To Have and Have Not – How to Bridge the Gap in Opportunities

Report

22 September 2025

2. Levels and trends in inequality of opportunity: How fairly are opportunities distributed in OECD countries?

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Abstract

This chapter applies the measure developed in Chapter 1 to a large subset of OECD Member and accession countries for which comparable data are available. It assesses levels and recent trends in inequality of opportunity across these countries. The chapter also provides evidence on which circumstances matter most as determinants of economic opportunity. In order to do so, it analyses the relative importance of different key inherited and individual factors (e.g., country of birth, parents’ socioeconomic status, family composition at age 14…) in shaping income. Furthermore, the chapter considers for whom these circumstances matter by looking at the way in which opportunities vary across different population groups, with a specific focus on generational differences and differences between men and women.

2.1. Analysis of levels and trends in inequality of opportunity

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2.1.1. Levels and trends in inequality of opportunity in OECD countries

This chapter presents and discusses new evidence on inequality of opportunity across a large subset of OECD countries, based on the measure developed in this report (see Chapter 1). The analysis in this chapter covers 29 OECD Member countries and 3 accession countries.1 While the estimates are based on different data sources and questions (see Annex 1.B for further detail), efforts have been made to enhance ex post cross-country comparability. The chapter is organised as follows. Section 2.1 presents the observed levels and trends in inequality of opportunity across the OECD countries studied. Section 2.2 examines the relative importance of different circumstances (e.g., parents’ socioeconomic status, family composition…) in shaping opportunities. Section 2.3 analyses the variation in opportunities across different population groups, with a focus on generational differences and differences between men and women. Finally, Section 2.4 provides a summary of the chapter’s key findings.

On average, across the OECD, over a quarter at least of today’s inequality in household market income can be attributed to circumstances beyond people’s control, such as their sex and country of birth or their parents’ socio-economic background (see bars in Figure 2.1).2 This suggests that a significant share of income disparity is shaped by factors that individuals inherit rather than by factors that reflect their own efforts or merit. This result confirms the persistent influence of socio-economic background on life outcomes, as highlighted in previous OECD work (OECD, 2018[1]). In doing so, it underlines the importance of effective policy responses to promote opportunities and ensure a more level playing field. As discussed in the chapter, it also points to the role that societal attitudes have to play in achieving these goals.

There is considerable variation across OECD countries in terms of the extent of inequality of opportunity. Switzerland and several Nordic countries have the lowest levels of relative inequality of opportunity, with shares below 15%. By contrast, inequality of opportunity tends to be higher in Southern and Eastern Europe, as well as some non-EU countries. In countries including Belgium, Chile, Ireland, Luxembourg, Poland, Portugal, Spain and the United States, the share is above 35% of total income inequality. These results are in line with previous estimates (Brunori, Hufe and Mahler, 2023[2]). Country rankings remain largely consistent when moving from relative levels of inequality of opportunity (represented by bars in Figure 2.1) to absolute levels of inequality of opportunity (represented by diamonds) – that is, the level of inequality that would prevail in a given country if outcomes were determined only by the set of circumstances measured.3

While absolute and relative inequality of opportunity tend to be aligned, discrepancies may nonetheless be observed for countries with comparatively high or low levels of income inequality. For instance, some countries with higher-than-average market income inequality (e.g., the United States and Chile) exhibit lower relative inequality of opportunity than would be expected based on their absolute levels. Conversely, some countries with lower-than-average market income inequality (e.g., Czechia and the Slovak Republic) display comparatively higher levels of relative inequality of opportunity than expected based on absolute levels. What these latter cases show is that, where income disparity is low, even modest levels of absolute inequality of opportunity may represent a large share of total inequality. This in turn will translate into high levels of relative inequality of opportunity. These differences underline the value of considering absolute and relative measures when assessing inequality of opportunity.4 Taking account of both type of measure offers a more balanced view of the intergenerational transmission of advantage and disadvantage. It can also help identify priority areas for policy intervention – a point further illustrated in the discussion of trends below.

Figure 2.1. Inequality of opportunity in household market income varies greatly in OECD countries
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Relative and absolute inequality of opportunity, individuals aged 25-59, by country, 2019 or latest available year

National averages may hide significant within-country differences in the distribution of opportunities and resulting inequality. While most of the available sub-national evidence focuses on the experience of the United States (Chetty et al., 2014[3]), Box 2.1 extends the analysis of inequality of opportunity to a subset of OECD countries. The large variation in regional levels of inequality of opportunity depicted in Figure 2.2 and Figure 2.3 reflects economic differences, as well as differences in the degree of regional autonomy in education and health policy. As discussed in Chapter 3, regions and places with stronger economies often have more resources and therefore greater means to improve access to quality public services.

Box 2.1. Going beyond national averages

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As discussed at greater length in Chapter 3 of this report, geographic location is an important source of inequality of opportunity. Local contextual factors – and differences between them across regions, but also within cities – play an important role during childhood and continue to affect people’s opportunities over the life-cycle through their access to public services and job, training and digital opportunities. If people were able to freely move without constraint and according to their preferences, geographic differences would be the result of residential self-selection: people would live where locally available facilities and resources suit their preferred lifestyle. However, several factors – including high housing prices and family ties – often limit people’s ability to move to areas with better opportunities, thereby constraining the extent to which geographic mobility can help overcome spatial inequalities.

Within-country comparisons are therefore important for informing policy decisions and for monitoring, as they can help identify where the key barriers lie and which local factors need to be targeted as a priority. Ideally, the analysis would draw on a very fine-grained territorial grid (possibly down to the neighbourhood-level). However, data sources are rarely designed to allow for such granular territorial disaggregation. Additionally, the focus should be on the place of residence during childhood, not only because children have no control over where they grow up, but also due to the “dosage effect” described in Chetty and Hendren (2018[4]). This effect posits that the younger children are when they move to a high-opportunity area, the stronger their chance of moving up the income ladder as adults. In practice, however, retrospective questions available in large and comparable surveys typically do not include information on the place of residence at age 14. While the analysis below highlights regional variation in inequality of opportunity, it is important to note that a change in the territorial framework could yield different results. This is because the influence of location on opportunities varies depending on the context – e.g., the neighbourhood level may be more relevant than the regional level when it comes to analysing access to education.

Figure 2.2 shows differences in levels of relative inequality of opportunity within regions for countries with available geographical information. The geographical grid used varies depending on the size of the regional samples. Although the estimates are computed for the current region of residence, the latter can serve as a proxy for the place of residence at age 14. Even though the region of residence in adulthood is typically a matter of choice, many people stay close to their birthplace throughout their adult life, as noted in Chapter 3, and internal mobility has been declining in a number of OECD European countries (Alvarez, Bernard and Lieske, 2021[5]).

Figure 2.2 reveals significant cross-country differences in regional inequality of opportunity, which points to the role played by factors operating at territorial level. Inequality of opportunity is particularly high in several Spanish and Polish regions, reflecting the relatively high national levels in these countries. It is also pronounced in the French regions of Ile de France, Hauts-de-France and Auvergne-Rhône-Alpes, as well as in the Brussels-Capital region in Belgium. Generally, larger European countries exhibit wider cross-regional disparities, with the exception of Belgium where relative inequality of opportunity in the Brussels-Capital region is nearly double that in Flanders. In Figure 2.2 each region is considered as a separate jurisdiction. An analysis of the way in which opportunities are distributed between regions can shed light on the importance of geographic location relative to other key determinants of inequality of opportunity.

