Unpackaging universities’ local economic impact through a systematic literature review: between fragmentation and unclear boundaries

Abstract

This paper systematizes the literature on universities’ local economic impact, using the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ (PRISMA) approach and bibliometric techniques to identify key aspects underlying this concept. Although the analysis identifies three essential dimensions of economic impact—Technology transfer, Human capital, and Local development—it also reveals the literature’s struggle to examine their interrelationships. Furthermore, while most scholars define local impact as regional, the literature finds also challenging to operationally define its boundaries. This paper contributes to this debate by emphasizing the importance of understanding universities’ economic impact as an interplay among various dimensions at multiple interrelated levels. It is crucial to adopt a holistic and multidisciplinary approach to formulate policies that leverage universities’ full potential as drivers of local economic growth. Additionally, we must acknowledge that local impacts often permeate multiple levels in an increasingly interconnected world, requiring a balance with the policy-driven need for clear and definite boundaries.

1. Introduction

The idea that universities contribute to the economic development and growth of local territories has become a ‘mantra’ in both policy and academic world. This idea has been significantly advanced by the proliferation of neoliberal ideas in national/supranational policy discourses through the concept of ‘knowledge economy’ (Chatterton and Goddard 2000; Keeling 2006): universities can indeed enhance human capital and productivity (Hermannsson et al. 2017; Smętkowski 2018) as well as facilitate knowledge transfer and innovation (Huggins and Cooke 1997; Guerrero, Cunningham and Urbano 2015; Del Giudice et al. 2016).

At the same time, the literature has extensively debated models of interaction among universities, governments, and industries (Etzkowitz and Leydesdorff 2000), highlighting the need for universities to turn into entrepreneurial entities (Etzkowitz et al. 2000; Quintero and Serrano 2023) and engage with external local stakeholders (Petersen, Kruss and van Rheede 2022).

However, despite the indisputable widespread interest in this topic, our understanding of what we truly mean by a university’s local economic impact often remains vague and difficult to define in operational terms (Uyarra 2010; Brekke 2021).

Firstly, the concept of a university’s economic impact is undeniably multidimensional and multidisciplinary. Applied economists tend to explore only specific aspects (e.g. human capital), while management scholars predominantly focus on others (e.g. technology transfer activities).

Secondly, the economic impact of universities can be examined from diverse analytical levels. Each level focuses on a specific meaning of impact: the macro-level perspective considers the effects of the higher education (HE) sector/institutions on labour markets or regional competitiveness. The ecosystemic approach examines the networks of actors generating an economic impact (Huggins and Thompson 2014; Pugh et al. 2016). Lastly, from a meso-individual-level perspective, the focus is on the organizations and individuals that are creating or benefiting from the generated impact (Garrido-Yserte and Gallo-Rivera 2010; Cooke 2005).

Thirdly, although universities’ economic impacts have predominantly been viewed as local (Lawton Smith 2003; Siegfried, Sanderson and McHenry 2007), the conceptual and empirical boundaries defining local impact are topics of debate (Beck et al. 1995). Where does a local impact begin and end? Is it strictly tied to a specific administrative or political boundary? As Chatterton and Goddard (2000: 476) state, ‘territoriality is an extremely complex and problematic concept for universities’ due to the interconnectedness of varying levels (global, national, and local) shaping their activities. Moreover, Beck et al. (1995: 249) emphasized that economic impacts ‘do not stop at political boundaries’. Even though researchers have specifically focused on regional and city levels, the empirical operationalization of these is often unclear (Cooke and Leydesdorff 2006).

In this context, scholars rarely endeavour to systematize the literature on universities’ local economic impact to identify its fundamental dimensions and the degree to which multiple (disciplinary and analytical) perspectives inform each other (there are reviews but on specific topics, see e.g. Santos, Dias and Mendonça 2023; Syed, Singh and Spicer 2023). This systematic approach is useful for both scholars, who can broaden or better contextualize their understanding of the economic impact of universities and policymakers. The latter often tackle this topic lacking a comprehensive understanding of its multidimensional nature and the distinctive local dynamics (Rossi and Goglio 2020; Papazoglou et al. 2024).

Thus, we strive to bridge this gap by systematically reviewing the literature on the local economic impact of universities, focusing on three research questions:

RQ1: What dimensions consistently define the local economic impact of universities according to the current literature? How do these dimensions relate to each other?

RQ2: How and by whom is the local economic impact of universities investigated in the current literature?

RQ3: How is the local dimension of universities’ impact conceived in the current literature?

To conduct the literature review, we adhere to the PRISMA approach (Shamseer et al. 2015) and execute various quantitative and qualitative analyses. The findings of this review underscore the local economic impact of universities as a multifaceted concept, covering various key dimensions. These dimensions, however, are predominantly studied in isolation, signalling high fragmentation in the relevant scholarly community. Therefore, our findings emphasize the necessity of viewing universities’ economic impact as a complex interplay of multiple and equally significant factors, rather than a monolithic concept. Moreover, this study emphasizes that the definition of local impact often lacks clarity, presenting challenges for evaluation and comparisons while stressing the need for multilevel and flexible approaches capable of recognizing that universities’ impacts occur at multiple levels in an increasingly interconnected world.

2. Literature review methodology

To address the research questions, we executed a systematic literature review on the economic impact of university adoptions using the PRISMA methodology (Shamseer et al. 2015).

This approach ensures a thorough, transparent, and repeatable approach (Littell, Corcoran and Pillai 2008) (see the PRISMA checklist— Appendix A).

2.1 Literature search and eligibility criteria

To identify relevant publications, a literature search was conducted from September to October 2023 using Scopus (Fig. 1). Scopus is one of the most widely utilized and extensive bibliographic databases, along with the Web of Science (WoS). However, Scopus provides more comprehensive information on authors and citations than WoS does (Visser et al. 2021). Therefore, considering the importance of these data in examining the ‘social structure’ of publications through Bibliometrix, the literature search was performed exclusively using Scopus.

The query ( Appendix B) consists of two sets of keywords underscoring the concepts of economic impact and local impact. Regarding the former, we recognize that multiple terms can indicate the idea of economic impact. Therefore, in addition to the term ‘economic impact’, we include recurrent synonyms such as ‘economic effect’, ‘economic development’, and ‘economic growth’ to be as comprehensive as possible. Regarding the latter, in addition to using the term ‘local’, we include more specific denominations, like ‘region’ and ‘city’.

Following this query, we identified 4,262 publications. To enhance our bibliographic analysis, we established four main eligibility criteria to effectively screen the obtained publications.

• Only articles written in English: book chapters, conference papers, books, or editorials are excluded.

• Only articles from the social science field: only articles from the ‘social sciences’, ‘economics’, and ‘business management’ fields (Scopus categories) are included. This is necessary because Scopus often includes articles from diverse fields such as medicine and natural sciences, which do not align closely with our research focus. Based on these 2 eligibility criteria, 1,877 articles were excluded.

• Only articles published in prestigious international journals: we select only those articles that are published in journals listed in the Academic Journal Guide by the Chartered Association of Business Schools and had a rating of two or above. These journals demand indeed high conceptual and empirical clarity as key requirements in order to be published and this criterion is highly consistent with the objective of this paper—systematizing the concept of universities’ local economic impact. As a result, 1,989 articles were excluded.

• Only articles addressing a local dimension of universities’ economic impact: to obtain only papers focusing on a local dimension of economic impact, the abstracts of each article were screened. Consequently, we excluded papers that either described universities’ economic impact only at the national/supranational level or analysed the impact of researchers or research groups without linking it to the university level.

