CC BY
3DOI: 10.29141/2658-5081-2023-24-2-5 EDN: GYZVCZ JEL classification: J24, 010, O12, O15, C81
Artem S. Shcherbakov State University of Vladimir named after Alexander
and Nikolay Stoletovs, Vladimir, Russia
Factual models for human capital assessment
Abstract. The socioeconomic development of society goes hand in hand with accumulation and enhancement of human capital, yet many of the issues concerning its assessment are still subject to research. At present, human capital is estimated with the help of abstract models that indirectly assess its possessors based on statistical and actual measurements. Such models are applicable at macro level and to a certain extent at meso level, though cannot be used at microlevel. By contrast, factual models assess directly the possessors of human capital and allow for individual economic, physiological, sociological, psychological indicators. The research aims to identify the prerequisites for designing and create a factual model for human capital assessment. Methodologically, the study rests on the human capital theory and labour economics. To examine the World Bank's statistics, the study applied a set of general scientific methods: comparative analysis, synthesis, induction and deduction, modelling, the mathematical method. The paper proposes a factual assessment model that allows digitizing the value of human capital, both general and special, based on a set of 56 indicators and sub-measurements. Its distinctive feature is a comprehensive consideration of all components of human capital: physiological, cognitive, social and emotional. The model puts emphasis on the measurement of social and emotional component with the use of a social psychological test and a comparative index of achievements and penalties received by an employee at a previous job. The results of applying the proposed model are consistent with the estimates obtained within the framework of generally accepted cost measurements, and can be used at the micro level both by individual businesses and households.
Keywords: human capital; knowledge-based economy; abstract models of assessment; factual models of assessment; employees; value of human capital.
Acknowledgments: the author expresses sincere gratitude to Prof. Muhammad Mubarik, rector of the College of Business Administration at the Karachi Institute of Management and Business (Pakistan) for providing access to the results of his research. The author thanks Prof. Irina Teslenko and Prof. Pavel Zakharov for recommendations on optimising the measurements of human capital and Associate-Prof. Vasily Krylov for discussing the mathematical part of the study.
For citation: Shcherbakov A. S. (2023). Factual models for human capital assessment. Journal of New Economy, vol. 24, no. 2, pp. 86-103. DOI: 10.29141/2658-5081-202324-2-5. EDN: GYZVCZ.
Article info: received January 20, 2023; received in revised form March 22, 2023; accepted March 28, 2023
Introduction
The growing importance of human capital (HC) as the key driving force behind the economic, technological and social development is beyond doubt [Akindinova et al., 2019; Biryukova et al., 2018]. As indicated in the target-oriented scenario for the development of the Russian economy until 2035 [Akindinova et al., 2019, p. 37], the contribution of HC and the dynamics of its components will lead to an increase in estimates of the human capital index from 0.76 to 0.95 by 2035. Such advances can expand the contribution of HC to an annual GDP increase up to 0.7-0.8 percentage points [Ibid., pp. 36-38], which will entail a rise in total factor productivity of at least 1.25 percentage points per year due to measures that boost competition and the quality of vocational education and reduce costs and non-market risks of doing business. If this trend persists, we will be witnessing a gain in productivity and earnings in the coming years.
The market value of a modern corporation is increasingly measured by the ability of the 'minds' it hires to create new ideas, goods, and services [Biryukova et al., 2018, p. 6]. Experts see HC as an opportunity to diversify the economy [Atangana, 2022, p. 66], and generally recognise it as the main source of competitive advantage and value creation for the enterprise [Chen, 2022, p. 3]. According to the World Bank, Russia has high-quality human capital, which allows counting on the potential growth of related indicators. As stated in the World Bank's 2020 report on human capital, the lowest human capital index (HCI) was recorded in the Central African Republic (0.29), followed by Pakistan (0.41). Among the leaders of the list were China (0.65), the Russian Federation (0.68), the USA (0.70), Germany (0.75), and Finland (0.80). Singapore topped the ranking with the HCI of 0.881.
Against this background, it is natural that the Government of the Russian Federation strives to enhance the share of human capital in the structure of national wealth, with a subsequent outstripping of the OECD countries. The Forecast of the long-term socioeconomic development of the Russian Federation for the period up to 2030 stipulates that the innovative nature of the economy's development will be ensured by higher government expenditure on HC as well as more significant spending
1 World Bank. (2020). The Human Capital Index 2020 update: Human capital in the time of COVID-19. Washington, DC. 229 p. DOI: 10.1596/978-1-4648-1552-2.
by citizens and organisations that are expected to grow to 13.6 % of GDP by 2030 (9.2 % in 2011)1.
