the effect of income inequality essay

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Causes and Consequences of Income Inequality – An Overview

Rising income inequality is one of the greatest challenges facing advanced economies today. Income inequality is multifaceted and is not the inevitable outcome of irresistible structural forces such as globalisation or technological development. Instead, this review shows that inequality has largely been driven by a multitude of political choices. The embrace of neoliberalism since the 1980s has provided the key catalyst for political and policy changes in the realms of union regulation, executive pay, the welfare state and tax progressivity, which have been the key drivers of inequality. These preventable causes have led to demonstrable harmful outcomes that are not explicable solely by material deprivation. This review also shows that inequality has been linked on the economic front with reduced growth, investment and innovation, and on the social front with reduced health and social mobility, and greater violent crime.

1 Introduction

Income inequality has recently come to be viewed as one of the greatest challenges facing the world today. In recent years, the topic has dominated the agenda of the World Economic Forum (WEF), where the world’s top political and business leaders attend. Their global risks report, drawn from over 700 experts in attendance, pronounced inequality to be the greatest threat to the world economy in 2017 ( Elliott 2017 ). Likewise, the past decade has seen leading global figures such as former American President Barack Obama, Pope Francis, Chinese President Xi Jinping, and the former head of the International Monetary Fund (IMF), Christine Lagarde, all undertake speeches on the gravity of income inequality and the need to address its rise. This is because, as this research note shows, income inequality engenders harmful consequences that are not explicable solely by material deprivation.

The general dynamics of income inequality include a tendency to rise slowly and fluctuate over time. For instance, Japan had one of the highest rates in the world prior to the Second World War and the United States (US) one of the lowest, which has since completely reversed for both. The United Kingdom (UK) was also the second most equitable large European country in the 1970s but is now the most inequitable ( Dorling 2018 : 27–28).

High rates of inequality are rarely sustained for long periods because they tend to lead to or become punctuated by man-made disasters that lead to a levelling out. Scheidel (2017) posits that there in fact exists a violent ‘Four Horseman of Leveling’ (mass mobilisation warfare, transformation revolutions, state collapse, and lethal pandemics) for inequality, which have at times dramatically reduced inequalities because they can lead to the alteration of existing power structures or wipe out the wealth of elites and redistribute their resources. For instance, the pronounced shocks of the two world wars led to the ‘Great Compression’ of income throughout the West in the post-war years. There is already some evidence that the current global pandemic caused by the novel Coronavirus, has led to greater aversion to income inequality ( Asaria, Costa-Font, and Cowell 2021 ; Wiwad et al. 2021 ).

Thus, greater aversion to inequality has been able to reduce inequality in the past, this is because, as this review also shows, income inequality does not result exclusively from efficient market forces but arises out of a set of rules that is shaped by those with political power. Inequality’s rise is not inevitable, nor beyond the control of governments and policymakers, as they can affect distributional outcomes and inequality through public policy.

It is the purpose of this review to outline the causes and consequences of income inequality. The paper begins with an analysis of the key structural and institutional determinants of inequality, followed by an examination into the harmful outcomes of inequality. It then concludes with a discussion of what policymakers can do to arrest the rise of inequality.

2 Causes of Income Inequality

Broadly speaking, explanations for the increase in income inequality have largely been classified as either structural or institutional. Historically, economists emphasised structural causes of increasing income inequality, with globalisation and technological change at the forefront. However, in recent years opinion has shifted to emphasise more institutional political factors to do with the adoption of neoliberal reforms such as privatisation, deregulation and tax and welfare reductions since the early 1980s. They were first embraced and most heavily championed by the UK and US, spreading globally later, and which provide the crucial catalysts of rising income inequality ( Atkinson 2015 ; Brown 2017 ; Piketty 2020 ; Stiglitz 2013 ). I discuss each of these key factors in turn.

2.1 Globalisation

One of the earliest, and most prominent explanations for the rise of income inequality emphasised the role of globalisation ( Borjas, Freeman, and Katz 1992 ; Revenga 1992 ). Globalisation has led to the offshoring of many goods and services that used to be produced or completed domestically in the West, which has created downward pressures on the wages of lower skilled workers. According to the ‘market forces hypothesis,’ increasing inequality is a response to the rising demand for skills at the top, in which the spread of globalisation and technological progress have been facilitated through reduced barriers to trade and movement.

Proponents of globalisation as the leading cause of inequality have argued that globalisation has constrained domestic state choices and left governments collectively powerless to address inequality. Detractors admit that globalisation has indeed had deep structural effects on Western economies but its impact on the degree of agency available to domestic governments has been mediated by individual policy choices ( Thomas 2016 : 346). A key problem with attributing the cause of inequality to globalisation, is that the extent of the inequality increase has varied considerably across countries, even though they have all been exposed to the same effects of globalisation. The US also has the highest inequality amongst rich countries, but it is less reliant on international trade than most other developed countries ( Brown 2017 : 56). Moreover, a recent meta-analysis by Heimberger (2020) found that globalisation has a “small-to-moderate” inequality-increasing effect, with financial globalisation displaying the largest impact.

2.2 Technology

A related explanation for inequality draws attention to the impact of technology specifically. The advent of the digital age has placed a higher premium on the skills needed for non-routine work and reduced the value placed on lower skilled routine work, as it has enabled machines to replace jobs that could be routinised. This skill-biased technological change (SBTC) has led to major changes in the organisation of work, as many full-time permanent jobs with benefits have given way to part-time flexible work without benefits, that are often centred around the completion of short ‘gigs’ such as a car journey or food delivery. For instance, the Organisation for Economic Co-operation and Development (OECD) estimated in 2015 that since the 1990s, roughly 60% of all job creation has been in the form of non-standard work due to technological changes and that those employed in such jobs are more likely to be poor ( Brown 2017 : 60).

Relatedly, a prevailing doctrine in economics is ‘marginal productivity theory,’ which holds that people with greater productivity levels will earn higher incomes. This is due to the belief that a person’s productivity is equated to their societal contribution ( Stiglitz 2013 : 37). Since technology is a leading determinant in the productivity of different skills and SBTC has led to increased productivity, it has also become a justification for inequality. However, it is very difficult to separate any one person’s contribution to society from that of others, as even the most successful businessperson owes their success to the rule of law, good infrastructure, and a state educated workforce ( Stiglitz 2013 : 97–98).

Further criticisms of the SBTC explanation, are that there was still substantial SBTC when inequality first fell dramatically and then stabilised in the period from 1930 to 1980, and it has failed to explain the perpetuation of both the gender and racial wage gap, “or the dramatic rise in education-related wage gaps for younger versus older workers” ( Brown 2017 : 67). Although it is difficult to decouple globalisation and technology, as they each have compounding tendencies, it is most likely that globalisation and technology are important explanatory factors for inequality, but predominantly facilitate and underlie the following more determinant institutional factors that happen to be already present, such as reduced tax progressivity, rising executive pay, and union decline. It is to these factors that I now turn.

2.3 Tax Policy

Taxes overwhelmingly comprise the primary source of revenue that governments can use for redistribution, which is fundamental to alleviating income inequality. Redistribution is defended on economic grounds because the marginal utility of money declines as income rises, meaning that the benefit derived from extra income is much higher for the poor than the rich. However, since the late 1970s, a major rethinking surrounding redistributive policy occurred. This precipitated ‘trickle-down economics’ theory achieving prominence amongst American and British policymakers, whereby the benefits from tax cuts on the wealthy would trickle-down to everyone. Subsequently, expert opinion has determined that tax cuts do not actually spur economic growth ( CBPP 2017 ).

Personal income tax progressivity has declined sharply in the West, as the average top income tax rate for OECD members fell from 62% in 1981 to 35% in 2015 ( IMF 2017 : 11). However, the decline has been most pronounced in the UK and the US, which had top rates of around 90% in the 1960s and 1970s. Corporate tax rates have also plummeted by roughly one half across the OECD since 1980 ( Shaxson 2015 : 4). Recent International Monetary Fund (IMF) research found that between 1985 and 1995, redistribution through the tax system had offset 60% of the increase in market inequality but has since failed to respond to the continuing increase in inequality ( IMF 2017 ). Moreover, in a sample of 18 OECD countries encompassing 50 years, Hope and Limberg (2020) found that tax reforms even significantly increased pre-tax income inequality, while having no significant effect on economic growth.

This decline in tax progressivity has been a leading cause of rising income inequality, which has been compounded by the growing problem of tax avoidance. A complex global web of shell corporations has been constructed by international brokers in offshore tax havens that is able to keep wealth hidden from tax collectors. The total hidden amount in tax havens is estimated to be $7.6 trillion US dollars and rising, or roughly 8% of total global household wealth ( Zucman 2015 : 36). Recent research has revealed that tax havens are overwhelmingly used by the immensely rich ( Alstadsæter, Johannesen, and Zucman 2019 ), thus taxing this wealth would substantially reduce income inequality and increase revenue available for redistribution. The massive reduction in income tax progressivity in the Anglo world, after it had been amongst its leaders in the post-war years, also “probably explains much of the increase in the very highest earned incomes” since 1980 ( Piketty 2014 : 495–496).

2.4 Executive Pay

The enormous rising pay of executives since the 1980s, has also fuelled income inequality and more specifically the gap between executives and their employees. For example, the gap between Chief Executive Officers (CEO) and their workers at the 500 leading US companies in 2016, was 335 times, which is nearly 10 times larger than in 1980. It is a similar story in the UK, with a pay ratio of 131 for large British firms, which has also risen markedly since 1980 ( Dorling 2017 ).

Piketty (2014 : 335) posits that the dramatic reduction in top income tax has had an amplifying effect on top executives pay since it provides them with much greater incentive to seek larger remuneration, as far less is then taken in tax. It is difficult to objectively measure an individual’s contribution to a company and with the onset of trickle-down economics and accompanying business-friendly climate since the 1980s, top executives have found it relatively easy to convince boards of their monetary worth ( Gabaix and Landier 2008 ).

The rise in executive pay in both the UK and US, is far larger than the rest of the OECD. This may partially be explained by the English-speaking ‘superstar’ theory, whereby the global market demand for top CEOs is much higher for native English speakers due to English being the prime language of the global economy ( Deaton 2013 : 210). Saez and Veall (2005) provide support for the theory in a study of the top 1% of earners from the Canadian province of Quebec, which showed that English speakers were able to increase their income share over twice as much as their French-speaking counterparts from 1980 to 2000. This upsurge of income at the top of the labour market has been accompanied by stagnation or diminishing returns for the middle and lower parts of the labour market, which has been affected by the dramatic decline of union influence throughout the West.

2.5 Union Decline

Trade unions have typically been viewed as an important force for moderating income inequality. They “contribute to wage compression by restricting wage decline among low-wage earners” and restrain wage surges among high-wage earners ( Checchi and Visser 2009 : 249). The mere presence of unions can also drive up the wages of non-union employees in similar industries, as employers tend to give in to wage demands to keep unions out. Union density has also been proven to be strongly associated with higher redistribution both directly and indirectly, through its influence on left party governments ( Haddow 2013 : 403).

There had broadly existed a ‘social contract’ between labour and business, whereby collective bargaining establishes a wage structure in many industries. However, this contract was abandoned by corporate America in the mid-1970s when large-scale corporate donations influenced policymakers to oppose pro-union reform of labour law, leading to political defeats for unions ( Hacker and Pierson 2010 : 58–59). The crackdown of strikes culminating in the momentous Air Traffic Controllers’ strike (1981) in the US and coal miner’s strike (1984–85) in the UK, caused labour to become de-politicised, which was self-reinforcing, because as their political power dispersed, policymakers had fewer incentives to protect or strengthen union regulations ( Rosenfeld and Western 2011 ). Consequently, US union density has plummeted from around a third of the workforce in 1960, down to 11.9% last decade, with the steepest decline occurring in the 1980s ( Stiglitz 2013 : 81).

Although the decline in union density is not as steep cross-nationally, the pattern is still similar. Baccaro and Howell (2011 : 529) found that on average the unionisation rate decreased by 0.39% a year since 1974 for the 15 OECD members they surveyed. Increasingly, the decline in the fortunes of labour is being linked with the increase in inequality and the sharpest increases in income inequality have occurred in the two countries with the largest falls in union density – the UK and US. Recent studies have found that the weakening of organised unions accounts for between a third and a fifth of the total rise in income inequality in the US ( Rosenfeld and Western 2011 ), and nearly one half of the increase in both the Gini rate and the top 10%’s income share amongst OECD members ( Jaumotte and Buitron 2015 ).

To illustrate the changing relationship between inequality and unionisation, Figure 1 displays a local polynomial smoother scatter plot of union density by income inequality, for 23 OECD countries, 1980–2018. They are negatively correlated, as countries with higher union density have much lower levels of income inequality. Figure 2 further plots the time trends of both. Income inequality (as measured via the Gini coefficient) has climbed over 0.02 percentage points on average in these countries since 1980, which is roughly a one-tenth rise. Whereas union density has fallen on average from 44 to 35 percentage points, which is over one-fifth.

Gini coefficient by union density, OECD 1980–2018. Data on Gini coefficients from SWIID ( Solt 2020 ); data on union density from ICTWSS Database ( Visser 2019 ).

Gini coefficient by union density, 1980–2018. Data on Gini coefficients from SWIID ( Solt 2020 ); data on union density from ICTWSS Database ( Visser 2019 ).

In sum, income inequality is multifaceted and is not the inevitable outcome of irresistible structural forces such as globalisation or technological development. Instead, it has largely been driven by a multitude of political choices. Tridico (2018) finds that the increases in inequality from 1990 to 2013 in 26 OECD countries, was largely owing to increased financialisation, deepening labour flexibility, the weakening of trade unions and welfare state retrenchment. While Huber, Huo, and Stephens (2019) recently reveals that top income shares are unrelated to economic growth and knowledge-intensive production but is closely related to political and policy changes surrounding union density, government partisanship, top income tax rates, and educational investment. Lastly, Hager’s (2020) recent meta-analysis concludes that the “empirical record consistently shows that government policy plays a pivotal role” in shaping income inequality.

These preventable causes that have given rise to inequality have created socio-economic challenges, due to the demonstrably negative outcomes that inequality engenders. What follows is a detailed analysis of the significant mechanisms that income inequality induces, which lead to harmful outcomes.

3 Consequences of Income Inequality

Escalating income inequality has been linked with numerous negative outcomes. On the economic front, negative results transpire beyond the obvious poverty and material deprivation that is often associated with low incomes. Income inequality has also been shown to reduce growth, innovation, and investment. On the social front, Wilkinson and Pickett’s ground-breaking The Spirit Level ( 2009 ), found that societies that are more unequal have worse social outcomes on average than more egalitarian societies. They summarised an extensive body of research from the previous 30 years to create an Index of Health and Social Problems, which revealed a host of different health and social problems (measuring life expectancy, infant mortality, obesity, trust, imprisonment, homicide, drug abuse, mental health, social mobility, childhood education, and teenage pregnancy) as being positively correlated with the level of income inequality across rich nations and across states within the US. Figure 3 displays the cross-national findings via a sample of 21 OECD countries.

Index of health and social problems by Gini coefficient. Data on health and social problems index from The Equality Trust (2018) ; data on Gini coefficients from OECD (2020) .

3.1 Economic

Income inequality is predominantly an economic subject. Therefore, it is understandable that it can engender pervasive economic outcomes. Foremost economically speaking, it has been linked with reduced growth, investment and innovation. Leading international organisations such as the IMF, World Bank and OECD, pushed for neoliberal reforms beginning in the 1980s, although they have recently started to substantially temper their views due to their own research into inequality. A 2016 study by IMF economists, noted that neoliberal policies have delivered benefits through the expansion of global trade and transfers of technology, but the resulting increases in inequality “itself undercut growth, the very thing that the neo-liberal agenda is intent on boosting” ( Ostry, Loungani, and Furceri 2016 : 41). Cingano’s (2014) OECD cross-national study, found that once a country’s income inequality reaches a certain level it reduces growth. The growth rate in these countries would have been one-fifth higher had income inequality not increased, while the greater equality of the other countries included in the study helped to increase their growth rates.

Consumer spending is good for economic growth but rising income inequality shifts more money to the top of the income distribution, where higher income individuals have a much smaller propensity to consume than lower-income individuals. The wealthy save roughly 15–25% of their income, whereas low income individuals spend their entire income on consumer goods and services ( Stiglitz 2013 : 106). Therefore, greater inequality reduces demand in an economy and is a major contributor to the ‘secular stagnation’ (persistent insufficient demand relative to aggregate private savings) that the largest Western economies have been experiencing since the financial crisis. Inequality also increases the level of debt, as lower-income individuals borrow more to maintain their standard of living, especially in a climate of low interest rates. Combined with deregulation, greater debt increases instability and “was a major contributor to, if not the underlying cause of, the 2008 financial crash” ( Brown 2017 : 35–36).

Another key economic effect of income inequality is that it leads to reduced welfare spending and public investment. Since a greater share of the income distribution is earned by the very wealthy, governments have less income available to fund education, public amenities, and other services that the poor rely heavily on. This creates social separation, whereby the wealthy opt out in publicly funding services because their private equivalents are of better quality. This causes a cycle of increasing income inequality that is likely to eventually lead to a situation of “private affluence and public squalor” ( Marmot 2015 : 39).

Lastly, it has been proven that economic instability is a by-product of increasing inequality, which harms innovation. Both countries and American states with the highest inequality have been found to be the least innovative in terms of the amount of Intellectual Property (IP) patents they produce ( Dorling 2018 : 129–130). Although income inequality is predominantly an economic subject, its effects are so pervasive that it has also been linked to a host of negative health and societal outcomes.

Wilkinson and Pickett found key associations between income inequality for both physical and mental health. For example, they discovered that on average the life expectancy gap is more than four years between the least and most equitable richest nations (Japan and the US). Since their revelations, overall life expectancy has been reported to be declining in the US ( Case and Deaton 2020 ). It has held or declined every year since 2014, which has led to a cumulative drop of 1.13 years ( Andrasfay and Goldman 2021 ). Marmot (2015) has provided evidence that there exists a social gradient whereby differences in affluence translate into increasing health inequalities, which can be shown even down to the neighbourhood level, as more affluent areas have higher life expectancy on average than deprived areas, and a clear gradient appears where life expectancy increases in line with affluence.

