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Academic engagement with industry: the role of research quality and experience

TL;DR: In this article, the role of university department characteristics in academic engagement with industry is explored. And the role played by research quality and previous experience in academic engagements across different scientific disciplines is investigated.
Abstract: This work explores the role of university department characteristics in academic engagement with industry. In particular, we investigate the role played by research quality and previous experience in academic engagement across different scientific disciplines. We test our hypotheses on a dataset of public sponsored university-industry partnerships in the United Kingdom, combined with data from the UK Research Assessment Exercises 2001 and 2008. Our analysis reveals a negative link between academic quality and the level of engagement with industry for departments in the basic sciences and a positive relationship for departments in the applied sciences. Our results further show that the role of research quality for academic engagement strictly depends on the level of the department’s previous experience in university-industry partnerships, notably in the basic sciences, where experience acts as a moderating factor. The findings of this work are highly relevant for policy makers and university managers and contribute to the innovation literature focused on the investigation of the determinants of valuable knowledge transfer practices in academia.

Summary (4 min read)

1 Introduction

  • Universities are key agents of economic and social progress.
  • The trade-off between motivations for collaboration is particularly important when the research pursued inside academia is aligned to basic research while the research and development activities (R&D) inside companies mostly involve applied research (Bodas-Freitas & Verspagen, 2017).
  • The paper is organised as follows: the authors review the relevant literature and develop their empirical hypotheses in Sect. 2; in Sect. 3 they illustrate data sources, variables and methodology; the empirical results along with robustness checks are presented in Sect.

2.1 Motivations for U–I collaboration

  • Universities carry out a wide range of collaborative initiatives, often labelled academic engagement.
  • The alignment of motivations is particularly important when the partners are driven by different incentives to collaborate, as typically in collaborations between university and companies.
  • The output of basic research is characterised by low marketability and applicability as the knowledge generated mostly originates from blue-sky research that is far from industrial application: such knowledge is most often at the frontier, highly tacit, hence less codifiable by those who do not command the field of investigation (Aghion et al., 2008; Dasgupta & David, 1994).
  • Substantial disciplinary effects stand out in the extant literature on academic engagement, including the specific case of research partnerships (e.g. Schartinger et al., 2002; Bekkers & Bodas-Freitas, 2008; D’Este & Iammarino, 2010).

2.2 The role of research quality for academic engagement across basic and applied sciences

  • Innovation scholars have devoted a great deal of attention to the role of research quality among the many determinants of U–I interactions.
  • The reason is that the different ways of pursuing academic research across disciplines determine the potential benefits that researchers derive from collaborating with industry (Perkmann et al., 2011; Filippetti & Savona, 2017), hence influencing the motivations for collaborating and, as a consequence, the extent and the characteristics of collaborations.
  • The relationship between research quality and academic engagement for the  basic and applied sciences is driven by a number of elements that can be ascribed to the motivations for academic departments to collaborate with businesses.
  • Firstly, the degree of internal resources available at department level plays a key role in shaping such motivations.
  • Following these considerations, the authors expect that the higher the academic standing of university departments belonging to applied sciences, the higher the extent of engagement with industry.

2.3 The role of cumulated experience in academic engagement

  • Notwithstanding the importance of research quality, even across different scientific domains, this alone cannot fully explain the occurrence and level of U–I interaction.
  • Therefore, academic departments with established collaborations with companies reflect an institutional environment favouring interactions with industry (D’Este & Patel, 2007).
  • Given the relevance of experience in U–I interactions, the authors expect it to compensate for the lack of attractiveness that basic sciences departments of high quality may have for businesses.
  • Hypothesis 2b Experience amplifies the positive relationship between research quality and academic engagement in the applied sciences.

