The Gini Coefficient and related concepts

The Gini Coefficient (Gini) was developed in 1912 by Corrado Gini. Gini is the most widely used single measure of inequality, recognized for its attractive theoretical and statistical properties and for being technically sound (World Bank Institute, 2005; De Maio, Reference De Maio2007; Ceriani & Verme, Reference Ceriani and Verme2012; Cobham, et al., Reference Cobham, Schlogl and Sumner2015; Gastwirth, Reference Gastwirth2016; Davies, Reference Davies2016). It is used to measure inequality of income or wealth distribution within a certain population or region, or across populations. Gini does not, however, measure the wealth of a population – a wealthy and a poor country can have the same Gini. Wealth of nations is measured by the Gross Domestic Product (GDP) – a monetary measure of the market value of all final goods and services produced in a year (or another period). The GDP per capita is GDP divided by the number of people in the country, which does not reflect differences in the cost of living and the inflation rates of the countries. These are taken into account in the Purchasing Power Parity (PPP) measure. The PPP is an exchange rate at which the currency of one country is converted into that of the second country in order to purchase the same volume of goods and services in both countries (International Monetary Fund; imf.org). The PPP is expressed in international dollars, which have the same purchasing power as US dollars have in the United States. The PPP per capita is GDP converted into international dollars, divided by total population. For some countries, the per capita GDP and PPP are very similar, whereas for others GDP and PPP can differ significantly. Unlike Gini, GDP and PPP measures tell nothing about how resources are distributed within countries.

Gini ranges from 0 to 1, where 0 means perfect equality (everyone has the same income/wealth) and 1 means absolute inequality (one person gets the whole income/wealth and the others get nothing). Gini can also be expressed in percentages (Gini Coefficient×100), which is referred to as the Gini Index. However, the two terms (Coefficient and Index) are commonly used interchangeably. Gini is derived from the Lorenz curve, which shows the cumulative proportion of the population, sorted from poorest to richest (or lowest earners to highest earners), on the horizontal axis; and the cumulative proportion of wealth or income on the vertical axis. So, the point (0.6, 0.2) would indicate that the bottom 60% of earners of the selected population earn 20% of the total income. One way to understand the meaning of Gini is as a percentage scale. So, if income Gini is 0.7, there is a 70% inequality and only 30% equality (equality scoring 30 out of 100). As an example, in the US in 2014, where income Gini was 0.4, 0.1% of the population earned on average US$6,087,113 each per annum, 1% of the population earned on average US$1,260,508 each per annum and 90% of the population earned on average US$33,068 each per annum (Piketty & Saez, Reference Piketty and Saez2003; Saez, Reference Saez2015).

Gini has some limitations. For example, the total Gini of a society is not equal to the sum of the Gini Coefficients of its subgroups (World Bank Institute, 2005). In addition, as Gini depends only on reliable income data, it does not take into account shadow or informal economies. There is also some controversy on whether Gini is more sensitive to changes at the centre of the distribution (Atkinson et al., Reference Atkinson, Piketty and Saez2011; Cobham et al., Reference Cobham, Schlogl and Sumner2015) or to changes in the lower and upper parts of the distribution (Gastwirth, Reference Gastwirth2016). Despite the relative strengths and weaknesses of different inequality measures, research has shown that they are in high agreement. For example, one study (Kawachi & Kennedy, Reference Kawachi and Kennedy1997) found an average correlation of 0.94 among Gini and five other measures of inequality (the decile ratio; the proportions of total income earned by the bottom 50%, 60% and 70% of households; the Robin Hood Index; the Atkinson Index; and Theil’s entropy measure).

The currently observed range of inequality across the world is very wide, with income Ginis in 2014 varying from 0.25 (Norway) to 0.64 (Comoros); and wealth Ginis varying from 0.42 (Slovakia) to 0.90 (the Russian Federation) (UNDP, 2013; Credit Suisse, 2014). Wealth Ginis are always more extreme than income Ginis. For example, the wealth share of the poorest 50% of the population is always less than 5%, whereas their income share generally falls in the 20% to 30% range (Piketty & Saez, Reference Piketty and Saez2014). In 2014, the wealth and income Ginis were: 0.85 vs 0.4 in the US; 0.73 vs 0.36 in the UK; and 0.56 vs 0.25 in Norway. Income and wealth inequality are reciprocally linked and both lead to consumption inequality (Credit Suisse, 2014; Crawford et al., Reference Crawford, Innes and O’Dea2016; OECD, 2017).

It is possible that income and wealth inequality have different impacts on people. In conditions of high average wealth (e.g. high per capita PPP) and low income Gini, a high wealth Gini may not significantly affect people’s lives. This is because most people would have good access to quality services and resources. Modern Sweden is a good example of such conditions: the per capita PPP is high (US$46,445), the income Gini is relatively low (0.27) and the wealth Gini is very high (0.80). In contrast, in conditions of high PPP coupled with high income Gini and wealth Gini, the impact of inequality on people’s lives may be very strong, both in terms of lack of resources and negative psychological experiences. The US exemplifies these conditions: the per capita PPP is high (54,539), income Gini is high (0.41) and wealth Gini is very high (0.86). The negative impact of inequality can be further inflated when average wealth is low because people who are disadvantaged by inequality have even fewer resources available to them. For example, in the Russian Federation, the per capita PPP is significantly lower (US$25,498), income Gini is high (0.42) and wealth Gini is very high (0.90) (Credit Suisse, 2014; UNDP, 2014; World Bank, 2017a, b).

