experimental estimate of human capital for 2017

EXPERIMENTAL ESTIMATE OF HUMAN CAPITAL FOR 2017

Milena Jankovič

Research Papers

December 2019

Experimental estimate of human capital for 2017 2

Experimental estimate of human capital for 2017 Authors: Milena Jankovič Published by: Statistical Office of the Republic of Slovenia, Litostrojska 54, SI-1000 Ljubljana Publication date: December 2019 Collection: Research Papers Available on the website of the Statistical Office of the Republic of Slovenia. Use and publication of data, text and figures is allowed provided the source is acknowledged. Data and views in the paper do not necessarily reflect the official statistical data or official position of the Statistical Office of the Republic of Slovenia.

http://www.stat.si/statweb/en/Methods/ResearchReports


Experimental estimate of human capital for 2017

Milena Jankovič1

Abstract

The Task Force on Measuring Human Capital was established in 2013 at the United Nations. It involved the following institutions: UNECE, OECD, universities, institutes and statistical offices of various countries, including the Statistical Office of the Republic of Slovenia (hereinafter SURS). The Task Force prepared the first manual entitled Guide on Measuring Human Capital, which provides guidelines and recommendations to help countries estimate the value of human capital.

SURS prepared an experimental estimate of the value of the stock of human capital in line with the so-called lifetime labour income method for 2017. This method is presented in the mentioned guide as one of the direct approaches for measuring the stock of human capital (in addition to the cost-based method and the indicators-based method).

Keywords:

human capital, life time labour income method

1 Statistical Office of the Republic of Slovenia, [email protected]

mailto:[email protected]


Eksperimentalna ocena človeškega kapitala za leto 2017

Milena Jankovič2

Povzetek

Leta 2013 je bila pri Združenih narodih ustanovljena strokovna skupina za merjenje človeškega kapitala. V njej so s svojimi predstavniki sodelovale različne institucije: UNECE, OECD, univerze, inštituti in statistični uradi različnih držav, med njimi tudi Statistični urad Republike Slovenije (dalje SURS). Omenjena skupina je pripravila tudi priročnik z naslovom Guide on Measuring Human Capital (slov. Smernice za merjenje človeškega kapitala), ki je prvi priročnik te vrste.

Na SURS smo pripravili eksperimentalno oceno vrednosti človeškega kapitala po metodi življenjskih dohodkov (angl. lifetime labour income approach) za leto 2017. Ta metoda je v omenjenem priročniku predstavljena kot ena izmed neposrednih metod vrednotenja človeškega kapitala (poleg stroškovne metode in metode fizičnih kazalnikov).

Ključne besede:

človeški kapital, metoda življenjskih dohodkov

2 Statistični urad Republike Slovenije, [email protected].

mailto:[email protected]


1. Introduction

The Task Force on Measuring Human Capital was established in 2013 at the United Nations. It involved the following institutions: UNECE, OECD, universities, institutes and statistical offices of various countries, including the Statistical Office of the Republic of Slovenia (hereinafter SURS). The Task Force prepared the first manual entitled Guide on Measuring Human Capital, which provides guidelines and recommendations to help countries estimate the value of human capital.

As economies have become more knowledge-based and globalised, human capital has become increasingly economically important. Individuals have become increasingly competitive on the labour market and economic success of countries has become more significant than ever. The importance of knowing and calculating the value of human capital is manifold: for understanding better what drives economic growth, assessing long-term sustainability of a country’s development path, and estimating production and productivity of the educational sector.

SURS prepared an experimental estimate of the value of the stock of human capital in line with the so-called lifetime labour income method for 2017. This method is presented in the mentioned guide as one of the direct approaches for measuring the stock of human capital (in addition to the cost-based method and the indicators-based method).

2. Methodology

2.1. Definition of human capital

An individual’s human capital is defined by OECD as the knowledge, skills, competencies and other characteristics that the individual obtained through learning and experience as well as innate abilities. A broader definition of human capital includes some other aspects of individual’s motivation and behaviour as well as their psychological, physical and emotional health.

2.2. Methods of measuring human capital

In general, the methods of measuring human capital are divided into methods based on physical indicators and methods based on monetary valuation. Physical indicators can be quantitative (average number of years of schooling, number of persons by educational attainment, etc.) or qualitative (class size by the number of persons included, results of tests, etc.). The methods of monetary valuation are divided into indirect and direct methods of estimating the stock of human capital.

