The Production of Scientific Output by

Early-Career Researchers

M a r t i n Ry a n a †

a S c h o o l o f E c o n o m i c s a n d G e a r y I n s t i t u t e , U n i v e r s i t y C o l l e g e D u b l i n , I r e l a n d .

1 8 / 0 1 / ’ 1 2

Abstract: This study investigates the production of scientific output by early-career researchers in the university setting. The expectations of these individuals - in relation to the commercialisation of their research - are also examined. To date, few studies have examined the individual-level determinants of publication and patent production. Most of the studies on academic scientists’ careers are based upon U.S. data; and not much is known about the individual-level determinants of academic scientists’ output in Europe. In addition, this is the first study to examine expectations related to research-commercialisation: that the author is aware of. The key results (based on a sample from the seven universities in Ireland) show that institutional affiliation, gender, interest in area of research and years of experience all play a role in the postdoctoral production function. In particular, institutional affiliation and gender are the most economically significant drivers of scientific output. Notably, males are twenty-one percent more likely to expect that they will commercialise their research.

JEL Classification: I23, J24, C81, O31, O38

Keywords: Ph.D. outcomes, research output, publishing, patenting, commercial expectations, commercialisation, scientific careers, human capital, economics of science, science policy, research and development

†Corresponding author: Martin Ryan, Desk 7.1, 2nd Floor, Geary Institute, University College Dublin, Dublin 4, Ireland. Tel: 00 -

353-1-716-4615. Fax: 00-353-1-716-1108. Email: martin.f.ryan@gmail.com. The corresponding author is a Ph.D. student at the UCD School of Economics. He was supported for three years of his Ph.D. by the Irish Research Council for the Humanities and Social Sciences (IRCHSS).

Acknowledgements: Thanks to seminar participants at the UCD School of Economics and the Geary Institute for providing comments; to participants at the COST/STRIKE/DIME conference on ‘The Organisation, Economics and Policy of Scientific Research’ (Turin; February 2011); and to participants at the annual conference of the Irish Society of New Economists (Dublin; August 2011). Thanks to Aldo Geuna for discusant’s comments in Turin (February 2011). Thanks to the Irish Universities Association for sponsoring the field-work that produced the data for this paper.

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1 . I n t r o d u c t i o n

This study examines postdoctoral scholars (that is, those who have completed doctorates but who

have not yet obtained long-term research positions) working at the seven Irish universities; with a particular

focus on the publication and patent output of these early-career researchers.1 The expectations of these

individuals - in relation to the commercialisation of their research - are also examined. A major contribution

of this paper is to extend the range of evidence that exists on the determinants of scientific output. Few

studies have examined the individual-level process of publication and patent production. In fact, most of the

studies on academic scientists’ productivity and careers are based upon U.S. data; and not much is known

about the determinants of academic scientists’ productivity in Europe (Lissoni, Mairesse, Montobbio &

Pezzoni; 2011). Furthermore, minimal systematic empirical research has been conducted at the individual-

level to investigate determinants of patenting behaviour of academic scientists (Huang, Feeney and Welch,

2011). Most studies of academic patenting focus on the university as the unit of analysis (Azagra-Caro,

Carayol & Llerena, 2006); and surprisingly little is known about the determinants of individual research

productivity ( Kelchtermans & Veugelers, 2011; Gonzalez-Brambila & Veloso, 2007).2 In addition, this is the

first study to examine expectations related to research-commercialisation: that the author is aware of.

Stephan and El-Ganainy (2007) document the substantial gender gap that exists among university scientists

with regard to entrepreneurial activity, focusing on biomedical sciences. Given this, it is expected that a

gender gap in commercial expectations (across disciplines) might also be present.

Notably, there is a scarcity of research on the individual-level determinants of commercial activity;

such as licensing, setting up a joint venture or setting up a spin-out company (Audretsch & Kayalar-Erdem,

1 A patent is a temporary monopoly awarded to inventors for the commercial use of an invention. For a patent to be granted, the

invention must be non-trivial, meaning that it would not appear obvious to a skilled practitioner of the relevant technology; and it must be useful, meaning that it has potential commercial value. If a patent is granted, an extensive public document is created, containing detailed information about the invention, the inventor, the organisation (if any) to which the inventor assigns the patent property right, and the technological antecedents of the invention (Jaffe, Fogarty & Banks, 1998). 2 Scientific research unravelling the determinants of research productivity has not been abundant, but is gradually receiving more

attention, both at the level of the individual researcher and at more aggregate levels (Kelchtermans & Veugelers, 2011).

3

2004; Landry, Amara & Rherrand, 2006).3 These activities are sometimes referred to as academic

entrepreneurship.4 Spin-outs (or spin-offs) are created by researchers to exploit the results of their work;5

these often arise through the commercialisation of intellectual property (typically patents, trade secrets or

copyrights) and the transfer of technology developed within academic institutions.6 7 Many companies,

universities and governmental organisations now have a Technology Transfer Office (TTO) dedicated to

identifying research which has potential commercial interest; and strategies for how to exploit it. Studies

focusing on TTOs have appeared only recently; results show that the size and experience of a TTO has

positive influence on spinout activity (Powers & McDougall, 2005); also that patent output relates positively

to the presence of a TTO (Gurmu, Black & Stephan, 2010). On several occasions, the European Commission

has argued that while European research institutions are good at producing academic research outputs, they

are not successful in transferring these outputs to the economy – the so called ‘European Paradox’

(European Commission 2007).8 Understanding more about scholars’ expectations to commercialise their

research is helpful in this regard. Furthermore, university researchers generally choose academic careers

based on a vocation for research and teaching, and the prospect of a stable and relatively well-paid job; this

3 A license involves signing over rights to another entity; it commonly has several component parts, including a term, territory,

renewal provisions, and other limitations deemed vital to the licensor. Although university patents, spin-off company creation, consulting and joint research agreements are often addressed as separate, alternative transfer mechanisms, in practice, commercialising a piece of university research may require a variable mix of instruments (Franzoni & Lissoni, 2006). For instance, in a recent survey on commercialisation of U.S. academic research, it emerged that licensing contracts made by technology transfer offices in the majority of cases involve royalties, annual fees, equity, milestones and consulting agreements (Dechenaux, Thursby & Thursby, 2009).

4 An academic entrepreneur is defined by Franzoni and Lissoni (2006) as a “university scientist, most often a professor, sometimes

a Ph.D. student or a post-doc researcher, who sets up a business company in order to commercialise the results of her research. It is the nearest possible definition to the classical one of entrepreneur, enriched of the qualifying adjective ‘academic’, to stress that the innovations introduced by the entrepreneur originate from the research she conducted as part of her ‘other job’ as a university scientist.” 5 Early contributions to the literature on spin-out companies tended to stress that university-based scientists own a specific set of

knowledge and information, enabling them to spot valuable opportunities of investment, thanks to the idiosyncratic knowledge gained while working on a scientific discovery (Franzoni & Lissoni, 2006).

6 A trade secret is a formula, practice, process, design, instrument, pattern, or compilation of information which is not generally

known or reasonably ascertainable, by which a business can obtain an economic advantage. A copyright is a set of exclusive rights granted by a state to the creator of an original work for a limited period of time in exchange for public disclosure of the work. 7 Technology transfer is the process of transferring knowledge, technologies, methods of manufacturing and samples of

manufacturing among governments or universities and other institutions to ensure that scientific and technological developments are accessible to a wider range of users.

8 Dosi, Llerena and Labini (2006) suggest that European weaknesses reside in its system of academic research and in weak

industry. Nonetheless, the United States is recognised as the world leader in technology transfer (Lockett, Siegel, Wright & Ensley, 2005).

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does not translate into a propensity to create enterprises (Etzkowitz, 1998). This study addresses the

propensity of university researchers to pursue academic enterprise by examining expectations related to

research-commercialisation.

Understanding what drives researcher productivity is of interest to policy makers as it allows for

more informed decisions regarding the design of science systems and career paths (Kelchtermans &

Veugelers, 2011).9 With respect to the wider economy, early-career researchers are particularly important

because they are often at the forefront of innovation (U.S. National Academies, 2010). Furthermore, it has

been demonstrated that Ph.D.-trained researchers facilitate the absorption of foreign knowledge (Mowery &

Oxley, 1995; Keller, 1996; Dorwick, 2003; Veugelers, 2007); and that this is an important factor for economic

growth in smaller economies (Bye et al., 2009). Therefore, if large economies wish to pursue innovation, or

small economies aim to absorb foreign knowledge; then both must have Ph.D. graduates who have been

trained in scientific research. In typical models of educational production, there is usually some measure of

academic achievement that enables one to differentiate between students. However, it is difficult to

differentiate between the human capital of Ph.D. graduates without considering whether they have

published or secured a patent. If a postdoctoral researcher has produced either of these outputs then they

have more human capital; compared to the graduate who has not produced any scientific output. Hence

there is an emphasis in this study on publications and patents as outcomes of the Ph.D. production process

(in addition to the outcome of expecting to commercialise research).

