely to take place between firms that are geographically proximate. Aggravated by China’s geography (mountains and hills, few plains), transportation costs have a bigger impact on local firms’ costs. However, the coastal region has geographical advantages over inland regions. For example, seven Coastal provinces, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan, have 82% percent of their population living within 100 km of the sea or navigable rivers (Demurger, 2001). The low cost of water transportation makes it easier to exchange goods within the Coastal region and export goods to foreign countries. Aside from these geographical advantages, the Eastern region also enjoys advantages in transportation infrastructure, including highways and railways. For instance, as shown in Fig. 3, the Eastern region has the highest highway mileage among all Chinese regions over 1995–2004. In 1998, the average transportation network density in the East is over 500km/1000 km2, whereas the equivalent density in the interior is 350–500 km/1000 km2 and the density in the West is even lower than 350km/1000 km2 (D emurger, 2001). The advantage of transportation facilities helps firms in the Eastern region receive more significant cost-savings within-region spillover effect than those in other regions of China. Absorptive ability is essential to determine how well firms can recognize, digest, and apply new knowledge to commercial ends. We use technology development personnel to roughly measure firms’ absorptive abilities. For the year 2001, our dataset indicates that the ratio of technology development personnel to total employees is 4.7% in the Eastern region; this is higher than the 3.6% in the Northern region, 3.2% in the Northeastern region, 4.4% in the Southern region, and 4.3% in the Southwestern region. Moreover, as shown in Fig. 3, the number of S&T workers in the Eastern region is higher than those in other regions. This advantage in absorptive ability relative to other regions contributes to the result that the Eastern region receives more significant cost-savings within-region spillover effects than other Chinese regions. In the early 1980s, in order to let coastal regions grow first, China’s

5.2.2. Infrastructure and cost-saving between-region spillover effects in the southwestern region Table 7 shows that firms in the Southwest not only gain largely from within-region total spillover effect (production cost reduced by 218%) but also receive cost-saving effect from total between-region spillover (production cost reduced by 108%). We search for regional policies that may partially explain the results, the Grand Western Development Program stands out. In order to analyze the impact of the Grand Western Development Program on the technological spillover and economic development in the Southwest, we divide the dataset into two time periods: 1995–1999 and 2000–2004. We then run almost identical regressions on these two datasets separately. The results are reported in Table 8. From the comparison of these two tables, we find two main changes: between-region total spillover effects change from being cost-increasing during the period 1995–1999 to being cost-saving during the period 2000–2004, and within-region total spillover effects change from being cost-saving during the period 1995–1999 to being cost-increasing during the period 2000–2004. The first change is largely caused by a change in the spillover effect of between-region downstream internal technology development. We will explore it below through a policy discussion. The investigation will also analyze the second change–the within-region total spillover effect turns from cost-saving during the period 1995–1999 to cost-increasing during the period 2000–2004. This change is mainly due to the spillover effect of within-region imported technology: its magnitude becomes smaller although it remains cost-saving during the two periods. In order to help the lagging Western region to catch up with the Coastal region, the China State Council launched the “Grand Western Development Program” in January 2000. There is evidence of success for the “Grand Western Development Program” at some levels. For example, statistical data shows substantial growth in foreign investment in fixed assets in the Southwestern region: from 4.5 billion Chinese Yuan in 1995 to 10.5 billion Chinese Yuan in 2004 (China Statistical Yearbook, 1995–2004). Real GDP per capita in the Southwestern region increases from 3425 Chinese Yuan in 1995–7696 Chinese Yuan in 2004. The real average growth rate over the period 2000–2004 is 10.4%, close to the 10.6% in the Northeastern region and not far behind the 11.8% recorded for the Northern region (China Statistical Yearbook, 1995–2004). As we introduced in section 2, the foundation stage of the “Grand Western Development Program” helps to improve the transportation infrastructure between the southwestern region and other regions. These 15

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transportation projects shorten the traffic distance between the Western and Coastal regions, enhance the mobility of labor force, and thereby promote economic exchange between the Western and the Coastal regions (Bottazzi and Peri, 2003; Breschi and Lissoni, 2009). Hence, the Western region will receive more cost-saving spillover effects from the Coastal region through a more convenient transportation infrastructure. However, these increasing spillover effects from outside the Western region will dilute the spillover effects within the region. This is consistent with our finding that the magnitude of the within-region spillover effect of imported technology becomes smaller after the implementation of the “Grand Western Development Program.” On the other hand, the inward flow of talent and improved infrastructure increase the absorptive capacity of the Western region. Transportation costs for upstream suppliers and downstream consumers become less costly. These changes help the Western region’s firms to receive more benefits from the closer relationship between the Western region and the Coastal region. Therefore, the Coastal region has a bigger positive impact on the Western region after the implementation of “Grand Western Development program”. Hence, the Grand Western Development Program may be one of the factors causing the between-region spillover effects in the Southwest to change from cost-increasing effects prior to1999 to cost-saving effects after 2000. Several previous studies also find the positive impact of Western development program on the Western region. For instance, Yang et al. (2018) find that the implementation of Western development program improved local ecological economic development. Deng, Hu, and Ma (2019) find that Western development program has promoted the tourism development in the western region through infrastructure construction and tax incentives. Compared with these studies, our empirical findings indicate that Western development program may be one of the factors improving the between-region spillover effects in the Southwest.

Fig. 7. Average new product innovation expenditure and total investment per firm in the Northeast.

5.2.3. Market structure and cost-increasing between-region spillover effects in the North In the Northeastern region, three technology stocks–internal technology expenditure, imported technology expenditure, and FDI–all have significant spillover effects on the local firms. Applying the growth accounting method, we find that technology development activities outside the Northeast have a negative impact on the firms operating within the Northeast. As listed in Table 9, the between-region total spillover effect increases production costs of firms in the Northeast by 462% (4th column and 27th row of Table 9). Although the within-region total spillover effect in the Northeast is cost-saving (0.042), its magnitude is the smallest among the five regions. Therefore, as our empirical results show, withinregion spillovers only bring a few benefits to local firms, and betweenregion spillovers increase the total costs of firms operating in the Northeastern region. Many factors may drive firms to increase their total cost. For example, the global financial crisis in 1998 might significantly influence firms’ technology spillovers and performances. Therefore, we divide the dataset into two periods: 1995–1998 and 1999–2004, then run two almost identical regressions on these two sub-samples. The two regression results are consistent (the regression results are available upon request), which implies that we cannot conclude that the crisis drives firms in the Northeastern region to increase their total cost. Other factors may induce firms to increase production cost. For instance, when facing intensified competition from the R&D activities of firms outside the Northeastern region, firms in the Northeast region are likely to innovate new products, which may largely increase their total costs. However, this type of costincreasing between-region spillover effect is different from true negative productivity effects, because the former helps firms to attain higher profits, especially in the long term. Therefore, it is necessary to investigate to what extent this cost-increasing spillover effect is due to firms’ innovating new products. In Fig. 7, we use our dataset to summarize average new product innovation expenditures for firms in the Northeastern region. The results

