Attraction or repulsion? Testing coagglomeration of innovation between firm and university

Rudkin, Simon; He, Ming; Chen, Yang Working Paper Attraction or repulsion? Testing coagglomeration of innovation between firm and university ADB Economics Working Paper Series, No. 608 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Rudkin, Simon; He, Ming; Chen, Yang (2020) : Attraction or repulsion? Testing coagglomeration of innovation between firm and university, ADB Economics Working Paper Series, No. 608, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS200067-2 This Version is available at: https://hdl.handle.net/10419/230362 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Testing Coagglomeration of Innovation between Firm and University Agglomeration theory suggests the geographical proximity of firms in production activities. The authors add to the literature by identifying whether universities are attracted by firms in patents production and the size of such attraction. Using a large patent dataset from Shenzhen, the first innovation-led city in the People’s Republic of China, and employing a spatial point process analysis technique, the authors found varying attraction and repulsion distances between the same type of innovative units and across university-firm innovation pairs. Attractions are shown within identical technology fields and across different technology fields. Weak support is offered to the integration of firms into the university-led innovation clusters in science parks. Firm innovations in the technological fields like Human Necessities, Physics, and Electrical should deserve more policy attention. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ATTRACTION OR REPULSION? TESTING COAGGLOMERATION OF INNOVATION BETWEEN FIRM AND UNIVERSITY Simon Rudkin, Ming He, and Yang Chen ADB ECONOMICS WORKING PAPER SERIES NO. 608 February 2020 ASIAN DEVELOPMENT BANK ADB Economics Working Paper Series Attraction or Repulsion? Testing Coagglomeration of Innovation between Firm and University Simon Rudkin, Ming He, and Yang Chen No. 608 | February 2020 Simon Rudkin (s.[email protected]) is a senior lecturer at the School of Management, Bay Campus, Skewen, Swansea, United Kingdom. Ming He (Ming.H[email protected]) is a lecturer and Yang Chen (Yang.[email protected]) is a lecturer at the International Business School Suzhou, Xi’an Jiaotong-Liverpool University, People’s Republic of China. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2019 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2020. ISSN 2313-6537 (print), 2313-6545 (electronic) Publication Stock No. WPS200067-2 DOI: http://dx.doi.org/10.22617/WPS200067-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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Since papers in this series are intended for quick and easy dissemination, the content may or may not be fully edited and may later be modified for final publication. CONTENTS TABLES AND FIGURES iv ABSTRACT v I. INTRODUCTION 1 II. BACKGROUND AND LITERATURE 2 A. Background of Shenzhen 2 B. Industrial Agglomeration and Coagglomeration 3 C. Coagglomeration of Industry and University Innovation 4 III. DATA 6 A. Patent Data 6 B. Mapping Innovation 7 IV. METHODOLOGY 10 V. COAGGLOMERATION 11 VI. POLICY IMPLICATIONS 25 VII. CONCLUSIONS 26 REFERENCES 29 TABLES AND FIGURES TABLES 1 Patent Applications by Applicant Type and Field, 2011–2015 7 2 Summary of Patent Colocalization Patterns 22 FIGURES 1 Total Applications by Applicant Type, 2000–2015 6 2 Shenzhen Area Map with Counties 7 3 Locations of Patent Applicants in Shenzhen, 2011–2015 8 4 Patent Application Locations, 2011–2015 9 5 Coagglomeration of Firm Patent A with University Patent A–H 13 6 Coagglomeration of Firm Patent B with University Patent A–H 14 7 Coagglomeration of Firm Patent C with University Patent A–H 15 8 Coagglomeration of Firm Patent D with University Patent A–H 17 9 Coagglomeration of Firm Patent E with University Patent A–H 18 10 Coagglomeration of Firm Patent F with University Patent A–H 19 11 Coagglomeration of Firm Patent G with University Patent A–H 20 12 Relative Colocation of Firm Patent with University Patent A–H 21 13 Relative Colocation of Firms and Universities in the Same Field 24 ABSTRACT Agglomeration theory supports and existing findings confirm the geographical proximity of similar firms and spatial attraction of firms to universities. In addition to that, we are able to identify whether universities as one type of innovative units are attracted by firm-type innovators and the size of such attraction. Testing the bidirectional spatial innovation linkage contributes to the debate on firmor university-led innovation. Using a large patent dataset from Shenzhen, the first innovation-led city in the People’s Republic of China, and employing a spatial point process analysis technique, underutilized in the literature that allows the bidirectional testing of coagglomeration, we find varying attraction distances between the same type of innovative units and across university–firm innovation pairs. Attractions are not only limited to identical technology fields but also generate coagglomerations across different technology fields of firms and universities. We find the attraction from firms to universities is more than that from universities to firms. Support is offered to the integration of firms into the university-led innovation clusters in science parks; firm innovation in patent fields like human necessities, physics, and electrical deserve more policy focus to benefit university research and innovation. Keywords: agglomeration, innovation, patents, spatial distribution, universities JEL codes: O31, R11, R12 I. INTRODUCTION In an ever automating and technology-driven world, the university–firm innovation linkage attracts clear academic, industry, and policy attention. The earlier literature suggests that firms benefit from academic knowledge and the attraction forces might be localized (Jaffe 1989; Jaffe, Trajtenberg, and Henderson 1993; Rosa and Mohnen 