The key results in this paper (based on a sample from the seven universities in Ireland) show that

institutional affiliation, gender, interest in area of research and years of experience all play a role in the

postdoctoral production function. In particular, institutional affiliation and gender are the most economically

significant drivers of scientific output. Remarkably, males are 21 percent more likely to expect that they will

commercialise their research. The next section of the paper outlines traditional motivations relating to public

investment in scientific research, and associated manpower requirements. Particular consideration is given

to the institutional setting of this study: the Irish economy. The third section discusses previous studies on

the production of publications and patents: at the individual level of the researcher. These are placed in the

context of educational production and human capital acquisition. The fourth section presents the data; these

were collected through a web-survey that the author helped to design: the first official feedback mechanism

for researchers working at Irish universities. The fifth section presents the method and results; the sixth

9 In addition, research performance plays an increasingly important role in the funding of research projects and institutions, as

well as the promotion of individual researchers (Kelchtermans & Veugelers, 2011).

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section concludes with a discussion.

2 . Public Investment in Research Labour

This section outlines recent trends and traditional motivations relating to public investment in

scientific research. The R&D spend in the Irish economy has trebled in the last decade and now stands at

approximately €2.5 billion. One third of the overall R&D spend (or approximately €0.83 billion) is financed by

the Irish Government. This is short of the Lisbon target of 3 percent, (Irish investment in R&D is 0.66 percent

of GNP); but represents an improvement in the situation compared to a decade ago. In 2007, the EU27 spent

€229 billion on R&D; this expenditure equates to 1.85 percent of EU27 GDP. Germany (€62 billion), France

(€39 billion) and the United Kingdom (€37 billion) accounted for 60 percent of total R&D expenditure in the

EU27 in 2007. In relation to manpower-training, the annual number of Ph.D. awards recently topped the

1,000 mark in Ireland; almost double the number compared to a decade ago. It is official Government policy

for this upward trajectory in Ph.D. numbers to continue. In 2005, approximately 100,000 doctoral degrees

were conferred in the EU-27, compared to 53,000 in the U.S. and 15,000 in Japan. From 1998 to 2005, the

number of doctoral degrees increased (per year, on average) by 4.4 percent in the EU-27, and by 2 percent in

the U.S. (Moguerou and Di Pietrogiacomo, 2008).

Since Ireland’s Technology Transfer Strengthening Initiative was established in 2007, 55 new spin-out

companies have emerged from State-funded research in Ireland; over 1,300 inventions have been disclosed,

more than 470 new patents filed and close to 220 deals have been signed between companies and

researchers to licence new technologies (Irish Dept. of Enterprise, Trade and Employment, 2010). The

Enterprise Ireland /Science Foundation Ireland Commercialisation Fund was introduced in 2009 to accelerate

the realisation of national economic benefits through active commercialisation of research outputs.

However, following the 2009 report by Ireland’s Special Group on Public Service Numbers and Expenditure

Programmes (colloquially known in Ireland as the report of An Bord Snip Nua), and in the context of limited

resources for supporting scientific research, a growing number of policy-makers and academics have become

interested in understanding the relationships between scientific research and economic activity in Ireland.

Similar concerns have also come to the fore internationally; for example: “Science economics: What science

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is really worth” (Nature, 2010), “Assessing the Impact of Science Funding” (Science, 2009) and “U.S. economic

stimulus plan is all about science” (New Scientist, 2009).

The case for the government’s role in promoting research and development was recently outlined by

Bernanke (2011). The primary economic rationale for a government role in R&D is that the private market

would not adequately supply certain types of research (Arrow, 1962).10 On the micro-economic level, it is

thought that knowledge spreads from universities to local firms in the form of spillovers (Lester, 2005);11 and

there is suggestive evidence that the development of spin-out companies follows government investment in

basic scientific research (U.S. Science Colaition, 2010).12 There is also evidence that publicly funded university

research translates into patents, particularly in pharmaceuticals, chemicals and electronics (Jaffe, 1998). On

an aggregate level, there is evidence pointing to positive returns from government investment in R&D; for

example: Toole (2008) and Hall, Mairesse and Mohnen (2009).13 Also, there is research pointing to much

larger (though more difficult to measure) indirect returns to government investment in R&D. Examples

include Griliches (1992), Jones and Williams (1998), Hall, Mairesse and Mohnen (2009) and the U.S. National

Academies (2010).14

However, in relation to the Irish case, all of the evidence mentioned above is heavily contextualised

by the fact that Ireland is a small open economy. The openness of the Irish economy is a notable concern in

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This argument applies particularly strongly to basic research. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view. Sometimes concerns are raised about crowding out in relation to public investment – that is, that government investment in scientific research would crowd out private investment (see David, Hall & Toole, 2000). However, the (U.S.) Congressional Budget Office (2007) has shown that firms’ spending on scientific research increases in response to federal spending on scientific research.

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A spillover is a cost or benefit, not transmitted through prices, incurred by a party who did not agree to the action causing the cost or benefit. In a competitive market, where prices do not reflect the full costs or benefits of producing or consuming a product or service, producers and consumers may either not bear all of the costs or not reap all of the benefits of the economic activity, and too much or too little of the good will be produced or consumed in terms of overall costs and benefits to society. Jaffe (1998) provides a useful discussion on spillover effects.

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The roots of 100 companies can be directly traced to basic research conducted at a U.S. university and sponsored by a federal agency. Some examples are Cisco Systems, FluGen, Google, SAS and Sun Microsystems (U.S. Science Coalition, 2010). 13

There is a very strong body of evidence pointing to positive returns from private investment in research and development; Mansfield (1993) is a prominent example. 14

In addition, due to time-lags (as discussed by Mansfield, 1998) and unexpected outcomes (as discussed by the U.S. Committee on Science, Engineering and Public Policy, 1999), estimates of the return to government investment in basic scientific research may be downward-biased. The Internet revolution of the 1990s was based on scientific investments made in the 1970s and 1980s. And today's widespread commercialization of biotechnology was based, in part, on key research findings developed in the 1950s. Thus, governments that choose to provide support for R&D are likely to get better results if that support is stable, avoiding a pattern of feast or famine (Bernanke, 2011; Freeman & van Reenen, 2009).

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relation to the scope for multiplier effects (see Ilzetzki, Mendoza & Veigh, 2010).15 There are also concerns

as to whether Ireland has the necessary scale to develop clusters of scientific research; it is known that

biotechnology firms, for example, tend to cluster (Audretsch & Stephan, 1996). A central idea in the Irish

case is to focus on being a clever copycat rather than developing its own R&D capacity; that is, Ireland

should just do the 'D' in R&D (Innovation Task Force, 2010). This approach is motivated by the scale of Irish

investment and the evidence that absorption of foreign knowledge (or absorptive capacity) is an important

factor for economic growth (Bye, Fæhn and Heggedal, 2009).16 Furthermore, it has been demonstrated that

Ph.D.-trained researchers facilitate the absorption of foreign knowledge (Mowery & Oxley, 1995; Keller,

1996; Dorwick, 2003; Veugelers, 2007);17 and therefore, a pool of high-quality researchers should help to

stimulate economic growth.18 Overall, if large economies wish to pursue innovation, or small economies aim

to absorb foreign knowledge; then both must have Ph.D. graduates who have been trained in scientific

research.

3 . P r o d u c t i o n o f P u b l i c a t i o n s a n d P a t e n t s

This section discusses previous studies on the production of publications and patents - at the

individual level of the researcher. 19 To begin, these studies are placed in the context of educational

production and human capital acquisition. Gurmu, Black and Stephan (2010) discuss the estimation of a

15

The multiplier effect is the idea that an initial amount of spending (usually by the government) leads to increased consumption spending; and results in an increase in national income greater than the initial amount of spending. In other words, an initial change in demand causes a change in output for the economy that is a multiple of the initial change. 16

Absorptive capacity is related to the ability of individuals in an organisation to assimilate, process and transform external knowledge flows (Escribano & Tribo, 2009). Escribano and Tribo (2009) show that firms with higher levels of absorptive capacity manage external knowledge flows more efficiently.

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Dorwick (2003) demonstrates that the absorption of foreign knowledge is a function of human capital. Mowery and Oxley (1995) and Keller (1996) measure absorptive capacity using investment in scientific and technical training, and the number of scientists and engineers employed. Similarly, Veugelers (1997) uses the number of doctorates within the R&D department as a measure of absorptive capacity.

18

Recent studies have highlighted the importance of intangible capital, such as the knowledge embodied in the workforce, for economic growth (Corrado, Hulten & Sichel, 2009; Corrado & Hulten, 2010).