Fig. 8. Ratios of total new product innovation expenditure over total cost.

show that there is a significant increase in new product innovation expenditures for firms in the Northeast during the period 1999–2003. Specifically, average expenditure on new product innovation per firm increases from 974 Chinese Yuan to 3559 Chinese Yuan. This may explain why between-region R&D activities have cost-increasing effects on firms located in the Northeast: firms in the Northeast adopt the strategy of innovating new products to face the competition from firms in other regions. However, because Fig. 7 only presents the new product innovation expenditure for firms in the Northeast, we need to do further analysis by summarizing the average ratios of new product innovation expenditure over total cost for firms in each of the five Chinese regions separately. As shown in Fig. 8, except for the year 1999, there is no significant difference between the ratio in the Northeast and those in other Chinese regions during 1995–2003. Thus, there may be other potential factors influencing between-region spillover effects in the Northeast. Since the Northeastern region has the highest ratio of the number of state-owned enterprises to the number of total enterprises, we are interested in whether this distinctive feature of ownership distribution in the Northeast matters in the between-region spillovers. The Northeastern region has the most important state-owned base. Begun in 1949, it is

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average, the total technology innovation has a positive spillover effect on any single firm in the East. However, technology innovations of firms in other regions have negative impacts on firms in the Northeastern region. Our empirical results also show that the between-region total spillover effect in the Southwest changes from cost-increasing during the period 1995–1999 to cost-saving during the period 2000–2004; thus, the Coastal region has a bigger positive impact on the Southwestern region since the implementation of the “Grand Western Development Program” in the year 2000. Several limitations indicate starting points for future research. First, this paper uses a dataset from 1995 to 2004 to investigate regional spillover effects; therefore, the results only apply to our period of study and will be biased to some extent. Future research using a longer time span and updated dataset will broaden the applicability of our results. Second, the limited dataset and length of this paper only allows us to cover some spillover channels. For example, we do not take into account the diffusion channels such as human capital exchange, product trade, and others. Our empirical findings raise many interesting policy implications. For instance, the empirical results on the comparison of vertical spillovers and horizontal spillovers have the following policy implications: First, it is essential to provide a better environment for the communication between upstream and downstream industries. Firms across the region should be encouraged to cooperate with each other in the same value chain. Second, it is important to improve firms’ ability to absorb and digest technology spillover. Firms should optimize the structure of human capital and pay great attention to the recruitment, training and promotion of talent including researchers, technicians and managers. Third, more R&D investment should be devoted to upstream manufacturing industries since they have a fundamental role in the vertical value chain. Fourth, there is a need to strengthen the in-depth connections between FDI and the local economy. In addition, preferential policy should be given to FDI with a higher level of cooperation with domestic firms, and foreign firms should be encouraged to purchase intermediate products or service from local firms. Fifth, the government needs to strengthen the protection of intellectual property rights. In the case of significant vertical linkage spillover, a sound system of intellectual property protection will spur upstream, horizontal and downstream firms to widely share knowledge through industrial linkage. Our empirical results show that within-region and between-region spillover are different across regions. This result also raises many valuable regional policy implications. First, we suggest that more investment should be devoted to support the development of the Central and Western region in China. Second, the development policy for each region should be established based on its individual comparative advantage. For instance, with abundant natural resources and labor, the Western region in China should focus on energy development and infrastructure construction. With fertile soil, the Northeastern region should place priority on developing agriculture. With a higher level of economic development, the Eastern region should focus on reconstructing and upgrading existing industries and develop highly technology industries. Third, it is necessary to increase the mobility of talent across regions. The government should encourage talent to move inside a corporation and across regions and set up relevant policies for less developed areas to attract talent such as offering stipends and education for children. Fourth, market barriers across regions should be eliminated. A favorable environment should be created for firms across regions to share information, talent and technologies. In addition, national market integration should be strengthened. Fifth, we suggest that the government should improve transportation infrastructure, especially for the Central and Western region. Good transport facilities can break market fragmentation and reduce barriers across regions, and hence increase the spatial transmission of regional innovation and between-region spillover.

known as the Northeast Old Industrial Base. Until 2002, the total value of assets of state-owned and state-holding enterprises in the Northeast is the largest among all Chinese regions and reaches 1324 billion Yuan, 14.86% of the total assets of all Chinese state-owned and state holding enterprises. Moreover, Assets of state-owned and state-holding enterprises account for 79.34% of the total assets of all enterprises in the Northeast Old Industrial Base, which is much higher than the national average of 60.93% (Huang and Ge, 2012). However, SOEs have many shortcomings, such as lack of an effective incentive mechanism, lack of survival of consciousness, and tendency to ignore policy implementation. As shown in Fig. 5, the provinces in the Northeastern region, especially Heilongjiang province, have the relatively low market competition and high financing constraint. Moreover, SOEs in the Northeast Old Industrial Base have their own disadvantages. Over the years, SOEs in the Northeastern region have experienced debt problems and a significant shortage of funds. Overstaffing problems have also hindered the product efficiency of SOEs in the Northeast. Recent structural reforms try to alleviate this problem by dismissing employees with bad performance records. However, this results in huge unemployment in the Northeast (Huang and Ge, 2012). The layoff of many employees also results in huge placement fees. Moreover, over the years, the expenditure of the subsidiary units of SOEs in the Northeast, such as schools and hospitals, has been a heavy burden on the development of these SOEs in the Northeast. Therefore, as we introduce in section 2, to accelerate economic development in the Northeast, Chinese government issued a policy for “revitalizing of the Old Industrial Base in the Northeast” in 2003. The principle of this policy is to deepen reform and expansion. It focuses on the strategic adjustment of SOEs, formulating a mechanism to promote the concentration of state-owned capital in national economic lifelines of important and advantageous industries and reforming the SOEs in accordance with the requirements of modern enterprises. 6. Conclusions By considering the firm’s regional attributes, industry attributes and integration with the connection between different industrial sectors, this paper combines industrial linkage and regional technology spillover together and provides a whole new perspective for theoretical and empirical studies of technology spillover. First, our theoretical model explores the impact of technology spillover on firms’ costs by incorporating regional factors and R&D input into a classic spatial model with industrial linkages, which expands the new economic geography theory. Second, our theoretical model obtains several attractive properties, such as vertical channel is the most important channel through which withinregion and between-region technological stocks affecting a local firm’s production costs and regional characteristics have greater impacts on vertical spillovers than on horizontal spillovers, which are verified by our empirical results. Third, our theoretical model provides a valuable modeling framework for future research on linkage spillover, particularly in an environment with regional differences and heterogeneous firms. Our empirical results discover the impact of regional factors on industrial linkage (vertical and horizontal) spillovers, which indicates that the study of regional characteristics can be used as a new entry point for the study of vertical spillovers, and provide a new research perspective for technology spillover. Our empirical results show that within-region and between-region spillovers vary across regions. For example, this study finds that the within-region total spillover effect in the East reduces the average firm’s production costs by 179%, and weak cost-saving within-region technology spillovers, and extremely strong costincreasing between-region spillovers take place in the Northeastern region. If we sum up technology innovations of all firms in the East, then on