Figure 2.2. Geographical variation in inequality of opportunity can be large within countries
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Relative inequality of opportunity (as a % of total inequality right vertical axis), by region, selected OECD countries, 2019

In Figure 2.3, the region of residence is also considered when measuring inequality of opportunity. Although this analysis is exploratory and limited to a selection of countries with large regional samples, it highlights the critical role that territorial factors play in shaping inequality of opportunity. When including region of residence among the set of circumstances, levels of relative inequality of opportunity increase significantly in some countries. For instance, in Finland, France, Italy and Spain, the level rises by over 8 percentage points, compared to the baseline scenario in which region of residence is not considered. Moreover, in terms of relative importance, the effect of region of residence in France is comparable to the combined effect of individual factors and parents’ country of birth, and in Finland it exceeds the effect of parental background (Figure 2.3). See Section 2.2 below for further analysis and discussion of the role of different circumstances.

Figure 2.3. The region of residence has a significant impact on inequality of opportunity
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Relative inequality of opportunity (% of total inequality, diamonds, RHS) and Shapley-Shorrocks decomposition of the relative (predictive) importance of different circumstances (%, LHS), 2019

It is important to consider both inter-regional and intra-regional inequality when assessing the distribution of opportunities at the sub-national level. Italy is an interesting case in point. Although inequality of opportunity within regions is more uniform in Italy than in other countries that exhibit similar or even lower levels of inequality of opportunity, such as France (see Figure 2.2), the disparity between Italian regions is significant. This is reflected in the substantial impact that the respondent’s current large region of residence has on inequality of opportunity.

Figure 2.4. Across countries, inequality of opportunity in household market income has converged towards a higher average level
Copy link to Figure 2.4. Across countries, inequality of opportunity in household market income has converged towards a higher average level

Relative inequality of opportunity (% of total inequality), individuals aged 25-59, by country and year

Trends in relative inequality of opportunity vary considerably across OECD countries. Recent patterns suggest a general convergence towards higher levels. While available data do not allow for long-run cross-country comparisons,5 Figure 2.4 shows that countries where relative inequality of opportunity has increased, such as Austria, the Netherlands, Portugal, Spain and some of the Nordic countries, also tended to start from lower initial levels – typically below 30% of total inequality in the mid-2000s (see Panels D to G). Conversely, countries that experienced a decline in inequality of opportunity tended to start from higher baseline levels, as can be seen for Estonia, Poland, the United Kingdom and the United States (Panel A). Overall, this has translated into a general upward convergence in inequality of opportunity across countries.6 Specifically, the median increase in inequality of opportunity is of 7 percentage points, compared to a median decrease of 3 percentage points. As a result, on average, relative inequality of opportunity is higher today than it was 20 years ago.

Available country data on long-term trends suggest that inequality of opportunity is affected both by cyclical and structural factors. The short-term trends observed here may illustrate how cyclical factors, such as economic crises and their policy responses, can influence existing inequalities of opportunity. Research focusing on the role played by the Global Financial Crisis also provides evidence suggesting that it has had a significant impact on trends in inequality of opportunity, particularly by affecting the most vulnerable segments of society. For instance, Brzezinski (2015[6]) confirms an overall increase in inequality of opportunity in Europe in the immediate aftermath of the Global Financial Crisis, while highlighting strong regional differences. Looking at its effects on vulnerable populations, Brzezinski (2015[6]) notably finds that in Belgium, which experienced a significant increase in inequality of opportunity between 2005 and 2011, the relative position of migrants deteriorated even further over that period, despite already being among the worst-off prior to the crisis.

Research on the medium- to long-term evolution of inequality of opportunity has been conducted in a small number of countries where data are available. These data confirm that intergenerational mobility is also influenced by structural changes. For example, the decline in intergenerational mobility observed in Denmark since the late 1950s can be attributed primarily to demographic changes, including an increase in single parenthood and delayed childbearing among higher-income parents. These factors have had a negative impact on mobility. Delayed childbearing allows parents to accumulate more financial resources and stability, enabling greater investments in their children’s education and well-being. Furthermore, wealthier parents tend to have fewer children overall, allowing for a more concentrated allocation of resources and attention, which enhances the developmental outcomes of their children. These disparities in investment between higher- and lower-income families also widen gaps in educational outcomes by socio-economic background, thereby entrenching inequality across generations. As an additional element to consider, changes in work experience and in economic policies may have contributed to further disadvantage low-income families (Harding and Munk, 2019[7]).

Intergenerational mobility also depends on how income and skills are distributed among the parents’ generation. While the initial impact of a change in social and demographic dynamics may not be significant for the first generation, its effects can become stronger and more significant for future generations (Harding and Munk, 2019[7]). For instance, a shift towards a more meritocratic society – where the influence of one's own skill and effort becomes more important relative to that of parental background – will benefit talented children from poor families. However, while mobility increases in the first generation affected, it is likely to decline again in subsequent generations if the more highly rewarded skills of the upwardly mobile are passed on to their children.

Also of interest, the observed increase in the average level of inequality of opportunity across OECD countries has come at a time when inequality of outcomes has tended to fall. A decomposition of inequality trends suggests that inequality of outcomes and inequality of opportunities have diverged in the period following the Global Financial Crisis (see Figure 2.5). Different dynamics can be observed when comparing trends in income inequality (Panel B) and in absolute inequality of opportunity (Panel C) over this period. Both measures rose markedly in the immediate aftermath of the crisis. However, the rate at which they have declined from their post-crisis peak has differed significantly. Whereas income inequality quickly returned to pre-crisis levels and continued to decline, the decrease in absolute levels of inequality of opportunity from peak levels has been more gradual and limited. As a result of these divergent trends, relative levels of inequality of opportunity have risen (Panel A), suggesting that, overall, the role of inherited circumstances in shaping outcomes has increased and continues to increase despite the observed fall in inequality of outcomes. These results confirm previous OECD research which indicated a long-term decline in equality of opportunity.7

Further research is needed to explain the different dynamics observed in the recovery between inequality of outcomes and inequality of opportunity. Possible explanations could include the following. The post-crisis job recovery may have helped reduce income inequality without addressing the structural barriers affecting opportunities. Furthermore, the stabilisation policies put in place during the crisis may have been effective in supporting income and limiting disparities in outcomes but may have placed less emphasis on longer-term measures to promote opportunities, such as investment in education. Alternatively, rising inequality of opportunity may primarily reflect the effects of long-term structural trends that are largely independent of the post-crisis recovery, such as digitalisation and changing patterns in employment.

Figure 2.5. Inequality in household market income and inequality of opportunity have diverged following the Global Financial Crisis
Copy link to Figure 2.5. Inequality in household market income and inequality of opportunity have diverged following the Global Financial Crisis

Relative inequality of opportunity (% of total inequality) (Rel. IOp, Panel A), Gini at household market income (Panel B) and absolute inequality of opportunity (Abs IOp, Panel C), individuals aged 25-59, OECD average

Box 2.2. Long-term trend in inequality of opportunity: The case of the United States

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The Panel Study of Income Dynamics (PSID) is the longest-running longitudinal household survey in the world (see Annex 1.B for more detail). It provides a consistent and reliable source of information for monitoring the evolution of inequality in outcomes and opportunity over an extended period of time. The PSID has helped document the long-term rise in income inequality in the United States, as covered extensively in the economic literature (Saez and Zucman, 2020[8]).

