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Papers or Patents: Channels of University Effect on Regional Innovation by * Robin Cowan ** Natalia Zinovyeva DOCUMENTO DE TRABAJO 2009-20

June 2009

* BETA, Université de Strasbourg. ** FEDEA.

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Jorge Juan, 46 28001 Madrid -España Tel.: +34 914 359 020 Fax: +34 915 779 575 infpub@fedea.es

University Efects on Regional Innovation^∗

Robin Cowan^†

BETA, UNIVERSITY OF STRASBOURG,

61 avenue de la Fort Noire, 67085 Strasbourg, France

UNU-MERIT, MAASTRICHT UNIVERSITY

Keizer Karelplein 19, 6211 TC Maastricht, The Netherlands

Natalia Zinovyeva INSTITUTE OF PUBLIC GOODS AND POLICIES SPANISH NATIONAL RESEARCH COUNCIL (IPP-CSIC) Calle Albasanz 26-28, Madrid, 28037, Spain

June 17, 2011

Abstract

This paper analyzes empirically whether expansion of a university system afects local industry innovation. We examine how the opening of new university schools in Italy during 1985-2000 afected regional innovation. We find that creation of new schools increased regional innovation activity already within five years. On average, an opening of a new school has led to a seven percent change in the number of patents filed by regional firms. In relatively more industrialized regions engineering schools have the strongest impact on innovation, whereas in relatively less industrialized regions medical, pharmaceutical, chemistry, agricultural and veterinary schools generate the strongest efect. Traditional measures of academic research activity — publications and patents — can only partly explain this efect.

Keywords: University research, regional innovation, publications, industry-university interaction

^∗We would like to acknowledge the inputs of members of the KEINS project, and particularly Francesco Lissoni and Bulat Sanditov, for their gracious openness and valuable help with the data. We are also grateful to Francesco Quatraro who provided us with the historic data on Italian regional R&D collected from various issues of ISTAT. We also acknowledge the helpful comments of Bronwyn Hall, Jacques Mairesse, Joel Baum and all the participants of XXXII Symposium of Economic Analysis in Granada and DIME Conference “Knowledge Based Entrepreneurship: Innovation, Networks, and System” in Milan. This research was supported by the DYREC Chaire d’ Excellence of Robin Cowan, funded by the French ANR, and grants from ESF COST and APE-INV projects.

^†Corresponding author at: UNU-MERIT, Keizer Karelplein 19, 6211 TC Maastricht, The Netherlands. Tel.: +31(0)433884408. Email addresses: r.cowan@merit.unimaas.nl (R. Cowan), nzinovieva@fedea.es (N. Zinovyeva).

1 Introduction

Between 1960 and 2000, there was a large expansion in universities in the industrialized countries. Early expansion was to deal with the baby boom coming of university age; later expansion was driven by the desire to increase the proportion of the population receiving tertiary education.¹ Naturally, a rise in student numbers tended to be accompanied by a rise in the size of the professoriate and an increase in the sizes and numbers of universities. The clearest efect was just that: an increase in the general education level of the labor force.

University expansion coincided with spectacular rise of innovation activity in industrialized world. If in 1963 US Patent Ofice granted around 45 thousand patents; by the end of nineties the yearly number of granted patents approached 160 thousand (Hall et al., 2001). How to maintain this competitiveness and get more innovation out of a knowledge system has become a hotly debated issue. Following the line taken in the literature on innovation systems, it is often suggested that stimulating academic research and close interactions between academia, industry and government are necessary to promote knowledge flows and innovation. These policy suggestions are often based on the idea that universities have within them some of the keys to increasing innovative activity.²

If it is true that the innovation performance of an economy is determined in part by participation of universities in the innovation system, one might expect that the increase of innovation activity during past decades is partially attributable to the increase in the size of the university sector. This hypothesis motivates our analysis.

What are the mechanisms through which university afects innovation? The answer to this question is of paramount importance for policy decisions. While there is a strong presumption that an increase in the skill level of the labor force will be good for economic performance, and in particular innovation, there may be efects of the universities other than through those means. In fact, one strong policy focus in recent decades has been on technology transfer and knowledge spillovers that exist independently of graduates. In this paper we ask whether there is an identifiable efect of universities on regional industrial innovation, other than those that operate through the education and subsequent employment of university graduates.

¹According to the Global Education Digest 2009 by UNESCO Institute of Statistics, the share of students in North America and Western Europe that enroll in tertiary education during five years after the end of secondary education increased by 41 percentage points from 30% in 1970 to 71% in 2007.

²An OECD 2007 report “Higher Education and Regions: Globally Competitive, Locally Engaged” estimates that only 10% of UK firms currently interact with universities with most university-industry links focusing on big business and a few hi-tech fields. Report concludes that “the potential of higher education institutions to contribute to the economic, social and cultural development of their regions is far from being fully realised”.

There is a broad empirical literature addressing the general efects of university research on industrial innovation. However, a challenge that runs throughout this literature is the problem identifying causation in a system rife with endogeneity. In fact, the positive association between academic research and industrial innovation does not necessarily imply that universities increase local innovation activity. Potentially, increases in university R&D output may be “caused by” increase in industrial R&D and associated to it easy access to industrial inputs such as equipment or materials. Increase in university research production might also be reinforced by the self-selection of academics able to benefit from interaction with industry into highly innovative industrial districts. In this paper we are able to use an unusual policy episode to solve some of these identification issues.

We analyze empirically the role of universities and academic research for regional innovation systems in Italy. In particular, we study how the creation of new science, engineering and medical schools during the period 1985-2000 afected industrial innovation activities in the corresponding geographical areas.³ The identification strategy relies on the fact that during the analyzed period there was a significant expansion in the supply of higher education and academic research in Italy. Many new academic units were opened but, as was acknowledged later by policy makers, the distribution of new schools across regions was largely independent of the properties of the regional economy. In fact, no significant correlation can be observed between the number of new schools in a region and observed regional characteristics including population, share of graduates in the labor force, regional private and public investment in research and development, and value added produced by diferent economic sectors.

