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Academic top earners. Research productivity, prestige generation, and salary patterns in European universities

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This article examines highly paid academics—or top earners—employed across universities in ten European countries based on large-scale international survey data regarding the academic profession. It examines the relationships between salaries and academic behaviors and productivity, as well as the predictors of becoming an academic top earner. While, in the Anglo-Saxon countries, the university research mission typically pays off at an individual level, in Continental Europe, it pays off only in combination with administrative and related duties. Seeking future financial rewards solely through research does not seem to be a viable strategy in Europe, but seeking satisfaction in research through solving research puzzles is also becoming difficult, with the growing emphasis on the 'relevance' and 'applicability' of fundable research. Thus, both the traditional 'in-vestment motivation' and 'consumption motivation' to perform research decrease, creating severe policy implications. The primary data come from 8,466 usable cases.
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Academic top earners. Research productivity,
prestige generation, and salary patterns in
European universities
Marek Kwiek*
Center for Public Policy Studies, Director, UNESCO Chair in Institutional Research and Higher Education Policy,
University of Poznan, ul. Szamarzewskiego 89, 60-569 Poznan, Poland
*Corresponding author. Email: kwiekm@amu.edu.pl
Abstract
This article examines highly paid academics—or top earners—employed across universities in ten
European countries based on large-scale international survey data regarding the academic profes-
sion. It examines the relationships between salaries and academic behaviors and productivity, as
well as the predictors of becoming an academic top earner. While, in the Anglo-Saxon countries,
the university research mission typically pays off at an individual level, in Continental Europe, it
pays off only in combination with administrative and related duties. Seeking future financial re-
wards solely through research does not seem to be a viable strategy in Europe, but seeking satis-
faction in research through solving research puzzles is also becoming difficult, with the growing
emphasis on the ‘relevance’ and ‘applicability’ of fundable research. Thus, both the traditional ‘in-
vestment motivation’ and ‘consumption motivation’ to perform research decrease, creating severe
policy implications. The primary data come from 8,466 usable cases.
Key words: academic salaries; highly paid academics; European universities; working time distribution; research productivity;
predictors of academic incomes.
1. Introduction
This research examines an emergent class of highly paid aca-
demics—or top earners—employed across European universities. It
differs from existing salary studies in its focus, sample, and method.
It goes beyond previous work that has studied academic salaries ei-
ther in single institutions (Katz 1973;Ferber 1974;Fox 1985), mul-
tiple institutions (Hamermesh et al. 1982;Konrad and Pfeffer 1990;
Ward 2001) or national systems (mostly the USA, as in McLaughlin
et al. (1979);Gomez-Mejia and Balkin (1992);Bellas (1993);
Fairweather (1993);Barbezat and Hughes (2005);Fairweather
(2005);Melguizo and Strober (2007)). Also, it explores cross-
national differences in salary patterns in ten European countries
based on large-scale international survey data regarding the aca-
demic profession (N¼17,211). This research also goes beyond a
more traditional approach, which examines the relationships be-
tween academic salary and its correlates through only bivariate cor-
relational analyses, by using both logistic regression analyses and
bivariate correlational analyses. This research examines the relation-
ships between academic salaries and academic behaviors and prod-
uctivity in a single institutional type, the European university,
exploring one subcategory of academics: academics employed
full-time and involved in both teaching and research. Finally, this
article explores predictors of becoming an academic top earner from
a comparative cross-national European perspective.
The massification of higher education inevitably leads to the mas-
sification of the academic profession, with dramatic consequences in
terms of its social and financial standing. As a global collection of 28
country studies emphasized, ‘without conditions that permit a secure
career, competitive with alternatives in the labor market, the entire
academic enterprise will falter’ (Altbach et al. 2012: 3). Even though
salaries are only a single element in a larger picture of the academic
research environment, it is a highly important element and a major
component of institutional budgets. For a long time, academic salaries
have been under-valued as a research topic and, consequently, largely
under-researched in higher education studies. However, in the last
two decades, academic salary studies have been booming, with more
than a hundred publications. The financial instability of the academic
profession as part of the traditional core component of the middle
classes across developed countries has helped to drive this research.
However, there are only a few cross-national comparative salary stud-
ies focusing on more than two countries (Shen and Xiong (2015); see
also reports based largely on descriptive statistics: Idea Consult 2013,
V
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Science and Public Policy, 45(1), 2018, 1–13
doi: 10.1093/scipol/scx020
Advance Access Publication Date: 20 May 2017
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produced within the MORE 2 project; European Commission 2007
on salaries in academia and industry; Deloitte 2012 on salaries in
Anglo-Saxon countries; and European Commission and Deloitte 2014
on working conditions and salaries across Europe), in addition to two
collections of national case studies (Rumbley et al. 2008;Altbach
et al. 2012).
We use two complementary approaches: statistical inference
(t-tests for the equality of means performed on two independent sam-
ples of academic top earners, defined as the upper 20 percent of aca-
demics on the academic income scales on disciplinary and national
bases as compared to the rest of academics, with these t-tests being
applied to various aspects of working time distribution and research
productivity, Section 4.1) and a multidimensional logistic regression
model (Section 4.2). While most previous studies rely on linear regres-
sion models, this research relies on a logistic regression model in seek-
ing country-specific predictors of becoming a highly paid academic
across Europe. The primary data analyzed come from two large-scale
global and European research projects on the academic profession,
CAP: Changing Academic Profession and EUROAC: Academic
Profession in Europe, with 8,466 usable cases for our purposes. The
data refer to highly paid academics (the upper 20 percent, N¼649),
who are contrasted with the remaining 80 percent of academics
(N¼2,937); in both cases, the term ‘academics’ refers only to those
who are employed full-time in the university sector, indicate both
teaching and research involvement and provide income data.
2. Analytical framework
2.1 Differences in salary regimes
The reward structure in science consists of two components
(Stephan 2010). First, science is governed by the priority system, a
reward system that encourages the production and sharing of know-
ledge. Scientists are motivated to perform research ‘by a desire to es-
tablish priority of discovery’ (Stephan 2010: 2) because recognition
in science depends on ‘being first’ (Stephan 1996: 1202). Also, se-
cond, the reward structure in science consists of remuneration.
Academic positions provide both extrinsic rewards (salaries and
other material benefits) and intrinsic rewards (derived from aca-
demic work) (Blau 1994;Stern 2004). Poor salaries are a major im-
pediment to effective faculty recruitment. National academic labor
markets (Williams et al. 1974;Fairweather 1995) determine who
academics are and who they will become in the future. They produce
or fail to produce the requisite talent in academe.
Institutions with more open salary systems, notably in the USA,
are more able to attract top-quality researchers from institutions with
less open salary systems, notably those in Continental Europe.
Academics across large parts of Continental Europe are still typically
civil servants paid largely based on a single well-defined fixed-salary
system (Altbach et al. 2012). Consequently, ‘universities in the US
have greater leeway than those in most other places to reward per-
formance and to pay high salaries to attract star researchers’ (Stephan
2012: 1). However, in the last two decades, most European systems
have tended to introduce various forms of merit pay, moving very
slowly away from fixed-salary systems (Enders and de Weert 2004:
18–19; and national case studies in Altbach et al. 2012).
2.2 Models of academic salaries
Existing theories of faculty pay can be categorized into market mod-
els and institutional models, which view academic pay as a function
of either market competition or institutional forces, respectively
(Fairweather 2005: 403). Two market models attribute changes in
faculty salaries, at least in part, to changes in supply and demand:
one school emphasizes the homogeneity of national academic mar-
kets based on research and prestige (research output is highly valued
in research-oriented institutions, and top research performers are
paid more), and the other school emphasizes the segmented charac-
ter of national academic markets (teaching-oriented institutions
value teaching over research, and top teachers are paid more). The
institutional theories of faculty pay emphasize that pay is an expres-
sion of institutional norms and values, regardless of institutional
missions, and that institutional forces can dictate salary levels: ‘insti-
tutions that actually value teaching will pay their productive teach-
ers the most, whereas institutions valuing research will pay their
productive researchers the most’ (Fairweather 2005: 403). In a
standard human capital model (as used by Hamermesh et al.
(1982)), academic earnings are a function of research productivity
(on the demand side), as well as any factors that affect the equilib-
rium supply of labor. Faculty salary is also viewed as indirectly
related to productivity because more productive academics are likely
to be promoted faster and promotions mean higher financial
rewards.
Scientists’ engagement in research can be either investment-
motivated (seeking future financial rewards), consumption-
motivated (seeking research puzzles) or both (Thursby et al. 2007).
While the investment motive implies a decline in research productiv-
ity over one’s career, the consumption motive does not imply such a
decline (Levin and Stephan 1991). A ‘taste for science’ (Roach and
Sauermann 2010)—that is, for nonpecuniary returns—causes scien-
tists to choose academia over industry. Academics with different
abilities and tastes in terms of nonpecuniary returns choose different
careers: basic or applied research in academia or industry (Agarwal
and Ohyama 2012). Time spent on research reduces current earn-
ings but increases future earnings, as in investment models of human
capital. On average, scientists become less productive as they age
(Over 1982;Kyvik 1990;Levin and Stephan 1991;Stephan and
Levin 1992), with changing research productivity over the lifecycle
and productivity in systems with aging academic cohorts being im-
portant research focuses.
From the perspective of the economics of higher education, specif-
ically the concepts of labor economics, academic compensation is
influenced by a number of factors related to the demand for higher
education services and the supply of qualified individuals for faculty
positions (Toutkoushian and Paulsen 2016: 324). From this perspec-
tive, academics have acquired human capital (skills and talents that
an individual can obtain through education, training and experience
in the labor market) and endowed human capital (natural ability and
talent) (Toutkoushian and Paulsen 2016: 351). In academic labor
markets, academics can have large variations in their levels of both
types of human capital. The connection between human capital and
academic salaries follows from the effects of human capital on prod-
uctivity, which in turn influences pay (Toutkoushian and Paulsen
2016: 353). As Toutkoushian and Paulsen point out, ‘if faculty com-
pensation is determined in part by an individual’s productivity in
teaching, research, and public service, then salaries should be corre-
lated with productivity. Human capital theory would thus predict
that faculty with more acquired and endowed human capital would,
on average, have higher salaries than other faculty’ (2016: 353).