At the same time, it is worth noting a number of controversial issues related to the assessment of human capital, which have not yet been resolved.
For example, Becker, Huselid and Ulrich rightly notice that "designing any new measurement system for intangible assets isn't easy - if it were, most companies would already have done it. Embracing this challenge takes time and a lot of careful thought" [Becker, Huselid, Ulrich, 2001, p. 34].
Mubarik, Chandran and Devadason argue that, despite the importance of human capital in the structure of income generation of modern organisations, it is still difficult to measure it and the proposals presented in the scientific literature are contradictory. In addition, attempts to construct an HC index are too simplistic [Mubarik, Chandran, Devadason, 2018, p. 606].
Voronov and co-authors emphasise that most of country development strategies recognise the importance of human capital and investment in it, however, the problems of its formation, development and use are considered in a rather declarative (fragmentary) manner [Voronov et al., 2020, p. 40].
Due to these challenges, the ability of economic entities to independently deal with assessment issues is becoming more and more in demand. The President of the Russian Federation Vladimir Putin notes: "Successful leadership of a region, industry, company in today's world requires a data-driven management model to be used. This means that the decision-making process is largely based not on intuition in the first place"2.
However, the current models of human capital assessment imply using actual and statistical data when performing calculations, i.e., information that is difficult to process. This does not allow evaluating HC at the micro and meso levels by administration efforts, and the paradigm of intuition-based management still exists within the framework of the HC assessment.
Moreover, these models are not focused on working with the possessor of human capital and, therefore, can be considered abstract [Shcherbakov, 2023]. To overcome this problem, it is necessary to concentrate on the development and popularisation of models of a new type, that is factual ones [Ibid., 2023]. Foreign researchers also stick to this point and advocate for flexibility when determining the indicators to measure human capital and avoid applying actual methods of assessment (in terms of organisation costs) that are less effective [Bullen, Eyler, 2010].
1 The forecast of the long-term socioeconomic development of the Russian Federation for the period up to 2030. http://www.consultant.ru/document/cons_doc_LAW_144190/. (In Russ.)
2 The official website of the RF President. Conference on Artificial Intelligence. November 24, 2022. http://www. kremlin.ru/events/president/news/69927/. (In Russ.)
Evaluating HC on the basis of the existing abstract models, such as the generally accepted Jorgenson-Fraumeni method, is a difficult economic and mathematical problem [Kapelyushnikov, 2012; Fraumeni, Christian, 2019; Mendoza, Borsi, Comim, 2022]. The growing role of human capital in the economy, intensifying competition, as well as such phenomena as a multivariate career and population aging require a prompt solution to the assessment issues and, consequently, control over this kind of capital.
The purpose of the study is to establish the prerequisites for the factual model for human capital assessment and provide its description taking into account the experience in using previous models.
To attain the purpose, the article aims to achieve the following objectives:
1) to identify the prerequisites for the emergence of a factual model for human capital assessment;
2) to discuss the substance of the model;
3) to suggest a method to classify the results of the model's application using a continuum of human capital assessment.
Methodologically, the study rests on the human capital theory and related theories of labour economics, economic psychology, and personality management. A set of theoretical and specific scientific methods was used, such as comparative analysis, synthesis, induction and deduction, modelling, and the mathematical method. The study is based both on classic works of Russian and foreign researchers, and on modern developments of practitioners and theorists, World Bank statistics and specialised literature on human capital theory and the knowledge economy.
Human capital assessment: A review of studies
The theory of human capital received scientific recognition in the middle of the 20th century thanks to works of American scientists Gary Becker, Theodore Schultz and many others. At the end of the 1990s, Jacob Mincer, based on the ideas of the founders of human capital theory Gary Becker, Charles Cobb, Paul Douglas, Robert Slow, etc., made a great contribution to the development of labour economics and proposed the Mincer equation.
In Russia, studies in the field of human capital and labour economics were pioneered by Goylo and Kapelyushnikov. At the turn of the 20th and 21st centuries, their ideas were further developed by Kritsky, Dobrynin, Dyatlov, Smirnov, Kurgansky, Simkina, Tuguskina.