19

There is a dearth of research on the individual-level determinants of commercial activities - such as licensing or setting up a spin-out company. Studies on this topic are scarce and mostly focus on the analysis of firms rather than the decision of scientists and engineers to create companies (Audretsch & Kayalar-Erdem, 2004; Landry, Amara & Rherrand, 2006). However, there is evidence which suggests that scientists with the strongest academic credentials are the most likely to be involved in commercial activity; Lowe and Gonzales-Brambila (2007) find that faculty entrepreneurs are usually star scientists, who are more productive in terms of publications and citations.

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knowledge production function. However, Gurmu, Black and Stephan (2010) are focused on all individuals

involved in the process of scientific production; whereas this paper is focused solely on early-career

researchers.

A simple production model lies behind much of the analysis in the economics of education; and can

be transposed here in the context of outcomes from higher education (specifically, Ph.D. training). There is a

precedent for the theoretical consideration of the doctoral production function: Breneman, Jamison and

Radner’s (1976) model of the Ph.D. production process proceeds as follows. The vast majority of graduate

students view the decision to enter graduate school as an investment, following the literature on human

capital investment. Breneman et al. (1976) make five assumptions about the Ph.D. production process, as

follows:

(i) The student, regardless of field, is viewed as an investor rather than a consumer of graduate education.

(ii) The investment requires the earning of the Ph. D. degree for its successful completion, i.e. the student attaches little if any value to incomplete degree work.

(iii) The investment is not properly evaluated in financial terms alone, but is viewed by the student as an investment necessary for entry into certain occupations requiring the doctorate.

(iv) The potential graduate student has very limited information and assumes that the demand for Ph.D.'s in his field will be strong when he graduates.

(v) The rational student may have sound reasons for lengthening his time to degree.20

In human capital theory, both Becker (1964) and Schultz (1971) recognise the role of scientific

research in human capital formation (Bozeman, Dietz & Gaughan, 2001). Schultz (1971) asserts that scientific

research yields two forms of capital: that which is transformed into new skills and human capabilities of

economic value (human capital), and that which is transformed into new materials of economic value (non

human capital). Becker (1964) argues that growth in scientific knowledge has raised the productivity of

labour and increased the value of education and training as embodied in scientists, technicians, managers,

and other workers. Recent studies have highlighted the importance of intangible capital, such as the

knowledge embodied in the workforce, for economic growth (Corrado, Hulten & Sichel, 2009; Corrado &

Hulten, 2010). Life cycle models of scientific production view scientific careers as a longitudinal function of

20

Stephan and Ma (2005) document a lengthening of postdoc duration at U.S. universities. Their results suggest that increased duration can be explained in part by the increasing proportion of Ph.D.s awarded to temporary residents and the increased number of degrees being awarded in the life sciences. Adverse job market conditions also appear to play a role. The authors also find the duration of the postdoc experience to be positively related to the provision of fringe benefits.

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individual skill levels and various incentives to act productively, mitigated by the effects of human ageing

(Bozeman, Dietz & Gaughan, 2001).21 However, life cycle models are not salient here as the focus is

exclusively on early-career researchers.

The outcome of interest in Breneman et al. (1976) is the number of Ph.D.’s produced per university

department. In this paper we are concerned with the individual outcome: that is, whether the student gets

the doctoral degree or not (and especially, whether they proceed to publish and/or patent). In typical

models of educational production, there is usually some measure of academic achievement that enables one

to differentiate between students. However, it is difficult to differentiate between the human capital (or

quality) of Ph.D. graduates without considering whether they have published or secured a patent. If a

postdoctoral researcher has produced either of these outputs then they have more human capital;

compared to the graduate who has not produced any scientific output. Hence, there is an emphasis in this

study on publications and patents as outcomes of the Ph.D. production process.

A gender gap in publication output is reported across several studies; and is the most widespread

finding in the literature on scientific production at the individual-level.22 23 Hamovitch and Morgenstern

(1977) control for teaching-duties and number of children; and find that women publish about 20 percent

fewer articles than men. Blackburn, Behymer and Hall (1978) find that men publish more than women

irrespective of academic field. In a more recent study using a sample of American biochemists, Long (1992)

finds that gender differences in the number of publications (and citations) are bigger during the first decade

of scientists’ careers; but subside later. Kyvik and Teigen (1996) also document a gender gap in publication

output that diminishes over time. Further evidence of the gender gap is provided by Xie and Shauman (1998)

and Prpic (2002). Even when controlling for giving birth to children and other personal characteristics, Stack

(2004) finds that women publish significantly less than men. However, while Penas and Willett (2006) find

evidence for differences in the productivity of men and women, they find no difference in the quality of their

work (as measured by citation counts). Sonnert and Holton (2006), Koplin and Singell (2006) and Symonds,

Gemmell, Braisher, Garringe and Elgar (2006) suggest that although men tend to publish more papers than

women, women’s papers are of higher quality.

21

Age effects in scientific production are a stylised fact; productivity declines with age following a predictable pattern (Bonaccorsi & Daraio, 2006). Using data from Mexico, Gonzalez-Brambila and Vesolo (2007) report that publishing peaks when researchers are approximately 53 years old, 5 or 10 years later than what prior studies have shown. 22

However, the reason(s) for the gender gap in the effectiveness of human capital are not clear-cut (Frietsch, Haller, Frunken-Vohlings & Grupp, 2009). 23

Borrego, Barrios, Villarorya and Olle (2010) is a recent study that finds no gender gap in postdoctoral researchers’ scientific output.

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According to Hogan (1981) and Tien and Blackburn (1996), early-career publishing success is

influenced by the rank of the Ph.D. program where an individual receives his or her degree. Buchmueller,

Dominitz and Hansen (1999), using data on U.S. Ph.D. economists, suggest that graduates of the top ten

Ph.D. programs, and those that had some experience as research assistants, are more productive. However,

more recent research, again using data on U.S. Ph.D. economists, suggests that the situation relating to

institutional inputs is more nuanced (Hilmer & Hilmer, 2011). According to Hilmer and Hilmer (2011), the

observed correlation between (dissertation) advisor prominence and early-career publishing is due more to

the higher innate potential required to gain admission to top programs; than to working with a more

prominent advisor. As a result, unobservable factors in early-career publishing may be explained more by

innate ability than by the quality of institutional inputs in the Ph.D. training process (where these

institutional inputs are unobserved).

Azoulay, Stuart and Ding (2007) is one of the few studies to concentrate on the determinants of

patenting activity at the individual-level.24 Azoulay, Stuart and Ding (2007) document that patenting is often

accompanied by a flurry of publication activity in the year preceding the patent application, even after

accounting for the lagged stock of publications, or controlling for scientist fixed effects. Given this, one might

expect publications to be more prevalent than patents among early-career researchers.25 Stephan, Gurmu,

Sumell and Black (2007) use the (U.S.) Survey of Doctorate Recipients to examine the individual

determinants of patenting. They find that individual patent output is positively and significantly related to

the number of researchers’ publications. Ding, Murray and Stuart (2006), using a random sample of 4,227 life

scientists over a 30-year period, show that women faculty members patent at about 40 percent of the rate

of men. McMillan (2009) suggests that although men tend to patent more than women, women’s patents

are of higher quality.

Kitkou and Gulbrandsen (2010) discuss the reinforcement effect (or complementarity effect) where

patenting and publishing mutually complement and/or reinforce each other. This may happen in either

direction. Patenting may open up new scientific opportunities, lead to new ideas, create scientific networks

etc; or patenting may the result of such opportunities and networks . Some researchers have indicated in

interviews that patents are sometimes based on the first draft of a scientific paper—and that the patent

application is written by a specialised professional (Gulbrandsen 2005). Breschi, Lissoni and Montobbio

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The number of patents related to academic research results has grown over the last 20 years (1986-2006), both in the U.S. and in Europe. These ‘academic patents’ account for 4 percent of total domestic patents in the U.S.; and similar figures have been estimated for France, Italy, Sweden, Finland and Norway (Franzoni & Lissoni, 2006). Morgan, Kruytbosch, and Kannankutty (2001) find that patenting and commercialisation rates are much higher in industry than the academic sector. 25

Indeed, this is borne out in the data examined in this paper.

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(2007) and Carayol (2007) find a strong and positive relationship between patenting and publishing; and

most of the empirical evidence supports the reinforcement effect (Kitkou & Gulbrandsen, 2010). This

suggests that the same early-career researchers who produce one of the outputs (patent or publication) may

be likely to produce the other output as well.

The substitution effect is where patenting suppresses scientific publishing (Kitkou & Gulbrandsen,

2010). Patenting often involves a delay of publications. It could be that this makes it more difficult to publish

a scientific paper—the research frontier could have shifted, the researcher’s attention could have moved on

to other problems, it may be intellectually or psychologically challenging to start working on a delayed paper,

etc. (Kitkou &Gulbrandsen, 2010). The patenting process also involves some degree of secrecy. In interviews,

some academic inventors in Norway have stated that they could not talk about their most recent research

because the relevant patents had not yet been secured (Kitkou & Gulbrandsen, 2005). However, most of the

empirical evidence does not support the substitution effect (Kitkou & Gulbrandsen, 2010).

