copy the linklink copied!2. Inequalities in health and its determinants

People in poor health have a lower probability of being employed

Across countries on average, and everything else being equal, the labour market participation rates of people who have no deteriorated health indicators and those with at least one differ by 8 percentage points (Figure 2.2). In this section, unless otherwise mentioned, the term “in poor health” will be used to describe those with at least one deteriorated health indicator. Box 2.4 highlights relevant results from analyses carried out using other specifications of the health variable.

In all countries, and as in previous studies (Orchard, 2015[6]; Knebelmann and Prinz, 2016[12]; OECD, 2016[5]), ill health translates into lower employment, but the scale of the problem varies considerably across countries (Figure 2.2). Romania, Norway and Bulgaria stand out as countries where the difference in labour market participation between those is good and poor health is much larger than on average (19, 16, and 14 percentage points, respectively). However, in these countries, the proportion of the population in poor health is relatively small (20%, 33% and 29%, respectively).

Portugal, Finland and Latvia present an opposite pattern, where the share of the population in poor health is comparatively high (53%, 59% and 56%, respectively) but the gap in employment rates between those in good and poor health relatively small (4, 6 and 2 percentage points).

Sweden and Germany stand out as well-performing countries where despite fairly high proportions of the population in poor health (42% in Sweden, and 52% in Germany), the gap in employment rates between those in poor and good health is small and the overall employment rate of the population is high.

Differences in labour market participation rates between people in good and bad health relate to country-specific labour market and health policies. General and specific activation labour market policies can for example facilitate access to paid work for people in poor health. In Sweden, active labour policies and employment protection are found to increase the opportunities for people with chronic illness to remain in work (Burström et al., 2005[13]). Policies encouraging employers to remove physical barriers to work, providing equal training opportunities for people in poor health, providing flexible work arrangements, or reinforcing employment protection regulations might also reduce differences in labour market participation rates between people in good and poor health. At the same time, public health policies designed to better prevent and manage chronic conditions at work can also make the workforce healthier and more productive, reducing differences in labour market participation. In Belgium, for example, prevention advisers give guidance to workplaces on psychological well-being and support the preparation of risk assessment plans to reduce stress and improve working conditions (OECD/EU, 2018[14]). Such actions support workers experiencing mental ill health to stay in work and maximise opportunities and fulfilment for employees.

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Figure 2.2. Probability of being in employment among the working-age population, by health status, European and OECD countries

Note: Data from 29 European countries, Canada, Chile and the United States. Results correspond to predicted probabilities estimated with logit models (all else being equal) (see Box 2.3). The dots refer to the probability of being in employment by health status. The size of the lower dot reflects the proportion of people in poor health in the working-age population (slightly underestimated for Chile). The difference in the probability of being employed between people in good and poor health is significant at the 95% confidence level in all countries except Croatia, Latvia, Luxembourg and Sweden.

Source: OECD estimates based on national health survey data.

In the absence of ill health, labour market participation could increase by 3 percentage points

Across European and OECD countries, the labour market participation rates could increase by 3 percentage points in the absence of ill health. This estimate, which does not factor in labour market dynamics, is simply computed as the difference between the estimated employment rate if everyone was in good health and the actual employment rate. Figure 2.3 shows that the impact of poor health on labour market participation is smallest in Sweden, Latvia, Italy and Croatia (with an impact of around 1 percentage point) and greatest in Chile and Norway. In these countries, labour market participation rates could increase by around 5 percentage points if everyone was in good health.

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Figure 2.3. Impact of poor health on labour market participation, European and OECD countries

Note: Data from 29 European countries, Canada, Chile and the United States. The impact of poor health on labour market participation is the difference between the estimated employment rate if everyone was in good health (people have none of the four health issues) and the actual weighted employment rate.

Source: OECD estimates based on national health survey data.