When reviewing the literature on inequality, one needs to be aware that studies use different indices and procedures to correct for data limitations. For example, analyses are often based on what is commonly called Earned Income – income received in return for work. Other studies also take into account Capital Income, Capital Gains Income and Business Income, which together account for the greater part of income inequality (Atkinson et al., Reference Atkinson, Piketty and Saez2011), but are more difficult measures to ascertain (Alvaredo et al., Reference Alvaredo, Atkinson, Piketty and Saez2013). As a result, reports often provide somewhat different estimates of Gini. For example, wealth Gini estimated for the same year in the Credit Swiss report was overall higher than that estimated by Allianz (Credit Suisse, 2016; Allianz, 2016). However, despite these differences across studies and reports, the patterns of results are consistent.

Heritability

Understanding heritability has become particularly important following recent scientific advancements in genetic science. Although widely used, the concept of heritability is still commonly misunderstood (Kovas & Malykh, Reference Kovas and Malykh2016). For example, many hold the mistaken view that heritability relates to a specific person. So, a heritability of 60% is mistakenly taken to mean that 60% of an individual’s trait (e.g. academic achievement) is determined by the person’s genes. In reality, it means that 60% of the differences in this trait among people within a particular population is explained by their genetic differences (see Kovas & Malykh, Reference Kovas and Malykh2016, for a detailed explanation). Heritability of a trait within a population can be estimated (quantified) by using a variety of quantitative genetic methods, such as comparing family members of different genetic relatedness. One of the most common methods is studying a sample of monozygotic (MZ) and dizygotic (DZ) twins drawn from a particular population. Comparing average similarities on a trait of MZ twins (who are genetically identical) with average similarities of DZ twins (who share on average 50% of the variable DNA), gives an indication of the relative contribution of genetic and environmental factors to variation in this trait in this particular population. Another quantitative genetic method – the Genomewide Complex Trait Analysis (GCTA) – estimates heritability directly by comparing DNA and trait similarities of unrelated individuals (Plomin & Deary, Reference Plomin and Deary2015).

Today we know that variation in any trait stems from multiple, possibly hundreds of DNA variants, and multiple environments, making human traits polyfactorial: polygenic and polyenvironmental. Heritability reflects differences in DNA among all people within a population. Approximately 0.01% of the human DNA sequence is different among people, with the rest of the sequence being identical for all humans. Although 0.01% is a small proportion of the genome sequence, it represents millions of DNA variants, each potentially contributing to the observed differences in behaviour. Moreover, recent research suggests that we may be even more different from each other genetically, with each person’s DNA containing many rare or completely new (De novo) mutations. It is estimated that the DNA sequence of each person contains an average of 8579 novel genetic variants (Telenti et al., Reference Telenti, Pierce, Biggs, DiIulio, Wong and Fabani2016). Importantly, these differences do not refer to race, countries or other population groups, but to differences between any two individuals, even in the same family.

An important aspect of the definition of heritability is that it refers to genetic contribution to differences among people within a particular population at a particular time. This implies that genetic factors can explain a different proportion of individual differences in different populations (even if the overall amount of trait variability is similar). For example, children differ greatly in their academic achievement within all countries. The Programme for International Student Assessment (PISA) study showed that students’ performance in mathematics at age 15 varies more within a country than between countries (OECD, 2013). Within a country, the gap between the highest- and the lowest-achieving students was over 300 points, the average score in OECD countries being around 500 points. This difference is enormous, as a performance gap of about 70 score points represents a large difference in skills and knowledge – the equivalent of about 2 years of schooling in a typical OECD country (OECD, 2013). The results also suggested that, although most of the variability overlaps across countries, there are also significant differences, especially in proportions of students achieving low and high scores (OECD, 2014).

Between-population variability could arise from differences in allelic frequencies and differences in environmental provisions across populations. Similarly, within-population differences are also a product of both genetic and environmental factors. In fact, genetic variability within a population is so vast that two randomly selected individuals within a population can be more genetically different from each other than two randomly selected individuals from two diverse populations (Jorde & Wooding, Reference Jorde and Wooding2004; Witherspoon et al., Reference Witherspoon, Wooding, Rogers, Marchani, Watkins, Batzer and Jorde2007). Environmental provisions also vary greatly within and between populations. An important question is whether the vast variability in achievement within each population is explained to a different extent by genetic and environmental factors in different populations. For example, if two countries show comparable variances but differ in average performance, variability in one country may be largely due to environmental disparities, whereas in the other it may largely be due to genetic variability (free from environmental limitations). Therefore, interpretations of sources of differences across populations may be complicated by the Gene–Gini interplay. This review evaluates whether environmental provision, and more specifically, inequality, moderates the effects of genes on variation in traits.