One of the indirect methods of estimating human capital is the residual method, which was first used by the World Bank (2006, 2011), Norway and some other countries. According to this approach, human capital is calculated as the difference between the total discounted value of future consumption flows in the country (as a proxy for total wealth of the country) and the sum of all capital goods that comprise the wealth of the country (produced capital and market value of natural resources).


On the other hand, direct methods of estimating human capital are the cost-based approach and the lifetime income-based approach.

According to the cost-based approach, human capital is estimated as the value of the stream of past investments in knowledge of the individual, the household, the employer and the government. The method is relatively simple for estimating human capital if it focuses on public and private expenditure for formal education. The estimate can include monetary expenditure for on-the-job training, and adult education and training. According to this method it is difficult to distinguish between investment expenditure and consumption expenditure. A problem is also selecting appropriate price indices for calculating the values of stocks in line with the perpetual inventory method and selecting the depreciation rate, which is usually determined randomly.

The lifetime income-based approach is closer to the concept of economic theory of capital than the cost-based approach and is the most appropriate approach in terms of data availability.

The document describes the methodology and the procedure of preparing estimates of the stock of human capital, data sources, assumptions in calculations, and results shown by sex, age and educational attainment.

The method was developed by Dale W. Jorgenson and Barbara M. Fraumeni (1989, 1992a and 1992b). In line with this method, an individual’s human capital measured with their lifetime labour income equals the sum of present annual labour income and discounted values of expected future income that they will generate on the labour market during the rest of their life. Labour income is the valuation of investment in human capital from education. If an individual achieves a higher educational attainment level, the expected income grows and so does their human capital. The entire stock of human capital in a country is calculated as the sum of lifetime income of all working age population in a country (labour force).

Further on the methodology of estimation, the procedure of preparing the first estimate of the value of human capital for Slovenia and data sources are presented, and results are analysed.

2.3. Estimation of human capital by the lifetime income method

The lifetime income method measures the stock of human capital as the sum of discounted present value of all future income streams that an individual in the population can expect to earn during their lifetime. The method focuses on the expected return and is therefore forward looking rather than backward looking method using historical costs of production for human capital. The method focuses on economic return for an individual as the main benefit due to investment in human capital. Taking care of health influences better return on human capital but from the aspect of national accounts health as an economic asset is not taken into consideration among economic returns (UNECE, 2016).

Better health and longer life, civic awareness and participation, job quality and satisfaction, social connections, subjective well-being and personal security are personal non-monetary benefits that an individual has due to investment in human capital. Public non-monetary benefits to society include higher productivity, lower social spending, better public health and safety, and stronger social inclusion. Education has a positive impact on all these benefits.

The method is partly adjusted as it excludes non-market activities that also bring returns on the use of human capital because it is difficult to determine their value due to lack of prices, necessary unanswered assumptions and a relatively large set of data. Non-market activities


bringing return on human capital are unpaid household production and leisure. Including non-market activities makes the comparison of the stock of human and physical capital possible (UNECE, 2016).

Other types of investment in human capital, which include working experience and on-the-job training, are included in the model indirectly via labour income (namely, those with more working experience receive higher earnings).

In Slovenien experimental estimate of human capital we used OECD model of the estimation human capital (Liu3, 2011). We distinguished three phases in a person’s lifecycle: schooling and work phase (persons aged 15–39 years), work phase (persons aged 40–64 years) and retirement phase (persons aged 65+).

Lifetime labour income of an individual was calculated using the following equations:

A. Persons aged 65+, who are retired, are outside the labour market so their future labour income equals 0.

B. For persons aged 40–64 years, who are in the work phase, lifetime labour income is estimated according to this formula:

(1) 𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 = 𝐸𝐸𝐸𝐸𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 𝐴𝐴𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 + 𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+1𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎+1𝑎𝑎𝑒𝑒𝑒𝑒 {(1 + 𝑟𝑟) ÷ (1 + 𝛿𝛿)}

whereby:

𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 is the present value of lifetime labour income of a person with certain educational attainment “edu” and age “age”.

𝐸𝐸𝐸𝐸𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 is the employment rate of a person with certain educational attainment “edu” and age “age”.

𝐴𝐴𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 is the present annual labour income of a person with certain educational attainment “edu” and age “age” if they are employed.

𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+1 is the probability of survival of a person aged “'age” in the next year.

𝑟𝑟 is the annual labour income growth rate in the future for a person with certain educational attainment “edu” and age “age”.

𝛿𝛿 is the annual discount rate.

C. For persons aged 15–39 years, who are in the schooling and work phase, labour income is estimated according to the following equation:

3 Gang Liu was the leader of the OECD human capital poject, ''Masuring the stock of human capital for comparative analysis; an application of the lifetime income approach to selected countries''.


(2) 𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 = 𝐸𝐸𝐸𝐸𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒𝐴𝐴𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 + �1 − ∑ 𝐸𝐸𝐿𝐿𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒𝑎𝑎𝑒𝑒𝑒𝑒 � 𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+1𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒{(1 + 𝑟𝑟) ÷

(1 + 𝛿𝛿)} + ∑ 𝐸𝐸𝐿𝐿𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒𝑎𝑎𝑒𝑒𝑒𝑒 ��∑ 𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡

𝑎𝑎𝑎𝑎𝑎𝑎 {(1 + 𝑟𝑟) ÷ 1 + 𝛿𝛿)}𝑡𝑡𝑡𝑡𝑒𝑒𝑒𝑒𝑒𝑒−𝑒𝑒𝑒𝑒𝑒𝑒𝑡𝑡−1 �÷

𝑡𝑡𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒�

whereby:

𝐸𝐸𝐿𝐿𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒 is the enrolment rate of a person with certain educational attainment “edu” who studies for a higher educational attainment ''𝑒𝑒𝑒𝑒𝑒𝑒′′.

𝑡𝑡𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒 is the duration of schooling of a person with certain educational attainment “edu” to finish studies for a higher educational attainment ''𝑒𝑒𝑒𝑒𝑒𝑒′′.

In the next year the individual in the schooling and work phase will tackle two situations. The first one is to continue schooling, and after schooling and achieved higher educational attainment they start earning income ��∑ 𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡

𝑎𝑎𝑎𝑎𝑎𝑎 {(1 + 𝑟𝑟) ÷ 1 + 𝛿𝛿)}𝑡𝑡𝑡𝑡𝑒𝑒𝑒𝑒𝑒𝑒−𝑒𝑒𝑒𝑒𝑒𝑒𝑡𝑡−1 �÷

𝑡𝑡𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒� with probability ∑ 𝐸𝐸𝐿𝐿𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒𝑎𝑎𝑒𝑒𝑒𝑒 .

The second one is that the individual starts work with the same educational attainment as a year earlier and earns income 𝑆𝑆𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎+1𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒{(1 + 𝑟𝑟) ÷ (1 + 𝛿𝛿)} with probability 1 −

∑ 𝐸𝐸𝐿𝐿𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒−𝑎𝑎𝑒𝑒𝑒𝑒𝑎𝑎𝑒𝑒𝑒𝑒 . Therefore, the individual’s income in the next year is the sum of the expected

values of the two situations described above.

The value of the stock of human capital is calculated according to the equation:

(3) 𝐻𝐻𝐻𝐻𝐻𝐻 = ∑ ∑ 𝐿𝐿𝐿𝐿𝐿𝐿𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒𝐿𝐿𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒

𝑎𝑎𝑒𝑒𝑒𝑒𝑎𝑎𝑎𝑎𝑎𝑎

whereby:

𝐿𝐿𝑆𝑆𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑒𝑒 is the number of persons in the group with appropriate educational attainment

and age.

Real labour income growth rate is the estimate of the annual productivity growth rate, and the discount rate is the estimate of the long-term return rate in the private sector. The data were calculated and used by Jorgenson and Fraumeni (1992a) in estimating human capital for the United States of America.

In the comparative analysis of the estimate of the stock of human capital of selected countries the OECD used the same rates for the countries for which the data were not available. Due to the comparability of results with the OECD study, we also decided to use the same rates. Thus the 1.32% real annual income growth rate and the 4.58% discount rate were used.


3. Data sources and assumptions

3.1. Assumptions in the calculation

The central assumption of the method is that a worker is paid according to marginal productivity. The next assumption is that educational attainment is the main driving force of human capital return. In reality, earnings are influenced by many factors such as market power, trade unions, discrimination, etc. On-the-job training is included indirectly; it is assumed that those with more experience earn more (HC, 2016).