4 . D a t a

The Contract Researchers’ Survey component of the Irish Universities Study is examined in this

paper. The survey was conducted by the UCD Geary Institute between November 2008 and December

2009; targeting researchers on fixed term contracts at the seven Irish universities. The data were collected

through a web-survey that the authors designed. The survey received 687 responses, which equates to a

response rate of 24 percent. The field-work involved dissemination of the web-survey to the entire

population of contract researchers (in Ireland’s seven universities) by the Irish Universities Association

(IUA). Based on university figures (provided by the IUA), it is known that the number of contract

researchers in the seven Irish universities was 2,871 as at September 2008.26 The distribution of

institutional affiliation in the sample is broadly in line with population figures.27 Of the 687 researchers in

the field-work sample, most fill out the survey comprehensively. There are at least 630 observations for

questions relating to full-time status, academic rank, job title, interest in area of research, teaching duties,

first year of employment, and contract details. This is not an exhaustive list; but is used for illustrative

26

The institutional breakdown from the university figures (in order of magnitude) was as follows: Trinity College Dublin: 648; University College Dublin: 589; University College Cork: 518; NUI Galway: 424; NUI Maynooth: 277; Dublin City University: 243; University Limerick: 172. 27

The only deviation from the population distribution arises from over-representation for one institution: University Limerick.

12

purposes because all of the questions mentioned above are non-sensitive; and they relate to items which

every respondent can answer. Overall, the indication is that this is a high-quality data; without any major

prevalence of missing values. Adjustments are made to produce an analytical sample, as follows.

Of the 687 researchers in the field-work sample, 129 fail to give information on the year that they

graduated from their Ph.D. programme. In addition, only 503 (of the 687) report that they have a doctoral

degree. 51 (out of 687) researchers report their academic rank as “research assistant” – which is usually

indicative of a doctoral qualification being absent. 125 (out of 687) researchers report “none of the above”

for academic rank, being unable to categorise themselves between postdoctoral researcher (302),

research fellow (117) or senior research fellow (41). A similar picture appears if one examines job title.28 If

respondents report that they have a highest qualification which is not a Ph.D. then they are dropped from

the sample. Exceptions are made in 10 cases for professional qualifications such as accountancy, which are

required to do research in professional fields. Some caution is also taken in relation to the available

information on year of Ph.D. graduation. Of respondents who do not report to hold a Ph.D. qualification,

three report a year of Ph.D. graduation.29 Therefore, respondents are only dropped from the sample if

they fail to provide a year of Ph.D. graduation in addition to stating that they do not hold a Ph.D. Finally,

there are 37 respondents who do not report holding a Ph.D., but who do report holding the academic rank

of postdoctoral researcher, research fellow or senior research fellow. These individuals are also retained

for the analytical sample. These combined adjustments result in an analytical sample of 609 individuals; all

of whom work as researchers in Irish universities; and all of whom hold Ph.D. qualifications.

Some further adjustments are necessary, as follows. 26 individuals report themselves to be

working on a part-time basis; these individuals are dropped from the analytical sample as they are a

characteristically different group (with a potential disadvantage in the process of scientific production). In

addition, 34 individuals claim to have graduated from their Ph.D. prior to 1998. These individuals are also

dropped from the sample, as there could be something characteristically different about researchers

working on short-term contracts more than a decade after attaining their Ph.D. qualification. (The

intention here is to examine a sample of post-doctoral researchers). Also, 17 individuals claim to have

started their current job prior to 1998. As these individuals are not likely to be postdoctoral researchers,

28

Out of 687 researchers, 50 report their job title as “research assistant” – which is usually indicative of a doctoral qualification being absent. 120 researchers report “other” for job title, being unable to categorise themselves between research associate (9), postdoctoral researcher (250), research fellow (121), senior research fellow (22), assistant professor (2), lecturer (40), assistant lecturer (3) or senior researcher (2). Lecturers and assistant professors are usually in permanent positions uncharacterised by short-term contracts. However, it is possible for some lecturers to be hired on a temporary basis. 29

Of respondents who do not report a year of Ph.D. graduation, 9 report that they hold a Ph.D. qualification.

13

they are also dropped from the sample. Another group unlikely to be postdoctoral researchers are those

claiming to be on contracts of 5 years or longer. This group of 40 individuals are also dropped from the

sample. All of these adjustments result in the final version of the analytical sample, which contains 490

observations. This sample represents postdoctoral researchers (those who have completed doctorates but

who have not yet obtained permanent research positions) working at the seven Irish universities.

Of the 490 postdoctoral researchers (postdocs) in the analytical sample, 319 (65%) report that

they have published articles in academic or professional journals. 61 (12%) report that they have secured

patents.30 Unfortunately there is no continuous information about number of publications, or number of

patents. However, this is less of a drawback when examining early-career researchers, as the primary

interest is in what drives the difference between those researchers who produce academic output, and

those who do not.31 The output-question in the survey asks whether the Ph.D. graduate has managed to

patent or publish "over the last two years".32 The question about commercial expectations asks if the

graduate expects to commercialise their research over the next five years. Of those who answer this

question (n=295), 29 percent expect to commercialise their work in the future. Appendix A shows Venn

diagrams for the correlations between the dependent variables. Of those who report that they produce a

publication, patent or have commercial expectations, 26 percent are in all three categories. 12 percent of

researchers report that they have produced both a publication and a patent. 12 percent of researchers

report that they have produced a patent as well as having commercial expectations.

The independent variables in the study are grouped into two categories: situational and

demographic. The former refers to the situation that the postdocs find themselves in; this includes: the

interest that they have in the area they are researching, the duration of their contract, whether they do

any teaching, and whether they are working in a field different to that of their Ph.D. The demographic

variables are as follows: whether the postdoc was ever unemployed, whether they have a partner, the

number of children that they have, their age, gender, and years since graduation (which approximates to

their experience). Interest in area of research is measured on a 10-point scale, with 45 percent of those

30

114 (out of 492) report that they have published chapters in edited volumes. 46 (out of 492) report that they have published books, manuals or monographs. The majority of the sample (90 percent) works in STEM (science, technology, engineering or maths). Therefore, the paper focuses on those outputs (publications, patents and commercialisation) which are most salient to STEM research. 31

Bland , Center, Finstad, Ribsey and Staples (2006) show that faculty with a tenure position produce more than faculty who do not have tenure.

32

The quality of publications and patents are not observed in this study. One way to determine the quality of journal publications is by number of citations; Garfield (2006) reviews the history of the “journal impact factor”. Another useful reference in this area is Hirsch (2005). Hirsch proposes the index h, defined as the number of papers with citation number ≥h, as an index to characterise scientific output, allowing for quality.

14

who answered reporting to be at the highest point on the scale.33 Duration of contract is a measure of

stability which may help researchers to focus on scientific production. This variable has five categories and

indicates longer duration when moving from one category to the next. Most researchers are on contracts

lasting one year (24 percent of those who answer) or two years (33 percent of those who answer). The

teaching variable indicates whether the postdoc has any teaching duties; of those who answered this

question, 53 percent do some teaching. Satisfaction with post-doctoral position is measured on a five-

point scale; 47 percent of postdocs are either satisfied or very satisfied with their situation.34 Less than a

third of postdocs are working in a field different to their Ph.D.

Table 1: Summary Statistics: Contract Researchers in Irish Universities

Variable Form Scale Mean S.D. N

Incidence of publication Binary 0-1 0.65 0.47 490

Incidence of patent Binary 0-1 0.12 0.33 490

Expect to commercialise Binary 0-1 0.28 0.45 295

Interest in area of research Cont. 0-10 8.61 1.73 445

Duration of contract Category Years 2.98 0.95 399

Incidence of teaching duties Binary 0-1 0.53 0.49 432

Satisfaction with post-doc Category 1-5 4.10 1.90 338

Working in different field to Ph.D. Binary 0-1 0.30 0.45 383

Natural science Binary 0-1 0.42 0.49 395

Ever unemployed, and duration Category Months 0.79 1.25 320

Postdoc has a partner Binary 0-1 0.62 0.48 391

Postdoc’s number of children Cont. 0-4 0.43 0.78 390

Age of postdoc Cont. 0-67 32.6 5.68 392

Whether the postdoc is male Binary 0-1 0.41 0.49 393

Irish citizen Binay 0-1 0.50 0.50 490

Years since graduation Category Years 2.87 2.87 490

Notes: The incidence of publication is over the previous two years.

Expectations to commercialise research are over the next five years. No researchers in the analytical sample have permanent employment contracts.

Approximately 90 percent of researchers are working in STEM subject areas. STEM = science, technology, engineering or maths.