After applying the previously mentioned criteria, our final dataset included 204 articles ( Appendix C contains the complete list of eligible studies, given that not all studies could be cited within the text). We collected citation information, author details, abstracts, keywords, and references for each article. Lastly, we downloaded the PDFs of the full texts for ensuing qualitative analyses.

2.2 Analysis of the literature review dataset

To analyse the retrieved publications and address the research questions, we conducted various analyses. We executed a bibliometric analysis using Bibliometrix (Aria and Cuccurullo 2017), an open-source software equipped with the R programming language package for quantitative analysis. Furthermore, we conducted a qualitative analysis of the articles to gain more comprehensive information (the coding structure is displayed in  Appendix D).

For the first research question (RQ1), we examined the ‘conceptual structure’ of the literature review dataset, namely the main recurring themes and conceptual dimensions. Hence, we initially utilized the Multiple Correspondence Analysis (MCA) function offered by Bibliometrix. This factor analysis technique helps identify three thematic clusters via the proximity relationship of the articles’ keywords (see Fig. 5). The distance between individual topics within each thematic cluster (represented as small triangles in Fig. 5) mirrors their association/dissociation. We verified the appropriateness of the software’s grouping of papers by looking at a sample of the papers of each cluster. Subsequently, a qualitative analysis, consisting of the screening of the abstract (and often of the full text), was then conducted on all the papers of the three distinct clusters. This process allowed us to deepen our analytical scope in two complementary directions.

Firstly, we analyse the papers of each cluster and then grouped them together to inductively identify thematic subclusters. These are groups of papers sharing a similar research question/goal and object of study, thus pointing to recurrent topics that underpin each macro thematic cluster (Table 2). For each subcluster, we summarized its main contributions to the literature and future research topics (Section 3.2.1).

Secondly, our qualitative analysis determined that some papers (13% of the dataset, see Table 2) cannot be singularly categorized into one single cluster as they encompass more than one topic within their research inquiry or conceptual/analytical framework. This approach allowed us to illuminate areas of intersection and complementarity between the pre-identified conceptual categories (the three clusters) by spotting those papers that bridge two clusters (Section 3.2.2).

For the second research question (RQ2), we performed empirical analyses on the ‘social structure’ of our literature dataset through a coauthorship analysis. This sheds light on the relationships between researchers, and, on a larger scale, between different countries who have investigated the local economic impact of universities (Figs 6 and 7). Additionally, to comprehend how this topic has been studied, we qualitatively reviewed all the articles (Table 3) and subsequently categorized their nature (empirical-vs.-conceptual-review articles) and methodologies (qualitative-vs.-quantitative-vs.-mixed-methods). Finally, we matched the clusters recognized through MCA (Fig. 5) with the methodology type employed by researchers (Table 4).

Finally, for the third research question (RQ3), we performed a qualitative analysis of the articles to understand how scholars operationally define the ‘local’ dimension of impact (Table 5). To this end, we classified the papers abductively, according to both predefined categories (like regional level and city level) and others that emerged from the analysis. The absence of clear definitions or precise boundaries was also deemed pertinent. Furthermore, we matched the three conceptual clusters recognized via the MCA (Fig. 5) with the categories of the ‘local’ dimension found in Table 6.

3. Results

The findings of the literature review are presented according to the three research questions. Before that, some descriptive analyses of the literature dataset are first provided in Section 3.1.

3.1 General description of the corpus of articles

The dataset comprises 204 articles published between 1974 and August 2023. The research on this topic is therefore relatively recent, presenting an annual growth rate of 3.72%. Although only 24% of the articles are single-authored, Table 1 demonstrates that most coauthored papers are not internationally coauthored, which suggests a predominantly nationally oriented and fragmented community of scholars.

This is further supported by Fig. 2, which distinguishes single-country publications from multiple-country publications, based on the authors’ university locations. A significant proportion of these articles stems from authors based in Anglo-Saxon countries, which are also the most collaborative nations in international terms. Notably, very few countries from Africa, Asia, and South America are represented in the figure.

Figure 3 illustrates the growing interest of scholars in the economic impact of universities since the early years of the new millennium, peaking in 2017. This significant uptick can be understood by considering not only the profound influence of the Triple Helix model and the concept of the entrepreneurial university (Etzkowitz and Leydesdorff 2000) but also the critical role of universities’ economic impact in supranational policy documents/processes such as the Lisbon strategy (2000) and the Bologna Process (1999) (Keeling 2006).

Ultimately, Fig. 4 presents the journals that most frequently published articles on this topic. Roughly 60% of the entire literature dataset (123 of 204) were published in just 14 journals, with 3 journals (‘European Planning Studies’, ‘Regional Studies’, and ‘Journal of Technology Transfer’) accounting for nearly 30% of the dataset. Although Fig. 4 only represents a portion of the obtained literature, it indicates that the economic impact of universities is examined across various types of journals (e.g. HE and science, technology transfer, and regional studies). These journals predominantly demonstrate a multi-method and multidisciplinary character.

3.2 RQ1: What conceptual dimensions consistently define the local economic impact of universities? How do these dimensions relate to each other?

This section presents the findings related to the ‘conceptual structure’ of existing literature on the local economic impact of universities. Specifically, we first present the results of the MCA conducted on the article’s keywords (Fig. 5), followed by the qualitative analysis carried out on the three resulting clusters (Section 3.2.1). Subsequently, we illustrate the interaction between these three clusters, focusing on those papers (approximately 13% of the dataset) that concurrently deal with two or more clusters (Section 3.2.2).

3.2.1 The thematic clusters: ‘Technology transfer’, ‘Human capital’, and ‘Local development’

Following the MCA, three thematic clusters were discerned (Fig. 5), accounting for 70% of the literature dataset’s heterogeneity. The red cluster is associated with ‘Technology transfer’ aspects, such as academic entrepreneurship, patents, and knowledge management.

The green cluster includes articles that address the topic of ‘Human capital’. These papers consider how universities improve the productivity or economic development of local areas by providing high-skilled graduates.

Finally, the purple cluster relates to ‘Local development’. Here most of the papers focus on the regional or urban economic impact of universities, linking it to the themes of innovation and the policies promoted by national/local policymakers to enhance universities’ impact on local territories.

Therefore, Fig. 5 illustrates three overarching dimensions: ‘Technology transfer’, ‘Human capital’, and ‘Local development’. These underpin and contribute to the pragmatic definition of the concept of universities’ economic impact. Each cluster is detailed here, highlighting the main subclusters (in italics) identified through qualitative analysis (Table 2).

3.3 Cluster 1: ‘Technology transfer’

This cluster encompasses approximately 20% of the literature review dataset and is comprised of three main subclusters: ‘Academic entrepreneurship’, ‘University–industry interactions for technology transfer’, and ‘Models and strategies for technology transfer’. The significant contribution of this cluster is to underscore the positive outcomes that occur when research and entrepreneurial activities by universities align with industry requirements (Urbano and Guerrero 2013).

The first subcluster (Academic entrepreneurship) pays special attention to universities’ spin-off companies (USOs) and students’ start-ups as local development catalysts (Benneworth and Charles 2005; Prokop and Kitagawa 2022). Scholars have, for example, examined the role and process of academics/universities in aiding the creation of successful academic spin-offs (Steffensen, Rogers and Speakman 2000), as well as the factors promoting or impeding the establishment of USOs (e.g. the role of technology transfer offices, access to funding, or local entrepreneurial culture) (Civera, Meoli and Vismara 2020; Iacobucci, Micozzi and Piccaluga 2021). Other studies lean more towards the significance of coordinating university commercialization activities with regional economic needs, implying that well-supported academic spin-offs can indeed bolster resilient local economies (Guerrero, Cunningham and Urbano 2015). For instance, Fuster et al. (2019) portray USOs in Andalusia as bridges between academia and industry, capable of initiating new ventures, creating employment opportunities, and drawing in technological players. Future research is urged to delve deeper into the institutional and organizational factors that most significantly influence USOs’ long-term effectiveness and sustainability and to enhance understanding of the various multiple impacts these ventures can yield on regional development.