The named researchers' works not only allow tracing the genesis of the human capital theory development, but also identifying trends that are expected to dominate in this scientific field in the nearest future.
When analysing statistical reports of international and Russian organisations, as well as empirical and theoretical research papers, we found that the transition from traditional models of human capital assessment to models of a new type is becoming more and more noticeable. The first signs of this shift were manifested in the statements by a number of scientists, for example, about the relationship between economics and psychology getting stronger [Zhuravlev, Poznyakov, 2004, p. 59], the need to apply the method of expert assessments when calculating the health capital based on medical reports, examinations, case records, etc. [Smirnov et al., 2005, p. 136], the opportunity to cover new measurement methods in the financial statements of the future [Bullen, Eyler, 2010].
At the current stage, we can highlight several models that promote the ideas of assessing human capital (factual) through the combination of classical indicators with indicators of a new type [Myasoedova, 2006; Mubarik, Chandran, Devadason, 2018; Voronov et al., 2020].
We believe that the main prerequisites for the formation of the above trend are phenomena, such as the growth of the median value of human capital in the structure of national wealth, the recognition of its multicomponent structure, and the lack of assessment models available to business entities. Consider the identified prerequisites in more detail.
Growth of the median value of human capital in the structure of national wealth.
According to Goldin, Robert Slow was the first researcher who drew attention to human capital in the structure of national wealth [Goldin, 2016, p. 56].
The assessment of human capital was repeatedly performed by Kapelyushnikov. He calculated the HC value using the Jorgenson-Fraumeni method and concluded that "for the period 2002-2010, Russia's human capital in real terms more than doubled, which implies an annual growth rate of about 10 percent" [Kapelyushnikov, 2012, p. 61] and that every Russian owns human assets in the average amount of about 6 million rubles [Ibid., p. 60].
The World Bank experts note that HC, measured as the current value of future incomes of the workforce, employed and self-employed, is the largest asset in all income groups accounting for 64 % of total wealth in 2018, which is 2 % higher than in 1995. Human capital remains the most important component of wealth. The share of produced capital decreased from 32 % to 31 %, and the share of renewable natural capital - from 4 % to 3 %, while the share of non-renewable natural capital increased from 2 % to 3 %.
While the share of HC in total wealth generally increases with development, it has declined in some countries, especially in China and non-OECD high-income
countries. According to the World Bank, this is due to population aging, slow salary growth and other factors such as technology1.
We assume that the above statistical data and empirical studies allow us to draw an affirmative conclusion regarding the median growth in the value of human capital. This trend is also reflected in the growing urge of the possessors of this capital to invest it more profitably. Another important aspect is the striving of organisations to learn to assess the existing HC using available tools in order to plan, organise, motivate and control it. The aforementioned aspirations can be satisfied by applying factual models for human capital assessment.
Recognition of the multicomponent structure of human capital. Even the fundamental human capital theory, not to mention the theory of multicomponents, was initially perceived by the academic society rather coldly. According to Goldin, one of the founders of human capital theory and her research advisor Gary Becker "hesitated to use the term 'human capital' in the title of his book and employed a long subtitle to guard against criticism" [Goldin, 2016, p. 57]. Later, this theory took its rightful place among other economic paradigms.
In Russia, the multicomponent structure of HC was recognised at the end of the 20th century following the works of Dobrynin, Smirnov, Kritsky, Simkina, and Tu-guskina. In its structure, the researchers identify the physiological, intellectual, labour and social components, while focusing on their correlation dynamics [Voronov et al., 2020, 2022].
Zaloznaya and Morgunov emphasise a certain peculiarity in human capital: the 'possessor' of value is an immediate personality, and their cultural level, education, motivation, attitudes, decisions and actions predetermine not only the intensity of human efforts and their transformation into a creative capital value, but any creative process [Zaloznaya, Morgunov, 2014, p. 76].
Skoblyakova et al. [2022, p. 610], when assessing the reproduction cycles of various types of human capital, distinguish between health, cultural, moral, labour and intellectual capital.
In order to examine how an increase in human capital investment affects economic growth, as well as childhood experience formation, labour discipline, and the current climate crisis, foreign researchers are increasingly using scientific works on sociology, economics and educational history in addition to census statistics and statistical yearbooks [Forsyth, 2023].