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Appendix A Data construction 1. Within-region horizontal technology development expenditure (imported technology or foreign capital intensity) Rtl h ig represents the within-region horizontal internal technology development expenditure of the target firm, which is constructed as follows: We apply the perpetual inventory method to convert flows to stocks, then find all firms located in the same region and same industry (three-digit SIC)as the target firm, and sum up all the internal technology development expenditures of these firms. Rtm h ig (Ffl h ig Ftl h ig ) represents the within-region horizontal imported technology (foreign capital stock, total capital stock) of the target firm, which is constructed in a similar way. Fkn h ig is the within-region horizontal foreign capital intensity of thetarget firm, which is constructed as:Fkn

h ig

¼

Ffl Ftl

h ig h ig

.

2. Within-region upstream (or downstream) technology development expenditure (imported technology or foreign capital intensity) Rtl up ig is the within-region upstream internal technology development expenditure. The construction procedure is as follows. Suppose the targeted firm’s two-digit SIC upstream industry is industry i, where i ranges from 1 to I. The input–output share for industry i to the targeted firm is si . Then we sum up the stocks of internal technology development expenditure of all firms that belong to industry i and are located in the same region as the targeted P firm. Suppose the result is fi , then Rtl up ig ðfirmÞ ¼ Ii¼1 fi si . We apply similar procedures to construct Rtm up ig ;Rtl down ig ;Rtm down ig ;Fkn up ig ;Fkn down ig . 3. Between-region horizontal technology development expenditure (imported technology or foreign capital intensity) We approximate the distance between two provinces by the distance between their capitals (Kuo and Yang, 2008) and use the following equation to P construct the distance between two regions which usually contain several provinces:dkl ¼ αki βlj Dij , where dkl is the distance between region k and i;j

region l; Dij is the distance between province i (within region k) and province j (within region l); αki is the GDP share of province i among region k; and βlj is the GDP share of province j among region l. As in Kuo and Yang (2008), the distance weight wkl is an exponential function with a distance decay , where Dmin is the average distance parameter β: wkl ¼ expð  βdkl Þ. Adopting the specification in Funke and Niebuhr (2005), we define β as:β ¼ lnð1γÞ D min

between two adjacent regions and γ is the transformed distance decay parameter. Usually, γ ranges from 0 to 1; we use only 0.5 in our study. Rtl h bg represents the between-region horizontal internal technology development expenditure. We assume that the targeted firm Λ is in the Eastern region. wr1 , as we define above, is the distance weight between region r and the Eastern region, where r belongs to the set Ω ¼ {Northern, Northeastern, Southern, Southwestern}. We then sum up all the flows of internal technology development expenditure of firms that belong to the same industry as the P targeted firm and are located in region r. We label the result as fr . Then Rtl h bg flow ðΛÞ ¼ fr wr1 . We then use the perpetual inventory method to r2Ω

convert Rtl

h bg f

to obtain Rtl

h bg .

Similar methods are applied to construct Rtm

h bg ,

Fkn

h bg .

4. Between-region upstream (or downstream) technology development expenditure (imported technology or foreign capital intensity) Rtl up bg represents the between-region upstream internal technology development expenditure. We assume that the targeted firm Λ is in the Eastern region. The target firm’s two-digit SIC upstream industry is industry i, where i ranges from 1 to I. The regional input-output share in region r from industry i to the targeted firm Λ is sir .We sum up the stocks of internal technology development expenditure of firms that not only are located in region r P PI but also belong to industry i. We label the result as fir , then Rtl up bg ðΛÞ ¼ i¼1 wr1 sir fir , where wr1 is the distance weight between region r and the r2Ω

Eastern region, and r belongs to the set Ω ¼ {Northern, Northeastern, Southern, Southwestern}. Similar approaches are applied to construct Rtm up bg Ffl up bg Ftl up bg Fkn up bg Rtl down bg Rtm down bg ,Fkn down bg Explanations on the values in Tables 6–9 In Table 6, the fourth column represents the sum of corresponding values in the fifth and sixth columns. The values in the sixth column are the sum of the corresponding values in the seventh, eighth, and ninth columns. We will focus on the fourth column now. The value “0.0199” in the second row indicates that the targeted firm receives a cost-increasing (cost increased by 1.99%7) spillover effect of within-region horizontal internal technology development expenditure, i.e. a cost-increasing spillover effect of Rtl h ig (Table A5). The value “0.3543” in the third row implies that the targeted firm receives a cost-increasing (cost increased by 35%) spillover effect of within-region upstream internal technology development expenditure, i.e. a costincreasing spillover effect of Rtl up ig (Table A5). Similarly, the value “–1.0575” in the fourth row indicates that the targeted firm receives a cost-saving (cost reduced by 105%) spillover effect of within-region downstream internal technology development expenditure, i.e. a cost-saving spillover effect of Rtl down ig (Table A5). The value “–0.6834” in the fifth row implies that the targeted firm receives a cost-saving (cost reduced by 68%) spillover effect of within-region internal technology development expenditure (the sum of the spillover effects of within-region horizontal, upstream, and downstream internal technology development expenditure). The value “–1.7973” in the fourteenth row indicates that the targeted firm receives a cost-saving (cost reduced by 179%) within-region total spillover effect (the sum of the spillover effects of within-region internal technology development expenditure, spillover effect of within-region imported technology, and the spillover effect of within-region foreign capital intensity). The other figures can be explained in similar ways.