Economic models of academic salary determination have been
predominantly based on the human capital theory (embedded in
analyses of for-profit firms and treating higher education institutions
accordingly); in the prestige model of salary determination,
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universities behave as both firms and nonprofit institutions, or ‘hy-
brids’ (Melguizo and Strober 2007: 634). In the prestige model, aca-
demic salaries are viewed as returns on the generation of prestige
(for the individual academic, as well as the institution). Nonprofit
higher education institutions act largely as ‘prestige maximizers’,
just like for-profit companies act as ‘profit maximizers’: ‘not only
are institutions seeking to maximize prestige, so are departments
and faculty members’ (Melguizo and Strober 2007: 635). While
human capital salary models focus on individuals’ research, teaching
and public service productivity, a potential alternative model focuses
on individuals’ prestige generation, mostly through publications, re-
search grants, patents and awards, that is, productivity. Both the
human capital model and the prestige model state that higher prod-
uctivity (defined for different areas, with publications being at the
forefront) should lead to higher academic incomes.
Prestige is largely a rival good, based on relative, rather than ab-
solute, measurement, and accumulating prestige is a zero-sum game
(Brewer et al. 2002: 30). Academia is becoming ever more competi-
tive, and competitiveness is encouraged by deliberate government
policies: ‘at the centre of all this is prestige, at all levels from the na-
tional system to the individual’ (Blackmore 2016: 1). Universities—
as well as academics—compete in prestige markets. In particular,
there is a strong link between individual and institutional prestige:
‘in maximizing their individual prestige, faculty members simultan-
eously maximize the prestige of their departments and institutions’
(Melguizo and Strober 2007: 635).
The maximization of prestige, in this theoretical framework, is
strongly correlated with faculty salaries. Academics who help their
institution to become prestigious are rewarded by the institution
with higher salaries: more articles and books published in presti-
gious outlets, more prestigious research grants, more patents, etc.
lead to higher institutional prestige, which consequently, albeit not
directly, leads to higher individual salaries. That is, ‘the currency in
which institutions are paid for faculty research is prestige. As a re-
sult, institutions provide financial rewards to scholarly output’, with
faculty salaries being viewed as returns on the generation of prestige
(Melguizo and Strober 2007: 639).
Following the logic of this salary model, in the context of our re-
search, highly productive academics should be disproportionately
overrepresented among highly paid academics. Because more time
spent on teaching means less time spent on research and vice versa, or
there being only ‘research’ and ‘non-research’ time investments (Levin
and Stephan 1991: 115), academics spending, on average, more time
on research should be receiving higher average salaries. Spending more
time on teaching, in turn, should have a negative or, at best, neutral ef-
fect on one’s salary (Katz 1973;Dillon and Marsh 1981;Konrad and
Pfeffer 1990;Fairweather 1993). However, there is a difference be-
tween spending time on research, being research-focused, and being
highly productive as compared with one’s peers: highly productive aca-
demics can also have more formal responsibilities as leaders, deans,
heads of departments, etc. and still have coauthored publications with
their post-docs or other early-stage researchers. The chance to have
longer lists of coauthored publications may increase for selected aca-
demics with more institutional power, and institutional power in-
creases with age and seniority (Stephan and Levin 1992).
3. Data and methods
The data for this research project were collected from 2007 to
2010 through a survey administered in eleven European countries;
however, income data were only available in the following ten
countries: Austria, Finland, Germany, Italy, the Netherlands,
Norway, Poland, Portugal, Switzerland, and the UK. The survey
(conducted within two large-scale research projects: CAP ‘Changing
Academic Profession’ and EUROAC ‘Academic Profession in
Europe: Responses to Societal Challenges’) has been widely used in
higher education research; however, to date, academic incomes have
not been explored in detail based on this survey (except for Shen
and Xiong (2015), which is mostly descriptive, and Nanbu and
Amano (2015) for a single-nation research, Japan). The eleven na-
tional datasets were cleaned, weighted, and merged into a single
European dataset, which is now the most comprehensive cross-
national source of data on academic views, attitudes, perceptions,
and behaviors in Europe. The author acted as a Principal
Investigator in Poland. The total number of usable returned surveys
in Europe was 17,211 and included between 1,000 and 1,700 re-
turned surveys from all the countries studied, except for Poland,
where the total number of responses was higher. The overall quality
of the dataset is high (Teichler et al. 2013: 35; Teichler and Ho¨ hle
2013: 9).
In technical terms, we divided the sample of all academics who
reported their incomes (as elsewhere throughout the article, only
those employed full-time in the university sector) in the ten above-
mentioned countries into academic ‘top earners’ and ‘the rest’. Top
earners are defined as those in the 80th percentile of gross academic
income—the upper 20 percent of academics in each of the five major
clusters of academic fields (separately), in each country (separately),
cut-off points permitting. We did not want to combine all full-time
academics from the university sector into a single subset, because
the vast majority of top earners would then come from Switzerland
and none would come from Poland or Portugal (based on the nom-
inal values of their salaries). We also wanted to explore all the sys-
tems and examine national-level top earners cross-nationally.
Additionally, we restricted our subsample of top earners and the
rest: we have explored only academics who are at least 40 years old
and have at least 10years of academic experience. This was done to
avoid comparing academics across radically different age cohorts
with different seniority levels and, especially, different job characteris-
tics. Analyzing top earners and the rest of academics across all age co-
horts and all career stages and seniority levels would significantly
increase the number of observations but would also lead to potentially
erroneous results with regard to the time spent on research. Analyzing
only older academics with longer academic experience (longer time
passed since first full-time employment) leads to more robust results.
However, this is the second-best approach, the best approach being a
study of smaller age cohorts and seniority and career-stage cohorts
separately, which is not possible due to the radically decreased num-
bers of observations by country and academic discipline cluster in
such a case. In a lifecycle view, faculty devote more time to research
early in their careers and less time to research later on (see, e.g.
Thursby et al. 2007;Levin and Stephan 1991). In the US context,
achieving tenure is a critical point, as is a Habilitation degree (a post-
doctoral degree that exists in various versions) in a number of
European systems, including the majority in our sample: Germany,
Poland, Finland, Switzerland, Austria, Portugal, and Italy.
Consequently, in Europe, academics aged 40years and over are al-
ready a relatively homogenous cohort. ‘The rest’ of academics is
defined here as the remaining 80 percent of academics who are at least
40 years old and have at least 10 years of academic experience.
The entire sample includes 17,211 cases, and the number of valid
cases with income and disciplinary data in the selected cohort is
3,586, including both top earners (N(TE) ¼649, or 18.1 percent) and
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the rest of academics (N(R) ¼2,937, or 81.9 percent) (see Table 8 in
the Supplementary Material). About two-thirds of academics in the
sample are male, with the age cohorts for academics aged at least
40 years and academic experience cohorts being relatively evenly dis-
tributed. The distribution of the sample population by country and
the frequencies of selected demographic characteristics are shown in
Table 1 (all academics) and Table 2 (top earners only). Table 9 in the
Supplementary Material provides data on the nonresponse rate per-
taining to the gross annual salary question, which ranged from as low
as 0.6 percent in the UK and 1.1 percent in Poland to as high as 31.2
percent in Switzerland and 47.4 percent in Austria.
3.1 Limitations of this research
The results of this research should be interpreted in light of several
limitations, specifically those related to the sample, methods, pro-
cedures, and dataset. First, while this research goes beyond the limi-
tation of being a single-institution study, as is the case with the
majority of papers on academic incomes published in the last four
decades, and thus it is not a case study, the national-level sampling
techniques differed across the ten countries studied (see Teichler and
Ho¨ hle (2013: 6–9); and RIHE (2008: 89–178)), as did the ways in
which the survey questionnaire was distributed (paper, on-line, or
combined), the response rates, the distribution of academics across
various institutional types and various forms of employment, etc.
These could have influenced the final results. Second, the analyses
are based on self-declared data that were provided by academics vol-
untarily, with some items potentially being viewed as more sensitive
in some countries than in others (e.g. income and research product-
ivity). Specifically, the analyses are based on self-reported annual
gross income, and there are substantial differences between taxation
systems across Europe, with the same gross income leading to vastly
different net incomes.
Third, we use a rather crude measure of research productivity,
which is defined as the number of peer-reviewed articles published
in a 3-year reference period. The survey does not allow an examin-
ation of journal quality (and especially does not allow to a distinc-
tion between top-tier journals and others) or the examination of
citation counts. However, to strengthen the robustness of our prod-
uctivity analyses, we have used three fractionalized versions of the
dependent variable: peer-reviewed article equivalents (also capturing
authored and edited books), internationally coauthored article
equivalents, and foreign language (here, English) article equivalents.
We were unable to examine individual academics’ research outputs
and link them to institutional tiers based on national prestige ladders
in the ten countries, rather than merely linking them to institutional
types (such as ‘universities’ in out case, as opposed to ‘polytechnics’
and ‘other’). We were also unable to define the selectivity of the em-
ploying institution (and of the PhD graduation institution) or its
wealth, size or current national or international ranking. Finally,
with the dataset in question, we were also unable to study how pat-
terns of academic salaries change over time intra-nationally and
cross-nationally.
Table 1. Sample description: frequencies of selected demographic characteristics, all countries combined.
Full-time employed in
the university sector, involved in
both teaching and research
(all responses)
Full-time employed in the
university sector, involved in
both teaching and research
(responses with valid income data)
N (%) N (%)
Gender Male 3,174 (65.9) 2,488 (66.9)
Female 1,639 (34.1) 1,230 (33.1)
Age (years) 40–44 1,035 (21.4) 796 (21.3)
45–49 1,098 (22.7) 789 (21.2)
50–54 754 (15.6) 599 (16.1)
55–59 938 (19.4) 723 (19.4)
60 1,011 (20.9) 825 (22.1)
Academic experience
a
(years) 10–19 1,527 (41.3) 1,188 (39.6)
20–29 1,176 (31.8) 958 (32.0)
30 992 (26.8) 852 (28.4)
Clusters of academic fields
b
Hard sciences 2,558 (55.5) 2,031 (56.6)
Soft sciences 2,051 (44.5) 1,554 (43.4)
a
A cluster of ‘hard sciences’ includes ‘physical sciences and mathematics’, ‘life sciences and medical sciences’, and ‘engineering’, a cluster of ‘soft sciences’ in-
cludes ‘humanities and social sciences’ and ‘professions’ (Question A2).
b
‘Academic experience’ means the number of years since first full-time employment (‘beyond research and teaching assistant in higher education/research sec-
tor’, Question A6).