It is worth paying attention to the extremely popular works of the Nobel laureate James Heckman. In the article Some contributions of economics to the study of
1 The changing wealth of nations 2021: Managing assets for the future. (2021). Washington, DC. P. 460. DOI: 10.1596/978-1-4648-1590-4.
personality, the researcher and his co-authors write that currently there are several taxonomies applied by specialists for measuring cognitive and non-cognitive skills, these are IQ tests, the Big Five, the Big Three, the Big Nine, and the MPQ. Accordingly, "the search is on for a minimal set of skills required to characterise empirically based human differences" [Heckman, Jagielka, Kautz, 2019, pp. 4-5]. To further the previously declared ideas, Heckman and Zhou publish empirical studies on measuring knowledge with the help of testing [Heckman, Zhou, 2022] and the impact of early childhood stimulation trial on the physiological, cognitive, social and emotional components of HC [Walker et al., 2022].
The above facts show an increase in the number of tools for assessing human capital, which makes it possible to talk about a new type of models that will be using factual data obtained from an individual possessor, rather than actual economic indicators, as their core.
Lack of models for human capital assessment available to business entities. When classifying models for HC assessment, economists highlight that they generally rely on one of three approaches. According to Kapelyushnikov [2012, p. 9], the three approaches are:
1) indicator-based, which allows for various actual characteristics of human capital;
2) cost-based, which implies accounting for the costs associated with the formation of this capital;
3) income-based, which implies accounting for the income generated by human capital.
Foreign researchers differentiate between the following approaches [Abraham, Mallatt, 2022, p. 105]:
1) the indicator approach that uses simple measures such as average years of schooling or creates indices comprised of several measures;
2) the cost approach values investments in human capital based on education spending;
3) the income approach values these investments by looking forward to the increment to expected future earnings.
The mentioned classifications clearly demonstrate the dominance of abstract models for human capital assessment. The calculations mainly utilise actual indicators (population literacy rate; average years of training per person) or take into account human capital costs and income.
Some foreign economists offer models that apply abstract and factual indicators with an emphasis on the method of expert assessments and sociological surveys. One of such models is developed by Mubarik, Chandran and Devadason [2018] (see Figure).
Analytic hierarchy process (AHP) model of human capital1: HC represents overall human capital; A, B, C, and N represent the dimensions of HC; a, b, c, and n represent the sub-measurements of HC
When creating the model, the authors conducted a series of sociological surveys, and the data collected were used for grouping and prioritising sub-measurements of HC. The core groups of sub-measurements included such characteristics as education, experience, professional skills, relationships, personal attributes, stability, health, and compliance.
Of greatest interest for our purposes are non-standard sub-measurements of personal attributes based on sociological tests, which can be called factual, but not abstract. For example, these include satisfaction, engagement, collaboration, commitment, creativity, risk-taking, diversity, leadership, intelligence, etc.
The authors propose that to carry out such sub-measurements, "one can use close-ended questionnaire carrying at least three questions on each HC dimensions" [Mubarik, Chandran, Devadason, 2018, p. 616].
Business entities can apply Mubarik, Chandran, and Devadason's model to perform an independent assessment of human capital of employees at the micro and meso levels. Since it partially uses factual measurements, but not only actual and statistical data, it can be called a mixed model.
At the same time, the model does not take into account that the value of an organisation's human capital is influenced by external cultural and social capital. For example, the human potential index and the World Bank's Human Capital Index are not considered. Within a certain system, organisation depends on macroeconomic indicators that reflect the socioeconomic situation in the country. For instance, with limited access to health care and education institutions, the indices of human capital and potential will decrease, which will also lead to a fall in the value of organisation's human capital. Without such coefficients included in analysis, there is a risk of overestimating the data on HC value. Moreover, Mubarik, Chandran, and Devadason's model attaches great importance to the method of expert assessments, and the lack of clear boundaries in the description of sub-measurements can cause an increase in the ambivalence of indicators at various enterprises and distort the results obtained. We
1 Source: [Mubarik, Chandran, Devadason, 2018].
believe that the assessment potential in this case is unlocked insufficiently due to the lack of social psychological measurements affecting the labour economy.