33

Researchers are asked: “How would you rate your interest in your current area of research?” 34

Researchers are asked: “Overall, how satisfied are you with your job conditions?”

15

Natural sciences are the most popular area for research; 34 percent of the sample is working in

this area. Almost 90 percent of researchers in the sample are working in the areas of science, technology,

engineering or maths (and these researchers are the focus of this paper). Of those who answered about

ever being unemployed, 34 percent have been. There are five categories of previous unemployment: less

than one month unemployed (8 percent), one month to three months unemployed (13 percent), three

months to six months unemployed (8 percent), six months to one year unemployed (4 percent), one year

to two years unemployed (1 percent).

The “partner” variable indicates whether a postdoc is married or is living with a long-term partner

(62 percent are). Of those who answered the question about number of children, 72 percent have none.

The average reported age is 32 years; 52 percent of respondents who answered the gender question

report themselves to be male. Half of respondents who answer the citizenship question report themselves

to be Irish. The average number of years since Ph.D. graduation is 3.4; so on average, researchers attained

their Ph.D. about three years prior to being surveyed. Summary statistics related to the analytical sample

are presented in Table 1. Visuals relating to descriptive statistics are presented in Figure 1.

5 . M e t h o d a n d R e s u l t s

The determinants of publications, patents and commercial expectations are estimated using the

following econometric model:

Yij = αi + β1Aij + β2Bij + μij (1)

where Yij is the instance of publication, patent or commercial expectation for postdoc i at

university j ; Aij is a matrix of variables relating to the postdoctoral researcher; Bij is a matrix of variables

relating to institutional characteristics. The matrix of variables relating to the postdoctoral researcher

refers to both situational and demographic characteristics. The situational characteristics are as follows:

16

Figure 1: Descriptive Statistics: Contract Researchers in Irish Universities

A: Year of Graduation B: Interest in Subject Area

02

04

06

08

0

fre

qu

en

cy

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year grad PhD

C: Duration of Contract D: Satisfaction with Post-Doc

05

01

00

15

0

fre

qu

en

cy

6 months or less 1 year 2 years 3 years 4 years

duration of contract

E: Subject Area F: Duration of Previous Unemployment

0 50 100 150 200frequency

Humanities

Social Sciences

Agricultural Sciences

Medical and Health Sciences

Engineering and Technology

Natural Sciences Including Computer Science

gen

era

l a

rea o

f fo

cus

0 10 20 30 40frequency

Yes for one year to two years

Yes for six months to one year

Yes for three months to six months

Yes for one month to three months

Yes for less than one month

une

mp

loye

d

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 Interest in area

0

50

100

150

1 2 3 4 5 satisfaction

17

(i) the interest that the postdoc has in the area they are researching35, (ii) the duration of their contract36,

(iii) whether they have to do any teaching, (iv) their satisfaction with their post-doctoral situation, and (v)

whether they are working in a field different to their Ph.D.

The demographic characteristics are as follows: (i) whether the postdoc is working in the natural

sciences, (ii) whether the postdoc was ever unemployed, (iii) whether the postdoc has a partner, (iv)

postdoc’s number of children, (v) age, (vi) gender, (vii) whether the postdoc is an Irish citizen, and (viii) the

number of years since the postdoc’s Ph.D. graduation (this is a measure of research experience since

graduation). Unobservable factors captured by the error term include, but are not limited to, the prestige

of the institution attended for doctoral study, the rigour of the doctoral program, and the reputation of

the doctoral advisor.37 The matrix of variables relating to institutional characteristics includes university-

affiliation, subject area (faculty) and an interaction between university and subject area – which captures

local institutional effects.38 Subject areas are defined broadly, similar to typical faculty structures - as

follows: natural sciences (including computer sciences), engineering and technology, medical and health

sciences, agricultural sciences (and social sciences, and humanities). This keeps the number of subject-

categories parsimonious – while still capturing the impact of faculty-specific factors in each university.

The analysis is conducted on a sample restricted to researchers working in science, technology,

engineering and maths (STEM); as these subject areas are more salient to patents and commercialisation

(n=440). A series of probit regressions (columns 1-3 in Table 2) are estimated as the dependent variables

are all binary indicators.39 It is important to allow for correlation across the multiple outcomes of scientific

output; Appendix A shows that there are correlations between the dependent variables.40

35

See Schiefele (1991) for a discussion on interest in subject area and intrinsic motivation. 36

Duration of contract is a measure of stability which may help researchers to focus on academic production. This variable has five categories and indicates longer duration when moving from one category to the next. Most researchers are on contracts lasting one year (24 percent of those who answer) or two years (33 percent of those who answer). 37

Hilmer and Hilmer (2011) suggest that the observed correlation between dissertation advisor prominence and early-career publishing results more from the higher innate potential required to gain admission to top programs; rather than working with a more prominent advisor. If this is true, then unobservable factors in early-career scientific production may be driven more by innate ability than by the quality of institutional inputs in the Ph.D. training process. 38

According to Ding, Murray and Stuart (2006), formal institutional support is particularly important for women. “Many women commented that their TTO (technology transfer office) provided industry contacts, advice, and encouragement to develop the commercial aspects of their research." 39

The incidence of publication or patenting could have a reverse-effect on the following independent variables: contract duration, whether the researcher has ever been unemployed, and/or what institution they are working at. If these independent variables are omitted from the specification there is no change in the overall pattern of results. 40

Of those who report that they produce a publication, patent or have commercial expectations, 26 percent are in all three categories. 12 percent of researchers report that they have produced both a publication and a patent. 12 percent of researchers report that they have produced a patent as well as having commercial expectations.

18

Table 2: Postdoctoral Production Functions: Probit Regressions

PROBIT MULTIVARIATE PROBIT

(1) (2) (3) (4) (5) (6)

Publish Patent Commerce Publish Patent Commerce

Interest in areaa 0.005 0.014** 0.058** 0.056 0.104 0.128**

(0.008) (0.006) (0.027) (0.063) (0.069) (0.064)

Duration of contract -0.015 -0.013 0.026 -0.045 -0.097 0.143

(0.017) (0.012) (0.035) (0.135) (0.116) (0.106)

Teaching dutiesb 0.030 -0.009 0.067* 0.224 0.069 0.193

(0.022) (0.015) (0.036) (0.227) (0.211) (0.189)

Satisfactionc 0.003 -0.011*** -0.012 0.014 -0.064 -0.031

(0.010) (0.004) (0.009) (0.058) (0.053) (0.047)

Different fieldd 0.034 -0.051*** 0.092 0.012 -0.389 0.173

(0.032) (0.010) (0.109) (0.246) (0.248) (0.211)

Unemploymente -0.017 -0.009 -0.024 -0.152* -0.056 -0.074

(0.016) (0.016) (0.044) (0.085) (0.087) (0.074)

Partner 0.046 0.038* 0.030 0.037 0.385* 0.253

(0.037) (0.022) (0.055) (0.237) (0.227) (0.200)

Number of children -0.003 0.021 0.002 -0.150 0.094 -0.045

(0.021) (0.018) (0.076) (0.177) (0.164) (0.157)

Age -0.003 -0.009 -0.009 -0.013 -0.061* -0.039

(0.002) (0.006) (0.012) (0.026) (0.031) (0.028)

Gender 0.044* 0.050* 0.211*** 0.176 0.353* 0.655***

(0.023) (0.026) (0.064) (0.214) (0.213) (0.186)

Irish citizen -0.017 -0.021 -0.092 0.008 -0.170 -0.342*

(0.036) (0.029) (0.063) (0.243) (0.222) (0.197)

Years since Ph.D. 0.010* 0.020*** 0.003 0.058 0.145*** 0.013

0.005 (0.008) (0.014) (0.050) (0.050) (0.046)

Observations 306 332 248 266 266 266

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Note: Results are shown for “STEM researchers” only: those working in the areas of science, technology, engineering and maths. Marginal effects are displayed in columns 1-3, only. Missing values are replaced by a value of zero: to keep as many observations in the sample as possible. Dummy variables are included to take account of where this is done; this method is known as dummy variable adjustment. Where they apply to incomplete cases, control variables for missing value adjustment are not shown above. Outliers and missing values are adjusted for independent variables only. Controls for subject area, university and local institutional effects (subject area interacted with university) are not shown above. Robust standard errors are clustered by university. a“Interest in area” refers to interest in area of research. b”Teaching duties” refers to the binary instance of teaching duties. c“Satisfaction”

refers to overall satisfaction with post-doctoral situation. d”Different field” refers to working in a field different to postdoc’s Ph.D. e”Unemployment” refers to prior unemployment.

19

It is likely that there is correlation between the error terms of the equations for publishing,

patenting and commercial expectations; this should be taken into account in order to avoid inconsistent

estimates. Results from multivariate (specifically, trivariate) probit regression are presented across

columns 4-6 in Table 2. These regressions take account of any unobservable characteristics affecting all

three outcomes in the scientific production process.