³In the Italian system teaching is organized into schools (facolt`a) and research is organized into departments (dipartimenti). Departments and schools may or may not coincide. To simplify presentation, we refer only to “schools”, and our measure of the date of opening of a new school is the year at which the first class was registered within a newly formed school. This should not be read to imply that university expansion affected only teaching. A new school in most cases implied creation of a new department. This conflation of schools and departments, teaching and research units, is not an issue for our analysis, as both measure university presence in the region.

We focus on the short-term efects of academic research. We do it for two reasons. First, it is likely that regional collaboration networks grow fastest in the first few years after opening of new university schools. Second, considering the short-run efect of universities allows us to identify the direct influence of academic research on innovation activity and to exclude other channels. In particular, it permits us to avoid the issue of how graduates contribute to innovation. The efects of an increased quantity and quality of graduates in a region are likely to be very difuse, and so hard to identify. However, they will certainly only emerge more than five years after a school opens: the oficial duration of most degrees in Italy (in analyzed period) is five years, less than 20% of graduates complete education on time and, on average, students take two more years to graduate after the end of the oficial program (Bagues et al. 2008). So by focusing on the short term efects, we can identify direct knowledge spillover efect from university faculty to local industries.

Our results suggest that there is a significant efect of the creation of new university schools on regional research and innovation activity. Industrial patenting increases following the introduction of a new school to a region: on average, one new school has led to about a seven percent increase in the number of patents filed by regional firms five years later. Less developed regions benefit more from university-industry interactions. We find that regional specialization conditions the extent to which industrial sectors can benefit from new scientific research in a given field. Engineering and science schools generate stronger efects in relatively more industrialized regions, whereas medical, chemistry, pharmaceutical, agricultural and veterinary schools afect innovation activity in relatively less industrialized areas with stronger agricultural sector. Traditional measures of academic research activity — academic publishing and patenting — can only partly explain this efect, suggesting that university role in transferring knowledge to industrial sector is not totally accounted for by skills related to academic knowledge production.

The rest of the paper is organized as follows. Section 2 reviews the existent empirical findings concerning the role of academic research in innovation systems. Section 3 describes the data. Section 4.1 introduces the empirical model and comments on the main identification assumptions. The results of the empirical analysis are provided in sections 4.2 and 4.3. Finally, section 4.4. discusses the findings, and section 6 concludes.

2 Background literature

There exists a large literature analyzing the relationship between academic research and industrial innovation activity. As early as the 1980s it was suggested that technology clusters such as those in Massachusetts and California would be impossible without the technology transfer from universities in these areas (Saxenian, 1985; Dorfman, 1983). It was not long though, before several case studies questioned the generality of the role of university as an accelerator of regional innovation (Feldman, 1994a; Rogers and Larsen, 1984) and suggested that various characteristics of regional technological infrastructure (business services, technologically related firms, etc.) are necessary for development of university research outputs.

To understand the magnitudes of possible efects of university research on industrial innovation, a more aggregate econometric analysis was necessary. Jafe (1989) provided one of the earliest studies of this kind. He used data for 8 years for 29 US states to test whether there is an impact of university R&D on industrial patenting, and found a significant positive efect. Several later studies confirmed Jafe’s finding, using firms product and process innovations instead of patents as a measure of innovative activity (Acs et al., 1993; Feldman, 1994b; Feldman and Florida, 1994).

Identifying emmpirically the spill-over efect of university R&D on innovation is not straightforward. First, both industrial and academic research might be simultaneously afected by other factors, such as economic growth, which would lead to a positive relation between university R&D and innovation. Second, academic research activity might be itself afected by the intensity of industrial innovation activity. Superior academic researchers might move to universities in regions providing better commercialization opportunities for their results. Universities may invest more in R&D when possibilities for collaboration with industry are higher.

In order to address above problems, Jafe (1989) estimated a system of three equations: first equation characterizing the efect on industrial and university R&D on patenting, and the two equations describing the determinants of, respectively, industrial R&D and university R&D. To identify the model, Jafe assumed that industry R&D does not depend on the number of private and public institutions and that university R&D does not depend on manufacturing value added, once, respectively, university and industrial R&D are taken into account. Thus the consistency of Jafe’s findings depends on the validity of these assumptions.

Econometric analysis of university efects on industrial innovation at the regional level relies on the assumption that knowledge difusion and spillovers are geographically localized. Some studies explicitly analyzed the degree of this localization by considering the geography of patent citations. The overall conclusion in this respect is that geographical proximity does matter for knowledge difusion (Jafe et al., 1993; Peri, 2005). Botazzi and Peri (2003), estimating the efect of external R&D on firms’ innovation, confirmed that in Europe spillovers are very localized and exist only within a distance of 300 km. Andersson et al. (2009) provide evidence suggesting that spillovers from university investment might be even more localized. They analyze the efects of changes in the Swedish university system and find that roughly half of the productivity gains from aggregate university investments are manifest within 5-8 km of the community in which they are made. Some authors claimed that the evidence of firms’ disproportionate location in areas close to universities already suggests that the potential positive interactions between industry and university are likely to be quite localized (Abramovsky et al., 2007; Audretsch and Stephan, 1996). There is also evidence suggesting that university R&D might be related to patenting activity much further away following the collaboration networks of university professors (Ponds et al., 2010).

Many studies at the firm level have confirmed that collaboration with university is associated with higher propensity to innovate (Loof and Brostrom, 2008; Zucker et al., 1998), especially in those technological areas that require frontier scientific knowledge (Hall et al., 2003).

