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Determinants of the incidence of non-academic staff in European and US HEIs

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In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011-2016 for Europe and 2012-2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance. Supplementary information: The online version contains supplementary material available at 10.1007/s10734-022-00819-7.
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Higher Education
https://doi.org/10.1007/s10734-022-00819-7
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Determinants oftheincidence ofnon‑academic staff
inEuropean andUS HEIs
AlessandroAvenali1· CinziaDaraio1· JoannaWolszczak‑Derlacz2
Accepted: 24 January 2022
© The Author(s) 2022
Abstract
In this article, we contribute to the scant literature covering quantitative studies on the
determinants of the non-academic staff incidence in higher education institutions by ana-
lysing how the proportion of non-academic staff is related to key features such as size,
prestige, year of foundation and financial structure of universities. We apply nonlinear
regression analysis to compare HEIs across Europe and the USA, taking into account time
and cross-country heterogeneity of the two balanced panel datasets concerning European
and American universities over a period of 6years (2011–2016 for Europe and 2012–2017
for the USA). Evidence suggests that in both Europe and the USA, public and larger (if
sufficiently large) as well as more research-oriented units are characterised by a higher
proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of
the share of non-personnel expenditure and the foundation year on the proportion of non-
academic staff, while the proportion of non-academic staff decreases with the share of core
and third-party funding. For the USA, we obtain similar findings except that the share of
core funding and third-party funding is characterised by a U-shaped effect, and the impact
of the share of non-personnel expenditure has no empirical effect on the proportion of non-
academic staff. Additionally, we discover that some factors that contribute to the propor-
tion of non-academic staff may constitute indicators of performance, suggesting the need
for further research to extend our knowledge on the complex issue of the role played by
non-academic staff in university performance.
Keywords Higher education institutions· Proportion of non-academic staff· Determinants
of non-academic staff· Europe· USA
JEL I22· I23· C14
* Joanna Wolszczak-Derlacz
jwo@zie.pg.gda.pl
1 Dipartimento Di Ingegneria Informatica Automatica E Gestionale Antonio Ruberti (DIAG),
Sapienza University ofRome, Rome, Italy
2 Faculty ofManagement andEconomics, Gdańsk University ofTechnology, Narutowicza 11/12,
80-233Gdańsk, Poland
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Introduction
A key factor determining the success of any organisation is its staff. According to the tasks
they perform, employees of higher education institutions (HEIs) can be divided into several
organisational domains: researchers and teachers (the main executives of university pri-
mary processes), administrative and technical staff (in charge of organising and supporting
primary processes) and high professional administrators (who coordinate and organise the
activities of employees of the other domains). However, quantitative data presented in the
relevant literature often oversimplify personnel division by applying a binary model where
total staff is divided into academic staff (researchers and teachers) and non-academic staff
(all other employees different from researchers and teachers).1
While the role of academics in university performance is usually underlined and
explored in-depth, the impact of non-academic staff on academics’ outcomes related to
institutional outputs cannot be neglected. Unfortunately, recent investigations and assess-
ments of the activities and roles of non-academic staff in the functioning of universities
are not very favourable. Ginsberg (2011) dedicates an entire book to describe ongoing and
disturbing changes in the operation of universities, with an increasingly higher impact of
(senior and middle) professional administrators’ decisions on the rules and the priorities
of academic life.2 Next, as one of the main problems concerning the functioning of today’s
universities, Martin (2016) points to the growth of bureaucratic procedures and, as a result,
a disproportionate increase in the number of employees holding administrative positions.
However, despite the growth in non-academic staff, academics employed by universities
spend more and more time on non-academic (bureaucratic) activities (Kwiek, 2018).
Surprisingly, there is a scarcity of literature devoted to developing quantitative studies
aimed at investigating key factors affecting the size of non-academic staff (i.e. the num-
ber and the share of non-academic personnel employed by HEIs) or finding empirical evi-
dence concerning the role of non-academic staff in university performance. In an empirical
analysis of universities in 11 European countries for the 2011/2012 academic year, Baltaru
and Soysal (2018) find that the main factor influencing the growth of administration is the
engagement of HEIs in multiple activities and undertaking various (new) missions and ini-
tiatives. Indeed, the proliferation of non-academic staff is mainly the result of transforma-
tion of universities into organisations that pursue strategic missions related both to primary
tasks assigned to universities such as teaching, research and third mission activities and
to public-policy goals such as inclusion and equality issues.3 Furthermore, a recent study
1 In our empirical analysis, we will refer to the simple binary division (academic versus non-academic
staff), which is still the predominant division in many universities and continues to be an important catego-
risation, e.g. as far as distinct contractual terms for academic/non-academic staff are considered. Addition-
ally, the binary division makes the operationalisation of different aspects of HEIs easier. However, it should
be underlined that the division of HEI personnel into specific categories is a complex issue and the binary
division is even more problematic especially if one takes into account: (a) development of administrative
roles in universities, with administrative work becoming more and more professional, (b) conducting part
of administrative work by academic staff with a double role, e.g. deans/heads of departments/faculties who
keep their academic position and manage units at the same time. See the “Relevant literature” section for a
wider discussion about the nomenclature and heterogeneity of non-academic staff.
2 Specifically, he writes: “For many of these career managers, promoting teaching and research is less
important than expanding their own administrative domains. Under their supervision, the means have
become the end.” (Grinsberg, 2011 p. 2).
3 See Pruvot and Estermann (2018). In the UK, for instance, race, disability and gender equality duties may
have an impact on universities’ professional staff (Baltaru 2019b).
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concerning the sample of 100 British universities conducted by Baltaru (2019a) examines
whether changes in the staffing structure between 2003 and 2011 affected the performance
of the universities. She finds that universities that moderately increased the number of non-
academic personnel were characterised by higher rates of course completion, but finds no
such effect on research quality, good honours degrees or graduate employability. Baltaru
(2019a) concludes that university performance is solely determined by the reputation and
prestige of the institution.
Aim andcontribution
The main purpose of this paper is to shed new light on the main factors that determine the
incidence of non-academic staff in higher education institutions (HEIs). We contribute to
the scant existing literature covering quantitative studies on the determinants of the non-
academic staff incidence in higher education institutions by developing an empirical analy-
sis, based on the estimation of nonlinear regressions, to compare the employment structure
(ratio of non-academic staff to total staff) of European HEIs and their US counterparts.
Two unique databases are used for the analysis. For Europe, we merge individual informa-
tion on HEIs from the European Tertiary Education Register (ETER) with bibliometric
indicators from the Centre for Science and Technology Studies (CWTS) at Leiden Univer-
sity.4 For the USA, the main data source is the Integrated Postsecondary Education Data
System (IPEDS) merged with publication data from the CWTS and information on the
year of foundation obtained directly from the websites of institutions.
Our objective is to extend pioneering cross-sectional results on determining factors
concerning non-academic staff in European HEIs explored by Baltaru and Soysal (2018)
for the 2011/2012 academic year by analysing the dynamics over time in European HEIs,
including the most updated data available for Europe, and providing an indirect compari-
son between European and US HEIs by taking into account time and cross-country het-
erogeneity. This means that we will analyse the two systems (European and American)
separately but use the most similar variables available for each system in our elaborations.
To the best of our knowledge, this is the first study to focus on the comparison concerning
the incidence of non-academic staff in HEIs. On the other hand, Wolszczak-Derlacz (2017)
provides a comparison of the efficiency of the two systems, while Lepori etal. (2019) ana-
lyse European and American scientific production.
Comparing Europe with the USA is useful and interesting for several reasons. First of
all, many European countries are implementing reforms of their higher education systems
based on the American model even if there is a lack of quantitative and systematic analyses
to understand which elements of the American system are considered most suitable for the
European context and which ones should be disregarded (see also Aghion et al., 2010).
Second, the preliminary step for conducting the comparison is the collection and analysis
of existing data on the two systems. As a result, carrying out the comparison enables us to
thoroughly investigate the availability and consistency of data and provides us with useful
suggestions on which data could be improved or integrated in future data collections. As
we will see, the US data available on non-academic staff are much more detailed than the
4 Furthermore, specific missing data in ETER were imputed (see the “Data and descriptive analysis” sec-
tion).
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European data included in ETER. Finally, an indirect comparison, as the one carried out in
this paper, can provide an overall and updated view on the incidence of non-academic staff
in the two systems while keeping track of their heterogeneity.
The rest of the paper is structured as follows. The “Relevant literature” section includes
a summary of the previous studies, taking into account universities’ features that may
affect the incidence of non-academic staff. In the “Data and descriptive analysis” section,
we describe two panel datasets, along with key descriptive statistics on the European and
American HEIs. The “Methods” section illustrates the nonlinear regression approach fol-
lowed in the empirical analysis to model the nonlinear relationship that we find between
the share of non-academic staff (the dependent variable) and its potential determinants.
The “Results” section includes the main results, while the “Discussion and further develop-
ments” section focuses on the discussion of the findings and the outline of further research.
The “Concluding remarks” section contains the concluding remarks.
Relevant literature
The long-observed increase in the number of administrative and technical personnel in
higher education institutions is a universal trend that emerged in many countries around
the world (Grove, 2012; Gumport & Pusser, 1995; Hansen & Guidugli, 1990; Krücken,
2011; Krücken etal., 2013; Rhoades & Sporn, 2002; Sebalj etal., 2012). Nowadays, non-
academic staff usually comprises a large part of the total staff of universities and, in many
cases, is even larger than academic staff. Indeed, new and complex external and internal
challenges in the last decades caused administrative and technical activities within HEIs to
increase considerably and evolve accordingly. However, most of these activities were allo-
cated to administrative or technical personnel of universities (usually referred to as “gen-
eral staff” or “non-academic staff”), while several of them were outsourced or left under
the charge of academic staff.
