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The relationship between academic seniority and scientific production at the organisational level

21/04/2023| By
Özgür Kadir Özgür Kadir Özer,
+ 1
Benedetto Benedetto Lepori

This study examines the relationship between academic seniority in rank and research productivity using a cross-national dataset covering 1,180 higher education organisations from 13 countries for 2011-2020. Our results, estimated using fixed-effect OLS models, show that organisational research productivity increases as the share of senior-level academic personnel rises. Increase in senior-level academic personnel also positively affects the impact of scientific publications.

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The relationship between academic seniority and scientific production at the organisational level

Özgür Kadir Özer*, Pedro Pineda** and Benedetto Lepori***



Science and Technology Policy Studies (TEKPOL), Middle East Technical University, Turkey



Department of Education, University of Bath, United Kingdom



Università della Svizzera Italiana, Faculty of Communication, Culture and Society, Switzerland

Abstract: This study examines the relationship between academic seniority in rank and research productivity using a cross-national dataset covering 1,180 higher education organisations from 13 countries for 2011-2020. Our results, estimated using fixed-effect OLS models, show that organisational research productivity increases as the share of senior-level academic personnel rises. The increase in senior-level academic personnel also positively affects the impact of scientific publications.

1. Introduction

Trends in academic employment are a contentious topic in academia and public policy. In a recent report, OECD highlights the rapid change in academic employment. According to the report, the “hyper-competitive” environment causes increases in short-term employment and academics in the lowest positions (OECD, 2021a). Groups of academics from several countries with different higher education histories, such as Germany, Denmark, Switzerland, the UK, and the US, have also voiced their discontent on that issue in recent years (#IchBinHanna, 2021; Petition to the Federal Assembly, 2021; University and College Union, 2017; Zieler, 2017; Camacho & Rhoads, 2015). Although there are some scholarly efforts to understand these trends in academic employment, they tend to be atheoretical or are mostly limited to national boundaries and unrepresentative samples of higher education (Clark, 1986; Ben-David & Zloczower, 1961; Dietz & Bozeman, 2005; Höhle & Teichler, 2013; Stromquist et al., 2007; Teichler et al., 2013; Waaijer et al., 2016; Banscherus et al., 2017; European Commission/Eurydice, 2017; Jongmanns, 2011; Kwiek, 2003). The literature needs empirical and theoretically based studies that shed light on these trends by comparing countries with different higher education systems. There is also a gap for studies that analyse the potential effects of the changes in academic employment. This study aims to respond to that need by examining the relationship between academic seniority in rank, considered a dimension of organisational characteristics of higher education institutes (HEIs), and scientific publication productivity.

The literature on research productivity tends to discuss academic seniority at the individual level. The concept is addressed with two related terms: age and academic rank. The literature is not clear about the relationship between age and research productivity. While some studies, such as Costas et al. (2010), Lissoni et al. (2011), and Abramo et al. (2016), show that age affects research performance negatively, some do not report any significant relationship between these two variables, such as Kyvik and Olsen (2008) and Carayol and Matt (2006), reported by Abramo et al. (2011).

On the other hand, the results of studies that consider seniority as academic rank or position are more evident. The literature indicates that a higher academic position generally brings more research and impact. Abramo et al.’s (2011) analysis of all faculty in the natural sciences, engineering, and medical sciences in Italian universities over 2004–2008 shows that full professors perform better than associate and assistant professors in publications and impact. A study by Abramo, D’Angelo, and Murgia (2016) analysing more than 11 thousand full professors in Italian universities shows that seniority positively affects research performance except in some fields. Baccini et al. (2014) also report a positive relationship between academic position and research productivity in their study concerning 942 permanent researchers from the University of Siena. Mishra and Smyth (2013) report that senior academics in Australian law schools are not more productive than junior academics in publishing top journals but produce publications with higher citations. Rørstad and Aksnes (2015) examine 12,403 personnel of Norwegian HEIs and show that professors are more prolific personnel in all fields analysed. Puuska (2010) also concludes that professors are the most productive in all disciplines by analysing 1,367 scholars from the University of Helsinki.