2008). Recent findings confirm that geographical proximity is more relevant for collaboration between firms and universities, than for the purely academic sector (Abramovsky, Harrison, and Simpson 2007; Abramovsky and Simpson 2011). From the policy perspective, governments in the United States, the United Kingdom, and many other countries emphasize the interaction between business and academic institutions (Branstetter and Ogura 2005; Griffith, Harrison, and Van Reenen 2006). The Government of the People’s Republic of China (PRC) has also paid increasing attention to innovation-led economic transition and policy highlights the role of geographic innovative clusters with close interaction between research institution and business in improving innovation performance. To explore the spatial innovation linkage (attraction versus dispersion) of university and firm, we employ a new relative spatial measure on the most detailed patent data including different types of organizations—firms and academic institutions. The use of a bivariate 𝑀 function (Marcon et al. 2015) directly incorporates the point data and avoids the concern with regard to the modifiable areal unit problem. With a focus on Shenzhen—the most innovative urban area in the PRC, we provide evidence about the distance up to which patent applications from universities and firms cluster together.1 We find that (i) among a range of patent technologies, not all patent technologies in academic institutions colocate with firm patents, nor are all firm patent types universally attracted to innovation from universities; and (ii) that there are many pairs of fields over which significant attraction occurs, echoing the coagglomeration message of Forman, Goldfarb, and Greenstein (2016) and others. Variation across patent technologies sparks broader discussion of organization for new industrial or science parks. A vast body of research documents spatial agglomeration of production activities of firms and industries across various levels of spatial units such as states, cities and counties (Ellison and Glaeser 1997; Ellison, Glaeser, and Kerr 2010; Kerr and Kominers 2015; Klaus Desmet 2017).2 However, there is a paucity of studies that examine the spatial innovation distribution of organizations with different institutional backgrounds, in particular, within a single urban area. This paper is an example of such, with its attention on firms and universities. Difficulty studying such arises from two aspects: methods and data availability (Kerr and Kominers 2015, Marcon and Puech 2017). We meet these difficulties with a microlevel patent database capturing the location of each patenting organization, and a relative bivariate analysis that gives a complete picture of coagglomeration patterns of patents across universities and firms in eight technology categories. The relative index M statistic provides nonsymmetric estimations; different results are derived when firms are the reference and universities are the neighbors compared to when universities are the reference and firms are the neighbors. Relative to the distance-based spatial measures by Duranton and Overman (2005, 2008), this approach offers a unique opportunity to show that the colocalization of firm innovation relative to academic institution and that of university innovation relative to business are directional. 1 Shenzhen was appointed the PRC’s first special economic zone since 1980 that grew fast in the manufacturing sector during the 1990s and the 2000s before transforming itself into a vibrant innovation hub and home to entrepreneurs, innovators, and tech firms. 2 Also see good reviews by Duranton and Puga (2004) and Rosenthal and Strange (2004). 2 | ADB Economics Working Paper Series No. 608 Our contribution is made in two key dimensions at a critical period (the 12th Five-Year Plan, 2011–2015) in the PRC’s development. First, we demonstrate that there are varied distance horizons over which university and firm innovations attract and repel each other; often repulsion occurring over shorter distances. Second, we are able to demonstrate that the attraction of university innovation to firms is stronger than that of firm innovation to universities. This asymmetric coagglomeration pattern recognizes a bidirectional relationship not modeled in the literature. Third, we show that proximity to university is important for firms to access research ideas. Advances in internet communication might be expected to reduce the strength of geographic agglomeration. However, the data do not show this to be happening during the 12th Five-Year Plan period. Data for this paper specifically address the 12th Five-Year Plan period (2011–2015), a time when policy was particularly focused on enabling innovation to cement the industrial development of previous decades.3 Our results align with a growing importance of coagglomeration across patent technology fields, and an increased recognition of the importance of universities as an innovative force. The remainder of the paper begins with a look at current understandings of agglomeration, innovation, and the role that universities play within the process in section II. Our dataset and context in Shenzhen are discussed in section III, with section IV providing a foundation for the empirical approach. Our colocalization results are presented in section V, with a brief policy discussion provided in section VI. Finally, section VII concludes. II. BACKGROUND AND LITERATURE A. The Background of Shenzhen The city of Shenzhen is located in the southern Guangdong province and was the first special economic zone created during the PRC’s economic reform and opening in 1978. It has 923.25 square kilometers (km2) of built-up area as of 2016 and a permanent population of 13.02 million. As of 2016, Shenzhen comprised nine administrative districts and one new district.4 With its rapid economic development and transition, Shenzhen was designated as the first national-level innovation-led city by the National Development and Reform Commission in 