Russian researchers have repeatedly proposed to measure human capital through factual indicators. According to Smirnov, health capital can be judged by medical examinations and reports, children's medical history, sick leave, expert estimates of life expectancy; cultural and moral capital - by school certificates and school characteristics; intellectual capital - using psychological methods. The moral aspect of behaviour is associated with co-workers, relatives, and individuals who maintain contact with a person and know him/her personally. Law enforcement bodies, such as juvenile delinquents' departments, keep files on people who have committed misdemeanours or crimes. Labour capital is confirmed by certificates and vocational education diplomas. Work experience is recorded in the employment record book, work achievements are indicated in the personal profile and letters of recommendation [Smirnov et al., 2005, p. 136].
Voronov et al. [2020] propose the following model for national welfare assessment, which includes human capital:
NW = f (HC) = f (PhC + LC + IC + SC - FhC), (1)
where f presents the functional dependence of NW on HC; NW is national welfare (gross domestic product); HC is human capital; PhC is physical component; LC is labour component; IC is intellectual component; SC is social component; FhC is fictitious human capital [Ibid., p. 54].
When calculating the components, researchers mainly rely on using cost-based and actual indicators that directly and indirectly characterise the components, as well as qualimetric indicators [Voronov et al., 2020, pp. 54-58]. It is worth noting that Voronov, when deciphering the social component, includes the results of sociological surveys, i.e., direct interaction with the possessor of capital. This allows us to talk about the minimal distortion of the data, which can be characterised as factual. "Social components are sociological surveys: index of trust in society, personal freedom and choice, government performance, personal rights, citizens' assessment of executive authorities, smart co-management" [Voronov et al., 2020, p. 58].
Myasoedova [2006] noted that the probability of achieving the highest possible value of human capital depends on the probability of achieving a high value of each of its characteristics (natural abilities, health, knowledge, professional skills, work and study motivation, mindset). She theoretically substantiated that significant results in the development of HC can be achieved only with balanced investments in knowledge, health care, motivation to work and study, and human culture [Ibid., p. 9].
This statement was expressed by Tuguskina [2021] as formula with multiplicative properties:
Phum.cap. ~ Pnat. abilit. X Phealth X Pknowl. X Pmotiv. X Psoc. cultur., (2)
where p is probabilistic values of human capital components [Tuguskina, p. 92].
Myasoedova provided a description of the key characteristics of human capital, but devising sub-measurements of the identified characteristics was beyond the scope of the research, which also did not allow revealing the essence of the factual models for assessment to the fullest.
We believe that the formula with a multiplicative effect used by Tuguskina most successfully emphasises the close relationship between the human capital values described by Myasoedova. For instance, there is no doubt that the value of human capital is significantly reduced in the presence of congenital cognitive disorders that negatively affect health and knowledge indicators.
Tuguskina [2022] analysed a variety of domestic methods for assessing human capital at enterprises, which, in our standpoint, are variations of abstract assessment models differing exclusively in the level of its implementation (macro, meso, micro).
The analysis of empirical and theoretical studies allows us to conclude that over the past decades scientists have repeatedly proposed using factual models to assess human capital.
The AHP model by Mubarik, Chandran and Devadason [2018], based on traditional actual indicators and the method of expert assessments and sociological surveys, expands the toolkit for determining human capital and promotes the measurement paradigm through factual indicators. However, other researchers paid scant attention to factual measurements describing them only indirectly.
The above facts allow us to deduce that the application of factual HC assessment models today is relevant, much in demand and at the same time understudied. So far, there is no tool to carry out this assessment on the basis of sociological, psychological, physiological, and socioeconomic indicators. The model proposed by the author of the article makes it possible to close this gap.
Research methods
To design our model, we identified two types of human capital, each of which consists of three components.
By human capital, we mean a certain stock of health, knowledge, skills, abilities, and motivations formed through investments and accumulated by a person, which are expediently utilised in the labour process contributing to the growth of both his/ her productivity and earnings [Smirnov et al., 2005, p. 99].
Human capital can be divided into two types:
1) general that encompasses knowledge and skills acquired in the course of general training and not alienated from a person;
2) special, i.e., knowledge and skills acquired in the course of special training in an organisation that cannot be used in its pure form outside this organisation.
The correlation of these types of human capital is described by formula:
H = (HG + Hs)/2; H = HG Hs = 0, (3)
where H is human capital; HG is general human capital; HS is special human capital.
