copy the linklink copied!15. Human Capital

Human Capital refers to the knowledge, competencies, skills and health status of individuals, which are viewed here from the perspective of their contribution to future well-being. The performance of OECD countries regarding human capital is mixed. While progress has been made in raising the educational attainment of the youth population, large gaps between countries remain. Labour market underutilisation, which poses risks to human capital through the degradation of skills, has improved since 2010 for most OECD countries. Only one country experienced an increase in premature mortality over the past decade. In terms of risk to future health status, smoking prevalence has declined steadily since 2005 in all but two OECD countries. However, obesity remains a major risk to human capital, with the large majority of OECD countries experiencing rising obesity rates over that same period.

    
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Figure 15.1. Human Capital snapshot: current levels, and direction of change since 2010
Figure 15.1. Human Capital snapshot: current levels, and direction of change since 2010

Note: The snapshot depicts data for 2018, or the latest available year, for each indicator. The colour of the circle indicates the direction of change, relative to 2010, or the closest available year: improvement is shown in blue, deterioration in orange, no clear or consistent change in grey and insufficient time series to determine trends in white. For each indicator, the OECD country with the lowest (on the left) and highest (on the right) well-being level are labelled, along with the OECD average. For full details of the methodology, see the Reader’s Guide.

Sources: OECD Educational attainment and labour-force status (database), http://stats.oecd.org/Index.aspx?DataSetCode=EAG_NEAC; OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH and OECD Health Status (database), https://stats.oecd.org/Index.aspx?DatasetCode=HEALTH_STAT.

copy the linklink copied!Educational attainment among young adults

Educational attainment among young adults reflects the stock of knowledge and skills likely to be available to future generations. The share of young adults (aged 25 to 34) with at least an upper secondary education has been rising for the majority of OECD countries over the past four years (Figure 15.2). The OECD average rate was 84.9% in 2018, ranging from over 95% in Korea and the Russian Federation to less than 70% in Turkey, Spain and Colombia, and 50% in Mexico.

Since 2014, the OECD average upper secondary attainment rate for young adults has increased by 2 percentage points. Some of the largest improvements occurred in countries furthest behind the OECD average in 2014, thus narrowing the attainment gap between countries. For example, Turkey gained 7.7 percentage points, Portugal 6.9 and Iceland 6.8. By contrast, the largest falls occurred in the United Kingdom (by around 1.3 percentage points), followed by Austria (1.1).

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Figure 15.2. The educational attainment of young adults is rising in most OECD countries
Share of people aged 25-34 with at least an upper secondary education, percentage
Figure 15.2. The educational attainment of young adults is rising in most OECD countries

Note: The latest available data is 2018 for all countries, except for Brazil, Chile, Israel and the Russian Federation (2017). The OECD average does not include Chile or Japan, giving missing data and/or incomplete time series for these countries. 2014 is used as the base year, as opposed to 2010, due to changes in education classification in 2014 for 19 OECD countries.

Source: OECD Educational attainment and labour-force status (database), http://stats.oecd.org/Index.aspx?DataSetCode=EAG_NEAC and Russian Federal State Statistics Service (Rosstat).

 StatLink https://doi.org/10.1787/888934082860

copy the linklink copied!Labour underutilisation rate

The labour underutilisation rate describes the share of the labour force that is either unemployed, underemployed (e.g. those who are involuntarily working part-time) or discouraged (i.e. persons not in the labour force who wish to and are available to work, but who did not actively seek work in the previous four weeks). It therefore provides a wider view of joblessness and unrealised potential compared to unemployment alone, with underutilisation rates typically between 1.5 and 4 times higher than the standard unemployment rate. There are large differences in labour underutilisation across OECD countries (Figure 15.3), with a gap of over 24 percentage points between Greece (where over 27% of the population is underutilised) and the Czech Republic (with only 3.6%).

Labour underutilisation has improved for all but five OECD countries since 2010 (Figure 15.4), and of these, only two (Italy and Greece) have worsened by more than one percentage point. Latvia has recorded the largest improvement, with labour underutilisation falling by 18.8 percentage points.