Moreover, Marmot’s famous Whitehall studies, which are large-scale longitudinal studies of Whitehall employees of UK central government, found an inverse-relationship between salary grade and ill-health, whereby low-grade workers were four times as likely as high-grade workers to suffer from ill-health ( 2015 : 11). Health steadily improves with rank and the correlation is little affected by lifestyle controls such as tobacco and alcohol usage. However, the leading factor that seems to make the most difference in ill-health is job stress and a person’s sense of control over their work, including the variety of work and the use and development of skills ( Schrecker and Bambra 2015 : 54–55).

‘Psychosocial stresses,’ like those appearing in the Whitehall studies, have been found to be more common and frequent amongst low-income individuals, beyond just the workplace ( Jensen and van Kersbergen 2017 : 24). Wilkinson and Pickett (2019) posit that greater income inequality engenders low self-esteem, chronic stress and depression, stemming from status anxiety. This occurs because more importance is placed on where people fit in a hierarchy with greater inequality. For evidence, they outline a clear relationship of a much higher percentage of the population suffering from mental illness in more unequal countries. Meticulous research has shown that huge inequalities in income result in the poor having feelings of shame across a range of environments. Furthermore, Dickerson and Kemeny’s (2004) meta-analysis of 208 studies found that stress-hormone (cortisol) levels were raised particularly “when people felt that others were making negative judgements about them” ( Rowlingson 2011 : 24).

These effects on both mental and physical health can be best illustrated via the ‘absolute income’ and ‘relative income’ hypotheses ( Daly, Boyce, and Wood 2015 ). The relative income hypothesis posits that when an individual’s income is held constant, the relative income of others can affect a person’s health depending on how they view themselves in comparison to those above them ( Wilkinson 1996 ). This pattern also holds when income inequality increases at the societal level, because if such changes lead to increases in chronic stress, it can increase ill-health nationally. Whereas the absolute income hypothesis predicts that health gains from an extra unit of income diminish as an individual’s income rises ( Kawachi, Adler, and Dow 2010 ). A mean preserving transfer from a richer to poorer individual raises the health of the poorer individual more than it lowers the health of the richer person. This occurs because there is an optimum threshold of income required to maintain good health. Thus, when holding total income constant, a more equal distribution of income should improve overall population health. This pattern also applies at the country-wide level, as the “effect of income on health appears substantial as countries move from about $15,000 to 25,000 US dollars per capita,” but appears non-existent beyond that point ( Leigh, Jencks, and Smeeding 2009 : 386–387).

Income inequality also impacts happiness and wellbeing, as the happiest nations are routinely the ones with low inequality, such as Denmark and Norway. Happiness has been proven to be affected by the law of diminishing returns in economics. It states that higher income incrementally improves happiness but only up to a certain point, as any individual income earned beyond roughly $70,000 US dollars, does not bring about greater happiness ( Deaton 2013 : 53). The negative physical and mental health outcomes that income inequality provoke, also impact key societal areas such as crime, social mobility and education.

Rates of violent crime are lower in more equal countries ( Hsieh and Pugh 1993 ; Whitworth 2012 ). This is largely because more equal countries have less poverty, which leads to less people being desperate about their situation, as lower-income individuals have been shown to commit more crime. Relatedly, according to strain theory, more unequal societies place higher social value in achieving economic success, while providing lower means to achieve it ( Merton 1938 ). This generates strain, which may lead more individuals to pursue crime as a means of attaining financial success. At the opposite end of the income spectrum, the wealthy in more equal countries are also less likely to exploit others and commit fraud or exhibit other anti-social behaviour, partly because they feel less of a need to cut corners to get ahead, or to make money ( Dorling 2017 : 152–153). Homicides also tend to rise with inequality. Daly (2016) reveals that inequality predicts homicide rates better than any other variable and accounts for around half of the variance in murder rates between countries and American states. Roughly 90% of American homicides are committed by men, and since the majority of homicides occur over status, inequality raises the stakes of disputes over status amongst men.

Studies have also shown that there is a marked negative relationship between income inequality and social mobility. Utilising Intergenerational Earnings Elasticity data from Blanden, Gregg, and Machin (2005) , Wilkinson and Pickett (2009) first outline this relationship cross-nationally for eight OECD countries. Corak (2013) famously expanded on this with his ‘Great Gatsby Curve’ for 22 countries using the same measure. I update and expand on these studies in Figure 4 to include all 36 OECD members, utilising the WEF’s inaugural 2020 Social Mobility Index. It clearly shows that social mobility is much lower on average in more unequal countries across the entire OECD.

Index of social mobility by Gini coefficient. Data on social mobility index from World Economic Forum (2020) ; data on Gini coefficients from SWIID ( Solt 2020 ).

A primary driver for the negative relationship between inequality and social mobility, derives from the availability of resources during early childhood. Life chances have been shown to be determined in early childhood to a disproportionately large extent ( Jensen and van Kersbergen 2017 : 29). Children in more equitable regions such as Scandinavia, have better access to resources, as they go to similar schools, receive similar educational opportunities, and have access to a wider range of career options. Whereas in the UK and US, a greater number of jobs at the top are closed off to those at the bottom and affluent parents are far more likely to send their children to private schools and fund other ‘child enrichment’ goods and services ( Dorling 2017 : 26). Therefore, as income inequality rises, there is a greater disparity in the resources that rich and poor parents can invest in their children’s education, which has been shown to substantially affect “cognitive development and school achievement” ( Brown 2017 : 33–34).

4 Conclusions

The causes and consequences of income inequality are multifaceted. Income inequality is not the inevitable outcome of irresistible structural forces such as globalisation or technological development. Instead, it has largely been driven by a multitude of institutional political choices. These preventable causes that have given rise to inequality have created socio-economic challenges, due to the demonstrably negative outcomes that inequality engenders.

The neoliberal political consensus poses challenges for policymakers to arrest the rise of income inequality. However, there are many proven solutions that policymakers can enact if the appropriate will can be summoned. Restoring higher levels of labour protections would aid in reversing the declining trend of labour wage share. Similarly, government promotion and support for new corporate governance models that give trade unions and workers a seat at the table in ownership decisions through board memberships, would somewhat redress the increasing power imbalance between capital and labour that is generating more inequality. Greater regulation aimed at limiting the now dominant shareholder principle of maximising value through share buy-backs and instead offering greater incentives to pursue maximisation of stakeholder value, long-term financial stability and investment, can reduce inequality. Most importantly, tax policy can be harnessed to redress income inequality. Such policies include restoring higher marginal income and corporate tax rates, setting higher corporate tax rates for firms with higher ratios of CEO-to-worker pay, and establishing luxury taxes on spiralling compensation packages. Finally, a move away from austerity, which has gripped the West since the financial crisis, and a move towards much greater government investment and welfare state spending, would also lift growth and low-wages.

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On the Impact of Inequality on Growth, Human Development, and Governance

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Ines A Ferreira, Rachel M Gisselquist, Finn Tarp, On the Impact of Inequality on Growth, Human Development, and Governance, International Studies Review , Volume 24, Issue 1, March 2022, viab058, https://doi.org/10.1093/isr/viab058

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Inequality is a major international development challenge. This is so from an ethical perspective and because greater inequality is perceived to be detrimental to key socioeconomic and political outcomes. Still, informed debate requires clear evidence. This article contributes by taking stock and providing an up-to-date overview of the current knowledge on the impact of income inequality, specifically on three important outcomes: (1) economic growth; (2) human development, with a focus on health and education as two of its dimensions; and (3) governance, with emphasis on democracy. With particular attention to work in economics, which is especially developed on these topics, this article reveals that the existing evidence is somewhat mixed and argues for further in-depth empirical work across disciplines. It also points to explanations for the lack of consensus embedded in data quality and availability, measurement issues, and shortcomings of the different methods employed. Finally, we suggest promising future research avenues relying on experimental work for microlevel analysis and reiterate the need for more region- and country-specific studies and improvements in the availability and reliability of data.

La desigualdad es un desafío importante para el desarrollo internacional. Esto es así desde una perspectiva ética y debido a que la mayor desigualdad se percibe como perjudicial para los resultados políticos y socioeconómicos clave. Aun así, los debates informados requieren pruebas claras. Esta revisión contribuye estudiando la situación y ofreciendo un resumen actualizado del conocimiento actual sobre el impacto de la desigualdad de ingresos, específicamente en tres resultados importantes: (1) el crecimiento económico; (2) el desarrollo humano, con un enfoque en la salud y la educación como dos de sus dimensiones; y (3) la gobernanza, con énfasis en la democracia. Prestando especial atención al trabajo en economía que se desarrolla particularmente sobre estos temas, este ensayo demuestra que las pruebas existentes están mezcladas de alguna manera y argumenta a favor de promover el trabajo empírico en profundidad en todas las disciplinas. También señala las explicaciones para la falta de consenso que están integradas en la calidad y la disponibilidad de los datos, los problemas de medición y los defectos de los diferentes métodos empleados. Finalmente, sugerimos prometedoras vías de investigación para el futuro que dependen del trabajo experimental para el análisis a pequeña escala, y reiteramos la necesidad de realizar más estudios específicos de la región y el país, así como mejoras en la disponibilidad y la confiabilidad de los datos.

L'inégalité est un défi majeur du développement international. Il en est ainsi d'un point de vue éthique et parce qu'une plus grande inégalité est perçue comme allant au détriment des principaux résultats socio-économiques et politiques. Toutefois, des preuves claires sont nécessaires pour débattre en connaissance de cause. Cette analyse y contribue en faisant le bilan et en offrant une présentation à jour des connaissances actuelles sur l'impact de l'inégalité des revenus, en particulier sur trois résultats importants: (1) la croissance économique, (2) le développement humain, en se concentrant sur la santé et l’éducation en tant que deux de ses dimensions, et (3) la gouvernance, en mettant l'accent sur la démocratie. Cet essai accorde une attention particulière aux travaux en économie qui sont particulièrement développés sur ces sujets et révèle que les preuves existantes sont quelque peu mitigées et plaide pour un travail empirique plus approfondi dans toutes les disciplines. Il met également en évidence des explications du manque de consensus inhérent à la qualité et à la disponibilité des données, aux problèmes de mesure et aux lacunes des différentes méthodes employées. Enfin, nous suggérons des pistes de recherches futures prometteuses qui s'appuieraient sur des travaux expérimentaux pour l'analyse au niveau micro et nous réitérons la nécessité de réaliser davantage d’études spécifiques aux régions et aux pays et d'améliorer la disponibilité et la fiabilité des données.

Recent decades have witnessed sharp rises in inequality of income and wealth in many countries (though neither globally nor everywhere) as well as in the observed level of inequality of opportunities in access to basic services, such as health and education. Concern with these trends is paramount in Goal 10 of the Sustainable Development Goals approved by the United Nations General Assembly in 2015, aiming at “reducing inequality within and among countries.” The COVID-19 pandemic, which has both reflected and exacerbated inequalities, further spotlights this objective.

Pursuing this goal can obviously be justified from an ethical perspective. The case is also made in instrumental terms, with reference to potential negative effects of inequality on a variety of socioeconomic and political outcomes. The World Development Report (2006) drew attention to the implications of high levels of inequality for long-term development ( World Bank 2006 ). Indeed, economists in particular have long been concerned with the relationship between equity and efficiency 1 ; interestingly, the old classical view, contrary to the 2006 report, suggests a contradiction between equality and development.

Informed policy debate requires clear evidence on these impacts. This analytical essay provides a “state-of-art” on research on this big question. While recent reviews of the literature tend to focus on the impact of inequality on one specific outcome, we have a broader scope; we aim to bring new clarity to the debate by taking stock of the current knowledge on the effects on three important outcomes: (1) economic growth; (2) human development, with a focus on health and education as two of its dimensions; and (3) governance, with emphasis on democracy. While we start by highlighting how the various processes are connected, we address the impacts of inequality on these outcomes separately, developing an overview of the core arguments and underlying mechanisms, and of the existing evidence, with a particular focus on cross-country insights.

We draw in particular on the large and well-developed literature on these topics in economics while also taking key insights from other disciplines. 2 Our focus is on broad outcomes that are of particular importance for international development and that received great attention in studies examining the impact of inequality across disciplines. The effects of inequality on economic growth have been extensively debated in economics, the main disciplinary focus of this article. However, health and education—two important channels with high policy relevance—have also been the object of investigation in public health studies. Moreover, the field of political science has greatly contributed to the debate addressing the effects of inequality on political aspects, including those related to democratic governance. 3

Building on previous reviews focusing on specific outcomes (e.g., Voitchovsky 2011 ; Neves and Silva 2014 ; O'Donnell, van Doorslaer, and van Ourti 2015 ; Scheve and Stasavage 2017 ), but adopting the broader outlook of the seminal review by Thorbecke and Charumilind (2002) , this article provides an updated and comprehensive perspective on the consequences of inequality in three core areas of concern for international studies. 4

We combine the main theoretical arguments on the impact of inequality and underlying transmission channels in a general framework, providing a simplified view while emphasizing the connections between different processes. Overall, our review of an extensive body of work suggests there is no clear consensus emerging from the empirical evidence, and we argue there is room for additional in-depth work to uncover the effects through specific mechanisms of transmission. In particular, there is no consensus from the results of studies using reduced-form equations to examine the effect on growth, and less work has been dedicated to exploring the channels of transmission. Moreover, the negative link between inequality and secondary school enrolment is confirmed by the evidence, but further research is needed in terms of other education outcomes. The economic and public health literatures disagree on whether the negative effect of inequality on health is confirmed by the existing evidence, and there are mixed results emerging from political scientists for the effects of inequality on democracy and political participation. We advance the underlying explanations for this state of affairs, related to the challenges inherent in data quality and availability, measurement issues, and shortcomings of the different estimation methods employed, and suggest avenues for further research.

In the second section, we offer an outline of the main theoretical predictions of the effects of inequality on socioeconomic outcomes and on governance, presenting different channels of transmission. The third section follows the same structure and reviews the existing empirical evidence. We reflect on key empirical challenges of estimating the effects of inequality in the fourth section. The fifth section concludes.

Several theoretical explanations exist across disciplines for the effects of inequality on socioeconomic and political outcomes. Before we describe in more detail these channels of influence and the resulting outcomes, we highlight a broader set of arguments, which act as a roadmap for the rest of the section.  Figure 1 provides a schematic overview.

Diagram with main outcomes of inequality

Source : Authors’ elaboration.

Starting from the left- to the right-hand side, the diagram represents different channels of transmission of the effects of higher levels of inequality, their intermediate effects, and the resulting positive or negative impact on our three outcomes of interest: growth, 5 human development, and democracy. We broadly divide these channels according to their underlying drivers: the poor, the population at large or the average, and the wealthy.

Overall, the diagram suggests that high inequality has predominantly harmful effects on our three outcomes of interest, according to theoretical explanations advanced in the literature. The dominant view then runs contra the expectations of the classical theorists, i.e., that inequality has a positive impact on growth, via savings and investment (shown at the top of  figure 1 ). We highlight six main transmission channels.

First, inequality affects incentives for savings and investment and the overall level of institutional quality through its influence on policy making and increased political instability, and consequent effects on property rights and the regulatory framework. This has implications for growth both directly and indirectly via governance.

Second, by favoring private over public investment, inequality affects investment in public goods, namely health and education, with implications across the three outcomes. Third, and related, inequality results in underinvestment on human capital resulting from credit constraints, and high fertility, which affects education levels and overall economic growth.

Fourth, high taxation will be demanded by a well-endowed median voter and the likelihood of transition to and stability of democracy will also depend on the pressure for redistribution, which is higher with lower levels of equality. Moreover, and fifth, a small middle class will affect the demand not only for democracy but also for manufactures.

Finally, high levels of polarization will lead to weak social cohesion via their effects on social capital, as well as low trust and potential high levels in violent crime, which affect health directly and indirectly via investment in public health. Additionally, the concentration of power on the rich leads to increased probability of political violence and affects political engagement.

Some of these channels affect all of the outcomes. For instance, the effect through investment in public goods has detrimental effects on human development, and on growth and democracy. Moreover, the resulting polarization and social discontent, which increase the chances of political violence, again negatively impact the three outcomes. However, there is also some indication that, when it comes to growth, the effect might be ambiguous depending on the predominance of the effects of transmission mechanisms. The channel through savings (and investment) points to a potential positive effect, while the different effects through public investment, taxation, the structure of demand, imperfect credit markets, fertility, and social discontent suggest potential negative consequences for growth.

This section uncovers more details about these different theoretical predictions. It starts by introducing the main hypotheses advanced for the effects of inequality on growth. While the approach in this article considers the three outcomes separately, we recognize that they are not disjointed or orthogonal and refer to the links between them. Nevertheless, a full discussion of these interlinkages is beyond the scope of this article. As suggested in  figure 1 and described in more detail below, some of these channels point to the impact of inequality on our remaining outcomes of interest, namely education and health, or governance. We return to them in the remaining two subsections, where we expand to consider the insights from other strands of literature.

How Inequality Affects Growth

An extensive literature examines the effects of inequality on growth, 6 highlighting multiple channels of transmission. 7 The early studies, referred to as the classical approach, argued that there is a positive effect of inequality on growth, explained via savings or incentives. However, subsequent work questioned this view, challenging some of its assumptions and proposing different channels of influence. Most of this work has predicted a negative effect of inequality. We briefly outline these channels in the next paragraphs and refer to Bourguignon (2015) , Neves and Silva (2014) , and Voitchovsky (2011) for complementary detail and reviews. 8

High inequality is growth enhancing

We start by drawing attention to the view of classical economists on income inequality, according to which there was a contradiction between equality and development (for a discussion of the trade-off between efficiency and equity, see Thorbecke 2016 ). Adam Smith defended that inequality had benefits based on arguments of (1) “trickle-down effects”—the increase in wealth will eventually benefit the poor, (2) incentive effects—inequality is necessary to encourage competition and to provide incentives for innovation, and (3) social stability—the different ranks in wealth distribution ensure peace and stability in society ( Walraevens 2021 , 3–6). The famous Kuznets curve ( Kuznets 1955 ), shaped like an inverted U-relationship between growth and inequality (as per capita income increases), seemed to reinforce this view. 9

Developed in the 1950s and 1960s, the so-called classical approach followed a similar line of thinking, based on arguments related to savings and incentives. The prominent work by Kaldor (1956) suggests a positive link between inequality and growth via saving rates, based on the assumption that the higher the level of income, the higher is the marginal propensity to save ( Aghion, Caroli, and García-Peñalosa 1999 , 1620). At the core of this assumption that the rich have a higher marginal propensity to save relative to the poor are two hypotheses: (1) consumption smoothing cannot occur unless the subsistence level of consumption is achieved, and therefore the poor cannot save, and (2) the possibility to save is conditioned by the previous generations, which leads to a concentration of savings in rich households ( Thorbecke and Charumilind 2002 , 1483).