Everywhere, the least educated women are more likely to be overweight

Overweight and obesity, which are known risk factors for numerous health problems (including for example hypertension, diabetes, cardiovascular diseases and some forms of cancer) has increased in many European and OECD countries over the past decades (OECD, 2017[23]; OECD/EU, 2018[14]). The prevalence of overweight and obesity is generally higher among men than women. On average across countries, 60% of men were overweight compared to 47% of women. Among men, the prevalence varies from 53% in France, Italy and the Netherlands to more than 67% in Croatia and the United States (see Annex Table 2.A.3). Among women, the prevalence ranges between less than 40% in Italy, Cyprus, and Luxembourg and 55% or more in the United States and Malta.

In all countries, the most educated women are less likely to be overweight than the least educated (Figure 2.7). On average across all countries, 52% of women with less than a high school degree are overweight or obese compared to 36% of those with tertiary education. Slovenia, Luxembourg, Spain, France, Croatia, Portugal, and Austria display the largest gap, with differences of more than 20 percentage points. In Latvia, Malta, Finland, Canada and Norway these gaps are relatively small (10 percentage points or less).

Taking into account the entire distribution of education levels of the female population, the overall gradient of inequality is significant in all countries. Absolute education-related inequalities to the detriment of the least educated women are highest in Luxembourg, Spain and Slovenia. The lowest inequalities can be observed in Latvia, Malta and Canada (Table 2.3).

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Figure 2.7. Age-standardised probability to be overweight by education level, women

Note: Data from 30 European countries, Canada and the United States. Results correspond to the age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for all countries, except Malta.

Source: OECD estimates based on national health survey data.

The differences in the probability of being overweight between education groups are smaller for men than for women (Figure 2.8). On average across European and OECD countries, the probabilities of being overweight display inequalities to the detriment of the least educated men: 58% of men with the lowest level of education are overweight or obese compared to 54% of those with the highest level. The largest gaps between education levels are found in Luxembourg, Sweden, Spain and Denmark with differences of more than 10 percentage points. In the United States, Canada, Lithuania, Romania, Iceland and Bulgaria, this gap is smaller (less than 2 percentage points). In Latvia, Poland, Slovenia, Cyprus, Hungary, Estonia and Croatia men with the highest level of education have a higher risk of being overweight than those with the lowest level of education.

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Figure 2.8. Age-standardised probability to be overweight by education level, men

Note: Data from 30 European countries, Canada and the United States. Results correspond to the age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for Denmark, France, Germany, Greece, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, the United Kingdom and Canada.

Source: OECD estimates based on national health survey data.

Computed over the whole population, inequalities to the disadvantage of the least educated men are smaller than those for women, but not significant in 13 out of 32 countries. Absolute education related-inequalities are highest in Luxembourg, Denmark and Sweden (Table 2.3). Canada and the United States, display the smallest absolute education-related inequalities, followed by Portugal and Finland. In Poland, the gradient is reverse: the likelihood of being overweight and obese rises with educational attainment.

Interestingly, Luxembourg, France, Spain, Italy and Denmark are high inequality countries to the detriment of the least educated people among both men and women.

Overall, the results found here are generally aligned with (Devaux and Sassi, 2013[9]), which, across 11 OECD countries, found larger education-related inequalities in overweight in women than in men. Within Europe, their study showed that the largest inequalities existed in France, Sweden and Austria among men and in Spain, Italy and France among women. These countries are ranked similarly in Table 2.3.

Gender differences in education-related inequalities in overweight might relate to the fact that highly educated women are more concerned about weight control than men with the same level of education, notably because obesity has more adverse impact on labour market outcomes among women than men. Other possible explanations relate to the pattern of physical activity, where low-paid job for women are less physically demanding than those taken up by men (Devaux and Sassi, 2013[9]).

In most EU and OECD countries, the people with the lowest level of education are more likely to smoke

Tobacco consumption, which is the largest avoidable health risk in OECD countries, has been decreasing over the past decade from 23% in 2006 to 18% in 2017 (OECD Health Statistics 2019). Sill, tobacco consumption is a major public health issue worldwide. In European and OECD countries, men smoke more than women. In total, nearly one in three men were daily or occasional smokers compared to one in five women (see Annex Table 2.A.4). Among men, smoking rates vary from more than 40% in Romania, Greece, Lithuania, Latvia, Cyprus, and Bulgaria to 23% (or less) in Northern European countries (Sweden, Iceland, Norway, Finland, Denmark, and the United Kingdom) as well as Canada and the United States. Among women, the prevalence is highest in Croatia, Austria, Greece, and Bulgaria (above 25%). Smoking rates are comparably low in the United States, Portugal, Lithuania, and Romania (below 15%).