Income inequality has been on a consistent upward trajectory since the late 1960s, peaking in the mid-2000s and remaining stable over the following decades. Prior to the mid-1970s, inequality of opportunity was declining, even as income inequality continued to rise. However, from the mid-1980s onwards, income inequality and absolute inequality of opportunity have increased in parallel and at a similar pace. This resulted in a period where relative inequality of opportunity remained stable (see Figure 2.6).

Part of the observed evolution in inequality of opportunity may reflect long-term developments in income mobility between groups, which have been shaped by major historical events. For example, mobility increased significantly among individuals born between the 1910s and 1940s, with the narrowing of Black-White income gaps accounting for approximately half of this improvement (Jácome, Kuziemko and Naidu, 2021[9]). This rise in mobility coincided with the Great Northward Migration, during which around 6 million African Americans relocated from rural Southern states to other regions of the United States over the periods 1910-1940 and 1945-1970 (Kelly-Hall and Ruggles, 2004[10]). However, mobility trends for cohorts born after the 1940s are less clear and more complex to interpret. While a short-term improvement was observed in the late 1960s, possibly related to the 1964 Civil Rights Act, it is important to note that race is not included among the circumstances used to estimate inequality of opportunity in Figure 2.6.

Figure 2.6. Over the past half century in the United States, inequality in household market income and inequality of opportunity have tended to rise hand in hand
Copy link to Figure 2.6. Over the past half century in the United States, inequality in household market income and inequality of opportunity have tended to rise hand in hand

Household market income inequality (Gini) and absolute (Abs. IOp) and relative (Rel. IOp) measures of inequality of opportunity in household market income in the United States, individuals aged 25-59, 1968 = 100

2.1.2. Interpretation of the results

Public perceptions align closely with the levels of inequality of opportunity observed in the analysis. As highlighted in OECD (2018[1]), the comparison with perceptual indicators can help contextualise the evidence collected on levels and trends in inequality of opportunity across countries. It can also help explore the impact they may have on the broader political economy. Figure 2.7 shows that inequality of opportunity is positively correlated with the perception that coming from a wealthy family matters for success (Panel A), as well as with preferences for greater action to promote opportunities (Panel B). These correlations underline the consistency of the proposed measure with established findings in the empirical literature on public perceptions and attitudes towards inequality of opportunity (OECD, 2023[11]; 2023[12]; 2023[13]).8

Figure 2.7. Measuring inequality of opportunity can shed light on cross-country differences in public attitudes and preferences
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Relation between relative inequality of opportunity (as % of total inequality) and beliefs on the role of parental socio-economic background in getting ahead in life (Panel A) and preference for more opportunities (Panel B), by country, 2019 or latest available year

Inequality of outcomes and inequality of opportunity often go hand in hand. The so-called “Great Gatsby Curve” helps visually illustrate the negative relation between intergenerational mobility and income inequality across countries.9 It shows that income mobility (proxied by earnings mobility between fathers and sons) is lower where inequality of outcomes (proxied by Gini coefficients of income inequality) is higher. There is no clear and established theoretical link between income mobility across generations, inequality of opportunity and income inequality at a point in time (Durlauf, Kourtellos and Tan, 2022[14]). However, Figure 2.8 does confirm that the Great Gatsby Curve holds when moving from intergenerational income persistence to inequality of opportunity, even though the relative homogeneity of OECD countries may weaken the strength of this relation.10 The observed correlation does not prove that there is a causal relation between inequality of outcomes and inequality of opportunity, but its strength and stability suggest a link. One possible explanation is that societies with high income inequality may also exhibit structural features that impede social mobility. Recent research suggests the possibility of a cyclical dynamic: high inequality in the present can result in reduced mobility in subsequent generations, thereby further intensifying inequality in the future (Durlauf, Kourtellos and Tan, 2022[14]; Narayan et al., 2018[15]). Despite this correlation, there are a number of notable exceptions. In the Nordic countries, for instance, market income inequality appears to be mid-range, while inequality of opportunity is much lower than in the majority of other countries. Conversely, in Bulgaria and Romania, the opposite is true, with inequality of opportunity being much higher than expected given their level of market income inequality.

Figure 2.8. More unequal countries also exhibit higher inequality of opportunity
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Relative inequality of opportunity (as % of total inequality) and household market income inequality (Gini), individuals aged 25-59, 2019 or latest available year

Differences in policy frameworks and labour market structures can help explain some of the variation in inequality of opportunity across OECD countries. Prior research has underlined the pivotal role of early childhood in human development. Findings indicate that investment in early childhood education not only generates substantial long-term benefits but also contributes to mitigate the impact of family background on students' academic performance by the eighth grade (OECD, 2017[16]). As shown in Figure 2.9, relative inequality of opportunity in household market income is negatively related to current public spending in early childhood education and care (ECEC) as a share of GDP (Panel A). This underlines the importance of early investment in setting children up for success and reducing intergenerational disadvantage. This negative correlation is also observed when looking at public investment in ECEC since the early 1980s (Panel B). However, explaining the long-term correlation would require further analysis. The fact that it holds over time could reflect the dynamic nature of inequality of opportunity and the long-term benefits of investment in early education and care on labour market outcomes. It may also reflect continuity in levels of investment by countries (i.e., relative stability in the country ranking in terms of public spending on early childhood education and care).

Figure 2.9. Public spending on early childhood education and care is associated with lower levels of inequality of opportunity
Copy link to Figure 2.9. Public spending on early childhood education and care is associated with lower levels of inequality of opportunity

2.2. Decomposing inequality of opportunity: Which circumstances matter most in life?

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Understanding the relative importance of different circumstances is crucial to design effective and targeted policies for promoting opportunities and ensuring a more level playing field. The evidence presented below does not identify the channels through which circumstances affect the distribution of household market income and only highlights patterns rather than cause-and-effect relationships.11 However, it still offers valuable insights into how inherited disadvantage is transmitted and can inform opportunity-enhancing policies.

The analysis confirms that parents’ socio-economic background is a key driver of inequality of opportunity. In around half of the countries studied, parents’ educational level and parents’ occupation each account for one-sixth of total inequality of opportunity, significantly more than the 9% rate that would be expected if all the factors considered were of equal importance (see Figure 2.10, Panel A). In three-quarters of the countries, over 60% of the observed inequality of opportunity can be attributed to the total combined effect of these factors12 and in a quarter of countries that total exceeds 75% (see Figure 2.10, Panel B). Overall, paternal educational background tends to play a slightly larger role, with a median importance of 19%, while maternal occupational background has a slightly lower influence, with a median impact of 13%. However, this difference may simply reflect the weaker labour market ties for women in previous generations.

By contrast, the impact of individual factors and parents’ country of birth on inequality of opportunity is less pronounced overall. For example, the measured effect of gender on inequality of opportunity is minimal in most OECD countries (Panel A). While this result may seem counterintuitive, it partly reflects the fact that the welfare concept used – household market income – does not account for intra-household inequality in earnings and the allocation of resources and duties. To investigate the role of gender in greater depth, Section 2.3 below complements this analysis by computing measures of inequality of opportunity in individual earnings (i.e., wages) for full-time employees. Finally, childhood environment factors offer a more mixed picture. The presence of parents and homeownership status have a minimal impact in most countries. The relative importance of the degree of urbanisation of the childhood environment varies across countries. While it has an average importance in half of the countries studied (with a median of 9%), it contributes to more than 20% of inequality of opportunity in some cases.

