The indicators in Table 1 give a snapshot of the key characteristics of Serbia's human capital, in a form that make them comparable with other countries. The indicators point to a good level of human capital, while showing potential for fostering its continuous and lifelong development.

Table 1 – Human capital development indicators, Serbia

	Year	Value
(1) Population structure (%)
0–24	2015	29.3
25–64		54.4
65+		16.3
0–24	2025	27.5
25–64		52.4
65+		20.1
(2) Average years of schooling	2017	11.1
(3) Expected years of schooling	2017	14.6
(4) Learning-adjusted years of schooling	2017	11.1
(5) Adult literacy	2015	98.1
(6) Global Innovation Index rank (x/126)	2018	55
(7) Global Competitiveness Index rank (x/137)	2017–18	65
(8) Digital Readiness Index rank (x/118)	2018	44
(9) Occupational mismatch	2016
% of upper-secondary graduates working in low-skilled jobs (ISCO 9)		7.1
% of tertiary graduates working in semi-skilled jobs (ISCO 4–9)		24.2

Sources: (1) UN Population Division, World Population Prospects – 2017 revision; (2) United Nations Educational, Scientific and Cultural Organization (UNESCO) Institute for Statistics (UIS) database; (3) and (4) World Bank, Human Capital Index, 2018; (5) UNESCO UIS database; (6) World Economic Forum (WEF), The Global Innovation Index, 2018; (7) WEF, Global Competitiveness Index 4.0, 2018; (8) Cisco, Country Digital Readiness, 2018; (9) ETF, skills mismatch measurement in the ETF Partner Countries.

Note: ISCO = International Standard Classification of Occupations

Indicator 1 shows an ageing population trend, considering that the age group 65+ is the only one that will be increasing between 2015 and 2025.

Indicators 2 to 4 focus on the years of schooling, which appear to be below the expected or planned number of years on average. Indicator 5 reassures about adult literacy, which continues to be almost universal in Serbia.

Indicators 6 to 8 position the country in the global rankings of innovation, competitiveness and digital readiness. They are associated with human capital with regard to the current use of it, as opposed to the potential contribution that human capital may make to these sectors in future.

Indicator 9 points to an occupational mismatch, which also relates to human capital, specifically in the use, or in this case under-use, of human capital, notably in the context of the labour market and contribution to the economy at large.

Serbia has had increasing participation in higher education over the last generation. Low educational attainment has decreased in parallel, painting a more positive picture overall. The historical characteristics of a predominant secondary education level have, however, been maintained. In fact, secondary education attainment remained almost stable from 2010 to 2017. Gendered statistics indicate that secondary education was attained by 62.2% of men and 51.8% of women, while more women than men attained higher education in 2017: 30.6% versus 20.5%.

The International Standard Classification of Education (ISCED) for low, medium and high attainment is often taken as a proxy for skills. With this understanding, we conclude from Table 2 that almost 84% of the active population aged 15 years or over in Serbia possesses a medium or high skill level. Close to 60% have a medium level of skills. The distribution of educational attainment by age was not available at the time this report was written.

Table 2 – Educational attainment of active population aged 15 to 74, Serbia 2010 to 2018 (%)

	2010	2011	2012	2013	2014	2015	2016	2017	2018
Total
LOW	21.2	20.4	19.7	19.8	17.7	16.5	17.2	16.8	16.2
MEDIUM	59.4	59.6	59.5	58.7	59.5	59.1	58.2	58.0	58.0
HIGH	19.3	19.9	20.8	21.5	22.8	24.4	24.6	25.2	25.9
Male
LOW	20.9	20.3	19.5	19.3	17.7	16.9	17.0	16.6	16.3
MEDIUM	63.7	63.2	63.2	62.8	63.9	63.3	63.1	62.7	62.7
HIGH	15.4	16.5	17.2	17.9	18.4	19.7	19.9	20.7	21.0
Female
LOW	21.6	20.6	19.9	20.4	17.7	16.0	17.4	17.0	16.0
MEDIUM	53.9	54.8	54.6	53.4	53.9	53.6	52.1	52.2	52.1
HIGH	24.5	24.6	25.6	26.2	28.5	30.4	30.5	30.8	31.9

Source: Eurostat

Years of schooling or educational attainment are also used as an estimate of the human capital stock, besides being considered a proxy for skills levels. The underlying assumption is that schooling forms an individual's own capital, comprising their own skills which they use in employment and for further development.