3.1 Data sources

  • The data for the empirical analysis consists of a set of U–I research grants awarded to UK Universities by the Engineering and Physical Sciences Research Council between 1992 and 2007, combined with university and department level information gathered from the UK Higher Education Funding Councils’ Research Assessment Exercise (RAE) 2001 and 2008.
  • The EPSRC provides funding to national research through a wide range of grant schemes.
  • 1 3 partnerships aimed at contributing to joint upstream research for the creation of new knowledge and, therefore, they are far from industrial applications.
  • RAE results are considered reliable because they follow an expert review process conducted by assessment panels, whose members are nominated by a wide range of organisations.
  • First, the authors link each academic department involved to a UoA6; secondly, they merge the data from the RAE 2001 and 2008.

3.2 Dependent variable

  • The authors measure U–I collaboration by the volume of funding that university departments receive from companies in the second period under investigation (2001–2007).
  • The authors consider the total cumulated level of funding in the main analysis, while the average amount of funding per project is employed for a robustness check.
  • Exploiting the level of private funding, as reported by the funding agency, allows to overcome limitations in prior research, mostly related to the use of indirect proxies of U–I collaboration (Perkmann et al., 2011, 2013).
  • Importantly, the amount of resources provided by private partners within collaborating projects provides a measurable account of the value that industry places on university knowledge.

3.3 Independent variables

  • The submission of each department to the RAE 2001 was rated on a seven-point scale from 1 to 5*, with 5* being the highest score, indicating that research quality achieved international excellence in more than a half of the departments’ submitted output, and the remaining output reached national excellence.
  • Given the concentration of departments in the highest ratings in their sample, the authors build an additional measure that allows to distinguish between departments whose research quality is extremely high from those whose quality is high.
  • The authors decided to keep the observations in the sample of non-basic and non-applied departments as this allows to test the relationship between the basic/applied dummy indicators and the dependent variable in the same model.
  • 13 Past EPSRC funding may be related to academic standing because better departments may have higher interaction with public funding agencies, hence capturing a very similar effect to that of Research quality on industry funding.

3.4 Control variables

  • The authors include a number of controls in the attempt to properly isolate the relationships between the dependent and independent variables (Table 1).
  • In the first place, to account for other streams of funding that each department received from the private sectors and that may be related to the volume of funds raised from industry through the EPSRC collaboration schemes, the authors control for the level of total private funding obtained in the period 1992–2000 .
  • Third, the authors control for department size by adding the count of research active staff in the department at the time of the RAE 2001 submissions (Size).
  • The following region level dummies are included: East Midlands, East of England, London, North East, North West, Northern Ireland, Scotland, South East, South West, Wales, West Midlands and Yorkshire and the Humber.

3.5 Methodology

  • The authors estimate two models that allow to test hypotheses 1a and 1b, and hypotheses 2a and 2b, respectively.
  • In the first model, the authors test the interaction effect between research quality and the basic or applied sciences dummy variables.
  • This allows to investigate whether departmental academic standing is negatively related to engagement with industry for basic sciences departments and positively related to that for applied sciences departments.
  • 1 3 Since the dependent variable is continuous, the authors estimate OLS regressions with robust standard error to account for potential heteroskedasticity of the error terms (Angrist & Pischke, 2008).
  • The VIFs are always fairly low (below 2) with the exception of the interaction and interacted terms.

4.1 Main results

  • In Table 6 the authors present the results of the OLS regressions testing hypotheses 1a and 1b, while the results of the split sample analysis carried out to test hypotheses 2a and 2b are shown in Table 7.
  • Except for very small values of y, the inverse sine can be interpreted as a standard logarithmic variable.
  • The graphs further confirms the findings from Table 6, hence supporting hypotheses 1a and 1b.
  • Figure 2 shows the predictive margins for the interaction terms displayed in Table 7.15 Graphs (a) and (c) show the plots of the statistically significant coefficients [columns (1) and (3) in Table 7].