Table 2. Sample description: frequencies of selected demographic
characteristics, all ten countries combined, top earners only.
N(%)
Gender Male 489 (82.8)
Female 102 (17.2)
Age (years) 40–44 44 (7.4)
45–49 87 (14.7)
50–54 88 (14.8)
55–59 146 (24.5)
60 230 (38.6)
Academic experience
a
(years) 10–19 127 (23.2)
20–29 179 (32.7)
30 241 (44.1)
Clusters of academic fields
b
Hard sciences 346 (58.1)
Soft sciences 249 (41.9)
a
A cluster of ‘hard sciences’ includes ‘physical sciences and mathematics’,
‘life sciences and medical sciences’, and ‘engineering’, a cluster of ‘soft sciences’
includes ‘humanities and social sciences’ and ‘professions’ (Question A2).
b
‘Academic experience’ means the number of years since first full-time em-
ployment (‘beyond research and teaching assistant in higher education/re-
search sector’, Question A6).
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On top of these specific limitations, more generic limitations
linked to cross-country comparisons in higher education in general,
given the differences in academic traditions across national systems,
must be mentioned. Comparative higher education has its poten-
tials, as well as its limits (Teichler 1996;Altbach 2002), and moving
from single-nation studies to cross-national studies, which involves
the emergence of international datasets and the institutionalization
of cross-national research, introduces new challenges. Analytical
frameworks in higher education research have mostly been produced
for national, rather than cross-national, interpretive purposes. The
knowledge base for cross-national studies increases (and the CAP/
EUROAC dataset used here provides a new type of comparative
data: primary, disaggregated and self-produced by researchers, ra-
ther than the secondary, national-level, aggregated data commonly
collected by the state), but international comparative research in
higher education is seldom grounded in ideal research designs with
clearly defined hypotheses. Datasets such as ours are clearly pro-
duced in heterogeneous national higher education settings: national
academic traditions lead to strong differences in national career
opportunities, research funding availability, dominant missions in
various institutional types, dominant academic activities in various
system subsectors, preferred academic role orientations, favored
publication outlets, etc. The meanings of such basic terms as, e.g.,
‘professor’, ‘young academic’, ‘competitive research funding’ and
‘academic duties’ differ from country to country and must still be
translated into a common set of concepts to organize data analysis.
Consequently, the best approach is to use a semi-structured research
design that reflects the wealth of options in higher education (with
enough room for the application of internationally incompatible
variables; in the EUROAC project, 480 interviews were conducted
across seven European countries, but the qualitative material is not
used in this research project). All in all, however, despite inherent
limitations, cross-national studies of the academic profession create
new insights with interesting institutional policy and national policy
implications.
4. Results
4.1 Bivariate analysis: academic top earners vs. the rest
of academics
In this section, we explore differences in working time distribution
and productivity differentials between academic top earners and the
rest of academics, specifically among older (aged 40 years and more)
and more academically experienced (10 or more years since the first
employment) cohorts to avoid the exploration of groups of aca-
demics with significantly different job profiles.
4.1.1 Bivariate analysis: academic income and working
time distribution
Here, we explore five dimensions of academic work that were cap-
tured by the CAP/EUROAC dataset: teaching, research, service, ad-
ministration, and other academic activities. We focus on weekly
hours in the teaching periods of the academic year, as well as in its
nonteaching periods. We annualize these hours, assuming that
60 percent for the former period and 40 percent for the latter period
represents a good approximation for the majority of the ten
European systems studied (regarding the average length of the teach-
ing and nonteaching periods during an academic year across
Europe, see Bentley and Kyvik (2013), who use a similar 66.6/33.3
ratio in a global study of thirteen countries). We explore the
differences in the means of various categories of working hours (by
academic activity) between the two subpopulations in each country
(Table 3). Our results are based on two-sided tests assuming equal
differences in arithmetic means with a significance level of a¼0.05.
For each pair with a mean difference significantly different from
zero, the symbol of the larger category (‘Top’ for top earners and
‘Rest’ for the rest of academics) appears in the column. Tests are ad-
justed for all pairwise comparisons within a row for each innermost
subtable using the Bonferroni correction. T-tests for the equality of
two arithmetic means (‘Top’ vs. ‘Rest’) were performed for each
country for each of the five types of academic activities studied, as
well as for total working hours.
Previous studies on academic salaries have strongly suggested
that longer research hours contribute strongly to higher salaries (e.g.
Katz 1973;Hamermesh et al. 1982;Fairweather 2005): our study
shows that while top earners in three European countries indeed
work statistically significantly longer ‘total hours’, most import-
antly, in six countries, they work longer ‘service’ (four countries)
and/or ‘administration’ hours (four countries; see either ‘Rest’ or
‘Top’ symbols for each country).
Interestingly, statistically significant working time differentials
between top earners and the rest of academics do not exist for teach-
ing and research time investments, except in the UK, where there is
clearly a different pattern: top earners, on average, spend more than
6.5 hours more per week on research, and the rest of academics
spend, on average, more than 5 hours more on teaching (see Table
10 in the Supplementary Material for details). Previous research
findings generally showed a strong positive correlation between re-
search hours and salary levels and also a negative or no correlation
between teaching hours and salary levels (Katz 1973;Dillon and
Marsh 1981;Hamermesh et al. 1982;Konrad and Pfeffer 1990;
Fairweather 2005;Melguizo and Strober 2007). Our research on the
European sample does not confirm these findings. The traditional
link between higher time investments in research and higher in-
comes—consistently demonstrated (mostly for Anglo-Saxon coun-
tries, especially the USA) in the last half a century—does not
currently seem to hold across Continental Europe. Interestingly,
from the perspective of future academic careers, top earners tend to
spend more time (than the rest of academics) on all academic activ-
ities except for teaching and research, and they especially spend
more time on administration and service. There is one qualification,
however: the time measured is the current time, not the time spent a
decade earlier, which may have led to their current positions.
Table 3. Working hours differentials. Results of t-tests for the equal-
ity of means, top earners (Top) vs. the rest of academics (Rest), in
ten countries. Question B1: ‘Considering all your professional
work, how many hours do you spend in a typical week on each of
the following activities’? (when ‘classes are in session’ and when
‘classes are not in session’), only academics employed full-time in
universities and involved in both teaching and research (annual-
ized mean weekly hours).
PL DE AT FI IT NL NO PT CH UK
Teaching hours Rest
Research hours Top
Service hours Top Top Top Top
Administration
hours
Top Top Top Top
Other hours Top Top
Total hours Top Top Top
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4.2.2 Bivariate analysis: academic income and
research productivity
The analysis of individual research productivity involves three meas-
ures: ‘peer-reviewed article equivalents’ (PRAE), ‘internationally co-
authored article equivalents’ (ICAE), and ‘English language article
equivalents’ (EAE). The PRAE measure is calculated (following
Bentley (2015)) as the weighted sum of self-reported articles in
books or journals (1 point), edited books (2 points) and authored
books (5 points) published over the period of 3years prior to the
survey execution. An individually provided share of peer-reviewed
publications is applied to each observation. The PRAE measure cap-
tures publishing through various outlets and does not focus only on
articles, leaving room for authored books and edited books, which
are still a major outlet in the social sciences and humanities in some
European systems. The ICAE measure applies the individually pro-
vided share of publications coauthored with international col-
leagues, and the EAE measure applies the individually-provided
share of publications published in a foreign language (assuming that
the language in question in all countries, except for the UK, is pre-
dominantly English, as descriptive statistics shows). In the majority
of the countries studied (for Austria, the Netherlands and
Switzerland, there are no statistically significant results), top earners
are, on average, much more academically productive in the three-
year reference period (see ‘Top’ in all lines in Table 4).
The productivity differential between top earners and the rest of
academics, as defined in this article, is high and statistically signifi-
cant, mostly at a high level (P <0.001), especially in the case of
peer-reviewed article equivalents. In seven countries—Poland,
Germany, Finland, Italy, Norway, Portugal and the UK– it is, on
average, in the 80–140 percent range. Only in Italy is the differential
lower, at 43 percent (the reason could be that Italian academics
show the highest average individual productivity among the
European countries studied; see Kwiek (2015a;2016a)). For in-
stance, in the UK, the average number of peer-reviewed article
equivalents published in the 3-year reference period by the rest of
academics is 4.63, while the equivalent number for top earners is
11.3, or 144.06 percent more. In the case of internationally coau-
thored article equivalents, the difference is even higher: 180.49 per-
cent for Poland, 178.05 percent for the UK, 145.56 percent for
Germany and 100 percent for Finland (the 95 percent confidence
interval for the difference of means and other statistical details are
provided in Table 11 in the Supplementary Material).
Our analysis shows (see graphically Figs 1 and 2) that the upper
20 percent of academics in terms of incomes (or our ‘top earners’) in
the majority of countries studied are substantially more productive
and produce much more internationally coauthored publications than
the rest of academics (from the same older age cohort). While they
work on average longer ‘administrative’ and ‘service’ hours (rather
than research hours), they are much more academically productive.
Is higher academic income positively correlated with better re-
search performance, even though it does not seem to be positively
correlated with research time investments? Are top earners dispropor-
tionately represented among top research performers (defined here in
a similar manner as top earners, as academics located in the upper 20
percent of research productivity, a pool created separately for major
academic disciplines and for each country and then merged; regarding
predictors of becoming ‘top research performers’ across Europe, see
Table 4. Research productivity and high academic income, sum-
mary. Results of t-tests for the equality of means, top earners (Top)
vs. the rest of academics (Rest) in ten countries. Group with a sig-
nificantly larger mean (Top or Rest) shown by country. Question
D4/3: ‘How many of the following scholarly contributions have you
completed in the past three years?’ combined with Question D5:
‘Which percentage of your publications in the last three years were
– peer-reviewed’ (PRAE), ‘were – published in a language different
from the language of instruction at your current institution’ (EAE),
and ‘were – co-authored with colleagues located in other (foreign)
countries’ (ICAE): ‘Articles published in an academic book or jour-
nal’, ‘Scholarly books you authored or co-authored’ and ‘Scholarly
books you edited or co-edited’. Only full-time academics employed
in universities and involved in both teaching and research.
