Quantitative assessment for training of senior scientific staff

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Abstract


The human society since ancient times has questions “Whom to teach?”, “What to teach?”, “How long to teach?”. The answers to these questions are changing along with the structure of human society, the structure of the economy. Currently, the economy requires qualified staff with a good education, and progress requires new technological ideas, the generators of which are representatives of the scientific community. Staff training for the scientific community is a separate complex and spending task. An insufficient number of graduated scientific staff entails inhibition in innovative development, and an oversupply of such graduates is a waste of state money and causes problems for the career path of the graduates themselves.


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I. Introduction

The task of predicting the optimal number of scientific staff becomes relevant. The international scientific community concerns about the significant growth in the number of PhD graduate students [1]. In 2017, the European Science Foundation conducted a study of career paths of people who graduated with a PhD degree [2], in which took part about 23% of those who graduated with a degree in the last 6 years preceding the study. The survey showed that only a little more than a half of respondents work in the academic sector. Less than half of polled people work in non-academic sector and hold a position corresponding to their academic degree or at least a master's degree. The result of the study indicates a certain redundancy of trained specialists with academic degrees.

In Russia, from 2011 to 2018, 121 thousand of doctoral research scholars defended their thesis. Their average age is 32 years. At the same time, the number of researchers under the age of 40 reaches 23 thousand [3], and university workers – 24 thousand [4]. It turns out that less than a quarter of PhD graduates work in science and education, where their academic degree is in demand first of all.

Thus, the process of forecasting the demand for senior scientific staff is necessary for planning the number of enrollment to graduate school and graduation from it with the defense of a thesis.

Forecasting the needs for candidates and doctors of sciences [5] is based on the calculation of the annual additional needs, for the assessment of which it is necessary to know the amount of natural retirement of staff.

Depending on the forecasting models used, two approaches are used to assess the natural staff retirement.

II. Natural-age retirement during the labor flow

For dynamic models describing the labor flow, natural retirement includes two components: death during the performance of labor functions and retirement by age. To calculate the first component, one can use ageing factor obtained from official statistics on the annual age structure of the Russian population over the past 10 years (Table 1).

 

Table 1. Ageing factor

Age

Ageing factor

before 25

1

25-29

1

30-34

0,999963

35-39

0,997889

40-44

0,988042

45-49

0,964374

50-54

0,925329

55-59

0,869745

60-64

0,795848

65-69

0,709277

70-75

0,579284

 

Thus, among researchers up to 70 years old with a scientific degree of a candidate of science, up to 5 thousand people can leave up annually at working age, among researchers who have a scientific degree of a doctor of science – up to 2.7 thousand.

Candidates of science who have reached the age of 70 years and over reach a number of  10.8 thousand people, doctors of science – 9.4 thousand [3], of them at the age of 70 years – about 3.2 thousand candidates of science and 0.9 thousand doctors of science.

If we assume that reaching the age of 70 years is the reason for the retirement of the researcher worker, then annually the natural-age retirement of candidates of sciences will be about 8.5 thousand people, and doctors of sciences – about 3.6 thousand people.

We will carry out similar calculations for the higher education sector. The annual retirement, subjected to termination of employment at the age of 65, is equal to 11.8 thousand for candidates of science, 3.7 thousand for doctors of science.

III. The rotation coefficient

The second approach is associated with the calculation of the rotation coefficient, which shows the share of annually retired personnel of the senior scientific staff (candidates of science and doctors of science) in relation to their total number in the current year and is equal to the inverse ratio of the average duration of work of these staff. For its calculation it is necessary to know the age of the beginning of labor activity, which coincides with the age of the thesis defense, and the age of retirement. If we assume that the age of the retirement for a candidate of science is 65 years, and for a doctor of science - 70 years, then the rotation coefficients on average will be 0.031 and 0.043.

It is important to know that for different branches of science and different sectors of the economy, these values differ significantly. Table 2 shows the average age of defense of a candidate thesis in the context of groups of scientific specialties and places of work of doctoral research scholars.

 

Table 2. The average age of defense of a candidate thesis

Specialties

Universities

Academy of Sciences

Other Research Institutes, Industrial Enterprises

Other organizations

01.01.00 Mathematics

30

28

28

29

01.02.00 Mechanics

30

30

31

31

01.03.00 Astronomy

33

30

39

39

01.04.00 Physics

30

31

32

31

02.00.00 Chemistry

29

28

30

30

03.01.00 Physicochemical Biology

30

29

31

32

03.02.00 General biology

31

32

33

34

03.03.00 Physiology

31

30

33

33

05.01.00 Engineering geometry and computer graphics

31

 

34

31

05.02.00 Mechanical engineering

31

40

31

33

05.04.00 Power, metallurgy and chemical engineering

31

29

39

33

05.05.00 Transport, mining and construction engineering

31

29

30

34

05.07.00 Aircraft, rocket and space technology

30

 

33

35

05.08.00 Shipbuilding

34

31

31

33

05.09.00 Electrical Engineering

31

28

31

32

05.11.00 Instrument-making, metrology and information-measuring devices and systems

29

33

34

32

05.12.00 Radio engineering and communication

30

31

32

32

05.13.00 Computer Science, Computer Engineering and Control

30

30

30

31

05.14.00 Energetics

30

32

33

32

05.16.00 Metallurgy and materials science

30

32

32

35

05.17.00 Chemical Technology

29

32

34

32

05.18.00 Food Technology

31

29

34

34

05.19.00 Technology of materials and products of textile and light industry

31

 