Each type of human capital incorporates the following components:
1) physical skills. These are abilities for physical work preconditioned by individual characteristics. For example, physique, height, weight, pulse, muscle mass index, and handgrip strength are evaluated mainly empirically or using a questionnaire;
2) cognitive skills. These are the ability of a human brain to absorb and process information about the world around. They embrace memory, attention, cognitive flexibility, imagination, speech, the ability to reason logically, perceive information with the senses. These skills are mainly determined through psychological tests. Indirectly, they can be measured by the level of education, its quality, state awards, patents, etc.;
3) social-emotional skills. These are the abilities that enable people to recognise and control their emotions, handle conflicts successfully, solve interpersonal problems, be sensible and emphatic, establish and maintain positive relationships, follow ethical principles, make a constructive contribution to reference communities, set goals and achieve them. Psychological tests are used to measure these skills. Indirectly, they can be evidenced by state awards and elected government positions.
The author's model for human capital assessment develops the ideas of other researchers, such as [Smirnov, 2005; Myasoedova, 2006; Mubarik, Chandran, Devadason, 2018; Heckman, Jagielka, Kautz, 2019; Voronov et al., 2020, 2022; Tuguskin, 2022; Heckman, Zhou 2022; Mubarik, Shahbaz, Abbas, 2023]. It can be represented in the form of formulas (4) and (5), which allow calculating the general and special human capital of its possessor, respectively.
HG = MMCI x PhN x PhA x PhH x CN x C A x CH x SEN x SEA x SEH x CIG, (4)
where HG is general human capital;
MMCI is median per capita monetary income calculated by the Federal State Statistics Service of the Russian Federation;
PhN is natural physical abilities (hereinafter, in the explication to formula (4), skills are indicated within the framework of general human capital) characterised by the coefficient obtained through a sociological survey, which aims to identify congenital
diseases limiting everyday activity, or health categories based on the results of voluntary medical examination;
PhGA is acquired physical skills, which are indicated by the coefficient obtained through a sociological survey (revealing the presence of bad habits) and medical measurements (heart rate monitoring, blood pressure, body mass index, athletic competence);
PhGH is hybrid physical skills described by the coefficient obtained through a sociological survey to identify the physical characteristics of the body. The specific features of the organism are assessed using tests for the nervous system's sensitivity, calculating the coefficient based on the results of the physical training standards (GTO), and measuring the biological age coefficient using Gimpelson's approach [Gimpelson, 2018].
CGN is natural cognitive skills described by the coefficient obtained through a sociological survey to identify congenital cognitive disorders limiting everyday activity or the amount of expenses incurred in specialised treatment;
CGA is acquired cognitive skills, which are indicated by the coefficient obtained through a social psychological test to identify the cognitive peculiarities of an individual, such as the level of education, academic performance, alternative degree, state awards, licenses, sports titles, academic degrees, copyrights for publications, including scholarly ones, registered inventions, citizenship, as well as knowledge of foreign languages, and elected positions in legislative or representative authorities;
CGH is hybrid cognitive skills characterised by a coefficient derived from a social psychological personality test, such as the cognitive reflection test (CRT) [Frederick, 2005] or an age-related coefficient [Kremen et al., 2019].
SEGN is natural social and emotional skills expressed as a coefficient obtained through a social psychological test to identify psychological disorders that limit everyday communication and motivation manifested before ten years of age (the fewer restrictions, the higher the coefficient);
SEGA is acquired social and emotional skills expressed as a coefficient obtained through a test of social psychological personality characteristics (may include Boyko's test for the type of emotional reaction, Zharikov's test for determining the leadership qualities, a test for the level of stress, Panina's index of life satisfaction in the adaptation);
SEGH is hybrid social and emotional skills indicated by the coefficient obtained through a test aimed at identifying the social psychological characteristics of a personality, such as the MBTI test, Shmelev's test of sixteen Russian-language factors and their dynamics during the latest measurements;
CIG is a general index expressed as a coefficient based on the human potential index of the country where the study is conducted, and the sum of bonus indices for certain indicators that are significant for society (state awards, level of education, personal R&Ds, family status, etc.).