The relative importance of the different factors in shaping inequality of opportunity varies considerably between countries. Figure 2.11 shows that in Norway less than 30% of overall inequality of opportunity can be attributed to parental background, whereas in Hungary it accounts for over 80%. A cluster analysis identifies four distinct groups:

1. The first group (Group 1 in Figure 2.11), which includes Austria, Belgium, Norway, Spain and Sweden, is characterised by a strong impact of individual factors and parents’ country of birth on inequality of opportunity. Furthermore, for countries in this group, maternal background plays a larger role than paternal background, both in terms of education and occupation.

2. The second group (Group 2 in the chart), which includes Denmark, Finland and Portugal is characterised by the high importance of both parents' occupation in shaping inequality of opportunity, as well as by the influence of childhood environment factors, particularly homeownership status and the degree of urbanisation.

3. The third group (Group 3 in the chart), which includes Lithuania and Eastern and Central European countries, is characterised by the key influence of parental background (both father’s and mother’s) and the degree of urbanisation of the area where individuals grew up. Conversely, in these countries, individual circumstances and parents’ country of birth only play a marginal role.

4. Finally, a last group (Group 4 in the chart), including all remaining OECD EU countries and the United States, is characterised by a higher-than-average influence of paternal background (both in terms of educational level and occupation), along with a moderate effect of individual factors and parents’ country of birth. These results align with previous research (Brunori, Hufe and Mahler, 2023[2])

Figure 2.10. Parents’ background explains most of the inequality of opportunity in household market income
Copy link to Figure 2.10. Parents’ background explains most of the inequality of opportunity in household market income

Distribution of the Shapley-Shorrocks decomposition (percentages) of the relative (predictive) contribution of different circumstances to inequality of opportunity in household market income, OECD 24, 2019

The relative importance of different circumstances varies with the overall level of inequality of opportunity. Examining the correlation at country-level between the contribution of these various factors and the measured level of inequality of opportunity suggests that the relative importance of the respondent’s sex and of the presence of parents is higher in countries with lower levels of inequality of opportunity. Similarly, the relative contribution of parents’ background increases as inequality of opportunity grows larger. These patterns may reflect the fact that the effects of certain factors are harder to address through policy means alone. Progress in reducing overall levels of inequality of opportunity may therefore often result from successful efforts to address systemic issues and compensate for the effect of external circumstances in areas where effective policy levers exist, such as education and skills, social policy, urban and territorial development. This may include, for example, efforts to reduce inequalities in access, such as investing in affordable quality education to limit the influence of parental background on individual outcomes. Conversely, the remaining drivers of inequality of opportunity tend to be less tractable for policy in the short-term as they involve more deeply rooted factors, such as personal characteristics and the effect of social norms (for instance on gender roles and household decisions). Finally, it should be noted that the observed variation across countries may also be driven by differences in societal structures.

Figure 2.11. The role of various factors in shaping inequality of opportunity differs considerably between countries
Copy link to Figure 2.11. The role of various factors in shaping inequality of opportunity differs considerably between countries

Shapley-Shorrocks decomposition (percentages points difference to the OECD average) of the relative (predictive) contribution of different circumstances to inequality of opportunity in household market income, by country, 2019

2.3. Inequality of opportunity across demographic groups: For whom do specific circumstances matter most?

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Aggregate measures of inequality of opportunity can mask disparities in individual outcomes and circumstances across various demographic groups, including gender and age. This section supplements the evidence discussed above by delving deeper into how different cohorts and genders experience inequality of opportunity.

2.3.1. Inequality of opportunity across generations and over time

Age and cohort effects influence inequality of opportunity in distinct and interacting ways. First, inequality of opportunity can be expected to differ between age groups, due to age-related income dynamics (OECD, 2018[1]) and the gradual weakening of intergenerational disadvantage as people grow older. Second, different levels of inequality of opportunity are also likely to be observed across generations, as people born in different periods experience distinct economic and social conditions. Structural and cyclical factors, such as changes in policy settings or economic crises, can affect opportunities differently across cohorts. For instance, while younger generations have benefitted from expanded access to education, they may also face new challenges arising from increased competition or precarious labour market conditions. Finally, demographic shifts, including population ageing and declining fertility rates, may have differentiated effects on opportunities. For example, population ageing puts pressure on public finances (OECD, 2021[17]; Rawdanowicz et al., 2021[18]), which can in turn limit the resources available for investment in areas of social spending that are key to fostering equal opportunity, such as early child education and care and higher education.

The interplay between age and cohort effects is complex, making their combined impact on inequality of opportunity difficult to predict. To shed light on the way in which inequality of opportunity, measured in terms of household market income, varies at different ages and across cohorts, the sample is split into four 10-year birth cohorts, from those born after 1949 to those born before 1991.13 As a general caveat, it should be noted that, due to the short timeframe over which repeated cross-sectional data are available (mainly from 2005 to 2023), the analysis does not allow for the computation of complete age profiles (i.e., how inequality of opportunity evolves over the life cycle) for any of the cohorts studied. In particular, the more recent the cohort, the shorter the observation window on their working lives will be. Moreover, comparisons across cohorts are limited to two data points (around the ages of 30 and 40 for the 1980 and the 1970 cohorts; around 40 and 50 for the 1970 and the 1960 cohorts; and around 50 and 60 for the 1960 and the 1950 cohorts). Despite these limitations, the analysis can help shed light on some of the mechanisms affecting inequality of opportunity across generations (see Figure 2.12). To ensure sufficient sample sizes, OECD EU countries are grouped into four regional clusters: Northern, Western, Southern, and Central and Eastern Europe.

Figure 2.12. In a majority of countries, inequality of opportunity is higher for younger cohorts
Copy link to Figure 2.12. In a majority of countries, inequality of opportunity is higher for younger cohorts

Relative inequality of opportunity (% of total inequality) by cohort and age

Several trajectories are possible when considering how inequality of opportunity evolves with age.14 To understand these patterns, it is important to contextualise them within the broader socio-economic developments that influence opportunities over time. Economic shocks can introduce long-term discontinuities in income trajectories, especially for cohorts entering the labour market during downturns (Karonen and Niemelä, 2020[19]). Japan's “lost generation” of the 1990s, for example, provides an illustration of how economic downturns can lead to lasting disadvantage. While some cohorts may eventually “catch up” and avoid permanent scarring effects (Freedman, 2024[20]), within-cohort inequality tends to increase as people age (OECD, 2017[21]) – a process that may be exacerbated by severe business cycle fluctuations (Crystal, 2021[22]).

Looking at the evolution of inequality of opportunity over the life cycle, different cohort- and region-specific patterns emerge, as highlighted in Figure 2.12:

In Northern European countries, (shown in Panel A), inequality of opportunity has remained relatively stable at a low level for the cohort born in the 1960s, suggesting that age has had a constant effect for that cohort. Conversely, for the cohort born in the 1970s, there are signs of a scarring effect, with relative inequality of opportunity increasing by about 7% between ages 30 and 50 – a period which coincides with the Global Financial Crisis.
In Western European countries (Panel B), trends in inequality of opportunity for the cohort born in the 1970s exhibit a pronounced inverted U-shaped effect. Here, an increase in inequality of opportunity is initially observed, peaking around the time of the Global Financial Crisis, and followed by a relative decrease in inequality of opportunity, though the overall effect results in higher inequality of opportunity at older ages compared to earlier years.
A similar pattern is observed for the 1960s and 1970s cohorts in Southern Europe (Panel C), while the effect of age remains relatively constant for the 1980s cohort during their 30s and 40s.
In Central and Eastern European countries (Panel D), a compensation effect is observed, with inequality of opportunity steadily declining with age for most generations.
In the United States (Panel E), inequality of opportunity for the cohorts born in the 1960s and 1970s also exhibits an inverted U-shaped effect.
By contrast, in the United Kingdom (Panel F), inequality of opportunity declines markedly with age for all cohorts.