The second subcluster (University–industry interactions for technology transfer) examines how universities and companies interact through avenues such as partnerships, networks, and incubators to simplify knowledge and technology transfer. Scholars scrutinize the dynamic interplay between formal and informal channels (Azagra-Caro et al. 2017), underscoring the intricate processes through which local economic impact is generated. Additional research underscores the effects of knowledge spillovers and market-mediated channels on regional development, pointing out the geographic concentration of knowledge flows stemming from university inventions (Mowery and Ziedonis Arvids 2015). Additionally, some studies emphasize the roles universities play in regional innovation systems (RIS), where universities function as hubs for knowledge transmission, thus promoting technological advancements and industrial growth (Bramwell and Wolfe 2008; Aksoy, Pulizzotto and Beaudry 2022). However, the success of these networks relies on aligning research agendas with industry needs, which presents challenges to academic independence (Muscio, Quaglione and Scarpinato 2012). Future research could aim to fine-tune transfer channels to balance economic outcomes with research autonomy while investigating the role of informal relationships in knowledge/technology transfer processes.

The third subcluster (Models and strategies for technology transfer) focuses on the frameworks and strategies that universities use to facilitate technology transfer, and, therefore, to optimize regional economic impact (McAdam et al. 2012). This subcluster examines the development of effective technology transfer models, addressing the challenges universities encounter when trying to align their mission with regional economic demands while evaluating the return on investment in university innovation activities (Heher 2006). The literature also underlines the importance of stakeholder relationships in technology transfer and advocates for collaborative networks between universities and regional development agencies (RDAs) to boost the efficacy of these processes (Kirby, Guerrero and Urbano 2011). Future research may delve into understanding which institutional and organizational strategies can strengthen universities’ entrepreneurial orientations and explore which models best improve stakeholder engagement across various geographic settings.

3.4 Cluster 2: ‘Human capital’

This cluster encompasses approximately 22% of the literature dataset. It consists of three main subclusters: ‘Impact on local labour markets’, ‘Impact of students’ mobility on local territories’, and ‘Impact of institutions/individuals expenditures on local economies’. Hence, this cluster discusses the crucial role of universities in augmenting human capital, and how this fosters positive economic impacts on employment growth, wages, productivity, and innovation (Drucker and Goldstein 2007; Gennaioli et al. 2013).

The first subcluster (Impact on local labour markets) examines the ways universities contribute to local labour markets and economic growth by providing graduates with skills and competencies that closely align with economic needs (Coulombe and Tremblay 2001; Keep, Mayhew and Payne 2006). In this context, these papers critically evaluate if and how the role of universities, educational policies, and creativity enhance productivity and economic performance (Mellander and Florida 2011). Similarly, some research conduction longitudinal analyses assess the impact of tertiary education on regional economic growth, highlighting universities’ role as both direct and indirect catalysts for local economic development and competitiveness (Marrocu, Paci and Usai 2022). This subcluster emphasizes the crucial role that universities play in addressing skill gaps and fostering economic resilience at the regional level (Di Liberto 2008; Delgado, Henderson and Parmeter 2014). Future research may further contribute to the literature by not only developing more tailored measures/metrics (e.g. on teaching outputs and quality) but also by exploring whether the type and quality of research activities conducted by universities equally affect a local region’s human capital stock.

The second subcluster (Impact of students’ mobility on local territories) probes into how the mobility and migration of students and graduates affect regional economic dynamics, in terms of brain drain, local talent retention, and attraction strategies. For instance, some studies have investigated whether the quality and reputation of universities (Ciriaci 2014; Tano 2014) or other factors (e.g. living costs or job prospects) influence students’ mobility decisions for both education and subsequent employment (Ma, Kang and Kwon 2017). Simultaneously, some papers have pinpointed specific challenges faced by smaller and medium-sized cities, which find it challenging to retain high-skilled graduates due to the powerful allure of larger and economically vibrant urban centres (Plöger and Weck 2014). However, more research is essential to understand which policies and strategies are most effective in reversing and mitigating mobility/migration trends. Furthermore, it is crucial to figure out how collaborations between universities, public administrations, and local businesses can be fortified to bridge regional/local skill gaps and make peripheral areas more appealing to skilled graduates (Lim, Lee and Kim 2015).

The third subcluster of papers (Impact of institutions/individuals’ expenditures on local economies) explores the effect of expenditures made by academic institutions, students, and staff on local economic growth (Garrido-Yserte and Gallo-Rivera 2010; Vaiciukevičiūtė, Stankevičienė and Bratčikovienė 2019). Several studies suggest that these expenditures can be strategically leveraged to maximize local economic benefits (Steinacker 2005). Several studies have therefore endeavoured to measure and quantify the direct, indirect, and induced economic contributions of universities. These focus on spending patterns and how they stimulate local businesses and job opportunities (Siegfried, Sanderson and McHenry 2007). However, future studies are required to improve the metrics and methodologies for effectively assessing the economic impact of universities through data on their expenditures.

3.5 Cluster 3: ‘Local development’

This cluster comprises nearly half of the literature review data, emphasizing the role of universities in nurturing local economic development. This is primarily achieved by encouraging innovative processes and stimulating local demand for knowledge-intensive services. The three main subclusters presented below are: ‘Universities as engines of local development’, ‘Governance and policies on innovation ecosystem’, and ‘Contributions of universities to peripheral/disadvantaged areas’.

The first subcluster (Universities as engines of local development) explores how universities serve as catalysts for regional innovation and economic growth by fostering long-term relationships with industry and various local stakeholders (Power and Malmberg 2008; Huggins and Johnston 2009). Central research questions addressed include how universities and RDAs can collaborate to boost local economic growth, and how universities can strike a balance between their research and educational roles to maximize their regional contributions (Goddard and Chatterton 1999; Agasisti, Barra and Zotti 2019). A primary strand of this literature underlines the role of ‘entrepreneurial universities’ (Pugh et al. 2022) in fostering local innovation ecosystems via proactive engagement strategies (Benneworth, Charles and Madanipour 2010) and the potential leadership role they can assume within these ecosystems (Chen and Kenney 2007; Brito 2018). Similarly, scholars broadly emphasize that robust management commitment is crucial for successful university–industry cooperation (Uyarra 2010; Galán-Muros et al. 2017). Future research directions could investigate what types of national/local policies (incentives, rules, etc.) and governance arrangements can more effectively enhance the entrepreneurial role of universities as engines of local development.

The second subcluster (Governance and policies on innovation ecosystem) takes a more macro-level and multi-actor approach. It focuses on the relationships and networks between universities, businesses, and other entities promoting local development (Cooke and Leydesdorff 2006), as well as the policies that shape these ecosystems (Andersson, Quigley and Wilhelmsson 2009; McAdam, Miller and McAdam 2016). This subcluster majorly emphasizes the application of the Triple Helix models (Etzkowitz and Leydesdorff 2000; Carayannis and Campbell 2009) in various contexts. Many scholars demonstrate the adaptability of this model to diverse regional settings by incorporating a broader range of stakeholders and addressing local economic peculiarities (Goldstein and Glaser 2012; Pugh 2017). Other research explores the influence of university involvement in local/regional policymaking and governance, demonstrating its significant impact on economic development (Cooke and Leydesdorff 2006; Pugh et al. 2016). Scholars also analysed the consequences of national/regional HE policies on universities’ strategic collaboration capacity with regional partners, as well as on productivity and innovation. It is worth noting that although educational investment boosts productivity, these gains tend to decrease with increasing geographic distance from innovation hubs (Kaufmann et al. 2003; Cooke 2005). Future studies might delve into understanding the mechanisms and structures that best facilitate universities’ governance roles and how these vary across different political and regional settings.