2008. It is expected to evolve to meet the definition of an internationally competitive innovative city by 2020. Against this backdrop, there has been wide policy debate and academic interest in the mechanism to promote development of a “regional innovation system” that integrates industrial clusters and higher education institutes. In addition to providing financial incentives and public resources to foster collaboration between firms and universities, the Shenzhen government built the Shenzhen Virtual University Park (SZVUP) in 1999 in the model of one campus for multiple universities. Sixty prestigious universities and research institutes from home and abroad are located within the SZVUP.5 A total of 1,265 high-technology enterprises are incubated within the park. In 2002, SZVUP was given the distinction of being a national university science park having a total area reaching more than 480,000 square meters. As a vehicle to facilitate innovation and a common service platform, SZVUP has gradually developed into a cluster of high-technology small and medium3 The State Council of the People’s Republic of China issued the National Patent Development Strategy since 2010. See the report from http://www.sipo.gov.cn/gk/gzyd/201111/t20111128_633501.html. 4 They are Futian, Luohu, Nanshan, Yantian, Bao’an, Longgang, Pingshan, Longhua, Guangming, and Dapeng new district. 5 There are 44 mainland universities; six universities from Hong Kong, China; seven international universities; and three research institutes such as Chinese Academy of Sciences, academician’s center Chinese Academy of Engineering, and Graduate School of Chinese Academy of Social Sciences. Notes: Pa t textiles a n markers f o Source: T h Shenzhen t ent field types a r n d paper (D), fixe o r clarity. h e patent applic a is from a comme Figure 4: r e denoted as in T d construction ( E a tion data is sour c rcial source. Patent Appl T able 1: human ne E ), mechanical en g c ed from the Nat i ication Loc a cessities (A), op e g ineering (F), ph y i onal Intellectual a tions, 2011 – e rations and trans y sics (G), electric Property Admini s Attractio n – 2015 port (B), chemis t al (H). Universit y s tration (2011-2 0 n or Repulsion t ry and metallurg y y plots use larger p 0 15). The shapefi l ? | 9 y (C), p oint l e for 10 | ADB Economics Working Paper Series No. 608 IV. METHODOLOGY Possessing detailed data information on patenting sites, we adopt distance-based methods to identify the spatial innovation linkage between firms and universities. The distance-based methods are developed as continuous function of space, providing information about concentration at all scales simultaneously. Critically, they do not rely on zoning. The seminal work by Ripley (1976, 1977) provided a univariate 𝐾(𝑟) function which sums 𝑔 on the range 0 to 𝑟 to compare the distribution of a set of points with a random distribution, where 𝑔 is a ratio of the joint probability of finding two points in a particular observed pair of locations and the marginal probabilities of each point being in the observed location and 𝑟 denotes distance. The 𝐾 function has been used in the ecology literature to characterize interactions assuming a homogeneous point process for which the probability to find a point is the same everywhere. Its use remained incidental in spatial economics until the works of Marcon and Puech (2003, 2009) and Duranton and Overman (2005). Two key limitations with 𝐾(𝑟) are noted by Marcon and Puech (2017). Firstly, the assumption that points could be randomly distributed fails to recognize the physical impediments to certain locations that prevent their development; Shenzhen, as mapped in Figure 2, has clear mountainous and sea regions that are not suitable for location. Secondly, 𝐾(𝑟) does not include any options for weighting, though this second limitation does not impact our analysis due to the lack of obvious weighting within the patent dataset. Addressing the limitations of 𝐾(𝑟), Marcon, Puech, and Traissac (2012) propose an 𝑀 function which compares the number of neighbors of interest to the total number of neighbors for a reference point up to a certain distance 𝑟 relative to the same ratio in the whole area of analysis, where the reference points are indexed by 𝑓, neighbor points by 𝑛, all points whatever their type by 𝑎; their numbers are 𝑁, 𝑁, and 𝑁, respectively. The numerator is the average ratio of neighbor points around the reference points and the denominator is the average ratio of neighbor points in the population. With a simple interpretation, 𝑀,(𝑟) > 1 implies that the reference and neighbor points attract each other, while a value of 𝑀,(𝑟) below 1 suggests repulsion. A further feature of this function is that the value does depend on which point type is chosen as the reference; this is evidenced very clearly in our application. 𝑀,(𝑟)=𝑁−1 𝑁𝑁∑𝟏(𝑥𝑥≤𝑟)   ∑𝟏(𝑥𝑥≤𝑟)  ,   Consider firms located in an area. If proximity to universities is attractive to a firm, we could expect that firm has more neighbors being universities around it than if it were locating randomly. On the contrary, finding fewer neighbors of universities than expected implies that firms locate away from universities. Similarly, consider universities located in an area. In the benchmark case known as complete spatial randomness, universities could locate at any place with constant density and they locate independently of each other. Now suppose that the location choice for university is not random but relies on the location of other universities, research institutes, firms, and other organizations. If proximity to firms is attractive to a university, that university will have more firm neighbors around it than if it were to locate randomly; on the contrary, finding fewer firm neighbors than expected implies that universities locate away from firms. We follow Marcon and Puech (2017) in viewing the process of colocalization identification as being a five-step operation. First, we calculate the number of neighbors of interests within a certain