PL DE AT FI IT NL NO PT CH UK
Peer-reviewed article
equivalent (PRAE)
Top Top Top Top Top Top Top
Internationally
coauthored article
equivalent (ICAE)
Top Top Top Top Top
Foreign language article
equivalent (EAE)
Top Top Top Top Top Top
Figure 1. Academic productivity and high academic income: top earners vs. the rest of academics. The average number of ‘peer-reviewed article equivalents’
published in a three-year reference period (top earners in blue, the rest of academics in red). Only full-time academics employed in universities and involved in
both teaching and research are included. Only countries with statistically significant results are included.
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Kwiek (2016a))? Yes, they definitely are. For instance, in Germany,
on average, 43.1 percent of research top performers are highly paid
academics (top earners), and on average, 73.3 percent of the rest of
academics are not top earners (Table 5). Our analysis shows that on
average, 31.8 percent of national top research performers are among
national top earners—from almost 80 percent in the UK to about 40
percent in Finland, Germany, and Portugal and 30 percent in
Norway. The only exception is Poland, where top performers are not
overrepresented among top earners, constituting merely 22.9 percent
of top earners. Poland has a strict national-level fixed salary scheme
(see Kwiek (2012;2016b)). A chi-square test of independence was
conducted: there is significant interdependence between the two vari-
ables in the six above-mentioned countries. The significance level for
all the countries is 0.10 (Table 6).
However, the above results are not multidimensional (the con-
clusions from the t-test analyses are independent of one another),
and as such, can be misleading (for instance, the relationship be-
tween salary and publication numbers may be influenced by senior-
ity). A study of multidimensional relationships requires the model
approach presented below.
4.2 Logistic regression analysis
4.2.1 Procedures and variables in the model
Like all regression analyses, logistic regression is a form of predictive
analysis. It is used to explain the relationship between one depend-
ent binary variable and one or more independent variables. Logistic
regression assumes that the dependent variable is a stochastic event
and proceeds in terms of likelihoods. The log odds of an event are
estimated. The question in this section is how does the probability of
being in the upper 20 percent of academic incomes change with
changes in various independent variables.
Studies on academic salaries focus on various aspects of aca-
demic work: publications and teaching/research abilities (Katz
1973), male and female salaries vis-
a-vis research performance
(Ferber et al. 1978), the role of citations (Hamermesh et al. 1982),
institutional emphasis on research (Konrad and Pfeffer 1990), publi-
cations in top-tier journals (Gomez-Mejia and Balkin 1992), journal
rankings (Gibson et al. 2014), the maximization of prestige
(Melguizo and Strober 2007), academic ranks and strategies to suc-
ceed financially (McLaughlin et al. 1979), teaching vs. research
(Fairweather 1993, 2005) and others.
One persistent dimension of earnings inequality is gender.
The gender salary gap has drawn research attention (initially in
Anglo-Saxon countries) since the early 1970s (as summarized in
Bellas (1993);Fox (1985);Barbezat and Hughes (2005)). While it is
uncertain whether gender differences ‘reflect differences in human
capital or productivity between individuals, discrimination by uni-
versities or supply decisions by workers’, a common thread that runs
through the literature is the ‘evidence of the existence of gender dif-
ferences in salary’ (Ward 2001). ‘Sex stratification’ is reported
within the academic profession, and the ‘cost of being female’ in sci-
ence is being explored (Bellas 1993: 62). The picture of the USA in
the 1980s, as reported by Fox (1985), was as follows: academic
tasks (teaching and research) were divided by sex, workplaces were
segregated by sex (institutions, fields, and areas) and activities were
stratified by sex. Men occupied more superordinate academic ranks
and positions, and women occupied more subordinate ranks and
positions. Empirical studies have consistently shown a ‘substantial
unexplained wage gap’ in favor of men, and gender is still reported
to determine faculty salaries (Toutkoushian et al. 2007: 574). There
are also growing cross-disciplinary disparities in academic salaries in
more open salary systems (as exemplified by the US system). The
disparities across generally less open systems, such as European sys-
tems, seem to be more restrained (Stephan 2012).
Figure 2. Academic productivity and high academic income: top earners vs. the rest of academics. The average number of ‘internationally co-authored article
equivalents’ published in a three-year reference period (top earners in blue, the rest of academics in red). Only full-time academics employed in universities and
involved in both teaching and research are included. Only countries with statistically significant results are included.
Table 5. The share of top earners among research top performers,
only countries with statistically significant results.
Rest (nontop earners) Top earners
Finland Rest (nontop performers) 74.5 25.5
Top performers 59.5 40.5
Germany Rest (nontop performers) 73.3 26.7
Top performers 56.9 43.1
Norway Rest (nontop performers) 82.7 17.3
Top performers 66.5 33.5
Poland Rest (nontop performers) 85.3 14.7
Top performers 77.1 22.9
Portugal Rest (nontop performers) 79.9 20.1
Top performers 57.3 42.7
UK Rest (nontop performers) 69.4 30.6
Top performers 22.0 78.0
Total Rest (nontop performers) 77.5 22.5
Top performers 68.2 31.8
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Research consistently shows scholarly productivity to be a strong
correlate of faculty pay. Teaching has been typically shown to be un-
related to or to be a negative factor in faculty compensation (see Katz
(1973);McLaughlin et al. (1979);Hamermesh et al. (1982); Konrad
and Pfeffer (1990);Fairweather (1993);Gomez-Mejia and Balkin
(1992);Fairweather (2005);Melguizo and Strober (2007);and
Gibson et al. (2014)). The quality of research (as measured by cit-
ations) is rewarded by the academic market (Hamermesh et al. 1982:
481): ‘an additional reference adds more to salary than does the publi-
cation of an additional book or article.’ The regression results show
that spending more hours in the classroom is related to a lower basic
salary and that publishing productivity is a significant positive factor
in pay at all types of institutions (Fairweather 2005: 416–17).
Based on the research literature and given the limitations of the
dataset at our disposal, specifically the lack of a link between individ-
ual observations and bibliometric data, we developed an analytical
model to study academic salaries. Specifically, we employed selected
variables found in McLaughlin et al. (1979),Gomez-Mejia and Balkin
(1992),Fairweather (1993),Melguizo and Strober (2007),andShen
and Xiong (2015). In this multivariate analysis, we dichotomized all
category variables through a recoding procedure. We started with
forty-two personal and institutional characteristics, which were
grouped into eight clusters: personal/demographics, socialization,
internationalization, academic behaviors, academic attitudes and role
orientation, overall research engagement, institutional policies, and in-
stitutional support. We then conducted Pearson Rho’s correlation tests
to find significantly correlated predictors of the dependent variable.
High intercollerations among the predictors (multicollinearity) were
tested using an inverse correlation matrix because a correlation matrix
refers only to pairwise correlations of independent variables. On the
main diagonal of an inverse correlation matrix, there are values with-
out unequivocal interpretation; however, they show how strongly a
given variable is correlated with all other variables. The interpretation
is performed in such a way that all variables with diagonal values
higher than 4 are removed from the analysis. In our case, there was
only one such value (‘peer-reviewed article equivalent’, diagonal value:
4.222); however, because it did not significantly exceed the conven-
tional boundary value of 4, it was left in the model. Diagonal values of
the inverse correlation matrix are given in Table 12 in the
Supplementary Material. Also, principal component analysis (PCA)
was performed to determine whether any variables, due to their high
level of correlation, could be grouped into homogenous groups. No
significant interdependence between any of the variables was found.
We also used two robustness checks. The first was an examin-
ation of pairwise correlations, particularly between the status of
professors, age and the productivity measures. It may be that most
of the effect of research productivity is captured by the professor
variable. The status of professors was moderately and positively cor-
related with age (r¼0.33), which is understandable. We used five
productivity measures: scholarly books authored or coauthored,
peer-reviewed article equivalents, internationally coauthored article
equivalents, foreign language article equivalents, and papers pre-
sented at scholarly conference. The correlation between the status of
professor with our productivity measures was indeed positive but
weak (in the r¼0.11–0.18 range). The second robustness check was
to run the model without the professor variable and see how the
results changed. We ran a pooled model across all countries with the
professor variable and without it. In both models, all productivity-
related variables were statistically insignificant. However, in the for-
mer model, the professor variable somehow inherently included several
variables traditionally associated with being a professor (such as being
a peer reviewer, being an editor of journals or book series or belonging
to national or international committees, boards and bodies, which
were all statistically insignificant). When the professor variable was
removed from the model, all these variables emerged as significant.
Surprisingly, there was a difference between productivity-related vari-
ables (which were insignificant) and prestige-related variables (which
were significant only in the model without the professor variable).
The model was estimated using a stepwise backward elimination
based on the Wald criteria, so only significant variables were included
in the models for each country. We estimated a regression model for
eight of the ten countries to highlight cross-national differences
among top earners (for Portugal and the UK, the character of data did
not allow the use of the maximum likelihood estimator). The models
were fitted correctly: neither over-fitting nor under-fitting occurred.
Only meaningful variables were included, and all meaningful vari-
ables were included. Each observation was independent. The predict-
ive power of the fourth model, as measured by Nagelkerke’s R
2
,was
generally high, and it was the highest for Finland (0.82), followed by
Switzerland, Germany, and the Netherlands (in the 0.60–061 range);
it was 0.45 for Italy and in the range of 0.29–0.30 for Austria,
Norway, and Poland. The total average variance demonstrated for
the eight countries was 49.66 percent. In Table 7, we present the re-
sults of the fourth model, which is the final model.