Gini’s effects on life outcomes

A large number of studies and reports have concluded that inequality, beyond poverty, has a strong impact on people’s lives, including education, health and well-being (e.g. Ryan & Adams, Reference Ryan and Adams1998; Wilkinson & Pickett, Reference Wilkinson and Pickett2006, Reference Wilkinson and Pickett2009, Reference Wilkinson and Pickett2011; Rowlingson, Reference Rowlingson2011; Reardon, Reference Reardon2011; Duncan & Murnane, Reference Duncan and Murnane2011, Reference Duncan and Murnane2016; Bischoff & Reardon, Reference Bischoff and Reardon2014; Carter & Reardon, Reference Carter and Reardon2014; Pickett & Wilkinson, Reference Pickett and Wilkinson2015; County Health Rankings and Roadmaps, 2015). This section provides a brief summary of evidence for the impact of inequality on educational and related outcomes.

The impact of inequality on children’s education, health and well-being is partly mediated by parental psychological and physical well-being. Negative psychobiological pathways may be activated by inequality-related feelings of injustice, stress, anxiety about relative social status, loss of social connections and cohesion within communities and insecurity (Kaplan et al., Reference Kaplan, Salonen, Cohen, Brand, Syme and Puska1988; Singer & Ryff, Reference Singer and Ryff2001; Wilkinson & Marmot, Reference Wilkinson and Marmot2003; Wilkinson & Picket, Reference Wilkinson and Pickett2011; County Health Rankings and Roadmaps, 2015). Inequality may have direct impact on maternal health and stress levels during pregnancy, and on the overall health and mental health of parents. For example, research suggests that the most socially disorganized neighbourhoods, rather than the poorest neighbourhoods, have the highest incidence of schizophrenia (Pinto et al., Reference Pinto, Ashworth and Jones2008). Inequality has been linked to increased prevalence of hallucinations and delusional ideation, depression and other mental health problems (Ryan & Adams, Reference Ryan and Adams1998; Wilkinson & Picket, 2009; Duncan & Murnane, Reference Duncan and Murnane2016). These can contribute to parenting problems, such as use of hostile parenting practices (Johnson et al., Reference Johnson, Wibbels and Wilkinson2015).

Inequality gets under people’s skin by activating many biological processes. Research has shown that relative access to social and material resources is related to differences among people in integrity of their distributed brain networks. This link is mediated by multiple processes, including chronic inflammation (e.g. Gianaros et al., Reference Gianaros, Marsland, Sheu, Erickson and Verstynen2012). Chronic inflammation arises as a result of many factors, including smoking, adiposity and social integration and support (e.g. Loucks et al., Reference Loucks, Berkman, Gruenewald and Seeman2006; Ford et al., Reference Ford, Loucks and Berkman2006) – which themselves are associated with socioeconomic status (SES) (e.g. Gianaros et al., Reference Gianaros, Marsland, Sheu, Erickson and Verstynen2012). For example, psychosocial stress can directly provoke pro-inflammatory cytokine production (Kiecolt-Glaser et al., Reference Kiecolt-Glaser, Gouin and Hantsoo2010). In addition, exposure to pollution, allergens and lead, as well as noise-related sleep disruptions – more frequently experienced in more disadvantaged neighbourhoods – all trigger long-term biological reactions (Braveman & Gottlieb, Reference Braveman and Gottlieb2014).

Early environment can be linked to later development through epigenetic regulation (e.g. Meaney, Reference Meaney2010). For example, several studies have found that negative life events in early childhood can affect expression of genes involved in immune function (Bick et al., Reference Bick, Naumova, Hunter, Barbot, Lee, Luthar and Grigorenko2012). Other studies found differences in expression of genes involved in inflammation and anti-inflammation between those who grew up in low- and middle-income families (Miller & Chen, Reference Miller and Chen2007). Socioeconomic disadvantage has been linked to increased expression of inflammatory molecules and proteins, transcriptional changes in genes and intracellular signalling cascades promoting systemic inflammation (Gianoros et al., Reference Gianaros, Marsland, Sheu, Erickson and Verstynen2012). The effects can emerge already pre-birth and affect the whole path of development. Mother’s stress and anxiety during pregnancy can suppress or enhance genetic expression (e.g. reviewed in Kovas et al., Reference Kovas, Henry, Feklicheva, Soldatova and Malykh2016). For example, elevated stress and lack of social support during pregnancy is linked to increased inflammatory activity, affecting immune function, with increased risk of pregnancy and labour complications (Coussons-Read et al., Reference Coussons-Read, Okun and Nettles2007).