HS = MMCI x Ph^ x Phi x PhH x C% x Cj x x SEN x SEA x SEH x CIS, (5)
where HS is special human capital;
MMCI is median per capita monetary income calculated by the Federal State Statistics Service of the Russian Federation;
PhSN is natural physical abilities (hereinafter, in the explication to formula (5), skills are indicated within the framework of special human capital) characterised by the coefficient obtained on the basis of health care expenses from the previous employer, the degree of absenteeism at the previous job, the amount of time actually worked;
Phj is acquired physical skills, which are indicated by the coefficient obtained by measuring health care expenses at the current employment, the degree of absenteeism at the current job, the number of safety violations;
PhSH is hybrid physical skills in the form of a coefficient calculated as a difference between the coefficients Ph_SAA and Ph_SAH, as well as the results of a sociological survey on employees' satisfaction with physical workload, measuring the heart rate under load and establishing the compliance of the results with the norm, measuring the compliance of physiological characteristics with those required for the job;
CSN is natural cognitive skills described by a coefficient calculated on the basis of data about the effectiveness of training in the previous job;
Cj is acquired cognitive skills expressed as a coefficient obtained on the basis of data about the in-house training costs and training effectiveness, the results of the MBTI test for job relevance, and a sociological survey of the company's leader about employee innovative activities within the firm;
CSH is hybrid cognitive skills characterised by a coefficient based on data about the latest actual advanced training and continuous education programmes attended over the past five years, the continuous innovation activity in its quantitative terms (number of publications, registered patents);
SEN is natural social and emotional skills expressed as a coefficient obtained through a comparative index, which is formed on the basis of the number of reprimands, complaints, penalties, violations of corporate secrets at the previous job;
SESA is acquired social and emotional skills indicated by (1) a coefficient obtained through a social psychological test determining the levels of energy and motivation for work, leadership qualities, readiness for change and flexibility, job performance, and (2) a comparative index formed according to the number of tasks fulfilled,
innovations, advanced training courses, reprimands, complaints, penalties, violations of corporate secrets;
SEH is hybrid social and emotional skills expressed by a coefficient derived from a comparative index when calculating the difference between SESN and SESA;
CIS is a general index described by a coefficient based on the human potential index of the country, where the study is performed, and the sum of bonus indices for certain indicators that are significant for organisations.
All the indicators and sub-measurements were developed by the author on the basis of, among other things, the aforementioned scientific studies. In addition, the applicability of the metrics of the factual model for human capital assessment was discussed within the framework of the 25th International Scientific and Practical Conference "Actual Problems of the Global Economy"1.
Research results
We can classify the obtained data on the HC value using the continuum of human capital assessment developed by the author (see Table). It serves as a unified way of ranking the organisation's personnel and population.
The continuum, designed on the basis of the Spiral Dynamics model by Clare Graves2, allows one to determine the current values of any person (organisation) and forecast their development. In prospect, nine groups of the continuum of human capital assessment will make it possible to classify all employees of the organisation according to not only economic criteria, but also physiological, cognitive, social and emotional characteristics of the possessor of human capital, as well as to determine the efficiency in the use of this capital.
The maximum value of the interval of the HC assessment continuum is calculated by formula:
A++ = MMCI x ALE, (6)
where А++ is the maximum value of the interval of the human capital assessment continuum; MMCI is median per capita monetary income calculated by the Federal State Statistics Service of the Russian Federation, rubles; ALE is average life expectancy, in months.
Accordingly, this value for the Russian Federation as of the end of 2022 can be calculated as follows:
А++ = 35,370 rubles x (72.7 x 12). (7)
1 Digital transformation in Russia and the world: Realities and prospects. Official website of Business Informatics and Economics Department of Vladimir State University. http://bie.vlsu.ru/news/?id=531. (In Russ.)
2 Spiral Dynamics is a model of the evolutionary development of individuals, organisations and societies developed by Don Beck, Christopher Cowan and Frederic Laloux. It is based on the emergent cyclical theory of double helix of the biopsychosocial development of the mature human (ECLET) by Clare W. Graves.
To measure the value of each group (from maximum to minimum), it is necessary to determine the arithmetic progression step (Vd) from the maximum value of the interval to the minimum one (from A++ to D+) using the formula:
Vd = A++ : (ng - 1); Vc = 30,856,788 rubles : (9 - 1), (8)
where Vd is the arithmetic progression step in the continuum; A++ is the maximum value of the interval of the HC assessment continuum; ng is the number of groups of the HC assessment continuum (in our case, nine groups with fixed indicators are identified).