The results from multivariate (trivariate) probit (columns 4-6 in Table 2) are principally used for

comparison with the univariate probit regressions (columns 1-3 in Table 2). Marginal effects are not

presented in the regression results for multivariate probit;41 so the main purpose of the trivariate

estimation is to focus attention on the statistically significant univariate results that are also statistically

significant in the trivariate results. Given this, the discussion that follows focuses on the following

independent variables: interest in area of research, whether the researcher has a partner, gender, years

since Ph.D. graduation (approximating experience), and institutional affiliation.42 A strong interest in one’s

area of research predicts patenting and commercial expectations; this is considered to reflect intrinsic

motivation. A one unit increase in motivation predicts that a researcher is one percent more likely to patent;

and six percent more likely to expect commercialisation. Researchers who have a partner are four percent

more likely to patent. Males are more likely to publish, to patent, and to expect that they will commercialise

their research. Specifically, males are four percent more likely to publish; five percent more likely to patent;

and 21 percent more likely to have commercial expectations. More experienced researchers (measured by

years since Ph.D. graduation) are more likely to publish and to patent. One extra year of experience predicts

that a researcher is one percent more likely to publish, and two percent more likely to produce a patent.

Finally, certain university-affiliations predict that researchers are more likely to publish, to patent, or

to expect commercialisation (this effect occurs after controlling for interactions between university and

subject area). University-affiliation coefficients are not shown in Table 2; however they are presented in a

longer results table in Appendix B.43 One university has researchers who are more likely to produce

publications. One university has researchers who are more likely to produce patents. Two universities have

41

While the estimation of uni- and bivariate probit models is straightforward and feasible with all standard software packages, the estimation of multivariate probit models with more than two dependent variables is more complex and requires the inclusion of simulators in the maximum likelihood method. Ultimately, there is no “canned routine” in statistical software packages for calculating marginal effects after trivariate probit; which is why marginal effects for trivariate probit are not presented in this paper.

42

Only independent variables that are statistically significant in both univariate and trivariate regression are discussed. Once an independent variable meets this criterion, its statistical significance for each of the univariate probit regressions is reported. The seven variables relating to institutional affiliation are not shown in Table 2 (for the purpose of brevity); however, many of these variables are statistically significant: across all univariate regressions as well as the trivariate regression.

43

The identity of these institutions is not revealed due to a need to preserve confidentiality.

20

researchers who are less likely to produce patents. Two universities have researchers who are more likely to

have commercial expectations. Three universities have researchers who are less likely to have commercial

expectations. The size of the coefficient on university-affiliation in the (commercial) expectations equation

ranges from -0.25 to 0.32. This indicates that researchers from certain university-affiliations are 25 percent

less likely, or 32 percent more likely, to have commercial expectations (while researchers from other

university-affiliations lie somewhere in between ). However, the sizes of the coefficients on university-

affiliation are much larger in the publishing and patenting equations. The size of the coefficients on

university-affiliation in the publishing equation ranges from -0.2 to -0.99; that is, there are six universities

whose researchers are less likely to publish, where that likelihood ranges from 20% to 99%. The size of the

coefficients on university-affiliation in the patenting equation ranges from -0.11 to 0.98; that is, researchers

from certain university-affiliations are 11 percent less likely, or 98 percent more likely, to produce patents

(while researchers from other university-affiliations lie somewhere in between ).

Overall, the effect of university-affiliation is the most economically significant driver of scientific

output, followed by researchers’ gender, their interest in their area of research, whether they have a partner,

and their years of experience. Of course, some of these variables are quite likely to be endogenous.

Researchers have some choice about what university they are affiliated to; and this is more likely to be the

case the more productive a researcher is. Also, researchers who are more productive are more likely to have a

high level of intrinsic motivation (or interest in their area of research); and to gain more years of experience.

However, researchers do not choose their gender, so at least this independent variable is free from any

concerns about endogeneity.

6 . C o n c l u s i o n

The key results from this paper show that institutional affiliation, gender, interest in area of research

and years of experience all play a role in the postdoctoral production function (in that order in terms of

economic significance).44 In particular, institutional affiliation and gender are the most economically

significant drivers of scientific output. It has been suggested that more effective use of female human capital

is potentially one of the best ways to enhance the competitiveness and quality of the knowledge-based

society (Frietsch, Haller, Frunken-Vohlings & Grupp, 2009). The results of this paper support this idea;

44

If the researcher has a partner they are also more likely to produce scientific output.

21

especially in relation to research-commercialisation. However, the reason(s) for the gender gap in the

effectiveness of human capital are not clear-cut (Frietsch, Haller, Frunken-Vohlings & Grupp, 2009).

According to Ding, Murray and Stuart (2006), formal institutional support is particularly important for

women.45 The results from this paper document a very strong role for institutional affiliation, after controlling

for local institutional effects (subject area interacted with university) as well as other covariates. This suggests

that some universities in Ireland may need to improve the range of supports that they offer to postdoctoral

researchers; alternatively, it may be necessary for some universities to develop a stronger culture around

aspects of the scientific process.

Future research should extend the study of scientific output by early-career researchers to settings

outside the university. Future research should also examine the frequency, and more importantly the quality,

of early-career research output. One limitation of this study is a lack of information about patent quality;

indeed, McMillan (2009) suggests that although men tend to patent more than women, women’s patents

are of higher quality. This is an issue which cannot be addressed in this paper. Nonetheless, the gender

difference in commercial expectations is clear in this study; and it is much larger than the gender difference

in patenting. Remarkably, males are 21 percent more likely to expect that they will commercialise their

research at some stage over the next five years of their career. Future research should investigate whether

this expectation actually reflects higher rates of commercial activity by males. Evidence in this area is scarce;46

for now this paper is the main source of evidence on the apparent gender gap in academic entrepreneurship.

Future research should also investigate whether the gap in commercial expectations is due to

underlying motivations, or perhaps due to perceived barriers to the successful pursuit of commercial

activities. In this regard, the role of the TTO (technology transfer office) is potentially very important.47 These

offices are designed to provide advice and encouragement to university staff who wish to develop the

commercial aspects of their research. On several occasions, the European Commission has argued that while

European research institutions are good at producing academic research outputs, they are not successful in

45

“Many women commented that their TTO (technology transfer office) provided industry contacts, advice, and encouragement to develop the commercial aspects of their research." (Ding, Murray & Stuart, 2006 – reporting on the results of qualitative research for an article in Science).

46

Studies on this topic mostly focus on the analysis of firms rather than the decision of scientists and engineers to create companies (Audretsch & Kayalar-Erdem, 2004; Landry, Amara & Rherrand, 2006). Stephan and El-Ganainy (2007) document the substantial gender gap that exists among university scientists with regard to entrepreneurial activity, focusing on biomedical sciences. 47

Studies focusing on TTOs have appeared only recently; results show that the size and experience of a TTO has positive influence on spinout activity (Powers & McDougall, 2005); also that patent output relates positively to the presence of a TTO (Gurmu, Black & Stephan, 2010).

22

transferring these outputs to the economy – the so called ‘European Paradox’ (European Commission 2007).48

Operational improvements to technology transfer offices could go some way to addressing this paradox. Such

operational improvements could be rolled out and evaluated on an experimental basis, in other future

research.

Finally, self-reported data on scientific outputs are highly desirable in micro-level studies on scientists

and researchers. The (U.S.) National Science Foundations’s Survey of Doctorate Recipients (SDR) should re-

instate questions on self-reported scientific output (publications and patents); these have been absent from

recent rounds of the survey. The SDR should also ask survey respondents about their expectations to

commercialise their research; and indeed if senior respondents have done so. Cross-country comparisons

would be highly valuable as the gender gap in commercial expectations may not exist in other countries;

particularly in an environment such as the United States - which is renowned for having a particularly strong

culture of enterprise. There is compelling evidence in this paper for a gender gap in commercial expectations

-- amongst early-career researchers in one European country.

References

48

Dosi, Llerena and Labini (2006) suggest that European weaknesses reside in its system of academic research and in weak industry. Nonetheless, the United States is recognised as the world leader in technology transfer (Lockett, Siegel, Wright & Ensley, 2005).

Arrow, K.J. (1962). The Economic Implications of Learning by Doing. Review of Economic Studies, 29 (3), 155-73.

Audretsch, D., & Kayalar-Erdem, D. (2004). Determinants of Scientist Entrepreneurship: An Integrative Research Agenda. Discussion Papers on Entrepreneurship, Growth and Public Policy, No. 2004-42, Max Planck Institute of Economics, Group for Entrepreneurship, Growth and Public Policy.

Azagra-Caro, Carayol & Llerena, (2006). Patent Production at a European Research University: Exploratory Evidence at the Laboratory Level. The Journal of Technology Transfer, 31 (2), 257-268.