Lastly, the third subcluster (Contributions of universities to peripheral/disadvantaged areas) encompasses studies investigating how universities stimulate economic and social development in marginalized and underdeveloped regions, often by formulating specific conceptual frameworks (Kruss and Gastrow 2017). This subcluster underscores the fundamental role of universities in emerging economies and peripheral areas, where they function not solely as educational and research institutions but also as catalysts of local economic development and inclusive growth (Fischer, Schaeffer and Silveira 2018). Research has specifically examined how universities can bridge the gap between international science and local industries in developing contexts, identified facilitating factors for meaningful interactions with marginalized communities, and explored ways for universities to attract and retain knowledge-intensive investments in disadvantaged areas (Čábelková, Normann and Pinheiro 2017). Still, the conditions that enable or impede effective university engagement in these regions remain underexplored. Therefore, comparative studies across different socioeconomic contexts might help identify common factors that amplify their positive impact.

3.5.1 The relationships between the three thematic clusters

Our qualitative analysis identified that some papers (about 13% of the dataset, according to Table 2) encompass more than one thematic cluster within their research inquiry or conceptual/analytical framework. In this regard, we delineated three groups of articles.

A first group focuses on the connection between human capital and technology transfer, particularly via academic entrepreneurship (Mason et al. 2020). These research questions revolve around the factors that contribute to successful student entrepreneurship programs and the crucial role that graduate students play in promoting technology transfer activities (Audretsch et al. 2022). Some studies, for instance, explore the impact of entrepreneurship programs in fostering student start-ups, and the influence of graduate students on university spin-offs, emphasizing human capital as a critical pillar of successful technology transfer initiatives (Hayter, Lubynsky and Maroulis 2017).

A second set of articles emphasizes universities’ dual roles as catalysts for human capital formation and drivers of local development. Scholars using international data have instead studied the diverse impacts of universities on regional economic growth (Smętkowski 2018). In this context, the analysis by Valero and Van Reenen (2019) unambiguously demonstrates that human capital generated by universities, in conjunction with technology transfer, propels the economic growth of regions. Another example is illustrated by Kitagawa et al. (2022), who demonstrate how universities contribute to regional economies by examining two types of graduate retention: labour retention (i.e. graduates employed in the region where they studied) and entrepreneurship retention (i.e. graduates starting businesses in the region where they studied). Finally, Fonseca (2023) highlights how counter-flows of students to peripheral regions in Portugal contribute to human capital enhancement and innovation, acting as a catalyst for regional development.

Lastly, a third group of papers intersects local development research with technology transfer studies. Scholars focus, for example, on how university-driven entrepreneurial activities such as spin-offs, patents, consultancies, and student start-ups contribute to regional competitiveness and economic growth across various local areas (Benneworth and Charles 2005; Guerrero, Urbano and Fayolle 2016). In a similar vein, some studies highlight the importance of innovation ecosystems as boosters of regional competitiveness, supporting the creation of spin-offs and knowledge-based initiatives that foster a conducive environment for innovation and economic development (Bramwell and Wolfe 2008; Breznitz and Feldman 2012).

3.6 RQ2: How and by whom is the local economic impact of universities investigated in the current literature?

We will now address how the local economic impact of universities is explored in current literature (Tables 3 and 4) and by whom (Figs 7 and 8).

Therefore, Table 3 presents information on the types of articles (conceptual vs. empirical) and the research designs adopted in the papers included in this review. While around 30% of the articles are purely conceptual, most (69%) present an empirical analysis. In this respect, scholars have primarily employed quantitative methodologies (around 70%), while qualitative methods are used significantly less. Mixed-method studies remain a minority. Notably, only four literature reviews emerge from this classification, suggesting that few attempts have been made to systematize research on universities’ economic impact.

Furthermore, we aligned the classification based on the methodological approach (quantitative vs. qualitative) with the three previously identified conceptual clusters (Fig. 5) to determine whether certain topics are more naturally investigated using specific methodologies. In this respect, Table 4 demonstrates that the studies belonging to the human capital cluster have been predominantly researched using quantitative methods (97%). Qualitative approaches are predominantly used to investigate local development topics and, to a lesser extent, technology/knowledge transfer, although quantitative approaches still represent the majority of the papers within these clusters. Articles that cover two clusters primarily use quantitative methods (78%), often merging datasets on different topics or creating multivariate econometric models.

Finally, we also verified whether empirical papers tend to adopt a single or comparative perspective. In this regard, we found that most studies (65%) analyse individual contexts (single regions or local areas), while approximately 30% of the articles aim to compare multiple contexts. In the latter scenario, 18 studies compare local areas from different countries, whereas 32 compare areas within the same country.

On the ‘who question’, namely, the ‘social structure’ of current literature, we conducted two distinct coauthorship analyses. Figure 6 illustrates the individual connections among the authors included in our review (i.e. the network’s nodes), while Fig. 7 represents the same analysis at the country level of the authors. Both figures unequivocally indicate a significantly fragmented scholars’ community.

Figure 6 demonstrates several small clusters of authors (16 clusters), ranging from a minimum of 2 to a maximum of 5 authors per cluster. However, the interrelation among these clusters is weak, signifying that authors rarely interact systematically. Further analysis of these clusters reveals that they predominantly comprise scholars from either the same university (or country) or the same disciplinary community. The most noticeable examples include the pink cluster of economists specializing in education and labour market topics from UK universities, who have collaborated on several papers, and the orange cluster composed of entrepreneurship scholars from Lancaster University.

Similarly, Fig. 7 visually demonstrates the interconnections among various countries through collaborative efforts on one or more articles. The graphic further underscores the central role of English-speaking nations in this research field, reiterating the point made earlier in Fig. 2. The UK and US nodes are notably larger and centrally positioned within the green and red clusters. Nonetheless, Fig. 7 also reveals a rather fragmented community, corroborating the observations made at the scholarly level. Robust links among countries (i.e. the ties are thicker) are primarily found between English-speaking nations, especially the USA and the UK, and some European countries, such as Sweden and the Netherlands. The network analysis also brought some other clusters to light. However, these clusters exhibit sparse collaborative connections with the two major red and green clusters. Asian, African, and South American nations, and by and large, developing countries, are almost entirely absent from these interactions.

3.7 RQ3: How is the local dimension of universities’ impact conceived in the current literature?

To understand what authors specifically mean when they refer to ‘local impact’, we conducted a qualitative analysis of the literature dataset. Our process involved abductively identifying five main scenarios, which are detailed in Table 5.

Firstly, as Table 5 indicates, the local economic impact of universities is primarily seen at the ‘regional level’ (57%). However, an in-depth analysis of the articles reveals that the regional level is defined in a variety of ways. For instance, some authors have adopted Nomenclature of Units for Territorial Statistics (NUTS) normative definitions for defining a region, while others have considered a portion or an entire state, as observed in the USA. Interestingly, Benneworth and Charles (2005) examined the impact of Newcastle University in the New East England region, one of England’s nine official regions. Yet, they also considered the case of Twente (Lazzeretti and Tavoletti 2005), even though Twente is not an administrative region but a geographically homogeneous area within the province of Overijssel.