However, across areas there is a relatively homogeneous trend towards rising inequality of opportunity over time. With a few exceptions, younger generations tend to exhibit higher levels of inequality of opportunity at a specific age than preceding generations. For instance, in Southern European countries, at age 40, people born in the 1970s faced levels of inequality of opportunity over 60% higher than for the previous cohort at the same age.15 Similarly, in the United States and in Southern Europe, at age 30, the 1980s cohort faced greater inequality than the 1970s cohort, with differences as large as 20% (see Figure 2.12).

The expansion of higher education could have been expected to result in lower inequality of opportunity for younger generations, but other factors may have counterbalanced its effects. Greater access to tertiary education has also meant that younger generations tend to spend more time in school, with many delaying their entry into full-time employment until their 30s (Häusermann and Schwander, 2010[23]).16 Here again, it is important to consider broader labour market dynamics and issues of timing when analysing trends in inequality of opportunity. For example, structural changes in the labour market, such as the rise in non-standard work, have predominantly affected younger workers.17 Moreover, higher levels of inequality of opportunity among younger cohorts may be due to the specific timing of economic shocks. For instance, a large share of the cohort born in the 1980s entered the labour market during or in the immediate aftermath of the Global Financial Crisis. While economic downturns affect all workers, they tend to have a stronger impact on young people’s ability to find or stay in work, as they are more likely to have temporary contracts and fewer company-specific skills (Escalonilla, Cueto and Pérez-Villadóniga, 2022[24]; Karonen and Niemelä, 2020[19]; Carcillo et al., 2015[25]).

Survey data provide some evidence of cohort effects, with changes in opportunities giving rise to significant differences in perceptions and attitudes between generations. Data collected through the Opportunities Module of the 2022 OECD Risks that Matter Survey reveal age-related differences in meritocratic beliefs. These findings align with the analysis conducted in this section, which shows that younger generations experience higher levels of inequality of opportunity in several countries. Younger respondents were more likely to view individual factors as important determinants for success in life and to place less emphasis on factors related to effort and merit in determining life outcomes (OECD, 2023[11]). The importance of early experience in shaping core beliefs about inequality throughout life is widely recognised in the literature on the formation of attitudes (Mijs, 2018[26]; Almås et al., 2010[27]).

2.3.2. The gender dimensions of inequality of opportunity

Gender dimensions have been a relative blind-spot in the analysis of inequality of opportunity for theoretical and methodological reasons. At a conceptual level, luck egalitarianism has been criticised for the limited ability of its central distinction between luck and effort to account for the experience of women and the structural factors that shape their opportunities and outcomes (Stark, 2020[28]; Anderson, 1999[29]). At a methodological level, research on the intergenerational persistence of earnings has largely been based on analysis of the relation between fathers and sons. This uneven focus is due to issues of data availability and to the historical structure of the labour market. Extending the analysis to cover the gender dimensions of inequality of opportunity requires a tailored strategy for measurement. In order to do so, the measure of inequality of opportunity used in this chapter has been adapted in a way that can help address these challenges and better capture gender differences in opportunities. This notably implies (i) a change in the observation unit, from the household to the individual level18; and (ii) a change in the welfare metric, from market income to earnings. The method used in this sub-section is presented in Box 2.3.

Box 2.3. Understanding better how opportunities vary between women and men – What should be measured and how?

Copy link to Box 2.3. Understanding better how opportunities vary between women and men – What should be measured and how?

In the remainder of the analysis, inequality of opportunity is measured for individual earnings instead of household market income, as was the case up to this point throughout the chapter. The changes introduced in the observation unit (from the household to the individual) and in the primary welfare metric (from market income to earnings) are designed to capture important gender-specific dimensions that are lost when measuring inequality of opportunity in terms of household market income. These changes also illustrate the broader point that the measurement of inequality of opportunity needs to be adapted to the populations studied and to the type of question asked and policy insights sought.

Shifting the analysis from household market income to individual earnings provides a clearer picture of gender differences and their impact on economic outcomes: The approach used in previous sections (i) focuses on inequality of opportunity as measured by market income, in order to capture income-generating capacity and inequalities of opportunity that relate to the labour market; and (ii) takes account of income pooling and economies of scale within the household, which provides a more accurate estimation of an individual's standard of living. Despite its advantages, allowing for income pooling at the household level is much less appropriate for analysing inequality from a gender perspective, where disparities within the household are of key importance. It assumes that resources are shared equally among household members and that joint decisions such as those relating to childcare and labour supply, which significantly affect individuals’ opportunities and economic outcomes, are made on an equal basis. Capturing these dynamics requires “opening the black box” of the household and focusing on incomes generated at the individual level. In turn, this implies a change in the welfare metric from market income to earnings, for reasons of data availability and comparability.

While it sheds valuable light on the gender dimensions of inequality of opportunity, this approach also has some limitations:

First, individual earnings provide a narrower metric of economic welfare than market income and exclude several important sources of income from the analysis. These sources of income – such as self-employment income, capital gains and property income streams – may represent substantial shares of overall economic resources, especially among wealthier individuals or households.
Second, the analysis is limited to full-time employees, meaning that part-time workers, the unemployed and those outside the labour force are excluded. Another consequence of shifting to individual earnings is a reduction in the scope of the population considered. This is particularly relevant from a gender perspective, as women are more likely to work part-time, experience career interruptions, or exit the labour force due to caregiving responsibilities. By excluding these groups, the analysis runs the risk of (i) introducing a self-selection bias by looking only at employed women;19 and (ii) overlooking key aspects of gender inequality that may be experienced by those who are unable to engage in full-time work.1

1. One approach that can enable the inclusion of part-time workers in the analysis would be to use hourly earnings as a measure of welfare. However, due to data limitations and inconsistencies in the measurement and reference periods for income and hours worked, this would lead to reduced country coverage, lower accuracy of estimates and reduced cross-country comparability.

Overall, measures based on individual earnings reveal higher levels of inequality of opportunity. In a large majority of OECD countries, the share of total inequality explained by circumstances beyond individuals’ control is higher when measured in terms of individual earnings rather than for household market income (Figure 2.13). This finding suggests that disparities in individual earnings capture deeper structural issues that relate to gender differences in labour market opportunities, such as differences in female labour market participation or unequal access to high-paying jobs.20 Some of these individual disparities are partly offset at the household level, through income pooling and a more equal distribution of resources within the family unit. However, the results confirm the analytical value of focusing on individually generated income as done in this section.