7 To be more accurate, it should be 1.99% of the change of the log of total cost over 1995–2004. Nevertheless, we assume that the change of the log of total cost equals to the original value of log of cost in year 1995. This assumption only normalizes our results and will not affect our explanation.

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Table A1 Industry code Code

Industry

Code

Industry

Code

Industry

13 14 15 16

Food processing Food production Beverage production Tobacco processing

23 24 25 26

33 34 35 36

Smelting and pressing of nonferrous metals Metal products Ordinary machinery manufacturing Special purpose equipment manufacturing

17 18 19 20

Textile industry Garments and other fiber products Leather, furs, down and related products Timber, bamboo, cane, palm fiber and straw products Furniture manufacturing Papermaking and paper products

27 28 29 30

Printing and record medium reproduction Cultural, educational and sports goods Petroleum processing and coking Raw chemical materials and chemical products Medical and pharmaceutical products Chemical fiber Rubber products Plastic products

37 40 41 42

31 32

Nonmetal mineral products Smelting and pressing of ferrous metals

43

Transport equipment manufacturing Electric equipment and machinery Electronic and telecommunications equipment Instruments, meters, cultural and office machinery Other manufacturing

21 22

Table A2 Firm distribution by region and industry

Mining Food and beverages Textiles, apparel and leather products Timber, furniture and paper products Petroleum processing and coking Chemicals Rubber and plastic products Non-mental products Metal processing and products Machinery, equipment and instruments Electric power other industry Total industry

North

Northeast

East

South

Southwest

24.6* 23.9 19.7 21.1 66 35.7 5.9 19.5 6.2 120.5 71.2 15 370

20 9.3 11 8.6 3.6 14.9 7.7 15.4 10.2 71.7 27.6 17 217

32.4 82.1 135.7 62.2 15.1 147.2 38.2 63.6 54 396.5 83.7 35.2 1146

19.2 66 49.5 28 5.9 63.5 10.1 47.2 13.9 162.7 49.1 29.8 545

19.6 20.1 12.9 8 0.7 37.3 4 12.4 9.3 82.5 9.3 12.9 229

Notes: *This figure is the average number of enterprises which belong to the Mining industry and are located in the North during 1995–2004. Figures are obtained from our balanced dataset.

Table A3 Technology development expenditure by region, 1995–2004 Region

Internal technology expenditure*

Imported technology expenditure*

Ratio of technology development expenditure to total output at constant price

North Northeast East South Southwest

83.2% 82.5% 83.4% 85.5% 84.4%

16.8% 17.5% 16.6% 14.5% 15.6%

23.5% 14% 17.4% 20.5% 21.4%

(11.6%) (12.8%) (51.4%) (20.1%) (4.18%)

(10.2%) (18.5%) (48.7%) (17%) (5.7%)

Notes: *Figures not in parenthesis are shares of internal technology expenditure, imported technology expenditure, in total technology development expenditure within each region, respectively (for each region, the sum of first two columns is 100%); Figures in parenthesis are ratios of internal technology expenditure (imported technology expenditure) of each region to total internal technology expenditure (total imported technology expenditure) of five regions.

Table A4 Foreign Capital Shares by Region, 1995–2004

North Northeast East South Southwest China

Relative to Total Capital

Share of Total foreign capital

8.35% 7.08% 9.54% 9.6% 5.77% 8.69%

19.2% 12.9% 46.1% 17.8% 4.0% 100%

Notes: Figures are shares obtained from our balanced dataset.

Table A5 Horizontal and Vertical Industry Stocks

Internal technology development expenditure Imported technology expenditure

Within- region Between-region Within- region Between-region

Horizontal

Upstream

Downstream

Rtl h ig Rtl h bg Rtm h ig Rtm h bg

Rtl up ig Rtl up bg Rtm up ig Rtm up bg

Rtl down ig Rtl down ig Rtm down ig Rtm down bg (continued on next column)

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Table A5 (continued )

Foreign capital intensity

Within- region Between-region

Horizontal

Upstream

Downstream

Fkn Fkn

Fkn Fkn

Fkn Fkn

h ig h bg

up ig up bg

down ig down bg

Notes: Rtl h ig represents the within-region horizontal internal technology development expenditure of the target firm.Rtm up ig represents the within-region upstream imported technology of the target firm.Fkn down bg represents the between-region downstream foreign capital intensity of the target firm. All others are explained in a similar way.

Table A6 Summary statistics for key variables variable

North

Northeast

East

South

Southwest

lnRtl lnRtm Fkn lnRtl h ig lnRtl up ig lnRtl down ig lnRtm h ig lnRtm up ig lnRtm down ig Fkn h ig Fkn up ig Fkn down ig lnRtl h bg lnRtl up bg lnRtl down bg lnRtm h bg lnRtm up bg lnRtm down bg Fkn h bg Fkn up bg Fkn down bg

0.362 (0.944) 0.604 (1.058) 0.068 (0.211) 1.498 (0.116) 1.598 (0.065) 1.657 (0.032) 1.387 (0.324) 1.651 (0.023) 1.623 (0.036) 0.135 (0.115) 0.102 (0.04) 0.14 (0.039) 1.582 (0.132) 1.735 (0.046) 1.721 (0.042) 1.426 (0.214) 1.795 (0.035) 1.719 (0.019) 0.153 (0.136) 0.113 (0.053) 0.157 (0.047)

0.525 (0.828) 0.434 (1.111) 0.114 (0.298) 1.425 (0.143) 1.453 (0.056) 1.579 (0.047) 1.203 (0.219) 1.546 (0.038) 1.538 (0.031) 0.113 (0.126) 0.103 (0.037) 0.132 (0.042) 1.512 (0.105) 1.725 (0.029) 1.852 (0.039) 1.425 (0.112) 1.729 (0.017) 1.673 (0.063) 0.193 (0.214) 0.157 (0.025) 0.146 (0.037)

0.541 (0.841) 0.389 (1.112) 0.081 (0.207) 1.439 (0.168) 1.67 (0.069) 1.676 (0.069) 1.338 (0.115) 1.642 (0.053) 1.638 (0.011) 0.139 (0.067) 0.114 (0.026) 0.144 (0.033) 1.327 (0.138) 1.543 (0.056) 1.581 (0.041) 1.273 (0.207) 1.469 (0.039) 1.592 (0.027) 0.127 (0.121) 0.104 (0.037) 0.137 (0.097)