The estimate of lifetime labour income takes into account the following assumptions:

• Individuals can continue schooling only in a higher educational attainment level than the one they already have.

• Individuals who have already achieved the highest educational attainment cannot continue schooling.

• For students enrolled in educational programs where it takes over a year to finish schooling it was assumed that they are distributed equally throughout the schooling period (duration of schooling). It was thus assumed that each year the same percent of enrolled students finished their schooling and got a job.

• It was assumed that in the total schooling period a student did not abandon schooling, jump a year or postpone schooling for a specific time.

3.2. Description of data and sources Survival rate

The survival rate is necessary for adjusting future lifetime income. The survival rate is the probability that a person living in year t will also live in year t+1. The data on the survival rate are from the Eurostat database https://ec.europa.eu/eurostat/data/database (Database by theme/Population and social conditions/Demography and migration/Dataset: Mortality, life expectation/Probability of surviving between exact ages). Survival rates are broken down by age and sex but not by educational attainment. Some studies show that people with higher educational attainment have longer life expectancy and a higher survival rate (OECD, 2010a). People with higher educational attainment live healthier (they exercise more, they eat healthier), have better working and living conditions, and greater opportunities for better health care.

Educational attainment

The source of data on educational attainment is the statistical survey Socioeconomic Characteristics of Population and Migrants (SEL-SOC)4. Educational attainment is classified by age and sex. The data on the type of education completed are classified by the Classification of Types of Educational Activities/Qualifications (KLASIUS-SRV) and for the needs of the experimental study of the value of the stock of human capital translated into

4 The data for the SEL-SOC survey are obtained with the help of various statistical surveys, registers, administrative records and collections and the population census.

https://ec.europa.eu/eurostat/data/database


ISCED 2011. As regards educational attainment, individuals are broken down into three groups (ISCED 2011; first level):

• Basic or less (no education, incomplete basic education, basic education) • Upper secondary (lower and middle vocational, professional, general) • Tertiary (1st, 2nd and 3rd cycle of higher, professional higher (former) and academic

(former), “magisterij” of science and doctorate of science).

Employment rate

The source of data on the number of persons in paid employment by sex, age and educational attainment is the statistical survey Socioeconomic Characteristics of Population and Migrants (SEL-SOC), which was adjusted to the data on employment in the statistical survey Labour Costs by Socioeconomic Characteristics of Employees and Self-Employed Persons, which is based on the data on employment in line with the national accounts methodology. The employment rate is calculated as the ratio between the number of persons in employment (employed and self-employed) and working age population.

Enrolment rate and duration of schooling

The enrolment rate is calculated as the ratio between the number of pupils/students and working age population by sex, age and educational attainment. The source of data for calculating the enrolment rate is the statistical survey Socioeconomic Characteristics of Population and Migrants (SEL-SOC).

The duration of schooling is the theoretical number of years necessary to complete the educational program. Sources of data for the calculation are various surveys on education.

Annual labour income

For calculating annual labour income we used the data from the statistical survey Labour Costs by Socioeconomic Characteristics of Employees and Self-Employed Persons. Sources of data are income tax returns collected by the Financial Administration of the Republic of Slovenia, the data from the Statistical Register of Employment (SRDAP) and national accounts data (employment, consumption of employees and mixed income of the self-employed). Annual labour income of employees includes gross earnings, gross wage compensations and reimbursement of costs related to work, and employers’ social security contributions. Annual labour income of self-employed persons is estimated and equals labour costs that would be incurred by the employer for one employee under the condition that the self-employed person and the employee have the same socioeconomic characteristics.

3.3. Calculation

Lifetime income for a group of individuals was calculated by age, sex, and educational attainment. Lifetime income of a group of individuals is the sum of current income and the present value of lifetime labour income in the active period taking into account the survival rate, the real labour income growth rate and the discount rate.

The empirical estimate of lifetime income is based on backwards recursion. With this approach lifetime labour income is first calculated for a group of individuals aged 64 years who are about to retire. For this group it is true that their lifetime labour income equals labour income in the


last year before retirement. What follows is the calculation for the group of individuals aged 63 years, for whom lifetime labour income equals income in this year and the present value of labour income at age 64 years multiplied by the survival rate. The calculation of lifetime income in year t in line with backwards recursion, in which part of the value (with appropriate adjustment) is taken from year t+1, continues up to the age of 40 years.