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Figure 15.3. Large discrepancies in labour force underutilisation across the OECD
Labour underutilisation, as a share of the total labour force, 2018
Figure 15.3. Large discrepancies in labour force underutilisation across the OECD

Note: The overall labour underutilisation rate includes the unemployed, discouraged workers (i.e. persons not in the labour force who did not actively seek work during in the previous four weeks but who wish to and are available to work) and the underemployed (full-time workers working less than usual during the survey reference week for economic reasons and part-time workers who wanted but could not find full-time work), expressed as a ratio of the total labour force. The OECD average does not include Chile, Colombia, Israel, Korea or Mexico.

Source: OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH.

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Figure 15.4. Labour underutilisation has been improving in all but five OECD countries
Share of unemployed, discouraged or underemployed workers in the total labour force, percentage
Figure 15.4. Labour underutilisation has been improving in all but five OECD countries

Note: Latest available data is 2018. The 2018 OECD average does not include Chile, Colombia, Israel, Korea or Mexico. Earliest available data is 2010, aside from 2011 for the Czech Republic, Germany, Portugal, and Turkey; 2012 for Japan; and 2013 for the Netherlands. The 2010 or earliest available OECD average does not include Belgium, Chile, Colombia, Denmark, France, Ireland, Israel, Luxembourg, Korea, Mexico, the Netherlands or Turkey, due to missing data or breaks in the series.

Source: OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH.

 StatLink https://doi.org/10.1787/888934082898

copy the linklink copied!Premature mortality

Potential years of life lost (PYLL) is a measure of premature mortality, due to a range of medical conditions or fatal accidents. Among OECD countries, Switzerland, Japan, Luxembourg and Norway have the lowest incidence of premature mortality, with rates below 3 200 years lost per 100 000 inhabitants, while Latvia and Mexico have the highest rates (8 733 and 8 661, respectively) – almost three times higher than the top performers (Figure 15.5). Premature mortality has improved in most OECD countries over the past decade, with the greatest fall in years of potential life lost in Lithuania (a 24% fall), Korea (22%), Luxembourg (19%) and Finland (18%). By contrast, premature mortality increased by 5% in the United States. Beyond OECD countries, South Africa saw a very large improvement (almost 28%) between 2010 and 2015.

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Figure 15.5. Premature mortality has been reduced in all but one OECD country
Potential years of life lost per 100 000 population (age standardised)
Figure 15.5. Premature mortality has been reduced in all but one OECD country

Note: Potential years of life lost places greater weight on deaths that occur at a younger age. The indicator is created by summing up deaths that occur at each age, and multiplying this sum by the remaining years up to a pre-determined age limit (OECD Health Statistics uses age 75). PYLL measures for each country are computed based on the OECD age-structure of the population (i.e. age standardised). Latest available data is 2016 for most countries; 2017 for Austria, the Czech Republic, Hungary, Iceland and Lithuania; 2015 for Canada, Colombia, Denmark, France, Ireland, Italy, Latvia, Slovenia, Brazil and South Africa; and 2014 for New Zealand, the Slovak Republic, Costa Rica and the Russian Federation. The earliest available data is 2010 for all countries. The OECD average does not include Turkey, due to missing data.

Source: OECD (2020), “Potential years of life lost” (indicator), https://doi.org/10.1787/193a2829-en.

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copy the linklink copied!Smoking prevalence

Smoking is a risk factor for human capital, as it damages future health through links to cancer, heart disease, respiratory problems and birth defects. In OECD countries on average, 19% of people report that they smoke tobacco at least once a day. In Greece, Turkey and Hungary, more than one-quarter of the population smokes daily, while in Mexico and Iceland fewer than 10% do. Since 2005, smoking rates have generally fallen most in the OECD countries already doing comparatively well. The fall has been steepest in Norway (13 percentage points), followed by Greece (12.7 points), Estonia, New Zealand and Denmark (10.6, 9.4 and 9.1 points, respectively). Costa Rica has the lowest level of daily smoking prevalence of all countries included in Figure 15.6 (at 4%), having more than halved its smoking rate since 2005. Only Austria and the Slovak Republic have experienced an increase in smoking since 2005 (by 1.1 and 3.4 percentage points, respectively).