Under this assumption, the redistribution of resources toward the rich leads to higher savings, which, in turn, improves growth via investment. This link is particularly important if one considers limited borrowing possibilities, initial setup costs, and the large investments involved in risky and high-return opportunities ( Aghion, Caroli, and García-Peñalosa 1999 , 1620; Voitchovsky 2011 , 558). Big investment projects involve large sunk costs, and therefore investment relies on the concentration of wealth in individuals to be able to afford them.

A second argument drew on the role of incentives and on the trade-off between efficiency and social justice mentioned earlier ( Aghion, Caroli, and García-Peñalosa 1999 , 1620). At the microlevel, in a simple moral hazard model, if output depends on unobserved effort, then setting a constant reward (in the form of wage) discourages effort, whereas linking the reward to output can be inefficient due to agents’ risk aversion. The same argument maintains at the aggregate level, assuming identical agents and/or perfect capital markets. As explained by Aghion, Caroli, and García-Peñalosa (1999 , 1620), redistribution will have a direct negative effect on growth as well as a negative indirect effect through the reduction in the incentives to accumulate wealth (resulting from redistribution through income tax).

High inequality has a negative effect on growth

Credit market imperfections and fertility.

The effects of inequality on growth via credit market imperfections and via fertility are linked by their focus on the circumstances of the poor and on human capital investment ( Voitchovsky 2011 ). The first channel addresses the impact of credit imperfections on investment decisions. If one considers the high fixed costs associated with, for instance, education, limitations on the access to credit may lead to underinvestment in human capital, which implies a negative impact on growth ( Neves and Silva 2014 , 3). This was the argument resulting from the Galor and Zeira (1993) model. Assuming that credit markets are imperfect and that investment in human capital is indivisible, they conclude that the distribution of wealth has an impact on aggregate investment in human capital and therefore on growth, both in the short and in the long run.

The reasoning behind the link between inequality and growth through fertility was similar. Poor families might not have the resources to invest in their children's education and, thus, their income depends on having bigger families; for richer families, it might be optimal to invest more in education and, consequently, to have fewer children ( Gründler and Scheuermeyer 2018 , 295). In this line of thinking, de la Croix and Doepke (2003) argued that a high fertility differential between the rich and the poor lowered average education. Thus, inequality leads to lower levels of human capital accumulation via the increased fertility differential and, therefore, to lower growth.

Taxation and regulatory policies

Seminal work by Alesina and Rodrik (1994) as well as Persson and Tabellini (1994) pointed to a negative link between inequality and growth through government expenditure and taxation, combining endogenous growth theory with political economy insights. They proposed two different mechanisms that Perotti (1996 , 151) termed “political” and “economic,” respectively. The Alesina and Rodrik (1994) model drew on the median voter theorem and considered tax revenues equally distributed among all individuals. Given that the tax rate is proportional to income, individuals with a lower share of capital income (relative to labor income) prefer higher taxes. Thus, the more equitable the distribution in the economy, the better endowed is the median voter, and the lower the equilibrium level of taxation. A lower rate of tax corresponds to a higher growth rate, which led them to conclude that there is an inverse relationship between inequality and subsequent economic growth.

Persson and Tabellini (1994) reached the same conclusion considering the role of incentives for productive accumulation and for growth. According to them, the incentives necessary for private savings and investment rely on individuals’ ability to “appropriate privately the fruits of their efforts” ( Persson and Tabellini 1994 , 600), which are in turn influenced by tax and regulatory policies. Inequality gives rise to policies that do not protect property rights or allow full appropriation of returns to investment and is therefore associated with lower economic growth.

Still, this result was defied by Li and Zou (1998) . They offered a more general framework than that proposed by Alesina and Rodrik (1994) , considering that government spending could be directed not only to production services—which entered the production function—but also to consumption services—which entered the utility function. Adding this extension, they showed that a more equal distribution could lead to lower growth via higher taxation and that the effect of income inequality on growth is, therefore, ambiguous.

The view outlined in Alesina and Rodrik (1994) and in Persson and Tabellini (1994) was also challenged by an alternative perspective suggesting that redistributive policies might also have a positive effect on growth in the presence of imperfect credit and insurance markets and that the popular support for these policies decreases with inequality ( Bénabou 2000 ). When combined, these two mechanisms could lead to multiple steady states, while the correlation with growth depends on the balance between incentive distortions and credit constraints ( Neves and Silva 2014 , 4). Voitchovsky (2011 , 556) lists the criticism toward the median voter argument and highlights how the channel through redistribution does not gather consensus.

The structure of demand

Zweimüller (2000) described the role of redistribution on growth through innovation. Building on the assumption of hierarchical preferences, the distribution of income affects the structure of demand: poor people spend mainly on basic needs whereas rich people spend on luxury goods. According to the author, inequality affects growth through its effect on the time path faced by an innovator. When a new and expensive good is introduced in the market, only rich consumers can afford it, until the increasing demand drives the price–wage ratio down (due to economies of scale), opening up the market to mass consumers ( Voitchovsky 2011 , 557). The optimal consumption levels of those affected by redistribution dictate the overall effect of changes in income inequality on long-run growth ( Zweimüller 2000 ). An earlier study by Murphy, Shleifer, and Vishny (1989) had already highlighted the importance of the middle class to the consumption of domestic manufactures and, therefore, to industrialization.

Sociopolitical instability and rent seeking

Another group of studies suggested a link between inequality and growth through sociopolitical instability, drawing attention to the effects on property rights. According to Alesina and Perotti (1996) , social unrest—resulting from social discontent caused by income inequality—can lead to an increasing probability of political violence as well as policy uncertainty and threats to property rights, which, in turn, have a negative impact on investment and thus on growth. Keefer and Knack (2002) claimed that income inequality leads to instability in government policies, namely those related to security of property rights, which affects the decisions of economic actors, and consequently slows the rate of growth. Relatedly, the Glaeser, Scheinkman, and Shleifer (2003) model showed a detrimental effect of inequality on property rights through the subversion of political regulatory and legal institutions by the rich for their own benefit.

The effect depends

Finally, we highlight contributions suggesting that different mechanisms might be present at different points. Galor and Moav (2004) proposed a unified theory between the credit market imperfections and the saving rate channels described earlier. According to them, the positive effect of inequality on growth suggested by classical theories corresponded to early stages of industrialization when physical capital accumulation is the primary driver of economic growth. However, at later stages, human capital accumulation becomes the main determinant of growth and credit constraints are largely binding, which explains the negative link between inequality and growth through credit market imperfections. As credit constraints become less binding due to wage increases, the aggregate effect of income distribution on growth is less significant.

A decade later, Halter, Oechslin, and Zweimüller (2014) presented a parsimonious theoretical model that takes into account both a short-term and a long-term effect of asset inequality. According to them, the short-term effect is positive and it occurs through an economic channel, whereas the long-term effect is negative and stems from a political economy channel.

How Inequality Affects Education and Health

Inequality can have both positive and negative effects on education.

While the literature examining the effects of education on inequality is extensive, the same is not true for studies looking at the other direction of causality. We distinguish between the arguments on the effects of inequality through expenditure on education and through school enrolment and attainment.

The provision of education depends on the willingness of citizens to redistribute resources via taxation, in line with Alesina and Rodrik (1994) and Perotti (1996 ). According to this political economy mechanism, increasing inequality will lead to lower availability of resources, as the rich will prefer not to contribute to public education, favoring private schools ( Mayer 2001 , 5). 10 Gutiérrez and Tanaka (2009) modeled the effect of inequality on school enrolment, and the preferred tax rate and expenditure per student focusing on parents’ decisions in developing countries. According to the authors, beyond a certain level of inequality, there is no longer support for public education. The model shows that, when considering the fact that parents can make a choice of sending their children either to work or to private or public schools, high inequality results in exiting public education, which has implications for the tax rate and expenditure per student. 11

According to the credit market imperfections’ channel discussed in section “How Inequality Affects Growth,” inequality creates obstacles in terms of access to education. In the presence of imperfect credit markets, the distribution of wealth affects the aggregate investment in human capital ( Galor and Zeira 1993 ; García-Peñalosa 1995 ). Additionally, inequality can affect enrolment by determining the number of poor who are able to substitute the return of child labor for school attendance ( Gutiérrez and Tanaka 2009 , 56). The Tanaka (2003) model shows that in contexts of high inequality, there is low support for public provision of schooling, which, in equilibrium, leads to a higher level of child labor.

The expected returns to the family from schooling will also affect the demand for education, as educated children are likely to have higher future income ( Birdsall 1999 , 17). If inequality is induced in part by increased returns to schooling, then there will be an incentive for children to stay in school and one could expect a positive relationship between an increase in inequality and educational attainment ( Mayer 2001 ; Thorbecke and Charumilind 2002 ; Dabla-Norris et al. 2015 ). 12

Inequality negatively affects health

The interest in understanding how income inequality affects health has instigated a broad range of work both in economics and in the fields of public health and sociology, 13 and different hypotheses are available. Generally, they suggest that inequality negatively affects health. Following O'Donnell, van Doorslaer, and van Ourti (2015) and Leigh, Jencks, and Smeeding (2011) , we distinguish between hypotheses that imply that the health of all individuals is affected and those that do not require that the health of every individual in society is under threat. 14

The first group of hypotheses proposes three different channels: public goods provision, social capital, and violent crime. 15 The effect through public goods provision can be negative or positive ( Leigh, Jencks, and Smeeding 2011 , 390). There will be a negative effect if inequality causes a reduction in the average value of publicly provided goods due to more heterogeneous preferences or if it enables the rich to acquire more political influence and, consequently, to pressure for a reduction in public spending on health. However, it can also be positive, given that as inequality increases among voters, the median voter will tend to support spending on health.

The effect through social capital builds on the assumption that income inequality leads to decreased social cohesion and, therefore, affects health through social 16 and psychosocial support, mechanisms of informal insurance, and diffusion of information ( O'Donnell, van Doorslaer, and van Ourti 2015 , 1501). Low trust can lead to disbelief about the improvements in health via public spending and links to higher mortality via smaller friendship networks as well ( Leigh, Jencks, and Smeeding 2011 , 390). Finally, although only a small percentage of deaths in developed countries results from violent crime, Leigh, Jencks, and Smeeding (2011 , 389) highlight the potentially larger secondary effects via increased stress about experiencing crime in the future. 17

In the second group of hypotheses, health depends on income at the individual level. The Wagstaff and van Doorslaer (2000) seminal review describes different interpretations. First, the absolute income hypothesis, which was also termed the “income artefact” hypothesis, suggests that the observed correlation between inequality and health is a result of the concave relationship between income and health; that is, the health gains of an additional unit of income are diminishing in an individual's income level. The term “artefact” applies to the fact that a redistribution of income leads to an increase in average population health even though there is no effect on the health of any individual, given their income. Second, the relative income hypothesis builds on the idea that psychosocial effects that result from individuals comparing their income with that of others (the mean income of the population or the community) affect health. Third, the deprivation hypothesis is a variation of the relative income hypothesis, and it argues that the crucial aspect is the extent of deprivation measured by the income gap. Fourth, and related, the relative position hypothesis states that what is important is the position of the individual in the income distribution.

How Inequality Affects Democratic Governance

In this section, we delve more deeply into the relationship between inequality and governance outcomes, democracy in particular, which have attracted considerable attention, especially within political economy and political science (see Bermeo 2009 ; Karl 2000 ). We start by focusing on the effects on democratic stability and democratic transition and then zoom in on the effects on political participation.

First, we refer back to the link between inequality and growth through political instability and social conflict described in section “High inequality has a negative effect on growth”. As highlighted by Fukuyama (2011 , 84), “[a] more likely reason why inequality is bad for growth is directly political: highly unequal countries are polarized between rich and poor, and the resulting social conflict destabilizes them, undermines democratic legitimacy, and reduces economic growth.” The summary in Thorbecke and Charumilind (2002 , 1486) suggests two main mechanisms: the relative deprivation hypothesis and resource mobilization. According to the first, discontent resulting from the gap between individual expected and achieved well-being leads to collective political violence. Inequality might deepen the grievances of certain groups or reduce the opportunity cost of engaging in violent conflict ( Dabla-Norris et al. 2015 , 9). Nevertheless, the second mechanism points to the ability of dissident groups to organize themselves as the key element.

The theoretical literature largely suggests negative effects of inequality on the likelihood of transition to and stability of democracy. It attributes an important role to democratic values and access to education, which are more likely to characterize citizens and the situation in equal societies, and to the middle class, which is more likely to promote tolerance and avoid extremist positions ( Houle 2015 , 145).

Two of the most prominent arguments for the link between inequality and democracy were presented in Boix (2003) and Acemoglu and Robinson (2006) . 18 The former argues that increasing levels of economic equality lead to a higher probability of democracy through redistribution. According to the theoretical predictions, the pressure for redistribution from the poor decreases with higher levels of equality, which means that a turn to democracy would be less costly for the holders of the most productive assets; that is, the payment of tax is less costly than repression.

The Acemoglu and Robinson (2006) predictions indicate a nonlinear, inverted U-shaped relationship. On the one hand, greater intergroup inequality increases the appeal of a revolution for citizens to increase their share in the income of the economy, thus increasing the likelihood of democracy. On the other hand, higher inequality also means higher aversion to democracy by elites as their tax burden is greater, thus discouraging democratization. Accordingly, the authors suggested that, for high levels of equality, there is no incentive for citizens to challenge the system and the interests of the elites are preserved. In societies with high levels of inequality, citizens try to rise up against the system, but this meets great repression from the elite, leading to a repressive non-democracy or a revolution, in certain cases. Therefore, the likelihood of democracy is higher for middle levels of inequality.

However, Houle (2009) highlighted three problems with these theories. First, they do not apply to transitions that are driven from above (e.g., from intra-elite competition). Second, the net effect of inequality is ambiguous because it makes redistribution more costly for the elites but, at the same time, it increases the population's demand for regime change. Finally, they ignore collective action problems and the challenges of mobilizing the population. More recently, Ansell and Samuels (2010) departed from Boix (2003) and Acemoglu and Robinson (2006) and proposed a contractarian approach that placed the focus on the citizens’ demand for protection against expropriation. According to these authors, democracy emerges from land equality and income inequality.

We briefly refer to a related group of studies examining the link from inequality to institutional quality and refer to Chong and Gradstein (2019) for details. Chong and Gradstein (2007 , 2019 ) argue that there is double causality: while inequality leads to subversion of institutions through the political power of the elite, poor institutional quality also causes a higher level of inequality. Furthermore, Kotschy and Sunde (2017) have proposed that inequality interacts with political institutions in shaping institutional quality. Some have also suggested that a link exists between inequality and corruption, via self-reinforcing mechanisms and social norms (e.g., Jong-sung and Khagram 2005 ) as well as via low trust (e.g., Rothstein and Uslaner 2005 ). 19

Finally, a strand of studies in political science has argued that there is a link between inequality and political participation. As reviewed in Solt (2008) , the theoretical predictions lead to different possible outcomes of economic inequality on political engagement 20 : a negative effect, a positive effect, or an effect that depends on the level of income of the individual. The first outcome is a result of the concentration of power: societies that are more unequal have a higher concentration of power, which has implications for how the issues that separate the rich from the poor are addressed in the political sphere. The rich will have a lower need to engage in the political process whereas the poor will feel removed from politics. The prediction of a positive effect results from the fact that the divergence in the views of the rich and the poor will be more apparent in societies with higher inequality, which should lead to higher participation in the political process. Finally, the last prediction hinges on the fact that political engagement entails the use of resources. Thus, with higher levels of inequality, one should expect greater engagement from the rich, who have more resources available, and lower political engagement from the poor. 21

We now move on to discuss the main insights from empirical analyses following the structure of the previous section. Although we focus here on cross-country analysis, which makes up a significant part of the evidence base, we also refer to studies examining these links at the regional level, especially in the United States.

Direct link

where |$g$| is the average annual growth rate, frequently measured as the log difference of gross domestic product (GDP) per capita; INEQ is a measure of income inequality (usually the Gini coefficient); Z m is a set of other variables commonly used in standard growth regressions; and u is the usual error term. This was then estimated, typically using basic ordinary least squares. To avoid reverse causation, inequality was measured at the beginning of the time span for growth, which usually considers a period of twenty to thirty years, and in some cases, authors employed instrumental variables to address endogeneity concerns.

Summary of results from selected empirical work testing the link between inequality and growth

Notes : DS, Deininger and Squire (1996) ; LIS, Luxemburg Income Study; OLS, ordinary least squares; 2SLS, two-stage least squares; WLS, weighted least squares; 3SLS, three-stage least squares; LSDV, least squares dummy variable; FE, fixed effects; RE, random effects; Sys-GMM, system GMM; Diff-GMM, difference GMM.

Source : Authors’ elaboration, inspired from Cingano (2014) and Neves and Silva (2014) .

The aim was to estimate the coefficient of the income inequality variable δ , and most of these studies found a negative effect of inequality on growth. Persson and Tabellini (1994) obtained evidence for this effect using historical panel data and postwar cross-sectional analysis. Both the studies by Alesina and Rodrik (1994) and Clarke (1995) confirm this relationship using data from, among others, Jain (1975) and Lecaillon et al. (1984) . Clarke (1995) showed that this was robust to different measures and empirical specifications.

Given the challenges imposed by scarce data, some authors turned to an analysis between states in the United States. Partridge (1997) tested the robustness of the Persson and Tabellini (1994) findings, and the results suggested a positive link between inequality and subsequent growth when considering either the Gini coefficient or the share of income of the middle quintile. 23 Using tax data at the state level for the period 1940–1980, Panizza (2002) warned that both the data and the methodology used led to significant differences in the estimated coefficients for the effect of inequality on growth.