While viewing human capital as a stock serves for measurement purposes, human capital is not crystallised but develops through work and life activities. This applies by analogy to skills, as they are formed through education, training and experience and not on a one-off basis. Skills may decline or depreciate along with inactivity, adverse life and occupational circumstances, or factors like under-employment and informal employment, among others. Skills improvement results in human capital gain; skills that weaken entail a loss of human capital.

Taking the above measures and considerations into account, this report will now highlight issues and trends that relate to Serbia's human capital development in the short to medium term.

The opening of this chapter has pointed to the ageing profile of the population in Serbia. Population growth has been negative for both men and women (see Table 3). According to the national statistical office (SORS), the tendency is projected to continue in the foreseeable future.

Table 3 – Total population in Serbia

	2010	2011	2012	2013	2014	2015	2016	2017	2018
Total	7 306 677	7 251 549b	7 216 649	7 181 505	7 146 759	7 114 393	7 076 372	7 040 272	7 001 444
Male	3 553 575	3 530 925b	3 514 420	3 497 008	3 479 863	3 464 399	3 446 258	3 429 027	3 410 592
Female	3 753 102	3 720 624b	3 702 229	3 684 497	3 666 896	3 649 994	3 630 114	3 611 245	3 590 852

Source: Eurostat

Note: b = break in time series

Table 4 focuses on 15- to 24-year-olds, highlighting that the young cohorts have shrunk significantly during the period under review. The table also shows a visible gender gap, with young men outnumbering young women. The TRP national report drew attention to the implications of the demographic dynamic for the country's human capital. It noted the speed of the transition towards a silver population (i.e. those aged 65+), considering that the proportion of the 15–24 age group was significantly lower if compared to the 15-74 population than if compared to the 15–64 age group.

Table 4 – Population aged 15 to 24 relative to population aged 15 to 74, in Serbia (%)

	2010	2011	2012	2013	2014	2015	2016	2017	2018
Total	15.0	14.9	14.4	13.9	14.4	14.1	13.9	13.7	13.5
Male	15.9	15.9	15.4	15.0	15.0	14.8	14.6	14.3	14.2
Female	14.2	13.9	13.4	12.9	13.8	13.5	13.3	13.1	12.9

Source: ETF calculations based on Eurostat data

The 40- to 64-year-olds who completed the formal education cycle at least 15 years ago will increasingly be represented in the active population of Serbia. The questions that an ageing population pose in terms of human capital and use of it in the labour market directly connect with the skills updating issue, and with the reskilling and upskilling opportunities that are needed to keep pace with the changing economy.

In other words, an ageing active population may bring ageing of skills. Since skills may deteriorate or become obsolete rather than developing over time, access to learning is critical to mitigate the potential disadvantages, and turn ageing into an opportunity for human capital gains.

The changes ongoing in the real economy have an influence on the labour market dynamics, including the occupational trends and skills demand. Here we look at the data that documents changes in the real economy, and appraise how these relate to human capital, its development and use. Ultimately, we ought to compare the direction of the real economy changes with the vision and goals that the government of Serbia set in the ERP 2019–21 (briefly summarised in Chapter 1).

The service sector has become a strong driver for Serbia's economic growth. In 2017, services accounted for more than half of the actual economic activity increase of the year. The most important positive change was observed in trade, which increased by 5.2%, and in the ICT sector, which created 3.8% more gross value added than in the same period of the previous year.

The respective contribution of agriculture, industry and services production to growth has remained relatively stable since 2015 (see Table 5).

Table 5 – Gross value added by broad economic sectors (%)

	2015	2016	2017	2018
Agriculture	6.7	6.8	6.0	6.2
Industry	25.7	25.7	26.1	25.9
Services	50.9	50.4	50.9	51.0
Other	16.7	17.1	17.0	16.9

Source: World Bank, World Development Indicators database

Industry and medium-value services still represent the backbone of the Serbian economy. Labour Force Survey (LFS) data of 2017 reported an increase of people employed as plant and machine operators, assemblers and professionals. The manufacturing sector recorded the highest increase in 2017, against a backdrop of employment increases in most economic sectors (ETF, 2019a).

Changes have nonetheless occurred, albeit to a moderate extent, as noted by the TRP national report. Economic restructuring and growth of new sectors, such as ICT, have affected the number and share of employed people in different activities. In 2017, there were more registered job vacancies in services, as a sum of all the activities in services, than all of the job vacancies in manufacturing activities. Trade, which correlates with different economic activities, stood out for the high number of job vacancies. Next was construction, followed by accommodation and food services activities. Agriculture has experienced employment destruction, notably in occupations like skilled agricultural, forestry and fisheries workers (ETF, 2019a).