Practically everywhere across European and OECD countries, men and women of low education are more likely to smoke than the ones with higher education (Figure 2.9 and Figure 2.10). On average, 24% of the least educated women smoke compared to 12% of the most educated women (Figure 2.9). The gaps in smoking rates are the largest in the Czech Republic, Austria, Norway, the Slovak Republic, Hungary, Belgium and Ireland with differences of more than 17 percentage points. By contrast, these gaps are smaller than 5 percentage points in Bulgaria, Portugal, Greece, and Italy. Only Romania and Cyprus display a reverse pattern: Here, the most educated women are more likely to smoke than the least educated.

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Figure 2.9. Age-standardised probability of smoking by education level, women

Note: Data from 30 European countries, Canada and the United States. Results correspond to age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for all countries except Bulgaria, Cyprus, Greece and Portugal.

Source: OECD estimates based on national health survey data.

When the entire population is taken into account, inequalities in smoking among women are to the detriment of the least educated in a majority of countries. The overall gradient of education-related inequality in smoking, measured with the slope index of inequality that takes into account the full distribution across education groups, is significant, to the detriment of the least educated women, in 26 out of 32 countries. Inequalities are comparably large in Hungary and Ireland (Table 2.4). They are relatively small in Italy, Spain, Lithuania, France and the United States. In three countries (Romania, Portugal, and Cyprus), the gradient is reverse and significant. In other words, as education increases overall, so does the likelihood to smoke.

Social disparities in smoking are larger among men and more clearly play in favour of the most educated (Figure 2.10). On average across all countries, 39% of the least educated men smoke compared to 20% of the most educated men. The largest gaps are found in Hungary, Lithuania, Latvia, Belgium, Estonia, Poland and the Slovak Republic, with differences of more than 23 percentage points between the least and the most educated men. The smallest gaps can be observed in Romania, Greece and Italy with differences of less than 12 percentage points.

The overall gradient of absolute education-related inequality in smoking is significant in all countries. It shows a higher degree of inequalities in Lithuania, Hungary and Latvia, whereas inequalities are smaller in Romania, Greece, Italy, Sweden, France, Germany, and the United States (Table 2.4).

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Figure 2.10. Age-standardised probability of smoking by education level, men

Note: Data from 30 European countries, Canada and the United States. Results correspond to the age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for all countries and the average.

Source: OECD estimates based on national health survey data.

Cross-country variation in the risk of smoking by education level has been explored in previous research. Huijts et al., 2017_[25] show among a pool sample of men and women that the risk of being a daily smoker is higher when respondents’ level of education is lower in each country. Large education-related inequalities in smoking in favour of the most educated people have been found in Northern European countries, while a reverse gradient could be observed for Southern European countries (Huisman, Kunst and Mackenbach, 2005[24]). Less recently, Schaap (2010_[25]) showed that socio-economic inequalities in smoking were larger in Northern and Eastern European countries (Schaap, 2010[25]).

The results described in this chapter are generally in line with these studies showing that some Northern European countries (Ireland, and Norway) and Central and Eastern European countries (Hungary, the Slovak Republic, and Estonia) are high inequality countries in smoking to the detriment of the least educated people. Results also support a reverse gradient among women in some Southern European countries (including Portugal, and Cyprus).

While tobacco consumption is an addictive behaviour, differences in health literacy may also partly explain why people of lower education smoke more often than others. The least educated are less informed about the risks of smoking and are less able to act upon this information. People with low education have significantly higher initiation rates and are less likely to stop smoking than people with higher levels of education (OECD, 2017[16]). In addition, education may provide the necessary resources (both cultural and psychosocial) to cope with adverse personal events in a more healthy way than through smoking (Huisman, Kunst and Mackenbach, 2005[24]).