Figure 2.13. Inequality of opportunity is higher when measured at the individual level
Copy link to Figure 2.13. Inequality of opportunity is higher when measured at the individual level

Relative inequality of opportunity (Rel. IOp), as percentage of total inequality in household market income and individual earnings, full-time employees aged 25-59, by country, 2019 or latest available year

Gender is the most significant single factor explaining inequality of opportunity in earnings. Overall, its effect surpasses that of the different components of parental socioeconomic background, which were typically the largest contributors to inequality of opportunity when measured in terms of household market income (Figure 2.14). In the median OECD country covered in the analysis, gender accounts for a quarter of inequality of opportunity in earnings – a tenfold increase compared to the share observed when using household market income (see Figure 2.10 above). In some countries, gender contributes nearly half of the total inequality of opportunity in earnings. This increase in the relative importance of gender reduces the influence of other factors, notably parental education and household homeownership status when the respondent was 14. These findings indicate that a substantial portion of the disparities in earnings between men and women is driven by gender-related factors, such as occupational segregation, biases in hiring and promotion practices, and socio-cultural norms that shape career choices and opportunities.

Figure 2.14. In a large majority of countries, gender is the most significant single factor explaining inequality of opportunity in individual earnings
Copy link to Figure 2.14. In a large majority of countries, gender is the most significant single factor explaining inequality of opportunity in individual earnings

Shapley-Shorrocks decomposition (percentages) of the relative (predictive) contribution of different circumstances to inequality of opportunity in individual earnings, full-time employees aged 25-59, OECD 24, 2019

Gender gaps in opportunities and wages go hand in hand. As expected, Figure 2.15 confirms that the relative importance of gender as a driver of inequality of opportunity (light blue squares) is closely linked to gender disparities in full-time wages. In countries with larger gender wage gaps, gender also accounts for a greater share of inequality of opportunity in earnings. Adjusting for the absolute level of inequality of opportunity (blue diamonds) reinforces this association.21 This suggest that further progress in reducing inequality of opportunity is contingent on addressing gender-based disparities in the labour market. This also lends support to the hypothesis, discussed in Section 2.2 above, that countries with lower inequality of opportunity have been more successful in addressing systemic issues such as access to education, skills development and territorial development, while remaining disparities are more likely to stem from deep-seated structural factors, such as social norms and gender roles, that are less tractable for policy intervention alone.

Figure 2.15. Gender disparities account for a larger share of inequality of opportunity in countries where the gender wage gap is higher
Copy link to Figure 2.15. Gender disparities account for a larger share of inequality of opportunity in countries where the gender wage gap is higher

Relative contribution of sex to inequality of opportunity in individual earnings and mean gender wage gap, full-time employees aged 25-59, 2019

2.4. Conclusion

Copy link to 2.4. Conclusion

This chapter introduces a new and robust measure of inequality of opportunity that aligns well with public perceptions of fairness and with the insights from contemporary theories of distributive justice. It confirms that socio-economic inequalities are deeply entrenched across generations. On average, at least over one-quarter of household market income inequality in OECD countries is due to factors beyond an individual's control, such as their sex, country of birth or parents’ socio-economic background. These results underscore the fact that, in many countries, a large proportion of both economic advantage and disadvantage are inherited rather than earned. However, the extent of inequality of opportunity varies considerably across countries. Some Nordic countries and Switzerland report the lowest levels with rates below 15% of total inequality, while Southern and Central and Eastern European countries, along with Chile and the United States, often have shares exceeding 35%. These persistent inequalities highlight the structural barriers faced by different groups and challenge the fundamental principle of equality of opportunity, which is a core component of the social contract in liberal democracies.

Understanding the relative importance of inherited and personal circumstances is crucial for designing policies that can effectively ensure a more level playing field for all. Although the evidence does not establish direct causality, it offers valuable insights into how advantage and disadvantage are transmitted across generations, and into the extent to which these differences in outcomes are likely to be perceived as fair and acceptable or not. In most OECD countries, parental socio-economic background – i.e., their educational level and occupation – accounts for more than 60% of inequality of opportunity in household market income and in some countries over 75%. By contrast, individual factors, such as sex and country of birth, have a much smaller impact. However, shifting the focus from household income to individual earnings tells a different story. Inequality of opportunity is generally higher when measured by individual earnings than by household market income, as income pooling within households offsets some inequalities. When focusing on individuals rather than households, gender emerges as the main driver of inequality of opportunity. In the median OECD country, gender accounts for about a quarter of inequality of opportunity in earnings, and in some cases nearly half.

Reducing inequality of opportunity requires ensuring universal access to essential services. Promoting human capital development from early childhood and throughout an individual’s life is key to lessening the influence of parental background on a child's future. Addressing regional and local disparities in access to education, healthcare and economic resources is also critical, as unequal access can exacerbate inequalities in rural and economically underserved areas. Expanding these services in disadvantaged regions is essential for creating a more level playing field and mitigating the impact of socio-economic background on economic outcomes. Chapter 3 provides a more detailed focus on the important geographic dimensions of equal opportunity.

Improving access is a necessary condition for equal opportunity, but not a sufficient one. Addressing the role played by other structural factors, such as social norms and discrimination, is also essential to ensure a more level playing field. Countries with low levels of inequality of opportunity have often implemented comprehensive policies covering key areas such as education, skills and territorial development. Yet, even in these contexts, persistent disparities remain. Here, barriers to further progress may be related to social norms related to gender and individual characteristics rather than to issues of access. Taking account of these underlying cultural and structural factors is essential to understand how a more equitable distribution of opportunities can be achieved in specific national contexts and what role policy can play in doing so. In this perspective, Chapter 4 considers a broad range of policies that can suit the different challenges encountered by countries and help develop comprehensive strategies for promoting equal opportunity.

References

[27] Almås, I. et al. (2010), “Fairness and the Development of Inequality Acceptance”, Science, Vol. 328/5982, pp. 1176-1178, https://doi.org/10.1126/science.1187300.

[5] Alvarez, M., A. Bernard and S. Lieske (2021), “Understanding Internal Migration Trends in OECD Countries”, Population, Space and Place, Special Issue: Declining internal migration? Patterns, causes and prospects, https://doi.org/10.1002/psp.2451.

[29] Anderson, E. (1999), “What Is the Point of Equality?”, Ethics, Vol. 109/2, pp. 287-337, https://doi.org/10.1086/233897.

[32] Brunori, P., F. Ferreira and P. Salas-Rojo (2024), “Inherited Inequality: A general framework and a ’beyond-averages’ application to South Africa”, IZA Discussion Paper Series IZA DP No. 17203, https://www.iza.org/publications/dp/17203/inherited-inequality-a-general-framework-and-a-beyond-averages-application-to-south-africa.

[2] Brunori, P., P. Hufe and D. Mahler (2023), “The Roots of Inequality: Estimating inequality of opportunity from regression trees and forests”, The Scandinavian Journal of Economics, Vol. 125/4, pp. 900-932, https://doi.org/10.1111/sjoe.12530.

[30] Brunori, P., F. Palmisano and V. Peragine (2019), “Inequality of Opportunity in sub-Saharan Africa”, Applied Economics, Vol. 51/60, pp. 6428-6458, https://doi.org/10.1080/00036846.2019.1619018.

[6] Brzezinski, M. (2015), “Inequality of Opportunity in Europe before and after the Great Recession”, ECINEQ Working Paper Series, Vol. 353, https://ideas.repec.org/p/inq/inqwps/ecineq2015-353.html.

[25] Carcillo, S. et al. (2015), “NEET Youth in the Aftermath of the Crisis: Challenges and Policies”, OECD Social, Employment and Migration Working Papers, No. 164, OECD Publishing, Paris, https://doi.org/10.1787/5js6363503f6-en.