While the quality and reliability of the data are important challenges pertaining to early studies ( Knowles 2005 ), the introduction of an improved and expanded dataset by Deininger and Squire (1996) led to a surge in new studies using panel estimators. In contrast with previous work, these studies found a positive link between inequality and growth. Li and Zou (1998) showed that the coefficient for lagged Gini has a positive sign and is significant in most growth regressions. Forbes (2000) confirmed this result using similar data and generalized method of moments (GMM) estimators. 24 Still, using the same dataset, Deininger and Squire (1998) found a negative effect of initial income inequality on growth, although the coefficient lost significance once they add regional dummies to the specification.

Offering a starting point to reconcile the differing views, some studies have argued that the relationship between inequality and growth depends on other factors. According to Barro (2000) , the effect of inequality on growth depends on the level of income of the country: panel evidence suggests growth-enhancing effects of inequality in richer countries (GDP per capita: above $2,000, 1985 US dollars) and negative effects in poorer countries (below $2,000). Moreover, Banerjee and Duflo (2003) have raised concerns about the functional form used in the literature, arguing against using a linear specification for the relationship between inequality and growth. Their empirical work suggests an inverted U-shaped function between changes in inequality and lower future growth rates. Using a small sample of industrialized countries, Voitchovsky (2005) showed empirical support for the hypothesis that the profile of inequality influenced its relationship with growth: top-end inequality seems to have a positive effect and bottom-end inequality a negative effect.

The debate has continued in the literature ever since. Cingano (2014) lends support to a negative effect of inequality on growth using data from the Organization for Economic Co-operation and Development (OECD) income distribution dataset. Additionally, the author suggests that reducing inequality by focusing on income disparities at the bottom of the income distribution has a greater positive effect on growth than by focusing on the top of the distribution. The Castelló-Climent (2010) results concur with this when considering the full sample of countries, but the results also find support for the argument of a differentiated effect according to the level of development. Halter, Oechslin, and Zweimüller (2014) argue that there is a time dimension to the link between inequality and growth, showing a positive coefficient for the current Gini coefficient and a negative coefficient for lagged Gini.

Some studies have used data from an additional dataset proposed by Solt (2009) , the Standardized World Income Inequality Database (SWIID). Yet, results also mirror the lack of consensus of earlier work. Applying system GMM, work from the International Monetary Fund finds a robust negative effect of inequality on growth ( Ostry, Berg, and Tsangarides 2014 ; Berg et al. 2018 ). While Gründler and Scheuermeyer (2018) concur with this result, Jäntti, Pirtillä, and Rönkkö (2020) raise concerns about the results in Berg et al. (2018) , resulting from the use of the SWIID dataset. El-Shagi and Shao (2019) criticize previous studies using system GMM and argue for the advantages of using a least-squares dummy variable estimation instead. In contrast, their results show a positive effect of inequality on growth over the medium term, primarily driven by market-based inequality.

Barro's (2000) view that the effect depends on the level of development in the country, confirmed in later analysis by the same author using the WIID dataset ( Barro 2008 ), has also been verified in some recent work. Gründler and Scheuermeyer (2018) see a negative and significant marginal effect of net inequality on growth in poor economies, which is, however, nonsignificant in high-income countries. 25

Channels of transmission

As discussed in section “How Inequality Affects Growth,” the theory proposes different channels through which inequality may affect growth. Although these specific mechanisms have received less attention in empirical work, we highlight the main findings, also summarized in  table 2 .

Summary of empirical evidence on the different channels linking inequality and growth

Starting with the savings channel, while there is evidence of a positive link between inequality and personal savings when using household micro-data, studies based on cross-country aggregate data have found mixed results (see references in Thorbecke and Charumilind, 2002 ). Barro (2000) found that the investment ratio does not depend significantly on inequality. The channel via market imperfections and borrowing constraints found support in Deininger and Squire (1998) , who added that the effect through the investment in human capital seems more important than that via physical capital, as well as to some extent in Perotti (1996 ). 26 This channel also suggests that asset inequality matters for growth ( Ravallion 2001 , 1810), shown in both Birdsall and Londoño (1997) and Deininger and Olinto (2000) .

Moreover, there is published support for the channels related to sociopolitical instability ( Perotti 1996 ). Using data from a sample of seventy-one countries over the period 1960–1985, Alesina and Perotti (1996) found that a wealthy middle class is associated with lower levels of political instability, conducive to higher investment. Keefer and Knack (2002) showed evidence of a negative effect of inequality on growth and suggested that property rights are an important channel for this relationship.

Perotti (1996 ) confirmed the link between inequality and growth via fertility. Testing the same hypothesis, de la Croix and Doepke (2003) used Deininger and Squire's (1996) improved dataset and showed that the negative and significant effect of initial inequality on subsequent growth does not survive the inclusion of the differential fertility variable, which is negative and significant. They interpret this as meaning that the differential fertility is an important factor explaining the link between inequality and growth.

The fiscal policy channel received less support by Perotti (1996 ) while Persson and Tabellini (1994) also obtained coefficients with the expected sign but statistically insignificant for the links from inequality to redistributive policies and from redistribution to growth. Sylwester (2000) showed results from cross-country analysis that indicated that higher inequality is associated with higher subsequent expenditures for public education relative to GDP, which in turn has a negative effect on current growth but a long-term positive impact.

Recent studies have shown evidence that corroborates the theoretical effects via human capital accumulation ( Berg et al. 2018 ), via credit market imperfections ( Gründler and Scheuermeyer 2018 ), and via fertility ( Berg et al. 2018 ; Gründler and Scheuermeyer 2018 ) as channels through which inequality affects growth. Using data from twenty-one OECD countries over the period 1870–2011, Madsen, Islam, and Doucouliagos (2018) find support for the hypothesis that income inequality affects growth through different channels, namely savings, investment, education, and ideas production. Additionally, they concur with the arguments on differentiated effects. Although the negative impacts are significant in financially underdeveloped countries, there is little effect of inequality on the four outcomes in countries with highly developed financial markets.

Education and Health

In a recent paper, Castells-Quintana, Royuela, and Thiel (2019) estimated the effects of the Gini coefficient on the human development index (HDI) and found a negative effect in the long run, whereas in the short run the results change for different components of the index: a positive effect on income and a negative effect on educational outcomes. Moreover, they concur with the aforementioned studies that found distinct effects depending on the level of development. We are not aware of any other studies pursuing a similar analysis for the HDI, but in the remainder of this section, we discuss the empirical results on the link between inequality and education and health. We summarize the main conclusions in  table 3 .

Summary of empirical evidence on the different hypotheses on the effects of inequality on education and health

Although there is an extensive body of empirical literature examining education as a determinant of income inequality, the evidence on the link from income inequality to educational outcomes is scarcer ( Thorbecke and Charumilind 2002 , 1488; Gutiérrez and Tanaka 2009 , 56). However, there is evidence that income inequality is reproduced in inequality in education, both in terms of achievements in primary and secondary school and in terms of access to tertiary education (see Buchmann and Hannum 2001 and references in Stewart 2016 ).

Regarding the links proposed in the theoretical work reviewed in the previous section, Sylwester (2000) reported a positive link between inequality and public expenditures on education. Considering the demand side, some studies have found a negative link between inequality and secondary school enrolment. Flug, Spilimbergo, and Wachtenheim (1998) and Easterly (2007) used cross-country analysis, while Esposito and Villaseñor (2018) used data from the 2010 Mexican Census. The study by Madsen, Islam, and Doucouliagos (2018) shows a negative impact of inequality on the combined primary, secondary, and tertiary school enrolment rate in financially underdeveloped countries (using a sample from OECD). Concurring with these findings, Berg et al. (2018) show a negative correlation between inequality and human capital, measured as the average years of primary and secondary schooling. Checchi (2003) provided support for the link between inequality and growth via borrowing constraints and showed evidence of a negative effect of inequality on access to secondary education. 27 Finally, using data from the United States for the period 1970–1990, Mayer (2001) found that the increase in inequality aggravates the gap in educational attainment between rich and poor children.

Given that the literature is extensive and stems from different fields of literature (including, public health), we summarize the main conclusions from different reviews, which distinguish between aggregate level and multilevel studies as well as cross-country and within-country empirical analyses. 28 Wagstaff and van Doorslaer (2000) highlighted that studies at the population level are limited in what they can reveal about the effects on individual health and that data at the individual level are required to disentangle the effects of the different hypotheses described in section “Inequality negatively affects health.” Still, existing evidence on these different channels remains inconclusive.

Lynch et al. (2004) found weak support for a direct effect of income inequality on health, although inequality contributes directly to some health outcomes (e.g., homicides). Furthermore, they underlined that the reduction of income inequality via income rises for the more disadvantaged contributes to improved health of these individuals and increases average population health. Rowlingson (2011) concludes that there is some evidence of an independent effect on health and social problems, but in line with Subramanian and Kawachi (2004) , also highlights the lack of consensus in the results and the need for further work. Still, from a systematic review of 155 published peer-review studies, Wilkinson and Pickett (2006) concluded that there is a link between greater income inequality and poorer health. Almost ten years later, the authors provided further support for the existence of a causal link between income inequality and health and reinforced their argument of the size of status and social class differences as an important mechanism ( Pickett and Wilkinson 2015 ).

The conclusions from the economics literature have pointed to no evidence of a causal relationship ( Nolan and Valenzuela 2019 ). From a detailed review of the literature, Deaton (2003 , 150) argued that “the stories about income inequality affecting health are stronger than the evidence” and that there is no robust evidence showing that income inequality in itself is an important determinant of population health, although it had effects through poverty. The review in Leigh, Jencks, and Smeeding (2011) concurred. However, they warned that given the data challenges and the limitations of the methods used to test the link between inequality and health, one should not jump to definite conclusions. Focusing on morbidity and mortality, the comprehensive review of empirical literature by O'Donnell, van Doorslaer, and van Ourti (2015) concludes that even though population health is negatively associated with income inequality, there is little evidence to support the hypothesis of a negative impact of income inequality on health.

We start this section by noting that the focus on voting underlying the political economy mechanism linking inequality and growth suggests that the effects should be observed in democracies ( Houle 2015 , 143). Thus, some of the early empirical literature on the relationship between inequality and growth also tested whether this effect was dependent on the regime type (e.g., see Alesina and Rodrik 1994 ; Persson and Tabellini 1994 ; Clarke 1995 ; Perotti 1996 ; Deininger and Squire 1998 ).

The results were mixed. Persson and Tabellini (1994) suggested that the negative link between inequality and growth is only present in democracies and that the transmission channel through government redistributive policies should be further investigated. However, Perotti (1996 ) counterargued that, although the data showed a stronger relationship between equality and growth in democracies, the effect of the democracy variable did not appear to be robust. Further criticism was advanced by Knack and Keefer (1997) , who, after some regime reclassification and deletion of doubtful observations, concluded that there is no evidence of a differential effect of inequality on growth in democracies and non-democracies. Østby (2013) and Stewart (2016) argued that there is compelling evidence for the link between horizontal inequality (i.e., inequality among groups) and civil conflict as well as other forms of group violence. However, more recent reviews suggest that the evidence on the link between inequality and political violence is mixed ( Lengfelder 2019 ).

We now turn to what the empirical evidence on the government outcomes described in section “How inequality affects democratic governance” shows, and summarize the main conclusions in  table 4 . Using data from two panels on the periods 1950–1990 and 1850–1980, Boix (2003) showed empirical evidence for a positive link between equality (proxied by an adjusted Gini coefficient) and democratization and, particularly, democratic consolidation. In an extension of this analysis, Boix and Stokes (2003) concluded that economic equality, proxied by farm ownership (distribution of agricultural property) and literacy rates (quality of human capital), has a positive effect on both the probability of a democratic transition and the stability of democracy.

Summary of empirical evidence on the effects of inequality on different governance outcomes

Others found low support for a significant link between the two (e.g., Bollen and Jackman 1985 ). 29 Barro (1999) showed a negative, but only marginally significant coefficient for the effect of inequality on democracy, proxied as electoral rights and civil liberties, for the period 1972–1995. However, when entered alongside the share of income accruing to the middle class, the coefficient is nonsignificant. The empirical analysis in Houle (2009) went against previous results on the negative link between inequality and democracy and showed a weak positive and nonsignificant relationship. Using the capital share of the value added in the industrial sector as a measure of inequality to overcome the data limitations in previous studies, the author also did not find support for Acemoglu and Robinson (2006) ’s inverted U-shaped relationship but rather for a weakly U-shaped one.

More recently, Haggard and Kaufman (2012) used causal process observation to examine the association between inequality and transitions to and from democratic rule and found limited evidence supporting the link via distributive conflict between elites and masses. Additionally, the evidence in Scheve and Stasavage (2017) does not support the hypothesis of a link between wealth inequality and democracy. Dorsch and Maarek (2020) offer an explanation for the abundancy of null results found for the link between inequality and democratization, showing that higher levels of inequality are associated with higher probabilities of democratic improvements following economic downturns (“windows of opportunity”). However, following growth periods, the effect of inequality is null or small and negative.

Considering a broader approach to governance, we briefly refer to the literature linking inequality and institutional quality. 30 Both Easterly (2007) and Chong and Gradstein (2007 ) tested the causal relationship between these variables using an instrumental variables approach and system GMM methods, respectively, and found support for the effect of inequality on institutions. More recently, Kotschy and Sunde (2017) showed evidence of the importance of equality as a determinant of the effect of democratic institutions on institutional quality, measured by an index of economic freedom and an indicator of civil liberties. 31 It has also been shown that countries with more income inequality have more corruption ( Jong-Sung and Khagram 2005 ), and, in particular, survey evidence links perceptions of corruption and inequality to lower political trust ( Uslaner 2017 ).

Finally, there is evidence from advanced industrial democracies of a negative link between inequality and political participation ( Lengfelder 2019 ). Solt (2008) showed a negative effect of economic inequality on political engagement, namely political interest, the frequency of political discussion, and participation in elections among all citizens except the richest, using data from advanced industrial countries. Using cross-sectional data from OECD countries and within-country data for Germany and a range of methods, the recent study by Schäfer and Schwander (2019) finds support for the negative link between economic inequality and political participation. Relatedly, empirical work suggests that economic inequality harms support for democracy (e.g., Andersen 2012 ; Krieckhaus et al. 2014 ) and political inequality (e.g., Houle 2018 ). Still, there appears to be limited evidence of an effect of inequality on electoral turnout ( Stockemer and Scruggs 2012 ; Cancela and Geys 2016 ).

The lack of consensus in the literature, especially about the effect of inequality on growth, is notable. What explains this divergence, and what can be done to contribute to the existing knowledge? In this section, we discuss the key empirical challenges of estimating the effects of inequality: data quality and availability, conceptual and measurement issues, and the methodological difficulties of dealing with confounding variables and endogeneity.

Data quality and availability

Early studies drew on secondary datasets provided, for example, by the World Bank ( Jain 1975 ) or the International Labour Office ( Lecaillon et al. 1984 ). The expanded dataset proposed by Deininger and Squire (1996) was crucial in opening possibilities for panel methods. Additionally, databases offering secondary data compilations on income inequality provided by the United Nations University World Institute for Development Economics Research, WIID (based on household surveys), and SWIID, developed by Solt (2020) and resulting from multiple imputations of the WIID data, have been frequently used in empirical studies. The World Inequality Database ( WID.world 2017 ) has emerged as an additional database providing data on income shares captured by top income groups.

Atkinson and Brandolini (2001 , 2009 ) and Ferreira, Lustig, and Teles (2015) offer comprehensive analyses on secondary datasets on income distribution, drawing attention to issues of data quality and consistency linked to differences in the definitions used, sources of data, and the processing used to obtain “ready-made” income distribution statistics. 32 Atkinson and Brandolini (2001 ) focused mainly on the Deininger and Squire dataset and on data for OECD member countries. Jenkins (2015) follows a similar line of reasoning and compares the WIID and the SWIID, noting that for the latter it is also critical to consider issues relating to the quality of imputations. Jäntti, Pirtillä, and Rönkkö (2020) stress that, in most developing countries, the actual redistribution is only rarely measured, so figures in the SWIID reflect questionable imputations.

As demonstrated in Atkinson and Brandolini (2001 , 2009 ) and Jenkins (2015) , issues of noncomparability have consequences for econometric analysis and for trends over time. Voitchovsky (2011 , 566) warns that data scarcity and limitations in terms of data availability may lead to a trade-off between sources of bias and precision in inequality studies. Ravallion (2001 , 1809) notes, however, that measurement errors, including those resulting from comparability problems, will have a greater impact on analyses that allow for country fixed-effects rather than on standard growth regressions given that the signal-to-noise ratio is likely to be low for changes in measured inequality.

The challenges are even more striking for tests that require data at the individual level, namely those related to the relative hypotheses linking inequality to health. These hypotheses also lead to questions about the appropriate reference groups—how they are defined and formed—as well as in terms of endogeneity, as the position of the individual in relation to the reference may be affected by group membership ( O'Donnell, van Doorslaer, and van Ourti 2015 , 1505).

Concept and measurement of inequality

Issues of concept and measurement are also consequential. 33 Atkinson and Brandolini (2001 ) provide a useful summary of eight parameters to be chosen when defining an income distribution, among which are the unit of observation, concept of resource (e.g., income versus expenditure), and tax treatment of income. These closely link to measurement choices. Different mechanisms require a specific concept of inequality and this should be reflected in the measure of inequality used in the empirical analysis ( Voitchovsky 2011 , 567). Additionally, different parts of the distribution receive importance depending on the inequality measure used, and even the concept of income is open to measurement issues ( Deaton 2003 , 135).

Knowles's (2005) account of the relationship between inequality and growth illustrates these concerns. The author warns that the results in previous studies should be regarded with some degree of caution given that they failed to measure inequality in a consistent manner, mixing measures of the distributions of income before and after tax and the distribution of expenditure. Considering six different measures of inequality (three Gini coefficients and three top ten income shares), a recent study by Blotevogel et al. (2020) shows that the choice of the inequality indicator has important implications for the results obtained in empirical analysis, namely when considering different transmission channels between inequality and growth. In terms of the link between inequality and democratic governance, there is a concern that frequently used measures do not capture interclass inequality, which precludes the testing of theoretical hypotheses that hinge on this ( Houle 2015 , 147).