distance. Second, we compare it with the number of all neighbors within fixed radius circles of each reference point. The 𝑀 function uses the full circle to identify neighbors rather than only those on the Attraction or Repulsion? | 11 edge like Ripley (1977). Third, we calculate the average ratio of neighbors around the reference point. Fourth, we compare the average ratio within a certain distance with the average ratio of neighbor points in the whole area. Finally, a null hypothesis of independence is needed to create a significance test for the characteristics of interest as suggested by Marcon, Puech, and Traissac (2012). Practically the implementation of the bivariate 𝑀 function involves the assumption that any point within either the reference set or the neighbor set may possibly be located at any position where there is a data point in the overall set of patents. This is an important advance because it ensures that the model is only trying to place points in locations where innovation could possibly occur. Independence in this sense is testing whether the points are independently distributed across the set of possible patent locations and not over the land area of Shenzhen. It is seen in Figure 3 that there are obvious blank areas and other areas where patent activity is much denser; the method used here reflects that and does not apportion attraction simply because a university–firm pair exists within one of the denser areas. Thus, random datasets are generated for the bivariate 𝑀 function by redistributing the actual point set on the actual location set (coordinates). Following Loosmore and Ford (2006)’s goodnessof-fit to obtain a correct p value to reject the null hypothesis, they first compute the average value of 𝑀(𝑟) on all simulations, where 𝑠 is the number of simulations and 𝑀(𝑟) is the value of 𝑀(𝑟) in the 𝑖th simulations. The statistic 𝑢 is computed for the 𝑖th simulation by summing up all values of 𝑟, where ∆𝑟 is the difference between the next value of 𝑟 and the present one, 𝑀=1 𝑠−1𝑀(𝑟)   𝑢=[𝑀 (𝑟)−𝑀]∆r  The same statistics for the actual data 𝑢 is compared to 𝑢 to get the p value, 𝑃≈∑𝟏(𝑢>𝑢)   𝑠 To avoid 𝑃=0 or 𝑃=1 for p values if 𝑢 is always greater or smaller than 𝑢, we could assume that another simulation would have given a value of 𝑢 higher or lower than 𝑢 and express 𝑃< 1/𝑠 or 𝑃> 1–1/𝑠. Using the R package, dbmss, (Marcon et al. 2015) the 𝑀 function is estimated for each of the eight different patent fields, with each of the universities and firms as reference type and neighbor type. This results in our studying 128 combinations.8 In each case, all other patent applications within the Shenzhen area are used as the possible locations for innovation to be randomized over. V. COAGGLOMERATION To fully appreciate coagglomeration between universities and firm patent applications, we must both recognize the bidirectional nature of the relationship and the potential for applications in one field to influence the location of applications in other fields. This latter need was evidenced in the literature in the strong coagglomeration forces discussed by Forman, Goldfarb, and Greenstein (2016) and others. Working systematically through the 128 combinations, we split the presentation by the field of the firm 8 University type A as reference to firm type A as neighbor is different from firm type A as reference to university type A as neighbor. 12 | ADB Economics Working Paper Series No. 608 patent; splitting by firm recognizes the interest in how university innovation transfers to firms. A final choice for the modeling is the distance 𝑟 to use for the search. We perform the analysis over a distance of 50 km but find that almost every spatial pattern has dissipated by around 40 km, and that in order to see the effects closer to the reference points, 30 km is an optimal choice for the plotting.9 Figures 5–12 provide the 𝑀 functions as estimated together with the confidence interval around independence. Plots are labeled according to their reference category and then the neighbor category, the letters corresponding to those of Table 1. Hence Firm A Uni A means that firm patent applications in the human necessities field are the reference category and university patent applications in the same field are the neighbor type. In every case where the 𝑀 function line sits below the shaded confidence interval, we have repulsion of the two types of application, and where it sits above, we have attraction. In order to test the significance of observed patterns, a goodness-of-fit test is available within dbmss (Marcon et al. 2015), but this returns a p value below 0.001 in every case and so we do not report the figures individually. Figure 5 plots the 𝑀 functions related to applications from firms in the human necessities field. In the first and third rows of the figure, firm patents in field A are taken as the reference category such that the figure label begins with Firm A. In each figure, there is an initial area of repulsion which exists over the first 1 km to 2 km, followed by a large attraction range. In most cases, the attraction range continues past 25 km. Many of the 𝑀 functions display a small repulsion region after the initial attraction, this appears between 4 km and 5 km. Many of these early patterns may be attributed to the campus nature of university design in the PRC and hence, we should not place too much importance to such short-lived effects. What is clear is that firm innovation in the human necessities category is not only attracted by university innovation in the same field but also by other patent fields. Reversing the order, the effects are notably different, although there is an attraction, it dissipates after around 17 km to be replaced by a repulsion over the next 10 km. Plot Uni D Firm A is perhaps the hardest to observe this pattern within, but even with the low number of university patent applications in textiles and paper (D), there is still an attraction to human necessity patents over the medium distance. Moving to consider firm