4.2 Robustness checks

  • Firstly, the authors estimate the models displayed in Tables 6 and 7 after employing a different dependent variable.
  • Given the rather different distribution of the RAE 2001 rating across disciplines, the authors exploit the median of each sub-group of departments (basic sciences, applied sciences, social sciences and humanities, and medical sciences).
  • Comparing the estimates obtained using differently coded variables allows to check both for the robustness of the results and for the reliability of the quality measures.
  • The results shown in Tables 8 and 9 are highly in line with those from Sect. 4.1, with the exception of a slightly different magnitude of the coefficients.
  • Therefore, the first set of the robustness checks implemented confirms a negative relationship between research quality and academic engagement in the basic sciences (hypothesis 1a), a positive relationship in 16 Out of 92 academic departments in basic sciences, 30 have a cumulated past experiences higher than the identified threshold (1.8 m GBP).

5 Discussion and conclusion

  • This paper investigated the relationship between university departments’ characteristics and academic engagement with businesses in the form of U–I collaboration.
  • In particular, the authors find that the higher the level of departmental cumulated experience in academic engagement, the weaker the negative relationship between research quality and U–I collaboration in the basic sciences.
  • In addition, given the focus on one specific channel of U–I interaction, namely formalised joint research partnerships, their findings may not be straightforwardly extended to other channels—most notably the less formalised ones.
  • Second, a negative relationship between research quality and university engagement with industry in the basic sciences may result in the adverse selection of academic institutions into cooperation with businesses.
  • This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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Vol.:(0123456789)
The Journal of Technology Transfer
https://doi.org/10.1007/s10961-021-09867-0
1 3
Academic engagement withindustry: therole ofresearch
quality andexperience
AlessandraScandura
1
· SimonaIammarino
2
Accepted: 9 June 2021
© The Author(s) 2021
Abstract
This work explores the role of university department characteristics in academic engage-
ment with industry. In particular, we investigate the role played by research quality and
previous experience in academic engagement across different scientific disciplines. We
test our hypotheses on a dataset of public sponsored university-industry partnerships in the
United Kingdom, combined with data from the UK Research Assessment Exercises 2001
and 2008. Our analysis reveals a negative link between academic quality and the level of
engagement with industry for departments in the basic sciences and a positive relationship
for departments in the applied sciences. Our results further show that the role of research
quality for academic engagement strictly depends on the level of the departments previous
experience in university-industry partnerships, notably in the basic sciences, where experi-
ence acts as a moderating factor. The findings of this work are highly relevant for policy
makers and university managers and contribute to the innovation literature focused on the
investigation of the determinants of valuable knowledge transfer practices in academia.
Keywords Academic engagement· Academic quality· Experience· University-industry
collaboration
JEL Classification I23· O30
1 Introduction
Universities are key agents of economic and social progress. Their mission has gradually
been extended to interactions with industry, and with society more generally, beyond the
traditional goals of teaching and research (e.g. Bercovitz & Feldman, 2006; Archibugi
& Filippetti, 2018; Giuri etal., 2019). The role of universities so conceived has attracted
* Simona Iammarino
s.iammarino@lse.ac.uk
Alessandra Scandura
alessandra.scandura@unito.it
1
Department ofEconomics andStatistics “Cognetti de Martiis”, University ofTorino, Turin, Italy
2
Department ofGeography andEnvironment, London School ofEconomics andPolitical Science,
London, UK