Criticism has also been directed at specific measures, in particular the widely used Gini coefficient. In light of the observations above, Gini coefficients will provide different information depending on how they are calculated, for example, if based on net income or on gross income ( Houle 2015 , 147). Moreover, some have argued that the use of absolute rather than relative measures might better capture perceptions of inequality on the ground (e.g., Bosmans et al. 2014 ; Atkinson and Brandolini 2004 ; Niño-Zarazúa, Roope, and Tarp 2017 ).

Estimation methods

A review of empirical studies on the inequality–growth link highlights contrasting findings between the early cross-country studies and those that employed panel estimation techniques, after the Deininger and Squire (1998) dataset became available. Some explanations have been advanced for this divergence.

Measurement error may affect the estimation results in cross-country estimation (country- or regional-specific measurement error), and also in panel data estimation, given that inequality tends to be persistent over time; thus, this method relies on more limited time-series variation in the data. The coefficients in cross-country studies may be biased due to time-invariant omitted variables ( Voitchovsky 2011 , 565), while if we consider that inequality is related to underlying determinants of development that are persistent, then fixed-effect estimates may be biased upward when considering long-run effects ( Castells-Quintana, Royuela, and Thiel 2019 , 454).

Additional explanations included the argument for the misspecification of the linearity in the effect of inequality and growth ( Banerjee and Duflo 2003 ) and the suggestion that the two methods capture different time effects, given the short- and long-term lag structures in panel and cross-country analyses, respectively ( Voitchovsky 2011 , 565).

Finally, several concerns have been raised regarding the use of different instruments to tackle reverse causality in the relationship between inequality and growth (see Easterly 2007 ) as well as health ( O'Donnell, van Doorslaer, and van Ourti 2015 , 1505) and democracy ( Houle 2015 , 147). While different attempts have been made using instrumental variable approaches, finding a valid instrument for inequality is certainly not straightforward. Furthermore, even if GMM has often been used to try to tackle these issues, Roodman (2009) warns about the risk of instrument proliferation and the possibility for generating false-positive results. As an illustration, he reexamined the analysis in Forbes (2000) and raised concerns over the positive effect of inequality on growth found in the original paper.

This review combined the different theoretical hypotheses concerning the impact of inequality on three core socioeconomic and political outcomes in a simplified framework and highlighted the mixed empirical evidence. We summarize the main conclusions as follows. First, in line with previous findings, the debate on whether there is a positive or a negative effect on growth remains open, with recent studies mirroring the disagreement in decades of empirical work. With the exception of the classical approach, most of the transmission channels between inequality and growth point to a negative effect of inequality. However, the evidence from reduced-form equations is not consensual and the channels of transmission have received less attention.

Second, while there seems to be some consensus in the evidence that there is a negative link between inequality and secondary school enrolment, there is need for further research in terms of other education outcomes. Although theory generally points toward a negative effect of inequality on health, the existing evidence does not provide clear support to this relationship, in the economic literature in particular, and there is a lot to be uncovered in terms of the mechanisms of transmission at the individual level. Third, theoretical predictions and empirical evidence show mixed results for the effects of inequality on democracy and political participation.

In understanding the diversity and divergence in theoretical and empirical results, a number of empirical challenges remain. Problems with data quality and availability are well understood in the literature, as are those related to the concept and measurement of inequality, and the shortcomings of different estimation methods.

In terms of potential avenues for future work, our review points for one to the value of further attention to different transmission channels (highlighted in  figure 1 ). We first propose a methodological suggestion. While advances in econometric analysis will shed light on the analysis across countries, this could be complemented with the use of experimental work to understand specific channels in particular contexts. While not a substitute for empirical cross-country analysis, experiments can be employed to understand microlevel behavior. The controlled nature of this work avoids biases in econometric studies and mitigates issues of endogeneity and measurement errors.

The second avenue relates to the focus of the analysis. While this review mainly concentrated on cross-country analysis, there is indication that disaggregating the level of analysis might provide useful insights in terms of channels of transmission and underlying cases. For instance, it might be that in Africa, competition over natural resources is the main driver of inequality and in turn slower growth, while in Latin America, inequality may be the main driver for political instability. Furthering regional and country-specific analysis might help dig deeper into these effects.

Finally, despite the existing efforts to compile new—and improve on the existing—secondary datasets, problems persist with the available data. Thus, in light of the importance of data availability and reliability for the analysis of the trends and effects of inequality, we stress that earlier calls for more and better data continue to be both relevant and important for progress in our search for better understanding of the impact of inequality.

Equity here refers to equality of opportunities to pursue a life of one's choosing and protection from extreme deprivation in outcomes ( World Bank 2006 , 18–19). Efficiency refers to economic efficiency, underpinning economic growth ( Thorbecke 2016 ).

Given the multidimensionality of inequality and that its effects are in focus in different disciplines, we follow an interdisciplinary approach. Yet, in the empirical section, we focus on strands of work that employ similar (quantitative) methodologies.

We focus on the main arguments that have attracted attention in these disciplines and have made a concerted effort to address the gender citation gap that exists, for instance, in international relations scholarship (e.g., Maliniak, Powers, and Walter 2013 ).

Throughout, we refer to “income inequality” and “inequality” interchangeably. Although we recognize the multidimensionality of the concept, we focus on literature considering income inequality, which remains a dominant measure ( Stewart 2016 , 64), and refer to more extensive work on other aspects, in particular, the relevance of poverty rates (e.g., Ravallion 2012 ), inequality of opportunity (e.g., Marrero and Rodriguez 2013 ; Ferreira et al. 2018 ), gender inequality (e.g., Bandiera and Natraj 2013 ; Kabeer 2015 ), and horizontal inequalities ( Stewart 2005 ).

We use “growth” and “economic growth” interchangeably.

We highlight that there is expanding work on different facets of economic performance, such as growth volatility (e.g., Iyigun and Owen 2004 ) or the occurrence of crises (e.g., Morelli and Atkinson 2015 ).

Kuznets (1955) argued that the early stages of the development process would experience rising inequality, which would then fall as the country reached higher levels of per capita income. This relationship, known as the “Kuznets curve,” and other work looking at this direction of causality are not covered here.

See also a review of early studies in Bénabou (1996) and Aghion, Caroli, and García-Peñalosa (1999 ) and a more recent overview in Ehrhart (2009) .

Sandmo (2015) reviews the history of theories of income distribution, from Adam Smith until the 1970s.

For a summary of theoretical work on the choice between a public and a private education system, see García-Peñalosa (1995) .

Gutiérrez and Tanaka (2009) review previous theoretical models.

Additional mechanisms relate to social comparison and include relative deprivation and gratification in the context of neighborhood and school effects, and economic segregation ( Mayer 2001 ). The first refers to the fact that people compare themselves with those who are more disadvantaged, which in the case of children can lead to feeling less willing to study or stay in school and in the case of parents can cause stress and alienation. The second suggests that increases in inequality are likely to lead to more geographic segregation as the rich and poor have less in common. See Mayer (2001 , 4–7) for more details.

See Deaton (2003 ) and Lynch et al. (2004) for detailed descriptions of the emergence of debate on the link between income inequality and health.

We do not cover studies on the link between inequality and homicides and between inequality and life satisfaction and happiness ( Graham 2014 ).

Lynch et al. (2004 , 15–16) refer to additional nuances, related to the effects of inequality through psychosocial processes and through the differential accumulation of exposures deriving from material sources rather than from perceptions of disadvantage. They also mention the weak and strong versions of this hypothesis proposed by Mellor and Milyo (2002) .

For a study on the effects of inequality on group participation, see La Ferrara (2002) .

Thorbecke and Charumilind (2002) review the evidence and causal mechanisms linking inequality and crime.

For a review of the theoretical arguments developed earlier, see Bollen and Jackman (1985) .

This line of reasoning can be linked to the work by Glaeser, Scheinkman, and Shleifer (2003) mentioned in section “How inequality affects growth,” which discusses the negative effects of inequality on growth through institutional subversion (including corruption).

For further details, see Solt (2008 , 48–50).

It is also useful to refer here to studies examining the impact of inequality on electoral turnout (e.g., Stockemer and Scruggs 2012 ), support for democracy (e.g., Andersen 2012 ; Krieckhaus et al. 2014 ), and, more generally, political inequality (e.g., Houle 2018 ).

A more complete list of studies is available from the authors.

Studies in the 1990s also focus on determining whether there was a differential effect of inequality on growth in democracies and non-democracies ( Persson and Tabellini 1994 ; Alesina and Rodrik 1994 ; Perotti 1996 ; Clarke 1995 ; Deininger and Squire 1998 ). We discuss this in Section ”Governance.”

Two recent studies build on Forbes (2000) , attempting to overcome some of the remaining estimation challenges. Aiyar and Ebeke (2020) draw attention to the importance of considering equality of opportunity and find empirical support for their hypothesis that the negative effect of income inequality is greater in countries with low levels of equality of opportunity (measured by intergenerational mobility). Scholl and Klasen (2019) replicate Forbes’ (2000) finding but show that it disappears once they control for the experience of transition countries.

Islam and McGillivray (2020) highlight the increasing interest in wealth inequality and investigate its effect on growth using wealth data from Forbes Magazine and Credit Suisse over the period 2000–2012. The results suggest a negative effect.

Perotti (1996 ) empirically tested the channels of transmission, estimating different structural models: first, using each of these channels in a growth model and, then, estimating the effects of inequality on each of the channels.

With the exception of Flug, Spilimbergo, and Wachtenheim (1998) , all these studies employ the Gini coefficient as one of their measures of inequality. Flug, Spilimbergo, and Wachtenheim (1998) used the ratio of the income shares of the top quintile to the bottom two quintiles of the population, and the shares of income accruing to the top quintile and the lowest quintile were used, respectively, by Easterly (2007) and Checchi (2003) . In their robustness checks, Esposito and Villaseñor (2018) used the Atkinson and Theil indices.

We do not offer a comprehensive overview of the measures used in the literature. According to the review in Lynch et al. (2004) , the majority of the studies employ the Gini coefficient or different shares of income. In the list of studies reviewed by these authors, we counted sixty-nine out of ninety-eight using the Gini as (one of) the measure(s) of inequality.

The review of the initial studies in Bollen and Jackman (1985) argued that problems of specification, measurement, and sample composition led to inconclusive results in the existing empirical analyses.

Savoia, Easaw, and McKay (2010) reviewed the arguments linking inequality to institutional quality directly and via democracy and argued that the limited existing work suggests a negative link between inequality and institutions, noting there is a need for further research.

When considering the role of governance (using different indicators), the estimates in Islam and McGillivray (2020) indicate that improved governance may contribute to reduced wealth inequality and higher growth.

See also discussions of these shortcomings in Deaton (2003 ), Voitchovsky (2011) , and Houle (2015) .

As illustrated in section “What the empirical evidence says,” issues of concept and measurement for our outcome variables also matter to consideration of theories and hypothesis testing.

This study was prepared within the project “The impacts of inequality on growth, human development, and governance - @EQUAL.” Support by the Novo Nordisk Foundation Grant NNF19SA0060072 is acknowledged.

We are grateful to the editors and three anonymous referees for insightful and useful suggestions. We thank Anustup Kundu for excellent research assistance as well as Klarizze Puzon, Miguel Niño-Zarazúa, Carlos Gradín, and participants at an internal project workshop for valuable comments. The usual caveats apply.

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Wilkinson Richard G. , Pickett Kate E. . 2006 . “ Income Inequality and Population Health: A Review and Explanation of the Evidence .” Social Science & Medicine 62 ( 7 ): 1768 – 84 .

World Bank . 2006 . World Development Report 2006: Equity and Development . Washington, DC : World Bank .

Zweimüller Josef . 2000 . “ Schumpeterian Entrepreneurs Meet Engel's Law: The Impact of Inequality on Innovation-Driven Growth .” Journal of Economic Growth 5 : 185 – 206 .

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Page One Economics ®

Income and wealth inequality.

"For we, the people, understand that our country cannot succeed when a shrinking few do very well and a growing many barely make it. We believe that America's prosperity must rest upon the broad shoulders of a rising middle class." 

—President Barack Obama 1

Introduction

There are many different types of inequality among people: educational attainment, work experience, and health—to name a few. This essay discusses economic inequality: its causes, measurement, and the potential impact of its growth in the U.S. economy.

Economists directly link differences in educational attainment and work experience, also known as human capital, to differences in economic outcomes. That is, formal education and job skills determine how likely a person is to find and hold a stable job that pays good wages. The flow of money from wages is the most important source of income for most people. Over time, regular income from employment allows people to own assets such as a home or a retirement financial portfolio. That stock of assets is called wealth .

Data collected by federal organizations such as the Census Bureau and the Board of Governors of the Federal Reserve System (BOG) allow us to measure how unequal the distributions of income and wealth are in the United States. Those data show that, since the 1970s, some individuals and families are earning much more income and accumulating much larger amounts of wealth than the typical family does. 

Data reported by the World Bank allow us to compare the distribution of income across countries. As of 2018, the available data show large international differences in income inequality. Although not all countries in the world have data on income inequality, among those that do, the United States ranks among the top 25% most unequal.

What Causes Inequality?

The root cause of differences in income and wealth across individuals and households is a combination of personal and social factors. Personal factors are unique to individuals and include talent, effort, and luck. Such factors can be either nurtured or hindered by the family upbringing of the individual. Social factors affect groups of people and include education policies, labor market laws, tax codes, and financial regulations. At any moment in time, social factors can overpower personal factors to determine individual prosperity and increase inequality among people. 2

For example, as gradually more married women started working outside the home between 1960 and 2000, their family incomes increased and the differences in income between households became larger depending on whether they had one or two people earning wages. At the same time, differences in the types of jobs women and men tend to hold also contribute to income inequality between genders. 3

Because wealth is accumulated over time, older people are generally wealthier than younger people. For that reason, if there are many more young people than old people in the general population and the old hold more wealth than the young, overall wealth inequality will be high. 4

Finally, some people argue that the type of monetary policy used to ensure steady access to credit by households and businesses during recent economic contractions may contribute to higher levels of income inequality. However, that claim is hotly disputed. 5

How Is Income Inequality Measured?

There are different ways to measure how unequal income is in a country. The U.S. Census measures income inequality as the ratio of the mean, or average, income for the highest quintile (top 20 percent) of earners divided by the mean income of the lowest quintile (bottom 20 percent) of earners in a particular area. Let's say a small county has 500 people earning an income. To measure how unequal those incomes are, the Census surveys and sorts each person's income from highest to lowest, calculates the average income of the 100 people earning the most and the average income of the 100 people earning the least, and divides the first figure by the second figure. 

Figure 1 Income Inequality by County 

SOURCE: U.S. Census via FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/graph/?m=QRCJ , accessed June 23, 2021.

Figure 1 shows average county-level income inequality measured between 2016 and 2020. The Census considers the average income over a five-year period to account for the fact that peoples' income changes from year to year. Measured this way, income inequality can be as high as 130 or as low as 5. These measurements mean that the most affluent households in a particular county can earn as much as 130 times or as little as 5 times as much as the least affluent households do.

Another way to measure income inequality in a population is to calculate the Gini index . The World Bank uses that index to measure how much the distribution of income among households deviates from a perfectly equal distribution. The Gini index can take any value between 0 and 100. A value of 0 represents perfect equality: All households earn the same income. A value of 100 indicates perfect inequality: One household earns all the income, and all other households earn nothing.

Figure 2 Gini Index by Nation

SOURCE: World Bank via FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/graph/?m=QRFh , accessed April 6, 2021.

Figure 2 shows country-level income inequality measured with a Gini index in 2018. The highest value is 54, and the lowest value is 25. It is important to note that two countries can have very similar Gini indexes despite having very different distributions of income. For example, in 2018, the Gini index for the United States was 41.4 and for Bulgaria was 41.3, despite the fact that those two countries' economic and social histories are very different.

In the United States, since the 1970s, the Gini index has increased at a steady rate, indicating greater income inequality across families. 6 Some research suggests that this growing difference is related to the increased value of the stock market. Wealthier households hold more stocks than poorer households. So, when stock market prices rise, the income of wealthier households grows relatively more and overall income inequality increases. 7  

How Is Wealth Inequality Measured?

The BOG combines information from two different surveys to measure how wealth is distributed among households: It takes the value of a household's assets (e.g., the current market price of a home) and liabilities (e.g., the unpaid part of a mortgage for a home) and calculates the difference between the two, which is called net worth . Next, the BOG sorts household wealth from highest to lowest and reports the net worth of four different groups: the wealthiest 1% of the population, the next 9%, the next 40%, and the bottom 50%.

Figure 3 Share of Total Net Worth Held by Population Groups

SOURCE: Board of Governors of the Federal Reserve System via FRED ® , Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/graph/?g=O2Kq , accessed April 6, 2021.

Figure 3 shows the share of total net worth held by each of those four groups. In 2021, the wealthiest 1% of the population (about 3.3 million households) held about one-third of total net worth; the next 9% (almost 30 million households) held a little more than one-third; the next 40% (about 133 million households) and the bottom 50% (about 166 million households) together held the rest—less than one-third of total net worth.

The data from the BOG show increasing wealth concentration since 1989, when the data first became available. 8 It is important to note that, over time, some individual households can move up or down between wealth groups, depending on the changing value of their assets. Also, some research suggests the particular nature of some economic fluctuations impacts some households' net worth more than others. For example, the real estate crash associated with the 2007-09 recession resulted in large losses for the poorest 50% of the population. 9

Does Inequality Matter?

The economic impact of growing income and wealth inequality in the United States is an intensely studied question. Economists are debating how to answer that question by analyzing data and creating mathematical models to study it. Because this is ongoing work, there is no single answer.