As a consequence, the last decades saw a wide development of general staff within uni-
versities in terms of its growth, higher responsibilities and more crucial roles. General staff
contributes to the definition of strategic targets and internal rules and to the overall perfor-
mance (e.g. by collaborating with academic staff on complex projects, providing techno-
logical support to teaching and student learning). Therefore, non-academic staff is usually
located in every organisational structure of the university (e.g. departments, faculties, cen-
tral offices). It is also typically characterised by a multitude of very different professional
roles. For instance, general staff may include technical personnel, maintenance staff, office
workers and high professional administrative personnel5 (for the discussion on the general
staff evolution, see, e.g. Szekeres, 2004, 2011; Whitchurch, 2006, 2007, 2017; Sebalj etal.,
2012; Kallenberg, 2018; Smith etal., 2021).
5 This group of non-academic professionals mainly consists of senior administrators such as, for instance,
the Director of Operations Management, Director of Quality Assurance, Director of Educational and Stu-
dent Affairs or Head of Strategic Projects. Together with several academics holding managerial positions,
these high professional administrators represent a third organisational domain that has developed to ensure
better coordination and organisation of the activities of other academic and non-academic employees in
order to balance their different—and sometimes conflicting—interests with the pursue of valuable outcomes
for HEIs (Kallenberg 2018; Smith etal., 2021; Whitchurch 2007).
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Despite the involvement of general staff in many different skills and roles, quantitative
models and data presented in the relevant literature are unable to represent the complexity
underlying non-academic staff for many HEIs considered in our analysis. In particular, the
binary staff classification (academic/non-academic) does not provide the full picture of the
complexity of personnel classification within HEIs (Sebalj etal., 2012; Smith etal., 2021;
Szekeres, 2011; Whitchurch, 2006, 2007, 2013). In this study, however, we will still refer
to it because most of the available quantitative staff data are provided in binary format.
The identification of key factors that may play a role in affecting the level of the overall
non-academic personnel inside universities is an interesting research issue, which can be
investigated by applying binary staff data.
Some first crucial reasons may be explained by taking inspiration from the long-term
goals of any HEI. Indeed, to deal with the strong domestic and international competi-
tion in terms of attracting more students and external funding for large research projects
(above all, due to a shortage of core funding such as government grants), universities must
strengthen their non-academic structures with highly professional administrative personnel
who can take part in the strategic planning process (Baltaru, 2019a; Gornitzka & Larsen,
2004; Graham, 2013; Kallenberg, 2018; Mcinnis, 1998; Ramirez & Christensen, 2013;
Tolofari, 2005; Veles & Carter, 2016). Therefore, a higher number of students and larger
external funding could trigger an increase in non-academic staff. The management of a
growing number of students and complex research projects also poses a difficult challenge
in terms of efficiency, possibly requiring an administrative structure that includes larger
staff with many different skills (Baltaru & Soysal, 2018; Gibb etal., 2012).6
In recent decades, the new public management paradigm also led universities to change
their organisation and governance in such a way to conduct their operations in a similar
fashion to private organisations, where an effective and sufficiently large administrative
structure, supporting primary processes, is crucial to ensure that an organisation is success-
ful (Baltaru & Soysal, 2018; Bleiklie etal., 2011; De Boer etal., 2007; Deem etal., 2007;
Pollitt & Bouckaert, 2004; Szekeres, 2004, 2006, 2011; Tolofari, 2005). This led to a rela-
tionship between academics and general staff, which is often described as conflicting and
characterised by scarce understanding (Anderson, 2008).7 This corporatisation process of
universities can be usually observed in countries where public education systems are pre-
dominant. Thus, the country of origin too might affect the general staff increase observed
in last decades.
Furthermore, important worldwide rankings unavoidably provide universities with
suitable incentives to control and, if necessary, modify their organisational behaviour in
order to improve their position in such rankings. An effective management control system
requires non-academic staff to be empowered in terms of their number and competences
(Bromley & Meyer, 2014; Sauder & Espeland, 2009).
It is worth noting that the increase in the number of non-academic staff is in some cases
related to a quite large increment of highly qualified administrative personnel and to a dec-
rement of technical and clerical staff, most likely due to the outsourcing of non-core activi-
ties (Gornitzka & Larsen, 2004; Whitchurch, 2006, 2007). With this in mind, increased
6 Baltaru and Soysal (2018) investigate several different factors, but they do not find empirical evidence
that the proportion of non-academic staff to total staff depends on student enrolments, budget cuts or dereg-
ulation.
7 According to some authors, this conflicting relationship does not necessarily jeopardise university out-
comes (Bacon, 2009).
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expenditures on tenders of services may be associated with a decrement of technical and
clerical personnel.
Still, most of the studies included in the relevant literature are based on qualitative anal-
yses and fail to investigate whether geographical and institutional (Europe vs. USA) fea-
tures may have an impact on the non-academic staff expansion. A notable exception is the
paper by Baltaru and Soysal (2018), where the authors apply a multivariate linear regres-
sion model to study factors associated with the variation in the percentage of non-academic
staff across European universities for the 2011/2012 academic year. However, they still fail
to consider the potential role of different geographical and institutional contexts.
Data anddescriptive analysis
As far as European HEIs are considered, the main source of data is the European Tertiary
Education Register (ETER, www. eter- proje ct. com), which provides open access to a cross-
country database at the level of individual HEIs, containing information about their charac-
teristics such as financial resources, staff, student enrolment and graduates. Currently (as at
June 2021), the data are available for 3,280 HEIs from 37 countries and for academic years
from 2011 to 2016.8 The establishment of one, common and publicly available database of
HEIs in Europe such as ETER is a milestone for data collection. However, the level of data
completeness significantly varies between countries, domains and variables. In this paper,
we merge data on HEIs characteristics from ETER with bibliometric information (based on
the number of publications of academics affiliated with a given institution indexed in the
Web of Science) from the Centre for Science and Technology Studies (CWTS) at Leiden
University.9
Our analysis is restricted to the sample of universities defined as academic institutions
with the right to award doctoral degree (as opposed to university of applied sciences, col-
leges, vocational schools such as Fachhochschule in Austria and Germany, Hogescholen in
the Netherlands, colleges in Norway, Szkoły Zawodowe in Poland).10 Hence, specialised
units such as arts/music/sport/police and theological academies are not taken into account.
Additionally, distance education universities are excluded where off-campus teaching (e.g.
through online courses) constitutes a substantial component of the educational offer, affect-
ing the staff structure and student-faculty ratios.11 Furthermore, the sample is limited to the
balanced panel of those HEIs reported for the subsequent 6years—from 2011 to 2016—
and with the minimum number of 500 students to avoid the inclusion of smaller outliers in
11 The Distance education institution variable in ETER includes three categories: no or limited amount of
distance education (< 20% of students of distance programmes), a substantial share of distance education
and mostly distance education (> 90% of students of distance programmes). The mean value of the number
of students for the distance education institutions reported in ETER is 30,000 and the mean number of stu-
dents per FTE academic staff is 78.5. When comparing these values with the statistics reported in Table2,
clear differences between distance and non-distance institutions can be observed. These differences demand
a separate analysis of these two types of institutions.
8 Additionally, some data concerning French HEIs are provided for 2017.
9 The bibliometric data can be obtained for research purposes through the EU-FP RISIS2 project (https://
rcf. risis2. eu/ datas ets). We would like to thank Josephine Bergmans and Ed Noyons from the CWTS, Leiden
University, for providing the data.
10 The restriction is based on the variable, Institution category standardised in ETER, which classified
institutions into three groups: universities, university of applied sciences/colleges and others.
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the analysis. It is very often the case that some data recorded in ETER are not complete.
Also, for some countries, the number of academic and non-academic staff is reported in
FTE (full-time equivalent) and for others in HC (head count). To overcome these problems,
basic variables (if missing) were imputed.12 We calculate the ratio of non-academic to total
staff (Non_acad) and treat FTE as the basic classification.13
In the Appendix, in Table4, the European coverage of our final sample after an initial
outliers’ detection is presented.14 As a result, information on 675 HEIs from 26 countries
were obtained. In Fig.1, cross-country differences of the proportion of non-academic staff
to total staff are presented. The mean value is 45%, with Iceland, Cyprus and the UK being
the countries with the highest share of non-academic staff to total staff, whereas Greece,
Belgium and Switzerland with the lowest. For larger countries in which the number of
reported HEIs is relatively high, the dispersion is substantial; for example, in Italy, there
are institutions where non-academic staff constitutes less than 20% of total staff, as well as
institutions with the majority of non-academic staff, i.e. 60% of total staff, similarly in Ger-
many and the UK. Interestingly, the mean share of non-academic staff in total staff is stable
over time (see Table5 in the Appendix).
The main source of data for higher education institutions from the USA is the Integrated
Postsecondary Education Data System (IPEDS). The IPEDS covers all higher education
institutions in the USA. In 2017, for instance, data were reported for 7,153 institutions. The
IPEDS contains very detailed information on students, staff, salary, finance, etc. and it was
merged with CWTS data on publication records as in the case of European HEIs. The final
sample includes institutions classified as public or private not-for-profit 4-year or longer,
including institutions conducting research, excluding specialist (one-field) institutions such
as theological seminaries or medical schools (according to the Carnegie Classification), for
which the CWTS provides data on publication records and which are recorded in all ana-
lysed years (balanced panel). The period of analysis is limited to years 2012–2017, which
overlaps with the data for Europe. In the final sample of 341 institutions, there are 215
HEIs classified as public and 126 as private not-for-profit. The sample does not include
private for-profit institutions whose financial structure differs significantly from those
analysed.