These results are not unexpected. Researchers who have spent more time in academia have had more opportunities to conduct research, build collaborations, enhance their social networks, and establish themselves as experts in their field. The main factors that may increase senior personnel's research productivity are summarised as follows: (i) Accumulating knowledge and experience through academic career help identify better research topics. (ii) Larger networks ease accessing resources, including graduate students who generally cite their supervisors. (iii) Senior personnel may spend less time on teaching-related activities than junior personnel. (iv) Senior personnel are more likely to lead research projects and be involved in many research projects simultaneously. (v) Senior positions are assigned to researchers who already have good publication activity (pre-selection effect) (Abramo et al., 2011, 2016; Mishra & Smyth, 2013; Puuska, 2010; Rørstad & Aksnes, 2015). On the other hand, the administrative tasks, which are generally assigned to senior personnel, and higher ‘publish or perish’ pressure on early career researchers are among the factors that may increase junior personnel’s research productivity (Mishra & Smyth, 2013).

By creating a dataset using cross-national data of 1,180 HEIs from 13 countries and analysing the data using regression models, we offer a new perspective to understand the relationship between academic employment and research productivity. The analysis was conducted at the organisational level.

2. Method and data

We estimated a fixed-effect OLS panel regression to examine the relationship between academic seniority and scientific publication production between 2011 and 2020. We used fixest package on R to estimate our models (Berge, 2018).

Our primary outcome variable is publication density, calculated by dividing the number of publications published in a calendar year by the total number of academic staff in HEI. We also used the share of publications among the most cited one percent and category normalised citation impact (CNCI) to estimate the effect of seniority on the publication impact, and the share of articles that contain one or more international co-authors to estimate the effect of seniority on international co-authorship. The data on scientific publications was retrieved from InCites in April 2023, which provides aggregated data at the organisational level. We limited our study to articles with up to 100 authors.

Our main independent variable is the share of senior-level academics over total academic personnel in a university. The data about academic ranks are from the statistics office of each country. We recoded national academic ranks according to the categories proposed by the OECD (2021b), except that we classified scientific assistants among junior staff. We also classified doctoral students employed by universities under the junior level. All numbers are full-time equivalent (FTE) except Norway and Italy. For Germany, France and Finland, we imputed FTE by changing unlabelled staff in a staff category. Then, we merged academic employment data with data from the European Tertiary Education Register (ETER) (2023) and IPEDS (2023). We focussed on Europe and the US because these are the two regions where we could collect relevant data for as many possible countries as possible under our rationale of searching for national and cross-national trends. The dataset includes 1,180 HEIs from 13 countries (Austria: 16, Denmark: 8, Germany: 96, Finland: 25, France: 67, Italy: 81, Norway: 25, Poland: 76, Portugal: 30, Sweden: 30, Switzerland:15, United Kingdom: 141, United States: 570).

We also included some variables in our models to control organisational characteristics by following Bonaccorsi, Belingheri, and Secondi (2021).

Since variables on scientific publication are our outcome variables, we needed to limit our study to HEIs on which Incites provides data. Also, we grouped HEIs in our dataset using the US Carnegie classification by following Lepori, Geuna, and Mira (2019). Because InCites does not provide data for a sufficient number of HEIs from other categories, we included only “doctoral universities” and “masters’ colleges and universities” in the analysis, which are defined HEIs with at least 20 ISCED 8 degrees in a year and HEIs with less than 20 ISCED8 degrees and at least 50 ISCED 8 degrees in a year, respectively.

Since publishing results from research takes time, we used one year lag between dependent and independent variables. All variables used in our estimations and their definitions are summarised in Table 1, correlations between variables are in Table 2, and descriptive statistics are in Table 3.