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Figure 15.6. Smoking prevalence is falling across the OECD
Share of people aged 15 or over who report smoking tobacco every day, percentage
Figure 15.6. Smoking prevalence is falling across the OECD

Note: The OECD average excludes Belgium, Canada, Colombia, Finland, Iceland, Ireland, Israel and the Netherlands, due to breaks in the series. Earliest available data is 2005, except for Austria, Estonia, Greece, Israel, Mexico, Portugal, Spain, Turkey and Brazil (2006); Australia, Ireland, Slovenia and Switzerland (2007); Belgium, Colombia and Latvia (2008); and Chile, Hungary, Poland, the Slovak Republic and the Russian Federation (2009). There is no earliest available data for Canada, Finland, Iceland, Ireland, Israel, the Netherlands and the Russian Federation, due to breaks in the series. Latest available data is 2018, except for Canada, the Czech Republic, Denmark, Germany, Israel, Italy, Japan, Korea, Mexico, the Netherlands, Spain, Sweden, Switzerland, the United Kingdom, the United States and Brazil (2017); Australia, Chile, Turkey and the Russian Federation (2016); South Africa (2015); and Austria, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, the Slovak Republic and Slovenia (2014). There is no latest available data for Belgium and Colombia, due to missing data.

Source: OECD Non-medical determinants of health (database), http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_LVNG.

 StatLink https://doi.org/10.1787/888934082936

Men have higher smoking rates than women in all but one OECD country: Iceland (Figure 15.7). Korea has by far the largest gender gap, with men over nine times more likely to smoke than women. Japan, Lithuania, Mexico and Turkey also have large gaps, with men more than three times as likely to smoke as women.

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Figure 15.7. Men smoke more than women in almost all OECD countries
Ratio of male to female smoking prevalence, 2018 or latest available year
Figure 15.7. Men smoke more than women in almost all OECD countries

Note: Gender ratios are expressed such that higher values (greater than 1) indicate better outcomes for women. Latest available data is 2018, except for Canada, the Czech Republic, Denmark, Germany, Israel, Italy, Japan, Korea, Mexico, the Netherlands, Spain, Sweden, Switzerland, the United Kingdom, the United States and Brazil (2017); Australia, Chile, Turkey and the Russian Federation (2016); South Africa (2015); and Austria, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, the Slovak Republic and Slovenia (2014). The OECD average does not include Belgium or Colombia due to missing data.

Source: OECD Non-medical determinants of health (database), http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_LVNG.

 StatLink https://doi.org/10.1787/888934082955

copy the linklink copied!Obesity prevalence

Obesity is another major risk to human capital: it increases the risk of heart disease, diabetes and some types of cancer. One in every five people are obese in OECD countries, on average (where obesity is defined as a Body Mass Index of 30 or higher). Differences across countries are large (Figure 15.8), ranging from 5% or less in Japan and Korea, to more than 40% in the United States (OECD, 2017[1]).

Over the past 15 years, obesity rates have been rising in most OECD countries. Of the 27 countries with time series data, none showed a fall in obesity rates, and only 2 maintained the same rate (Ireland and France). Chile showed the steepest increase, with obesity prevalence rising by 9.3 percentage points. Countries with higher levels of obesity have also recorded some of the largest increases over the past 15 years, suggesting that the problem is compounding rather than reaching a plateau. Even countries with relatively low levels of obesity – such as Switzerland, Norway, Sweden and Korea – have also experienced increases over the past decade.

The picture for gender gaps in obesity prevalence across OECD countries is mixed. Men have a higher obesity rate than women in 15 countries (with rates 20% higher in Switzerland, Slovenia and Italy). On the other hand, obesity prevalence among women is higher than that for men in 19 OECD countries, with the largest gaps in Turkey and Colombia.