12 The imputation method is based on Bruni etal. (2021) from which the imputed data were taken.
13 Previous studies included in the relevant literature consider the number of non-academics relative to the
number of academics/total staff (Baltaru and Soysal 2018; Gornitzka and Larsen 2004).
14 The initial data examination showed that there is an enormous variation in the proportion of non-aca-
demic staff of the analysed institutions. We identified institutions with a proportion of non-academic staff
equal to zero as well as institutions with an extremely high proportion. For example, there are three univer-
sities reporting zero non-academic staff, namely Webster University Vienna, a private university in Austria;
Link Campus University in Italy; and International Hellenic University in Greece. On the other hand, the
universities with the highest proportion of non-academic staff, i.e. above 85%, include London Business
School in the UK, Campus Bio-Medico University in Italy and International University of Andalucía in
Spain. For this reason, to ensure that the results were not driven by outliers, we decided to eliminate these
extreme observations by considering those institutions with Non_acad either lower than 0.1 or higher than
0.85 as outliers. However, to check the robustness of these identified thresholds, we repeated the empirical
analysis considering two alternative methods of outlier detection as well. The first method is based on per-
centiles (and identifies outliers as those observations that are either smaller than the 1st percentile or larger
than the 99th percentile), while the other considers the distance from the mean (considering those observa-
tions that have values of Non_acad three standard deviations away from the mean as outliers). As described
in the “Results” section, the choice of the methodology for detecting outliers does not affect our empirical
results.
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We calculate the ratio of non-academic staff to total staff (Non_acad) which is
expressed in FTE.15 For the USA, there is more detailed information available compared
to Europe.16 The group of variables on non-academic staff is in fact detailed in the fol-
lowing occupational categories: librarians, management, business and financial operations,
IT (Information Technologies), engineering and science; community, social service, legal,
arts, design; entertainment, sports and media; healthcare practitioners and technical; ser-
vice occupations; sales and related occupations; office and administrative support; natural
resources, construction and maintenance; production, transportation and material moving.
The employment structure in American HEIs is presented in Table1. Overall, the average
share of non-academic staff across American universities is around 64%, while office and
administrative support constitutes the highest share of non-academic staff, which is 22% on
average. Similar to European institutions, the share of non-academic staff to total staff is
constant over time.
Table2 includes basic descriptive statistics concerning our sample of European and US
HEIs: the number of students, the number of students per FTE academic staff, the share of
core revenues,17 the share of third-party revenues,18 the share of non-personnel expendi-
ture,19 publications per academic staff member, proportion of publications belonging to top
10% most cited in the field and year, the average number of citations (impact) per publica-
tions (normalised by year and field).
Total revenues of HEIs are composed of two basic categories: core and third-party rev-
enues. The classification of a given type of revenue into core and third-party funding is
based on ETER and IPEDS documentation. For European universities, in ETER, core fund-
ing includes basic government allocations, gifts and donations, interests and investment
income. For US universities, to ensure some comparability with European universities, the
following IPEDS items were included in core funding: federal, state and local appropria-
tions, federal, state and local non-operating grants, gifts, other revenues and investments.
15 Using IPEDS, FTE for each of the occupational category is derived by adding the full-time staff head-
count to 1/3 of the part-time headcount for each occupational category.
16 ETER provides the classification of staff in binary format: academic versus non-academic staff (giving
total staff when summed up). Non-academic staff includes HEI-level management staff such as the director,
administrative director and head of service; HEI-level administrative staff, including both central-level and
department-level staff; staff engaged in maintenance and operations, including special services such as IT
(Information Technologies); and undergraduate students employed for teaching assistance or research.
17 In the case of Europe, core revenues refer to funding available for the operations of the institution as a
whole, which are not earmarked for specific activities and whose internal allocation can be decided fairly
freely by the institution itself. They are further divided into basic government allocation and other revenues,
source: ETER (2018). For the USA, they are comprised of federal, state and local appropriations and fed-
eral, state and non-operating grants; for public institutions: according to accounting standards established
by the Governmental Accounting Standards Board (GASB), for private not-for-profit: according to the
Financial Accounting Standards Board (FASB), source: IPEDS (2020).
18 In the case of Europe, third-party revenues refer to funds for specific activities and institutional units;
they are the sum of grants from national and international funding agencies for research activities; funds
from charities and non-profit organisations for specific research and educational purposes; contracts con-
cluded with public bodies, non-profit organisations and private companies for specific research and ser-
vices; fees/payments from companies for educational services and research; and service grants from
companies, source: ETER (2018). For the USA, in the case of public institutions: federal, state, and local
operating grants and contracts, in the case of private not-for-profit: federal, state, and local grants and con-
tracts, source: IPEDS (2020).
19 In Europe, non-personnel expenditure includes expenditure on goods and services other than staff com-
pensation, source: ETER (2018). In USA, all expenses not included in salaries and wages as compensation
for services to all employees, source: IPEDS (2020).
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For European universities, third-party funding includes public and private grants and con-
tracts and grants and contracts from abroad, while for US universities, it covers federal,
state and local grants and private gifts, grants and contracts. This correspondence between
European and US revenues as divided into core and third-party categories, although not
perfect, proved to be effective also in other studies such as Lepori etal. (2019), from which
it is derived.20
On average, the US institutions are larger (the average number of students in the US
HEIs and the European HEIs is 21,600 and approx. 17,000, respectively), while the largest
units are similar in size as far as the number of students is concerned. The average number
of students per academic staff is greater in the USA (20 students versus 17 students per
academic staff). As far as the structure of finance, revenue sources and expenditure alloca-
tion are concerned, there are some differences between the European and American univer-
sities. Firstly, non-personnel expenditure as a share of total expenditure is higher in the US
units (53% on average), while the average share of core budget in the total budget is lower
in the US units, with the average share of third-party budget being equal to 17% and 15%
for Europe and the USA, respectively. The indications of research outcomes measured by
bibliometric records are similar for the European and American samples: 0.62 to 0.68 pub-
lications per academic staff (yearly), 11 to 13% share of top publications and citations from
1.15 to 1.3 comparing the average of European indicators to the American ones.
Methods
We apply regression analysis to examine determinants of the share of non-academic staff
in HEIs. Independent variables (potential determinants of the share of non-academic staff)
are mostly selected based on the theoretical discussion in the “Relevant literature” sec-
tion. In particular, the number of students and the share of third-party revenues may be
considered significant potential explanatory variables since attracting a higher number of
Fig. 1 Proportion of non-
academic staff to total staff
across different countries (all
years pooled together). Source:
authors’ own elaboration based
on ETER data
0.2 .4 .6 .8
GR BE CH IT AT BG DK ES NO SE NL DE HR CZ EE PT PL MT IE SK LI FR LT UK CY IS
20 See TableS1 in the supplementary materials for details about the mapping of core and third-party rev-
enues of European and US HEIs derived from Lepori etal. (2019) and applied in this paper.
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Table 1 Employment structure of the US HEIs
Source: authors’ own elaboration based on IPEDS.
Variable Obs Mean Std. Dev Min Max
Non-academic staff to total staff (Non_acad)2,046 0.639 0.07 0.343 0.856
Share of a given non-academic occupational category in total non-academic staff, FTE
Librarians, archivists, curators and museum/student
and academic affairs 2,046 0.084 0.061 0.000 0.469
Management 2,046 0.142 0.079 0.000 0.567
Business and financial operations 2,046 0.113 0.071 0.000 0.674
IT, Engineering and Science 2,046 0.127 0.067 0.000 0.479
Community, social service, legal, arts, design,
entertainment, sports and media 2,046 0.093 0.054 0.000 0.482
Healthcare practitioners and technical 2,046 0.037 0.056 0.000 0.501
Service occupations 2,046 0.125 0.053 0.000 0.308
Sales and related occupations 2,046 0.003 0.01 0.000 0.204
Office and administrative support 2,046 0.219 0.079 0.012 0.861
Natural resources, construction and maintenance 2,046 0.045 0.029 0.000 0.3
Production, transportation and material moving 2,046 0.012 0.012 0.000 0.094
Table 2 Descriptive statistics, yearly averages
Notes: Descriptive statistics refer to yearly averages, calculated on the sample of 675 universities in Europe
(2011–2016) and 341 in the USA (2012–2017).
Source: authors’ own elaboration based on ETER and IPEDS.