A.Scandura, S.Iammarino
1 3
considerable attention from scholars and policy-makers (e.g. Hsu et al., 2015; Trune &
Goslin, 1998; Kochenkova et al., 2016). As a matter of fact, university engagement in
knowledge transfer and dissemination of research results (“third mission” activities) is
investigated by various streams of the academic literature, including economics of innova-
tion, economic geography, geography of innovation, and economics of science. Univer-
sity engagement activities take various forms, including employment channels, intellectual
property rights (IPRs) related interactions, research collaboration, and informal direct/indi-
rect contacts (e.g. Geuna & Rossi, 2013; Rossi & Rosli, 2013). However, whilst university
IPR-related activities and academic entrepreneurship have attracted major attention both
within the academic literature and the policy community (Phan & Siegel, 2006; Rothaer-
mel etal., 2007; O’Shea etal., 2008), other types of university–industry (U–I) interaction
have become more prevalent (Perkmann etal., 2013). This is notably the case of research
partnerships, which refer to a specific typology of university interaction with industry
entailing firms and university joint research and financial effort within a specific collabora-
tive project (e.g. D’Este & Iammarino, 2010; Scandura, 2016).
Despite the well known benefits accruing from U–I interaction for both parties as well
as for the society as a whole, linkages are often hampered by differences in the research
missions of university and industry (Bodas-Freitas & Verspagen, 2017; Dasgupta & David,
1994). In particular, due to different research-related incentive structures in academia and
industry, the diverse research missions and motivations for interaction can be hard to rec-
oncile in a collaborative framework. Yet, the innovation literature has underlined that the
alignment between motivations for collaboration is fundamental for the successful set up
of a collaboration (Foray & Steinmuller, 2003; Ankrah etal., 2013). The trade-off between
motivations for collaboration is particularly important when the research pursued inside
academia is aligned to basic research while the research and development activities (R&D)
inside companies mostly involve applied research (Bodas-Freitas & Verspagen, 2017).
In the attempt to understand what influences the realisation of U–I interactions and
their success, the innovation literature has scrutinized in depth the determinants of U–I
partnerships (e.g. Schartinger et al., 2002; Fontana et al., 2006; D’Este & Iammarino,
2010; D’Este et al., 2013). However, while the role of individual-level factors is rather
well explored, the empirical evidence is scant about the context in which U–I partnerships
occur, mostly with respect to the characteristics of the university departments involved
(Perkmann etal., 2013). Yet, the department routines, together with the university culture
and policies, are likely to have the largest influence on researchers’ behaviour, including
their attitudes towards U–I interactions (D’Este & Patel, 2007). Interestingly, whilst the
relevance of the research standing of academic departments has been investigated (e.g.
Mansfield, 1995, 1997; Mansfield & Lee, 1996; Tornquist & Kallsen, 1994), its joint effect
with other contextual factors on U–I interactions remains mostly unexplored. In particu-
lar, it is beyond doubt that the patterns in U–I partnerships depend on the scientific origin
of academic departments and researchers (e.g. Bekkers & Bodas Freitas, 2008; D’Este &
Iammarino, 2010; Mansfield & Lee, 1996), but the empirical evidence is still scarce about
the joint effect of research quality and scientific disciplines. A few contributions point to
differences between hard sciences and humanities as well as between applied and basic sci-
ences, suggesting that the effects of research quality and scientific disciplines on U–I part-
nerships may be interdependent (D’Este & Iammarino, 2010; Olmos-Peñuela etal., 2014).
Similarly, while cumulated experience in academic engagement has been shown to be a
predictor of future engagement, its influence on the link between research quality and U–I
partnerships is an underexplored issue in the literature (Boardman & Ponomariov, 2009;
Bozeman & Gaughan, 2007). In particular, whether the influence of research quality holds