Secondly, papers that operationalize the local impact at the ‘subregional level’ or ‘city level’ are, indeed, a minority (7% and 6%,, respectively). These impacts often pertain to a section or an entire administrative province that extends beyond urban and metropolitan territories. For instance, Harris (1997) examines the Portsmouth travel-to-work area, situated between the city and the region. (Garrido-Yserte and Gallo-Rivera 2010) adopt a similar approach in exploring the impact generated by the University of Alcala. The analysis of universities’ economic impact at the ‘city level’ (accounting for 6% of papers) is more clearly defined than that at the aforementioned ‘subregional level’. Here, authors often pinpoint the critical role of universities in fostering urban regeneration and stimulating economic growth (Benneworth, Charles and Madanipour 2010). However, in some instances, the term ‘city level’ is used interchangeably with ‘metropolitan’ or ‘urban areas’, leading to undefined boundaries (Steinacker 2005).

Third, only 10 of 204 papers (5%) analyse the economic impact of universities on multiple local levels. For instance, Fonseca (2023) highlights the need to identify multiple levels of analysis, ranging from the regional macro-area down to the city and district levels, when exploring the counterflows of students and their effects as drivers of innovation and economic growth. Similarly, (Kruss and Gastrow 2017) study case reports from a multilevel perspective. These papers seem to suggest that to effectively capture the economic effects of universities, multiple levels of local analysis should be considered jointly.

Lastly, it is noteworthy that approximately one-quarter of the examined papers present ‘no clear definition or operationalization’ of local impact, with 70% of these papers being empirical. For instance, Breznitz and Feldman (2012) and Galán-Muros et al. (2017) explore the analysis of a local context and themes, yet specific local boundaries are not clarified. It is also probable that these papers encompass initiatives that may transcend traditional administrative boundaries (e.g. research projects/partnerships between universities and companies located in different areas) or involve cross-regional collaboration.

If we split the categorization of Table 5 into the three clusters (Fig. 5), we get Table 6. This table depicts the percentage of papers corresponding to each cluster, as defined by the adopted local impact criterion. As Table 6 illustrates, all three clusters, as well as papers that focus on multiple clusters, are primarily studied at a regional analytical level. This is especially noticeable in the local development and human capital clusters, where roughly 70% of studies concentrate on the economic impact of universities within a specified region or subregional area. For example, Cooke and Leydesdorff (2006) underscore the importance of universities in encouraging RIS and local development, while Di Liberto (2008) probes the role of human capital in regional economic evolution in Italy, notably shedding light on the development-boosting function of primary education in the southern regions. The human capital cluster contains the highest percentage of articles focusing on city- or metropolitan-level analysis (14%). This cluster includes a collection of studies examining the impact of student and graduate mobility/migration on diverse urban environments.

Interestingly, the cluster related to technology transfer presents approximately half of its papers without a clear definition of local impact. This percentage is notably lower in the other two clusters.

4. Discussion

In this paper, we reviewed the current literature on the economic impact of universities on local areas. Through a combination of quantitative bibliometric techniques and qualitative analysis we examined the crucial conceptual dimensions underpinning universities’ economic impact (RQ1), the social structure of the community studying this topic (RQ2), and the definition of the local dimension (RQ3). Our findings critically contribute to this debate in three ways.

4.1 Contribution #1: ‘The local economic impact of universities as an umbrella concept’

The analysis of the conceptual structure (Section 3.2) reveals that the local economic impact of universities can be broadly defined as an ‘umbrella concept’ under which several, but equally relevant dimensions, coexist. These are ‘Technology transfer’, ‘Human capital’, and ‘Local development’ (Fig. 5). Our findings also highlight that these three key conceptual dimensions interact only to a limited extent. Most of the papers (87%) tend to focus on only one of the three conceptual clusters, with limited adoption of more holistic and synergistic approaches. Two main factors may concurrently contribute to these limited interactions.

Firstly, our findings show that specific conceptual dimensions are primarily examined through quantitative methodologies (Tables 3 and 4), while others are more inclined towards qualitative approaches. Therefore, this methodological compartmentalization could represent a barrier to assessing universities’ economic impact in a more holistic way.

Secondly, the analysis of social structure (Section 3.3) suggests the existence of several loosely connected communities of scholars, as opposed to a cohesive, dialoguing network (Figs 6 and 7). Indeed, strong connections and relationships tend to exist almost exclusively among scholars from the same scientific community, who usually adopt a specific understanding of economic impact, within universities, or among those with prior collaborations.

Therefore, what appears to be missing is a comprehensive framework capable of uniting the multiple, yet equally relevant, aspects that characterize the concept of universities’ economic impact. In this regard, Fig. 8 represents our attempt to synthesize the three dimensions of economic impact into a single conceptual framework, emphasizing their interconnections.

The framework emphasizes three principal dimensions—‘Technology transfer’, ‘Human capital’, and ‘Local development’, all of which collectively fall under the larger concept of the local economic impact of universities. These dimensions are interconnected, as evidenced in Section 3.2.2. Nonetheless, the strength of the relationships between them varies, at least based on the findings of this literature review.

The relationship between ‘Human capital’ and ‘Technology transfer’ predominantly centres on how the former impacts the latter. Graduates equipped with entrepreneurial skills and businesses-related competencies are indeed claimed to be a crucial channel of knowledge and technology transfer (Hayter, Lubynsky and Maroulis 2017; Audretsch et al. 2022). Conversely, the question of whether and how technology transfer activities affect human capital has been sporadically addressed and with controversial results (Guerrero, Urbano and Fayolle 2016).

The connection between ‘Human capital’ and ‘Local development’ centres on the beneficial association between stocks of human capital and regional/local economic advancement (Valero and Van Reenen 2019). Other aspects of this relationship are represented by universities’ contribution to local economies in terms of graduate retention (Kitagawa et al. 2022) or students/graduates’ mobility as a catalyst for local development (Fonseca 2023).

The strongest link appears to be represented in papers focusing on both ‘Technology transfer’ and ‘Local development’. Technology transfer activities, particularly spin-offs, are frequently seen to boost regional competitiveness (Guerrero, Urbano and Fayolle 2016). Concurrently, the proximity of, or participation by, universities in regional or local innovation ecosystems is a crucial factor in encouraging the development and spread of technology transfer activities (Bramwell and Wolfe 2008; Breznitz and Feldman 2012).

One notable gap in the existing literature is the limited research exploring the interplay of all three dimensions—Human capital, Technology transfer, and Local development—in an integrated manner (exceptions are, e.g. Cox and Taylor 2006; Rossi and Goglio 2020). Future studies could address this by examining whether the mechanisms driving universities’ impact in one dimension align or diverge across others. Interdisciplinary initiatives, such as multinational research projects and international conferences, might play a pivotal role in fostering a holistic understanding of these dimensions. They could also encourage collaborative approaches that address multiple aspects of universities’ economic impact simultaneously. These efforts would create a more comprehensive framework for leveraging the multifaceted roles of universities in regional development.

4.2 Contribution #2: ‘The definition of local impact boundaries: towards a paradox?’

Our analysis reveals that defining and operationalizing ‘local’ can be a complex issue. A significant part of the current literature struggles to provide a clear and empirically grounded definition of local impact (Table 5). Ambiguity persists even when terms like region or city are used, as the conceptual framing often deviates from the geographical scope (Cooke and Leydesdorff 2006). This leads to vague operationalizations of local boundaries (Pugh et al. 2016). Definitions of regional and local can vary based on disciplinary perspectives, with economists, geographers, and sociologists giving different interpretations. Economists may prioritize administrative boundaries following the operationalization of metrics like Gross Domestic Product (Giuliani and Rabellotti 2012) or employment rates. Conversely, geographers and sociologists emphasize sociocultural and functional dimensions (Brekke 2021). This disciplinary divergence results in inconsistencies in how impacts are measured and compared across studies.