Azoulay, P. Ding, W. & Stuart, T. (2007). The Determinants of Faculty Patenting Behavior: Demographics or Opportunities? Journal of Economic Behavior & Organizations, 63 (4), 599-623.

Azoulay, P., Ding, W., & Stuart, T. (2009). The Impact of Academic Patenting on the Rate, Quality and Direction of (Public) Research Output. The Journal of Industrial Economics, 57 (4), 637-676.

Becker, G. (1964). Human Capital: A Theoretical and Empirical Analysis, with a Special Reference to Education, Chicago, University of Chicago Press.

23

Bernanke, B. (2011). Promoting Research and Development: The Government's Role. At the Conference on "New Building Blocks for Jobs and Economic Growth," Washington, D.C., May 16.

Blackburn, R. T., Behymer, C. E. & Hall, D. E. (1978). Research Note: Correlates of Faculty Publications. Sociology of Education, 51 (2), 132-141.

Bland, C. J., Center, B.A., Finstad, D.A., Risbey, K. R., & Staples, J. (2006). The Impact of Appointment Type on the Productivity and Commitment of Full-Time Faculty in Research and Doctoral Institutions. The Journal of Higher Education, 77 (1), 89-123.

Bonaccorsi A. & Daraio C. (2003). Age effects in scientific productivity. The case of the Italian National Research Council (CNR). Scientometrics, 58, (1), 47-88.

Borrego, A., Barrios, M. Villarroya, A. & Olle, C. (2010). Scientific output and impact of postdoctoral scientists: a gender perspective. Scientometrics, 83, 93-101

Bozeman, B., Dietz, J.S. & Gaughan, M. (2001). Scientific and technical human capital: an alternative model for research evaluation. Int. J. Technology Management, 22, (7/8), 716-740.

Breneman, D.W., Jamison, D.T & Radner, R. (1976). Chapter in NBER book Education as an Industry, Joseph T. Froomkin, Dean T. Jamison and Roy Radner, eds. (p. 1 - 52). NBER.

Breschi, Lissoni and Montobbio (2007). The Scientific Productivity of Academic Inventors: New Evidence from Italian Data. Economics of Innovation and New Technology, 16 (2), 101-118.

Buchmueller, T., Dominitz, J. & Hansen, L. (1999). Graduate Training and the Early Career Productivity of PhD Economists. Economics of Education Review, 14, 65-77.

Bye, B., Fæhn, T. & Heggedal, T.R., (2009). Welfare and growth impacts of innovation policies in a small, open economy: An applied general equilibrium analysis. Economic Modelling, 26, 1075–1088.

Carayol, N. (2007). Academic Incentives, Research Organization and Patenting at A Large French University. Economics of Innovation and New Technology, 16 (2), 119-138.

Committee on Science, Engineering, and Public Policy, National Academy of Sciences (1999), Evaluating Federal Research Programs: Research and the Government Performance and Results Act.

Congressional Budget Office (2007), Federal Support for Research and Development.

Corrado, C., Hulten, C. & Sichel, D. (2009). Intangible Capital and U.S. Economic Growth. The Review of Income and Wealth, 55 (September), 661-85.

Corrado, C., and Hulten, C.R. (2010). How Do You Measure a 'Technological Revolution? American Economic Review, 100 (May), 99-104.

24

David, P.A., Hall, B.H. & Toole, A.A. (2000). Is Public R&D a Complement or Substitute for Private R&D? A Review of the Econometric Evidence. Research Policy, 29 (4-5), 497-529.

Dechenaux, E. Thursby, M. & Thursby, J. (2009). Shirking, sharing risk and shelving: The role of university license contracts. International Journal of Industrial Organization, 27 (1), 80-91.

Dept. of Enterprise, Trade and Employment (2010). Jobs and Growth.

Ding, Murray & Stuart (2006). Gender Differences in Patenting in the Academic Life Sciences. Science, 313 (5787, Aug 4): 665-667.

Dowrick, S., (2003). A Review of the Evidence on Science, R&D and Productivity. ANU, Paper prepared for the Department of Education, Science and Training, Australian Government.

Dosi G., Llerena P, & Labini M.S. (2006). The relationships between science, technologies and their industrial exploitation: an illustration through the myths and realities of the so-called 'European Paradox’. Research Policy, 35 (10), 1450-1464.

Etzkowitz, H. (1998). The norms of entrepreneurial science: cognitive effects of the new university–industry linkages. Research Policy, 27 (8), 823-833.

Escribano A., Fosfuri, A. & Tribo, J.A. (2009). Managing External Knowledge Flows: The Moderating Role of Absorptive Capacity. Research Policy, 38, 96‐105.

European Commission (2007), "Improving knowledge transfer between research institutions and industry across Europe: embracing open innovation --Implementing the Lisbon Agenda", Communication to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions.

Franzoni, C, & Lissoni, F. (2006). Academic entrepreneurship, patents and spin-offs: critical issues and lessons for Europe. Centro di Ricerca sui Processi di Innovazione e Internazionalizzazione CESPRI, Università Commerciale Luigi Bocconi. Working Paper No. 80.

Frietsch, R., Haller, I., Frunken-Vohlings, M. & Grupp, H. (2009). Gender-specific patterns in patenting and publishing. Research Policy, 38 (4), 590-599

Freeman, R. & Van Reenen, J. (2009). What if Congress Doubled R&D Spending on the Physical Sciences? Innovation Policy and the Economy, 9, 1-38.

Garfield (2006). The History and Meaning of the Journal Impact Factor. Journal of the American Medical Association. 295 (1), 90-93.

Gonzalez-Brambila, C. & Veloso, F. (2007). The Determinants of Research Productivity: A Study of Mexican Researchers. Research Policy, 36 (7), 1035-1051.

Government of Ireland (2008). Building Ireland’s Smart Economy, Government Publications Office, Dublin.

25

Griliches, Z. (1992). The Search for R&D Spillovers. Scandinavian Journal of Economics, 94 (supplement), 29-47.

Gurmu, Black & Stephans (2010). The Knowledge Production Function For University Patenting. Economic Inquiry, 48 (1), 192-213.

Hall, Mairesse & Mohnen (2009). Measuring the Returns to R&D. NBER Working Paper No. 15622.

Hamovitch, W. & Morgenstern, R.D. (1977). Children and the productivity of academic women. Journal of Higher Education 47 (6), 633–645.

Hilmer, M.J. & Hilmer, C.E. (2011). Is it where you go or who you know? On the relationship between students, Ph.D. program quality, dissertation advisor prominence, and early career publishing success. Economics of Education Review, 30 (5), 991-996.

Hirsch, J.E. (2005). An index to quantify an individual's scientific research output. Proc Natl Acad Sci, 102 (46), 16569–16572.

Hogan, T. D. (1981). Faculty Research Activity and the Quality of Graduate Training. Journal of Human Resources, 16 (3), 400-415.

Huang, W., Feeney, M.K. & Welch, E.W. (2011). Organizational and individual determinants of patent production of academic scientists and engineers in the United States. Science & Public Policy, forthcoming.

Ilzetzki, E., Mendoza, E.G. & Végh, C.A. (2010). How Big (Small?) are Fiscal Multipliers? NBER Working Papers, 16479.

Innovation Taskforce Report, (2010). Innovation Ireland: Report of the Innovation Taskforce, Government Publications Office, Dublin.

Jaffe, A. B. (1998). The importance of ‘Spillovers’ in the policy mission of the advanced technology program. Journal of Technology Transfer, 23 (2), 11–19.

Jaffe, Fogarty & Banks (1998). Evidence from Patents and Patent Citations on the Impact of NASA and Other Federal Labs on Commercial Innovation. .Journal of Industrial Economics, 46 (2), 183-205.

Jones, C.I., & Williams, J.C. (1998). Measuring the Social Return to R&D. Quarterly Journal of Economics, 113 (4), 1119-35.

Keller, W., (1996). Absorptive capacity: on the creation and acquisition of technology in development. Journal of Development, 49, 199-227.

Klitkou, A. & Gulbrandsen, M. (2010). The relationship between academic patenting and scientific publishing in Norway. Scientometrics 82(1), 93-108.

26

Koplin, V. W. & Singell, L.D. (1996). The gender composition and scholarly performance of economics departments: A test for employment discrimination. Industrial and Labor Relations Review, 49 (3), 408–423.

Kyvik, S. & Teigen, M. (1996). Child Care, Research Collaboration, and Gender Differences in Scientific Productivity. Science, Technology, and Human Values, 21 (1), 54-71.

Kelchtermans, S. & Veugelers, R. (2011). The great divide in scientific productivity: why the average scientist does not exist. Industrial and Corporate Change, 20 (1), 295-336

Landry, E., Amara, N., & Rherrand, I. (2006). Why are some university researchers more likely to create spin-offs than others? Evidence from Canadian universities. Research Policy, 35 (10), 1599-1615.