Variable Obs Mean Std. Dev Min Max
Europe
Students total 4050 16,787 13,149 525 112,472
Students per academic staff 4050 17.43 9.57 1.18 99.40
Non-personnel expenditure in total 2742 0.33 0.10 0.05 0.78
Core budget in total 2902 0.56 0.25 0.00 1.00
Third-party budget in total 2710 0.17 0.13 0.00 0.96
Publications per academic staff 4050 0.62 0.55 0.00 7.06
Top 10% publications 4050 0.11 0.05 0.00 0.50
Citation 4050 1.15 0.51 0.00 11.78
USA
Students total 2046 21,623 14,636 1,529 112,984
Students per academic staff 2046 20.05 8.99 0.78 74.39
Non-personnel expenditure in total 2042 0.53 0.05 0.31 0.83
Core budget in total 2042 0.22 0.20 0.00 0.92
Third-party budget in total 2042 0.15 0.11 0.00 0.58
Publications per academic staff 2046 0.69 0.62 0.00 7.48
Top 10% publications 2046 0.13 0.05 0.00 0.50
Citation 2046 1.30 0.49 0.00 10.99
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1 3
students and larger external funds for complex research projects is typical strategic goals
of any HEI, which thus require to be effectively supported by general staff (Baltaru, 2019a;
Baltaru & Soysal, 2018; Gibb etal., 2012; Gornitzka & Larsen, 2004). The share of core
revenues may be considered an interesting variable to be considered as well. Indeed, on
the one hand, larger core revenues for institutional activities could increase the total effort
for non-academic staff and, consequently, the size of overall non-academic staff (Brom-
ley & Meyer, 2014; Ginsberg, 2011; Graham, 2013). On the other hand, a lower level of
core revenues could demand a larger efficiency of administrative staff (Bleiklie etal., 2011;
Tolofari, 2005). With this in mind, in terms of the share of non-academic staff, the overall
relationship with core revenues could be not univocally determined and could, most likely,
be nonlinear. In addition, several worldwide rankings push universities to improve their
outcomes such as, in particular, publications and graduate students, which, in turn, may
require non-academic staff to be empowered in order to support and control the pursuit of
these challenges (Sauder & Espeland, 2009). Therefore, the number of publications per
academic staff member (considered a proxy of university’s scientific production and contri-
bution to university’ rankings and prestige) is also expected to be an important determinant
(in this paper, emphasis is not put on graduate students as they are correlated with the
number of students). In some cases, several non-core activities supporting primary pro-
cesses can be outsourced, the goal of which is to save money (mostly when the scale of
these activities is large) and to obtain operational efficiency (Gornitzka & Larsen, 2004;
Whitchurch, 2006, 2007). The share of non-personnel expenditure could partially21 capture
this aspect, showing some negative impact on the incidence of non-academic staff. Fur-
thermore, the corporatisation process of many HEIs (due to the spread of the new public
management paradigm) generally extended the role and responsibilities of general staff,
while its implementation can be dependent on the specific country and be observed mostly
in areas characterised by a prevailing incidence of public HEIs (De Boer etal., 2007; Deem
etal., 2007; Pollitt & Bouckaert, 2004; Szekeres, 2006; Tolofari, 2005). The country of
origin and the public/private nature of the HEI could, therefore, help capturing the impact
of this organisation and governance change on the incidence of non-academic staff. Finally,
the foundation year and time fixed effects are considered to be further potential explanatory
variables related to the age of the HEI and to the role of time, respectively.
Unlike previous studies, which use linear regressions (Baltaru & Soysal, 2018), we
carried out nonparametric analysis before defining our model. It was based on locally
weighted regressions (lowess) between some potential explanatory variables and the share
of non-academic staff (dependent variable). The objective of this preliminary nonparamet-
ric analysis is to identify the relationship between the dependent variable and the explana-
tory variables without assuming it a priori. Another aspect that makes our analysis a novel
contribution to the field is that the elaborations are based on data on individual HEIs from
several years (2011/2012–2016/2017).
Figure2 illustrates lowess scatterplots separately for the European sample (the upper
panel) and the US sample (the lower panel). Following the detection of evidence of non-
linear relationships that can be seen in Fig.2, a nonlinear regression model is developed in
order to analyse the determinants of the share of non-academic staff for each dataset availa-
ble. Conducting two separate analyses allows us to make an indirect comparison useful for
21 This variable also includes expenditures different from outsourced activities.
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1 3
keeping track of the heterogeneity of the datasets22 and, at the same time, makes it easier to
carry out a comparative evaluation of the results for the two systems.
Generally, nonlinear trends are observed for both the European and the American sam-
ple. The first analysed variable is the number of students to see if larger institutions are
characterised by a higher share of non-academic staff. For Europe and the US alike, an
increasing trend can be observed up to the largest institutions where the trend is reversed.
The next sub-graphs present the relationship between the share of non-personnel expendi-
ture to total expenditure and the share of non-academic staff. The share of non-personnel
expenditure can be considered a proxy of outsourcing activities of universities. In the case
of the European institutions, the relationship appears to be nonlinear—the higher the non-
personnel expenditure, the higher the share of non-academic staff up to a threshold above
which the trend is reversed. For the US HEIs, the graph shows a rather flat line. Further-
more, a similar approach is used to analyse the relationship between the share of core fund-
ing and the share of third-party funding. Core funding refers to the funds available for the
operations of the institution as a whole; usually, it is not earmarked for specific activities.
In both cases, there are some nonlinear relationships against the share of non-academic
staff. On the other hand, third-party funding appears to be deterministic to the share of
non-academic staff only for the US HEIs.23 The relationship between the year of foun-
dation and the share of non-academic staff (Non_acad) and the relationship between the
publication per academic staff member and Non_acad are also presented. Publications per
academic staff are considered a proxy of research orientation of the unit and prestige of the
institution.
Using lowess smoothing-graph analyses pointing to certain nonlinear relationships
between the potential explanatory variables and Non_acad (our dependent variable), we
estimate of a polynomial function based on the variables proposed in previous studies, in
particular by Baltaru and Soysal (2018), and on the relevant variables that were available in
our databases.
The following model is proposed:
where i is the individual university, c is either the country or state in the case of the Ameri-
can sample and t is the time. The dependent variable is the share of non-academic staff
to total staff (Non_acad), while the independent variables include the number of students
enrolled (Stud), the foundation year of an institution (YearFound), indication whether the
institution is a private one (Private), the number of publications per academic staff (Publ_
Acad), the share of non-personnel expenditure to total expenditure (Non_personal), the
share of core revenues (Core_budget) and the share of third-party revenues (Third_party)
to their totals.
(1)
Non
_acadict =𝛼+𝛽1Studit +𝛽2(Studit )
2
+𝛽3YearFoundi+𝛽4(YearFoundi)2+𝛽5Privatei+𝛽6Publ_Acadit +𝛽7Non_personal
it
+𝛽
8
(
Non
_
personalit
)2+𝛽
9Core
_
budgetit
+𝛽
10
(
Core
_
budgetit
)2+𝛽
11Third
_
partyit
+𝛽
12
(
Third
_
partyit
)2+
Dc
+
Dt
+𝜖
ict
22 It would be very difficult to merge the two datasets and treat them as a unique dataset due to definitional
and comparability problems between the variables.
23 In the case of European HEIs, there are some outliers for third-party funding with extremely high values,
e.g. third-party funding being higher than 90% (see also statistics in Table2). To check the robustness of
our results and to be sure that the results of our empirical analysis are not driven by those outliers, we rerun
estimations of the analyses without those observations. We find that the results still hold. See TableS10 in
the supplementary materials.
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The data available refer to a short panel that covers 6years of observations, meaning
that we can only estimate a pooled model with country (or state) and time effects.24 More
specifically, the independent variables include both time-variant and time-invariant regres-
sors; the latter include the foundation year and type of institution (private versus pubic).
For this reason, we cannot use a fixed-effects estimation method; otherwise, the time-invar-
iant regressors would be dropped. Hence, in this paper, we consider individual effects for
time (in order to gauge time-specific trends) represented by variable Dt and country (or
state in the US sample case) effects represented by variable Dc in order to capture country/
state specific characteristics (e.g. institutional factors of higher education systems). With
this modelling strategy, our model can include all the variables in which we are interested.
In addition, we employ a robust variance estimator in order to take into account the pos-
sible presence of heteroscedasticity. Finally, we calculate partial correlations among the
independent variables to find if the regressors are independent among them—as they
should be—in order to avoid the problem of multicollinearity that originates from cor-
related covariates and can affect the estimate reliability. Tables6 and 7 in the Appendix
include the partial correlations calculated for the European and US samples, respectively.25
Considering data heterogeneity and to check if the European and American institutions
differ in relation to the determinants of Non_acad, we run Eq.1 separately for these two
groups of institutions.
Results
The results of the nonlinear regression analyses carried out in relation to the European and
American samples are presented in Table3. Here, the results of the final regression with
all covariates are given, while the Appendix (Tables8 and 9) includes different versions of
the specification, starting from a slimmer regression (with fewer right-hand variables and
without financial variables) separately for the European and US HEIs. It should be noted
that when additional variables are added, the number of observations in the European sam-
ple decreases, with the final specification being then estimated for HEIs from 20 (out of 26)
European countries.
The results can also be illustrated with plots showing the predicted values of the depend-
ent variable (Non_acad) at specified values of covariates and with plots of marginal effects
24 Following the suggestion of a reviewer, we attempted to estimate a dynamic model with a lagged
dependent variable using an appropriate two-step generalised method of moments (GMM). For an intro-
duction and more details on GMM, see e.g. Mátyás etal. (1999). However, it turned out that the intro-
duced lagged dependent variable, which was highly significant, absorbed all the effects of the independent
variables in which we are interested. It is most likely that the introduced lagged value of the share of non-
academic staff to total staff captured the effects of past independent variables mainly due to a very per-
sistent dependent variable. This is a well-known problem in the literature when autoregressive parameters
dominate regression and swap other independent variables (see e.g. Keele and Kelly 2006). This problem is
also aggravated by (i) a very persistent dependent variable (see Table5 in the Appendix), (ii) the short time
period of our analysis and (iii) a rather demanding structure of the polynomial model. Additional details
and the obtained results are available from the authors upon request. Even though we do not possess data
covering a sufficient number of years to carry out dynamic analysis yet, it would be an interesting extension
of the study when additional data will be collected and available.
25 To tackle possible structural multicollinearity that arises in models that include interaction terms, we
also run estimations with mean-centred continuous regressors. The results are presented in TableS11 of the
supplementary materials.