Once again, even if the empirical evidence suggests that industrial innovation and university research tend to cluster in the same locations and university-industry collaborations are more frequent in highly innovative firms, it is dificult to claim empirically that the intensity of university research influences the innovativeness of industrial sector, and not vice versa.

There also exists a broad literature that discusses specific mechanisms through which universities might spur regional innovation activity. As pointed out above, aside from their efects through graduates, universities have roughly two channels by which they might influence industrial innovation: applied research; and basic research. In the past decade both channels have been addressed in the literature.

Academic patents are often discussed, especially by policy makers, as one of the main sources of applied knowledge and technology transfer from university. In part, this belief motivated the U.S. Bayh-Dole Act (1980), (and subsequent, similar, European legislation), which gave permission for US universities to patent technology developed with federal funds. The underlying rationale was that this should speed up technology transfer by bringing new commercialization opportunities to the market.⁴ But at the same time, many academics expressed concerns about potential detrimental efects of incentives to patent on the type and the quality of the research output produced (Lundvall, 1992; Henderson et al., 1998). But contrary to the apparent belief of policy makers, the empirical evidence tends to suggest that academic patenting per se is not a key factor in explaining the positive association between academic research and innovation (Agrawal and Henderson, 2002; Arundel and Geuna, 2004; Cohen et al., 2002; D’Este and Patel, 2007).

Transfer of university basic knowledge is also widely discussed in the empirical literature as a potential channel of university efect on innovation. It is argued that the academic knowledge could be spread through traditional academic channels such as seminars, face-to-face interactions or (more measurably) scientific publications. In fact, this latter channel (as well as co-publishing with industry) is often claimed to be among the most important channels of knowledge transfer from academia as perceived by survey respondents (Cohen et al., 2002; Cassiman et al., 2008; Bekkers and Freitas, 2008).

⁴Market failure theory suggests that due to the public good nature of knowledge private companies have little incentive to invest in developing an invention that is not protected by a patent.

Whether knowledge flows from university to industrial innovation exist and via which channels these spillovers occur, is an important empirical question. Still, as discussed above, the empirical analysis of these issues face several methodological problems related to the identification of the efects of university research.

The presence of the above methodological problems often opens a window for criticisms of the existent empirical findings on university efects on industry and especially on the channels of these efects. The literature based on survey data have an additional problem, as one might argue that, given the subjectiveness of survey responses, very distinct opinions about channels’ importance might be generated, depending on who is asked to evaluate it. For instance, Bekkers and Freitas (2008) compare perceptions of the importance of academic patents (among other things) as a channel of technology transfer among academics and private sector R&D workers and report that the private sector considers them to be twice as important as does academia.⁵

In what follows we perform a statistical analysis to examine whether a rapid growth of the university system in Italy had an efect on local industrial innovation activity. Because, as discussed below, the location of new university schools was relatively independent of local economic structure, the data allow us to avoid many of the endogeneity problems typically attendant on this type of study.

We also explore whether the extent to which the industrial sector can benefit from new scientific research in a given field depends on regional specialization. One might argue that any industrial innovative activity is built upon its own particular type of human capital, knowledge, or technology. For any industry to be efective in innovating, there must be a match among all these ancillary inputs. As a consequence, it seems probable that some regions will be more susceptible to university-industry spillovers than others. A university focused on one discipline in a region whose industrial activity is specialized in another is not likely to create large spillover efects. When university activity and industrial activity are aligned in terms of discipline, though, we might reasonably expect to see an efect in industrial activity of the presence of the university.

⁵Note that the revealed importance of academic patents as perceived by the private sector does not allow to discard the possibility that the knowledge flows from academia are endogenous. For instance, it is possible that 1) most developed businesses exert high demand for skilled labor and lobby new universities at their locations, 2) university professors in these locations benefit from local industry and produce more publications and patents than academics in other locations, 3) better research output helps universities to attract more public research funds and, finally, 4) firms reorient their R&D investments towards publi research given the cost eficiency concerns.

Finally, our third goal is to address the issue of “channels of interaction” between universities and industry. In particular we are interested in understanding the extent to which industrial innovation is afected by the activities and human capital associated with professors’ publishing as opposed to those associated with the more applied activities associated with patenting.

3 Data

The analysis is performed using Italian data at the regional level. The database includes characteristics of the university system, indicators of industrial and academic innovation activity and economic indicators observed for 20 Italian regions between 1984 and 2000.

Our main indicator of the university presence in the region is the number of university schools in science, medicine and engineering.⁶ We consider the date students were first enrolled in the degree program of the school as the date of the creation of this school. Information about the number of first-year students at the school level was obtained from diferent issues of the Italian National Statistical Bureau bulletins (L’universit`a in cifre and Lo Stato dell’Universit`a).⁷

According to this definition, 65 schools in science, medicine and engineering were opened for enrollment between 1985 and 2000. (Figure 1 describes the dynamics of uni versity expansion across time and Figure 2 shows the geographical distribution of new schools.) Out of the total of 65 new schools, 29 schools were in civil and industrial engineering, 12 in sciences, 11 in agriculture and veterinary, and 13 in medicine, pharmaceutics and chemistry. An average Italian region has nine schools and every fifth region in a given year had opened a new school (Table 1). This rapid growth in the number of schools was a result of the plan to expand educational supply, which was adopted by the Italian government in the beginning of the 1980s.⁸ The plan was seeking to unload overcrowded universities and to rise generally low graduation rates. In most cases the new schools were opened within previously existing universities, but often located in diferent towns. With time some of these schools became independent universities (for example, what is now the University of Eastern Piedmont was founded in 1998 on the basis of schools of the University of Turin located in Vercelli, Alessandria and Novara). In a few cases the new units appeared as a result of the split of overcrowded universities in big megalopolises (the University of Rome III was founded in 1992 simply by taking part of the staf from University of Rome La Sapienza). There are very few examples of opening completely new universities from scratch (one such is the University of Teramo, founded in 1993).