Lester (2005). Universities, Innovation, and the Competitiveness of Local Economies: A Summary Report from the Local Innovation Systems Project – Phase I, MIT IPC Working Paper IPC-05-010

Lissoni, F., Mairesse, J. Montobbio, F. & Pezzoni, M. (2011). Scientific productivity and academic promotion: A study on French and Italian physicists. Industrial and Corporate Change, forthcoming.

Long J.S. (1992). Measures of sex-differences in scientific productivity. Social Forces, 71, 159–178.

Mansfield, E. (1991). Academic Research and Industrial Innovation. Research Policy, 20, 1-12.

McMillan, G.S. (2009). Gender differences in patenting activity: An examination of the US biotechnology industry. Scientometrics, 80 (3), 683-91.

Moguero, P. & Di Pietrogiacomo, M.P. (2008). Stock, Career and Mobility of Researchers in the EU. European Commission, Joint Research Centre Scientific and Technical Reports.

Morgan, R.P. Kruytbosch, C. & Kannankutty, N. (2001). Patenting and Invention Activity of US Scientists and Engineers in the Academic Sector: Comparisons with Industry. Journal of Technology Transfer. 76, 173-183.

Mowery, D. C., & Oxley, J. E., (1995). Inward technology transfer and competitiveness: The role of national innovation systems. Cambridge Journal of Economics, 19, 67-93.

Nature Editorial (2010). Science economics: What science is really worth. Nature, 465, 682-684.

New Scientist Editorial (2009). US economic stimulus plan is all about science. New Scientist, issue 2692.

Penas, C.S. & Willett, P. (2006). Gender differences in publication and citation counts in librarianship and information science research. Journal of Information Science, 32 (5), 480–485.

Powers, J. B., & McDougall, P. P. (2005),. University start-up formation and technology licensing with firms that go public: A resource-based view of academic entrepreneurship. Journal of Business Venturing, 20 (3), 291–311.

27

Prpic, K. (2002). Gender and productivity differentials in science. Scientometrics, 55 (1), 27–58.

Schiefele, U. (1991). Interest, Learning and Motivation. Educational Psychologist, 26 (3 & 4), 299-323.

Schultz, T.W. (1971). Investment in human capital, in Kiker, B.F. (Ed.), Investment in Human Capital, Columbia, University of South Carolina Press.

Science Editorial (2000). Assessing the Impact of Science Funding. Science, 324 (5932), 1273–1275.

Sonnert, G., & Holton, G. (1995). Gender Differences in Science Careers: The Project Access Study. New Brunswick, NJ: Rutgers University Press.

Stephan & El-Ganainy (2007). The entrepreneurial puzzle: explaining the gender gap. The Journal of Technology Transfer, 32 (5), 475-487.

Stephan, P. & Ma, J. (2005). The Increased Frequency and Duration of the Postdoctorate Career Stage. American Economic Review, 95 (2): 71–75.

Symonds, M.R., Gemmell, N.J., Braisher, T.L., Gorringe, K.L. & Elgar, M.A. (2006). Gender Differences in Publication Output: Towards an Unbiased Metric of Research Performance. PLoS ONE, 1 (1), e127.

Tien, F. F., & Blackburn, R. T. (1996). Faculty Rank System, Research Motivation, and Faculty Research Productivity: Measure Refinement and Theory Testing. The Journal of Higher Education, 67 (1), 2-22.

Toole, A. A. (2008). The impact of public basic research on industrial innovation: evidence from the pharmaceutical industry. SIEPR Discussion Paper No. 00-07. Stanford, CA.

U.S. National Academies (2010). Rising Above the Gathering Storm.

U.S. Science Colaition (2010). Sparking Economic Growth.

Veugelers, R. (1997). Internal R & D expenditures and external technology sourcing. Research Policy, 26 (3), 303-315,

Xie, Y. & Shauman, K. A. (1998). Sex Differences in Research Productivity: New Evidence about and Old Puzzle. The American Sociological Review, 63 (6), 847-870.

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Appendix A: Corre l at i ons B etween Dependent Var i ab les

Correlation between Publications, Patents and Commercial Expectations

N = 295

publications patents

(83 %) (18 %)

(29 %) expect to commercialise research

38 (13 %)

19 6 %

1 0 %

152 52 %

31 11 %

9 3 %

42 14 %

3 1 %

29

Correlation between Publications and Patents N = 490

publications patents

(65 %) (12 %)

167 (34 %)

57 12 %

4 1 %

262 53 %

Correlation between Patents and Commercial Expectations N = 295

patents expect to commercialise research

(18 %) (29 %)

190 (64 %)

34 12 %

51 17 %

20 7 %

30

Appendix B : Anal ys i s Showi ng U ni vers i ty -Af f i l i at i on Coef f i c i ents

(Postdoctoral Production Functions: Probit Regressions – Shown over Two Pages)

PROBIT MULTIVARIATE PROBIT

(1) (2) (3) (4) (5) (6)

Publish Patent Commerce Publish Patent Commerce

Interest in areaa 0.005 0.014** 0.058** 0.056 0.104 0.128**

(0.008) (0.006) (0.027) (0.063) (0.069) (0.064)

Duration of contract -0.015 -0.013 0.026 -0.045 -0.097 0.143

(0.017) (0.012) (0.035) (0.135) (0.116) (0.106)

Teaching dutiesb 0.030 -0.009 0.067* 0.224 0.069 0.193

(0.022) (0.015) (0.036) (0.227) (0.211) (0.189)

Satisfactionc 0.003 -0.011*** -0.012 0.014 -0.064 -0.031

(0.010) (0.004) (0.009) (0.058) (0.053) (0.047)

Different fieldd 0.034 -0.051*** 0.092 0.012 -0.389 0.173

(0.032) (0.010) (0.109) (0.246) (0.248) (0.211)

Unemploymente -0.017 -0.009 -0.024 -0.152* -0.056 -0.074

(0.016) (0.016) (0.044) (0.085) (0.087) (0.074)

Partner 0.046 0.038* 0.030 0.037 0.385* 0.253

(0.037) (0.022) (0.055) (0.237) (0.227) (0.200)

Number of children -0.003 0.021 0.002 -0.150 0.094 -0.045

(0.021) (0.018) (0.076) (0.177) (0.164) (0.157)

Age -0.003 -0.009 -0.009 -0.013 -0.061* -0.039

(0.002) (0.006) (0.012) (0.026) (0.031) (0.028)

Gender 0.044* 0.050* 0.211*** 0.176 0.353* 0.655***

(0.023) (0.026) (0.064) (0.214) (0.213) (0.186)

Irish citizen -0.017 -0.021 -0.092 0.008 -0.170 -0.342*

(0.036) (0.029) (0.063) (0.243) (0.222) (0.197)

Years since Ph.D. 0.010* 0.020*** 0.003 0.058 0.145*** 0.013

0.005 (0.008) (0.014) (0.050) (0.050) (0.046)

University 1 -0.966*** 0.981*** 0.248** 3.783 -0.305 0.220

(0.002) (0.002) (0.119) (252.229) (0.438) (0.372)

University 3 -0.996*** -0.057*** -0.003 -1.166*** 0.507 -0.336

(0.000) (0.011) (0.048) (0.384) (0.347) (0.368)

University 4 -0.951*** 0.018 -0.140*** -0.680 0.433 0.492

(0.002) (0.020) (0.049) (0.528) (0.436) (0.373)

University 5 -0.995*** -0.117*** -0.252*** -0.927*** -0.165 -0.111

(0.002) (0.009) (0.020) (0.335) (0.293) (0.270)

University 6 -0.202*** 0.012 0.328** -0.693 -0.760 0.502

(0.038) (0.047) (0.138) (0.462) (0.649) (0.369)

University 7 -0.997*** 0.020 -0.119** -0.943*** 0.764*** 0.871***

(0.000) (0.020) (0.046) (0.356) (0.288) (0.269)

Observations 306 332 248 266 266 266

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

31

Note: University 2 is the base category. The identity of universities is not revealed due to a need to preserve confidentiality. Results are shown for “STEM researchers” only: those working in the areas of science, technology, engineering and maths. Marginal effects are displayed in columns 1-3, only. Missing values are replaced by a value of zero: to keep as many observations in the sample as possible. Dummy variables are included to take account of where this is done; this method is known as dummy variable adjustment. Where they apply to incomplete cases, control variables for missing value adjustment are not shown above. Outliers and missing values are adjusted for independent variables only. Controls for subject area and local institutional effects (subject a rea interacted with university) are not shown above. Robust standard errors are clustered by university. a“Interest in area” refers to interest in area of research. b”Teaching duties” refers to the binary instance of teaching duties. c“Satisfaction”

refers to overall satisfaction with post-doctoral situation. d”Different field” refers to working in a field different to postdoc’s Ph.D. e”Unemployment” refers to prior unemployment.