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0
.2 .4 .6 .8
050000100000
Students
.2 .4 .6 .8
0 .2 .4 .6 .8
Non-personal expenditure
.2 .4 .6 .8
0 .2 .4 .6 .8 1
Core budget
0.2 .4 .6 .8
0 .2 .4 .6 .8 1
Third party funding
0.2 .4 .6 .8
1000 12001400160018002000
Foundation year
0.2 .4 .6 .8
0 2 4 6 8
Publications per academic staff
Europe
.2 .4 .6 .8 1
050000100000
Students
.2 .4 .6 .8 1
.3 .4 .5 .6 .7 .8
Non-personal expenditure
.2 .4 .6 .8 1
0 .2 .4 .6 .8 1
Core public budget
.2 .4 .6 .8 1
0 .2 .4 .6
Third party funding
.2 .4 .6 .8 1
1600 1700 1800 1900 2000
Foundation year
.2 .4 .6 .8 1
0 2 4 6 8
Publications per academic staff
USA
Fig. 2 Proportion of non-academic staff to total staff (on the y-axes) versus the analysed variables (the num-
ber of students, non-personnel expenditure, core budget, third-party funding, foundation year, publications
per academic staff). Upper panel, Europe; lower panel, USA. Source: authors’ own elaboration based on
ETER and IPEDS
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of covariates. For example, the marginal effect of Non-personnel expenditures
(
Non_personal)
on the proportion of non-academic staff (y) is calculated by computing the
first derivative of y in Eq.1 that corresponds to
𝜕Non_acad
𝜕Non_personal
=𝛽7+2𝛽8Non_
personal
. Fig-
ure3 presents the plots of predicted Non_acad, while Fig.4 shows respective marginal
effects illustrating the results from Table3.
The analysis points to a number of similarities between Europe and the USA, but also
several noteworthy differences. The results reveal that larger universities—with more stu-
dents—have a higher proportion of non-academic staff, and for European universities, this
trend begins with a negative starting-point relationship, which is quickly reversed. The year
of foundation shows an inverted U-shape relationship, which is true for both the European
and US samples—the youngest and oldest universities are characterised by a lower share
of non-academic staff. Similarly, for the European and American HEIs, private institutions
have a lower share of non-academic staff, while HEIs with a higher share of non-academic
staff boast a higher number of publications per academic staff. Turning to the potential
Table 3 Determinants of Non_
acad (dependent variable: ratio
of non-academic staff to total
staff), European and US samples
*p < 0.10, **p < 0.05, ***p < 0.01, country (European sample)/state
(US sample) and time fixed effects included (not reported). Robust
standard errors.
Europe USA
(1) (2)
Studentsit − 0.008*** − 0.004
[0.003] [0.003]
Studentsit20.001*** 0.001**
[0.000] [0.000]
YearFoundi0.067*** 0.356***
[0.012] [0.095]
YearFoundi 2 − 0.002*** − 0.010***
[0.000] [0.003]
Privatei − 0.028** − 0.035***
[0.014] [0.010]
Publ_Acadit 0.024*** 0.052***
[0.004] [0.004]
Non_personalit 0.922*** − 0.39
[0.136] [0.412]
Non_personalit2 − 1.243*** 0.343
[0.184] [0.390]
Core_budgetit − 0.100** − 0.173***
[0.047] [0.048]
Core_budgetit20.06 0.177***
[0.047] [0.058]
Third_ partyit − 0.111*** 0.177***
[0.037] [0.045]
Third_partyit20.088 − 0.489***
[0.057] [0.101]
N2570 2042
R20.46 0.43
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impact of the funding structure on the share of non-academic staff, there are some interest-
ing results. For the European sample, an inverted U-shape relationship between the share
of non-personnel expenditure and Non_acad can be observed. However, this is not true for
the American institutions, for which the relationship is not statistically significant. Further-
more, in the case of the European sample, there is a negative relationship between the share
of core funding, while for the American universities, after the initial negative relationship,
there is a rebound in the other direction and the nonlinear relationship is confirmed. In
contrast, the third-party budget is negatively correlated with the share of non-academic
staff in the European HEIs, while in the case of the American sample, the relationship is
nonlinear—positive at the beginning and negative for higher shares of third-party budget.
The robustness of our findings was verified in a number of ways. Firstly, to prevent
potential endogeneity problems, we run regressions analogous to Eq.(1), in which we con-
sidered time-varying independent variables, now including these variables as lagged vari-
ables (called lag students, lag Publ_Acad, lag financial variables). The obtained results are
presented in TablesS2 (for Europe) and S3 (for the USA) included in the supplementary
materials. They generally confirm the aforementioned correlations between the specified
variables and the share of non-academic staff. Secondly, we verify the robustness of our
findings by adding some extra covariates such as a dummy indicating whether the univer-
sity has an hospital or whether it is a multisite institution and the number of EU-FP par-
ticipation (for the European HEIs). In all of these cases, the main findings still hold. In the
next step, we rerun the estimation using samples to which different outlier detection meth-
ods were applied, and no significant impact on the final results is observed. The obtained
results are available as supplementary materials (see TablesS8 and S9). For the European
HEIs, we also confirmed the results considering the model specification in relation to the
third-party funding variable (with and without its square term) and excluding observations
with extremely high values of Third partyit (TableS10 in the supplementary materials)
from the sample. To tackle possible structural multicollinearity that arises in models that
include interaction terms, we also ran estimations with mean-centred continuous regressors
(TableS11 in the supplementary materials).
Discussion andfurther developments
In this work, we attempted to address, with an empirical analysis, a stylised fact observed
in many countries of the world: the increase in the number of non-academic staff within
higher education institutions. We are aware of the limitations of empirical analyses on this
subject linked to the complexity of non-academic staff and the roles that they play in uni-
versities. There are, in fact, very few empirical studies on this issue, which is not surprising
taking into account the limitation of data available on the subject.
The potential determinants of the share of non-academic staff considered in the quan-
titative analysis include the number of students enrolled (as a proxy of size of the insti-
tution), year of foundation of institution (age of the unit), publication per academic staff
member (illustrating university’s research profile) and the share of non-personnel expendi-
ture and budget structure (the share of third-party and core revenues). The choice of the
potential determinants was driven by the theoretical background provided in the “Relevant
literature” section and by the availability of data described in the “Data and descriptive
analysis” section. We perform the comparative analysis of European versus American
HEIs, which is a different approach than that in previous studies. The comparison between
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Higher Education
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the two systems reveals that we can expect a higher proportion of non-academic staff in
both HEI systems with a sufficiently large number of students. Similarly, the number of
publications per academic staff considered a proxy of university’s scientific production and
Fig. 3 Plots of predicted Non_acad at specific values of covariates for Europe and USA. Notes: Predicted
y – predicted Non_acad, based on the results from Table3. Source: authors’ own elaboration based on data
from ETER and IPEDS
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contribution to university rankings and prestige might be a determinant of the proportion
of non-academic staff because more active (from a scientific point of view) academic staff
might require higher technical and administrative support for handling laboratories and an
Fig. 4 Plots of marginal effects illustrating the results from Table3 for Europe and USA. Source: authors
own elaboration based on data from ETER and IPEDS
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easier publication process. On the contrary, a lower share of non-academic staff in Europe
and the USA can be observed for private HEIs that can capture the impact of organisa-
tional and governance differences between public/private units. In the European case, the
nonlinear relationship between the share of non-personnel expenditure and the incidence
of non-academic staff is confirmed. This result can be a sign that several non-core activi-
ties previously performed by non-academic staff are outsourced and conducted outside the
institution. At the same time, it should be noted that there is a possibility that previous lev-
els of administrative staff may be a confounding variable and that further research should
take this into account.
In both systems (Europe and the USA), it can be observed that the share of non-
academic staff reduces when the share of core revenues raises and is sufficiently low,
while in the case of the American sample, it increases with the share of core revenues
if the latter is sufficiently high. These nonlinear relationships might suggest that there is
a certain (superiorly bounded) scale effect, in the sense that low values of the share of
core revenues provide HEIs with incentives to increase their organisational efficiency,
while for high values of the share of core revenues, these incentives could be jeopard-
ised. Finally, the relationship between the share of third-party revenues and the share
of non-academic staff is different for the European and American HEIs. In Europe, the
share of non-academic staff goes down as the share of third-party revenues increases.
However, in the case of the American sample, it is true only for higher shares of third-
party budget.
The increase in third-party funding that decreases the share of non-academic staff in
Europe is a problematic result to interpret. This is because we would expect that as third-
party funding increases, universities will increase their involvement in third mission activi-
ties and would therefore require more technical and administrative support. Over time, this
could also lead to an increase in academic staff to support third mission activities involving
local councils, industry and other sponsors.
A first rough interpretation of this evidence could be that there is a substantial differ-
ence between European and American universities. In Europe, the Humboldtian model of
university devoted mainly to teaching and research may still be the prevailing one. On the
contrary, in the USA, the universities are more innovative and have adopted a more wide-
spread entrepreneurial model, through which they are better able to be active in the sphere
of third mission activities. This could explain the observed differences, although a more
in-depth analysis would be required to more accurately interpret the results obtained and
this is left to future work.
Third-party revenues may include different lines of funds, such as due to projects com-
missioned by public agencies or private companies as well as due to large projects involv-
ing several universities or to small ones. Therefore, the overall impact of the third-party
revenues on the non-academic staff may be complex to analyse and would require much
more unbundled data to be study in-depth. For instance, when new researchers are hired
and they activate new research projects, the impact on non-academic staff may be very
different. On one hand, in the case projects are not too big and their duration is short, non-
academic personnel of the department may be asked to work overtime (instead of assuming
new people), or temporary office workers are paid to provide technical and administrative
assistance for the projects, or outsourced professional services are purchased to manage
projects. In all these cases, the share of third-party budget raises while the share of non-
academic staff reduces (as the measured non-academic staff essentially remains constant).