Reviews, based on recent large studies, provide evidence for a causal connection between inequality and poor health (Pickett & Wilkinson, Reference Pickett and Wilkinson2015). A recent large study ranked the health of 3061 of 3143 counties/county equivalents in the US (County Health Rankings and Roadmaps, 2015). The results showed that inequality (not just poverty) contributes to increased risk of mortality and poor health. For example, higher ratio between high and low earners within a county was associated with lower life expectancy and higher rates of smoking, cardiovascular disease and obesity. Inequality’s effect was present even when communities of similar average income and racial composition were compared (County Health Rankings and Roadmaps, 2015). In a recent book (Ansell, Reference Ansell2017) inequality is described as ‘a societal disease’ that leads to unequally dispersed biological disease. For example, an average life expectancy gap of 35 years has been estimated between the richest and the most deprived US neighbourhoods (Ansell, Reference Ansell2017). This is consistent with a US study that found substantial correlations (0.50–0.66) between mortality and inequality (on six different measures), even after adjustment for median income and poverty (Kawachi & Kennedy, Reference Kawachi and Kennedy1997). Data from the Office for National Statistics in the UK (www.ons.gov.uk) also show that inequality, beyond poverty, affects life expectancy: the gap in life expectancy at birth increased between the local areas, despite improvements across these areas over the last two decades. Moreover, people in higher socioeconomic positions live longer than those in positions slightly lower than themselves (Marmot et al., Reference Marmot, Rose, Shipley and Hamilton1978; Marmot et al., Reference Marmot, Shipley and Rose1984; Rowlingson, Reference Rowlingson2011). It has also been shown that rates of infant mortality can be reduced with improvements in Gini, including in wealthy countries (Hales et al., Reference Hales, Howden-Chapman, Salmond, Woodward and Mackenbach1999). For example, income Ginis of 0.25 (Sweden, Norway, and Finland), 0.35 (UK), and 0.40 (US) are associated with mortality rates of 2, 4, and 6 per 1,000, respectively (World Bank, 2015). Taken together, this evidence suggests that, beyond absolute levels of wealth, people’s experience of inequality, such as feelings of injustice, unfairness and envy, may lead to elevated stress. In turn, this may trigger cascading negative biological reactions.

The strength of effects of inequality changes as a function of absolute level of wealth. Poverty may override the effect of inequality due to its dramatic limiting effects on development. However, above the poverty line, relative wealth/income may have a stronger effect. For example, data for 21 regions of Taiwan showed that, as living standards rose during the course of rapid economic growth (between 1976 and 1995), income inequality replaced absolute median income as the best predictor of mortality (Chiang, Reference Chiang1999). In developed countries, people on average have better health in countries with lower income Gini, than in those with the highest GDP/PPP (Wilkinson, Reference Wilkinson1996; Wilkinson & Pickett, Reference Wilkinson and Pickett2006). One large international study, with data from 23 high-income countries, found a strong relationship between income Ginis and the Index of Health and Social Problems endured by populations, including indicators such as life expectancy, mental illness, obesity, educational scores, teenage birth rates, social mobility and education (Wilkinson & Pickett, Reference Wilkinson and Pickett2009). Analysis of data from 50 US states showed very similar results (Kerry et al., Reference Kerry, Pickett and Wilkinson2010).

In developed countries, a clear correlation has also been shown between income inequality and the UNICEF Index of Child Well-Being (Wilkinson & Picket, 2009). Children in more unequal societies are more likely to be overweight, experience more mental health problems, experience more bullying and victimization and become teenage parents; and less likely to continue education beyond compulsory schooling and have on average lower achievement (Ryan & Adams, Reference Ryan and Adams1998; Pickett & Wilkinson, Reference Pickett and Wilkinson2007; Wilkinson & Picket, 2009; Reardon, Reference Reardon2011; Duncan & Murnane, Reference Duncan and Murnane2011, Reference Duncan and Murnane2016; Bischoff & Reardon, Reference Bischoff and Reardon2014; Pickett & Wilkinson, Reference Pickett and Wilkinson2015; UNICEF, 2016).

Several studies suggest that the pattern of increase in inequality and associated educational investment gap corresponds to the increase in educational achievement gap (e.g. Magnuson & Waldfogel, Reference Magnuson and Waldfogel2008; Reardon, Reference Reardon2011; Piketty & Saez, Reference Piketty and Saez2003). In the US, the change in inequality has followed a U-shaped trajectory over the last 100 years: high in the beginning of the 20th century, falling between 1929 and the late 1970s, and continuously increasing since then (e.g. 0.40 in 1976 and 0.47 in 2006), with the 2012 Gini being almost as high as in 1929 (Piketty & Saez, Reference Piketty and Saez2003; Saez & Zucman, Reference Saez and Zucman2014; Sommeiller & Price, Reference Sommeiller and Price2015; Atkinson et al., Reference Atkinson, Piketty and Saez2011).

This increase in overall inequality is accompanied by an increase in the educational investment gap. For example, in 1970, a family with school-age children at the 90th percentile of the income distribution earned five times as much as a family at the 10th percentile; in 2013 the high-income families earned eleven times more than the low-income ones (Reardon, Reference Reardon2013). Consequently, in 1972 high-income families were spending five times as much per child as low-income families, and in 2007, they were spending nine times more (Kornrich & Furstenberg, Reference Kornrich and Furstenberg2013). One study in the US showed an increase in income-based gaps in a range of parenting behaviours over a 25-year period (beginning in 1979), such as teaching children letters or attending museums. For the most part, these gaps arose from top-income families pulling away from middle- and low-income families (Kalil et al., Reference Kalil, Ziol-Guest, Ryan and Markowitz2016).