patents in operations and transport (B), we see a very different pattern emerging. In every case, there is significant repulsion of innovation almost throughout the entire 30 km range. A small exception is where we also consider Type B applications from universities, as here, a short attraction around 20 km is seen. We can see that type B innovation in firms is weakly attracted by university innovation in physics (A) and electrical (H) over the 20 km to 25 km range. However, the broad message of repulsion is entirely at odds with the attraction picked up above. When reversing the relationship, taking university as the reference category, there is even stronger evidence of repulsion. Aside from a small insignificant region of attraction around 4 km in the case of university patents in mechanical engineering (F), there is nothing but high levels of significance in the results. Across the two firm patent types A and B, two very distinct patterns emerged, the first is an attraction pattern and the second is a clear repulsion. When considering firm patents in the chemistry and metallurgy (C) field, the pattern is very close to the second type (Figure 7 ). Taking the firm as the reference category, there are small ranges of attraction around the 20 km mark as there were in Figure 6. Firm innovation in field C is significantly attracted by university innovation in field B, G, and H, but surprisingly not by the same patent field. When reversing the relationship, once again there are no regions of insignificance; university innovation repels firm innovation across the full 30 km. 9 We make 50 km plots available on request. Plots are not included as an appendix within the paper as this would take the file size over the 3 megabyte limit. Attraction or Repulsion? | 13 Figure 5: Coagglomeration of Firm Patent A with University Patent A–H Notes: The figures plot the coagglomeration of firm patent A with university patent A–H. The first and third rows take firm as the reference; the second and fourth rows take university as the reference. Labels are written below the plots and show the reference point type followed by the neighbor category. Shaded areas represent 95% confidence intervals around the null hypothesis of zero relation. Plots are generated over the 30 km radii. Source: The patent application data is sourced from the National Intellectual Property Administration (2011–2015). Firm A Uni B Firm A Uni C Firm A Uni D Uni C Firm A Uni D Firm AUni A Firm A Uni B Firm A Firm A Uni E Firm A Uni F Firm A Uni G Firm A Uni H Uni E Firm A Uni F Firm A Uni G Firm A Uni H Firm A 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) Firm A Uni A 0 5,000 10,000 15,000 20,000 25,000 30,000 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r M (r) r 0.0 0.5 1.0 1.5 2.0 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) r 0.0 0.5 1.0 1.5 2.0 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r M (r) 0.0 0.5 1.0 1.5 2.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0.0 0.5 1.0 1.5 2.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r M (r) 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M (r) r 0 5,000 10,000 15,000 20,000 25,000 30,000 0 1 2 3 4 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 4 3 2 1 0 r M (r) Mobs(r) ^M(r) Mhi(r) ^Mlo(r) ^ 14 | ADB Economics Working Paper Series No. 608 Figure 6: Coagglomeration of Firm Patent B with University Patent A–H Notes: The figures plot the coagglomeration of firm patent B with university patent A–H. The first and third rows take firm as the reference; the second and fourth rows take university as the reference. Labels are written below the plots and show the reference point type followed by the neighbor category. Shaded areas represent 95% confidence intervals around the null hypothesis of zero relation. Plots are generated over the 30 km radii. Source: The patent application data is sourced from the National Intellectual Property Administration (2011–2015). Firm B Uni A Uni A Firm B Firm B Uni E Uni E Firm B Uni F Firm B Uni G Firm B Uni H Firm B Firm B Uni F Firm B Uni G Firm B Uni H Uni B Firm B Uni C Firm B Uni D Firm B Firm B Uni B Firm B Uni C Firm B Uni D 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 2.5 3.0 r M(r) M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.5 1.0 1.5 2.0 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 M(r) r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 r M(r) r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.5 1.0 1.5 2.0 2.5 3.0 r M(r) 0.0 0.2 0.4 0.6 0.8 1.0 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r M(r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Mobs(r) ^M(r) Mhi(r) ^Mlo(r) ^ Attraction or Repulsion? | 15 Figure 7: Coagglomeration of Firm Patent C with University Patent A–H Notes: The figures plot the coagglomeration of firm patent C with university patent A–H. The first and third rows take firm as the reference; the second and fourth rows take university as the reference. Labels are written below the plots and show the reference point type followed by the neighbor category. Shaded areas represent 95% confidence intervals around the null hypothesis of zero relation. Plots are generated over the 30 km radii. Source: The patent application data is sourced from the National Intellectual Property Administration (2011–2015). Firm C Uni A Uni A Firm C Firm C Uni E Uni E Firm C Uni F Firm C Uni G Firm C Uni H Firm C Firm C Uni F Firm C Uni G Firm C Uni H Uni B Firm C Uni C Firm C Uni D Firm C Firm C Uni B Firm C Uni C Firm C Uni D r 0.2 0.4 0.6 0.8 1.0 1.2 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0 1 2 3 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 r M(r) 0.0 0.5 1.0 1.5 2.0 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.5 1.0 1.5 M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.2 0.4 0.6 0.8 1.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.2 0.4 0.6 0.8 1.0 1.2 M(r) r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0 1 2 3 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.5 1.0 1.5 2.