Furthermore, the spatial boundaries defining a region (or other local territories) are intrinsically ambiguous and dependent on context. For example, the concept of a region may include administrative units such as NUTS in Europe, but these frequently fail to coincide with actual functional economic areas where interdependencies are most conspicuous. Similarly, the term ‘local’ may reference a small urban area or an extensive rural community, thereby complicating comparative analyses (Cheshire and Magrini 2000).

The difficulty in defining clear, comparable local boundaries underscores a critical challenge: the requirement for a more adaptable concept of ‘local’ that can recognize the dynamic and interrelated nature of local contexts and universities’ impacts. In today’s increasingly interconnected world, cross-boundary interactions and economic flows are persistently redefining geographical and functional boundaries (Chatterton and Goddard 2000; Brekke 2021). This challenges traditional administrative boundaries and blurs the demarcations between what is considered local, national, and global. As local networks expand through multilevel interactions, the importance of strict geographic definitions lessens, creating a need for a more relational understanding of territories as interconnected nodes within a larger network-based system (Power and Malmberg 2008). Furthermore, the impacts of local actions often traverse administrative borders, accentuating the necessity for adaptable definitions and frameworks that account for these cross-boundary dynamics (Cooke 2005). These dynamics are particularly evident in the diffusion and transfer of knowledge/technology, mobility of human capital, and innovation networks and ecosystems, which are rarely confined to a single local area. Essentially, the fluidity of spatial relationships and impacts somehow undermines stringent territorial definitions (Cheshire and Magrini 2000).

However, there is an equally significant practical need to define the boundaries of a local impact as precisely as possible to support the implementation and evaluation of policies that are measurable and outcome-oriented (Cooke and Leydesdorff 2006; Isaksen and Trippl 2017). Local and national policymakers require well-defined structures and frameworks for effectively allotting responsibilities and attributing resources, essentials for future evaluation exercises (Goddard and Chatterton 1999).

The definition and operationalization of local impact seem to open a paradox. While there is an increasing awareness and need for flexible and multilevel conceptualizations of local impacts, there is also a requirement for clear boundaries to effectively support the design, implementation, and evaluation of policies. Future studies are essential to discuss and develop innovative solutions capable of balancing these divergent yet equally relevant demands.

4.3 Contribution #3: ‘Emerging literature gaps: negative impacts, qualitative approaches, and comparative studies’

Our findings also highlight three main gaps that future studies need to address.

Firstly, the negative, unintended, or controversial impacts of universities on the local economy have often been overlooked. There are only a few papers dealing with this topic. Notable exceptions are, Ciriaci (2014) and Ma, Kang and Kwon (2017), who explain that the ‘brain drain’ phenomenon of students and the commercialization of research can negatively impact less developed regions or areas. Similarly, Acebo, Miguel-Dávila and Nieto (2021) critically analyse the commonly accepted relationship between university and industry, showing a slight effect on firms’ performance. Conversely, Papazoglou et al. (2024) demonstrate that regional actors appear to be nearly unaffected by university research activities. Florida and Gaetani (2020) argue that though universities are key sources of talent and stimulate innovation and economic growth, they can also contribute to economic and spatial inequalities. Future studies could further explore these often-overlooked issues, providing a more nuanced understanding of university-based economic impact.

Secondly, our findings emphasize the dominance of quantitative approaches in studying the economic impact of universities (Tables 3 and 4). These frameworks typically excel in quantifying and demonstrating this impact, illuminating its main drivers and moderating variables. Nonetheless, qualitative or mixed-methods approaches can more readily investigate other relevant aspects of a university’s economic impact such as stakeholders’ experiences and relationships, as well as those institutional/organizational dynamics/factors that generate, aid, or hinder the generation of these impacts. For instance, qualitative studies may support scholars in better acknowledging local stakeholders’ perspectives on what they believe the economic impact of universities to be, or what benefits they expect from universities’ activities (McAdam, Miller and McAdam 2016; Gianiodis and Meek 2020). Hence, this study advocates for increased use of qualitative and mixed-method approaches, leveraging the strengths of both methodologies to explore the intersection between measurable outcomes and contextual dynamics. These methods have the potential to uncover dimensions frequently ignored by purely quantitative approaches, or mechanisms less suited to quantification. Both viewpoints appear essential in bolstering evidence-based policymaking and a more comprehensive understanding of universities’ economic impact.

Lastly, comparative studies are scarce, likely hindered by the high fragmentation of the scholarly community on this topic. Future research adopting a comparative perspective could investigate whether universities have distinct impacts in various local contexts, highlighting common trends and disparities (Nieth and Radinger-Peer 2023), and promoting effective policy translation. Furthermore, there are minimal studies on universities’ economic impact on (and by the authors of) developing and emerging countries Giuliani and Rabellotti (2012). Since universities are critical actors in local development, it is crucial to determine whether and how universities foster growth in less developed countries, as suggested by studies focusing on peripheral regions in Western countries (Čábelková, Normann and Pinheiro 2017; Rossi and Goglio 2020).

5. Concluding remarks

This paper synthesizes how the local economic impact of universities is conceptualized and analysed in the existing literature. Our analysis yields two main contributions to the research. Firstly, it underscores the importance of investigating a university’s economic impact in a more holistic manner, namely, analysing the various yet equally relevant dimensions underpinning this concept in conjunction (see Fig. 8).

Secondly, we underscore the paradoxical tension that arises from the need for flexible and multilevel approaches in examining local impact, while concurrently requiring well-defined local boundaries for policy development and evaluation.

However, our findings also entail some policy implications. Firstly, policymakers should encourage dialogue among national and supranational statistical agencies to effectively harmonize and integrate data collection efforts on this topic. Developing consolidated multivariate datasets on universities’ economic impacts is essential not just for local and national policymakers, who require precise data to design and implement policies, but also for universities, who are increasingly called upon to prove their value to local communities and governments. Integrated datasets would enable universities, as well as local and national policymakers, to better illustrate how public funds engender positive externalities. The need for rich, robust, and integrated panel datasets to accurately measure and evaluate the economic impacts in their multiple nuances seems thus crucial. Therefore, improving the availability and comparability of datasets about universities’ impacts would pave the way for more effective policy design and benchmarking. The establishment of standardized analytical frameworks and the fostering of comparability across multiple contexts would help identify transferable and scalable best practices.

Secondly, the paradoxical tension about the boundaries of local impacts (flexible and multilevel vs. clear and fixed) highlights the multilevel and interconnected nature of universities’ economic impact (Brekke 2021). This perspective advocates for new governance models rooted in relational and ecosystem-focused approaches (Bryson et al. 2017) and clearly aligning with the Helix models. Such models underscore the importance of fostering network-based collaborations and dialogue across a wide array of stakeholders (policymakers, universities, businesses, and local communities) and levels (Cooke 2005). An ecosystemic model allows distributing and sharing responsibilities, thereby promoting horizontal accountability and also helps to integrate diverse competencies and resources, ultimately enhancing the effectiveness and sustainability of universities’ actions. By acknowledging these dynamic interconnections, policymakers can create more adapted and nuanced policies that address the intricacies of local and regional development while ensuring accountability and long-term impacts.

In conclusion, it is important to note some limitations associated with the current analysis.

Firstly, like all literature review methodologies, the PRISMA approach has both its strengths and shortcomings. On the one hand, this approach ensures precision and rigour, fostering transparency and reproducibility of the major processes (Littell, Corcoran and Pillai 2008). On the other hand, the choice to employ stringent eligibility criteria could lessen the flexibility of the literature search and the scope of the retrieved literature. For example, pertinent articles may have been omitted from our review based on their structure: articles discussing the economic impact of universities within the text but not explicitly using the terms from our query in the title, keywords, or abstract may have been overlooked. Similarly, our search strategy may have excluded some studies that focus on specific economic impacts without explicitly using the term ‘economic impact’ (and synonymous). Furthermore, we have not taken into account certain forms of publications (e.g. grey literature) which could further enhance comprehension of economic impact but necessitate different review techniques.