0.403 (0.935) 0.488 (1.089) 0.08 (0.225) 1.325 (0.152) 1.567 (0.037) 1.582 (0.046) 1.214 (0.126) 1.539 (0.028) 1.542 (0.013) 0.136 (0.109) 0.105 (0.025) 0.136 (0.037) 1.537 (0.129) 1.724 (0.037) 1.692 (0.052) 1.425 (0.158) 1.759 (0.053) 1.726 (0.019) 0.179 (0.113) 0.137 (0.025) 0.165 (0.023)

0.339 (0.714) 0.421 (1.12) 0.018 (0.093) 1.221 (0.132) 1.458 (0.051) 1.562 (0.047) 1.212 (0.203) 1.424 (0.036) 1.501 (0.027) 0.102 (0.113) 0.092 (0.032) 0.128 (0.047) 1.782 (0.191) 1.932 (0.102) 1.869 (0.092) 1.725 (0.316) 1.912 (0.052) 1.823 (0.045) 0.292 (0.193) 0.316 (0.102) 0.297 (0.076)

Notes: The figures in parenthesis are stand deviation of key variables. The figures not in parenthesis are means of key variables.

Table A7 Estimation results variable

North

Northeast

East

South

Southwest

lnRtl h ig lnRtl up ig lnRtl down ig lnRtm h ig lnRtm up ig lnRtm down ig Fkn h ig Fkn up ig Fkn down ig lnRtl h bg lnRtl up bg lnRtl down ig lnRtm h bg lnRtm up bg lnRtm down bg Fkn h bg Fkn up bg Fkn down bg

0.713 (0.137) 2.027 (0.653) 0.789 (0.849) 0.037 (0.65) 6.941 (0.004) 5.231 (0.071) 0.399 (0.187) 2.276 (0.269) 0.874 (0.588) 0.11 (0.87) 0.939 (0.844) 0.201 (0.968) 0.454 (0.02) 8.359 (0.064) 4.463 (0.15) 0.501 (0.361) 4.207 (0.234) 1.552 (0.548)

0.914 (0.068) 15.569 (0.003) 8.702 (0.086) 0.031 (0.844) 3.635 (0.286) 5.711 (0.086) 0.226 (0.762) 2.161 (0.697) 17.47 (0) 1.14 (0.322) 45.877 (0.001) 17.71 (0.175) 0.07 (0.903) 8.344 (0.218) 10.35 (0.164) 0.636 (0.474) 14.373 (0.023) 13.108 (0.02)

0.175 (0.431) 4.58 (0.019) 1.474 (0.493) 0.189 (0.004) 7.096 (0) 3.727 (0.007) 0.163 (0.275) 0.191 (0.828) 0.712 (0.294) 0.21 (0.193) 2.804 (0.184) 3.151 (0.153) 0.184 (0.004) 6.186 (0) 1.82 (0.121) 0.249 (0.112) 2.353 (0.039) 0.835 (0.355)

0.418 (0.017) 1.152 (0.525) 3.323 (0.05) 0.004 (0.945) 0.617 (0.527) 0.404 (0.699) 0.036 (0.86) 2.364 (0.092) 1.259 (0.292) 0.638 (0.003) 2.275 (0.461) 4.063 (0.012) 0.364 (0.001) 0.282 (0.909) 0.37 (0.839) 0.087 (0.693) 1.014 (0.446) 2.873 (0.018)

0.135 (0.455) 19.75 (0.009) 14.418 (0.002) 0.052 (0.469) 5.238 (0.101) 1.758 (0.478) 0.488 (0.315) 4.047 (0.48) 1.791 (0.691) 0.787 (0.104) 7.141 (0.236) 4.594 (0.496) 0.102 (0.646) 6.382 (0.038) 0.309 (0.923) 0.469 (0.352) 2.635 (0.26) 2.519 (0.286)

Notes: The figures in parenthesis are P-values. The figures not in parenthesis are estimated coefficients.

Table A8 Contribution to the change in total cost, 1995–2004

Within-region

Between-region

Horizontal Upstream Downstream Horizontal Upstream Downstream

East

Northeast

Southwest

0.0297* 1.0462 0.7808 0.0506 0.0854 0.3384

0.116 2.743 2.591 0.331 11.235 6.279

0.061 2.3562 0.109 0.0318 2.1531 1.1025

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Notes: The number (0.0297) is the sum of the spillover effect of within-region horizontal internal technology development expenditure in the East, the spillover effect of within-region horizontal imported technology in the East, and the spillover effect of within-region horizontal foreign capital intensity in the East. All others are explained in similar ways.

Appendix B Robustness Checks

TFP and R&D development expenditure In order to test the robustness of our main results– spillover effects generated through vertical channels are more significant than effects generated through horizontal channel, both within and between-regions–we change the dependent variable to be the total factor productivity (TFP). Among many TFP estimation methods, the ACF method (Ackerberg et al., 2015) solves the problems of endogeneity, sample selection bias, and collinearity. Therefore, we adopt the ACF method to obtain firms’ TFPs (see Bournakis and Mallick, 2018, for an overview of the method, which is briefly discussed below). Consider the following production function: yit ¼ β0 þ βk kit þ βl lit þ ωit þ εit

(B1)

In equation (B1),y; k; l are the output, capital, and labor, respectively. ω represents the productivity and is assumed to follow an AR (1) processωit ¼ ρωit1 þ θit with jρj < 1. A firm’s intermediate input demand is given bymit ¼ gt ðkit ; lit ; ωit Þ. Assuming that the function is strictly increasing inω, we obtain the following equation: yit ¼ β0 þ βk kit þ βl lit þ gt 1 ðkit ; lit ; mit Þ þ εit ¼ ξt ðkit ; lit ; mit Þ þ εit (B2)

We then apply a simple OLS regression of output on a high-order polynomial in capital, labor, and intermediate inputs to obtain the estimates ξbt ðkit ;lit ; mit Þofξt ðkit ; lit ; mit Þ in the first stage. In the second stage, we apply GMM to obtain the estimate of βk ; βl following the moment restrictions: b it ¼ yit  b E½kit θit  ¼ 0,E½lit1 θit  ¼ 0. Finally, we derive TFP estimates from the expression ω a l lit  b a k kit . The main estimation model is constructed as follows: 1 0 0 0 ln TFP ¼ α0 þ NðR; F; TÞ þ BðR; F; T; ZÞ þ αZ lnZ þ αQ ln Q þ lnZ βZZ lnZ þ ln QβQZ lnZ þ αQ2 ðln QÞ2 þ ε 2