⁶We exclude social science and humanities as they seem unlikely to be involved in research that leads to patenting activity either within the university or in industry.

⁷The data are available from 1984 from the printed annual editions of L’universit`a in cifre and Lo Stato dell’Universit`a (available in most university libraries in Italy). Data for years from 1988 on were accessed at http://ionio.cineca.it, in November 2007.

We measure regional innovation productivity by the number of patents registered in the European Patent Ofice, using the location of inventors to determine region.^9,10 In order to disentangle the knowledge spillover efect of universities from the direct efect of university R&D investment on patenting, we split patents into two groups: the ones that are produced by universities (or academic patents), and the rest of patents (industrial patents). Note that until recently it has been dificult to attach patenting activity to university research. In fact, in contrast to the US case, up to the present, in Italy universities did not generally retain the property rights on inventions done by their researchers. Often “IPRs over inventions derived from sponsored research programmes were left to the

⁸Law August 14, 1982, n. 590 “Istituzione di Nuove Universit`a”.

⁹Specifically, the database includes all patent applications that passed a preliminary examination in the EPO. The assigned date of the patent is the priority date, which is the date of the first filing world-wide.

¹⁰Patents with inventors from two diferent regions are counted twice.

Figure 1: Number of new university schools (excluding schools in humanities and social sciences), 1985-2000

Source: National Statistical Bureau (ISTAT) bulletins L’universit`a in cifre and Lo Stato dell’Universit`a.

Table 1: Descriptive statistics by region

Notes: (*) Total number of regions is 20. Regions classified as “Northern” are Piedmont (PIE), Aosta Valley (AOS), Lombardy (LOM), Friuli-Venezia-Giulia (FVG), Trentino-Alto Adige (TAA), Veneto (VEN), Emilia-Romagna (EMR), Liguria (LIG); regions classified as “Central" are Tuscany (TUS). Umbria (UMB). Marche (MAR). Lazio (LAZ). Sardinia (SAR): regions classified as “Southern” are Abruzzo (ABR), Basilicata (BAS), Calabria (CAL), Campania (CAM), Molise (MOL), Apulia (APU), Sicily (SIC). Mean values for the period 1984-2000.

sponsors”(see Balconi et al., 2004). The recent KEINS EP-INV database on academic patenting (Lissoni et al., 2006) matches the names of the inventors of the patents with a list of university professors. Thanks to this methodology, the KEINS database includes not only any patent owned by universities, but also all patents that involve university scientists, whether the patents are owned by firms, public research organizations, universities, or the scientists themselves. In the following we use the KEINS database to identify industrial and academic patents.

We measure the quality of innovation by the average number of citations received by these patents before 2004. Naturally, series on patent citations are subject to a truncation bias since the number of citations any patent receives grows with time, and our data include citations received only until 2004.¹¹ We correct for truncation bias following the method developed by Jafe and Trajtenberg (1996) in the version where the difusion process is assumed to have the same shape in all technological sectors. Figure 3 shows the evolution of the number of patents, patent citations and corrected citations in industrial and academic sectors. Patents, both industrial and academic, and citations to them grew steadily over the period. An average region produced annually 142 patents and only seven of them were produced with the participation of academic inventors.

Figure 3: Evolution of academic (left panel) and industrial (right panel) innovation outcomes in Italy, 1985-2000

The number of publications in journals listed in Science Citation Index (SCI) is used to measure the scientific output of regions.¹² We also disaggregate information on publications and patents for diferent disciplinary groups: engineering, science, and medicine, agriculture, veterinary, chemistry and pharmacy. The extent to which technological innovations rely on scientific knowledge is measured by the propensity of patents to cite non-patent literature (NPL). Not surprisingly, we observe that patents with inventors from academia draw more on scientific knowledge than pure industrial patents (column 1, Table 1).

¹¹More precisely, citation variables count the number of citations received by regional patents from all Italian patents until 2004.

¹²The Thomson ISI data on publications was obtained for 1985-1994 from the National Science Indica-

We use information from the Italian National Statistical Bureau on several regional characteristics including private and public spending on research and development, valueadded produced by diferent economic sectors (industry, services, agriculture and construction), population, population of the age 19, and the proportion of university graduates in the labor force.

Note that the intensity of innovation activity is very heterogeneous across Italian regions (columns 3-8, Table 1). Between 1984 and 2000, regions in the North of Italy were investing almost ten times more in R&D than Southern regions. These diferences are also reflected in the number of patent applications done by inventors from these regions. The gap in the innovation activity across these regions are not due to the size efect: there are no important population diferences across the regions. There are also no substantial diferences in the university presence or in the educational level of the labor force. Largely the diferences in the innovation activity could be attributed to a relatively low income level in the South and to the diferences in the industrial structure: in the North manufacturing has a larger weight in the economy than in the South, whereas in the South service sector and agriculture are relatively more important.

4 Empirical Analysis

We start by observing simple correlations between the number of new schools opened in a region and the variation in various indicators of research and innovation activity (Table 2). First, we analyze whether the opening of new schools during analyzed period is associated with the increase in the academic research activity. We find that university expansion is associated with the growth of university R&D investment in the region observed about three-five years later (column 1). Similarly, higher university presence is associated with the rise of the number of scientific publications already about three years later (column 2). We do not observe any clear (significant at standard levels) relationship between university expansion and the growth of academic patenting within first five years (column 3). Second, we analyze the relationship between university expansion and the growth of industrial innovation activity. We find that there is a positive correlation between university expansion and the growth of industrial patenting few years later (column 4), even though new university units are not associated with the growth of private R&D (column 5).

tors on Diskette (NSIOD) at UNU-MERIT and for 1995-1999 from the Conference of Italian University Rectors (CRUI) aggregated data (Breno et al., 2002).