Secondly, despite its integration with Bibliometrix offering certain advantages (Aria and Cuccurullo 2017), data from Scopus also display limitations, particularly in relation to the disciplinary classification of articles. Therefore, replicating this review using WoS or alternative databases might help refine the findings obtained.

Conflict of interest

The authors report there are no competing interests to declare.

Funding

The authors acknowledge financial support within the ‘Fund for Departments of Excellence Academic Funding’ provided by the Italian Ministry of Education (MIIM), established by the Stability Law, namely, ‘Legge di Stabilità n.232/2016-2017’ - Project of the Department of Economics, Management, and Quantitative Methods, University of Milan.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Appendix A PRISMA checklist

Appendix B Query used in the literature search strategy

TITLE-ABS-KEY (‘economic impact’) OR TITLE-ABS-KEY (‘economic effect’) OR TITLE-ABS-KEY (‘economic growth’) OR TITLE-ABS-KEY (‘economic development’) OR TITLE-ABS-KEY (‘economic development’) AND TITLE-ABS-KEY (‘universit*’) OR TITLE-ABS-KEY (‘higher education’) OR TITLE-ABS-KEY (‘higher education institution*’) AND TITLE-ABS-KEY (‘local’) OR TITLE-ABS-KEY (‘cit*’) OR TITLE-ABS-KEY (‘region*’)

Appendix C List of eligible studies

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3. Agasisti, T., & Bertoletti, A. (2022). Higher education and economic growth: A longitudinal study of European regions 2000–2017. Socio-Economic Planning Sciences, 81, 100940.

4. Agasisti, T., Barra, C., & Zotti, R. (2019). Research, knowledge transfer, and innovation: The effect of Italian universities’ efficiency on local economic development 2006–2012. Journal of Regional Science, 59(5), 819–849.

5. Agasisti, T., Egorov, A., Zinchenko, D., & Leshukov, O. (2021). Efficiency of regional higher education systems and regional economic short-run growth: Empirical evidence from Russia. Industry and Innovation, 28(4), 507–534.

6. Aksoy, A. Y., Pulizzotto, D., & Beaudry, C. (2022). University-industry partnerships in the smart specialisation era. Technological Forecasting and Social Change, 176, 121438.

7. Amendola, A., Barra, C., & Zotti, R. (2020). Does graduate human capital production increase local economic development? An instrumental variable approach. Journal of Regional Science, 60(5), 959–994.

8. Andersson, R., Quigley, J. M., & Wilhelmson, M. (2004). University decentralization as regional policy: The Swedish experiment. Journal of Economic Geography, 4(4), 371–388.

9. Andersson, R., Quigley, J. M., & Wilhelmsson, M. (2009). Urbanization, productivity, and innovation: Evidence from investment in higher education. Journal of Urban Economics, 66(1), 2–15.

10. Armstrong, H. W. (1993). The local income and employment impact of Lancaster University. Urban Studies, 30(10), 1653–1668.

11. Artis, M. J., Miguelez, E., & Moreno, R. (2012). Agglomeration economies and regional intangible assets: An empirical investigation. Journal of Economic Geography, 12(6), 1167–1189.

12. Audretsch, D. B., Belitski, M., Guerrero, M., & Siegel, D. S. (2022). Assessing the impact of the UK’s Research Excellence Framework on the relationship between university scholarly output and education and regional economic growth. Academy of Management Learning & Education, 21(3), 394–421.

13. Avanesova, A. A., & Shamliyan, T. A. (2018). Comparative trends in research performance of the Russian universities. Scientometrics, 116, 2019–2052.

14. Avnimelech, G., & Feldman, M. P. (2015). The stickiness of university spin-offs: A study of formal and informal spin-offs and their location from 124 US academic institutions. International Journal of Technology Management, 68(1–2), 122–149.

15. Azagra-Caro, J. M., Barberá-Tomás, D., Edwards-Schachter, M., & Tur, E. M. (2017). Dynamic interactions between university-industry knowledge transfer channels: A case study of the most highly cited academic patent. Research Policy, 46(2), 463–474.

16. Baade, R. A., Baumann, R. W., & Matheson, V. A. (2011). Big men on campus: Estimating the economic impact of college sports on local economies. Regional Studies, 45(3), 371–380.

17. Baltzopoulos, A., & Broström, A. (2013). Attractors of entrepreneurial activity: Universities, regions and alumni entrepreneurs. Regional Studies, 47(6), 934–949.

18. Bathelt, H., Kogler, D. F., & Munro, A. K. (2010). A knowledge-based typology of university spin-offs in the context of regional economic development. Technovation, 30(9–10), 519–532.

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20. Beer, A., & Cooper, J. (2007). University–regional partnership in a period of structural adjustment: Lessons from Southern Adelaide’s response to an automobile plant closure. European Planning Studies, 15(8), 1063–1084.

21. Benneworth, P., & Charles, D. (2005). University spin-off policies and economic development in less successful regions: Learning from two decades of policy practice. European Planning Studies, 13(4), 537–557.

22. Benneworth, P., Charles, D., & Madanipour, A. (2010). Building localized interactions between universities and cities through university spatial development. European Planning Studies, 18(10), 1611–1629.

23. Benneworth, P., Young, M., & Normann, R. (2017). Between rigour and regional relevance? Conceptualising tensions in university engagement for socio-economic development. Higher Education Policy, 30, 443–462.

24. Bertoletti, A., Berbegal-Mirabent, J., & Agasisti, T. (2022). Higher education systems and regional economic development in Europe: A combined approach using econometric and machine learning methods. Socio-Economic Planning Sciences, 82, 101231.

25. Bonander, C., Jakobsson, N., Podestà, F., & Svensson, M. (2016). Universities as engines for regional growth? Using the synthetic control method to analyze the effects of research universities. Regional Science and Urban Economics, 60, 198–207.

26. Bramwell, A., & Wolfe, D. A. (2008). Universities and regional economic development: The entrepreneurial University of Waterloo. Research Policy, 37(8), 1175–1187.

27. Breznitz, S. M. (2011). Improving or impairing? Following technology transfer changes at the University of Cambridge. Regional Studies, 45(4), 463–478.

28. Breznitz, S. M., & Feldman, M. P. (2012). The engaged university. The Journal of Technology Transfer, 37, 139–157.

29. Breznitz, S. M., O’Shea, R. P., & Allen, T. J. (2008). University commercialization strategies in the development of regional bioclusters. Journal of Product Innovation Management, 25(2), 129–142.

30. Brito, C. M. (2018). Promoting the creation of Innovation Ecosystems: The case of the University of Porto. Journal of Innovation Management, 6(3), 8–16.

31. Čábelková, I., Normann, R., & Pinheiro, R. (2017). The role of higher education institutions in fostering industry clusters in peripheral regions: Strategies, actors and outcomes. Higher Education Policy, 30, 481–498.

32. Calcagnini, G., Favaretto, I., Giombini, G., Perugini, F., & Rombaldoni, R. (2016). The role of universities in the location of innovative start-ups. The Journal of Technology Transfer, 41, 670–693.

33. Canal Domínguez, J. F. (2021). Higher education, regional growth and cohesion: insights from the Spanish case. Regional Studies, 55(8), 1403–1416.

34. Cantu, F. J., Bustani, A., Molina, A., & Moreira, H. (2008). A knowledge-based development model: The research chair strategy. Journal of Knowledge Management, 12(6), 1.

35. Carree, M., Malva, A. D., & Santarelli, E. (2014). The contribution of universities to growth: Empirical evidence for Italy. The Journal of Technology Transfer, 39, 393–414.