Academic engagement withindustry: therole ofresearch quality
1 3
when academia cumulates experience in U–I interactions remains an open question. In this
paper, we intend to fill these gaps and extend the existing literature in various directions.
Firstly, we investigate the role played by university department research quality for the
level of academic engagement in U–I partnerships, by distinguishing between departments
in basic and applied hard sciences. We test the hypotheses that academic quality is nega-
tively related to U–I partnerships in basic sciences departments, and positively related to it
in applied sciences departments. Our hypotheses build on the argument that the successful
realisation of a collaboration depends on the alignment of research motivations and expec-
tations of the partners (Foray & Steinmuller, 2003; Ankrah etal., 2013). We focus on the
analysis from the university side and argue that the alignment process plays out differently
across scientific disciplines and quality levels, due to the diverse degree of resource availa-
bility and institutional norms and values inside university departments. Given the different
motivations driving basic and applied sciences departments toward academic engagement,
we posit that researchers in basic sciences departments of high quality are pushed away
from U–I interactions, whilst their peers in top applied sciences departments are highly
engaged. Secondly, we focus on department-level cumulated experience in U–I partner-
ships as a joint determinant of academic engagement together with research quality. We
postulate that department past experience weakens the negative relationship between aca-
demic quality and engagement in thebasic sciences, while it amplifies the positive link in
theapplied disciplines. We base our hypotheses on the argument that department experi-
ence in academic engagement influences the capability to fruitfully establish and main-
tain connections with firms and that the extent of such influence changes across scientific
disciplines.
To address these issues, we carry out regression analyses on a dataset of U–I partner-
ships funded by the UK Engineering and Physical Sciences Research Council (EPSRC),
combined with data on academic institutions from the UK Research Assessment Exercises
(RAE) developed by the UK Higher Education Funding Councils. In the empirical analy-
sis, we account for academic departments’ engagement in U–I partnerships by considering
the level of financial resources involved. In doing so, we overcome one of the limitations
in extant research, namely the lack of information on the amount of financial flows at stake
(Perkmann et al., 2011). Yet, income flows that university departments receive for their
knowledge transfer activities may reflect the value placed by external partners on academic
knowledge, thus providing a measure of the economic value created through knowledge
transfer (Rossi & Rosli, 2013).
The paper is organised as follows: we review the relevant literature and develop our
empirical hypotheses in Sect.2; in Sect.3 we illustrate data sources, variables and method-
ology; the empirical results along with robustness checks are presented in Sect.4; finally,
we discuss our findings and offer some concluding remarks in Sect.5.
2 Literature andhypotheses development
2.1 Motivations forU–I collaboration
Universities carry out a wide range of collaborative initiatives, often labelled academic
engagement. As defined by Perkmann etal. (2013), this refers to inter-organisational col-
laboration that links universities with other organisations, especially firms, and includes
both formal activities (e.g. collaborative research, contract research and consulting) and

A.Scandura, S.Iammarino
1 3
informal activities such as networking with practitioners. Although there is extensive
research on university IPRs activity and academic entrepreneurship, it is widely recognized
that other forms of academic engagement are more pervasive (Perkmann etal., 2013). In
this respect, U–I collaborative research partnerships stand out: these are a specific channel
of inter-organisational knowledge flows and potential spillovers from (and to) academic
research, aimed at carrying out R&D projects, mainly involving pre-competitive and basic
research and often subsidized with public funding (D’Este & Fontana, 2007; D’Este etal.,
2013; OECD, 1998, 2002; Scandura, 2016). U–I research partnerships represent one of the
most frequent policy instruments put in place by policy-makers to incentivize U–I knowl-
edge transfer and foster pre-competitive research (Fisher etal., 2009).
The successful set up of a collaboration depends on the alignment of research motiva-
tions and expectations of the partners (Foray & Steinmuller, 2003; Ankrah etal., 2013).
Bodas-Freitas and Verspagen (2017) refer to it as the integration of the objectives of part-
ners belonging to different technological and institutional environments into joint projects
that may benefit both parties. The alignment of motivations is particularly important when
the partners are driven by different incentives to collaborate, as typically in collaborations
between university and companies. Indeed, the different incentive frameworks in academia
and industry are often cited as a constraining factor of U–I interactions and their outcome
(Dasgupta & David, 1994; Rosenberg & Nelson, 1994).
Academic scientists collaborate with companies to search for practical applications of
their research results, to advance and widen their research agendas, to get funding for their
research, for graduate students and for purchasing equipment, and to increase the chances
for future collaboration opportunities (D’Este & Perkmann, 2011; Lam, 2011; Lee, 1996,
2000; Lee and Bozeman, 2005; Perkmann & Walsh, 2009). Firms are motivated to col-
laborate with universities to access and develop interdisciplinary scientific capabilities to
solve complex industry problems, to get support for the product development phase of their
R&D activities, to access public funding, to pursue exploratory research to generate new
ideas for new products, technologies and markets as well as to get access to highly skilled
labour force, most notably qualified engineers (Meyer-Krahmer & Schmoch, 1998; Lee,
1996, 2000; Feller et al., 2002; Carayol, 2003; Lam, 2005; Balconi & Laboranti, 2006;
Arza, 2010; Subramanian etal., 2013).
Some of these motivations are expected to easily converge in a collaborative frame-
work because complementary to each other. For instance, academic scientists’ search for
industrial application of their inventions can match firms’ product development objectives
(Bodas-Freitas & Verspagen, 2017). However, motivations may also conflict, hence pre-
venting full accordance between university and industry.
1
A typical example of conflict
is the clash between the university objective of opening up new research paths and firms’
product development goals: while the exploration of new research lines is aligned to basic
research, product development involves applied research building on the results of basic
research (Bodas-Freitas & Verspagen, 2017).
Both the theoretical and empirical innovation literature helps understanding the differ-
ence between basic/fundamental research and applied/practical research, and how it relates
to university and industry diverse motives for collaborating. Investigating the advantages
1
Extant research shows that diverse motivations to U–I collaboration are not always nor necessarily in
conflict (Ankrah etal., 2013; Lee, 2000). When in contrast, industry and university motivations can be rec-
onciled into a collaborative project with well-defined technological objectives and organisation, eventually
relying on different institutions (e.g. technology transfer offices) (Lam, 2011; Subramanian etal., 2013).