Some research shows that, in richer countries, more unequal income makes economic fluctuations more pronounced. 10 That finding means that the changes in overall income and employment known as business cycles become more dramatic. Moreover, statistical evidence suggests increased income inequality undermines economic growth due to lower educational achievements (and human capital) among poorer individuals and households. 11 As discussed earlier, education builds a person's human capital and is rewarded with higher income from employment. Finally, research suggests the increasing income and wealth inequality can undermine the use of monetary policy (as we know it) to maximize employment and ensure price stability. 12  

Inequality in individual economic outcomes arises from a combination of personal traits and social conditions. The distributions of income and wealth in a society can be measured in multiple ways: comparing the highest to the lowest earners, calculating an index describing how unequal income is among all individuals, and assessing people's financial wellbeing according to the value of their wealth holdings. Regardless of how we measure income and wealth inequality, their distributions in the United States are becoming more unequal. This trend is likely to impact economic life as we know it. More research is needed to figure out precisely how that may happen.

1 Obama, Barack. "Inaugural Address." January 21, 2013; https://obamawhitehouse.archives.gov/the-press-office/2013/01/21/inaugural-address-president-barack-obama .

2 For an example of how the use of city maps to assess lending risk after the Great Depression influenced homeownership rates across population groups for decades afterward, see the following article: Mendez-Carbajo, Diego. "Neighborhood Redlining, Racial Segregation, and Homeownership." Federal Reserve Bank of St. Louis Page One Economics , September 2021; https://research.stlouisfed.org/publications/page1-econ/2021/09/01/neighborhood-redlining-racial-segregation-and-homeownership .

3 For more on gender and labor markets, see the following article: Mendez-Carbajo, Diego. "Gender and Labor Markets." Federal Reserve Bank of St. Louis Page One Economics , January 2022; https://research.stlouisfed.org/publications/page1-econ/2022/01/03/gender-and-labor-markets .

4 For more on aging and wealth inequality, see the following article: Vandenbroucke, Guillaume and Zhu, Heting. "Aging and Wealth Inequality." Federal Reserve Bank of St. Louis Economic Synopses , 2017, No. 2; https://research.stlouisfed.org/publications/economic-synopses/2017/02/24/aging-and-wealth-inequality/ .

5 For a contribution to the ongoing debate about the relationship between monetary policy and income inequality, see the following article: Bullard, James. "Income Inequality and Monetary Policy: A Framework with Answers to Three Questions." Presented at the C. Peter McColough Series on International Economics, Council on Foreign Relations, New York, June 26, 2014; http://research.stlouisfed.org/econ/bullard/pdf/Bullard_CFR_26June2014_Final.pdf .

6 The following FRED® graph shows the income Gini ratio of all families, reported by the U.S. Census Bureau since 1947: https://fred.stlouisfed.org/graph/?g=MKYg .

7 For more on income inequality and the stock market, see the following articles: 

Bennett, Julie and Chien, YiLi. "The Large Gap in Stock Market Participation Between Black and White Households." Federal Reserve Bank of St. Louis Economic Synopses , 2022, No. 7; https://research.stlouisfed.org/publications/economic-synopses/2022/03/28/the-large-gap-in-stock-market-participation-between-black-and-white-households/ . 

Owyang, Michael T. and Shell, Hannah G. "Taking Stock: Income Inequality and the Stock Market." Federal Reserve Bank of St. Louis Economic Synopses , 2016, No. 7; https://research.stlouisfed.org/publications/economic-synopses/2016/04/29/taking-stock-income-inequality-and-the-stock-market/ .

8 For more about the change in wealth distribution over time, see the following post: Federal Reserve Bank of St. Louis. "Comparing the Assets of the Rich, Poor, and Middle Class." FRED ® Blog , October 21, 2019; https://fredblog.stlouisfed.org/2019/10/comparing-the-assets-of-the-rich-poor-and-middle-class/ .

9 For more on how recessions impact household net worth, see the following article: Mendez-Carbajo, Diego. "How Recessions Have Affected Household Net Worth, 1990-2017: Uneven Experiences by Wealth Quantile." Federal Reserve Bank of St. Louis Economic Synopses , 2020, No. 38; https://research.stlouisfed.org/publications/economic-synopses/2020/08/07/how-recessions-have-affected-household-net-worth-1990-2017-uneven-experiences-by-wealth-quantile .

10 For more on the relationship between inequality and economic fluctuations, see the following article: Iyigun, Murat F. and Owen, Ann L. "Income Inequality and Macroeconomic Fluctuations." Board of Governors of the Federal Reserve System International Finance Discussion Papers , July 1997; https://www.federalreserve.gov/econres/ifdp/income-inequality-and-macroeconomic-fluctuations.htm .

11 For more on the relationship between income inequality and economic growth, see the following article: Cingano, Federico. "Trends in Income Inequality and its Impact on Economic Growth." Organisation for Economic Co-operation and Development OECD Social, Employment, and Migration Working Papers , 2014, No. 163; https://www.oecd.org/els/soc/trends-in-income-inequality-and-its-impact-on-economic-growth-sem-wp163.pdf .

12 For more on the relationship between income inequality and monetary policy, see the following article: Cairo, Isabel and Sim, Jae W. "Income Inequality, Financial Crises, and Monetary Policy." Board of Governors of the Federal Reserve System Finance and Economics Discussion Series , July 2018; https://www.federalreserve.gov/econres/feds/income-inequality-financial-crises-and-monetary-policy.htm .

© 2022, Federal Reserve Bank of St. Louis. The views expressed are those of the author(s) and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Asset: A resource with economic value that an individual, corporation, or country owns with the expectation that it will provide future benefits.

Gini index: A statistical measure of income inequality in a population that ranges from 0 (indicating absolute income equality) to 100 (indicating a perfectly inequal income distribution).

Household: A group of people living in the same home, regardless of their relationship to one another.

Income: The payment people receive for providing resources in the marketplace. When people work, they provide human resources (labor) and in exchange they receive income in the form of wages or salaries. People also earn income in the forms of rent, profit, and interest.

Liability: A legal responsibility to pay back money from a loan or other type of debt.

Net worth: The value of a person's assets minus the value of his or her liabilities.

Quintile: Any of five equal groups into which a population can be divided according to the distribution of values of a particular variable.

Wealth: The value of a person's assets accumulated over time.

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How does income inequality affect economic growth?

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The relationship between aggregate output and the distribution of income is an important topic in macroeconomics (Galor 2011). The role that income inequality plays in economic growth has also received quite a bit of attention in policy circles and the press recently. For instance, the World Bank Group has included among its key global objective for development the eradication of extreme poverty and boosting the incomes of the bottom 40% of developing countries. The IMF has weighed in with a discussion on the role of income distribution as a cause and consequence of economic growth (see, for example, Ostry et al. 2014).

In a recent paper (Brueckner and Lederman 2015), we provide estimates of the within-country effect that income inequality has on aggregate output. Our empirical analysis starts from the premise that the effect of changes in income inequality on GDP per capita may differ between rich and poor countries. This premise is grounded in economic theory. In a seminal contribution, Galor and Zeira (1993) proposed a model with credit market imperfections and indivisibilities in investment to show that inequality affects GDP per capita in the short run as well as in the long run. Galor and Zeira’s model predicts that the effect of rising inequality on GDP per capita is negative in relatively rich countries but positive in poor countries. We test this prediction by introducing in the panel model an interaction term between income inequality and countries’ initial (i.e. beginning of sample) GDP per capita.

How large are the effects?

Our empirical analysis shows that for the average country in the sample during 1970-2010, increases in income inequality reduce GDP per capita.

Specifically, we find that, on average, a 1 percentage point increase in the Gini coefficient reduces GDP per capita by around 1.1% over a five-year period; the long-run (cumulative) effect is larger and amounts to about -4.5%.

To be clear, this finding implies that, on average, increases in the level of income inequality lead to lower transitional GDP per capita growth. Increases in the level of income inequality have a negative long-run effect on the level of GDP per capita. We document the robustness of this result to alternative measures of income inequality, alternative income inequality data sources, splitting the sample between pre- and post-1990 period (end of the Cold War), and restricting the sample to countries located in Latin America and the Caribbean or Asia.

While the average effect of income inequality on GDP per capita is negative and significantly different from zero, it varies with countries’ initial income level. In an econometric model that includes an interaction term between initial GDP per capita and income inequality, the coefficient on the interaction term is negative and significantly different from zero at the 1% level. Quantitatively, the size of the coefficient on the interaction term implies that differences in initial income induce a substantial effect on the impact that changes in income inequality have on GDP per capita. For example, at the 25th percentile of initial income the predicted effect of a 1 percentage point increase in the Gini coefficient on GDP per capita is 2.3% (with a corresponding standard error of 0.6%); at the 75th percentile of initial income the effect is -5.3% (the corresponding standard error is 0.8%).

The estimates from the interaction model thus suggest that in poor countries, increases in income inequality raise GDP per capita while the opposite is the case in high- and middle-income countries.

Effects of inequality on human capital

Additional evidence that our empirical results are in line with Galor and Zeira’s (1993) model comes from the response of investment and human capital. 1  Our panel estimates show that within-country increases in income inequality significantly increase the investment-to-GDP ratio in poor countries but decrease it in high- and middle-income countries. Furthermore, within-country increases in income inequality significantly increase human capital (measured by the average years of schooling and share of the population with a secondary and tertiary education) in poor countries. On the other hand, in high- and middle-income countries increases in income inequality reduce human capital.

Identification

Identification of the causal effect of income inequality on aggregate output is complicated by the endogeneity of the former variable. Income inequality may be affected by countries’ GDP per capita as well as other variables related to deep-rooted differences in their geography and history. We address this issue by estimating a panel model with country and time fixed effects. We instrument income inequality with variation in that variable not driven by GDP per capita building on the work of Brueckner et al. (2015).

Our empirical analysis is motivated by the theoretical work of Galor and Zeira (1993). who examined the relationship between inequality and aggregate output in the presence of credit market imperfections and indivisibilities in human capital investment. Galor and Zeira’s model predicts heterogeneity in the effects of inequality on aggregate output across countries’ initial income levels. Taking this prediction seriously, our econometric model included an interaction between measures of income inequality and countries’ initial level of GDP per capita. Instrumental variables estimates showed that income inequality has a significant negative effect on aggregate output for the average country in the sample. However, for poor countries income inequality has a significant positive effect. We document that this heterogeneity is also present when considering investment – in particular, investment in human capital – as a channel through which inequality affects aggregate output. Overall, our empirical results provide support for the hypothesis that income inequality is beneficial to economic growth in poor countries, but that it is detrimental to economic growth in advanced economies.

Disclaimer: The findings, interpretations, and conclusions expressed are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organisations, or those of the Executive Directors of the World Bank or the governments they represent.

Brueckner, M and D Lederman (2015), “Effects of Income Inequality on Aggregate Output”, World Bank Policy Discussion Paper 7317.

Brueckner, M, E Dabla Norris, M Gradstein (2015), “National Income and Its Distribution”,  Journal of Economic Growth 20: 149-175.

Galor, O (2011), “Inequality, Human Capital Formation, and the Process of Development”, Brown University working papers 2011-7.

Galor, O and J Zeira (1993), “Income Distribution and Macroeconomics”,  Review of Economic Studies 60: 35-52.

Ostry, J D, A Berg, and G D Tsangarides (2014), “Redistribution, Inequality, and Growth”, IMF Staff Discussion Note No. SDN/14/02, February.

Perotti, R (1996), “Growth, Income Distribution, and democracy: what the Data say?”,  Journal of Economic Growth 1(2): 149—187.

1 Ideally, in the cross-country time series context, we would like to use data on the distribution of wealth rather than income since wealth inequality is the relevant measure in theoretical models with credit market imperfections. Unfortunately, data on wealth inequality are not available to generate a long time-series for a large number of countries. As noted in previous empirical research (e.g. Perotti 1996), income inequality and wealth inequality are highly positively correlated.

This article is published in collaboration with VoxEU . Publication does not imply endorsement of views by the World Economic Forum.

To keep up with the Agenda  subscribe to our weekly newsletter .

Authors: Markus Brückner is an Associate Professor in economics at the National University of Singapore. Daniel Lederman is Lead Economist in the World Bank’s International Trade Department (PRMTR).

Image: U.S. one dollar bills blow near the Andalusian capital of Seville in this photo illustration taken on November 16, 2014. REUTERS/Marcelo Del Pozo.

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• Most Americans Say There Is Too Much Economic Inequality in the U.S., but Fewer Than Half Call It a Top Priority
• 1. Trends in income and wealth inequality

Table of Contents

• 2. Views of economic inequality
• 3. What Americans see as contributors to economic inequality
• 4. Views on reducing economic inequality
• Acknowledgments
• Methodology

Barely 10 years past the end of the Great Recession in 2009, the U.S. economy is doing well on several fronts . The labor market is on a job-creating streak that has rung up more than 110 months straight of employment growth, a record for the post-World War II era. The unemployment rate in November 2019 was 3.5%, a level not seen since the 1960s. Gains on the jobs front are also reflected in household incomes, which have rebounded in recent years.

But not all economic indicators appear promising. Household incomes have grown only modestly in this century, and household wealth has not returned to its pre-recession level. Economic inequality, whether measured through the gaps in income or wealth between richer and poorer households, continues to widen.

Household incomes are growing again after a lengthy period of stagnation

With periodic interruptions due to business cycle peaks and troughs, the incomes of American households overall have trended up since 1970. In 2018, the median income of U.S. households stood at $74,600. 5 This was 49% higher than its level in 1970, when the median income was $50,200. 6 (Incomes are expressed in 2018 dollars.)

But the overall trend masks two distinct episodes in the evolution of household incomes (the first lasting from 1970 to 2000 and the second from 2000 to 2018) and in how the gains were distributed.

Most of the increase in household income was achieved in the period from 1970 to 2000. In these three decades, the median income increased by 41%, to $70,800, at an annual average rate of 1.2%. From 2000 to 2018, the growth in household income slowed to an annual average rate of only 0.3%. If there had been no such slowdown and incomes had continued to increase in this century at the same rate as from 1970 to 2000, the current median U.S. household income would be about $87,000, considerably higher than its actual level of $74,600.

The shortfall in household income is attributable in part to two recessions since 2000. The first recession, lasting from March 2001 to November 2001, was relatively short-lived. 7  Yet household incomes were slow to recover from the 2001 recession and it was not until 2007 that the median income was restored to about its level in 2000.

But 2007 also marked the onset of the Great Recession, and that delivered another blow to household incomes. This time it took until 2015 for incomes to approach their pre-recession level. Indeed, the median household income in 2015 – $70,200 – was no higher than its level in 2000, marking a 15-year period of stagnation, an episode of unprecedented duration in the past five decades. 8

More recent trends in household income suggest that the effects of the Great Recession may finally be in the past. From 2015 to 2018, the median U.S. household income increased from $70,200 to $74,600, at an annual average rate of 2.1%. This is substantially greater than the average rate of growth from 1970 to 2000 and more in line with the economic expansion in the 1980s and the dot-com bubble era of the late 1990s.

Why economic inequality matters

The rise in economic inequality in the U.S. is tied to several factors. These include , in no particular order, technological change, globalization, the decline of unions and the eroding value of the minimum wage. Whatever the causes, the uninterrupted increase in inequality since 1980 has caused concern among members of the public , researchers , policymakers and politicians .

One reason for the concern is that people in the lower rungs of the economic ladder may experience diminished economic opportunity and mobility in the face of rising inequality, a phenomenon referred to as The Great Gatsby Curve . Others have highlighted inequality’s negative impact on the political influence of the disadvantaged, on geographic segregation by income, and on economic growth itself. The matter may not be entirely settled, however, as an opposing viewpoint suggests that income inequality does not harm economic opportunity.

Alternative estimates of economic inequality

This report presents estimates of income inequality based on household income as estimated in the Current Population Survey (CPS), a survey of households conducted by the U.S. Census Bureau in partnership with the Bureau of Labor Statistics. These estimates refer to gross (pretax) income and encompass most sources of income. A key omission is the value of in-kind services received from government sources. Because income taxes are progressive and in-kind services also serve to boost the economic wellbeing of (poorer) recipients, not accounting for these two factors could overstate the true gap in the financial resources of poorer and richer households.

The Congressional Budget Office (CBO) offers an alternative estimate of income inequality that accounts for federal taxes and a more comprehensive array of cash transfers and in-kind services than is possible with Current Population Survey data. The CBO finds that the Gini coefficient in the U.S. in 2016 ranged from 0.595, before accounting for any forms of taxes and transfers, to 0.423, after a full accounting of taxes and transfers. These estimates bracket the Census Bureau’s estimate of 0.481 for the Gini coefficient in 2016. By either estimate, income inequality in the U.S. is found to have increased by about 20% from 1980 to 2016 (The Gini coefficient ranges from 0 to 1, or from perfect equality to complete inequality). Findings from other researchers show the same general rise in inequality over this period regardless of accounting for in-kind transfers.

Yet another alternative is to focus on inequality in consumption, which implicitly accounts for all forms and sources of incomes, taxes and transfers. Some estimates based on consumption show that inequality in the U.S. increased by less than implied by estimates based on income, but other estimates suggest the trends based on consumption and income are similar. Empirically, consumption can be harder to measure than income.

Upper-income households have seen more rapid growth in income in recent decades

The growth in income in recent decades has tilted to upper-income households. At the same time, the U.S. middle class , which once comprised the clear majority of Americans, is shrinking. Thus, a greater share of the nation’s aggregate income is now going to upper-income households and the share going to middle- and lower-income households is falling. 9

The share of American adults who live in middle-income households has decreased from 61% in 1971 to 51% in 2019. This downsizing has proceeded slowly but surely since 1971, with each decade thereafter typically ending with a smaller share of adults living in middle-income households than at the beginning of the decade.

The decline in the middle-class share is not a total sign of regression. From 1971 to 2019, the share of adults in the upper-income tier increased from 14% to 20%. Meanwhile, the share in the lower-income tier increased from 25% to 29%. On balance, there was more movement up the income ladder than down the income ladder.

But middle-class incomes have not grown at the rate of upper-tier incomes. From 1970 to 2018, the median middle-class income increased from $58,100 to $86,600, a gain of 49%. 10  This was considerably less than the 64% increase for upper-income households, whose median income increased from $126,100 in 1970 to $207,400 in 2018. Households in the lower-income tier experienced a gain of 43%, from $20,000 in 1970 to $28,700 in 2018. (Incomes are expressed in 2018 dollars.)