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Figure 15.8. One in every five people are obese in OECD countries, and rates are rising
Share of the population aged 15 or older, as reported or measured, percentage
Figure 15.8. One in every five people are obese in OECD countries, and rates are rising

Note: Points in grey indicate the data come from health interview surveys; points in blue indicate the data come from health examinations. Earliest available data are from 2005, except for Austria, France, Greece, Spain and the United States (2006); Australia, New Zealand, Slovenia and Switzerland (2007); Poland and South Africa (2008); Chile and Hungary (2009); Finland and Turkey (2011); Germany (2012) and Brazil (2013). There is no earliest available data for Belgium, Canada, Estonia, Iceland, Israel, Latvia, the Netherlands, Portugal, Costa Rica and the Russian Federation, due to missing data and breaks in the time series. Latest available data are from 2017, except for New Zealand (2018); Chile, Latvia, Mexico and the United States (2018); Colombia, France, Israel, Norway and Portugal (2015) and Austria, Belgium, Estonia, Greece, Hungary, Lithuania, Luxembourg, Poland, Slovenia, Costa Rica and South Africa (2014). There is no latest available data for the Czech Republic, Germany, the Slovak Republic and Brazil, due to missing data. The OECD average is a simple average and excludes Belgium, Canada, the Czech Republic, Estonia, Germany, Iceland, Israel, Latvia, the Netherlands, Portugal and the Slovak Republic, due to missing data or breaks in the time series.

Source: OECD Non-medical determinants of health (database), http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_LVNG and INE for the 2014 value for Spain.

 StatLink https://doi.org/10.1787/888934082974

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Box 15.1. Measurement and the statistical agenda ahead

Human Capital broadly refers to the skills, competencies (including education and tacit knowledge) and health status of individuals (OECD, 2015[2]). Many researchers and institutions are currently using definitions of human capital that emphasise its value to economic production and income generation, particularly regarding the importance of the quality of labour (Boarini, Mira d’Ercole and Liu, 2012[3]). Beyond technical skills, the concept of human capital has since been expanded to include aspects of motivation and behaviour, as well as the physical, emotional and mental health of individuals (OECD, 2009[4]). Both health and education are also outcomes of intrinsic value in their own right, as well as contributing extensively to the production of other well-being outcomes (OECD, 2011[5]).

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Table 15.1. Human capital indicators considered in this chapter

Indicator 

Unit of measurement

Stock

Flow

Risk factor

Resilience factor

Educational attainment among young adults

Share of people aged 25-34 who have attained at least an upper secondary education

Labour underutilisation rate

Share of unemployed, discouraged workers and underemployed workers in the total labour force

Premature mortality

Years of potential life lost due to a range of medical conditions and fatal accidents per 100 000 population (age standardised)

Smoking prevalence

Share of people aged 15 or over who report smoking every day

Obesity prevalence

Share of the population aged 15 or older who are obese, either self-reported or measured through health interviews

Educational attainment among young adults is measured as the share of people aged 25 to 34 that have attained at least upper secondary education. Upper secondary education uses the International Standard Classification of Education (ISCED) definition, of education at or above level 3. This includes both general programmes geared towards preparation for higher education, as well as vocational education and training (VET) programmes (OECD, 2018[6]). Data are drawn from the OECD Education at a Glance database.

Labour underutilisation rate aims to capture the permanent effects of labour market slack in reducing the skills and learning opportunities available to people. It includes in the numerator the unemployed, the discouraged (i.e. persons not in the labour force who did not actively look for work during the past four weeks but who wish and are available to work) and underemployed workers (i.e. full-time workers working less than usual during the survey reference week for economic reasons and part-time workers who wanted but could not find full-time work), expressed as a ratio of the labour force. It therefore provides a wider view of joblessness and unrealised potential, beyond unemployment rates. Data are drawn from the OECD Household Dashboard database.

Premature mortality refers to deaths occurring before the age of 75. The indicator PYLL is calculated by subtracting the selected age of premature mortality (75 years in OECD calculations) from the actual age of death of each person, then multiplying this by the number of deaths at each age, and finally adding up the numbers across all age groups to come up with an overall total. Implicit in this approach is that deaths occurring at a younger age are weighted more heavily than deaths at an older age (e.g. in the case of an infant dying in its first year of life, PYLL is 75 – 1, i.e. 74, while for someone dying at 74, PYLL is 75 – 74, i.e. 1). The indicator takes into account differences in population structure by age across OECD countries (by applying the OECD population structure) to avoid reporting higher scores for countries that have the same age-specific death rates as others but a younger population structure (i.e., data are age standardised). Data are drawn from the OECD Health Statistics database.