In calculating lifetime income for a group of individuals aged less than 40 years, we took into account that some of them were in education. Lifetime income for these age groups is calculated as the sum of three components. The first one is labour income in this year (t), the second one is adjusted income from year t+1 for persons who are not in education in year t, and the third one is adjusted lifetime income of persons in education in year t. In this case we take as the basis lifetime income in year t+n, whereby n is the average duration of schooling for a specific educational attainment.

Simply put, we calculated how much we earn today and how much we will earn in all the years until retirement, taking into account the real income growth rate and the discount rate.

4. Analysis of results

4.1. Employment rate and survival rate

In 20175 there were 987,792 persons in employment in Slovenia, 562,742 or 57% of them men and 425,050 or 43% of them women. The average employment rate was 72.9% and differed both by sex and educational attainment. It was higher for men (80.6%) than for women (64.7%). Among young people aged up to 30 years the employment rate for women with basic education was much lower than for men with the same educational attainment (comparison of Charts 1 and 2). The most probable reason is that girls in most cases decide to continue schooling. As regards people aged 27–30 years with upper secondary and tertiary education, more men than women were employed. Women of this age more frequently decide to start a family and have children, and some stay at home and take care of children and the household.

Among older employees who are expected to retire in a few years the employment rate was rapidly declining, which shows that a large share of employees retired immediately after they fulfilled minimum conditions for retirement. Both for men and for women the decline in the employment rate was the sharpest after 57 years of age and for women with basic education even before 54 years of age. The employment rate for men with tertiary education aged 63 was almost 60%, while for women it was only about 20%.

Between the ages of 30 and 57 the employment rate was the highest for men with tertiary education, while for women with tertiary education it was the highest between the ages of 42 and 57. For women with upper secondary education the employment rate started to grow after 30 years of age and was growing up to 51 years of age, but it was still lower than the employment rate of men of the same age and with the same educational attainment.

5 Data on labour costs by socioeconomic characteristics of employees and self-employed persons for

2017 were published on 20 December 2018.


Chart 1: Employment rate by educational attainment, men, Slovenia, 2017

Source: SURS

Chart 2: Employment rate by educational attainment, women, Slovenia, 2017

Source: SURS

Both for men and for women the survival rate falls with age, particularly in the last years of employment. The survival rate for women is higher than for men at all ages.

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Chart 3: Survival rate by sex, Slovenia, 2017

Source: SURS

4.2. Annual income and value of human capital

In 2017 the total annual labour income of employed and self-employed persons was EUR 24,153 million or EUR 24,452 per employee.

Annual income per employee was above average for employees with tertiary education both for men and for women (comparison of Charts 4 and 5). Labour income of women with tertiary education was lower than of men but the share of employed women with tertiary education was higher than the share of men. This indicates a general phenomenon that labour income received by women with tertiary education is on average lower than labour income of men with the same educational attainment.

Labour income was quite low and varied up to age 30, irrespective of educational attainment. There were various reasons for this. Most young people were still in education, whereas the number of employed young people was lower than at later ages (after 30 years of age) and it is possible that they were paid less than older employees.

At the end of active life, income of persons with tertiary education again increases both for women and men. This is a result of a small number of persons employed after 60 years of age who are not retired and have above average income.

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Chart 4: Annual labour income per person in paid employment by educational attainment, men, Slovenia, 2017

Source: SURS

Chart 5: Annual labour income per person in paid employment by educational attainment, women, Slovenia, 2017

Source: SURS

Human capital for 2017 was estimated at EUR 447,337 million. As expected, the estimated value of human capital was the highest in the group with the highest educational attainment. Employees with tertiary education received on average higher income than employees with lower education and their employment rate was very high.

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The largest part of human capital is that of men with upper secondary education. They represent a higher value of human capital than men with tertiary education. The reason is that the number of employed men with upper secondary education is higher than the number of employed men with tertiary education. Although women have on average lower income than men, the estimated human capital for women with tertiary education is slightly higher than for men.