Academic engagement withindustry: therole ofresearch quality
1 3
and disadvantages of academic and private-sector research, Aghion etal. (2008) argue that
the critical trade-off between academia and industry is one of creative control versus focus.
Because of its commitment to keep creative control in the hands of scientists, academia is
indispensable for early stage basic research aimed at fostering new research lines; at the
same time, the private sector’s focus on higher payoff activities makes it more useful for
later-stage applied research, aimed at producing profitable innovations and introducing
them to the market. The divergence in incentive structures—but also norms, language and
purposes—between the two worlds is likely to be particularly strong when the academic
partner is most oriented towards upstream blue-sky research as compared to research closer
to the context of application (Dasgupta & David, 1994). Relatedly, the characteristics of the
knowledge stemming from research activities play a key role in shaping the link between
academia and industry (Meyer-Krahmer & Schmoch, 1998). The output of basic research
is characterised by low marketability and applicability as the knowledge generated mostly
originates from blue-sky research that is far from industrial application: such knowledge is
most often at the frontier, highly tacit, hence less codifiable by those who do not command
the field of investigation (Aghion etal., 2008; Dasgupta & David, 1994). Companies are
generally only scarcely interested in this typology of research because of its high riskiness
and intrinsic low appropriability: given firms’ profit maximisation objectives, they will be
less interested in new knowledge that is likely to be less marketable (Aghion etal., 2008).
On the contrary, the output of applied research activities is by definition closer to the busi-
ness community (Meyer-Khramer & Schmoch, 1998). The artefacts in applied sciences are
tangible and thus open to direct, experience-based manipulation, as opposed to the prod-
ucts of basic sciences. Therefore, applied research pursued in fields such as engineering is
highly applicable for industrial purposes as it generates knowledge with high technical and
market related content (Meyer-Khramer & Schmoch, 1998).
The orientation of academic researchers towards basic or applied research is naturally
related to the scientific discipline they are affiliated to. Substantial disciplinary effects stand
out in the extant literature on academic engagement, including the specific case of research
partnerships (e.g. Schartinger etal., 2002; Bekkers & Bodas-Freitas, 2008; D’Este & Iam-
marino, 2010). Academic affiliation to a scientific discipline shapes the norms relevant for
researchers as these are the rules of conduct that prevail within the so-called “invisible
colleges” in which academic scientists operate (Crane, 1972). The disciplinary origin of
an academic department has been shown to be an important factor affecting the typology
and the extent of engagement with industry (Bekkers & Bodas-Freitas, 2008; Martinelli
et al., 2008). The literature suggests that collaboration and engagement in entrepreneur-
ial activities are more likely to happen in applied fields of research as compared to less
applied domains (Perkmann et al., 2013). For instance, informal contacts, collaborative
and contract research, patents and licensing are important channels of knowledge transfer
for engineering-related departments, while researchers oriented at basic research tend to
value much less patents and licensing. Conversely, academic departments of economics
and other social sciences tend to transfer knowledge through publication, personal contacts,
labour mobility and specific organised activities (Bekkers & Bodas-Freitas, 2008). In the
medical sciences, clinical researchers are more likely to interact with firms with respect
to their non-clinical peers, but the latter are more engaged in commercialisation activities
(Louis etal., 2001).
Against the complex process of convergence between university and industry’s research
missions across different scientific disciplines, public grants may create incentives for spe-
cific motivations for U–I collaboration. Participation to public sponsored U–I collabora-
tions may not be critical to firms’ competitive position, but it may provide both university