[4] Chetty, R. and N. Hendren (2018), “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood exposure effect”, The Quarterly Journal of Economics, Vol. 133/3, pp. 1107-1162., https://doi.org/10.1093/qje/qjy007.

[3] Chetty, R. et al. (2014), “Where is the Land of Opportunity? The geography of intergenerational mobility in the United States”, The Quarterly Journal of Economics, Vol. 129/4, pp. 1553-1623, https://doi.org/10.1093/qje/qju022.

[31] Corak, M. (2006), Do Poor Children Become Poor Adults? Lessons from a cross-country comparison of generational earnings mobility, Emerald Group Publishing, https://doi.org/10.1016/S1049-2585(06)13006-9.

[22] Crystal, S. (2021), “Impact of Economic Shocks on Cumulative Advantage Processes: Recession, recovery, and trajectories of inequality”, Innovation in Aging, Vol. 5/Supplement 1, pp. 506-507, https://doi.org/10.1093/geroni/igab046.1957.

[14] Durlauf, S., A. Kourtellos and C. Tan (2022), “The Great Gatsby Curve”, The Annual Review of Economics, Vol. 14, pp. 571-605, https://doi.org/10.1146/annurev-economics-082321-122703.

[24] Escalonilla, M., B. Cueto and M. Pérez-Villadóniga (2022), “Is the Millennial Generation Left Behind? Inter-cohort labour income inequality in a context of economic shock”, Social Indicator Research, Vol. 164/1, pp. 285–321, https://doi.org/10.1007/s11205-022-02958-x.

[20] Freedman, M. (2024), “Earnings, Cohort Effects, and Inter-Generational Inequality: Evidence from the Luxembourg Income Study”, The Review of Income and Wealth, Vol. 70/2, pp. 278-290, https://doi.org/10.1111/roiw.12641.

[7] Harding, D. and M. Munk (2019), “The Decline of Intergenerational Income Mobility in Denmark: Returns to education, demographic change, and labor market experience”, Social Forces, Vol. 98/4, pp. 1436-1464, https://doi.org/10.1093/sf/soz108.

[23] Häusermann, S. and H. Schwander (2010), Explaining Welfare Preferences in Dualized Societies, http://schwander-hanna.ch/files/Hausermann_Schwander_CES2010.pdf.

[9] Jácome, E., I. Kuziemko and S. Naidu (2021), “Mobility for All: Representative intergenerational mobility estimates over the 20th century”, NBER Working Paper Series, https://doi.org/10.3386/w29289.

[19] Karonen, E. and M. Niemelä (2020), “Life Course Perspective on Economic Shocks and Income Inequality Through Age-Period-Cohort Analysis: Evidence from Finland”, The Review of Income and Wealth, Vol. 66/2, pp. 287-310, https://doi.org/10.1111/roiw.12409.

[10] Kelly-Hall, P. and S. Ruggles (2004), ““Restless in the Midst of Their Prosperity”: New evidence on the internal migration of Americans, 1850-2000”, Journal of American History, Vol. 91/3, pp. 829-846, https://doi.org/10.2307/3662857.

[26] Mijs, J. (2018), “Inequality Is a Problem of Inference: How people solve the social puzzle of unequal outcomes”, Societies, Vol. 8/3, https://doi.org/10.3390/soc8030064.

[15] Narayan, A. et al. (2018), Fair progress? Economic mobility across generations around the World (English), World Bank Group, https://doi.org/10.1596/978-1-4648-1210-1.

[11] OECD (2023), “Hard work, privilege or luck? Exploring people’s views of what matters most to get ahead in life”, OECD Policy Insights on Well-being, Inclusion and Equal Opportunity, No. 12, OECD Publishing, Paris, https://doi.org/10.1787/e1903ab2-en.

[36] OECD (2023), Joining Forces for Gender Equality: What is Holding us Back?, OECD Publishing, Paris, https://doi.org/10.1787/67d48024-en.

[37] OECD (2023), OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market, OECD Publishing, Paris, https://doi.org/10.1787/08785bba-en.

[38] OECD (2023), OECD Regional Outlook 2023: The Longstanding Geography of Inequalities, OECD Publishing, Paris, https://doi.org/10.1787/92cd40a0-en.

[13] OECD (2023), “Working hand in hand? Exploring people’s views of the role of different actors in fighting inequality”, OECD Policy Insights on Well-being, Inclusion and Equal Opportunity, No. 13, OECD Publishing, Paris, https://doi.org/10.1787/dbd54315-en.

[12] OECD (2023), “Working hand in hand? Exploring people’s views of the role of different actors in fighting inequality”, OECD Policy Insights on Well-being, Inclusion and Equal Opportunity, No. 13, OECD Publishing, Paris, https://doi.org/10.1787/dbd54315-en.

[40] OECD (2022), Education at a Glance 2022: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/3197152b-en.

[17] OECD (2021), Delivering Quality Education and Health Care to All: Preparing Regions for Demographic Change, OECD Rural Studies, OECD Publishing, Paris, https://doi.org/10.1787/83025c02-en.

[1] OECD (2018), A Broken Social Elevator? How to Promote Social Mobility, OECD Publishing, Paris, https://doi.org/10.1787/9789264301085-en.

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

[16] OECD (2017), Educational Opportunity for All: Overcoming Inequality throughout the Life Course, Educational Research and Innovation, OECD Publishing, Paris, https://doi.org/10.1787/9789264287457-en.

[21] OECD (2017), Preventing Ageing Unequally, OECD Publishing, Paris, https://doi.org/10.1787/9789264279087-en.

[39] OECD (2017), The Pursuit of Gender Equality: An Uphill Battle, OECD Publishing, Paris, https://doi.org/10.1787/9789264281318-en.

[18] Rawdanowicz, Ł. et al. (2021), “Constraints and demands on public finances: Considerations of resilient fiscal policy”, OECD Economics Department Working Papers, No. 1694, OECD Publishing, Paris, https://doi.org/10.1787/602500be-en.

[8] Saez, E. and G. Zucman (2020), “The Rise of Income and Wealth Inequality in America: Evidence from distributional macroeconomic accounts”, Journal of Economic Perspectives, Vol. 34/4, pp. 3-26, https://doi.org/10.1257/jep.34.4.3.

[33] Shapley, L. (1953), A Value for n-Person Games, Princeton University Press, https://www.jstor.org/stable/j.ctt1b9x1zv.24.

[34] Shorrocks, A. (2013), “Decomposition Procedures For Distributional Analysis: A unified framework based on the Shapley Value”, The Journal of Economic Inequality, Vol. 11/1, https://doi.org/10.1007/s10888-011-9214-z.

[28] Stark, C. (2020), “Why Luck Egalitarianism Fails in Condemning Oppression”, Feminist Philosophy Quarterly, Vol. 6/4, pp. 1-17, https://doi.org/10.5206/fpq/2020.4.8101.

Notes

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← 1. The analysis includes all of the European OECD Member countries (Austria; Belgium; Czechia; Denmark; Estonia; Finland; France; Germany; Greece; Hungary; Iceland; Ireland; Italy; Latvia; Lithuania; Luxembourg; the Netherlands; Norway; Poland; Portugal; the Slovak Republic; Slovenia; Spain; Sweden; Switzerland; the United Kingdom), 3 non-European OECD Member countries for which comparable data are available (Australia; Chile; the United States) and three European OECD accession countries (Bulgaria; Croatia; Romania).