On the other hand, in the case whereby projects are big and their duration is years long,
additional non-academic units could be hired. The different mix of research projects and
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different strategies in terms of insourcing or outsourcing activities to manage projects
could also partially explain the difference among European and American HEIs.
Our analysis focuses on the evaluation of the potential determinants of the share of
non-academic staff in HEIs, but the presented results serve as a clear indication that some
determinants of the incidence of non-academic staff, which include size, prestige, year of
foundation and financial structure of the universities, are usually considered to be perfor-
mance measures for HEIs. This evidence may provide a motivation for investigating the
relationship between non-academic staff and university performance. However, there are
conceptual, methodological and critical data problems to be addressed with regard to this
further relevant issue (mainly oversimplifying academic versus non-academic personnel
data representation in HEIs).
The impact of non-academic staff on university performance may be difficult to evalu-
ate due to the participation of the faculty in the complex decision-making process in
universities. For instance, McCormick and Meiners (1988) find that involving adminis-
trative personnel in the decision-making process is usually associated with better univer-
sity performance, while faculty participation may lead to low-quality decisions. Kaplan
(2004) observes that faculty participation in academic issues yields decisions which are
not beneficial to the university, being made in the interest of faculty members instead.
Brown (2001) carries out more in-depth analyses and finds that faculty participation
in the academic decision-making process may lead to high-quality decisions if finan-
cial decisions and day-to-day management are under the exclusive control of adminis-
trative personnel. In particular, Cunningham (2009) reveals that faculty participation in
academic decisions is correlated with better performance only when it concerns faculty
personnel and student matters. Brown (2014) analyses a potential relationship between
university board characteristics (an important segment of non-academic staff) and uni-
versity performance through quantitative data related to the US universities. On one side,
Brown’s findings confirm that the size and composition of the board are affected by uni-
versity size, religious affiliation and university type (liberal arts colleges versus institu-
tions that offer doctoral degrees). On the other side, the results show that a larger size of
the board and a higher fraction of the board chosen directly by alumni have a positive
impact on university performance.
The results obtained in this paper point to the presence of several nonlinear relation-
ships and for this reason, we think that further research should employ nonparametric
methods (Daraio, 2018) to accomplish the difficult task of modelling the impact of
non-academic staff on performance. In addition, investing in European data appears
timely. The collection and integration of more detailed information on the composition
of non-academic staff in European HEIs—taking into account the information avail-
able for the USA—could be another step towards a better understanding of both the
determinants of the share of non-academic staff and the complex relationship existing
between non-academic staff and university performance. With the results obtained in
this paper, we can surmise that the determinants of the share of non-academic staff
vary depending on the role that different non-academic staff categories play in the per-
formance of HEIs.
An interesting extension of this piece of research could be the estimation of dynamic
models including the lagged dependent variable given that previous levels of non-aca-
demic staff may drive subsequent levels. The investigation of this relationship would be of
key interest for HE researchers in the field. At present, longitudinal data available do not
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Higher Education
1 3
provide us with a sufficient number of years to carry out such analysis, so future research
will be required when additional data will be available.
Finally, in future studies, it will be interesting to analyse the impact of COVID-19 on
the functioning of HEIs. The COVID-19 pandemic combined with introduced restrictions
and lockdowns affected the functioning of HEIs in many aspects such as closing units,
moving teaching and learning online and slowing down internationalisation and introduc-
tion of working from home for their staff (Marinoni etal., 2020). Additionally, in view of
the possible budget cuts (Blankenberger & Williams, 2020) not only in relation to public
funds but also private revenues (e.g. drop in tuition fees), HEIs, in the long run, will have
to adjust to a new financial constraint also with a possible restructuration of the employ-
ment structure (e.g. Burki, 2020 reports for HEIs in the UK that the most unfavourable
situation is for short-term contracted staff and PhDs whose funding is precarious). Need-
less to say, it would be very interesting to verify how COVID-19 impacts not only the
model of work (teaching and learning), but also other aspects of HEI functioning such as
the structure of employment in HEIs and possible pressure on employment cuts, on which
there is not much evidence yet.26
Concluding remarks
Our investigation focuses on a neglected topic, i.e. the determining factors concern-
ing non-academic staff of HEIs. Considering the new public management accountancy
needs and the scarce resources of universities everywhere, this is a very important sub-
ject from a policy-making point of view, on which many more quantitative and sys-
tematic analyses are needed. Building and extending the existing scant literature on the
topic, we examine two unique datasets to provide updated empirical evidence and an
indirect comparative analysis on the structure and heterogeneity of HEIs in Europe and
the USA. Our analyses shed some light on the determinants of the share (incidence) of
non-academic staff, which include size, prestige, year of foundation and the financial
structure of the analysed HEIs. Interestingly, these determinants are typically consid-
ered proxies of HEI performance. The empirical results reported in this paper can there-
fore be considered the first step towards a more extended analysis to try to understand
the relationship existing between non-academic staff and university performance. To
address this relevant research question, conceptual, methodological and data problems
should be analysed further and deeper. In this paper, we provide some updated empiri-
cal evidence that offers certain insights to facilitate thorough investigations and further
research into the matter at hand.
An interesting extension of this piece of research could be the estimation of dynamic
models including the lagged dependent variable given that previous levels of non-academic
staff may drive subsequent levels. The investigation of this relationship would be of key
interest for HE researchers in the field. At present, longitudinal data available do not pro-
vide us with a sufficient number of years to carry out such analysis, so future research will
be required when additional data will be available.
26 Changes at the level of individual HEIs will also depend on how the governments of different countries
have responded to the COVID-19 crisis in terms of university funding.
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Higher Education
1 3
Appendix
Table 4 The final European
sample—the number of
universities by country
Source: own elaboration based on ETER data.
Country 2011 2012 2013 2014 2015 2016 Total
AT 19 19 19 19 19 19 114
BE 66666636
BG 11 11 11 11 11 11 66
CH 12 12 12 12 12 12 72
CY 1111116
CZ 22 22 22 22 22 22 132
DE 84 84 84 84 84 84 504
DK 88888848
EE 44444424
ES 69 69 69 69 69 69 414
FR 62 62 62 62 62 62 372
GR 18 18 18 18 18 18 108
HR 55555530
IE 77777742
IS 22222212
IT 69 69 69 69 69 69 414
LI 1111116
LT 12 12 12 12 12 12 72
MT 1111116
NL 13 13 13 13 13 13 78
NO 88888848
PL 60 60 60 60 60 60 360
PT 17 17 17 17 17 17 102
SE 27 27 27 27 27 27 162
SK 18 18 18 18 18 18 108
UK 119 119 119 119 119 119 714
Total 675 675 675 675 675 675 4050
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Higher Education
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Table 5 The share of non-
academic staff to total staff
across countries over years,
country-average
Source: own elaboration based on data from ETER.
Country 2011 2012 2013 2014 2015 2016
AT 0.37 0.39 0.38 0.38 0.36 0.37
BE 0.30 0.30 0.29 0.29 0.30 0.30
BG 0.38 0.38 0.38 0.41 0.41 0.41
CH 0.32 0.32 0.32 0.32 0.32 0.32
CY 0.59 0.57 0.57 0.58 0.58 0.56
CZ 0.42 0.43 0.44 0.44 0.46 0.46
DE 0.47 0.47 0.47 0.47 0.47 0.47
DK 0.43 0.42 0.40 0.38 0.37 0.36
EE 0.42 0.42 0.42 0.43 0.46 0.47
ES 0.41 0.41 0.41 0.41 0.39 0.40
FR 0.54 0.53 0.52 0.53 0.52 0.53
GR 0.24 0.22 0.20 0.20 0.17 0.16
HR 0.39 0.41 0.42 0.43 0.43 0.44
IE 0.46 0.45 0.47 0.45 0.46 0.45
IS 0.76 0.76 0.76 0.77 0.79 0.80
IT 0.37 0.37 0.38 0.39 0.39 0.38
LI 0.47 0.51 0.54 0.44 0.45 0.46
LT 0.51 0.53 0.53 0.53 0.52 0.52
MT 0.49 0.47 0.46 0.44 0.44 0.42
NL 0.42 0.42 0.42 0.41 0.41 0.41
NO 0.41 0.42 0.42 0.42 0.42 0.41
PL 0.45 0.45 0.45 0.45 0.45 0.44
PT 0.46 0.46 0.46 0.45 0.46 0.45
SE 0.42 0.42 0.42 0.42 0.41 0.41
SK 0.46 0.47 0.46 0.46 0.46 0.46
UK 0.55 0.54 0.54 0.53 0.53 0.53
Mean 0.45 0.45 0.45 0.45 0.45 0.45
Table 6 Partial correlation of covariates used in the analysis—European sample
Source: own elaboration based on data from ETER.
Studentsit YearFoundiPrivateiPubl_Acadit Non_personalit Core budgetit
Studentsit 1.000
YearFoundi − 0.437 1.000
Privatei − 0.269 0.166 1.000
Publ_Acadit 0.247 − 0.211 − 0.157 1.000
Non_personalit − 0.051 0.007 0.250 − 0.082 1.000
Core_budgetit 0.096 − 0.032 − 0.359 − 0.031 − 0.259 1.000
Third_partyit − 0.022 − 0.132 − 0.022 0.348 0.014 − 0.235
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Table 7 Partial correlation of variables used in the analysis—US sample
Source: own elaboration based on data from IPEDS.