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.0 0.2 0.4 0.6 0.8 1.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0.2 0.4 0.6 0.8 1.0 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 Mobs(r) ^M(r) Mhi(r) ^Mlo(r) ^ 16 | ADB Economics Working Paper Series No. 608 A low number of patent applications within the textiles and paper (D) field mean that the initial confidence intervals over low radii are very large. Graphs become compressed to accommodate the high values of the upper bound of the confidence interval as a consequence, as seen in Figure 8. Across the 30 km, there are regions of attraction and repulsion, typically more than one of each type covering a distance of around 5 km per region. Tests that the overall effect differs from random remain significant in almost all cases with p values ranging from 0 to 0.06. Of those not significant at the 5% level, all have firms as the reference category, the fields being textiles and paper (D), fixed construction (E), and mechanical engineering (F). Figure 9 displays the coagglomeration pattern for patents in the field of fixed construction (E). There is an attraction played out over a longer distance with the repulsion, for the firm as reference category, that ended around 2 km in Figure 5 persisting until almost 10 km in every case. Attractions are found from 10 km until around 25 km and once again, all of these patterns are highly significant. Taking the university applications as reference, there is again strong repulsion over the first 10 km, with weak attraction around the 15 km mark. Unlike the case with firms as reference, there is then a significant repulsion evidenced between 20 km and 30 km, appearing most clearly in the plots for Uni A Firm E, Uni B Firm E, Uni G Firm E, and Uni H Firm E. Patent applicants from field E, fixed construction, include developers of roads, railways, and bridges demanding and occupying larger space, which drives them further from universities, research institutes, and where innovation in other categories takes place. This may explain why there is a broader initial repulsion region than there was in the cases studied elsewhere in this paper. Mechanical engineering is another field where we might expect a large amount of innovation to take place in larger factories and construction sites with their associated larger distances from other innovation. In Figure 10, with firms as the reference category, regions of repulsion can be seen extending beyond 15 km in every case; this is even further than for fixed construction (E). After a brief significant attraction, we then see returns to repulsion by the end of the 30 km range. Firm innovation in mechanical engineering is strongly attracted by university innovation in operations and transport (B), chemistry and metallurgy (C), physics (G), and electrical (H) fields. Switching the order such that university innovation is the reference category removes the attraction region significantly, we do not see any evidence of universities surrounding the innovative firms in patent field F. Such a result is entirely intuitive since seldom would we expect universities to locate to serve such traditional sectors of industry. Figure 11 displays the attraction pattern once more for firm patents in physics (G), albeit with an initial repulsion range that stretches to 5 km, being 7 km for university patents in chemistry and metallurgy (C) and just 3 km when the university patent is in physics (G). Firm innovation in physics is attracted by university innovation within the 30 km radius and are pronounced in almost all cases; 𝑀(𝑟) peaks above 1.2 around 15 km for many patent types. It should be noted that a peak this high represents an aggregation at least 20% higher than would be the case under random allocation. Reversing to consider universities as the reference category still displays attraction beyond 5km, but the magnitude is much smaller than in the firm reference case. This implies that the innovation spillover from university to firm is more than that from firm to university. Although the values of 𝑀(𝑟) do get closer to 1, the attraction remains significant according to the goodness-of-fit tests. Attraction or Repulsion? | 17 Figure 8: Coagglomeration of Firm Patent D with University Patent A–H Notes: The figures plot the coagglomeration of firm patent C with university patent A–H. The first and third rows take firm as the reference; the second and fourth rows take university as the reference. Labels are written below the plots and show the reference point type followed by the neighbor category. Shaded areas represent 95% confidence intervals around the null hypothesis of zero relation. Plots are generated over the 30 km radii. Source: The patent application data is sourced from the National Intellectual Property Administration (2011-2015). Firm D Uni A Firm D Uni B Firm D Uni C Firm D Uni D Uni A Firm D Uni B Firm D Uni C Firm D Uni D Firm D Firm D Uni E Firm D Uni F Firm D Uni G Firm D Uni H Uni E Firm D Uni F Firm D Uni G Firm D Uni H Firm D M(r) M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 r 0.0 0.5 1.0 1.5 2.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0 2 4 6 8 10 12 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 4 3 2 1 0 r M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 6 4 2 0 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 4 3 2 1 0 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0 2 4 6 8 10 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 r M (r)M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 r 0.0 0.5 1.0 1.5 2.0 2.5 M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 10 5 0 15 20 25 30 35 r M (r) 0 5,000 10,000 15,000 20,000 25,000 30,000 0.0 0.5 1.0 1.5 2.0 r M (r) r 0 5,000 10,000 15,000 20,000 25,000 30,000 0 2 3 4 5 6 7 M (r) r M(r) 4 3 2 1 0 0 5,000 10,000 15,000 20,000 25,000 30,000 4 3 2 1 0 r M(r) 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0.5 1.0 1.5 2.0 2.5 3.0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 15 10 5 0 r 0 5,000 