Table 2: Correlation between new school opening and variations in the indicators of private and public research activity

Notes: * p-value <0.100, ** p-value <0.050, *** p-value <0.010.

Even though being indicative, the results of Table 2 should be considered with caution. In principle, the creation of new schools could be not independent of regional innovation activity. In addition, the above correlations could be confounded by some characteristics of economy which vary simultaneously with university expansion and regional innovation. In the following we make explicit the assumptions of our identification strategy and analyze the above finding in more detail.

4.1 Empirical model and identification strategy

The main problem that we seek to address is the possibility of circular causation between university research and industrial innovation. In order to address this issue, we analyze and build upon the standard reduced-form relationship between industrial innovation output, , and the number of schools in the region, ,

\[P _ {i, t} = \alpha + \beta U _ {i, t} + \mathbf {X _ {i , t}} \gamma + c _ {t} + c _ {i} + \epsilon_ {i, t}\tag{1}\]

The simplest way to avoid potential simultaneity problem in model (1) is to consider the right-hand-side variables – including university presence – with a time lag. The time lag between university presence and innovation activity could be also justified since the efects of institutional changes could take time to realize (see the above findings in Table 2). Below we consider the right-hand-side variables with a 5-year lag. Nevertheless, given the presence of strong autocorrelation in the data, lagging independent variables is likely to be insuficient to avoid endogeneity.

In order to capture some diferences across regions, we include extensive list of observable regional characteristics among controls, . The size of the region – both in terms of population, and especially younger population, and in terms of economic production – may reflect inherent local demand for higher education as well as the propensity to innovate. Therefore we control for population, the proportion of 19 year olds, and aggregate value-added. Industrial composition afects the propensity of a region to patent, and it may afect the value of certain types of high-skilled human capital and so the ability of a region to lobby the central government for more university resources. We include proportions of regional value-added in industry, construction, services and agriculture. An additional control for industrial structure is the share of graduates in the local labor force. Public non-university R&D and private R&D both afect industrial patenting, and may reflect a general attitude towards the value of knowledge production and training, thus again afecting the ability to lobby for more university resources. We include both controls in our estimations.

University expansion was stronger in the 1990’s than in the 1980’s coinciding with the rapid growth of innovation activity (see Figures 1 and 3). In other words, the timing of university expansion generally might be not independent of the time trend afecting innovation activity, . In order to account for time efects influencing all regions simultaneously, we introduce a set of year dummies among controls.

Notwithstanding the inclusion of the above controls, one might suspect that regions difer on other perhaps non-observable dimensions; and these diferences might explain both the university presence in the region and the development of innovation activity. In other words, the unobserved regional efects, , might be correlated with the university presence. Given the panel structure of our data, we can account for in two ways: using a fixed efect estimator (or, equivalently, including the regional dummies among controls) or using a diference estimator. Below we report results obtained using the first-diference estimator, even though fixed efect estimation produces results that are statistically similar to the ones presented here. We prefer diference estimator to the fixed efect specification since the former does not require strictly exogenous regressors (that is, it does not require that industrial innovation has no impact on future right-hand-side variables included in , such as value added and R&D) (Wooldridge, 2002).

In regard to the count nature of our dependent variable, in the following we adopt the traditional approach of modeling regional innovation activity using a log-log relationship between university presence and regional patents (Jafe, 1989; Feldman and Florida, 1994). We prefer this model to a negative binomial specification for two reasons. First, given that the mean number of regional patents is quite high (134 patents, see Table 1 for more details), the negative binomial distribution is essentially normal and the loglog model provides a good approximation. Second, the linear model allows capturing region-specific efects without imposing the strong exogeneity assumption. Performing a negative binomial estimation with predetermined regressors requires a GMM estimation for which our sample size is not suficiently large (Blundell et al. 2002).

Ultimately, our identifying assumption is that the error term, , is uncorrelated with university presence in the region once regional time-invariant efects, time efects and observable time-varying characteristics are taken into account. In other words, we assume that during the analyzed period no variations in regional characteristics (apart from the ones included in afected both the variation in the number of university schools and the variation in the regional innovation. Is this assumption justified?

To answer this question we need to understand the factors that influenced university expansion. As was acknowledged by policy makers ex post, the distribution of new units across the regions was largely independent of regional labor market demands. The openings seemed to be associated with an indiscriminate allocation of funds across the regions. In this regard, the Observatory for the evaluation of the university system in the Ministry of Education and Research (MURST) after analyzing the expansion of university system in the beginning of 90’s concludes:

The rules by which new institutions were created does not seem to have followed any logic tailoring university development to territorial specificities. It seems not to have made reference to a demand for university education (that is, responding to the potential scope of use of the new initiatives), nor does it seem to have made reference to the demand for graduates (the formative needs of the country) or to existing infrastructure. In substance, no rigorous evaluations of the initiatives were done, either in absolute terms, or concerning compatibility with the rest of the system. The criterion actually favored was geographical re-equilibrium, which aimed to bring the ofer of university education and subjects near to the demand, ignoring not only the “real” size of this demand (which sometimes turned out to be less than the minimum requirements for the initiative to be eficient and efective), but also the importance of the transportation system, the receptive capacity of the population of students and students’ financial support in determining access to university establishments. So, [. . . ] at least to a large extent, the prevalent logic was the one of incremental expansion and distribution “by drops of rain”, without evaluating other initiatives that were suppressed [. . . ]. (p.3, Verifica dei piani di sviluppo dell’universita 1986-90 e 1991-93, Osservatorio per la valutazione del sistema universitario, MURST, 1997; authors’ translation).