There is no clear social gradient in heavy drinking in the majority of countries

Heavy alcohol consumption (i.e. having a daily amount of pure alcohol of 20 grams or more for women, and 40 grams or more for men) increases the risk of harmful consequences in terms of both morbidity and mortality4 (Sassi, 2015[26]). Rates of heavy drinking display a large degree of variation across countries, and are generally more common among men than women. On average across European and OECD countries, 5% of men report heavy drinking compared to 3% among women (Annex Table 2.A.5). Among men, the United Kingdom, Romania, and Portugal have the highest proportion of heavy drinkers (above 10%), while these shares stand only at 1% in Cyprus and Iceland. For women, the share of heavy alcohol consumers is highest in the United Kingdom (17%), Ireland and Belgium (7-8%). By comparison, in Croatia and Estonia no heavy female drinkers were included in the EHIS-2 sample.

Contrary to the risk of smoking and of being overweight, the social gradient in heavy alcohol consumption is not clear (Figure 2.11). For women, the differences in heavy drinking between the least and most educated population groups are only significant in nine countries: in Lithuania, Portugal, Romania and Canada the least educated women drink more; in Austria, Belgium, Germany, Luxembourg and the United Kingdom the most educated women have a higher risk of heavy drinking (Annex Table 2.A.5).

At population level, heavy drinking is a greater problem as education decreases for women in three countries (Lithuania, Romania and Canada). In seven countries, better education is significantly associated with increased heavy drinking overall (Austria, Germany, Norway, Denmark, Belgium, the United Kingdom and Luxembourg) (Table 2.5). In the remaining 17 countries, there is no social gradient in heavy drinking.

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Figure 2.11. Age-standardised probability to be a heavy drinker by education level, women

Note: Data from 27 European countries and Canada. Results correspond to the age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for Austria, Belgium, Germany, Lithuania, Luxembourg, Portugal, Romania, the United Kingdom and Canada.

Source: OECD calculations based on national health surveys.

For men, the gradient is more consistent across countries and in the expected direction. In 25 countries, the highest educated men are less at risk of heavy drinking compared to those with the lowest level of education (although the relationship between education and heavy drinking is significant in only eight countries – see Annex Table 2.A.5). Lithuania, Romania, Portugal, the Slovak Republic, Belgium and Slovenia present the largest gap in heavy drinking between education levels – more than 6 percentage points (Figure 2.12).

For men, at population level, heavy drinking becomes less frequent as education rises in 13 countries. Inequalities are higher in Portugal, Lithuania, and Romania and smaller in Canada and Spain (Table 2.5). The social gradient is not significant in 15 countries.

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Figure 2.12. Age-standardised probability to be heavy drinker by education level, men

Note: Data from 27 European countries and Canada. Results correspond to the age-standardised probabilities estimated with logit models. Difference between education groups is significant at the 95% confidence level for Belgium, Bulgaria, Estonia, Hungary, Lithuania, Luxembourg, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain and Canada.

Source: OECD calculations based on national health surveys.

The difference of patterns in the social gradient in drinking behaviour between genders has already been demonstrated elsewhere. OECD (2015) showed that higher educated women are more likely to be heavy drinkers than their counterparts in lower education groups, while the reverse pattern is found for men in seven countries (least educated men are more at risk of heavy drinking) (Sassi, 2015[26]). The reverse social gradient among women is also confirmed by a meta-analysis covering 33 countries (Grittner et al., 2013[27]). Most plausibly, for women with higher education, drinking behaviour is associated with a wider range of social activities. They might have more opportunities of socialising and going out with colleagues compared to women with a lower level of education. It might also be possible that women with higher education drink more heavily because they take better paid jobs which involves higher degrees of responsibility and level of stress (Sassi, 2015[26]).