The rates in Table 6 provide a synthetic picture of the evolving share of employment across broad sectors, since 2011. This and additional statistical information is published in the key indicators on education, skills and employment accessible on the ETF website (ETF, 2019b).

Table 6 – Employment by broad economic sectors (%)

	2011	2012	2013	2014	2015	2016	2017	2018
Agriculture	20.6	20.2	20.2	19.1b	18.9	18.1	16.8	15.5
Industry	27.0	26.7	26.7	24.9b	24.7	24.7	25.6	27.2
Services	52.4	53.1	53.1	55.9b	56.4	57.2	57.6	57.3

Source: ETF, 2019b

Note: b = break in time series

These shifts registered by labour market and economic data delineate a transition in the real economy. Together with services reinforcing its lead role as a growth contributor, manufacturing has been developing in the last few years through investments and opening of new plants, with beneficial effects on employment. At the same time, the structure of manufacturing is still characterised by low-added value products and upstream products. In agriculture, productivity has been increasing although remaining below its potential; consequently, its contribution to growth is rather stable.

The EU accession process plays a role in the economic transition, by means of pull and push factors. The Digital Agenda, the Common Agricultural Policy, the path to a single market and the consumer protection policy all come with a combination of normative benchmarks and supporting actions, including technical and financial support. These are not the only drivers for the transition in the real economy, but it would be a mistake to understate the role of these EU-driven incentives, and the effects that together with national incentives they have on the real economy.

Chapter 3 will further discuss the consequences of the real economy change, and what these changes mean for the skills demands in the labour market.

The Sustainable Development Goal on education (SDG4) sets out to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, by the year 2030. The SDG4 establishes an agenda that challenges all countries in all continents. Starting points and achievements obviously vary; however, so far no country can claim success against all the targets. Equitable and accessible opportunities for quality learning is the area where even the richest countries have some road to travel towards meeting the goal (Ward, 2019).

Serbia is not an exception to the rule. To appreciate equity and inclusiveness of quality education and lifelong learning opportunities, we should look into data related to both education and the labour market. Within overall positive educational attainment and relatively high skills levels, indicators such as completion rates bring to light areas of underachievement that call for the attention of decision-makers.

Table 7 assembles selected statistics on education completion, early school leaving, participation, low attainment, and vulnerable employment in Serbia. The data highlights limited development and use of human capital for young and adult population groups of significant size, which expose them to actual or potential risks of exclusion.

Table 7 – Vulnerability in education and employment, selected indicators, Serbia

	Year	Value (%)
(1) Completion rate in secondary vocational education by programme 3-year programme 4-year programme	2016/17	77.6 86.7
(2) Early school leavers – aged 18–24	2010 2018	8.2 6.8
(3) Participation in secondary education of children from poorest households Roma households – total Roma households – girls	ns	68.2 22.0 15.0
(4) Low educational attainment of active population – aged 15+ total population – aged 15+	2018	16.2 25.3
(5) Incidence of vulnerable employment – aged 15+, total	2016 2017 2018	28.2 27.2 24.6

Sources: (1) SORS; (2) Eurostat; (3) UNICEF, quoted by the TRP national report; (4) Eurostat; (5) ETF calculations based on Eurostat data.

ns: not specified

Disparities in access to education and the labour market come to the surface when data is broken down by age and gender, socio-economic status, ethnicity and geographical location. This level of granularity is, however, not always available or not on a regular basis, meaning that the assessment of risk areas is at times complex.

The 2019 Riga policy reporting questionnaire contained references to inequity associated with Serbia's existing disparities, although with no great details (GoS, 2019b). The TRP national report noted constraints to educational attainment are greater in rural areas. These include fewer schools compared to cities and, as regards VET, a very limited offer. Dropout rates are particularly high in three-year vocational education, most often attended by students from a vulnerable background (GoS, 2019a).

Vulnerability in education is often associated with subsequent vulnerable employment. In addition to vulnerable employment, the issue of non-participation in the labour market is relatively important in Serbia, demonstrated by high rates of inactivity, especially among women. In summary, factors such as geography, ethnicity, income and gender are to be considered to appreciate the disparities in development and use of human capital.