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01 Jan 2009
TL;DR: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes.
Abstract: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications

7,192 citations

Journal ArticleDOI
TL;DR: In this paper, the authors make use of insights from the theory of games of incomplete information to synthesize the classic approach of Arrow and Nelson in examining the implications of the characteristics of information for allocative efficiency in research activities, on the one hand, with the functionalist analysis of institutional structures, reward systems and behavioral norms of "open science" communities associated with the sociology of science in the tradition of Merton.

2,336 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a systematic review of research on academic scientists' involvement in collaborative research, contract research, consulting and informal relationships for university-industry knowledge transfer, which they refer as academic engagement.
Abstract: A considerable body of work highlights the relevance of collaborative research, contract research, consulting and informal relationships for university-industry knowledge transfer. We present a systematic review of research on academic scientists’ involvement in these activities to which we refer as ‘academic engagement’. Apart from extracting findings that are generalisable across studies, we ask how academic engagement differs from commercialization, defined as intellectual property creation and academic entrepreneurship. We identify the individual, organizational and institutional antecedents and consequences of academic engagement, and then compare these findings with the antecedents and consequences of commercialization. Apart from being more widely practiced, academic engagement is distinct from commercialization in that it is closely aligned with traditional academic research activities, and pursued by academics to access resources supporting their research agendas. We conclude by identifying future research needs, opportunities for methodological improvement and policy interventions. (Published version available via open access)

1,589 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a systematic review of research on academic scientists' involvement in collaborative research, contract research, consulting and informal relationships for university-industry knowledge transfer, which they refer as academic engagement.

1,470 citations

Frequently Asked Questions (2)
Q1. What are the future works in "Academic engagement with industry: the role of research quality and experience" ?

The choice to study U–I partnerships sponsored by the EPSRC, hence excluding other sources of U–I grants, may represent an additional limitation, because both universities and companies normally receive a multitude of public funding to conduct joint R & D activities. As a consequence, academic departments dedicated to more abstract research ( e. g. basic sciences ) may further reduce their interest in pursuing academic engagement, while departments in applied sciences may end up increasing their interaction patterns at the expense of their research quality. 19 Policy makers should acknowledge the possibility of adverse selection and consider whether it is a desirable outcome for the university system as well as for the whole economy. The authors thank an anonymous reviewer for suggesting the possibility of an adverse effect of the RAE framework. 

This work explores the role of university department characteristics in academic engagement with industry. In particular, the authors investigate the role played by research quality and previous experience in academic engagement across different scientific disciplines. The findings of this work are highly relevant for policy makers and university managers and contribute to the innovation literature focused on the investigation of the determinants of valuable knowledge transfer practices in academia. Their results further show that the role of research quality for academic engagement strictly depends on the level of the department ’ s previous experience in university-industry partnerships, notably in the basic sciences, where experience acts as a moderating factor.