In all countries, the least educated have a higher risk of assessing their own health as poor

Self-assessed-health5 and other reported measures of health are good summary measures which capture different dimensions of health including medical and physical functioning. They are recognised as good predictors of people’s future health care use and mortality (Palladino et al., 2016[34]). However, as any other survey-based indicator, these measures of health are subject to reporting bias of self-reported data such as under-diagnosis and reporting errors. In addition, there are cross-country comparisons issues due to cultural differences.

Across OECD and European countries, Lithuania, Latvia and Portugal report the highest percentages of poor self-assessed health. Here, one in two persons consider their health status as not good. By comparison, these shares are below 20% in Ireland and Malta (Annex Table 2.A.6).

As expected, there is a large gap in self-assessed health across education groups (Figure 2.15): 44% of the least educated people assess their health as poor, compared to 23% of the people in the highest education group. Differences of 25 percentage points or more between education groups can be observed in the United States, Iceland, the Slovak Republic, Hungary, Luxembourg, and Portugal. In Romania, Malta and Italy these gaps are less than 16 percentage points.

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Figure 2.15. Age-sex standardised probability of reporting a poor-self assessed health status by education level

Note: Data from 30 European countries, Canada, Chile and the United States. Results correspond to age-sex standardised probabilities. Differences between education groups are significant at the 95% confidence level for all countries.

Source: OECD estimates based on national health survey data.

Although smaller than for self-assessed health, differences between the least and most educated population groups in the probabilities of reporting limitations in daily activities and multi-morbidities go in the same direction. People in lower education groups are 16 percentage points more likely to report limitations in daily activities, and 11 percentage points more likely to report multi-morbidity compared to their counterparts in higher education groups (Annex Table 2.A.6 and 7). For the risk of reporting multi-morbidities, the highest differences between education groups are found in the Slovak Republic, Luxembourg, Cyprus and Iceland, while the lowest differences are found in Romania, Poland, Latvia, Estonia, and Germany. For the risk of reporting limitation in daily activities, the highest differences between education groups are found in the Slovak Republic, Luxembourg, Hungary and Lithuania, and the lowest in Malta, Canada, Italy, Chile, and Romania.

Absolute inequalities in self-assessed health are the highest in Portugal, Luxembourg Hungary, the Slovak Republic and the United States

All 33 countries included in this analysis display inequalities in health to the detriment of the poor for all measures available. To measure and compare the magnitude of health inequalities across countries taking into account the distribution over the whole population, Slope index of inequalities were calculated for all three health variables (Box 2.5 presents the methodology). Table 2.7 shows that inequalities to the detriment of the poor exist for all variables in all countries, and ranks them in each case from the lowest to highest degree of inequality. For self-assessed health, Portugal and Luxembourg present by far the largest absolute inequalities to the detriment of the least educated people. Hungary, the Slovak Republic and the United States also present very large absolute inequalities in self-assessed health. By contrast, Romania, Chile, Sweden, the Netherlands, Austria, Italy, Denmark, Estonia, and Norway are low inequality countries.

There is some consistency in country rankings when also considering inequalities in limitations in daily activities and in multi-morbidity. Luxembourg, Portugal, Hungary and the Slovak Republic display high level of absolute inequalities for all three analysed health variables. To some extent, this is also true for Greece and Iceland. On other hand, Romania and Sweden are faring relatively well in having low education-related inequalities across the different health outcomes. Absolute inequalities also appear more limited in Chile, the Netherlands and Canada (see all numerical values in Annex Table 2.A.6-7).

These results support recent findings exploring inequalities in poor self-reported health in 26 EU countries (Leão et al., 2018[31]). Using cross-sectional data from the EU-SILC survey, high level of inequalities were found in some Southern European countries (including Cyprus, Greece, and Portugal) and in some Central and Eastern European countries (including Hungary, Slovenia, and the Slovak Republic). The use of the European Social Survey also confirmed that Greece and Portugal have large inequalities in self-reported general health (Eikemo et al., 2008[35]). EHIS data used in this chapter, however, does not point to Spain and even less Italy as being among the highest inequality countries with regards to self-assessed health – or other health indicators for that matter. This result is, however, consistent with findings from Mackenbach et al (2008[36]) which showed that inequalities in mortality and in self-assessed health were smaller in those two countries.