(B3)

Firm productivity may affect the amount of funds it can invest in R&D; so endogeneity problem may exist in equation (B3). Internal technology development, imported technology, and foreign capital intensity may be endogenous. We take the variables lag behind by one period as instrumental variables and carry out several tests to demonstrate the validity of the instrumental variables (IVs). First, we need to check whether the original regression has an endogeneity problem. The Hausman test is not suitable for heteroscedasticity, so we adopt the Stata command “ivreg2” to perform a robust endogeneity test. The P-values of the test for the internal technology development, imported technology, and foreign capital intensity are 0.0572, 0.0398, and 0.0899, respectively; all three of these variables can therefore be considered as endogenous variables. Second, we need to verify the exogeneity of the IVs. We adopt the over-identifying test. The p-value of Hansen’s J chi2 is 0.732. Hence, the original Hypothesis holds, and all of the IVs are exogenous. Third, we need to confirm that the instrumental variables are correlated with the endogenous variables. We will explain by illustrating how the stocks are constructed, by taking the internal technology development as an example (the process is the same for imported technology). The stock of internal technology development is constructed as the accumulation of reported technology development expenditure minus depreciation; i.e., KR,i,t ¼ (1 – δ)KR,i,t-1 þ IR,i,t-1, where KR,i,t  the R&D stock of firm i at time t; IR,i,t-1  the flow of R&D expenditures of firm i at time t – 1; and δ  depreciation rate (assumed to be 15%). Hence, the stock of internal technology development at time period t is correlated with the stock of internal technology development at time period t – 1. We run the first-stage regression for the foreign capital intensity. The estimated coefficient of the instrumental variable is robust at the 5% significant level. Therefore, all of the IVs are correlated with their respective endogenous variables. We then use the GMM method to estimate the coefficients, and apply these coefficients to obtain the percentage change in TFP contributed by each type of technology development expenditure (see Table 6 footnotes for more details). Due to space limitations, we only list the results for the Eastern region in Table B1, consist of the neutral effect, factor-bias effect and total effect, for three channels through which three technology developments have spillover effects. Table B1 Contribution to the change in TFP, 1995–2004 (East)

Within-region spillover effect

Internal technology stock

Imported technology stock

Foreign capital intensity

Between-region spillover effect

Total Internal technology stock

Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream

Total effect

Neutral effect

Factor-biased effect

0.0365*** 0.2384*** 3.1836*** 2.9817*** 0.0265*** 1.8156*** 0.6853*** 1.1568*** 0.001 0.0107*** 0.9261*** 0.9144*** 5.0529*** 0.0209 1.3253***

0.0613** 1.0344 0.2925*** 1.3882*** 0.1078** 0.4528 0.3525** 0.0075*** 0.0031 0.0217 0.0652** 0.0838*** 1.4645*** 0.0216 0.3545**

0.0248*** 1.2728*** 2.8911*** 1.5935*** 0.1343*** 1.3628*** 0.3328*** 1.1643*** 0.0021*** 0.0324*** 0.8609*** 0.8306*** 3.5884*** 0.0425 1.6798*** (continued on next column)

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Table B1 (continued )

Imported technology stock

Foreign capital intensity

Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total

total

Total effect

Neutral effect

Factor-biased effect

1.6654*** 0.3192*** 0.0105 1.9019*** 0.937*** 0.9814*** 0.0078*** 0.0399*** 0.0325*** 0.0004*** 1.301***

0.5831 0.207*** 0.0104 1.1344 0.0035** 1.1413*** 0.0083 0.0358** 0.0563** 0.0288* 0.9631***

2.2485*** 0.5262*** 0.0001 0.7675*** 0.9335*** 0.1659*** 0.0005*** 0.0041*** 0.0238 0.0284*** 0.3319***

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at the 1% level.

From Table B1, we find that the spillover effect of within-region horizontal foreign capital and spillover effect of between-region horizontal import technology are insignificant. We also find that the spillover effect of within-region vertical (upstream and downstream) internal technology stock have a larger magnitude than the spillover effect of within-region horizontal internal technology stock. This feature also holds for the spillover effect of between-region internal technology stock and the spillover effects of within- and between-region import technology (foreign capital). Therefore, spillover effects generated through vertical channels are more significant than effects generated through horizontal channel, both within and betweenregions. Results based on the unbalanced dataset As a second robustness check, we run the same SUR including equations (18)–(21) on our unbalanced dataset of around 22,000 large and mediumsize firms. However, because we are not able to use the perpetual inventory method to convert the flow into stock due to the unbalanced nature of the dataset, we have to use flow instead of stock to measure R&D. The regression results are listed in Table B2. We find that the spillover effect of betweenregion horizontal internal technology flow is not significant, whereas all vertical (upstream and downstream) spillover effects, both within- and between-regions, are significant. Moreover, the spillover effect of vertical internal technology flow (imported technology flow or foreign capital intensity) has a larger magnitude than the spillover effect of horizontal internal technology flow (imported technology flow, or foreign capital intensity), both within- and between-regions. Compared with the competition effect of horizontal spillover, the technology transfer (Javorcik, 2004), labor training (Keller, 2010), and higher requirements on production quality of vertical spillover may be more important for firms in reducing cost or improving productivity. Therefore, both the robustness tests support our conclusions. Table B2 Contribution to the change in total cost, 1995–2004 (East)

Within-region spillover effect

Internal technology flow

Imported technology flow

Foreign capital intensity

Between-region spillover effect

Total Internal technology flow

Imported technology flow

Foreign capital intensity

Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total Horizontal Upstream Downstream Total

Total

Total effect

Neutral effect

Factor-biased effect

0.0156*** 1.7492*** 1.0584*** 0.7059*** 0.0021*** 0.6185*** 0.882*** 1.4984*** 0.0246*** 0.031*** 0.3432*** 0.3368*** 1.1293*** 0.0006 0.5641*** 0.1287*** 0.436*** 0.0552*** 2.3933*** 1.5351*** 0.803*** 0.0033*** 0.3367*** 0.1706*** 0.5106*** 0.1436***

0.0094** 1.2541 0.5619*** 0.7016*** 0.0014** 0.0512*** 0.1228 0.1754*** 0.0029 0.0239*** 0.1537*** 0.1805*** 0.3457*** 0.0025 0.0946** 0.4936 0.3965*** 0.054 1.4319 0.0028** 1.3807** 0.0052 0.2386*** 0.1682** 0.4016*** 1.3756***

0.0057*** 0.4951*** 0.4965*** 0.0043*** 0.0035*** 0.5673*** 0.7592*** 1.323*** 0.0217*** 0.0549*** 0.1895*** 0.1563*** 1.475*** 0.0019 0.4695*** 0.3649*** 0.8325*** 0.0012*** 0.9614*** 1.5379*** 0.577*** 0.0085*** 0.0981*** 0.0024 0.109*** 1.5185***

Notes: * significant at the 10% level; ** significant at the 5% level; ***significant at the 1% level.