These increases in income and educational investment gaps have been paralleled by a growing achievement gap. Test scores of children who were adolescents in the late 1960s showed an achievement gap of four-fifths of a standard deviation (about 80 points on a SAT-type test), with children from low-income families lagging behind their better-off peers. Forty years later, this gap was 50% larger, amounting to nearly 125 SAT-type points. Trends in maths skill gaps were similar (Reardon, Reference Reardon2011; Duncan & Murnane, Reference Duncan and Murnane2011). Further research similarly showed that the achievement gap between children from high- and low-income families was roughly 30% to 40% larger among children born in 2001 than among those born 25 years earlier (Reardon, Reference Reardon2011; Duncan & Murnane, Reference Duncan and Murnane2011). Another US study showed similar results: from 1970 to 2013, the achievement gap between children in families at the 90th percentile and those in families at the 10th percentile of the income distribution grew by 40% (Reardon, Reference Reardon2013).

Further support for the strong link between the achievement gap and inequality (beyond poverty) is the evidence that the decrease in inequality between black and white children after the 1950s and 1960s (when it was very high in the US) was accompanied by a reduction in the achievement gap between them (Piketty & Saez, Reference Piketty and Saez2003; Magnuson & Waldfogel, Reference Magnuson and Waldfogel2008). At the same time, increase in income inequality, which reached a historic high in the 21st century, was accompanied by a significant increase in the achievement gap between children from low- and high-earning families. With these changes, family income became a better predictor of children’s success in school than race, with the low–high income achievement gap being twice as large as the black–white achievement gap (Magnuson & Waldfogel, Reference Magnuson and Waldfogel2008). Consistent with these findings, a large longitudinal Canadian study (NLSCY) showed that, even once children are out of low income, every incremental increase in household income (in families with low income) is associated with better child development outcomes; and that there is no point above which high income ceases to benefit children’s development (Phipps & Lethbridge, Reference Phipps and Lethbridge2008).

Research has also shown that more inequality is associated with less mobility across generations. For example, among OECD countries (in the 1980s), striking associations were found when the most equal (Finland, Sweden, Norway and Denmark) were compared with the least equal (the UK and the US). In Finland, Norway and Denmark, less than one-fifth of economic advantage or disadvantage was passed on from father to son in adulthood. In the UK and the US, roughly 50% of any advantage or disadvantage was passed on (Corak, Reference Corak2013).

The PISA study shows an interesting pattern of results on educational equity and achievement. The results showed that approximately 15% of the variation in student performance in mathematics, reading and science was attributed to differences in students’ socioeconomic status. A more socioeconomically advantaged student scored 39 points higher in mathematics (measured by PISA) – the equivalent of nearly one year of schooling – than a less-advantaged student (OECD, 2013). Students attending socioeconomically advantaged schools outscored those in disadvantaged schools by more than 104 points in mathematics, on average across OECD countries. In some countries, small improvements in equity were observed over time (2003–2012): the extent to which students’ socioeconomic background predicts their academic performance was reduced; and the gap in performance between advantaged and disadvantaged students narrowed. However, in many countries, educational equity decreased over the same period. In some cases, the improved equity was accompanied by increased average performance of a nation. However, in other countries, increase in average performance was observed in the presence of deterioration of equity (OECD, 2013). As discussed in this review, national averages do not reflect experiences of individual students, just as GDP and PPP measures do not reflect socioeconomic circumstances of individuals. Focusing on inequality and heritability measures allows further insights to be gained into the sources of variation in performance within populations.

The trend of growing inequality has been seen in many countries across the world, with 7 out of 10 people living in a country that has seen a rise in inequality in the last 30 years (Oxfam, 2017b). Over the last 25 years, the top 1% (≃60 million people) have gained more income than the bottom 50% put together; and own more wealth than the rest of the planet (≃6 billion people). Wealth is consolidating in the hands of very few people: in 2010, the 388 richest people had as much wealth as the poorest half of the world (3.6 billion people); in 2017 as few as eight people had as much wealth (Oxfam, 2016, 2017a). In the US, between 2009 and 2012, all income growth happened in the top 1% of earners in eighteen of the states; and in New York and Connecticut, the top 1% of earners attained average incomes more than 48 times that of the bottom 99% (Sommeiller & Price, Reference Sommeiller and Price2015). Over 1989 and 2013 the top 10% owned 87.7% of household wealth, the top 1% owned 50.8% and the top 0.1% had 20.3% (Saez & Zucman, Reference Saez and Zucman2014). These trends have happened against a background of increases in absolute wealth, with per capita PPP increasing from US$37,062 in 1990 to US$52,549 in 2015 (UNDP, 2016), with the US remaining the richest region in the world (Allianz, 2016). At a global level, it is estimated that over the next 20 years, 500 people will hand over US$2.1 trillion to their heirs – a sum larger than the GDP of India (Oxfam, 2017a). These severe income inequality trends have been identified by the 2012 survey of the World Economic Forum as the most likely global risk to manifest in the next 10 years (World Economic Forum, 2012).