Outside of Europe, a recent analysis showed that gaps between the self-assessed health of groups with high and low education in various states of the United States were in general greater than in most European countries (Präg and Subramanian, 2017[37]). The population-level summary measure used here generally confirms this although some European countries (Portugal, Luxembourg, Hungary and the Slovak Republic) exhibit larger absolute educational inequalities.

Institutional determinants might partly explain these cross-country variations in socio-economic health inequalities. High level of income inequality, and fragmented social provision have indeed been associated with larger health inequalities (Eikemo et al., 2008[35]; Präg and Subramanian, 2017[37]; Ásgeirsdóttir and Ragnarsdóttir, 2013[38]; Leão et al., 2018[31]).

Some countries concentrate education-related inequalities in health status

Considering jointly the information on inequalities across different health status variables suggests inequalities are consistently higher (or lower) in some countries. Additional analyses combining the SIIs for self-assessed health, activity limitations and multiple chronic conditions was used to distinguish three groups of countries with low, intermediate and high levels of inequalities (see Box 2.6)6. Based on this, Figure 2.16 ranks countries by increasing life expectancy at birth while the colour of the bar indicates the level of inequality based on this grouping (darker shades correspond to more widespread inequalities to the detriment of the least educated). This analysis shows:

• The highest education-related inequalities in health status to the detriment of the least educated are found in Lithuania, Hungary, the Slovak Republic, Croatia, Portugal, Luxembourg, Iceland, Greece, Ireland, Belgium and Cyprus. In seven of these countries, the life expectancy at birth is above the average of all countries considered (80.2 years). The Slovak Republic, Hungary, Lithuania, and Croatia combine a relatively low life expectancy at birth with very large health inequalities to the detriment of the least educated.

• Northern European countries (Denmark, Sweden, the Netherlands, Germany), Poland, Romania, Estonia, Malta, Italy and Canada, Chile present the lowest level of education-related inequalities in health status to the detriment of the least educated. In four of these countries (Romania, Estonia, Poland, and Chile), the life expectancy at birth is below the average of all countries considered (80.2 years). Italy, Malta and Sweden combine a high life expectancy at birth but very low health inequalities to the detriment of the least educated.

• Bulgaria, Latvia, the United States, the Czech Republic, the United Kingdom, Finland, Austria, Slovenia, Norway, France, and Spain have more intermediate levels of inequalities in health.

Overall, the correlation between the overall health status in a country (as measured by life expectancy at birth) and the level of education-related health inequalities is very weak.

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Figure 2.16. Life expectancy at birth and summary level of education-related health inequalities across European and OECD countries

Note: *The data for multiple chronic conditions was not available for Chile, however various analyses based on comparisons of data for self-assessed health and activity limitations unambiguously demonstrate that the country belongs to the low inequalities group.

Source: OECD estimates based on national health survey data.

The ranking of countries is broadly consistent when using an alternative measure of inequalities

An alternative grouping using a similar methodology but comparing relative inequalities led to broadly consistent results with a few exception. Alongside SIIs, RIIs were computed which capture relative inequalities by education level (results are presented in Annex Table 2.A.6-7). The grouping obtained in the PCAs combining RIIs for self-assessed health, activity limitations and multiple chronic diseases is compared with the one obtained with SIIs in Figure 2.7. With some exceptions (Malta, Croatia and Lithuania), the pattern observed for absolute education-related inequalities in health status also generally applies to relative education-related inequalities in health status.

Malta would be clustered into the high inequality group if RIIs were used instead of SIIs to determine inequalities in health outcomes. This is due to the fact that the general prevalence in poor health outcomes in Malta–and hence the absolute differences between the different education groups- is comparably low. Looking at relative differences instead would signal higher overall inequalities. The reverse is true for Croatia and Lithuania: They would be grouped into the category of low inequality countries if RIIs were used instead of SIIs.