10,000 15,000 20,000 25,000 30,000 Mobs(r) ^M(r) Mhi(r) ^Mlo(r) ^ 18 | ADB Economics Working Paper Series No. 608 Figure 9: Coagglomeration of Firm Patent E with University Patent A–H Notes: The figures plot the coagglomeration of firm patent E with university patent A–H. The first and third rows take firm as the reference; the second and fourth rows take university as the reference. Labels are written below the plots and show the reference point type followed by the neighbor category. Shaded areas represent 95% confidence intervals around the null hypothesis of zero relation. Plots are generated over the 30 km radii. Source: The patent application data is sourced from the National Intellectual Property Administration (2011–2015). Mobs(r) ^M(r) Mhi(r) ^Mlo(r) ^ Firm E Uni A Firm E Uni B Firm E Uni C Firm E Uni D Uni A Firm E Uni B Firm E Uni C Firm E Uni D Firm E Firm E Uni E Uni E Firm E Uni F Firm E Uni G Firm E Uni H Firm E Firm E Uni F Firm E Uni G Firm E Uni H M(r) M(r) M(r) r r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0 1 2 3 4 M(r) 0 1 2 3 4 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 rrrr 0.0 0.5 1.0 1.5 M(r) M(r) M(r) M(r) 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 0 2 4 6 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) 0 1 2 3 4 5 M(r) M(r) M(r) 0 1 2 3 4 r r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 r 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 M(r) M(r) M(r) 0.0 0.5 1.0 1.5 r 0.0 0.5 1.0 1.5 rrr 0.0 0.5 1.0 1.5 M(r) 0.0 1.0 0.5 1.5 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,000 15,000 20,000 25,000 30,000 Attraction or Repulsion? | 25 VI. POLICY IMPLICATIONS Universities are by design centers for innovation, and this does spill over into the production of patents and intellectual property. Within the agglomeration literature, much is made of the knowledge spillovers that can come to firms who locate with others in similar fields. Combining these to gain benefits from the innovation of universities is a natural extension of the theory that has been given strong consideration in the literature. Results have been very mixed, however. In this paper, we employ spatial point pattern analysis of patent applications in Shenzhen, PRC, to question the extent of colocalization of innovation between firms and universities; our conclusions then inform on the strength of spillovers between the two. We verified that the insights gained are broadly robust to time. Forman, Goldfarb, and Greenstein (2016) is one of a growing number of studies to suggest that it is important to consider not only technology spillover within the same technology field but also that there will be coagglomeration across fields. Our results demonstrated consistency across all eight of the possible university patent types for each of the firm patent types. Such a robustness is encouraging for innovation policy to promote these areas and to obtain attraction for innovation in other fields. This cross field attraction is also consistent with the results of Abramovsky and Simpson (2011) and their assertion that precise field did not matter in the university–firm innovation relationship. Critically, the attraction of firms to universities is consistently larger than that of universities to firms, albeit that the presence of attraction was found to be similar in all cases. Consequently, the results presented here suggest that encouraging innovation in any one of the four attraction fields (human necessities [A], fixed construction [E], physics [G], and electrical [H]) could create development in the other four fields and bring benefit to the Shenzhen region. Jaffe (1989); Audretsch, Lehmann, and Warning (2005); and Abramovsky, Harrison, and Simpson (2007) are among many to identify the importance of geographic proximity to universities in promoting innovation; within the four attraction fields, our results agree with this. Given that science parks are often designed with universities as active collaborators, our evidence demonstrates the wisdom of this strategy for firms active in the electrical high-technology area. Li and Zhu (2017) suggested weak association with universities, something that we identify in a number of the other patent fields where repulsion is the dominant pattern. In the cases where repulsion was dominant, there was some evidence of longer distance attractions of the type found by Abramovsky and Simpson (2011); such longer distance attraction is present as part of the overall attraction in the attraction cases too. Shorter distance repulsion between universities and firms may be the result of zonal planning precluding certain types of innovation activity within the main industrial parks; that there is consistency across all eight university fields suggests this is not true, however. An alternative interpretation is that, over a short distance, attraction pattern is shown between universities since the knowledge distance between similar types of organizations is shorter than that between university and firm. Our evidence does support the use of universities to enable firm innovation but cannot provide a causality to the relationship. Within the PRC’s context, much of the development of innovation is managed through the creation of science parks, technology zones, and university towns that are all designed on blank canvases to align with the best available theory of the time. Shenzhen was the first innovation-led city, obtaining the designation in 2008, and is home to some of the PRC’s largest high-technology firms. Compared to major neighbors, Guangzhou and Hong Kong, China, Shenzhen is a new city and is highly planned in its expansion. Our work on attraction therefore represents a genuine environment in which firm locations can be considered to be less hindered than might be the case in an established urban ecosystem. Li and Wang (2019) observed that high-tech firms were often within 5 km of universities in 26 | ADB Economics Working Paper Series No. 608 Nanjing’s science parks; our low distance attractions