Genes’ effects on life outcomes

Decades of behavioural genetic research leave us with little doubt that genes have a profound effect on differences among people in all aspects of human functioning (Polderman et al., Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven, Visscher and Posthuma2015; Plomin et al., Reference Plomin, DeFries, Knopik and Neiderhiser2016). Recent adequately powered large-scale studies and meta-analyses have confirmed that the heritability of most traits is significant, ranging from moderate to substantial. The most comprehensive meta-analysis to date of the causes of individual differences in human traits examined 17,804 traits from 2748 publications including 14,558,903 twin pairs and concluded that average heritability across all traits is 0.49 (Polderman et al., Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven, Visscher and Posthuma2015). The most reliable estimates are available for traits that received much research attention and funding. In the meta-analysis, average (weighted) heritabilities for some of the most studied traits were: 0.54 for blood pressure; 0.50 for conduct disorder; 0.40 for depressive episode; 0.62 for general metabolic function; 0.52 for heart function; 0.50 for immune system function; 0.44 for mental and behavioural disorders due to the use of alcohol; 0.50 for mental and behavioural disorders due to the use of tobacco; 0.44 for other anxiety disorders; and 0.54 for higher-level cognitive functions (Polderman et al., Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven, Visscher and Posthuma2015). The Genomewide Complex Trait Analysis (GCTA) studies, which do not rely on data from family members, also confirm that common genetic variation among people explains part of the variance in observed traits. The GCTA heritability estimates are usually lower than those from family studies (e.g. half for intelligence). This is because GCTA analysis relies only on genetic effects tagged by common Single Nucleotide Polymorphisms (SNPs) and does not capture rare SNPs or other types of genetic variation, such as Common Number Variants (CNVs) (Plomin & Deary, Reference Plomin and Deary2015).

Research into individual differences in educational traits also clearly suggests a substantial role of genetic factors (e.g. reviewed in Krapohl et al., Reference Krapohl, Rimfeld, Nicholas, Shakeshaft, Trzaskowski and McMillan2014, 2015). For example, one large-scale UK study demonstrated that state examination performance at age 16 was highly heritable, with genetic factors explaining 62% of the variation in examination results (Krapohl et al., Reference Krapohl, Rimfeld, Nicholas, Shakeshaft, Trzaskowski and McMillan2014; Kovas et al., Reference Kovas, Garon-Carrier, Boivin, Petrill, Malykh and Spinath2015). There are several reasons for why genetic effects on educational achievement are strong. First, genetic effects on achievement have many indirect paths, including through genetic influences on cognition, emotion, personality and motivation (Krapohl et al., Reference Krapohl, Rimfeld, Nicholas, Shakeshaft, Trzaskowski and McMillan2014). Second, environmental effects, such as SES, are partly confounded by genetic effects (Selzam et al., Reference Selzam, Krapohl, von Stumm, O’Reilly, Rimfeld and Kovas2016). Third, initial genetic predispositions get enhanced through the processes of active selection and modification of environments (see below for further explanation of gene–environment correlations). Finally, a standardized educational set-up (as is the case of the UK) may lead to more pronounced genetic effects, as students respond differently to the same circumstances, based on their genetic make-up. This means that equalized circumstances (same resources, standard teacher training, standard curricula, standard class sizes) would reduce the proportion of the variance in achievement explained by environmental disparities, and therefore potentially enhance genetic effects on learning and achievement (Kovas & Malykh, Reference Kovas and Malykh2016).

High heritability of education may in turn be reflected in the variance of other traits. For example, a large twin study from the Finnish Twin Cohort found a 0.15 correlation between educational attainment and BMI (Silventoinen et al., Reference Silventoinen, Sarlio-Lahteenkorva, Koskenvuo, Lahelma and Kaprio2004). The correlation was mainly explained by common genetic factors (affecting both educational attainment and body weight). This may mean that genetic effects on education contribute to education-associated disparities in BMI in adulthood, although the direction of effects in such associations remains poorly understood.

Molecular genetic methods have begun to identify specific DNA variants that add up to the observed heritability of traits (Plomin & Deary, Reference Plomin and Deary2015). Many recent technological advances enable faster progress in this field. For example, microarray technology enables the genotyping of hundreds of thousands of DNA variants simultaneously. Microarrays can also be used to evaluate epigenetic processes across the genome at the same time. Whole-genome sequencing is becoming increasingly more accessible. It is likely that in the near future most DNA variants that drive the heritability of each trait will be established. Two recent large-scale molecular genetic studies, using DNA and attainment data from 127,000 and 329,000 participants, have already identified a number of DNA variants involved in educational attainment and achievement (Rietveld et al., Reference Rietveld, Medland, Derringer, Yang, Esko and Martin2013; Okbay et al., Reference Okbay, Beauchamp, Fontana, Lee, Pers and Rietveld2016).

Although the effect of each genetic variant is weak, a constellation of a large number of such genetic factors (polygenic score; Plomin & Deary, Reference Plomin and Deary2015) can be used for prediction. One recent study (Selzam et al., Reference Selzam, Krapohl, von Stumm, O’Reilly, Rimfeld and Kovas2016) aggregated polymorphisms implicated in educational outcomes by previous studies into a polygenic score and examined whether this score predicted variance in educational outcomes at ages 7, 12 and 16 in a sample of 5825 UK individuals. The score predicted up to 9% of the variation in achievement at age 16 (accounting for 15% of the heritable variance). Continued progress in genetic research is likely to improve people’s ability to predict learning outcomes from genetic data, enabling early diagnosis of learning disabilities. Moreover, this information will be invaluable in further refining of diagnoses and overall better understanding of the multidimensional nature of traits. Better understanding of the genetic architecture of complex traits will lead to new research into specific mechanisms of gene–environment co-action, ultimately contributing to tailoring learning environments to every student’s individual needs.