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Figure 2.17. Absolute and relative education-related inequality in health, European and OECD countries

Note: The groupings by level of absolute and relative inequalities in health status are derived from PCA analysis of the SIIs and RIIs (available in Annex Table 2.A.6-7). Data for multiple chronic conditions was not available for Chile so the country is not included.

Source: OECD estimates based on national health survey data.

Using income as a proxy of socio-economic status provides a different grouping of countries

While the focus of this section is on differences in health by education level, income is an alternative proxy of socio-economic status to measure health inequalities. Absolute and relative income-related inequalities for all three health indictors were estimated on the basis of SIIs and RIIs (results in Annex Table 2.A.8) and groupings by level of inequality were elaborated as above. As with education, results first show the existence income-related inequalities to the detriment of the poor in all countries considered. Figure 2.18 compares the grouping of countries by level of (absolute) education-related inequalities with that based on income. Income and education tend to be differently associated to health status across countries. In particular, results show that:

• There is a relatively weak correlation between absolute education- and income-related inequalities (correlation coefficient of 0.23).

• 14 out of 32 countries belong to the same groups of inequality whether inequalities are captured through education or income level (Romania, Poland, Malta, Germany, Italy, France, Latvia, Finland, Spain, Belgium, Croatia, Iceland, Hungary, Luxembourg).

• Among the other countries, Sweden, Estonia and Denmark have low levels of education-related inequalities in health status but have high levels of income-related inequalities. The same is true for Slovenia, the United Kingdom and the United States, which have all high levels of income-related health inequalities and intermediate levels of education-related health inequalities. In these countries, social health inequalities are thus more pronounced when income is used as a measure of the differences in the social structure of the countries. In line with these results, the United States, the United Kingdom, Estonia and Slovenia have previously been found as high income-related health inequalities countries (van Doorslaer et al., 1997[39]; Ásgeirsdóttir and Ragnarsdóttir, 2013[38]). More recently, Hero, Zaslavsky and Blendon (2017) have shown that the United States, Chile and Portugal have among the largest income-related differences in self-rated health (Hero, Zaslavsky and Blendon, 2017[40]). In that regards, results presented in this chapter for Chile differ from previous findings: SII measured by education and income point to low levels of inequalities in Chile compared to other OECD countries (results in Annex Table 2.A.8). Although health inequalities have been reduced recently in Chile (Cabieses et al., 2015[41]), further research would need to confirm this result.

• Greece, Lithuania, Ireland, Norway, Austria, Czech Republic, and Bulgaria show a reverse pattern. They have low levels of income-related health inequalities combined with high (Ireland, Lithuania and Greece) or medium (Bulgaria, the Czech Republic, Austria and Norway) levels of education-related health inequality.

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Figure 2.18. Absolute inequalities in health status comparing education and income

Note: The rank of absolute inequalities in health status are derived from PCAs analyses presented above (box 2.6). Numerical results are available upon request. The data for multiple chronic conditions was not available for Chile so the country is not included.

Source: OECD estimates based on national health survey data.

The fact that countries have different levels of inequalities depending on whether the summary marker of people’s social position is income or education is not a surprise:

• Each country is characterised by a distribution of the population across education levels which can be more or less unequal;

• The wealth gap between people in the first and last income quintile is also likely to vary across countries;

• Finally, the marginal impacts of income and education on health are unlikely to be identical.

A similar set of arguments could be made to explain why patters between relative and absolute inequalities differ.

Systematically linking the differences in the above pattern with countries characteristics is clearly beyond the scope of this report. Their presentation, however, highlights a number of points:

Pretty much no matter how they are measured, inequalities in health status are a problem in all countries considered;

Some countries clearly have high levels of inequality, no matter how they are measured (e.g., Belgium, Croatia, Iceland, Hungary and Luxembourg). Others are doing better with low levels of inequalities (Romania, Poland, Malta, Germany, and Italy) but some of them need to figure out how to do better on average health outcomes while trying not to leave people behind (Romania and Poland);

At the end of the day though, deciding on priorities and policies to tackle health inequalities requires looking at various measures simultaneously –it is never enough to look at one type of measure of inequality for one indicator on one measure of welfare.