are consistent with this, but over a much larger area. The suggestion of our work is that such possibilities for attraction can yield significant spillovers and that embedding universities should remain a cornerstone of policy irrespective of the insignificance found elsewhere. Our contribution is to direct the fields of study that the universities would be best focused on. Some further questions are raised by our work that will require more exploration. Patents in fixed construction are necessarily linked to firms that operate far from existing developments, while those in mechanical engineering are also born of firms requiring bigger land takes. These fields need more analysis, but do display consistent patterns across the eight university patent types. Looking at the previous 5-year plan, insignificance was found and a curious attraction across short distances was detected in mechanical engineering. The evolution from close attraction to overall repulsion is the area where more analysis is needed to determine the true pattern. Overall attraction between university patenting activity and firm innovation is shown to be strong; use of universities as catalysts for development has clear promise. However, there are also channels which we are unable to evidence within this dataset. First, universities are increasingly opening graduate research centers, or research hubs and these have very different functionality to the traditional research institutes. Second, universities are also educational establishments producing graduates for roles in the very firms whose innovation performance is being considered. To what extent this creates spillover that is not spatial or is a secondary motivation for proximity is open to discussion; firms will certainly benefit from a skilled workforce on the doorstep. These two elements speak to the broader importance of knowledge spillovers and labor productivity in agglomeration, respectively. Our valuable insights thus have links to the long-standing colocation arguments (Ellison and Glaeser 1997; Ellison, Glaeser, and Kerr 2010; Kerr and Kominers 2015) and labor productivity in particular (He, Chen, and Schramm 2018; Melo et al. 2017). VII. CONCLUSIONS University innovation is a strong attractor of firms, particularly in the higher technology sectors where the line between theoretical research and commercializable intellectual property are closest together. Employing the spatial point pattern analysis of Marcon and Puech (2009), we find distances over which the promotion of innovation in the higher education sector will attract an increase in patenting by firms. Increased patenting is then part of a chain that leads to new markets, cementing of market power, and the ability to fund further innovation and hence, further future strength. The virtuous circle of innovation begetting profits repeats not only within particular industrial fields, but also across other areas. Diverse coagglomerations have been identified as most beneficial to the wider urban area giving planners further motivation to recognize these conclusions in designing science parks and spatial master plans. There are significant distance horizons over which university and firm innovation attracts each other, while firms (universities) attract firms (universities) over shorter distances. Further, we showed that these attraction distances are different for firms attracting universities compared to the stronger pull of university innovation on firms. Being able to discern this differential within the spatial point pattern is a selling point of the Marcon and Puech (2009) methodology to the economics literature and allows us to provide new evidence of universities also locating proximate to innovative firms especially in human necessities, physics, and electrical fields. Our work is set within the city of Shenzhen owing to the leading innovation status that the city has in the PRC’s development. Shenzhen’s flexible planning and regulation environment is hardly found, and nor could it be replicated in other cities. Fine graining the categories, the definitions of Attraction or Repulsion? | 27 university, and extending the period can represent potentially fruitful directions for further work. Notwithstanding these future research directions, this study has evidenced a number of important phenomenon in innovation coagglomeration along both directions, representing a significant advance over existing studies. Policy has therefore been justified in promoting universities as an integral part of new science park planning, and our results now allow that recommendation to be fine-tuned around the fields in which the universities will be innovating. Coagglomeration of university and firm innovation commands greater inclusion in development strategies. 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Testing Coagglomeration of Innovation between Firm and University Agglomeration theory suggests the geographical proximity of firms in production activities. The authors add to the literature by identifying whether universities are attracted by firms in patents production and the size of such attraction. Using a large patent dataset from Shenzhen, the first innovation-led city in the People’s Republic of China, and employing a spatial point process analysis technique, the authors found varying attraction and repulsion distances between the same type of innovative units and across university-firm innovation pairs. Attractions are shown within identical technology fields and across different technology fields. Weak support is offered to the integration of firms into the university-led innovation clusters in science parks. Firm innovations in the technological fields like Human Necessities, Physics, and Electrical should deserve more policy attention. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ATTRACTION OR REPULSION? TESTING COAGGLOMERATION OF INNOVATION BETWEEN FIRM AND UNIVERSITY Simon Rudkin, Ming He, and Yang Chen ADB ECONOMICS WORKING PAPER SERIES NO. 608 February 2020