Table 1: Human capital by educational attainment and sex, Slovenia, 2017

million EUR TOTAL Men Women

TOTAL 447,337 252,152 195,185

Basic (ISCED 2011; 1-2) 56,330 33,477 22,853

Upper secondary (ISCED 2011; 3-4) 188,519 120,149 68,370

Tertiary (ISCED 2011; 5-8) 202,488 98,526 103,962

Source: SURS

A comparison of the value of human capital by sex shows that the value of human capital for women with upper secondary education is much lower than the value of human capital for men with the same educational level. This is the result of a much lower annual labour income and a lower number of employed women with this educational attainment, too.

Chart 6: Human capital by educational attainment, men, Slovenia, 2017

Source: SURS

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Chart 7: Human capital by educational attainment, women, Slovenia, 2017

Source: SURS

4.3. International comparison

For comparing the estimate of human capital in Slovenia and in other countries, we considered the 2011 OECD study on the measurement of human capital. The study included 15 countries: Australia, Canada, New Zealand, some EU Member States and some other countries. The study estimates mainly refer to 2006 data.

In all selected European countries the value of human capital of men was higher than the value of human capital of women, which can be seen on Chart 8. In Slovenia the share of human capital of men (56%) was slightly higher than of women, which is comparable to most observed countries. Slightly higher shares of men were recorded in the Netherlands and Italy (about 68%).

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Chart 8: Distribution of human capital by sex, selected European countries, 20061)

Note 1: Estimate of human capital for Denmark for 2002, for Slovenia for 2017. Sources: OECD, SURS

In 2017 the ratio between human capital and gross domestic product was 10.4, meaning that the value of human capital was 10.4-times the value of GDP. Compared to other countries, Slovenia is in the group of countries with higher rates. According to the OECD study, the differences among countries were small; in most countries the ratio was between 9 and 10.

Chart 9: Ratio between human capital and GDP, 20061)


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The experimental estimate of the value of human capital was compared with the value of physical capital (Chart 10). As the value of physical capital we took the net value of fixed assets according to national accounts data. In Slovenia the ratio between human and physical capital in 2017 was 3.3. In the OECD study the ratio was between 3.6 in the Netherlands and Italy and 7.0 in the United Kingdom. Compared to other countries, Slovenia had the lowest ratio.

Chart 10: Ratio between human and physical capital, 20061)


Table 2: Comparison of human capital, gross domestic product and fixed assets, Slovenia, 2017

Value (million EUR) Ratio of lifetime income

Human capital 447,337

GDP, current prices 42,987 10.4

Net value of fixed assets 133,578 3.3

Source: SURS

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5. Possibilities of further development

Within the Medium-Term Programme of Statistical Surveys 2018–2022, SURS set up to prepare the estimate of the value of human capital. We sought calculations in the international environment, but so far, only some countries have prepared estimates. A similar study was conducted by the OECD, which included the largest number of European countries so far, so this was the basis for preparing our estimate. Study results were also used for the international comparison of our estimate of the value of human capital.

For the comparability of results with the OECD study, we used the same rates: for the real annual income growth rate 1.32% and for the discount rate 4.58%. The next step in improving the estimate of human capital will be to test various income growth rates and discount rates for Slovenia. In the future, we will continue work on estimating human capital for several years.

Good practice of countries will help us develop the detailed methodology for estimating human capital. SURS will continue to monitor the development and follow the guidelines.


Sources and literature

1. UNECE (2016). Guide on Measuring Human Capital. UN. Geneva. 2. Jorgenson, Dale W., Fraumeni, Barbara M. (1989). The Accumulation of Human and Non-

human Capital 3. Jorgenson, Dale W., Fraumeni, Barbara M. (1992a). Investment in Education and U.S.

Economic Growth 4. Jorgenson, Dale W., Fraumeni, Barbara M. (1992b). The Output of the Education Sector 5. Liu (2011). Measuring the stock of human capital for comparative analysis: an application

of the lifetime income approach to selected countries. OECD Statistics Working Papers No.41 (2011/6). OECD Publishing

6. OECD (2001). The well-being of nations: The role of human and social capital, OECD Publishing, Paris

7. OECD (2010a), Improving Health and Social Cohesion through Education, OECD Publishing, Paris

8. System of National Accounts – SNA 2008 9. World Bank (2006). Where is the Wealth of Nations? Washington, D.C., World Bank 10. World Bank (2011). The Changing Wealth of Nations: Measuring sustainable development

in the new millennium. Washington, D.c., World Bank

experimental estimate of human capital for 2017

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