4.2.2 Statistically significant variables
Institutional variables did not enter into the equation in any of the
countries studied. The importance of individual-level variables dif-
fers from country to country. In the first block of individual pre-
dictors (‘personal/ demographics’), age entered into the equation in
the majority of countries. Age is a strong predictor of being an aca-
demic top earner in Germany, Finland, Italy, Poland, and the
Netherlands: on average, a one-unit increase (i.e. 1 year) increases
the odds of being a top earner by as much as 29 percent in Finland
and 20 percent in the Netherlands, as well as by 17 percent in Italy,
12 percent in Germany and 4 percent in Poland (ceteris paribus) for
the specific older academic cohort explored here. This finding is
consistent with the conclusions of Melguizo and Strober (2007) and
McLaughlin et al. (1979), who emphasized positive correlations be-
tween age and salaries. In Norway, where age did not enter into the
equation, the predictor ‘years since first full-time employment’ (or
academic age) did: on average, a 1-year increase in academic age
increased the odds of becoming a top earner by 5 percent and, unex-
pectedly, decreases the odds of becoming a top earner by 18 percent
in Finland. The only exceptions in our pool of countries were
Table 6. Chi-square independence test statistics.
Statistics AT FI DE IT NL NO PL PT CH UK
Chi-square 0.455 3.637 3.493 1.364 0.229 7.636 7.666 3.41 0.54 10.698
df 1111111111
P-value 0.5 0.057 0.062 0.243 0.633 0.006 0.006 0.065 0.462 0.001
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Austria and Switzerland, where neither age nor academic age was
statistically significant.
Interestingly, being a female academic entered into the equation
only in Poland. It is a very strong predictor of not being a top earner
there. In Poland, the odds ratio value indicates that female aca-
demics are, on average, highly unlikely (about one-third as likely as
male academics) to be top earners (Exp(B) ¼0.379). It is important
to remember that the cohort studied is aged 40 years and more and
has a minimum of 10 years of academic experience. In all other
countries, being a female academic is not a statistically significant
variable. This finding does not confirm the conclusions of earlier
academic salary studies (Fox 1985;Bellas 1993;Ward 2001; and
Balkin and Gomez-Mejia 2002), specifically the research findings of
a long list of Anglo-Saxon pay equity studies focused on gender sal-
ary gaps (Barbezat 2002;Barbezat and Hughes 2005). However, it
is consistent with more recent studies (e.g. Melguizo and Strober
2007), which show limited correlation between gender and earnings
and the potential impact of institutional and/or national policy cor-
rective actions. This research explores highly paid European aca-
demics, rather than all academics, and the odds of entering this
peculiar group. Being a professor (or academic seniority) emerged as
an important variable in the model, with statistical significance in
all countries; however, the odds ratios for five of these countries
should be treated with caution (limited numbers of observations). In
the other countries, being of a senior rank increases the odds of
being a top earner by about eight-fold in Italy and about three-fold
in Norway and Poland. Again, consistent with previous studies,
European academics are certainly more likely to be promoted to
higher ranks if and when they are older (Barbezat and Donihue
1998) as their publication lists become longer, which is consistent
with human capital (Becker and Toutkoushian 2003;Toutkoushian
and Paulsen 2016) and prestige models of academic salaries
(Melguizo and Strober 2007). Professorship and academic seniority
certainly affect academic income levels, as we would expect from
human capital theory, and the relationship is reciprocal (Katz 1973;
Fairweather 2005): correlates of high incomes do not necessarily in-
dicate causal relationships. The cluster of hard sciences entered into
the equation in two countries, with ambiguous results. The decrease
in the odds of becoming a top earner in this cluster in Poland
(Exp(B)¼0.43) is consistent with recent analyses of the Polish aca-
demic profession in which university salaries in the natural sciences
and life sciences are lower than the institutional average (Kwiek
2015b,c).
In the block of ‘academic behaviors’, annualized mean research
hours per week did not emerge as a determinative predictor of
becoming a top earner in any country. In a similar vein, only in
Poland and Germany did annualized mean weekly teaching hours
emerge as a determinative predictor: on average, a unit increase of
1 hour decreased the odds of becoming a top earner by about 5 per-
cent in the former and 7 percent in the latter (ceteris paribus), which
Table 7. Odds ratio estimates by logistic regression for being in the top 20% in total gross academic income, all nine countries.
AT CH DE FI IT NO PL NL
Nagelkerke’s R
2
0.302 0.608 0.61 0.822 0.449 0.298 0.286 0.598
Personal/demographics
Age 1.124** 1.287* 1.174*** 1.043* 1.201*
Female 0.379**
Professor 12.419***
a
15.171**
a
77.322***
a
2588.019***
a
8.334*** 3.267** 3.219** 78.745**
a
Cluster of hard sciences 5.14* 44.523**
a
0.43**
Years since first full-time appointment 0.814* 1.045*
Academic behaviors
Annualized (proxy: 60% in session, 40%
not in session) mean weekly teaching hours
0.925* 0.953*
Annualized mean weekly research hours
Annualized mean weekly service hours 1.17** 1.104***
Annualized mean weekly administrative hours 1.466** 1.061* 1.07*
Annualized mean weekly other hours 1.241*
Academic attitudes and role orientation
Research-oriented (‘Primarily in research’) 68.817**
a
2.573*
Teaching-oriented (‘Primarily in teaching’) 34.68*
a
Basic/ theoretical research 7.39*
Applied/ practically-oriented
Commercially oriented/intended for technology transfer 0.004**
a
2.312* 5.656**
Internationalization and collaboration
Collaborating internationally in research
Research international in scope or orientation 33.982*
a
Overall research engagement
A peer reviewer 7.447*
Editor of journals/book series
National/international committees, boards, bodies
Being a research top-performer (the upper 20%) 2.521* 3.559**
Scholarly books authored or coauthored 0.598* 3.071*
Peer-reviewed article equivalent published 0.94*
Internationally coauthored article equivalent published 1.187* 0.611**
Foreign language article equivalent published 1.358** 1.034*
Papers presented at a scholarly conference 0.885**
Patent or invention 2.283* 9.99*
Constant 0.199*** 0.005*** 0*** 0** 0*** 0.038*** 0.015** 0**
Results that are not statistically significant are not shown in the Table.
*P <0.05, **P <0.01, ***P <0.001.
a
These odds ratios need to be treated with caution.
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is consistent with the vast teaching/research trade-off literature. This
is perhaps the most perplexing finding of this research: contrary to
most of the extant literature (specifically Katz (1973);Dillon and
Marsh 1981;Hamermesh et al. (1982);Fairweather (1993)) in the
majority of the European countries studied, neither teaching nor re-
search hours are statistically significant predictors of higher aca-
demic incomes.
In the case of teaching/research time investments, the results of
the bivariate analysis and regression analysis point in the same direc-
tion. Traditionally, long research hours were reported to be strongly
correlated with higher academic incomes, and long teaching hours
were reported to be correlated with lower academic incomes. Our
research does not support these claims in the specific case of
European academics. However, previous research was predomin-
antly focused on Anglo-Saxon academics and on all incomes rather
than higher incomes. There is an important qualification: the meas-
ure used refers to the current time only; past time investments in re-
search cannot be grasped with the instrument used. Annualized
mean weekly service hours, in turn, entered into the equation in two
countries (Switzerland, where a unit-increase of 1 hour per week in-
creases the odds of becoming a top earner by about 17 percent on
average, and Poland, where such an increase in mean weekly service
hours increased the odds of becoming a top earner by about 10 per-
cent on average). Annualized mean weekly administrative hours
entered the equation in three countries (increasing the odds by about
47 percent in Finland, 6 percent in Norway, and 7 percent in Poland
on average and ceteris paribus). Finally, ‘other’ hours emerged as a
strong predictor in Switzerland (increasing the odds of becoming a
top earner by about 24 percent on average). However, ‘total work-
ing hours’ did not enter into the equation in any of the countries
studied, which may indicate the importance of a specific working
time distribution rather than long working hours (in contrast to the
case of European research top performers analyzed in Kwiek
(2015a), for whom all types of hours, except for teaching hours,
were longer; from a cross-generational perspective, see Kwiek
(2015c) and Kwiek and Antonowicz (2014)). In the case of teaching/
research time investments—as well as administration and service
time investments—the results of the bivariate analysis and the re-
gression analysis again point in the same direction, consistent with
McLaughlin et al. (1979: 32), who link individual strategies to suc-
ceed financially with academic rank ladders and suggest avoiding
too much teaching in the early stages of one’s career and focusing on
administration in the later stages of one’s academic career: ‘an ad-
ministrative assignment for an established professor will increase the
likelihood for salary increases.’
Being in the ‘academic attitudes and role orientation’ block that
was interested ‘primarily in research’ emerged as a strong predictor
in only one country, Italy (increasing the odds of becoming a top
earner by more than two-and-a-half-fold on average). This was sur-
prising in the context of the existing literature, in which the research
role orientation matters considerably in terms of higher salaries (e.g.
Stephan (1996);Konrad and Pfeffer (1990)). Characterizing one’s
primary research as ‘commercially-oriented or intended for technol-
ogy transfer’ substantially increases the odds of becoming a top
earner in two countries: by almost two-and-a-half-fold in Italy and
by about 5.7-fold in Norway. An interest in ‘basic/theoretical re-
search’ increases the odds of becoming a top earner by 7.4-fold in
Switzerland. Overall, different academic attitudes and role orienta-
tions (especially a preference for teaching or research) did not
emerge as statistically important (the same results were achieved in
the bivariate correlational analysis, which is not reported here due
to space limits). In the ‘internationalization and collaboration’
block, no predictors entered the equation.
Finally, inconsistent with the conclusions from most of the litera-
ture referred to in Section 2 (especially Gomez-Mejia and Balkin
(1992);Fairweather (1993); and McLaughlin et al. (1979)), in the
‘overall research engagement’ block, selected academic research-
related and prestige-related activities, as well as high research prod-
uctivity, did not emerge as highly determinative. High individual
academic productivity—being a research top performer, defined as
being in the upper 20 percent of the productivity scale and measured
separately for major clusters of academic fields—entered into the
equation in only two countries, increasing the odds in Poland (more
than three-and-a-half-fold on average) and in Norway (more than
two-and-a-half-fold on average, in both cases ceteris paribus). In
other countries, high academic productivity did not have a statistic-
ally significant effect on becoming a top earner. Being a peer re-
viewer emerged as a significant variable in the model in only one
country, Finland (with Exp(B) ¼7.447). Sitting on national and
international committees, boards and bodies did not emerge as sig-
nificant in any of the countries. The same was true for being an edi-
tor of a journal (or a book series). Having a patent or invention
within the reference period of 3years emerged as significant in two
countries (Austria, Exp(B) ¼2.283 and Finland, Exp(B)¼9.99).