More tepid growth in the income of middle-class households and the reduction in the share of households in the middle-income tier led to a steep fall in the share of U.S. aggregate income held by the middle class. From 1970 to 2018, the share of aggregate income going to middle-class households fell from 62% to 43%. Over the same period, the share held by upper-income households increased from 29% to 48%. The share flowing to lower-income households inched down from 10% in 1970 to 9% in 2018.

These trends in income reflect the growth in economic inequality overall in the U.S. in the decades since 1980.

Income growth has been most rapid for the top 5% of families

Even among higher-income families, the growth in income has favored those at the top. Since 1980, incomes have increased faster for the most affluent families – those in the top 5% – than for families in the income strata below them. This disparity in outcomes is less pronounced in the wake of the Great Recession but shows no signs of reversing.

From 1981 to 1990, the change in mean family income ranged from a loss of 0.1% annually for families in the lowest quintile (the bottom 20% of earners) to a gain of 2.1% annually for families in the highest quintile (the top 20%). The top 5% of families, who are part of the highest quintile, fared even better – their income increased at the rate of 3.2% annually from 1981 to 1990. Thus, the 1980s marked the beginning of a long and steady rise in income inequality.

A similar pattern prevailed in the 1990s, with even sharper growth in income at the top. From 1991 to 2000, the mean income of the top 5% of families grew at an annual average rate of 4.1%, compared with 2.7% for families in the highest quintile overall, and about 1% or barely more for other families.

The period from 2001 to 2010 is unique in the post-WWII era. Families in all strata experienced a loss in income in this decade, with those in the poorer strata experiencing more pronounced losses. The pattern in income growth from 2011 to 2018 is more balanced than the previous three decades, with gains more broadly shared across poorer and better-off families. Nonetheless, income growth remains tilted to the top, with families in the top 5% experiencing greater gains than other families since 2011.

The wealth of American families is currently no higher than its level two decades ago

Other than income, the wealth of a family is a key indicator of its financial security. Wealth, or net worth, is the value of assets owned by a family, such as a home or a savings account, minus outstanding debt, such as a mortgage or student loan. Accumulated over time, wealth is a source of retirement income, protects against short-term economic shocks, and provides security and social status for future generations.

The period from the mid-1990s to the mid-2000s was beneficial for the wealth portfolios of American families overall. Housing prices more than doubled in this period, and stock values tripled. 11 As a result, the median net worth of American families climbed from $94,700 in 1995 to $146,600 in 2007, a gain of 55%. 12  (Figures are expressed in 2018 dollars.)

But the run up in housing prices proved to be a bubble that burst in 2006. Home prices plunged starting in 2006, triggering the Great Recession in 2007 and dragging stock prices into a steep fall as well. Consequently, the median net worth of families fell to $87,800 by 2013, a loss of 40% from the peak in 2007. As of 2016, the latest year for which data are available, the typical American family had a net worth of $101,800, still less than what it held in 1998.

The wealth divide among upper-income families and middle- and lower-income families is sharp and rising

The wealth gap among upper-income families and middle- and lower-income families is sharper than the income gap and is growing more rapidly.

The period from 1983 to 2001 was relatively prosperous for families in all income tiers, but one of rising inequality. The median wealth of middle-income families increased from $102,000 in 1983 to $144,600 in 2001, a gain of 42%. The net worth of lower-income families increased from $12,3oo in 1983 to $20,600 in 2001, up 67%. Even so, the gains for both lower- and middle-income families were outdistanced by upper-income families, whose median wealth increased by 85% over the same period, from $344,100 in 1983 to $636,000 in 2001. (Figures are expressed in 2018 dollars.)

The wealth gap between upper-income and lower- and middle-income families has grown wider this century. Upper-income families were the only income tier able to build on their wealth from 2001 to 2016, adding 33% at the median. On the other hand, middle-income families saw their median net worth shrink by 20% and lower-income families experienced a loss of 45%. As of 2016, upper-income families had 7.4 times as much wealth as middle-income families and 75 times as much wealth as lower-income families. These ratios are up from 3.4 and 28 in 1983, respectively.

The reason for this is that middle-income families are more dependent on home equity as a source of wealth than upper-income families, and the bursting of the housing bubble in 2006 had more of an impact on their net worth. Upper-income families, who derive a larger share of their wealth from financial market assets and business equity, were in a better position to benefit from a relatively quick recovery in the stock market once the recession ended.

As with the distribution of aggregate income, the share of U.S. aggregate wealth held by upper-income families is on the rise. From 1983 to 2016, the share of aggregate wealth going to upper-income families increased from 60% to 79%. Meanwhile, the share held by middle-income families has been cut nearly in half, falling from 32% to 17%. Lower-income families had only 4% of aggregate wealth in 2016, down from 7% in 1983.

The richest are getting richer faster

The richest families in the U.S. have experienced greater gains in wealth than other families in recent decades, a trend that reinforces the growing concentration of financial resources at the top.

The tilt to the top was most acute in the period from 1998 to 2007. In that period, the median net worth of the richest 5% of U.S. families increased from $2.5 million to $4.6 million, a gain of 88%.

This was nearly double the 45% increase in the wealth of the top 20% of families overall, a group that includes the richest 5%. Meanwhile, the net worth of families in the second quintile, one tier above the poorest 20%, increased by only 16%, from $27,700 in 1998 to $32,100 in 2007. (Figures are expressed in 2018 dollars.)

The wealthiest families are also the only ones to have experienced gains in wealth in the years after the start of the Great Recession in 2007. From 2007 to 2016, the median net worth of the richest 20% increased 13%, to $1.2 million. For the top 5%, it increased by 4%, to $4.8 million. In contrast, the net worth of families in lower tiers of wealth decreased by at least 20% from 2007 to 2016. The greatest loss – 39% – was experienced by the families in the second quintile of wealth, whose wealth fell from $32,100 in 2007 to $19,500 in 2016.

As a result, the wealth gap between America’s richest and poorer families more than doubled from 1989 to 2016. In 1989, the richest 5% of families had 114 times as much wealth as families in the second quintile, $2.3 million compared with $20,300. By 2016, this ratio had increased to 248, a much sharper rise than the widening gap in income. 13

Income inequality in the U.S has increased since 1980 and is greater than in peer countries

Income inequality may be measured in a number of ways , but no matter the measure , economic inequality in the U.S. is seen to be on the rise.

One widely used measure – the 90/10 ratio – takes the ratio of the income needed to rank among the top 10% of earners in the U.S. (the 90th percentile) to the income at the threshold of the bottom 10% of earners (the 10th percentile). In 1980, the 90/10 ratio in the U.S. stood at 9.1, meaning that households at the top had incomes about nine times the incomes of households at the bottom. The ratio increased in every decade since 1980, reaching 12.6 in 2018, an increase of 39%. 14

Not only is income inequality rising in the U.S., it is higher than in other advanced economies. Comparisons of income inequality across countries are often based on the Gini coefficient , another commonly used measure of inequality. 15 Ranging from 0 to 1, or from perfect equality to complete inequality, the Gini coefficient in the U.S. stood at 0.434 in 2017, according to the Organization for Economic Cooperation and Development (OECD). 16  This was higher than in any other of the G-7 countries , in which the Gini ranged from 0.326 in France to 0.392 in the UK, and inching closer to the level of inequality observed in India (0.495). More globally, the Gini coefficient of inequality ranges from lows of about 0.25 in Eastern European countries to highs in the range of 0.5 to 0.6 in countries in southern Africa, according to World Bank estimates .

• The median income splits the income distribution into two halves – half the households earn less than the median and half the households earn more. Incomes are adjusted for household size and scaled to represent a household size of three. See methodology for details. ↩
• Percentage changes are estimated, and other calculations are made, before numbers are rounded. ↩
• The recession dates are as designated by the National Bureau of Economic Research . ↩
• It is likely that household incomes did not return to their 2000 level till 2016 or later. A redesign of income questions by the Census Bureau in 2014 is estimated to have given a boost of about 3% to median household income in the U.S. at the time of the redesign. ↩
• Middle-income” Americans are adults whose annual household income is two-thirds to double the national median, after incomes have been adjusted for household size. Lower-income households have incomes less than 67% of the median and upper-income households have incomes that are more than double the median. See methodology for details. Previous Pew Research Center reports have examined the state of the American middle class in greater detail, including trends within U.S. metropolitan areas. ↩
• The data source for these estimates is the Current Population Survey, Annual Social and Economic Supplement for 1971 to 2019. In the survey, respondents provide household income data for the previous calendar year. Thus, income data in this section refer to the 1970-2018 period and the counts of people from the same survey refer to the 1971-2019 period. ↩
• The S&P/Case-Shiller U.S. National Home Price Index increased from 80 in January 1995 to 185 in June 2006 (January 2000=100). It fell to 134 in February 2012 and climbed thereafter, reaching 212 in August 2019. At the start of the Great Recession in December 2007, the S&P 500 index stood at about 1,500, three times its level of about 500 in 1995. After the peak in 2007, the S&P 500 fell below 1,000 in 2009. As of November 2019, the index had reached a level of about 3,000. (S&P 500 historical values downloaded from Yahoo! on Nov. 21, 2019.) ↩
• Estimates of wealth are from the Survey of Consumer Finances (SCF). The SCF is conducted triennially by the Federal Reserve Board of Governors. It was first fielded in 1983 and the latest survey for which data are available was in 2016. ↩
• It is not possible to compute the ratio of the wealth of the top 5% of families to the wealth of the poorest 20% because the median wealth of the poorest families is either zero or negative in most years examined. ↩
• Per the U.S. Census Bureau , the source of these estimates, the 90th percentile household income in 2018 was $184,292 and the 10th percentile household income was $14,629 (incomes not adjusted for household size). ↩
• The Gini coefficient encapsulates the share of aggregate income held by each person or household. If everyone has the same income, or the same share of aggregate income, the Gini coefficient equals zero. If the income distribution is perfectly unequal, a single person or household holds all aggregate income, the Gini coefficient is equal to one. ↩
• The OECD is a group of 36 countries, including many of the world’s advanced economies. The OECD’s estimates of the Gini coefficient are for the following years: U.S. – 2017, UK – 2017, Italy – 2016, Japan – 2015, Canada – 2017, Germany – 2016, France – 2016, and India – 2011. ↩

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Beyond income: A multidimensional approach to tackling inequality

James foster, michael m. lokshin.

Rising income inequality in many developed and developing countries captures the attention of social activists and policy makers alike. Yet income is just one dimension of inequality. It exists in health, education, and social services, whose dimension-specific inequalities may reinforce or dampen the impact of income inequality. Focusing solely on income inequality offers only a partial view of what Amartya Sen has termed “ economic inequality ” limiting the scope and accuracy of a country’s policy responses.

Imagine a country or a region where the rich live in areas with the best public schools and the best publicly funded hospitals while the poor live where the schools and hospitals are of poor quality. Now, consider a counterfactual where the poor have access to top-quality public education and healthcare. The government implements a program to improve the quality of education in the lagging regions.

The impact of such an intervention can be assessed in two ways. In the first scenario, the poor will have access to better quality schooling, resulting in higher test scores for their children, while in the second scenario, the schools will be improved for the rich. Under some innocuous assumptions, we conclude that economic inequality would decline in the first case and rise in the second. But income and health inequalities are unchanged in both scenarios, while education inequality will be lower compared to the pre-policy situation. The so-called “dashboard” approach of measuring each dimension-specific inequality level separately (or taking a weighted average) ignores potential interactions across dimensions. However, such effects could be of critical importance as they convey valuable information on how people and societies experience inequality. Improving the quality of education for the poor will increase social mobility and allow many families to escape poverty, thus reducing income inequality. Investing in education in affluent areas might result in heightened political tensions and could stunt economic growth.

To account for these interactions and better gauge the extent of economic inequality, measures of multidimensional inequality have been developed. Despite significant advances in the range of tools available for measuring inequality across multiple dimensions, the policy impact of these measures has been muted.   

In our recent paper , we propose new multidimensional inequality measures that are easily implementable and transparent and overcome many deficiencies of existing measures. The paper aims to identify axiomatically sound multidimensional inequality measures with attributes well-suited for policy. The measures follow a traditional two-stage format, suggested by  Maasoumi (1986) , which aggregates dimensions first and then applies a unidimensional measure like the Gini coefficient to the distribution of aggregates.

We show that only a linear form can be used for aggregation of the individual-level components. Previous studies have considered linear aggregation, but this is the first paper to select this structure based on the axiomatic properties of its associated measures. We demonstrate that multidimensional inequality can be expressed as a weighted average of specific inequalities and a non-negative term reflecting the relevant aspects of the joint distribution across dimensions.

We also develop a calibration approach based on data in an initial period and normative policy weights. Once the multidimensional inequality measure has been calibrated, it can be used to gauge the multidimensional inequality through time and, with additional assumptions, through space.  

Application and Impact in Developing Countries

We illustrate the application of our methodology by analyzing changes in multidimensional inequality in Azerbaijan from 2016 to 2023. We use data from the second (2016) and the fourth (2023) rounds of the Life in Transition Survey ( LITS ). We construct the multidimensional inequality index based on three dimensions captured by the monthly per capita income, years of education, and respondent’s subjective health assessment. The measure is calibrated for 2016 using normative weights of ½ for income and ¼ for the education and health dimensions.

Table 1 presents the specific and multidimensional inequality levels for Azerbaijan. The mean monthly per capita income increased by almost 59 percent from about 852 PPP dollars in 2016 to 1350 PPP dollars in 2023. Income growth was accompanied by an increase in income inequality, from a Gini of 0.253 in 2016 to 0.339 in 2023. The average years of education and health self-assessment remained relatively stable. The inequality in years of education grew while the inequality in health assessment slightly declined.

Table 1: Specific and multidimensional inequalities in Azerbaijan, 2016–2023.

Multidimensional inequality M(x)   as measured by Gini, increased between 2016 and 2023, from 0.144 to 0.230. This increase is due to (i) changes in the specific inequalities, (ii) changes in the effective weights as dimensional means change, and (iii) changes in the rearrangement term R(x) . The rearrangement term fell slightly from 0.037 to 0.029, reflecting greater alignment of dimensions in the second year, meaning that people with higher incomes also got better education and health. To reduce economic inequality in the country, the government of Azerbaijan might invest in improving the quality of education in the country’s poorest regions.

Expanding Understanding of Inequality

By adopting such a multidimensional approach, developing countries, with their limited resources and high inequality rates, can better address their policy challenges. Tackling income inequality is notoriously difficult, and such policies often generate undesirable second-order effects. Our framework allows countries to reduce economic inequality by providing public goods to people experiencing poverty, which is straightforward from the implementation perspective and much more palatable politically. By recognizing the interconnectedness of various life dimensions, policy makers can devise more effective strategies to promote equitable growth and ensure that no one is left behind.

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Home — Essay Samples — Economics — Income Inequality — The Causes, Consequences And Solutions Of Income Inequality

The Causes, Consequences and Solutions of Income Inequality

• Categories: Economic Inequality Gender Wage Gap Income Inequality

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Words: 2000 |

10 min read

Published: Mar 18, 2021

Words: 2000 | Pages: 4 | 10 min read

• In 2010, the employment rate had stood at about 73% being those with an associate’s degree, then in 2017, rose to about 78% for those with a higher level of education.
• Having high education would NOT always guarantee an increase in income.

Works Cited

• “The Gender Wage Gap: 2018 Earnings Differences by Race and Ethnicity.” Institute for Women's Policy Research, iwpr.org/publications/gender-wage-gap-2018/.
• “How Immigration Makes Income Inequality Worse in the US.” USAPP, 16 Oct. 2015, blogs.lse.ac.uk/usappblog/2015/10/14/how-immigration-makes-income-inequality-worse-i n-the-us/.
• “Immigration and Rising Income Inequality.” Federation for American Immigration Reform, www.fairus.org/issue/publications-resources/immigration-and-rising-income-inequality.
• “Mass Immigration and the Growth of Inequality • Social Europe.” Social Europe, 8 Jan. 2019, www.socialeurope.eu/mass-immigration-growth-inequality.
• McCarthy, Niall. “Income Inequality Between White And Black Americans Is Worse Today Than In 1979 [Infographic].” Forbes, Forbes Magazine, 21 Sept. 2016, www.forbes.com/sites/niallmccarthy/2016/09/21/income-inequality-between-white-black- americans-is-worse-today-than-in-1979-infographic/#53d81f223740.
• “The NCES Fast Facts Tool Provides Quick Answers to Many Education Questions (National Center for Education Statistics).” The National Center for Education Statistics (NCES) Home Page, a Part of the U.S. Department of Education, nces.ed.gov/fastfacts/display.asp?id=77.
• Rogoff, Kenneth, et al. “The Link between Immigration and Inequality.” The World Economic Forum, www.weforum.org/agenda/2015/05/the-link-between-immigration-and-inequality/.
• Sepúlveda, Magdalena. “ Economic Inequality and Taxation Are Feminist Issues.”
• Common Dreams, 1 Mar. 2019, www.commondreams.org/views/2019/03/01/economic-inequality-and-taxation-are-femini st-issues.
• Shaer, Matthew. “The Archaeology of Wealth Inequality.” Smithsonian.com, Smithsonian Institution, 1 Mar. 2018, www.smithsonianmag.com/history/aracheology-wealth-inequality-180968072/.
• “Taxes, Inequality & Growth Archives.” Equitable Growth, equitablegrowth.org/issue/taxes-inequality-growth/.
• “The Distribution of Wealth in the United States and Implications for a Net Worth Tax.” Equitable Growth, 20 Mar. 2019, equitablegrowth.org/the-distribution-of-wealth-in-the-united-states-and-implications-for-a- net-worth-tax/.
• “The Effects of Taxes and Benefits on Income Inequality: 1977 to Financial Year Ending 2015.” Office for National Statistics, www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomea ndwealth/bulletins/theeffectsoftaxesandbenefitsonincomeinequality/1977tofinancialyearen ding2015.

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Income Inequality 101: Causes, Facts, Examples, Ways to Take Action

Billionaires are increasing their fortunes by $2.7 billion every day . Meanwhile, at least 1.7 billion workers live in areas where inflation is higher than wages. Income inequality is a global problem. It has several consequences, including financial crises, fragile economies, high inflation, poorer health outcomes, and violence. In this article, we’ll explore what causes income inequality, what it looks like, the most important facts everyone should know, and how to address it.