Gene–Gini interplay: how genetic effects on life outcomes change as a function of equality

This section examines whether the strength of genetic effects on educational outcomes differ across environmental conditions. As inequality is a major factor in creating differences in environments, the term ‘Gene–Gini interplay’ is used in this paper to describe the gene–environment processes. Heritability may be affected by different socio-cultural indices, including absolute wealth of the population (e.g. poverty-related limitations on development such as lack of nutrition, access to education), distribution of wealth (more or less equal opportunity) and educational policies (e.g. compulsory education, standardized curricula).

A disadvantaged environment may reduce genetic effects because it lacks ‘food’ (educational opportunity, resources, encouragement, choice of activities) for children’s genetic ‘appetite’ for learning. In other words, genetically influenced potentials for adaptive functioning may express more in enriched environments (see Tucker-Drob & Bates, Reference Tucker-Drob and Bates2016, for more detail on the Scarr-Rowe Hypothesis of Gene by SES interaction). Equal provision of quality education may buffer the effects of some of the negative environments and also enable evocative and active gene–environment correlation processes. Gene–environment correlation arises as children are exposed to environmental effects in a non-random manner depending on their genotypes (Plomin et al., Reference Plomin, DeFries and Loehlin1977; Plomin & Bergeman, Reference Plomin and Bergeman1991). In childhood, correlations are mostly passive and evocative: parents transmit genetic variants to their children and create environments for their children that partly reflect the parents’ genetic make-up; parents also modify their own behaviours responding to specific (partly genetically driven) behaviours of their children (e.g. Tucker-Drob & Harden, Reference Tucker-Drob and Harden2012; Henry et al., Reference Henry, Boivin and Brendgen2016). Active gene–environment correlations emerge when children actively choose their educational environment and activities that correspond with their genetically influenced abilities and interests. In the presence of gene–environment correlation, MZ twins’ similarity on a trait remains the same across development, as their genetic predispositions lead to similar environments. In contrast, DZ twins become less similar with age, as they experience progressively more and more different environments.

Limiting environments, such as lack of quality resources, may suppress gene–environment correlation processes by suppressing expression of the relevant genes. In addition, as discussed above, socioeconomic inequality may relate to educational disparities via inflammatory and other pathways impacting the structural integrity of brain networks (Gianaros et al., Reference Gianaros, Marsland, Sheu, Erickson and Verstynen2012). These processes may have cascading implications for heritability. Reduced structural integrity of brain networks may lead an individual’s developmental trajectory diverging from those enabled by their genetic propensities. For example, genetic predispositions for advanced reading ability may not be expressed if negative neuro-developmental processes affect brain structure and function, suppressing reading ability and motivation. In this case, genetic differences among people would not translate into differences in their reading ability.

Even in the presence of high educational standards and equity, available environments may be inadequate for some students. For example, in the Netherlands, average mathematical performance level in primary education is relatively high, largely because the weakest students are performing better than the weakest students from other countries participating in PISA and other international studies (Schwabe et al., Reference Schwabe, Boomsma and van den Berg2017). This is probably due to significant efforts of the Dutch educational system to help weaker students to achieve adequate educational levels (Schwabe et al., Reference Schwabe, Boomsma and van den Berg2017). A recent behavioural genetic study suggested that the overall heritability of mathematical ability among Dutch 12-year-olds was high (0.73), suggesting that much of the negative influences of home environments on achievement (particularly at the low end) have been reduced. However, there was an indication that environments explained more variance for gifted students, potentially because inadequate environments suppressed some of the genetically driven giftedness (Schwabe et al., Reference Schwabe, Boomsma and van den Berg2017). ‘Inadequate’ in this case refers to a lack of advanced opportunities that would allow for the expression of the relevant genes.

Good educational provisions may also suppress some of the negative genetic effects. For example, compulsory secondary education requires that students remain at school until 16 or even 18 years of age (e.g. in the UK). This may reduce the effects of genetic propensities on educational attainment (years in education) and associated outcomes. With genetic contributions to dropping out of school minimized, these effects cannot explain variance in whether a person progresses to higher education and for how long.

Because different processes unravel simultaneously and may have opposing effects (enhancing vs suppressing genetic propensities), average heritability differences across populations may be small and difficult to detect. Nevertheless, several specific predictions can be formulated in relation to the effects of wealth (e.g. PPP) and equality (Gini) on heritability of educational traits:

(1) When absolute wealth in a population is low (low PPP), heritability is low, as deprivation limits genetic expression. (2) When absolute wealth is high (high PPP), but resources are distributed unequally (high Gini), the overall heritability is lower than in populations with more equally distributed resources. This is because there are many more inequality-disadvantaged than inequality-advantaged people, and heritability within disadvantaged groups may be lower due to reduced opportunity and lack of gene–environment processes. For some traits, under certain conditions, heritability may also be reduced in advantaged environments, as greater education and opportunity may buffer some negative genetic propensities. (3) When inequality is high (high Gini), irrespective of absolute wealth, heritability is lower. This is because the experience of inequality (especially for those disadvantaged) may be an environmental factor that limits expression of relevant genetic factors. The experience of inequality may activate detrimental for health and education cascading psychological and biological reactions, including anxiety, depression and chronic stress. When environmentally triggered, such effects could contribute to lower heritability of achievement (especially in the disadvantaged groups). (4) When absolute wealth is high and more equally distributed (high PPP and low Gini), a very high proportion of the variance is explained by genetic factors.