Table 1. Variables and definitions

Variable name Definition Source
Research productivity Publication density (pubdensity) Number of articles per academic in a calendar year (Square root) InCites
Research impact Share of Top1 articles (wos_top1_share) Percentage of publications in the top 1% based on citations by category, year, and document type InCites
Category Normalized Citation Impact (cnci) Citation impact (citations per paper) normalized for subject, year and document type InCites
Share of internationally co-authored articles (wos_international_share) Percentage of publications that have international co-authors InCites
Academic employment Share of senior level personnel (senior_share) Share of senior academic personnel in rank over total academic staff (Square root) National statistics offices
Organizational characteristics Size (totalstudents) Number of students enrolled (Log) ETER, IPEDS
PhD Awarding (phdawarding) (Dummy variable) Yes, if a university awarded any doctorate degrees between 2011-2020 ETER, IPEDS
Institutional age (age) (Dummy) Old, if a university established before 1946 ETER, IPEDS, ROR, university websites
Control of university (legalstatus) (Dummy) Public or Private (private and mostly privately funded universities) ETER, IPEDS
Teaching load (education_intensity) Number of undergraduate students per academic staff (sqrt) ETER, IPEDS
Budget per enrolled student (budget)

Total core budget per student for European universities

Total all revenues and other additions or Total revenues and investment return or Total revenues and investment return for universities per student from the US (Log)

Subject specialization of university in social sciences and humanities (ssh_rca) Share of bachelor and master graduates (ISCED 5 to 7) in the social sciences and humanities (SSH) normalized by the average share in the whole sample (Lepori, 2021) ETER, IPEDS

Table 2. Correlation between variables

Variables 1 2 3 4 5 6 7 8 9
pubdensity (1) 1 0.488 0.308 0.408 0.203 0.376 -0.232 -0.319 0.448
wos_cnci (2) 1 0.641 0.350 0.075 0.250 -0.0004 -0.235 0.247
wos_top1_share (3) 1 0.226 0.076 0.148 -0.012 -0.134 0.184
wos_international_share (4) 1 -0.165 0.224 -0.236 -0.286 -0.006
senior_share (5) 1 0.015 0.124 0.085 0.287
Totalstudents (6) 1 0.067 0.053 0.017
ssh_rca (7) 1 0.228 -0.096
education_intensity (8) 1 -0.222
budget (9) 1

Table 3. Descriptive statistics for variables

Variables Min. Max. Mean Std. Dev.
pubdensity 0.05 3.67 0.70 0.40
wos_cnci 0.00 12.48 1.11 0.44
wos_top1_share 0.00 50.00 1.53 1.87
wos_international_share 0.00 100.00 36.26 16.13
senior_share 0.00 0.91 0.47 0.12
totalstudents 71.00 168215.00 15281.40 12734.40
ssh_rca -1.00 0.77 0.00 0.39
education_intensity 0.00 18.04 3.89 1.21
budget 29.08 1584552.26 27699.90 65787.92
phdawarding* 1.00 2.00 1.83 0.38
age* 1.00 2.00 1.61 0.49

* Categorical (dummy) variables

3. Results

Table 4 presents the estimations of our models on the relationship between academic seniority in rank and publication productivity at the organisational level. We estimated seven models and added relevant variables to the equation stepwise. The coefficients of almost all variables are significant in all models and have the expected signs with some exceptions.

The results show a significant and positive relationship between academic seniority in rank and research productivity for all models estimated. Senior-level academics' share is the most influential factor in our models. As abovementioned, that is not surprising. An increase in the share of experienced researchers with relatively wider social networks creates more opportunities for scientific publications.