Income inequality is a global issue with several causes, including historical racism, unequal land distribution, high inflation, and stagnant wages. As gaps increase thanks to crises like COVID-19, the world needs to take action in education, labor market policies, tax reforms, and higher wages.

What is income inequality?

When some people in society earn significantly more than others, it creates inequality. Inequality is more than just about the paychecks we take home, however. There’s also wealth inequality, which refers to uneven distributions of wealth. This includes the value of assets and possessions like stocks, property, boats, and so on. Someone may earn a lower income than a neighbor, but because they own stocks and land, they’re wealthier.

Income inequality is measured with factors like gender, ethnicity, location, historical income, and occupation. When identifying a country’s income inequality, there are measurements like the Gini index , which is also called the Gini coefficient. A score of 0 on the index means there’s no deviation; everyone is perfectly equal. A score of 100 means total inequality; a single person has all the country’s wealth. The index isn’t perfect . As Amanda Shendruck points out, Greece, Israel, Thailand, and the UK got the same score in 2015. However, poverty in these countries looks very different. The World Inequality Database avoids the index altogether. On its own, the Gini index may not be especially useful, but it can provide a quick snapshot that encourages more investigation.

The causes of income inequality: two case studies

There are global and country-specific factors that drive income inequality. To get a clearer idea of the causes, let’s look at two countries as examples: South Africa and the United States.

South Africa: The long shadow of apartheid and land ownership

Based on the Gini index, South Africa has the world’s highest income inequality at 63.0 . Apartheid is a big reason why. For almost 50 years, this formalized racial segregation restricted the activities and movements of Black South Africans, who made up most of the population. Black Africans couldn’t marry white people, travel without passbooks, or start businesses in white areas. Society was structured to uplift white people while trampling Black South Africans. When apartheid ended in the 1990s, inequality remained baked into the country’s foundation. South Africa has struggled to make progress on ending inequality. According to a 2022 World Bank report , the top 10% of South Africa’s population holds 71% of all income. Living in or near cities increases job opportunities, but South Africa’s growth has stalled and failed to create enough jobs. High unemployment is a significant driver of inequality, especially for young people.

Gender, race, and land ownership are three other main causes. In South Africa, women earn 38% less than men even when they have similar education levels. When race gets added to inequality analyses, it contributes 41% to income inequality. The World Bank report also studied land ownership, which is vital for addressing inequality among poor people in rural areas. Because of apartheid, there’s a long history of unequal land distribution which hasn’t been remedied yet. COVID-19 made all these factors worse.

The United States: The legacy of slavery and stagnant wages

The United States isn’t among the top most unequal countries in the world, but it has a much higher Gini coefficient when compared to similar economies. According to Statista , the top 10% of earners in the United States (in the third quarter of 2022) held 68% of the country’s total wealth. The lowest 50% held just 3.3.%. Like South Africa, the United States’ history of racial segregation plays a big role. Slavery made it impossible for Black people to build wealth, but even after emancipation, Jim Crow laws severely restricted economic opportunities. The effects resonate to this day. A 2018 analysis of incomes and wealth found that over the past 70 years, there’s been no progress in reducing income and wealth inequalities between Black and white households.

Inequality is also driven by the fact that wages haven’t kept pace with inflation. In June 2022, consumer prices hit 9.1% higher than the year before. This made it the largest annual increase since 1981. Wages have been going up, but they’ve been consistently at 4.5%. The federal minimum wage hasn’t increased since 2009: it’s just $7.25. A study found that in 91% of U.S. counties, a full-time minimum wage worker doesn’t make enough to afford a one-bedroom apartment rental.

What are the five main facts everyone should know about income equality?

There’s a lot to sift through when it comes to income and wealth inequality, but here are five of the most important facts to know:

#1. Inequalities within countries are getting worse

While global inequalities between countries are lowering, the gaps within countries are increasing. According to the World Inequality Database’s 2022 report , the gap between the average incomes of the bottom 50% and the top 10% of individuals has nearly doubled in the past two decades. The World Inequality Database frames it this way: “global inequalities seem to be about as great today as they were at the peak of Western imperialism in the early 20th century.”

#2. COVID-19 is erasing progress

According to groups like the IMF , COVID-19 is worsening inequalities within countries (the poor were hit harder than the rich), but also between countries. Wealthier countries had more resources to deal with the pandemic and could recover faster. According to the World Bank , progress was set back by about a decade.

#3. Inequality hits already-disenfranchised people the hardest

Income inequality is an intersectional issue. It affects disenfranchised groups like women, young people, informal industry workers, the elderly, and disabled people the most. As income inequality worsened in the UK , the disposable income for the poorest ⅕ of the population dropped by 3.8%. The average income for retired households also went down from £26,300 to £25,900.

#4. Over the last decade, the world’s richest 1% have gotten 54% of new wealth – and they’re getting richer

According to an Oxfam report , the world’s richest 1% captured $42 trillion of the new wealth created between December 2019-December 2021. $16 trillion got distributed to the bottom 99%. While the pandemic hit the poor the hardest, the world’s richest actually gained wealth. There was a slight dip in 2022, but in 2023, their wealth is increasing yet again.

#5. Income inequality is linked to climate change

Every year, humans emit around 6.6 tonnes of carbon dioxide equivalent per capita. However, the top 10% of emitters are releasing around 50% of all emissions. The bottom 50% are producing just 12%. Why does this matter to income inequality? The world’s biggest emitters are rich. While many of the world’s poorest countries emit significantly less CO2 , they’re enduring the worst climate change effects. Even within rich countries, the poorest half of the population have already met (or are close to meeting) the 2030 climate targets set by their nations. It’s the rich who need to change.

How to take action on income inequality

Income inequality is a deeply-entrenched, global problem that will take lots of work. Here are three ways countries can take action:

#1. Pay a living wage

Many countries are raising wages, but they’re not raising them enough to close income gaps. That’s why minimum wages need to be higher. In an article on the World Economic Forum about fair wages , the global director of human rights at Unilver emphasized the need for living wages. These are calculated based on what it takes to afford a decent standard of living. Currently, minimum wages in many countries don’t reflect reality. The United States is an example as its minimum wage won’t cover rent on a one-bedroom apartment.

#2. Invest in good public education

Study after study shows the positive impact of good public education. According to a report from Oxfam , a good education can reduce poverty, increase opportunities, and encourage a more democratic society. Education also improves gender equality, which is key to closing income inequality gaps. To successfully address income inequality, education must be universal, free, and public. If it isn’t, education can make inequalities worse as it divides students by traits like race, gender, and wealth.

#3. Make tax systems more redistributive

According to the IMF , addressing inequality more redistributive tax systems. What is a redistributive tax system ? It’s a system where high-income people pay higher taxes (positive taxes) and lower-income people receive more subsidies. In places like the United States, where legislation has designed tax codes to benefit corporations and the wealthiest individuals , wider inequality has followed. The rich are also allowed to get away with more. In 2014-2016, the IRS – which is famously underfunded – didn’t pursue over 300,000 high-income individuals who failed to file tax returns. If countries want to tackle inequality, their tax systems should be designed to help rather than make things worse. That includes spending more on social sectors like education, health, and social protection.

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About the author, emmaline soken-huberty.

Emmaline Soken-Huberty is a freelance writer based in Portland, Oregon. She started to become interested in human rights while attending college, eventually getting a concentration in human rights and humanitarianism. LGBTQ+ rights, women’s rights, and climate change are of special concern to her. In her spare time, she can be found reading or enjoying Oregon’s natural beauty with her husband and dog.

Opinion: Income inequality is gutting the middle class

I care about economic inequality. I teach a class on it. I wrote  a book  about it. Yet, recently, two different arguments have popped up with a seemingly similar conclusion: economic inequality hasn’t risen as much as conventional wisdom would suggest.

While these two arguments have the same implication, they look at vastly different ends of the income distribution. 

The first  focuses on the bottom 20 percent of households by income and suggests an actual  reduction  in inequality. The point is that through increased government transfers, the bottom has actually caught up to the middle over the last 40 years. The  second argument  focuses on the top 1 percent of the income distribution. It argues that estimates of the top 1 percent’s share of income make assumptions that may overstate that share’s growth and thus the growth of inequality.

I don’t necessarily disagree with either of these arguments. Over the last 50 years, the social safety net has expanded in ways that  reduce poverty  and benefit the bottom 20 percent relative to the middle. And, because most surveys of household finances miss out on the top 1 percent, calculating their share of income is hard. It  requires assumptions , and those assumptions will yield different estimates of growth. While I don’t profess to know which group is right — the one saying  lots  of growth or the one saying  less  — I know enough to admit that I don’t know.

So, if I don’t disagree with these arguments, then why am I writing this? Because I think a focus on the poles of income distribution distracts from the real story: the stagnation of the middle over the last 50 years. Sure, the bottom has caught up to the middle partially because of government programs. But the other part of the story is that the bottom has also caught up because the middle hasn’t seen much income growth at all.

The figure below illustrates this point. It shows how GDP per capita, the median earnings of female versus male full-time workers and the median income of Black versus white households have grown since 1975. While per capita income in the U.S. nearly doubled over this period, the same can’t be said for anyone in the middle. 

The median working man saw his real income  decline . And, while the median working women saw increases, this is largely because their education and work experience expanded rapidly. In any case, despite these gains, they still couldn’t keep up with the economy’s growth nor catch up completely to men — the gender wage gap today sits around  83 percent .

At the household level, things aren’t great either. Median income for white households grew about 25 percent. Black households saw even less growth. That’s right, since 1975, the Black-white household income gap  has grown . And while those making the first argument above could fairly point out that my numbers do not account for non-cash safety net programs like food stamps or Medicaid, the median household isn’t getting these programs anyway.

These data contain questions about the economy that demand answers. Why aren’t labor markets providing any economic growth for middle-earning men? Why haven’t middle-earning women — despite massive increases in human capital — been able to outpace the economy? Why are households not seeing much growth despite large increases in women’s earnings and labor force participation? And why did Black households actually  lose  ground?

Economists know the answers to some of these questions.  Technology ,  trade  and the  increased power  of large businesses all likely play some role in holding down median wages. Women have to trade pay for  flexibility  due to caregiving responsibilities. The  decline of marriage  in the middle — at least partially fueled by the economic performance of men — means fewer two-income households than would have occurred otherwise. And Black households still face  discrimination , a damaging  human capital gap  and economic conditions that can make marriage  tougher  than it is for white households.

These issues demand some consideration. Should we plan for the impact of AI to prevent another four decades lost for middle earners? Would universal pre-K help kids do better and help moms work and earn more if they want? Do we want to change housing policy to improve opportunities for Black households to unstick the racial income gap? 

These conversations are worth having, but they won’t be had if people think incorrectly that the issue is settled.

Geoffrey Sanzenbache r is an associate professor of the practice at Boston College, where he teaches a class called The Economics of Inequality. He is also a research fellow at The Center for Retirement Research at Boston College.

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High Interest Rates Are Hitting Poorer Americans the Hardest

The economy as a whole has proved resilient amid the highest rates in decades. But beneath the surface, many low- and moderate-income families are struggling.

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By Ben Casselman and Jeanna Smialek

High interest rates haven’t crashed the financial system, set off a wave of bankruptcies or caused the recession that many economists feared.

But for millions of low- and moderate-income families, high rates are taking a toll.

More Americans are falling behind on payments on credit card and auto loans, even as many are taking on more debt than ever before. Monthly interest expenses have soared since the Federal Reserve began raising interest rates two years ago. For families already strained by high prices, dwindling savings and slowing wage growth, increased borrowing costs are pushing them closer to the financial edge.

“It’s crazy,” said Ora Dorsey, a 43-year-old Army veteran in Clarksville, Tenn. “It does make it hard to get out of debt. It seems like you’re only paying the interest.”

Ms. Dorsey has been working for years to chip away at the debts she accrued when a series of health issues left her temporarily out of work. Now she is juggling three jobs to try to pay off thousands of dollars in credit card balances and other debts. She is making progress, but high rates aren’t helping.

“How am I supposed to retire?” she asked. “I’m not able to save, have that rainy-day fund, because I’m trying to take down the debt that I have.”

Ms. Dorsey isn’t likely to get relief soon. Fed officials have indicated that they expect to keep interest rates at their current level, the highest in decades, for months. And while policymakers still say they are likely to cut rates eventually, assuming inflation slows down as expected, they could consider raising them further if prices begin rising faster again. The latest evidence will come on Wednesday when the Labor Department releases data showing whether inflation cooled in April, or remained uncomfortably hot for a fourth straight month.

The overall economy has proved unexpectedly resilient to high interest rates. Consumers have continued spending on travel, restaurant meals and entertainment thanks to rising wages and debt levels that, despite their recent increase, remain manageable as a share of income for most people.

But aggregate figures obscure an underlying divide that is likely to widen the longer interest rates remain high. Affluent households, and even many in the middle class, have largely been insulated from the effects of the Fed’s policies. Many took out long-term mortgages when rates were at rock bottom in 2020 or earlier — if they don’t own their homes outright — and most have little if any variable-rate debt. And they are benefiting from higher returns on their savings.

For poorer families, it is different. They are likelier to carry a balance on credit cards, meaning they’re more likely to feel high rates. According to Fed data, about 56 percent of people earning less than $25,000 carried a credit card balance in 2022, compared with 38 percent of those earning more than $100,000. Black Americans, like Ms. Dorsey, and Latinos are also more likely to carry balances.

Recent economic research suggests that high borrowing costs may be one reason for Americans’ dim view of the state of the economy. In surveys, lower-income households remain particularly dour about their financial well-being.

Barbara L. Martinez, a financial counselor in Chicago who works at Heartland Alliance, a nonprofit group, said that for many of her low-income clients, debt is inescapable, especially since food prices and rents have soared. They don’t have savings to cover unexpected expenses like car repairs or illness. And while high borrowing costs aren’t necessarily causing their financial difficulties, they make dealing with debt much harder.

“You’re trying to get out of the ocean, but the waves keep pushing you back,” she said. “No matter how much you swim, you get tired.”

High interest rates are always tougher on borrowers than on savers. But most of the time, they also push down the value of stocks, houses and other assets. That means rate increases usually affect households across the income spectrum, albeit in different ways.

That isn’t how things have played out recently. Stock prices fell when the Fed began raising rates, but have rebounded and are near a record. Home prices have continued rising in most of the country.

The result is a growing divide. Fed data suggests that wealth for the upper half dipped after the Fed’s initial rate increase in 2022, but is again setting records. For the bottom half, however, wealth remains below its level before the Fed began raising rates, after subtracting credit card and mortgage debt and other liabilities.

“Higher-income households feel very flush,” said Brian Rose, senior economist at UBS. “They’ve seen such a huge run-up in the value of their house and the value of their portfolios that they feel like they can keep spending.”

Airlines, hotels and other industries that cater largely to higher-income consumers have generally reported strong profits of late. But mass-market brands like McDonald’s and KFC have reported slower sales , with many citing weakness among low-income consumers as part of the reason.

The divergence puts Fed officials in an uncomfortable position: Free spending by wealthy households means high interest rates have done little to curb consumer demand. But with few other inflation-fighting tools, policymakers have little choice but to keep interest rates high — even if those policies hurt families that are already struggling.

Virginia Diaz thought she was on track for a secure retirement when she moved to Florida from New York nearly 20 years ago. But she drew down her savings and built up credit card debt helping family members, including a niece with health issues. Now high prices and high interest rates are putting her retirement in jeopardy.

“Every time I make a payment to my credit card, most of the money is going to pay interest, and that just snowballs,” she said. “I’m at the end of my rope.”

Ms. Diaz, 74, said she has cut her spending to the bone — “If I want to buy a candle, I have to think about it,” she said — and the rest of her family is also struggling. Her nephew, 35, works full time in the insurance industry, but lives in an apartment in her garage because he can’t afford to buy a house, or even a car. A friend of her niece’s also lives with her, chipping in to pay bills.

Ms. Diaz practically begged Fed officials to cut interest rates.

“I know they mean well, but it’s not working,” she said. “Lower it, for God’s sake, so people can live. Give us half a chance to give us a decent level of living.”

Many liberal economists agree, arguing that inflation has fallen enough that the Fed should start cutting rates before it causes more severe economic damage.

“High interest rates really forced cracks in that recovery, and it’s folks who are on the margins of our economy who are hit first and hit hardest,” said Rakeen Mabud, chief economist at the Groundwork Collaborative, a progressive group. “They really serve as a bellwether for what could happen to the rest of our economy.”

But Fed officials argue it is essential to bring inflation under control, in part because it, too, has a bigger impact on the poor, who have little room in their budgets to accommodate higher prices.

“If you’re a person who’s living paycheck to paycheck, and suddenly all the things you buy, the fundamentals of life, go up in price, you are in trouble right away,” Jerome H. Powell, the Fed chair, said at a news conference this month. “And so, with those people in mind in particular, what we’re doing is we’re using our tools to bring down inflation.”

And while high interest rates have affected many families, they have not so far caused the widespread job losses that many progressive critics predicted and that have historically been hardest on lower-wage workers. The unemployment rate remains low, including for Black and Hispanic workers, who are often more prone to lose their jobs when the economy weakens. And wage growth over the past several years has been strongest for lower-paid workers.

For most people, “the big issue is whether you’re holding onto your job,” said C. Eugene Steuerle, a fellow at the Urban Institute who has studied how monetary policy affects inequality.

But high rates today could make it harder for many families to build wealth in the longer run by making homeownership more difficult. They could also curb the construction of apartments and houses, which over time could further push up rents.

The result: a generation of young adults who fear they can neither afford to buy nor rent.

Chris Nunn, 31, has accumulated more than $6,000 in credit card debt, most of it from moving expenses tied to rent increases. His rent in Louisville, Ky., keeps rising, and he sees little hope of paying off the debt with what he makes driving for DoorDash while completing a college degree.

“We don’t have the credit to be able to buy a house, and we have a bunch of debt, either student loans or credit card debt,” he said. “So we’re trapped.”

Ben Casselman writes about economics with a particular focus on stories involving data. He has covered the economy for nearly 20 years, and his recent work has focused on how trends in labor, politics, technology and demographics have shaped the way we live and work. More about Ben Casselman

Jeanna Smialek covers the Federal Reserve and the economy for The Times from Washington. More about Jeanna Smialek