Smoking prevalence is defined as the share of the population aged 15 or over that smokes tobacco daily. This indicator takes into account neither the quantity of tobacco smoked, beyond one cigarette per day (OECD, 2017[7]), nor the exposure to second-hand smoke; it also excludes the use of smokeless tobacco products (such as chewing tobacco). Data are drawn from the OECD Health Statistics database.

Obesity is defined using the body mass index (BMI), a single number that takes into account an individual’s height and weight. Based on WHO standards, an adult with a BMI of 30 or above is considered obese. While BMI is the most commonly-used metric for defining obesity, it is not without limits (e.g. different ethnic groups may have equivalent levels of health risks at different BMI values, (OECD, 2017[8])). Data are drawn from the OECD Health Statistics database.

Correlations among Human Capital indicators

Correlations among the Human Capital indicators are moderate to weak, and not statistically significant in a number of cases (Table 15.2). The main exception is labour market underutilisation and young adult educational attainment: countries with higher attainment rates have lower levels of underutilisation. Smoking prevalence and obesity are not significantly related across countries.

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Table 15.2. The indicators used in this chapter reflect different facets of Human Capital
Bivariate correlation coefficients among the Human Capital indicators

 

Educational attainment

Labour underutilisation rate

Premature mortality

Smoking prevalence

Obesity prevalence

Educational attainment

 

 

 

 

 

 

 

 

 

 

Labour underutilisation rate

-0.48***

(31)

Premature mortality

-0.10

-0.29

(39)

(31)

Smoking prevalence

0.34**

0.27

0.18

(38)

(31)

(38)

Obesity prevalence

-0.28

-0.10

0.27

-0.23

(36)

(29)

(36)

(35)

Note: Table shows the bivariate Pearson’s correlation coefficient; values in parentheses refer to the number of observations (countries). * Indicates that correlations are significant at the p<0.10 level, ** that they are significant at the p<0.05 level, and *** at the p<0.01 level.

Statistical agenda ahead

Upper secondary educational attainment among young adults may not be a particularly sensitive measure, given that almost 40% of adults in OECD countries have obtained at least a tertiary degree. It has been retained as an indicator, given the differential higher education vs. technical training paths present in OECD countries.

Labour market underutilisation may not inherently be a deprivation measure, in that the time an individual spends as a part of the underutilised labour force may not indicate skill loss. For example, if an individual is underemployed but using that time to volunteer in the community, or serve as an unpaid caregiver, this implies a contribution to the well-being of others. This aspect is currently not accounted for in the well-being framework.

Obesity data are compiled from two distinct survey types: health interview surveys (self-reported) and health exams, administered by medical professionals, which are considered to be more reliable (OECD, 2017[7]). The conflicting data sourcing makes cross-country comparisons difficult.

The lack of a consistent and regular time series for a number of human capital indicators, especially obesity and smoking prevalence, has made the measurement of performance over time and across countries difficult. For this reason, trends in obesity and smoking in this chapter are measured over the past fifteen years, rather than the past decade.

References

[3] Boarini, R., M. Mira d’Ercole and G. Liu (2012), “Approaches to Measuring the Stock of Human Capital: A Review of Country Practices”, OECD Statistics Working Papers, No. 2012/4, OECD Publishing, Paris, https://dx.doi.org/10.1787/5k8zlm5bc3ns-en.

[6] OECD (2018), Education at a Glance 2018: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/eag-2018-en.

[7] OECD (2017), Health at a Glance 2017: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/health_glance-2017-en.

[8] OECD (2017), How’s Life? 2017: Measuring Well-being, OECD Publishing, Paris, https://dx.doi.org/10.1787/how_life-2017-en.

[1] OECD (2017), Obesity Update 2017, OECD, Paris, http://oecd.org/health/obesity-update.htm.

[2] OECD (2015), How’s Life? 2015: Measuring Well-being, OECD Publishing, Paris, https://dx.doi.org/10.1787/how_life-2015-en.

[5] OECD (2011), How’s Life?: Measuring Well-being, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264121164-en.

[4] OECD (2009), Measuring Capital - OECD Manual 2009: Second edition, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264068476-en.

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