HEIs with more extensive financial means tend to produce more scientific publications. The positive and significant coefficients of the PhD awarding variable in Models 2 to 7 state that the likelihood of producing scientific articles is higher in HEIs that provide PhD programmes. The literature states that teaching load has a negative impact on research productivity. On the other hand, our estimates show a positive effect of education intensity measured by the undergraduate students/academic staff ratio on research productivity in Models 4,5 and 6. Nevertheless, the magnitudes and significance levels of the corresponding coefficients are questionable. The effect of HEI size measured by the number of students enrolled on research productivity is also arguable in the literature. Our estimates show a positive and significant relationship between these two variables. Models 5 and 6 indicate that public universities tend to have higher research productivity than privately controlled ones. That applies to the institutional age too. The research productivity of HEIs established before 1946 is likely higher than relatively new HEIs. Lastly, the universities with a larger share of social sciences and humanities graduates tend to produce fewer publications per academic staff, according to Model 7. It should be mentioned that adding the subject specialisation on social sciences and humanities in the estimation made the coefficients of education intensity and legal status insignificant.

We also estimated models to analyse the effect of academic seniority in rank on research impact. The results of our estimations are summarised in Table 5. The share of articles in the top 1% based on citations is the dependent variable in Model 1, category normalised citation impact in Model 2, and the share of articles with international co-authors in Model 3. Models 1 and 2 show that the impact of research measured by citation-related metrics rises as the share of senior-level academics increases in HEIs. While the size and financial resources positively affect the research impact, the teaching load has a negative effect. Model 3 in Table 5 indicates a positive and significant relationship between senior-level academics and international co-authorship.

Table 4. The effect of senior-level academic personnel share on research productivity with additional control variables

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Table 5. The effect of senior-level academic personnel share on research impact with additional control variables

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4. Discussion and concluding remarks

This study examined the relationship between research productivity and academic seniority in rank using a cross-national dataset. We aimed to contribute to the literature on the effect of changing trends in academic employment from a different perspective. In this regard, we carried out the analysis at the organisational level while the literature mainly addressed the issue at the individual level.

Our estimates confirm the literature about the relationship between academic seniority in rank and research productivity and impact. An increase in the share of senior-level academics in a HEI tends to enhance both the quantity and impact of scientific publications. Indeed, the share of senior academics in rank appeared as the most influential variable in our estimations. That underlines the role of experienced researchers in the scientific production of universities. The changes in academic employment towards favouring short-term employment and academics in the lowest positions over senior-level academics might adversely affect research productivity and impact.

Our study has some limitations. Our dataset allows a cross-national view of academic employment in universities. However, the recoding of data entails significant limitations in losing the level of detail that a single-country analysis may offer. Our re-categorisation of academic positions to allow comparison also brings different positions that are only partially equivalent because they may differ in status and working conditions. Another significant limitation of our study is the need for more standardisation for FTE. The lack of data we have for temporary and part-time positions for most of the countries we examine is related to the drawback on the comparatively of ranks. It should be indicated that the data on scientific articles is from InCites, which might bring biases, such as favouring some research fields and languages. Also, the study used aggregated data, while scientific disciplines have different publication behaviours.

Open science practices

The data on academic staff were extracted either from the websites of national statistics authorities or retrieved via correspondence with the employees of these authorities. All data about HEIs’ organisational characteristics are from openly available data sources, namely ETER and IPEDS. We employed InCites data, a subscription-based service of Clarivate, for bibliometric data since it provides aggregated data for HEIs analysed. Data curation was mainly carried out on R. We also estimated our models using a package named fixest on R.


We want to thank Christine Heisterberg, Daniel Wagner, Helena Wintgren, Kaja Wendt, and Elena Zafarana for supporting us in obtaining national statistics on academic staff.

Author contributions

Özgür K. Özer: Conceptualization, Data curation, Methodology, Formal analysis, Writing – original draft, Writing – review & editing.

Pedro Pineda: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing.

Benedetto Lepori: Conceptualization.

Competing interests

The authors state they have no competing interests.

Funding information

The authors state that no funding was involved in this study.


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