Studentsit YearFoundiPrivateiPubl_Acadit Non_personalit Core budgetit
Studentsit 1.000
YearFoundi − 0.066 1.000
Privatei − 0.440 − 0.156 1.000
Publ_Acadit 0.281 − 0.364 − 0.051 1.000
Non_personalit 0.099 0.027 − 0.243 0.150 1.000
Core_budgetit 0.194 − 0.010 − 0.521 0.253 0.310 1.000
Third_partyit 0.266 − 0.195 − 0.314 0.577 0.043 0.115
Table 8 Determinants of Non_acad (the dependent variable: the ratio of non-academic staff to total staff),
European sample
*p < 0.10, **p < 0.05, ***p < 0.01, country and time fixed effects included (not reported). Robust standard
errors. Specifications (2)–(5), no data on BG, ES, GR, HR, IS; specifications (6)–(7), additionally, no data
on CZ.
Source: own elaboration based on data from ETER.
(1) (2) (3) (4) (5) (6) (7)
Studentsit − 0.008*** − 0.007** − 0.009*** − 0.007*** − 0.007*** − 0.007*** − 0.007**
[0.002] [0.003] [0.003] [0.003] [0.003] [0.003] [0.003]
Studentsit20.001*** 0.001*** 0.002*** 0.001*** 0.001*** 0.001*** 0.001***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
YearFoundi0.076*** 0.077*** 0.072*** 0.079*** 0.082*** 0.078*** 0.078***
[0.011] [0.012] [0.012] [0.012] [0.012] [0.012] [0.012]
YearFoundi 2 − 0.002*** − 0.002*** − 0.002*** − 0.003*** − 0.003*** − 0.002*** − 0.003***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Privatei − 0.008 − 0.015 − 0.007 − 0.024* − 0.031** − 0.015 − 0.017
[0.008] [0.013] [0.013] [0.014] [0.014] [0.013] [0.013]
Publ_Acadit 0.013*** 0.021*** 0.019*** 0.022*** 0.023*** 0.025*** 0.026***
[0.004] [0.004] [0.004] [0.004] [0.004] [0.004] [0.004]
Non_person-
alit
0.002 0.795***
[0.035] [0.126]
Non_person-
alit2 − 1.033***
[0.169]
Core_budgetit − 0.016 − 0.098**
[0.014] [0.043]
Core_
budgetit20.081**
[0.039]
Third_partyit − 0.036** − 0.074**
[0.018] [0.036]
Third_partyit20.061
[0.060]
N 4050 2742 2742 2902 2902 2710 2710
No countries 26 21 21 21 21 20 20
R2 0.47 0.43 0.44 0.43 0.43 0.44 0.44
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Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s10734- 022- 00819-7.
Acknowledgements We thank the editor and two anonymous referees for their many insightful comments
and suggestions, allowing us to improve the quality of the paper considerably. We would like also to thank
participants of the DIAG Seminar held at University of Rome, La Sapienza, on 9 Jan 2020, and Seminar
of Department of Economics, Gdańsk University of Technology, Poland (5 March 2020). Cinzia Daraio
acknowledges the support of the Research Infrastructure for and Innovation Policy Studies 2 (RISIS2) Pro-
ject, funded by the European Union’s Horizon 2020 Research and Innovation programme, award number:
Table 9 Determinants of Non_acad (the dependent variable: the ratio of non-academic staff to total staff),
US sample
*p < 0.10, **p < 0.05, ***p < 0.01, state and time fixed effects included (not reported). Robust standard
errors.
Source: own elaboration based on data from IPEDS.
(1) (2) (3) (4) (5) (6) (7)
Studentsit − 0.002 − 0.002 − 0.002 − 0.003 − 0.001 − 0.002 − 0.004
[0.003] [0.003] [0.003] [0.003] [0.003] [0.003] [0.003]
Studentsit20.001* 0.001* 0.001* 0.001** 0.001 0.001* 0.001**
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
YearFoundi0.330*** 0.310*** 0.310*** 0.313*** 0.295*** 0.351*** 0.373***
[0.100] [0.100] [0.100] [0.097] [0.098] [0.102] [0.097]
YearFoundi
2 − 0.009*** − 0.008*** − 0.008*** − 0.008*** − 0.008*** − 0.010*** − 0.010***
[0.003] [0.003] [0.003] [0.003] [0.003] [0.003] [0.003]
Privatei − 0.002 − 0.004 − 0.004 − 0.014** − 0.027*** − 0.004 − 0.001
[0.004] [0.004] [0.004] [0.007] [0.010] [0.004] [0.004]
Publ_Acadit 0.053*** 0.054*** 0.054*** 0.052*** 0.051*** 0.055*** 0.053***
[0.003] [0.003] [0.003] [0.003] [0.003] [0.004] [0.004]
Non_per-
sonalit
− 0.043 − 0.047
[0.032] [0.333]
Non_per-
sonalit20.005
[0.309]
Core_
budgetit
− 0.029* − 0.119**
[0.015] [0.048]
Core_
budgetit20.118**
[0.058]
Third_
partyit
− 0.018 0.166***
[0.020] [0.044]
Third_
partyit2 − 0.425***
[0.099]
N 2046 2042 2042 2042 2042 2042 2042
R2 0.41 0.41 0.41 0.41 0.41 0.41 0.42
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824091. Joanna Wolszczak-Derlacz acknowledges the support of the Polish National Agency for Academic
Exchange (NAWA) under the Bekker programme.
Author contribution All authors contributed to the study conception and design. JWD was responsible for
data collection, data inspection and descriptive statistics. The choice of the final sample of HEIs to further
analysis was decided by the consensus of all authors. An empirical analysis was conducted by JWD and AA.
Literature review was performed by AA. CD was responsible for the “Discussion and further developments
and “Concluding remarks” sections. The first draft of the manuscript was written by JWD and AA and was
considerable revised and enriched by CD.
Funding This work is supported by RISIS2 (Research Infrastructure for and Innovation Policy Studies 2),
funded by the European Union’s Horizon 2020 Research and innovation programme under the grant number
n°824091. Joanna Wolszczak-Derlacz acknowledges the support of the Polish National Agency for Aca-
demic Exchange (NAWA) under the Bekker programme.
Data availability Data on European and US HEIs come from openly available sources: European Tertiary
Education Register project (https:// eter- proje ct. com/) and Integrated Postsecondary Education Dataset
(https:// nces. ed. gov/ ipeds/). The bibliometric data can be obtained for research purposes from the Centre
for Science and Technology Studies (CWTS) at University of Leiden through the EU-FP RISIS2 project
(https:// rcf. risis2. eu/ datas ets).
Code availability Upon request.
Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
References
Aghion, P., Dewatripont, M., Hoxby, C., Mas-Colell, A., & Sapir, A. (2010). The governance and perfor-
mance of universities: Evidence from Europe and the US. Economic Policy, 25(61), 7–59. https://
doi. org/ 10. 1111/j. 1468- 0327. 2009. 00238.x
Anderson, G. (2008). Mapping academic resistance in the Managerial University. Organization, 15(2),
251–270. https:// doi. org/ 10. 1177/ 13505 08407 086583
Bacon, E. (2009). Do professional managers have a profession: The specialist/generic distinction
amongst higher education professional services staff. Perspectives: Policy and Practice in Higher
Education, 13(1), 11–17. https:// doi. org/ 10. 1080/ 13603 10080 25970 07
Baltaru, R. D. (2019). Do non-academic professionals enhance universities’ performance? Reputation
vs. organisation. Studies in Higher Education, 44(7), 1183–1196. https:// doi. org/ 10. 1080/ 03075
079. 2017. 14211 56
Baltaru, R. D. (2019b). Universities’ pursuit of inclusion and its effects on professional staff: The
case of the United Kingdom. Higher Education, 77(4), 641–656. https:// doi. org/ 10. 1007/
s10734- 018- 0293-7
Baltaru, R. D., & Soysal, Y. N. (2018). Administrators in higher education: Organizational expan-
sion in a transforming institution. Higher Education, 76(2), 213–229. https:// doi. org/ 10. 1007/
s10734- 017- 0204-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Higher Education
1 3
Blankenberger, B., & Williams, A. M. (2020). COVID and the impact on higher education: The essential
role of integrity and accountability. Administrative Theory & Praxis, 42(3), 404–423. https:// doi.
org/ 10. 1080/ 10841 806. 2020. 17719 07
Bleiklie, I., Enders, J., Lepori, B., & Musselin, C. (2011). New public management, network governance
and the university as a changing professional organization. In T. Christensen & P. Laegreid (Eds.),
The Ashgate Research Companion to New Public Management (pp. 161–176). Ashgate.
Bromley, P., & Meyer, J. W. (2014). “They are all organizations”: The cultural roots of blurring between
the nonprofit, business, and government sectors. Administration and Society, 49(7), 939–966.
https:// doi. org/ 10. 1177/ 00953 99714 548268
Brown, W. O., Jr. (2001). Faculty participation in university governance and the effects on university
performance. Journal of Economic Behavior and Organization, 44(2), 129–143. https:// doi. org/ 10.
1016/ S0167- 2681(00) 00136-0
Brown, W. O., Jr. (2014). University board composition: Causes and consequences. Managerial and
Decision Economics, 35(5), 318–336. https:// doi. org/ 10. 1002/ mde. 2618. 10. 1002/ mde. 2618
Bruni, R., Daraio, C., & Aureli, D. (2021). Imputation techniques for the reconstruction of missing
interconnected data from higher educational institutions. Knowledge-Based Systems, 212, 106512.
https:// doi. org/ 10. 1016/j. knosys. 2020. 106512
Burki, T. K. (2020). COVID-19: Consequences for higher education. The Lancet Oncology, 21(6), 758.
https:// doi. org/ 10. 1016/ S1470- 2045(20) 30287-4
Cunningham, B. M. (2009). Faculty: Thy administrator’s keeper? Some evidence. Economic of Educa-
tion Review, 28(4), 444–453.