Also, the variables related to publications (such as scholarly
books authored or coauthored, peer-reviewed article equivalents
published, internationally coauthored article equivalents published,
and foreign language article equivalents published) did not emerge
as unambiguous determinative variables in most of the countries
studied. For instance, books published increased the odds of becom-
ing a top earner substantially (three-fold) in Finland, but decreased
them in Austria. Also, peer-reviewed article equivalents published
entered into the equation in only one country: they actually
decreased the odds of becoming a top earner by 6 percent in
Germany. Finally, another dimension of research activities—the
number of papers presented at a scholarly conference—actually
decreased the odds of being a top earner in Finland (by 11 percent)
and was statistically insignificant in all other countries.
Finally, a third robustness check was performed by running the
model with a higher cut-off point. We examined the upper
15 percent (instead of the upper 20 percent), as contrasted with the
rest of academics, and compared the differences. The predictive
power of the fourth model, as measured by Nagelkerke’s R
2
, was
roughly the same, being slightly higher for five countries (Austria,
Switzerland, Italy, Norway, and Poland) and slightly lower for the
remaining three (Germany, Finland, and the Netherlands). The total
average variance demonstrated for the eight countries was similar
(50.11 percent). As in the first model, being a female academic
emerged as a statistically significant variable (in Italy, Norway, and
Poland), and the odds ratio values indicate that female academics
are, on average, highly unlikely (about one-third to one-fourth as
likely as male academics) to become top earners (Exp(B)¼0.274–
0.379). The hard sciences cluster emerged as statistically significant,
substantially decreasing the odds of being among the top earners in
four countries (Austria, Italy, Norway, and Poland, with
Exp(B)¼0.248–0.360). Annualized mean weekly research hours
were shown to decrease the odds of becoming a top earner in
Austria, Italy, and Poland (with Exp(B)¼0.926–0.969). Annualized
mean weekly service hours, in turn, increased the odds of becoming
a top earner in all countries, except Austria and Finland (with
Exp(B)¼1.066–1.201), as did annualized mean weekly ‘other’ hours
in Germany and Italy (with Exp(B)¼1.057 and Exp(B)¼1.090,
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respectively). Also, similar to the first model, a research orientation
increases the odds of becoming a top earner (in Austria and Italy),
papers presented at scholarly conferences decrease these odds (in
Austria and Finland) and having a patent or invention increases
these odds (in Austria and Finland).
Thus, overall research engagement—as studied through many
variables in the model—proves to be largely statistically insignificant
as a predictor of belonging to the class highly paid academics.
Relatively weak correlations between various research-related activ-
ities (such as being a top research performer or the number of peer-
reviewed article equivalents, conference papers, etc.), various
prestige-related academic activities (such as being a journal editor or
sitting on national and international scientific committees) and aca-
demic incomes are largely inconsistent with earlier conclusions
based on studies of the American academic profession. These results
do not confirm the results of the bivariate analysis and indicate that
the use of parallel methods is more useful than a focus on single
methods. The European/American academic profession split re-
vealed in this research may be related both to the more general ideas
organizing research-based academic careers and academic salaries
and, more practically, to greater leeway due to the better funding of
American universities. There is also an important distinction to be
made between the determinants of academic incomes in general and
the predictors of high academic incomes studied in this article. A
tentative conclusion could be that the focus on older academic co-
horts and high academic incomes amplifies the differences between
the American academic profession (as traditionally explored in aca-
demic salary studies) and the European academic profession.
5. Discussion and conclusions
This article is an empirically based comparative study of highly paid
academics in Europe, and it uses a large dataset of behaviors, atti-
tudes, and perceptions of the European academic profession (CAP/
EUROAC, N¼17,211). We have examined ten European systems,
and our focus was restricted to full-time academics involved in both
teaching and research who were employed in a specific institutional
type: the university. A class of academic ‘top earners’ (from the age
cohort of 40 years old and more having a minimum of 10 years of
academic experience) was explored to explore various aspects of
their working time distribution and research productivity. Finally,
the predictors of becoming an academic top earner were examined
from a cross-national European perspective.
To a large extent, the findings obtained via a multidimensional
model approach support the findings of inferential statistics: inter-
estingly, in the context of previous (mostly) single-nation studies, re-
search time for the academic cohort studied is not positively
correlated with high incomes, teaching time is not negatively corre-
lated with high incomes, and there is almost no correlation between
the research role orientation or gender and high incomes.
Interestingly, the strong correlations between high productivity and
high incomes seen in the bivariate analysis are not confirmed in the
regression analysis. The research focus of this article was on high in-
comes in an older cohort of academics and the odds of receiving
them, rather than—as in traditional academic salary studies—all
academics and all academic incomes in general. Consequently, this
research explores cross-national academic salaries via new questions
(top earners, as contrasted with the rest of academics, and the pre-
dictors of being a top earner) applied to new (i.e. older) academic
cohorts in new (i.e. European) national settings.
This research project has two types of implications: implications
for current theoretical models and assumptions in salary studies, as
well as policy implications for institutions and national systems.
Starting with the former, our findings tend to suggest that the trad-
itionally explored link between higher time investments in research
and higher academic incomes—consistently demonstrated for Anglo-
Saxon countries over the last four decades (as in Katz (1973);Konrad
and Pfeffer (1990);Fairweather (1993);Gomez-Mejia and Balkin
(1992);Fairweather (2005);Melguizo and Strober (2007);and
Gibson et al. (2014))—may not hold across Europe today as strongly
as in Anglo-Saxon systems. As Fairweather (1993: 629) expressed, the
traditional view is that ‘faculty who spend more time on research and
who publish the most are paid more than their teaching-oriented col-
leagues’, and the American academe is moving toward ‘a single fac-
ulty reward structure, one dependent on publishing, spending time on
research, and minimizing involvement in instruction’. National aca-
demic labor markets in Europe, as is clear from this research, are
homogenous and research-based, rather than segmented (Fairweather
2005): teaching-oriented institutions do not seem to be paying their
top teachers more, while all institutions tend to pay their top per-
formers more. For European universities, academic pay does not seem
to be influenced—as in labor economics (Toutkoushian and Paulsen
2016)—by the demand for higher education services or the supply of
qualified individuals. According to our results, highly productive aca-
demics are disproportionately over-represented among highly paid
academics across Europe: on average, 31.8 percent of top national re-
search performers are among national top earners, with this percent-
age ranging from almost 80 percent in the UK to about 40 percent in
Finland, Germany, and Portugal. The correlations between high in-
comes and high performance are strong.
Given that European higher education research stands in the
shadow of its American counterpart, especially in terms of its basic
theoretical frameworks, this article suggests a more sustained focus
on cross-national (not to say cross-continental) differences in higher
education and on the role of various national traditions in the aca-
demic enterprise in the future. We suggest rethinking the potential
over-reliance on American research findings in discussing academic
salaries in non-American contexts. Some theoretical frameworks
and analytical concepts stand firm and are useful on both sides of
the Atlantic, while others may not be as useful.
“Large-scale” differences in the organization and funding of
Anglo-Saxon (and especially American) higher education systems
and European ones may suggest rethinking not only the analytical
frameworks used to explore academic salaries but also those used to
explore various aspects of higher education that are specifically
linked to national traditions. They include, for instance, cost-
sharing, fees and loans, public and private goods in higher educa-
tion, typologies of university governance models and others.
The results of our analysis are not consistent with traditional
(mostly US-focused) academic salary research, which tends to em-
phasize strong positive correlations between salaries and long re-
search hours, combined with a strong research focus; however, our
results are consistent with those of traditional research in terms of
showing correlations between high salaries and high productivity.
The interesting point is that individual productivity also includes
coauthored productivity. Therefore, high productivity must not ne-
cessarily be correlated with longer research hours. One indication
that the high earners in our sample may be involved in the extensive
supervision of collective research grants and/or leading research
groups, heading departments or faculties, etc. is the finding that they
tend to spend more time on administration, service, and other
Science and Public Policy, 2018, Vol. 45, No. 1 11
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academic duties. For instance, top earners in Germany and
Switzerland work, on average, 8 more hours per week than the rest
of academics in the same older cohorts. This indicates, on average, 5
more administrative hours in Germany and 10 more service hours in
Switzerland, with no statistically significant difference in tradition-
ally explored teaching and research hours.
Thus, the US salary system (with highly differentiated salary lev-
els across institutions) and European salary systems (with low na-
tional pay differentials) certainly have one point in common: the
higher average research productivity of highly paid academics.
However, while in the USA, longer research hours, a stronger re-
search focus and higher research productivity seem to pay off dir-
ectly, in Europe, only higher research productivity seems to matter
directly in determining individual salary levels. On top of that, high
productivity determines high salaries only in combination with more
time being spent on nonresearch academic activities outside the trad-
itional teaching and research dyad.
At a policy level, a more direct research-income link in the USA
as compared with European countries may result in the ever-
stronger siphoning of research-focused academics with higher ‘tastes
for science’ (Roach and Sauermann 2010) (those who want to have
better salaries and do not want to be involved in university adminis-
trative duties) from European to American universities. Academic
salaries and the distribution of research/nonresearch time are at the
core of the traditional university enterprise. The questions of what
to do (proportions of teaching, research and administration time
and whether to conduct basic or applied research) and where to be
(at which institution, in academia or industry and possibly in which
national system) are looming not only for individual academics but
also at the institutional and national levels, guiding institutional
(Pinheiro et al. 2012;Stensaker et al. 2012) and national (Enders
et al. 2011;Musselin and Teixeira 2014) higher education reform
agendas. Our research strongly supports findings about the ‘asym-
metric international mobility’ of talented scientists between Europe
and the USA, as recently studied by Janger and Nowotny (2016).
While top researchers certainly attract other top researchers and the
attractiveness of an academic job increases with its salary, job choice
is ‘not driven by the remuneration package alone’ (Janger and
Nowotny 2016: 1679). There seems to be a ‘“global” view on which
[academic] jobs are attractive, explaining the international mobility
of scientists towards countries where jobs with these characteristics
are more common’ (Janger and Nowotny 2016: 1681).