Based on our calculations, we obtain the continuum's boundaries for assessing human capital in the Russian Federation as of the end of 2022 (see Table), using the data of the Federal State Statistics Service1:
Continuum of human capital assessment in the Russian Federation
no. Group's code Interval, rubles Human capital use efficiency, %
1 A++ > 30,856,788.0 > 100.0
2 A+ > 26,999,689.5 > 87.5
3 A > 23,142,591.0 > 75.0
4 B+ > 19,285,492.5 > 62.5
5 B > 15,428,394.0 > 50.0
6 C+ > 11,571,295.5 > 37.5
7 C > 7,714,197.0 > 25.0
8 D+ > 3,857,098.5 > 12.5
9 D < 3,857,098.5 < 12.5
Source: Federal State Statistics Service of the Russian Federation. (2023). https://rosstat.gov.ru/la-bour_costs. (In Russ.); https://rosstat.gov.ru/storage/mediabank/ozhid_life_pr_2022.xlsx. (In Russ.)
Note: intervals are calculated by formula (8).
The given version of the continuum takes into account only the economic attribute, which allows identifying the efficiency of the human capital use. For Group A++, it is > 100 %, which is difficult to accomplish. For a healthy skilled worker, the standard efficiency is > 50 %, which corresponds to continuum Group B.
Thus, we can conclude that the purpose and objectives set in the introduction have been achieved.
In accordance with first calculations carried out on the basis of the author's factual model, the average value of human capital was found to be 15,000,000 rubles. These data correlate with the indicators obtained by Kapelyushnikov in the study conducted in 2010-2012 using the Jorgenson-Fraumeni method [Kapelyushnikov, 2012].
The value of human capital is 6,000,000 rubles, which, in the light of inflation between 2010 and 2023, amounts to 14,000,000 rubles at the beginning of 2023.
1 Federal State Statistics Service of the Russian Federation. (2023). https://rosstat.gov.ru/labour_costs; https:// rosstat.gov.ru/storage/mediabank/ozhid_life_pr_2022.xlsx. (In Russ.)
According to the author's model, the assessment of human capital per person resulted in an average of 15,000,000 rubles.
Based on this observation, we can deduce that the proposed factual model consists of new types of indicators and sub-measurements compared to the Jorgenson-Fraumeni method. At the same time, it makes it possible to digitise human capital and obtain values comparable to those produced according to the global practice of human capital assessment.
Conclusion
The new combination of indicators and sub-measurements will allow any business entity or individual to conduct assessment at the micro and meso levels for their own needs. The proposed model for evaluating human capital covers 56 sub-measurements: 39 economic (69.64 %), 6 social (10.71 %), 6 medical (10.71 %), and 5 psychological (8.92 %). At that, only four sub-measurements (7.14 %) are represented by macroeconomic indicators that are not founded on the specificities of the possessor of the capital under consideration: MMCIC, MMCIS, CIC 1, CIS1.
We revealed the following regularity: general human capital is more expensive than special one. The availability of cognitive, social and emotional, and social and economic markers significantly increased human capital, which singled out individuals with achievements by averaged 30-40 % of the HC value. This property of the model confirms its ability to reflect cognitive and social and emotional skills in the form of value, which is important for the existing models of HC assessment [Gimpelson, Zu-dina, Kapelyushnikov, 2020, p. 4].
The choice of a basic financial indicator for measuring the value of special human capital HS is a debatable issue within the proposed model. In the current study, the median per capita monetary income MMCI was applied as such an indicator and it helped to link the value of human capital with the macroeconomic indicators of the region (country) and minimise the income inequality across the country while unifying human capital calculations.
At the same time, such an indicator as MASP (monthly average salary per person) calculated for the possessor of human capital for the last 12 months allows one to individualise calculations of special capital and determine the degree of general human capital implementation in relation to special one. The dependence of the human capital value on wage size is increasing, which can lead to a distortion of the estimate in the highest- and lowest-income regions.
Since describing all 56 sub-measurements of the factual human capital assessment model is beyond the scope of the current study, this task can be regarded as a promising area for further research.
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Information about the author
Artem S. Shcherbakov, Postgraduate of Business Information Science and Economics Dept. State University of Vladimir named after Alexander and Nikolay Stoletovs, Vladimir, Russia. E-mail: sherbakov.artem@mail.ru
© Shcherbakov A. S., 2023