Daraio, C. (2018). Nonparametric methods and higher education. In J. C. Shin & P. Teixeira (Eds.),
Encyclopedia of International Higher Education Systems and Institutions. Springer.
De Boer, H., Enders, J., & Schimank, U. (2007). On the way towards new public management? The Gov-
ernance of University Systems in England, the Netherlands, Austria, and Germany. In D. Jansen
(Ed.), New Forms of Governance in Research Organizations (pp. 137–152). Springer. https:// doi.
org/ 10. 1007/ 978-1- 4020- 5831-8_5
Deem, R., Hillyard, S., & Reed, M. (2007). Knowledge, higher education, and the new managerialism:
The changing management of UK universities. Oxford University Press.
ETER (2018) Implementing and Disseminating the European Tertiary Education Register. Handbook
for data collection. Retrieved June 2, 2020, from https:// www. eter- proje ct. com/ assets/ pdf/ ETER_
Handb ook_ runni ng. pdf
Gibb, A., Haskins G., Hannon P., & Robertson I. (2012). Leading the Entrepreneurial University. The
National Centre for Entrepreneurship in Education (NCEE). Retrieved May 7, 2021, from https://
core. ac. uk/ downl oad/ pdf/ 28828 7534. pdf.
Ginsberg, B. (2011). The Fall of the Faculty: The Rise of the All-Administrative University and Why It
Matters. Oxford University Press.
Gornitzka, Å., & Larsen, I. M. (2004). Towards professionalisation? Restructuring of administrative
work force in universities. Higher Education, 47(4), 455–471. https:// doi. org/ 10. 1023/B: HIGH.
00000 20870. 06667. f1
Graham, C. (2013). Changing technologies, changing identities. Perspectives: Policy and Practice in
Higher Education, 17(2), 62–70.
Grove, J. (2012). University manager numbers rising ’twice as fast as academics’. Times Higher Educa-
tion. Retrieved June 7, 2020, from https:// www. times highe reduc ation. com/ news/ unive rsity- manag
er- numbe rs- rising- twice- as- fast- as- acade mics/ 419229. artic le
Gumport, P. J., & Pusser, B. (1995). A case of bureaucratic accretion: Context and consequences. The
Journal of Higher Education, 66(5), 493–520. https:// doi. org/ 10. 2307/ 29439 34
Hansen, W. L., & Guidugli, T. F. (1990). Comparing faculty and employment gains for higher educa-
tion administrators and faculty members. Journal of Higher Education, 61(2), 142–159. https://
doi. org/ 10. 2307/ 19819 59
IPEDS (2020), Finance data dictionary. Retrieved May 15, 2021, from https:// nces. ed. gov/ ipeds/ datac enter/
DataF iles. aspx? goToR eport Id=7
Kallenberg, T. (2018). Shadows of hierarchy: Managerial-administrative relationships within universities
under pressure. In P. N. Teixeira, A. Veiga, M. J. Rosa, & A. Magalhães (Eds.), under pressure (pp.
77–89). Brill. https:// doi. org/ 10. 1163/ 97890 04398 481_ 006
Kaplan, G. E. (2004). Do governance structures matter? New Directions for Higher Education, 127, 23–34.
https:// doi. org/ 10. 1002/ he. 153
Keele, L., & Kelly, N. J. (2006). Dynamic models for dynamic theories: The ins and outs of lagged depend-
ent variables. Political Analysis, 14(2), 186–205. https:// doi. org/ 10. 1093/ pan/ mpj006
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Higher Education
1 3
Krücken, G., Blümel, A., & Kloke, K. (2013). The managerial turn in higher education? On the interplay
of organizational and occupational change in German academia. Minerva, 51(4), 417–442. https:// doi.
org/ 10. 1007/ s11024- 013- 9240-z
Krücken, G. (2011). A European Perspective on New Modes of University Governance and Actorhood. Cen-
tre for Studies in Higher Education: Research & Occasional Paper Series CSHE.17.1. Retrieved July
15, 2020, from http:// files. eric. ed. gov/ fullt ext/ ED529 727. pdf
Kwiek, M. (2018). Changing European academics: A comparative study of social stratification, work pat-
terns and research productivity. Routledge.
Lepori, B., Geuna, A., & Mira, A. (2019). Scientific output scales with resources. A comparison of US and
European universities. PloS one, 14(10), e0223415. https:// doi. org/ 10. 1371/ journ al. pone. 02234 15
Marinoni, G., Van’t Land, H., & Jensen, T. (2020). The impact of Covid-19 on higher education around
the world. IAU Global Survey Report. Retrieved April 23, 2021, from https:// www. iau- aiu. net/
IMG/ pdf/ iau_ covid 19_ and_ he_ survey_ report_ final_ may_ 2020. pdf
Martin, B. (2016). What’s happening to our universities? Prometheus, 34(1), 7–24. https:// doi. org/ 10.
1080/ 08109 028. 2016. 12221 23
Mátyás, L., Gourieroux, C., & Phillips, P. C. (1999). Generalized method of moments estimation. Cam-
bridge University Press.
McCormick, R. E., & Meiners, R. E. (1988). University governance: A property rights perspective.
Journal of Law and Economics, 31(2), 423–442. https:// doi. org/ 10. 1086/ 467163
Mcinnis, C. (1998). Academics and Professional Administrators in Australian Universities: Dissolving
boundaries and new tensions. Journal of Higher Education Policy and Management, 20(2), 161–
173. https:// doi. org/ 10. 1080/ 13600 80980 200204
Pollitt, C., & Bouckaert, G. (2004). Public management reform. A comparative analysis. Oxford Univer-
sity Press.
Pruvot, E. B., & Estermann, T. (2018). University governance: Autonomy, structures and inclusiveness.
In A. Curaj, L. Deca, & R. Pricopie (Eds.), European Higher Education Area: The Impact of Past
and Future Policies (pp. 619–638). Springer. https:// doi. org/ 10. 1007/ 978-3- 319- 77407-7_ 37
Ramirez, O. F., & Christensen, T. (2013). The formalization of the university: Rules, roots, and routes.
Higher Education, 65(6), 695–708. https:// doi. org/ 10. 1007/ s10734- 012- 9571-y
Rhoades, G., & Sporn, B. (2002). New models of management and shifting modes and costs of produc-
tion. Europe and the United States. Tertiary Education Management, 8(1), 3–28.
Sauder, M., & Espeland, W. N. (2009). The discipline of rankings: Tight coupling and organizational
change. American Sociological Review, 74(1), 63–82. https:// doi. org/ 10. 1177/ 00031 22409 07400
104
Sebalj, D., Holbrook, A., & Bourke, S. (2012). The rise of ‘professional staff’ and demise of the ‘non-aca-
demic’: A study of university staffing nomenclature preferences. Journal of Higher Education Policy
and Management, 34(5), 463–472. https:// doi. org/ 10. 1080/ 13600 80X. 2012. 715994
Smith, C., Holden, M., Yu, E., & Hanlon, P. (2021). ‘So what do you do?’: Third space professionals navi-
gating a Canadian university context. Journal of Higher Education Policy and Management, 43, 1–15.
https:// doi. org/ 10. 1080/ 13600 80X. 2021. 18845 13
Szekeres, J. (2004). The invisible workers. Journal of Higher Education Policy and Management, 26(1),
7–22. https:// doi. org/ 10. 1080/ 13600 80042 00018 2500
Szekeres, J. (2006). General Staff Experiences in the Corporate University. Journal of Higher Education
Policy and Management, 28(2), 133–145. https:// doi. org/ 10. 1080/ 13600 80060 07509 62
Szekeres, J. (2011). Professional staff carve out a new space. Journal of Higher Education Policy and Man-
agement, 33(6), 679–691. https:// doi. org/ 10. 1080/ 13600 80X. 2011. 621193
Tolofari, S. (2005). New public management and education. Policy Futures in Education, 3(1), 75–98.
https:// doi. org/ 10. 2304/ pfie. 2005.3. 1. 11
Veles, N., & Carter, M. A. (2016). Imagining a future: Changing the landscape for third space profession-
als in Australian higher education institutions. Journal of Higher Education Policy and Management,
38(5), 519–533. https:// doi. org/ 10. 1080/ 13600 80X. 2016. 11969 38
Whitchurch, C. (2006). Who do they think they are? The changing identities of professional administrators
and managers in UK higher education. Journal of Higher Education Policy and Management, 28(2),
159–171. https:// doi. org/ 10. 1080/ 13600 80060 07510 02
Whitchurch, C. (2007). The changing roles and identities of professional managers in UK higher education.
Perspectives, 11(2), 53–60. https:// doi. org/ 10. 1080/ 13603 10070 12590 22
Whitchurch, C. (2013). Reconstructing identities in higher education: The rise of third space professionals.
Routledge.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Higher Education
1 3
Whitchurch, C. (2017). Professional staff identities in higher education. In A. Teixeira & J. C. Shin (Eds.), Ency-
clopedia of International Higher Education Systems and Institutions. Springer. https:// doi. org/ 10. 1007/
978- 94- 017- 9553-1_ 302-1
Wolszczak-Derlacz, J. (2017). An evaluation and explanation of (in)efficiency in higher education institutions
in Europe and the U.S. with the application of two-stage semiparametric DEA. Research Policy, 46(9),
1595–1605. https:// doi. org/ 10. 1016/j. respol. 2017. 07. 010
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