This research shows that while in Anglo-Saxon countries, the
university research mission traditionally pays off at an individual
level, in Europe, it pays off in combination with administrative and
related duties. Seeking future financial rewards through research in
Europe seems difficult, except for highly productive academics, but
seeking satisfaction through solving research puzzles is also becom-
ing more difficult than ever before because of the growing emphasis
on the relevance and applicability of fundable research (Teichler
et al. 2013). Thus, because both the traditional ‘investment motiv-
ation’ and the ‘consumption motivation’ for research (Levin and
Stephan 1991) are scarce in European academia today, national-
level and institutional-level policies may need to be rethought so
that the best and brightest will still seek and maintain academic em-
ployment in European higher education in the context of an ‘exodus
of European researchers’ (Docquier and Rapoport 2012: 715).
Certainly, the conditions of the academic profession vary from
country to country across the continent (Janger et al. 2013), with
Switzerland being the most attractive and Poland being the least at-
tractive in the sample when viewed through the proxies of average
academic job satisfaction, average salary level, and willingness to
leave the academic profession. There are many employment options
to choose from today, and studies of the academic profession show
an ever-shorter list of ‘non-pecuniary advantages’ and an ever-
longer list of ‘pecuniary disadvantages’ (Ward and Sloane 2000) for
academic positions in European universities. The intersection of
high research performance and high academic salaries is one of the
most consequential testing grounds for the attractiveness of the aca-
demic profession in the future.
Supplementary data
Supplementary data is available at Science and Public Policy Journal online
Acknowledgements
The author gratefully acknowledges the support of the National Research
Council (NCN, Grant DEC-2011/02/A/HS6/00183) and wishes to thank
Ulrich Teichler, the coordinator of the European Science Foundation
EUROAC research project, ‘Academic Profession in Europe: Responses to
Societal Challenges’, and Jung Cheol Shin, the organizer of ‘The Fourth
International Conference on Academic Profession in Knowledge Society’
(April 20–22, 2016, Seoul National University, Korea), as well as participants
in this conference for their valuable comments. Dr Wojciech Roszka’s assist-
ance was substantial. Finally, the author wishes to express his gratitude to an-
onymous reviewers for their highly constructive suggestions.
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Rośnie intensywność stratyfikacji według produktywności badawczej w ramach profesji akademickiej. Na poziomie indywidualnym, społeczna stratyfikacja w nauce oznacza, że społeczność naukowa nie jest „grupą równych”. Raczej jest tak, że niewielki odsetek naukowców „wnosi niewspółmiernie duży wkład w rozwój nauki i otrzymuje nieproporcjonalnie dużą część nagród i środków potrzebnych na badania” (Zuckerman 1988: 526). Na różnych poziomach analizy oznacza to, że „jednostki, grupy, laboratoria, instytuty, uniwersytety, czasopisma, pola i specjalności, teorie i metody są nieustannie oceniane i klasyfikowane według prestiżu”, który opiera się na badaniach i publikacjach (Zuckerman 1988: 526). Dla naukowców uznanie ich pracy jest „jedynym oczywistym dowodem na to, że to, co zrobili, ma znaczenie dla nauki” (Zuckerman 1988: 526). W nauce uznanie przekłada się na środki na dalsze badania, a dystrybucja nagród akademickich – oraz finansowanie badań – jest silnie podzielona. Jedni dostają wiele, drudzy dostają mniej, a jeszcze inni nie dostają ich (nagród i finansowania) wcale. Zawsze tak było i zawsze tak będzie, chyba że zmieni się koncepcja uprawiania nauki akademickiej, która w tej chwili – w ramach obowiązującego paradygmatu – wydaje się najbardziej racjonalna. Koncepcja ta sprawia, że naukowcy pracują po 50-60 godzin tygodniowo i najczęściej, jak pokazują wieloletnie międzynarodowe badania porównawcze kadry akademickiej, są ze swojej pracy zadowoleni. Mniej zadowoleni mogą z niej być wykładowcy akademiccy, zwłaszcza w systemach silnie rozgraniczających działalność dydaktyczną i naukową – których tradycyjny system nagradzania w nauce akademickiej siłą rzeczy nie obejmuje. Istotne jest rozróżnienie na pracę w szkolnictwie wyższym, między innymi na uniwersytecie, i zajmowanie się akademickimi badaniami naukowymi. Jedno z drugim nie musi się łączyć, to częsty przypadek w mniej konkurencyjnych systemach.
... Paula Stephan opisuje naturę nauki nie jako konkurencję typu zwycięzca bierze wszystko (w której nie ma nagród za bycie drugim czy trzecim), ale jako układ turniejowy (w którym przegrani również otrzymują pewne nagrody, co utrzymuje jednostki w grze naukowej nawet pomimo systematycznego, trwającego nieraz całe życie braku wygranej) (Stephan 2012: 29). Jednak pod względem wynagrodzeń osoby osiągające najlepsze wyniki w badaniach są wyraźnie nadreprezentowane wśród najlepiej zarabiających naukowców, przynajmniej w 10 badanych przez nas systemach europejskich (Kwiek 2018c): top research perfomers stanowią dużą część academic top earners, jak ich nazwaliśmy, zestawiając ze sobą górnych 20% naukowców pod względem produktywności i górnych 20% naukowców pod względem wynagrodzeń w oparciu o rozległe dane ankietowe (ponad 17 000 zwróconych ankiet). ...
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Struktura produkcji naukowej uczelni badawczych zostanie w tym raporcie przebadana w dwóch istotnych kontekstach: globalizacji nauki i globalizacji naukowców oraz stratyfikacji pionowej naukowców według produktywności badawczej, w tym zmieniającej się strukturze produkcji naukowej: od prac jednoautorskich do prac wieloautorskich oraz od prac krajowych do prac pisanych we współpracy międzynarodowej. Globalizacja nauki jest szerszym kontekstem, w ramach którego zajmujemy się w tym raporcie zmianami strategii publikacyjnych. Punktem wyjścia jest fakt, że struktura produkcji naukowej uczelni badawczej zależy od struktury produkcji naukowej poszczególnych, zatrudnionych tam naukowców. W ramach globalnej nauki rośnie rola indywidualnych naukowców i ich sposobów pracy naukowej: czy i w jakiej mierze współpracują międzynarodowo w prowadzonych badaniach; czy i w jakiej mierze publikują wyniki prowadzonych badań naukowych w prestiżowych czasopismach; jak jest ich indywidualna produktywność badawcza; jakie są ich dominujące strategie publikacyjne i strategie współpracy; oraz w jaki sposób – z racji różnych presji zewnętrznych – powyższe pytania znajdują odpowiedzi zmieniające się w czasie. Prezentowany raport pokazuje fundamentalną rolę pojedynczych naukowców w kształtowaniu struktury produkcji naukowej na poziomie wydziałów, uczelni, dyscyplin – oraz kraju.
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Publikacje jednoautorskie nadal odgrywają istotną rolę w jednoznacznym sygnalizowaniu zdolności badawczych naukowców, zwłaszcza w naukach społecznych i humanistycznych. Efekty wyborów strategii publikacyjnych – wyboru typu autorstwa, typu czasopisma, a zwłaszcza jego zasięgu, języka i prestiżu – mają wpływ na kształt indywidualnej kariery akademickiej przez dekady funkcjonowania w nauce. Działa tu efekt kumulacji korzyści (i sukcesów) stanowiący odwrotność efektu kumulacji niekorzyści (i porażek) w czasie, co prowadzi do zróżnicowanego dostępu z jednej strony do akademickiego uznania, a z drugiej do awansów naukowych i grantów badawczych. Mężczyźni i kobiety w nauce funkcjonują według tych samych reguł, ale zaprezentowana lista nierówności z pewnością się dzisiaj nie zmniejsza. O badaniach prowadzonych indywidualnie warto pamiętać zwłaszcza na wczesnych etapach kariery naukowej, kiedy, przynajmniej teoretycznie, wszystkie możliwości są jeszcze otwarte zgodnie w merytokratycznym postulatem funkcjonowania systemu nauki.
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Pierwsza na polskim ryku publikacja, w której autor analizuje funkcjonowanie globalnej nauki, pokazując: skrajne nierówności indywidualnych osiągnięć naukowych i niesprawiedliwy rozkład produkcji wiedzy, silne związki między dochodami naukowców i umiędzynarodowieniem nauki a produktywnością badawczą, rosnące znaczenie międzynarodowych publikacji w krajowych systemach nauki, różnicującą rolę badań w systemach szkolnictwa wyższego oraz rolę różnic między mężczyznami i kobietami naukowcami w różnych wymiarach kariery akademickiej. Wskazuje również na podstawową rolę dobrze przemyślanych fundamentów reform szkolnictwa wyższego we wspieraniu funkcjonowania polskich naukowców w światowym obiegu idei i innowacji. Pokazuje też, że do absorpcji globalnej wiedzy niezbędni są naukowcy – tylko oni są w stanie produkować globalną wiedzę i zarazem dokonywać jej przekładu na krajowe potrzeby. Globalizacja nauki stawia w centrum uwagi globalnego naukowca i jego indywidualne wybory zawodowe, takie jak sieci współpracy czy intensywność prowadzonych badań, pracującego na przecięciu nauki globalnej i krajowej. Globalizacja nauki promuje produktywnych uczestników globalnej konwersacji naukowej, funkcjonujących obok tradycyjnych naukowców lokalnych. Ponadto dzisiejsze wzorce publikacyjne silnie stratyfikują naukowców – pewne kanały publikacyjne liczą się radykalnie bardziej w karierze zawodowej naukowca niż inne; a niektóre kanały nie liczą się w niej wcale. Książka posługuje się niezwykle rozbudowanym materiałem empirycznym, poddając szczegółowej analizie dziesiątki tysięcy naukowców i setki tysięcy publikacji.