Appendix C. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.econmod.2019.11.018.

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Economic Modelling xxx (xxxx) xxx

References

Keller, W., 2010. Internationaltrade, foreign direct investment, and technology spillovers. In: Hall, B.H., Rosenberg, N. (Eds.), Handbook of the Economics of Innovation793–829. Elsevier North Holland, Amsterdam. Kesavayuth, D., Zikos, V., 2012. Upstream and downstream horizontal R&D networks. Econ. Modell. 29 (3), 742–750. Khachoo, Q., Sharma, R., 2016. FDI and innovation: an investigation into intra- and interindustry effects. Glob. Econ. Rev. 45 (4), 311–330. Kosova, R., 2010. Do foreign firms crowd out domestic firms? Evidence from the Czech Republic. Rev. Econ. Stat. 92 (4), 861–881. Kuo, C., Yang, C., 2008. Knowledge capital and spillover on regional economic growth: evidence from China. China Econ. Rev. 19 (4), 594–604. Lee, C., 2008. The relationship between innovation, productivity and exports: some preliminary evidence from the Malaysian manufacturing sector. Econ. Bull. 12 (31), 1–13. Li, Z., Yu, M., 2013. Exports, productivity, and credit constraints: a firm-level empirical investigation of China. Econ. Res. J. 48 (6), 85–99. Liang, J., Goetz, S.J., 2018. Technology intensity and agglomeration economies. Res. Policy 47 (10), 1990–1995. Lin, F., Zhang, C., Wang, L., 2013. Vertical spillover effects of multinationals on Chinese domestic firms via supplier-customer relationships. China World Econ. 21 (6), 37–57. Liu, Z., 2008. Foreign direct investment and technology spillovers: theory and evidence. J. Dev. Econ. 85 (1–2), 176–193. Lu, Y., Tao, Z., Zhu, L., 2017. Identifying FDI spillovers. J. Int. Econ. 107 (7), 75–90. Mariotti, S., Mutinelli, M., Nicolini, M., Piscitello, L., 2015. Productivity spillovers from foreign multinational enterprises to domestic manufacturing firms: to what extent does spatial proximity matter? Reg. Stud. (10), 1639–1653. Mendoza, O.V., 2016. Preferential policies and income inequality: evidence from special economic zones and open cities in China. China Econ. Rev. 40, 228–240. Nemlioglu, I., Mallick, S., 2017. Do managerial practices matter in innovation and firm performance relations? New evidence from the UK. Eur. Financ. Manag. 23, 1016–1061. Newman, C., Rand, J., Talbot, T., Tarp, F., 2015. Technology transfers, foreign investment and productivity spillovers. Eur. Econ. Rev. 76, 168–187. Ni, B., Spatareanu, M., Manole, V., Otsuki, T., Yamada, H., 2017. The origin of FDI and domestic firms’ productivity - evidence from Vietnam. J. Asian Econ. 52, 56–76. Ning, L.T., Wang, F., Li, J., 2016. Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: evidence from Chinese cities. Res. Policy 45, 830–843. Orlic, E., Hashi, I., Hisarciklilar, M., 2018. Cross sectoral FDI spillovers and their impact on manufacturing productivity. Int. Bus. Rev. 27 (4), 777–796. Peress, J., 2010. Product market competition, insider trading, and stock market efficiency. J. Financ. 65 (1), 1–43. Petersen, M., Rajan, R., 1994. The benefits of lending relationships: evidence from small business data. J. Financ. 49 (1), 3–37. Petit, M.L., Sanna-Randaccio, F., Sestini, R., 2009. Asymmetric knowledge flows and localization with endogenous R&D: a dynamic analysis. Econ. Modell. 26 (2), 536–547. Qi, P., Xu, H., Ai, H., 2008. The spillover effect of FDI on domestic enterprises: an empirical study of Chinese manufacturing enterprises. Manag. World 4, 58–68. Qi, J.H., Zheng, Y.M., Laurenceson, J., Li, H., 2009. Productivity spillover from FDI in China: regional differences and threshold effects. China World Econ. 17 (4), 18–35. Sari, D., Khalifah, N., Suyanto, S., 2016. The spillover effects of foreign direct investment on the firms’productivity performances. J. Prod. Anal. 46 (2–3), 199–233. Shibata, T., 2014. Market structure and R&D investment spillovers. Econ. Modell. 43, 321–329. Suyanto, Salim, R., 2013. Foreign direct investment spillovers and technical efficiency in the Indonesian pharmaceutical sector: firm level evidence. Appl. Econ. 45 (3), 383–395. Tambe, P., Hitt, L., 2014. Job hopping, information technology spillovers, and productivity growth. Manag. Sci. 60 (2), 338–355. Wang, C.F., 2008. FDI, inter-industry linkages and spillover effects evidence from panel data of China’s manufacturing sectors. World Economy Study 169 (3), 73–79. Wiboonchutikula, P., Phucharoen, C., Pruektanakul, N., 2016. Spillover effects of foreign direct investment on domestics manufacturing firms in Thailand. Singap. Econ. Rev. 61 (2). Wooldridge, J., 2009. Introductory Econometrics: A Modern Approach, fourth ed. SouthWestern, a part of Cengage Learning. Xu, X., Sheng, Y., 2012. Productivity spillovers from foreign direct investment: firm-level evidence from China. World Dev. 40 (1), 62–74. Yang, Y., Cai, W.J., Wang, C., 2014. Industrial CO2 intensity, indigenous innovation and R&D spillover in China’s province. Appl. Energy 131 (131), 117–127. Yang, F., Yang, M., Xue, B., Luo, Q., 2018. The effects of China’s western development strategy implementation on local ecological economic performance. J. Clean. Prod. 202, 925–933. Zhang, C., Guo, B., Wang, J., 2014. The different impacts of home countries characteristics in FDI on Chinese spillover effects: based on one-stage SFA. Econ. Modell. 38, 572–580. Zheng, W., Wang, X., Li, C., 2007. The spatial disparities of regional comprehensive urbanization level of vice provincial city in China from 1997. Econ. Geogr. 27 (2), 256–260.