← 2. The caveat “over a quarter at least” is necessary given that the measure only captures the effect of a selected set of circumstances and therefore represents a lower-bound estimate of actual levels of inequality of opportunity (see Section 1.2.3 in the previous chapter). Market income inequality and absolute inequality of opportunity are measured here using the Gini index (on a 0-1 scale). It is important to note that the lack of exact disaggregation of the Gini coefficient by population subgroups is not a significant limitation in this context. For further discussion of the merits of using the Gini index to assess inequality of opportunity, see Brunori, Palmisano and Peragine (2019[30]).

← 3. Absolute inequality of opportunity is calculated as the Gini coefficient for the counterfactual distribution. Relative inequality of opportunity is calculated by dividing the Gini of the counterfactual distribution (absolute inequality of opportunity) by the observed Gini for equivalised household market income (total inequality). Relative inequality of opportunity represents the share of the total inequality of outcomes that can be attributed to circumstances. See Box 1.4 in the previous chapter for further detail. The rank correlation between the two is above 0.90.

← 4. As mentioned in Chapter 1, absolute inequality of opportunity provides a direct measure of the impact of the set of selected circumstances on inequality of outcomes, while relative inequality of opportunity helps contextualise this impact within a country’s broader inequality landscape.

← 5. For a case study, see Box 2.2.

← 6. The relative standard deviation of relative inequality of opportunity, a measure (also known as sigma-convergence) used to assess how disparities among countries have evolved over time, fell from 0.29 in 2005 to 0.24 in 2019.

← 7. For example, OECD (2018[1]) finds that there has been a general tendency for income positions to become more persistent since the 1990s, particularly at the lower and upper ends of the distribution (see Box 1.1 for further detail).

← 8. Both Spearman rank correlations exceed 0.5 (0.63 for the perception that a wealthier background is needed to get ahead and 0.52 for the preference for more opportunities). Furthermore, when outliers such as Belgium and the United States are excluded, the rank correlation for preferences for more opportunities increases significantly, approaching 0.80. The results offer some empirical validation for the use of machine learning techniques as a reliable method for understanding inequality of opportunity in OECD countries.

← 9. The relation was originally formulated by Corak (2006[31]). The term “Great Gatsby Curve”, which it came to be popularly known by, was later coined by Alan Krueger, then-Chair of the White House Council of Economic Advisers.

← 10. The rank correlation between income inequality (as measured by the Gini coefficient) and relative inequality of opportunity, as computed in Figure 2.1, is 0.39. While this number may seem low, it should be borne in mind that the relative homogeneity of OECD countries may lower the significance of the relationship between total inequality and inequality in opportunities. For instance, in OECD (2018[1]), the correlation between intergenerational earnings persistence and income inequality jumps from 0.45, when restricting the analysis to OECD countries, up to 0.74, when including Argentina, Brazil, China, India and South Africa.

← 11. The relative contribution of each circumstance is computed through the Shapley-Shorrocks decomposition method (Brunori, Ferreira and Salas-Rojo, 2024[32]), which calculates the reduction in inequality of opportunity arising from the exclusion of a given circumstance from the prediction model (Shorrocks, 2013[34]; Shapley, 1953[33]). This is the only method that meets two essential criteria: first, the decomposition is an exact decomposition under addition (the sum of the relative effect of circumstances adds up to 100%); and secondly, the decomposition is symmetric with respect to the order of the arguments (the order in which circumstances are removed has no impact). See Annex 1.A for further detail. The authors are thankful to Paolo Brunori and Pedro Salas-Rojo for sharing the R code used to compute the Shapley-Shorrocks decomposition.

← 12. That is, father’s educational level and occupation and mother’s educational level and occupation.

← 13. To distinguish between age and cohort effects, a longitudinal sample would be ideal, allowing for the direct tracking of income inequality as it evolves throughout the life cycle of individuals from the same cohort. However, this option is rarely available over an extended timeframe. A feasible alternative involves treating repeated cross-sectional surveys as a pseudo-panel: if each survey is a random sample of the population, then each birth cohort within those surveys can be considered comparable across different survey waves.

← 14. Among plausible scenarios, age could notably have a constant effect over time, implying that the effect of initial disadvantages faced by a cohort remains constant as the cohort gets older. Conversely, its effects could differ over time, potentially giving rise to (i) a compensation effect, when the effect weakens as the cohort ages; (ii) a scarring effect, when unequal beginnings lead to cumulative disadvantage over the lifecycle; or (iii) an inverted U-shaped effect, with a scarring effect in younger years followed by a compensation effect later in life.

← 15. However, inequality of opportunity increased steeply with age for the cohort born in the 1960s, while it declined slightly for those born in the 1970s. As a result, both cohorts reached similar levels of inequality of opportunity around the age of 50.

← 16. Skill-biased demand for labour means that the returns on higher education remain substantial in terms of employment and earnings (OECD, 2022[40]). However, evidence suggests that increase in the supply of skilled labour may also contribute to diminish these returns, as the wage premium accruing to the tertiary-educated tends to be highest where their share is low (OECD, 2018[35]).

← 17. Looking forward to possible future trends, the changes brought about by Artificial Intelligence may expand opportunities for younger generations. However, at present the significant wage premium associated with specialised AI skills does not disproportionally benefit young workers. The share of young workers in the AI workforce is no larger than for the employed population with a tertiary degree (OECD, 2023[37]).

← 18. While using the household as a unit of observation presents several important advantages for analysis, it relies on the central assumption that resources are shared equally among household members. This assumption needs to be relaxed in order to account for intra-household disparities in earnings and allocation of resources and duties, which are of crucial importance for understanding gender differences in the opportunities available to men and women. See Section 1.2.2. in the previous chapter and Annex 1.B for a discussion on the implications of using the household as unit of analysis.

← 19. Although self-selection is a significant issue when considering labour force participation by women in past periods, its relevance has tended to decrease in recent times. It is therefore less restrictive to focus the analysis only on employed women when studying inequality of opportunity for the more recent generations. The rising trend in labour force participation among women observed in many countries means that problems of self-selection and representativeness should be lesser, although significant gender gaps still remain and affect labour market participation decisions by younger cohorts (OECD, 2023[36]; 2017[39]). If the decision to participate in the labour market is influenced by the perceived extent of inequality of opportunity – i.e., if a woman is less likely to enter into the labour market when she expects to be discriminated against on the basis of her socio-economic background, then the measure obtained by focusing on individual earnings will represent a lower bound of actual inequality of opportunity. To estimate the mobility of employed women, the analysis only considers education and occupation in the mother’s generation – not earnings, as would be required for the calculation of elasticities, for which the bias is likely to be stronger.

← 20. It should be noted, however, that some of these differences arise within the context of the household. For example, unequal participation in the labour market may reflect a joint decision by the couple.

← 21. This is done by multiplying the relative importance of a specific circumstance by the measured level of inequality of opportunity. From this perspective, a country where absolute inequality of opportunity (measured as Gini index of the counterfactual distribution) is 0.1 and where 50% of inequality of opportunity relates to gender would be considered similar to a country where absolute inequality of opportunity is 0.5 and where 10% of inequality of opportunity relates to gender. In this sense, adjusting for the absolute level of inequality means that the influence of a specific factor is less significant in countries with lower overall inequality of opportunity.

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