This evaluation of Italian Ministry of Education and Research supports our identifying assumption. In Table 3 we also analyze the contemporaneous correlation between observable regional characteristics included in and university expansion. Consistently with characterization done by the Italian Ministry, observable regional characteristics in the analyzed period seem to be poorly correlated with university expansion. Correlation between the number of new schools and the contemporaneous dynamics in observed regional characteristics, including the growth of regional patents, is very poor as well.

Similarly, the opening of new schools does not seem to be related to the demand for certain types of professions. In Figure 4 we plot the degree of the fit of educational supply to local demand for skilled labor in a region-discipline (measured as the number of new graduates incorporated in the labor force over the number of new graduates from local universities) versus the number of new schools in each region-discipline.¹³ An economically driven policy might aim to locate schools in regions that were importing skilled labor, leading to a positive correlation between university expansion and our measure of the fit of educational supply. Visually, no correlation is apparent, and indeed, the correlation between educational fit and the number of new schools in a corresponding discipline and region is on aggregate -0.055. Again, this is consistent with the MURST analogy between school creation and drops of rain.

Table 3: Correlations between the main variables

Notes: * p-value <0.100, ** p-value <0.050, *** p-value <0.010.

Figure 4: Number of new schools open between 1984 and 2000 by regional demand for corresponding professions

Overall, the above evidence implies that exploiting the variation in the number of schools within regions across time allows consistent estimation of the efect of university presence on regional innovation.

4.2 Regional innovation activity

Quantity The estimation results for model (1) with innovation activity being measured by the (log) number of industrial patents are presented in Table 4. We find that opening a new university school significantly increases regional innovation activity. The coeficients here are elasticities, so an increase of one percent in the number of schools in a region increases industrial patenting in that region by 0.6 percent. The mean number of schools per region is nine, and the mean number of patent applications per region per year is 134 (Table 1), so on average, one new university school brings about nine new patent applications by regional non-academic inventors five years later.

¹³New school openings are for the period from 1985 to 2000. The mismatch ratio uses data from the triannual Italian National Statistical Bureau (ISTAT) representative survey of graduates, the 1995 edition, which surveys students graduating in 1992. It covers information concerning graduates’ university-towork transition, asking, inter alia, where and in what discipline they graduated, and where they work. The description of the data could be found in Bagues et al. (2008).

Column 2 of Table 1 considers separately the efect of two groups of schools: engineering and science schools vs. agricultural, veterinary, medical, chemical, and pharmacy schools.¹⁴ We observe that the last group is responsible for the lion’s share of the increase in patenting activity.

A natural question to raise is whether these efects hold uniformly across regions, or whether regions with diferent economic development and diferent industrial structures respond diferently. In Table 5 we explore whether university spillovers difer by the type of region.

Panel (a) of Table 5 suggests that the strongest spillover efect occurs in the center and in the South of Italy (even though the efect is statistically insignificant for central regions). This efect is observed only for schools in agriculture, veterinary, medicine, chemistry and pharmacy, but not for schools in engineering and sciences. As we have mentioned before, Southern and central regions difer from the northern regions in the level of income, private R&D investment and the industrial structure. To test whether these characteristics of the economy are in fact conditioning the strength of the spillover efect, we analyze how the efect difers across regions with diferent loadings along these dimensions. Specifically, for each dimension, we split the regions into those that are above the median and those that are below the median. Note that regions are allowed to move from one group to another across time.

Results of this analysis are shown in panels (b)-(d) of Table 5. We observe that regions with low per capita income benefit from knowledge spillovers from university, whereas, on average, no positive university efect could be observed for high income regions (panel (b)). No statistically significant diferences can be observed in the strength of knowledge spillovers across regions with diferent level of private R&D investment (panel (c)). Panel (d) shows that knowledge spillovers difer significantly across regions with diferent industrial structure. In relatively less industrialized regions with stronger agricultural sector, agricultural, veterinary, medical and chemical-pharma schools have a significant efect on innovation. At the same time, in relatively more industrialized regions there exists a significant efect of engineering and science schools. This suggests that industrial specialization conditions the possibility of knowledge transfer from diferent types of schools.

¹⁴Given the overall low number of new schools in each specific discipline, there is not enough variation in the data to identify efects of more disaggregated groups of schools.

Table 4: The efect of the university expansion on the number of regional industrial patents

Notes: Dependent variable is the logarithm of regional industrial patents. The table shows the estimates of the first diference model. All independent variables are considered with a 5-year lag. In parentheses standard errors clustered by regions. p-value <0.100, ** p-value <0.050, *** p-value <0.010.

Quality The number of patents might be an appropriate indicator to capture the quantity of innovation, but it might hide important changes in quality. In fact, the observed increase in the quantity of patents as a result of a new university school in a region does not guarantee that the overall value of the regional innovation activity grows as well. Therefore it is important to analyze the efect of universities on the quality of the patents produced.

We check for efects on average patent quality, measured as the average number of received citations by regional patents. Evidence exists suggesting that patent citations represent a valid way to capture patent importance. (See Jafe et al. (2000) for example.) Specifically, we estimate equation (1) using the average number of citations to regional industrial patents as the dependent variable. Results are presented in columns 1-2 of Table 6. Opening a new school has no efect on the number of citations per regional patent.

One might also ask whether not just the quality, but also other patent characteristics has changed. For instance, one might be interested to know whether the presence of a university tends to bring industrial innovation closer to science. We measure the proximity of industrial innovation to science by the prevalence of non-patent-literature (NPL) citations in industrial patents. This is a very noisy measure (not least because many citations are actually added by patent examiners (Akers, 2000), but it can nevertheless reflect any substantial changes in patents’ reliance on basic knowledge. Still, we do not observe

Table 5: The efect of the university expansion on the number of regional industrial patents

Notes: Dependent variable is the logarithm of regional industrial patents. The estimates of the first diference model are shown. All regressors of Table 4 are included among controls. Independent variables are considered with a 5-year lag. In parentheses standard errors clustered by regions. * p-value <0.100, ** p-value <0.050, *** p-value <0.010.