Ackerberg, D.A., Caves, K., Frazer, G., 2015. Identification properties of recent productionfunction estimators. Econometrica 83 (6), 2411–2451. Ahmed, E.M., 2012. Are the FDI inflow spillover effects on Malaysia’s economic growth input driven? Econ. Modell. 29 (4), 1498–1504. Aitken, B.J., Harrison, A.E., 1999. Do domestic firms benefit from direct foreigninvestment? Evidence from Venezuela. Am. Econ. Rev. 89 (3), 605–618. Alder, S., Shao, L., Zilibotti, F., 2016. Economic reforms and industrial policy in a panel of Chinese cities. J. Econ. Growth 21 (4), 305–349. Anwar, S., Nguyen, L.P., 2014. Is foreign direct investment productive? A case study of the regions of Vietnam. J. Bus. Res. 67 (7), 1376–1387. Ben Hamida, L., 2013. Are there regional spillovers from FDI in Swiss manufacturing industry? Int. Bus. Rev. 22 (4), 754–769. Ben Hassine, H., Boudier, F., Mathieu, C., 2017. The two ways of FDI R&D spillovers: evidence from the French manufacturing industry. Appl. Econ. 49 (25), 2395–2408. Bengoa, M., Martinez-San Roman, V., Perez, P., 2017. Do R&D activities matter for productivity? A regional spatial approach assessing the role of human and social capital. Econ. Modell. 448–461. Benos, N., Karagiannis, S., karkalakos, S., 2015. Proximity and growth spillovers in European regions: the role of geographical, economic, and technological linkages. J. Macroecon. 43, 124–139. Berndt, E.R., 1991. The Practice of Econometrics: Classic and Contemporary. AddisonWesley Publishing Co. Biesebroeck, V.J., 2005. Firm size matters: growth and productivity growth in African manufacturing. Econ. Dev. Cult. Change 53 (3), 545–583. Bin, G., 2008. Technology acquisition channels and industry performance: an industrylevel analysis of Chinese large- and medium-size manufacturing enterprises. Res. Policy (2), 194–209. Blalock, G., Gertler, P.J., 2008. Welfare gains from foreign direct investment through technology transfer to local suppliers. J. Int. Econ. 74 (2), 402–421. Bottazzi, L., Peri, G., 2003. Innovation and spillovers in regions: evidence from European patent data. Eur. Econ. Rev. 47 (4), 687–710. Bournakis, I., Mallick, S., 2018. TFP estimation at firm level: the fiscal aspect of productivity convergence in the UK. Econ. Model. 70, 579–590. Bournakis, I., Christopoulos, D., Mallick, S., 2018. Knowledge spillovers and output per worker: an industry- level analysis for OECD countries. Econ. Inq. 56 (2), 1028–1046. Brandt, L., Johannes, V.B., Zhang, Y., 2014. Challenges of working with the Chinese NBS firm-level data. China Econ. Rev. 30 (C), 339–352. Breschi, S., Lissoni, F., 2009. Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows. J. Econ. Geogr. 9 (4), 439–468. Bronzini, R., Piselli, P., 2009. Determinants of long-run regional productivity with geographical spillovers: the role of R&D, human capital and public infrastructure. Reg. Sci. Urban Econ. 39 (2), 187–199. Cassiman, B., Golovko, E., Martínez-Ros, E., 2010. Innovation, exports and productivity. Int. J. Ind. Organ. 28 (4), 0–376. Chaudhry, A., Ikram, R., 2015. Does genetic proximity to high growth countries affect a country’s own growth? Econ. Modell. 51, 444–453. Deltas, G., Karkalakos, S., 2013. Similarity of R&D activities, physical proximity, and R&D spillovers. Reg. Sci. Urban Econ. 43 (1), 124–131. Demurger, S., 2001. Infrastructure development and economic growth: an explanation for regional disparities in China? J. Comp. Econ. 29 (1), 95–117. Deng, T., Hu, Y., Ma, M., 2019. Regional policy and tourism: a quasi-natural experiment. Ann. Tourism Res. 74, 1–16. Dixit, K., Stiglitz, J., 1977. Monopolistic competition and optimum product diversity. Am. Econ. Rev. 67, 297–308. Drivas, K., Economidou, C., Karkalakos, S., 2014. Spatial aspects of innovation activity in the US. Journal of the Knowledge Economy 5, 464–480. Elkins, T., Keller, R.T., 2003. Leadership in research and development organizations: a literature review and conceptual framework. Leadersh. Q. (4–5), 587–606. Fujita, M., Thisse, J.-F., 1996. Economics of agglomeration. J. Jpn. Int. Econ. (4), 339–378. Funke, M., Niebuhr, A., 2005. Regional geographic research and development spillovers and economic growth: evidence from West Germany. Reg. Stud. 39 (1), 143–153. Gorodnichenko, Y., Svejnar, J., Terrell, K., 2014. When does FDI have positive spillovers? Evidence from 17 transition market economies. J. Comp. Econ. 42 (4), 954–969. Hritonenko, N., Yatsenko, Y., 2012. Energy Substitutability and Modernization of Energyconsuming Technologies. Energy Econ. 34 (5), 1548–1556. Huang, S., Ge, Q., 2012. Strategy revival of state-owned enterprises in northeast old industrial base: from passive reform to active reform. Econ. Manag. 20, 4–10. Javorcik, B.S., 2004. Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. Am. Econ. Rev. 94 (3), 605–627. Javorcik, J.B., Spatareanu, M., 2005. Disentangling FDI spillover effects: what do firm perceptions tell us? In: Moran, T.H., Graham, E.M., Blomstr€ om, M. (Eds.), Does Foreign Direct Investment Promote Development? Institute for International Economics, Washington DC. Jeon, Y., Park, B., Ghauri, P., 2013. Foreign direct investment spillover effects in China: are they different across industries with different technological levels? China Econ. Rev. 26, 105–117. Jude, C., 2016. Technology spillovers from FDI, evidence on the intensity of different spillover channels. World Econ. 39 (12), 1947–1973.

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