29

35

05.20.00 Processes and machines for agroengineering systems

32

36

30

33

05.21.00 Technology, machinery and equipment for logging, forestry, wood processing and chemical processing of wood biomass

32

34

32

33

05.22.00 Transport

31

32

39

35

05.23.00 Sivil engineering and architecture

31

31

33

32

05.25.00 Documentary Information

35

36

34

38

05.26.00 Human Safety

32

30

36

35

05.27.00 Electronics

29

31

34

32

06.01.00 Agronomy

32

33

33

33

06.02.00 Veterinary and Zootechnics

31

32

33

35

06.03.00 Forestry

30

32

32

35

06.04.00 Fisheries

38

31

40

44

07.00.00 History and Archeology

34

33

39

33

08.00.00 Economy

32

31

33

32

09.00.00 Philosophy

35

34

43

35

10.01.00 Literature

33

37

36

32

10.02.00 Linguistics

33

34

33

31

12.00.00 Law

32

29

34

32

13.00.00 Pedagogy

38

40

44

39

14.01.00 Clinical medicine

33

32

33

35

14.02.00 Preventative medicine

35

30

37

40

14.03.00 Biomedical Sciences

33

32

34

37

14.04.00 Pharmacy

30

27

31

33

17.00.00 Art history

37

43

33

38

19.00.00 Psychology

34

32

36

35

22.00.00 Sociology

32

32

34

33

23.00.00 Political science

32

31

36

32

24.00.00 Cultural studies

36

39

34

36

25.00.00 Earth sciences

31

31

34

34

 

The youngest candidates of science are employees of research institutes engaged in research in the field of the group “01.01.00 Mathematics”. Researches from the group “13.00.00 Pedagogy” have the greatest age of thesis defense. Researches from the groups “09.00.00 Philosophy”, “06.04.00 Fisheries”, and “17.00.00 Art History” also have high age values.

Fig. 1 and 2 show the values of rotation coefficients for candidates and doctors of sciences in the context of groups of scientific specialties and sectors of the economy: universities, institutes of the Academy of Sciences, other research institutes, design bureaus, and other organizations.

 

Fig. 1. Values of the rotation coefficient for candidates of science

 

Fig. 2. The values of the rotation coefficient for doctors of science

 

With an equal age of graduation, the rotation coefficient is higher for those scientific specialties where the age of defense is higher. For doctors of sciences employed in universities, a high value of rotation coefficient is observed for scientific specialties from the group 05.08.00 Shipbuilding, for the Academy of Sciences – 05.17.00 Chemical Technology.

For applicants for a medical degree working in medical institutions, the defense of candidate thesis takes place at a more mature age (Table 3).

 

Table 3. Average age of defense of a thesis in medical specialties

Specialties

Medical institution

University

Academy of Sciences

14.01.00 Clinical medicine

35

32

32

14.02.00 Preventative medicine

41

35

30

14.03.00 Biomedical Sciences

37

33

32

 

As a result, the rotation coefficient for these senior scientific staff will be higher and will be equal to: 0.034, 0.042 and 0.036, respectively.

IV. Conclusion

To predict the needs of the economy for senior scientific staff, it is necessary to conduct quantitative assessments of the annual natural-age retirement of candidates and doctors of sciences, taking into account the specifics of scientific research and their place of work. The article presents quantitative calculations of these indicators for two types of models in the context of groups of scientific specialties and sectors of the economy.

About the authors

V. A. Gurtov

Petrozavodsk State University

Author for correspondence.
Email: vgurt@psu.karelia.ru

Russian Federation, Petrozavodsk

L. V. Shegoleva

Petrozavodsk State University

Email: schegoleva@petrsu.ru

Russian Federation, Petrozavodsk

G. I. Dmitriev

Saint Petersburg Electrotechnical University “LETI”

Email: sznmc@mail.ru

Russian Federation, St. Petersburg

References

  1. Cyranoski D., Gilbert N., Ledford H., Nayar A., Yahia M. The world is producing more PhDs than ever before. Is it time to stop? Nature. 2011. 472:276–279. doi: 10.1038/472276a
  2. Career Tracking Survey of Doctorate Holders. Project Report.
  3. Gohberg L.M., Ditkovskij K.A., D'yachenko E.L. and other. Indikatory nauki: 2019: statisticheskij sbornik. [Science Indicators: 2019: Statistical Digest]. Moscow. Publ. House of National Research University Higher School of Economics. 2019. 328 p. (in Russian).
  4. Bondarenko N.V., Gohberg L.M., Kovaleva N.V. and other. Indikatory obrazovaniya: 2018: statisticheskij sbornik [Education Indicators: 2018: Statistical Digest]. Moscow. Publ. House of National Research University Higher School of Economics. 2018. 400 p. (in Russian).
  5. Gurtov V.A., Shchegoleva L.V. Forecasting the Economic Need for Personnel with Higher Scientific Qualifications. Studies on Russian Economic Development. 2018. Vol. 29. No. 4. Рр. 415–422.

Supplementary files

Supplementary Files Action
1.
Fig. 1. Values of the rotation coefficient for candidates of science

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2.
Fig. 2. The values of the rotation coefficient for doctors of science

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Copyright (c) 1970 Gurtov V.A., Sheglova L.V., Dmitriev G.I.

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