Regions with high level of VA per capita (abbreviations are the same as in Table 1, in parentheses – the year since when a region gets above the median): ABR (1997), EMR (1991), FVG (1994), LAZ (1991), LIG (1995), LOM, MAR (1995), MOL (2000), PIE (1991), TAA, TUS (1993), UMB (1995), VEN (1995), AOS (1989). Regions with high level of private R&D: ABR (1992), CAM, EMR, FVG (1990), LAZ, LIG, LOM, PIE, SIC (1998), TUS, VEN. Regions with high ratio of industrial VA to agricultural VA: ABR (2000), EMR (1989), FVG (1988), LAZ (1986), LIG (1986), LOM, MAR (1991), PIE, TUS, UMB (1988), VEN, AOS.

any significant efect of new schools on the nature of industrial patenting as measured by NPL citations (columns 3-4, Table 6).

Table 6: The efect of university expansion on the quality of industrial patents and their proximity to scientific literature

Notes: Dependent variable is the number of NPL citations done by an average patent. The table shows the estimates of the first diference model. All regressors of Table 4 are included among controls. Independent variables are considered with a 5-year lag. In parentheses standard errors clustered by regions are shown. * p-value <0.100, ** p-value <0.050, *** p-value <0.010.

To summarize, we have observed that an increase in the number of schools is followed by an increase in industrial innovation activity in the region: numbers of industrial patents increase. The efect of new schools depends on the specialization of the region: engineering and science schools influence innovation in relatively more industrialized regions, whereas agricultural, veterinary, medical and chemical-pharmaceutical schools afect innovation in regions with relatively stronger agricultural sector. Interestingly, the average quality of industrial patents seems not to change with university presence, at least in the very short run.

4.3 Possible channels

In the previous section we found that at the regional level, university presence has a positive influence on the quantity of industrial innovation. In this section we ask whether it is possible to identify processes which underlie this efect. Traditional university research activity is expressed through publishing papers. Recently, in addition, there has been an emphasis on academic patenting: in a variety of ways academic researchers have been encouraged to patent their findings, possibly in collaboration with firms. Each of these represents a channel through which a university could afect local innovative activity. Furthermore, publications and academic patents are often used to measure both university quality, and especially university contribution to economic activity. In this section we ask to what extent these measures of academic research activity can explain the efects we have found in the previous section.

We do this through an accounting exercise. To the previous model, we add variables representing each of these channels, and ask how their inclusion afects the estimated coeficient of the number of schools. Specifically we include in model (1) publications and academic patents in diferent scientific fields,

\[P _ {i, t} = \alpha + \beta U _ {i, t} + \mathbf {X _ {i , t}} \gamma + \mathbf {P} _ {i, t} ^ {u n i} \delta + c _ {t} + c _ {i} + \epsilon_ {i, t}\tag{2}\]

Note that there is little a priori reason to assume a particular temporal structure of the relationship between school opening, academic scientific production and industrial patenting. On the one hand, if the mechanism of knowledge transfer is via reading academic papers by industrial inventors, we would expect a considerable lag between publication and a new patent. On the other hand, if industrial inventors and academic inventors are in relatively close contact, industrial inventors might access academic knowledge long before actual publication, and there would be essentially no time lag between publication and patent. Publications could be also interpreted as a proxy for certain types of human capital being present in a region. That human capital could in principle be accessed at any point in the invention/patenting process. Consequently, we allow the efect to be distributed in time and include several lags of academic research output in

A positive in equation (1) signals the existence of a causal relationship between the opening of a new school and regional innovation output. However, if the inclusion of in equation (2) reduces significantly the size of relative to its value in equation (1), we can claim that the corresponding university research output proxies some channels through which a university afects regional innovation. If, controlling for these indirect channels, we still observe a significant direct efect of universities on industrial patenting, we might conclude that there is something beyond these measurable efects.

The results are presented in Table 7. Column 1 shows the total efect of schools on industrial patenting, estimated using the model without publications and/or academic patents among regressors (and simply reproduces column 2 of Table 4). Column 2 of Table 7 includes academic publishing as an intervening variable, column 3 includes academic patenting, whereas column 4 includes both channels. Comparing the coeficients on schools in column 1 with that in column 2 shows the extent to which the overall efect is mediated by factors related to academic publishing. Similarly, comparing the coeficients in column 1 with that in columns 3 and 4 allows us to access which part of the overall efect is mediated, respectively, by factors related to patents, and by factors related to publications and/or patents. We observe that, at least in the short run analyzed here, an increase in academic publications accounts for about thirteen percent of the efect of the new schools in medicine, veterinary, agriculture, chemistry and pharmacy ([0.90 − 0.78]/0.90). Academic patents, can explain even less. Together publications and academic patents account for around twenty percent of the total university efect.

The potential of publications and academic patents to explain the efect of university expansion varies substantially depending on the disciplinary profile of new schools and regional specialization (columns 5-12). Recall that in section 4.2 we observed that engineering and science schools generate spillovers in relatively more industrialized areas, whereas medical, chemistry, pharmacy, veterinary and agricultural schools produce positive spillover efects in relatively less industrialized areas. Analysis of the channels of this knowledge transfer suggests that the efect of engineering and science schools in industrialized areas, if anything, could be better explained by academic patenting activity. However, overall including publications and academic patents among regressors has little efect on the estimated coeficient of engineering schools (columns 9-12). At the same time, in relatively less industrialized regions with stronger agricultural sector roughly thirty five percent of the positive efect of schools is mediated by factors related to university research. Here publications capture roughly three times more of the efect of new schools than academic patents do. Still, the diference between the explanatory power of publications and the explanatory power of academic patents is not statistically significant and should be considered with caution.