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It is impossible to overstate the importance of librarians in the intellectual development of any institution. The impact of this function can also be seen in librarians' productivity. In other words, when librarians are productive, they contribute significantly to the organization's long-term viability. If this isn't the case, the organization has a good chance of collapsing. In this light, the purpose of this study is to look into Electronic Information Resources (EIRs) Use and Research Productivity (RP) of lecturers in private universities in Oyo state. The study used a Descriptive research design. Population consists of 520 lecturers in private universities in Oyo state, Nigeria. After Krejcie and Morgan were applied, the sample size was 217 lecturers. After Krejcie and Morgan were used to establish the sample size, it was used as the sample size. To collect data, a multi-stage sampling technique was employed, as well as a validated questionnaire. For each variable, the reliability coefficient ranged from 0.72 to 0.81. Descriptive and inferential statistics were used to analyze the data. EIRs had a substantial influence on (RP) (Adj. R2 = 0.216; p = 0.000), according to the findings. According to the findings, EIRs has an influence on RP. The study concluded that EIR influence RP. The study recommended that academic libraries should educate lecturers about the importance of publishing in high-impact journals by utilizing the numerous (EIRs) as available in university libraries.
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Niniejszy raport prezentuje analizę bezprecedensowego wzrostu międzynarodowej współpracy badawczej w Europie pod kątem rozkładu współautorstwa i cytowań publikacji globalnie indeksowanych w ostatniej dekadzie (lata 2009-2018). Dynamika zmian wyłaniająca się z tej analizy jest następująca: rosnący poziom współpracy międzynarodowej odciąga najważniejsze systemy europejskie od współpracy instytucjonalnej, przy stabilnej i silnej współpracy krajowej. Krajowa produkcja naukowa, czyli całkowita liczba publikacji pozostaje na stałym poziomie, a cały wzrost liczby publikacji w badanym okresie należy przypisać międzynarodowym publikacjom współautorskim, które stają się już nie tyle najważniejszą, co jedyną siłą napędową wzrostu liczby publikacji w Europie. Bardzo to ważna konkluzja w kontekście Polski: potencjał rozwoju nauki w ramach współpracy krajowej – z którego aktualnie korzystamy – będzie się stopniowo wyczerpywał i wtedy kluczem do utrzymania konkurencyjności polskiej nauki będzie wyłącznie współpraca międzynarodowa. Na razie, podobnie jak inne kraje dołączające do globalnej nauki, korzystamy z renty opóźnionego startu do udziału w jej zmaganiach. W związku z pojawieniem się globalnej usieciowionej nauki, w której rola polityki krajowej we współpracy spada, a rola naukowców rośnie, kluczem do rozwoju współpracy w Europie (oraz w Polsce) jest gotowość poszczególnych naukowców do podejmowania współpracy międzynarodowej. Naukowcy współpracują na arenie międzynarodowej wtedy, kiedy jest to dla nich opłacalne pod względem prestiżu akademickiego, uznania naukowego i dostępu do finansowania badań, co sugerują trzy zaproponowane tutaj modele (model cyklu wiarygodności w nauce, model maksymalizacji prestiżu i model nauki globalnej). Łączna liczba analizowanych w tym raporcie artykułów indeksowanych w bazie Scopus wyniosła 5,5 miliona, w tym 2,2 miliona artykułów napisanych w ramach współpracy międzynarodowej.
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The skill-attracting policies encouraging the internationalisation of higher education are compatible with a modernisation discourse, at the heart of which lies a belief that international researchers are highly embedded super-achievers allured by targeted policies. By focusing on the life stories of foreign-born scholars working in Poland (100 in-depth interviews), and Polish department heads (20 interviews) this article revealed three paradoxes that should never have come to be according to the Western modernisation paradigm. The first paradox is related to the expectation that policies targeted at incoming scholars should be the first and foremost enticement for international scholars. The second paradox stems from the fact that some internationally mobile academics representing the humanities and social sciences—in their biographical narratives—highly criticise current academic policies focused on internationalisation. The third paradox is related to the fact that, counterintuitively, less embedded academic migrants perform better. This is an outcome of the life strategy of top-performing scholars, who decide to work in Poland only upon receiving a prestigious and temporary, often EU-funded, scholarship (e.g., Marie Curie). Building on the empirical material from Poland, this article introduces a new notion—the ‘internationalisation against the grain’ to embrace the paradoxes of internationalisation from many peripheral countries.
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Academic human capital (AHC) is a key element in the explanation of scientific productivity. However, few studies have analysed this topic in the academic context, and their conclusions about composition and measurement remain ambiguous. This study proposes a measurement scale to assess AHC, following a systemic procedure composed of two steps: qualitative and quantitative phases. First, the Delphi technique was applied to reach a consensus on the AHC factors, resulting in a scale of 22 items. Second, exploratory and confirmatory factor analyses were conducted to determine the underlying factorial structure of the scale, using a sample of 2,223 researchers in Spanish universities. The results provided a five-dimensional structure of AHC, measuring the knowledge and abilities required to perform research activities, as well as skills related to the organisation of scientific processes, alertness to research opportunities, and the openness to provide and receive criticism. This study poses interesting challenges for knowledge management in universities.
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This paper seeks to conceptualize the processes of de-privatization in higher education. Trends of de-privatization (and contraction in enrolments) are highly interesting because they go against global trends of privatization (and educational expansion). De-privatization means a decreasing role for the private component in the changing public–private dynamics. The paper studies its two dimensions (funding and provision) and distinguishes between seven potential empirical organizational/geographical levels of analysis. Empirically, the paper draws from data from Central Europe. The traditional dichotomous pairing of the public and the private is shown to still be useful in specific empirical contexts, despite it becoming blurred globally. Major approaches to privatization in higher education over the last two decades are rethought and redirected toward de-privatization. An empirically informed notion of de-privatization is being developed and its usefulness is briefly tested.
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The asymmetric international mobility of talented scientists is well documented, yet there is little evidence about the reasons why scientists choose particular jobs. Building on an extended human capital model of science, we unify a dispersed literature relevant for job choice to formulate hypotheses which we test in a unique international quasi-experiment among more than 10,000 researchers. We find that attractive jobs satisfy researchers’ “taste for science” and increase their expected scientific productivity, responding to both intrinsic and extrinsic motivations. In particular, while salaries, research funding and working with stimulating peers matter, we provide unique estimates of the importance of organisational and institutional factors: early stage researchers are willing to trade off a substantial amount of salary for early independence and tenure perspectives; later stage researchers favour jobs which make it easy to take up new lines of research. Research-only positions are considered as less attractive than jobs with a moderate amount of teaching. Our findings have important implications for the organisational design of research universities and the competitiveness of European science in light of the brain drain of highly talented scientists towards the U.S.
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In this paper, we focus on a rare scholarly theme of highly productive academics, statistically confirming their pivotal role in knowledge production across 11 systems studied. The upper 10 % of highly productive academics in 11 European countries studied (N = 17,211) provide on average almost half of all academic knowledge production. In contrast to dominating bibliometric studies of research productivity, we focus on academic attitudes, behaviors, and perceptions as predictors of becoming research top performers across European systems. Our paper provides a (large-scale and cross-country) corroboration of the systematic inequality in knowledge production, for the first time argued for by Lotka (J Wash Acad Sci 16:317–323, 1929) and de Solla Price (Little science, big science. Columbia University Press, New York, 1963). We corroborate the deep academic inequality in science and explore this segment of the academic profession. The European research elite is a highly homogeneous group of academics whose high research performance is driven by structurally similar factors, mostly individual rather than institutional. Highly productive academics are similar from a cross-national perspective, and they substantially differ intra-nationally from their lower-performing colleagues.
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This book provides an overview on the major findings of a questionnaire survey of academic profession in international perspective. More than 25,000 professors and junior staff at universities and other institutions of higher education at almost 20 countries from all over the world provide information on their working situation, their views and activities. The study “The Changing Academic Profession” is the second major study of its kind, and changes of views and activities are presented through a comparison of the findings with those of the earlier study undertaken in the early 1990s. Major themes are the academics’ perception of their societal and institutional environments, the views on the major tasks of teaching, research and services, their professional preferences and actual activities, their career, their perceived influence and their overall job satisfaction. Emphasis is placed on the influence of recent changes in higher education: the internationalisation and globalisation, the increasing expectation to provide evidence of the relevance of academic work, and finally the growing power of management at higher education institutions. Overall, the academics surveyed show that worldwide discourses and trends in higher education put their mark on the academic profession, but differences by country continue to be noteworthy. Academics consider themselves to be more strongly exposed to mechanism of regulations, incentives and sanctions as well as various assessments than in the past; yet their own freedom, and responsibilities and influence shape their identity more strongly and are reflected in widespread professional satisfaction.
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This book examines the many ways in which economic concepts, theories and models can be used to examine issues in higher education. The topics explored in the book include how students make college-going decisions, the payoffs to students and society from going to college, markets for higher education services, demand and supply in markets for higher education, why and how state and federal governments intervene in higher education markets, college and university revenues and expenditures, how institutions use net-pricing strategies and non-price product-differentiation strategies to pursue their goals and to compete in higher education markets, as well as issues related to faculty labor markets. The book is written for both economists and non-economists who study higher education issues and provides readers with background information and thorough explanations and illustrations of key economic concepts. In addition to reviewing the contributions economists have made to the study of higher education, it also examines recent research in each of the major topical areas. The book is policy-focused and each chapter analyses how contemporary higher education policies affect the behaviour of students, faculty and/or institutions of higher education. "Toutkoushian and Paulsen attempted a daunting task: to write a book on the economics of higher education for non-economists that is also useful to economists. A book that could be used for reference and as a textbook for higher education classes in economics, finance, and policy. They accomplish this tough balancing act with stunning success in a large volume that will serve as the go-to place for anyone interested in the history and current thinking on the economics of higher education." William E. Becker, Jr., Professor Emeritus of Economics, Indiana University
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This book analyzes the reforms that led to a differentiated landscape of higher education systems after university practices and governance were considered poorly adapted to contemporary settings and to their new missions. This has led to a growing institutional differentiation in many higher education systems. This differentiation has certainly contributed to making the institutional landscape more diverse across and within higher education systems. This book covers this diversity. Each part corresponds to a different but complementary way of looking at reforms and highlights what can be learnt on specific cases by adopting a specific perspective. The first part analyzes the ongoing reforms and their evolution, identifies their internal contradictions, as well as the redefinitions and reorientations they experience, and reveals the ideas, representations, ideologies and theories on which they are built. The second part includes comparison between countries but also other comparative perspectives such as how one reform is developed in different regions of the same country, as well as how comparable reforms are declined to different sectors. The last part addresses the impact of the reforms. What is known about the effectiveness of such instruments on higher education systems? This part shows that reforms provoke new power games and reconfigure power relations.