The best support for the existence and specific mechanisms of the Gene–Gini interplay would come from international studies examining heritability of the same traits in different populations. However, in practice, such research is extremely difficult to conduct: large twin studies are needed, representing comparable populations (e.g. school children of the same age), assessed using the same measures and procedures. The comparisons are further limited because international samples and birth cohorts differ on many parameters, such as educational set-ups, nutrition and other socio-cultural characteristics. This complicates drawing conclusions about the true sources of observed differences in heritability. There is another problem with testing the Gene–Gini interplay hypothesis through international comparisons. To date, most data have been collected in countries that are relatively similar to each other in terms of educational and cultural norms. This reduces the power to detect effects of these norms on heritability. In the most comprehensive meta-analysis to date, twin data were available from only 39 countries, of which most studies were based on US twin samples (34%). Samples from South America, Africa and Asia were heavily under-represented (5.7%, combined) (Polderman et al., Reference Polderman, Benyamin, de Leeuw, Sullivan, van Bochoven, Visscher and Posthuma2015).

These limitations preclude a robust test of the four predictions. However, available data allow a preliminary examination of the Gene–Gini interplay hypothesis. The following sections review three strands of evidence on whether heritability of educational traits differs: (1) among population subgroups within a country at a particular time (e.g. people of lower and higher socioeconomic status); (2) among birth and age cohorts within a country (e.g. people born before and after social reforms; pre-school and school children); and (3) across countries, at a particular time.

Afterword (Gini and eugenics)

Corrado Gini’s significance to this paper was limited to that of a statistician who developed the Gini Coefficient. However, learning about his interest in eugenics added an unexpected twist to the title of this review and called for a few words on eugenics. For Corrado Gini, statistics was a methodological lens through which it was possible to observe populations, economies, social classes, wars and other large-scale phenomena (Prévost, Reference Prévost2016). The application of statistics to eugenics was his particular passion (Berlivet, Reference Berlivet2016), culminating in his appointment in October 1935 as the inaugural president of the Latin International Federation of Eugenic Societies. This, together with his role in various academic and governmental offices before, during and after the fascist regime in Italy, makes Corrado Gini a controversial figure (Macuglia, Reference Macuglia2014).

Eugenics originated from Francis Galton’s theorizing and developed into a popular movement in the UK in the early 20^th century, spreading to many countries. The central purpose of the eugenic movement was described as ‘improving a human population (stock) by controlled breeding to increase the occurrence of desirable heritable characteristics’ (en.oxforddictionaries.com). Particularly in the Anglo-Saxon eugenic model, its purpose was formulated as: eliminating from subsequent generations beings ‘inferior by heredity’ (negative selection); and increasing the reproductivity of the ‘best’ (positive selection). This led to unethical practices in many countries, including forced sterilization of people deemed unfit and marriage prohibition, and encouraging particularly ‘fit’ individuals to reproduce (see Kevles, Reference Kevles1985, for a review).

It is necessary to mention that Corrado Gini himself did not support this agenda for eugenics. According to him, societies were too ignorant of the mechanisms of genetic and environmental influences to exercise social control on populations (Gini, Reference Gini1932). In fact, he recognized that attempts to reduce the genetic variability of a population may have catastrophic effects. In his view, the purpose of eugenics was to study successive generations, isolated populations and migrants – and how populations change over time. He believed that genetics and statistics provided the foundation of such study. In fact, his proposed programme and motto for eugenics in the immediate future was ‘facts, facts, facts’ (Gini, Reference Gini1932). Altogether, Gini’s theorizing can be viewed as accepting that environment and heredity interact in complex ways (Berlivet, Reference Berlivet2016). It therefore seems more appropriate to describe his interest as interdisciplinary, including sociology, demography, anthropology, genetics and statistics – with an unfortunate focus on eugenics. It is also worth mentioning that historically, the term eugenics applied also to benign practices, such as avoidance of inbreeding, or prenatal care for mothers.

Eugenics cast a long shadow over genetic research, with many people rightly worrying about the potential unethical applications of genetic knowledge. Apart from the dangers of selective breeding and sterilization, neo-eugenic risks include forced prenatal ‘people selection’ and gene editing. These and other risks can only be avoided by appropriate safeguards against misuses of genetic information, such as increasing genetic literacy (Chapman et al., in press), enacting enforceable laws and providing equal access to justice (Selita, Reference Selita2018).

Behavioural genetics is fundamentally different from any form of eugenics. Eugenics is concerned with factors through which societies rise and decline, and ways to maximize societies’ potential. In contrast, the purpose of behavioural genetics (and of this review) is to gain insight into the complex processes driving the development of each individual. This includes understanding the influences that societies have on individuals, such as the effects of inequality on people’s genetic potential.