36. Cash, P. R., Bhadury, J., McCrickard, D. L., & Weeks, J. K. (2010). In pursuit of the ‘Third Mission’: Strategic focus on regional economic development by a business school in the USA. Local Economy, 25(2), 148–153.

37. Chen, K., & Kenney, M. (2007). Universities/research institutes and regional innovation systems: the cases of Beijing and Shenzhen. World Development, 35(6), 1056–1074.

38. Cheshire, P., & Magrini, S. (2000). Endogenous processes in European regional growth: Convergence and policy. Growth and Change, 31(4), 455–479.

39. Christopherson, S., & Clark, J. (2010). Limits to ‘the learning region’: What university-centered economic development can (and cannot) do to create knowledge-based regional economies. Local Economy, 25(2), 120–130.

40. Chu, S., Kuroki, M., & Liu, X. (2022). Do research universities boost regional economic development?—A case study of University of Science and Technology of China. Applied Economics, 54(29), 3392–3411.

41. Ciriaci, D. (2014). Does university quality influence the interregional mobility of students and graduates? The case of Italy. Regional Studies, 48(10), 1592–1608.

42. Civera, A., Meoli, M., & Vismara, S. (2020). Engagement of academics in university technology transfer: Opportunity and necessity academic entrepreneurship. European Economic Review, 123, 103376.

43. Comunian, R., Faggian, A., & Li, Q. C. (2010). Unrewarded careers in the creative class: The strange case of bohemian graduates. Papers in Regional Science, 89(2), 389–411.

44. Comunian, R., Taylor, C., & Smith, D. N. (2014). The role of universities in the regional creative economies of the UK: Hidden protagonists and the challenge of knowledge transfer. European Planning Studies, 22(12), 2456–2476.

45. Cooke, P. (2005). Regionally asymmetric knowledge capabilities and open innovation: Exploring ‘Globalisation 2’—A new model of industry organisation. Research Policy, 34(8), 1128–1149.

46. Cooke, P. (2021). Generative growth with ‘thin’globalization: Cambridge’s crossover model of innovation. In Dislocation: Awkward Spatial Transitions (pp. 115–134). Routledge.

47. Cooke, P., & Leydesdorff, L. (2006). Regional development in the knowledge-based economy: The construction of advantage. The Journal of Technology Transfer, 31, 5–15.

48. Cooper, A. C. (1985). The role of incubator organizations in the founding of growth-oriented firms Journal of Business Venturing, 1(1), 75–86.

49. Copeland, P., & Diamond, P. (2022). From EU structural funds to levelling up: Empty signifiers, ungrounded statism and English regional policy. Local Economy, 37(1–2), 34–49.

50. Corsi, C., Prencipe, A., Rodríguez-Gulías, M. J., Rodeiro-Pazos, D., & Fernández-López, S. (2019). Growth of KIBS and non-KIBS firms: Evidences from university spin-offs. The Service Industries Journal, 39(1), 43–64.

51. Coulombe, S., & Tremblay, J. F. (2001). Human capital and regional convergence in Canada. Journal of Economic Studies, 28(3), 154–180.

52. Cox, S., & Taylor, J. (2006). The impact of a business school on regional economic development: A case study. Local Economy, 21(2), 117–135.

53. de La Mothe, J., & Mallory, G. (2004). Local knowledge and the strategy of constructing advantage: The role of community alliances. International Journal of Technology Management, 27(8), 809–820.

54. Del Monte, A., Moccia, S., & Pennacchio, L. (2020). Regional entrepreneurship and innovation: Historical roots and the impact on the growth of regions. Small Business Economics, 1–23.

55. Delgado, M. S., Henderson, D. J., & Parmeter, C. F. (2014). Does education matter for economic growth? Oxford Bulletin of Economics and Statistics, 76(3), 334–359.

56. Di Liberto, A. (2008). Education and Italian regional development. Economics of Education Review, 27(1), 94–107.

57. Drucker, J. (2016). Reconsidering the regional economic development impacts of higher education institutions in the United States. Regional Studies, 50(7), 1185–1202.

58. Engstrand, Å. K., & Sätre Åhlander, A. M. (2008). Collaboration for local economic development: Business networks, politics and universities in two Swedish cities. European Planning Studies, 16(4), 487–505.

59. Farzin, F. (2017). Localising the impact of techno-entrepreneurship in Eastern Iran: Birjand’s Science and Technology Park as a local innovation community. Local Economy, 32(7), 692–710.

60. Feldman, M., & Desrochers, P. (2003). Research universities and local economic development: Lessons from the history of the Johns Hopkins University. Industry and Innovation, 10(1), 5–24.

61. Fernández-Esquinas, M., & Pinto, H. (2014). The role of universities in urban regeneration: Reframing the analytical approach. European Planning Studies, 22(7), 1462–1483.

62. Fernandez, F., Fu, Y. C., Hu, X., & Moradel Vásquez, J. J. (2023). Examining the influence of Texas’ strategic plan for increasing university research: Loose coupling and research production at regional public universities. The Journal of Higher Education, 1–26.

63. Fischer, B. B., Schaeffer, P. R., & Silveira, J. P. (2018). Universities’ gravitational effects on the location of knowledge-intensive investments in Brazil. Science and Public Policy, 45(5), 692–707.

64. Florida, R., & Gaetani, R. (2020). The university’s Janus face: The innovation–inequality nexus. Managerial and Decision Economics, 41(6), 1097–1112.

65. Fonseca, M. (2023). Innovation in the peripheries: Counter-flows of students to second tier cities in Portugal. Geoforum, 141, 103732.

66. Forrant, R. (2001). Pulling together in Lowell: The university and the regional development process. European Planning Studies, 9(5), 613–628.

67. Fowkes, A. S. (1983). The economic impact of higher education in the Yorkshire and Humberside region of England. Higher Education, 12(5), 591–596.

68. Fuster, E., Padilla-Meléndez, A., Lockett, N., & del-Águila-Obra, A. R. (2019). The emerging role of university spin-off companies in developing regional entrepreneurial university ecosystems: The case of Andalusia. Technological Forecasting and Social Change, 141, 219–231.

69. Galán-Muros, V., van der Sijde, P., Groenewegen, P., & Baaken, T. (2017). Nurture over nature: How do European universities support their collaboration with business? The Journal of Technology Transfer, 42, 184–205.

70. Garrido-Yserte, R., & Gallo-Rivera, M. T. (2010). The impact of the university upon local economy: Three methods to estimate demand-side effects. The Annals of Regional Science, 44, 39–67.

71. Geiger, R. L. (2006). The quest for ‘economic relevance’ by US research universities. Higher Education Policy, 19, 411–431.

72. Ghignoni, E. (2021). Informal recruitment channels, family background and university enrolments in Italy. Higher Education, 81(4), 815–841.

73. Gianiodis, P. T., & Meek, W. R. (2020). Entrepreneurial education for the entrepreneurial university: A stakeholder perspective. The Journal of Technology Transfer, 45(4), 1167–1195.

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Appendix D Coding process

To establish our qualitative coding protocol (established only for those analytical dimensions that also required qualitative categorization; see Section 2.2), the three authors started by independently analysing the full texts of five randomly selected articles by considering each of the analytical dimensions reported in the table below. Once the 3 authors independently coded the 15 selected articles, we met to discuss the coding strategies we pursued, solving disagreements. The process was repeated another time, coding a total of 30 randomly selected articles. As a result of this iterative process, we reached saturation about our coding rules and consistency of the coding activity. The remaining articles were equally split between three authors and coded independently. The table below provides specific details on the coding process for each variable that was qualitatively analysed and manually coded by the authors. A data extraction form in Excel was used to report the information extracted from the articles and to report the final coding for each variable.

References