Table 7: Channels of the efect of the new schools on the number of regional patents

Notes: Dependent variable is the logarithm of regional industrial patents. The table shows the estimates of the first diference model. All regressors of Table 4 are included among controls. Independent variables are considered with a 5-year lag. In parentheses standard errors clustered by regions are shown. * p-value <0.100, ** p-value <0.050, *** p-value <0.010.

4.4 Discussion

Overall, our results suggest that the efect of universities on industrial innovative activity is only very partially mediated by activities measured by publication and academic patenting. On average, publication is more important, however, the importance of each activity depends on the discipline and regional specialization.

The diference between regions with lower and higher industrialization is striking. In the former, we see strong measurable efects of university research output; in the latter – much less, though in both types of regions there are positive efects of university presence on industrial innovation.

Which is the origin of this diference? We might think that some disciplines are such that the university research is, by its nature, close to, and readily used in industrial activity. The best-known of these are biotechnology and chemicals. Where these disciplines are important, we might expect to see that efects of universities are at least partially captured by measurable outputs such as papers and patents. By contrast in other disciplines corresponding industries might not look at the university as the provider of access to cutting edge research. Industries working in these disciplines might rely on specialized expertise, to which university professors might contribute only little. However, in these disciplines the value of academic knowledge could be in something else. For instance, university might contribute by collecting, generalizing, classifying existent knowledge, disseminating it in a comprehensive way, initially being driven by the needs of its didactic activity. In this case what is important for the industry is not professors ability to innovate or to produce new science, but their ability to collect, generalize and classify knowledge, to develop good criteria, which may or may not be fully captured by publications and patents. The transfer of such knowledge could occur in a variety of ways. Explicit consulting done by university researchers would be one form of transfer. Another way could be participation by industrial researchers in university activities such as seminars, or thesis supervision or examination. Finally, the transfer could occur via social interaction. University faculty can serve as a source of interaction that is tapped informally, intermittently, and through social contacts.

5 Conclusions

In this paper we focus on the economic efects of universities, and in particular on their efects on innovation. It is strongly believed that the presence of a university in a region would be beneficial for industrial innovation activity. We have taken advantage of certain unusual features of university expansion in Italy during the 1980s and 1990s in an attempt to identify the efect of university presence on regional innovation. According to ex post evaluation of the expansion programmes, university schools were created “like rain”, independently of underlying economic features of the regions. This experiment permits a nice way out of standard endogeneity problems.

Our first result indicates that there is indeed a significant efect of the creation of new university schools on the regional innovation activity. In response, industrial patenting activity in the region increases quite significantly even within five years of a new school opening. Thus the general impression seems to be correct: university activity is positively correlated with local innovation activity, and a policy tool to increase the latter is indeed to increase the former.

Still, there is no general recipe for how to increase innovation via university expansion. The efect of new schools strongly depends on the specialization of the region: engineering and science schools might influence innovation in industrialized regions, whereas agricultural, veterinary, medicine and chemical-pharmaceutical schools might afect innovation in less industrialized areas.

How are these benefits created? Marshall might suggest that it arises simply from the agglomeration of agents pursuing related activities; Mike Lazaridis¹⁵ asserts that it arises through the production of highly trained graduates; supporters of the Bayh-Dole act assert that it comes from controlled technology transfer through academic patenting. Given the time frame we examine, namely efects within 5 years of a school opening, we exclude the possibility that the efect of university occurs via the new graduates who enter regional labor markets. To address the question of other channels through which this influence flows, we have performed an accounting exercise estimating how the gross efect of increasing the number of universities is afected when we add to the model the proxies of factors that might be intermediary in the process. The factors that we focus on include professors’ ability to produce scientific research and their ability to produce patentable inventions. We measure the former by the number of ISI publications and the latter by the number of patent applications done with the participation of academic inventors.

We observe that, on average, the human capital related to new knowledge production that is brought to the region by university expansion captures around twenty percent of the overall university impact on industrial innovation. However, when we look at regions with diferent specialization, the picture appears to be more complex. In relatively less developed areas, both professors’ capability to perform pure scientific research and their ability to patent play a role in spurring regional innovation: together they explain more than 35% of the overall university efect. The human capital associated with traditional university production, as measured by scientific publications, has a stronger efect than innovativeness of academic researchers as measured by academic patents. In relatively more industrialized areas where engineering and science schools are more likely to produce spillovers, if anything, academic patenting activity seems to be more important. Still, overall academic research output can capture only a little part of the total university efect.

¹⁵Founder and CEO of Research in Motion, maker of the Blackberry.

According to our results, a big part of university efect could not be attributed neither to measurable academic research outputs. What could be driving the positive efect of university presence if not professors’ ability to produce new knowledge as measured by their publications and patents? It could be that this efect arises through activities that are both harder to observe and harder to measure. For instance, university might alter local innovation via collecting, generalizing and classifying existent knowledge. The development of this capability by university might be a byproduct of its research activity or it might be driven by the needs of its teaching activity. It should not necessarily be related to the ability of producing cutting edge research, and thus it may or may not be fully captured by academic publications and patents. Industry can draw on this university asset in a variety of ways. Firm employees might participate in university activities such as seminars, supervision and teaching, or, on the contrary, academics might get attracted to industrial activities such as short term visits or consulting. University professors could also share this knowledge informally via social interactions.

Generally, from our analysis we can conclude that already in the short run one could observe sizable efects of new higher education institutions on local innovation activity. Still, there is a lot more to technology or knowledge transfer than industry picking up new knowledge created by universities, and turning it into new products or processes. Perhaps Marshall was right, and that a big part of the efect of a university is to create something “in the air”.

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